diff --git a/Analysis_code/4.oversampling_data_test/analysis_for_oversampling_data.ipynb b/Analysis_code/4.oversampling_data_test/analysis_for_oversampling_data.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..cb91c11f911cbf1dd45ec5d8356f829229fb3bbf
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+++ b/Analysis_code/4.oversampling_data_test/analysis_for_oversampling_data.ipynb
@@ -0,0 +1,1627 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7a537eaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import joblib\n",
+    "import ast\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5d6f8ad1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(96, 7)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df= pd.read_csv(\"../../data/oversampled_data_test_for_model/combined_sampled_data_test.csv\")\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bf430e2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>region</th>\n",
+       "      <th>model</th>\n",
+       "      <th>data_sample</th>\n",
+       "      <th>CSI</th>\n",
+       "      <th>MCC</th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>fold_csi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>seoul</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.505041</td>\n",
+       "      <td>0.646992</td>\n",
+       "      <td>0.936174</td>\n",
+       "      <td>[[0.46595932802825235, 0.5771195097037204, 0.4...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.430188</td>\n",
+       "      <td>0.600801</td>\n",
+       "      <td>0.956971</td>\n",
+       "      <td>[[0.32824427480911017, 0.4782608695651431, 0.4...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>incheon</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.554663</td>\n",
+       "      <td>0.687951</td>\n",
+       "      <td>0.911954</td>\n",
+       "      <td>[[0.4845292955891715, 0.6037628278220865, 0.57...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.292340</td>\n",
+       "      <td>0.481989</td>\n",
+       "      <td>0.956964</td>\n",
+       "      <td>[[0.28124999999994504, 0.3320537428022395, 0.2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.478437</td>\n",
+       "      <td>0.625244</td>\n",
+       "      <td>0.932748</td>\n",
+       "      <td>[[0.43333333333329205, 0.4547920433995972, 0.5...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    region     model data_sample       CSI       MCC  Accuracy  \\\n",
+       "0    seoul  LightGBM        pure  0.505041  0.646992  0.936174   \n",
+       "1    busan  LightGBM        pure  0.430188  0.600801  0.956971   \n",
+       "2  incheon  LightGBM        pure  0.554663  0.687951  0.911954   \n",
+       "3    daegu  LightGBM        pure  0.292340  0.481989  0.956964   \n",
+       "4  daejeon  LightGBM        pure  0.478437  0.625244  0.932748   \n",
+       "\n",
+       "                                            fold_csi  \n",
+       "0  [[0.46595932802825235, 0.5771195097037204, 0.4...  \n",
+       "1  [[0.32824427480911017, 0.4782608695651431, 0.4...  \n",
+       "2  [[0.4845292955891715, 0.6037628278220865, 0.57...  \n",
+       "3  [[0.28124999999994504, 0.3320537428022395, 0.2...  \n",
+       "4  [[0.43333333333329205, 0.4547920433995972, 0.5...  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dfaca702",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "seoul      16\n",
+       "busan      16\n",
+       "incheon    16\n",
+       "daegu      16\n",
+       "daejeon    16\n",
+       "gwangju    16\n",
+       "Name: region, dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['region'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "887c4ae7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LightGBM    48\n",
+       "XGBoost     48\n",
+       "Name: model, dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['model'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8eebadba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pure                  12\n",
+       "smote                 12\n",
+       "ctgan20000            12\n",
+       "ctgan10000            12\n",
+       "ctgan7000             12\n",
+       "smotenc_ctgan7000     12\n",
+       "smotenc_ctgan10000    12\n",
+       "smotenc_ctgan20000    12\n",
+       "Name: data_sample, dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['data_sample'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7eb4d347",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['fold_csi'] = df['fold_csi'].apply(lambda x: ast.literal_eval(x)[0] if isinstance(x, str) else x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c790c54b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_expanded = []\n",
+    "for idx, row in df.iterrows():\n",
+    "    fold_csi_list = row['fold_csi']\n",
+    "    for fold_num, csi_score in enumerate(fold_csi_list, start=1):\n",
+    "        csi_score = float(csi_score)\n",
+    "        new_row = row.copy()\n",
+    "        new_row['fold'] = fold_num\n",
+    "        new_row['csi_score'] = csi_score\n",
+    "        df_expanded.append(new_row)\n",
+    "\n",
+    "df_fold = pd.DataFrame(df_expanded)\n",
+    "df_fold = df_fold.drop(columns=['fold_csi'])\n",
+    "df_fold['fold'] = df_fold['fold'].astype(\"category\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "692be7e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>region</th>\n",
+       "      <th>model</th>\n",
+       "      <th>data_sample</th>\n",
+       "      <th>CSI</th>\n",
+       "      <th>MCC</th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>fold</th>\n",
+       "      <th>csi_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>seoul</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.505041</td>\n",
+       "      <td>0.646992</td>\n",
+       "      <td>0.936174</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.465959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>seoul</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.505041</td>\n",
+       "      <td>0.646992</td>\n",
+       "      <td>0.936174</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.577120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>seoul</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.505041</td>\n",
+       "      <td>0.646992</td>\n",
+       "      <td>0.936174</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.472045</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.430188</td>\n",
+       "      <td>0.600801</td>\n",
+       "      <td>0.956971</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.328244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.430188</td>\n",
+       "      <td>0.600801</td>\n",
+       "      <td>0.956971</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.478261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>smotenc_ctgan20000</td>\n",
+       "      <td>0.489587</td>\n",
+       "      <td>0.636099</td>\n",
+       "      <td>0.933698</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.478610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>smotenc_ctgan20000</td>\n",
+       "      <td>0.489587</td>\n",
+       "      <td>0.636099</td>\n",
+       "      <td>0.933698</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.544531</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>smotenc_ctgan20000</td>\n",
+       "      <td>0.487791</td>\n",
+       "      <td>0.635238</td>\n",
+       "      <td>0.941375</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.392500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>smotenc_ctgan20000</td>\n",
+       "      <td>0.487791</td>\n",
+       "      <td>0.635238</td>\n",
+       "      <td>0.941375</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.567148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>smotenc_ctgan20000</td>\n",
+       "      <td>0.487791</td>\n",
+       "      <td>0.635238</td>\n",
+       "      <td>0.941375</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.503726</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>288 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     region     model         data_sample       CSI       MCC  Accuracy fold  \\\n",
+       "0     seoul  LightGBM                pure  0.505041  0.646992  0.936174    1   \n",
+       "0     seoul  LightGBM                pure  0.505041  0.646992  0.936174    2   \n",
+       "0     seoul  LightGBM                pure  0.505041  0.646992  0.936174    3   \n",
+       "1     busan  LightGBM                pure  0.430188  0.600801  0.956971    1   \n",
+       "1     busan  LightGBM                pure  0.430188  0.600801  0.956971    2   \n",
+       "..      ...       ...                 ...       ...       ...       ...  ...   \n",
+       "94  daejeon   XGBoost  smotenc_ctgan20000  0.489587  0.636099  0.933698    2   \n",
+       "94  daejeon   XGBoost  smotenc_ctgan20000  0.489587  0.636099  0.933698    3   \n",
+       "95  gwangju   XGBoost  smotenc_ctgan20000  0.487791  0.635238  0.941375    1   \n",
+       "95  gwangju   XGBoost  smotenc_ctgan20000  0.487791  0.635238  0.941375    2   \n",
+       "95  gwangju   XGBoost  smotenc_ctgan20000  0.487791  0.635238  0.941375    3   \n",
+       "\n",
+       "    csi_score  \n",
+       "0    0.465959  \n",
+       "0    0.577120  \n",
+       "0    0.472045  \n",
+       "1    0.328244  \n",
+       "1    0.478261  \n",
+       "..        ...  \n",
+       "94   0.478610  \n",
+       "94   0.544531  \n",
+       "95   0.392500  \n",
+       "95   0.567148  \n",
+       "95   0.503726  \n",
+       "\n",
+       "[288 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_fold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6eb8d9ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x1800 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 지역과 모델 목록 가져오기\n",
+    "regions = sorted(df_fold['region'].unique())\n",
+    "models = sorted(df_fold['model'].unique())\n",
+    "data_samples = sorted(df_fold['data_sample'].unique())\n",
+    "\n",
+    "# 색상 팔레트 생성 (data_sample별로 다른 색상)\n",
+    "colors = plt.cm.tab20(np.linspace(0, 1, len(data_samples)))\n",
+    "\n",
+    "# 6행 2열 subplot 생성 (6개 지역 × 2개 모델)\n",
+    "fig, axes = plt.subplots(6, 2, figsize=(14, 18))\n",
+    "fig.suptitle('CSI Score by Fold, Region, and Model (Grouped by Data Sample)', \n",
+    "             fontsize=16, y=0.995)\n",
+    "\n",
+    "# 각 지역과 모델 조합에 대해 plot 생성\n",
+    "for region_idx, region in enumerate(regions):\n",
+    "    for model_idx, model in enumerate(models):\n",
+    "        # subplot 위치 계산\n",
+    "        ax = axes[region_idx, model_idx]\n",
+    "        \n",
+    "        # 해당 지역과 모델의 데이터 필터링\n",
+    "        filtered_df = df_fold[(df_fold['region'] == region) & (df_fold['model'] == model)]\n",
+    "        \n",
+    "        # 각 data_sample별로 line plot 생성\n",
+    "        for sample_idx, data_sample in enumerate(data_samples):\n",
+    "            sample_data = filtered_df[filtered_df['data_sample'] == data_sample].sort_values('fold')\n",
+    "            \n",
+    "            # 데이터가 있는 경우에만 plot\n",
+    "            if len(sample_data) > 0:\n",
+    "                # 숫자 타입으로 명시적 변환\n",
+    "                x_data = sample_data['fold'].astype(float)\n",
+    "                y_data = pd.to_numeric(sample_data['csi_score'], errors='coerce')\n",
+    "                \n",
+    "                # NaN이 아닌 데이터만 plot\n",
+    "                valid_mask = ~y_data.isna()\n",
+    "                if valid_mask.sum() > 0:\n",
+    "                    ax.plot(x_data[valid_mask], y_data[valid_mask], \n",
+    "                           marker='o', linewidth=2, markersize=6, \n",
+    "                           color=colors[sample_idx], label=data_sample)\n",
+    "        \n",
+    "        # subplot 제목 설정\n",
+    "        ax.set_title(f\"{region} - {model}\", fontsize=11)\n",
+    "        ax.set_xlabel('Fold', fontsize=9)\n",
+    "        ax.set_ylabel('CSI Score', fontsize=9)\n",
+    "        ax.grid(True, alpha=0.3)\n",
+    "        ax.set_xticks([1, 2, 3])\n",
+    "        \n",
+    "        # 첫 번째 subplot에만 범례 표시\n",
+    "        if region_idx == 0 and model_idx == 0:\n",
+    "            ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=8)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "65c17011",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(96, 6)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df= pd.read_csv(\"../../data/oversampled_data_test_for_model/combined_sampled_data_test.csv\")\n",
+    "df.drop(columns=['fold_csi'], inplace=True)\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b596686e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data shape: (96, 6)\n",
+      "\n",
+      "Columns: ['region', 'model', 'data_sample', 'CSI', 'MCC', 'Accuracy']\n",
+      "\n",
+      "Region order: ['busan', 'daegu', 'daejeon', 'gwangju', 'incheon', 'seoul']\n",
+      "\n",
+      "First few rows:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>region</th>\n",
+       "      <th>model</th>\n",
+       "      <th>data_sample</th>\n",
+       "      <th>CSI</th>\n",
+       "      <th>MCC</th>\n",
+       "      <th>Accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>seoul</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.505041</td>\n",
+       "      <td>0.646992</td>\n",
+       "      <td>0.936174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.430188</td>\n",
+       "      <td>0.600801</td>\n",
+       "      <td>0.956971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>incheon</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.554663</td>\n",
+       "      <td>0.687951</td>\n",
+       "      <td>0.911954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.292340</td>\n",
+       "      <td>0.481989</td>\n",
+       "      <td>0.956964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.478437</td>\n",
+       "      <td>0.625244</td>\n",
+       "      <td>0.932748</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    region     model data_sample       CSI       MCC  Accuracy\n",
+       "0    seoul  LightGBM        pure  0.505041  0.646992  0.936174\n",
+       "1    busan  LightGBM        pure  0.430188  0.600801  0.956971\n",
+       "2  incheon  LightGBM        pure  0.554663  0.687951  0.911954\n",
+       "3    daegu  LightGBM        pure  0.292340  0.481989  0.956964\n",
+       "4  daejeon  LightGBM        pure  0.478437  0.625244  0.932748"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# CSI 평균값을 기준으로 region별 시각화를 위한 데이터 준비\n",
+    "# Cell 10에서 생성한 df 사용 (CSI 컬럼이 이미 fold 1,2,3의 평균값)\n",
+    "\n",
+    "# region을 알파벳 순서로 정렬하기 위해 category 타입으로 변환\n",
+    "df['region'] = df['region'].astype('category')\n",
+    "df['region'] = df['region'].cat.reorder_categories(sorted(df['region'].cat.categories))\n",
+    "\n",
+    "# 데이터 확인\n",
+    "print(\"Data shape:\", df.shape)\n",
+    "print(\"\\nColumns:\", df.columns.tolist())\n",
+    "print(\"\\nRegion order:\", df['region'].cat.categories.tolist())\n",
+    "print(\"\\nFirst few rows:\")\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "fca3bcf2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Region별 CSI 평균값 시각화\n",
+    "# x축: region, y축: CSI, hue: data_sample\n",
+    "# XGBoost와 LightGBM을 별도의 subplot으로 분리\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(18, 7))\n",
+    "fig.suptitle('CSI Score by Region (Grouped by Data Sample)', \n",
+    "             fontsize=16, fontweight='bold', y=1.02)\n",
+    "\n",
+    "models = sorted(df['model'].unique())\n",
+    "\n",
+    "for model_idx, model in enumerate(models):\n",
+    "    ax = axes[model_idx]\n",
+    "    \n",
+    "    # 해당 모델의 데이터만 필터링\n",
+    "    model_df = df[df['model'] == model]\n",
+    "    \n",
+    "    # Seaborn lineplot 사용\n",
+    "    sns.lineplot(\n",
+    "        data=model_df,\n",
+    "        x='region',\n",
+    "        y='CSI',\n",
+    "        hue='data_sample',\n",
+    "        marker='o',\n",
+    "        markersize=8,\n",
+    "        linewidth=2.5,\n",
+    "        palette='tab20',  # data_sample별 색상\n",
+    "        ax=ax\n",
+    "    )\n",
+    "    \n",
+    "    ax.set_title(f'{model}', fontsize=14, fontweight='bold', pad=15)\n",
+    "    ax.set_xlabel('Region', fontsize=12)\n",
+    "    ax.set_ylabel('CSI Score', fontsize=12)\n",
+    "    ax.tick_params(axis='x', rotation=45)\n",
+    "    ax.grid(True, alpha=0.3, linestyle='--')\n",
+    "    ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=9, framealpha=0.9)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e3f192c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================================================================================================\n",
+      "지역별, 모델별 data_sample 성능 순위 (CSI 기준 내림차순)\n",
+      "====================================================================================================\n",
+      "\n",
+      "📍 BUSAN\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "\n",
+      "  🔹 LightGBM:\n",
+      "\n",
+      "  순위     Data Sample               CSI        MCC        Accuracy  \n",
+      "  ----------------------------------------------------------------------\n",
+      "  1      ctgan10000                0.467663   0.631435   0.959516  \n",
+      "  2      ctgan7000                 0.466374   0.634410   0.960050  \n",
+      "  3      ctgan20000                0.466346   0.626520   0.957503  \n",
+      "  4      smote                     0.466021   0.631880   0.950198  \n",
+      "  5      smotenc_ctgan10000        0.456299   0.624299   0.957919  \n",
+      "  6      smotenc_ctgan7000         0.448540   0.617030   0.957082  \n",
+      "  7      smotenc_ctgan20000        0.433627   0.608014   0.956400  \n",
+      "  8      pure                      0.430188   0.600801   0.956971  \n",
+      "\n",
+      "  🔹 XGBoost:\n",
+      "\n",
+      "  순위     Data Sample               CSI        MCC        Accuracy  \n",
+      "  ----------------------------------------------------------------------\n",
+      "  1      smotenc_ctgan20000        0.467128   0.630099   0.958033  \n",
+      "  2      smote                     0.466966   0.629257   0.952444  \n",
+      "  3      ctgan7000                 0.464988   0.633134   0.959059  \n",
+      "  4      ctgan20000                0.463713   0.629221   0.958679  \n",
+      "  5      ctgan10000                0.462157   0.623260   0.957997  \n",
+      "  6      pure                      0.460244   0.626085   0.959745  \n",
+      "  7      smotenc_ctgan10000        0.459860   0.625067   0.957994  \n",
+      "  8      smotenc_ctgan7000         0.459674   0.624993   0.957958  \n",
+      "\n",
+      "📍 DAEGU\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "\n",
+      "  🔹 LightGBM:\n",
+      "\n",
+      "  순위     Data Sample               CSI        MCC        Accuracy  \n",
+      "  ----------------------------------------------------------------------\n",
+      "  1      smote                     0.447354   0.616921   0.963730  \n",
+      "  2      ctgan20000                0.440419   0.608625   0.967447  \n",
+      "  3      ctgan7000                 0.426128   0.599566   0.968853  \n",
+      "  4      smotenc_ctgan20000        0.422337   0.591076   0.963494  \n",
+      "  5      smotenc_ctgan10000        0.411417   0.589803   0.967411  \n",
+      "  6      ctgan10000                0.406700   0.578623   0.966763  \n",
+      "  7      smotenc_ctgan7000         0.402243   0.583238   0.966424  \n",
+      "  8      pure                      0.292340   0.481989   0.956964  \n",
+      "\n",
+      "  🔹 XGBoost:\n",
+      "\n",
+      "  순위     Data Sample               CSI        MCC        Accuracy  \n",
+      "  ----------------------------------------------------------------------\n",
+      "  1      smote                     0.454066   0.620948   0.964908  \n",
+      "  2      ctgan7000                 0.437178   0.609142   0.970487  \n",
+      "  3      ctgan20000                0.422417   0.595599   0.966613  \n",
+      "  4      ctgan10000                0.422093   0.598276   0.967489  \n",
+      "  5      smotenc_ctgan10000        0.416966   0.592889   0.966614  \n",
+      "  6      pure                      0.416651   0.600957   0.968324  \n",
+      "  7      smotenc_ctgan7000         0.410981   0.591435   0.965780  \n",
+      "  8      smotenc_ctgan20000        0.408470   0.583915   0.965550  \n",
+      "\n",
+      "📍 DAEJEON\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "\n",
+      "  🔹 LightGBM:\n",
+      "\n",
+      "  순위     Data Sample               CSI        MCC        Accuracy  \n",
+      "  ----------------------------------------------------------------------\n",
+      "  1      smote                     0.521335   0.656321   0.930621  \n",
+      "  2      smotenc_ctgan20000        0.482738   0.627963   0.931189  \n",
+      "  3      ctgan20000                0.480839   0.625832   0.931760  \n",
+      "  4      pure                      0.478437   0.625244   0.932748  \n",
+      "  5      ctgan10000                0.478241   0.626177   0.932976  \n",
+      "  6      smotenc_ctgan7000         0.476309   0.625184   0.932141  \n",
+      "  7      ctgan7000                 0.470145   0.619110   0.931494  \n",
+      "  8      smotenc_ctgan10000        0.468342   0.620789   0.931759  \n",
+      "\n",
+      "  🔹 XGBoost:\n",
+      "\n",
+      "  순위     Data Sample               CSI        MCC        Accuracy  \n",
+      "  ----------------------------------------------------------------------\n",
+      "  1      smote                     0.516494   0.651203   0.931456  \n",
+      "  2      pure                      0.504450   0.647193   0.935752  \n",
+      "  3      ctgan10000                0.499208   0.644695   0.935751  \n",
+      "  4      smotenc_ctgan7000         0.497847   0.641809   0.934080  \n",
+      "  5      ctgan7000                 0.490323   0.635876   0.933508  \n",
+      "  6      smotenc_ctgan20000        0.489587   0.636099   0.933698  \n",
+      "  7      ctgan20000                0.486799   0.632363   0.932787  \n",
+      "  8      smotenc_ctgan10000        0.486735   0.634073   0.933356  \n",
+      "\n",
+      "📍 GWANGJU\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "\n",
+      "  🔹 LightGBM:\n",
+      "\n",
+      "  순위     Data Sample               CSI        MCC        Accuracy  \n",
+      "  ----------------------------------------------------------------------\n",
+      "  1      smote                     0.522731   0.660423   0.936850  \n",
+      "  2      ctgan20000                0.493713   0.637313   0.936783  \n",
+      "  3      ctgan7000                 0.486034   0.634803   0.942667  \n",
+      "  4      smotenc_ctgan10000        0.485951   0.638436   0.942780  \n",
+      "  5      pure                      0.482777   0.636815   0.943236  \n",
+      "  6      ctgan10000                0.481417   0.630243   0.941869  \n",
+      "  7      smotenc_ctgan20000        0.475348   0.618830   0.934692  \n",
+      "  8      smotenc_ctgan7000         0.471552   0.623710   0.940309  \n",
+      "\n",
+      "  🔹 XGBoost:\n",
+      "\n",
+      "  순위     Data Sample               CSI        MCC        Accuracy  \n",
+      "  ----------------------------------------------------------------------\n",
+      "  1      smote                     0.512112   0.652238   0.935221  \n",
+      "  2      ctgan7000                 0.497729   0.642781   0.942971  \n",
+      "  3      ctgan10000                0.494866   0.639831   0.941983  \n",
+      "  4      smotenc_ctgan10000        0.492042   0.640042   0.942248  \n",
+      "  5      pure                      0.490036   0.637981   0.943429  \n",
+      "  6      smotenc_ctgan20000        0.487791   0.635238   0.941375  \n",
+      "  7      smotenc_ctgan7000         0.484990   0.631557   0.940424  \n",
+      "  8      ctgan20000                0.483538   0.630085   0.941072  \n",
+      "\n",
+      "📍 INCHEON\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "\n",
+      "  🔹 LightGBM:\n",
+      "\n",
+      "  순위     Data Sample               CSI        MCC        Accuracy  \n",
+      "  ----------------------------------------------------------------------\n",
+      "  1      smote                     0.583560   0.706137   0.910464  \n",
+      "  2      ctgan20000                0.566681   0.688934   0.907626  \n",
+      "  3      smotenc_ctgan20000        0.564798   0.688999   0.909293  \n",
+      "  4      pure                      0.554663   0.687951   0.911954  \n",
+      "  5      ctgan10000                0.553099   0.687651   0.912108  \n",
+      "  6      smotenc_ctgan7000         0.551851   0.686446   0.911078  \n",
+      "  7      smotenc_ctgan10000        0.547445   0.682151   0.910282  \n",
+      "  8      ctgan7000                 0.540065   0.677611   0.909713  \n",
+      "\n",
+      "  🔹 XGBoost:\n",
+      "\n",
+      "  순위     Data Sample               CSI        MCC        Accuracy  \n",
+      "  ----------------------------------------------------------------------\n",
+      "  1      smote                     0.589146   0.709143   0.912136  \n",
+      "  2      smotenc_ctgan10000        0.563561   0.693149   0.912638  \n",
+      "  3      ctgan7000                 0.561770   0.692611   0.913133  \n",
+      "  4      smotenc_ctgan7000         0.561186   0.692362   0.912675  \n",
+      "  5      ctgan10000                0.558826   0.690054   0.912600  \n",
+      "  6      pure                      0.558144   0.689218   0.913058  \n",
+      "  7      smotenc_ctgan20000        0.557241   0.687968   0.911421  \n",
+      "  8      ctgan20000                0.553268   0.685576   0.911232  \n",
+      "\n",
+      "📍 SEOUL\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "\n",
+      "  🔹 LightGBM:\n",
+      "\n",
+      "  순위     Data Sample               CSI        MCC        Accuracy  \n",
+      "  ----------------------------------------------------------------------\n",
+      "  1      smote                     0.578939   0.708499   0.939995  \n",
+      "  2      ctgan10000                0.548902   0.686531   0.943140  \n",
+      "  3      ctgan7000                 0.548318   0.687390   0.943140  \n",
+      "  4      ctgan20000                0.543010   0.678072   0.940934  \n",
+      "  5      smotenc_ctgan10000        0.539145   0.680238   0.941963  \n",
+      "  6      smotenc_ctgan20000        0.535881   0.678979   0.941392  \n",
+      "  7      smotenc_ctgan7000         0.535260   0.678219   0.941240  \n",
+      "  8      pure                      0.505041   0.646992   0.936174  \n",
+      "\n",
+      "  🔹 XGBoost:\n",
+      "\n",
+      "  순위     Data Sample               CSI        MCC        Accuracy  \n",
+      "  ----------------------------------------------------------------------\n",
+      "  1      smote                     0.582266   0.710502   0.942040  \n",
+      "  2      ctgan10000                0.572645   0.705046   0.945992  \n",
+      "  3      pure                      0.565012   0.700483   0.945572  \n",
+      "  4      ctgan20000                0.561291   0.696774   0.944776  \n",
+      "  5      ctgan7000                 0.560659   0.697024   0.944889  \n",
+      "  6      smotenc_ctgan10000        0.558459   0.696414   0.944813  \n",
+      "  7      smotenc_ctgan20000        0.557444   0.695379   0.944244  \n",
+      "  8      smotenc_ctgan7000         0.556605   0.695629   0.944015  \n",
+      "\n",
+      "====================================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 지역별, 모델별로 data_sample 성능 순위 (CSI 기준 내림차순)\n",
+    "regions = sorted(df['region'].unique())\n",
+    "models = sorted(df['model'].unique())\n",
+    "\n",
+    "# 각 지역-모델 조합별로 data_sample을 CSI 값 기준으로 정렬\n",
+    "results = []\n",
+    "\n",
+    "for region in regions:\n",
+    "    for model in models:\n",
+    "        # 해당 지역과 모델의 데이터 필터링\n",
+    "        region_model_df = df[(df['region'] == region) & (df['model'] == model)]\n",
+    "        \n",
+    "        # CSI 값 기준으로 내림차순 정렬\n",
+    "        sorted_df = region_model_df.sort_values('CSI', ascending=False)\n",
+    "        \n",
+    "        # 순위 추가\n",
+    "        sorted_df = sorted_df.copy()\n",
+    "        sorted_df['rank'] = range(1, len(sorted_df) + 1)\n",
+    "        \n",
+    "        # 결과 저장\n",
+    "        for idx, row in sorted_df.iterrows():\n",
+    "            results.append({\n",
+    "                'region': region,\n",
+    "                'model': model,\n",
+    "                'rank': row['rank'],\n",
+    "                'data_sample': row['data_sample'],\n",
+    "                'CSI': row['CSI'],\n",
+    "                'MCC': row['MCC'],\n",
+    "                'Accuracy': row['Accuracy']\n",
+    "            })\n",
+    "\n",
+    "# 결과를 DataFrame으로 변환\n",
+    "ranking_df = pd.DataFrame(results)\n",
+    "\n",
+    "# 표 형태로 출력\n",
+    "print(\"=\" * 100)\n",
+    "print(\"지역별, 모델별 data_sample 성능 순위 (CSI 기준 내림차순)\")\n",
+    "print(\"=\" * 100)\n",
+    "\n",
+    "for region in regions:\n",
+    "    print(f\"\\n📍 {region.upper()}\")\n",
+    "    print(\"-\" * 100)\n",
+    "    \n",
+    "    for model in models:\n",
+    "        print(f\"\\n  🔹 {model}:\")\n",
+    "        model_data = ranking_df[(ranking_df['region'] == region) & (ranking_df['model'] == model)]\n",
+    "        \n",
+    "        # 표 형식으로 출력\n",
+    "        print(f\"\\n  {'순위':<6} {'Data Sample':<25} {'CSI':<10} {'MCC':<10} {'Accuracy':<10}\")\n",
+    "        print(\"  \" + \"-\" * 70)\n",
+    "        \n",
+    "        for _, row in model_data.iterrows():\n",
+    "            print(f\"  {int(row['rank']):<6} {row['data_sample']:<25} {row['CSI']:<10.6f} {row['MCC']:<10.6f} {row['Accuracy']:<10.6f}\")\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2e5ab8d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>region</th>\n",
+       "      <th>model</th>\n",
+       "      <th>rank</th>\n",
+       "      <th>data_sample</th>\n",
+       "      <th>CSI</th>\n",
+       "      <th>MCC</th>\n",
+       "      <th>Accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>0.467663</td>\n",
+       "      <td>0.631435</td>\n",
+       "      <td>0.959516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>2</td>\n",
+       "      <td>ctgan7000</td>\n",
+       "      <td>0.466374</td>\n",
+       "      <td>0.634410</td>\n",
+       "      <td>0.960050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>ctgan20000</td>\n",
+       "      <td>0.466346</td>\n",
+       "      <td>0.626520</td>\n",
+       "      <td>0.957503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>0.466021</td>\n",
+       "      <td>0.631880</td>\n",
+       "      <td>0.950198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>5</td>\n",
+       "      <td>smotenc_ctgan10000</td>\n",
+       "      <td>0.456299</td>\n",
+       "      <td>0.624299</td>\n",
+       "      <td>0.957919</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>6</td>\n",
+       "      <td>smotenc_ctgan7000</td>\n",
+       "      <td>0.448540</td>\n",
+       "      <td>0.617030</td>\n",
+       "      <td>0.957082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>7</td>\n",
+       "      <td>smotenc_ctgan20000</td>\n",
+       "      <td>0.433627</td>\n",
+       "      <td>0.608014</td>\n",
+       "      <td>0.956400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>8</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.430188</td>\n",
+       "      <td>0.600801</td>\n",
+       "      <td>0.956971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>1</td>\n",
+       "      <td>smotenc_ctgan20000</td>\n",
+       "      <td>0.467128</td>\n",
+       "      <td>0.630099</td>\n",
+       "      <td>0.958033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>2</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>0.466966</td>\n",
+       "      <td>0.629257</td>\n",
+       "      <td>0.952444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>3</td>\n",
+       "      <td>ctgan7000</td>\n",
+       "      <td>0.464988</td>\n",
+       "      <td>0.633134</td>\n",
+       "      <td>0.959059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>4</td>\n",
+       "      <td>ctgan20000</td>\n",
+       "      <td>0.463713</td>\n",
+       "      <td>0.629221</td>\n",
+       "      <td>0.958679</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>5</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>0.462157</td>\n",
+       "      <td>0.623260</td>\n",
+       "      <td>0.957997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>6</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.460244</td>\n",
+       "      <td>0.626085</td>\n",
+       "      <td>0.959745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>7</td>\n",
+       "      <td>smotenc_ctgan10000</td>\n",
+       "      <td>0.459860</td>\n",
+       "      <td>0.625067</td>\n",
+       "      <td>0.957994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>busan</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>8</td>\n",
+       "      <td>smotenc_ctgan7000</td>\n",
+       "      <td>0.459674</td>\n",
+       "      <td>0.624993</td>\n",
+       "      <td>0.957958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>1</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>0.447354</td>\n",
+       "      <td>0.616921</td>\n",
+       "      <td>0.963730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>2</td>\n",
+       "      <td>ctgan20000</td>\n",
+       "      <td>0.440419</td>\n",
+       "      <td>0.608625</td>\n",
+       "      <td>0.967447</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>ctgan7000</td>\n",
+       "      <td>0.426128</td>\n",
+       "      <td>0.599566</td>\n",
+       "      <td>0.968853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>smotenc_ctgan20000</td>\n",
+       "      <td>0.422337</td>\n",
+       "      <td>0.591076</td>\n",
+       "      <td>0.963494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>5</td>\n",
+       "      <td>smotenc_ctgan10000</td>\n",
+       "      <td>0.411417</td>\n",
+       "      <td>0.589803</td>\n",
+       "      <td>0.967411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>6</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>0.406700</td>\n",
+       "      <td>0.578623</td>\n",
+       "      <td>0.966763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>7</td>\n",
+       "      <td>smotenc_ctgan7000</td>\n",
+       "      <td>0.402243</td>\n",
+       "      <td>0.583238</td>\n",
+       "      <td>0.966424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>8</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.292340</td>\n",
+       "      <td>0.481989</td>\n",
+       "      <td>0.956964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>1</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>0.454066</td>\n",
+       "      <td>0.620948</td>\n",
+       "      <td>0.964908</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>2</td>\n",
+       "      <td>ctgan7000</td>\n",
+       "      <td>0.437178</td>\n",
+       "      <td>0.609142</td>\n",
+       "      <td>0.970487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>3</td>\n",
+       "      <td>ctgan20000</td>\n",
+       "      <td>0.422417</td>\n",
+       "      <td>0.595599</td>\n",
+       "      <td>0.966613</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>4</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>0.422093</td>\n",
+       "      <td>0.598276</td>\n",
+       "      <td>0.967489</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>5</td>\n",
+       "      <td>smotenc_ctgan10000</td>\n",
+       "      <td>0.416966</td>\n",
+       "      <td>0.592889</td>\n",
+       "      <td>0.966614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>6</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.416651</td>\n",
+       "      <td>0.600957</td>\n",
+       "      <td>0.968324</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>7</td>\n",
+       "      <td>smotenc_ctgan7000</td>\n",
+       "      <td>0.410981</td>\n",
+       "      <td>0.591435</td>\n",
+       "      <td>0.965780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>daegu</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>8</td>\n",
+       "      <td>smotenc_ctgan20000</td>\n",
+       "      <td>0.408470</td>\n",
+       "      <td>0.583915</td>\n",
+       "      <td>0.965550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>1</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>0.521335</td>\n",
+       "      <td>0.656321</td>\n",
+       "      <td>0.930621</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>2</td>\n",
+       "      <td>smotenc_ctgan20000</td>\n",
+       "      <td>0.482738</td>\n",
+       "      <td>0.627963</td>\n",
+       "      <td>0.931189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>3</td>\n",
+       "      <td>ctgan20000</td>\n",
+       "      <td>0.480839</td>\n",
+       "      <td>0.625832</td>\n",
+       "      <td>0.931760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>4</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.478437</td>\n",
+       "      <td>0.625244</td>\n",
+       "      <td>0.932748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>5</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>0.478241</td>\n",
+       "      <td>0.626177</td>\n",
+       "      <td>0.932976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>6</td>\n",
+       "      <td>smotenc_ctgan7000</td>\n",
+       "      <td>0.476309</td>\n",
+       "      <td>0.625184</td>\n",
+       "      <td>0.932141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>7</td>\n",
+       "      <td>ctgan7000</td>\n",
+       "      <td>0.470145</td>\n",
+       "      <td>0.619110</td>\n",
+       "      <td>0.931494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>8</td>\n",
+       "      <td>smotenc_ctgan10000</td>\n",
+       "      <td>0.468342</td>\n",
+       "      <td>0.620789</td>\n",
+       "      <td>0.931759</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>1</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>0.516494</td>\n",
+       "      <td>0.651203</td>\n",
+       "      <td>0.931456</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>2</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>0.504450</td>\n",
+       "      <td>0.647193</td>\n",
+       "      <td>0.935752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>3</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>0.499208</td>\n",
+       "      <td>0.644695</td>\n",
+       "      <td>0.935751</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>4</td>\n",
+       "      <td>smotenc_ctgan7000</td>\n",
+       "      <td>0.497847</td>\n",
+       "      <td>0.641809</td>\n",
+       "      <td>0.934080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>5</td>\n",
+       "      <td>ctgan7000</td>\n",
+       "      <td>0.490323</td>\n",
+       "      <td>0.635876</td>\n",
+       "      <td>0.933508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>6</td>\n",
+       "      <td>smotenc_ctgan20000</td>\n",
+       "      <td>0.489587</td>\n",
+       "      <td>0.636099</td>\n",
+       "      <td>0.933698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>7</td>\n",
+       "      <td>ctgan20000</td>\n",
+       "      <td>0.486799</td>\n",
+       "      <td>0.632363</td>\n",
+       "      <td>0.932787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>8</td>\n",
+       "      <td>smotenc_ctgan10000</td>\n",
+       "      <td>0.486735</td>\n",
+       "      <td>0.634073</td>\n",
+       "      <td>0.933356</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>1</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>0.522731</td>\n",
+       "      <td>0.660423</td>\n",
+       "      <td>0.936850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>2</td>\n",
+       "      <td>ctgan20000</td>\n",
+       "      <td>0.493713</td>\n",
+       "      <td>0.637313</td>\n",
+       "      <td>0.936783</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     region     model  rank         data_sample       CSI       MCC  Accuracy\n",
+       "0     busan  LightGBM     1          ctgan10000  0.467663  0.631435  0.959516\n",
+       "1     busan  LightGBM     2           ctgan7000  0.466374  0.634410  0.960050\n",
+       "2     busan  LightGBM     3          ctgan20000  0.466346  0.626520  0.957503\n",
+       "3     busan  LightGBM     4               smote  0.466021  0.631880  0.950198\n",
+       "4     busan  LightGBM     5  smotenc_ctgan10000  0.456299  0.624299  0.957919\n",
+       "5     busan  LightGBM     6   smotenc_ctgan7000  0.448540  0.617030  0.957082\n",
+       "6     busan  LightGBM     7  smotenc_ctgan20000  0.433627  0.608014  0.956400\n",
+       "7     busan  LightGBM     8                pure  0.430188  0.600801  0.956971\n",
+       "8     busan   XGBoost     1  smotenc_ctgan20000  0.467128  0.630099  0.958033\n",
+       "9     busan   XGBoost     2               smote  0.466966  0.629257  0.952444\n",
+       "10    busan   XGBoost     3           ctgan7000  0.464988  0.633134  0.959059\n",
+       "11    busan   XGBoost     4          ctgan20000  0.463713  0.629221  0.958679\n",
+       "12    busan   XGBoost     5          ctgan10000  0.462157  0.623260  0.957997\n",
+       "13    busan   XGBoost     6                pure  0.460244  0.626085  0.959745\n",
+       "14    busan   XGBoost     7  smotenc_ctgan10000  0.459860  0.625067  0.957994\n",
+       "15    busan   XGBoost     8   smotenc_ctgan7000  0.459674  0.624993  0.957958\n",
+       "16    daegu  LightGBM     1               smote  0.447354  0.616921  0.963730\n",
+       "17    daegu  LightGBM     2          ctgan20000  0.440419  0.608625  0.967447\n",
+       "18    daegu  LightGBM     3           ctgan7000  0.426128  0.599566  0.968853\n",
+       "19    daegu  LightGBM     4  smotenc_ctgan20000  0.422337  0.591076  0.963494\n",
+       "20    daegu  LightGBM     5  smotenc_ctgan10000  0.411417  0.589803  0.967411\n",
+       "21    daegu  LightGBM     6          ctgan10000  0.406700  0.578623  0.966763\n",
+       "22    daegu  LightGBM     7   smotenc_ctgan7000  0.402243  0.583238  0.966424\n",
+       "23    daegu  LightGBM     8                pure  0.292340  0.481989  0.956964\n",
+       "24    daegu   XGBoost     1               smote  0.454066  0.620948  0.964908\n",
+       "25    daegu   XGBoost     2           ctgan7000  0.437178  0.609142  0.970487\n",
+       "26    daegu   XGBoost     3          ctgan20000  0.422417  0.595599  0.966613\n",
+       "27    daegu   XGBoost     4          ctgan10000  0.422093  0.598276  0.967489\n",
+       "28    daegu   XGBoost     5  smotenc_ctgan10000  0.416966  0.592889  0.966614\n",
+       "29    daegu   XGBoost     6                pure  0.416651  0.600957  0.968324\n",
+       "30    daegu   XGBoost     7   smotenc_ctgan7000  0.410981  0.591435  0.965780\n",
+       "31    daegu   XGBoost     8  smotenc_ctgan20000  0.408470  0.583915  0.965550\n",
+       "32  daejeon  LightGBM     1               smote  0.521335  0.656321  0.930621\n",
+       "33  daejeon  LightGBM     2  smotenc_ctgan20000  0.482738  0.627963  0.931189\n",
+       "34  daejeon  LightGBM     3          ctgan20000  0.480839  0.625832  0.931760\n",
+       "35  daejeon  LightGBM     4                pure  0.478437  0.625244  0.932748\n",
+       "36  daejeon  LightGBM     5          ctgan10000  0.478241  0.626177  0.932976\n",
+       "37  daejeon  LightGBM     6   smotenc_ctgan7000  0.476309  0.625184  0.932141\n",
+       "38  daejeon  LightGBM     7           ctgan7000  0.470145  0.619110  0.931494\n",
+       "39  daejeon  LightGBM     8  smotenc_ctgan10000  0.468342  0.620789  0.931759\n",
+       "40  daejeon   XGBoost     1               smote  0.516494  0.651203  0.931456\n",
+       "41  daejeon   XGBoost     2                pure  0.504450  0.647193  0.935752\n",
+       "42  daejeon   XGBoost     3          ctgan10000  0.499208  0.644695  0.935751\n",
+       "43  daejeon   XGBoost     4   smotenc_ctgan7000  0.497847  0.641809  0.934080\n",
+       "44  daejeon   XGBoost     5           ctgan7000  0.490323  0.635876  0.933508\n",
+       "45  daejeon   XGBoost     6  smotenc_ctgan20000  0.489587  0.636099  0.933698\n",
+       "46  daejeon   XGBoost     7          ctgan20000  0.486799  0.632363  0.932787\n",
+       "47  daejeon   XGBoost     8  smotenc_ctgan10000  0.486735  0.634073  0.933356\n",
+       "48  gwangju  LightGBM     1               smote  0.522731  0.660423  0.936850\n",
+       "49  gwangju  LightGBM     2          ctgan20000  0.493713  0.637313  0.936783"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ranking_df.head(50)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Analysis_code/4.sampling_data_test/lgb_sampled_test.ipynb b/Analysis_code/4.oversampling_data_test/lgb_sampled_test.ipynb
similarity index 100%
rename from Analysis_code/4.sampling_data_test/lgb_sampled_test.ipynb
rename to Analysis_code/4.oversampling_data_test/lgb_sampled_test.ipynb
diff --git a/Analysis_code/4.sampling_data_test/xgb_sampled_test.ipynb b/Analysis_code/4.oversampling_data_test/xgb_sampled_test.ipynb
similarity index 100%
rename from Analysis_code/4.sampling_data_test/xgb_sampled_test.ipynb
rename to Analysis_code/4.oversampling_data_test/xgb_sampled_test.ipynb
diff --git a/Analysis_code/4.sampling_data_test/analysis.ipynb b/Analysis_code/4.sampling_data_test/analysis.ipynb
deleted file mode 100644
index 06269c0eff9ee514286b56d2e9e2024a7a355806..0000000000000000000000000000000000000000
--- a/Analysis_code/4.sampling_data_test/analysis.ipynb
+++ /dev/null
@@ -1,244 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "70effd7a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f38ce7d1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df= pd.read_csv(\"../../data/oversampled_data_test_for_model/combined_sampled_data_test.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "2bae91e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "      <th>fold_csi</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.505041</td>\n",
-       "      <td>0.646992</td>\n",
-       "      <td>0.936174</td>\n",
-       "      <td>[[0.46595932802825235, 0.5771195097037204, 0.4...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.430188</td>\n",
-       "      <td>0.600801</td>\n",
-       "      <td>0.956971</td>\n",
-       "      <td>[[0.32824427480911017, 0.4782608695651431, 0.4...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  region     model data_sample       CSI       MCC  Accuracy  \\\n",
-       "0  seoul  LightGBM        pure  0.505041  0.646992  0.936174   \n",
-       "1  busan  LightGBM        pure  0.430188  0.600801  0.956971   \n",
-       "\n",
-       "                                            fold_csi  \n",
-       "0  [[0.46595932802825235, 0.5771195097037204, 0.4...  \n",
-       "1  [[0.32824427480911017, 0.4782608695651431, 0.4...  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "6893a958",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.467663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.454066</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.521335</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.522731</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.589146</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.582266</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    region     model data_sample       CSI\n",
-       "0    busan  LightGBM  ctgan10000  0.467663\n",
-       "1    daegu   XGBoost       smote  0.454066\n",
-       "2  daejeon  LightGBM       smote  0.521335\n",
-       "3  gwangju  LightGBM       smote  0.522731\n",
-       "4  incheon   XGBoost       smote  0.589146\n",
-       "5    seoul   XGBoost       smote  0.582266"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 지역별로 CSI가 가장 높은 model과 data_sample 조합 보기\n",
-    "top_csi_per_region = df.loc[df.groupby('region')['CSI'].idxmax()][['region', 'model', 'data_sample', 'CSI']]\n",
-    "top_csi_per_region = top_csi_per_region.sort_values('region').reset_index(drop=True)\n",
-    "top_csi_per_region"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2942ba86",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d55af59c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_daegu.py b/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_daegu.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d5a39d710cea7d01ca8ccc2e2e31d3c98c4eae1
--- /dev/null
+++ b/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_daegu.py
@@ -0,0 +1,97 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="deepgbm", region="daegu", data_sample='ctgan10000'), 
+    n_trials=100
+,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/deepgbm_ctgan10000_daegu_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="deepgbm",
+        region="daegu",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_daejeon.py b/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_daejeon.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8efef41d596aaaf953f543e366e57b528b92275
--- /dev/null
+++ b/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_daejeon.py
@@ -0,0 +1,97 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="deepgbm", region="daejeon", data_sample='ctgan10000'), 
+    n_trials=100
+,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/deepgbm_ctgan10000_daejeon_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="deepgbm",
+        region="daejeon",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_gwangju.py b/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_gwangju.py
new file mode 100644
index 0000000000000000000000000000000000000000..c540a056a07f7393ca64a8b9e089ea4ae9549963
--- /dev/null
+++ b/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_gwangju.py
@@ -0,0 +1,97 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="deepgbm", region="gwangju", data_sample='ctgan10000'), 
+    n_trials=100
+,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/deepgbm_ctgan10000_gwangju_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="deepgbm",
+        region="gwangju",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_incheon.py b/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_incheon.py
new file mode 100644
index 0000000000000000000000000000000000000000..a922d8672e246c19407c698ac1e176ed04faa68e
--- /dev/null
+++ b/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_incheon.py
@@ -0,0 +1,97 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="deepgbm", region="incheon", data_sample='ctgan10000'), 
+    n_trials=100
+,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/deepgbm_ctgan10000_incheon_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="deepgbm",
+        region="incheon",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_seoul.py b/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_seoul.py
new file mode 100644
index 0000000000000000000000000000000000000000..857377bc5eee04cba68aa40fa45771dd5c3233ac
--- /dev/null
+++ b/Analysis_code/5.optima/deepgbm_ctgan10000/deepgbm_ctgan10000_seoul.py
@@ -0,0 +1,97 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="deepgbm", region="seoul", data_sample='ctgan10000'), 
+    n_trials=100
+,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/deepgbm_ctgan10000_seoul_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="deepgbm",
+        region="seoul",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_daegu.py b/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_daegu.py
new file mode 100644
index 0000000000000000000000000000000000000000..e771cc7f1f435b4832f1284f467ebb8f9fc031a4
--- /dev/null
+++ b/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_daegu.py
@@ -0,0 +1,96 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="ft_transformer", region="daegu", data_sample='ctgan10000'), 
+    n_trials=100,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/ft_transformer_ctgan10000_daegu_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="ft_transformer",
+        region="daegu",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_daejeon.py b/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_daejeon.py
new file mode 100644
index 0000000000000000000000000000000000000000..e589cd02bb6ace4d63d8998ffc248387f6db4498
--- /dev/null
+++ b/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_daejeon.py
@@ -0,0 +1,96 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="ft_transformer", region="daejeon", data_sample='ctgan10000'), 
+    n_trials=100,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/ft_transformer_ctgan10000_daejeon_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="ft_transformer",
+        region="daejeon",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_gwangju.py b/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_gwangju.py
new file mode 100644
index 0000000000000000000000000000000000000000..c286b90507bea60f87d6ea4bc884af5ac0b14c82
--- /dev/null
+++ b/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_gwangju.py
@@ -0,0 +1,96 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="ft_transformer", region="gwangju", data_sample='ctgan10000'), 
+    n_trials=100,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/ft_transformer_ctgan10000_gwangju_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="ft_transformer",
+        region="gwangju",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_incheon.py b/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_incheon.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee055b57e8f9a56011aa47bafc16888a44441204
--- /dev/null
+++ b/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_incheon.py
@@ -0,0 +1,96 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="ft_transformer", region="incheon", data_sample='ctgan10000'), 
+    n_trials=100,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/ft_transformer_ctgan10000_incheon_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="ft_transformer",
+        region="incheon",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_seoul.py b/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_seoul.py
new file mode 100644
index 0000000000000000000000000000000000000000..85c3d9ae25fadb185b5d1d82e8a6f3b6e145ad03
--- /dev/null
+++ b/Analysis_code/5.optima/ft_transformer_ctgan10000/ft_transformer_ctgan10000_seoul.py
@@ -0,0 +1,96 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="ft_transformer", region="seoul", data_sample='ctgan10000'), 
+    n_trials=100,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/ft_transformer_ctgan10000_seoul_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="ft_transformer",
+        region="seoul",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_busan.py b/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_busan.py
index 47acd0a69908453923c69724197875a9f8ddd16c..af4a616acfe854c9456096523f2e2cefbfe6b025 100644
--- a/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_busan.py
+++ b/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_busan.py
@@ -281,6 +281,7 @@ print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
 import os
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/lgb_ctgan10000_busan_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_daegu.py b/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_daegu.py
new file mode 100644
index 0000000000000000000000000000000000000000..185e5f534a94e794a98c52c851c2746372c48dca
--- /dev/null
+++ b/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_daegu.py
@@ -0,0 +1,317 @@
+import pandas as pd
+import numpy as np
+import joblib
+import os
+from warnings import filterwarnings
+from sklearn.metrics import confusion_matrix
+from lightgbm import LGBMClassifier
+from hyperopt import fmin, tpe, Trials, hp
+
+filterwarnings('ignore')
+
+# 상수 정의
+RANDOM_STATE = 42
+N_ESTIMATORS = 4000
+EARLY_STOPPING_ROUNDS = 400
+MAX_EVALS = 100
+DEVICE = 'gpu'
+OBJECTIVE = 'multiclassova'
+
+# Fold 설정: (train_years, val_year)
+FOLD_CONFIGS = [
+    ([2018, 2019], 2020),  # Fold 1
+    ([2018, 2020], 2019),  # Fold 2
+    ([2019, 2020], 2018),  # Fold 3
+]
+
+def calculate_csi(y_true, y_pred):
+    """CSI(Critical Success Index) 점수를 계산합니다.
+    
+    Args:
+        y_true: 실제 레이블
+        y_pred: 예측 레이블
+        
+    Returns:
+        CSI 점수 (0~1 사이의 값)
+    """
+    cm = confusion_matrix(y_true, y_pred)
+    
+    # 혼동 행렬에서 H(Hit), F(False alarm), M(Miss) 추출
+    H = cm[0, 0] + cm[1, 1]
+    F = cm[1, 0] + cm[2, 0] + cm[0, 1] + cm[2, 1]
+    M = cm[0, 2] + cm[1, 2]
+    
+    # CSI 계산
+    csi = H / (H + F + M + 1e-10)
+    return csi
+
+
+def csi_metric(y_true, pred_prob):
+    """LightGBM용 CSI 메트릭 함수.
+    
+    Args:
+        y_true: 실제 레이블
+        pred_prob: 예측 확률 (shape: [n_samples, n_classes])
+        
+    Returns:
+        ('CSI', score, higher_better) 튜플
+    """
+    y_pred_binary = np.argmax(pred_prob, axis=1)
+    score = calculate_csi(y_true, y_pred_binary)
+    return 'CSI', score, True
+
+def add_derived_features(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    제거했던 파생 변수들을 복구
+    
+    Args:
+        df: 데이터프레임
+    
+    Returns:
+        파생 변수가 추가된 데이터프레임
+    """
+    df = df.copy()
+    df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
+    df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
+    df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
+    df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)
+    df['ground_temp - temp_C'] = df['groundtemp'] - df['temp_C']
+    return df
+
+def preprocessing(df):
+    """데이터 전처리 함수.
+    
+    Args:
+        df: 원본 데이터프레임
+        
+    Returns:
+        전처리된 데이터프레임
+    """
+    df = df[df.columns].copy()
+    df['year'] = df['year'].astype('int')
+    df['month'] = df['month'].astype('int')
+    df['hour'] = df['hour'].astype('int')
+    df = add_derived_features(df).copy()
+    df['multi_class'] = df['multi_class'].astype('int')
+    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
+    df['wind_dir'] = df['wind_dir'].astype('int')
+    df = df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
+       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
+       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
+       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
+       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
+       'month_sin', 'month_cos','multi_class']].copy()
+    return df
+
+
+def split_data(df_sampled, df_original, train_years, val_year):
+    """데이터를 학습용과 검증용으로 분할합니다.
+    
+    Args:
+        df_sampled: 샘플링된 데이터프레임
+        df_original: 원본 데이터프레임
+        train_years: 학습에 사용할 연도 리스트
+        val_year: 검증에 사용할 연도
+        
+    Returns:
+        (X_train, X_val, y_train, y_val) 튜플
+    """
+    # 학습 데이터: 샘플링된 데이터에서 train_years에 해당하는 데이터
+    train_mask = df_sampled['year'].isin(train_years)
+    X_train = df_sampled.loc[train_mask, df_sampled.columns != 'multi_class'].copy()
+    y_train = df_sampled.loc[train_mask, 'multi_class']
+    
+    # 검증 데이터: 원본 데이터에서 val_year에 해당하는 데이터
+    val_mask = df_original['year'] == val_year
+    X_val = df_original.loc[val_mask, df_original.columns != 'multi_class'].copy()
+    y_val = df_original.loc[val_mask, 'multi_class']
+    
+    # 'year' 컬럼 제거
+    X_train = X_train.drop(columns=['year'])
+    X_val = X_val.drop(columns=['year'])
+    
+    return X_train, X_val, y_train, y_val
+
+
+def create_lgb_model(search_space=None, best_params=None):
+    """LightGBM 모델을 생성합니다.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간 (objective_func에서 사용)
+        best_params: 최적화된 하이퍼파라미터 (최종 모델 학습에서 사용)
+        
+    Returns:
+        LGBMClassifier 인스턴스
+    """
+    base_params = {
+        'n_estimators': N_ESTIMATORS,
+        'device': DEVICE,
+        'objective': OBJECTIVE,
+        'random_state': RANDOM_STATE,
+        'early_stopping_rounds': EARLY_STOPPING_ROUNDS,
+        'verbose': -1,
+    }
+    
+    if search_space is not None:
+        # 하이퍼파라미터 최적화 중
+        params = {
+            **base_params,
+            'learning_rate': search_space['learning_rate'],
+            'max_depth': int(search_space['max_depth']),
+            'num_leaves': int(search_space['num_leaves']),
+            'min_child_weight': int(search_space['min_child_weight']),
+            'subsample': search_space['subsample'],
+            'colsample_bytree': search_space['colsample_bytree'],
+            'reg_alpha': search_space['reg_alpha'],
+            'reg_lambda': search_space['reg_lambda'],
+        }
+    elif best_params is not None:
+        # 최적화된 파라미터로 최종 모델 생성
+        params = {
+            **base_params,
+            'learning_rate': best_params['learning_rate'],
+            'max_depth': int(best_params['max_depth']),
+            'num_leaves': int(best_params['num_leaves']),
+            'min_child_weight': int(best_params['min_child_weight']),
+            'subsample': best_params['subsample'],
+            'colsample_bytree': best_params['colsample_bytree'],
+            'reg_alpha': best_params['reg_alpha'],
+            'reg_lambda': best_params['reg_lambda'],
+        }
+    else:
+        params = base_params
+    
+    return LGBMClassifier(**params)
+
+# 데이터 로딩
+print("데이터 로딩 중...")
+# 파일 위치 기반으로 데이터 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+data_base_dir = os.path.abspath(os.path.join(current_file_dir, '../../../data'))
+df_daegu = pd.read_csv(os.path.join(data_base_dir, "data_for_modeling/daegu_train.csv"))
+df_ctgan_daegu_1 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_1_daegu.csv"))
+df_ctgan_daegu_2 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_2_daegu.csv"))
+df_ctgan_daegu_3 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_3_daegu.csv"))
+
+# 데이터 전처리
+print("데이터 전처리 중...")
+df_ctgan_daegu_1 = preprocessing(df_ctgan_daegu_1)
+df_ctgan_daegu_2 = preprocessing(df_ctgan_daegu_2)
+df_ctgan_daegu_3 = preprocessing(df_ctgan_daegu_3)
+df_daegu = preprocessing(df_daegu)
+
+# CTGAN 데이터 리스트 (fold 순서와 일치)
+df_ctgan_list = [df_ctgan_daegu_1, df_ctgan_daegu_2, df_ctgan_daegu_3]
+
+# 하이퍼파라미터 검색 공간 정의
+lgb_search_space = {
+    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.2)),
+    'max_depth': hp.quniform('max_depth', 3, 15, 1),
+    'num_leaves': hp.quniform('num_leaves', 20, 150, 1),  # 2^max_depth 보다는 작게
+    'min_child_weight': hp.quniform('min_child_weight', 1, 20, 1),
+    'subsample': hp.uniform('subsample', 0.6, 1.0),
+    'colsample_bytree': hp.uniform('colsample_bytree', 0.6, 1.0),
+    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),
+    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0)
+}
+
+
+def objective_func(search_space):
+    """하이퍼파라미터 최적화를 위한 목적 함수.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간
+        
+    Returns:
+        평균 CSI 점수의 음수값 (hyperopt는 최소화를 수행하므로)
+    """
+    lgb_model = create_lgb_model(search_space=search_space)
+    csi_scores = []
+
+    # 각 fold에 대해 교차 검증 수행
+    for df_ctgan, (train_years, val_year) in zip(df_ctgan_list, FOLD_CONFIGS):
+        X_train, X_val, y_train, y_val = split_data(
+            df_ctgan, df_daegu, train_years, val_year
+        )
+        
+        lgb_model.fit(
+            X_train, y_train,
+            eval_set=[(X_val, y_val)],
+            eval_metric=csi_metric
+        )
+        
+        csi = calculate_csi(y_val, lgb_model.predict(X_val))
+        csi_scores.append(csi)
+
+    # 평균 CSI의 음수값 반환 (hyperopt는 최소화를 수행)
+    return -1 * round(np.mean(csi_scores), 4)
+
+
+# 하이퍼파라미터 최적화
+print("하이퍼파라미터 최적화 시작...")
+trials = Trials()
+lgb_best = fmin(
+    fn=objective_func,
+    space=lgb_search_space,
+    algo=tpe.suggest,
+    max_evals=MAX_EVALS,
+    trials=trials
+)
+
+# 최적화 결과 분석 및 출력
+print(f"\n최적화 완료. 최적 파라미터: {lgb_best}")
+
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+import os
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_ctgan10000_daegu_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
+# 최적화된 하이퍼파라미터로 최종 모델 학습
+print("최종 모델 학습 시작...")
+models = []
+
+for fold_idx, (df_ctgan, (train_years, val_year)) in enumerate(
+    zip(df_ctgan_list, FOLD_CONFIGS), start=1
+):
+    print(f"Fold {fold_idx} 학습 중... (학습 연도: {train_years}, 검증 연도: {val_year})")
+    
+    X_train, X_val, y_train, y_val = split_data(
+        df_ctgan, df_daegu, train_years, val_year
+    )
+    
+    lgb_model = create_lgb_model(best_params=lgb_best)
+    lgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)])
+    
+    # 검증 성능 출력
+    val_csi = calculate_csi(y_val, lgb_model.predict(X_val))
+    print(f"Fold {fold_idx} 검증 CSI: {val_csi:.4f}")
+    
+    models.append(lgb_model)
+
+# 모델 저장
+print("모델 저장 중...")
+model_save_path = os.path.join(base_dir, "save_model/lgb_optima/lgb_ctgan10000_daegu.pkl")
+joblib.dump(models, model_save_path)
+print(f"모델이 {model_save_path}에 저장되었습니다.")
+
diff --git a/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_daejeon.py b/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_daejeon.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0cbd62d4751c94d7d1268109c55fd63381ac0c5
--- /dev/null
+++ b/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_daejeon.py
@@ -0,0 +1,317 @@
+import pandas as pd
+import numpy as np
+import joblib
+import os
+from warnings import filterwarnings
+from sklearn.metrics import confusion_matrix
+from lightgbm import LGBMClassifier
+from hyperopt import fmin, tpe, Trials, hp
+
+filterwarnings('ignore')
+
+# 상수 정의
+RANDOM_STATE = 42
+N_ESTIMATORS = 4000
+EARLY_STOPPING_ROUNDS = 400
+MAX_EVALS = 100
+DEVICE = 'gpu'
+OBJECTIVE = 'multiclassova'
+
+# Fold 설정: (train_years, val_year)
+FOLD_CONFIGS = [
+    ([2018, 2019], 2020),  # Fold 1
+    ([2018, 2020], 2019),  # Fold 2
+    ([2019, 2020], 2018),  # Fold 3
+]
+
+def calculate_csi(y_true, y_pred):
+    """CSI(Critical Success Index) 점수를 계산합니다.
+    
+    Args:
+        y_true: 실제 레이블
+        y_pred: 예측 레이블
+        
+    Returns:
+        CSI 점수 (0~1 사이의 값)
+    """
+    cm = confusion_matrix(y_true, y_pred)
+    
+    # 혼동 행렬에서 H(Hit), F(False alarm), M(Miss) 추출
+    H = cm[0, 0] + cm[1, 1]
+    F = cm[1, 0] + cm[2, 0] + cm[0, 1] + cm[2, 1]
+    M = cm[0, 2] + cm[1, 2]
+    
+    # CSI 계산
+    csi = H / (H + F + M + 1e-10)
+    return csi
+
+
+def csi_metric(y_true, pred_prob):
+    """LightGBM용 CSI 메트릭 함수.
+    
+    Args:
+        y_true: 실제 레이블
+        pred_prob: 예측 확률 (shape: [n_samples, n_classes])
+        
+    Returns:
+        ('CSI', score, higher_better) 튜플
+    """
+    y_pred_binary = np.argmax(pred_prob, axis=1)
+    score = calculate_csi(y_true, y_pred_binary)
+    return 'CSI', score, True
+
+def add_derived_features(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    제거했던 파생 변수들을 복구
+    
+    Args:
+        df: 데이터프레임
+    
+    Returns:
+        파생 변수가 추가된 데이터프레임
+    """
+    df = df.copy()
+    df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
+    df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
+    df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
+    df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)
+    df['ground_temp - temp_C'] = df['groundtemp'] - df['temp_C']
+    return df
+
+def preprocessing(df):
+    """데이터 전처리 함수.
+    
+    Args:
+        df: 원본 데이터프레임
+        
+    Returns:
+        전처리된 데이터프레임
+    """
+    df = df[df.columns].copy()
+    df['year'] = df['year'].astype('int')
+    df['month'] = df['month'].astype('int')
+    df['hour'] = df['hour'].astype('int')
+    df = add_derived_features(df).copy()
+    df['multi_class'] = df['multi_class'].astype('int')
+    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
+    df['wind_dir'] = df['wind_dir'].astype('int')
+    df = df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
+       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
+       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
+       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
+       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
+       'month_sin', 'month_cos','multi_class']].copy()
+    return df
+
+
+def split_data(df_sampled, df_original, train_years, val_year):
+    """데이터를 학습용과 검증용으로 분할합니다.
+    
+    Args:
+        df_sampled: 샘플링된 데이터프레임
+        df_original: 원본 데이터프레임
+        train_years: 학습에 사용할 연도 리스트
+        val_year: 검증에 사용할 연도
+        
+    Returns:
+        (X_train, X_val, y_train, y_val) 튜플
+    """
+    # 학습 데이터: 샘플링된 데이터에서 train_years에 해당하는 데이터
+    train_mask = df_sampled['year'].isin(train_years)
+    X_train = df_sampled.loc[train_mask, df_sampled.columns != 'multi_class'].copy()
+    y_train = df_sampled.loc[train_mask, 'multi_class']
+    
+    # 검증 데이터: 원본 데이터에서 val_year에 해당하는 데이터
+    val_mask = df_original['year'] == val_year
+    X_val = df_original.loc[val_mask, df_original.columns != 'multi_class'].copy()
+    y_val = df_original.loc[val_mask, 'multi_class']
+    
+    # 'year' 컬럼 제거
+    X_train = X_train.drop(columns=['year'])
+    X_val = X_val.drop(columns=['year'])
+    
+    return X_train, X_val, y_train, y_val
+
+
+def create_lgb_model(search_space=None, best_params=None):
+    """LightGBM 모델을 생성합니다.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간 (objective_func에서 사용)
+        best_params: 최적화된 하이퍼파라미터 (최종 모델 학습에서 사용)
+        
+    Returns:
+        LGBMClassifier 인스턴스
+    """
+    base_params = {
+        'n_estimators': N_ESTIMATORS,
+        'device': DEVICE,
+        'objective': OBJECTIVE,
+        'random_state': RANDOM_STATE,
+        'early_stopping_rounds': EARLY_STOPPING_ROUNDS,
+        'verbose': -1,
+    }
+    
+    if search_space is not None:
+        # 하이퍼파라미터 최적화 중
+        params = {
+            **base_params,
+            'learning_rate': search_space['learning_rate'],
+            'max_depth': int(search_space['max_depth']),
+            'num_leaves': int(search_space['num_leaves']),
+            'min_child_weight': int(search_space['min_child_weight']),
+            'subsample': search_space['subsample'],
+            'colsample_bytree': search_space['colsample_bytree'],
+            'reg_alpha': search_space['reg_alpha'],
+            'reg_lambda': search_space['reg_lambda'],
+        }
+    elif best_params is not None:
+        # 최적화된 파라미터로 최종 모델 생성
+        params = {
+            **base_params,
+            'learning_rate': best_params['learning_rate'],
+            'max_depth': int(best_params['max_depth']),
+            'num_leaves': int(best_params['num_leaves']),
+            'min_child_weight': int(best_params['min_child_weight']),
+            'subsample': best_params['subsample'],
+            'colsample_bytree': best_params['colsample_bytree'],
+            'reg_alpha': best_params['reg_alpha'],
+            'reg_lambda': best_params['reg_lambda'],
+        }
+    else:
+        params = base_params
+    
+    return LGBMClassifier(**params)
+
+# 데이터 로딩
+print("데이터 로딩 중...")
+# 파일 위치 기반으로 데이터 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+data_base_dir = os.path.abspath(os.path.join(current_file_dir, '../../../data'))
+df_daejeon = pd.read_csv(os.path.join(data_base_dir, "data_for_modeling/daejeon_train.csv"))
+df_ctgan_daejeon_1 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_1_daejeon.csv"))
+df_ctgan_daejeon_2 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_2_daejeon.csv"))
+df_ctgan_daejeon_3 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_3_daejeon.csv"))
+
+# 데이터 전처리
+print("데이터 전처리 중...")
+df_ctgan_daejeon_1 = preprocessing(df_ctgan_daejeon_1)
+df_ctgan_daejeon_2 = preprocessing(df_ctgan_daejeon_2)
+df_ctgan_daejeon_3 = preprocessing(df_ctgan_daejeon_3)
+df_daejeon = preprocessing(df_daejeon)
+
+# CTGAN 데이터 리스트 (fold 순서와 일치)
+df_ctgan_list = [df_ctgan_daejeon_1, df_ctgan_daejeon_2, df_ctgan_daejeon_3]
+
+# 하이퍼파라미터 검색 공간 정의
+lgb_search_space = {
+    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.2)),
+    'max_depth': hp.quniform('max_depth', 3, 15, 1),
+    'num_leaves': hp.quniform('num_leaves', 20, 150, 1),  # 2^max_depth 보다는 작게
+    'min_child_weight': hp.quniform('min_child_weight', 1, 20, 1),
+    'subsample': hp.uniform('subsample', 0.6, 1.0),
+    'colsample_bytree': hp.uniform('colsample_bytree', 0.6, 1.0),
+    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),
+    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0)
+}
+
+
+def objective_func(search_space):
+    """하이퍼파라미터 최적화를 위한 목적 함수.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간
+        
+    Returns:
+        평균 CSI 점수의 음수값 (hyperopt는 최소화를 수행하므로)
+    """
+    lgb_model = create_lgb_model(search_space=search_space)
+    csi_scores = []
+
+    # 각 fold에 대해 교차 검증 수행
+    for df_ctgan, (train_years, val_year) in zip(df_ctgan_list, FOLD_CONFIGS):
+        X_train, X_val, y_train, y_val = split_data(
+            df_ctgan, df_daejeon, train_years, val_year
+        )
+        
+        lgb_model.fit(
+            X_train, y_train,
+            eval_set=[(X_val, y_val)],
+            eval_metric=csi_metric
+        )
+        
+        csi = calculate_csi(y_val, lgb_model.predict(X_val))
+        csi_scores.append(csi)
+
+    # 평균 CSI의 음수값 반환 (hyperopt는 최소화를 수행)
+    return -1 * round(np.mean(csi_scores), 4)
+
+
+# 하이퍼파라미터 최적화
+print("하이퍼파라미터 최적화 시작...")
+trials = Trials()
+lgb_best = fmin(
+    fn=objective_func,
+    space=lgb_search_space,
+    algo=tpe.suggest,
+    max_evals=MAX_EVALS,
+    trials=trials
+)
+
+# 최적화 결과 분석 및 출력
+print(f"\n최적화 완료. 최적 파라미터: {lgb_best}")
+
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+import os
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_ctgan10000_daejeon_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
+# 최적화된 하이퍼파라미터로 최종 모델 학습
+print("최종 모델 학습 시작...")
+models = []
+
+for fold_idx, (df_ctgan, (train_years, val_year)) in enumerate(
+    zip(df_ctgan_list, FOLD_CONFIGS), start=1
+):
+    print(f"Fold {fold_idx} 학습 중... (학습 연도: {train_years}, 검증 연도: {val_year})")
+    
+    X_train, X_val, y_train, y_val = split_data(
+        df_ctgan, df_daejeon, train_years, val_year
+    )
+    
+    lgb_model = create_lgb_model(best_params=lgb_best)
+    lgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)])
+    
+    # 검증 성능 출력
+    val_csi = calculate_csi(y_val, lgb_model.predict(X_val))
+    print(f"Fold {fold_idx} 검증 CSI: {val_csi:.4f}")
+    
+    models.append(lgb_model)
+
+# 모델 저장
+print("모델 저장 중...")
+model_save_path = os.path.join(base_dir, "save_model/lgb_optima/lgb_ctgan10000_daejeon.pkl")
+joblib.dump(models, model_save_path)
+print(f"모델이 {model_save_path}에 저장되었습니다.")
+
diff --git a/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_gwangju.py b/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_gwangju.py
new file mode 100644
index 0000000000000000000000000000000000000000..650e3f23f52129e13d34e8a46bc1f2a3b00465ab
--- /dev/null
+++ b/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_gwangju.py
@@ -0,0 +1,317 @@
+import pandas as pd
+import numpy as np
+import joblib
+import os
+from warnings import filterwarnings
+from sklearn.metrics import confusion_matrix
+from lightgbm import LGBMClassifier
+from hyperopt import fmin, tpe, Trials, hp
+
+filterwarnings('ignore')
+
+# 상수 정의
+RANDOM_STATE = 42
+N_ESTIMATORS = 4000
+EARLY_STOPPING_ROUNDS = 400
+MAX_EVALS = 100
+DEVICE = 'gpu'
+OBJECTIVE = 'multiclassova'
+
+# Fold 설정: (train_years, val_year)
+FOLD_CONFIGS = [
+    ([2018, 2019], 2020),  # Fold 1
+    ([2018, 2020], 2019),  # Fold 2
+    ([2019, 2020], 2018),  # Fold 3
+]
+
+def calculate_csi(y_true, y_pred):
+    """CSI(Critical Success Index) 점수를 계산합니다.
+    
+    Args:
+        y_true: 실제 레이블
+        y_pred: 예측 레이블
+        
+    Returns:
+        CSI 점수 (0~1 사이의 값)
+    """
+    cm = confusion_matrix(y_true, y_pred)
+    
+    # 혼동 행렬에서 H(Hit), F(False alarm), M(Miss) 추출
+    H = cm[0, 0] + cm[1, 1]
+    F = cm[1, 0] + cm[2, 0] + cm[0, 1] + cm[2, 1]
+    M = cm[0, 2] + cm[1, 2]
+    
+    # CSI 계산
+    csi = H / (H + F + M + 1e-10)
+    return csi
+
+
+def csi_metric(y_true, pred_prob):
+    """LightGBM용 CSI 메트릭 함수.
+    
+    Args:
+        y_true: 실제 레이블
+        pred_prob: 예측 확률 (shape: [n_samples, n_classes])
+        
+    Returns:
+        ('CSI', score, higher_better) 튜플
+    """
+    y_pred_binary = np.argmax(pred_prob, axis=1)
+    score = calculate_csi(y_true, y_pred_binary)
+    return 'CSI', score, True
+
+def add_derived_features(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    제거했던 파생 변수들을 복구
+    
+    Args:
+        df: 데이터프레임
+    
+    Returns:
+        파생 변수가 추가된 데이터프레임
+    """
+    df = df.copy()
+    df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
+    df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
+    df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
+    df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)
+    df['ground_temp - temp_C'] = df['groundtemp'] - df['temp_C']
+    return df
+
+def preprocessing(df):
+    """데이터 전처리 함수.
+    
+    Args:
+        df: 원본 데이터프레임
+        
+    Returns:
+        전처리된 데이터프레임
+    """
+    df = df[df.columns].copy()
+    df['year'] = df['year'].astype('int')
+    df['month'] = df['month'].astype('int')
+    df['hour'] = df['hour'].astype('int')
+    df = add_derived_features(df).copy()
+    df['multi_class'] = df['multi_class'].astype('int')
+    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
+    df['wind_dir'] = df['wind_dir'].astype('int')
+    df = df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
+       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
+       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
+       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
+       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
+       'month_sin', 'month_cos','multi_class']].copy()
+    return df
+
+
+def split_data(df_sampled, df_original, train_years, val_year):
+    """데이터를 학습용과 검증용으로 분할합니다.
+    
+    Args:
+        df_sampled: 샘플링된 데이터프레임
+        df_original: 원본 데이터프레임
+        train_years: 학습에 사용할 연도 리스트
+        val_year: 검증에 사용할 연도
+        
+    Returns:
+        (X_train, X_val, y_train, y_val) 튜플
+    """
+    # 학습 데이터: 샘플링된 데이터에서 train_years에 해당하는 데이터
+    train_mask = df_sampled['year'].isin(train_years)
+    X_train = df_sampled.loc[train_mask, df_sampled.columns != 'multi_class'].copy()
+    y_train = df_sampled.loc[train_mask, 'multi_class']
+    
+    # 검증 데이터: 원본 데이터에서 val_year에 해당하는 데이터
+    val_mask = df_original['year'] == val_year
+    X_val = df_original.loc[val_mask, df_original.columns != 'multi_class'].copy()
+    y_val = df_original.loc[val_mask, 'multi_class']
+    
+    # 'year' 컬럼 제거
+    X_train = X_train.drop(columns=['year'])
+    X_val = X_val.drop(columns=['year'])
+    
+    return X_train, X_val, y_train, y_val
+
+
+def create_lgb_model(search_space=None, best_params=None):
+    """LightGBM 모델을 생성합니다.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간 (objective_func에서 사용)
+        best_params: 최적화된 하이퍼파라미터 (최종 모델 학습에서 사용)
+        
+    Returns:
+        LGBMClassifier 인스턴스
+    """
+    base_params = {
+        'n_estimators': N_ESTIMATORS,
+        'device': DEVICE,
+        'objective': OBJECTIVE,
+        'random_state': RANDOM_STATE,
+        'early_stopping_rounds': EARLY_STOPPING_ROUNDS,
+        'verbose': -1,
+    }
+    
+    if search_space is not None:
+        # 하이퍼파라미터 최적화 중
+        params = {
+            **base_params,
+            'learning_rate': search_space['learning_rate'],
+            'max_depth': int(search_space['max_depth']),
+            'num_leaves': int(search_space['num_leaves']),
+            'min_child_weight': int(search_space['min_child_weight']),
+            'subsample': search_space['subsample'],
+            'colsample_bytree': search_space['colsample_bytree'],
+            'reg_alpha': search_space['reg_alpha'],
+            'reg_lambda': search_space['reg_lambda'],
+        }
+    elif best_params is not None:
+        # 최적화된 파라미터로 최종 모델 생성
+        params = {
+            **base_params,
+            'learning_rate': best_params['learning_rate'],
+            'max_depth': int(best_params['max_depth']),
+            'num_leaves': int(best_params['num_leaves']),
+            'min_child_weight': int(best_params['min_child_weight']),
+            'subsample': best_params['subsample'],
+            'colsample_bytree': best_params['colsample_bytree'],
+            'reg_alpha': best_params['reg_alpha'],
+            'reg_lambda': best_params['reg_lambda'],
+        }
+    else:
+        params = base_params
+    
+    return LGBMClassifier(**params)
+
+# 데이터 로딩
+print("데이터 로딩 중...")
+# 파일 위치 기반으로 데이터 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+data_base_dir = os.path.abspath(os.path.join(current_file_dir, '../../../data'))
+df_gwangju = pd.read_csv(os.path.join(data_base_dir, "data_for_modeling/gwangju_train.csv"))
+df_ctgan_gwangju_1 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_1_gwangju.csv"))
+df_ctgan_gwangju_2 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_2_gwangju.csv"))
+df_ctgan_gwangju_3 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_3_gwangju.csv"))
+
+# 데이터 전처리
+print("데이터 전처리 중...")
+df_ctgan_gwangju_1 = preprocessing(df_ctgan_gwangju_1)
+df_ctgan_gwangju_2 = preprocessing(df_ctgan_gwangju_2)
+df_ctgan_gwangju_3 = preprocessing(df_ctgan_gwangju_3)
+df_gwangju = preprocessing(df_gwangju)
+
+# CTGAN 데이터 리스트 (fold 순서와 일치)
+df_ctgan_list = [df_ctgan_gwangju_1, df_ctgan_gwangju_2, df_ctgan_gwangju_3]
+
+# 하이퍼파라미터 검색 공간 정의
+lgb_search_space = {
+    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.2)),
+    'max_depth': hp.quniform('max_depth', 3, 15, 1),
+    'num_leaves': hp.quniform('num_leaves', 20, 150, 1),  # 2^max_depth 보다는 작게
+    'min_child_weight': hp.quniform('min_child_weight', 1, 20, 1),
+    'subsample': hp.uniform('subsample', 0.6, 1.0),
+    'colsample_bytree': hp.uniform('colsample_bytree', 0.6, 1.0),
+    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),
+    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0)
+}
+
+
+def objective_func(search_space):
+    """하이퍼파라미터 최적화를 위한 목적 함수.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간
+        
+    Returns:
+        평균 CSI 점수의 음수값 (hyperopt는 최소화를 수행하므로)
+    """
+    lgb_model = create_lgb_model(search_space=search_space)
+    csi_scores = []
+
+    # 각 fold에 대해 교차 검증 수행
+    for df_ctgan, (train_years, val_year) in zip(df_ctgan_list, FOLD_CONFIGS):
+        X_train, X_val, y_train, y_val = split_data(
+            df_ctgan, df_gwangju, train_years, val_year
+        )
+        
+        lgb_model.fit(
+            X_train, y_train,
+            eval_set=[(X_val, y_val)],
+            eval_metric=csi_metric
+        )
+        
+        csi = calculate_csi(y_val, lgb_model.predict(X_val))
+        csi_scores.append(csi)
+
+    # 평균 CSI의 음수값 반환 (hyperopt는 최소화를 수행)
+    return -1 * round(np.mean(csi_scores), 4)
+
+
+# 하이퍼파라미터 최적화
+print("하이퍼파라미터 최적화 시작...")
+trials = Trials()
+lgb_best = fmin(
+    fn=objective_func,
+    space=lgb_search_space,
+    algo=tpe.suggest,
+    max_evals=MAX_EVALS,
+    trials=trials
+)
+
+# 최적화 결과 분석 및 출력
+print(f"\n최적화 완료. 최적 파라미터: {lgb_best}")
+
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+import os
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_ctgan10000_gwangju_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
+# 최적화된 하이퍼파라미터로 최종 모델 학습
+print("최종 모델 학습 시작...")
+models = []
+
+for fold_idx, (df_ctgan, (train_years, val_year)) in enumerate(
+    zip(df_ctgan_list, FOLD_CONFIGS), start=1
+):
+    print(f"Fold {fold_idx} 학습 중... (학습 연도: {train_years}, 검증 연도: {val_year})")
+    
+    X_train, X_val, y_train, y_val = split_data(
+        df_ctgan, df_gwangju, train_years, val_year
+    )
+    
+    lgb_model = create_lgb_model(best_params=lgb_best)
+    lgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)])
+    
+    # 검증 성능 출력
+    val_csi = calculate_csi(y_val, lgb_model.predict(X_val))
+    print(f"Fold {fold_idx} 검증 CSI: {val_csi:.4f}")
+    
+    models.append(lgb_model)
+
+# 모델 저장
+print("모델 저장 중...")
+model_save_path = os.path.join(base_dir, "save_model/lgb_optima/lgb_ctgan10000_gwangju.pkl")
+joblib.dump(models, model_save_path)
+print(f"모델이 {model_save_path}에 저장되었습니다.")
+
diff --git a/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_incheon.py b/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_incheon.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcc0450aece8e03959f6980b4bd3ff82510d3cfc
--- /dev/null
+++ b/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_incheon.py
@@ -0,0 +1,317 @@
+import pandas as pd
+import numpy as np
+import joblib
+import os
+from warnings import filterwarnings
+from sklearn.metrics import confusion_matrix
+from lightgbm import LGBMClassifier
+from hyperopt import fmin, tpe, Trials, hp
+
+filterwarnings('ignore')
+
+# 상수 정의
+RANDOM_STATE = 42
+N_ESTIMATORS = 4000
+EARLY_STOPPING_ROUNDS = 400
+MAX_EVALS = 100
+DEVICE = 'gpu'
+OBJECTIVE = 'multiclassova'
+
+# Fold 설정: (train_years, val_year)
+FOLD_CONFIGS = [
+    ([2018, 2019], 2020),  # Fold 1
+    ([2018, 2020], 2019),  # Fold 2
+    ([2019, 2020], 2018),  # Fold 3
+]
+
+def calculate_csi(y_true, y_pred):
+    """CSI(Critical Success Index) 점수를 계산합니다.
+    
+    Args:
+        y_true: 실제 레이블
+        y_pred: 예측 레이블
+        
+    Returns:
+        CSI 점수 (0~1 사이의 값)
+    """
+    cm = confusion_matrix(y_true, y_pred)
+    
+    # 혼동 행렬에서 H(Hit), F(False alarm), M(Miss) 추출
+    H = cm[0, 0] + cm[1, 1]
+    F = cm[1, 0] + cm[2, 0] + cm[0, 1] + cm[2, 1]
+    M = cm[0, 2] + cm[1, 2]
+    
+    # CSI 계산
+    csi = H / (H + F + M + 1e-10)
+    return csi
+
+
+def csi_metric(y_true, pred_prob):
+    """LightGBM용 CSI 메트릭 함수.
+    
+    Args:
+        y_true: 실제 레이블
+        pred_prob: 예측 확률 (shape: [n_samples, n_classes])
+        
+    Returns:
+        ('CSI', score, higher_better) 튜플
+    """
+    y_pred_binary = np.argmax(pred_prob, axis=1)
+    score = calculate_csi(y_true, y_pred_binary)
+    return 'CSI', score, True
+
+def add_derived_features(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    제거했던 파생 변수들을 복구
+    
+    Args:
+        df: 데이터프레임
+    
+    Returns:
+        파생 변수가 추가된 데이터프레임
+    """
+    df = df.copy()
+    df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
+    df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
+    df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
+    df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)
+    df['ground_temp - temp_C'] = df['groundtemp'] - df['temp_C']
+    return df
+
+def preprocessing(df):
+    """데이터 전처리 함수.
+    
+    Args:
+        df: 원본 데이터프레임
+        
+    Returns:
+        전처리된 데이터프레임
+    """
+    df = df[df.columns].copy()
+    df['year'] = df['year'].astype('int')
+    df['month'] = df['month'].astype('int')
+    df['hour'] = df['hour'].astype('int')
+    df = add_derived_features(df).copy()
+    df['multi_class'] = df['multi_class'].astype('int')
+    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
+    df['wind_dir'] = df['wind_dir'].astype('int')
+    df = df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
+       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
+       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
+       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
+       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
+       'month_sin', 'month_cos','multi_class']].copy()
+    return df
+
+
+def split_data(df_sampled, df_original, train_years, val_year):
+    """데이터를 학습용과 검증용으로 분할합니다.
+    
+    Args:
+        df_sampled: 샘플링된 데이터프레임
+        df_original: 원본 데이터프레임
+        train_years: 학습에 사용할 연도 리스트
+        val_year: 검증에 사용할 연도
+        
+    Returns:
+        (X_train, X_val, y_train, y_val) 튜플
+    """
+    # 학습 데이터: 샘플링된 데이터에서 train_years에 해당하는 데이터
+    train_mask = df_sampled['year'].isin(train_years)
+    X_train = df_sampled.loc[train_mask, df_sampled.columns != 'multi_class'].copy()
+    y_train = df_sampled.loc[train_mask, 'multi_class']
+    
+    # 검증 데이터: 원본 데이터에서 val_year에 해당하는 데이터
+    val_mask = df_original['year'] == val_year
+    X_val = df_original.loc[val_mask, df_original.columns != 'multi_class'].copy()
+    y_val = df_original.loc[val_mask, 'multi_class']
+    
+    # 'year' 컬럼 제거
+    X_train = X_train.drop(columns=['year'])
+    X_val = X_val.drop(columns=['year'])
+    
+    return X_train, X_val, y_train, y_val
+
+
+def create_lgb_model(search_space=None, best_params=None):
+    """LightGBM 모델을 생성합니다.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간 (objective_func에서 사용)
+        best_params: 최적화된 하이퍼파라미터 (최종 모델 학습에서 사용)
+        
+    Returns:
+        LGBMClassifier 인스턴스
+    """
+    base_params = {
+        'n_estimators': N_ESTIMATORS,
+        'device': DEVICE,
+        'objective': OBJECTIVE,
+        'random_state': RANDOM_STATE,
+        'early_stopping_rounds': EARLY_STOPPING_ROUNDS,
+        'verbose': -1,
+    }
+    
+    if search_space is not None:
+        # 하이퍼파라미터 최적화 중
+        params = {
+            **base_params,
+            'learning_rate': search_space['learning_rate'],
+            'max_depth': int(search_space['max_depth']),
+            'num_leaves': int(search_space['num_leaves']),
+            'min_child_weight': int(search_space['min_child_weight']),
+            'subsample': search_space['subsample'],
+            'colsample_bytree': search_space['colsample_bytree'],
+            'reg_alpha': search_space['reg_alpha'],
+            'reg_lambda': search_space['reg_lambda'],
+        }
+    elif best_params is not None:
+        # 최적화된 파라미터로 최종 모델 생성
+        params = {
+            **base_params,
+            'learning_rate': best_params['learning_rate'],
+            'max_depth': int(best_params['max_depth']),
+            'num_leaves': int(best_params['num_leaves']),
+            'min_child_weight': int(best_params['min_child_weight']),
+            'subsample': best_params['subsample'],
+            'colsample_bytree': best_params['colsample_bytree'],
+            'reg_alpha': best_params['reg_alpha'],
+            'reg_lambda': best_params['reg_lambda'],
+        }
+    else:
+        params = base_params
+    
+    return LGBMClassifier(**params)
+
+# 데이터 로딩
+print("데이터 로딩 중...")
+# 파일 위치 기반으로 데이터 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+data_base_dir = os.path.abspath(os.path.join(current_file_dir, '../../../data'))
+df_incheon = pd.read_csv(os.path.join(data_base_dir, "data_for_modeling/incheon_train.csv"))
+df_ctgan_incheon_1 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_1_incheon.csv"))
+df_ctgan_incheon_2 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_2_incheon.csv"))
+df_ctgan_incheon_3 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_3_incheon.csv"))
+
+# 데이터 전처리
+print("데이터 전처리 중...")
+df_ctgan_incheon_1 = preprocessing(df_ctgan_incheon_1)
+df_ctgan_incheon_2 = preprocessing(df_ctgan_incheon_2)
+df_ctgan_incheon_3 = preprocessing(df_ctgan_incheon_3)
+df_incheon = preprocessing(df_incheon)
+
+# CTGAN 데이터 리스트 (fold 순서와 일치)
+df_ctgan_list = [df_ctgan_incheon_1, df_ctgan_incheon_2, df_ctgan_incheon_3]
+
+# 하이퍼파라미터 검색 공간 정의
+lgb_search_space = {
+    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.2)),
+    'max_depth': hp.quniform('max_depth', 3, 15, 1),
+    'num_leaves': hp.quniform('num_leaves', 20, 150, 1),  # 2^max_depth 보다는 작게
+    'min_child_weight': hp.quniform('min_child_weight', 1, 20, 1),
+    'subsample': hp.uniform('subsample', 0.6, 1.0),
+    'colsample_bytree': hp.uniform('colsample_bytree', 0.6, 1.0),
+    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),
+    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0)
+}
+
+
+def objective_func(search_space):
+    """하이퍼파라미터 최적화를 위한 목적 함수.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간
+        
+    Returns:
+        평균 CSI 점수의 음수값 (hyperopt는 최소화를 수행하므로)
+    """
+    lgb_model = create_lgb_model(search_space=search_space)
+    csi_scores = []
+
+    # 각 fold에 대해 교차 검증 수행
+    for df_ctgan, (train_years, val_year) in zip(df_ctgan_list, FOLD_CONFIGS):
+        X_train, X_val, y_train, y_val = split_data(
+            df_ctgan, df_incheon, train_years, val_year
+        )
+        
+        lgb_model.fit(
+            X_train, y_train,
+            eval_set=[(X_val, y_val)],
+            eval_metric=csi_metric
+        )
+        
+        csi = calculate_csi(y_val, lgb_model.predict(X_val))
+        csi_scores.append(csi)
+
+    # 평균 CSI의 음수값 반환 (hyperopt는 최소화를 수행)
+    return -1 * round(np.mean(csi_scores), 4)
+
+
+# 하이퍼파라미터 최적화
+print("하이퍼파라미터 최적화 시작...")
+trials = Trials()
+lgb_best = fmin(
+    fn=objective_func,
+    space=lgb_search_space,
+    algo=tpe.suggest,
+    max_evals=MAX_EVALS,
+    trials=trials
+)
+
+# 최적화 결과 분석 및 출력
+print(f"\n최적화 완료. 최적 파라미터: {lgb_best}")
+
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+import os
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_ctgan10000_incheon_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
+# 최적화된 하이퍼파라미터로 최종 모델 학습
+print("최종 모델 학습 시작...")
+models = []
+
+for fold_idx, (df_ctgan, (train_years, val_year)) in enumerate(
+    zip(df_ctgan_list, FOLD_CONFIGS), start=1
+):
+    print(f"Fold {fold_idx} 학습 중... (학습 연도: {train_years}, 검증 연도: {val_year})")
+    
+    X_train, X_val, y_train, y_val = split_data(
+        df_ctgan, df_incheon, train_years, val_year
+    )
+    
+    lgb_model = create_lgb_model(best_params=lgb_best)
+    lgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)])
+    
+    # 검증 성능 출력
+    val_csi = calculate_csi(y_val, lgb_model.predict(X_val))
+    print(f"Fold {fold_idx} 검증 CSI: {val_csi:.4f}")
+    
+    models.append(lgb_model)
+
+# 모델 저장
+print("모델 저장 중...")
+model_save_path = os.path.join(base_dir, "save_model/lgb_optima/lgb_ctgan10000_incheon.pkl")
+joblib.dump(models, model_save_path)
+print(f"모델이 {model_save_path}에 저장되었습니다.")
+
diff --git a/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_seoul.py b/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_seoul.py
new file mode 100644
index 0000000000000000000000000000000000000000..2af44f1c108b052821920b226c8772a9ef792f93
--- /dev/null
+++ b/Analysis_code/5.optima/lgb_ctgan10000/LGB_ctgan10000_seoul.py
@@ -0,0 +1,317 @@
+import pandas as pd
+import numpy as np
+import joblib
+import os
+from warnings import filterwarnings
+from sklearn.metrics import confusion_matrix
+from lightgbm import LGBMClassifier
+from hyperopt import fmin, tpe, Trials, hp
+
+filterwarnings('ignore')
+
+# 상수 정의
+RANDOM_STATE = 42
+N_ESTIMATORS = 4000
+EARLY_STOPPING_ROUNDS = 400
+MAX_EVALS = 100
+DEVICE = 'gpu'
+OBJECTIVE = 'multiclassova'
+
+# Fold 설정: (train_years, val_year)
+FOLD_CONFIGS = [
+    ([2018, 2019], 2020),  # Fold 1
+    ([2018, 2020], 2019),  # Fold 2
+    ([2019, 2020], 2018),  # Fold 3
+]
+
+def calculate_csi(y_true, y_pred):
+    """CSI(Critical Success Index) 점수를 계산합니다.
+    
+    Args:
+        y_true: 실제 레이블
+        y_pred: 예측 레이블
+        
+    Returns:
+        CSI 점수 (0~1 사이의 값)
+    """
+    cm = confusion_matrix(y_true, y_pred)
+    
+    # 혼동 행렬에서 H(Hit), F(False alarm), M(Miss) 추출
+    H = cm[0, 0] + cm[1, 1]
+    F = cm[1, 0] + cm[2, 0] + cm[0, 1] + cm[2, 1]
+    M = cm[0, 2] + cm[1, 2]
+    
+    # CSI 계산
+    csi = H / (H + F + M + 1e-10)
+    return csi
+
+
+def csi_metric(y_true, pred_prob):
+    """LightGBM용 CSI 메트릭 함수.
+    
+    Args:
+        y_true: 실제 레이블
+        pred_prob: 예측 확률 (shape: [n_samples, n_classes])
+        
+    Returns:
+        ('CSI', score, higher_better) 튜플
+    """
+    y_pred_binary = np.argmax(pred_prob, axis=1)
+    score = calculate_csi(y_true, y_pred_binary)
+    return 'CSI', score, True
+
+def add_derived_features(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    제거했던 파생 변수들을 복구
+    
+    Args:
+        df: 데이터프레임
+    
+    Returns:
+        파생 변수가 추가된 데이터프레임
+    """
+    df = df.copy()
+    df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
+    df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
+    df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
+    df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)
+    df['ground_temp - temp_C'] = df['groundtemp'] - df['temp_C']
+    return df
+
+def preprocessing(df):
+    """데이터 전처리 함수.
+    
+    Args:
+        df: 원본 데이터프레임
+        
+    Returns:
+        전처리된 데이터프레임
+    """
+    df = df[df.columns].copy()
+    df['year'] = df['year'].astype('int')
+    df['month'] = df['month'].astype('int')
+    df['hour'] = df['hour'].astype('int')
+    df = add_derived_features(df).copy()
+    df['multi_class'] = df['multi_class'].astype('int')
+    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
+    df['wind_dir'] = df['wind_dir'].astype('int')
+    df = df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
+       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
+       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
+       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
+       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
+       'month_sin', 'month_cos','multi_class']].copy()
+    return df
+
+
+def split_data(df_sampled, df_original, train_years, val_year):
+    """데이터를 학습용과 검증용으로 분할합니다.
+    
+    Args:
+        df_sampled: 샘플링된 데이터프레임
+        df_original: 원본 데이터프레임
+        train_years: 학습에 사용할 연도 리스트
+        val_year: 검증에 사용할 연도
+        
+    Returns:
+        (X_train, X_val, y_train, y_val) 튜플
+    """
+    # 학습 데이터: 샘플링된 데이터에서 train_years에 해당하는 데이터
+    train_mask = df_sampled['year'].isin(train_years)
+    X_train = df_sampled.loc[train_mask, df_sampled.columns != 'multi_class'].copy()
+    y_train = df_sampled.loc[train_mask, 'multi_class']
+    
+    # 검증 데이터: 원본 데이터에서 val_year에 해당하는 데이터
+    val_mask = df_original['year'] == val_year
+    X_val = df_original.loc[val_mask, df_original.columns != 'multi_class'].copy()
+    y_val = df_original.loc[val_mask, 'multi_class']
+    
+    # 'year' 컬럼 제거
+    X_train = X_train.drop(columns=['year'])
+    X_val = X_val.drop(columns=['year'])
+    
+    return X_train, X_val, y_train, y_val
+
+
+def create_lgb_model(search_space=None, best_params=None):
+    """LightGBM 모델을 생성합니다.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간 (objective_func에서 사용)
+        best_params: 최적화된 하이퍼파라미터 (최종 모델 학습에서 사용)
+        
+    Returns:
+        LGBMClassifier 인스턴스
+    """
+    base_params = {
+        'n_estimators': N_ESTIMATORS,
+        'device': DEVICE,
+        'objective': OBJECTIVE,
+        'random_state': RANDOM_STATE,
+        'early_stopping_rounds': EARLY_STOPPING_ROUNDS,
+        'verbose': -1,
+    }
+    
+    if search_space is not None:
+        # 하이퍼파라미터 최적화 중
+        params = {
+            **base_params,
+            'learning_rate': search_space['learning_rate'],
+            'max_depth': int(search_space['max_depth']),
+            'num_leaves': int(search_space['num_leaves']),
+            'min_child_weight': int(search_space['min_child_weight']),
+            'subsample': search_space['subsample'],
+            'colsample_bytree': search_space['colsample_bytree'],
+            'reg_alpha': search_space['reg_alpha'],
+            'reg_lambda': search_space['reg_lambda'],
+        }
+    elif best_params is not None:
+        # 최적화된 파라미터로 최종 모델 생성
+        params = {
+            **base_params,
+            'learning_rate': best_params['learning_rate'],
+            'max_depth': int(best_params['max_depth']),
+            'num_leaves': int(best_params['num_leaves']),
+            'min_child_weight': int(best_params['min_child_weight']),
+            'subsample': best_params['subsample'],
+            'colsample_bytree': best_params['colsample_bytree'],
+            'reg_alpha': best_params['reg_alpha'],
+            'reg_lambda': best_params['reg_lambda'],
+        }
+    else:
+        params = base_params
+    
+    return LGBMClassifier(**params)
+
+# 데이터 로딩
+print("데이터 로딩 중...")
+# 파일 위치 기반으로 데이터 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+data_base_dir = os.path.abspath(os.path.join(current_file_dir, '../../../data'))
+df_seoul = pd.read_csv(os.path.join(data_base_dir, "data_for_modeling/seoul_train.csv"))
+df_ctgan_seoul_1 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_1_seoul.csv"))
+df_ctgan_seoul_2 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_2_seoul.csv"))
+df_ctgan_seoul_3 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_3_seoul.csv"))
+
+# 데이터 전처리
+print("데이터 전처리 중...")
+df_ctgan_seoul_1 = preprocessing(df_ctgan_seoul_1)
+df_ctgan_seoul_2 = preprocessing(df_ctgan_seoul_2)
+df_ctgan_seoul_3 = preprocessing(df_ctgan_seoul_3)
+df_seoul = preprocessing(df_seoul)
+
+# CTGAN 데이터 리스트 (fold 순서와 일치)
+df_ctgan_list = [df_ctgan_seoul_1, df_ctgan_seoul_2, df_ctgan_seoul_3]
+
+# 하이퍼파라미터 검색 공간 정의
+lgb_search_space = {
+    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.2)),
+    'max_depth': hp.quniform('max_depth', 3, 15, 1),
+    'num_leaves': hp.quniform('num_leaves', 20, 150, 1),  # 2^max_depth 보다는 작게
+    'min_child_weight': hp.quniform('min_child_weight', 1, 20, 1),
+    'subsample': hp.uniform('subsample', 0.6, 1.0),
+    'colsample_bytree': hp.uniform('colsample_bytree', 0.6, 1.0),
+    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),
+    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0)
+}
+
+
+def objective_func(search_space):
+    """하이퍼파라미터 최적화를 위한 목적 함수.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간
+        
+    Returns:
+        평균 CSI 점수의 음수값 (hyperopt는 최소화를 수행하므로)
+    """
+    lgb_model = create_lgb_model(search_space=search_space)
+    csi_scores = []
+
+    # 각 fold에 대해 교차 검증 수행
+    for df_ctgan, (train_years, val_year) in zip(df_ctgan_list, FOLD_CONFIGS):
+        X_train, X_val, y_train, y_val = split_data(
+            df_ctgan, df_seoul, train_years, val_year
+        )
+        
+        lgb_model.fit(
+            X_train, y_train,
+            eval_set=[(X_val, y_val)],
+            eval_metric=csi_metric
+        )
+        
+        csi = calculate_csi(y_val, lgb_model.predict(X_val))
+        csi_scores.append(csi)
+
+    # 평균 CSI의 음수값 반환 (hyperopt는 최소화를 수행)
+    return -1 * round(np.mean(csi_scores), 4)
+
+
+# 하이퍼파라미터 최적화
+print("하이퍼파라미터 최적화 시작...")
+trials = Trials()
+lgb_best = fmin(
+    fn=objective_func,
+    space=lgb_search_space,
+    algo=tpe.suggest,
+    max_evals=MAX_EVALS,
+    trials=trials
+)
+
+# 최적화 결과 분석 및 출력
+print(f"\n최적화 완료. 최적 파라미터: {lgb_best}")
+
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+import os
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_ctgan10000_seoul_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
+# 최적화된 하이퍼파라미터로 최종 모델 학습
+print("최종 모델 학습 시작...")
+models = []
+
+for fold_idx, (df_ctgan, (train_years, val_year)) in enumerate(
+    zip(df_ctgan_list, FOLD_CONFIGS), start=1
+):
+    print(f"Fold {fold_idx} 학습 중... (학습 연도: {train_years}, 검증 연도: {val_year})")
+    
+    X_train, X_val, y_train, y_val = split_data(
+        df_ctgan, df_seoul, train_years, val_year
+    )
+    
+    lgb_model = create_lgb_model(best_params=lgb_best)
+    lgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)])
+    
+    # 검증 성능 출력
+    val_csi = calculate_csi(y_val, lgb_model.predict(X_val))
+    print(f"Fold {fold_idx} 검증 CSI: {val_csi:.4f}")
+    
+    models.append(lgb_model)
+
+# 모델 저장
+print("모델 저장 중...")
+model_save_path = os.path.join(base_dir, "save_model/lgb_optima/lgb_ctgan10000_seoul.pkl")
+joblib.dump(models, model_save_path)
+print(f"모델이 {model_save_path}에 저장되었습니다.")
+
diff --git a/Analysis_code/5.optima/lgb_pure/LGB_pure_daegu.py b/Analysis_code/5.optima/lgb_pure/LGB_pure_daegu.py
index f483b231a1b2d89ed49d7c16fa60ea4581bbf120..85f7c24d7c94c5cd46bde577772b5884c64b3b0c 100644
--- a/Analysis_code/5.optima/lgb_pure/LGB_pure_daegu.py
+++ b/Analysis_code/5.optima/lgb_pure/LGB_pure_daegu.py
@@ -271,6 +271,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/lgb_pure_daegu_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/lgb_pure/LGB_pure_daejeon.py b/Analysis_code/5.optima/lgb_pure/LGB_pure_daejeon.py
index bab9bb892a48fd6590253b8eeec4350749dda2ea..495f7f47cc99b9256e47d222bd6eeb0e535bc63a 100644
--- a/Analysis_code/5.optima/lgb_pure/LGB_pure_daejeon.py
+++ b/Analysis_code/5.optima/lgb_pure/LGB_pure_daejeon.py
@@ -271,6 +271,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/lgb_pure_daejeon_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/lgb_pure/LGB_pure_gwangju.py b/Analysis_code/5.optima/lgb_pure/LGB_pure_gwangju.py
index 719ff56681d4b15bd55535b468f5bb023df33bd9..6827f82cba339bf23d71d1eacbc01dd01bfd1fd4 100644
--- a/Analysis_code/5.optima/lgb_pure/LGB_pure_gwangju.py
+++ b/Analysis_code/5.optima/lgb_pure/LGB_pure_gwangju.py
@@ -271,6 +271,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/lgb_pure_gwangju_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/lgb_pure/LGB_pure_incheon.py b/Analysis_code/5.optima/lgb_pure/LGB_pure_incheon.py
index 153b134824d846bcde39e8f69d07237e54272863..80972fdf2126daedce421aaef7dc513bbd569f26 100644
--- a/Analysis_code/5.optima/lgb_pure/LGB_pure_incheon.py
+++ b/Analysis_code/5.optima/lgb_pure/LGB_pure_incheon.py
@@ -271,6 +271,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/lgb_pure_incheon_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/lgb_pure/LGB_pure_seoul.py b/Analysis_code/5.optima/lgb_pure/LGB_pure_seoul.py
index 658801664267e3d0a1ea6523a3e3be403e36845c..e5dbc2bd887f77170c6c4a205d204f52b6469967 100644
--- a/Analysis_code/5.optima/lgb_pure/LGB_pure_seoul.py
+++ b/Analysis_code/5.optima/lgb_pure/LGB_pure_seoul.py
@@ -271,6 +271,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/lgb_pure_seoul_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/lgb_smote/LGB_smote_busan.py b/Analysis_code/5.optima/lgb_smote/LGB_smote_busan.py
index 5dc4befc188d6f0338ebca270ca5fffe9bd35fc3..03afa0878d9f251f0d6a7e9f698a276d73576a7d 100644
--- a/Analysis_code/5.optima/lgb_smote/LGB_smote_busan.py
+++ b/Analysis_code/5.optima/lgb_smote/LGB_smote_busan.py
@@ -259,6 +259,31 @@ lgb_best = fmin(
 )
 print(f"최적화 완료. 최적 파라미터: {lgb_best}")
 
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_smote_busan_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
 # 최적화된 하이퍼파라미터로 최종 모델 학습
 print("최종 모델 학습 시작...")
 models = []
@@ -283,6 +308,7 @@ for fold_idx, (df_smote, (train_years, val_year)) in enumerate(
 
 # 모델 저장
 print("모델 저장 중...")
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 model_save_path = os.path.join(base_dir, "save_model/lgb_optima/lgb_smote_busan.pkl")
 joblib.dump(models, model_save_path)
 print(f"모델이 {model_save_path}에 저장되었습니다.")
\ No newline at end of file
diff --git a/Analysis_code/5.optima/lgb_smote/LGB_smote_daegu.py b/Analysis_code/5.optima/lgb_smote/LGB_smote_daegu.py
index b5e338f425c4c97e638beb9c3e4f7c2170636422..d632305cb3f92fff7382a25adfb7ddaa1b79a5f4 100644
--- a/Analysis_code/5.optima/lgb_smote/LGB_smote_daegu.py
+++ b/Analysis_code/5.optima/lgb_smote/LGB_smote_daegu.py
@@ -260,6 +260,31 @@ lgb_best = fmin(
 )
 print(f"최적화 완료. 최적 파라미터: {lgb_best}")
 
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_smote_daegu_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
 # 최적화된 하이퍼파라미터로 최종 모델 학습
 print("최종 모델 학습 시작...")
 models = []
@@ -284,6 +309,7 @@ for fold_idx, (df_smote, (train_years, val_year)) in enumerate(
 
 # 모델 저장
 print("모델 저장 중...")
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 model_save_path = os.path.join(base_dir, "save_model/lgb_optima/lgb_smote_daegu.pkl")
 joblib.dump(models, model_save_path)
 print(f"모델이 {model_save_path}에 저장되었습니다.")
diff --git a/Analysis_code/5.optima/lgb_smote/LGB_smote_daejeon.py b/Analysis_code/5.optima/lgb_smote/LGB_smote_daejeon.py
index c413f2c4806625fc554d0aa63106a80a4dbcd224..f180e86f63057eb924b08b72f6d36ab2d86557d7 100644
--- a/Analysis_code/5.optima/lgb_smote/LGB_smote_daejeon.py
+++ b/Analysis_code/5.optima/lgb_smote/LGB_smote_daejeon.py
@@ -260,6 +260,31 @@ lgb_best = fmin(
 )
 print(f"최적화 완료. 최적 파라미터: {lgb_best}")
 
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_smote_daejeon_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
 # 최적화된 하이퍼파라미터로 최종 모델 학습
 print("최종 모델 학습 시작...")
 models = []
@@ -284,6 +309,7 @@ for fold_idx, (df_smote, (train_years, val_year)) in enumerate(
 
 # 모델 저장
 print("모델 저장 중...")
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 model_save_path = os.path.join(base_dir, "save_model/lgb_optima/lgb_smote_daejeon.pkl")
 joblib.dump(models, model_save_path)
 print(f"모델이 {model_save_path}에 저장되었습니다.")
diff --git a/Analysis_code/5.optima/lgb_smote/LGB_smote_gwangju.py b/Analysis_code/5.optima/lgb_smote/LGB_smote_gwangju.py
index 315e49d2a3ffb6bafb194accf52c6577d4ce9984..638df8a22652e5f95db7db4b06a924872464c739 100644
--- a/Analysis_code/5.optima/lgb_smote/LGB_smote_gwangju.py
+++ b/Analysis_code/5.optima/lgb_smote/LGB_smote_gwangju.py
@@ -260,6 +260,31 @@ lgb_best = fmin(
 )
 print(f"최적화 완료. 최적 파라미터: {lgb_best}")
 
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_smote_gwangju_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
 # 최적화된 하이퍼파라미터로 최종 모델 학습
 print("최종 모델 학습 시작...")
 models = []
@@ -284,6 +309,7 @@ for fold_idx, (df_smote, (train_years, val_year)) in enumerate(
 
 # 모델 저장
 print("모델 저장 중...")
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 model_save_path = os.path.join(base_dir, "save_model/lgb_optima/lgb_smote_gwangju.pkl")
 joblib.dump(models, model_save_path)
 print(f"모델이 {model_save_path}에 저장되었습니다.")
diff --git a/Analysis_code/5.optima/lgb_smote/LGB_smote_incheon.py b/Analysis_code/5.optima/lgb_smote/LGB_smote_incheon.py
index 3e9e562c7e792c0f8c0f25c29f2c2809ff143f78..0be32d2805b845718335afa74062b003e33ad4a7 100644
--- a/Analysis_code/5.optima/lgb_smote/LGB_smote_incheon.py
+++ b/Analysis_code/5.optima/lgb_smote/LGB_smote_incheon.py
@@ -260,6 +260,31 @@ lgb_best = fmin(
 )
 print(f"최적화 완료. 최적 파라미터: {lgb_best}")
 
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_smote_incheon_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
 # 최적화된 하이퍼파라미터로 최종 모델 학습
 print("최종 모델 학습 시작...")
 models = []
@@ -284,6 +309,7 @@ for fold_idx, (df_smote, (train_years, val_year)) in enumerate(
 
 # 모델 저장
 print("모델 저장 중...")
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 model_save_path = os.path.join(base_dir, "save_model/lgb_optima/lgb_smote_incheon.pkl")
 joblib.dump(models, model_save_path)
 print(f"모델이 {model_save_path}에 저장되었습니다.")
diff --git a/Analysis_code/5.optima/lgb_smote/LGB_smote_seoul.py b/Analysis_code/5.optima/lgb_smote/LGB_smote_seoul.py
index 84fde035b37e31db7a8cf69c73394359f7218753..b2f4293cf0df3b591d67ea6740da8b4c1f9c8ee2 100644
--- a/Analysis_code/5.optima/lgb_smote/LGB_smote_seoul.py
+++ b/Analysis_code/5.optima/lgb_smote/LGB_smote_seoul.py
@@ -260,6 +260,31 @@ lgb_best = fmin(
 )
 print(f"최적화 완료. 최적 파라미터: {lgb_best}")
 
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_smote_seoul_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
 # 최적화된 하이퍼파라미터로 최종 모델 학습
 print("최종 모델 학습 시작...")
 models = []
@@ -284,6 +309,7 @@ for fold_idx, (df_smote, (train_years, val_year)) in enumerate(
 
 # 모델 저장
 print("모델 저장 중...")
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 model_save_path = os.path.join(base_dir, "save_model/lgb_optima/lgb_smote_seoul.pkl")
 joblib.dump(models, model_save_path)
 print(f"모델이 {model_save_path}에 저장되었습니다.")
diff --git a/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_busan.py b/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_busan.py
index c3dff8ce53fecb1ec8ee85469dac2537e735198e..51bcb9bff7576cb7f970c24d6538052ec6a9b05e 100644
--- a/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_busan.py
+++ b/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_busan.py
@@ -260,6 +260,31 @@ lgb_best = fmin(
 )
 print(f"최적화 완료. 최적 파라미터: {lgb_best}")
 
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_smotenc_ctgan20000_busan_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
 # 최적화된 하이퍼파라미터로 최종 모델 학습
 print("최종 모델 학습 시작...")
 models = []
diff --git a/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daegu.py b/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daegu.py
index 065cd754427517eb7144ea92f04cdedd673aebae..c8e165396022647ae12adfe34e418ebfecd68a9c 100644
--- a/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daegu.py
+++ b/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daegu.py
@@ -260,6 +260,31 @@ lgb_best = fmin(
 )
 print(f"최적화 완료. 최적 파라미터: {lgb_best}")
 
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_smotenc_ctgan20000_daegu_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
 # 최적화된 하이퍼파라미터로 최종 모델 학습
 print("최종 모델 학습 시작...")
 models = []
diff --git a/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daejeon.py b/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daejeon.py
index d001409dd4139649416437472ff73fd97a24c0f6..f022cfd63e9f7f4efe4b320b0490b596d311aab2 100644
--- a/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daejeon.py
+++ b/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daejeon.py
@@ -260,6 +260,31 @@ lgb_best = fmin(
 )
 print(f"최적화 완료. 최적 파라미터: {lgb_best}")
 
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_smotenc_ctgan20000_daejeon_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
 # 최적화된 하이퍼파라미터로 최종 모델 학습
 print("최종 모델 학습 시작...")
 models = []
diff --git a/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_gwangju.py b/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_gwangju.py
index 9b3090388614343cea9ee188dc1feb2504c63b17..a33d9707ef8ef85f5b32e6de6ffa7a7db23f3da9 100644
--- a/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_gwangju.py
+++ b/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_gwangju.py
@@ -260,6 +260,31 @@ lgb_best = fmin(
 )
 print(f"최적화 완료. 최적 파라미터: {lgb_best}")
 
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_smotenc_ctgan20000_gwangju_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
 # 최적화된 하이퍼파라미터로 최종 모델 학습
 print("최종 모델 학습 시작...")
 models = []
diff --git a/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_incheon.py b/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_incheon.py
index 0d8c2942ca46c2a233939857074c7ae38647d0a9..26d1d612d61cb2923a92870d94a3473486180991 100644
--- a/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_incheon.py
+++ b/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_incheon.py
@@ -260,6 +260,31 @@ lgb_best = fmin(
 )
 print(f"최적화 완료. 최적 파라미터: {lgb_best}")
 
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_smotenc_ctgan20000_incheon_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
 # 최적화된 하이퍼파라미터로 최종 모델 학습
 print("최종 모델 학습 시작...")
 models = []
diff --git a/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_seoul.py b/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_seoul.py
index c432b6ffde78f5f7b80b3b82efa30cbbaf344e94..5ad162a92df74000c2f71f46ab3d8c15742da55e 100644
--- a/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_seoul.py
+++ b/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_seoul.py
@@ -260,6 +260,31 @@ lgb_best = fmin(
 )
 print(f"최적화 완료. 최적 파라미터: {lgb_best}")
 
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/lgb_smotenc_ctgan20000_seoul_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
 # 최적화된 하이퍼파라미터로 최종 모델 학습
 print("최종 모델 학습 시작...")
 models = []
diff --git a/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_daegu.py b/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_daegu.py
new file mode 100644
index 0000000000000000000000000000000000000000..32634ab2b1674291ce9b138d2dac53576401a5d3
--- /dev/null
+++ b/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_daegu.py
@@ -0,0 +1,97 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="resnet_like", region="daegu", data_sample='ctgan10000'), 
+    n_trials=100
+,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/resnet_like_ctgan10000_daegu_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="resnet_like",
+        region="daegu",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_daejeon.py b/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_daejeon.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f6ae18683f6aa105a98c2faa287a3dbc01bc430
--- /dev/null
+++ b/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_daejeon.py
@@ -0,0 +1,97 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="resnet_like", region="daejeon", data_sample='ctgan10000'), 
+    n_trials=100
+,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/resnet_like_ctgan10000_daejeon_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="resnet_like",
+        region="daejeon",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_gwangju.py b/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_gwangju.py
new file mode 100644
index 0000000000000000000000000000000000000000..92706176c6357eb0aa2fb3fd0c14cefa6d48815e
--- /dev/null
+++ b/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_gwangju.py
@@ -0,0 +1,97 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="resnet_like", region="gwangju", data_sample='ctgan10000'), 
+    n_trials=100
+,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/resnet_like_ctgan10000_gwangju_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="resnet_like",
+        region="gwangju",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_incheon.py b/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_incheon.py
new file mode 100644
index 0000000000000000000000000000000000000000..196d54c4e4c7b6ea8e7b38606cf6c461f8672389
--- /dev/null
+++ b/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_incheon.py
@@ -0,0 +1,97 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="resnet_like", region="incheon", data_sample='ctgan10000'), 
+    n_trials=100
+,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/resnet_like_ctgan10000_incheon_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="resnet_like",
+        region="incheon",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_seoul.py b/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_seoul.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd6f3c96a204dfc3cc910c65dc6dd0022b987fbc
--- /dev/null
+++ b/Analysis_code/5.optima/resnet_like_ctgan10000/resnet_like_ctgan10000_seoul.py
@@ -0,0 +1,97 @@
+import optuna
+import numpy as np
+import random
+import pandas as pd
+import joblib
+import os
+import torch
+from utils import *
+# Python 및 Numpy 시드 고정
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+
+
+# 1. Study 생성 시 'maximize'로 설정
+study = optuna.create_study(
+    direction="maximize",  # CSI 점수가 높을수록 좋으므로 maximize
+    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10) # 초반 10에폭은 지켜보고 이후 가지치기
+)
+# Trial 완료 시 상세 정보 출력하는 callback 함수
+def print_trial_callback(study, trial):
+    """각 trial 완료 시 best value를 포함한 상세 정보 출력"""
+    print(f"\n{'='*80}")
+    print(f"Trial {trial.number} 완료")
+    print(f"  Value (CSI): {trial.value:.6f}" if trial.value is not None else f"  Value: {trial.value}")
+    print(f"  Parameters: {trial.params}")
+    print(f"  Best Value (CSI): {study.best_value:.6f}" if study.best_value is not None else f"  Best Value: {study.best_value}")
+    print(f"  Best Trial: {study.best_trial.number}")
+    print(f"  Best Parameters: {study.best_params}")
+    print(f"{'='*80}\n")
+
+
+
+# 2. 최적화 실행
+study.optimize(
+    lambda trial: objective(trial, model_choose="resnet_like", region="seoul", data_sample='ctgan10000'), 
+    n_trials=100
+,
+    callbacks=[print_trial_callback]
+)
+
+# 3. 결과 확인 및 요약
+print(f"\n최적화 완료.")
+print(f"Best CSI Score: {study.best_value:.4f}")
+print(f"Best Hyperparameters: {study.best_params}")
+
+try:
+    # 모든 trial의 CSI 점수 추출
+    csi_scores = [trial.value for trial in study.trials if trial.value is not None]
+    
+    if len(csi_scores) > 0:
+        print(f"\n최적화 과정 요약:")
+        print(f"  - 총 시도 횟수: {len(study.trials)}")
+        print(f"  - 성공한 시도: {len(csi_scores)}")
+        print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+        print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+        print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+        print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+        print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+    
+    # Study 객체 저장
+    # 파일 위치 기반으로 base 디렉토리 경로 설정
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+    os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+    study_path = os.path.join(base_dir, "optimization_history/resnet_like_ctgan10000_seoul_trials.pkl")
+    joblib.dump(study, study_path)
+    print(f"\n최적화 Study 객체가 {study_path}에 저장되었습니다.")
+    
+    # 최적화된 하이퍼파라미터로 최종 모델 학습 및 저장
+    print("\n" + "="*50)
+    print("최적화된 하이퍼파라미터로 최종 모델 학습 시작")
+    print("="*50)
+    
+    best_params = study.best_params
+    model_path = train_final_model(
+        best_params=best_params,
+        model_choose="resnet_like",
+        region="seoul",
+        data_sample='ctgan10000',
+        target='multi',
+        n_folds=3,
+        random_state=seed
+    )
+    
+    print(f"\n최종 모델 학습 및 저장 완료!")
+    print(f"저장된 모델 경로: {model_path}")
+        
+except Exception as e:
+    print(f"\n⚠️  최적화 결과 분석 중 오류 발생: {e}")
+    import traceback
+    traceback.print_exc()
+
+# 정상 종료
+import sys
+sys.exit(0)
+
diff --git a/Analysis_code/5.optima/run_bash/deepgbm/deepgbm_ctgan10000.log b/Analysis_code/5.optima/run_bash/deepgbm/deepgbm_ctgan10000.log
new file mode 100644
index 0000000000000000000000000000000000000000..c06e5c042ae64e651c656263a7d0336882e17da5
--- /dev/null
+++ b/Analysis_code/5.optima/run_bash/deepgbm/deepgbm_ctgan10000.log
@@ -0,0 +1,8016 @@
+nohup: ignoring input
+/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)
+==========================================
+DeepGBM CTGAN10000 파일 실행 시작
+시작 시간: 2025-12-28 12:36:02
+GPU: 0번 (CUDA_VISIBLE_DEVICES=0)
+==========================================
+
+----------------------------------------
+실행 중: deepgbm_ctgan10000/deepgbm_ctgan10000_busan.py
+시작 시간: 2025-12-28 12:36:02
+----------------------------------------
+[I 2025-12-28 12:36:03,350] A new study created in memory with name: no-name-f8f8163b-0d6b-44d7-b45d-92727874dc8c
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:38:32,701] Trial 0 finished with value: 0.38770751821594523 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.13084881692315956, 'lr': 0.00022258541076412326, 'weight_decay': 0.004868686949203451, 'batch_size': 32}. Best is trial 0 with value: 0.38770751821594523.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.387708
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.13084881692315956, 'lr': 0.00022258541076412326, 'weight_decay': 0.004868686949203451, 'batch_size': 32}
+  Best Value (CSI): 0.387708
+  Best Trial: 0
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.13084881692315956, 'lr': 0.00022258541076412326, 'weight_decay': 0.004868686949203451, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:39:54,979] Trial 1 finished with value: 0.3882440532876936 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.30754261616949313, 'lr': 0.0004192024392841491, 'weight_decay': 0.0005190956971438232, 'batch_size': 128}. Best is trial 1 with value: 0.3882440532876936.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.388244
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.30754261616949313, 'lr': 0.0004192024392841491, 'weight_decay': 0.0005190956971438232, 'batch_size': 128}
+  Best Value (CSI): 0.388244
+  Best Trial: 1
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.30754261616949313, 'lr': 0.0004192024392841491, 'weight_decay': 0.0005190956971438232, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:41:10,524] Trial 2 finished with value: 0.42343062277312127 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3415568705386193, 'lr': 0.00164419474736584, 'weight_decay': 0.0035618619985765265, 'batch_size': 64}. Best is trial 2 with value: 0.42343062277312127.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.423431
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3415568705386193, 'lr': 0.00164419474736584, 'weight_decay': 0.0035618619985765265, 'batch_size': 64}
+  Best Value (CSI): 0.423431
+  Best Trial: 2
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3415568705386193, 'lr': 0.00164419474736584, 'weight_decay': 0.0035618619985765265, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:41:55,262] Trial 3 finished with value: 0.3909864259403904 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.34359376018237037, 'lr': 0.0025310441867235213, 'weight_decay': 0.0018249918792258464, 'batch_size': 256}. Best is trial 2 with value: 0.42343062277312127.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.390986
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.34359376018237037, 'lr': 0.0025310441867235213, 'weight_decay': 0.0018249918792258464, 'batch_size': 256}
+  Best Value (CSI): 0.423431
+  Best Trial: 2
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3415568705386193, 'lr': 0.00164419474736584, 'weight_decay': 0.0035618619985765265, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:42:42,125] Trial 4 finished with value: 0.4302411821678192 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.35845930914090496, 'lr': 0.0009607581451670014, 'weight_decay': 0.07737669684354931, 'batch_size': 256}. Best is trial 4 with value: 0.4302411821678192.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.430241
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.35845930914090496, 'lr': 0.0009607581451670014, 'weight_decay': 0.07737669684354931, 'batch_size': 256}
+  Best Value (CSI): 0.430241
+  Best Trial: 4
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.35845930914090496, 'lr': 0.0009607581451670014, 'weight_decay': 0.07737669684354931, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:45:12,111] Trial 5 finished with value: 0.3897682738591593 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.21560699439533046, 'lr': 0.0002706781911763059, 'weight_decay': 0.012669446375571386, 'batch_size': 64}. Best is trial 4 with value: 0.4302411821678192.
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.389768
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.21560699439533046, 'lr': 0.0002706781911763059, 'weight_decay': 0.012669446375571386, 'batch_size': 64}
+  Best Value (CSI): 0.430241
+  Best Trial: 4
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.35845930914090496, 'lr': 0.0009607581451670014, 'weight_decay': 0.07737669684354931, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:46:39,025] Trial 6 finished with value: 0.4123227960635745 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3940862484422013, 'lr': 0.0006379474533784767, 'weight_decay': 0.0014063697626802458, 'batch_size': 64}. Best is trial 4 with value: 0.4302411821678192.
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.412323
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3940862484422013, 'lr': 0.0006379474533784767, 'weight_decay': 0.0014063697626802458, 'batch_size': 64}
+  Best Value (CSI): 0.430241
+  Best Trial: 4
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.35845930914090496, 'lr': 0.0009607581451670014, 'weight_decay': 0.07737669684354931, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:47:47,595] Trial 7 finished with value: 0.40585947078618845 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.28524742235282563, 'lr': 0.0033161250617509287, 'weight_decay': 0.002063637723064899, 'batch_size': 128}. Best is trial 4 with value: 0.4302411821678192.
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.405859
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.28524742235282563, 'lr': 0.0033161250617509287, 'weight_decay': 0.002063637723064899, 'batch_size': 128}
+  Best Value (CSI): 0.430241
+  Best Trial: 4
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.35845930914090496, 'lr': 0.0009607581451670014, 'weight_decay': 0.07737669684354931, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 12:47:59,157] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.263520
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.2900111759899311, 'lr': 0.00012717483644731376, 'weight_decay': 0.0027471295555674966, 'batch_size': 64}
+  Best Value (CSI): 0.430241
+  Best Trial: 4
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.35845930914090496, 'lr': 0.0009607581451670014, 'weight_decay': 0.07737669684354931, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:49:07,360] Trial 9 finished with value: 0.4179289942714603 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.334367210848393, 'lr': 0.0007013057642028161, 'weight_decay': 0.07876526285484178, 'batch_size': 128}. Best is trial 4 with value: 0.4302411821678192.
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.417929
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.334367210848393, 'lr': 0.0007013057642028161, 'weight_decay': 0.07876526285484178, 'batch_size': 128}
+  Best Value (CSI): 0.430241
+  Best Trial: 4
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.35845930914090496, 'lr': 0.0009607581451670014, 'weight_decay': 0.07737669684354931, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 12:49:12,033] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.215686
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.3913367761643475, 'lr': 3.309298885680783e-05, 'weight_decay': 0.08312517396355139, 'batch_size': 256}
+  Best Value (CSI): 0.430241
+  Best Trial: 4
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.35845930914090496, 'lr': 0.0009607581451670014, 'weight_decay': 0.07737669684354931, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 12:49:17,492] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.363165
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.36258305078281133, 'lr': 0.008716866625548174, 'weight_decay': 0.00016533806804300337, 'batch_size': 256}
+  Best Value (CSI): 0.430241
+  Best Trial: 4
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.35845930914090496, 'lr': 0.0009607581451670014, 'weight_decay': 0.07737669684354931, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:51:45,203] Trial 12 finished with value: 0.44544694808774704 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.24783461805657933, 'lr': 0.0015947013660579806, 'weight_decay': 0.020109718744301845, 'batch_size': 32}. Best is trial 12 with value: 0.44544694808774704.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.445447
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.24783461805657933, 'lr': 0.0015947013660579806, 'weight_decay': 0.020109718744301845, 'batch_size': 32}
+  Best Value (CSI): 0.445447
+  Best Trial: 12
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.24783461805657933, 'lr': 0.0015947013660579806, 'weight_decay': 0.020109718744301845, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:54:01,205] Trial 13 finished with value: 0.416416711695267 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.23356519277597973, 'lr': 0.0012986320012849447, 'weight_decay': 0.022312140682743595, 'batch_size': 32}. Best is trial 12 with value: 0.44544694808774704.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.416417
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.23356519277597973, 'lr': 0.0012986320012849447, 'weight_decay': 0.022312140682743595, 'batch_size': 32}
+  Best Value (CSI): 0.445447
+  Best Trial: 12
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.24783461805657933, 'lr': 0.0015947013660579806, 'weight_decay': 0.020109718744301845, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:57:40,919] Trial 14 finished with value: 0.46241360858882524 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2611744671484639, 'lr': 0.006538644629674235, 'weight_decay': 0.028806140621950027, 'batch_size': 32}. Best is trial 14 with value: 0.46241360858882524.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.462414
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2611744671484639, 'lr': 0.006538644629674235, 'weight_decay': 0.028806140621950027, 'batch_size': 32}
+  Best Value (CSI): 0.462414
+  Best Trial: 14
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2611744671484639, 'lr': 0.006538644629674235, 'weight_decay': 0.028806140621950027, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:00:52,126] Trial 15 finished with value: 0.4464678677018112 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.25075848648773574, 'lr': 0.008919562113582653, 'weight_decay': 0.0191937053680462, 'batch_size': 32}. Best is trial 14 with value: 0.46241360858882524.
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.446468
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.25075848648773574, 'lr': 0.008919562113582653, 'weight_decay': 0.0191937053680462, 'batch_size': 32}
+  Best Value (CSI): 0.462414
+  Best Trial: 14
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2611744671484639, 'lr': 0.006538644629674235, 'weight_decay': 0.028806140621950027, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:04:31,687] Trial 16 finished with value: 0.45661394213281636 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.2092523244707838, 'lr': 0.008616714482940543, 'weight_decay': 0.009619886172702378, 'batch_size': 32}. Best is trial 14 with value: 0.46241360858882524.
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.456614
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.2092523244707838, 'lr': 0.008616714482940543, 'weight_decay': 0.009619886172702378, 'batch_size': 32}
+  Best Value (CSI): 0.462414
+  Best Trial: 14
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2611744671484639, 'lr': 0.006538644629674235, 'weight_decay': 0.028806140621950027, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:05:06,150] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.352151
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.19446812848501352, 'lr': 0.004211874982670483, 'weight_decay': 0.008085903867602962, 'batch_size': 32}
+  Best Value (CSI): 0.462414
+  Best Trial: 14
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2611744671484639, 'lr': 0.006538644629674235, 'weight_decay': 0.028806140621950027, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:08:55,815] Trial 18 finished with value: 0.4503917494198751 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.1793479886428485, 'lr': 0.0050731893729288975, 'weight_decay': 0.041205522471970624, 'batch_size': 32}. Best is trial 14 with value: 0.46241360858882524.
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.450392
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.1793479886428485, 'lr': 0.0050731893729288975, 'weight_decay': 0.041205522471970624, 'batch_size': 32}
+  Best Value (CSI): 0.462414
+  Best Trial: 14
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2611744671484639, 'lr': 0.006538644629674235, 'weight_decay': 0.028806140621950027, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:09:20,993] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.257967
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1507539012482862, 'lr': 0.009617177351847864, 'weight_decay': 0.009986063118577686, 'batch_size': 32}
+  Best Value (CSI): 0.462414
+  Best Trial: 14
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2611744671484639, 'lr': 0.006538644629674235, 'weight_decay': 0.028806140621950027, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:11:49,054] Trial 20 finished with value: 0.4569849465850864 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10725593660748117, 'lr': 0.0026297398066231557, 'weight_decay': 0.03510283410124723, 'batch_size': 32}. Best is trial 14 with value: 0.46241360858882524.
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.456985
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10725593660748117, 'lr': 0.0026297398066231557, 'weight_decay': 0.03510283410124723, 'batch_size': 32}
+  Best Value (CSI): 0.462414
+  Best Trial: 14
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2611744671484639, 'lr': 0.006538644629674235, 'weight_decay': 0.028806140621950027, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:15:15,661] Trial 21 finished with value: 0.46479094271864296 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.464791
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:18:48,885] Trial 22 finished with value: 0.45189603407528794 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10307647778905539, 'lr': 0.0026343990438759675, 'weight_decay': 0.038011678265953766, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.451896
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10307647778905539, 'lr': 0.0026343990438759675, 'weight_decay': 0.038011678265953766, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:21:39,824] Trial 23 finished with value: 0.4609580591447227 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.11105704375127494, 'lr': 0.004508760543446181, 'weight_decay': 0.03463906506027731, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.460958
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.11105704375127494, 'lr': 0.004508760543446181, 'weight_decay': 0.03463906506027731, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:24:57,375] Trial 24 finished with value: 0.4535577950195207 and parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.15115644174778628, 'lr': 0.005516124987225362, 'weight_decay': 0.051992726822051, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.453558
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.15115644174778628, 'lr': 0.005516124987225362, 'weight_decay': 0.051992726822051, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:27:54,788] Trial 25 finished with value: 0.44588746175292643 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.10071702532912205, 'lr': 0.0051029023957843015, 'weight_decay': 0.02371691397616606, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.445887
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.10071702532912205, 'lr': 0.0051029023957843015, 'weight_decay': 0.02371691397616606, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:30:24,396] Trial 26 finished with value: 0.4544073306182926 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.1617136172194275, 'lr': 0.002272032039904807, 'weight_decay': 0.047697142041128085, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.454407
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.1617136172194275, 'lr': 0.002272032039904807, 'weight_decay': 0.047697142041128085, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:33:34,350] Trial 27 finished with value: 0.43677552914507783 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.12751392106341228, 'lr': 0.005515105989642216, 'weight_decay': 0.01444822109613692, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.436776
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.12751392106341228, 'lr': 0.005515105989642216, 'weight_decay': 0.01444822109613692, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:33:43,536] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.331135
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.17668828464095798, 'lr': 0.0037596969295730326, 'weight_decay': 0.005879323998441681, 'batch_size': 128}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:36:02,370] Trial 29 finished with value: 0.411336615785038 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.12604147478902364, 'lr': 0.0017261908985914732, 'weight_decay': 0.00537341089595442, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.411337
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.12604147478902364, 'lr': 0.0017261908985914732, 'weight_decay': 0.00537341089595442, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:38:33,719] Trial 30 finished with value: 0.436291531392975 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.13807232179753698, 'lr': 0.001244116531033127, 'weight_decay': 0.02804924444213796, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.436292
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.13807232179753698, 'lr': 0.001244116531033127, 'weight_decay': 0.02804924444213796, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:39:22,087] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.384286
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.11819095619538351, 'lr': 0.002951936508432688, 'weight_decay': 0.031125097849156198, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:41:54,149] Trial 32 finished with value: 0.44661138426546776 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.11113022352947247, 'lr': 0.0037075977040380297, 'weight_decay': 0.05344766485153693, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.446611
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.11113022352947247, 'lr': 0.0037075977040380297, 'weight_decay': 0.05344766485153693, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:42:26,321] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.383126
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.13771451569054052, 'lr': 0.0020245633410206974, 'weight_decay': 0.015324674011086075, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:42:57,737] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.364985
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.11642584261961077, 'lr': 0.006218554854528793, 'weight_decay': 0.03113403361514693, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:43:44,500] Trial 35 finished with value: 0.4281789509609424 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.1042205746603048, 'lr': 0.0026188889433300987, 'weight_decay': 0.09312064192113352, 'batch_size': 128}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.428179
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.1042205746603048, 'lr': 0.0026188889433300987, 'weight_decay': 0.09312064192113352, 'batch_size': 128}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:44:13,582] Trial 36 finished with value: 0.42336660243247803 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.13564114396729246, 'lr': 0.0036374913181047137, 'weight_decay': 0.05564359252440134, 'batch_size': 256}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.423367
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.13564114396729246, 'lr': 0.0036374913181047137, 'weight_decay': 0.05564359252440134, 'batch_size': 256}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:44:34,690] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.394109
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.15652875443434233, 'lr': 0.006423209304883422, 'weight_decay': 0.026512056853480997, 'batch_size': 64}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:47:07,391] Trial 38 finished with value: 0.42500595760098253 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.12135498813409618, 'lr': 0.0020470207567328236, 'weight_decay': 0.01411206115390187, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.425006
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.12135498813409618, 'lr': 0.0020470207567328236, 'weight_decay': 0.01411206115390187, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+[I 2025-12-28 13:47:59,072] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.416890
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.13986533039171933, 'lr': 0.000928463224866854, 'weight_decay': 0.06465418678751561, 'batch_size': 64}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:48:39,803] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.371191
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.16959807766757182, 'lr': 0.0004155789775893521, 'weight_decay': 0.03775287060763421, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:52:15,800] Trial 41 finished with value: 0.45185650590929055 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.19584517151561193, 'lr': 0.00737049002941897, 'weight_decay': 0.009034137281013032, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.451857
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.19584517151561193, 'lr': 0.00737049002941897, 'weight_decay': 0.009034137281013032, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:52:43,461] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.327965
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.11779860638751649, 'lr': 0.009670958943346705, 'weight_decay': 0.01645535886994672, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:53:22,342] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.367199
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10302603776219135, 'lr': 0.0067005924419238465, 'weight_decay': 0.010029700937724319, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:53:27,228] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.281609
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.2752722876172996, 'lr': 0.0040187047639353105, 'weight_decay': 0.022673099239101477, 'batch_size': 256}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:54:32,507] Trial 45 finished with value: 0.45284617110666797 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.2161907226349917, 'lr': 0.007181153307994011, 'weight_decay': 0.06544232529977383, 'batch_size': 128}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.452846
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.2161907226349917, 'lr': 0.007181153307994011, 'weight_decay': 0.06544232529977383, 'batch_size': 128}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:55:57,440] Trial 46 finished with value: 0.4433229672705871 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.1417831167136468, 'lr': 0.002916701166854368, 'weight_decay': 0.035568413022994606, 'batch_size': 64}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.443323
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.1417831167136468, 'lr': 0.002916701166854368, 'weight_decay': 0.035568413022994606, 'batch_size': 64}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:56:28,960] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.285498
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.12721300165658223, 'lr': 0.009946420702544395, 'weight_decay': 0.09895065633513092, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:56:57,512] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.326409
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.26981512509188, 'lr': 0.00456976348098232, 'weight_decay': 0.019484460456292055, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:57:26,976] Trial 49 finished with value: 0.44084978723353174 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.15907736145781765, 'lr': 0.0045340101411759795, 'weight_decay': 0.042833173761078674, 'batch_size': 256}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.440850
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.15907736145781765, 'lr': 0.0045340101411759795, 'weight_decay': 0.042833173761078674, 'batch_size': 256}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:57:47,716] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.226754
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.18952506525604182, 'lr': 0.007338802903305222, 'weight_decay': 0.004133073092078845, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:58:28,723] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.341679
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.11667768333099689, 'lr': 0.0021385759700771325, 'weight_decay': 0.051036187440769876, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:58:59,209] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.384058
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.1665605516326069, 'lr': 0.0027114013323183165, 'weight_decay': 0.030081803114934132, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:59:44,641] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.414209
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.21017050098317785, 'lr': 0.003173476165684324, 'weight_decay': 0.018886807968091716, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:03:21,691] Trial 54 finished with value: 0.44251862842971423 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.15174339152956673, 'lr': 0.005240063937049505, 'weight_decay': 0.011619861616332576, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.442519
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.15174339152956673, 'lr': 0.005240063937049505, 'weight_decay': 0.011619861616332576, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:06:10,772] Trial 55 finished with value: 0.442869983119529 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.13069244429924026, 'lr': 0.007684214921380267, 'weight_decay': 0.02516485977504387, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.442870
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.13069244429924026, 'lr': 0.007684214921380267, 'weight_decay': 0.02516485977504387, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:09:38,036] Trial 56 finished with value: 0.46273493643091584 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.1123814822905133, 'lr': 0.004330519124921856, 'weight_decay': 0.07259394855034221, 'batch_size': 32}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.462735
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.1123814822905133, 'lr': 0.004330519124921856, 'weight_decay': 0.07259394855034221, 'batch_size': 32}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:10:24,428] Trial 57 finished with value: 0.4531857523738472 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.11007150474436368, 'lr': 0.004960571020790247, 'weight_decay': 0.06613949240889548, 'batch_size': 128}. Best is trial 21 with value: 0.46479094271864296.
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.453186
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.11007150474436368, 'lr': 0.004960571020790247, 'weight_decay': 0.06613949240889548, 'batch_size': 128}
+  Best Value (CSI): 0.464791
+  Best Trial: 21
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10122178387986465, 'lr': 0.005261462040436964, 'weight_decay': 0.026181584673643014, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:12:47,837] Trial 58 finished with value: 0.46990461592330696 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10343915151139849, 'lr': 0.003938328222874651, 'weight_decay': 0.04150194552991327, 'batch_size': 32}. Best is trial 58 with value: 0.46990461592330696.
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.469905
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10343915151139849, 'lr': 0.003938328222874651, 'weight_decay': 0.04150194552991327, 'batch_size': 32}
+  Best Value (CSI): 0.469905
+  Best Trial: 58
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10343915151139849, 'lr': 0.003938328222874651, 'weight_decay': 0.04150194552991327, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:15:41,492] Trial 59 finished with value: 0.4670418888225205 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11026359600292551, 'lr': 0.0016035834439267957, 'weight_decay': 0.0707050534496364, 'batch_size': 32}. Best is trial 58 with value: 0.46990461592330696.
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.467042
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11026359600292551, 'lr': 0.0016035834439267957, 'weight_decay': 0.0707050534496364, 'batch_size': 32}
+  Best Value (CSI): 0.469905
+  Best Trial: 58
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10343915151139849, 'lr': 0.003938328222874651, 'weight_decay': 0.04150194552991327, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:16:56,665] Trial 60 finished with value: 0.46646606942286123 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10062690897206789, 'lr': 0.0037346237439991998, 'weight_decay': 0.06930667304688505, 'batch_size': 64}. Best is trial 58 with value: 0.46990461592330696.
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.466466
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10062690897206789, 'lr': 0.0037346237439991998, 'weight_decay': 0.06930667304688505, 'batch_size': 64}
+  Best Value (CSI): 0.469905
+  Best Trial: 58
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10343915151139849, 'lr': 0.003938328222874651, 'weight_decay': 0.04150194552991327, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:18:09,887] Trial 61 finished with value: 0.46876379797793605 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10315232437297442, 'lr': 0.0037298848727084825, 'weight_decay': 0.07763738944658308, 'batch_size': 64}. Best is trial 58 with value: 0.46990461592330696.
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.468764
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10315232437297442, 'lr': 0.0037298848727084825, 'weight_decay': 0.07763738944658308, 'batch_size': 64}
+  Best Value (CSI): 0.469905
+  Best Trial: 58
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10343915151139849, 'lr': 0.003938328222874651, 'weight_decay': 0.04150194552991327, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:19:26,138] Trial 62 finished with value: 0.4738494121182732 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}. Best is trial 62 with value: 0.4738494121182732.
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.473849
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:19:49,319] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.426367
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10284306859644116, 'lr': 0.0016398667092600656, 'weight_decay': 0.07902567899317137, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:20:12,866] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.417952
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.125395286308761, 'lr': 0.0031725733828850966, 'weight_decay': 0.07959297170092797, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:21:47,750] Trial 65 finished with value: 0.4711780415622433 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1002600080532499, 'lr': 0.0035002739099976644, 'weight_decay': 0.09814292599730294, 'batch_size': 64}. Best is trial 62 with value: 0.4738494121182732.
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.471178
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1002600080532499, 'lr': 0.0035002739099976644, 'weight_decay': 0.09814292599730294, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:23:14,397] Trial 66 finished with value: 0.4556448111158719 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10033705896488646, 'lr': 0.0013636874909160426, 'weight_decay': 0.09334606931126206, 'batch_size': 64}. Best is trial 62 with value: 0.4738494121182732.
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.455645
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10033705896488646, 'lr': 0.0013636874909160426, 'weight_decay': 0.09334606931126206, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:24:31,240] Trial 67 finished with value: 0.45686511845331457 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.14583548989340966, 'lr': 0.0023115777231187593, 'weight_decay': 0.05734222305518099, 'batch_size': 64}. Best is trial 62 with value: 0.4738494121182732.
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.456865
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.14583548989340966, 'lr': 0.0023115777231187593, 'weight_decay': 0.05734222305518099, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:25:37,920] Trial 68 finished with value: 0.4483865038460711 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.13048935407867213, 'lr': 0.003367474008078338, 'weight_decay': 0.04266885489892994, 'batch_size': 64}. Best is trial 62 with value: 0.4738494121182732.
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.448387
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.13048935407867213, 'lr': 0.003367474008078338, 'weight_decay': 0.04266885489892994, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:26:43,027] Trial 69 finished with value: 0.45320364869775914 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.10007619348051804, 'lr': 0.0019504842734048885, 'weight_decay': 0.09866549740570842, 'batch_size': 64}. Best is trial 62 with value: 0.4738494121182732.
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.453204
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.10007619348051804, 'lr': 0.0019504842734048885, 'weight_decay': 0.09866549740570842, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:27:46,316] Trial 70 finished with value: 0.4708613257998487 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.12230284519911813, 'lr': 0.0036400093971619965, 'weight_decay': 0.07259447688909491, 'batch_size': 64}. Best is trial 62 with value: 0.4738494121182732.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.470861
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.12230284519911813, 'lr': 0.0036400093971619965, 'weight_decay': 0.07259447688909491, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:29:06,943] Trial 71 finished with value: 0.4731921537729096 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11135448839665767, 'lr': 0.003581153818834269, 'weight_decay': 0.07152234550254163, 'batch_size': 64}. Best is trial 62 with value: 0.4738494121182732.
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.473192
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11135448839665767, 'lr': 0.003581153818834269, 'weight_decay': 0.07152234550254163, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:30:14,645] Trial 72 finished with value: 0.46805698597531925 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.12031078641920644, 'lr': 0.0035623507869844894, 'weight_decay': 0.07398905588782931, 'batch_size': 64}. Best is trial 62 with value: 0.4738494121182732.
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.468057
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.12031078641920644, 'lr': 0.0035623507869844894, 'weight_decay': 0.07398905588782931, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:30:36,230] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.372635
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.12284981061725049, 'lr': 0.0025909269665593026, 'weight_decay': 0.0791659736809234, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:31:33,459] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.418367
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11295129709500419, 'lr': 0.00333118017356316, 'weight_decay': 0.048185147779518274, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:31:55,280] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.423913
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.13403363164525656, 'lr': 0.005860579013208639, 'weight_decay': 0.05991812168220642, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:33:04,291] Trial 76 finished with value: 0.44953428367571197 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.12112144472725093, 'lr': 0.0023460409305517743, 'weight_decay': 0.07957573506983952, 'batch_size': 64}. Best is trial 62 with value: 0.4738494121182732.
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.449534
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.12112144472725093, 'lr': 0.0023460409305517743, 'weight_decay': 0.07957573506983952, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:34:01,668] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.391946
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10885326124419174, 'lr': 0.001735083487313999, 'weight_decay': 0.05824872350565738, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:35:13,713] Trial 78 finished with value: 0.4717733984888035 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.14553078594895047, 'lr': 0.004004563667245331, 'weight_decay': 0.09985783529457035, 'batch_size': 64}. Best is trial 62 with value: 0.4738494121182732.
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.471773
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.14553078594895047, 'lr': 0.004004563667245331, 'weight_decay': 0.09985783529457035, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:35:33,735] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.367781
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.13143458094008578, 'lr': 0.004094599159352513, 'weight_decay': 0.046042477082152124, 'batch_size': 64}
+  Best Value (CSI): 0.473849
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10516290838010475, 'lr': 0.0032132950134573578, 'weight_decay': 0.07963486715216589, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:37:00,702] Trial 80 finished with value: 0.475435864212341 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}. Best is trial 80 with value: 0.475435864212341.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.475436
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:38:35,389] Trial 81 finished with value: 0.4690446720663903 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11932083077605173, 'lr': 0.005327508564084069, 'weight_decay': 0.0955542128094541, 'batch_size': 64}. Best is trial 80 with value: 0.475435864212341.
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.469045
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11932083077605173, 'lr': 0.005327508564084069, 'weight_decay': 0.0955542128094541, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:38:50,092] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.360996
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1466236461260512, 'lr': 0.00581030931160281, 'weight_decay': 0.09580554083173486, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:40:09,920] Trial 83 finished with value: 0.472596433737188 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.13864853760774892, 'lr': 0.0045085669563948464, 'weight_decay': 0.09993409982821864, 'batch_size': 64}. Best is trial 80 with value: 0.475435864212341.
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.472596
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.13864853760774892, 'lr': 0.0045085669563948464, 'weight_decay': 0.09993409982821864, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:41:30,779] Trial 84 finished with value: 0.46617379263776587 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.14271446405676863, 'lr': 0.007843433754653706, 'weight_decay': 0.0885179361034867, 'batch_size': 64}. Best is trial 80 with value: 0.475435864212341.
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.466174
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.14271446405676863, 'lr': 0.007843433754653706, 'weight_decay': 0.0885179361034867, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:41:44,591] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.350340
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.13498956855610997, 'lr': 0.00608829177479784, 'weight_decay': 0.09873587514278988, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:42:06,149] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.435933
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.15197546959070507, 'lr': 0.005053531333723819, 'weight_decay': 0.06145208310737065, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:42:27,824] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.365662
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1163624835329164, 'lr': 0.0029144293825210956, 'weight_decay': 0.047220000884809654, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:43:52,567] Trial 88 finished with value: 0.46356758019824423 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.12258940699752771, 'lr': 0.008439770077051094, 'weight_decay': 0.05342405064288168, 'batch_size': 64}. Best is trial 80 with value: 0.475435864212341.
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.463568
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.12258940699752771, 'lr': 0.008439770077051094, 'weight_decay': 0.05342405064288168, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:45:34,660] Trial 89 finished with value: 0.47435816635082356 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1384057322190941, 'lr': 0.004651191677613838, 'weight_decay': 0.0854022089004165, 'batch_size': 64}. Best is trial 80 with value: 0.475435864212341.
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.474358
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1384057322190941, 'lr': 0.004651191677613838, 'weight_decay': 0.0854022089004165, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:45:49,395] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.373752
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.13906027469882676, 'lr': 0.004158496008141223, 'weight_decay': 0.03945712957909421, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:47:19,835] Trial 91 finished with value: 0.4634292315789265 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.12713761786644925, 'lr': 0.004717639209683438, 'weight_decay': 0.08401856519228396, 'batch_size': 64}. Best is trial 80 with value: 0.475435864212341.
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.463429
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.12713761786644925, 'lr': 0.004717639209683438, 'weight_decay': 0.08401856519228396, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:48:39,581] Trial 92 finished with value: 0.473573455381163 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11235688414616969, 'lr': 0.006265638139969192, 'weight_decay': 0.06397821422856823, 'batch_size': 64}. Best is trial 80 with value: 0.475435864212341.
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.473573
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11235688414616969, 'lr': 0.006265638139969192, 'weight_decay': 0.06397821422856823, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:48:54,286] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.378601
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11254898329467997, 'lr': 0.006723318974407071, 'weight_decay': 0.06476623144532767, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:49:08,127] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.357616
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10760543728653212, 'lr': 0.008536974636213146, 'weight_decay': 0.06023221056166393, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:50:44,209] Trial 95 finished with value: 0.47511877713247946 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.14624060851532672, 'lr': 0.004378197360170068, 'weight_decay': 0.08505725369582863, 'batch_size': 64}. Best is trial 80 with value: 0.475435864212341.
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.475119
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.14624060851532672, 'lr': 0.004378197360170068, 'weight_decay': 0.08505725369582863, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:51:05,973] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.430707
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.15650543364430916, 'lr': 0.0061115730042290315, 'weight_decay': 0.08722889830883064, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:52:22,109] Trial 97 finished with value: 0.4711528919596078 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1473454102000562, 'lr': 0.002963275714592843, 'weight_decay': 0.06796800285755306, 'batch_size': 64}. Best is trial 80 with value: 0.475435864212341.
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.471153
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1473454102000562, 'lr': 0.002963275714592843, 'weight_decay': 0.06796800285755306, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:52:44,032] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.394366
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.14698174253583804, 'lr': 0.0028240234823678225, 'weight_decay': 0.05104707902052777, 'batch_size': 64}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:52:50,486] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.408163
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.16378835933153857, 'lr': 0.004665472770577183, 'weight_decay': 0.06618511843932078, 'batch_size': 256}
+  Best Value (CSI): 0.475436
+  Best Trial: 80
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4754
+Best Hyperparameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.3877
+  - 최종 CSI: 0.4082
+  - 최고 CSI: 0.4754
+  - 최저 CSI: 0.2157
+  - 평균 CSI: 0.4151
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/deepgbm_ctgan10000_busan_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 27, Best CSI: 0.4443
+  Fold 1 학습 완료 (검증 CSI: 0.4443)
+Fold 2 학습 중...
+  Early stopping at epoch 40, Best CSI: 0.4866
+  Fold 2 학습 완료 (검증 CSI: 0.4866)
+Fold 3 학습 중...
+  Early stopping at epoch 27, Best CSI: 0.4576
+  Fold 3 학습 완료 (검증 CSI: 0.4576)
+
+모든 모델 저장 완료: ../save_model/deepgbm_optima/deepgbm_ctgan10000_busan.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/deepgbm_optima/scaler/deepgbm_ctgan10000_busan_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/deepgbm_optima/deepgbm_ctgan10000_busan.pkl
+
+✓ 완료: deepgbm_ctgan10000/deepgbm_ctgan10000_busan.py (소요 시간: 8294초)
+
+----------------------------------------
+실행 중: deepgbm_ctgan10000/deepgbm_ctgan10000_daegu.py
+시작 시간: 2025-12-28 14:54:16
+----------------------------------------
+[I 2025-12-28 14:54:17,294] A new study created in memory with name: no-name-1e4d0e56-5aa9-4335-b0a9-44df45f7ae9e
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:54:59,889] Trial 0 finished with value: 0.33960186302143996 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.10048263954190001, 'lr': 0.0002745507870780533, 'weight_decay': 0.001496979780233946, 'batch_size': 256}. Best is trial 0 with value: 0.33960186302143996.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.339602
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.10048263954190001, 'lr': 0.0002745507870780533, 'weight_decay': 0.001496979780233946, 'batch_size': 256}
+  Best Value (CSI): 0.339602
+  Best Trial: 0
+  Best Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.10048263954190001, 'lr': 0.0002745507870780533, 'weight_decay': 0.001496979780233946, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:58:12,514] Trial 1 finished with value: 0.378187050254666 and parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.22155329979083938, 'lr': 0.0023309763802577183, 'weight_decay': 0.0009183235132857968, 'batch_size': 32}. Best is trial 1 with value: 0.378187050254666.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.378187
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.22155329979083938, 'lr': 0.0023309763802577183, 'weight_decay': 0.0009183235132857968, 'batch_size': 32}
+  Best Value (CSI): 0.378187
+  Best Trial: 1
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.22155329979083938, 'lr': 0.0023309763802577183, 'weight_decay': 0.0009183235132857968, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:00:30,934] Trial 2 finished with value: 0.35026477913685633 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.24210179642763666, 'lr': 0.000742302130252828, 'weight_decay': 0.0003503412323504509, 'batch_size': 32}. Best is trial 1 with value: 0.378187050254666.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.350265
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.24210179642763666, 'lr': 0.000742302130252828, 'weight_decay': 0.0003503412323504509, 'batch_size': 32}
+  Best Value (CSI): 0.378187
+  Best Trial: 1
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.22155329979083938, 'lr': 0.0023309763802577183, 'weight_decay': 0.0009183235132857968, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:04:38,292] Trial 3 finished with value: 0.3301398002787137 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.10234419782441666, 'lr': 0.00016253902796375507, 'weight_decay': 0.0006230230664403558, 'batch_size': 32}. Best is trial 1 with value: 0.378187050254666.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.330140
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.10234419782441666, 'lr': 0.00016253902796375507, 'weight_decay': 0.0006230230664403558, 'batch_size': 32}
+  Best Value (CSI): 0.378187
+  Best Trial: 1
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.22155329979083938, 'lr': 0.0023309763802577183, 'weight_decay': 0.0009183235132857968, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:05:40,296] Trial 4 finished with value: 0.33245678200602513 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.3887969411422143, 'lr': 0.00012747702678318117, 'weight_decay': 0.0002873462291200247, 'batch_size': 128}. Best is trial 1 with value: 0.378187050254666.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.332457
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.3887969411422143, 'lr': 0.00012747702678318117, 'weight_decay': 0.0002873462291200247, 'batch_size': 128}
+  Best Value (CSI): 0.378187
+  Best Trial: 1
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.22155329979083938, 'lr': 0.0023309763802577183, 'weight_decay': 0.0009183235132857968, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:06:06,739] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.216428
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3900404617305159, 'lr': 5.6741912267302266e-05, 'weight_decay': 0.00010384733848756253, 'batch_size': 32}
+  Best Value (CSI): 0.378187
+  Best Trial: 1
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.22155329979083938, 'lr': 0.0023309763802577183, 'weight_decay': 0.0009183235132857968, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:06:11,806] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.189189
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.30698604510312133, 'lr': 0.00029992139445755725, 'weight_decay': 0.03440661181292897, 'batch_size': 256}
+  Best Value (CSI): 0.378187
+  Best Trial: 1
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.22155329979083938, 'lr': 0.0023309763802577183, 'weight_decay': 0.0009183235132857968, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:06:24,384] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.173953
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.2256099266265833, 'lr': 1.7323035220239333e-05, 'weight_decay': 0.00046577441537449225, 'batch_size': 64}
+  Best Value (CSI): 0.378187
+  Best Trial: 1
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.22155329979083938, 'lr': 0.0023309763802577183, 'weight_decay': 0.0009183235132857968, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:06:29,235] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.281713
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.12815968983554082, 'lr': 0.00030377436541432297, 'weight_decay': 0.060474452061187116, 'batch_size': 256}
+  Best Value (CSI): 0.378187
+  Best Trial: 1
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.22155329979083938, 'lr': 0.0023309763802577183, 'weight_decay': 0.0009183235132857968, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:06:39,815] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.144216
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2369923546373676, 'lr': 1.1467383066888542e-05, 'weight_decay': 0.00018409455028139236, 'batch_size': 64}
+  Best Value (CSI): 0.378187
+  Best Trial: 1
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.22155329979083938, 'lr': 0.0023309763802577183, 'weight_decay': 0.0009183235132857968, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:06:48,783] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.279107
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.17658483625448684, 'lr': 0.005871469039710539, 'weight_decay': 0.005421816186441453, 'batch_size': 128}
+  Best Value (CSI): 0.378187
+  Best Trial: 1
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.22155329979083938, 'lr': 0.0023309763802577183, 'weight_decay': 0.0009183235132857968, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:07:52,242] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.339545
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.27372157370147204, 'lr': 0.0016312172583869298, 'weight_decay': 0.001606280657702201, 'batch_size': 32}
+  Best Value (CSI): 0.378187
+  Best Trial: 1
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.22155329979083938, 'lr': 0.0023309763802577183, 'weight_decay': 0.0009183235132857968, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:10:03,778] Trial 12 finished with value: 0.3575303736547419 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.1987882323157377, 'lr': 0.0010111621718003714, 'weight_decay': 0.0006788363622574628, 'batch_size': 32}. Best is trial 1 with value: 0.378187050254666.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.357530
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.1987882323157377, 'lr': 0.0010111621718003714, 'weight_decay': 0.0006788363622574628, 'batch_size': 32}
+  Best Value (CSI): 0.378187
+  Best Trial: 1
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.22155329979083938, 'lr': 0.0023309763802577183, 'weight_decay': 0.0009183235132857968, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:12:08,854] Trial 13 finished with value: 0.380394119570451 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.18477220842484451, 'lr': 0.0022024613385582784, 'weight_decay': 0.006098907755954173, 'batch_size': 32}. Best is trial 13 with value: 0.380394119570451.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.380394
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.18477220842484451, 'lr': 0.0022024613385582784, 'weight_decay': 0.006098907755954173, 'batch_size': 32}
+  Best Value (CSI): 0.380394
+  Best Trial: 13
+  Best Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.18477220842484451, 'lr': 0.0022024613385582784, 'weight_decay': 0.006098907755954173, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:12:28,091] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.142020
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.16258022061872707, 'lr': 0.009270721771587715, 'weight_decay': 0.007325433261845298, 'batch_size': 32}
+  Best Value (CSI): 0.380394
+  Best Trial: 13
+  Best Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.18477220842484451, 'lr': 0.0022024613385582784, 'weight_decay': 0.006098907755954173, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:14:55,717] Trial 15 finished with value: 0.39461737943964487 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.1924651597461154, 'lr': 0.0030885495511274873, 'weight_decay': 0.010611090514443111, 'batch_size': 32}. Best is trial 15 with value: 0.39461737943964487.
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.394617
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.1924651597461154, 'lr': 0.0030885495511274873, 'weight_decay': 0.010611090514443111, 'batch_size': 32}
+  Best Value (CSI): 0.394617
+  Best Trial: 15
+  Best Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.1924651597461154, 'lr': 0.0030885495511274873, 'weight_decay': 0.010611090514443111, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:15:36,502] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.328571
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.1692930291006192, 'lr': 0.0031251988683074893, 'weight_decay': 0.013115400056001382, 'batch_size': 32}
+  Best Value (CSI): 0.394617
+  Best Trial: 15
+  Best Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.1924651597461154, 'lr': 0.0030885495511274873, 'weight_decay': 0.010611090514443111, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:16:53,214] Trial 17 finished with value: 0.4000563717405678 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}. Best is trial 17 with value: 0.4000563717405678.
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.400056
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:17:13,935] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.325444
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.13852886777007783, 'lr': 0.0049130774112810475, 'weight_decay': 0.019039109136041434, 'batch_size': 64}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:17:36,091] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.325820
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.14378125310157108, 'lr': 0.000853084962796281, 'weight_decay': 0.08374313816560215, 'batch_size': 64}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:17:56,872] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.315197
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.20430798181732893, 'lr': 0.009481253488026937, 'weight_decay': 0.003510821113638696, 'batch_size': 64}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:18:11,231] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.323587
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.18849418379310373, 'lr': 0.0037008101568512786, 'weight_decay': 0.009709737963720209, 'batch_size': 128}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:18:34,712] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.315682
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.15679242807934113, 'lr': 0.001762002658743736, 'weight_decay': 0.023012999699570953, 'batch_size': 64}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:19:15,853] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.334004
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.18552991449244122, 'lr': 0.004314857338256199, 'weight_decay': 0.004054433717502271, 'batch_size': 32}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:21:19,243] Trial 24 finished with value: 0.3727207971690169 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.13495439472033993, 'lr': 0.0023581672617071455, 'weight_decay': 0.00925572156828387, 'batch_size': 32}. Best is trial 17 with value: 0.4000563717405678.
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.372721
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.13495439472033993, 'lr': 0.0023581672617071455, 'weight_decay': 0.00925572156828387, 'batch_size': 32}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:21:30,130] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.299228
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2023411205642721, 'lr': 0.0013934789006436795, 'weight_decay': 0.005917705091244245, 'batch_size': 64}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:21:37,006] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.281588
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.16082080619188416, 'lr': 0.0007285685942847825, 'weight_decay': 0.014537818071242145, 'batch_size': 128}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:22:02,727] Trial 27 finished with value: 0.3580498689706458 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.12046690457923395, 'lr': 0.00285404198894877, 'weight_decay': 0.032440512033505245, 'batch_size': 256}. Best is trial 17 with value: 0.4000563717405678.
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.358050
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.12046690457923395, 'lr': 0.00285404198894877, 'weight_decay': 0.032440512033505245, 'batch_size': 256}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+[I 2025-12-28 15:23:21,663] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.364070
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.1594196831432449, 'lr': 0.005012351768452008, 'weight_decay': 0.0024195409645248636, 'batch_size': 32}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:23:56,226] Trial 29 finished with value: 0.3418937482231015 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.10026137768943591, 'lr': 0.0005071182078194477, 'weight_decay': 0.0025616810444000424, 'batch_size': 256}. Best is trial 17 with value: 0.4000563717405678.
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.341894
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.10026137768943591, 'lr': 0.0005071182078194477, 'weight_decay': 0.0025616810444000424, 'batch_size': 256}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:24:19,863] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.283422
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.18096919005163525, 'lr': 0.0014103091954209583, 'weight_decay': 0.010927837450943074, 'batch_size': 64}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:26:58,999] Trial 31 finished with value: 0.37935287568317616 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.21905504111672588, 'lr': 0.002465022033722438, 'weight_decay': 0.0013269388987709764, 'batch_size': 32}. Best is trial 17 with value: 0.4000563717405678.
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.379353
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.21905504111672588, 'lr': 0.002465022033722438, 'weight_decay': 0.0013269388987709764, 'batch_size': 32}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:27:44,971] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.314991
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.20884665205982916, 'lr': 0.0022217758252304656, 'weight_decay': 0.0013357889768425624, 'batch_size': 32}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:30:45,257] Trial 33 finished with value: 0.3811670100504399 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.2200406257411401, 'lr': 0.0031756337291676904, 'weight_decay': 0.005099832816906901, 'batch_size': 32}. Best is trial 17 with value: 0.4000563717405678.
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.381167
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.2200406257411401, 'lr': 0.0031756337291676904, 'weight_decay': 0.005099832816906901, 'batch_size': 32}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:31:40,437] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.339583
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.18033519508295007, 'lr': 0.006467974921124283, 'weight_decay': 0.005628950349856029, 'batch_size': 32}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:34:07,661] Trial 35 finished with value: 0.38558896650370217 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.24809515970884113, 'lr': 0.003314829018161552, 'weight_decay': 0.004163944482652498, 'batch_size': 32}. Best is trial 17 with value: 0.4000563717405678.
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.385589
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.24809515970884113, 'lr': 0.003314829018161552, 'weight_decay': 0.004163944482652498, 'batch_size': 32}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:34:53,121] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.297794
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.25497131234602605, 'lr': 0.0037933452381971084, 'weight_decay': 0.0038310440362187485, 'batch_size': 32}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:35:22,208] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.308129
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.25670370008547383, 'lr': 0.006934659694851737, 'weight_decay': 0.008708763672140601, 'batch_size': 32}
+  Best Value (CSI): 0.400056
+  Best Trial: 17
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14549554721861246, 'lr': 0.0042283154042823574, 'weight_decay': 0.01600657222055848, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:37:53,008] Trial 38 finished with value: 0.40295645783222445 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.2331537495643457, 'lr': 0.003807932680873466, 'weight_decay': 0.016284937836895323, 'batch_size': 32}. Best is trial 38 with value: 0.40295645783222445.
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.402956
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.2331537495643457, 'lr': 0.003807932680873466, 'weight_decay': 0.016284937836895323, 'batch_size': 32}
+  Best Value (CSI): 0.402956
+  Best Trial: 38
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.2331537495643457, 'lr': 0.003807932680873466, 'weight_decay': 0.016284937836895323, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:38:07,573] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.338947
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.2801036692348856, 'lr': 0.007012452889600593, 'weight_decay': 0.015688854761380218, 'batch_size': 128}
+  Best Value (CSI): 0.402956
+  Best Trial: 38
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.2331537495643457, 'lr': 0.003807932680873466, 'weight_decay': 0.016284937836895323, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:38:17,298] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.311321
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.2386862425847814, 'lr': 0.004478105841634324, 'weight_decay': 0.025823948725600267, 'batch_size': 256}
+  Best Value (CSI): 0.402956
+  Best Trial: 38
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.2331537495643457, 'lr': 0.003807932680873466, 'weight_decay': 0.016284937836895323, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:41:22,270] Trial 41 finished with value: 0.40664550112368597 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.22224770351769335, 'lr': 0.0031850146121884565, 'weight_decay': 0.012819131240404945, 'batch_size': 32}. Best is trial 41 with value: 0.40664550112368597.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.406646
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.22224770351769335, 'lr': 0.0031850146121884565, 'weight_decay': 0.012819131240404945, 'batch_size': 32}
+  Best Value (CSI): 0.406646
+  Best Trial: 41
+  Best Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.22224770351769335, 'lr': 0.0031850146121884565, 'weight_decay': 0.012819131240404945, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:42:05,266] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.333333
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.22485139046358923, 'lr': 0.0034303581376202356, 'weight_decay': 0.012504761765884595, 'batch_size': 32}
+  Best Value (CSI): 0.406646
+  Best Trial: 41
+  Best Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.22224770351769335, 'lr': 0.0031850146121884565, 'weight_decay': 0.012819131240404945, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:42:45,376] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.316633
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23564010646592504, 'lr': 0.0013473863399158202, 'weight_decay': 0.017353789613487547, 'batch_size': 32}
+  Best Value (CSI): 0.406646
+  Best Trial: 41
+  Best Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.22224770351769335, 'lr': 0.0031850146121884565, 'weight_decay': 0.012819131240404945, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:43:06,863] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.302120
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2123574084395499, 'lr': 0.005857530768100317, 'weight_decay': 0.04076612701676704, 'batch_size': 32}
+  Best Value (CSI): 0.406646
+  Best Trial: 41
+  Best Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.22224770351769335, 'lr': 0.0031850146121884565, 'weight_decay': 0.012819131240404945, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:43:53,003] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.325349
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.19375415461702583, 'lr': 0.0018377324818930816, 'weight_decay': 0.01983152938067292, 'batch_size': 32}
+  Best Value (CSI): 0.406646
+  Best Trial: 41
+  Best Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.22224770351769335, 'lr': 0.0031850146121884565, 'weight_decay': 0.012819131240404945, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:45:06,371] Trial 46 finished with value: 0.37992762877813924 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.24701408663501526, 'lr': 0.009864887333526164, 'weight_decay': 0.007671297932674995, 'batch_size': 64}. Best is trial 41 with value: 0.40664550112368597.
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.379928
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.24701408663501526, 'lr': 0.009864887333526164, 'weight_decay': 0.007671297932674995, 'batch_size': 64}
+  Best Value (CSI): 0.406646
+  Best Trial: 41
+  Best Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.22224770351769335, 'lr': 0.0031850146121884565, 'weight_decay': 0.012819131240404945, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:45:47,317] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.345416
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.26824946056927285, 'lr': 0.002635588805095361, 'weight_decay': 0.01055055196168097, 'batch_size': 32}
+  Best Value (CSI): 0.406646
+  Best Trial: 41
+  Best Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.22224770351769335, 'lr': 0.0031850146121884565, 'weight_decay': 0.012819131240404945, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:48:39,163] Trial 48 finished with value: 0.42145417609942265 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.421454
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:49:29,063] Trial 49 finished with value: 0.4068470091998704 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23226018453531014, 'lr': 0.005206590835911559, 'weight_decay': 0.04146263223013787, 'batch_size': 128}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.406847
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23226018453531014, 'lr': 0.005206590835911559, 'weight_decay': 0.04146263223013787, 'batch_size': 128}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:49:41,875] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.364606
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23249384831817896, 'lr': 0.007857233876157093, 'weight_decay': 0.04779274217898951, 'batch_size': 128}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:49:55,191] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.349036
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.21274418709485665, 'lr': 0.004610268512836974, 'weight_decay': 0.029319070111060556, 'batch_size': 128}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:50:07,983] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.351515
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.19619780462285477, 'lr': 0.005012276268645425, 'weight_decay': 0.02343045489049477, 'batch_size': 128}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:50:20,925] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.325758
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2258126480060682, 'lr': 0.005788950250086275, 'weight_decay': 0.036082202296057, 'batch_size': 128}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:50:41,799] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.338677
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.20151910041807816, 'lr': 0.001985057449992942, 'weight_decay': 0.056769697494184486, 'batch_size': 64}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:51:20,761] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.372043
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23158974564775106, 'lr': 0.00398657424787013, 'weight_decay': 0.015653997838963384, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:51:30,250] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.342553
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.17079815320472425, 'lr': 0.0073595517888106945, 'weight_decay': 0.021819150180444478, 'batch_size': 256}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:52:06,930] Trial 57 finished with value: 0.3687659290710921 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.21336219315347604, 'lr': 0.002925132271490882, 'weight_decay': 0.031280854643638986, 'batch_size': 128}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.368766
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.21336219315347604, 'lr': 0.002925132271490882, 'weight_decay': 0.031280854643638986, 'batch_size': 128}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:53:33,366] Trial 58 finished with value: 0.39936790701198577 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.1905684088156614, 'lr': 0.008469987984473362, 'weight_decay': 0.012933105493036955, 'batch_size': 64}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.399368
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.1905684088156614, 'lr': 0.008469987984473362, 'weight_decay': 0.012933105493036955, 'batch_size': 64}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:54:14,485] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.382096
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.24173706505852102, 'lr': 0.008762272393309338, 'weight_decay': 0.04626382716456711, 'batch_size': 64}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:55:36,538] Trial 60 finished with value: 0.3848774718042837 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.1487706625022873, 'lr': 0.005805394947233362, 'weight_decay': 0.013095605303222464, 'batch_size': 64}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.384877
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.1487706625022873, 'lr': 0.005805394947233362, 'weight_decay': 0.013095605303222464, 'batch_size': 64}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:55:59,453] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.367059
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.1958102282739253, 'lr': 0.009919418871469524, 'weight_decay': 0.018543587937345493, 'batch_size': 64}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:56:25,483] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.375286
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.18685303040593396, 'lr': 0.00399249857796903, 'weight_decay': 0.024774146741234238, 'batch_size': 64}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:56:36,394] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.295539
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.1717267812500946, 'lr': 0.002740554054051416, 'weight_decay': 0.007621198778933801, 'batch_size': 64}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:58:57,314] Trial 64 finished with value: 0.39822335379713375 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.20360696974109407, 'lr': 0.005455502272971773, 'weight_decay': 0.026327451893355905, 'batch_size': 32}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.398223
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.20360696974109407, 'lr': 0.005455502272971773, 'weight_decay': 0.026327451893355905, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:01:26,250] Trial 65 finished with value: 0.4100355390772889 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2044385538858529, 'lr': 0.007565609810659757, 'weight_decay': 0.028303648802007017, 'batch_size': 32}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.410036
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2044385538858529, 'lr': 0.007565609810659757, 'weight_decay': 0.028303648802007017, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:04:06,989] Trial 66 finished with value: 0.406772216728585 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.21973247126026793, 'lr': 0.007196792694573845, 'weight_decay': 0.03925927159021245, 'batch_size': 32}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.406772
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.21973247126026793, 'lr': 0.007196792694573845, 'weight_decay': 0.03925927159021245, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:07:15,372] Trial 67 finished with value: 0.4139072734590785 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.22733156939039767, 'lr': 0.007122653237615367, 'weight_decay': 0.037180831575158044, 'batch_size': 32}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.413907
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.22733156939039767, 'lr': 0.007122653237615367, 'weight_decay': 0.037180831575158044, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 16:08:17,808] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.383908
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.22439658111306635, 'lr': 0.006919926873655944, 'weight_decay': 0.07690462320789974, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:11:08,649] Trial 69 finished with value: 0.41299611339727144 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.21866999306443635, 'lr': 0.0076109067142395665, 'weight_decay': 0.0367317698662934, 'batch_size': 32}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.412996
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.21866999306443635, 'lr': 0.0076109067142395665, 'weight_decay': 0.0367317698662934, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 16:11:27,222] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.283654
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2183329985014209, 'lr': 0.008112519762534414, 'weight_decay': 0.035679005495839425, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:13:33,932] Trial 71 finished with value: 0.41905412149125865 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2287060274529267, 'lr': 0.00547709275724029, 'weight_decay': 0.053076506127540495, 'batch_size': 32}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.419054
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2287060274529267, 'lr': 0.00547709275724029, 'weight_decay': 0.053076506127540495, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:16:16,657] Trial 72 finished with value: 0.4186014599900927 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.20770082226395975, 'lr': 0.00606807599628275, 'weight_decay': 0.06567129952153568, 'batch_size': 32}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.418601
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.20770082226395975, 'lr': 0.00606807599628275, 'weight_decay': 0.06567129952153568, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:18:47,045] Trial 73 finished with value: 0.40864056369216456 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.20924041176583225, 'lr': 0.006376913704037816, 'weight_decay': 0.06702888297269836, 'batch_size': 32}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.408641
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.20924041176583225, 'lr': 0.006376913704037816, 'weight_decay': 0.06702888297269836, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:21:55,741] Trial 74 finished with value: 0.41871023894758524 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24142825573496157, 'lr': 0.005427245020870652, 'weight_decay': 0.09447814212358446, 'batch_size': 32}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.418710
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24142825573496157, 'lr': 0.005427245020870652, 'weight_decay': 0.09447814212358446, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:25:12,038] Trial 75 finished with value: 0.4116528566426125 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.20830097869648045, 'lr': 0.006340004304316706, 'weight_decay': 0.09054060245619273, 'batch_size': 32}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.411653
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.20830097869648045, 'lr': 0.006340004304316706, 'weight_decay': 0.09054060245619273, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:28:16,259] Trial 76 finished with value: 0.42023024352411414 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24551386128059804, 'lr': 0.004670464514431808, 'weight_decay': 0.09122776608314831, 'batch_size': 32}. Best is trial 48 with value: 0.42145417609942265.
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.420230
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24551386128059804, 'lr': 0.004670464514431808, 'weight_decay': 0.09122776608314831, 'batch_size': 32}
+  Best Value (CSI): 0.421454
+  Best Trial: 48
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23116500347660948, 'lr': 0.0043001569401369295, 'weight_decay': 0.02502971721388179, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:31:46,788] Trial 77 finished with value: 0.4336459584801961 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24410041707618135, 'lr': 0.004732944297044856, 'weight_decay': 0.0974623452373224, 'batch_size': 32}. Best is trial 77 with value: 0.4336459584801961.
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.433646
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24410041707618135, 'lr': 0.004732944297044856, 'weight_decay': 0.0974623452373224, 'batch_size': 32}
+  Best Value (CSI): 0.433646
+  Best Trial: 77
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24410041707618135, 'lr': 0.004732944297044856, 'weight_decay': 0.0974623452373224, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:34:15,274] Trial 78 finished with value: 0.4088962099990748 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24879222983892657, 'lr': 0.004637999773823381, 'weight_decay': 0.09422939828215657, 'batch_size': 32}. Best is trial 77 with value: 0.4336459584801961.
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.408896
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24879222983892657, 'lr': 0.004637999773823381, 'weight_decay': 0.09422939828215657, 'batch_size': 32}
+  Best Value (CSI): 0.433646
+  Best Trial: 77
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24410041707618135, 'lr': 0.004732944297044856, 'weight_decay': 0.0974623452373224, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:37:26,807] Trial 79 finished with value: 0.4194113008104796 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2579362616190441, 'lr': 0.004608913401491574, 'weight_decay': 0.07371571181410137, 'batch_size': 32}. Best is trial 77 with value: 0.4336459584801961.
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.419411
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2579362616190441, 'lr': 0.004608913401491574, 'weight_decay': 0.07371571181410137, 'batch_size': 32}
+  Best Value (CSI): 0.433646
+  Best Trial: 77
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24410041707618135, 'lr': 0.004732944297044856, 'weight_decay': 0.0974623452373224, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:40:11,988] Trial 80 finished with value: 0.4149447358213268 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2620328161630937, 'lr': 0.0035364091522824192, 'weight_decay': 0.09924501792907728, 'batch_size': 32}. Best is trial 77 with value: 0.4336459584801961.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.414945
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2620328161630937, 'lr': 0.0035364091522824192, 'weight_decay': 0.09924501792907728, 'batch_size': 32}
+  Best Value (CSI): 0.433646
+  Best Trial: 77
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24410041707618135, 'lr': 0.004732944297044856, 'weight_decay': 0.0974623452373224, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:43:08,486] Trial 81 finished with value: 0.4267709732798906 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2627913385028543, 'lr': 0.0038005715120737068, 'weight_decay': 0.07462341088349138, 'batch_size': 32}. Best is trial 77 with value: 0.4336459584801961.
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.426771
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2627913385028543, 'lr': 0.0038005715120737068, 'weight_decay': 0.07462341088349138, 'batch_size': 32}
+  Best Value (CSI): 0.433646
+  Best Trial: 77
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24410041707618135, 'lr': 0.004732944297044856, 'weight_decay': 0.0974623452373224, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:46:19,377] Trial 82 finished with value: 0.43887148648963953 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.26535220341982607, 'lr': 0.003651382593765993, 'weight_decay': 0.07077194775760214, 'batch_size': 32}. Best is trial 82 with value: 0.43887148648963953.
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.438871
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.26535220341982607, 'lr': 0.003651382593765993, 'weight_decay': 0.07077194775760214, 'batch_size': 32}
+  Best Value (CSI): 0.438871
+  Best Trial: 82
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.26535220341982607, 'lr': 0.003651382593765993, 'weight_decay': 0.07077194775760214, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:48:53,951] Trial 83 finished with value: 0.43350709219849737 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2805473184485389, 'lr': 0.0024966441505826693, 'weight_decay': 0.07471475397380463, 'batch_size': 32}. Best is trial 82 with value: 0.43887148648963953.
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.433507
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2805473184485389, 'lr': 0.0024966441505826693, 'weight_decay': 0.07471475397380463, 'batch_size': 32}
+  Best Value (CSI): 0.438871
+  Best Trial: 82
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.26535220341982607, 'lr': 0.003651382593765993, 'weight_decay': 0.07077194775760214, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:51:36,633] Trial 84 finished with value: 0.4237787808223923 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.28033901284256657, 'lr': 0.002520707722208588, 'weight_decay': 0.07175925280524864, 'batch_size': 32}. Best is trial 82 with value: 0.43887148648963953.
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.423779
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.28033901284256657, 'lr': 0.002520707722208588, 'weight_decay': 0.07175925280524864, 'batch_size': 32}
+  Best Value (CSI): 0.438871
+  Best Trial: 82
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.26535220341982607, 'lr': 0.003651382593765993, 'weight_decay': 0.07077194775760214, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:54:20,276] Trial 85 finished with value: 0.430459197811045 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.27955479232846503, 'lr': 0.0023079514190269324, 'weight_decay': 0.07795637111854702, 'batch_size': 32}. Best is trial 82 with value: 0.43887148648963953.
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.430459
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.27955479232846503, 'lr': 0.0023079514190269324, 'weight_decay': 0.07795637111854702, 'batch_size': 32}
+  Best Value (CSI): 0.438871
+  Best Trial: 82
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.26535220341982607, 'lr': 0.003651382593765993, 'weight_decay': 0.07077194775760214, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 16:54:50,744] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.343137
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.28231132052934665, 'lr': 0.0023232322340101673, 'weight_decay': 0.07289883843242868, 'batch_size': 32}
+  Best Value (CSI): 0.438871
+  Best Trial: 82
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.26535220341982607, 'lr': 0.003651382593765993, 'weight_decay': 0.07077194775760214, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:57:04,672] Trial 87 finished with value: 0.4390250453058284 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}. Best is trial 87 with value: 0.4390250453058284.
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.439025
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+  Best Value (CSI): 0.439025
+  Best Trial: 87
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:59:31,109] Trial 88 finished with value: 0.42285998951389975 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2911536842638407, 'lr': 0.0020898070139619045, 'weight_decay': 0.058777191590226804, 'batch_size': 32}. Best is trial 87 with value: 0.4390250453058284.
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.422860
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2911536842638407, 'lr': 0.0020898070139619045, 'weight_decay': 0.058777191590226804, 'batch_size': 32}
+  Best Value (CSI): 0.439025
+  Best Trial: 87
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:00:10,850] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.356077
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.28347326164910897, 'lr': 0.0021568717913645714, 'weight_decay': 0.05838825867869429, 'batch_size': 32}
+  Best Value (CSI): 0.439025
+  Best Trial: 87
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:00:15,321] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.311538
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2935139811267915, 'lr': 0.0016911140289507722, 'weight_decay': 0.06322308068596527, 'batch_size': 256}
+  Best Value (CSI): 0.439025
+  Best Trial: 87
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:02:42,027] Trial 91 finished with value: 0.4309823523652951 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.27171099481746264, 'lr': 0.002468130755060003, 'weight_decay': 0.08041006342147479, 'batch_size': 32}. Best is trial 87 with value: 0.4390250453058284.
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.430982
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.27171099481746264, 'lr': 0.002468130755060003, 'weight_decay': 0.08041006342147479, 'batch_size': 32}
+  Best Value (CSI): 0.439025
+  Best Trial: 87
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:05:42,588] Trial 92 finished with value: 0.4245009568961313 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.26983249591031966, 'lr': 0.002606599914736537, 'weight_decay': 0.07890547702174526, 'batch_size': 32}. Best is trial 87 with value: 0.4390250453058284.
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.424501
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.26983249591031966, 'lr': 0.002606599914736537, 'weight_decay': 0.07890547702174526, 'batch_size': 32}
+  Best Value (CSI): 0.439025
+  Best Trial: 87
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:08:13,837] Trial 93 finished with value: 0.42238462025647566 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2732389590474488, 'lr': 0.0026170588598050178, 'weight_decay': 0.07791997294371439, 'batch_size': 32}. Best is trial 87 with value: 0.4390250453058284.
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.422385
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2732389590474488, 'lr': 0.0026170588598050178, 'weight_decay': 0.07791997294371439, 'batch_size': 32}
+  Best Value (CSI): 0.439025
+  Best Trial: 87
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:08:33,459] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.335470
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29101147699248886, 'lr': 0.001955437589587563, 'weight_decay': 0.07933581475488519, 'batch_size': 32}
+  Best Value (CSI): 0.439025
+  Best Trial: 87
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:08:50,498] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.315888
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2687392075330634, 'lr': 0.0015733024558478752, 'weight_decay': 0.052703816757787056, 'batch_size': 32}
+  Best Value (CSI): 0.439025
+  Best Trial: 87
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:11:32,329] Trial 96 finished with value: 0.4231507763328064 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.3035920634866548, 'lr': 0.0028693459620136768, 'weight_decay': 0.061463871632928636, 'batch_size': 32}. Best is trial 87 with value: 0.4390250453058284.
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.423151
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.3035920634866548, 'lr': 0.0028693459620136768, 'weight_decay': 0.061463871632928636, 'batch_size': 32}
+  Best Value (CSI): 0.439025
+  Best Trial: 87
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:14:15,498] Trial 97 finished with value: 0.42410064172874024 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.3020755379445224, 'lr': 0.002925937276035293, 'weight_decay': 0.08297738255488343, 'batch_size': 32}. Best is trial 87 with value: 0.4390250453058284.
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.424101
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.3020755379445224, 'lr': 0.002925937276035293, 'weight_decay': 0.08297738255488343, 'batch_size': 32}
+  Best Value (CSI): 0.439025
+  Best Trial: 87
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:16:41,182] Trial 98 finished with value: 0.42293613199096675 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.26562598128935794, 'lr': 0.003422584309241772, 'weight_decay': 0.07999711628016662, 'batch_size': 32}. Best is trial 87 with value: 0.4390250453058284.
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.422936
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.26562598128935794, 'lr': 0.003422584309241772, 'weight_decay': 0.07999711628016662, 'batch_size': 32}
+  Best Value (CSI): 0.439025
+  Best Trial: 87
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:16:59,948] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.333333
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2562045077269126, 'lr': 0.0025101616710141274, 'weight_decay': 0.047830149039415085, 'batch_size': 32}
+  Best Value (CSI): 0.439025
+  Best Trial: 87
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4390
+Best Hyperparameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.3396
+  - 최종 CSI: 0.3333
+  - 최고 CSI: 0.4390
+  - 최저 CSI: 0.1420
+  - 평균 CSI: 0.3576
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/deepgbm_ctgan10000_daegu_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 40, Best CSI: 0.4199
+  Fold 1 학습 완료 (검증 CSI: 0.4199)
+Fold 2 학습 중...
+  Early stopping at epoch 33, Best CSI: 0.4505
+  Fold 2 학습 완료 (검증 CSI: 0.4505)
+Fold 3 학습 중...
+  Early stopping at epoch 39, Best CSI: 0.4099
+  Fold 3 학습 완료 (검증 CSI: 0.4099)
+
+모든 모델 저장 완료: ../save_model/deepgbm_optima/deepgbm_ctgan10000_daegu.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/deepgbm_optima/scaler/deepgbm_ctgan10000_daegu_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/deepgbm_optima/deepgbm_ctgan10000_daegu.pkl
+
+✓ 완료: deepgbm_ctgan10000/deepgbm_ctgan10000_daegu.py (소요 시간: 8754초)
+
+----------------------------------------
+실행 중: deepgbm_ctgan10000/deepgbm_ctgan10000_daejeon.py
+시작 시간: 2025-12-28 17:20:10
+----------------------------------------
+[I 2025-12-28 17:20:11,320] A new study created in memory with name: no-name-964a1216-a290-4083-912e-508929ca66c4
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:21:19,419] Trial 0 finished with value: 0.47091280722131773 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.17390268033497497, 'lr': 0.004799108459451018, 'weight_decay': 0.0006664019304836773, 'batch_size': 128}. Best is trial 0 with value: 0.47091280722131773.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.470913
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.17390268033497497, 'lr': 0.004799108459451018, 'weight_decay': 0.0006664019304836773, 'batch_size': 128}
+  Best Value (CSI): 0.470913
+  Best Trial: 0
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.17390268033497497, 'lr': 0.004799108459451018, 'weight_decay': 0.0006664019304836773, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:23:48,448] Trial 1 finished with value: 0.3966883031027069 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.15238152933956228, 'lr': 5.9296846344998454e-05, 'weight_decay': 0.0025513910498295525, 'batch_size': 64}. Best is trial 0 with value: 0.47091280722131773.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.396688
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.15238152933956228, 'lr': 5.9296846344998454e-05, 'weight_decay': 0.0025513910498295525, 'batch_size': 64}
+  Best Value (CSI): 0.470913
+  Best Trial: 0
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.17390268033497497, 'lr': 0.004799108459451018, 'weight_decay': 0.0006664019304836773, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:25:22,593] Trial 2 finished with value: 0.38109723850909494 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.20306034610770032, 'lr': 1.711285001790452e-05, 'weight_decay': 0.0008665871853341875, 'batch_size': 128}. Best is trial 0 with value: 0.47091280722131773.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.381097
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.20306034610770032, 'lr': 1.711285001790452e-05, 'weight_decay': 0.0008665871853341875, 'batch_size': 128}
+  Best Value (CSI): 0.470913
+  Best Trial: 0
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.17390268033497497, 'lr': 0.004799108459451018, 'weight_decay': 0.0006664019304836773, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:26:48,345] Trial 3 finished with value: 0.46919425923951713 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.357622005513733, 'lr': 0.0008432612884923642, 'weight_decay': 0.02771504880690769, 'batch_size': 64}. Best is trial 0 with value: 0.47091280722131773.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.469194
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.357622005513733, 'lr': 0.0008432612884923642, 'weight_decay': 0.02771504880690769, 'batch_size': 64}
+  Best Value (CSI): 0.470913
+  Best Trial: 0
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.17390268033497497, 'lr': 0.004799108459451018, 'weight_decay': 0.0006664019304836773, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:29:57,645] Trial 4 finished with value: 0.4172821776028532 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.16094916613594742, 'lr': 8.457040341630836e-05, 'weight_decay': 0.0005325836021360593, 'batch_size': 32}. Best is trial 0 with value: 0.47091280722131773.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.417282
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.16094916613594742, 'lr': 8.457040341630836e-05, 'weight_decay': 0.0005325836021360593, 'batch_size': 32}
+  Best Value (CSI): 0.470913
+  Best Trial: 0
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.17390268033497497, 'lr': 0.004799108459451018, 'weight_decay': 0.0006664019304836773, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:30:09,696] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.313209
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.3757101393534783, 'lr': 3.13631846883115e-05, 'weight_decay': 0.000167927293001174, 'batch_size': 64}
+  Best Value (CSI): 0.470913
+  Best Trial: 0
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.17390268033497497, 'lr': 0.004799108459451018, 'weight_decay': 0.0006664019304836773, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:31:49,735] Trial 6 finished with value: 0.46694427548927225 and parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.38560501080196474, 'lr': 0.0007003334636300012, 'weight_decay': 0.006145169968373231, 'batch_size': 64}. Best is trial 0 with value: 0.47091280722131773.
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.466944
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.38560501080196474, 'lr': 0.0007003334636300012, 'weight_decay': 0.006145169968373231, 'batch_size': 64}
+  Best Value (CSI): 0.470913
+  Best Trial: 0
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.17390268033497497, 'lr': 0.004799108459451018, 'weight_decay': 0.0006664019304836773, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:31:54,727] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.175655
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.12475260003909754, 'lr': 2.8443065231174017e-05, 'weight_decay': 0.00012100742042434264, 'batch_size': 256}
+  Best Value (CSI): 0.470913
+  Best Trial: 0
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.17390268033497497, 'lr': 0.004799108459451018, 'weight_decay': 0.0006664019304836773, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:34:06,654] Trial 8 finished with value: 0.4865570363639032 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3335192709099133, 'lr': 0.001658609697657929, 'weight_decay': 0.02935973246541922, 'batch_size': 32}. Best is trial 8 with value: 0.4865570363639032.
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.486557
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3335192709099133, 'lr': 0.001658609697657929, 'weight_decay': 0.02935973246541922, 'batch_size': 32}
+  Best Value (CSI): 0.486557
+  Best Trial: 8
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3335192709099133, 'lr': 0.001658609697657929, 'weight_decay': 0.02935973246541922, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:34:31,875] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.390077
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.39374589522465897, 'lr': 0.001594977694675972, 'weight_decay': 0.002162277029541997, 'batch_size': 64}
+  Best Value (CSI): 0.486557
+  Best Trial: 8
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3335192709099133, 'lr': 0.001658609697657929, 'weight_decay': 0.02935973246541922, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:37:57,982] Trial 10 finished with value: 0.5022344337726664 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.29955819082733437, 'lr': 0.009777072830587137, 'weight_decay': 0.08870699223556335, 'batch_size': 32}. Best is trial 10 with value: 0.5022344337726664.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.502234
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.29955819082733437, 'lr': 0.009777072830587137, 'weight_decay': 0.08870699223556335, 'batch_size': 32}
+  Best Value (CSI): 0.502234
+  Best Trial: 10
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.29955819082733437, 'lr': 0.009777072830587137, 'weight_decay': 0.08870699223556335, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:38:17,430] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.330309
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.30401618192102503, 'lr': 0.009996718108260706, 'weight_decay': 0.08874969033278796, 'batch_size': 32}
+  Best Value (CSI): 0.502234
+  Best Trial: 10
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.29955819082733437, 'lr': 0.009777072830587137, 'weight_decay': 0.08870699223556335, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:41:42,016] Trial 12 finished with value: 0.5101248146449113 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.510125
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:42:06,472] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.366667
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.26903915626912817, 'lr': 0.00844809745303296, 'weight_decay': 0.0815958464589299, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:44:34,916] Trial 14 finished with value: 0.49774830612605087 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2515666618686739, 'lr': 0.003421675045911773, 'weight_decay': 0.019692998745633985, 'batch_size': 32}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.497748
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2515666618686739, 'lr': 0.003421675045911773, 'weight_decay': 0.019692998745633985, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:45:04,791] Trial 15 finished with value: 0.4833623544177786 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2927453433088182, 'lr': 0.0035600698291556017, 'weight_decay': 0.09934047385924903, 'batch_size': 256}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.483362
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2927453433088182, 'lr': 0.0035600698291556017, 'weight_decay': 0.09934047385924903, 'batch_size': 256}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:48:00,027] Trial 16 finished with value: 0.4482004416498409 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.22622434320527612, 'lr': 0.0002503313245716792, 'weight_decay': 0.012362275435169177, 'batch_size': 32}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.448200
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.22622434320527612, 'lr': 0.0002503313245716792, 'weight_decay': 0.012362275435169177, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:48:24,354] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.379859
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.3183736308593409, 'lr': 0.009288432268907354, 'weight_decay': 0.049761678664403425, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:49:14,710] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.400000
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.27308844430955515, 'lr': 0.002224066549745564, 'weight_decay': 0.012027983137257506, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:49:23,062] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.365091
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.3423545521620392, 'lr': 0.00039961875580443626, 'weight_decay': 0.044343959617037194, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:49:56,807] Trial 20 finished with value: 0.4818067155568582 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.2996520802406873, 'lr': 0.004474181005361122, 'weight_decay': 0.04940550045195796, 'batch_size': 256}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.481807
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.2996520802406873, 'lr': 0.004474181005361122, 'weight_decay': 0.04940550045195796, 'batch_size': 256}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:52:16,120] Trial 21 finished with value: 0.4887879197351149 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.24330859482971923, 'lr': 0.003254579554696712, 'weight_decay': 0.01980122640043668, 'batch_size': 32}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.488788
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.24330859482971923, 'lr': 0.003254579554696712, 'weight_decay': 0.01980122640043668, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:55:30,946] Trial 22 finished with value: 0.501421797238383 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.26532681366022953, 'lr': 0.004489141122021316, 'weight_decay': 0.050438880265888004, 'batch_size': 32}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.501422
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.26532681366022953, 'lr': 0.004489141122021316, 'weight_decay': 0.050438880265888004, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:58:17,800] Trial 23 finished with value: 0.49621995589922263 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.27805030708182077, 'lr': 0.006711710926520925, 'weight_decay': 0.057370255990679325, 'batch_size': 32}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.496220
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.27805030708182077, 'lr': 0.006711710926520925, 'weight_decay': 0.057370255990679325, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:58:42,882] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.362657
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.301443361558529, 'lr': 0.005488303614799007, 'weight_decay': 0.09125198786125734, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:01:15,568] Trial 25 finished with value: 0.4998545913389673 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.31958061358918466, 'lr': 0.002439363924374219, 'weight_decay': 0.033923419303727435, 'batch_size': 32}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.499855
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.31958061358918466, 'lr': 0.002439363924374219, 'weight_decay': 0.033923419303727435, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:01:39,864] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.387067
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.25302440875857213, 'lr': 0.005559924145512478, 'weight_decay': 0.055993497515554436, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:02:24,743] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.389243
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.22403523433985434, 'lr': 0.001304932830912518, 'weight_decay': 0.017698278694635167, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:03:10,712] Trial 28 finished with value: 0.471591099326581 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2761121710210888, 'lr': 0.0026088435027735606, 'weight_decay': 0.03219341983681844, 'batch_size': 128}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.471591
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2761121710210888, 'lr': 0.0026088435027735606, 'weight_decay': 0.03219341983681844, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:03:37,070] Trial 29 finished with value: 0.46719755465755125 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.18903884737313537, 'lr': 0.004891352962031701, 'weight_decay': 0.007494667956670961, 'batch_size': 256}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.467198
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.18903884737313537, 'lr': 0.004891352962031701, 'weight_decay': 0.007494667956670961, 'batch_size': 256}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:04:36,431] Trial 30 finished with value: 0.5100542409277865 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.35528646597883196, 'lr': 0.005975833641923651, 'weight_decay': 0.06727342355696113, 'batch_size': 128}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.510054
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.35528646597883196, 'lr': 0.005975833641923651, 'weight_decay': 0.06727342355696113, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:04:45,946] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.368587
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.33237051240871407, 'lr': 0.005997564155847514, 'weight_decay': 0.06841381155916106, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:04:54,100] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.367958
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.3204440244271537, 'lr': 0.009289873900452232, 'weight_decay': 0.09812734956435876, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:05:49,288] Trial 33 finished with value: 0.49464760143239683 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.35919867400242994, 'lr': 0.004160465014675709, 'weight_decay': 0.04390312310592777, 'batch_size': 128}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.494648
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.35919867400242994, 'lr': 0.004160465014675709, 'weight_decay': 0.04390312310592777, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:06:41,172] Trial 34 finished with value: 0.5042487635648454 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.3418867157570947, 'lr': 0.006554115370437086, 'weight_decay': 0.06391484731798987, 'batch_size': 128}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.504249
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.3418867157570947, 'lr': 0.006554115370437086, 'weight_decay': 0.06391484731798987, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:07:00,605] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.421333
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3546571688594399, 'lr': 0.006791951636010245, 'weight_decay': 0.06912814216580676, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:07:46,505] Trial 36 finished with value: 0.48231200140552555 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3695806147496655, 'lr': 0.002675856042792232, 'weight_decay': 0.0338497342171349, 'batch_size': 128}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.482312
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3695806147496655, 'lr': 0.002675856042792232, 'weight_decay': 0.0338497342171349, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:08:06,688] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.420964
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.3456201153862546, 'lr': 0.006970257331670231, 'weight_decay': 0.0684213971982247, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:08:27,100] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.430944
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.37405254512499014, 'lr': 0.009583360216170903, 'weight_decay': 0.024572289856664965, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:08:44,795] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.392122
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.39227918934741374, 'lr': 0.001025120766394647, 'weight_decay': 0.038741989126993344, 'batch_size': 64}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:08:54,754] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.408574
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.3575502149755919, 'lr': 0.001808902525630881, 'weight_decay': 0.02522669014727317, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:09:48,036] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.429373
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.28690753592736007, 'lr': 0.005412098639235286, 'weight_decay': 0.06127566287517714, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:10:15,384] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.415867
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.30884013570704605, 'lr': 0.0038155021048132823, 'weight_decay': 0.04715393262917385, 'batch_size': 64}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:11:10,660] Trial 43 finished with value: 0.5022258467402851 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2883613434066421, 'lr': 0.004578872815216211, 'weight_decay': 0.07211212035819524, 'batch_size': 128}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.502226
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2883613434066421, 'lr': 0.004578872815216211, 'weight_decay': 0.07211212035819524, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:11:18,305] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.395703
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.33327468649108233, 'lr': 0.0029322067646842527, 'weight_decay': 0.09870449787234076, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:11:28,726] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.418100
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.28704773943534345, 'lr': 0.007373105822288396, 'weight_decay': 0.07161839773563837, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:11:40,312] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.411492
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3098514773753347, 'lr': 0.0018733901616811233, 'weight_decay': 0.03686992559432701, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:12:42,165] Trial 47 finished with value: 0.4992038039340496 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.3988887227740137, 'lr': 0.003917603560231747, 'weight_decay': 0.07323294816190767, 'batch_size': 128}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.499204
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.3988887227740137, 'lr': 0.003917603560231747, 'weight_decay': 0.07323294816190767, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:12:49,594] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.419087
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.29270400317795936, 'lr': 0.0068450702908284785, 'weight_decay': 0.027349624455736424, 'batch_size': 256}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+[I 2025-12-28 18:13:20,288] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.468977
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.3252494732917137, 'lr': 0.0012924941723493337, 'weight_decay': 0.07829298866756979, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:13:33,514] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.385274
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.34202871483796915, 'lr': 0.0031787484372776693, 'weight_decay': 0.04311551375059074, 'batch_size': 64}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:13:55,637] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.382022
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.26185761290678383, 'lr': 0.00451124646439791, 'weight_decay': 0.05398042073561988, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:14:14,536] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.376442
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.309520172455306, 'lr': 0.007914455386396757, 'weight_decay': 0.058481470543485, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:17:07,244] Trial 53 finished with value: 0.5061308419442494 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2622742326037925, 'lr': 0.004753571970329188, 'weight_decay': 0.09691894696122295, 'batch_size': 32}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.506131
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2622742326037925, 'lr': 0.004753571970329188, 'weight_decay': 0.09691894696122295, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:17:33,906] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.389039
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.27742879474011145, 'lr': 0.00544644839283288, 'weight_decay': 0.0945740049533425, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:17:57,932] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.365601
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.29706448611415515, 'lr': 0.009837410962295422, 'weight_decay': 0.0690034953566541, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:18:09,427] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.414758
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.2830128153780071, 'lr': 0.0022502002602343284, 'weight_decay': 0.08091008387533014, 'batch_size': 256}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:18:20,024] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.412065
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2658579889688746, 'lr': 0.0036533557518305788, 'weight_decay': 0.0388911068095591, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:18:45,061] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.387011
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.38235613021567505, 'lr': 0.00744518133489163, 'weight_decay': 0.0560710681278779, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:21:31,593] Trial 59 finished with value: 0.5053330514211721 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.29560956336451355, 'lr': 0.003040081756009423, 'weight_decay': 0.09580403460928869, 'batch_size': 32}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.505333
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.29560956336451355, 'lr': 0.003040081756009423, 'weight_decay': 0.09580403460928869, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:21:59,659] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.408889
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.3011232392438384, 'lr': 0.003000504428500985, 'weight_decay': 0.09784326499069082, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:22:23,162] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.389365
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2963775737071764, 'lr': 0.00506861775703181, 'weight_decay': 0.08161289629084556, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:22:54,964] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.416065
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2846157658372228, 'lr': 0.002151200731835764, 'weight_decay': 0.057036118781110826, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:23:41,198] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.416593
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.31405966282369663, 'lr': 0.005829428451562356, 'weight_decay': 0.04253416839807944, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:24:01,667] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.359927
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.3263409897720465, 'lr': 0.004145444708752362, 'weight_decay': 0.0999954549096991, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:24:32,406] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.406032
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.25762015133634975, 'lr': 0.007949438236119473, 'weight_decay': 0.0792248724215335, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:24:41,192] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.385035
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2508265201052113, 'lr': 0.003020886681352587, 'weight_decay': 0.03064112512487593, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:24:58,382] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.398866
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.27407198003924643, 'lr': 0.005028515362871348, 'weight_decay': 0.050038444995749616, 'batch_size': 64}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:25:30,164] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.422078
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.24333586523145578, 'lr': 0.0060810759425103545, 'weight_decay': 0.06673177649380793, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:26:11,152] Trial 69 finished with value: 0.4870335755832722 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2682809073630958, 'lr': 0.003599631844528232, 'weight_decay': 0.04685069695109793, 'batch_size': 128}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.487034
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2682809073630958, 'lr': 0.003599631844528232, 'weight_decay': 0.04685069695109793, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:26:47,595] Trial 70 finished with value: 0.5019749248092794 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.3159129816495217, 'lr': 0.009147405439382734, 'weight_decay': 0.08337005549467671, 'batch_size': 256}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.501975
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.3159129816495217, 'lr': 0.009147405439382734, 'weight_decay': 0.08337005549467671, 'batch_size': 256}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:27:29,066] Trial 71 finished with value: 0.4998149860292238 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.304585707332184, 'lr': 0.007580811594324356, 'weight_decay': 0.07606563639129538, 'batch_size': 256}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.499815
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.304585707332184, 'lr': 0.007580811594324356, 'weight_decay': 0.07606563639129538, 'batch_size': 256}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:28:13,329] Trial 72 finished with value: 0.4990164575619962 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.31505339518768294, 'lr': 0.009886834565477558, 'weight_decay': 0.0632719113834685, 'batch_size': 256}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.499016
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.31505339518768294, 'lr': 0.009886834565477558, 'weight_decay': 0.0632719113834685, 'batch_size': 256}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:28:20,717] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.409649
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.2911121024149011, 'lr': 0.004504295139028827, 'weight_decay': 0.07851203008208621, 'batch_size': 256}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:28:28,703] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.417697
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3290848599662435, 'lr': 0.0060926616573028815, 'weight_decay': 0.06166131238023137, 'batch_size': 256}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:28:53,723] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.379588
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.3209433660504459, 'lr': 0.008346520081415048, 'weight_decay': 0.08503921218042387, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:29:50,856] Trial 76 finished with value: 0.4952577805657872 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.3415048419518644, 'lr': 0.006223673717125244, 'weight_decay': 0.03855374688828488, 'batch_size': 128}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.495258
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.3415048419518644, 'lr': 0.006223673717125244, 'weight_decay': 0.03855374688828488, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:29:57,552] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.402878
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2807002058742021, 'lr': 0.0027024763217727205, 'weight_decay': 0.0489026436060842, 'batch_size': 256}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:30:18,897] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.435626
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.3063696719669886, 'lr': 0.008380282104991515, 'weight_decay': 0.09839814459188238, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:30:51,610] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.420552
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.2902530822374183, 'lr': 0.004839406787464274, 'weight_decay': 0.034564523990167385, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:31:09,825] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.424645
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.3164527313882728, 'lr': 0.0037687053454149755, 'weight_decay': 0.058521063197785037, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:31:37,362] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.410887
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.27109701178242446, 'lr': 0.004457679284564059, 'weight_decay': 0.08313239637185656, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:33:47,562] Trial 82 finished with value: 0.48825418527056746 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2624569320696532, 'lr': 0.006280747597616649, 'weight_decay': 0.06652184549352809, 'batch_size': 32}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.488254
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2624569320696532, 'lr': 0.006280747597616649, 'weight_decay': 0.06652184549352809, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:36:17,247] Trial 83 finished with value: 0.5061719330403941 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2996658956135587, 'lr': 0.003438306768301007, 'weight_decay': 0.04423976242941889, 'batch_size': 32}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.506172
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2996658956135587, 'lr': 0.003438306768301007, 'weight_decay': 0.04423976242941889, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:37:08,056] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.431858
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.29985267665654475, 'lr': 0.003223076085807653, 'weight_decay': 0.049650873217483155, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:37:38,537] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.405201
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.29612017548052844, 'lr': 0.0021204320924548877, 'weight_decay': 0.08151750374184714, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:37:56,062] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.426510
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.33745565488740514, 'lr': 0.006831185241651979, 'weight_decay': 0.06746145752511458, 'batch_size': 64}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:38:43,051] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.422974
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.28106674636282875, 'lr': 0.0025255775471508275, 'weight_decay': 0.04215124729566637, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:38:52,920] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.404914
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.32690375455095805, 'lr': 0.008514977587267531, 'weight_decay': 0.08914422755756038, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:39:00,048] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.399831
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.31175893236224994, 'lr': 0.005249788141357181, 'weight_decay': 0.05513959648210336, 'batch_size': 256}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:39:50,890] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.413603
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.3503783114843492, 'lr': 0.003402090045197869, 'weight_decay': 0.02792141894735297, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:40:20,669] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.427822
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.28888325596262904, 'lr': 0.00413708071505135, 'weight_decay': 0.06913474835995947, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 18:42:56,567] Trial 92 finished with value: 0.5033539046145203 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.27445581376558154, 'lr': 0.004774432138629257, 'weight_decay': 0.047537114023331516, 'batch_size': 32}. Best is trial 12 with value: 0.5101248146449113.
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.503354
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.27445581376558154, 'lr': 0.004774432138629257, 'weight_decay': 0.047537114023331516, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:43:15,772] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.346681
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.3047317329477646, 'lr': 0.009909781348570993, 'weight_decay': 0.050787228372132905, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:43:36,309] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.373246
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2812721779928454, 'lr': 0.006899512521297028, 'weight_decay': 0.08475016723298338, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:43:58,853] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.386262
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2730530822866347, 'lr': 0.005448444461461762, 'weight_decay': 0.03264842224458182, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:44:07,617] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.419183
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.29533616834646614, 'lr': 0.004122429282259635, 'weight_decay': 0.059492291256100095, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:44:57,803] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.431166
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.3641854375809598, 'lr': 0.002815556835940789, 'weight_decay': 0.04227223614147624, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:45:27,754] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.405757
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.31939572403757643, 'lr': 0.004840558741651972, 'weight_decay': 0.07521658115475248, 'batch_size': 32}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 18:45:38,541] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.422455
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.33485466177643086, 'lr': 0.007815277211069609, 'weight_decay': 0.09843197744086116, 'batch_size': 128}
+  Best Value (CSI): 0.510125
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5101
+Best Hyperparameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4709
+  - 최종 CSI: 0.4225
+  - 최고 CSI: 0.5101
+  - 최저 CSI: 0.1757
+  - 평균 CSI: 0.4269
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/deepgbm_ctgan10000_daejeon_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 21, Best CSI: 0.4333
+  Fold 1 학습 완료 (검증 CSI: 0.4333)
+Fold 2 학습 중...
+  Early stopping at epoch 22, Best CSI: 0.5188
+  Fold 2 학습 완료 (검증 CSI: 0.5188)
+Fold 3 학습 중...
+  Early stopping at epoch 36, Best CSI: 0.5534
+  Fold 3 학습 완료 (검증 CSI: 0.5534)
+
+모든 모델 저장 완료: ../save_model/deepgbm_optima/deepgbm_ctgan10000_daejeon.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/deepgbm_optima/scaler/deepgbm_ctgan10000_daejeon_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/deepgbm_optima/deepgbm_ctgan10000_daejeon.pkl
+
+✓ 완료: deepgbm_ctgan10000/deepgbm_ctgan10000_daejeon.py (소요 시간: 5261초)
+
+----------------------------------------
+실행 중: deepgbm_ctgan10000/deepgbm_ctgan10000_gwangju.py
+시작 시간: 2025-12-28 18:47:51
+----------------------------------------
+[I 2025-12-28 18:47:52,960] A new study created in memory with name: no-name-01b1a4b9-20d0-4156-8138-c38636494102
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 18:49:07,207] Trial 0 finished with value: 0.45743779147083535 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.14717721520045274, 'lr': 0.0015945354588365946, 'weight_decay': 0.00032422769721220253, 'batch_size': 64}. Best is trial 0 with value: 0.45743779147083535.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.457438
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.14717721520045274, 'lr': 0.0015945354588365946, 'weight_decay': 0.00032422769721220253, 'batch_size': 64}
+  Best Value (CSI): 0.457438
+  Best Trial: 0
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.14717721520045274, 'lr': 0.0015945354588365946, 'weight_decay': 0.00032422769721220253, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 18:50:38,923] Trial 1 finished with value: 0.3044843465528707 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.113608865076364, 'lr': 2.849025643295352e-05, 'weight_decay': 0.00045410524985854965, 'batch_size': 128}. Best is trial 0 with value: 0.45743779147083535.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.304484
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.113608865076364, 'lr': 2.849025643295352e-05, 'weight_decay': 0.00045410524985854965, 'batch_size': 128}
+  Best Value (CSI): 0.457438
+  Best Trial: 0
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.14717721520045274, 'lr': 0.0015945354588365946, 'weight_decay': 0.00032422769721220253, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 18:54:26,647] Trial 2 finished with value: 0.4959397723639696 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.36010406104672654, 'lr': 0.007599512357460581, 'weight_decay': 0.03531587966911193, 'batch_size': 32}. Best is trial 2 with value: 0.4959397723639696.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.495940
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.36010406104672654, 'lr': 0.007599512357460581, 'weight_decay': 0.03531587966911193, 'batch_size': 32}
+  Best Value (CSI): 0.495940
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.36010406104672654, 'lr': 0.007599512357460581, 'weight_decay': 0.03531587966911193, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 18:58:14,411] Trial 3 finished with value: 0.4324411750429358 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.2566049316218987, 'lr': 0.00011622308664845337, 'weight_decay': 0.00411247906351364, 'batch_size': 32}. Best is trial 2 with value: 0.4959397723639696.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.432441
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.2566049316218987, 'lr': 0.00011622308664845337, 'weight_decay': 0.00411247906351364, 'batch_size': 32}
+  Best Value (CSI): 0.495940
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.36010406104672654, 'lr': 0.007599512357460581, 'weight_decay': 0.03531587966911193, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:00:43,120] Trial 4 finished with value: 0.46261403048889216 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.21728285466905572, 'lr': 0.0007765448864084281, 'weight_decay': 0.009455845611329351, 'batch_size': 32}. Best is trial 2 with value: 0.4959397723639696.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.462614
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.21728285466905572, 'lr': 0.0007765448864084281, 'weight_decay': 0.009455845611329351, 'batch_size': 32}
+  Best Value (CSI): 0.495940
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.36010406104672654, 'lr': 0.007599512357460581, 'weight_decay': 0.03531587966911193, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:01:06,396] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.323746
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2866442522811028, 'lr': 1.0841707181535189e-05, 'weight_decay': 0.03746678424700817, 'batch_size': 32}
+  Best Value (CSI): 0.495940
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.36010406104672654, 'lr': 0.007599512357460581, 'weight_decay': 0.03531587966911193, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:01:10,679] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.317241
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.245480203575578, 'lr': 0.0003822605247828503, 'weight_decay': 0.022059437220133093, 'batch_size': 256}
+  Best Value (CSI): 0.495940
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.36010406104672654, 'lr': 0.007599512357460581, 'weight_decay': 0.03531587966911193, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:01:33,365] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.281807
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.25875377854530024, 'lr': 2.459051496954693e-05, 'weight_decay': 0.027852413459059405, 'batch_size': 32}
+  Best Value (CSI): 0.495940
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.36010406104672654, 'lr': 0.007599512357460581, 'weight_decay': 0.03531587966911193, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:03:31,451] Trial 8 finished with value: 0.46999725590122 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.14172372640790123, 'lr': 0.0004988308571126208, 'weight_decay': 0.003552889484955622, 'batch_size': 32}. Best is trial 2 with value: 0.4959397723639696.
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.469997
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.14172372640790123, 'lr': 0.0004988308571126208, 'weight_decay': 0.003552889484955622, 'batch_size': 32}
+  Best Value (CSI): 0.495940
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.36010406104672654, 'lr': 0.007599512357460581, 'weight_decay': 0.03531587966911193, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:05:53,773] Trial 9 finished with value: 0.4770368761308679 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.1843173655160542, 'lr': 0.00185842092477608, 'weight_decay': 0.0011518304447969625, 'batch_size': 32}. Best is trial 2 with value: 0.4959397723639696.
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.477037
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.1843173655160542, 'lr': 0.00185842092477608, 'weight_decay': 0.0011518304447969625, 'batch_size': 32}
+  Best Value (CSI): 0.495940
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.36010406104672654, 'lr': 0.007599512357460581, 'weight_decay': 0.03531587966911193, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:08:10,306] Trial 10 finished with value: 0.5053558761865672 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.384980080114916, 'lr': 0.00925024038802788, 'weight_decay': 0.055896684002468056, 'batch_size': 64}. Best is trial 10 with value: 0.5053558761865672.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.505356
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.384980080114916, 'lr': 0.00925024038802788, 'weight_decay': 0.055896684002468056, 'batch_size': 64}
+  Best Value (CSI): 0.505356
+  Best Trial: 10
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.384980080114916, 'lr': 0.00925024038802788, 'weight_decay': 0.055896684002468056, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:10:12,441] Trial 11 finished with value: 0.5199080740628351 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.3881056771222904, 'lr': 0.007412799581213635, 'weight_decay': 0.098616753638106, 'batch_size': 64}. Best is trial 11 with value: 0.5199080740628351.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.519908
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.3881056771222904, 'lr': 0.007412799581213635, 'weight_decay': 0.098616753638106, 'batch_size': 64}
+  Best Value (CSI): 0.519908
+  Best Trial: 11
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.3881056771222904, 'lr': 0.007412799581213635, 'weight_decay': 0.098616753638106, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:12:38,852] Trial 12 finished with value: 0.5204031176113428 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.520403
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:14:35,275] Trial 13 finished with value: 0.511117993558572 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3998589722411654, 'lr': 0.003933938973051474, 'weight_decay': 0.0875561412763022, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.511118
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3998589722411654, 'lr': 0.003933938973051474, 'weight_decay': 0.0875561412763022, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:16:21,254] Trial 14 finished with value: 0.5080411436842638 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.3332621306917933, 'lr': 0.0033857813776511235, 'weight_decay': 0.08394133011073669, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.508041
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.3332621306917933, 'lr': 0.0033857813776511235, 'weight_decay': 0.08394133011073669, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:18:28,448] Trial 15 finished with value: 0.501066549545124 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3265857487248327, 'lr': 0.007806120877395297, 'weight_decay': 0.011139326332018678, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.501067
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3265857487248327, 'lr': 0.007806120877395297, 'weight_decay': 0.011139326332018678, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:19:09,302] Trial 16 finished with value: 0.4918268246952901 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.398193382094987, 'lr': 0.0016598203825901974, 'weight_decay': 0.07969200184494632, 'batch_size': 256}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.491827
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.398193382094987, 'lr': 0.0016598203825901974, 'weight_decay': 0.07969200184494632, 'batch_size': 256}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:20:06,632] Trial 17 finished with value: 0.4800523116396036 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3591385897551574, 'lr': 0.0038795861579728878, 'weight_decay': 0.016090387672401216, 'batch_size': 128}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.480052
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3591385897551574, 'lr': 0.0038795861579728878, 'weight_decay': 0.016090387672401216, 'batch_size': 128}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:22:10,202] Trial 18 finished with value: 0.5116760961997822 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.33721762564528424, 'lr': 0.009933408894517578, 'weight_decay': 0.09889253243637368, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.511676
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.33721762564528424, 'lr': 0.009933408894517578, 'weight_decay': 0.09889253243637368, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:23:30,459] Trial 19 finished with value: 0.4651229430660629 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2992064004470284, 'lr': 0.0008238636802500891, 'weight_decay': 0.00012118299714530565, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.465123
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2992064004470284, 'lr': 0.0008238636802500891, 'weight_decay': 0.00012118299714530565, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:23:44,925] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.325507
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3700174880178303, 'lr': 0.00017979913464595, 'weight_decay': 0.04605876780378306, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:24:06,749] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.404908
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3411540823524105, 'lr': 0.009510521958097012, 'weight_decay': 0.09304744568353097, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:24:48,291] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.446934
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.3750657095350376, 'lr': 0.0045721363736998, 'weight_decay': 0.04625643937639819, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:26:19,098] Trial 23 finished with value: 0.5170982255559309 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3500003701256375, 'lr': 0.005477077118286817, 'weight_decay': 0.09959296853057667, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.517098
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3500003701256375, 'lr': 0.005477077118286817, 'weight_decay': 0.09959296853057667, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:26:24,959] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.350446
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3995102689199929, 'lr': 0.002500039361204995, 'weight_decay': 0.026550619886303597, 'batch_size': 256}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:26:46,026] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.431285
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.3609304211829338, 'lr': 0.0049790046166129556, 'weight_decay': 0.04892268311422608, 'batch_size': 128}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:27:23,020] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.435976
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.31402987742933763, 'lr': 0.005448633008595281, 'weight_decay': 0.01564241389619987, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:28:02,554] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.436034
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3588065818147843, 'lr': 0.002258142222328861, 'weight_decay': 0.05659625530811039, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:28:36,790] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.436205
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.30961753632547556, 'lr': 0.005563776228929742, 'weight_decay': 0.02700555807675681, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:29:08,516] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.426894
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.3830012013755625, 'lr': 0.0013755361919013287, 'weight_decay': 0.05750401993276429, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:29:45,678] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.398278
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3443202106944347, 'lr': 0.002876283189771078, 'weight_decay': 0.00690752387785095, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:31:49,794] Trial 31 finished with value: 0.5176978246543809 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.34220512150959276, 'lr': 0.006677798109302621, 'weight_decay': 0.09892723183934939, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.517698
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.34220512150959276, 'lr': 0.006677798109302621, 'weight_decay': 0.09892723183934939, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:33:48,322] Trial 32 finished with value: 0.5081249069634662 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3769301620579597, 'lr': 0.004996929463025663, 'weight_decay': 0.06794618120793647, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.508125
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3769301620579597, 'lr': 0.004996929463025663, 'weight_decay': 0.06794618120793647, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:34:08,662] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.447095
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.3505453699572437, 'lr': 0.005998206793397177, 'weight_decay': 0.0964058067730205, 'batch_size': 128}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:34:48,396] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.433036
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3722618203665385, 'lr': 0.0072047428155664305, 'weight_decay': 0.03900775573121464, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:35:22,392] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.429907
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3228156502649429, 'lr': 0.002980973985833703, 'weight_decay': 0.03435142505939458, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:35:28,190] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.376485
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.3520670707733077, 'lr': 0.0014862420355476493, 'weight_decay': 0.061611426953799335, 'batch_size': 256}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:36:32,103] Trial 37 finished with value: 0.5044889449664779 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.3856623911830082, 'lr': 0.006823704920271817, 'weight_decay': 0.03740612290843219, 'batch_size': 128}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.504489
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.3856623911830082, 'lr': 0.006823704920271817, 'weight_decay': 0.03740612290843219, 'batch_size': 128}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:38:06,166] Trial 38 finished with value: 0.506440163335977 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.2810932856255846, 'lr': 0.0034473837651149654, 'weight_decay': 0.05961893721568134, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.506440
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.2810932856255846, 'lr': 0.0034473837651149654, 'weight_decay': 0.05961893721568134, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:39:40,865] Trial 39 finished with value: 0.502510350337439 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.32894589631606913, 'lr': 0.009732789830784249, 'weight_decay': 0.019794287929635966, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.502510
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.32894589631606913, 'lr': 0.009732789830784249, 'weight_decay': 0.019794287929635966, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:39:46,686] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.379977
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3677425353944939, 'lr': 0.0011607579562786057, 'weight_decay': 0.030406269617775872, 'batch_size': 256}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:41:25,338] Trial 41 finished with value: 0.5034208506091843 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3473503172141952, 'lr': 0.006987761614334529, 'weight_decay': 0.09577222910774748, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.503421
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3473503172141952, 'lr': 0.006987761614334529, 'weight_decay': 0.09577222910774748, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:43:50,304] Trial 42 finished with value: 0.5086736536039177 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3407549871808069, 'lr': 0.008877451941847277, 'weight_decay': 0.07721072964520237, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.508674
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3407549871808069, 'lr': 0.008877451941847277, 'weight_decay': 0.07721072964520237, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:45:39,013] Trial 43 finished with value: 0.5150498433084334 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3900501995426364, 'lr': 0.002177143528597078, 'weight_decay': 0.09845970535203796, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.515050
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3900501995426364, 'lr': 0.002177143528597078, 'weight_decay': 0.09845970535203796, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:48:20,734] Trial 44 finished with value: 0.5021726224447053 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.3907544036636971, 'lr': 0.0021642182681941066, 'weight_decay': 0.0691194717563556, 'batch_size': 32}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.502173
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.3907544036636971, 'lr': 0.0021642182681941066, 'weight_decay': 0.0691194717563556, 'batch_size': 32}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:50:13,397] Trial 45 finished with value: 0.5004065455793114 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3841291380226531, 'lr': 0.004310026808714425, 'weight_decay': 0.04409591947695909, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.500407
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3841291380226531, 'lr': 0.004310026808714425, 'weight_decay': 0.04409591947695909, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:51:47,366] Trial 46 finished with value: 0.5079051495459682 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3674735083721002, 'lr': 0.0029273384918499803, 'weight_decay': 0.07016013088540546, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.507905
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3674735083721002, 'lr': 0.0029273384918499803, 'weight_decay': 0.07016013088540546, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:52:10,976] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.421329
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.38946409727911796, 'lr': 0.006355604523829454, 'weight_decay': 0.03289101832683543, 'batch_size': 32}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:53:50,525] Trial 48 finished with value: 0.5059740512548597 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3986515076146073, 'lr': 0.0041429917652249775, 'weight_decay': 0.09931130157498179, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.505974
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3986515076146073, 'lr': 0.0041429917652249775, 'weight_decay': 0.09931130157498179, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:54:04,556] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.408016
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.35497029070060426, 'lr': 0.0019073106378240394, 'weight_decay': 0.0020564244775307765, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:54:21,353] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.428058
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3773156905989508, 'lr': 0.0036568958982546706, 'weight_decay': 0.0478866685367857, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:54:37,716] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.432658
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3676909346208033, 'lr': 0.007932443093623398, 'weight_decay': 0.09803464820400955, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:54:54,189] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.384615
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3394647166307227, 'lr': 0.009768570790528338, 'weight_decay': 0.06312181544589911, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:55:09,069] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.426096
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.33147154266516576, 'lr': 0.005728460947095907, 'weight_decay': 0.07191086796752749, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 19:57:30,045] Trial 54 finished with value: 0.508911125648539 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.3617603791368776, 'lr': 0.0072359233646866255, 'weight_decay': 0.08101064744545525, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.508911
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.3617603791368776, 'lr': 0.0072359233646866255, 'weight_decay': 0.08101064744545525, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:57:38,718] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.402373
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.38845977883892696, 'lr': 0.004337115924785338, 'weight_decay': 0.04330609701996728, 'batch_size': 128}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:57:55,176] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.426060
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.39979300903690446, 'lr': 0.0027480933860287555, 'weight_decay': 0.05203480123349231, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:58:27,893] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.430998
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3751722139545968, 'lr': 0.005196821091551595, 'weight_decay': 0.07850419215127004, 'batch_size': 32}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 19:58:33,686] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.394111
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3474796173754939, 'lr': 0.0035529842587499745, 'weight_decay': 0.0216526854527647, 'batch_size': 256}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 20:00:17,970] Trial 59 finished with value: 0.5088051330627582 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.31807444949166586, 'lr': 0.008269296703923002, 'weight_decay': 0.05234547965214455, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.508805
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.31807444949166586, 'lr': 0.008269296703923002, 'weight_decay': 0.05234547965214455, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 20:01:37,307] Trial 60 finished with value: 0.5041930391312742 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.35826086966277776, 'lr': 0.006024463382471898, 'weight_decay': 0.09975303675746902, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.504193
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.35826086966277776, 'lr': 0.006024463382471898, 'weight_decay': 0.09975303675746902, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:01:52,166] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.390547
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.39339640691194744, 'lr': 0.004522221264429776, 'weight_decay': 0.07709033967068188, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:02:26,661] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.440476
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3797558899228809, 'lr': 0.002357416200781271, 'weight_decay': 0.06226967323248316, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 20:04:30,286] Trial 63 finished with value: 0.5166375925228691 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3805126512796094, 'lr': 0.007158943594021254, 'weight_decay': 0.08183199905323905, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.516638
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3805126512796094, 'lr': 0.007158943594021254, 'weight_decay': 0.08183199905323905, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:04:44,145] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.405286
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.33594168187673046, 'lr': 0.007837992761350792, 'weight_decay': 0.03914224483638975, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:04:55,951] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.392265
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.3645588384042396, 'lr': 0.00608143170501572, 'weight_decay': 0.08004634258009205, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:05:08,867] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.391032
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3806279489908581, 'lr': 0.009899980612007819, 'weight_decay': 0.05185735622181592, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 20:06:59,967] Trial 67 finished with value: 0.5032839050901277 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.35335652649777266, 'lr': 0.0051508066889706305, 'weight_decay': 0.028356610636243052, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.503284
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.35335652649777266, 'lr': 0.0051508066889706305, 'weight_decay': 0.028356610636243052, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:07:07,915] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.395349
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3710738162347643, 'lr': 0.003374167354308752, 'weight_decay': 0.09902248698973977, 'batch_size': 128}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 20:08:46,163] Trial 69 finished with value: 0.5045980532417332 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3058634332483628, 'lr': 0.007647998183362028, 'weight_decay': 0.062172097749050866, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.504598
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3058634332483628, 'lr': 0.007647998183362028, 'weight_decay': 0.062172097749050866, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:08:51,465] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.368293
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3906560588498733, 'lr': 0.006578461867642616, 'weight_decay': 0.04006887533784696, 'batch_size': 256}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:09:05,536] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.403867
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.39729521499778986, 'lr': 0.004096324922086911, 'weight_decay': 0.07963015121522148, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:09:18,543] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.380328
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.38388079840317463, 'lr': 0.004775941018164819, 'weight_decay': 0.08265809754921269, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:09:31,590] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.330442
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3766161705152674, 'lr': 0.008674091121096817, 'weight_decay': 0.05811512619123711, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:09:43,640] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.381531
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3619962901342814, 'lr': 0.009978027527024782, 'weight_decay': 0.07147563810715965, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:09:56,596] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.396396
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.399796099634898, 'lr': 0.00550559733648782, 'weight_decay': 0.04894645144100689, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 20:11:20,551] Trial 76 finished with value: 0.5062130595844381 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.389793156021762, 'lr': 0.003195443595202035, 'weight_decay': 0.08107323235573854, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.506213
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.389793156021762, 'lr': 0.003195443595202035, 'weight_decay': 0.08107323235573854, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:11:43,646] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.436492
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.34578383036128174, 'lr': 0.0069806713462778665, 'weight_decay': 0.09955220678899207, 'batch_size': 32}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 20:12:49,671] Trial 78 finished with value: 0.5082231389897766 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3704746268178663, 'lr': 0.0038930792018943613, 'weight_decay': 0.0657254661836288, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.508223
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3704746268178663, 'lr': 0.0038930792018943613, 'weight_decay': 0.0657254661836288, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 20:14:18,922] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.465830
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3225595329386204, 'lr': 0.002569397503968321, 'weight_decay': 0.03550951700125492, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 20:15:52,249] Trial 80 finished with value: 0.5096813875615697 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.35452999786506867, 'lr': 0.006342425153953135, 'weight_decay': 0.05599566181101483, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.509681
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.35452999786506867, 'lr': 0.006342425153953135, 'weight_decay': 0.05599566181101483, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 20:17:18,539] Trial 81 finished with value: 0.5046004283096744 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.3541279811789449, 'lr': 0.006453351298458861, 'weight_decay': 0.08618260375637439, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.504600
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.3541279811789449, 'lr': 0.006453351298458861, 'weight_decay': 0.08618260375637439, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:17:30,320] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.421986
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.38292209580662984, 'lr': 0.005041842975123803, 'weight_decay': 0.0666670338382839, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 20:18:47,364] Trial 83 finished with value: 0.5099096075821573 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.3383571923899666, 'lr': 0.008165408527606194, 'weight_decay': 0.053596564033258666, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.509910
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.3383571923899666, 'lr': 0.008165408527606194, 'weight_decay': 0.053596564033258666, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:18:59,095] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.424808
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.32883171317145266, 'lr': 0.008411782966371094, 'weight_decay': 0.043737586459351066, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:19:07,201] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.403681
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.33871355415942905, 'lr': 0.008039782885410398, 'weight_decay': 0.088523724585261, 'batch_size': 128}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 20:20:30,678] Trial 86 finished with value: 0.5095629775063902 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3741070360149087, 'lr': 0.004185701812380822, 'weight_decay': 0.06971742318226766, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.509563
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3741070360149087, 'lr': 0.004185701812380822, 'weight_decay': 0.06971742318226766, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:20:45,223] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.439614
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.36508987886450606, 'lr': 0.00574015205955465, 'weight_decay': 0.0885151169252504, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:20:50,293] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.428902
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3919604914336178, 'lr': 0.007245477909339212, 'weight_decay': 0.05378963211164918, 'batch_size': 256}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:21:16,593] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.422007
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.3448586961766934, 'lr': 0.003162417904567774, 'weight_decay': 0.06751461721588688, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:21:48,329] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.457424
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.38523321401740646, 'lr': 0.004645304598476381, 'weight_decay': 0.04598278871423093, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:22:04,759] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.438576
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.3591861909604221, 'lr': 0.006688083071799077, 'weight_decay': 0.059125180589416085, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:22:16,679] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.423163
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.35293037835917845, 'lr': 0.008663685736743518, 'weight_decay': 0.0878068488380457, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 20:23:52,859] Trial 93 finished with value: 0.511811444479353 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.3337772978611656, 'lr': 0.005873939465557939, 'weight_decay': 0.05530202022867509, 'batch_size': 64}. Best is trial 12 with value: 0.5204031176113428.
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.511811
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.3337772978611656, 'lr': 0.005873939465557939, 'weight_decay': 0.05530202022867509, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:24:04,633] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.430733
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.3338894398817575, 'lr': 0.005379871238429161, 'weight_decay': 0.07393759792663299, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:24:25,430] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.438515
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.32383959018102515, 'lr': 0.0036146494538285374, 'weight_decay': 0.08636106054022934, 'batch_size': 32}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:24:51,653] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.448619
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.34022208575779045, 'lr': 0.00847648418996258, 'weight_decay': 0.09986260182361341, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:25:04,580] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.416092
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3784231007827543, 'lr': 0.0045227156739380365, 'weight_decay': 0.050991099005961625, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:25:16,379] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.434637
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.39434691555411766, 'lr': 0.0072289292457852925, 'weight_decay': 0.0358452974515514, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 20:25:28,197] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.401075
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.36941442601879393, 'lr': 0.009921348963153302, 'weight_decay': 0.07098423143762086, 'batch_size': 64}
+  Best Value (CSI): 0.520403
+  Best Trial: 12
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5204
+Best Hyperparameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4574
+  - 최종 CSI: 0.4011
+  - 최고 CSI: 0.5204
+  - 최저 CSI: 0.2818
+  - 평균 CSI: 0.4456
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/deepgbm_ctgan10000_gwangju_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 50, Best CSI: 0.4590
+  Fold 1 학습 완료 (검증 CSI: 0.4590)
+Fold 2 학습 중...
+  Early stopping at epoch 33, Best CSI: 0.5391
+  Fold 2 학습 완료 (검증 CSI: 0.5391)
+Fold 3 학습 중...
+  Early stopping at epoch 34, Best CSI: 0.5169
+  Fold 3 학습 완료 (검증 CSI: 0.5169)
+
+모든 모델 저장 완료: ../save_model/deepgbm_optima/deepgbm_ctgan10000_gwangju.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/deepgbm_optima/scaler/deepgbm_ctgan10000_gwangju_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/deepgbm_optima/deepgbm_ctgan10000_gwangju.pkl
+
+✓ 완료: deepgbm_ctgan10000/deepgbm_ctgan10000_gwangju.py (소요 시간: 5981초)
+
+----------------------------------------
+실행 중: deepgbm_ctgan10000/deepgbm_ctgan10000_incheon.py
+시작 시간: 2025-12-28 20:27:32
+----------------------------------------
+[I 2025-12-28 20:27:33,401] A new study created in memory with name: no-name-8edaa145-8bb7-48ef-a771-758b35d3f973
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:28:37,462] Trial 0 finished with value: 0.5035583243789783 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.10281805062299142, 'lr': 0.0004094547946910562, 'weight_decay': 0.006084708900936006, 'batch_size': 128}. Best is trial 0 with value: 0.5035583243789783.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.503558
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.10281805062299142, 'lr': 0.0004094547946910562, 'weight_decay': 0.006084708900936006, 'batch_size': 128}
+  Best Value (CSI): 0.503558
+  Best Trial: 0
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.10281805062299142, 'lr': 0.0004094547946910562, 'weight_decay': 0.006084708900936006, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:29:52,625] Trial 1 finished with value: 0.5114078009850181 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.16675316028175197, 'lr': 0.00026360224664090707, 'weight_decay': 0.005503476811570524, 'batch_size': 64}. Best is trial 1 with value: 0.5114078009850181.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.511408
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.16675316028175197, 'lr': 0.00026360224664090707, 'weight_decay': 0.005503476811570524, 'batch_size': 64}
+  Best Value (CSI): 0.511408
+  Best Trial: 1
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.16675316028175197, 'lr': 0.00026360224664090707, 'weight_decay': 0.005503476811570524, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:30:42,562] Trial 2 finished with value: 0.5218245071097826 and parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.17741559083803368, 'lr': 0.0008052752456691312, 'weight_decay': 0.0008816500025451825, 'batch_size': 64}. Best is trial 2 with value: 0.5218245071097826.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.521825
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.17741559083803368, 'lr': 0.0008052752456691312, 'weight_decay': 0.0008816500025451825, 'batch_size': 64}
+  Best Value (CSI): 0.521825
+  Best Trial: 2
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.17741559083803368, 'lr': 0.0008052752456691312, 'weight_decay': 0.0008816500025451825, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:32:25,707] Trial 3 finished with value: 0.4519947920478018 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.22975447608056218, 'lr': 1.679045813497873e-05, 'weight_decay': 0.0015285353589354915, 'batch_size': 128}. Best is trial 2 with value: 0.5218245071097826.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.451995
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.22975447608056218, 'lr': 1.679045813497873e-05, 'weight_decay': 0.0015285353589354915, 'batch_size': 128}
+  Best Value (CSI): 0.521825
+  Best Trial: 2
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.17741559083803368, 'lr': 0.0008052752456691312, 'weight_decay': 0.0008816500025451825, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:34:02,766] Trial 4 finished with value: 0.4638606519912772 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.10077230027579337, 'lr': 1.4508941024024848e-05, 'weight_decay': 0.0012147781600427053, 'batch_size': 128}. Best is trial 2 with value: 0.5218245071097826.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.463861
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.10077230027579337, 'lr': 1.4508941024024848e-05, 'weight_decay': 0.0012147781600427053, 'batch_size': 128}
+  Best Value (CSI): 0.521825
+  Best Trial: 2
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.17741559083803368, 'lr': 0.0008052752456691312, 'weight_decay': 0.0008816500025451825, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:34:46,082] Trial 5 finished with value: 0.5206829575178441 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.11049435817265445, 'lr': 0.0006684809043764866, 'weight_decay': 0.034577590638617, 'batch_size': 128}. Best is trial 2 with value: 0.5218245071097826.
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.520683
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.11049435817265445, 'lr': 0.0006684809043764866, 'weight_decay': 0.034577590638617, 'batch_size': 128}
+  Best Value (CSI): 0.521825
+  Best Trial: 2
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.17741559083803368, 'lr': 0.0008052752456691312, 'weight_decay': 0.0008816500025451825, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:35:02,454] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.405694
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.2929604526456821, 'lr': 0.00011432999981178968, 'weight_decay': 0.026160192865894515, 'batch_size': 64}
+  Best Value (CSI): 0.521825
+  Best Trial: 2
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.17741559083803368, 'lr': 0.0008052752456691312, 'weight_decay': 0.0008816500025451825, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:35:42,118] Trial 7 finished with value: 0.5175670562898792 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.388394709939174, 'lr': 0.00013044892881589949, 'weight_decay': 0.03361641678858114, 'batch_size': 256}. Best is trial 2 with value: 0.5218245071097826.
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.517567
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.388394709939174, 'lr': 0.00013044892881589949, 'weight_decay': 0.03361641678858114, 'batch_size': 256}
+  Best Value (CSI): 0.521825
+  Best Trial: 2
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.17741559083803368, 'lr': 0.0008052752456691312, 'weight_decay': 0.0008816500025451825, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:36:13,179] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.429080
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.2387566055950624, 'lr': 0.0004484398377982173, 'weight_decay': 0.051286402108193754, 'batch_size': 64}
+  Best Value (CSI): 0.521825
+  Best Trial: 2
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.17741559083803368, 'lr': 0.0008052752456691312, 'weight_decay': 0.0008816500025451825, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:36:20,178] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.263276
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.23023134914783494, 'lr': 1.1223329568539183e-05, 'weight_decay': 0.051230003255330725, 'batch_size': 128}
+  Best Value (CSI): 0.521825
+  Best Trial: 2
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.17741559083803368, 'lr': 0.0008052752456691312, 'weight_decay': 0.0008816500025451825, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:37:02,007] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.471853
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.17651674688923996, 'lr': 0.004374698606638214, 'weight_decay': 0.00013233360202172123, 'batch_size': 32}
+  Best Value (CSI): 0.521825
+  Best Trial: 2
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.17741559083803368, 'lr': 0.0008052752456691312, 'weight_decay': 0.0008816500025451825, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:37:38,852] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.456005
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.15903182312222638, 'lr': 0.002230379249302043, 'weight_decay': 0.09218640657939937, 'batch_size': 32}
+  Best Value (CSI): 0.521825
+  Best Trial: 2
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.17741559083803368, 'lr': 0.0008052752456691312, 'weight_decay': 0.0008816500025451825, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:38:05,424] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.504042
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.146393204316876, 'lr': 0.0014310228865868407, 'weight_decay': 0.0126276360797855, 'batch_size': 256}
+  Best Value (CSI): 0.521825
+  Best Trial: 2
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.17741559083803368, 'lr': 0.0008052752456691312, 'weight_decay': 0.0008816500025451825, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:39:09,645] Trial 13 finished with value: 0.5525887443618827 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}. Best is trial 13 with value: 0.5525887443618827.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.552589
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:39:33,904] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.450758
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.19498200900332885, 'lr': 0.009893611778306687, 'weight_decay': 0.0004338299706188968, 'batch_size': 64}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:40:00,627] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.456703
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.13535999933645929, 'lr': 0.009248265759705006, 'weight_decay': 0.0006625876622580232, 'batch_size': 64}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:40:21,808] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.488000
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.19870200024636508, 'lr': 0.0028956090097254483, 'weight_decay': 0.0020779444761752515, 'batch_size': 64}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:41:11,403] Trial 17 finished with value: 0.5275211063263553 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.1464431349235381, 'lr': 0.001373487342070769, 'weight_decay': 0.00038985610024115994, 'batch_size': 64}. Best is trial 13 with value: 0.5525887443618827.
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.527521
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.1464431349235381, 'lr': 0.001373487342070769, 'weight_decay': 0.00038985610024115994, 'batch_size': 64}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+[I 2025-12-28 20:42:04,013] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.572043
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.14543241966993234, 'lr': 0.00621609641985209, 'weight_decay': 0.00040571717146254847, 'batch_size': 64}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:42:27,472] Trial 19 finished with value: 0.5277419807061007 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.12611740317819922, 'lr': 0.0038539989258168355, 'weight_decay': 0.00017455043966780616, 'batch_size': 256}. Best is trial 13 with value: 0.5525887443618827.
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.527742
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.12611740317819922, 'lr': 0.0038539989258168355, 'weight_decay': 0.00017455043966780616, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:42:49,397] Trial 20 finished with value: 0.5363796260473088 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.12837074699695597, 'lr': 0.0038529643611446254, 'weight_decay': 0.00010111382854533909, 'batch_size': 256}. Best is trial 13 with value: 0.5525887443618827.
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.536380
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.12837074699695597, 'lr': 0.0038529643611446254, 'weight_decay': 0.00010111382854533909, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:43:14,718] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.512080
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.12884370776307674, 'lr': 0.004726120780706167, 'weight_decay': 0.00011345485774027207, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:43:36,224] Trial 22 finished with value: 0.5378022212421132 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11956309976931895, 'lr': 0.0032185244622791416, 'weight_decay': 0.0002378583050787927, 'batch_size': 256}. Best is trial 13 with value: 0.5525887443618827.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.537802
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11956309976931895, 'lr': 0.0032185244622791416, 'weight_decay': 0.0002378583050787927, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:44:00,149] Trial 23 finished with value: 0.521566981008169 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11960937608096459, 'lr': 0.0023342401190555744, 'weight_decay': 0.0002205002445413614, 'batch_size': 256}. Best is trial 13 with value: 0.5525887443618827.
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.521567
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11960937608096459, 'lr': 0.0023342401190555744, 'weight_decay': 0.0002205002445413614, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:44:25,011] Trial 24 finished with value: 0.5310643305162138 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10106281200861522, 'lr': 0.007240658610974353, 'weight_decay': 0.00024569987550007667, 'batch_size': 256}. Best is trial 13 with value: 0.5525887443618827.
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.531064
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10106281200861522, 'lr': 0.007240658610974353, 'weight_decay': 0.00024569987550007667, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:44:50,104] Trial 25 finished with value: 0.5385497513139267 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.1332549919788319, 'lr': 0.005443883549428839, 'weight_decay': 0.0002301110670036112, 'batch_size': 256}. Best is trial 13 with value: 0.5525887443618827.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.538550
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.1332549919788319, 'lr': 0.005443883549428839, 'weight_decay': 0.0002301110670036112, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:45:13,574] Trial 26 finished with value: 0.5513822481901878 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.15746769345916276, 'lr': 0.009936298633893755, 'weight_decay': 0.0007183498875337535, 'batch_size': 256}. Best is trial 13 with value: 0.5525887443618827.
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.551382
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.15746769345916276, 'lr': 0.009936298633893755, 'weight_decay': 0.0007183498875337535, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:45:34,671] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.406946
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.16144100170670295, 'lr': 0.006507288328540219, 'weight_decay': 0.0007074983235018028, 'batch_size': 32}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:46:03,680] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.524291
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.1947422358422172, 'lr': 0.00957308106416948, 'weight_decay': 0.0022228895721241753, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:46:29,420] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.512218
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.10759421679650272, 'lr': 0.005807783064775652, 'weight_decay': 0.0036816594060894697, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:46:51,627] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.375486
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.14481769963212696, 'lr': 0.009885012893340085, 'weight_decay': 0.0010679321706356304, 'batch_size': 32}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:47:14,616] Trial 31 finished with value: 0.5334388665940594 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.12312150998341437, 'lr': 0.004260319336614774, 'weight_decay': 0.00027742952350576526, 'batch_size': 256}. Best is trial 13 with value: 0.5525887443618827.
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.533439
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.12312150998341437, 'lr': 0.004260319336614774, 'weight_decay': 0.00027742952350576526, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:47:22,774] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.445841
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.11812917319439357, 'lr': 0.006032144409797176, 'weight_decay': 0.0005622279041040653, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:47:31,048] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.442883
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.16336013012062325, 'lr': 0.002421148683661409, 'weight_decay': 0.0008337646690032803, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:47:36,247] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.431442
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.10110369446073636, 'lr': 0.003096452269201278, 'weight_decay': 0.0003148508107841231, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:47:41,064] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.443684
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.17695118708292357, 'lr': 0.0015028743862582161, 'weight_decay': 0.0005236099374172995, 'batch_size': 256}
+  Best Value (CSI): 0.552589
+  Best Trial: 13
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12022799581247412, 'lr': 0.0074950162350266485, 'weight_decay': 0.0009847165297610762, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:48:14,300] Trial 36 finished with value: 0.552627867752313 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13804213033188278, 'lr': 0.00666980471505662, 'weight_decay': 0.00018744416376778518, 'batch_size': 128}. Best is trial 36 with value: 0.552627867752313.
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.552628
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13804213033188278, 'lr': 0.00666980471505662, 'weight_decay': 0.00018744416376778518, 'batch_size': 128}
+  Best Value (CSI): 0.552628
+  Best Trial: 36
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13804213033188278, 'lr': 0.00666980471505662, 'weight_decay': 0.00018744416376778518, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:48:57,039] Trial 37 finished with value: 0.5516927856928084 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1402324842234663, 'lr': 0.006636739687073565, 'weight_decay': 0.00016182492203133933, 'batch_size': 128}. Best is trial 36 with value: 0.552627867752313.
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.551693
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1402324842234663, 'lr': 0.006636739687073565, 'weight_decay': 0.00016182492203133933, 'batch_size': 128}
+  Best Value (CSI): 0.552628
+  Best Trial: 36
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13804213033188278, 'lr': 0.00666980471505662, 'weight_decay': 0.00018744416376778518, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:49:13,762] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.484663
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.15841307765272056, 'lr': 0.0069671211314324966, 'weight_decay': 0.0001537450779196032, 'batch_size': 128}
+  Best Value (CSI): 0.552628
+  Best Trial: 36
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13804213033188278, 'lr': 0.00666980471505662, 'weight_decay': 0.00018744416376778518, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:49:27,052] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.479738
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.20791938600145665, 'lr': 0.0019037293031849319, 'weight_decay': 0.0012668679979656464, 'batch_size': 128}
+  Best Value (CSI): 0.552628
+  Best Trial: 36
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13804213033188278, 'lr': 0.00666980471505662, 'weight_decay': 0.00018744416376778518, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:50:06,378] Trial 40 finished with value: 0.5434342159450714 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.18222127522923576, 'lr': 0.003852184714086002, 'weight_decay': 0.0008802825988495726, 'batch_size': 128}. Best is trial 36 with value: 0.552627867752313.
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.543434
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.18222127522923576, 'lr': 0.003852184714086002, 'weight_decay': 0.0008802825988495726, 'batch_size': 128}
+  Best Value (CSI): 0.552628
+  Best Trial: 36
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13804213033188278, 'lr': 0.00666980471505662, 'weight_decay': 0.00018744416376778518, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:50:47,575] Trial 41 finished with value: 0.5438192585622175 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.1770580475070806, 'lr': 0.004528158262329327, 'weight_decay': 0.0009106646664395158, 'batch_size': 128}. Best is trial 36 with value: 0.552627867752313.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.543819
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.1770580475070806, 'lr': 0.004528158262329327, 'weight_decay': 0.0009106646664395158, 'batch_size': 128}
+  Best Value (CSI): 0.552628
+  Best Trial: 36
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13804213033188278, 'lr': 0.00666980471505662, 'weight_decay': 0.00018744416376778518, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:51:30,023] Trial 42 finished with value: 0.5502364039892521 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.150486901812303, 'lr': 0.007264732590789507, 'weight_decay': 0.0014740223707663718, 'batch_size': 128}. Best is trial 36 with value: 0.552627867752313.
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.550236
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.150486901812303, 'lr': 0.007264732590789507, 'weight_decay': 0.0014740223707663718, 'batch_size': 128}
+  Best Value (CSI): 0.552628
+  Best Trial: 36
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13804213033188278, 'lr': 0.00666980471505662, 'weight_decay': 0.00018744416376778518, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:52:05,647] Trial 43 finished with value: 0.5395463832387208 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.1510478381812438, 'lr': 0.007310521170910806, 'weight_decay': 0.0018681007141710157, 'batch_size': 128}. Best is trial 36 with value: 0.552627867752313.
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.539546
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.1510478381812438, 'lr': 0.007310521170910806, 'weight_decay': 0.0018681007141710157, 'batch_size': 128}
+  Best Value (CSI): 0.552628
+  Best Trial: 36
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13804213033188278, 'lr': 0.00666980471505662, 'weight_decay': 0.00018744416376778518, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:52:50,844] Trial 44 finished with value: 0.5526658397235988 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1381605783099045, 'lr': 0.009993818847757572, 'weight_decay': 0.000146558800057306, 'batch_size': 128}. Best is trial 44 with value: 0.5526658397235988.
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.552666
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1381605783099045, 'lr': 0.009993818847757572, 'weight_decay': 0.000146558800057306, 'batch_size': 128}
+  Best Value (CSI): 0.552666
+  Best Trial: 44
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1381605783099045, 'lr': 0.009993818847757572, 'weight_decay': 0.000146558800057306, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:52:58,071] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.409012
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.13761231654135025, 'lr': 0.0009986456042909958, 'weight_decay': 0.00016328005892009723, 'batch_size': 128}
+  Best Value (CSI): 0.552666
+  Best Trial: 44
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1381605783099045, 'lr': 0.009993818847757572, 'weight_decay': 0.000146558800057306, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:53:38,200] Trial 46 finished with value: 0.5428695905026595 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.16578408733573052, 'lr': 0.009748857333802688, 'weight_decay': 0.00010115269326018381, 'batch_size': 128}. Best is trial 44 with value: 0.5526658397235988.
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.542870
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.16578408733573052, 'lr': 0.009748857333802688, 'weight_decay': 0.00010115269326018381, 'batch_size': 128}
+  Best Value (CSI): 0.552666
+  Best Trial: 44
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1381605783099045, 'lr': 0.009993818847757572, 'weight_decay': 0.000146558800057306, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:53:53,357] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.449970
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.10704114904448213, 'lr': 0.005099756611182526, 'weight_decay': 0.0003192811083118881, 'batch_size': 128}
+  Best Value (CSI): 0.552666
+  Best Trial: 44
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1381605783099045, 'lr': 0.009993818847757572, 'weight_decay': 0.000146558800057306, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:54:27,799] Trial 48 finished with value: 0.5309709300204094 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.11634655419178058, 'lr': 0.0031116093331770792, 'weight_decay': 0.00018344646159815345, 'batch_size': 128}. Best is trial 44 with value: 0.5526658397235988.
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.530971
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.11634655419178058, 'lr': 0.0031116093331770792, 'weight_decay': 0.00018344646159815345, 'batch_size': 128}
+  Best Value (CSI): 0.552666
+  Best Trial: 44
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1381605783099045, 'lr': 0.009993818847757572, 'weight_decay': 0.000146558800057306, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:56:04,937] Trial 49 finished with value: 0.5512153584620476 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.13759436474325948, 'lr': 0.00801475269334421, 'weight_decay': 0.00014296985811924757, 'batch_size': 64}. Best is trial 44 with value: 0.5526658397235988.
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.551215
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.13759436474325948, 'lr': 0.00801475269334421, 'weight_decay': 0.00014296985811924757, 'batch_size': 64}
+  Best Value (CSI): 0.552666
+  Best Trial: 44
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1381605783099045, 'lr': 0.009993818847757572, 'weight_decay': 0.000146558800057306, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:56:51,356] Trial 50 finished with value: 0.5444042024748891 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.26131374001608554, 'lr': 0.0053223541304732265, 'weight_decay': 0.00038785448496057916, 'batch_size': 128}. Best is trial 44 with value: 0.5526658397235988.
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.544404
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.26131374001608554, 'lr': 0.0053223541304732265, 'weight_decay': 0.00038785448496057916, 'batch_size': 128}
+  Best Value (CSI): 0.552666
+  Best Trial: 44
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1381605783099045, 'lr': 0.009993818847757572, 'weight_decay': 0.000146558800057306, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 20:57:19,572] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.487635
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.13881393587773386, 'lr': 0.00904373630047427, 'weight_decay': 0.00013723037792367547, 'batch_size': 64}
+  Best Value (CSI): 0.552666
+  Best Trial: 44
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1381605783099045, 'lr': 0.009993818847757572, 'weight_decay': 0.000146558800057306, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:58:41,522] Trial 52 finished with value: 0.5522679238658653 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.13198411605031007, 'lr': 0.007771443815937695, 'weight_decay': 0.00013605329702851807, 'batch_size': 64}. Best is trial 44 with value: 0.5526658397235988.
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.552268
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.13198411605031007, 'lr': 0.007771443815937695, 'weight_decay': 0.00013605329702851807, 'batch_size': 64}
+  Best Value (CSI): 0.552666
+  Best Trial: 44
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1381605783099045, 'lr': 0.009993818847757572, 'weight_decay': 0.000146558800057306, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 20:59:58,883] Trial 53 finished with value: 0.5461612245473196 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.11417811994182488, 'lr': 0.007256187932963692, 'weight_decay': 0.00019558196385629736, 'batch_size': 64}. Best is trial 44 with value: 0.5526658397235988.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.546161
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.11417811994182488, 'lr': 0.007256187932963692, 'weight_decay': 0.00019558196385629736, 'batch_size': 64}
+  Best Value (CSI): 0.552666
+  Best Trial: 44
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1381605783099045, 'lr': 0.009993818847757572, 'weight_decay': 0.000146558800057306, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:01:18,161] Trial 54 finished with value: 0.5511642815842203 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12927572221608072, 'lr': 0.005368295486019479, 'weight_decay': 0.00012809372043801339, 'batch_size': 64}. Best is trial 44 with value: 0.5526658397235988.
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.551164
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12927572221608072, 'lr': 0.005368295486019479, 'weight_decay': 0.00012809372043801339, 'batch_size': 64}
+  Best Value (CSI): 0.552666
+  Best Trial: 44
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1381605783099045, 'lr': 0.009993818847757572, 'weight_decay': 0.000146558800057306, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:02:08,825] Trial 55 finished with value: 0.5382657651058985 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.15467731108980148, 'lr': 0.0037310700818680253, 'weight_decay': 0.0003397931916256659, 'batch_size': 64}. Best is trial 44 with value: 0.5526658397235988.
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.538266
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.15467731108980148, 'lr': 0.0037310700818680253, 'weight_decay': 0.0003397931916256659, 'batch_size': 64}
+  Best Value (CSI): 0.552666
+  Best Trial: 44
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1381605783099045, 'lr': 0.009993818847757572, 'weight_decay': 0.000146558800057306, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:02:56,214] Trial 56 finished with value: 0.5644485264432356 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.564449
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:03:30,821] Trial 57 finished with value: 0.5401849496176356 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16884541712051412, 'lr': 0.004480434929250742, 'weight_decay': 0.0005006702682682651, 'batch_size': 128}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.540185
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16884541712051412, 'lr': 0.004480434929250742, 'weight_decay': 0.0005006702682682651, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:04:12,057] Trial 58 finished with value: 0.5583019600461835 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1403443515202148, 'lr': 0.0073746308956520144, 'weight_decay': 0.00018858367651560897, 'batch_size': 128}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.558302
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1403443515202148, 'lr': 0.0073746308956520144, 'weight_decay': 0.00018858367651560897, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:04:21,162] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.448757
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.12801695707228683, 'lr': 0.002861767671521901, 'weight_decay': 0.0002560839447216127, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:04:51,902] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.468176
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.11393178193721737, 'lr': 0.007611046874514047, 'weight_decay': 0.00021636367672213627, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:05:08,735] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.462668
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.14205573912216174, 'lr': 0.005747599274205612, 'weight_decay': 0.00017585722568585284, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:05:44,974] Trial 62 finished with value: 0.5466624781627548 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.14863606700740828, 'lr': 0.007818373948897735, 'weight_decay': 0.00011539263451515848, 'batch_size': 128}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.546662
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.14863606700740828, 'lr': 0.007818373948897735, 'weight_decay': 0.00011539263451515848, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:06:22,646] Trial 63 finished with value: 0.5464776373208452 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.13315542605462802, 'lr': 0.006130577652585583, 'weight_decay': 0.00030809857554184864, 'batch_size': 128}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.546478
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.13315542605462802, 'lr': 0.006130577652585583, 'weight_decay': 0.00030809857554184864, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:07:07,381] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.467228
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.10979940005355489, 'lr': 0.003614732664716501, 'weight_decay': 0.00019022633032522626, 'batch_size': 32}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:07:16,207] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.470944
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.12664916293655118, 'lr': 0.004520623088306594, 'weight_decay': 0.00013194437841817169, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:07:59,377] Trial 66 finished with value: 0.5591047207082122 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.14297398821729423, 'lr': 0.00791153374632006, 'weight_decay': 0.00025820616073126237, 'batch_size': 128}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.559105
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.14297398821729423, 'lr': 0.00791153374632006, 'weight_decay': 0.00025820616073126237, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:09:26,460] Trial 67 finished with value: 0.5563757274389706 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.12223487335508174, 'lr': 0.00843563094272057, 'weight_decay': 0.0004567724897560505, 'batch_size': 64}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.556376
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.12223487335508174, 'lr': 0.00843563094272057, 'weight_decay': 0.0004567724897560505, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:09:33,961] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.446903
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.12060625335562272, 'lr': 0.008534689512597497, 'weight_decay': 0.0006110478654668953, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:09:51,032] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.475030
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10199904803106835, 'lr': 0.009929520965415998, 'weight_decay': 0.0004739526975566487, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:10:18,353] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.412976
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.152570100154129, 'lr': 0.004980637643241967, 'weight_decay': 0.00037276498335706324, 'batch_size': 32}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:11:33,848] Trial 71 finished with value: 0.549818206883998 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.13573956575055868, 'lr': 0.0063179594810047045, 'weight_decay': 0.00025379494809594377, 'batch_size': 64}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.549818
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.13573956575055868, 'lr': 0.0063179594810047045, 'weight_decay': 0.00025379494809594377, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:12:03,483] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.478342
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.12432316115593231, 'lr': 0.008210270879621546, 'weight_decay': 0.0004080807834238328, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:13:12,965] Trial 73 finished with value: 0.5507199744219553 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.1457916000663571, 'lr': 0.008127576078909146, 'weight_decay': 0.00027152595883661297, 'batch_size': 64}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.550720
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.1457916000663571, 'lr': 0.008127576078909146, 'weight_decay': 0.00027152595883661297, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:14:13,914] Trial 74 finished with value: 0.5525845465691134 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.11212983631965275, 'lr': 0.005963619508207302, 'weight_decay': 0.00021374867440367628, 'batch_size': 64}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.552585
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.11212983631965275, 'lr': 0.005963619508207302, 'weight_decay': 0.00021374867440367628, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:14:24,998] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.458665
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.11073889268550598, 'lr': 0.005925005612992019, 'weight_decay': 0.00043136417956511126, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:14:36,108] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.445768
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10022271521420009, 'lr': 0.003972959551450464, 'weight_decay': 0.00021054783163272541, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:15:47,829] Trial 77 finished with value: 0.5413655997299855 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.11884193149946026, 'lr': 0.004755900670655561, 'weight_decay': 0.0006608760350254661, 'batch_size': 64}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.541366
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.11884193149946026, 'lr': 0.004755900670655561, 'weight_decay': 0.0006608760350254661, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:16:27,141] Trial 78 finished with value: 0.5488693099109199 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.1687992270797759, 'lr': 0.006279245789470673, 'weight_decay': 0.0003034539394951718, 'batch_size': 128}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.548869
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.1687992270797759, 'lr': 0.006279245789470673, 'weight_decay': 0.0003034539394951718, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:17:33,549] Trial 79 finished with value: 0.5387880992326108 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.14247799789447735, 'lr': 0.0026473358404261902, 'weight_decay': 0.0004869065094112459, 'batch_size': 64}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.538788
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.14247799789447735, 'lr': 0.0026473358404261902, 'weight_decay': 0.0004869065094112459, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:17:42,359] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.432561
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.15869021629446295, 'lr': 0.002260366358397602, 'weight_decay': 0.0002365933925797734, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:18:12,111] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.500614
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.13088681017005405, 'lr': 0.0083268641155692, 'weight_decay': 0.00015120553279700493, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:18:38,210] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.481149
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.12412305681560211, 'lr': 0.006471869151337779, 'weight_decay': 0.00020159727795449086, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:19:42,237] Trial 83 finished with value: 0.5425596378076011 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.13380365490975396, 'lr': 0.003293840445705116, 'weight_decay': 0.00011500435998749942, 'batch_size': 64}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.542560
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.13380365490975396, 'lr': 0.003293840445705116, 'weight_decay': 0.00011500435998749942, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:20:10,877] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.471346
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.10826970740460654, 'lr': 0.008698171465099503, 'weight_decay': 0.00017171963428280472, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:20:21,060] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.459920
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.11642496678569736, 'lr': 0.005079738235353199, 'weight_decay': 0.00028007026272535156, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:20:35,863] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.447103
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.14759474016350158, 'lr': 0.009867683548540415, 'weight_decay': 0.0003539531954940543, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:21:19,505] Trial 87 finished with value: 0.5393082296797388 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.13546148701172994, 'lr': 0.0069468501421974015, 'weight_decay': 0.00022685517637057858, 'batch_size': 128}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.539308
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.13546148701172994, 'lr': 0.0069468501421974015, 'weight_decay': 0.00022685517637057858, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:23:47,408] Trial 88 finished with value: 0.5539615439342908 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.15432134461796648, 'lr': 0.004368718128055782, 'weight_decay': 0.0005933406319756243, 'batch_size': 32}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.553962
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.15432134461796648, 'lr': 0.004368718128055782, 'weight_decay': 0.0005933406319756243, 'batch_size': 32}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+[I 2025-12-28 21:25:09,687] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.574559
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.1557769379230941, 'lr': 0.0036440132761718894, 'weight_decay': 0.0005584553456636461, 'batch_size': 32}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+[I 2025-12-28 21:26:38,006] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.571205
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.14107327296414077, 'lr': 0.0043826980063356395, 'weight_decay': 0.0006949914528162474, 'batch_size': 32}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:27:00,637] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.414397
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.12764769254487093, 'lr': 0.007033835266080436, 'weight_decay': 0.00010035222447790803, 'batch_size': 32}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:27:23,243] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.471710
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.12149022163376003, 'lr': 0.005735976033430046, 'weight_decay': 0.000332401136807744, 'batch_size': 32}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:28:33,882] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.527356
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.16088968004904097, 'lr': 0.008117725108960324, 'weight_decay': 0.0001606794604031563, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:28:42,748] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.459903
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.15309450190472612, 'lr': 0.005485578686672786, 'weight_decay': 0.00042030885444263355, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:29:10,087] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.411533
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.14445497223448572, 'lr': 0.006990923413303166, 'weight_decay': 0.0007674298137507321, 'batch_size': 32}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+[I 2025-12-28 21:29:44,629] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.566069
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.10954693544660513, 'lr': 0.008405647449623596, 'weight_decay': 0.0002791517339263404, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:30:15,412] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.472309
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.16622570817251361, 'lr': 0.00981338571024433, 'weight_decay': 0.0005828781242231709, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 21:30:29,267] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.468301
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.13338427187704002, 'lr': 0.005102294337218716, 'weight_decay': 0.00018592584892562257, 'batch_size': 128}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 21:31:41,689] Trial 99 finished with value: 0.5398996117646979 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.11444183072574908, 'lr': 0.0041157111018092895, 'weight_decay': 0.0010113978851207346, 'batch_size': 64}. Best is trial 56 with value: 0.5644485264432356.
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.539900
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.11444183072574908, 'lr': 0.0041157111018092895, 'weight_decay': 0.0010113978851207346, 'batch_size': 64}
+  Best Value (CSI): 0.564449
+  Best Trial: 56
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5644
+Best Hyperparameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5036
+  - 최종 CSI: 0.5399
+  - 최고 CSI: 0.5746
+  - 최저 CSI: 0.2633
+  - 평균 CSI: 0.5004
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/deepgbm_ctgan10000_incheon_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 27, Best CSI: 0.5119
+  Fold 1 학습 완료 (검증 CSI: 0.5119)
+Fold 2 학습 중...
+  Early stopping at epoch 24, Best CSI: 0.5883
+  Fold 2 학습 완료 (검증 CSI: 0.5883)
+Fold 3 학습 중...
+  Early stopping at epoch 24, Best CSI: 0.5554
+  Fold 3 학습 완료 (검증 CSI: 0.5554)
+
+모든 모델 저장 완료: ../save_model/deepgbm_optima/deepgbm_ctgan10000_incheon.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/deepgbm_optima/scaler/deepgbm_ctgan10000_incheon_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/deepgbm_optima/deepgbm_ctgan10000_incheon.pkl
+
+✓ 완료: deepgbm_ctgan10000/deepgbm_ctgan10000_incheon.py (소요 시간: 3893초)
+
+----------------------------------------
+실행 중: deepgbm_ctgan10000/deepgbm_ctgan10000_seoul.py
+시작 시간: 2025-12-28 21:32:25
+----------------------------------------
+[I 2025-12-28 21:32:26,789] A new study created in memory with name: no-name-fdf0f58a-0d25-4898-b54e-1a0ac9760424
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 21:36:02,943] Trial 0 finished with value: 0.4703025378092154 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.22117231179668684, 'lr': 2.2371365327741396e-05, 'weight_decay': 0.005447814868861018, 'batch_size': 32}. Best is trial 0 with value: 0.4703025378092154.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.470303
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.22117231179668684, 'lr': 2.2371365327741396e-05, 'weight_decay': 0.005447814868861018, 'batch_size': 32}
+  Best Value (CSI): 0.470303
+  Best Trial: 0
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.22117231179668684, 'lr': 2.2371365327741396e-05, 'weight_decay': 0.005447814868861018, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 21:36:39,726] Trial 1 finished with value: 0.4782679190593994 and parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.1177310104629107, 'lr': 0.00015740664660188913, 'weight_decay': 0.01044268185401063, 'batch_size': 256}. Best is trial 1 with value: 0.4782679190593994.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.478268
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.1177310104629107, 'lr': 0.00015740664660188913, 'weight_decay': 0.01044268185401063, 'batch_size': 256}
+  Best Value (CSI): 0.478268
+  Best Trial: 1
+  Best Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.1177310104629107, 'lr': 0.00015740664660188913, 'weight_decay': 0.01044268185401063, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 21:37:28,118] Trial 2 finished with value: 0.5455396057210445 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.35307077202667325, 'lr': 0.005328589578343898, 'weight_decay': 0.0038473436240858876, 'batch_size': 128}. Best is trial 2 with value: 0.5455396057210445.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.545540
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.35307077202667325, 'lr': 0.005328589578343898, 'weight_decay': 0.0038473436240858876, 'batch_size': 128}
+  Best Value (CSI): 0.545540
+  Best Trial: 2
+  Best Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.35307077202667325, 'lr': 0.005328589578343898, 'weight_decay': 0.0038473436240858876, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 21:40:22,948] Trial 3 finished with value: 0.5620475905480543 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}. Best is trial 3 with value: 0.5620475905480543.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.562048
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 21:41:10,277] Trial 4 finished with value: 0.5052463257058484 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3233917068741079, 'lr': 0.0012901844148085352, 'weight_decay': 0.00010791010452093161, 'batch_size': 128}. Best is trial 3 with value: 0.5620475905480543.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.505246
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3233917068741079, 'lr': 0.0012901844148085352, 'weight_decay': 0.00010791010452093161, 'batch_size': 128}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:41:21,330] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.386049
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.24338330110286954, 'lr': 0.0002981406736392642, 'weight_decay': 0.0435257538860857, 'batch_size': 64}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:41:27,208] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.399249
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.32779218232717294, 'lr': 0.0015166860628446337, 'weight_decay': 0.0005085687904213193, 'batch_size': 256}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:41:34,832] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.365982
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.3684422721230156, 'lr': 2.9109909960689975e-05, 'weight_decay': 0.012069255334381067, 'batch_size': 128}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:42:00,225] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.387931
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.16933043492079625, 'lr': 0.00012546124259549425, 'weight_decay': 0.017151725646977315, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:42:06,172] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.397704
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.23303260737828432, 'lr': 0.0014137451570338272, 'weight_decay': 0.04853179100400946, 'batch_size': 256}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 21:44:27,672] Trial 10 finished with value: 0.5326173924885841 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.2863503544985895, 'lr': 0.007321720573037164, 'weight_decay': 0.000977185408305386, 'batch_size': 32}. Best is trial 3 with value: 0.5620475905480543.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.532617
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.2863503544985895, 'lr': 0.007321720573037164, 'weight_decay': 0.000977185408305386, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:44:43,884] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.456771
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.3890093922021857, 'lr': 0.006150209277111024, 'weight_decay': 0.0016888917996709712, 'batch_size': 128}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:45:10,811] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.460000
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2876718339564919, 'lr': 0.009433469713448798, 'weight_decay': 0.003169850483575423, 'batch_size': 64}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:45:21,685] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.443243
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.39496107079554615, 'lr': 0.002902326843206018, 'weight_decay': 0.0004136177088423997, 'batch_size': 128}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 21:47:49,852] Trial 14 finished with value: 0.5530388425925558 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.1942780607301383, 'lr': 0.0031231786739227144, 'weight_decay': 0.002264776124164396, 'batch_size': 32}. Best is trial 3 with value: 0.5620475905480543.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.553039
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.1942780607301383, 'lr': 0.0031231786739227144, 'weight_decay': 0.002264776124164396, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:48:11,027] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.394036
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.19193895709295405, 'lr': 0.0007569776979325284, 'weight_decay': 0.001305061027099884, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:49:11,843] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.427252
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.17694472428740324, 'lr': 0.0028799138178403293, 'weight_decay': 0.0001514470608266138, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:49:33,070] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.417489
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.27957823003342624, 'lr': 0.0006083444395893277, 'weight_decay': 0.00029526595315422586, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:50:23,410] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.431526
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.2003002380151626, 'lr': 0.0026517474434389087, 'weight_decay': 0.0008709722825504827, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:51:18,967] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.448889
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.1477948472804278, 'lr': 0.003449391306108875, 'weight_decay': 0.0020365913228150433, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:51:31,541] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.316417
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.21741538505954397, 'lr': 0.008670832340904256, 'weight_decay': 0.00019763252947298753, 'batch_size': 64}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:51:37,596] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.438844
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.25574433936427143, 'lr': 0.004265850964536, 'weight_decay': 0.0035315398616997083, 'batch_size': 128}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:51:52,882] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.438972
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2635805814605773, 'lr': 0.005716008854633256, 'weight_decay': 0.0006751277630046174, 'batch_size': 128}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:52:19,193] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.451635
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.21152017185820635, 'lr': 0.0018433456093989839, 'weight_decay': 0.001973883298781508, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 21:54:29,927] Trial 24 finished with value: 0.5306565524756649 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.24836995128034023, 'lr': 0.004581375265898128, 'weight_decay': 0.0002689930299836126, 'batch_size': 32}. Best is trial 3 with value: 0.5620475905480543.
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.530657
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.24836995128034023, 'lr': 0.004581375265898128, 'weight_decay': 0.0002689930299836126, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:54:46,589] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.437765
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.22757168259469565, 'lr': 0.009965681124996623, 'weight_decay': 0.0006037254364241354, 'batch_size': 128}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:55:18,285] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.447018
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3124985979081019, 'lr': 0.00482736172287, 'weight_decay': 0.005486498588848869, 'batch_size': 64}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:55:23,118] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.394050
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.3572839024193176, 'lr': 0.002210041760749496, 'weight_decay': 0.0012452870980601593, 'batch_size': 256}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:55:48,638] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.430894
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2712046572294762, 'lr': 0.0009246527103353629, 'weight_decay': 0.0008927080053397817, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 21:57:31,122] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.503226
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.23353177648878198, 'lr': 0.002265255415792327, 'weight_decay': 0.003951447423395857, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:57:41,551] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.439847
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3024163989002844, 'lr': 0.003968212512522471, 'weight_decay': 0.0021836364287323557, 'batch_size': 128}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:58:16,783] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.445091
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.28943846539936097, 'lr': 0.0065329781776994845, 'weight_decay': 0.0010084125741559639, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 21:59:17,690] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.457122
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.342068082982951, 'lr': 0.0061860776465791625, 'weight_decay': 0.0056176475067692775, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:00:18,523] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.448042
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.2631292658658078, 'lr': 0.003373264901847713, 'weight_decay': 0.00042153133749043236, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:01:14,022] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.438860
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.30134311278438647, 'lr': 0.0017789818378242674, 'weight_decay': 0.0012865682141716096, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:01:18,613] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.409594
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.32753555712545135, 'lr': 0.0010856137660270359, 'weight_decay': 0.0007675629090892598, 'batch_size': 256}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:01:38,586] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.372708
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.24507992637407922, 'lr': 0.007930549535741033, 'weight_decay': 0.002704755854775388, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:01:45,820] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.398058
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.27612305161537387, 'lr': 0.004279145876258088, 'weight_decay': 0.006957355250702544, 'batch_size': 128}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:01:58,927] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.437899
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.3411125002404894, 'lr': 0.0013021735711344013, 'weight_decay': 0.0006000926524871888, 'batch_size': 64}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:02:04,534] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.438974
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.31620778399144445, 'lr': 0.006314552074196195, 'weight_decay': 0.0016021867751390463, 'batch_size': 256}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:02:21,371] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.433060
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.37284411785517035, 'lr': 0.002125472389231902, 'weight_decay': 0.0026613109838984727, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:03:01,430] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.444934
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.24411461733570816, 'lr': 0.004619659234152188, 'weight_decay': 0.000330874073539053, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:03:41,314] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.438241
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.25404620178690934, 'lr': 0.0034909630080711277, 'weight_decay': 0.0002635397557913019, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:03:55,684] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.322434
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.23653372289701638, 'lr': 0.009757009730980513, 'weight_decay': 0.00011268254240914734, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 22:06:21,218] Trial 44 finished with value: 0.5603929479636361 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.21886426127489722, 'lr': 0.0052312368820888605, 'weight_decay': 0.0005188908329885096, 'batch_size': 32}. Best is trial 3 with value: 0.5620475905480543.
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.560393
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.21886426127489722, 'lr': 0.0052312368820888605, 'weight_decay': 0.0005188908329885096, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:06:28,139] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.419118
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.22101976267114848, 'lr': 0.006454106084272818, 'weight_decay': 0.0005569254543235998, 'batch_size': 128}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:06:47,297] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.436610
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.20544510579160744, 'lr': 0.00271693202841955, 'weight_decay': 0.0010127879107889736, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:07:37,770] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.446512
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.181442220699703, 'lr': 0.0014255881393654367, 'weight_decay': 0.0016481262051730098, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:08:06,008] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.428091
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.1979998169543779, 'lr': 0.007623543771169269, 'weight_decay': 0.00051987717429119, 'batch_size': 64}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:08:12,630] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.419332
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.22216805293532454, 'lr': 0.0031635558796524895, 'weight_decay': 0.0004524733516007845, 'batch_size': 128}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:08:17,782] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.382767
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.15707112203333234, 'lr': 0.00056361375632203, 'weight_decay': 0.004094238990782842, 'batch_size': 256}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:08:32,009] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.368421
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.25481513070975503, 'lr': 0.005400753686732274, 'weight_decay': 0.00021852620873257008, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:09:17,381] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.450622
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.21396035456604992, 'lr': 0.0049504546500309735, 'weight_decay': 0.00035869284651317615, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 22:11:29,166] Trial 53 finished with value: 0.5503224344986251 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.24354084836010303, 'lr': 0.0037886796385869113, 'weight_decay': 0.00025918523440955524, 'batch_size': 32}. Best is trial 3 with value: 0.5620475905480543.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.550322
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.24354084836010303, 'lr': 0.0037886796385869113, 'weight_decay': 0.00025918523440955524, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 22:13:32,375] Trial 54 finished with value: 0.5481211899784894 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2349153945283539, 'lr': 0.002642755066461728, 'weight_decay': 0.0007479154284589059, 'batch_size': 32}. Best is trial 3 with value: 0.5620475905480543.
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.548121
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2349153945283539, 'lr': 0.002642755066461728, 'weight_decay': 0.0007479154284589059, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 22:15:44,400] Trial 55 finished with value: 0.5413516107549424 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2331075105545691, 'lr': 0.0026779308719154186, 'weight_decay': 0.0003764709158128595, 'batch_size': 32}. Best is trial 3 with value: 0.5620475905480543.
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.541352
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2331075105545691, 'lr': 0.0026779308719154186, 'weight_decay': 0.0003764709158128595, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:16:07,401] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.446388
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.20802773442935416, 'lr': 0.0016529522347821521, 'weight_decay': 0.0001796590299176935, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:16:33,794] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.457496
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.1908681845263833, 'lr': 0.003639186447670738, 'weight_decay': 0.000780191324017324, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:16:43,572] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.430425
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.2256412623268874, 'lr': 0.002295320064472111, 'weight_decay': 0.00024553804314374954, 'batch_size': 128}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:17:23,631] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.450450
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.23799836602570099, 'lr': 0.0029148146861254476, 'weight_decay': 0.00031298615727994006, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:17:49,595] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.452681
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2674596935208798, 'lr': 0.0018627412451897927, 'weight_decay': 0.00047066737020138466, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 22:19:47,265] Trial 61 finished with value: 0.5562218245470287 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23170266377001675, 'lr': 0.0024849146998217842, 'weight_decay': 0.00036656655057741686, 'batch_size': 32}. Best is trial 3 with value: 0.5620475905480543.
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.556222
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23170266377001675, 'lr': 0.0024849146998217842, 'weight_decay': 0.00036656655057741686, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:20:32,252] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.437922
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.21445980842694135, 'lr': 0.003966491852749469, 'weight_decay': 0.0006805350097843359, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:20:55,382] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.433155
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.22663616047802956, 'lr': 0.007627647166894666, 'weight_decay': 0.0001719158921806444, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:21:24,841] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.467513
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.24174369152426978, 'lr': 0.004813772622652762, 'weight_decay': 0.0003922058078536395, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:21:35,755] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.436865
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.252444347772953, 'lr': 0.0025989824596362524, 'weight_decay': 0.0002831566072012636, 'batch_size': 64}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:22:20,726] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.470183
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2612588301700684, 'lr': 0.0054547042503612, 'weight_decay': 0.000601168068237752, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:22:26,851] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.418754
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.19953239839761286, 'lr': 0.003449357478300865, 'weight_decay': 0.0002182200000717981, 'batch_size': 128}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:22:53,004] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.438822
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.22968910338097914, 'lr': 0.002017260853687685, 'weight_decay': 0.0004906274793717003, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:23:14,383] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.440933
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.27747113112358435, 'lr': 0.0011358865805161363, 'weight_decay': 0.0010501968223815147, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:23:19,899] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.420708
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.24710700222277662, 'lr': 0.0014776813260005683, 'weight_decay': 0.00030929117850830493, 'batch_size': 256}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 22:25:18,452] Trial 71 finished with value: 0.5527063019876816 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2374883615748952, 'lr': 0.0025648369617845, 'weight_decay': 0.0003729523751117234, 'batch_size': 32}. Best is trial 3 with value: 0.5620475905480543.
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.552706
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2374883615748952, 'lr': 0.0025648369617845, 'weight_decay': 0.0003729523751117234, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:26:03,437] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.448378
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2177769181899023, 'lr': 0.003906992113028214, 'weight_decay': 0.0003999065295626568, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:26:48,183] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.446442
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23521398070413804, 'lr': 0.0030414575905052007, 'weight_decay': 0.0007245138185713923, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:27:33,234] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.449376
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2586058636179824, 'lr': 0.005457460371337615, 'weight_decay': 0.0002337834582572608, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:27:56,145] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.470772
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.24330207022925252, 'lr': 0.0023681128889006696, 'weight_decay': 0.0003501210630469124, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:28:02,922] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.420220
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.2235148545508787, 'lr': 0.001653585936561436, 'weight_decay': 0.00015173214498392476, 'batch_size': 128}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:28:32,065] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.463933
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.20659535094166062, 'lr': 0.004357256519075715, 'weight_decay': 0.00079925933422267, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:29:11,556] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.462500
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.25003388648291147, 'lr': 0.00729989555120143, 'weight_decay': 0.0005366043616712213, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:29:23,490] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.447785
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.23313724887706339, 'lr': 0.002044433233223082, 'weight_decay': 0.0002836600314209355, 'batch_size': 64}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:29:42,535] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.422862
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.26411735130763625, 'lr': 0.0033709147987772176, 'weight_decay': 0.0013643076702286668, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 22:31:49,109] Trial 81 finished with value: 0.5558661836412612 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23646998988200052, 'lr': 0.002512206490926576, 'weight_decay': 0.0004476731977099644, 'batch_size': 32}. Best is trial 3 with value: 0.5620475905480543.
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.555866
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23646998988200052, 'lr': 0.002512206490926576, 'weight_decay': 0.0004476731977099644, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:32:08,252] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.412961
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23936683299117223, 'lr': 0.0025507477397768495, 'weight_decay': 0.0005004234220988308, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 22:34:45,425] Trial 83 finished with value: 0.553362968787864 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.21468391172642293, 'lr': 0.0038842305407804493, 'weight_decay': 0.00044073421328091607, 'batch_size': 32}. Best is trial 3 with value: 0.5620475905480543.
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.553363
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.21468391172642293, 'lr': 0.0038842305407804493, 'weight_decay': 0.00044073421328091607, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:35:11,757] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.471299
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.21728173922860716, 'lr': 0.0029758273856928447, 'weight_decay': 0.00041455333169100703, 'batch_size': 32}
+  Best Value (CSI): 0.562048
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.24378639566603055, 'lr': 0.0032011721381719196, 'weight_decay': 0.0003798946802423518, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 22:37:12,405] Trial 85 finished with value: 0.5652632820174084 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}. Best is trial 85 with value: 0.5652632820174084.
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.565263
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:37:31,378] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.446873
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.20915288466397505, 'lr': 0.004004039591117556, 'weight_decay': 0.0004499394315111278, 'batch_size': 32}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:37:50,427] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.398437
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.1915000777145732, 'lr': 0.0047043310019172745, 'weight_decay': 0.0006183023021636469, 'batch_size': 32}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:38:40,768] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.449819
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.22704103323465272, 'lr': 0.006077462590548687, 'weight_decay': 0.0003236915611164814, 'batch_size': 32}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:38:59,962] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.443966
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.2185043404150587, 'lr': 0.001972563928737882, 'weight_decay': 0.00027015839794317174, 'batch_size': 32}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 22:41:03,497] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.549296
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.20066925110093495, 'lr': 0.0037434173209479806, 'weight_decay': 0.00019963838758493388, 'batch_size': 32}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:41:48,745] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.443452
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2294606756105856, 'lr': 0.0024161714969528976, 'weight_decay': 0.0006533549791388401, 'batch_size': 32}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 22:44:00,733] Trial 92 finished with value: 0.5579868620185066 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2468107137142098, 'lr': 0.0030513613777232436, 'weight_decay': 0.0008727125163609398, 'batch_size': 32}. Best is trial 85 with value: 0.5652632820174084.
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.557987
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2468107137142098, 'lr': 0.0030513613777232436, 'weight_decay': 0.0008727125163609398, 'batch_size': 32}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:44:19,749] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.451812
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.24442773199082987, 'lr': 0.0034409839901405476, 'weight_decay': 0.0003670916633367426, 'batch_size': 32}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:45:03,138] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.465558
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2563096279676195, 'lr': 0.0030681168612820696, 'weight_decay': 0.0005158527566906674, 'batch_size': 32}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:45:46,485] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.449926
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.24852356304335238, 'lr': 0.004178472892000451, 'weight_decay': 0.0009594083516398163, 'batch_size': 32}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:46:30,074] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.467269
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.21342476040806263, 'lr': 0.0049975412484920476, 'weight_decay': 0.00042266624033273384, 'batch_size': 32}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:46:35,517] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.389715
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.22121448769131613, 'lr': 0.001684079881540931, 'weight_decay': 0.0005730806650962919, 'batch_size': 256}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:46:56,796] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.336916
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.24146942128955776, 'lr': 0.008925330383976348, 'weight_decay': 0.0003498236440129695, 'batch_size': 32}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 22:47:22,929] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.459877
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.21168673025532303, 'lr': 0.002265459607689244, 'weight_decay': 0.0006478976303694914, 'batch_size': 32}
+  Best Value (CSI): 0.565263
+  Best Trial: 85
+  Best Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5653
+Best Hyperparameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2113225708109025, 'lr': 0.0038029027900849553, 'weight_decay': 0.0005824342055366228, 'batch_size': 32}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4703
+  - 최종 CSI: 0.4599
+  - 최고 CSI: 0.5653
+  - 최저 CSI: 0.3164
+  - 평균 CSI: 0.4514
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/deepgbm_ctgan10000_seoul_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 30, Best CSI: 0.4693
+  Fold 1 학습 완료 (검증 CSI: 0.4693)
+Fold 2 학습 중...
+  Early stopping at epoch 36, Best CSI: 0.5956
+  Fold 2 학습 완료 (검증 CSI: 0.5956)
+Fold 3 학습 중...
+  Early stopping at epoch 33, Best CSI: 0.5764
+  Fold 3 학습 완료 (검증 CSI: 0.5764)
+
+모든 모델 저장 완료: ../save_model/deepgbm_optima/deepgbm_ctgan10000_seoul.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/deepgbm_optima/scaler/deepgbm_ctgan10000_seoul_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/deepgbm_optima/deepgbm_ctgan10000_seoul.pkl
+
+✓ 완료: deepgbm_ctgan10000/deepgbm_ctgan10000_seoul.py (소요 시간: 4642초)
+
+==========================================
+DeepGBM CTGAN10000 파일 실행 완료
+종료 시간: 2025-12-28 22:49:47
+총 소요 시간: 10시간 13분 45초
+성공: 6개
+실패: 0개
+==========================================
diff --git a/Analysis_code/5.optima/run_bash/deepgbm/deepgbm_smotenc_ctgan20000.log b/Analysis_code/5.optima/run_bash/deepgbm/deepgbm_smotenc_ctgan20000.log
index d5765f82ecf1040690497abf342b364f88f9416e..cd5ed667a1d7f6e4061d6294914eeee4486f9013 100644
--- a/Analysis_code/5.optima/run_bash/deepgbm/deepgbm_smotenc_ctgan20000.log
+++ b/Analysis_code/5.optima/run_bash/deepgbm/deepgbm_smotenc_ctgan20000.log
@@ -651,635 +651,635 @@ Trial 50 완료
   Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
-  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-ut': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
-================================================================================
-
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
   Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
-  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
-[I 2025-12-25 16:21:35,227] Trial 50 finished with value: 0.4189061362739133 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.10702365279418882, 'lr': 0.007817047784089107, 'weight_decay': 0.010992417433931895, 'batch_size': 64}. Best is trial 24 with value: 0.4302129848817923.
-
-================================================================================
-Trial 50 완료
-  Value (CSI): 0.418906
-  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.10702365279418882, 'lr': 0.007817047784089107, 'weight_decay': 0.010992417433931895, 'batch_size': 64}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
-================================================================================
-
-  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:22:22,041] Trial 51 pruned. 
+[I 2025-12-25 17:45:17,876] Trial 51 pruned. 
 
 ================================================================================
 Trial 51 완료
-  Value (CSI): 0.315152
-  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.10675256623056373, 'lr': 0.007225067641766694, 'weight_decay': 0.011195785028922937, 'batch_size': 64}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.401370
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.10109737567989377, 'lr': 0.003028165899051981, 'weight_decay': 0.0027504027042338417, 'batch_size': 32}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:22:48,029] Trial 52 pruned. 
+[I 2025-12-25 17:46:33,363] Trial 52 pruned. 
 
 ================================================================================
 Trial 52 완료
-  Value (CSI): 0.268817
-  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.12863743103196137, 'lr': 0.009963698301545788, 'weight_decay': 0.01681389901654019, 'batch_size': 64}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.320346
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.13129793914369212, 'lr': 0.001481532884357565, 'weight_decay': 0.0018890375815431247, 'batch_size': 32}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:23:34,552] Trial 53 pruned. 
+[I 2025-12-25 17:48:54,808] Trial 53 pruned. 
 
 ================================================================================
 Trial 53 완료
-  Value (CSI): 0.378917
-  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.10032926402706926, 'lr': 0.004897209018455356, 'weight_decay': 0.02200313283130308, 'batch_size': 64}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.295031
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.11549644536193453, 'lr': 0.004046619329973408, 'weight_decay': 0.0037065849177916597, 'batch_size': 32}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:24:22,056] Trial 54 pruned. 
+[I 2025-12-25 17:49:34,159] Trial 54 pruned. 
 
 ================================================================================
 Trial 54 완료
-  Value (CSI): 0.304933
-  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.11791941563052726, 'lr': 0.0035178854000125635, 'weight_decay': 0.008589087774902776, 'batch_size': 64}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.251908
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.15425463648448123, 'lr': 0.009796278594102578, 'weight_decay': 0.0022875593476935716, 'batch_size': 128}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:24:48,433] Trial 55 pruned. 
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+[I 2025-12-25 17:56:25,722] Trial 55 pruned. 
 
 ================================================================================
 Trial 55 완료
-  Value (CSI): 0.284375
-  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.14236828236532537, 'lr': 0.006798542004134109, 'weight_decay': 0.005311876683372322, 'batch_size': 64}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.397622
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.13681335810227524, 'lr': 0.002786936085766453, 'weight_decay': 0.00163223881691868, 'batch_size': 32}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:25:11,429] Trial 56 pruned. 
+[I 2025-12-25 17:57:05,878] Trial 56 pruned. 
 
 ================================================================================
 Trial 56 완료
-  Value (CSI): 0.326087
-  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.10946941815087365, 'lr': 0.00253747222404387, 'weight_decay': 0.05819322566716805, 'batch_size': 128}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.318862
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.11069002012224309, 'lr': 0.004984763551845672, 'weight_decay': 0.0001291019440928184, 'batch_size': 128}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:26:40,526] Trial 57 pruned. 
+[I 2025-12-25 17:58:16,969] Trial 57 pruned. 
 
 ================================================================================
 Trial 57 완료
-  Value (CSI): 0.352134
-  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.1319047087204743, 'lr': 0.0038688812688380957, 'weight_decay': 0.03205508355721769, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.349640
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.31113124319006835, 'lr': 0.001870353353578518, 'weight_decay': 0.0011146207794431311, 'batch_size': 32}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:26:57,436] Trial 58 pruned. 
+[I 2025-12-25 17:59:20,076] Trial 58 pruned. 
 
 ================================================================================
 Trial 58 완료
-  Value (CSI): 0.361516
-  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.16239978319800713, 'lr': 0.008259564174326143, 'weight_decay': 0.01963105787220931, 'batch_size': 256}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.191223
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.10105045439970599, 'lr': 0.007513096860505891, 'weight_decay': 0.0007535007523173838, 'batch_size': 32}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:27:24,046] Trial 59 pruned. 
+[I 2025-12-25 17:59:40,488] Trial 59 pruned. 
 
 ================================================================================
 Trial 59 완료
-  Value (CSI): 0.302147
-  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.1481384451544694, 'lr': 0.005678311652858847, 'weight_decay': 0.012099109378015565, 'batch_size': 64}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.326951
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.12702920491781292, 'lr': 0.003863363949300385, 'weight_decay': 0.0012088754394051124, 'batch_size': 128}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:28:07,816] Trial 60 pruned. 
+[I 2025-12-25 18:01:09,199] Trial 60 pruned. 
 
 ================================================================================
 Trial 60 완료
-  Value (CSI): 0.294207
-  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.17529853770888407, 'lr': 0.0022127926102930165, 'weight_decay': 0.01592920478746801, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.317529
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.2723068108847165, 'lr': 0.0026421842641347637, 'weight_decay': 0.001593135583905912, 'batch_size': 32}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:28:36,264] Trial 61 pruned. 
+[I 2025-12-25 18:02:10,833] Trial 61 pruned. 
 
 ================================================================================
 Trial 61 완료
-  Value (CSI): 0.363768
-  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.12314429431047245, 'lr': 0.007621182937703196, 'weight_decay': 0.007413414714726313, 'batch_size': 128}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.382022
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.11969669753274281, 'lr': 0.0008297313909699557, 'weight_decay': 0.00020769538994261697, 'batch_size': 128}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:29:04,368] Trial 62 pruned. 
+[I 2025-12-25 18:03:03,939] Trial 62 pruned. 
 
 ================================================================================
 Trial 62 완료
-  Value (CSI): 0.330871
-  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.11269415532479991, 'lr': 0.0047534280103731594, 'weight_decay': 0.00999308614030292, 'batch_size': 128}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.329609
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.13670540424386163, 'lr': 0.0011691650901202856, 'weight_decay': 0.0008860998832163965, 'batch_size': 64}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:29:33,714] Trial 63 pruned. 
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:05:08,138] Trial 63 finished with value: 0.38830989839578073 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.1570856791181315, 'lr': 0.005315054576773846, 'weight_decay': 0.0006728305820472547, 'batch_size': 256}. Best is trial 5 with value: 0.40671898361820197.
 
 ================================================================================
 Trial 63 완료
-  Value (CSI): 0.344928
-  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.1350107000007198, 'lr': 0.003772623877331189, 'weight_decay': 0.0029909652749106055, 'batch_size': 128}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.388310
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.1570856791181315, 'lr': 0.005315054576773846, 'weight_decay': 0.0006728305820472547, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:30:01,302] Trial 64 pruned. 
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+[I 2025-12-25 18:06:17,509] Trial 64 pruned. 
 
 ================================================================================
 Trial 64 완료
-  Value (CSI): 0.351825
-  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.10002735186722521, 'lr': 0.0029481114878395377, 'weight_decay': 0.055505025181126925, 'batch_size': 128}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.415775
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.15577111562404, 'lr': 0.005644041929587199, 'weight_decay': 0.0006229430589220063, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:31:35,750] Trial 65 pruned. 
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:07:45,937] Trial 65 finished with value: 0.38839365211378957 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.1279018225231914, 'lr': 0.0077457943418727205, 'weight_decay': 0.0004969208612056993, 'batch_size': 256}. Best is trial 5 with value: 0.40671898361820197.
 
 ================================================================================
 Trial 65 완료
-  Value (CSI): 0.386124
-  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.12072613175771055, 'lr': 0.0058564881925697665, 'weight_decay': 0.09727960144578884, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.388394
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.1279018225231914, 'lr': 0.0077457943418727205, 'weight_decay': 0.0004969208612056993, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:32:01,824] Trial 66 pruned. 
+[I 2025-12-25 18:08:01,278] Trial 66 pruned. 
 
 ================================================================================
 Trial 66 완료
-  Value (CSI): 0.341390
-  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.10958657904209147, 'lr': 0.007628295412549454, 'weight_decay': 0.024288334119965824, 'batch_size': 128}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.340599
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.14413996986051586, 'lr': 0.008358316154697815, 'weight_decay': 0.00040228522229636643, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:32:57,995] Trial 67 pruned. 
+[I 2025-12-25 18:08:34,614] Trial 67 pruned. 
 
 ================================================================================
 Trial 67 완료
-  Value (CSI): 0.296923
-  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.1517839976213043, 'lr': 0.004454034604222775, 'weight_decay': 0.040554075557005326, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.338150
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.12870097776983516, 'lr': 0.0064279439737954, 'weight_decay': 0.0004952350834768033, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:33:36,831] Trial 68 pruned. 
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:10:07,137] Trial 68 pruned. 
 
 ================================================================================
 Trial 68 완료
-  Value (CSI): 0.309707
-  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1254130823539338, 'lr': 0.001625781637932773, 'weight_decay': 0.08263927724710225, 'batch_size': 64}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.366265
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.1786797959722209, 'lr': 0.0052234708273334735, 'weight_decay': 0.0005361500092919291, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:34:47,677] Trial 69 pruned. 
+[I 2025-12-25 18:10:22,308] Trial 69 pruned. 
 
 ================================================================================
 Trial 69 완료
-  Value (CSI): 0.289269
-  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.13830527177098956, 'lr': 0.0024651297700038354, 'weight_decay': 0.07616381596546044, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.245877
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.16522318177024176, 'lr': 0.008465923402403201, 'weight_decay': 0.0007061943937561288, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:35:01,307] Trial 70 pruned. 
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+[I 2025-12-25 18:11:28,932] Trial 70 pruned. 
 
 ================================================================================
 Trial 70 완료
-  Value (CSI): 0.321113
-  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.11086018030627125, 'lr': 0.0005022112918568984, 'weight_decay': 0.031166630423000063, 'batch_size': 256}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.413924
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.10571807854649783, 'lr': 0.0035216075002353488, 'weight_decay': 0.00031046562933354275, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:35:43,586] Trial 71 pruned. 
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:13:10,468] Trial 71 finished with value: 0.4011772244854422 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.11908960868684904, 'lr': 0.006117552079172186, 'weight_decay': 0.0009129071249698151, 'batch_size': 256}. Best is trial 5 with value: 0.40671898361820197.
 
 ================================================================================
 Trial 71 완료
-  Value (CSI): 0.280675
-  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.21379584635665705, 'lr': 0.003512156437799224, 'weight_decay': 0.0050454721381640415, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.401177
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.11908960868684904, 'lr': 0.006117552079172186, 'weight_decay': 0.0009129071249698151, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
   Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
-  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
-[I 2025-12-25 16:40:12,710] Trial 72 finished with value: 0.40688386424163325 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1949889952030475, 'lr': 0.005211232360544129, 'weight_decay': 0.004849810177572028, 'batch_size': 32}. Best is trial 24 with value: 0.4302129848817923.
+[I 2025-12-25 18:14:03,872] Trial 72 pruned. 
 
 ================================================================================
 Trial 72 완료
-  Value (CSI): 0.406884
-  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1949889952030475, 'lr': 0.005211232360544129, 'weight_decay': 0.004849810177572028, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.412983
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.12340326831532598, 'lr': 0.006382813882890589, 'weight_decay': 0.0009380580138075627, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
   Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
   Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
-[I 2025-12-25 16:44:10,757] Trial 73 finished with value: 0.40747774092372463 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1932652833870413, 'lr': 0.0046685776611646935, 'weight_decay': 0.004076403715749991, 'batch_size': 32}. Best is trial 24 with value: 0.4302129848817923.
+[I 2025-12-25 18:15:06,449] Trial 73 pruned. 
 
 ================================================================================
 Trial 73 완료
-  Value (CSI): 0.407478
-  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1932652833870413, 'lr': 0.0046685776611646935, 'weight_decay': 0.004076403715749991, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.343646
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.14243132125038444, 'lr': 0.00987564463759118, 'weight_decay': 0.0006861427099044916, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:45:06,678] Trial 74 pruned. 
+[I 2025-12-25 18:15:17,207] Trial 74 pruned. 
 
 ================================================================================
 Trial 74 완료
-  Value (CSI): 0.317771
-  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1994726664589557, 'lr': 0.005407543936447907, 'weight_decay': 0.0035433300046623546, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.336986
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.1514195754247138, 'lr': 0.00436633570565048, 'weight_decay': 0.00044390499935163417, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
-[I 2025-12-25 16:47:05,393] Trial 75 pruned. 
+[I 2025-12-25 18:15:28,205] Trial 75 pruned. 
 
 ================================================================================
 Trial 75 완료
-  Value (CSI): 0.358108
-  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1666944101426796, 'lr': 0.008431383906804833, 'weight_decay': 0.0063454158677240064, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.184615
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.13052268585282767, 'lr': 0.007736480374361614, 'weight_decay': 0.0014537740550699852, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:48:17,482] Trial 76 pruned. 
+[I 2025-12-25 18:15:41,956] Trial 76 pruned. 
 
 ================================================================================
 Trial 76 완료
-  Value (CSI): 0.375533
-  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.17646185955939953, 'lr': 0.003151970126071883, 'weight_decay': 0.009217005169408867, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.353096
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.10849905859140657, 'lr': 0.005508695408396211, 'weight_decay': 0.0005813895318419942, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:49:15,204] Trial 77 pruned. 
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:17:16,193] Trial 77 finished with value: 0.39102092604728816 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.11959130541721946, 'lr': 0.003457043811124447, 'weight_decay': 0.00034744669098163726, 'batch_size': 128}. Best is trial 5 with value: 0.40671898361820197.
 
 ================================================================================
 Trial 77 완료
-  Value (CSI): 0.287736
-  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.19703406081073918, 'lr': 0.004455055841752981, 'weight_decay': 0.055996014513633, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.391021
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.11959130541721946, 'lr': 0.003457043811124447, 'weight_decay': 0.00034744669098163726, 'batch_size': 128}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:49:57,409] Trial 78 pruned. 
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:18:49,265] Trial 78 finished with value: 0.38039690710557844 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12159998388585104, 'lr': 0.003371286199246197, 'weight_decay': 0.0008305118804503275, 'batch_size': 128}. Best is trial 5 with value: 0.40671898361820197.
 
 ================================================================================
 Trial 78 완료
-  Value (CSI): 0.239130
-  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.23974207792701685, 'lr': 0.006175456315206872, 'weight_decay': 0.004441254859254637, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.380397
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12159998388585104, 'lr': 0.003371286199246197, 'weight_decay': 0.0008305118804503275, 'batch_size': 128}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:50:59,561] Trial 79 pruned. 
+[I 2025-12-25 18:19:08,892] Trial 79 pruned. 
 
 ================================================================================
 Trial 79 완료
-  Value (CSI): 0.304154
-  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.15926629753409988, 'lr': 0.0022270104936239826, 'weight_decay': 0.0023953010409125345, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.320242
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10063182861391519, 'lr': 0.004499713151713611, 'weight_decay': 0.001034342713745363, 'batch_size': 128}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:51:38,498] Trial 80 pruned. 
+[I 2025-12-25 18:19:27,118] Trial 80 pruned. 
 
 ================================================================================
 Trial 80 완료
-  Value (CSI): 0.318386
-  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.19388305299590636, 'lr': 0.0028382714083252606, 'weight_decay': 0.006536126693137277, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.315485
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.11481715230868643, 'lr': 0.0025411037797610225, 'weight_decay': 0.0002475132862843732, 'batch_size': 128}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:52:21,409] Trial 81 pruned. 
+[I 2025-12-25 18:19:42,644] Trial 81 pruned. 
 
 ================================================================================
 Trial 81 완료
-  Value (CSI): 0.331288
-  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1313726742592781, 'lr': 0.006380318218653235, 'weight_decay': 0.009991774237101377, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.329882
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.13249965071391034, 'lr': 0.0065790205061311705, 'weight_decay': 0.000374622685292399, 'batch_size': 128}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 16:52:57,939] Trial 82 pruned. 
+[I 2025-12-25 18:19:57,888] Trial 82 pruned. 
 
 ================================================================================
 Trial 82 완료
-  Value (CSI): 0.321646
-  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12359154217028238, 'lr': 0.004766871495966046, 'weight_decay': 0.01205612261057229, 'batch_size': 64}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.309659
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.10880613051170143, 'lr': 0.005830277894761178, 'weight_decay': 0.00046472833226147324, 'batch_size': 128}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
-  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
-[I 2025-12-25 16:58:59,565] Trial 83 finished with value: 0.417566904533699 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10672492199867661, 'lr': 0.009376096371456843, 'weight_decay': 0.007980109853611072, 'batch_size': 32}. Best is trial 24 with value: 0.4302129848817923.
+[I 2025-12-25 18:20:18,369] Trial 83 pruned. 
 
 ================================================================================
 Trial 83 완료
-  Value (CSI): 0.417567
-  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10672492199867661, 'lr': 0.009376096371456843, 'weight_decay': 0.007980109853611072, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.342606
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.13923265391015635, 'lr': 0.003894792201659016, 'weight_decay': 0.0002808148770939549, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
-[I 2025-12-25 17:01:55,168] Trial 84 pruned. 
+[I 2025-12-25 18:20:33,878] Trial 84 pruned. 
 
 ================================================================================
 Trial 84 완료
-  Value (CSI): 0.411079
-  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10628248224853874, 'lr': 0.0075038776231885745, 'weight_decay': 0.007744396567681024, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.348178
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12102770447487761, 'lr': 0.0031385586187517014, 'weight_decay': 0.00034240382870881215, 'batch_size': 128}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 17:02:51,706] Trial 85 pruned. 
+[I 2025-12-25 18:20:44,184] Trial 85 pruned. 
 
 ================================================================================
 Trial 85 완료
-  Value (CSI): 0.287023
-  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.14576340507682778, 'lr': 0.00904468699382788, 'weight_decay': 0.006330182066785134, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.352861
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.1474079139521567, 'lr': 0.004828548262909157, 'weight_decay': 0.0006450887134128369, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 17:03:38,563] Trial 86 pruned. 
+[I 2025-12-25 18:21:02,373] Trial 86 pruned. 
 
 ================================================================================
 Trial 86 완료
-  Value (CSI): 0.305847
-  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1151881703729369, 'lr': 0.004016017443746976, 'weight_decay': 0.008675856982076924, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.317437
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.11589415618001286, 'lr': 0.008448712954074471, 'weight_decay': 0.001335415875601387, 'batch_size': 128}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 17:04:31,353] Trial 87 pruned. 
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:21:58,981] Trial 87 finished with value: 0.3971331455468843 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12660032068742688, 'lr': 0.006652881762385428, 'weight_decay': 0.0005063524673281321, 'batch_size': 256}. Best is trial 5 with value: 0.40671898361820197.
 
 ================================================================================
 Trial 87 완료
-  Value (CSI): 0.323620
-  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.13599254398269864, 'lr': 0.005266106480048947, 'weight_decay': 0.004415308053167378, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.397133
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12660032068742688, 'lr': 0.006652881762385428, 'weight_decay': 0.0005063524673281321, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 17:05:17,172] Trial 88 pruned. 
+[I 2025-12-25 18:22:08,888] Trial 88 pruned. 
 
 ================================================================================
 Trial 88 완료
-  Value (CSI): 0.312012
-  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.15456597283997137, 'lr': 0.00646705911792937, 'weight_decay': 0.046321874649736014, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.264122
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.10823807206037883, 'lr': 0.007457762145157222, 'weight_decay': 0.0007622729327164687, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 17:06:02,204] Trial 89 pruned. 
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+[I 2025-12-25 18:22:54,335] Trial 89 pruned. 
 
 ================================================================================
 Trial 89 완료
-  Value (CSI): 0.337243
-  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10637901833478834, 'lr': 0.003408332620674264, 'weight_decay': 0.01421197890799066, 'batch_size': 64}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.396053
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.13111030156739334, 'lr': 0.006452167483957661, 'weight_decay': 0.0005476882121211639, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 17:06:41,970] Trial 90 pruned. 
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:24:00,424] Trial 90 finished with value: 0.3770461945105574 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.12292335041091423, 'lr': 0.002001594101355978, 'weight_decay': 0.0004227959796655832, 'batch_size': 256}. Best is trial 5 with value: 0.40671898361820197.
 
 ================================================================================
 Trial 90 완료
-  Value (CSI): 0.296296
-  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.12329722995608361, 'lr': 0.009707417661640228, 'weight_decay': 0.018090322732533547, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.377046
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.12292335041091423, 'lr': 0.002001594101355978, 'weight_decay': 0.0004227959796655832, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 17:08:07,176] Trial 91 pruned. 
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:25:24,079] Trial 91 finished with value: 0.38503613354788896 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.14071661041360847, 'lr': 0.005102591888743379, 'weight_decay': 0.0003148545208828977, 'batch_size': 256}. Best is trial 5 with value: 0.40671898361820197.
 
 ================================================================================
 Trial 91 완료
-  Value (CSI): 0.274603
-  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.11781094279512412, 'lr': 0.008039399207054822, 'weight_decay': 0.0072217700860683935, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.385036
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.14071661041360847, 'lr': 0.005102591888743379, 'weight_decay': 0.0003148545208828977, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 17:08:34,249] Trial 92 pruned. 
+[I 2025-12-25 18:25:35,713] Trial 92 pruned. 
 
 ================================================================================
 Trial 92 완료
-  Value (CSI): 0.341791
-  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.12743994145294735, 'lr': 0.005308773708493135, 'weight_decay': 0.005624407552403323, 'batch_size': 128}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.341667
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.14003966837361392, 'lr': 0.0048752652556196, 'weight_decay': 0.0010676842647772719, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 17:09:20,862] Trial 93 pruned. 
+[I 2025-12-25 18:26:00,193] Trial 93 pruned. 
 
 ================================================================================
 Trial 93 완료
-  Value (CSI): 0.350954
-  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10551887014607182, 'lr': 0.004182661150342164, 'weight_decay': 0.01107445579882637, 'batch_size': 64}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.280236
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.11728008203167606, 'lr': 0.0038204833281482967, 'weight_decay': 0.0003296299284006589, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 17:09:37,667] Trial 94 pruned. 
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:26:57,203] Trial 94 finished with value: 0.38335452279464954 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12642856896410576, 'lr': 0.0056033122384444795, 'weight_decay': 0.0002293502591008029, 'batch_size': 256}. Best is trial 5 with value: 0.40671898361820197.
 
 ================================================================================
 Trial 94 완료
-  Value (CSI): 0.282209
-  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.1468597356599491, 'lr': 0.006430774383855066, 'weight_decay': 0.08315334200981778, 'batch_size': 256}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.383355
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.12642856896410576, 'lr': 0.0056033122384444795, 'weight_decay': 0.0002293502591008029, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 17:11:02,641] Trial 95 pruned. 
+[I 2025-12-25 18:27:09,124] Trial 95 pruned. 
 
 ================================================================================
 Trial 95 완료
-  Value (CSI): 0.273994
-  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.13480773386060452, 'lr': 0.008318091729049298, 'weight_decay': 0.03622180101926556, 'batch_size': 32}
-  Best Value (CSI): 0.430213
-  Best Trial: 24
-  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12642891069348194, 'lr': 0.003991429900223644, 'weight_decay': 0.09309925180854568, 'batch_size': 32}
+  Value (CSI): 0.268366
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.10781027151787033, 'lr': 0.008888886303763142, 'weight_decay': 0.00017724027785152356, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
-  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
-[I 2025-12-25 17:16:51,907] Trial 96 finished with value: 0.4356716825666811 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.11201244362332098, 'lr': 0.004812382291062589, 'weight_decay': 0.06014336738768738, 'batch_size': 32}. Best is trial 96 with value: 0.4356716825666811.
+[I 2025-12-25 18:27:20,745] Trial 96 pruned. 
 
 ================================================================================
 Trial 96 완료
-  Value (CSI): 0.435672
-  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.11201244362332098, 'lr': 0.004812382291062589, 'weight_decay': 0.06014336738768738, 'batch_size': 32}
-  Best Value (CSI): 0.435672
-  Best Trial: 96
-  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.11201244362332098, 'lr': 0.004812382291062589, 'weight_decay': 0.06014336738768738, 'batch_size': 32}
+  Value (CSI): 0.335165
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.15029645112606724, 'lr': 0.0027955809759112687, 'weight_decay': 0.0005010071305988381, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 17:18:10,408] Trial 97 pruned. 
+[I 2025-12-25 18:27:41,055] Trial 97 pruned. 
 
 ================================================================================
 Trial 97 완료
-  Value (CSI): 0.351014
-  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.11401829642594447, 'lr': 0.0036781141395967752, 'weight_decay': 0.0705422170378469, 'batch_size': 32}
-  Best Value (CSI): 0.435672
-  Best Trial: 96
-  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.11201244362332098, 'lr': 0.004812382291062589, 'weight_decay': 0.06014336738768738, 'batch_size': 32}
+  Value (CSI): 0.268702
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.13534038476992516, 'lr': 0.007051733601595868, 'weight_decay': 0.0008220966142636274, 'batch_size': 128}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 17:19:36,423] Trial 98 pruned. 
+[I 2025-12-25 18:28:07,891] Trial 98 pruned. 
 
 ================================================================================
 Trial 98 완료
-  Value (CSI): 0.363770
-  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.10174459004894057, 'lr': 0.0027876050601992317, 'weight_decay': 0.044131441037995454, 'batch_size': 32}
-  Best Value (CSI): 0.435672
-  Best Trial: 96
-  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.11201244362332098, 'lr': 0.004812382291062589, 'weight_decay': 0.06014336738768738, 'batch_size': 32}
+  Value (CSI): 0.362745
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.1600183052181945, 'lr': 0.004496298701220488, 'weight_decay': 0.00030149607638163875, 'batch_size': 256}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
-[I 2025-12-25 17:21:01,893] Trial 99 pruned. 
+[I 2025-12-25 18:28:24,551] Trial 99 pruned. 
 
 ================================================================================
 Trial 99 완료
-  Value (CSI): 0.338346
-  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12063832255409532, 'lr': 0.0045688297593179114, 'weight_decay': 0.06093146459705058, 'batch_size': 32}
-  Best Value (CSI): 0.435672
-  Best Trial: 96
-  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.11201244362332098, 'lr': 0.004812382291062589, 'weight_decay': 0.06014336738768738, 'batch_size': 32}
+  Value (CSI): 0.330137
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.1124738917266832, 'lr': 0.0033584912705342668, 'weight_decay': 0.00039786642153368825, 'batch_size': 128}
+  Best Value (CSI): 0.406719
+  Best Trial: 5
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 ================================================================================
 
 
 최적화 완료.
-Best CSI Score: 0.4357
-Best Hyperparameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.11201244362332098, 'lr': 0.004812382291062589, 'weight_decay': 0.06014336738768738, 'batch_size': 32}
+Best CSI Score: 0.4067
+Best Hyperparameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}
 
 최적화 과정 요약:
   - 총 시도 횟수: 100
   - 성공한 시도: 100
-  - 최초 CSI: 0.3873
-  - 최종 CSI: 0.3383
-  - 최고 CSI: 0.4357
-  - 최저 CSI: 0.2251
-  - 평균 CSI: 0.3392
+  - 최초 CSI: 0.3820
+  - 최종 CSI: 0.3301
+  - 최고 CSI: 0.4176
+  - 최저 CSI: 0.1846
+  - 평균 CSI: 0.3369
 
 최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/deepgbm_smotenc_ctgan20000_busan_trials.pkl에 저장되었습니다.
 
@@ -1288,48 +1288,21 @@ Best Hyperparameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout':
 ==================================================
 최종 모델 학습 시작...
 Fold 1 학습 중...
-  Early stopping at epoch 24, Best CSI: 0.3765
-  Fold 1 학습 완료 (검증 CSI: 0.3765)
+  Early stopping at epoch 34, Best CSI: 0.3801
+  Fold 1 학습 완료 (검증 CSI: 0.3801)
 Fold 2 학습 중...
-  Early stopping at epoch 16, Best CSI: 0.4313
-  Fold 2 학습 완료 (검증 CSI: 0.4313)
+  Early stopping at epoch 23, Best CSI: 0.4721
+  Fold 2 학습 완료 (검증 CSI: 0.4721)
 Fold 3 학습 중...
-  Early stopping at epoch 38, Best CSI: 0.4564
-  Fold 3 학습 완료 (검증 CSI: 0.4564)
+  Early stopping at epoch 38, Best CSI: 0.4197
+  Fold 3 학습 완료 (검증 CSI: 0.4197)
 
 모든 모델 저장 완료: ../save_model/deepgbm_optima/deepgbm_smotenc_ctgan20000_busan.pkl (총 3개 fold)
 Scaler 저장 완료: ../save_model/deepgbm_optima/scaler/deepgbm_smotenc_ctgan20000_busan_scaler.pkl (총 3개 fold)
 
 최종 모델 학습 및 저장 완료!
 저장된 모델 경로: ../save_model/deepgbm_optima/deepgbm_smotenc_ctgan20000_busan.pkl
-
-✓ 완료: deepgbm_smotenc_ctgan20000/deepgbm_smotenc_ctgan20000_busan.py (소요 시간: 8984초)
-
-----------------------------------------
-실행 중: deepgbm_smotenc_ctgan20000/deepgbm_smotenc_ctgan20000_daegu.py
-시작 시간: 2025-12-25 17:26:49
-----------------------------------------
-[I 2025-12-25 17:26:50,318] A new study created in memory with name: no-name-e66e283a-09e0-493a-a62f-970a0e5236a9
-  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
-  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
-  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
-[I 2025-12-25 17:28:31,433] Trial 0 finished with value: 0.36467219457887107 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3017085979899251, 'lr': 0.0030065297190371167, 'weight_decay': 0.00025062843235395106, 'batch_size': 128}. Best is trial 0 with value: 0.36467219457887107.
-
-================================================================================
-Trial 0 완료
-  Value (CSI): 0.364672
-  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3017085979899251, 'lr': 0.0030065297190371167, 'weight_decay': 0.00025062843235395106, 'batch_size': 128}
-  Best Value (CSI): 0.364672
-  Best Trial: 0
-  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3017085979899251, 'lr': 0.0030065297190371167, 'weight_decay': 0.00025062843235395106, 'batch_size': 128}
-================================================================================
-
-  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
-  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
-  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
-[I 2025-12-25 17:30:39,470] Trial 1 finished with value: 0.3856790077158836 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2848573987586112, 'lr': 0.007340360080223874, 'weight_decay': 0.016783140287706385, 'batch_size': 64}. Best is trial 1 with value: 0.3856790077158836.
-
-================================================================================
+========================================
 Trial 1 완료
   Value (CSI): 0.385679
   Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2848573987586112, 'lr': 0.007340360080223874, 'weight_decay': 0.016783140287706385, 'batch_size': 64}
@@ -1409,3 +1382,6464 @@ Trial 6 완료
   Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
   Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
   Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 17:40:02,161] Trial 7 finished with value: 0.3543437698570853 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.38043745238576543, 'lr': 0.0007226450013182126, 'weight_decay': 0.0015903753489726108, 'batch_size': 128}. Best is trial 1 with value: 0.3856790077158836.
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.354344
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.38043745238576543, 'lr': 0.0007226450013182126, 'weight_decay': 0.0015903753489726108, 'batch_size': 128}
+  Best Value (CSI): 0.385679
+  Best Trial: 1
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2848573987586112, 'lr': 0.007340360080223874, 'weight_decay': 0.016783140287706385, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 17:42:07,170] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.301766
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.2773607825626969, 'lr': 0.0005859019881779902, 'weight_decay': 0.0016385428838565678, 'batch_size': 64}
+  Best Value (CSI): 0.385679
+  Best Trial: 1
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2848573987586112, 'lr': 0.007340360080223874, 'weight_decay': 0.016783140287706385, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 17:42:44,619] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.210393
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.18534018537983848, 'lr': 2.9683462284887478e-05, 'weight_decay': 0.0008225148626672035, 'batch_size': 32}
+  Best Value (CSI): 0.385679
+  Best Trial: 1
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2848573987586112, 'lr': 0.007340360080223874, 'weight_decay': 0.016783140287706385, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 17:45:19,240] Trial 10 finished with value: 0.42421665871258857 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23749084860307185, 'lr': 0.009370159012270448, 'weight_decay': 0.07499307287280156, 'batch_size': 64}. Best is trial 10 with value: 0.42421665871258857.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.424217
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23749084860307185, 'lr': 0.009370159012270448, 'weight_decay': 0.07499307287280156, 'batch_size': 64}
+  Best Value (CSI): 0.424217
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23749084860307185, 'lr': 0.009370159012270448, 'weight_decay': 0.07499307287280156, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 17:48:42,261] Trial 11 finished with value: 0.4307323196661675 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.430732
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 17:52:01,098] Trial 12 finished with value: 0.42153393601603795 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.22315479556256956, 'lr': 0.008812284431471919, 'weight_decay': 0.06667295762377227, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.421534
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.22315479556256956, 'lr': 0.008812284431471919, 'weight_decay': 0.06667295762377227, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 17:54:47,339] Trial 13 finished with value: 0.4034030872912259 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23311295737234916, 'lr': 0.0021838499272363848, 'weight_decay': 0.08628139019391722, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.403403
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23311295737234916, 'lr': 0.0021838499272363848, 'weight_decay': 0.08628139019391722, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 17:57:55,358] Trial 14 finished with value: 0.40968728720923303 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.19754713188569664, 'lr': 0.009945449525975654, 'weight_decay': 0.012854075488803903, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.409687
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.19754713188569664, 'lr': 0.009945449525975654, 'weight_decay': 0.012854075488803903, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:00:34,943] Trial 15 finished with value: 0.3952509776109269 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.2522596763481665, 'lr': 0.0013070974614867957, 'weight_decay': 0.043461785851368936, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.395251
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.2522596763481665, 'lr': 0.0013070974614867957, 'weight_decay': 0.043461785851368936, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:03:20,076] Trial 16 finished with value: 0.4088328396770544 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.2544338555138684, 'lr': 0.004677267709926582, 'weight_decay': 0.0807044470169193, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.408833
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.2544338555138684, 'lr': 0.004677267709926582, 'weight_decay': 0.0807044470169193, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:03:48,453] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.332636
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.18648436361058998, 'lr': 0.0011608089602853637, 'weight_decay': 0.0290344780015774, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:04:32,303] Trial 18 finished with value: 0.37793174270817165 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.20525289617103892, 'lr': 0.005047197238627658, 'weight_decay': 0.009627468404161656, 'batch_size': 256}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.377932
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.20525289617103892, 'lr': 0.005047197238627658, 'weight_decay': 0.009627468404161656, 'batch_size': 256}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:05:11,479] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.305927
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.10106350188299201, 'lr': 0.0002799389297057265, 'weight_decay': 0.006096906325289003, 'batch_size': 32}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:05:31,216] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.163721
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.1602911793555841, 'lr': 1.0031812145737914e-05, 'weight_decay': 0.02179490620499538, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:08:34,657] Trial 21 finished with value: 0.4231529582097365 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.22828990125210155, 'lr': 0.009423829800023327, 'weight_decay': 0.09967290709828559, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.423153
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.22828990125210155, 'lr': 0.009423829800023327, 'weight_decay': 0.09967290709828559, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:11:54,429] Trial 22 finished with value: 0.4067124559746507 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2325376947826469, 'lr': 0.005046363063258625, 'weight_decay': 0.05133246763235007, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.406712
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2325376947826469, 'lr': 0.005046363063258625, 'weight_decay': 0.05133246763235007, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:14:06,669] Trial 23 finished with value: 0.39671492049207974 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.2632891730669977, 'lr': 0.00965365890406693, 'weight_decay': 0.04042888183333823, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.396715
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.2632891730669977, 'lr': 0.00965365890406693, 'weight_decay': 0.04042888183333823, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:17:04,081] Trial 24 finished with value: 0.42720967059261244 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.21863509492149125, 'lr': 0.0023248878957764013, 'weight_decay': 0.08571852973548237, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.427210
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.21863509492149125, 'lr': 0.0023248878957764013, 'weight_decay': 0.08571852973548237, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:19:40,035] Trial 25 finished with value: 0.40237437877823495 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.21357424996120083, 'lr': 0.002144093961873882, 'weight_decay': 0.04667294659064852, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.402374
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.21357424996120083, 'lr': 0.002144093961873882, 'weight_decay': 0.04667294659064852, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:20:21,214] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.363825
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.24340594288189873, 'lr': 0.003556438413357864, 'weight_decay': 0.021931615736668155, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:23:53,630] Trial 27 finished with value: 0.4160426954620579 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.17231829954982364, 'lr': 0.00545503090987752, 'weight_decay': 0.05916776181579717, 'batch_size': 32}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.416043
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.17231829954982364, 'lr': 0.00545503090987752, 'weight_decay': 0.05916776181579717, 'batch_size': 32}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:24:02,885] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.309055
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.20670480300462657, 'lr': 0.001770409831421198, 'weight_decay': 0.029703394623959854, 'batch_size': 256}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:25:52,378] Trial 29 finished with value: 0.40383060707703633 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.27201085743598696, 'lr': 0.0035933768127341874, 'weight_decay': 0.09310316747227268, 'batch_size': 128}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.403831
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.27201085743598696, 'lr': 0.0035933768127341874, 'weight_decay': 0.09310316747227268, 'batch_size': 128}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:26:26,782] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.328571
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2962818062314527, 'lr': 0.0032074278337085077, 'weight_decay': 0.01341143901405962, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:29:18,612] Trial 31 finished with value: 0.40881139068605893 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2252976071001744, 'lr': 0.0060078748811469495, 'weight_decay': 0.08824532971780802, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.408811
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2252976071001744, 'lr': 0.0060078748811469495, 'weight_decay': 0.08824532971780802, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:31:50,561] Trial 32 finished with value: 0.40214826942393067 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.24649037880923885, 'lr': 0.007605691257890702, 'weight_decay': 0.05053192197241287, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.402148
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.24649037880923885, 'lr': 0.007605691257890702, 'weight_decay': 0.05053192197241287, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:34:36,065] Trial 33 finished with value: 0.42049143430056074 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.21865870207257745, 'lr': 0.005960575335137106, 'weight_decay': 0.09954427849242226, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.420491
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.21865870207257745, 'lr': 0.005960575335137106, 'weight_decay': 0.09954427849242226, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:37:13,265] Trial 34 finished with value: 0.4078006416230125 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.23928927245235473, 'lr': 0.007733959982864252, 'weight_decay': 0.059511656793378075, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.407801
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.23928927245235473, 'lr': 0.007733959982864252, 'weight_decay': 0.059511656793378075, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:37:47,238] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.363420
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.27134027308812675, 'lr': 0.003857826684909271, 'weight_decay': 0.031212835147525864, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:38:55,926] Trial 36 finished with value: 0.40836705758590797 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.2914542789532121, 'lr': 0.009801969979271827, 'weight_decay': 0.06653467916239415, 'batch_size': 256}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.408367
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.2914542789532121, 'lr': 0.009801969979271827, 'weight_decay': 0.06653467916239415, 'batch_size': 256}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:39:11,495] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.352814
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.1937653358822804, 'lr': 0.0029958003067072424, 'weight_decay': 0.0369298365507583, 'batch_size': 128}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:41:46,985] Trial 38 finished with value: 0.41279691251749534 and parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.2138135828046362, 'lr': 0.005123055780222242, 'weight_decay': 0.020402972377308332, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.412797
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.2138135828046362, 'lr': 0.005123055780222242, 'weight_decay': 0.020402972377308332, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:42:08,605] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.344828
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.2574704694359799, 'lr': 0.0066053293068389475, 'weight_decay': 0.09936317765147569, 'batch_size': 128}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:48:38,004] Trial 40 finished with value: 0.41128349249131935 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.30627768480981177, 'lr': 0.0022323582181983433, 'weight_decay': 0.06687823910316276, 'batch_size': 32}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.411283
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.30627768480981177, 'lr': 0.0022323582181983433, 'weight_decay': 0.06687823910316276, 'batch_size': 32}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:48:55,922] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.299807
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.22353751960608617, 'lr': 0.00724204792874194, 'weight_decay': 0.06546966847844535, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:49:15,854] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.346491
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23477593164959804, 'lr': 0.009602638935963578, 'weight_decay': 0.037764357386960615, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:51:57,820] Trial 43 finished with value: 0.41808661723681856 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.20197808275072954, 'lr': 0.004253784220987185, 'weight_decay': 0.06601402406138104, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.418087
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.20197808275072954, 'lr': 0.004253784220987185, 'weight_decay': 0.06601402406138104, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:52:20,399] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.323529
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2218459105267715, 'lr': 0.007253516732757605, 'weight_decay': 0.047146360946487186, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:52:50,474] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.379913
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.24159014299193804, 'lr': 0.0025396691082781478, 'weight_decay': 0.026292851128864047, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:53:25,984] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.391204
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.18433001821122869, 'lr': 0.004092991016348934, 'weight_decay': 0.07468036277031787, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:54:30,284] Trial 47 finished with value: 0.40036502003894486 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.2848488530576282, 'lr': 0.006760840868383694, 'weight_decay': 0.036148244190647834, 'batch_size': 256}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.400365
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.2848488530576282, 'lr': 0.006760840868383694, 'weight_decay': 0.036148244190647834, 'batch_size': 256}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:54:52,342] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.370370
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.25523746156992333, 'lr': 0.0016002909095353512, 'weight_decay': 0.016912832498253125, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:57:28,978] Trial 49 finished with value: 0.41828104724060117 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2302632322923392, 'lr': 0.0027603176153533403, 'weight_decay': 0.048966604591236654, 'batch_size': 64}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.418281
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2302632322923392, 'lr': 0.0027603176153533403, 'weight_decay': 0.048966604591236654, 'batch_size': 64}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:01:21,478] Trial 50 finished with value: 0.42426212563397964 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.212330681339756, 'lr': 0.004205333666457319, 'weight_decay': 0.09816136632085802, 'batch_size': 32}. Best is trial 11 with value: 0.4307323196661675.
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.424262
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.212330681339756, 'lr': 0.004205333666457319, 'weight_decay': 0.09816136632085802, 'batch_size': 32}
+  Best Value (CSI): 0.430732
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23178114116339094, 'lr': 0.007074238908032745, 'weight_decay': 0.055751033425856605, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:06:37,449] Trial 51 finished with value: 0.43407422794617584 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.434074
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:10:42,290] Trial 52 finished with value: 0.42082832354028327 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.21300573900718572, 'lr': 0.004727438186661075, 'weight_decay': 0.09835883461428876, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.420828
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.21300573900718572, 'lr': 0.004727438186661075, 'weight_decay': 0.09835883461428876, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:15:56,722] Trial 53 finished with value: 0.41949280928743987 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2020438360977361, 'lr': 0.003090471271376684, 'weight_decay': 0.07967114753019448, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.419493
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2020438360977361, 'lr': 0.003090471271376684, 'weight_decay': 0.07967114753019448, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:21:09,510] Trial 54 finished with value: 0.427132534648724 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24803947359680517, 'lr': 0.005927786012811929, 'weight_decay': 0.07490316217111197, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.427133
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24803947359680517, 'lr': 0.005927786012811929, 'weight_decay': 0.07490316217111197, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:24:59,595] Trial 55 finished with value: 0.4197194348106534 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2490046390973562, 'lr': 0.0038285638564561596, 'weight_decay': 0.05398506440503409, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.419719
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2490046390973562, 'lr': 0.0038285638564561596, 'weight_decay': 0.05398506440503409, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:28:30,745] Trial 56 finished with value: 0.39940673759607087 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2637445488023327, 'lr': 0.0057400718659215925, 'weight_decay': 0.07486684542201437, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.399407
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2637445488023327, 'lr': 0.0057400718659215925, 'weight_decay': 0.07486684542201437, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:29:07,704] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.322799
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.19272047479576604, 'lr': 0.0010204042812459282, 'weight_decay': 0.04109061550913597, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:32:44,326] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.348243
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.17590732962824757, 'lr': 0.001886731907218819, 'weight_decay': 0.058328735729890877, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:33:16,324] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.303901
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.239995074916181, 'lr': 0.002633112385391826, 'weight_decay': 0.027583019436489237, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:38:31,383] Trial 60 finished with value: 0.4308991188902496 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.21270708382121373, 'lr': 0.005277156599127611, 'weight_decay': 0.07100057734691462, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.430899
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.21270708382121373, 'lr': 0.005277156599127611, 'weight_decay': 0.07100057734691462, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:39:11,897] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.376658
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.20932512088998403, 'lr': 0.004888466579318628, 'weight_decay': 0.07587152926378347, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:39:50,296] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.374046
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2189093809995362, 'lr': 0.00780340408333206, 'weight_decay': 0.07967094584167915, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:41:13,543] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.416185
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23266325827139642, 'lr': 0.005993168204026863, 'weight_decay': 0.04698579566995875, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:46:34,987] Trial 64 finished with value: 0.41763777750982234 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.20356247756032733, 'lr': 0.00471789488310128, 'weight_decay': 0.056636190295222766, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.417638
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.20356247756032733, 'lr': 0.00471789488310128, 'weight_decay': 0.056636190295222766, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:50:00,536] Trial 65 finished with value: 0.4070351765655115 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.22729944987771575, 'lr': 0.0036550016598206132, 'weight_decay': 0.035371927474825884, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.407035
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.22729944987771575, 'lr': 0.0036550016598206132, 'weight_decay': 0.035371927474825884, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:50:35,321] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.259317
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24436651563167983, 'lr': 0.007875940360112839, 'weight_decay': 0.08348010762488307, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:51:26,682] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.380723
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.19584489315215609, 'lr': 0.005858802186624893, 'weight_decay': 0.04281084185898097, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:51:38,304] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.344086
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.2122139164905525, 'lr': 0.0031579606963262397, 'weight_decay': 0.056797423553631404, 'batch_size': 128}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:52:13,700] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.310658
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.23477195854993635, 'lr': 0.004198451015060448, 'weight_decay': 0.07333535112188005, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:58:14,732] Trial 70 finished with value: 0.4032492135727754 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2518471429537309, 'lr': 0.009004625048552001, 'weight_decay': 0.09762585069517558, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.403249
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2518471429537309, 'lr': 0.009004625048552001, 'weight_decay': 0.09762585069517558, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:58:35,870] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.388889
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.2228576383603036, 'lr': 0.008007373959863484, 'weight_decay': 0.08715529138598666, 'batch_size': 256}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:01:57,902] Trial 72 finished with value: 0.41996973409792965 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.21631459337182884, 'lr': 0.006382293155322122, 'weight_decay': 0.09994078754523282, 'batch_size': 64}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.419970
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.21631459337182884, 'lr': 0.006382293155322122, 'weight_decay': 0.09994078754523282, 'batch_size': 64}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:02:29,415] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.175565
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.22879083305022235, 'lr': 0.009827351717226991, 'weight_decay': 0.06177496236319921, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:03:00,691] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.402913
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.20612033452910047, 'lr': 0.005693282558569551, 'weight_decay': 0.06801769507785034, 'batch_size': 64}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:03:20,293] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.369710
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.2374748230479039, 'lr': 0.004478186993895352, 'weight_decay': 0.051903959821338067, 'batch_size': 128}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:03:38,369] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.221649
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.24535886083356154, 'lr': 0.00812611875407826, 'weight_decay': 0.07993779922953767, 'batch_size': 64}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:04:12,572] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.335556
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.21911958312498603, 'lr': 0.006534878012297473, 'weight_decay': 0.04409438383706514, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:04:20,856] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.305357
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.25975254845165796, 'lr': 0.003649796319945675, 'weight_decay': 0.03408499025373447, 'batch_size': 256}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:04:50,639] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.396373
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.19011943260264244, 'lr': 0.005159114067066938, 'weight_decay': 0.06353554019523935, 'batch_size': 64}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:05:10,342] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.359580
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.22710442124516608, 'lr': 0.009869676687408024, 'weight_decay': 0.0871070741602575, 'batch_size': 64}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:05:28,525] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.292929
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.20887823288945753, 'lr': 0.007127703094232709, 'weight_decay': 0.07036069938847393, 'batch_size': 64}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:05:47,718] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.314961
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.19671465103788788, 'lr': 0.00846359060423983, 'weight_decay': 0.05275568207687822, 'batch_size': 64}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:06:06,796] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.278293
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.23695540480940538, 'lr': 0.006463414966220404, 'weight_decay': 0.0849445334042718, 'batch_size': 64}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:06:25,927] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.338422
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.22275974426732878, 'lr': 0.005398972184782542, 'weight_decay': 0.09884881636930135, 'batch_size': 64}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:06:59,191] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.379233
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2491550488922752, 'lr': 0.0033583043263127202, 'weight_decay': 0.061317073522414764, 'batch_size': 64}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:08:31,838] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.388626
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.2126823549603015, 'lr': 0.004224392324596293, 'weight_decay': 0.04215650420945348, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:08:52,507] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.350365
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.20047294591397416, 'lr': 0.007012384015873315, 'weight_decay': 0.030103887265738143, 'batch_size': 64}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:09:10,421] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.385681
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.22958427269187817, 'lr': 0.008615158759380797, 'weight_decay': 0.06831267809545653, 'batch_size': 128}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:09:48,900] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.372549
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.18510270795623265, 'lr': 0.004690531455264671, 'weight_decay': 0.05053497484686443, 'batch_size': 64}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:15:47,690] Trial 90 finished with value: 0.4339155939492993 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.2661337802700446, 'lr': 0.0023284381209485055, 'weight_decay': 0.08342499411622561, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.433916
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.2661337802700446, 'lr': 0.0023284381209485055, 'weight_decay': 0.08342499411622561, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:19:52,943] Trial 91 finished with value: 0.4214778719462193 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.26563931257895024, 'lr': 0.002574107371872439, 'weight_decay': 0.08443682533307036, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.421478
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.26563931257895024, 'lr': 0.002574107371872439, 'weight_decay': 0.08443682533307036, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:23:25,318] Trial 92 finished with value: 0.41716036262214184 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.2560738006923415, 'lr': 0.0031229574669147096, 'weight_decay': 0.07285488406601091, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.417160
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.2560738006923415, 'lr': 0.0031229574669147096, 'weight_decay': 0.07285488406601091, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:24:42,548] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.382536
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.24141143177477284, 'lr': 0.0022482348483322647, 'weight_decay': 0.06078606510237429, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:29:46,314] Trial 94 finished with value: 0.42500326725304527 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.21701059651915255, 'lr': 0.008675808310166717, 'weight_decay': 0.08091015753193935, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.425003
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.21701059651915255, 'lr': 0.008675808310166717, 'weight_decay': 0.08091015753193935, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:30:23,265] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.346260
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.21891960812528677, 'lr': 0.0054107833201511414, 'weight_decay': 0.0805991363417232, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:31:17,116] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.390995
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.27814244682301226, 'lr': 0.0037286443285049816, 'weight_decay': 0.08730717310482743, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:31:53,566] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.365789
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.2504994296100569, 'lr': 0.008837309315090603, 'weight_decay': 0.04689625748476855, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:36:08,169] Trial 98 finished with value: 0.41104829724454256 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.20586397015001542, 'lr': 0.006795712557385779, 'weight_decay': 0.05589865761672832, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.411048
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.20586397015001542, 'lr': 0.006795712557385779, 'weight_decay': 0.05589865761672832, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:41:47,650] Trial 99 finished with value: 0.4198799524659114 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2262534272124283, 'lr': 0.004211137199121187, 'weight_decay': 0.09992308248709598, 'batch_size': 32}. Best is trial 51 with value: 0.43407422794617584.
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.419880
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2262534272124283, 'lr': 0.004211137199121187, 'weight_decay': 0.09992308248709598, 'batch_size': 32}
+  Best Value (CSI): 0.434074
+  Best Trial: 51
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4341
+Best Hyperparameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2100809214334562, 'lr': 0.0045042768238180075, 'weight_decay': 0.09966580216480231, 'batch_size': 32}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.3647
+  - 최종 CSI: 0.4199
+  - 최고 CSI: 0.4341
+  - 최저 CSI: 0.1637
+  - 평균 CSI: 0.3694
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/deepgbm_smotenc_ctgan20000_daegu_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 45, Best CSI: 0.4680
+  Fold 1 학습 완료 (검증 CSI: 0.4680)
+Fold 2 학습 중...
+  Early stopping at epoch 37, Best CSI: 0.4349
+  Fold 2 학습 완료 (검증 CSI: 0.4349)
+Fold 3 학습 중...
+  Early stopping at epoch 27, Best CSI: 0.3607
+  Fold 3 학습 완료 (검증 CSI: 0.3607)
+
+모든 모델 저장 완료: ../save_model/deepgbm_optima/deepgbm_smotenc_ctgan20000_daegu.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/deepgbm_optima/scaler/deepgbm_smotenc_ctgan20000_daegu_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/deepgbm_optima/deepgbm_smotenc_ctgan20000_daegu.pkl
+
+✓ 완료: deepgbm_smotenc_ctgan20000/deepgbm_smotenc_ctgan20000_daegu.py (소요 시간: 11983초)
+
+----------------------------------------
+실행 중: deepgbm_smotenc_ctgan20000/deepgbm_smotenc_ctgan20000_daejeon.py
+시작 시간: 2025-12-25 20:46:32
+----------------------------------------
+[I 2025-12-25 20:46:33,619] A new study created in memory with name: no-name-fca98561-827d-4155-be33-7d2f5e601517
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:49:28,839] Trial 0 finished with value: 0.4262594899720438 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.21101723172705014, 'lr': 0.00032904926177824474, 'weight_decay': 0.0005335686459176832, 'batch_size': 64}. Best is trial 0 with value: 0.4262594899720438.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.426259
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.21101723172705014, 'lr': 0.00032904926177824474, 'weight_decay': 0.0005335686459176832, 'batch_size': 64}
+  Best Value (CSI): 0.426259
+  Best Trial: 0
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.21101723172705014, 'lr': 0.00032904926177824474, 'weight_decay': 0.0005335686459176832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:51:43,445] Trial 1 finished with value: 0.37761252619367713 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.37637668416255254, 'lr': 4.0591580335925086e-05, 'weight_decay': 0.0003313851061364189, 'batch_size': 256}. Best is trial 0 with value: 0.4262594899720438.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.377613
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.37637668416255254, 'lr': 4.0591580335925086e-05, 'weight_decay': 0.0003313851061364189, 'batch_size': 256}
+  Best Value (CSI): 0.426259
+  Best Trial: 0
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.21101723172705014, 'lr': 0.00032904926177824474, 'weight_decay': 0.0005335686459176832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:54:38,537] Trial 2 finished with value: 0.4284431001824986 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.20135809721865588, 'lr': 0.0004408130658105509, 'weight_decay': 0.015761320258034627, 'batch_size': 64}. Best is trial 2 with value: 0.4284431001824986.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.428443
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.20135809721865588, 'lr': 0.0004408130658105509, 'weight_decay': 0.015761320258034627, 'batch_size': 64}
+  Best Value (CSI): 0.428443
+  Best Trial: 2
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.20135809721865588, 'lr': 0.0004408130658105509, 'weight_decay': 0.015761320258034627, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:56:03,134] Trial 3 finished with value: 0.46394179016547543 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.18046371845819598, 'lr': 0.007259702087428587, 'weight_decay': 0.0006423878376771966, 'batch_size': 128}. Best is trial 3 with value: 0.46394179016547543.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.463942
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.18046371845819598, 'lr': 0.007259702087428587, 'weight_decay': 0.0006423878376771966, 'batch_size': 128}
+  Best Value (CSI): 0.463942
+  Best Trial: 3
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.18046371845819598, 'lr': 0.007259702087428587, 'weight_decay': 0.0006423878376771966, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:57:42,922] Trial 4 finished with value: 0.3761129480891374 and parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.2614339955176517, 'lr': 3.423086082730074e-05, 'weight_decay': 0.01883085482178125, 'batch_size': 256}. Best is trial 3 with value: 0.46394179016547543.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.376113
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.2614339955176517, 'lr': 3.423086082730074e-05, 'weight_decay': 0.01883085482178125, 'batch_size': 256}
+  Best Value (CSI): 0.463942
+  Best Trial: 3
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.18046371845819598, 'lr': 0.007259702087428587, 'weight_decay': 0.0006423878376771966, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:59:10,311] Trial 5 finished with value: 0.4495286719767657 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.38323514145036885, 'lr': 0.001649216556756964, 'weight_decay': 0.0002454822547733107, 'batch_size': 128}. Best is trial 3 with value: 0.46394179016547543.
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.449529
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.38323514145036885, 'lr': 0.001649216556756964, 'weight_decay': 0.0002454822547733107, 'batch_size': 128}
+  Best Value (CSI): 0.463942
+  Best Trial: 3
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.18046371845819598, 'lr': 0.007259702087428587, 'weight_decay': 0.0006423878376771966, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:00:19,820] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.386473
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.2445687166150087, 'lr': 0.00365514189324657, 'weight_decay': 0.0037412826746722216, 'batch_size': 64}
+  Best Value (CSI): 0.463942
+  Best Trial: 3
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.18046371845819598, 'lr': 0.007259702087428587, 'weight_decay': 0.0006423878376771966, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:00:47,242] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.322247
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.22483504459808512, 'lr': 0.00013226940862088685, 'weight_decay': 0.002378529339802384, 'batch_size': 32}
+  Best Value (CSI): 0.463942
+  Best Trial: 3
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.18046371845819598, 'lr': 0.007259702087428587, 'weight_decay': 0.0006423878376771966, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:01:34,718] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.339660
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.1442942912044119, 'lr': 0.0020903096064110466, 'weight_decay': 0.06641112594814864, 'batch_size': 32}
+  Best Value (CSI): 0.463942
+  Best Trial: 3
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.18046371845819598, 'lr': 0.007259702087428587, 'weight_decay': 0.0006423878376771966, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:02:59,293] Trial 9 finished with value: 0.48118035803105963 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}. Best is trial 9 with value: 0.48118035803105963.
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.481180
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:03:33,983] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.400189
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10597827716797298, 'lr': 0.00882626295819218, 'weight_decay': 0.07192763765931887, 'batch_size': 256}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:04:20,739] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.411527
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.2883670748062774, 'lr': 0.007167506741677508, 'weight_decay': 0.0011192823464008421, 'batch_size': 128}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:04:38,226] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.361029
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.169652388778825, 'lr': 0.008949523184205159, 'weight_decay': 0.00011464824351005747, 'batch_size': 128}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:06:28,402] Trial 13 finished with value: 0.4394321600142265 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.29551756773465965, 'lr': 0.0010634968828297107, 'weight_decay': 0.0040550946798717956, 'batch_size': 128}. Best is trial 9 with value: 0.48118035803105963.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.439432
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.29551756773465965, 'lr': 0.0010634968828297107, 'weight_decay': 0.0040550946798717956, 'batch_size': 128}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:07:13,787] Trial 14 finished with value: 0.4489298908426979 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.166127693361648, 'lr': 0.0038123506633168753, 'weight_decay': 0.001147575656494536, 'batch_size': 256}. Best is trial 9 with value: 0.48118035803105963.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.448930
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.166127693361648, 'lr': 0.0038123506633168753, 'weight_decay': 0.001147575656494536, 'batch_size': 256}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:07:28,222] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.376562
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.19711303928102408, 'lr': 0.0008780118155778272, 'weight_decay': 0.011499804927084611, 'batch_size': 128}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:07:41,839] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.381434
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.11337836414869765, 'lr': 0.002937375032802072, 'weight_decay': 0.039785435855219854, 'batch_size': 256}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:08:26,022] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.314642
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.2555086163169648, 'lr': 0.004902898336806034, 'weight_decay': 0.007243489545013331, 'batch_size': 32}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:08:34,764] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.361277
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.16913222083933588, 'lr': 0.00984632956276224, 'weight_decay': 0.09803672587215481, 'batch_size': 256}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:08:57,883] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.390459
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.13695033170144744, 'lr': 0.0020079242671124844, 'weight_decay': 0.032355058380779574, 'batch_size': 128}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:09:27,672] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.412963
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.321153785679115, 'lr': 0.0049622960755986295, 'weight_decay': 0.0077296703769385595, 'batch_size': 128}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:09:39,924] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.364381
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.39871399752923675, 'lr': 0.0014913703555511349, 'weight_decay': 0.00023616474927675284, 'batch_size': 128}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:09:59,653] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.394440
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.34675629933932606, 'lr': 0.0027554004223360818, 'weight_decay': 0.000762245220937114, 'batch_size': 128}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:10:28,825] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.409914
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.3272991197932211, 'lr': 0.0054557870911055234, 'weight_decay': 0.0019511420664850153, 'batch_size': 128}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:10:40,543] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.357280
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.2858771496548033, 'lr': 0.0011578528241657075, 'weight_decay': 0.00010958405583395432, 'batch_size': 128}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:10:52,839] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.372633
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.23081055907541315, 'lr': 0.004329572611625722, 'weight_decay': 0.00037741101749830887, 'batch_size': 256}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:11:23,459] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.390110
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.26446881975398895, 'lr': 0.002132870624871925, 'weight_decay': 0.0002121976779071309, 'batch_size': 64}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:12:30,210] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.359204
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.23357188257378922, 'lr': 0.0060255307071021245, 'weight_decay': 0.0006647488934516185, 'batch_size': 32}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:12:47,349] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.382382
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.35756642474718736, 'lr': 0.0006939727100590357, 'weight_decay': 0.0014369605147767754, 'batch_size': 128}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:13:18,164] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.360656
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2144213488034115, 'lr': 0.0015988615068126945, 'weight_decay': 0.0005025255070221872, 'batch_size': 64}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:13:28,138] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.366071
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.31353135492791917, 'lr': 0.002940274812184209, 'weight_decay': 0.0008186730463566985, 'batch_size': 256}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:13:36,695] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.370674
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.1874617235650119, 'lr': 0.003644482818412619, 'weight_decay': 0.0009164362720786244, 'batch_size': 256}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:13:50,371] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.365274
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.21532987233517925, 'lr': 0.006453568114558021, 'weight_decay': 0.0014047247844893988, 'batch_size': 256}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:14:04,505] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.382166
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.18403611540317585, 'lr': 0.003561473438297543, 'weight_decay': 0.000607534155459557, 'batch_size': 256}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:14:12,703] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.371622
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.20439664568407634, 'lr': 0.009751937369184794, 'weight_decay': 0.0005054826248978718, 'batch_size': 256}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:14:21,533] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.377953
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.24273347310142515, 'lr': 0.006148030878072652, 'weight_decay': 0.00035097393175730995, 'batch_size': 256}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:15:02,491] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.378578
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.2664895402934331, 'lr': 0.0006521694070273213, 'weight_decay': 0.0030959861678185427, 'batch_size': 64}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:15:20,916] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.395108
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.21807802058442796, 'lr': 0.0037961161200202737, 'weight_decay': 0.0011713877841381041, 'batch_size': 256}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:16:09,841] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.383117
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.2439763909261289, 'lr': 0.0003514440663251721, 'weight_decay': 0.0017701740005099463, 'batch_size': 32}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:16:21,648] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.290848
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.15070470124058294, 'lr': 1.5515931942954363e-05, 'weight_decay': 0.002407732439647774, 'batch_size': 128}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:16:28,637] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.339016
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3763585315960131, 'lr': 0.00020369967356384842, 'weight_decay': 0.000986042239241149, 'batch_size': 256}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:17:56,428] Trial 41 finished with value: 0.4548972752875507 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.28611498964467275, 'lr': 0.002555313921001397, 'weight_decay': 0.004922653582178948, 'batch_size': 128}. Best is trial 9 with value: 0.48118035803105963.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.454897
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.28611498964467275, 'lr': 0.002555313921001397, 'weight_decay': 0.004922653582178948, 'batch_size': 128}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:19:45,373] Trial 42 finished with value: 0.4739067741664577 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2813690380757722, 'lr': 0.007364108060332943, 'weight_decay': 0.021392391804391238, 'batch_size': 128}. Best is trial 9 with value: 0.48118035803105963.
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.473907
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2813690380757722, 'lr': 0.007364108060332943, 'weight_decay': 0.021392391804391238, 'batch_size': 128}
+  Best Value (CSI): 0.481180
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.24489832566128836, 'lr': 0.008409036563040598, 'weight_decay': 0.08915563100169602, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:22:24,903] Trial 43 finished with value: 0.4811835602195654 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.284338319363738, 'lr': 0.006962757874257798, 'weight_decay': 0.02081649568092699, 'batch_size': 128}. Best is trial 43 with value: 0.4811835602195654.
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.481184
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.284338319363738, 'lr': 0.006962757874257798, 'weight_decay': 0.02081649568092699, 'batch_size': 128}
+  Best Value (CSI): 0.481184
+  Best Trial: 43
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.284338319363738, 'lr': 0.006962757874257798, 'weight_decay': 0.02081649568092699, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:24:35,232] Trial 44 finished with value: 0.4808147654352907 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.27640451898897334, 'lr': 0.007543093358568035, 'weight_decay': 0.02174392535437996, 'batch_size': 128}. Best is trial 43 with value: 0.4811835602195654.
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.480815
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.27640451898897334, 'lr': 0.007543093358568035, 'weight_decay': 0.02174392535437996, 'batch_size': 128}
+  Best Value (CSI): 0.481184
+  Best Trial: 43
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.284338319363738, 'lr': 0.006962757874257798, 'weight_decay': 0.02081649568092699, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:26:57,239] Trial 45 finished with value: 0.48167020129419075 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.27259933582257756, 'lr': 0.007743526940984613, 'weight_decay': 0.02282125726521158, 'batch_size': 128}. Best is trial 45 with value: 0.48167020129419075.
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.481670
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.27259933582257756, 'lr': 0.007743526940984613, 'weight_decay': 0.02282125726521158, 'batch_size': 128}
+  Best Value (CSI): 0.481670
+  Best Trial: 45
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.27259933582257756, 'lr': 0.007743526940984613, 'weight_decay': 0.02282125726521158, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:27:31,157] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.407372
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2728281848814167, 'lr': 0.007676036251741119, 'weight_decay': 0.02311928369727232, 'batch_size': 128}
+  Best Value (CSI): 0.481670
+  Best Trial: 45
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.27259933582257756, 'lr': 0.007743526940984613, 'weight_decay': 0.02282125726521158, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:30:33,545] Trial 47 finished with value: 0.4848928597361463 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}. Best is trial 47 with value: 0.4848928597361463.
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.484893
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:31:06,338] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.408203
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2529559393458279, 'lr': 0.009570690790397043, 'weight_decay': 0.046040703833816386, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:33:16,448] Trial 49 finished with value: 0.47913001410952166 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.29815367806371273, 'lr': 0.006187347395753423, 'weight_decay': 0.014508047935757468, 'batch_size': 128}. Best is trial 47 with value: 0.4848928597361463.
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.479130
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.29815367806371273, 'lr': 0.006187347395753423, 'weight_decay': 0.014508047935757468, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:33:37,985] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.374625
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.27096416013403246, 'lr': 0.004748062861203474, 'weight_decay': 0.024083777865791935, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:35:30,117] Trial 51 finished with value: 0.4826587132041303 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.3024196472520981, 'lr': 0.007326820636660214, 'weight_decay': 0.014222989318425297, 'batch_size': 128}. Best is trial 47 with value: 0.4848928597361463.
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.482659
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.3024196472520981, 'lr': 0.007326820636660214, 'weight_decay': 0.014222989318425297, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:35:53,929] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.393459
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.29998234436199417, 'lr': 0.007143216561088489, 'weight_decay': 0.015634499797199195, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:36:28,939] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.422101
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.25522400001303186, 'lr': 0.009667479832295336, 'weight_decay': 0.03135578226930194, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:37:12,562] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.420434
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.27682698116455784, 'lr': 0.004913042404566072, 'weight_decay': 0.05050830064356937, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:37:40,039] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.385017
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2642514982646438, 'lr': 0.007542982794204647, 'weight_decay': 0.01211705783362002, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:40:19,098] Trial 56 finished with value: 0.4844240760703545 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.29357489655063185, 'lr': 0.005304077634705985, 'weight_decay': 0.027520750919757787, 'batch_size': 128}. Best is trial 47 with value: 0.4848928597361463.
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.484424
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.29357489655063185, 'lr': 0.005304077634705985, 'weight_decay': 0.027520750919757787, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:42:01,456] Trial 57 finished with value: 0.474693972288278 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.30866576502511606, 'lr': 0.0049975745374650615, 'weight_decay': 0.0633397449513323, 'batch_size': 128}. Best is trial 47 with value: 0.4848928597361463.
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.474694
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.30866576502511606, 'lr': 0.0049975745374650615, 'weight_decay': 0.0633397449513323, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:43:43,307] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.417594
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.2924814695550796, 'lr': 0.0039023688488274556, 'weight_decay': 0.03045252614907524, 'batch_size': 32}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:44:08,046] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.317443
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.2603850545785468, 'lr': 0.005692969086004813, 'weight_decay': 0.09927239872441294, 'batch_size': 64}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:44:20,114] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.329126
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.30616232566337487, 'lr': 0.00989853160778781, 'weight_decay': 0.037739709955277624, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:46:10,376] Trial 61 finished with value: 0.4768835026777287 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2910184690088797, 'lr': 0.007377411355590754, 'weight_decay': 0.01834454750166698, 'batch_size': 128}. Best is trial 47 with value: 0.4848928597361463.
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.476884
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2910184690088797, 'lr': 0.007377411355590754, 'weight_decay': 0.01834454750166698, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:46:29,794] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.402737
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2768622119677462, 'lr': 0.00792461111596149, 'weight_decay': 0.024055848571056256, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:46:48,805] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.399048
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2822245971644818, 'lr': 0.0055737783980538275, 'weight_decay': 0.010497952569368052, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:47:19,694] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.410867
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.24899395949250894, 'lr': 0.004370513814320774, 'weight_decay': 0.017835974270869213, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:47:53,536] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.431267
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.32249418176575506, 'lr': 0.00302011768170808, 'weight_decay': 0.026530407624991714, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:48:32,518] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.433457
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.29774963308584923, 'lr': 0.006289378651212446, 'weight_decay': 0.04214701796900457, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:48:58,551] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.400374
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.27094056510424347, 'lr': 0.008189418597413558, 'weight_decay': 0.054248915579466254, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:50:25,365] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.403509
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.28730386633631966, 'lr': 0.0021924216689570577, 'weight_decay': 0.034627821018889254, 'batch_size': 32}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:52:10,227] Trial 69 finished with value: 0.47228056504486066 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.24013881327330386, 'lr': 0.0034067009095296624, 'weight_decay': 0.07930598637701577, 'batch_size': 128}. Best is trial 47 with value: 0.4848928597361463.
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.472281
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.24013881327330386, 'lr': 0.0034067009095296624, 'weight_decay': 0.07930598637701577, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:55:58,201] Trial 70 finished with value: 0.4848639846173235 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2627805323298385, 'lr': 0.004401145415017848, 'weight_decay': 0.02866171134528252, 'batch_size': 64}. Best is trial 47 with value: 0.4848928597361463.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.484864
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2627805323298385, 'lr': 0.004401145415017848, 'weight_decay': 0.02866171134528252, 'batch_size': 64}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:56:31,969] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.403272
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.27856385182863547, 'lr': 0.004494259472352582, 'weight_decay': 0.028410085677772864, 'batch_size': 64}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:57:11,262] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.401932
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2607551004188599, 'lr': 0.005778210353964066, 'weight_decay': 0.020233331288262664, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:57:46,827] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.346275
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.25395857966710816, 'lr': 0.007472302309428705, 'weight_decay': 0.03247929244628862, 'batch_size': 64}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:58:32,686] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.404473
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2297869718741404, 'lr': 0.00430805586725335, 'weight_decay': 0.04174234083827567, 'batch_size': 64}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:59:17,610] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.379541
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.26500783956404583, 'lr': 0.00856380349860931, 'weight_decay': 0.017957061370711593, 'batch_size': 64}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 22:02:12,303] Trial 76 finished with value: 0.46907975651404676 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.3039982142293721, 'lr': 0.006447303150290649, 'weight_decay': 0.026741460619738318, 'batch_size': 64}. Best is trial 47 with value: 0.4848928597361463.
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.469080
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.3039982142293721, 'lr': 0.006447303150290649, 'weight_decay': 0.026741460619738318, 'batch_size': 64}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:02:31,233] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.399829
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.2922652626293276, 'lr': 0.005203751412469963, 'weight_decay': 0.013171026544706189, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 22:04:30,085] Trial 78 finished with value: 0.47558042800929096 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.3147219122357237, 'lr': 0.0030458707822316423, 'weight_decay': 0.057395842279937645, 'batch_size': 128}. Best is trial 47 with value: 0.4848928597361463.
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.475580
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.3147219122357237, 'lr': 0.0030458707822316423, 'weight_decay': 0.057395842279937645, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:04:45,030] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.411815
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.2720800369212929, 'lr': 0.00834566861020941, 'weight_decay': 0.009960479698931135, 'batch_size': 256}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 22:06:42,509] Trial 80 finished with value: 0.46250877478901514 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.28580434235054003, 'lr': 0.004066707231488422, 'weight_decay': 0.014173168749111774, 'batch_size': 128}. Best is trial 47 with value: 0.4848928597361463.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.462509
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.28580434235054003, 'lr': 0.004066707231488422, 'weight_decay': 0.014173168749111774, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:07:08,503] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.397612
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.3003029128473533, 'lr': 0.006311411661529209, 'weight_decay': 0.01606354770042392, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:07:34,486] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.394429
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2984490288372278, 'lr': 0.006790874619870127, 'weight_decay': 0.02062554731749367, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:07:54,959] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.368164
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2822265875270956, 'lr': 0.008693289138954476, 'weight_decay': 0.014631455389729361, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:08:21,033] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.396709
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.29345930067793174, 'lr': 0.005477261317380699, 'weight_decay': 0.036743627418298015, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:08:55,802] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.435018
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.3119630470505606, 'lr': 0.00363774233944351, 'weight_decay': 0.04752629967758706, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:11:12,349] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.421632
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.24809868960274103, 'lr': 0.0025541097467457956, 'weight_decay': 0.026256301191778297, 'batch_size': 32}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:11:24,739] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.370256
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2691385466742335, 'lr': 0.006331549332167146, 'weight_decay': 0.016477888176712648, 'batch_size': 256}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:11:50,571] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.382766
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.25905727435126585, 'lr': 0.009520057453971319, 'weight_decay': 0.020920058753506828, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:12:21,652] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.318584
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.27675606764485633, 'lr': 0.004882510664088383, 'weight_decay': 0.009178839907191778, 'batch_size': 64}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:12:54,729] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.422263
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.33118813426411803, 'lr': 0.007196826612193463, 'weight_decay': 0.012680933537315387, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:13:11,694] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.361742
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.29118734794277984, 'lr': 0.0071946063936407356, 'weight_decay': 0.017549756448256203, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:13:26,477] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.336266
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.28770740608994494, 'lr': 0.009902328925614918, 'weight_decay': 0.01912514382337648, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 22:16:09,983] Trial 93 finished with value: 0.4782856871746312 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.30399221197018894, 'lr': 0.005587344078483064, 'weight_decay': 0.03086909231574752, 'batch_size': 128}. Best is trial 47 with value: 0.4848928597361463.
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.478286
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.30399221197018894, 'lr': 0.005587344078483064, 'weight_decay': 0.03086909231574752, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:16:35,816] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.387812
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.26663731666077267, 'lr': 0.005651492535082516, 'weight_decay': 0.022975238951654577, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:17:18,565] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.399431
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.27946131687001685, 'lr': 0.004426067031478167, 'weight_decay': 0.030510826027656587, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:17:39,991] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.406764
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.30588821276698747, 'lr': 0.008450743971849828, 'weight_decay': 0.035970417318175844, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:17:52,507] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.403226
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.315723666768097, 'lr': 0.005240085946549746, 'weight_decay': 0.042652992628428595, 'batch_size': 256}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 22:19:22,601] Trial 98 finished with value: 0.46361115734747677 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2996171042730319, 'lr': 0.00323290218084579, 'weight_decay': 0.025058497000452918, 'batch_size': 128}. Best is trial 47 with value: 0.4848928597361463.
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.463611
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2996171042730319, 'lr': 0.00323290218084579, 'weight_decay': 0.025058497000452918, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 22:19:44,013] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.406946
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.2736092397710576, 'lr': 0.003967032998237749, 'weight_decay': 0.014496634087094515, 'batch_size': 128}
+  Best Value (CSI): 0.484893
+  Best Trial: 47
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4849
+Best Hyperparameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4263
+  - 최종 CSI: 0.4069
+  - 최고 CSI: 0.4849
+  - 최저 CSI: 0.2908
+  - 평균 CSI: 0.4044
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/deepgbm_smotenc_ctgan20000_daejeon_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 42, Best CSI: 0.4353
+  Fold 1 학습 완료 (검증 CSI: 0.4353)
+Fold 2 학습 중...
+  Early stopping at epoch 19, Best CSI: 0.4869
+  Fold 2 학습 완료 (검증 CSI: 0.4869)
+Fold 3 학습 중...
+  Early stopping at epoch 36, Best CSI: 0.5338
+  Fold 3 학습 완료 (검증 CSI: 0.5338)
+
+모든 모델 저장 완료: ../save_model/deepgbm_optima/deepgbm_smotenc_ctgan20000_daejeon.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/deepgbm_optima/scaler/deepgbm_smotenc_ctgan20000_daejeon_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/deepgbm_optima/deepgbm_smotenc_ctgan20000_daejeon.pkl
+
+✓ 완료: deepgbm_smotenc_ctgan20000/deepgbm_smotenc_ctgan20000_daejeon.py (소요 시간: 5740초)
+
+----------------------------------------
+실행 중: deepgbm_smotenc_ctgan20000/deepgbm_smotenc_ctgan20000_gwangju.py
+시작 시간: 2025-12-25 22:22:12
+----------------------------------------
+[I 2025-12-25 22:22:13,993] A new study created in memory with name: no-name-d2c3d161-8daf-41cf-933a-f5374fc41acb
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:24:08,266] Trial 0 finished with value: 0.3914969362009899 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.1005969536528481, 'lr': 9.106420380441998e-05, 'weight_decay': 0.00024631062407710143, 'batch_size': 256}. Best is trial 0 with value: 0.3914969362009899.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.391497
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.1005969536528481, 'lr': 9.106420380441998e-05, 'weight_decay': 0.00024631062407710143, 'batch_size': 256}
+  Best Value (CSI): 0.391497
+  Best Trial: 0
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.1005969536528481, 'lr': 9.106420380441998e-05, 'weight_decay': 0.00024631062407710143, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:27:01,897] Trial 1 finished with value: 0.4490234094416914 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2800153824343252, 'lr': 0.0006426978978963236, 'weight_decay': 0.0002771814864448882, 'batch_size': 64}. Best is trial 1 with value: 0.4490234094416914.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.449023
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2800153824343252, 'lr': 0.0006426978978963236, 'weight_decay': 0.0002771814864448882, 'batch_size': 64}
+  Best Value (CSI): 0.449023
+  Best Trial: 1
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2800153824343252, 'lr': 0.0006426978978963236, 'weight_decay': 0.0002771814864448882, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:32:01,283] Trial 2 finished with value: 0.4832850681063274 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.483285
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:33:17,138] Trial 3 finished with value: 0.46209732074500826 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.15382979787722542, 'lr': 0.0048153117236909805, 'weight_decay': 0.00836147136484166, 'batch_size': 256}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.462097
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.15382979787722542, 'lr': 0.0048153117236909805, 'weight_decay': 0.00836147136484166, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:38:35,880] Trial 4 finished with value: 0.44662669651384457 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.2012826167008137, 'lr': 0.0004745986869838708, 'weight_decay': 0.030997494280022273, 'batch_size': 32}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.446627
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.2012826167008137, 'lr': 0.0004745986869838708, 'weight_decay': 0.030997494280022273, 'batch_size': 32}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:39:05,897] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.355042
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.33300494559993876, 'lr': 0.00010467119807363395, 'weight_decay': 0.019421753007736225, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:39:29,452] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.395445
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.10418943609452273, 'lr': 0.0003482604316403126, 'weight_decay': 0.016655087073069005, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:40:53,895] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.378251
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.3791838796178063, 'lr': 0.0007944001031089349, 'weight_decay': 0.0005643542319288052, 'batch_size': 32}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:41:40,262] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.379787
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.15981188089818552, 'lr': 0.0001780970969454513, 'weight_decay': 0.00030343867571144313, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:42:59,361] Trial 9 finished with value: 0.44560810472430235 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.20554963703698184, 'lr': 0.0012819942638136947, 'weight_decay': 0.0005796746714362216, 'batch_size': 64}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.445608
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.20554963703698184, 'lr': 0.0012819942638136947, 'weight_decay': 0.0005796746714362216, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:43:19,318] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.297587
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.29188775703175424, 'lr': 0.009913974040037141, 'weight_decay': 0.08066185951083446, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:43:28,991] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.359801
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.23977557359531745, 'lr': 0.007764804235280423, 'weight_decay': 0.004156952695486679, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:43:51,645] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.364169
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.33612673067815574, 'lr': 0.003018909056657062, 'weight_decay': 0.0027964716579474604, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:44:00,822] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.268085
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.15920244233429115, 'lr': 1.5556000751724564e-05, 'weight_decay': 0.004649100730992759, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:44:22,063] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.277062
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.3866630328335943, 'lr': 0.0034424027684260487, 'weight_decay': 0.0019971894495344348, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:44:37,220] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.363423
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.27203148114404235, 'lr': 0.003292922224233365, 'weight_decay': 0.006752045900919438, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:44:50,181] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.280285
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.3188051025491085, 'lr': 0.009398000920422278, 'weight_decay': 0.0015622435851774016, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:45:26,958] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.348079
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.362209808885587, 'lr': 0.0017615741745661787, 'weight_decay': 0.00010224554980671716, 'batch_size': 32}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:45:44,250] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.330539
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.2467967864538977, 'lr': 0.005071423052666507, 'weight_decay': 0.008463836149891074, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:46:06,207] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.332117
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.3109575217251736, 'lr': 0.001907568706004695, 'weight_decay': 0.008974569643014452, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:47:12,863] Trial 20 finished with value: 0.47467736850738945 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.3391713729243251, 'lr': 0.005154123509073468, 'weight_decay': 0.0013100023348637823, 'batch_size': 256}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.474677
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.3391713729243251, 'lr': 0.005154123509073468, 'weight_decay': 0.0013100023348637823, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:47:20,761] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.335758
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.35228792761741656, 'lr': 0.0051072777364021575, 'weight_decay': 0.0014676161052040227, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:47:28,516] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.332924
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2979545427569133, 'lr': 0.0058937096622344145, 'weight_decay': 0.0030233829962338613, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:48:37,525] Trial 23 finished with value: 0.46152269174610555 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.34429415752022674, 'lr': 0.0026196531142408963, 'weight_decay': 0.0012077808614528581, 'batch_size': 256}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.461523
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.34429415752022674, 'lr': 0.0026196531142408963, 'weight_decay': 0.0012077808614528581, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:48:45,096] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.357569
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.317729291448378, 'lr': 0.0012642392640698458, 'weight_decay': 0.004744950473102117, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:49:04,459] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.347500
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.3941388199404078, 'lr': 0.005306849436722333, 'weight_decay': 0.0024501012977334985, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:50:25,248] Trial 26 finished with value: 0.46354769238393456 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.36530496476049584, 'lr': 0.004301550316843092, 'weight_decay': 0.010891762518604346, 'batch_size': 128}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.463548
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.36530496476049584, 'lr': 0.004301550316843092, 'weight_decay': 0.010891762518604346, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:51:55,375] Trial 27 finished with value: 0.4763354759206342 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.36628641331682654, 'lr': 0.009339719796417296, 'weight_decay': 0.015259278594557491, 'batch_size': 128}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.476335
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.36628641331682654, 'lr': 0.009339719796417296, 'weight_decay': 0.015259278594557491, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:52:05,385] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.271028
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.36968901928401093, 'lr': 0.007777489527765142, 'weight_decay': 0.037300833162262674, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:53:31,766] Trial 29 finished with value: 0.4619882052837505 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.39870152813014903, 'lr': 0.009527536002147115, 'weight_decay': 0.0009342753933426574, 'batch_size': 128}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.461988
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.39870152813014903, 'lr': 0.009527536002147115, 'weight_decay': 0.0009342753933426574, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:53:48,522] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.339576
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.352760459215956, 'lr': 0.0021457899903276558, 'weight_decay': 0.005016171330066203, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:54:00,637] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.343001
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.38132903853031946, 'lr': 0.00393090055438325, 'weight_decay': 0.012353747865064593, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:55:03,762] Trial 32 finished with value: 0.4572021950070324 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.366777294382283, 'lr': 0.006377225437427263, 'weight_decay': 0.011120970962374573, 'batch_size': 128}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.457202
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.366777294382283, 'lr': 0.006377225437427263, 'weight_decay': 0.011120970962374573, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:55:16,675] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.360494
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.33434358053781027, 'lr': 0.004461418942109357, 'weight_decay': 0.006784460113560662, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:56:48,494] Trial 34 finished with value: 0.4644290417231065 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3735571099790338, 'lr': 0.003011791421017191, 'weight_decay': 0.0038057823600348276, 'batch_size': 128}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.464429
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3735571099790338, 'lr': 0.003011791421017191, 'weight_decay': 0.0038057823600348276, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:57:26,228] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.223013
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.34426565258489045, 'lr': 0.009986582264340087, 'weight_decay': 0.0032469985955951583, 'batch_size': 32}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:59:40,145] Trial 36 finished with value: 0.4639132985072327 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.3211828540908625, 'lr': 0.0026204213264570046, 'weight_decay': 0.001938240829847954, 'batch_size': 64}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.463913
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.3211828540908625, 'lr': 0.0026204213264570046, 'weight_decay': 0.001938240829847954, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:01:01,545] Trial 37 finished with value: 0.47373144078881024 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.38190964257972215, 'lr': 0.005919744704054083, 'weight_decay': 0.00587910421633594, 'batch_size': 128}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.473731
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.38190964257972215, 'lr': 0.005919744704054083, 'weight_decay': 0.00587910421633594, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:03:25,221] Trial 38 finished with value: 0.47021249124259645 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3978868128232501, 'lr': 0.00658770674575674, 'weight_decay': 0.02177455515477852, 'batch_size': 64}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.470212
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3978868128232501, 'lr': 0.00658770674575674, 'weight_decay': 0.02177455515477852, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:04:01,433] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.324353
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.35846075390041665, 'lr': 0.006770136651049132, 'weight_decay': 0.006123847802988946, 'batch_size': 32}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:04:29,009] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.373536
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.3810640500274266, 'lr': 0.0006742690891153435, 'weight_decay': 0.015862860392165647, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:06:31,538] Trial 41 finished with value: 0.4656580313645577 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3981208758027062, 'lr': 0.006694479310270003, 'weight_decay': 0.019126848493060813, 'batch_size': 64}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.465658
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3981208758027062, 'lr': 0.006694479310270003, 'weight_decay': 0.019126848493060813, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:06:56,785] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.323810
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3783770341662833, 'lr': 0.0043431001487228334, 'weight_decay': 0.03136625934126597, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:07:22,890] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.332889
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.34898520498150337, 'lr': 0.0063337501378291205, 'weight_decay': 0.021831553254289832, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:10:08,116] Trial 44 finished with value: 0.4665117600046857 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3383766980998423, 'lr': 0.007852289710540488, 'weight_decay': 0.012795706280567063, 'batch_size': 64}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.466512
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3383766980998423, 'lr': 0.007852289710540488, 'weight_decay': 0.012795706280567063, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:10:34,878] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.345616
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.3882820272494991, 'lr': 0.0035247635520507226, 'weight_decay': 0.006410367279124853, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:10:50,852] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.384431
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.33111005629953494, 'lr': 0.005045059420725095, 'weight_decay': 0.025862282204055672, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:11:27,991] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.266791
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.35682885403615, 'lr': 0.009787646180378253, 'weight_decay': 0.016225044749668637, 'batch_size': 32}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:14:38,282] Trial 48 finished with value: 0.4563438542159066 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3710330570584067, 'lr': 0.0012604974616036385, 'weight_decay': 0.04320465339749285, 'batch_size': 64}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.456344
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3710330570584067, 'lr': 0.0012604974616036385, 'weight_decay': 0.04320465339749285, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:16:06,987] Trial 49 finished with value: 0.4773549731338256 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.38711196363843936, 'lr': 0.007262066409069914, 'weight_decay': 0.04972496383809627, 'batch_size': 128}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.477355
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.38711196363843936, 'lr': 0.007262066409069914, 'weight_decay': 0.04972496383809627, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:16:23,265] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.348168
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.3858111512386037, 'lr': 0.0035612870727074466, 'weight_decay': 0.04786149768006714, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:16:39,974] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.358612
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.39406056514955823, 'lr': 0.007234862223556834, 'weight_decay': 0.08235827642267513, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:16:56,899] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.375309
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.3632830769082415, 'lr': 0.005820155647848395, 'weight_decay': 0.05770847719352885, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:17:57,607] Trial 53 finished with value: 0.4754556034362705 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.39834704273126204, 'lr': 0.007924421840144552, 'weight_decay': 0.02402252926556552, 'batch_size': 128}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.475456
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.39834704273126204, 'lr': 0.007924421840144552, 'weight_decay': 0.02402252926556552, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:18:13,386] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.329502
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3758321489359507, 'lr': 0.008079322743907671, 'weight_decay': 0.027579971716862512, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:18:27,685] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.349105
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3533508379987766, 'lr': 0.0042702306918374995, 'weight_decay': 0.060241627385951714, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:18:38,325] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.313850
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.3853512946509898, 'lr': 0.0025968322426770177, 'weight_decay': 0.032172857786145356, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:18:56,074] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.329936
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.32858461241902387, 'lr': 0.005183914418428007, 'weight_decay': 0.008855669122951128, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:20:12,665] Trial 58 finished with value: 0.4814271786552286 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.30948560445510426, 'lr': 0.009970385564610116, 'weight_decay': 0.013887119168940188, 'batch_size': 128}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.481427
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.30948560445510426, 'lr': 0.009970385564610116, 'weight_decay': 0.013887119168940188, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:20:23,277] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.334586
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.3086343099289128, 'lr': 0.009793571447098469, 'weight_decay': 0.09712343862421366, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:20:39,172] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.363218
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.32482956953765735, 'lr': 0.00802582984966101, 'weight_decay': 0.014782189794453789, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:20:53,166] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.347607
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.34338505821613385, 'lr': 0.0055458452598045865, 'weight_decay': 0.019702223539195412, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:21:09,305] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.383275
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.28090582673994446, 'lr': 0.008082100426959142, 'weight_decay': 0.010516745296717181, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:22:30,629] Trial 63 finished with value: 0.46435118648017415 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3655885292214273, 'lr': 0.0038325042950142774, 'weight_decay': 0.007658490402361315, 'batch_size': 128}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.464351
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3655885292214273, 'lr': 0.0038325042950142774, 'weight_decay': 0.007658490402361315, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:22:47,102] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.363073
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3394163211556534, 'lr': 0.005322155248779461, 'weight_decay': 0.004830084699129099, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:22:59,239] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.336306
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.3062555620062305, 'lr': 0.007517754971596113, 'weight_decay': 0.01361337878801424, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:23:14,658] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.319629
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.37309470978775383, 'lr': 0.009761755892159208, 'weight_decay': 0.02592825284344551, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:24:47,617] Trial 67 finished with value: 0.46534020382452906 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3598614822213377, 'lr': 0.004575031218885208, 'weight_decay': 0.009972131898757152, 'batch_size': 128}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.465340
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3598614822213377, 'lr': 0.004575031218885208, 'weight_decay': 0.009972131898757152, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:25:55,465] Trial 68 finished with value: 0.4687949301483345 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.38900220192855833, 'lr': 0.005914949882280429, 'weight_decay': 0.003932154573111431, 'batch_size': 128}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.468795
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.38900220192855833, 'lr': 0.005914949882280429, 'weight_decay': 0.003932154573111431, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:26:06,643] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.330729
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3496613695814781, 'lr': 0.008300023069876402, 'weight_decay': 0.007944676456925443, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:26:56,714] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.339130
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.37954403140892984, 'lr': 0.0031642233400748926, 'weight_decay': 0.017084109188035906, 'batch_size': 32}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:27:18,280] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.335484
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.39792185561611537, 'lr': 0.006637945463801181, 'weight_decay': 0.02299450556287274, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:27:42,233] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.357881
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.3872270414285178, 'lr': 0.006359425662464759, 'weight_decay': 0.03646840203585204, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:29:06,081] Trial 73 finished with value: 0.4726118234463648 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3934191933618364, 'lr': 0.004762012143515294, 'weight_decay': 0.019600871187309132, 'batch_size': 128}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.472612
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3934191933618364, 'lr': 0.004762012143515294, 'weight_decay': 0.019600871187309132, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:29:23,064] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.351250
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.37018320667678395, 'lr': 0.004224403989096567, 'weight_decay': 0.012069197619808604, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:29:40,989] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.392361
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3589510913349602, 'lr': 0.005216355816743394, 'weight_decay': 0.005527383921710401, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:29:57,162] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.316883
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.378679110842704, 'lr': 0.00839063022877975, 'weight_decay': 0.01786275773214014, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:30:11,811] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.338028
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3920309812244853, 'lr': 0.009917516331123678, 'weight_decay': 0.014527006007614786, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:30:30,488] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.283290
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.33453920305894835, 'lr': 0.007020823375409739, 'weight_decay': 0.009957977252845003, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:30:41,774] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.396249
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.3167916362544814, 'lr': 0.0037705320451241336, 'weight_decay': 0.00258281359221636, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:30:58,248] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.352798
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.34732018987698293, 'lr': 0.0045986188384404166, 'weight_decay': 0.0076715134016013846, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+[I 2025-12-25 23:31:55,313] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.498282
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.39599511728348813, 'lr': 0.00621006172030482, 'weight_decay': 0.022125193154988884, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:32:21,044] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.341207
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.38997814662187885, 'lr': 0.0073691794018006795, 'weight_decay': 0.01976563378910939, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:32:35,368] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.312420
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.38304220126073135, 'lr': 0.008519484273262776, 'weight_decay': 0.02887991300588204, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:33:31,415] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.376721
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.398945376417183, 'lr': 0.005897569873484095, 'weight_decay': 0.02381470091512575, 'batch_size': 32}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:33:47,712] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.367104
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3724762433085644, 'lr': 0.0029422060599869656, 'weight_decay': 0.014457701083360192, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:34:16,915] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.310479
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3661097828696568, 'lr': 0.004817554977301824, 'weight_decay': 0.012102343898671321, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:34:25,660] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.250000
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.3806915863216654, 'lr': 0.0069584949262775355, 'weight_decay': 0.03634768789445125, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:34:43,584] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.325611
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.39100668663900123, 'lr': 0.008917205470864187, 'weight_decay': 0.0034373627583911886, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:34:58,005] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.300475
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.36099600314289704, 'lr': 0.0034235712944343123, 'weight_decay': 0.030630504073245374, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:35:25,949] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.296392
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.37665379448560177, 'lr': 0.0056550981795301354, 'weight_decay': 0.01974024623897317, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:35:41,325] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.384988
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.38861855684218577, 'lr': 0.006430244013697935, 'weight_decay': 0.005555524203764986, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:35:55,465] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.342965
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.399874395094421, 'lr': 0.004071208607427997, 'weight_decay': 0.0029628906611872583, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:36:11,085] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.363962
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.3862285334885257, 'lr': 0.007371126705309927, 'weight_decay': 0.002269814891939352, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+[I 2025-12-25 23:36:46,349] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.501029
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.36937544967024094, 'lr': 0.008792824740803541, 'weight_decay': 0.004178520445195884, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:38:19,920] Trial 95 finished with value: 0.4745812926591617 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3899684211463129, 'lr': 0.00508619492869192, 'weight_decay': 0.0042134905428849794, 'batch_size': 128}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.474581
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.3899684211463129, 'lr': 0.00508619492869192, 'weight_decay': 0.0042134905428849794, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:38:36,167] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.353165
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3550580974713845, 'lr': 0.0055344663322735126, 'weight_decay': 0.01617834254615003, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:38:46,710] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.326276
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.37516935677893853, 'lr': 0.004657364323796894, 'weight_decay': 0.00675832014038623, 'batch_size': 256}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:39:02,800] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.326873
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.38178102745337067, 'lr': 0.009971262404789526, 'weight_decay': 0.009299117286042031, 'batch_size': 128}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:41:38,073] Trial 99 finished with value: 0.454180750021214 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3933961142810686, 'lr': 0.0071016030591518646, 'weight_decay': 0.01107627951948891, 'batch_size': 64}. Best is trial 2 with value: 0.4832850681063274.
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.454181
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.3933961142810686, 'lr': 0.0071016030591518646, 'weight_decay': 0.01107627951948891, 'batch_size': 64}
+  Best Value (CSI): 0.483285
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4833
+Best Hyperparameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3184031589315668, 'lr': 0.00908394353608693, 'weight_decay': 0.005195807637314873, 'batch_size': 64}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.3915
+  - 최종 CSI: 0.4542
+  - 최고 CSI: 0.5010
+  - 최저 CSI: 0.2230
+  - 평균 CSI: 0.3758
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/deepgbm_smotenc_ctgan20000_gwangju_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 47, Best CSI: 0.4493
+  Fold 1 학습 완료 (검증 CSI: 0.4493)
+Fold 2 학습 중...
+  Early stopping at epoch 49, Best CSI: 0.5379
+  Fold 2 학습 완료 (검증 CSI: 0.5379)
+Fold 3 학습 중...
+  Early stopping at epoch 38, Best CSI: 0.5036
+  Fold 3 학습 완료 (검증 CSI: 0.5036)
+
+모든 모델 저장 완료: ../save_model/deepgbm_optima/deepgbm_smotenc_ctgan20000_gwangju.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/deepgbm_optima/scaler/deepgbm_smotenc_ctgan20000_gwangju_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/deepgbm_optima/deepgbm_smotenc_ctgan20000_gwangju.pkl
+
+✓ 완료: deepgbm_smotenc_ctgan20000/deepgbm_smotenc_ctgan20000_gwangju.py (소요 시간: 5011초)
+
+----------------------------------------
+실행 중: deepgbm_smotenc_ctgan20000/deepgbm_smotenc_ctgan20000_incheon.py
+시작 시간: 2025-12-25 23:45:43
+----------------------------------------
+[I 2025-12-25 23:45:44,808] A new study created in memory with name: no-name-ca31852a-8e14-4a7d-b3d5-5beadf95467a
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:47:26,918] Trial 0 finished with value: 0.54044559241215 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3667052648040591, 'lr': 0.0019007657888356479, 'weight_decay': 0.0022412385655779226, 'batch_size': 64}. Best is trial 0 with value: 0.54044559241215.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.540446
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3667052648040591, 'lr': 0.0019007657888356479, 'weight_decay': 0.0022412385655779226, 'batch_size': 64}
+  Best Value (CSI): 0.540446
+  Best Trial: 0
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3667052648040591, 'lr': 0.0019007657888356479, 'weight_decay': 0.0022412385655779226, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:49:00,886] Trial 1 finished with value: 0.5059505530556793 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.28950660981777837, 'lr': 0.0001201402245665964, 'weight_decay': 0.0001727707593349708, 'batch_size': 128}. Best is trial 0 with value: 0.54044559241215.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.505951
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.28950660981777837, 'lr': 0.0001201402245665964, 'weight_decay': 0.0001727707593349708, 'batch_size': 128}
+  Best Value (CSI): 0.540446
+  Best Trial: 0
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3667052648040591, 'lr': 0.0019007657888356479, 'weight_decay': 0.0022412385655779226, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:50:06,634] Trial 2 finished with value: 0.5341442398644815 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.20134548227098573, 'lr': 0.001866779904833721, 'weight_decay': 0.0010820972195939363, 'batch_size': 128}. Best is trial 0 with value: 0.54044559241215.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.534144
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.20134548227098573, 'lr': 0.001866779904833721, 'weight_decay': 0.0010820972195939363, 'batch_size': 128}
+  Best Value (CSI): 0.540446
+  Best Trial: 0
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3667052648040591, 'lr': 0.0019007657888356479, 'weight_decay': 0.0022412385655779226, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:54:05,324] Trial 3 finished with value: 0.5287648277131348 and parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.15336000999474075, 'lr': 0.0021730156931134563, 'weight_decay': 0.0013088301573688959, 'batch_size': 32}. Best is trial 0 with value: 0.54044559241215.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.528765
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.15336000999474075, 'lr': 0.0021730156931134563, 'weight_decay': 0.0013088301573688959, 'batch_size': 32}
+  Best Value (CSI): 0.540446
+  Best Trial: 0
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3667052648040591, 'lr': 0.0019007657888356479, 'weight_decay': 0.0022412385655779226, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:55:20,977] Trial 4 finished with value: 0.5289736293053533 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.21509639780137682, 'lr': 0.0012927196883953352, 'weight_decay': 0.04148662110058731, 'batch_size': 128}. Best is trial 0 with value: 0.54044559241215.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.528974
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.21509639780137682, 'lr': 0.0012927196883953352, 'weight_decay': 0.04148662110058731, 'batch_size': 128}
+  Best Value (CSI): 0.540446
+  Best Trial: 0
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3667052648040591, 'lr': 0.0019007657888356479, 'weight_decay': 0.0022412385655779226, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:55:36,808] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.447813
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.3959963040577539, 'lr': 0.00011182494828586522, 'weight_decay': 0.002136410736447527, 'batch_size': 128}
+  Best Value (CSI): 0.540446
+  Best Trial: 0
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3667052648040591, 'lr': 0.0019007657888356479, 'weight_decay': 0.0022412385655779226, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:59:12,600] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.438788
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 4, 'dropout': 0.12302605992272903, 'lr': 2.8884236978658085e-05, 'weight_decay': 0.0051450772150614344, 'batch_size': 32}
+  Best Value (CSI): 0.540446
+  Best Trial: 0
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.3667052648040591, 'lr': 0.0019007657888356479, 'weight_decay': 0.0022412385655779226, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:01:40,793] Trial 7 finished with value: 0.5408824531335323 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.2066212300577071, 'lr': 0.003323662463686277, 'weight_decay': 0.0025328064540124278, 'batch_size': 64}. Best is trial 7 with value: 0.5408824531335323.
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.540882
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.2066212300577071, 'lr': 0.003323662463686277, 'weight_decay': 0.0025328064540124278, 'batch_size': 64}
+  Best Value (CSI): 0.540882
+  Best Trial: 7
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.2066212300577071, 'lr': 0.003323662463686277, 'weight_decay': 0.0025328064540124278, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:02:41,576] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.438084
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.22571976062080706, 'lr': 6.134396216911527e-05, 'weight_decay': 0.010502763907771492, 'batch_size': 32}
+  Best Value (CSI): 0.540882
+  Best Trial: 7
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.2066212300577071, 'lr': 0.003323662463686277, 'weight_decay': 0.0025328064540124278, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:04:46,040] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.491772
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.293472508247844, 'lr': 0.0002718024634711874, 'weight_decay': 0.010615902352143382, 'batch_size': 64}
+  Best Value (CSI): 0.540882
+  Best Trial: 7
+  Best Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.2066212300577071, 'lr': 0.003323662463686277, 'weight_decay': 0.0025328064540124278, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:05:42,367] Trial 10 finished with value: 0.5442597491498226 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10769839287398476, 'lr': 0.009525131239793653, 'weight_decay': 0.07430773407010675, 'batch_size': 256}. Best is trial 10 with value: 0.5442597491498226.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.544260
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10769839287398476, 'lr': 0.009525131239793653, 'weight_decay': 0.07430773407010675, 'batch_size': 256}
+  Best Value (CSI): 0.544260
+  Best Trial: 10
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10769839287398476, 'lr': 0.009525131239793653, 'weight_decay': 0.07430773407010675, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:06:30,727] Trial 11 finished with value: 0.5438698344359757 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10797291582631306, 'lr': 0.008277150236091012, 'weight_decay': 0.09761956663509892, 'batch_size': 256}. Best is trial 10 with value: 0.5442597491498226.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.543870
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10797291582631306, 'lr': 0.008277150236091012, 'weight_decay': 0.09761956663509892, 'batch_size': 256}
+  Best Value (CSI): 0.544260
+  Best Trial: 10
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10769839287398476, 'lr': 0.009525131239793653, 'weight_decay': 0.07430773407010675, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:07:22,635] Trial 12 finished with value: 0.5454211999115509 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}. Best is trial 12 with value: 0.5454211999115509.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.545421
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:08:11,909] Trial 13 finished with value: 0.544122117522429 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.10308919524155724, 'lr': 0.008765344334768232, 'weight_decay': 0.07944364874867733, 'batch_size': 256}. Best is trial 12 with value: 0.5454211999115509.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.544122
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.10308919524155724, 'lr': 0.008765344334768232, 'weight_decay': 0.07944364874867733, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:08:23,445] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.433395
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.15887615464281568, 'lr': 0.0005803030601695833, 'weight_decay': 0.03715146424395707, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:08:34,469] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.481622
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1529391312910724, 'lr': 0.009242987414399018, 'weight_decay': 0.028154723850654588, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:08:46,342] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.447088
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.13222004068614257, 'lr': 0.004459319732899502, 'weight_decay': 0.09998847856475053, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:09:07,583] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.556604
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.17030358937171652, 'lr': 0.000882950119227547, 'weight_decay': 0.025076832389689236, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:09:49,899] Trial 18 finished with value: 0.529326218580414 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.10405617420506673, 'lr': 0.004165233163183057, 'weight_decay': 0.015430079468391332, 'batch_size': 256}. Best is trial 12 with value: 0.5454211999115509.
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.529326
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.10405617420506673, 'lr': 0.004165233163183057, 'weight_decay': 0.015430079468391332, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:09:58,378] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.327622
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.17890001679453238, 'lr': 1.0919634385681597e-05, 'weight_decay': 0.04994289332535135, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:10:09,095] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.445879
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.1371796324607018, 'lr': 0.0005888443024477293, 'weight_decay': 0.0554355495193096, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:10:19,066] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.428387
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.10001084990755102, 'lr': 0.00851752884982044, 'weight_decay': 0.0629985588672389, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:10:29,010] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.427284
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.12836603089669577, 'lr': 0.005426813208439263, 'weight_decay': 0.09431668470773782, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:10:40,786] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.463023
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.10646024314196056, 'lr': 0.009422942961845163, 'weight_decay': 0.02249564832452241, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:11:01,041] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.570136
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.13462358717992445, 'lr': 0.0036481434195272796, 'weight_decay': 0.05494537199885521, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:11:59,452] Trial 25 finished with value: 0.5356927816012992 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.181240125595102, 'lr': 0.005545841210289506, 'weight_decay': 0.028862928599039286, 'batch_size': 256}. Best is trial 12 with value: 0.5454211999115509.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.535693
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.181240125595102, 'lr': 0.005545841210289506, 'weight_decay': 0.028862928599039286, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:12:09,924] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.437229
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.14944290934737137, 'lr': 0.0028741768902834346, 'weight_decay': 0.06843083558879022, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:13:01,190] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.409567
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12036785571074947, 'lr': 0.00595543977240383, 'weight_decay': 0.01660922370705496, 'batch_size': 32}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:13:29,645] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.450695
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.10106244395208261, 'lr': 0.002553503037575875, 'weight_decay': 0.06282082102273907, 'batch_size': 64}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:13:56,447] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.464832
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.14158012633839195, 'lr': 0.0018287077063598811, 'weight_decay': 0.037138023905174546, 'batch_size': 64}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:14:05,114] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.466624
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.17148322591128828, 'lr': 0.006064997061384621, 'weight_decay': 0.09589021330825949, 'batch_size': 256}
+  Best Value (CSI): 0.545421
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1019145630795297, 'lr': 0.009473145037889064, 'weight_decay': 0.09377828327575906, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:14:54,322] Trial 31 finished with value: 0.548082869767588 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.11810161420146811, 'lr': 0.005626161255766633, 'weight_decay': 0.07813595965260633, 'batch_size': 256}. Best is trial 31 with value: 0.548082869767588.
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.548083
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.11810161420146811, 'lr': 0.005626161255766633, 'weight_decay': 0.07813595965260633, 'batch_size': 256}
+  Best Value (CSI): 0.548083
+  Best Trial: 31
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.11810161420146811, 'lr': 0.005626161255766633, 'weight_decay': 0.07813595965260633, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:15:14,582] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.583805
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1196883182041709, 'lr': 0.009231259496872554, 'weight_decay': 0.03994868882735761, 'batch_size': 256}
+  Best Value (CSI): 0.548083
+  Best Trial: 31
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.11810161420146811, 'lr': 0.005626161255766633, 'weight_decay': 0.07813595965260633, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:15:57,597] Trial 33 finished with value: 0.5496514767249386 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}. Best is trial 33 with value: 0.5496514767249386.
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.549651
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:17:04,516] Trial 34 finished with value: 0.5391510656805605 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1440196627983718, 'lr': 0.003918233945876215, 'weight_decay': 0.053582605029085746, 'batch_size': 128}. Best is trial 33 with value: 0.5496514767249386.
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.539151
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1440196627983718, 'lr': 0.003918233945876215, 'weight_decay': 0.053582605029085746, 'batch_size': 128}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:17:15,413] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.440122
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12125628535109054, 'lr': 0.00205771861570579, 'weight_decay': 0.03765043519065167, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:17:40,307] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.573415
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.15582312694613848, 'lr': 0.0015301742172401072, 'weight_decay': 0.06686969189870928, 'batch_size': 128}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:19:21,405] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.584624
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.18661236561140981, 'lr': 0.0027138712796909363, 'weight_decay': 0.020148793273370078, 'batch_size': 32}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:19:31,296] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.441103
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.11691325658117041, 'lr': 0.005059497387616477, 'weight_decay': 0.00011463260645479976, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:19:47,465] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.437925
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1321856276295156, 'lr': 0.0012119410759001772, 'weight_decay': 0.0007447268103318002, 'batch_size': 128}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:22:05,886] Trial 40 finished with value: 0.5450780727734363 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.1627381954613088, 'lr': 0.003053118654783677, 'weight_decay': 0.03208414716624912, 'batch_size': 64}. Best is trial 33 with value: 0.5496514767249386.
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.545078
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.1627381954613088, 'lr': 0.003053118654783677, 'weight_decay': 0.03208414716624912, 'batch_size': 64}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:22:34,478] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.424321
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.11763077084774717, 'lr': 0.003136654209320503, 'weight_decay': 0.06968439212950128, 'batch_size': 64}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:23:02,438] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.433138
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.13979633165120775, 'lr': 0.006484002731884658, 'weight_decay': 0.03824784233278941, 'batch_size': 64}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:23:31,328] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.436482
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.16028541086882056, 'lr': 0.003969660167201104, 'weight_decay': 0.09998370263815894, 'batch_size': 64}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:24:00,280] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.408730
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.11775871666351921, 'lr': 0.0070655583004284464, 'weight_decay': 0.05080300277860471, 'batch_size': 64}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:24:46,463] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.417772
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.19780340972592655, 'lr': 0.0022044168879631725, 'weight_decay': 0.07861419812841668, 'batch_size': 32}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:25:07,632] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.355731
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.14812429697728946, 'lr': 0.009974439328352243, 'weight_decay': 0.03245233114100497, 'batch_size': 64}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:26:00,667] Trial 47 finished with value: 0.5392433468956933 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.16578046533882615, 'lr': 0.006417645399657734, 'weight_decay': 0.04557757070587515, 'batch_size': 256}. Best is trial 33 with value: 0.5496514767249386.
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.539243
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout': 0.16578046533882615, 'lr': 0.006417645399657734, 'weight_decay': 0.04557757070587515, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:26:50,680] Trial 48 finished with value: 0.5399853400567142 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12870986052120786, 'lr': 0.004508473370455997, 'weight_decay': 0.02875539371865502, 'batch_size': 256}. Best is trial 33 with value: 0.5496514767249386.
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.539985
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12870986052120786, 'lr': 0.004508473370455997, 'weight_decay': 0.02875539371865502, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:27:10,518] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.583987
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.1100386507119037, 'lr': 0.003155297085711408, 'weight_decay': 0.07640358141392502, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:27:41,192] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.511041
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.23528595317438578, 'lr': 0.00731541676552626, 'weight_decay': 0.04725055252177155, 'batch_size': 128}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:27:51,357] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.438295
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1027705791733015, 'lr': 0.004749677885665039, 'weight_decay': 0.07753517392239728, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:28:01,409] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.417910
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1147415771269165, 'lr': 0.007734953512221603, 'weight_decay': 0.07151311136696416, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:28:22,206] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.540719
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10068109528194977, 'lr': 0.009676687429529544, 'weight_decay': 0.04630342462547213, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:28:42,614] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.551995
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1310536058663749, 'lr': 0.006904084411649362, 'weight_decay': 0.09691173066578251, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:28:52,425] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.441846
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.11322150267841806, 'lr': 0.003897281937271959, 'weight_decay': 0.030160311017941175, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:29:03,478] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.419924
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.14638794791825438, 'lr': 0.00510723662645598, 'weight_decay': 0.061487483603322804, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:29:14,583] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.444725
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.12680373183997196, 'lr': 0.007252924279052623, 'weight_decay': 0.010515785849892587, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:30:36,123] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.549150
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.15795620947363545, 'lr': 0.0033438481642037933, 'weight_decay': 0.023339873234751174, 'batch_size': 32}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:30:46,018] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.426158
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.11082832524254957, 'lr': 0.002540209625358045, 'weight_decay': 0.0835191096932168, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:31:08,259] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.373663
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.13917345047918933, 'lr': 0.005317191441900549, 'weight_decay': 0.05597436315191188, 'batch_size': 64}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:31:18,639] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.423607
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12610694921241553, 'lr': 0.00997569639363915, 'weight_decay': 0.07955990395203798, 'batch_size': 256}
+  Best Value (CSI): 0.549651
+  Best Trial: 33
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12040597835986357, 'lr': 0.004042336529579042, 'weight_decay': 0.07140326236268495, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:32:08,028] Trial 62 finished with value: 0.5502041022090476 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}. Best is trial 62 with value: 0.5502041022090476.
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.550204
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:32:52,988] Trial 63 finished with value: 0.5457134941573479 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.11031039765826121, 'lr': 0.007676885182154922, 'weight_decay': 0.061355325210686165, 'batch_size': 256}. Best is trial 62 with value: 0.5502041022090476.
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.545713
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.11031039765826121, 'lr': 0.007676885182154922, 'weight_decay': 0.061355325210686165, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:33:03,785] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.456413
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.11001122502119348, 'lr': 0.005404304693574052, 'weight_decay': 0.04419722440085392, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:33:14,438] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.442821
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1353187797757808, 'lr': 0.007158751322376289, 'weight_decay': 0.0566421358322544, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:33:25,095] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.435682
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1253576753104251, 'lr': 0.007987829951502273, 'weight_decay': 0.032853505080592725, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:33:44,970] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.583651
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10060998089687596, 'lr': 0.00440008982492711, 'weight_decay': 0.09966191484143383, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:34:05,314] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.583799
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10849359754330477, 'lr': 0.0034453922287061073, 'weight_decay': 0.06376740723385275, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:34:16,266] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.443746
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.1497564333327584, 'lr': 0.005910539177815773, 'weight_decay': 0.03804338333821477, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:34:44,695] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.407234
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.11553986976503003, 'lr': 0.007937117632203283, 'weight_decay': 0.08168078625508159, 'batch_size': 64}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:34:55,341] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.440689
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10022917851452216, 'lr': 0.0061416904675594625, 'weight_decay': 0.06434764338722652, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:35:06,063] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.420812
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.12042825119211578, 'lr': 0.008766254634445655, 'weight_decay': 0.05297788475663313, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:35:16,064] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.442623
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.13713161077692956, 'lr': 0.004413554433040063, 'weight_decay': 0.08299596256234486, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:35:32,443] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.398802
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10963185631578243, 'lr': 0.007757577868385006, 'weight_decay': 0.042569952575620205, 'batch_size': 128}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:35:52,633] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.585076
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.12490884963697325, 'lr': 0.005528026327736817, 'weight_decay': 0.06532510146163475, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:36:43,429] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.357992
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1425370901758967, 'lr': 0.009964660587062926, 'weight_decay': 0.08814597477479234, 'batch_size': 32}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:37:04,625] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.544153
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.1233840146144245, 'lr': 0.004247683398321402, 'weight_decay': 0.0517623137806593, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:37:15,354] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.445783
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.11006304474219661, 'lr': 0.0064979149300802095, 'weight_decay': 0.07336333406642745, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:37:43,656] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.399061
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.13242996360599582, 'lr': 0.00809935985840178, 'weight_decay': 0.09687188549048982, 'batch_size': 64}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:38:04,971] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.570538
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10661119293721677, 'lr': 0.0035784644734490356, 'weight_decay': 0.05916319332085249, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:38:46,465] Trial 81 finished with value: 0.5333310685623037 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1177818759383586, 'lr': 0.008488625796027119, 'weight_decay': 0.08365886973578984, 'batch_size': 256}. Best is trial 62 with value: 0.5502041022090476.
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.533331
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1177818759383586, 'lr': 0.008488625796027119, 'weight_decay': 0.08365886973578984, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:38:57,907] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.468593
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10681616712877211, 'lr': 0.006129368867997624, 'weight_decay': 0.06944829036999534, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:39:18,398] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.563960
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10062730050858844, 'lr': 0.005370522193284279, 'weight_decay': 0.04639958044589333, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:39:57,812] Trial 84 finished with value: 0.5443993450467787 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1169199247244405, 'lr': 0.004791684959471504, 'weight_decay': 0.09655373884293487, 'batch_size': 256}. Best is trial 62 with value: 0.5502041022090476.
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.544399
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1169199247244405, 'lr': 0.004791684959471504, 'weight_decay': 0.09655373884293487, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:40:19,081] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.584164
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.11703355861532837, 'lr': 0.002910539698355419, 'weight_decay': 0.035298492893928626, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:40:38,908] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.562464
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.12995485681765323, 'lr': 0.004776994113049706, 'weight_decay': 0.09972233342994503, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:40:54,537] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.419293
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.15019824752377078, 'lr': 0.0037711488016715588, 'weight_decay': 0.07238097585331865, 'batch_size': 128}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:41:22,936] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.385942
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.1407645225566533, 'lr': 0.006735207664263645, 'weight_decay': 0.056226553216804274, 'batch_size': 64}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:41:41,368] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.553030
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.12256297330998901, 'lr': 0.00246241436833551, 'weight_decay': 0.04302285790993002, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:42:30,548] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.326102
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.11151658723158726, 'lr': 0.008637301440924958, 'weight_decay': 0.08351033466774432, 'batch_size': 32}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:42:51,151] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.586462
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10697556321790372, 'lr': 0.00997027125478668, 'weight_decay': 0.06678521291341949, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:43:12,055] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.581963
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.11639625853095183, 'lr': 0.006848142410680243, 'weight_decay': 0.08634441869139166, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:43:32,127] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.538040
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1288092937971269, 'lr': 0.004852312502239798, 'weight_decay': 0.05337612867027623, 'batch_size': 256}
+  Best Value (CSI): 0.550204
+  Best Trial: 62
+  Best Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10029619278557904, 'lr': 0.007931406644933343, 'weight_decay': 0.09626294183442244, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 00:44:16,036] Trial 94 finished with value: 0.5515777046032605 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10014845072807586, 'lr': 0.008578508208533469, 'weight_decay': 0.07361767414227417, 'batch_size': 256}. Best is trial 94 with value: 0.5515777046032605.
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.551578
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10014845072807586, 'lr': 0.008578508208533469, 'weight_decay': 0.07361767414227417, 'batch_size': 256}
+  Best Value (CSI): 0.551578
+  Best Trial: 94
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10014845072807586, 'lr': 0.008578508208533469, 'weight_decay': 0.07361767414227417, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:44:27,248] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.440099
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10053509412887335, 'lr': 0.008332550797411463, 'weight_decay': 0.07464042355316727, 'batch_size': 256}
+  Best Value (CSI): 0.551578
+  Best Trial: 94
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10014845072807586, 'lr': 0.008578508208533469, 'weight_decay': 0.07361767414227417, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:44:48,939] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.520998
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.12173712529228915, 'lr': 0.005323374999248873, 'weight_decay': 0.06124698468709382, 'batch_size': 256}
+  Best Value (CSI): 0.551578
+  Best Trial: 94
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10014845072807586, 'lr': 0.008578508208533469, 'weight_decay': 0.07361767414227417, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 00:45:00,089] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.449784
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.11399391880866064, 'lr': 0.005926063042462059, 'weight_decay': 0.049246166062706515, 'batch_size': 256}
+  Best Value (CSI): 0.551578
+  Best Trial: 94
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10014845072807586, 'lr': 0.008578508208533469, 'weight_decay': 0.07361767414227417, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:45:21,839] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.544170
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.13461647462442722, 'lr': 0.0040740327396090945, 'weight_decay': 0.0275813257120184, 'batch_size': 256}
+  Best Value (CSI): 0.551578
+  Best Trial: 94
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10014845072807586, 'lr': 0.008578508208533469, 'weight_decay': 0.07361767414227417, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 00:46:15,170] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.546789
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.1065302958243868, 'lr': 0.007218393562947236, 'weight_decay': 0.038322882595800314, 'batch_size': 64}
+  Best Value (CSI): 0.551578
+  Best Trial: 94
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10014845072807586, 'lr': 0.008578508208533469, 'weight_decay': 0.07361767414227417, 'batch_size': 256}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5516
+Best Hyperparameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.10014845072807586, 'lr': 0.008578508208533469, 'weight_decay': 0.07361767414227417, 'batch_size': 256}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5404
+  - 최종 CSI: 0.5468
+  - 최고 CSI: 0.5865
+  - 최저 CSI: 0.3261
+  - 평균 CSI: 0.4873
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/deepgbm_smotenc_ctgan20000_incheon_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 14, Best CSI: 0.4799
+  Fold 1 학습 완료 (검증 CSI: 0.4799)
+Fold 2 학습 중...
+  Early stopping at epoch 30, Best CSI: 0.5939
+  Fold 2 학습 완료 (검증 CSI: 0.5939)
+Fold 3 학습 중...
+  Early stopping at epoch 25, Best CSI: 0.5610
+  Fold 3 학습 완료 (검증 CSI: 0.5610)
+
+모든 모델 저장 완료: ../save_model/deepgbm_optima/deepgbm_smotenc_ctgan20000_incheon.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/deepgbm_optima/scaler/deepgbm_smotenc_ctgan20000_incheon_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/deepgbm_optima/deepgbm_smotenc_ctgan20000_incheon.pkl
+
+✓ 완료: deepgbm_smotenc_ctgan20000/deepgbm_smotenc_ctgan20000_incheon.py (소요 시간: 3683초)
+
+----------------------------------------
+실행 중: deepgbm_smotenc_ctgan20000/deepgbm_smotenc_ctgan20000_seoul.py
+시작 시간: 2025-12-26 00:47:06
+----------------------------------------
+[I 2025-12-26 00:47:07,713] A new study created in memory with name: no-name-03175cf9-1d5b-4a6e-bdf9-7ac88c0b6582
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 00:49:09,404] Trial 0 finished with value: 0.49860941539715525 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.25254539424314093, 'lr': 0.0005617369324276381, 'weight_decay': 0.00010662009745115875, 'batch_size': 64}. Best is trial 0 with value: 0.49860941539715525.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.498609
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.25254539424314093, 'lr': 0.0005617369324276381, 'weight_decay': 0.00010662009745115875, 'batch_size': 64}
+  Best Value (CSI): 0.498609
+  Best Trial: 0
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.25254539424314093, 'lr': 0.0005617369324276381, 'weight_decay': 0.00010662009745115875, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 00:50:16,411] Trial 1 finished with value: 0.47295026545449703 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29891826584209974, 'lr': 0.00012475855901992078, 'weight_decay': 0.028620301098035702, 'batch_size': 256}. Best is trial 0 with value: 0.49860941539715525.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.472950
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29891826584209974, 'lr': 0.00012475855901992078, 'weight_decay': 0.028620301098035702, 'batch_size': 256}
+  Best Value (CSI): 0.498609
+  Best Trial: 0
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.25254539424314093, 'lr': 0.0005617369324276381, 'weight_decay': 0.00010662009745115875, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 00:52:15,940] Trial 2 finished with value: 0.44787046029452576 and parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.3109816773506793, 'lr': 1.1386714319911892e-05, 'weight_decay': 0.029572949896356834, 'batch_size': 128}. Best is trial 0 with value: 0.49860941539715525.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.447870
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.3109816773506793, 'lr': 1.1386714319911892e-05, 'weight_decay': 0.029572949896356834, 'batch_size': 128}
+  Best Value (CSI): 0.498609
+  Best Trial: 0
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.25254539424314093, 'lr': 0.0005617369324276381, 'weight_decay': 0.00010662009745115875, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 00:52:50,514] Trial 3 finished with value: 0.509879094136492 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.15716668876308398, 'lr': 0.002505562535491622, 'weight_decay': 0.0010247794805652143, 'batch_size': 256}. Best is trial 3 with value: 0.509879094136492.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.509879
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.15716668876308398, 'lr': 0.002505562535491622, 'weight_decay': 0.0010247794805652143, 'batch_size': 256}
+  Best Value (CSI): 0.509879
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.15716668876308398, 'lr': 0.002505562535491622, 'weight_decay': 0.0010247794805652143, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 00:55:19,042] Trial 4 finished with value: 0.4743948646027698 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.21828929004180328, 'lr': 4.7808266938063634e-05, 'weight_decay': 0.0007282296523659097, 'batch_size': 64}. Best is trial 3 with value: 0.509879094136492.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.474395
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.21828929004180328, 'lr': 4.7808266938063634e-05, 'weight_decay': 0.0007282296523659097, 'batch_size': 64}
+  Best Value (CSI): 0.509879
+  Best Trial: 3
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.15716668876308398, 'lr': 0.002505562535491622, 'weight_decay': 0.0010247794805652143, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 00:56:08,504] Trial 5 finished with value: 0.5477355590816262 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.35897357546772146, 'lr': 0.00796023516397056, 'weight_decay': 0.09321148861405949, 'batch_size': 256}. Best is trial 5 with value: 0.5477355590816262.
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.547736
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.35897357546772146, 'lr': 0.00796023516397056, 'weight_decay': 0.09321148861405949, 'batch_size': 256}
+  Best Value (CSI): 0.547736
+  Best Trial: 5
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.35897357546772146, 'lr': 0.00796023516397056, 'weight_decay': 0.09321148861405949, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:56:37,501] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.342052
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.2680992918953701, 'lr': 1.8788968520392145e-05, 'weight_decay': 0.006335177828729479, 'batch_size': 32}
+  Best Value (CSI): 0.547736
+  Best Trial: 5
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.35897357546772146, 'lr': 0.00796023516397056, 'weight_decay': 0.09321148861405949, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:57:10,123] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.282649
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.3321038344813615, 'lr': 1.2267942572129646e-05, 'weight_decay': 0.0009986446948957897, 'batch_size': 32}
+  Best Value (CSI): 0.547736
+  Best Trial: 5
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.35897357546772146, 'lr': 0.00796023516397056, 'weight_decay': 0.09321148861405949, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:01:23,173] Trial 8 finished with value: 0.5083167404655692 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3876707058786192, 'lr': 0.00048622617842201005, 'weight_decay': 0.00655077730227745, 'batch_size': 32}. Best is trial 5 with value: 0.5477355590816262.
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.508317
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3876707058786192, 'lr': 0.00048622617842201005, 'weight_decay': 0.00655077730227745, 'batch_size': 32}
+  Best Value (CSI): 0.547736
+  Best Trial: 5
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.35897357546772146, 'lr': 0.00796023516397056, 'weight_decay': 0.09321148861405949, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:02:01,341] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.360717
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.38403558315891784, 'lr': 9.21998724832433e-05, 'weight_decay': 0.002033165405891799, 'batch_size': 32}
+  Best Value (CSI): 0.547736
+  Best Trial: 5
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.35897357546772146, 'lr': 0.00796023516397056, 'weight_decay': 0.09321148861405949, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:02:57,907] Trial 10 finished with value: 0.5657974901513136 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.565797
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:04:08,789] Trial 11 finished with value: 0.5582078162847205 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.10780724766196015, 'lr': 0.009656432736570138, 'weight_decay': 0.0991079507676134, 'batch_size': 256}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.558208
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.10780724766196015, 'lr': 0.009656432736570138, 'weight_decay': 0.0991079507676134, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:05:00,636] Trial 12 finished with value: 0.536267682969468 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.10215728926137342, 'lr': 0.00845654349268458, 'weight_decay': 0.06580097512788673, 'batch_size': 256}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.536268
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.10215728926137342, 'lr': 0.00845654349268458, 'weight_decay': 0.06580097512788673, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:05:17,500] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.439189
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.11309829273299493, 'lr': 0.003682051195654335, 'weight_decay': 0.094717980809892, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:05:30,194] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.413344
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.1535546776052753, 'lr': 0.00160584857514054, 'weight_decay': 0.025214492313235234, 'batch_size': 128}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:06:24,044] Trial 15 finished with value: 0.5352402302912023 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.19512312295471101, 'lr': 0.009926931946755257, 'weight_decay': 0.015341727578467426, 'batch_size': 256}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.535240
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.19512312295471101, 'lr': 0.009926931946755257, 'weight_decay': 0.015341727578467426, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:06:32,428] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.423139
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.13522570579652243, 'lr': 0.0013706372261094609, 'weight_decay': 0.043802071659648584, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:06:41,789] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.437819
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.10018764448984192, 'lr': 0.003877116656013006, 'weight_decay': 0.014971578579742906, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:07:23,976] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.433588
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.18820530103634028, 'lr': 0.0011255825612293481, 'weight_decay': 0.05130603807321384, 'batch_size': 64}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:07:48,603] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.447077
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1412441021884708, 'lr': 0.004235828251133253, 'weight_decay': 0.09819586692416048, 'batch_size': 128}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:08:06,981] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.449811
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.17614802356724185, 'lr': 0.005608086654008918, 'weight_decay': 0.0422245782452127, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:09:05,153] Trial 21 finished with value: 0.5631324644186134 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.12923381952984464, 'lr': 0.009342728310721199, 'weight_decay': 0.08524338742076888, 'batch_size': 256}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.563132
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.12923381952984464, 'lr': 0.009342728310721199, 'weight_decay': 0.08524338742076888, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:09:55,560] Trial 22 finished with value: 0.536965981414783 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.12496580784462663, 'lr': 0.009641271126421522, 'weight_decay': 0.05742792101682836, 'batch_size': 256}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.536966
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.12496580784462663, 'lr': 0.009641271126421522, 'weight_decay': 0.05742792101682836, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:10:03,303] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.444618
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.12473726849896447, 'lr': 0.0028495836527775457, 'weight_decay': 0.017442043733128635, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:10:20,029] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.451852
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.10050766022350172, 'lr': 0.005080275559900223, 'weight_decay': 0.0976436467513916, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:10:28,266] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.433735
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.14824886619041971, 'lr': 0.002114816499987826, 'weight_decay': 0.04689863993221635, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:10:35,676] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.451484
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.16473409594137764, 'lr': 0.0059036327729321365, 'weight_decay': 0.030292108591217844, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:12:29,838] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.501274
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.12542687243388767, 'lr': 0.0026649678377323495, 'weight_decay': 0.0627510282424863, 'batch_size': 64}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:12:41,491] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.405152
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.20966599109861195, 'lr': 0.005798891553711698, 'weight_decay': 0.017618226227701404, 'batch_size': 128}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:14:46,694] Trial 29 finished with value: 0.5233306342708856 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1698102302932065, 'lr': 0.001363507182787418, 'weight_decay': 0.008529289969318454, 'batch_size': 64}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.523331
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.1698102302932065, 'lr': 0.001363507182787418, 'weight_decay': 0.008529289969318454, 'batch_size': 64}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:14:53,964] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.392359
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.1293003618398662, 'lr': 0.0011157823272516727, 'weight_decay': 0.06518695815967147, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:15:01,337] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.447735
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2312603994764229, 'lr': 0.0072762201538325525, 'weight_decay': 0.0933477093819344, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:15:42,739] Trial 32 finished with value: 0.5303323712328316 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.14823788001465182, 'lr': 0.00728075208772201, 'weight_decay': 0.03939816243951985, 'batch_size': 256}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.530332
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.14823788001465182, 'lr': 0.00728075208772201, 'weight_decay': 0.03939816243951985, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:15:55,152] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.455810
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.24756117454943752, 'lr': 0.00989624777755689, 'weight_decay': 0.03049380047640989, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:16:31,015] Trial 34 finished with value: 0.529993910246214 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.1194331604252038, 'lr': 0.004100994586737723, 'weight_decay': 0.0690443264930513, 'batch_size': 256}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.529994
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.1194331604252038, 'lr': 0.004100994586737723, 'weight_decay': 0.0690443264930513, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:16:38,359] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.461073
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.18332667802088354, 'lr': 0.005659088298021359, 'weight_decay': 0.0334210602206272, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:16:44,067] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.409825
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.1604443447882551, 'lr': 0.0027460443918513313, 'weight_decay': 0.0705588216812739, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:16:53,286] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.394825
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.27635410151360057, 'lr': 0.009934371332939828, 'weight_decay': 0.021308226053985385, 'batch_size': 128}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:17:07,124] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.395529
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout': 0.1117511036453075, 'lr': 0.006387600810723465, 'weight_decay': 0.040420035058477646, 'batch_size': 64}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:17:39,432] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.410601
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.1421156861320548, 'lr': 0.000607249814094972, 'weight_decay': 0.027164302357791364, 'batch_size': 32}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:17:45,517] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.452492
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.3206582962238784, 'lr': 0.003226852110642448, 'weight_decay': 0.09805718915821852, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:18:28,196] Trial 41 finished with value: 0.5422970899509355 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.11672476117135427, 'lr': 0.007776642750170445, 'weight_decay': 0.05595658806326156, 'batch_size': 256}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.542297
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.11672476117135427, 'lr': 0.007776642750170445, 'weight_decay': 0.05595658806326156, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:19:18,336] Trial 42 finished with value: 0.5425734316686407 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.11409458082717218, 'lr': 0.007034332204610135, 'weight_decay': 0.06763567411107038, 'batch_size': 256}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.542573
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.11409458082717218, 'lr': 0.007034332204610135, 'weight_decay': 0.06763567411107038, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:19:25,088] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.425178
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.10946563388336114, 'lr': 0.0042511183589127675, 'weight_decay': 0.074382543611822, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:19:51,944] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.331683
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.1316710538402613, 'lr': 0.007146820667453135, 'weight_decay': 0.049286585317073174, 'batch_size': 32}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:19:58,616] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.439024
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.3485630367992857, 'lr': 0.004476924390328516, 'weight_decay': 0.08076200791991599, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:20:05,394] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.402256
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.15432789267365343, 'lr': 0.0019259873866568024, 'weight_decay': 0.07073601909719147, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:20:12,414] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.439476
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.10053866688834341, 'lr': 0.003450693198416156, 'weight_decay': 0.03536147245214299, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:20:41,467] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.297173
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.13610691050138274, 'lr': 0.0076283214691741895, 'weight_decay': 0.051197606203410885, 'batch_size': 32}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:21:05,720] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.456386
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.11448245308155942, 'lr': 0.004982548626260849, 'weight_decay': 0.09755409511522563, 'batch_size': 128}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:21:11,787] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.415219
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.19792179970604962, 'lr': 0.0022539730627043797, 'weight_decay': 0.05781551904129336, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:21:18,232] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.428459
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.11005618200380084, 'lr': 0.007960589364416262, 'weight_decay': 0.0552320451339676, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:21:24,685] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.431545
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.11720147714746906, 'lr': 0.007837274018201994, 'weight_decay': 0.077797146076287, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:21:31,733] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.467136
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13844306670700557, 'lr': 0.0061581521619264775, 'weight_decay': 0.040235382551265714, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:21:38,131] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.415739
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.12363447642442357, 'lr': 0.009408190483971443, 'weight_decay': 0.021838090274106114, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:21:44,846] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.408654
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.17321124126460036, 'lr': 0.0033579983154139073, 'weight_decay': 0.05022984187865108, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:21:52,187] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.456869
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.14310325294649373, 'lr': 0.005324089517180886, 'weight_decay': 0.07729038077536858, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:22:05,237] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.466561
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.1559183849571359, 'lr': 0.00998027254454398, 'weight_decay': 0.05875832721338847, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:22:21,564] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.415748
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.10817496117090926, 'lr': 0.0041436778124385905, 'weight_decay': 0.09815900390004917, 'batch_size': 64}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:23:14,883] Trial 59 finished with value: 0.5369375015100614 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.12906354081419563, 'lr': 0.006550131806153976, 'weight_decay': 0.03816050334944652, 'batch_size': 256}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.536938
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.12906354081419563, 'lr': 0.006550131806153976, 'weight_decay': 0.03816050334944652, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:23:21,230] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.434180
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.1012763013058034, 'lr': 0.004779057318065506, 'weight_decay': 0.026841812267314333, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:23:27,788] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.428571
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.12165966610020537, 'lr': 0.008037675741030096, 'weight_decay': 0.06292000756850075, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:23:41,704] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.463659
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.1328433502183065, 'lr': 0.008345177425980826, 'weight_decay': 0.04868936309899401, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:24:29,011] Trial 63 finished with value: 0.548895187665947 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.11728532038581209, 'lr': 0.006319507377618241, 'weight_decay': 0.07874528187658836, 'batch_size': 256}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.548895
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.11728532038581209, 'lr': 0.006319507377618241, 'weight_decay': 0.07874528187658836, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:24:35,389] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.446178
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.11485381061797043, 'lr': 0.0060166011000717945, 'weight_decay': 0.07758408291819936, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:24:44,676] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.409722
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.14504114351788294, 'lr': 0.003512413711868491, 'weight_decay': 0.08360424446232625, 'batch_size': 128}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:24:51,577] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.445341
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.16395355136514023, 'lr': 0.004840124590846542, 'weight_decay': 0.061651536300328144, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:25:47,683] Trial 67 finished with value: 0.5512789377857006 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.100031475942269, 'lr': 0.006826955927321861, 'weight_decay': 0.09973844949972972, 'batch_size': 256}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.551279
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.100031475942269, 'lr': 0.006826955927321861, 'weight_decay': 0.09973844949972972, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:26:20,003] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.341134
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 6, 'dropout': 0.1066292494176178, 'lr': 0.0064638740516152204, 'weight_decay': 0.09939395036544936, 'batch_size': 32}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:26:27,251] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.427451
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.1245896740517777, 'lr': 0.002802832718454946, 'weight_decay': 0.03387067549897562, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:26:44,848] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.410046
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.10263763001480719, 'lr': 0.005256041605062858, 'weight_decay': 0.04367847932654872, 'batch_size': 64}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:26:51,265] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.435070
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.11721513234328487, 'lr': 0.008639652423499188, 'weight_decay': 0.07268246423719095, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:26:57,818] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.430189
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13192736964450763, 'lr': 0.006891488925908063, 'weight_decay': 0.08355737471964135, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:27:03,846] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.406342
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.11871447574881547, 'lr': 0.009786943614708192, 'weight_decay': 0.059219483920107084, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:27:11,029] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.420775
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.11166574549091715, 'lr': 0.003946037019721871, 'weight_decay': 0.04822363430279504, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:27:17,740] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.448361
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.13817128175084362, 'lr': 0.007167141744793665, 'weight_decay': 0.08244879630605882, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:27:23,425] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.441820
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.10003863571054683, 'lr': 0.005519774435953239, 'weight_decay': 0.08100124483650961, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:27:30,777] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.468983
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.1257279287882203, 'lr': 0.008392181566703839, 'weight_decay': 0.06730410171392288, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+[I 2025-12-26 01:27:57,861] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.536246
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.1112888297013719, 'lr': 0.004376909353610049, 'weight_decay': 0.045073882375685735, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:28:06,389] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.438627
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.15210130795935012, 'lr': 0.006670350143994888, 'weight_decay': 0.032467560701983623, 'batch_size': 128}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+[I 2025-12-26 01:28:37,384] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.542373
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.14518070963421165, 'lr': 0.0053536928906253965, 'weight_decay': 0.06249314376111498, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:28:43,760] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.362489
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.12436477302529395, 'lr': 0.009835525836034337, 'weight_decay': 0.0563283118691329, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:28:50,132] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.440406
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.1073598929029249, 'lr': 0.00846380379209577, 'weight_decay': 0.0999057115535506, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:29:48,863] Trial 83 finished with value: 0.5401008788879833 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13324159371395203, 'lr': 0.0071922642886845635, 'weight_decay': 0.06715454757946063, 'batch_size': 256}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.540101
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13324159371395203, 'lr': 0.0071922642886845635, 'weight_decay': 0.06715454757946063, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:30:29,459] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.513441
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13255650206383865, 'lr': 0.007096988680959557, 'weight_decay': 0.07013341495146849, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:30:35,787] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.436962
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.11582757179010825, 'lr': 0.0036901460210835776, 'weight_decay': 0.08563146469743467, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:31:02,465] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.424691
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.13501808815403032, 'lr': 0.005932747309339245, 'weight_decay': 0.05088794249852813, 'batch_size': 32}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:31:08,541] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.442760
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.10724492681878241, 'lr': 0.004753198134511028, 'weight_decay': 0.04232271331508332, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:31:25,099] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.336877
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.1204127890703194, 'lr': 0.008059572689508765, 'weight_decay': 0.08716314449906024, 'batch_size': 64}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:31:31,663] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.437792
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.1484800236032622, 'lr': 0.002980711108441323, 'weight_decay': 0.06792436946976993, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:31:37,382] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.443134
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.14106325517469248, 'lr': 0.006121576880151821, 'weight_decay': 0.03771856675495574, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:31:43,755] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.445205
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.12886082955520972, 'lr': 0.008585604748907214, 'weight_decay': 0.0577909420025908, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:31:57,831] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.451437
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.12012711196760774, 'lr': 0.007356204375862358, 'weight_decay': 0.0875071547245646, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:32:04,772] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.469167
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.10466426752205814, 'lr': 0.008540683427631602, 'weight_decay': 0.0705017292810407, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:32:11,124] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.420455
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.11388880219555003, 'lr': 0.006956168143656705, 'weight_decay': 0.05432528689989613, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:32:17,897] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.391020
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.16133135068014567, 'lr': 0.009682661627739526, 'weight_decay': 0.07484846021400264, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:32:23,903] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.425759
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.12692221992145408, 'lr': 0.00469144283887213, 'weight_decay': 0.08769734870064443, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:32:30,309] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.438982
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.29450116978879315, 'lr': 0.0059625171795270855, 'weight_decay': 0.09996156868326624, 'batch_size': 256}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 01:33:58,581] Trial 98 finished with value: 0.5501145869977568 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.13833322969096906, 'lr': 0.003951632515561313, 'weight_decay': 0.04461794241236279, 'batch_size': 128}. Best is trial 10 with value: 0.5657974901513136.
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.550115
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.13833322969096906, 'lr': 0.003951632515561313, 'weight_decay': 0.04461794241236279, 'batch_size': 128}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:34:07,945] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.444351
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout': 0.13743763081930566, 'lr': 0.0041304570249015475, 'weight_decay': 0.04400800237316215, 'batch_size': 128}
+  Best Value (CSI): 0.565797
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5658
+Best Hyperparameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4986
+  - 최종 CSI: 0.4444
+  - 최고 CSI: 0.5658
+  - 최저 CSI: 0.2826
+  - 평균 CSI: 0.4509
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/deepgbm_smotenc_ctgan20000_seoul_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 42, Best CSI: 0.4873
+  Fold 1 학습 완료 (검증 CSI: 0.4873)
+Fold 2 학습 중...
+  Early stopping at epoch 30, Best CSI: 0.5746
+  Fold 2 학습 완료 (검증 CSI: 0.5746)
+Fold 3 학습 중...
+  Early stopping at epoch 33, Best CSI: 0.5781
+  Fold 3 학습 완료 (검증 CSI: 0.5781)
+
+모든 모델 저장 완료: ../save_model/deepgbm_optima/deepgbm_smotenc_ctgan20000_seoul.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/deepgbm_optima/scaler/deepgbm_smotenc_ctgan20000_seoul_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/deepgbm_optima/deepgbm_smotenc_ctgan20000_seoul.pkl
+
+✓ 완료: deepgbm_smotenc_ctgan20000/deepgbm_smotenc_ctgan20000_seoul.py (소요 시간: 2883초)
+
+==========================================
+DeepGBM SMOTENC CTGAN20000 파일 실행 완료
+종료 시간: 2025-12-26 01:35:09
+총 소요 시간: 10시간 38분 4초
+성공: 6개
+실패: 0개
+==========================================
diff --git a/Analysis_code/5.optima/run_bash/deepgbm/run_deepgbm_ctgan10000.sh b/Analysis_code/5.optima/run_bash/deepgbm/run_deepgbm_ctgan10000.sh
new file mode 100755
index 0000000000000000000000000000000000000000..4ce18cec7d07cf11c42dd257f5edc2c622011efa
--- /dev/null
+++ b/Analysis_code/5.optima/run_bash/deepgbm/run_deepgbm_ctgan10000.sh
@@ -0,0 +1,80 @@
+#!/bin/bash
+
+# 스크립트 디렉토리로 이동 (상위 디렉토리인 5.optima로 이동)
+cd "$(dirname "$0")/../.."
+
+# 시작 시간 기록
+START_TIME=$(date +%s)
+echo "=========================================="
+echo "DeepGBM CTGAN10000 파일 실행 시작"
+echo "시작 시간: $(date '+%Y-%m-%d %H:%M:%S')"
+echo "GPU: 0번 (CUDA_VISIBLE_DEVICES=0)"
+echo "=========================================="
+echo ""
+
+# 실행할 파일 목록
+FILES=(
+    "deepgbm_ctgan10000_busan.py"
+    "deepgbm_ctgan10000_daegu.py"
+    "deepgbm_ctgan10000_daejeon.py"
+    "deepgbm_ctgan10000_gwangju.py"
+    "deepgbm_ctgan10000_incheon.py"
+    "deepgbm_ctgan10000_seoul.py"
+)
+
+# 에러 발생 시 중단 여부 (set -e를 사용하면 에러 발생 시 즉시 중단)
+set -e
+
+# 각 파일 실행
+SUCCESS_COUNT=0
+FAIL_COUNT=0
+
+for file in "${FILES[@]}"; do
+    filepath="deepgbm_ctgan10000/$file"
+    if [ ! -f "$filepath" ]; then
+        echo "⚠️  경고: $filepath 파일을 찾을 수 없습니다. 건너뜁니다."
+        FAIL_COUNT=$((FAIL_COUNT + 1))
+        continue
+    fi
+    
+    echo "----------------------------------------"
+    echo "실행 중: $filepath"
+    echo "시작 시간: $(date '+%Y-%m-%d %H:%M:%S')"
+    echo "----------------------------------------"
+    
+    FILE_START=$(date +%s)
+    
+    # Python 스크립트 실행 (GPU 0번 설정)
+    if CUDA_VISIBLE_DEVICES=0 python3 -u "$filepath"; then
+        FILE_END=$(date +%s)
+        FILE_DURATION=$((FILE_END - FILE_START))
+        echo ""
+        echo "✓ 완료: $filepath (소요 시간: ${FILE_DURATION}초)"
+        SUCCESS_COUNT=$((SUCCESS_COUNT + 1))
+    else
+        FILE_END=$(date +%s)
+        FILE_DURATION=$((FILE_END - FILE_START))
+        echo ""
+        echo "✗ 실패: $filepath (소요 시간: ${FILE_DURATION}초)"
+        FAIL_COUNT=$((FAIL_COUNT + 1))
+        echo "에러 발생으로 인해 스크립트를 중단합니다."
+        exit 1
+    fi
+    echo ""
+done
+
+# 종료 시간 기록
+END_TIME=$(date +%s)
+TOTAL_DURATION=$((END_TIME - START_TIME))
+HOURS=$((TOTAL_DURATION / 3600))
+MINUTES=$(((TOTAL_DURATION % 3600) / 60))
+SECONDS=$((TOTAL_DURATION % 60))
+
+echo "=========================================="
+echo "DeepGBM CTGAN10000 파일 실행 완료"
+echo "종료 시간: $(date '+%Y-%m-%d %H:%M:%S')"
+echo "총 소요 시간: ${HOURS}시간 ${MINUTES}분 ${SECONDS}초"
+echo "성공: ${SUCCESS_COUNT}개"
+echo "실패: ${FAIL_COUNT}개"
+echo "=========================================="
+
diff --git a/Analysis_code/5.optima/run_bash/ft_transformer/ft_transformer_ctgan10000.log b/Analysis_code/5.optima/run_bash/ft_transformer/ft_transformer_ctgan10000.log
new file mode 100644
index 0000000000000000000000000000000000000000..c33d31e2369e41c3b798779331261e4b57369994
--- /dev/null
+++ b/Analysis_code/5.optima/run_bash/ft_transformer/ft_transformer_ctgan10000.log
@@ -0,0 +1,7974 @@
+nohup: ignoring input
+/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)
+==========================================
+FT-Transformer CTGAN10000 파일 실행 시작
+시작 시간: 2025-12-28 12:36:22
+GPU: 1번 (CUDA_VISIBLE_DEVICES=1)
+==========================================
+
+----------------------------------------
+실행 중: ft_transformer_ctgan10000/ft_transformer_ctgan10000_busan.py
+시작 시간: 2025-12-28 12:36:22
+----------------------------------------
+[I 2025-12-28 12:36:23,448] A new study created in memory with name: no-name-e80e745d-9835-433d-945d-46785d8fe8b9
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:37:52,007] Trial 0 finished with value: 0.2937353546123581 and parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.15557858942827202, 'ffn_dropout': 0.16969935785804233, 'lr': 6.490237465988272e-05, 'weight_decay': 0.0011203727047510053, 'batch_size': 256}. Best is trial 0 with value: 0.2937353546123581.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.293735
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.15557858942827202, 'ffn_dropout': 0.16969935785804233, 'lr': 6.490237465988272e-05, 'weight_decay': 0.0011203727047510053, 'batch_size': 256}
+  Best Value (CSI): 0.293735
+  Best Trial: 0
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.15557858942827202, 'ffn_dropout': 0.16969935785804233, 'lr': 6.490237465988272e-05, 'weight_decay': 0.0011203727047510053, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:40:30,192] Trial 1 finished with value: 0.2592751845674596 and parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.11569357924954832, 'ffn_dropout': 0.2717601648117537, 'lr': 3.2412384190319987e-05, 'weight_decay': 0.045120013876145344, 'batch_size': 256}. Best is trial 0 with value: 0.2937353546123581.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.259275
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.11569357924954832, 'ffn_dropout': 0.2717601648117537, 'lr': 3.2412384190319987e-05, 'weight_decay': 0.045120013876145344, 'batch_size': 256}
+  Best Value (CSI): 0.293735
+  Best Trial: 0
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.15557858942827202, 'ffn_dropout': 0.16969935785804233, 'lr': 6.490237465988272e-05, 'weight_decay': 0.0011203727047510053, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:42:37,694] Trial 2 finished with value: 0.05763020768354185 and parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.27794328531059703, 'ffn_dropout': 0.38416020684121166, 'lr': 0.004195343130425373, 'weight_decay': 0.0034238609577708187, 'batch_size': 64}. Best is trial 0 with value: 0.2937353546123581.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.057630
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.27794328531059703, 'ffn_dropout': 0.38416020684121166, 'lr': 0.004195343130425373, 'weight_decay': 0.0034238609577708187, 'batch_size': 64}
+  Best Value (CSI): 0.293735
+  Best Trial: 0
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.15557858942827202, 'ffn_dropout': 0.16969935785804233, 'lr': 6.490237465988272e-05, 'weight_decay': 0.0011203727047510053, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:48:38,996] Trial 3 finished with value: 0.3349144718213748 and parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.32923827238769926, 'ffn_dropout': 0.35307454041648356, 'lr': 1.4972537950935246e-05, 'weight_decay': 0.019938736065690722, 'batch_size': 32}. Best is trial 3 with value: 0.3349144718213748.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.334914
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.32923827238769926, 'ffn_dropout': 0.35307454041648356, 'lr': 1.4972537950935246e-05, 'weight_decay': 0.019938736065690722, 'batch_size': 32}
+  Best Value (CSI): 0.334914
+  Best Trial: 3
+  Best Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.32923827238769926, 'ffn_dropout': 0.35307454041648356, 'lr': 1.4972537950935246e-05, 'weight_decay': 0.019938736065690722, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:50:35,888] Trial 4 finished with value: 0.2927190406070285 and parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22423571821376748, 'ffn_dropout': 0.17164042763282372, 'lr': 2.9118459062015985e-05, 'weight_decay': 0.002271616984005451, 'batch_size': 128}. Best is trial 3 with value: 0.3349144718213748.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.292719
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22423571821376748, 'ffn_dropout': 0.17164042763282372, 'lr': 2.9118459062015985e-05, 'weight_decay': 0.002271616984005451, 'batch_size': 128}
+  Best Value (CSI): 0.334914
+  Best Trial: 3
+  Best Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.32923827238769926, 'ffn_dropout': 0.35307454041648356, 'lr': 1.4972537950935246e-05, 'weight_decay': 0.019938736065690722, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 12:51:08,623] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.256831
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.32039892571893924, 'ffn_dropout': 0.31021361449208007, 'lr': 1.9551091797222194e-05, 'weight_decay': 0.0012362676536468661, 'batch_size': 64}
+  Best Value (CSI): 0.334914
+  Best Trial: 3
+  Best Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.32923827238769926, 'ffn_dropout': 0.35307454041648356, 'lr': 1.4972537950935246e-05, 'weight_decay': 0.019938736065690722, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 12:51:28,907] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.218088
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1351375555133366, 'ffn_dropout': 0.3729374972870594, 'lr': 7.518669733226845e-05, 'weight_decay': 0.001629924080956464, 'batch_size': 128}
+  Best Value (CSI): 0.334914
+  Best Trial: 3
+  Best Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.32923827238769926, 'ffn_dropout': 0.35307454041648356, 'lr': 1.4972537950935246e-05, 'weight_decay': 0.019938736065690722, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:52:59,420] Trial 7 finished with value: 0.45793006881540704 and parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.1948796003161526, 'ffn_dropout': 0.1414963905502595, 'lr': 0.000340207207119526, 'weight_decay': 0.08328209922823542, 'batch_size': 128}. Best is trial 7 with value: 0.45793006881540704.
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.457930
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.1948796003161526, 'ffn_dropout': 0.1414963905502595, 'lr': 0.000340207207119526, 'weight_decay': 0.08328209922823542, 'batch_size': 128}
+  Best Value (CSI): 0.457930
+  Best Trial: 7
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.1948796003161526, 'ffn_dropout': 0.1414963905502595, 'lr': 0.000340207207119526, 'weight_decay': 0.08328209922823542, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:53:44,117] Trial 8 finished with value: 0.45392645624637096 and parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.2667330749361459, 'ffn_dropout': 0.22311728808305645, 'lr': 0.0021386972798200883, 'weight_decay': 0.00026921701567811834, 'batch_size': 256}. Best is trial 7 with value: 0.45793006881540704.
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.453926
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.2667330749361459, 'ffn_dropout': 0.22311728808305645, 'lr': 0.0021386972798200883, 'weight_decay': 0.00026921701567811834, 'batch_size': 256}
+  Best Value (CSI): 0.457930
+  Best Trial: 7
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.1948796003161526, 'ffn_dropout': 0.1414963905502595, 'lr': 0.000340207207119526, 'weight_decay': 0.08328209922823542, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:04:40,180] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.441761
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.3267010025242002, 'ffn_dropout': 0.15475455757614773, 'lr': 0.00025400607654542845, 'weight_decay': 0.00015788892922449722, 'batch_size': 32}
+  Best Value (CSI): 0.457930
+  Best Trial: 7
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.1948796003161526, 'ffn_dropout': 0.1414963905502595, 'lr': 0.000340207207119526, 'weight_decay': 0.08328209922823542, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:05:50,018] Trial 10 finished with value: 0.47923052284165424 and parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3822134547243423, 'ffn_dropout': 0.10076460097758799, 'lr': 0.0006135331541667003, 'weight_decay': 0.09879218512830837, 'batch_size': 128}. Best is trial 10 with value: 0.47923052284165424.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.479231
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3822134547243423, 'ffn_dropout': 0.10076460097758799, 'lr': 0.0006135331541667003, 'weight_decay': 0.09879218512830837, 'batch_size': 128}
+  Best Value (CSI): 0.479231
+  Best Trial: 10
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3822134547243423, 'ffn_dropout': 0.10076460097758799, 'lr': 0.0006135331541667003, 'weight_decay': 0.09879218512830837, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:07:20,453] Trial 11 finished with value: 0.4727975682687428 and parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3636777556470543, 'ffn_dropout': 0.11969603042997873, 'lr': 0.0007594590074859042, 'weight_decay': 0.08838703826582563, 'batch_size': 128}. Best is trial 10 with value: 0.47923052284165424.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.472798
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3636777556470543, 'ffn_dropout': 0.11969603042997873, 'lr': 0.0007594590074859042, 'weight_decay': 0.08838703826582563, 'batch_size': 128}
+  Best Value (CSI): 0.479231
+  Best Trial: 10
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3822134547243423, 'ffn_dropout': 0.10076460097758799, 'lr': 0.0006135331541667003, 'weight_decay': 0.09879218512830837, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:08:29,833] Trial 12 finished with value: 0.48862241437211223 and parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.488622
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:08:41,008] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.018321
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3835958117783593, 'ffn_dropout': 0.10406955815052532, 'lr': 0.009649351588332316, 'weight_decay': 0.02292264602371786, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:10:14,550] Trial 14 finished with value: 0.4828620884196826 and parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.398593527317624, 'ffn_dropout': 0.11227519393087858, 'lr': 0.0009397711411085843, 'weight_decay': 0.013368406810182924, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.482862
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.398593527317624, 'ffn_dropout': 0.11227519393087858, 'lr': 0.0009397711411085843, 'weight_decay': 0.013368406810182924, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:12:02,986] Trial 15 finished with value: 0.47892304666149643 and parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.39934023699397664, 'ffn_dropout': 0.19901780511969835, 'lr': 0.0013371590916870385, 'weight_decay': 0.01029812650718879, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.478923
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.39934023699397664, 'ffn_dropout': 0.19901780511969835, 'lr': 0.0013371590916870385, 'weight_decay': 0.01029812650718879, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:12:18,405] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.224979
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3537029172304629, 'ffn_dropout': 0.13719813351905136, 'lr': 0.0022152440412436273, 'weight_decay': 0.008684437237961428, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:17:07,001] Trial 17 finished with value: 0.477121662577825 and parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3975972411997598, 'ffn_dropout': 0.1958197542142851, 'lr': 0.00021772411963727646, 'weight_decay': 0.035822356811918865, 'batch_size': 32}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.477122
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3975972411997598, 'ffn_dropout': 0.1958197542142851, 'lr': 0.00021772411963727646, 'weight_decay': 0.035822356811918865, 'batch_size': 32}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:19:17,984] Trial 18 finished with value: 0.4731414749109968 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.29655734971314573, 'ffn_dropout': 0.12834519629902674, 'lr': 0.0008224792902485263, 'weight_decay': 0.010692431388100394, 'batch_size': 64}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.473141
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.29655734971314573, 'ffn_dropout': 0.12834519629902674, 'lr': 0.0008224792902485263, 'weight_decay': 0.010692431388100394, 'batch_size': 64}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:21:14,455] Trial 19 finished with value: 0.4691148088213118 and parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3544147730892721, 'ffn_dropout': 0.1057413321888555, 'lr': 0.0004751644231175245, 'weight_decay': 0.04395853372433978, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.469115
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3544147730892721, 'ffn_dropout': 0.1057413321888555, 'lr': 0.0004751644231175245, 'weight_decay': 0.04395853372433978, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:23:55,030] Trial 20 finished with value: 0.4543474376518259 and parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.29467335552206775, 'ffn_dropout': 0.2538825352655031, 'lr': 0.00013896032431370185, 'weight_decay': 0.004693117908748101, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.454347
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.29467335552206775, 'ffn_dropout': 0.2538825352655031, 'lr': 0.00013896032431370185, 'weight_decay': 0.004693117908748101, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:25:14,193] Trial 21 finished with value: 0.45288146868894624 and parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.37324935720316477, 'ffn_dropout': 0.10258062620671786, 'lr': 0.0006034434277637087, 'weight_decay': 0.09864974255898239, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.452881
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.37324935720316477, 'ffn_dropout': 0.10258062620671786, 'lr': 0.0006034434277637087, 'weight_decay': 0.09864974255898239, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:26:55,538] Trial 22 finished with value: 0.47877914246023673 and parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39862093110642216, 'ffn_dropout': 0.13948398108079021, 'lr': 0.0014086356020661405, 'weight_decay': 0.04466897832152646, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.478779
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39862093110642216, 'ffn_dropout': 0.13948398108079021, 'lr': 0.0014086356020661405, 'weight_decay': 0.04466897832152646, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:28:45,940] Trial 23 finished with value: 0.47052982357083595 and parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.36003248851868197, 'ffn_dropout': 0.1236856672429474, 'lr': 0.00048330418585209336, 'weight_decay': 0.022288217122697844, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.470530
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.36003248851868197, 'ffn_dropout': 0.1236856672429474, 'lr': 0.00048330418585209336, 'weight_decay': 0.022288217122697844, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:29:58,661] Trial 24 finished with value: 0.46532419413769066 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3744309598855003, 'ffn_dropout': 0.16154502867835663, 'lr': 0.0009194327399256198, 'weight_decay': 0.06473618930229175, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.465324
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3744309598855003, 'ffn_dropout': 0.16154502867835663, 'lr': 0.0009194327399256198, 'weight_decay': 0.06473618930229175, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:31:37,667] Trial 25 finished with value: 0.46748993623212404 and parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3419144780919314, 'ffn_dropout': 0.11054517310900602, 'lr': 0.0003703085958582872, 'weight_decay': 0.05680123566625818, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.467490
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3419144780919314, 'ffn_dropout': 0.11054517310900602, 'lr': 0.0003703085958582872, 'weight_decay': 0.05680123566625818, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:31:45,543] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.324832
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.38313565359053947, 'ffn_dropout': 0.1489847708228845, 'lr': 0.0001834971490941133, 'weight_decay': 0.0281407878419934, 'batch_size': 256}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:32:52,920] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.375367
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.35015814008993285, 'ffn_dropout': 0.10045397880157811, 'lr': 0.000605384401215078, 'weight_decay': 0.016494441691168846, 'batch_size': 32}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:34:58,160] Trial 28 finished with value: 0.47448310453993897 and parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.31137637896890275, 'ffn_dropout': 0.12831232328394068, 'lr': 0.001190805560051262, 'weight_decay': 0.06062942301563714, 'batch_size': 64}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.474483
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.31137637896890275, 'ffn_dropout': 0.12831232328394068, 'lr': 0.001190805560051262, 'weight_decay': 0.06062942301563714, 'batch_size': 64}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:36:01,474] Trial 29 finished with value: 0.44950940247059146 and parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.3435125531676066, 'ffn_dropout': 0.17831475656208848, 'lr': 0.0003266200986608945, 'weight_decay': 0.029627013142545705, 'batch_size': 256}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.449509
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.3435125531676066, 'ffn_dropout': 0.17831475656208848, 'lr': 0.0003266200986608945, 'weight_decay': 0.029627013142545705, 'batch_size': 256}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:37:30,027] Trial 30 finished with value: 0.408310760440532 and parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.377088238754656, 'ffn_dropout': 0.15690366014269008, 'lr': 0.00011370341164801367, 'weight_decay': 0.09532204491176723, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.408311
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.377088238754656, 'ffn_dropout': 0.15690366014269008, 'lr': 0.00011370341164801367, 'weight_decay': 0.09532204491176723, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:39:14,237] Trial 31 finished with value: 0.4767446535696614 and parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.39626503551762093, 'ffn_dropout': 0.20131956009948843, 'lr': 0.001448901293109124, 'weight_decay': 0.011549947898817236, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.476745
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.39626503551762093, 'ffn_dropout': 0.20131956009948843, 'lr': 0.001448901293109124, 'weight_decay': 0.011549947898817236, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:41:13,761] Trial 32 finished with value: 0.45805689434716906 and parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.39207271092663626, 'ffn_dropout': 0.12112848462976264, 'lr': 0.001074341677073069, 'weight_decay': 0.008137105788653596, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.458057
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.39207271092663626, 'ffn_dropout': 0.12112848462976264, 'lr': 0.001074341677073069, 'weight_decay': 0.008137105788653596, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:41:31,842] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.004668
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.36670797643769487, 'ffn_dropout': 0.1849571432882215, 'lr': 0.002231993714314073, 'weight_decay': 0.005463704256909418, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:43:45,246] Trial 34 finished with value: 0.4798258194478366 and parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39965058525369457, 'ffn_dropout': 0.14739586305927058, 'lr': 0.0006205749036542376, 'weight_decay': 0.014224766444528116, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.479826
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39965058525369457, 'ffn_dropout': 0.14739586305927058, 'lr': 0.0006205749036542376, 'weight_decay': 0.014224766444528116, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:45:42,928] Trial 35 finished with value: 0.4562823082054032 and parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3363725402175345, 'ffn_dropout': 0.14098570631901305, 'lr': 0.0004625682501499456, 'weight_decay': 0.015560850747105432, 'batch_size': 256}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.456282
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3363725402175345, 'ffn_dropout': 0.14098570631901305, 'lr': 0.0004625682501499456, 'weight_decay': 0.015560850747105432, 'batch_size': 256}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:50:13,747] Trial 36 finished with value: 0.477092949628616 and parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.37653812274167003, 'ffn_dropout': 0.1691727284873077, 'lr': 0.0006172859330167775, 'weight_decay': 0.036866097074344524, 'batch_size': 64}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.477093
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.37653812274167003, 'ffn_dropout': 0.1691727284873077, 'lr': 0.0006172859330167775, 'weight_decay': 0.036866097074344524, 'batch_size': 64}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:52:16,158] Trial 37 finished with value: 0.4752609649920519 and parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3661654289685232, 'ffn_dropout': 0.119511925548937, 'lr': 0.0008233183516473982, 'weight_decay': 0.05912132060829927, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.475261
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3661654289685232, 'ffn_dropout': 0.119511925548937, 'lr': 0.0008233183516473982, 'weight_decay': 0.05912132060829927, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:00:12,051] Trial 38 finished with value: 0.47065244225262176 and parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.33143453523864835, 'ffn_dropout': 0.15175014374290963, 'lr': 0.0002590205863273558, 'weight_decay': 0.030162094562368017, 'batch_size': 32}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.470652
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.33143453523864835, 'ffn_dropout': 0.15175014374290963, 'lr': 0.0002590205863273558, 'weight_decay': 0.030162094562368017, 'batch_size': 32}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:00:34,146] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.095329
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3807640592593791, 'ffn_dropout': 0.11931221379075743, 'lr': 0.00329631959465686, 'weight_decay': 0.02165367800506811, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:02:26,310] Trial 40 finished with value: 0.4629778012593533 and parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.31630388259078185, 'ffn_dropout': 0.10100627277463958, 'lr': 0.00036460331240556785, 'weight_decay': 0.015084661260940655, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.462978
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.31630388259078185, 'ffn_dropout': 0.10100627277463958, 'lr': 0.00036460331240556785, 'weight_decay': 0.015084661260940655, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:03:52,815] Trial 41 finished with value: 0.4800643751467312 and parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.39540012038536, 'ffn_dropout': 0.1355910718164778, 'lr': 0.0012961498074751127, 'weight_decay': 0.006649890579718473, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.480064
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.39540012038536, 'ffn_dropout': 0.1355910718164778, 'lr': 0.0012961498074751127, 'weight_decay': 0.006649890579718473, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:04:14,890] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.402906
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.38675520932243945, 'ffn_dropout': 0.13212876365160164, 'lr': 0.0016047312028104235, 'weight_decay': 0.0025627660164038615, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:05:13,383] Trial 43 finished with value: 0.4746127292865954 and parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.362746278945248, 'ffn_dropout': 0.16624037774191047, 'lr': 0.0009357456572516605, 'weight_decay': 0.006241825908190311, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.474613
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.362746278945248, 'ffn_dropout': 0.16624037774191047, 'lr': 0.0009357456572516605, 'weight_decay': 0.006241825908190311, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:05:54,155] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.400888
+  Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.386872159728992, 'ffn_dropout': 0.14682206739850118, 'lr': 0.000658820341563418, 'weight_decay': 0.0036239532601317903, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:07:17,133] Trial 45 finished with value: 0.4614794755387323 and parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3529831490780354, 'ffn_dropout': 0.11413511882455141, 'lr': 0.0010880661052147054, 'weight_decay': 0.006529020169872523, 'batch_size': 128}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.461479
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3529831490780354, 'ffn_dropout': 0.11413511882455141, 'lr': 0.0010880661052147054, 'weight_decay': 0.006529020169872523, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:07:35,383] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.056218
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3979205222472862, 'ffn_dropout': 0.13534521470769326, 'lr': 0.0030409579164347657, 'weight_decay': 0.07123373111029556, 'batch_size': 64}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:07:57,602] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.135351
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.2433443547474716, 'ffn_dropout': 0.11459657812107736, 'lr': 0.0018325829414567692, 'weight_decay': 0.04583166411874843, 'batch_size': 128}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:13:14,777] Trial 48 finished with value: 0.4863966891820796 and parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.37101259980750617, 'ffn_dropout': 0.12868509252821492, 'lr': 0.0008107660526219226, 'weight_decay': 0.013713347427570575, 'batch_size': 32}. Best is trial 12 with value: 0.48862241437211223.
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.486397
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.37101259980750617, 'ffn_dropout': 0.12868509252821492, 'lr': 0.0008107660526219226, 'weight_decay': 0.013713347427570575, 'batch_size': 32}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:14:01,675] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.406944
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.34037543936774317, 'ffn_dropout': 0.15872154635147503, 'lr': 0.0011677103841223374, 'weight_decay': 0.012530804373857996, 'batch_size': 32}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:14:43,657] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.026543
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3693623679219078, 'ffn_dropout': 0.13171183121490296, 'lr': 0.0018634936864645399, 'weight_decay': 0.019261736253364643, 'batch_size': 32}
+  Best Value (CSI): 0.488622
+  Best Trial: 12
+  Best Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3940644252777227, 'ffn_dropout': 0.10144489779979293, 'lr': 0.000927760920937013, 'weight_decay': 0.08621819676348708, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:18:54,909] Trial 51 finished with value: 0.4960458104166546 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.496046
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:23:53,772] Trial 52 finished with value: 0.46589141195108974 and parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38796452002213944, 'ffn_dropout': 0.14667025480330814, 'lr': 0.0007849044294507805, 'weight_decay': 0.00817077613635832, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.465891
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38796452002213944, 'ffn_dropout': 0.14667025480330814, 'lr': 0.0007849044294507805, 'weight_decay': 0.00817077613635832, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:31:24,997] Trial 53 finished with value: 0.4810142381388513 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3737917793139734, 'ffn_dropout': 0.11392044025990927, 'lr': 0.0009761093807725675, 'weight_decay': 0.009214733134475187, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.481014
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3737917793139734, 'ffn_dropout': 0.11392044025990927, 'lr': 0.0009761093807725675, 'weight_decay': 0.009214733134475187, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:36:15,431] Trial 54 finished with value: 0.4918401751600367 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3582221003094483, 'ffn_dropout': 0.11204167603766357, 'lr': 0.000978955211134021, 'weight_decay': 0.0097903811788153, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.491840
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3582221003094483, 'ffn_dropout': 0.11204167603766357, 'lr': 0.000978955211134021, 'weight_decay': 0.0097903811788153, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:41:34,406] Trial 55 finished with value: 0.4914419586733148 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3574710981966181, 'ffn_dropout': 0.11515683866629714, 'lr': 0.0008795883495806496, 'weight_decay': 0.008962916854640188, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.491442
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3574710981966181, 'ffn_dropout': 0.11515683866629714, 'lr': 0.0008795883495806496, 'weight_decay': 0.008962916854640188, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:44:32,298] Trial 56 finished with value: 0.47513788872771934 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.35410940300587246, 'ffn_dropout': 0.124855963014258, 'lr': 0.0004672654251111365, 'weight_decay': 0.004500171607572147, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.475138
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.35410940300587246, 'ffn_dropout': 0.124855963014258, 'lr': 0.0004672654251111365, 'weight_decay': 0.004500171607572147, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:48:23,023] Trial 57 finished with value: 0.4847766356143078 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.327434937489462, 'ffn_dropout': 0.1103426934906433, 'lr': 0.0007771033367101665, 'weight_decay': 0.012154893746761264, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.484777
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.327434937489462, 'ffn_dropout': 0.1103426934906433, 'lr': 0.0007771033367101665, 'weight_decay': 0.012154893746761264, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:48:58,725] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.310606
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3296376390455716, 'ffn_dropout': 0.109908032088843, 'lr': 0.0008434689224837926, 'weight_decay': 0.011359710324373768, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 14:49:32,808] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.139330
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.349007959345058, 'ffn_dropout': 0.12652613223895007, 'lr': 0.001565042581478021, 'weight_decay': 0.017943368317390594, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:53:11,589] Trial 60 finished with value: 0.4752663406045627 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.360728155153809, 'ffn_dropout': 0.11070403167407866, 'lr': 0.0004836272468634505, 'weight_decay': 0.010128927956973718, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.475266
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.360728155153809, 'ffn_dropout': 0.11070403167407866, 'lr': 0.0004836272468634505, 'weight_decay': 0.010128927956973718, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 14:56:49,516] Trial 61 finished with value: 0.47569666929381427 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.36733691358981396, 'ffn_dropout': 0.10287756386749575, 'lr': 0.000689179169789766, 'weight_decay': 0.013609050014788092, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.475697
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.36733691358981396, 'ffn_dropout': 0.10287756386749575, 'lr': 0.000689179169789766, 'weight_decay': 0.013609050014788092, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+[I 2025-12-28 15:00:49,903] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.494024
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.38063962491110953, 'ffn_dropout': 0.12055166998740467, 'lr': 0.001008744812308412, 'weight_decay': 0.008760852552239136, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 15:01:27,107] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.367589
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3450312713423307, 'ffn_dropout': 0.10054861941694547, 'lr': 0.0012767838668696887, 'weight_decay': 0.023293834592426566, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:05:05,205] Trial 64 finished with value: 0.47999660057389587 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.32530813585499624, 'ffn_dropout': 0.13888277875893368, 'lr': 0.0007779768891092811, 'weight_decay': 0.012415489897094439, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.479997
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.32530813585499624, 'ffn_dropout': 0.13888277875893368, 'lr': 0.0007779768891092811, 'weight_decay': 0.012415489897094439, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:08:33,325] Trial 65 finished with value: 0.47769918666333644 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3610737211388382, 'ffn_dropout': 0.11070323565454437, 'lr': 0.0004987937809962737, 'weight_decay': 0.01929302562327261, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.477699
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3610737211388382, 'ffn_dropout': 0.11070323565454437, 'lr': 0.0004987937809962737, 'weight_decay': 0.01929302562327261, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 15:09:10,050] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.351391
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3883297304056102, 'ffn_dropout': 0.12894956215955364, 'lr': 0.0010607679437823626, 'weight_decay': 0.004309641117141332, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:12:22,279] Trial 67 finished with value: 0.480106374482691 and parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3769604130306578, 'ffn_dropout': 0.1539590487212119, 'lr': 0.0007205367629380758, 'weight_decay': 0.007668007575572236, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.480106
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3769604130306578, 'ffn_dropout': 0.1539590487212119, 'lr': 0.0007205367629380758, 'weight_decay': 0.007668007575572236, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 15:12:31,734] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.345228
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3567795049954773, 'ffn_dropout': 0.11977232552083573, 'lr': 0.00030390055259658857, 'weight_decay': 0.005489703755012369, 'batch_size': 256}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:16:32,994] Trial 69 finished with value: 0.46267872897529605 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.33744697121157147, 'ffn_dropout': 0.11045973584981589, 'lr': 0.0004040301179122586, 'weight_decay': 0.010251278930057731, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.462679
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.33744697121157147, 'ffn_dropout': 0.11045973584981589, 'lr': 0.0004040301179122586, 'weight_decay': 0.010251278930057731, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:20:16,767] Trial 70 finished with value: 0.4715439515523785 and parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.38324380708584715, 'ffn_dropout': 0.14104476415241649, 'lr': 0.0005684728837502738, 'weight_decay': 0.014209432417588819, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.471544
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.38324380708584715, 'ffn_dropout': 0.14104476415241649, 'lr': 0.0005684728837502738, 'weight_decay': 0.014209432417588819, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 15:20:52,265] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.370690
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.37170813240412115, 'ffn_dropout': 0.1143040672181226, 'lr': 0.0009362064476428578, 'weight_decay': 0.009653993393918292, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+[I 2025-12-28 15:24:07,639] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.502063
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.37235505973028693, 'ffn_dropout': 0.12742991014846708, 'lr': 0.0008857756015791197, 'weight_decay': 0.02545553001292108, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 15:24:51,224] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.027186
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3904887909060981, 'ffn_dropout': 0.10785977445913815, 'lr': 0.0012916915436174655, 'weight_decay': 0.007918283148256936, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:29:08,834] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.434229
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3764856938940627, 'ffn_dropout': 0.11757052816147007, 'lr': 0.0006924117433928905, 'weight_decay': 0.01643149853179107, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 15:29:45,521] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.362406
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3485598393765892, 'ffn_dropout': 0.10007510664548848, 'lr': 0.0010340307602541, 'weight_decay': 0.011574228942997795, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:32:08,707] Trial 76 finished with value: 0.4812105479665137 and parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3563441128686736, 'ffn_dropout': 0.12959915319151485, 'lr': 0.000560207656342641, 'weight_decay': 0.020323877437911343, 'batch_size': 64}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.481211
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3563441128686736, 'ffn_dropout': 0.12959915319151485, 'lr': 0.000560207656342641, 'weight_decay': 0.020323877437911343, 'batch_size': 64}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:34:30,260] Trial 77 finished with value: 0.4742636920564072 and parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3571794936713558, 'ffn_dropout': 0.13849309726138068, 'lr': 0.000528735691580323, 'weight_decay': 0.032708436709618836, 'batch_size': 64}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.474264
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3571794936713558, 'ffn_dropout': 0.13849309726138068, 'lr': 0.000528735691580323, 'weight_decay': 0.032708436709618836, 'batch_size': 64}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:37:08,500] Trial 78 finished with value: 0.46655083136932435 and parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.36304772056676266, 'ffn_dropout': 0.16234983084570767, 'lr': 0.0004146372598841977, 'weight_decay': 0.025912807570860816, 'batch_size': 64}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.466551
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.36304772056676266, 'ffn_dropout': 0.16234983084570767, 'lr': 0.0004146372598841977, 'weight_decay': 0.025912807570860816, 'batch_size': 64}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:39:40,213] Trial 79 finished with value: 0.4781811387060401 and parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.3933794042536188, 'ffn_dropout': 0.1315804814336779, 'lr': 0.0005360147970284623, 'weight_decay': 0.02041324408538275, 'batch_size': 64}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.478181
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.3933794042536188, 'ffn_dropout': 0.1315804814336779, 'lr': 0.0005360147970284623, 'weight_decay': 0.02041324408538275, 'batch_size': 64}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:42:16,189] Trial 80 finished with value: 0.4762260107765102 and parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3444553236728217, 'ffn_dropout': 0.14857277322123172, 'lr': 0.0006980823888271574, 'weight_decay': 0.016038409893148076, 'batch_size': 64}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.476226
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3444553236728217, 'ffn_dropout': 0.14857277322123172, 'lr': 0.0006980823888271574, 'weight_decay': 0.016038409893148076, 'batch_size': 64}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:43:19,718] Trial 81 finished with value: 0.4660245963783822 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3703518189908725, 'ffn_dropout': 0.12346411770726184, 'lr': 0.0008147905717846085, 'weight_decay': 0.009542979340541513, 'batch_size': 256}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.466025
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3703518189908725, 'ffn_dropout': 0.12346411770726184, 'lr': 0.0008147905717846085, 'weight_decay': 0.009542979340541513, 'batch_size': 256}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 15:43:54,835] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.195588
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3998967279272485, 'ffn_dropout': 0.10829283363692124, 'lr': 0.0014736043047723863, 'weight_decay': 0.012810186666718805, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 15:44:17,623] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.417630
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.38029297304843807, 'ffn_dropout': 0.11766019161101322, 'lr': 0.0005840189782815385, 'weight_decay': 0.007384794937248584, 'batch_size': 64}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 15:45:03,558] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.099222
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3844192924286456, 'ffn_dropout': 0.1308457441006873, 'lr': 0.0011364785362980806, 'weight_decay': 0.01845587731801211, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 15:45:58,380] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3673799032127775, 'ffn_dropout': 0.10678318700435284, 'lr': 0.0009824679272094257, 'weight_decay': 0.005787849510182729, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:50:01,747] Trial 86 finished with value: 0.4666107893598241 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.33533054976318616, 'ffn_dropout': 0.14470047159660998, 'lr': 0.000874708505268233, 'weight_decay': 0.03802186911462882, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.466611
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.33533054976318616, 'ffn_dropout': 0.14470047159660998, 'lr': 0.000874708505268233, 'weight_decay': 0.03802186911462882, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:52:23,318] Trial 87 finished with value: 0.46701544069958717 and parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3531749909563654, 'ffn_dropout': 0.11674410441233475, 'lr': 0.0012296745697082659, 'weight_decay': 0.007143927116599192, 'batch_size': 64}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.467015
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3531749909563654, 'ffn_dropout': 0.11674410441233475, 'lr': 0.0012296745697082659, 'weight_decay': 0.007143927116599192, 'batch_size': 64}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:56:27,421] Trial 88 finished with value: 0.4702611462644719 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3916757964429081, 'ffn_dropout': 0.1244705324160128, 'lr': 0.00041755130030716683, 'weight_decay': 0.009033455120720736, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.470261
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3916757964429081, 'ffn_dropout': 0.1244705324160128, 'lr': 0.00041755130030716683, 'weight_decay': 0.009033455120720736, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 15:58:00,928] Trial 89 finished with value: 0.46244481968980056 and parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.37795423555733976, 'ffn_dropout': 0.13490523112645492, 'lr': 0.000648786750939277, 'weight_decay': 0.014688902951160907, 'batch_size': 256}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.462445
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.37795423555733976, 'ffn_dropout': 0.13490523112645492, 'lr': 0.000648786750939277, 'weight_decay': 0.014688902951160907, 'batch_size': 256}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 15:58:34,883] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.108859
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3669920817279055, 'ffn_dropout': 0.15326164431750866, 'lr': 0.001777243343172068, 'weight_decay': 0.08266549285798874, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 16:03:05,722] Trial 91 finished with value: 0.4835326119899943 and parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3719642337170449, 'ffn_dropout': 0.1521286504855443, 'lr': 0.0007356980480054816, 'weight_decay': 0.007651810738369785, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.483533
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3719642337170449, 'ffn_dropout': 0.1521286504855443, 'lr': 0.0007356980480054816, 'weight_decay': 0.007651810738369785, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 16:07:35,561] Trial 92 finished with value: 0.48492679469640865 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3890193563287839, 'ffn_dropout': 0.14238429312598122, 'lr': 0.0007453628490743121, 'weight_decay': 0.01165308784942211, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.484927
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3890193563287839, 'ffn_dropout': 0.14238429312598122, 'lr': 0.0007453628490743121, 'weight_decay': 0.01165308784942211, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 16:11:08,508] Trial 93 finished with value: 0.4821037618562549 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3897073380382652, 'ffn_dropout': 0.13865237434908162, 'lr': 0.0008139640169396708, 'weight_decay': 0.011056588823901217, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.482104
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3897073380382652, 'ffn_dropout': 0.13865237434908162, 'lr': 0.0008139640169396708, 'weight_decay': 0.011056588823901217, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 16:11:43,933] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.361673
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3918534255353679, 'ffn_dropout': 0.1711094411336314, 'lr': 0.0008060925295025305, 'weight_decay': 0.011502400215589714, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 16:12:30,932] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.417740
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3867480208321956, 'ffn_dropout': 0.14396019899578133, 'lr': 0.0006778582835764893, 'weight_decay': 0.006286591872999541, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 16:13:03,685] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.134660
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.39944971009444413, 'ffn_dropout': 0.156470790720399, 'lr': 0.001370922450391433, 'weight_decay': 0.00511975091309368, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 16:13:53,224] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.402250
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.38216065392000453, 'ffn_dropout': 0.13784298384231627, 'lr': 0.0011681415595628931, 'weight_decay': 0.013086207668760732, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 16:17:46,159] Trial 98 finished with value: 0.482250152053455 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39253876688219974, 'ffn_dropout': 0.16459959583645678, 'lr': 0.0008032641460999773, 'weight_decay': 0.010575196237729921, 'batch_size': 32}. Best is trial 51 with value: 0.4960458104166546.
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.482250
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39253876688219974, 'ffn_dropout': 0.16459959583645678, 'lr': 0.0008032641460999773, 'weight_decay': 0.010575196237729921, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 16:18:32,101] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.411504
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.361561104405005, 'ffn_dropout': 0.12301382321024466, 'lr': 0.0007250806306714704, 'weight_decay': 0.006693394628608259, 'batch_size': 32}
+  Best Value (CSI): 0.496046
+  Best Trial: 51
+  Best Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4960
+Best Hyperparameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.2937
+  - 최종 CSI: 0.4115
+  - 최고 CSI: 0.5021
+  - 최저 CSI: 0.0000
+  - 평균 CSI: 0.3875
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/ft_transformer_ctgan10000_busan_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 30, Best CSI: 0.4803
+  Fold 1 학습 완료 (검증 CSI: 0.4803)
+Fold 2 학습 중...
+  Early stopping at epoch 29, Best CSI: 0.4923
+  Fold 2 학습 완료 (검증 CSI: 0.4923)
+Fold 3 학습 중...
+  Early stopping at epoch 31, Best CSI: 0.4888
+  Fold 3 학습 완료 (검증 CSI: 0.4888)
+
+모든 모델 저장 완료: ../save_model/ft_transformer_optima/ft_transformer_ctgan10000_busan.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/ft_transformer_optima/scaler/ft_transformer_ctgan10000_busan_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/ft_transformer_optima/ft_transformer_ctgan10000_busan.pkl
+
+✓ 완료: ft_transformer_ctgan10000/ft_transformer_ctgan10000_busan.py (소요 시간: 13596초)
+
+----------------------------------------
+실행 중: ft_transformer_ctgan10000/ft_transformer_ctgan10000_daegu.py
+시작 시간: 2025-12-28 16:22:58
+----------------------------------------
+[I 2025-12-28 16:22:59,553] A new study created in memory with name: no-name-ba0f68be-ffb5-47eb-8851-48a161add0c7
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:27:37,724] Trial 0 finished with value: 0.2778810650700827 and parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3701467009841284, 'ffn_dropout': 0.34426462320006423, 'lr': 1.3109360467028999e-05, 'weight_decay': 0.02423566308912537, 'batch_size': 64}. Best is trial 0 with value: 0.2778810650700827.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.277881
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3701467009841284, 'ffn_dropout': 0.34426462320006423, 'lr': 1.3109360467028999e-05, 'weight_decay': 0.02423566308912537, 'batch_size': 64}
+  Best Value (CSI): 0.277881
+  Best Trial: 0
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3701467009841284, 'ffn_dropout': 0.34426462320006423, 'lr': 1.3109360467028999e-05, 'weight_decay': 0.02423566308912537, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:33:19,450] Trial 1 finished with value: 0.38877054282869344 and parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3923824967760874, 'ffn_dropout': 0.366372620834721, 'lr': 7.008528604913917e-05, 'weight_decay': 0.0018675228609619104, 'batch_size': 32}. Best is trial 1 with value: 0.38877054282869344.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.388771
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3923824967760874, 'ffn_dropout': 0.366372620834721, 'lr': 7.008528604913917e-05, 'weight_decay': 0.0018675228609619104, 'batch_size': 32}
+  Best Value (CSI): 0.388771
+  Best Trial: 1
+  Best Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3923824967760874, 'ffn_dropout': 0.366372620834721, 'lr': 7.008528604913917e-05, 'weight_decay': 0.0018675228609619104, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:34:09,727] Trial 2 finished with value: 0.20377099349222924 and parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.26753797601240537, 'ffn_dropout': 0.3271404027274428, 'lr': 0.0036042968905664234, 'weight_decay': 0.010232940484755502, 'batch_size': 128}. Best is trial 1 with value: 0.38877054282869344.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.203771
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.26753797601240537, 'ffn_dropout': 0.3271404027274428, 'lr': 0.0036042968905664234, 'weight_decay': 0.010232940484755502, 'batch_size': 128}
+  Best Value (CSI): 0.388771
+  Best Trial: 1
+  Best Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3923824967760874, 'ffn_dropout': 0.366372620834721, 'lr': 7.008528604913917e-05, 'weight_decay': 0.0018675228609619104, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:42:13,846] Trial 3 finished with value: 0.39652656875481224 and parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.32948318116036757, 'ffn_dropout': 0.16821397984058944, 'lr': 3.332753256113301e-05, 'weight_decay': 0.0001802089324120185, 'batch_size': 32}. Best is trial 3 with value: 0.39652656875481224.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.396527
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.32948318116036757, 'ffn_dropout': 0.16821397984058944, 'lr': 3.332753256113301e-05, 'weight_decay': 0.0001802089324120185, 'batch_size': 32}
+  Best Value (CSI): 0.396527
+  Best Trial: 3
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.32948318116036757, 'ffn_dropout': 0.16821397984058944, 'lr': 3.332753256113301e-05, 'weight_decay': 0.0001802089324120185, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:47:38,475] Trial 4 finished with value: 0.4410366955005478 and parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.441037
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:49:23,863] Trial 5 finished with value: 0.4167220709912521 and parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.36944100174298133, 'ffn_dropout': 0.2718846156695268, 'lr': 0.0016406077621457548, 'weight_decay': 0.003318831477091509, 'batch_size': 128}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.416722
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.36944100174298133, 'ffn_dropout': 0.2718846156695268, 'lr': 0.0016406077621457548, 'weight_decay': 0.003318831477091509, 'batch_size': 128}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 16:50:34,348] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.16185476576351565, 'ffn_dropout': 0.1678240171917521, 'lr': 0.001615971637617205, 'weight_decay': 0.041108070508839516, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 16:51:16,423] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.210345
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.3252415669042098, 'ffn_dropout': 0.16169577174503838, 'lr': 2.5283965640193944e-05, 'weight_decay': 0.0018320496723356848, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:55:02,901] Trial 8 finished with value: 0.42372346160540614 and parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.32982919884014783, 'ffn_dropout': 0.11139095180519151, 'lr': 0.0006007911455383494, 'weight_decay': 0.0003596767220767154, 'batch_size': 64}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.423723
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.32982919884014783, 'ffn_dropout': 0.11139095180519151, 'lr': 0.0006007911455383494, 'weight_decay': 0.0003596767220767154, 'batch_size': 64}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 16:55:29,140] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.057243
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.38866032747452317, 'ffn_dropout': 0.2045138309129935, 'lr': 0.0009033782888554393, 'weight_decay': 0.0037844093813659916, 'batch_size': 64}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 16:55:40,025] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.098012
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.20093928279986067, 'ffn_dropout': 0.25603738269056797, 'lr': 0.0001425327108931291, 'weight_decay': 0.06477598259866986, 'batch_size': 256}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:59:34,031] Trial 11 finished with value: 0.42113167696589243 and parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2681726705516485, 'ffn_dropout': 0.12422923954998787, 'lr': 0.00032414744740194394, 'weight_decay': 0.0003062275289856915, 'batch_size': 64}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.421132
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2681726705516485, 'ffn_dropout': 0.12422923954998787, 'lr': 0.00032414744740194394, 'weight_decay': 0.0003062275289856915, 'batch_size': 64}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:00:06,917] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.360887
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.29914156961221017, 'ffn_dropout': 0.10095807426228082, 'lr': 0.0002890374276537757, 'weight_decay': 0.0006024297700590324, 'batch_size': 256}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:03:50,261] Trial 13 finished with value: 0.4364802844187287 and parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.10812121199780253, 'ffn_dropout': 0.10099119275616791, 'lr': 0.00042037574098368377, 'weight_decay': 0.0006794997511097631, 'batch_size': 64}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.436480
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.10812121199780253, 'ffn_dropout': 0.10099119275616791, 'lr': 0.00042037574098368377, 'weight_decay': 0.0006794997511097631, 'batch_size': 64}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:04:46,506] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10091839574773706, 'ffn_dropout': 0.20998567524708406, 'lr': 0.009297216714080368, 'weight_decay': 0.00010393929184419674, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:05:27,716] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.339827
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.21151647860428308, 'ffn_dropout': 0.13881680677224553, 'lr': 0.00011739530328211201, 'weight_decay': 0.0007217565325261509, 'batch_size': 64}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:05:46,154] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.368653
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.10984560242647123, 'ffn_dropout': 0.10018030775961752, 'lr': 0.00028070714229719984, 'weight_decay': 0.0008867163544360894, 'batch_size': 256}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:05:57,400] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.078691
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.23858937227457216, 'ffn_dropout': 0.20720987930572723, 'lr': 8.71878147562777e-05, 'weight_decay': 0.010531684125290508, 'batch_size': 128}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:06:38,122] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.306620
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.15370925792654455, 'ffn_dropout': 0.14238132059748346, 'lr': 5.4306182646121885e-05, 'weight_decay': 0.001075734073261604, 'batch_size': 64}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:12:58,600] Trial 19 finished with value: 0.43692922420067787 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.21503912723424526, 'ffn_dropout': 0.14097372155620447, 'lr': 0.00018317359898890786, 'weight_decay': 0.0059170113583524656, 'batch_size': 32}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.436929
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.21503912723424526, 'ffn_dropout': 0.14097372155620447, 'lr': 0.00018317359898890786, 'weight_decay': 0.0059170113583524656, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:17:55,572] Trial 20 finished with value: 0.43432573221224785 and parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2385513012130085, 'ffn_dropout': 0.18269155408995588, 'lr': 0.00015956194228066413, 'weight_decay': 0.005924410902479277, 'batch_size': 32}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.434326
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2385513012130085, 'ffn_dropout': 0.18269155408995588, 'lr': 0.00015956194228066413, 'weight_decay': 0.005924410902479277, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:23:59,437] Trial 21 finished with value: 0.42600751953396715 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1402722670929168, 'ffn_dropout': 0.1475850064017891, 'lr': 0.0001863195703407688, 'weight_decay': 0.00277082718665622, 'batch_size': 32}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.426008
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1402722670929168, 'ffn_dropout': 0.1475850064017891, 'lr': 0.0001863195703407688, 'weight_decay': 0.00277082718665622, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:32:16,629] Trial 22 finished with value: 0.41353256500551744 and parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.19720492218749186, 'ffn_dropout': 0.12797651400816107, 'lr': 0.000495178758657061, 'weight_decay': 0.0015586334783531895, 'batch_size': 32}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.413533
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.19720492218749186, 'ffn_dropout': 0.12797651400816107, 'lr': 0.000495178758657061, 'weight_decay': 0.0015586334783531895, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:38:00,494] Trial 23 finished with value: 0.4173922855701848 and parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.17930384796010074, 'ffn_dropout': 0.12697276163810145, 'lr': 0.0002077983341525288, 'weight_decay': 0.005688923842675696, 'batch_size': 32}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.417392
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.17930384796010074, 'ffn_dropout': 0.12697276163810145, 'lr': 0.0002077983341525288, 'weight_decay': 0.005688923842675696, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:44:41,677] Trial 24 finished with value: 0.4270196526799979 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1305489220308568, 'ffn_dropout': 0.14707842034889101, 'lr': 0.00010074242146200202, 'weight_decay': 0.0012618637790405114, 'batch_size': 32}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.427020
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1305489220308568, 'ffn_dropout': 0.14707842034889101, 'lr': 0.00010074242146200202, 'weight_decay': 0.0012618637790405114, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:47:14,033] Trial 25 finished with value: 0.43164657438152854 and parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.17379594662419506, 'ffn_dropout': 0.18478909173009345, 'lr': 0.00044544989303464243, 'weight_decay': 0.0005054991023566345, 'batch_size': 64}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.431647
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.17379594662419506, 'ffn_dropout': 0.18478909173009345, 'lr': 0.00044544989303464243, 'weight_decay': 0.0005054991023566345, 'batch_size': 64}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:47:29,002] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.187016
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.22088417480497952, 'ffn_dropout': 0.11625574369543377, 'lr': 5.592297297582552e-05, 'weight_decay': 0.0019541343950212854, 'batch_size': 256}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:47:51,214] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.313297
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.13256132285394256, 'ffn_dropout': 0.22810700035390155, 'lr': 0.00014989052089656936, 'weight_decay': 0.0008678055373563038, 'batch_size': 128}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:54:41,185] Trial 28 finished with value: 0.4252268679431384 and parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.19001315087120063, 'ffn_dropout': 0.15614607708422013, 'lr': 0.0002654527590801228, 'weight_decay': 0.002573465027448971, 'batch_size': 32}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.425227
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.19001315087120063, 'ffn_dropout': 0.15614607708422013, 'lr': 0.0002654527590801228, 'weight_decay': 0.002573465027448971, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:55:09,779] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.227102
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.17348723074832184, 'ffn_dropout': 0.10018473858642078, 'lr': 1.6090750235341957e-05, 'weight_decay': 0.017946357754710474, 'batch_size': 64}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 17:55:35,028] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.291045
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2212145267074257, 'ffn_dropout': 0.13526300446824732, 'lr': 9.433966517880381e-05, 'weight_decay': 0.0011518173872887916, 'batch_size': 64}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 17:59:28,805] Trial 31 finished with value: 0.4174178706993557 and parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.24048536547188604, 'ffn_dropout': 0.17878178804673248, 'lr': 0.00015235865551818068, 'weight_decay': 0.004796727863596373, 'batch_size': 32}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.417418
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.24048536547188604, 'ffn_dropout': 0.17878178804673248, 'lr': 0.00015235865551818068, 'weight_decay': 0.004796727863596373, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:03:12,257] Trial 32 finished with value: 0.41701615999466024 and parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.25332940256998526, 'ffn_dropout': 0.18591533761726112, 'lr': 0.00019048421655226425, 'weight_decay': 0.007636466609754598, 'batch_size': 32}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.417016
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.25332940256998526, 'ffn_dropout': 0.18591533761726112, 'lr': 0.00019048421655226425, 'weight_decay': 0.007636466609754598, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 18:03:59,859] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.301184
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.277357626435737, 'ffn_dropout': 0.15530634965097306, 'lr': 4.526814702422805e-05, 'weight_decay': 0.005190336304289837, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 18:04:33,389] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.262794
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2226300009687655, 'ffn_dropout': 0.11997795303382583, 'lr': 0.00011267138880272823, 'weight_decay': 0.016262237150809038, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:10:54,070] Trial 35 finished with value: 0.43517580034057524 and parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2471485456410509, 'ffn_dropout': 0.17617170894663353, 'lr': 7.751791898412974e-05, 'weight_decay': 0.002536006307165387, 'batch_size': 32}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.435176
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2471485456410509, 'ffn_dropout': 0.17617170894663353, 'lr': 7.751791898412974e-05, 'weight_decay': 0.002536006307165387, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 18:11:12,918] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.291519
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.2908913120813284, 'ffn_dropout': 0.16515512627448653, 'lr': 8.210408773614566e-05, 'weight_decay': 0.0027277184725345055, 'batch_size': 128}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:20:37,046] Trial 37 finished with value: 0.4240695794218001 and parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.26092772989371105, 'ffn_dropout': 0.136825659477604, 'lr': 6.500846396521682e-05, 'weight_decay': 0.001971050216091294, 'batch_size': 32}. Best is trial 4 with value: 0.4410366955005478.
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.424070
+  Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.26092772989371105, 'ffn_dropout': 0.136825659477604, 'lr': 6.500846396521682e-05, 'weight_decay': 0.001971050216091294, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 18:21:09,876] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.152595
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.2441950476193616, 'ffn_dropout': 0.2868835205646733, 'lr': 3.1496752342310546e-05, 'weight_decay': 0.003389399270824248, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 18:21:52,380] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.245283
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1172512730086474, 'ffn_dropout': 0.3905775495921092, 'lr': 4.177661882470523e-05, 'weight_decay': 0.0014672429013298004, 'batch_size': 32}
+  Best Value (CSI): 0.441037
+  Best Trial: 4
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28149479497858065, 'ffn_dropout': 0.17739961891493267, 'lr': 0.00011721284975607486, 'weight_decay': 0.002095918030208876, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:24:21,692] Trial 40 finished with value: 0.4414200029420164 and parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.441420
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:26:28,837] Trial 41 finished with value: 0.40821319028067854 and parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11380741759786872, 'ffn_dropout': 0.17045346842793277, 'lr': 0.0008278682117330897, 'weight_decay': 0.009287643392166362, 'batch_size': 128}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.408213
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11380741759786872, 'ffn_dropout': 0.17045346842793277, 'lr': 0.0008278682117330897, 'weight_decay': 0.009287643392166362, 'batch_size': 128}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:29:02,436] Trial 42 finished with value: 0.43116866945239735 and parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.14155923997016856, 'ffn_dropout': 0.15612469395539055, 'lr': 0.0007106629281613418, 'weight_decay': 0.004127294067055026, 'batch_size': 128}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.431169
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.14155923997016856, 'ffn_dropout': 0.15612469395539055, 'lr': 0.0007106629281613418, 'weight_decay': 0.004127294067055026, 'batch_size': 128}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:31:32,482] Trial 43 finished with value: 0.43628994954246325 and parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.158485265303198, 'ffn_dropout': 0.1948554239438959, 'lr': 0.0003886845603145932, 'weight_decay': 0.0020910166437049463, 'batch_size': 128}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.436290
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.158485265303198, 'ffn_dropout': 0.1948554239438959, 'lr': 0.0003886845603145932, 'weight_decay': 0.0020910166437049463, 'batch_size': 128}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:33:22,350] Trial 44 finished with value: 0.40674682199865614 and parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.15291946337901252, 'ffn_dropout': 0.19768801929691726, 'lr': 0.0011338342463576556, 'weight_decay': 0.00369996902521847, 'batch_size': 128}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.406747
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.15291946337901252, 'ffn_dropout': 0.19768801929691726, 'lr': 0.0011338342463576556, 'weight_decay': 0.00369996902521847, 'batch_size': 128}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:36:34,033] Trial 45 finished with value: 0.42766893605926964 and parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.15934208840399025, 'ffn_dropout': 0.2272607462537528, 'lr': 0.0004657110496285893, 'weight_decay': 0.00692192893003497, 'batch_size': 128}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.427669
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.15934208840399025, 'ffn_dropout': 0.2272607462537528, 'lr': 0.0004657110496285893, 'weight_decay': 0.00692192893003497, 'batch_size': 128}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:38:54,495] Trial 46 finished with value: 0.41398727836554156 and parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.12087274464840478, 'ffn_dropout': 0.12135616935495028, 'lr': 0.00034238500243035996, 'weight_decay': 0.002092155408082985, 'batch_size': 128}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.413987
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.12087274464840478, 'ffn_dropout': 0.12135616935495028, 'lr': 0.00034238500243035996, 'weight_decay': 0.002092155408082985, 'batch_size': 128}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 18:39:27,972] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.364629
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.101331451260982, 'ffn_dropout': 0.11262176105916095, 'lr': 0.0006420092180808236, 'weight_decay': 0.003945287571753858, 'batch_size': 128}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:40:50,421] Trial 48 finished with value: 0.3930885634383961 and parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.16844787111446488, 'ffn_dropout': 0.16220069590992453, 'lr': 0.0010667698021979508, 'weight_decay': 0.01107175087779283, 'batch_size': 128}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.393089
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.16844787111446488, 'ffn_dropout': 0.16220069590992453, 'lr': 0.0010667698021979508, 'weight_decay': 0.01107175087779283, 'batch_size': 128}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 18:41:54,798] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.366071
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.186889128498951, 'ffn_dropout': 0.19357609419693145, 'lr': 0.00022611814481767533, 'weight_decay': 0.0004627903298386736, 'batch_size': 64}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:44:25,772] Trial 50 finished with value: 0.43668175181800956 and parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.1518201551781169, 'ffn_dropout': 0.14685664561297185, 'lr': 0.0003506868170730323, 'weight_decay': 0.0009054143173436034, 'batch_size': 256}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.436682
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.1518201551781169, 'ffn_dropout': 0.14685664561297185, 'lr': 0.0003506868170730323, 'weight_decay': 0.0009054143173436034, 'batch_size': 256}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 18:44:45,548] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.356838
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.1484801143814788, 'ffn_dropout': 0.14382823767962546, 'lr': 0.00036287085350915956, 'weight_decay': 0.0008554656323528077, 'batch_size': 256}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:46:59,162] Trial 52 finished with value: 0.4379298071713558 and parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.1601666767646172, 'ffn_dropout': 0.17156274236570754, 'lr': 0.000391827225278508, 'weight_decay': 0.0015144711926276676, 'batch_size': 256}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.437930
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.1601666767646172, 'ffn_dropout': 0.17156274236570754, 'lr': 0.000391827225278508, 'weight_decay': 0.0015144711926276676, 'batch_size': 256}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 18:47:33,047] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.359596
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.12925519172699754, 'ffn_dropout': 0.1707227907164825, 'lr': 0.0005783478484232665, 'weight_decay': 0.0007458088538710941, 'batch_size': 256}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 18:47:49,206] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.352342
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.18312804282897227, 'ffn_dropout': 0.13174521065987496, 'lr': 0.00027775969113076024, 'weight_decay': 0.0014546947160609855, 'batch_size': 256}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 18:48:12,654] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.338115
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.16560956294095042, 'ffn_dropout': 0.10892657153958425, 'lr': 0.000535584887452813, 'weight_decay': 0.0010309816539916476, 'batch_size': 256}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:50:30,159] Trial 56 finished with value: 0.4215606952038758 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1415709674621142, 'ffn_dropout': 0.14865399402704152, 'lr': 0.00023744338176008272, 'weight_decay': 0.0006606749020254069, 'batch_size': 256}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.421561
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1415709674621142, 'ffn_dropout': 0.14865399402704152, 'lr': 0.00023744338176008272, 'weight_decay': 0.0006606749020254069, 'batch_size': 256}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 18:51:05,855] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.370763
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.19947496063571546, 'ffn_dropout': 0.12938786534384153, 'lr': 0.0003676285877543154, 'weight_decay': 0.00038537532383469737, 'batch_size': 256}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 18:51:28,089] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.294400
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.17046375006303202, 'ffn_dropout': 0.1624687126833142, 'lr': 0.00012259062633890306, 'weight_decay': 0.0015711935449833978, 'batch_size': 256}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:55:07,926] Trial 59 finished with value: 0.4268494956568521 and parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.12227775694630182, 'ffn_dropout': 0.14035312682128326, 'lr': 0.0001961835162982108, 'weight_decay': 0.0011306724784946893, 'batch_size': 64}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.426849
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.12227775694630182, 'ffn_dropout': 0.14035312682128326, 'lr': 0.0001961835162982108, 'weight_decay': 0.0011306724784946893, 'batch_size': 64}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 18:55:22,213] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.234867
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.14738163162100482, 'ffn_dropout': 0.11035243668803918, 'lr': 0.00030344492212457667, 'weight_decay': 0.0005381824805471099, 'batch_size': 256}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 18:57:37,154] Trial 61 finished with value: 0.42434148574158215 and parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1618324906198552, 'ffn_dropout': 0.16972991540214574, 'lr': 0.00040064306856915535, 'weight_decay': 0.0022176667679372677, 'batch_size': 128}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.424341
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1618324906198552, 'ffn_dropout': 0.16972991540214574, 'lr': 0.00040064306856915535, 'weight_decay': 0.0022176667679372677, 'batch_size': 128}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 19:02:38,683] Trial 62 finished with value: 0.4306726222233921 and parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.15531911365798245, 'ffn_dropout': 0.14841974083141266, 'lr': 0.0004354097706023698, 'weight_decay': 0.0030311940515041855, 'batch_size': 64}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.430673
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.15531911365798245, 'ffn_dropout': 0.14841974083141266, 'lr': 0.0004354097706023698, 'weight_decay': 0.0030311940515041855, 'batch_size': 64}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 19:03:16,779] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.353846
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.13399888854527425, 'ffn_dropout': 0.18612436481955713, 'lr': 0.0002589953027207309, 'weight_decay': 0.0017574512841025413, 'batch_size': 256}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 19:05:26,995] Trial 64 finished with value: 0.4171097536811994 and parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1768568151838165, 'ffn_dropout': 0.15478515614876204, 'lr': 0.0005588875477666259, 'weight_decay': 0.0013077979895293028, 'batch_size': 128}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.417110
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1768568151838165, 'ffn_dropout': 0.15478515614876204, 'lr': 0.0005588875477666259, 'weight_decay': 0.0013077979895293028, 'batch_size': 128}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 19:06:00,787] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.347722
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1937437342711599, 'ffn_dropout': 0.179493072950223, 'lr': 0.0001608599354514207, 'weight_decay': 0.0009522080274236417, 'batch_size': 64}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 19:09:12,148] Trial 66 finished with value: 0.4253455382972498 and parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.1829266273626336, 'ffn_dropout': 0.13135931840257387, 'lr': 0.0007336456743598867, 'weight_decay': 0.000673853053209739, 'batch_size': 256}. Best is trial 40 with value: 0.4414200029420164.
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.425346
+  Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.1829266273626336, 'ffn_dropout': 0.13135931840257387, 'lr': 0.0007336456743598867, 'weight_decay': 0.000673853053209739, 'batch_size': 256}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 19:09:51,354] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.363636
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15504062242650196, 'ffn_dropout': 0.197143131197394, 'lr': 0.00031771024965177204, 'weight_decay': 0.0023441523271843586, 'batch_size': 128}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 19:11:41,737] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.357564
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.1095408727676423, 'ffn_dropout': 0.1727437198805319, 'lr': 0.0004430360440253659, 'weight_decay': 0.00469007692822832, 'batch_size': 32}
+  Best Value (CSI): 0.441420
+  Best Trial: 40
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1556353802013698, 'ffn_dropout': 0.16208534456557933, 'lr': 0.0007496145596734645, 'weight_decay': 0.008780582140665853, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 19:15:42,398] Trial 69 finished with value: 0.4431741248703596 and parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.16403219128792793, 'ffn_dropout': 0.12389945690707567, 'lr': 0.0002171309467375011, 'weight_decay': 0.0017934889765604998, 'batch_size': 64}. Best is trial 69 with value: 0.4431741248703596.
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.443174
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.16403219128792793, 'ffn_dropout': 0.12389945690707567, 'lr': 0.0002171309467375011, 'weight_decay': 0.0017934889765604998, 'batch_size': 64}
+  Best Value (CSI): 0.443174
+  Best Trial: 69
+  Best Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.16403219128792793, 'ffn_dropout': 0.12389945690707567, 'lr': 0.0002171309467375011, 'weight_decay': 0.0017934889765604998, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 19:19:41,439] Trial 70 finished with value: 0.4196754395078465 and parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2055732124683943, 'ffn_dropout': 0.12360387450684893, 'lr': 0.00012191830288681153, 'weight_decay': 0.003159863292954228, 'batch_size': 64}. Best is trial 69 with value: 0.4431741248703596.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.419675
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2055732124683943, 'ffn_dropout': 0.12360387450684893, 'lr': 0.00012191830288681153, 'weight_decay': 0.003159863292954228, 'batch_size': 64}
+  Best Value (CSI): 0.443174
+  Best Trial: 69
+  Best Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.16403219128792793, 'ffn_dropout': 0.12389945690707567, 'lr': 0.0002171309467375011, 'weight_decay': 0.0017934889765604998, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 19:23:39,461] Trial 71 finished with value: 0.4204887383965752 and parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.16403341479815875, 'ffn_dropout': 0.13958081240736597, 'lr': 0.00023035280209805352, 'weight_decay': 0.0017788389197751068, 'batch_size': 64}. Best is trial 69 with value: 0.4431741248703596.
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.420489
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.16403341479815875, 'ffn_dropout': 0.13958081240736597, 'lr': 0.00023035280209805352, 'weight_decay': 0.0017788389197751068, 'batch_size': 64}
+  Best Value (CSI): 0.443174
+  Best Trial: 69
+  Best Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.16403219128792793, 'ffn_dropout': 0.12389945690707567, 'lr': 0.0002171309467375011, 'weight_decay': 0.0017934889765604998, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 19:26:54,566] Trial 72 finished with value: 0.4300861950358712 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.14178259417799075, 'ffn_dropout': 0.10601497995132242, 'lr': 0.00017974206318143438, 'weight_decay': 0.0013829533626075142, 'batch_size': 64}. Best is trial 69 with value: 0.4431741248703596.
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.430086
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.14178259417799075, 'ffn_dropout': 0.10601497995132242, 'lr': 0.00017974206318143438, 'weight_decay': 0.0013829533626075142, 'batch_size': 64}
+  Best Value (CSI): 0.443174
+  Best Trial: 69
+  Best Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.16403219128792793, 'ffn_dropout': 0.12389945690707567, 'lr': 0.0002171309467375011, 'weight_decay': 0.0017934889765604998, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 19:30:51,014] Trial 73 finished with value: 0.43437788253710635 and parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.17435240403767915, 'ffn_dropout': 0.1184008817958567, 'lr': 0.00037441260460655196, 'weight_decay': 0.0011027074952704244, 'batch_size': 64}. Best is trial 69 with value: 0.4431741248703596.
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.434378
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.17435240403767915, 'ffn_dropout': 0.1184008817958567, 'lr': 0.00037441260460655196, 'weight_decay': 0.0011027074952704244, 'batch_size': 64}
+  Best Value (CSI): 0.443174
+  Best Trial: 69
+  Best Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.16403219128792793, 'ffn_dropout': 0.12389945690707567, 'lr': 0.0002171309467375011, 'weight_decay': 0.0017934889765604998, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 19:32:00,900] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.017208
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.12731653987602387, 'ffn_dropout': 0.1515501729584194, 'lr': 0.0005285236199010238, 'weight_decay': 0.002418961894724857, 'batch_size': 32}
+  Best Value (CSI): 0.443174
+  Best Trial: 69
+  Best Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.16403219128792793, 'ffn_dropout': 0.12389945690707567, 'lr': 0.0002171309467375011, 'weight_decay': 0.0017934889765604998, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 19:33:06,192] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.367483
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.18647066952118696, 'ffn_dropout': 0.16046344640671834, 'lr': 0.00014390368388044635, 'weight_decay': 0.0018984357045423708, 'batch_size': 64}
+  Best Value (CSI): 0.443174
+  Best Trial: 69
+  Best Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.16403219128792793, 'ffn_dropout': 0.12389945690707567, 'lr': 0.0002171309467375011, 'weight_decay': 0.0017934889765604998, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 19:33:47,093] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.373576
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.1343568783199494, 'ffn_dropout': 0.13412112266573673, 'lr': 0.0002958944519899403, 'weight_decay': 0.000835877698940035, 'batch_size': 256}
+  Best Value (CSI): 0.443174
+  Best Trial: 69
+  Best Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.16403219128792793, 'ffn_dropout': 0.12389945690707567, 'lr': 0.0002171309467375011, 'weight_decay': 0.0017934889765604998, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 19:34:01,752] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.327345
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.14735778164386165, 'ffn_dropout': 0.11725152093659434, 'lr': 0.00020102065510181985, 'weight_decay': 0.0012774312600350324, 'batch_size': 128}
+  Best Value (CSI): 0.443174
+  Best Trial: 69
+  Best Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.16403219128792793, 'ffn_dropout': 0.12389945690707567, 'lr': 0.0002171309467375011, 'weight_decay': 0.0017934889765604998, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 19:39:46,982] Trial 78 finished with value: 0.44918319157422554 and parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.449183
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 19:44:15,125] Trial 79 finished with value: 0.41077999210910804 and parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.16867100957054396, 'ffn_dropout': 0.12607406092029955, 'lr': 0.0002463472406391484, 'weight_decay': 0.003069763560343024, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.410780
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.16867100957054396, 'ffn_dropout': 0.12607406092029955, 'lr': 0.0002463472406391484, 'weight_decay': 0.003069763560343024, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 19:45:41,199] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.358051
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1811049257405468, 'ffn_dropout': 0.1454676274524241, 'lr': 0.00013992724247680573, 'weight_decay': 0.006163531217760437, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 19:50:42,000] Trial 81 finished with value: 0.4420117302971501 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.16040871939953077, 'ffn_dropout': 0.16367735877215775, 'lr': 0.0003327621798319041, 'weight_decay': 0.004645945169993859, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.442012
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.16040871939953077, 'ffn_dropout': 0.16367735877215775, 'lr': 0.0003327621798319041, 'weight_decay': 0.004645945169993859, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 19:51:33,735] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.329730
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1635470980524377, 'ffn_dropout': 0.15926548758387668, 'lr': 0.0003137323155267198, 'weight_decay': 0.00402301647510049, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 19:52:59,566] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.348269
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19529197167907367, 'ffn_dropout': 0.13824790418933847, 'lr': 0.00017644782749934415, 'weight_decay': 0.0034124911632735337, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 19:54:27,227] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.319419
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.14865115806371734, 'ffn_dropout': 0.15069977993817893, 'lr': 0.00022610838468533088, 'weight_decay': 0.004763659044386419, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 19:59:28,151] Trial 85 finished with value: 0.44052616442516523 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.17400292846833323, 'ffn_dropout': 0.10556144588768568, 'lr': 0.0005005052915230868, 'weight_decay': 0.0027131388409398997, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.440526
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.17400292846833323, 'ffn_dropout': 0.10556144588768568, 'lr': 0.0005005052915230868, 'weight_decay': 0.0027131388409398997, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 20:04:53,564] Trial 86 finished with value: 0.44178844339108014 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1712906287602601, 'ffn_dropout': 0.1238482631111626, 'lr': 0.000482944657085638, 'weight_decay': 0.0026962646562749535, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.441788
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1712906287602601, 'ffn_dropout': 0.1238482631111626, 'lr': 0.000482944657085638, 'weight_decay': 0.0026962646562749535, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 20:10:05,008] Trial 87 finished with value: 0.44260934523432666 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.17168181393330476, 'ffn_dropout': 0.12562169578849877, 'lr': 0.0006450790370654633, 'weight_decay': 0.0028769137213888445, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.442609
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.17168181393330476, 'ffn_dropout': 0.12562169578849877, 'lr': 0.0006450790370654633, 'weight_decay': 0.0028769137213888445, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 20:15:21,974] Trial 88 finished with value: 0.4225039235934051 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.17716253733065707, 'ffn_dropout': 0.10373222813602012, 'lr': 0.0008060110550786684, 'weight_decay': 0.002762115349932314, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.422504
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.17716253733065707, 'ffn_dropout': 0.10373222813602012, 'lr': 0.0008060110550786684, 'weight_decay': 0.002762115349932314, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 20:20:38,940] Trial 89 finished with value: 0.4331833072119837 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1890056926716874, 'ffn_dropout': 0.11396396282861, 'lr': 0.0005075379826556228, 'weight_decay': 0.0024884935711004977, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.433183
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1890056926716874, 'ffn_dropout': 0.11396396282861, 'lr': 0.0005075379826556228, 'weight_decay': 0.0024884935711004977, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 20:25:52,652] Trial 90 finished with value: 0.434780381610878 and parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15925184932964423, 'ffn_dropout': 0.12349497213164552, 'lr': 0.0006309746054727753, 'weight_decay': 0.0037050439827611397, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.434780
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15925184932964423, 'ffn_dropout': 0.12349497213164552, 'lr': 0.0006309746054727753, 'weight_decay': 0.0037050439827611397, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 20:30:49,451] Trial 91 finished with value: 0.4099034866820849 and parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.17264366410927948, 'ffn_dropout': 0.1317296908253193, 'lr': 0.0010044852010196881, 'weight_decay': 0.005478366456452128, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.409903
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.17264366410927948, 'ffn_dropout': 0.1317296908253193, 'lr': 0.0010044852010196881, 'weight_decay': 0.005478366456452128, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 20:35:59,004] Trial 92 finished with value: 0.41962453309776165 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.20777980930487486, 'ffn_dropout': 0.14179592016430473, 'lr': 0.0006598511598691256, 'weight_decay': 0.002868843656715645, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.419625
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.20777980930487486, 'ffn_dropout': 0.14179592016430473, 'lr': 0.0006598511598691256, 'weight_decay': 0.002868843656715645, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 20:39:58,711] Trial 93 finished with value: 0.4201215018677731 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1687561161704828, 'ffn_dropout': 0.11209935167464238, 'lr': 0.0004611785229799356, 'weight_decay': 0.004381972393210843, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.420122
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1687561161704828, 'ffn_dropout': 0.11209935167464238, 'lr': 0.0004611785229799356, 'weight_decay': 0.004381972393210843, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 20:40:46,449] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.366013
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.17926851582710424, 'ffn_dropout': 0.12645963096261686, 'lr': 0.00027446837330017006, 'weight_decay': 0.0017033773078440023, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 20:41:36,397] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.180041
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.16214201024242458, 'ffn_dropout': 0.167265015445544, 'lr': 0.0012821343910400659, 'weight_decay': 0.0022352506919120336, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 20:47:32,305] Trial 96 finished with value: 0.4388926315929929 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1912236184330946, 'ffn_dropout': 0.12009565204016064, 'lr': 0.0007288495141506138, 'weight_decay': 0.0033976339837676274, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.438893
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1912236184330946, 'ffn_dropout': 0.12009565204016064, 'lr': 0.0007288495141506138, 'weight_decay': 0.0033976339837676274, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 20:52:54,984] Trial 97 finished with value: 0.4274120121183942 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19035338143141867, 'ffn_dropout': 0.12092017089876318, 'lr': 0.0009174887374745283, 'weight_decay': 0.003374267126255662, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.427412
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19035338143141867, 'ffn_dropout': 0.12092017089876318, 'lr': 0.0009174887374745283, 'weight_decay': 0.003374267126255662, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 20:56:33,825] Trial 98 finished with value: 0.4347028957094918 and parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.15863340622012126, 'ffn_dropout': 0.10997161681174206, 'lr': 0.0007445219008485975, 'weight_decay': 0.004219024001386447, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.434703
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.15863340622012126, 'ffn_dropout': 0.10997161681174206, 'lr': 0.0007445219008485975, 'weight_decay': 0.004219024001386447, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 21:02:10,347] Trial 99 finished with value: 0.4191712639007423 and parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1718070721666482, 'ffn_dropout': 0.10358805464232193, 'lr': 0.0005550459078643854, 'weight_decay': 0.002587835766959848, 'batch_size': 32}. Best is trial 78 with value: 0.44918319157422554.
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.419171
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1718070721666482, 'ffn_dropout': 0.10358805464232193, 'lr': 0.0005550459078643854, 'weight_decay': 0.002587835766959848, 'batch_size': 32}
+  Best Value (CSI): 0.449183
+  Best Trial: 78
+  Best Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4492
+Best Hyperparameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.2779
+  - 최종 CSI: 0.4192
+  - 최고 CSI: 0.4492
+  - 최저 CSI: 0.0000
+  - 평균 CSI: 0.3587
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/ft_transformer_ctgan10000_daegu_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 17, Best CSI: 0.3962
+  Fold 1 학습 완료 (검증 CSI: 0.3962)
+Fold 2 학습 중...
+  Early stopping at epoch 38, Best CSI: 0.4349
+  Fold 2 학습 완료 (검증 CSI: 0.4349)
+Fold 3 학습 중...
+  Early stopping at epoch 28, Best CSI: 0.4252
+  Fold 3 학습 완료 (검증 CSI: 0.4252)
+
+모든 모델 저장 완료: ../save_model/ft_transformer_optima/ft_transformer_ctgan10000_daegu.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/ft_transformer_optima/scaler/ft_transformer_ctgan10000_daegu_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/ft_transformer_optima/ft_transformer_ctgan10000_daegu.pkl
+
+✓ 완료: ft_transformer_ctgan10000/ft_transformer_ctgan10000_daegu.py (소요 시간: 17131초)
+
+----------------------------------------
+실행 중: ft_transformer_ctgan10000/ft_transformer_ctgan10000_daejeon.py
+시작 시간: 2025-12-28 21:08:29
+----------------------------------------
+[I 2025-12-28 21:08:30,933] A new study created in memory with name: no-name-3f3c81fc-a71e-4897-bb9d-8d2f14daf07b
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 21:10:45,843] Trial 0 finished with value: 0.3515107336046827 and parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2910103348211043, 'ffn_dropout': 0.36754899903896976, 'lr': 2.3405541509068347e-05, 'weight_decay': 0.0001511155501594832, 'batch_size': 256}. Best is trial 0 with value: 0.3515107336046827.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.351511
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2910103348211043, 'ffn_dropout': 0.36754899903896976, 'lr': 2.3405541509068347e-05, 'weight_decay': 0.0001511155501594832, 'batch_size': 256}
+  Best Value (CSI): 0.351511
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2910103348211043, 'ffn_dropout': 0.36754899903896976, 'lr': 2.3405541509068347e-05, 'weight_decay': 0.0001511155501594832, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 21:12:53,317] Trial 1 finished with value: 0.48268241236605886 and parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2829185571256843, 'ffn_dropout': 0.29864292887671456, 'lr': 0.00023191180470194834, 'weight_decay': 0.000992272744848878, 'batch_size': 128}. Best is trial 1 with value: 0.48268241236605886.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.482682
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2829185571256843, 'ffn_dropout': 0.29864292887671456, 'lr': 0.00023191180470194834, 'weight_decay': 0.000992272744848878, 'batch_size': 128}
+  Best Value (CSI): 0.482682
+  Best Trial: 1
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2829185571256843, 'ffn_dropout': 0.29864292887671456, 'lr': 0.00023191180470194834, 'weight_decay': 0.000992272744848878, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 21:15:44,597] Trial 2 finished with value: 0.5013219159497612 and parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10565268869135563, 'ffn_dropout': 0.1512220025238161, 'lr': 0.0015391779544413461, 'weight_decay': 0.0034351346365071294, 'batch_size': 64}. Best is trial 2 with value: 0.5013219159497612.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.501322
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10565268869135563, 'ffn_dropout': 0.1512220025238161, 'lr': 0.0015391779544413461, 'weight_decay': 0.0034351346365071294, 'batch_size': 64}
+  Best Value (CSI): 0.501322
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10565268869135563, 'ffn_dropout': 0.1512220025238161, 'lr': 0.0015391779544413461, 'weight_decay': 0.0034351346365071294, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 21:16:50,130] Trial 3 finished with value: 0.3093077976149397 and parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.14840952108238487, 'ffn_dropout': 0.1453854539724981, 'lr': 1.238707618652678e-05, 'weight_decay': 0.025335452633787264, 'batch_size': 128}. Best is trial 2 with value: 0.5013219159497612.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.309308
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.14840952108238487, 'ffn_dropout': 0.1453854539724981, 'lr': 1.238707618652678e-05, 'weight_decay': 0.025335452633787264, 'batch_size': 128}
+  Best Value (CSI): 0.501322
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10565268869135563, 'ffn_dropout': 0.1512220025238161, 'lr': 0.0015391779544413461, 'weight_decay': 0.0034351346365071294, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 21:19:27,380] Trial 4 finished with value: 0.46030731800690755 and parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.17276075571553545, 'ffn_dropout': 0.3474022903607733, 'lr': 0.0001584714987491879, 'weight_decay': 0.0006814440446575935, 'batch_size': 64}. Best is trial 2 with value: 0.5013219159497612.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.460307
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.17276075571553545, 'ffn_dropout': 0.3474022903607733, 'lr': 0.0001584714987491879, 'weight_decay': 0.0006814440446575935, 'batch_size': 64}
+  Best Value (CSI): 0.501322
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10565268869135563, 'ffn_dropout': 0.1512220025238161, 'lr': 0.0015391779544413461, 'weight_decay': 0.0034351346365071294, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 21:20:20,632] Trial 5 finished with value: 0.4987061635154914 and parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3713927388668614, 'ffn_dropout': 0.3197649291495102, 'lr': 0.0009720779545266859, 'weight_decay': 0.0024427548553668595, 'batch_size': 128}. Best is trial 2 with value: 0.5013219159497612.
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.498706
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3713927388668614, 'ffn_dropout': 0.3197649291495102, 'lr': 0.0009720779545266859, 'weight_decay': 0.0024427548553668595, 'batch_size': 128}
+  Best Value (CSI): 0.501322
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10565268869135563, 'ffn_dropout': 0.1512220025238161, 'lr': 0.0015391779544413461, 'weight_decay': 0.0034351346365071294, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:21:59,694] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.365305
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.15076091579590556, 'ffn_dropout': 0.30415455879227615, 'lr': 2.1431908212236976e-05, 'weight_decay': 0.0005184691476675433, 'batch_size': 32}
+  Best Value (CSI): 0.501322
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10565268869135563, 'ffn_dropout': 0.1512220025238161, 'lr': 0.0015391779544413461, 'weight_decay': 0.0034351346365071294, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:22:35,075] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1361494845743928, 'ffn_dropout': 0.10745031387126859, 'lr': 0.008474590982147583, 'weight_decay': 0.02996632985549609, 'batch_size': 64}
+  Best Value (CSI): 0.501322
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10565268869135563, 'ffn_dropout': 0.1512220025238161, 'lr': 0.0015391779544413461, 'weight_decay': 0.0034351346365071294, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:22:44,846] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.142573
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3967395136156501, 'ffn_dropout': 0.3876093034675808, 'lr': 1.6615662574198014e-05, 'weight_decay': 0.0006295034375823504, 'batch_size': 256}
+  Best Value (CSI): 0.501322
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10565268869135563, 'ffn_dropout': 0.1512220025238161, 'lr': 0.0015391779544413461, 'weight_decay': 0.0034351346365071294, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 21:26:05,945] Trial 9 finished with value: 0.49564702440561703 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.32835210822772365, 'ffn_dropout': 0.1774733759381295, 'lr': 7.267523155451432e-05, 'weight_decay': 0.0011692465877522508, 'batch_size': 32}. Best is trial 2 with value: 0.5013219159497612.
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.495647
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.32835210822772365, 'ffn_dropout': 0.1774733759381295, 'lr': 7.267523155451432e-05, 'weight_decay': 0.0011692465877522508, 'batch_size': 32}
+  Best Value (CSI): 0.501322
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10565268869135563, 'ffn_dropout': 0.1512220025238161, 'lr': 0.0015391779544413461, 'weight_decay': 0.0034351346365071294, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:26:35,904] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.20990584109636984, 'ffn_dropout': 0.21857764222635895, 'lr': 0.0012364894857675441, 'weight_decay': 0.00662271549445519, 'batch_size': 64}
+  Best Value (CSI): 0.501322
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10565268869135563, 'ffn_dropout': 0.1512220025238161, 'lr': 0.0015391779544413461, 'weight_decay': 0.0034351346365071294, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 21:27:44,087] Trial 11 finished with value: 0.4917700703665459 and parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.11029183690839856, 'ffn_dropout': 0.2526676915697925, 'lr': 0.0012736609612595687, 'weight_decay': 0.004865858775688092, 'batch_size': 128}. Best is trial 2 with value: 0.5013219159497612.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.491770
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.11029183690839856, 'ffn_dropout': 0.2526676915697925, 'lr': 0.0012736609612595687, 'weight_decay': 0.004865858775688092, 'batch_size': 128}
+  Best Value (CSI): 0.501322
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10565268869135563, 'ffn_dropout': 0.1512220025238161, 'lr': 0.0015391779544413461, 'weight_decay': 0.0034351346365071294, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:28:40,886] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.453509
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2125488418345018, 'ffn_dropout': 0.21702726593221097, 'lr': 0.0008933109721431462, 'weight_decay': 0.003538699326323409, 'batch_size': 64}
+  Best Value (CSI): 0.501322
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10565268869135563, 'ffn_dropout': 0.1512220025238161, 'lr': 0.0015391779544413461, 'weight_decay': 0.0034351346365071294, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:29:05,311] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.439394
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.1054738506617745, 'ffn_dropout': 0.12192189326601019, 'lr': 0.0024070517609002355, 'weight_decay': 0.009393528589689108, 'batch_size': 128}
+  Best Value (CSI): 0.501322
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10565268869135563, 'ffn_dropout': 0.1512220025238161, 'lr': 0.0015391779544413461, 'weight_decay': 0.0034351346365071294, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 21:31:05,683] Trial 14 finished with value: 0.5026041056392309 and parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}. Best is trial 14 with value: 0.5026041056392309.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.502604
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:32:03,006] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.401718
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2499398319377708, 'ffn_dropout': 0.15989392516588427, 'lr': 0.0003236232054018821, 'weight_decay': 0.09619251418964923, 'batch_size': 64}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:33:04,025] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.34362470050957933, 'ffn_dropout': 0.10075486039854117, 'lr': 0.004018723029757962, 'weight_decay': 0.013157509850258141, 'batch_size': 32}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:33:22,186] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.392644
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.20565665060175314, 'ffn_dropout': 0.17811910410728996, 'lr': 0.0006387547861781145, 'weight_decay': 0.09698879829924102, 'batch_size': 256}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 21:34:57,018] Trial 18 finished with value: 0.4941770913736554 and parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.39249792924519195, 'ffn_dropout': 0.14120337052036003, 'lr': 0.0005190718069112197, 'weight_decay': 0.01829550455255961, 'batch_size': 128}. Best is trial 14 with value: 0.5026041056392309.
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.494177
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.39249792924519195, 'ffn_dropout': 0.14120337052036003, 'lr': 0.0005190718069112197, 'weight_decay': 0.01829550455255961, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:35:32,603] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2492674716467032, 'ffn_dropout': 0.18993060196715997, 'lr': 0.0022903006526314024, 'weight_decay': 0.04298668487060687, 'batch_size': 64}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:36:10,040] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.406854
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3244487197097661, 'ffn_dropout': 0.13060537613725406, 'lr': 0.0004065826720290648, 'weight_decay': 0.010029330910050725, 'batch_size': 64}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:36:36,174] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.411304
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.3677426936343995, 'ffn_dropout': 0.1580412151566468, 'lr': 0.0008496330702481001, 'weight_decay': 0.0020726867847233087, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 21:37:35,253] Trial 22 finished with value: 0.49875739273390646 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3542877777742037, 'ffn_dropout': 0.2460692956445881, 'lr': 0.001472427431370659, 'weight_decay': 0.003285940974074307, 'batch_size': 128}. Best is trial 14 with value: 0.5026041056392309.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.498757
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3542877777742037, 'ffn_dropout': 0.2460692956445881, 'lr': 0.001472427431370659, 'weight_decay': 0.003285940974074307, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:37:48,980] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.358862
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.35564032325385153, 'ffn_dropout': 0.23594284415411612, 'lr': 0.0022085021077608916, 'weight_decay': 0.0064032153627335085, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:37:59,422] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.387668
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3985602367797292, 'ffn_dropout': 0.2631548337992453, 'lr': 0.0005004521696077459, 'weight_decay': 0.003399743644493096, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:38:17,322] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.082650
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.31052494314229745, 'ffn_dropout': 0.2088412675644749, 'lr': 0.004390479209791537, 'weight_decay': 0.04677165064189165, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:38:58,207] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.057236
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.36912851683126746, 'ffn_dropout': 0.19691261949120098, 'lr': 0.0015559169987432939, 'weight_decay': 0.015435394035251434, 'batch_size': 32}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:39:28,201] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.433849
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.34368707966964024, 'ffn_dropout': 0.1728717862583832, 'lr': 0.0007186420135024457, 'weight_decay': 0.007955432604678436, 'batch_size': 256}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:39:37,914] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.292836
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2776459334353187, 'ffn_dropout': 0.1524554772207722, 'lr': 0.00022334670402665232, 'weight_decay': 0.004206578048017745, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:39:54,510] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.225598
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.30470697138666925, 'ffn_dropout': 0.23513303174167588, 'lr': 0.00010266013877038994, 'weight_decay': 0.000262085661526064, 'batch_size': 256}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:40:25,169] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.405123
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.267457712044999, 'ffn_dropout': 0.12374488813804471, 'lr': 0.0003558986388240892, 'weight_decay': 0.001747441667056421, 'batch_size': 64}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:40:36,493] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.396484
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.37584916598718954, 'ffn_dropout': 0.2694108829907798, 'lr': 0.0011213259159563631, 'weight_decay': 0.0023956216980094763, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 21:41:41,946] Trial 32 finished with value: 0.49591783564901526 and parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.35314992631609726, 'ffn_dropout': 0.32978910030022957, 'lr': 0.0006096988428593991, 'weight_decay': 0.0026893810504803025, 'batch_size': 128}. Best is trial 14 with value: 0.5026041056392309.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.495918
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.35314992631609726, 'ffn_dropout': 0.32978910030022957, 'lr': 0.0006096988428593991, 'weight_decay': 0.0026893810504803025, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:42:00,458] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.423009
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.38017144468210534, 'ffn_dropout': 0.28472842108333657, 'lr': 0.0015429396280446262, 'weight_decay': 0.0015217547008547867, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:42:11,841] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.391867
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.38169369565381134, 'ffn_dropout': 0.3142207084881544, 'lr': 0.0008694883297162419, 'weight_decay': 0.004028795601264474, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:42:25,131] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.346614
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.3586603633484885, 'ffn_dropout': 0.35078014827688264, 'lr': 0.00023199493693819677, 'weight_decay': 0.0013909771723235847, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:42:47,806] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.394221
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3327587060427215, 'ffn_dropout': 0.30455745947096213, 'lr': 0.0016959290025939783, 'weight_decay': 0.0008948118686046056, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:43:06,049] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.428300
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.399155228196549, 'ffn_dropout': 0.2832916149539943, 'lr': 0.0010146204054581878, 'weight_decay': 0.002808513081241631, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:44:06,720] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.429104
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.29767874892345975, 'ffn_dropout': 0.23612626042558887, 'lr': 0.0006319263484298835, 'weight_decay': 0.005459650297328778, 'batch_size': 32}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:44:28,446] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.37955220178179155, 'ffn_dropout': 0.36478353641755257, 'lr': 0.0034276076215243993, 'weight_decay': 0.00011243883532956182, 'batch_size': 64}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:44:42,681] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.3643136506230742, 'ffn_dropout': 0.14051477410992014, 'lr': 0.006349979396458491, 'weight_decay': 0.0008433932628501213, 'batch_size': 256}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:45:00,997] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.433980
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.34616787726431775, 'ffn_dropout': 0.3159080195023359, 'lr': 0.0005080600923839421, 'weight_decay': 0.0025555157104543884, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:45:19,481] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.412931
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3561814010611708, 'ffn_dropout': 0.33422496030519633, 'lr': 0.0011699146384217525, 'weight_decay': 0.0018801678207811022, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:45:29,947] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.388410
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3224479127568032, 'ffn_dropout': 0.3402329690182905, 'lr': 0.0007228391419247939, 'weight_decay': 0.0011911349873560914, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:45:48,291] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.416316
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.38709835627554073, 'ffn_dropout': 0.3994342256007215, 'lr': 0.00043833767359392853, 'weight_decay': 0.004818086425654556, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:46:29,473] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.430865
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.12659611651144798, 'ffn_dropout': 0.32607102624881745, 'lr': 0.002034543458756047, 'weight_decay': 0.003149787410563841, 'batch_size': 64}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:46:48,037] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.442593
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.17359990974929396, 'ffn_dropout': 0.29509720702954306, 'lr': 0.0013789563225031604, 'weight_decay': 0.0005476356471574887, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+[I 2025-12-28 21:48:54,537] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.473862
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3353963539538535, 'ffn_dropout': 0.3554934988966506, 'lr': 0.0010313342594078995, 'weight_decay': 0.007271917526062417, 'batch_size': 32}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:49:16,046] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.192003
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38771268593017516, 'ffn_dropout': 0.38155517642387754, 'lr': 0.002846663377088448, 'weight_decay': 0.0026271191035313305, 'batch_size': 64}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:49:34,090] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.391026
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.3569629550852389, 'ffn_dropout': 0.1623815444512328, 'lr': 0.0018052307344522424, 'weight_decay': 0.011941913524678527, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:49:51,228] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.359105
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.36985723078733546, 'ffn_dropout': 0.3281323842739015, 'lr': 0.0005855974849549153, 'weight_decay': 0.005307948419336565, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:50:25,690] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.392157
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3158093630462354, 'ffn_dropout': 0.17519705277780875, 'lr': 3.9476621197482796e-05, 'weight_decay': 0.0011615958868990786, 'batch_size': 32}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:51:01,931] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.394068
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3326067820124891, 'ffn_dropout': 0.19054448568110172, 'lr': 0.0008376667228978081, 'weight_decay': 0.0020766535524811137, 'batch_size': 32}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:52:02,048] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.425265
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.34922003532826523, 'ffn_dropout': 0.1096389840789711, 'lr': 0.0013292637699121057, 'weight_decay': 0.003454639021209991, 'batch_size': 32}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+[I 2025-12-28 21:54:06,260] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.455945
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3699930071841796, 'ffn_dropout': 0.14235980736103676, 'lr': 0.00037920091078743733, 'weight_decay': 0.001441788397419719, 'batch_size': 32}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:54:47,872] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.403263
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.10320806102268956, 'ffn_dropout': 0.20094253666451284, 'lr': 0.0007391130379886442, 'weight_decay': 0.0006681877382356347, 'batch_size': 64}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:55:06,486] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.443630
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3916426821962267, 'ffn_dropout': 0.18303405829345676, 'lr': 0.002488196044812075, 'weight_decay': 0.008844392367418616, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:55:23,868] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.403604
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.33908371343242466, 'ffn_dropout': 0.1668786339058096, 'lr': 0.0005792657099834926, 'weight_decay': 0.02488842177476815, 'batch_size': 256}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:56:22,602] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.421933
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.32473265507728605, 'ffn_dropout': 0.14977315571684574, 'lr': 0.00027944521712988316, 'weight_decay': 0.006206785631725239, 'batch_size': 32}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:56:51,273] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.404199
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.35504941019122477, 'ffn_dropout': 0.21716095486291476, 'lr': 0.00016514797041883663, 'weight_decay': 0.0021991470942317928, 'batch_size': 64}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:57:01,799] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.321154
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.37514920863848794, 'ffn_dropout': 0.16684354556917638, 'lr': 0.0004887068907532713, 'weight_decay': 0.004165144081633466, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:57:19,861] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.407447
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.39085618761924856, 'ffn_dropout': 0.1327227231970394, 'lr': 0.000931920219140957, 'weight_decay': 0.06447921001036785, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+[I 2025-12-28 21:58:27,691] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.441331
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.36248932678716794, 'ffn_dropout': 0.15279045626358187, 'lr': 0.0002923397710636246, 'weight_decay': 0.01651820895349918, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:59:01,075] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.443820
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3845126780585659, 'ffn_dropout': 0.14150886442378036, 'lr': 0.00041385813794535893, 'weight_decay': 0.0017030347501990352, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 21:59:14,456] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.404902
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.39608407881426566, 'ffn_dropout': 0.18582277408441594, 'lr': 0.0012371514755540366, 'weight_decay': 0.0035061646640506673, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+[I 2025-12-28 21:59:50,078] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.439929
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.37468182989004906, 'ffn_dropout': 0.17696022925393243, 'lr': 0.00172641510383182, 'weight_decay': 0.019468063499820557, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+[I 2025-12-28 22:00:36,864] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.451907
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.34732274775533084, 'ffn_dropout': 0.161536852029162, 'lr': 0.0007368969734184046, 'weight_decay': 0.033249677988333094, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+[I 2025-12-28 22:02:47,880] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.448663
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.3657419601143125, 'ffn_dropout': 0.11951832158139404, 'lr': 0.0010527814437938788, 'weight_decay': 0.0029369821562984415, 'batch_size': 64}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:03:13,843] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.380851
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.3988055509612349, 'ffn_dropout': 0.15525300928339614, 'lr': 0.000564817636098151, 'weight_decay': 0.01074115020290595, 'batch_size': 256}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:03:24,451] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.345487
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.290186967892228, 'ffn_dropout': 0.24927857773199208, 'lr': 0.0003413806629306943, 'weight_decay': 0.008653283107142463, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:03:36,264] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.404339
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.35094481955345497, 'ffn_dropout': 0.20808595753321998, 'lr': 0.0008570847763093429, 'weight_decay': 0.0068339091917421395, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:03:58,394] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.408253
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1198680387247807, 'ffn_dropout': 0.26961840983738394, 'lr': 0.0015209417732697755, 'weight_decay': 0.004504098948542499, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:04:12,234] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.411215
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.10927201880991169, 'ffn_dropout': 0.13612573739433703, 'lr': 0.0020333872229633372, 'weight_decay': 0.002265476951681874, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:04:22,871] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.360697
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.1480606012467573, 'ffn_dropout': 0.14620924696964935, 'lr': 0.0006741599423051107, 'weight_decay': 0.003796710980546308, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:04:39,887] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.405291
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.38024380973275806, 'ffn_dropout': 0.22765326842757222, 'lr': 0.001286292298604475, 'weight_decay': 0.0028806217162041514, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:05:39,369] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.416944
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3603872528710351, 'ffn_dropout': 0.19541552198280238, 'lr': 0.00048527339046402666, 'weight_decay': 0.0017370597613519446, 'batch_size': 32}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 22:06:29,583] Trial 76 finished with value: 0.49394858586906015 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3881244862768132, 'ffn_dropout': 0.17017775882110805, 'lr': 0.001079391824478875, 'weight_decay': 0.004814755859249107, 'batch_size': 128}. Best is trial 14 with value: 0.5026041056392309.
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.493949
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3881244862768132, 'ffn_dropout': 0.17017775882110805, 'lr': 0.001079391824478875, 'weight_decay': 0.004814755859249107, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:06:47,724] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.434199
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3868400151004187, 'ffn_dropout': 0.17299576236645445, 'lr': 0.0008181055907429058, 'weight_decay': 0.005717022654903751, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:07:20,083] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.447177
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.37444959968655794, 'ffn_dropout': 0.1266228230320316, 'lr': 0.0010516478657094893, 'weight_decay': 0.0025107616832460147, 'batch_size': 64}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:07:38,737] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.418025
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3660551763568543, 'ffn_dropout': 0.18448542998786427, 'lr': 0.0014187394276588183, 'weight_decay': 0.00477879207937681, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:07:49,934] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.418882
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3822630932452248, 'ffn_dropout': 0.16681631542122274, 'lr': 0.0006650093370076611, 'weight_decay': 0.0011798303513801513, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+[I 2025-12-28 22:08:26,092] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.490694
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.392156948685362, 'ffn_dropout': 0.15614378742747972, 'lr': 0.0010975406127280497, 'weight_decay': 0.0036007910733382993, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 22:09:48,875] Trial 82 finished with value: 0.49418024970628993 and parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2373559361034919, 'ffn_dropout': 0.20239764318595713, 'lr': 0.0009504068554256145, 'weight_decay': 0.004508710577828345, 'batch_size': 128}. Best is trial 14 with value: 0.5026041056392309.
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.494180
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2373559361034919, 'ffn_dropout': 0.20239764318595713, 'lr': 0.0009504068554256145, 'weight_decay': 0.004508710577828345, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 22:11:00,309] Trial 83 finished with value: 0.49819287954649605 and parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21878187910929067, 'ffn_dropout': 0.1473864176158939, 'lr': 0.0009030310046924134, 'weight_decay': 0.007771188015928922, 'batch_size': 128}. Best is trial 14 with value: 0.5026041056392309.
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.498193
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21878187910929067, 'ffn_dropout': 0.1473864176158939, 'lr': 0.0009030310046924134, 'weight_decay': 0.007771188015928922, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:11:26,359] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.414327
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.22716334693976697, 'ffn_dropout': 0.1468869901305848, 'lr': 0.0005551955375175151, 'weight_decay': 0.006769149574937204, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:11:38,154] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.348003
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2600316876520279, 'ffn_dropout': 0.17979839489120042, 'lr': 0.00042484608271293594, 'weight_decay': 0.00995306939630061, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+[I 2025-12-28 22:14:41,293] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.443478
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.2336223965590215, 'ffn_dropout': 0.160465544928828, 'lr': 0.0009122157703412441, 'weight_decay': 0.0030784258464602345, 'batch_size': 32}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:14:54,482] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.399163
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.19071457736005384, 'ffn_dropout': 0.13535742359860606, 'lr': 0.0007904563163174866, 'weight_decay': 0.0073981839819809, 'batch_size': 256}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+[I 2025-12-28 22:16:14,848] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.461538
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13910169279455387, 'ffn_dropout': 0.14924255100103456, 'lr': 0.0006394418458321396, 'weight_decay': 0.005536435492531891, 'batch_size': 64}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:16:38,231] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.415094
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.34035370337862636, 'ffn_dropout': 0.19116339067933386, 'lr': 0.001499978472548167, 'weight_decay': 0.012957618630418827, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:16:49,085] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.383699
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.16106227633524878, 'ffn_dropout': 0.20580835227392907, 'lr': 0.0004931414888703649, 'weight_decay': 0.0018751997872073704, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:17:06,930] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.453875
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.245525154146777, 'ffn_dropout': 0.1672492729628541, 'lr': 0.0011717576162025424, 'weight_decay': 0.004254525223372229, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:17:25,536] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.439614
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3996853322671304, 'ffn_dropout': 0.171879387014887, 'lr': 0.0018114367322291276, 'weight_decay': 0.005097082133074525, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:17:36,173] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.342301
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.27219858664747326, 'ffn_dropout': 0.1551487324433049, 'lr': 0.0007385784272149927, 'weight_decay': 0.008039255899523989, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:17:55,802] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.399015
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.35326333923400705, 'ffn_dropout': 0.20078686506419616, 'lr': 0.0008936180198610464, 'weight_decay': 0.002585353764523342, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:18:07,252] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.426618
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.37489883378705896, 'ffn_dropout': 0.18037850190251226, 'lr': 0.001077649116806185, 'weight_decay': 0.0038749915945665435, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:19:03,436] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.428571
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.2114159733284789, 'ffn_dropout': 0.14138325078465772, 'lr': 0.0005999376929795218, 'weight_decay': 0.005999710846949739, 'batch_size': 32}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:19:20,999] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.425723
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3072505767682264, 'ffn_dropout': 0.34537956728108543, 'lr': 0.0009562772057381145, 'weight_decay': 0.0030063086696620277, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:20:00,400] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.426195
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.38757747785496344, 'ffn_dropout': 0.18803207881264233, 'lr': 0.0013034441147882065, 'weight_decay': 0.004816764263709723, 'batch_size': 64}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 22:20:10,740] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.406297
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.365098772570804, 'ffn_dropout': 0.17289328415765787, 'lr': 0.00034651454356218785, 'weight_decay': 0.002116148456919586, 'batch_size': 128}
+  Best Value (CSI): 0.502604
+  Best Trial: 14
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5026
+Best Hyperparameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.3515
+  - 최종 CSI: 0.4063
+  - 최고 CSI: 0.5026
+  - 최저 CSI: 0.0000
+  - 평균 CSI: 0.3814
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/ft_transformer_ctgan10000_daejeon_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 27, Best CSI: 0.4417
+  Fold 1 학습 완료 (검증 CSI: 0.4417)
+Fold 2 학습 중...
+  Early stopping at epoch 22, Best CSI: 0.5159
+  Fold 2 학습 완료 (검증 CSI: 0.5159)
+Fold 3 학습 중...
+  Early stopping at epoch 34, Best CSI: 0.5334
+  Fold 3 학습 완료 (검증 CSI: 0.5334)
+
+모든 모델 저장 완료: ../save_model/ft_transformer_optima/ft_transformer_ctgan10000_daejeon.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/ft_transformer_optima/scaler/ft_transformer_ctgan10000_daejeon_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/ft_transformer_optima/ft_transformer_ctgan10000_daejeon.pkl
+
+✓ 완료: ft_transformer_ctgan10000/ft_transformer_ctgan10000_daejeon.py (소요 시간: 4428초)
+
+----------------------------------------
+실행 중: ft_transformer_ctgan10000/ft_transformer_ctgan10000_gwangju.py
+시작 시간: 2025-12-28 22:22:17
+----------------------------------------
+[I 2025-12-28 22:22:18,736] A new study created in memory with name: no-name-4864b88c-1265-49ab-bc7d-bad5a92fe5a7
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:24:43,148] Trial 0 finished with value: 0.30355860091147063 and parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1744549040386172, 'ffn_dropout': 0.3845778191742626, 'lr': 1.1416482526812583e-05, 'weight_decay': 0.033663094335532405, 'batch_size': 64}. Best is trial 0 with value: 0.30355860091147063.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.303559
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1744549040386172, 'ffn_dropout': 0.3845778191742626, 'lr': 1.1416482526812583e-05, 'weight_decay': 0.033663094335532405, 'batch_size': 64}
+  Best Value (CSI): 0.303559
+  Best Trial: 0
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1744549040386172, 'ffn_dropout': 0.3845778191742626, 'lr': 1.1416482526812583e-05, 'weight_decay': 0.033663094335532405, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:27:42,051] Trial 1 finished with value: 0.09806344498277031 and parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.17032841745359167, 'ffn_dropout': 0.2334634215576455, 'lr': 0.005839455349348195, 'weight_decay': 0.03884347556796034, 'batch_size': 32}. Best is trial 0 with value: 0.30355860091147063.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.098063
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.17032841745359167, 'ffn_dropout': 0.2334634215576455, 'lr': 0.005839455349348195, 'weight_decay': 0.03884347556796034, 'batch_size': 32}
+  Best Value (CSI): 0.303559
+  Best Trial: 0
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1744549040386172, 'ffn_dropout': 0.3845778191742626, 'lr': 1.1416482526812583e-05, 'weight_decay': 0.033663094335532405, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:30:33,179] Trial 2 finished with value: 0.459070633697188 and parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.13072872167536764, 'ffn_dropout': 0.1140845675514245, 'lr': 0.0012126801600609715, 'weight_decay': 0.005827068786727869, 'batch_size': 64}. Best is trial 2 with value: 0.459070633697188.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.459071
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.13072872167536764, 'ffn_dropout': 0.1140845675514245, 'lr': 0.0012126801600609715, 'weight_decay': 0.005827068786727869, 'batch_size': 64}
+  Best Value (CSI): 0.459071
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.13072872167536764, 'ffn_dropout': 0.1140845675514245, 'lr': 0.0012126801600609715, 'weight_decay': 0.005827068786727869, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:32:06,257] Trial 3 finished with value: 0.4837955602953958 and parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.13417611437554622, 'ffn_dropout': 0.101642562315039, 'lr': 0.0006671901431392045, 'weight_decay': 0.0252392379246892, 'batch_size': 256}. Best is trial 3 with value: 0.4837955602953958.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.483796
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.13417611437554622, 'ffn_dropout': 0.101642562315039, 'lr': 0.0006671901431392045, 'weight_decay': 0.0252392379246892, 'batch_size': 256}
+  Best Value (CSI): 0.483796
+  Best Trial: 3
+  Best Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.13417611437554622, 'ffn_dropout': 0.101642562315039, 'lr': 0.0006671901431392045, 'weight_decay': 0.0252392379246892, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:32:59,884] Trial 4 finished with value: 0.09167591968793302 and parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15909672442187694, 'ffn_dropout': 0.3717921952562553, 'lr': 0.005985171792316229, 'weight_decay': 0.0009709989182516932, 'batch_size': 256}. Best is trial 3 with value: 0.4837955602953958.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.091676
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15909672442187694, 'ffn_dropout': 0.3717921952562553, 'lr': 0.005985171792316229, 'weight_decay': 0.0009709989182516932, 'batch_size': 256}
+  Best Value (CSI): 0.483796
+  Best Trial: 3
+  Best Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.13417611437554622, 'ffn_dropout': 0.101642562315039, 'lr': 0.0006671901431392045, 'weight_decay': 0.0252392379246892, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:35:28,413] Trial 5 finished with value: 0.39117419231477824 and parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.39548683926693073, 'ffn_dropout': 0.24640408844891987, 'lr': 3.823039307863025e-05, 'weight_decay': 0.0002952964524635349, 'batch_size': 128}. Best is trial 3 with value: 0.4837955602953958.
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.391174
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.39548683926693073, 'ffn_dropout': 0.24640408844891987, 'lr': 3.823039307863025e-05, 'weight_decay': 0.0002952964524635349, 'batch_size': 128}
+  Best Value (CSI): 0.483796
+  Best Trial: 3
+  Best Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.13417611437554622, 'ffn_dropout': 0.101642562315039, 'lr': 0.0006671901431392045, 'weight_decay': 0.0252392379246892, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:35:45,971] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.407279
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.10787546013535586, 'ffn_dropout': 0.20269570904753575, 'lr': 0.0008350155464408088, 'weight_decay': 0.0005291762021911083, 'batch_size': 256}
+  Best Value (CSI): 0.483796
+  Best Trial: 3
+  Best Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.13417611437554622, 'ffn_dropout': 0.101642562315039, 'lr': 0.0006671901431392045, 'weight_decay': 0.0252392379246892, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:36:25,931] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.073201
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.30887545723357024, 'ffn_dropout': 0.39787040746232605, 'lr': 0.008957114036168577, 'weight_decay': 0.0018031616376532479, 'batch_size': 32}
+  Best Value (CSI): 0.483796
+  Best Trial: 3
+  Best Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.13417611437554622, 'ffn_dropout': 0.101642562315039, 'lr': 0.0006671901431392045, 'weight_decay': 0.0252392379246892, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:37:23,908] Trial 8 finished with value: 0.4962456778342523 and parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2427549664804898, 'ffn_dropout': 0.16526714996641406, 'lr': 0.004003287639796112, 'weight_decay': 0.00039140197253343913, 'batch_size': 256}. Best is trial 8 with value: 0.4962456778342523.
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.496246
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2427549664804898, 'ffn_dropout': 0.16526714996641406, 'lr': 0.004003287639796112, 'weight_decay': 0.00039140197253343913, 'batch_size': 256}
+  Best Value (CSI): 0.496246
+  Best Trial: 8
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2427549664804898, 'ffn_dropout': 0.16526714996641406, 'lr': 0.004003287639796112, 'weight_decay': 0.00039140197253343913, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:38:04,514] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.370732
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2591490653886718, 'ffn_dropout': 0.10574968794215048, 'lr': 9.775118420469087e-05, 'weight_decay': 0.00017499013641922043, 'batch_size': 256}
+  Best Value (CSI): 0.496246
+  Best Trial: 8
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2427549664804898, 'ffn_dropout': 0.16526714996641406, 'lr': 0.004003287639796112, 'weight_decay': 0.00039140197253343913, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:38:46,972] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.375000
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.23001732499596575, 'ffn_dropout': 0.1692137877502867, 'lr': 0.0025311727803787364, 'weight_decay': 0.00012230507505704623, 'batch_size': 128}
+  Best Value (CSI): 0.496246
+  Best Trial: 8
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2427549664804898, 'ffn_dropout': 0.16526714996641406, 'lr': 0.004003287639796112, 'weight_decay': 0.00039140197253343913, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:38:53,094] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.197179
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.10251961469272192, 'ffn_dropout': 0.14783375038099805, 'lr': 0.00039142875037926196, 'weight_decay': 0.00683189698280882, 'batch_size': 256}
+  Best Value (CSI): 0.496246
+  Best Trial: 8
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2427549664804898, 'ffn_dropout': 0.16526714996641406, 'lr': 0.004003287639796112, 'weight_decay': 0.00039140197253343913, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:40:15,857] Trial 12 finished with value: 0.4887356291415484 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.21451183634540438, 'ffn_dropout': 0.1696037775564281, 'lr': 0.0018943596581716322, 'weight_decay': 0.08085910067281002, 'batch_size': 256}. Best is trial 8 with value: 0.4962456778342523.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.488736
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.21451183634540438, 'ffn_dropout': 0.1696037775564281, 'lr': 0.0018943596581716322, 'weight_decay': 0.08085910067281002, 'batch_size': 256}
+  Best Value (CSI): 0.496246
+  Best Trial: 8
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2427549664804898, 'ffn_dropout': 0.16526714996641406, 'lr': 0.004003287639796112, 'weight_decay': 0.00039140197253343913, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:40:45,024] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.324427
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2214074976487779, 'ffn_dropout': 0.18123065483509015, 'lr': 0.0014631621805475724, 'weight_decay': 0.07830910675016767, 'batch_size': 256}
+  Best Value (CSI): 0.496246
+  Best Trial: 8
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2427549664804898, 'ffn_dropout': 0.16526714996641406, 'lr': 0.004003287639796112, 'weight_decay': 0.00039140197253343913, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:41:51,247] Trial 14 finished with value: 0.5001066701078627 and parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}. Best is trial 14 with value: 0.5001066701078627.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.500107
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:42:10,738] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.373063
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28236438023078103, 'ffn_dropout': 0.26781169969815066, 'lr': 0.00282033439405969, 'weight_decay': 0.0007357217243206521, 'batch_size': 256}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:43:13,135] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.398559
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.32594838996754927, 'ffn_dropout': 0.20210232078133653, 'lr': 0.0003193468849435769, 'weight_decay': 0.0020729762525110674, 'batch_size': 32}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:43:34,825] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2589613929991434, 'ffn_dropout': 0.2887930431420088, 'lr': 0.0038432203023356748, 'weight_decay': 0.00044623393047431525, 'batch_size': 64}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:43:53,635] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.375297
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.1984614586364219, 'ffn_dropout': 0.14263672594909627, 'lr': 0.0034446941689181675, 'weight_decay': 0.00022421823579855528, 'batch_size': 128}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:44:03,522] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2883263005658156, 'ffn_dropout': 0.21616130640562767, 'lr': 0.00823323375328018, 'weight_decay': 0.0010373564061809165, 'batch_size': 256}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:45:52,523] Trial 20 finished with value: 0.48444765777873006 and parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.24484920110323144, 'ffn_dropout': 0.2828868762985322, 'lr': 0.0007713408804542852, 'weight_decay': 0.00010125118763892607, 'batch_size': 256}. Best is trial 14 with value: 0.5001066701078627.
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.484448
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.24484920110323144, 'ffn_dropout': 0.2828868762985322, 'lr': 0.0007713408804542852, 'weight_decay': 0.00010125118763892607, 'batch_size': 256}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+[I 2025-12-28 22:46:37,235] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.474252
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.20795133100172977, 'ffn_dropout': 0.17425397547284796, 'lr': 0.001594031642863458, 'weight_decay': 0.004878996258385458, 'batch_size': 256}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:47:45,837] Trial 22 finished with value: 0.493836799397319 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.20004826003642076, 'ffn_dropout': 0.14348259573171573, 'lr': 0.002175165628458849, 'weight_decay': 0.012375227652865986, 'batch_size': 256}. Best is trial 14 with value: 0.5001066701078627.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.493837
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.20004826003642076, 'ffn_dropout': 0.14348259573171573, 'lr': 0.002175165628458849, 'weight_decay': 0.012375227652865986, 'batch_size': 256}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:48:05,328] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.407320
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.19541684200349002, 'ffn_dropout': 0.14849340783950735, 'lr': 0.0040876867415894904, 'weight_decay': 0.010300959587378518, 'batch_size': 256}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:48:33,658] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.422535
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2397898644805776, 'ffn_dropout': 0.13043230853213322, 'lr': 0.0020728845123684295, 'weight_decay': 0.0024919705179691502, 'batch_size': 256}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:50:19,699] Trial 25 finished with value: 0.44927154165643507 and parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.26062758267942754, 'ffn_dropout': 0.12948723936417045, 'lr': 0.0045272217475245905, 'weight_decay': 0.0014046972618234252, 'batch_size': 256}. Best is trial 14 with value: 0.5001066701078627.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.449272
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.26062758267942754, 'ffn_dropout': 0.12948723936417045, 'lr': 0.0045272217475245905, 'weight_decay': 0.0014046972618234252, 'batch_size': 256}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:51:23,375] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.486907
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.19537457846352377, 'ffn_dropout': 0.19950138967902994, 'lr': 0.001139501246004423, 'weight_decay': 0.0034806737502214805, 'batch_size': 128}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:52:10,669] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.073201
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.2300903455113604, 'ffn_dropout': 0.16057503887382626, 'lr': 0.009932825092885943, 'weight_decay': 0.0012032312597511328, 'batch_size': 32}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:52:42,735] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.393462
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2831612792151492, 'ffn_dropout': 0.188045527953837, 'lr': 0.0026549374073146685, 'weight_decay': 0.011642809062383366, 'batch_size': 64}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:54:46,905] Trial 29 finished with value: 0.49754604321956447 and parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.19493447109604495, 'ffn_dropout': 0.22307411149666334, 'lr': 0.0005505583305283389, 'weight_decay': 0.0033284605779323436, 'batch_size': 64}. Best is trial 14 with value: 0.5001066701078627.
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.497546
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.19493447109604495, 'ffn_dropout': 0.22307411149666334, 'lr': 0.0005505583305283389, 'weight_decay': 0.0033284605779323436, 'batch_size': 64}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:55:33,173] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.366629
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1726057958392631, 'ffn_dropout': 0.22227277625174308, 'lr': 0.0005672156835071591, 'weight_decay': 0.0024937014614530477, 'batch_size': 64}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:56:16,556] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.390156
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1908205918905234, 'ffn_dropout': 0.18869333030522734, 'lr': 0.001036010040708237, 'weight_decay': 0.003307410563802071, 'batch_size': 64}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 22:58:09,974] Trial 32 finished with value: 0.4977312410829639 and parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.213740741544501, 'ffn_dropout': 0.21845009975877583, 'lr': 0.0015630138398552303, 'weight_decay': 0.000685523600920735, 'batch_size': 64}. Best is trial 14 with value: 0.5001066701078627.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.497731
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.213740741544501, 'ffn_dropout': 0.21845009975877583, 'lr': 0.0015630138398552303, 'weight_decay': 0.000685523600920735, 'batch_size': 64}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:58:42,439] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.414141
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.2150945389082463, 'ffn_dropout': 0.23794295415084643, 'lr': 0.0004688807992686707, 'weight_decay': 0.0006928205565085322, 'batch_size': 64}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:59:15,656] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.409389
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.1728383002676006, 'ffn_dropout': 0.21209568802467998, 'lr': 0.0010771337309337828, 'weight_decay': 0.00044604722818240313, 'batch_size': 64}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 22:59:47,944] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.429796
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.15095830209416888, 'ffn_dropout': 0.22023469521178715, 'lr': 0.00022651010646390513, 'weight_decay': 0.0016052441684229095, 'batch_size': 64}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:00:08,853] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.307292
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.236865488044541, 'ffn_dropout': 0.23155622008342217, 'lr': 0.0014689681811972863, 'weight_decay': 0.0003402551144924845, 'batch_size': 64}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:00:25,441] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.031587
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.21893104835569907, 'ffn_dropout': 0.25423999387034074, 'lr': 0.005564802285039306, 'weight_decay': 0.0007982851563507056, 'batch_size': 64}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:01:06,659] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.390960
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.18716410839842074, 'ffn_dropout': 0.19415345710592774, 'lr': 0.000701048579507398, 'weight_decay': 0.0013409891742012228, 'batch_size': 64}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:01:37,065] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.086041
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.2516404130694109, 'ffn_dropout': 0.2341006969713482, 'lr': 0.006431610382192338, 'weight_decay': 0.0006515579176368732, 'batch_size': 32}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:01:58,490] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2269692653532709, 'ffn_dropout': 0.20679215181105004, 'lr': 0.0029838890416435905, 'weight_decay': 0.0009798599700912216, 'batch_size': 64}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:02:50,876] Trial 41 finished with value: 0.49817309664825915 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.18640263261436377, 'ffn_dropout': 0.16122032663611224, 'lr': 0.0021231277257470946, 'weight_decay': 0.01688572475573524, 'batch_size': 256}. Best is trial 14 with value: 0.5001066701078627.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.498173
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.18640263261436377, 'ffn_dropout': 0.16122032663611224, 'lr': 0.0021231277257470946, 'weight_decay': 0.01688572475573524, 'batch_size': 256}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:03:15,624] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.365269
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15405778746423404, 'ffn_dropout': 0.17952606619386152, 'lr': 0.0017890333818619064, 'weight_decay': 0.018635011852634466, 'batch_size': 256}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:03:43,095] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.389850
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.17920659778666367, 'ffn_dropout': 0.17051235941910464, 'lr': 0.0009233631758119583, 'weight_decay': 0.004776348210741444, 'batch_size': 128}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:03:58,880] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.376993
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.16455219603813523, 'ffn_dropout': 0.16287670978236635, 'lr': 0.0012632386144249806, 'weight_decay': 0.030895530146561694, 'batch_size': 256}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:04:53,331] Trial 45 finished with value: 0.48428792236162393 and parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13693275726090168, 'ffn_dropout': 0.19167065155210405, 'lr': 0.002039887112659745, 'weight_decay': 0.007738404653461596, 'batch_size': 256}. Best is trial 14 with value: 0.5001066701078627.
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.484288
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13693275726090168, 'ffn_dropout': 0.19167065155210405, 'lr': 0.002039887112659745, 'weight_decay': 0.007738404653461596, 'batch_size': 256}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:05:32,389] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2123719000500617, 'ffn_dropout': 0.2066227968338971, 'lr': 0.0032340357024603144, 'weight_decay': 0.001764298779913322, 'batch_size': 32}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:05:50,406] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.353482
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1818014109171604, 'ffn_dropout': 0.2489586538881031, 'lr': 0.004710573705223361, 'weight_decay': 0.0002958088423358973, 'batch_size': 256}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:06:23,454] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.403670
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.20911044808136287, 'ffn_dropout': 0.10067260791044297, 'lr': 0.0014452556494008153, 'weight_decay': 0.0005064115797420447, 'batch_size': 64}
+  Best Value (CSI): 0.500107
+  Best Trial: 14
+  Best Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2776549176345562, 'ffn_dropout': 0.2073819204835327, 'lr': 0.0017492735615692344, 'weight_decay': 0.0023623967295935363, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:07:22,437] Trial 49 finished with value: 0.500955876255065 and parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22595662460184046, 'ffn_dropout': 0.18072426018543497, 'lr': 0.00233421501453318, 'weight_decay': 0.0001873978639928311, 'batch_size': 256}. Best is trial 49 with value: 0.500955876255065.
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.500956
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22595662460184046, 'ffn_dropout': 0.18072426018543497, 'lr': 0.00233421501453318, 'weight_decay': 0.0001873978639928311, 'batch_size': 256}
+  Best Value (CSI): 0.500956
+  Best Trial: 49
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22595662460184046, 'ffn_dropout': 0.18072426018543497, 'lr': 0.00233421501453318, 'weight_decay': 0.0001873978639928311, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+[I 2025-12-28 23:08:07,882] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.493480
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.18237838074463372, 'ffn_dropout': 0.18241034624732622, 'lr': 0.0006746426196008369, 'weight_decay': 0.00014618701761166458, 'batch_size': 128}
+  Best Value (CSI): 0.500956
+  Best Trial: 49
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22595662460184046, 'ffn_dropout': 0.18072426018543497, 'lr': 0.00233421501453318, 'weight_decay': 0.0001873978639928311, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:08:29,345] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.374562
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22169374723206992, 'ffn_dropout': 0.1997662848958918, 'lr': 0.002503010590667555, 'weight_decay': 0.00021551991169730015, 'batch_size': 256}
+  Best Value (CSI): 0.500956
+  Best Trial: 49
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22595662460184046, 'ffn_dropout': 0.18072426018543497, 'lr': 0.00233421501453318, 'weight_decay': 0.0001873978639928311, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:08:41,508] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.24634900278319083, 'ffn_dropout': 0.15436805724223415, 'lr': 0.003347029659416572, 'weight_decay': 0.0001706727222444742, 'batch_size': 256}
+  Best Value (CSI): 0.500956
+  Best Trial: 49
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22595662460184046, 'ffn_dropout': 0.18072426018543497, 'lr': 0.00233421501453318, 'weight_decay': 0.0001873978639928311, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:09:23,421] Trial 53 finished with value: 0.48762834509332986 and parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2660804679686664, 'ffn_dropout': 0.16827319909748595, 'lr': 0.0017567766402478964, 'weight_decay': 0.0002329108103257573, 'batch_size': 256}. Best is trial 49 with value: 0.500955876255065.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.487628
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2660804679686664, 'ffn_dropout': 0.16827319909748595, 'lr': 0.0017567766402478964, 'weight_decay': 0.0002329108103257573, 'batch_size': 256}
+  Best Value (CSI): 0.500956
+  Best Trial: 49
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22595662460184046, 'ffn_dropout': 0.18072426018543497, 'lr': 0.00233421501453318, 'weight_decay': 0.0001873978639928311, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:09:47,208] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.041186
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2028991447901797, 'ffn_dropout': 0.17842433038241115, 'lr': 0.0025439054955018296, 'weight_decay': 0.0003705109855800235, 'batch_size': 256}
+  Best Value (CSI): 0.500956
+  Best Trial: 49
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22595662460184046, 'ffn_dropout': 0.18072426018543497, 'lr': 0.00233421501453318, 'weight_decay': 0.0001873978639928311, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:10:10,040] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.420225
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.23113495377384968, 'ffn_dropout': 0.2180161264745915, 'lr': 0.0012579888210722666, 'weight_decay': 0.000551238644273327, 'batch_size': 256}
+  Best Value (CSI): 0.500956
+  Best Trial: 49
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22595662460184046, 'ffn_dropout': 0.18072426018543497, 'lr': 0.00233421501453318, 'weight_decay': 0.0001873978639928311, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:10:23,595] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.446833
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.23852137461664685, 'ffn_dropout': 0.15358530662157838, 'lr': 0.0008800414174104112, 'weight_decay': 0.0008745308747049414, 'batch_size': 256}
+  Best Value (CSI): 0.500956
+  Best Trial: 49
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22595662460184046, 'ffn_dropout': 0.18072426018543497, 'lr': 0.00233421501453318, 'weight_decay': 0.0001873978639928311, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:10:54,040] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.363286
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2051821731821433, 'ffn_dropout': 0.1919634291113097, 'lr': 0.0037707014017494696, 'weight_decay': 0.0011724753850994264, 'batch_size': 256}
+  Best Value (CSI): 0.500956
+  Best Trial: 49
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22595662460184046, 'ffn_dropout': 0.18072426018543497, 'lr': 0.00233421501453318, 'weight_decay': 0.0001873978639928311, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:11:05,544] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.419522
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.2728658092886924, 'ffn_dropout': 0.13648294079132645, 'lr': 0.0022405748605553435, 'weight_decay': 0.0021725687202815945, 'batch_size': 256}
+  Best Value (CSI): 0.500956
+  Best Trial: 49
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22595662460184046, 'ffn_dropout': 0.18072426018543497, 'lr': 0.00233421501453318, 'weight_decay': 0.0001873978639928311, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:12:24,228] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.373541
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.22255961472236965, 'ffn_dropout': 0.1180496420466749, 'lr': 0.0017377240801767912, 'weight_decay': 0.06425982092571389, 'batch_size': 32}
+  Best Value (CSI): 0.500956
+  Best Trial: 49
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22595662460184046, 'ffn_dropout': 0.18072426018543497, 'lr': 0.00233421501453318, 'weight_decay': 0.0001873978639928311, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:12:49,419] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.25535433708338734, 'ffn_dropout': 0.1596546884928284, 'lr': 0.005710100780388977, 'weight_decay': 0.004482614877122853, 'batch_size': 64}
+  Best Value (CSI): 0.500956
+  Best Trial: 49
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22595662460184046, 'ffn_dropout': 0.18072426018543497, 'lr': 0.00233421501453318, 'weight_decay': 0.0001873978639928311, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:14:00,627] Trial 61 finished with value: 0.5013875965799505 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}. Best is trial 61 with value: 0.5013875965799505.
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.501388
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:14:28,318] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.398437
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.19010527013785894, 'ffn_dropout': 0.14760021593643738, 'lr': 0.003077712132696288, 'weight_decay': 0.007637730361597706, 'batch_size': 256}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:14:51,083] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.368485
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19575346235661628, 'ffn_dropout': 0.17475242560346416, 'lr': 0.0020275501504749965, 'weight_decay': 0.023683198811449067, 'batch_size': 256}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:15:10,648] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.420086
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.2121005009887643, 'ffn_dropout': 0.16105245272359592, 'lr': 0.0014743737567357983, 'weight_decay': 0.016293260584767866, 'batch_size': 256}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:16:06,791] Trial 65 finished with value: 0.49243876298257144 and parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.23437717998982613, 'ffn_dropout': 0.13842942804786482, 'lr': 0.004573049662470003, 'weight_decay': 0.009957506837230588, 'batch_size': 256}. Best is trial 61 with value: 0.5013875965799505.
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.492439
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.23437717998982613, 'ffn_dropout': 0.13842942804786482, 'lr': 0.004573049662470003, 'weight_decay': 0.009957506837230588, 'batch_size': 256}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:16:34,319] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.370513
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.16863935092390567, 'ffn_dropout': 0.21209372533464382, 'lr': 0.0024983669923680893, 'weight_decay': 0.0032279442840474847, 'batch_size': 128}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:16:49,633] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.451302
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.24485946320396842, 'ffn_dropout': 0.1846905801480751, 'lr': 0.0009713380634149733, 'weight_decay': 0.002516826681300192, 'batch_size': 256}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:17:10,512] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.20076063842224695, 'ffn_dropout': 0.1989018766916691, 'lr': 0.007243659623447381, 'weight_decay': 0.0061993348443998356, 'batch_size': 64}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:17:18,206] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.286802
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.22060617322167925, 'ffn_dropout': 0.22862580006854297, 'lr': 0.003595391780531467, 'weight_decay': 0.0015018084403389516, 'batch_size': 256}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:18:07,973] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.421348
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1888613183262093, 'ffn_dropout': 0.16913271178644232, 'lr': 0.0012918841753341076, 'weight_decay': 0.0006448145777512577, 'batch_size': 64}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:19:30,194] Trial 71 finished with value: 0.4947363505746171 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.204744852458924, 'ffn_dropout': 0.1279721805878679, 'lr': 0.002207732032827607, 'weight_decay': 0.014266081446455367, 'batch_size': 256}. Best is trial 61 with value: 0.5013875965799505.
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.494736
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.204744852458924, 'ffn_dropout': 0.1279721805878679, 'lr': 0.002207732032827607, 'weight_decay': 0.014266081446455367, 'batch_size': 256}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:19:52,900] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.427796
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.2055131802182044, 'ffn_dropout': 0.12634896266696083, 'lr': 0.0017526736307685773, 'weight_decay': 0.013824169046047932, 'batch_size': 256}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:20:20,524] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.351582
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.22946151257017075, 'ffn_dropout': 0.11830708485933376, 'lr': 0.002298997694773247, 'weight_decay': 0.009110737014005034, 'batch_size': 256}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:21:33,657] Trial 74 finished with value: 0.4992919156401956 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1782984853538817, 'ffn_dropout': 0.11022349336541981, 'lr': 0.0028451960181233504, 'weight_decay': 0.018193450041975326, 'batch_size': 256}. Best is trial 61 with value: 0.5013875965799505.
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.499292
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1782984853538817, 'ffn_dropout': 0.11022349336541981, 'lr': 0.0028451960181233504, 'weight_decay': 0.018193450041975326, 'batch_size': 256}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:21:55,602] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.397770
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1808859444528016, 'ffn_dropout': 0.14415345476015878, 'lr': 0.002765941830488035, 'weight_decay': 0.018043506549232913, 'batch_size': 256}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:22:05,961] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.17328298467210929, 'ffn_dropout': 0.11024934012463894, 'lr': 0.005275484331557514, 'weight_decay': 0.021463794992675374, 'batch_size': 256}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:22:46,418] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.367901
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.16099482551181665, 'ffn_dropout': 0.15297353488863297, 'lr': 0.00107676655962284, 'weight_decay': 0.03433704916529691, 'batch_size': 64}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:23:46,806] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.396396
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.2152702302087926, 'ffn_dropout': 0.10883553954662918, 'lr': 0.003961185454049697, 'weight_decay': 0.02740645980795988, 'batch_size': 32}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:24:02,826] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.405952
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19532442842301123, 'ffn_dropout': 0.1375330036783151, 'lr': 0.002976880384000001, 'weight_decay': 0.011997044104301618, 'batch_size': 256}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:25:28,743] Trial 80 finished with value: 0.4980693078653548 and parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22449747582521493, 'ffn_dropout': 0.20669896978515856, 'lr': 0.0007957641965844268, 'weight_decay': 0.048889848942813015, 'batch_size': 128}. Best is trial 61 with value: 0.5013875965799505.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.498069
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22449747582521493, 'ffn_dropout': 0.20669896978515856, 'lr': 0.0007957641965844268, 'weight_decay': 0.048889848942813015, 'batch_size': 128}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:25:56,471] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.423651
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22405046939496254, 'ffn_dropout': 0.21108138290965373, 'lr': 0.0014897288499596763, 'weight_decay': 0.0475172208698025, 'batch_size': 128}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:26:24,234] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.372842
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.24944695749258378, 'ffn_dropout': 0.2026586865779086, 'lr': 0.0006114274682924321, 'weight_decay': 0.043004622805300624, 'batch_size': 128}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:27:48,456] Trial 83 finished with value: 0.486987919595208 and parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.24078883891391925, 'ffn_dropout': 0.22153739027121797, 'lr': 0.000767517958336242, 'weight_decay': 0.023627084385686687, 'batch_size': 128}. Best is trial 61 with value: 0.5013875965799505.
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.486988
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.24078883891391925, 'ffn_dropout': 0.22153739027121797, 'lr': 0.000767517958336242, 'weight_decay': 0.023627084385686687, 'batch_size': 128}
+  Best Value (CSI): 0.501388
+  Best Trial: 61
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19828426115038106, 'ffn_dropout': 0.14321602580528056, 'lr': 0.00218720274175351, 'weight_decay': 0.01739688286070899, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:29:11,066] Trial 84 finished with value: 0.5052817328725289 and parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}. Best is trial 84 with value: 0.5052817328725289.
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.505282
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:30:14,199] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.486239
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21364624203038216, 'ffn_dropout': 0.1869596517584163, 'lr': 0.0019142264955753193, 'weight_decay': 0.027011044901116082, 'batch_size': 128}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:31:28,310] Trial 86 finished with value: 0.4937566261594135 and parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1950396127127738, 'ffn_dropout': 0.195164433568025, 'lr': 0.0008970031661372964, 'weight_decay': 0.03265615515284464, 'batch_size': 128}. Best is trial 84 with value: 0.5052817328725289.
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.493757
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1950396127127738, 'ffn_dropout': 0.195164433568025, 'lr': 0.0008970031661372964, 'weight_decay': 0.03265615515284464, 'batch_size': 128}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+[I 2025-12-28 23:32:01,311] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.481964
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.17716180990839395, 'ffn_dropout': 0.20606298319351526, 'lr': 0.0011609782029478946, 'weight_decay': 0.0038968153664484958, 'batch_size': 128}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:32:20,059] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.18547118031728013, 'ffn_dropout': 0.17562652152681163, 'lr': 0.0016406679962412021, 'weight_decay': 0.019400778821703775, 'batch_size': 128}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:32:43,045] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.407119
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.20803433905130264, 'ffn_dropout': 0.24025994211830787, 'lr': 0.0005441178548353954, 'weight_decay': 0.05125926990384529, 'batch_size': 128}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:33:40,971] Trial 90 finished with value: 0.4948383962339948 and parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.19986354036801487, 'ffn_dropout': 0.18580076009483934, 'lr': 0.0007811522916767445, 'weight_decay': 0.005678587648692619, 'batch_size': 128}. Best is trial 84 with value: 0.5052817328725289.
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.494838
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.19986354036801487, 'ffn_dropout': 0.18580076009483934, 'lr': 0.0007811522916767445, 'weight_decay': 0.005678587648692619, 'batch_size': 128}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:34:21,964] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.388398
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.23150336371131483, 'ffn_dropout': 0.1676580515506743, 'lr': 0.0028960759873974855, 'weight_decay': 0.039881485037680464, 'batch_size': 64}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:34:40,474] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.383220
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21752452045176462, 'ffn_dropout': 0.1799835538959502, 'lr': 0.0019529870047801138, 'weight_decay': 0.0008628585887442941, 'batch_size': 256}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:34:50,654] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.405192
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.22647814851654224, 'ffn_dropout': 0.19809027820887315, 'lr': 0.0013274499678661198, 'weight_decay': 0.09787615428518671, 'batch_size': 256}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:35:15,940] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.18940042688901407, 'ffn_dropout': 0.22490778790388583, 'lr': 0.003389406033099836, 'weight_decay': 0.0003922349133094853, 'batch_size': 64}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 23:36:02,227] Trial 95 finished with value: 0.4957523957124577 and parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.23801731382579366, 'ffn_dropout': 0.2171479623425568, 'lr': 0.001564709525674296, 'weight_decay': 0.01602921456848865, 'batch_size': 256}. Best is trial 84 with value: 0.5052817328725289.
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.495752
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.23801731382579366, 'ffn_dropout': 0.2171479623425568, 'lr': 0.001564709525674296, 'weight_decay': 0.01602921456848865, 'batch_size': 256}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:36:41,081] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2081682559647492, 'ffn_dropout': 0.1916114404057045, 'lr': 0.002457280668526897, 'weight_decay': 0.0011541661753339553, 'batch_size': 32}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:36:55,064] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.034229
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.2177110385364753, 'ffn_dropout': 0.211113230930637, 'lr': 0.004208437356842814, 'weight_decay': 0.021110179156677178, 'batch_size': 256}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:37:45,541] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.395466
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.25538258180866397, 'ffn_dropout': 0.18080055269367168, 'lr': 0.0021568035159304625, 'weight_decay': 0.00026077864873069, 'batch_size': 64}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 23:38:03,764] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.414712
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.18436782224804477, 'ffn_dropout': 0.1582756113214427, 'lr': 0.0010440759507552013, 'weight_decay': 0.00031600073662022525, 'batch_size': 256}
+  Best Value (CSI): 0.505282
+  Best Trial: 84
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5053
+Best Hyperparameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.3036
+  - 최종 CSI: 0.4147
+  - 최고 CSI: 0.5053
+  - 최저 CSI: 0.0000
+  - 평균 CSI: 0.3473
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/ft_transformer_ctgan10000_gwangju_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 16, Best CSI: 0.4397
+  Fold 1 학습 완료 (검증 CSI: 0.4397)
+Fold 2 학습 중...
+  Early stopping at epoch 26, Best CSI: 0.5254
+  Fold 2 학습 완료 (검증 CSI: 0.5254)
+Fold 3 학습 중...
+  Early stopping at epoch 25, Best CSI: 0.5187
+  Fold 3 학습 완료 (검증 CSI: 0.5187)
+
+모든 모델 저장 완료: ../save_model/ft_transformer_optima/ft_transformer_ctgan10000_gwangju.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/ft_transformer_optima/scaler/ft_transformer_ctgan10000_gwangju_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/ft_transformer_optima/ft_transformer_ctgan10000_gwangju.pkl
+
+✓ 완료: ft_transformer_ctgan10000/ft_transformer_ctgan10000_gwangju.py (소요 시간: 4618초)
+
+----------------------------------------
+실행 중: ft_transformer_ctgan10000/ft_transformer_ctgan10000_incheon.py
+시작 시간: 2025-12-28 23:39:15
+----------------------------------------
+[I 2025-12-28 23:39:17,014] A new study created in memory with name: no-name-a5b67fde-dd1a-4b35-8708-081520102ec1
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 23:42:23,103] Trial 0 finished with value: 0.5462797172926566 and parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.3510578172433737, 'ffn_dropout': 0.3165398010379043, 'lr': 0.0002464326184428672, 'weight_decay': 0.0021581132202383307, 'batch_size': 64}. Best is trial 0 with value: 0.5462797172926566.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.546280
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.3510578172433737, 'ffn_dropout': 0.3165398010379043, 'lr': 0.0002464326184428672, 'weight_decay': 0.0021581132202383307, 'batch_size': 64}
+  Best Value (CSI): 0.546280
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.3510578172433737, 'ffn_dropout': 0.3165398010379043, 'lr': 0.0002464326184428672, 'weight_decay': 0.0021581132202383307, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 23:49:36,930] Trial 1 finished with value: 0.5464301254312375 and parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2352796863064692, 'ffn_dropout': 0.36022215897032983, 'lr': 0.0010946082821419853, 'weight_decay': 0.007054410467014514, 'batch_size': 32}. Best is trial 1 with value: 0.5464301254312375.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.546430
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2352796863064692, 'ffn_dropout': 0.36022215897032983, 'lr': 0.0010946082821419853, 'weight_decay': 0.007054410467014514, 'batch_size': 32}
+  Best Value (CSI): 0.546430
+  Best Trial: 1
+  Best Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2352796863064692, 'ffn_dropout': 0.36022215897032983, 'lr': 0.0010946082821419853, 'weight_decay': 0.007054410467014514, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 23:52:53,593] Trial 2 finished with value: 0.5176309198936521 and parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.32693833818166396, 'ffn_dropout': 0.3329104229929004, 'lr': 9.531124017565615e-05, 'weight_decay': 0.0014548712158556664, 'batch_size': 64}. Best is trial 1 with value: 0.5464301254312375.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.517631
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.32693833818166396, 'ffn_dropout': 0.3329104229929004, 'lr': 9.531124017565615e-05, 'weight_decay': 0.0014548712158556664, 'batch_size': 64}
+  Best Value (CSI): 0.546430
+  Best Trial: 1
+  Best Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2352796863064692, 'ffn_dropout': 0.36022215897032983, 'lr': 0.0010946082821419853, 'weight_decay': 0.007054410467014514, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 23:54:37,223] Trial 3 finished with value: 0.5370048614998882 and parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.1744414651718707, 'ffn_dropout': 0.39689610586116497, 'lr': 0.00029908950950459617, 'weight_decay': 0.0014636452494366332, 'batch_size': 64}. Best is trial 1 with value: 0.5464301254312375.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.537005
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.1744414651718707, 'ffn_dropout': 0.39689610586116497, 'lr': 0.00029908950950459617, 'weight_decay': 0.0014636452494366332, 'batch_size': 64}
+  Best Value (CSI): 0.546430
+  Best Trial: 1
+  Best Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2352796863064692, 'ffn_dropout': 0.36022215897032983, 'lr': 0.0010946082821419853, 'weight_decay': 0.007054410467014514, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 23:56:04,086] Trial 4 finished with value: 0.437454940659255 and parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21300642605036968, 'ffn_dropout': 0.1505749801818388, 'lr': 4.7399689186158254e-05, 'weight_decay': 0.01212058668322202, 'batch_size': 128}. Best is trial 1 with value: 0.5464301254312375.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.437455
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21300642605036968, 'ffn_dropout': 0.1505749801818388, 'lr': 4.7399689186158254e-05, 'weight_decay': 0.01212058668322202, 'batch_size': 128}
+  Best Value (CSI): 0.546430
+  Best Trial: 1
+  Best Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2352796863064692, 'ffn_dropout': 0.36022215897032983, 'lr': 0.0010946082821419853, 'weight_decay': 0.007054410467014514, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 23:56:21,765] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.366454
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.17447741332140246, 'ffn_dropout': 0.2452934851930991, 'lr': 2.7897643402814155e-05, 'weight_decay': 0.00047731779370732756, 'batch_size': 64}
+  Best Value (CSI): 0.546430
+  Best Trial: 1
+  Best Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2352796863064692, 'ffn_dropout': 0.36022215897032983, 'lr': 0.0010946082821419853, 'weight_decay': 0.007054410467014514, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 23:56:30,112] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.199245
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10537557476906195, 'ffn_dropout': 0.1410387756704381, 'lr': 5.933039783426547e-05, 'weight_decay': 0.0001072532913548624, 'batch_size': 256}
+  Best Value (CSI): 0.546430
+  Best Trial: 1
+  Best Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2352796863064692, 'ffn_dropout': 0.36022215897032983, 'lr': 0.0010946082821419853, 'weight_decay': 0.007054410467014514, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 00:02:58,992] Trial 7 finished with value: 0.5482335521221479 and parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11120869422981193, 'ffn_dropout': 0.12617347554183356, 'lr': 0.00015776266193100045, 'weight_decay': 0.001820829144600586, 'batch_size': 32}. Best is trial 7 with value: 0.5482335521221479.
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.548234
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11120869422981193, 'ffn_dropout': 0.12617347554183356, 'lr': 0.00015776266193100045, 'weight_decay': 0.001820829144600586, 'batch_size': 32}
+  Best Value (CSI): 0.548234
+  Best Trial: 7
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11120869422981193, 'ffn_dropout': 0.12617347554183356, 'lr': 0.00015776266193100045, 'weight_decay': 0.001820829144600586, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:03:19,388] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.435490
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.24230920279189036, 'ffn_dropout': 0.12438456961443337, 'lr': 2.8385518438793184e-05, 'weight_decay': 0.03309078421669175, 'batch_size': 128}
+  Best Value (CSI): 0.548234
+  Best Trial: 7
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11120869422981193, 'ffn_dropout': 0.12617347554183356, 'lr': 0.00015776266193100045, 'weight_decay': 0.001820829144600586, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:04:16,260] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.3929595994118318, 'ffn_dropout': 0.3108415177081977, 'lr': 0.002373889830295486, 'weight_decay': 0.00710212083580161, 'batch_size': 32}
+  Best Value (CSI): 0.548234
+  Best Trial: 7
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11120869422981193, 'ffn_dropout': 0.12617347554183356, 'lr': 0.00015776266193100045, 'weight_decay': 0.001820829144600586, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:05:32,157] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.147427
+  Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11972846958028585, 'ffn_dropout': 0.1972941509831399, 'lr': 0.0075603924732496755, 'weight_decay': 0.06883168473442167, 'batch_size': 32}
+  Best Value (CSI): 0.548234
+  Best Trial: 7
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11120869422981193, 'ffn_dropout': 0.12617347554183356, 'lr': 0.00015776266193100045, 'weight_decay': 0.001820829144600586, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:06:26,273] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.28976331019395996, 'ffn_dropout': 0.10046102154516451, 'lr': 0.0010957127755507245, 'weight_decay': 0.0067622542171069154, 'batch_size': 32}
+  Best Value (CSI): 0.548234
+  Best Trial: 7
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11120869422981193, 'ffn_dropout': 0.12617347554183356, 'lr': 0.00015776266193100045, 'weight_decay': 0.001820829144600586, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:07:41,542] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.099383
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.270853924462793, 'ffn_dropout': 0.20096324957038045, 'lr': 0.0007817805141926115, 'weight_decay': 0.015460338023660657, 'batch_size': 32}
+  Best Value (CSI): 0.548234
+  Best Trial: 7
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11120869422981193, 'ffn_dropout': 0.12617347554183356, 'lr': 0.00015776266193100045, 'weight_decay': 0.001820829144600586, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:08:47,160] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.390043
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.15482396554564729, 'ffn_dropout': 0.260433248472379, 'lr': 1.2640391637559996e-05, 'weight_decay': 0.0034591324820598427, 'batch_size': 32}
+  Best Value (CSI): 0.548234
+  Best Trial: 7
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11120869422981193, 'ffn_dropout': 0.12617347554183356, 'lr': 0.00015776266193100045, 'weight_decay': 0.001820829144600586, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 00:11:05,854] Trial 14 finished with value: 0.5141720701802868 and parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.2403904204380791, 'ffn_dropout': 0.38140466429794534, 'lr': 0.00017455095805766422, 'weight_decay': 0.08960125142951682, 'batch_size': 256}. Best is trial 7 with value: 0.5482335521221479.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.514172
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.2403904204380791, 'ffn_dropout': 0.38140466429794534, 'lr': 0.00017455095805766422, 'weight_decay': 0.08960125142951682, 'batch_size': 256}
+  Best Value (CSI): 0.548234
+  Best Trial: 7
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11120869422981193, 'ffn_dropout': 0.12617347554183356, 'lr': 0.00015776266193100045, 'weight_decay': 0.001820829144600586, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:12:01,595] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.147427
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19943515826116465, 'ffn_dropout': 0.18133413105636048, 'lr': 0.0009616034287122157, 'weight_decay': 0.0007159513200565918, 'batch_size': 32}
+  Best Value (CSI): 0.548234
+  Best Trial: 7
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11120869422981193, 'ffn_dropout': 0.12617347554183356, 'lr': 0.00015776266193100045, 'weight_decay': 0.001820829144600586, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 00:18:52,166] Trial 16 finished with value: 0.5615761316445699 and parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1380493586467473, 'ffn_dropout': 0.24358844962569826, 'lr': 0.0004894899689411649, 'weight_decay': 0.004098449384519225, 'batch_size': 32}. Best is trial 16 with value: 0.5615761316445699.
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.561576
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1380493586467473, 'ffn_dropout': 0.24358844962569826, 'lr': 0.0004894899689411649, 'weight_decay': 0.004098449384519225, 'batch_size': 32}
+  Best Value (CSI): 0.561576
+  Best Trial: 16
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1380493586467473, 'ffn_dropout': 0.24358844962569826, 'lr': 0.0004894899689411649, 'weight_decay': 0.004098449384519225, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:20:51,495] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.490083
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.13400388386582784, 'ffn_dropout': 0.23885592609765183, 'lr': 0.00014383085817323593, 'weight_decay': 0.0037793176325993318, 'batch_size': 32}
+  Best Value (CSI): 0.561576
+  Best Trial: 16
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1380493586467473, 'ffn_dropout': 0.24358844962569826, 'lr': 0.0004894899689411649, 'weight_decay': 0.004098449384519225, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 00:23:12,276] Trial 18 finished with value: 0.5643832267965512 and parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}. Best is trial 18 with value: 0.5643832267965512.
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.564383
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 00:24:51,000] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.509234
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.14957954967348686, 'ffn_dropout': 0.2800870794477315, 'lr': 0.0005319785577291643, 'weight_decay': 0.0006412975874320264, 'batch_size': 256}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 00:25:53,325] Trial 20 finished with value: 0.5264481381947264 and parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.10089007890601638, 'ffn_dropout': 0.22243490483196152, 'lr': 0.00043524164569292014, 'weight_decay': 0.00026857826793736246, 'batch_size': 256}. Best is trial 18 with value: 0.5643832267965512.
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.526448
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.10089007890601638, 'ffn_dropout': 0.22243490483196152, 'lr': 0.00043524164569292014, 'weight_decay': 0.00026857826793736246, 'batch_size': 256}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 00:28:04,641] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.477580
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.12587702091007322, 'ffn_dropout': 0.171769138114872, 'lr': 0.00027810456590594726, 'weight_decay': 0.0012867254274542095, 'batch_size': 256}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:28:49,124] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.549041
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.14034425867940936, 'ffn_dropout': 0.1637852601775704, 'lr': 0.00014788804132516523, 'weight_decay': 0.0025419095030742144, 'batch_size': 128}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+[I 2025-12-29 00:30:08,054] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.576567
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.10132696074937578, 'ffn_dropout': 0.11838863742701045, 'lr': 0.00048375297059161706, 'weight_decay': 0.000982606423297445, 'batch_size': 256}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 00:37:57,647] Trial 24 finished with value: 0.5452319781737195 and parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.16979016934560456, 'ffn_dropout': 0.15038072988185297, 'lr': 0.00011500819385513623, 'weight_decay': 0.0003696986954667127, 'batch_size': 32}. Best is trial 18 with value: 0.5643832267965512.
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.545232
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.16979016934560456, 'ffn_dropout': 0.15038072988185297, 'lr': 0.00011500819385513623, 'weight_decay': 0.0003696986954667127, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:39:25,047] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.506422
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.12356542388230375, 'ffn_dropout': 0.2141113010735353, 'lr': 0.0002514440541562182, 'weight_decay': 0.0018424789539535846, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:39:42,393] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.362331
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1985323349101837, 'ffn_dropout': 0.1841158137450152, 'lr': 9.375521429618026e-05, 'weight_decay': 0.0009918336446864385, 'batch_size': 256}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 00:41:51,854] Trial 27 finished with value: 0.5523076108441733 and parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.13130877181998685, 'ffn_dropout': 0.16801967383763183, 'lr': 0.00020982994508162084, 'weight_decay': 0.0036049157731073123, 'batch_size': 128}. Best is trial 18 with value: 0.5643832267965512.
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.552308
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.13130877181998685, 'ffn_dropout': 0.16801967383763183, 'lr': 0.00020982994508162084, 'weight_decay': 0.0036049157731073123, 'batch_size': 128}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 00:43:37,714] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.540420
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15663569176095848, 'ffn_dropout': 0.22357225971691325, 'lr': 0.0004056937105550292, 'weight_decay': 0.004196219342154379, 'batch_size': 128}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:44:21,484] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.537367
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.13825230132267136, 'ffn_dropout': 0.20082399428944703, 'lr': 0.0002175853527812361, 'weight_decay': 0.0022474023401324356, 'batch_size': 128}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 00:46:01,509] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.531825
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.15512065281194876, 'ffn_dropout': 0.16872809060271243, 'lr': 0.0006777026148565629, 'weight_decay': 0.0029149259395310225, 'batch_size': 128}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 00:48:28,578] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.515233
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.12017243720235452, 'ffn_dropout': 0.13445131546928224, 'lr': 0.00019987726465314456, 'weight_decay': 0.002164062932968941, 'batch_size': 128}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:49:51,763] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.453292
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11729625762413762, 'ffn_dropout': 0.10277943559013536, 'lr': 0.000258276941044599, 'weight_decay': 0.005090692893771197, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:50:44,312] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.448900
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.13805123523139862, 'ffn_dropout': 0.15013617176449412, 'lr': 0.00035652314214680277, 'weight_decay': 0.001975910400366289, 'batch_size': 64}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 00:57:19,748] Trial 34 finished with value: 0.5526226590348261 and parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.17822697178187646, 'ffn_dropout': 0.16239233794709368, 'lr': 9.8948657760547e-05, 'weight_decay': 0.0013179676713396845, 'batch_size': 32}. Best is trial 18 with value: 0.5643832267965512.
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.552623
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.17822697178187646, 'ffn_dropout': 0.16239233794709368, 'lr': 9.8948657760547e-05, 'weight_decay': 0.0013179676713396845, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:57:40,592] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.448471
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.1775633042953697, 'ffn_dropout': 0.18434482937480196, 'lr': 7.939534366246458e-05, 'weight_decay': 0.0013018722931509364, 'batch_size': 128}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:58:26,633] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.494864
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.18756376006064038, 'ffn_dropout': 0.1491303741607855, 'lr': 0.00011715445856402639, 'weight_decay': 0.0052004952280321525, 'batch_size': 64}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:58:48,887] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.538505
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2201909760053963, 'ffn_dropout': 0.16562300977175326, 'lr': 0.0003639311793465039, 'weight_decay': 0.0033750476564611124, 'batch_size': 256}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 00:59:23,229] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.467086
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1651150601039373, 'ffn_dropout': 0.20977987006228813, 'lr': 6.95017313408398e-05, 'weight_decay': 0.010042484944057472, 'batch_size': 64}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:00:40,088] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.519093
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1773130090235038, 'ffn_dropout': 0.23536522710957605, 'lr': 0.00021484650929151007, 'weight_decay': 0.0014362896688472364, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+[I 2025-12-29 01:02:04,272] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.541386
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.14608759547348982, 'ffn_dropout': 0.2563168568723006, 'lr': 0.0001213425504160212, 'weight_decay': 0.002651002341320549, 'batch_size': 128}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:03:46,624] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.503071
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11759514848670445, 'ffn_dropout': 0.1312498827447781, 'lr': 0.00017585132728941089, 'weight_decay': 0.0015747445896388903, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 01:11:28,898] Trial 42 finished with value: 0.5591557623722259 and parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1328778764751705, 'ffn_dropout': 0.1222859064671967, 'lr': 5.4408215906146685e-05, 'weight_decay': 0.0010212712036367943, 'batch_size': 32}. Best is trial 18 with value: 0.5643832267965512.
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.559156
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1328778764751705, 'ffn_dropout': 0.1222859064671967, 'lr': 5.4408215906146685e-05, 'weight_decay': 0.0010212712036367943, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:13:14,803] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.496921
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.161131252874405, 'ffn_dropout': 0.15616650087799774, 'lr': 5.4343323363981646e-05, 'weight_decay': 0.0009404587718160067, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:16:08,624] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.515188
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.13121322223822776, 'ffn_dropout': 0.14053149897339584, 'lr': 3.8385743918216924e-05, 'weight_decay': 0.0005587549021746302, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:18:44,873] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.508177
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.14398264544705586, 'ffn_dropout': 0.11515393373204819, 'lr': 4.480248511612693e-05, 'weight_decay': 0.00038870570059338245, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:20:32,797] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.506242
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1084269413379796, 'ffn_dropout': 0.13550581147327595, 'lr': 8.878353905558894e-05, 'weight_decay': 0.0007381223389466994, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:20:46,409] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.288837
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.13075847136344396, 'ffn_dropout': 0.19331088301551502, 'lr': 6.339790025948526e-05, 'weight_decay': 0.0010741211141414974, 'batch_size': 256}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:22:09,585] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.469349
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.16457222274891387, 'ffn_dropout': 0.17582066036302907, 'lr': 0.00029719417264965117, 'weight_decay': 0.001759079541398814, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:22:53,043] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.18375308813694813, 'ffn_dropout': 0.1576603252870394, 'lr': 0.0012849111511400186, 'weight_decay': 0.002933562482816141, 'batch_size': 64}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:24:56,520] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.528695
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.14994934150580436, 'ffn_dropout': 0.12036010488584449, 'lr': 2.8849493235188442e-05, 'weight_decay': 0.007959992788597797, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:26:55,375] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.494678
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11139695325026643, 'ffn_dropout': 0.1402597802185082, 'lr': 0.00015898431903922707, 'weight_decay': 0.0016835136359699676, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 01:34:47,913] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.539526
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.10054385392614736, 'ffn_dropout': 0.10547076147603708, 'lr': 9.421887063399697e-05, 'weight_decay': 0.0007978168408181234, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:36:58,214] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.499688
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.13031920960313817, 'ffn_dropout': 0.12059613125394011, 'lr': 0.00031082253091144717, 'weight_decay': 0.0012402360570501585, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 01:43:38,592] Trial 54 finished with value: 0.551710375421777 and parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.12243005076664129, 'ffn_dropout': 0.1288444763745584, 'lr': 0.000601126530477638, 'weight_decay': 0.00423966027385117, 'batch_size': 32}. Best is trial 18 with value: 0.5643832267965512.
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.551710
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.12243005076664129, 'ffn_dropout': 0.1288444763745584, 'lr': 0.000601126530477638, 'weight_decay': 0.00423966027385117, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:44:26,542] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.470260
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.14800819499266915, 'ffn_dropout': 0.1623276439785605, 'lr': 0.0005709928186422164, 'weight_decay': 0.00399822712327428, 'batch_size': 256}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 01:48:36,596] Trial 56 finished with value: 0.49118031111281696 and parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.11265059719117237, 'ffn_dropout': 0.1769276931236695, 'lr': 0.0006733322518482248, 'weight_decay': 0.005720651807583956, 'batch_size': 32}. Best is trial 18 with value: 0.5643832267965512.
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.491180
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.11265059719117237, 'ffn_dropout': 0.1769276931236695, 'lr': 0.0006733322518482248, 'weight_decay': 0.005720651807583956, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:49:13,914] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.503056
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.12720094541459576, 'ffn_dropout': 0.1294672021085732, 'lr': 0.0004128748227254106, 'weight_decay': 0.0023769657096122526, 'batch_size': 128}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:50:02,862] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.508115
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.16471730684547087, 'ffn_dropout': 0.14743856744947034, 'lr': 0.0009480749798316357, 'weight_decay': 0.0031631583025376214, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:50:39,974] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.530814
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.14277665883065147, 'ffn_dropout': 0.19420345566058644, 'lr': 0.0002398823123829807, 'weight_decay': 0.004429821264610799, 'batch_size': 256}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 01:55:50,971] Trial 60 finished with value: 0.5468393054370795 and parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15724910010373874, 'ffn_dropout': 0.10928514775108408, 'lr': 0.0005048676561633345, 'weight_decay': 0.01362819348817963, 'batch_size': 32}. Best is trial 18 with value: 0.5643832267965512.
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.546839
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15724910010373874, 'ffn_dropout': 0.10928514775108408, 'lr': 0.0005048676561633345, 'weight_decay': 0.01362819348817963, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:57:24,307] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.457143
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11126207171006505, 'ffn_dropout': 0.1280061076917386, 'lr': 0.00012623528651466964, 'weight_decay': 0.001922736930672293, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 01:58:47,908] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.467803
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1219521748757495, 'ffn_dropout': 0.11114971583257492, 'lr': 0.00017571539737284977, 'weight_decay': 0.0066715937549738326, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:00:45,525] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.488540
+  Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.13516614795874016, 'ffn_dropout': 0.14330543081324593, 'lr': 0.00030328618845788585, 'weight_decay': 0.0035183493083197417, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 02:03:00,197] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.518965
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1260288740100909, 'ffn_dropout': 0.15656658076171365, 'lr': 0.00015372187684879247, 'weight_decay': 0.002481824317685139, 'batch_size': 128}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:05:48,775] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.500000
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.10734359414184927, 'ffn_dropout': 0.12252180332169352, 'lr': 7.294398541435345e-05, 'weight_decay': 0.0011426704710325013, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:06:25,763] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.496615
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.1414452528667507, 'ffn_dropout': 0.1715158961580739, 'lr': 0.0004268213510793753, 'weight_decay': 0.0015242835354650599, 'batch_size': 256}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:07:41,117] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.522975
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11652401308232883, 'ffn_dropout': 0.1868235732511662, 'lr': 0.00010823157718656925, 'weight_decay': 0.0009080870019577153, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 02:11:10,002] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.515107
+  Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.1526896427419394, 'ffn_dropout': 0.10191060853890357, 'lr': 0.00021029965699925508, 'weight_decay': 0.000612929701843668, 'batch_size': 128}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:13:21,628] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.490123
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.13536761547012877, 'ffn_dropout': 0.14006899025705527, 'lr': 0.00035161465569575663, 'weight_decay': 0.002083127600029381, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 02:16:12,281] Trial 70 finished with value: 0.5443605652632723 and parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.10052381846130058, 'ffn_dropout': 0.1699446826256683, 'lr': 0.0002750223134701451, 'weight_decay': 0.00427161233039884, 'batch_size': 64}. Best is trial 18 with value: 0.5643832267965512.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.544361
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.10052381846130058, 'ffn_dropout': 0.1699446826256683, 'lr': 0.0002750223134701451, 'weight_decay': 0.00427161233039884, 'batch_size': 64}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:17:38,793] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.513497
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15705726030287262, 'ffn_dropout': 0.11496696924824318, 'lr': 0.00048314472620145956, 'weight_decay': 0.013903301993387527, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:19:35,601] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.504926
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.12114851850750076, 'ffn_dropout': 0.1275246101576053, 'lr': 0.0005493998068400294, 'weight_decay': 0.03550651912674199, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:20:50,827] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.497342
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1727605375760804, 'ffn_dropout': 0.11244759751945095, 'lr': 0.000682120998931394, 'weight_decay': 0.008347055954910884, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:22:18,514] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.526102
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.14802037279166366, 'ffn_dropout': 0.13356660986361782, 'lr': 0.00038551727892383145, 'weight_decay': 0.0013717336899351962, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:23:46,638] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.494479
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.1596173262842347, 'ffn_dropout': 0.14981544426307608, 'lr': 0.000137159045818321, 'weight_decay': 0.0029413589942313967, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:23:57,735] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.380472
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.13347188459450113, 'ffn_dropout': 0.10732748336045642, 'lr': 0.00018492382374384568, 'weight_decay': 0.0008515724884633574, 'batch_size': 256}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:24:27,393] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.526229
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.12507757716508608, 'ffn_dropout': 0.15982659941414, 'lr': 0.00023374888252350107, 'weight_decay': 0.005625368837237321, 'batch_size': 128}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:26:09,810] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.490136
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.14042636486523313, 'ffn_dropout': 0.12093017296260274, 'lr': 0.0003188204950081463, 'weight_decay': 0.001077573974636095, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:27:24,466] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.526754
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.15306436366098672, 'ffn_dropout': 0.10081046699775781, 'lr': 8.476491417673307e-05, 'weight_decay': 0.00234832856682374, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:28:47,675] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.446154
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.1692188929787728, 'ffn_dropout': 0.13786665360625644, 'lr': 0.00048180307602545524, 'weight_decay': 0.0016949233785873952, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:29:35,206] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.498141
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2288172760053352, 'ffn_dropout': 0.27889871497197316, 'lr': 0.0008645306269682021, 'weight_decay': 0.009837973408712552, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:30:19,962] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.294456
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.19727717111484516, 'ffn_dropout': 0.396349307622322, 'lr': 0.0012998987357782103, 'weight_decay': 0.003720774254327264, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:31:21,343] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.483871
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.10955880960407603, 'ffn_dropout': 0.36312906768795217, 'lr': 0.0005922267416131876, 'weight_decay': 0.004823423670198022, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:31:39,518] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.467762
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.25022650565347765, 'ffn_dropout': 0.32503966175211096, 'lr': 0.0007793136807075203, 'weight_decay': 0.017245360892372698, 'batch_size': 256}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 02:34:44,175] Trial 85 finished with value: 0.5567263224724469 and parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1195142150753754, 'ffn_dropout': 0.20485175493792943, 'lr': 0.00010449506975161863, 'weight_decay': 0.0068670422769548985, 'batch_size': 64}. Best is trial 18 with value: 0.5643832267965512.
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.556726
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1195142150753754, 'ffn_dropout': 0.20485175493792943, 'lr': 0.00010449506975161863, 'weight_decay': 0.0068670422769548985, 'batch_size': 64}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:35:24,657] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.501531
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11897206157882803, 'ffn_dropout': 0.21170067527105896, 'lr': 0.00014368305858029076, 'weight_decay': 0.006475731335381718, 'batch_size': 64}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:36:05,158] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.507754
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1283153969343001, 'ffn_dropout': 0.16612440653798136, 'lr': 0.00010476102421687655, 'weight_decay': 0.0028147428386043265, 'batch_size': 64}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:36:31,791] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.384663
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1390035577366695, 'ffn_dropout': 0.14653953168715994, 'lr': 6.347006895519652e-05, 'weight_decay': 0.00356343101567134, 'batch_size': 64}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:37:18,400] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.462611
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.1129361105423105, 'ffn_dropout': 0.17853714064563014, 'lr': 0.00019025334891864082, 'weight_decay': 0.00510689662259767, 'batch_size': 64}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:38:46,554] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.537866
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.14867016963215005, 'ffn_dropout': 0.15484813659396807, 'lr': 8.000034343293483e-05, 'weight_decay': 0.002010552368835503, 'batch_size': 64}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:39:47,901] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.483118
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13157542666056743, 'ffn_dropout': 0.18850506380661813, 'lr': 0.00034755755506706546, 'weight_decay': 0.007083768226416792, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+[I 2025-12-29 02:41:00,196] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.559091
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.12350200171914191, 'ffn_dropout': 0.23102812496229302, 'lr': 0.00026606798188590785, 'weight_decay': 0.004047499294300202, 'batch_size': 128}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:42:31,662] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.516697
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.10586118038060947, 'ffn_dropout': 0.19656956457953234, 'lr': 0.00010196569592521579, 'weight_decay': 0.0012746211851924563, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:43:50,042] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.415405
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15643780051442444, 'ffn_dropout': 0.24532794565787674, 'lr': 0.0004740733237809556, 'weight_decay': 0.006290953007007396, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:44:16,681] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.510070
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1431511861753134, 'ffn_dropout': 0.20545245309209145, 'lr': 0.0001344080033631852, 'weight_decay': 0.004559317578369444, 'batch_size': 256}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:46:57,386] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.517346
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11432318316474932, 'ffn_dropout': 0.21792276875044492, 'lr': 5.505897101999206e-05, 'weight_decay': 0.0031531514373563823, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-29 02:49:22,023] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.532840
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.13307953023487068, 'ffn_dropout': 0.12445022691399608, 'lr': 0.00016354271660700285, 'weight_decay': 0.005543919940951279, 'batch_size': 128}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:51:03,331] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.505063
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.18112096949966985, 'ffn_dropout': 0.20361572905354947, 'lr': 0.00023312057927042648, 'weight_decay': 0.007806076538260478, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-29 02:52:18,433] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.512104
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.16739910791392276, 'ffn_dropout': 0.13299706143126586, 'lr': 0.0004017517571328272, 'weight_decay': 0.0025762049777413353, 'batch_size': 32}
+  Best Value (CSI): 0.564383
+  Best Trial: 18
+  Best Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5644
+Best Hyperparameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5463
+  - 최종 CSI: 0.5121
+  - 최고 CSI: 0.5766
+  - 최저 CSI: 0.0000
+  - 평균 CSI: 0.4693
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/ft_transformer_ctgan10000_incheon_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 29, Best CSI: 0.5478
+  Fold 1 학습 완료 (검증 CSI: 0.5478)
+Fold 2 학습 중...
+  Early stopping at epoch 39, Best CSI: 0.5743
+  Fold 2 학습 완료 (검증 CSI: 0.5743)
+Fold 3 학습 중...
+  Early stopping at epoch 28, Best CSI: 0.5100
+  Fold 3 학습 완료 (검증 CSI: 0.5100)
+
+모든 모델 저장 완료: ../save_model/ft_transformer_optima/ft_transformer_ctgan10000_incheon.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/ft_transformer_optima/scaler/ft_transformer_ctgan10000_incheon_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/ft_transformer_optima/ft_transformer_ctgan10000_incheon.pkl
+
+✓ 완료: ft_transformer_ctgan10000/ft_transformer_ctgan10000_incheon.py (소요 시간: 11710초)
+
+----------------------------------------
+실행 중: ft_transformer_ctgan10000/ft_transformer_ctgan10000_seoul.py
+시작 시간: 2025-12-29 02:54:25
+----------------------------------------
+[I 2025-12-29 02:54:26,928] A new study created in memory with name: no-name-cfc479f4-72df-4a6d-bfe2-c73a36ce6936
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 02:56:56,130] Trial 0 finished with value: 0.5579049839306286 and parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.31795400561331116, 'ffn_dropout': 0.30735230899827337, 'lr': 0.00023961964055182848, 'weight_decay': 0.021219153663130146, 'batch_size': 128}. Best is trial 0 with value: 0.5579049839306286.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.557905
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.31795400561331116, 'ffn_dropout': 0.30735230899827337, 'lr': 0.00023961964055182848, 'weight_decay': 0.021219153663130146, 'batch_size': 128}
+  Best Value (CSI): 0.557905
+  Best Trial: 0
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.31795400561331116, 'ffn_dropout': 0.30735230899827337, 'lr': 0.00023961964055182848, 'weight_decay': 0.021219153663130146, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 02:58:58,029] Trial 1 finished with value: 0.5467262445555292 and parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11909806403334228, 'ffn_dropout': 0.13639481788905272, 'lr': 0.0006551105132270649, 'weight_decay': 0.0007667624170607032, 'batch_size': 128}. Best is trial 0 with value: 0.5579049839306286.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.546726
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.11909806403334228, 'ffn_dropout': 0.13639481788905272, 'lr': 0.0006551105132270649, 'weight_decay': 0.0007667624170607032, 'batch_size': 128}
+  Best Value (CSI): 0.557905
+  Best Trial: 0
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.31795400561331116, 'ffn_dropout': 0.30735230899827337, 'lr': 0.00023961964055182848, 'weight_decay': 0.021219153663130146, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 02:59:49,871] Trial 2 finished with value: 0.3864559980847096 and parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.11263559437447485, 'ffn_dropout': 0.27517152473074846, 'lr': 1.7380463950187136e-05, 'weight_decay': 0.006545579805835805, 'batch_size': 128}. Best is trial 0 with value: 0.5579049839306286.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.386456
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.11263559437447485, 'ffn_dropout': 0.27517152473074846, 'lr': 1.7380463950187136e-05, 'weight_decay': 0.006545579805835805, 'batch_size': 128}
+  Best Value (CSI): 0.557905
+  Best Trial: 0
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.31795400561331116, 'ffn_dropout': 0.30735230899827337, 'lr': 0.00023961964055182848, 'weight_decay': 0.021219153663130146, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 03:02:28,192] Trial 3 finished with value: 0.09805561793547309 and parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.18168718401907408, 'ffn_dropout': 0.20410979838156046, 'lr': 0.0016164240937481325, 'weight_decay': 0.0014196037996539723, 'batch_size': 64}. Best is trial 0 with value: 0.5579049839306286.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.098056
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.18168718401907408, 'ffn_dropout': 0.20410979838156046, 'lr': 0.0016164240937481325, 'weight_decay': 0.0014196037996539723, 'batch_size': 64}
+  Best Value (CSI): 0.557905
+  Best Trial: 0
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.31795400561331116, 'ffn_dropout': 0.30735230899827337, 'lr': 0.00023961964055182848, 'weight_decay': 0.021219153663130146, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 03:11:31,407] Trial 4 finished with value: 0.5725894886897501 and parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}. Best is trial 4 with value: 0.5725894886897501.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.572589
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 03:11:43,097] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.140701
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.17062336854442037, 'ffn_dropout': 0.20579089383914406, 'lr': 2.506129524013788e-05, 'weight_decay': 0.004492904748965555, 'batch_size': 128}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 03:12:22,697] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.10207073250615734, 'ffn_dropout': 0.29690310611505, 'lr': 0.009656268309818652, 'weight_decay': 0.0014546192126293862, 'batch_size': 32}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 03:12:39,751] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.016741
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3295872195178423, 'ffn_dropout': 0.17756885112938722, 'lr': 0.004859822891995932, 'weight_decay': 0.0017263949348336745, 'batch_size': 64}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 03:12:49,078] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.274917
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2217329543480606, 'ffn_dropout': 0.27781370622021306, 'lr': 6.963644832947678e-05, 'weight_decay': 0.030179047730342516, 'batch_size': 128}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 03:13:08,792] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.38306233888478025, 'ffn_dropout': 0.10995227482973959, 'lr': 0.008065184798294404, 'weight_decay': 0.00011352903271818957, 'batch_size': 128}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 03:14:10,542] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.310973
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2597368590806157, 'ffn_dropout': 0.3977758216412729, 'lr': 1.0048739962758657e-05, 'weight_decay': 0.08730986665800299, 'batch_size': 32}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 03:14:27,678] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.286210
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.285749719359101, 'ffn_dropout': 0.32694390805598683, 'lr': 0.00015267879702870508, 'weight_decay': 0.015323620339219567, 'batch_size': 256}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 03:20:06,251] Trial 12 finished with value: 0.5672164910898182 and parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.2976224591819775, 'ffn_dropout': 0.3361044960918732, 'lr': 0.00017425335506144425, 'weight_decay': 0.023645411885796466, 'batch_size': 32}. Best is trial 4 with value: 0.5725894886897501.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.567216
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.2976224591819775, 'ffn_dropout': 0.3361044960918732, 'lr': 0.00017425335506144425, 'weight_decay': 0.023645411885796466, 'batch_size': 32}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 03:21:33,690] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.445783
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.2321795836727086, 'ffn_dropout': 0.3363290636408807, 'lr': 6.42124147086052e-05, 'weight_decay': 0.09619023699959023, 'batch_size': 32}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 03:27:38,678] Trial 14 finished with value: 0.571643543151814 and parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.2697960009937791, 'ffn_dropout': 0.24660136888593615, 'lr': 0.00010552571107046037, 'weight_decay': 0.009900681595458921, 'batch_size': 32}. Best is trial 4 with value: 0.5725894886897501.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.571644
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.2697960009937791, 'ffn_dropout': 0.24660136888593615, 'lr': 0.00010552571107046037, 'weight_decay': 0.009900681595458921, 'batch_size': 32}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 03:32:10,750] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.599809
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.25491446318232, 'ffn_dropout': 0.2418176728685899, 'lr': 4.681308129647074e-05, 'weight_decay': 0.008619004185613652, 'batch_size': 32}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 03:32:49,493] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.440933
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.21428712193808533, 'ffn_dropout': 0.245348029094218, 'lr': 0.0004950446686988365, 'weight_decay': 0.04065653412603421, 'batch_size': 256}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 03:33:29,056] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.337432
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.18818600124077917, 'ffn_dropout': 0.2277747169158896, 'lr': 7.869068402677907e-05, 'weight_decay': 0.010562171852071317, 'batch_size': 32}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 03:34:18,736] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.321859
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.2694319076765506, 'ffn_dropout': 0.27068687555165083, 'lr': 3.359304406488655e-05, 'weight_decay': 0.005217120838257398, 'batch_size': 32}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 03:37:56,964] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.587302
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2347602940713037, 'ffn_dropout': 0.19089315650019492, 'lr': 0.00012324335712764707, 'weight_decay': 0.011568811507437745, 'batch_size': 32}
+  Best Value (CSI): 0.572589
+  Best Trial: 4
+  Best Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21693351790691612, 'ffn_dropout': 0.28366608615803063, 'lr': 4.624919806815511e-05, 'weight_decay': 0.019396895465195362, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 03:40:53,619] Trial 20 finished with value: 0.5840041121927088 and parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15138336650637607, 'ffn_dropout': 0.2209876231731353, 'lr': 0.0003384467661077483, 'weight_decay': 0.04790408424933305, 'batch_size': 64}. Best is trial 20 with value: 0.5840041121927088.
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.584004
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15138336650637607, 'ffn_dropout': 0.2209876231731353, 'lr': 0.0003384467661077483, 'weight_decay': 0.04790408424933305, 'batch_size': 64}
+  Best Value (CSI): 0.584004
+  Best Trial: 20
+  Best Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15138336650637607, 'ffn_dropout': 0.2209876231731353, 'lr': 0.0003384467661077483, 'weight_decay': 0.04790408424933305, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 03:44:21,509] Trial 21 finished with value: 0.5692611869504968 and parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15260200801027626, 'ffn_dropout': 0.2299506016341118, 'lr': 0.00038648182971781327, 'weight_decay': 0.04521377059350505, 'batch_size': 64}. Best is trial 20 with value: 0.5840041121927088.
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.569261
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15260200801027626, 'ffn_dropout': 0.2299506016341118, 'lr': 0.00038648182971781327, 'weight_decay': 0.04521377059350505, 'batch_size': 64}
+  Best Value (CSI): 0.584004
+  Best Trial: 20
+  Best Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15138336650637607, 'ffn_dropout': 0.2209876231731353, 'lr': 0.0003384467661077483, 'weight_decay': 0.04790408424933305, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 03:47:34,905] Trial 22 finished with value: 0.5750868235368252 and parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.20213073075325172, 'ffn_dropout': 0.25276633710336677, 'lr': 0.0002699922387287163, 'weight_decay': 0.04848142422795926, 'batch_size': 64}. Best is trial 20 with value: 0.5840041121927088.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.575087
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.20213073075325172, 'ffn_dropout': 0.25276633710336677, 'lr': 0.0002699922387287163, 'weight_decay': 0.04848142422795926, 'batch_size': 64}
+  Best Value (CSI): 0.584004
+  Best Trial: 20
+  Best Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15138336650637607, 'ffn_dropout': 0.2209876231731353, 'lr': 0.0003384467661077483, 'weight_decay': 0.04790408424933305, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 03:48:10,171] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.029363
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.14473548544540732, 'ffn_dropout': 0.17297357140834846, 'lr': 0.0008652956534740518, 'weight_decay': 0.043336825146245116, 'batch_size': 64}
+  Best Value (CSI): 0.584004
+  Best Trial: 20
+  Best Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15138336650637607, 'ffn_dropout': 0.2209876231731353, 'lr': 0.0003384467661077483, 'weight_decay': 0.04790408424933305, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 03:51:23,418] Trial 24 finished with value: 0.5734361421451473 and parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.20759771548090097, 'ffn_dropout': 0.21468123752398455, 'lr': 0.00025420553866967237, 'weight_decay': 0.0647083701609805, 'batch_size': 64}. Best is trial 20 with value: 0.5840041121927088.
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.573436
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.20759771548090097, 'ffn_dropout': 0.21468123752398455, 'lr': 0.00025420553866967237, 'weight_decay': 0.0647083701609805, 'batch_size': 64}
+  Best Value (CSI): 0.584004
+  Best Trial: 20
+  Best Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15138336650637607, 'ffn_dropout': 0.2209876231731353, 'lr': 0.0003384467661077483, 'weight_decay': 0.04790408424933305, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 03:54:54,345] Trial 25 finished with value: 0.5728761684696874 and parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.19531877018222105, 'ffn_dropout': 0.2163469352356568, 'lr': 0.00026793978788309766, 'weight_decay': 0.060526039597060335, 'batch_size': 64}. Best is trial 20 with value: 0.5840041121927088.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.572876
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.19531877018222105, 'ffn_dropout': 0.2163469352356568, 'lr': 0.00026793978788309766, 'weight_decay': 0.060526039597060335, 'batch_size': 64}
+  Best Value (CSI): 0.584004
+  Best Trial: 20
+  Best Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15138336650637607, 'ffn_dropout': 0.2209876231731353, 'lr': 0.0003384467661077483, 'weight_decay': 0.04790408424933305, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 03:58:59,379] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.554714
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.14623903107326944, 'ffn_dropout': 0.15917179323107283, 'lr': 0.00027793459460336066, 'weight_decay': 0.06752963459670294, 'batch_size': 64}
+  Best Value (CSI): 0.584004
+  Best Trial: 20
+  Best Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15138336650637607, 'ffn_dropout': 0.2209876231731353, 'lr': 0.0003384467661077483, 'weight_decay': 0.04790408424933305, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 03:59:48,224] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.032840
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.16950012418427002, 'ffn_dropout': 0.20288908347600293, 'lr': 0.0010034527978017675, 'weight_decay': 0.09965754029836332, 'batch_size': 64}
+  Best Value (CSI): 0.584004
+  Best Trial: 20
+  Best Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15138336650637607, 'ffn_dropout': 0.2209876231731353, 'lr': 0.0003384467661077483, 'weight_decay': 0.04790408424933305, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:02:26,780] Trial 28 finished with value: 0.5788872867819307 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.2016068251445053, 'ffn_dropout': 0.2575884819768468, 'lr': 0.00043879747382487755, 'weight_decay': 0.03143945377816023, 'batch_size': 64}. Best is trial 20 with value: 0.5840041121927088.
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.578887
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.2016068251445053, 'ffn_dropout': 0.2575884819768468, 'lr': 0.00043879747382487755, 'weight_decay': 0.03143945377816023, 'batch_size': 64}
+  Best Value (CSI): 0.584004
+  Best Trial: 20
+  Best Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15138336650637607, 'ffn_dropout': 0.2209876231731353, 'lr': 0.0003384467661077483, 'weight_decay': 0.04790408424933305, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:05:29,066] Trial 29 finished with value: 0.5767424500570838 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1881056951475832, 'ffn_dropout': 0.2479988546153217, 'lr': 0.00048283484068757567, 'weight_decay': 0.025649327935511782, 'batch_size': 64}. Best is trial 20 with value: 0.5840041121927088.
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.576742
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1881056951475832, 'ffn_dropout': 0.2479988546153217, 'lr': 0.00048283484068757567, 'weight_decay': 0.025649327935511782, 'batch_size': 64}
+  Best Value (CSI): 0.584004
+  Best Trial: 20
+  Best Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15138336650637607, 'ffn_dropout': 0.2209876231731353, 'lr': 0.0003384467661077483, 'weight_decay': 0.04790408424933305, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:08:45,077] Trial 30 finished with value: 0.5871801671613369 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}. Best is trial 30 with value: 0.5871801671613369.
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.587180
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}
+  Best Value (CSI): 0.587180
+  Best Trial: 30
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:09:31,644] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.488751
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13051867347068974, 'ffn_dropout': 0.260281554563409, 'lr': 0.000429693868905566, 'weight_decay': 0.026803669176562836, 'batch_size': 64}
+  Best Value (CSI): 0.587180
+  Best Trial: 30
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:10:17,810] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.470204
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12757790062608004, 'ffn_dropout': 0.2331033463086535, 'lr': 0.0005328271924469928, 'weight_decay': 0.016758772543039376, 'batch_size': 64}
+  Best Value (CSI): 0.587180
+  Best Trial: 30
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:11:16,877] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1754021243433097, 'ffn_dropout': 0.25237838050557104, 'lr': 0.0007074769302769029, 'weight_decay': 0.03149193571375503, 'batch_size': 64}
+  Best Value (CSI): 0.587180
+  Best Trial: 30
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:11:38,842] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.169155
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1603374066104922, 'ffn_dropout': 0.26162791042510625, 'lr': 0.001330667342379479, 'weight_decay': 0.020322353307538312, 'batch_size': 64}
+  Best Value (CSI): 0.587180
+  Best Trial: 30
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:11:59,061] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.395439
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.18679409326783822, 'ffn_dropout': 0.22612479088663734, 'lr': 0.0001920615443389492, 'weight_decay': 0.031826385232525095, 'batch_size': 256}
+  Best Value (CSI): 0.587180
+  Best Trial: 30
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:14:51,811] Trial 36 finished with value: 0.5766925878416213 and parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11299131009819986, 'ffn_dropout': 0.28165584565671, 'lr': 0.00036423548077642363, 'weight_decay': 0.014944008461314653, 'batch_size': 64}. Best is trial 30 with value: 0.5871801671613369.
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.576693
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11299131009819986, 'ffn_dropout': 0.28165584565671, 'lr': 0.00036423548077642363, 'weight_decay': 0.014944008461314653, 'batch_size': 64}
+  Best Value (CSI): 0.587180
+  Best Trial: 30
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:15:33,307] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.485759
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13169794429282083, 'ffn_dropout': 0.2653366138640552, 'lr': 0.0005417823714042152, 'weight_decay': 0.021105546799304115, 'batch_size': 64}
+  Best Value (CSI): 0.587180
+  Best Trial: 30
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:16:02,263] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.013898
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1583171447334046, 'ffn_dropout': 0.28859050840028894, 'lr': 0.0017588952803970746, 'weight_decay': 0.0074232370696753975, 'batch_size': 64}
+  Best Value (CSI): 0.587180
+  Best Trial: 30
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:16:45,429] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.482400
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.17821616132165277, 'ffn_dropout': 0.3014862592759043, 'lr': 0.0007926725729142426, 'weight_decay': 0.03653475589452454, 'batch_size': 64}
+  Best Value (CSI): 0.587180
+  Best Trial: 30
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:17:18,551] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.462548
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1006318173978627, 'ffn_dropout': 0.19740064024336465, 'lr': 0.0003553565594700732, 'weight_decay': 0.012848431876657362, 'batch_size': 128}
+  Best Value (CSI): 0.587180
+  Best Trial: 30
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:20:11,445] Trial 41 finished with value: 0.5820980514045591 and parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11513504544675668, 'ffn_dropout': 0.2856617378633517, 'lr': 0.0003387630992399779, 'weight_decay': 0.015063109005040497, 'batch_size': 64}. Best is trial 30 with value: 0.5871801671613369.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.582098
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11513504544675668, 'ffn_dropout': 0.2856617378633517, 'lr': 0.0003387630992399779, 'weight_decay': 0.015063109005040497, 'batch_size': 64}
+  Best Value (CSI): 0.587180
+  Best Trial: 30
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:22:52,073] Trial 42 finished with value: 0.5826088856623229 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11829574259077641, 'ffn_dropout': 0.27786123422526277, 'lr': 0.00020436288643566148, 'weight_decay': 0.02724252332885922, 'batch_size': 64}. Best is trial 30 with value: 0.5871801671613369.
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.582609
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11829574259077641, 'ffn_dropout': 0.27786123422526277, 'lr': 0.00020436288643566148, 'weight_decay': 0.02724252332885922, 'batch_size': 64}
+  Best Value (CSI): 0.587180
+  Best Trial: 30
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13029659543170038, 'ffn_dropout': 0.26314272832988106, 'lr': 0.0004555995067335758, 'weight_decay': 0.02699427292151179, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:25:31,929] Trial 43 finished with value: 0.5885711803137631 and parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}. Best is trial 43 with value: 0.5885711803137631.
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.588571
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:25:57,129] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.440318
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.11584640365787176, 'ffn_dropout': 0.3124092221918302, 'lr': 0.0002047257510557689, 'weight_decay': 0.018458053146143304, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:26:20,280] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.369430
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.10098182860557439, 'ffn_dropout': 0.277769112110723, 'lr': 0.0001484778189344276, 'weight_decay': 0.006325728418314964, 'batch_size': 256}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:26:41,776] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.430407
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13759369907107238, 'ffn_dropout': 0.29152350266355476, 'lr': 0.00022651303159974983, 'weight_decay': 0.015595806785966313, 'batch_size': 128}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:27:25,188] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.475162
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.12109774498400552, 'ffn_dropout': 0.2745289628119233, 'lr': 0.00010345681716254672, 'weight_decay': 0.024383757729480055, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:27:42,372] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.325000
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.11654831088109424, 'ffn_dropout': 0.3147730944906184, 'lr': 0.00016085671842261403, 'weight_decay': 0.00351422848731665, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:28:22,295] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.418746
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.14076986155892818, 'ffn_dropout': 0.2960943334899248, 'lr': 0.0003050899124188452, 'weight_decay': 0.00910371467047018, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:29:02,738] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.474814
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.12738092712833432, 'ffn_dropout': 0.280369610149275, 'lr': 0.000632816329457544, 'weight_decay': 0.05484601218268662, 'batch_size': 128}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 04:30:45,687] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.595285
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.15874607498139368, 'ffn_dropout': 0.2633384217805401, 'lr': 0.00036649007898842693, 'weight_decay': 0.03350392478165871, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:33:37,213] Trial 52 finished with value: 0.5855357784884214 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11731164574725747, 'ffn_dropout': 0.23816056363935245, 'lr': 0.00022051718562417637, 'weight_decay': 0.037738985225110315, 'batch_size': 64}. Best is trial 43 with value: 0.5885711803137631.
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.585536
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11731164574725747, 'ffn_dropout': 0.23816056363935245, 'lr': 0.00022051718562417637, 'weight_decay': 0.037738985225110315, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:36:32,924] Trial 53 finished with value: 0.5825601143092648 and parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10813048504945645, 'ffn_dropout': 0.242520963163446, 'lr': 0.00020551838546086957, 'weight_decay': 0.07699102926582083, 'batch_size': 64}. Best is trial 43 with value: 0.5885711803137631.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.582560
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10813048504945645, 'ffn_dropout': 0.242520963163446, 'lr': 0.00020551838546086957, 'weight_decay': 0.07699102926582083, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:39:17,104] Trial 54 finished with value: 0.5822811640862801 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10677239084416629, 'ffn_dropout': 0.23685237483127486, 'lr': 0.00022358015308684168, 'weight_decay': 0.07702848410469723, 'batch_size': 64}. Best is trial 43 with value: 0.5885711803137631.
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.582281
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10677239084416629, 'ffn_dropout': 0.23685237483127486, 'lr': 0.00022358015308684168, 'weight_decay': 0.07702848410469723, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:39:32,981] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.318744
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1382497468923508, 'ffn_dropout': 0.23774639206303022, 'lr': 0.00014177240483837324, 'weight_decay': 0.056107001103240794, 'batch_size': 256}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:43:35,520] Trial 56 finished with value: 0.5786512956901767 and parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.12435888010929234, 'ffn_dropout': 0.22306922947797206, 'lr': 0.00018515400363972018, 'weight_decay': 0.07905435558307217, 'batch_size': 64}. Best is trial 43 with value: 0.5885711803137631.
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.578651
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.12435888010929234, 'ffn_dropout': 0.22306922947797206, 'lr': 0.00018515400363972018, 'weight_decay': 0.07905435558307217, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 04:45:06,285] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.576427
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10757793396127785, 'ffn_dropout': 0.2141371903919246, 'lr': 0.0001224192730610905, 'weight_decay': 0.04436120454446182, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:45:35,641] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.288565
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.12080037491831641, 'ffn_dropout': 0.24659577238328956, 'lr': 9.033209234643225e-05, 'weight_decay': 0.039622857291108905, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:46:00,834] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.260241
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.14744852855582782, 'ffn_dropout': 0.24006856896700993, 'lr': 6.703217259238753e-05, 'weight_decay': 0.07092868875380011, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:48:33,077] Trial 60 finished with value: 0.5771309635712614 and parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1346835159086081, 'ffn_dropout': 0.2714572708095556, 'lr': 0.00023054941246685432, 'weight_decay': 0.051940359954400196, 'batch_size': 64}. Best is trial 43 with value: 0.5885711803137631.
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.577131
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1346835159086081, 'ffn_dropout': 0.2714572708095556, 'lr': 0.00023054941246685432, 'weight_decay': 0.051940359954400196, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:51:24,211] Trial 61 finished with value: 0.5805523008484407 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10688235200661511, 'ffn_dropout': 0.2341942150214136, 'lr': 0.0001902569669852399, 'weight_decay': 0.0828880413798283, 'batch_size': 64}. Best is trial 43 with value: 0.5885711803137631.
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.580552
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10688235200661511, 'ffn_dropout': 0.2341942150214136, 'lr': 0.0001902569669852399, 'weight_decay': 0.0828880413798283, 'batch_size': 64}
+  Best Value (CSI): 0.588571
+  Best Trial: 43
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12221399429185369, 'ffn_dropout': 0.27723467741432656, 'lr': 0.0002004737157808501, 'weight_decay': 0.017798768457349907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:53:57,632] Trial 62 finished with value: 0.5902150483032744 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}. Best is trial 62 with value: 0.5902150483032744.
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.590215
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 04:56:31,967] Trial 63 finished with value: 0.5761304966321242 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12096545013730317, 'ffn_dropout': 0.2546782896684882, 'lr': 0.00031541584194649737, 'weight_decay': 0.051116998835743, 'batch_size': 64}. Best is trial 62 with value: 0.5902150483032744.
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.576130
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12096545013730317, 'ffn_dropout': 0.2546782896684882, 'lr': 0.00031541584194649737, 'weight_decay': 0.051116998835743, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 04:58:06,120] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.583821
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10017653710502612, 'ffn_dropout': 0.26741324277445516, 'lr': 0.00012685038392221408, 'weight_decay': 0.09933725451305633, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 04:58:57,380] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.421367
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.14831281617234934, 'ffn_dropout': 0.24683217060438484, 'lr': 0.0002792676196590625, 'weight_decay': 0.02665850119448124, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 05:01:01,890] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.595216
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.1293611674986793, 'ffn_dropout': 0.22477006225140303, 'lr': 0.00017979875442098418, 'weight_decay': 0.038481811830882715, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 05:01:14,022] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.329504
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11472780047346824, 'ffn_dropout': 0.21333210870995709, 'lr': 0.0002474172539387321, 'weight_decay': 0.06429726138634083, 'batch_size': 256}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 05:03:12,573] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.473045
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.16675521856447773, 'ffn_dropout': 0.2560450595649854, 'lr': 0.0004465645646684194, 'weight_decay': 0.023611578198663427, 'batch_size': 32}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 05:03:30,818] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.339135
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.14063210800796877, 'ffn_dropout': 0.27063906098569096, 'lr': 0.0001499614000064815, 'weight_decay': 0.044467955872826016, 'batch_size': 128}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 05:05:55,365] Trial 70 finished with value: 0.5823328307616414 and parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.15360930595994146, 'ffn_dropout': 0.24230138424159062, 'lr': 0.0001024483259337221, 'weight_decay': 0.06030002573700187, 'batch_size': 64}. Best is trial 62 with value: 0.5902150483032744.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.582333
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.15360930595994146, 'ffn_dropout': 0.24230138424159062, 'lr': 0.0001024483259337221, 'weight_decay': 0.06030002573700187, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 05:06:55,860] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.602980
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.15203814701138024, 'ffn_dropout': 0.2430930681806338, 'lr': 0.00011091474741207093, 'weight_decay': 0.06653768840583758, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 05:07:32,758] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.422105
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.12573660427253677, 'ffn_dropout': 0.25964629826940444, 'lr': 8.54189070699916e-05, 'weight_decay': 0.03547852627177405, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 05:09:40,237] Trial 73 finished with value: 0.5728515985023669 and parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.10942705800688653, 'ffn_dropout': 0.2300452021848057, 'lr': 0.0002892666055680068, 'weight_decay': 0.05704902722685733, 'batch_size': 64}. Best is trial 62 with value: 0.5902150483032744.
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.572852
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.10942705800688653, 'ffn_dropout': 0.2300452021848057, 'lr': 0.0002892666055680068, 'weight_decay': 0.05704902722685733, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 05:11:22,517] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.587488
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.13908456339849215, 'ffn_dropout': 0.25594781485281626, 'lr': 0.00021826036422696197, 'weight_decay': 0.07798666723959523, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 05:12:20,429] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.437042
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.13211634361608926, 'ffn_dropout': 0.2212168665246504, 'lr': 0.00016728373918987349, 'weight_decay': 0.04408488976547518, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 05:14:02,880] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.599412
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1517109686842802, 'ffn_dropout': 0.24967440573368807, 'lr': 0.0004183723127483666, 'weight_decay': 0.02851147482324035, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 05:14:19,926] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.327571
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.11037771074907918, 'ffn_dropout': 0.20678846877997326, 'lr': 0.00013459968027379216, 'weight_decay': 0.09332382514522398, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 05:15:37,873] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.610536
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.12146641640644601, 'ffn_dropout': 0.23789436331991326, 'lr': 0.00031924509726042043, 'weight_decay': 0.019934426047364052, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 05:16:42,782] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.449222
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.17107662061001871, 'ffn_dropout': 0.275952953779324, 'lr': 5.3078213949806096e-05, 'weight_decay': 0.03180543809107208, 'batch_size': 32}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 05:17:03,798] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.420601
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.16298545282238547, 'ffn_dropout': 0.2670184113208191, 'lr': 0.00024789366090929144, 'weight_decay': 0.06222291373745851, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 05:17:50,738] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.480125
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10881599624108843, 'ffn_dropout': 0.23055890424707653, 'lr': 0.00018783938344469822, 'weight_decay': 0.07755427091175272, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 05:19:30,156] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.585390
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10038281962591575, 'ffn_dropout': 0.24028858056728497, 'lr': 0.00021332151287445762, 'weight_decay': 0.04728373319341611, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 05:21:13,109] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.607619
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13312136615921247, 'ffn_dropout': 0.23378428584048425, 'lr': 0.00039210785204485084, 'weight_decay': 0.07553547869369046, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 05:23:43,670] Trial 84 finished with value: 0.5698066378405643 and parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11958995152624513, 'ffn_dropout': 0.2524170860645722, 'lr': 9.929962087270417e-05, 'weight_decay': 0.037442205872462525, 'batch_size': 64}. Best is trial 62 with value: 0.5902150483032744.
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.569807
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11958995152624513, 'ffn_dropout': 0.2524170860645722, 'lr': 9.929962087270417e-05, 'weight_decay': 0.037442205872462525, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 05:26:35,707] Trial 85 finished with value: 0.5813084162281533 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11125319716064018, 'ffn_dropout': 0.28561983502752886, 'lr': 0.00025584770257392435, 'weight_decay': 0.08726024119690849, 'batch_size': 64}. Best is trial 62 with value: 0.5902150483032744.
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.581308
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11125319716064018, 'ffn_dropout': 0.28561983502752886, 'lr': 0.00025584770257392435, 'weight_decay': 0.08726024119690849, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 05:28:17,385] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.585700
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.14556830671400348, 'ffn_dropout': 0.2643943009940297, 'lr': 0.00015977018799567377, 'weight_decay': 0.06026190580643869, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 05:29:47,474] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.595930
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.1283647803196793, 'ffn_dropout': 0.2468824135839423, 'lr': 0.0005502312009717997, 'weight_decay': 0.05114471586901889, 'batch_size': 128}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 05:29:59,561] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.398967
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10719128423118103, 'ffn_dropout': 0.22381310285198575, 'lr': 0.00033424510400721755, 'weight_decay': 0.022960715233175312, 'batch_size': 256}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 05:32:42,164] Trial 89 finished with value: 0.5837434608728763 and parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1549131596412708, 'ffn_dropout': 0.279386170889448, 'lr': 0.00012528789840496432, 'weight_decay': 0.029491179677383857, 'batch_size': 64}. Best is trial 62 with value: 0.5902150483032744.
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.583743
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1549131596412708, 'ffn_dropout': 0.279386170889448, 'lr': 0.00012528789840496432, 'weight_decay': 0.029491179677383857, 'batch_size': 64}
+  Best Value (CSI): 0.590215
+  Best Trial: 62
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10908300747104757, 'ffn_dropout': 0.253484998998862, 'lr': 0.00028652786262047274, 'weight_decay': 0.0765345214426832, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 05:35:43,739] Trial 90 finished with value: 0.5936576575682742 and parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}. Best is trial 90 with value: 0.5936576575682742.
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.593658
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}
+  Best Value (CSI): 0.593658
+  Best Trial: 90
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 05:36:10,517] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.333333
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1598610563184279, 'ffn_dropout': 0.28145271575862263, 'lr': 7.697713543246169e-05, 'weight_decay': 0.030015902236366993, 'batch_size': 64}
+  Best Value (CSI): 0.593658
+  Best Trial: 90
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 05:36:54,624] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.448544
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15582214283051204, 'ffn_dropout': 0.2927477479282527, 'lr': 0.00010947537998356849, 'weight_decay': 0.018691160709088876, 'batch_size': 64}
+  Best Value (CSI): 0.593658
+  Best Trial: 90
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 05:37:41,410] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.403498
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.14225563017621035, 'ffn_dropout': 0.3028678517514851, 'lr': 0.00014287765329235475, 'weight_decay': 0.038728006548534206, 'batch_size': 64}
+  Best Value (CSI): 0.593658
+  Best Trial: 90
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 05:40:05,716] Trial 94 finished with value: 0.5789642828373208 and parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.13446390801781316, 'ffn_dropout': 0.2751389696501385, 'lr': 0.00012279379067272055, 'weight_decay': 0.02615411408014481, 'batch_size': 64}. Best is trial 90 with value: 0.5936576575682742.
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.578964
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.13446390801781316, 'ffn_dropout': 0.2751389696501385, 'lr': 0.00012279379067272055, 'weight_decay': 0.02615411408014481, 'batch_size': 64}
+  Best Value (CSI): 0.593658
+  Best Trial: 90
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 05:42:57,914] Trial 95 finished with value: 0.5846675374753975 and parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1530043983274827, 'ffn_dropout': 0.2881867651786676, 'lr': 0.0001700874664617842, 'weight_decay': 0.033379049976407546, 'batch_size': 64}. Best is trial 90 with value: 0.5936576575682742.
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.584668
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1530043983274827, 'ffn_dropout': 0.2881867651786676, 'lr': 0.0001700874664617842, 'weight_decay': 0.033379049976407546, 'batch_size': 64}
+  Best Value (CSI): 0.593658
+  Best Trial: 90
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-29 05:45:10,924] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.579038
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.17285487875653333, 'ffn_dropout': 0.2962554931642661, 'lr': 0.00017088254580087918, 'weight_decay': 0.022659897944009497, 'batch_size': 64}
+  Best Value (CSI): 0.593658
+  Best Trial: 90
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 05:46:42,375] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.588813
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1816655638004124, 'ffn_dropout': 0.285345968404239, 'lr': 0.0002135626759827049, 'weight_decay': 0.01307917837419845, 'batch_size': 64}
+  Best Value (CSI): 0.593658
+  Best Trial: 90
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-29 05:48:17,539] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.583665
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.16608408233895347, 'ffn_dropout': 0.2900561254330309, 'lr': 0.0002809138175271737, 'weight_decay': 0.028304694978962264, 'batch_size': 64}
+  Best Value (CSI): 0.593658
+  Best Trial: 90
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-29 05:49:52,737] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.451685
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.11832376199069997, 'ffn_dropout': 0.27140107497959304, 'lr': 0.00019517907474982205, 'weight_decay': 0.0410477497510017, 'batch_size': 32}
+  Best Value (CSI): 0.593658
+  Best Trial: 90
+  Best Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5937
+Best Hyperparameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5579
+  - 최종 CSI: 0.4517
+  - 최고 CSI: 0.6105
+  - 최저 CSI: 0.0000
+  - 평균 CSI: 0.4571
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/ft_transformer_ctgan10000_seoul_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 19, Best CSI: 0.5223
+  Fold 1 학습 완료 (검증 CSI: 0.5223)
+Fold 2 학습 중...
+  Early stopping at epoch 36, Best CSI: 0.6397
+  Fold 2 학습 완료 (검증 CSI: 0.6397)
+Fold 3 학습 중...
+  Early stopping at epoch 19, Best CSI: 0.5893
+  Fold 3 학습 완료 (검증 CSI: 0.5893)
+
+모든 모델 저장 완료: ../save_model/ft_transformer_optima/ft_transformer_ctgan10000_seoul.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/ft_transformer_optima/scaler/ft_transformer_ctgan10000_seoul_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/ft_transformer_optima/ft_transformer_ctgan10000_seoul.pkl
+
+✓ 완료: ft_transformer_ctgan10000/ft_transformer_ctgan10000_seoul.py (소요 시간: 10708초)
+
+==========================================
+FT-Transformer CTGAN10000 파일 실행 완료
+종료 시간: 2025-12-29 05:52:53
+총 소요 시간: 17시간 16분 31초
+성공: 6개
+실패: 0개
+==========================================
diff --git a/Analysis_code/5.optima/run_bash/ft_transformer/ft_transformer_smote.log b/Analysis_code/5.optima/run_bash/ft_transformer/ft_transformer_smote.log
index fe0c0aefdaf28e35cb811f2fc89735a5e9f83928..d8174e0bc82044475524764541c8365ebdc6a4e3 100644
--- a/Analysis_code/5.optima/run_bash/ft_transformer/ft_transformer_smote.log
+++ b/Analysis_code/5.optima/run_bash/ft_transformer/ft_transformer_smote.log
@@ -6296,3 +6296,1672 @@ Trial 76 완료
   Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
   Fold 2 - 클래스별 가중치: {0: 1.3172, 1: 1.3172, 2: 0.6749333879893421} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14637})
   Fold 3 - 클래스별 가중치: {0: 1.3153333333333332, 1: 1.3153333333333332, 2: 0.6759164097293594} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14595})
+[I 2025-12-25 17:40:24,580] Trial 77 finished with value: 0.5575035380233367 and parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3786917685194243, 'ffn_dropout': 0.2521502050234237, 'lr': 0.00014587545184136568, 'weight_decay': 0.0008125924274305289, 'batch_size': 32}. Best is trial 30 with value: 0.5674050423289939.
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.557504
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3786917685194243, 'ffn_dropout': 0.2521502050234237, 'lr': 0.00014587545184136568, 'weight_decay': 0.0008125924274305289, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+[I 2025-12-25 17:41:31,078] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.538251
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3620517059987306, 'ffn_dropout': 0.28223413499562866, 'lr': 0.0004113629336124001, 'weight_decay': 0.000234633059784876, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+[I 2025-12-25 17:44:11,296] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.530312
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3362812592350845, 'ffn_dropout': 0.1878533770819069, 'lr': 0.00020744348198387848, 'weight_decay': 0.0003537807645318261, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+[I 2025-12-25 17:44:42,273] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.530955
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3240097676470653, 'ffn_dropout': 0.23884319722804678, 'lr': 0.00033725322895608974, 'weight_decay': 0.0005077914369808697, 'batch_size': 64}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.3172, 1: 1.3172, 2: 0.6749333879893421} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.3153333333333332, 1: 1.3153333333333332, 2: 0.6759164097293594} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14595})
+[I 2025-12-25 17:54:19,562] Trial 81 finished with value: 0.5619965753706834 and parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.35697243757182073, 'ffn_dropout': 0.22095096121680724, 'lr': 0.0002307971326262494, 'weight_decay': 0.00028207228908645125, 'batch_size': 32}. Best is trial 30 with value: 0.5674050423289939.
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.561997
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.35697243757182073, 'ffn_dropout': 0.22095096121680724, 'lr': 0.0002307971326262494, 'weight_decay': 0.00028207228908645125, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+[I 2025-12-25 17:55:32,695] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.540107
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.34836943438501483, 'ffn_dropout': 0.2045635899913562, 'lr': 0.000280099764641132, 'weight_decay': 0.00020604597149227657, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+[I 2025-12-25 17:56:46,245] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.536145
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.365400281041358, 'ffn_dropout': 0.22602945234155472, 'lr': 0.0001689391329359149, 'weight_decay': 0.0003378377079914079, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.3172, 1: 1.3172, 2: 0.6749333879893421} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.3153333333333332, 1: 1.3153333333333332, 2: 0.6759164097293594} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14595})
+[I 2025-12-25 18:05:11,437] Trial 84 finished with value: 0.5613987516497861 and parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.34205660394318593, 'ffn_dropout': 0.21869071039588409, 'lr': 0.0005283731813540785, 'weight_decay': 0.00045957798950018217, 'batch_size': 32}. Best is trial 30 with value: 0.5674050423289939.
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.561399
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.34205660394318593, 'ffn_dropout': 0.21869071039588409, 'lr': 0.0005283731813540785, 'weight_decay': 0.00045957798950018217, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+[I 2025-12-25 18:06:37,020] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.539978
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.37285392806271395, 'ffn_dropout': 0.22868398128387246, 'lr': 0.0002685402535155513, 'weight_decay': 0.00026630715233407534, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.3172, 1: 1.3172, 2: 0.6749333879893421} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.3153333333333332, 1: 1.3153333333333332, 2: 0.6759164097293594} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14595})
+[I 2025-12-25 18:15:28,649] Trial 86 finished with value: 0.5646619766490142 and parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.33168195072092793, 'ffn_dropout': 0.24205233579297242, 'lr': 0.0003313737620774728, 'weight_decay': 0.00036269017078117407, 'batch_size': 32}. Best is trial 30 with value: 0.5674050423289939.
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.564662
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.33168195072092793, 'ffn_dropout': 0.24205233579297242, 'lr': 0.0003313737620774728, 'weight_decay': 0.00036269017078117407, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+[I 2025-12-25 18:15:37,988] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.453095
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.33157240620352796, 'ffn_dropout': 0.2423061334990648, 'lr': 0.0004422495324936617, 'weight_decay': 0.0006494477592519485, 'batch_size': 256}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+[I 2025-12-25 18:15:51,975] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.512658
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.323261254375982, 'ffn_dropout': 0.26769016403497853, 'lr': 0.00033204447974512354, 'weight_decay': 0.0005672360091194626, 'batch_size': 128}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+[I 2025-12-25 18:16:50,735] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.523703
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.31760090403969976, 'ffn_dropout': 0.25668645068052154, 'lr': 0.000641453296369461, 'weight_decay': 0.0003789597611529661, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+[I 2025-12-25 18:17:40,608] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.527660
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3391995717623639, 'ffn_dropout': 0.25034177368355454, 'lr': 0.00022315865570545474, 'weight_decay': 0.0007452806759972192, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.3172, 1: 1.3172, 2: 0.6749333879893421} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.3153333333333332, 1: 1.3153333333333332, 2: 0.6759164097293594} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14595})
+[I 2025-12-25 18:24:36,749] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.514877
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.3522270455310675, 'ffn_dropout': 0.23592958394974028, 'lr': 0.0003142048850216142, 'weight_decay': 0.000457025097744545, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.3172, 1: 1.3172, 2: 0.6749333879893421} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.3153333333333332, 1: 1.3153333333333332, 2: 0.6759164097293594} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14595})
+[I 2025-12-25 18:31:29,596] Trial 92 finished with value: 0.5625301022558141 and parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3316778846124803, 'ffn_dropout': 0.2167509831934703, 'lr': 0.00025252017880273515, 'weight_decay': 0.00029690462852767106, 'batch_size': 32}. Best is trial 30 with value: 0.5674050423289939.
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.562530
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3316778846124803, 'ffn_dropout': 0.2167509831934703, 'lr': 0.00025252017880273515, 'weight_decay': 0.00029690462852767106, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.3172, 1: 1.3172, 2: 0.6749333879893421} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.3153333333333332, 1: 1.3153333333333332, 2: 0.6759164097293594} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14595})
+[I 2025-12-25 18:39:15,155] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.515712
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.35700542273779784, 'ffn_dropout': 0.2285144608615685, 'lr': 0.00041009333545977237, 'weight_decay': 0.00023785043517588253, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.3172, 1: 1.3172, 2: 0.6749333879893421} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14637})
+[I 2025-12-25 18:43:36,536] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.555556
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3459338394968773, 'ffn_dropout': 0.1968426831799045, 'lr': 0.00016825231175600458, 'weight_decay': 0.00033881607417409653, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.3172, 1: 1.3172, 2: 0.6749333879893421} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.3153333333333332, 1: 1.3153333333333332, 2: 0.6759164097293594} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14595})
+[I 2025-12-25 18:50:41,970] Trial 95 finished with value: 0.567399549943466 and parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3049224290468632, 'ffn_dropout': 0.262458911386045, 'lr': 0.0004705378123090436, 'weight_decay': 0.0002011568536459783, 'batch_size': 32}. Best is trial 30 with value: 0.5674050423289939.
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.567400
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3049224290468632, 'ffn_dropout': 0.262458911386045, 'lr': 0.0004705378123090436, 'weight_decay': 0.0002011568536459783, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.3172, 1: 1.3172, 2: 0.6749333879893421} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.3153333333333332, 1: 1.3153333333333332, 2: 0.6759164097293594} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14595})
+[I 2025-12-25 18:56:43,950] Trial 96 finished with value: 0.561816564837237 and parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.30896276854711474, 'ffn_dropout': 0.2630071033281542, 'lr': 0.00048624747270327744, 'weight_decay': 0.00020972461119679467, 'batch_size': 32}. Best is trial 30 with value: 0.5674050423289939.
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.561817
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.30896276854711474, 'ffn_dropout': 0.2630071033281542, 'lr': 0.00048624747270327744, 'weight_decay': 0.00020972461119679467, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+[I 2025-12-25 18:57:40,140] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.537549
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.29492872114426294, 'ffn_dropout': 0.14031063910948866, 'lr': 0.0005902338352850365, 'weight_decay': 0.0004111862436463382, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+[I 2025-12-25 18:58:34,222] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.536913
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28072763298617825, 'ffn_dropout': 0.3336802876880405, 'lr': 0.00076676123816529, 'weight_decay': 0.00016953305738751375, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.313511111111111, 1: 1.313511111111111, 2: 0.6768814987861298} (클래스별 샘플 수: {0: 7500, 1: 7500, 2: 14554})
+[I 2025-12-25 18:59:31,145] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.546421
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3236561361103351, 'ffn_dropout': 0.3031467734958405, 'lr': 0.00039230307339006967, 'weight_decay': 0.0005623894036993773, 'batch_size': 32}
+  Best Value (CSI): 0.567405
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5674
+Best Hyperparameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5290
+  - 최종 CSI: 0.5464
+  - 최고 CSI: 0.5674
+  - 최저 CSI: 0.0000
+  - 평균 CSI: 0.5042
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/ft_transformer_smote_incheon_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 25, Best CSI: 0.5545
+  Fold 1 학습 완료 (검증 CSI: 0.5545)
+Fold 2 학습 중...
+  Early stopping at epoch 22, Best CSI: 0.6011
+  Fold 2 학습 완료 (검증 CSI: 0.6011)
+Fold 3 학습 중...
+  Early stopping at epoch 20, Best CSI: 0.5260
+  Fold 3 학습 완료 (검증 CSI: 0.5260)
+
+모든 모델 저장 완료: ../save_model/ft_transformer_optima/ft_transformer_smote_incheon.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/ft_transformer_optima/scaler/ft_transformer_smote_incheon_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/ft_transformer_optima/ft_transformer_smote_incheon.pkl
+
+✓ 완료: ft_transformer_smote/ft_transformer_smote_incheon.py (소요 시간: 25906초)
+
+----------------------------------------
+실행 중: ft_transformer_smote/ft_transformer_smote_seoul.py
+시작 시간: 2025-12-25 19:04:39
+----------------------------------------
+[I 2025-12-25 19:04:40,322] A new study created in memory with name: no-name-7b6c1c0c-7f07-4abd-8acf-3eaf3c02983f
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 19:07:47,794] Trial 0 finished with value: 0.4938262435375931 and parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.17877587888051422, 'ffn_dropout': 0.25816851791180656, 'lr': 0.00013524525410014886, 'weight_decay': 0.000247082500026181, 'batch_size': 128}. Best is trial 0 with value: 0.4938262435375931.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.493826
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.17877587888051422, 'ffn_dropout': 0.25816851791180656, 'lr': 0.00013524525410014886, 'weight_decay': 0.000247082500026181, 'batch_size': 128}
+  Best Value (CSI): 0.493826
+  Best Trial: 0
+  Best Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.17877587888051422, 'ffn_dropout': 0.25816851791180656, 'lr': 0.00013524525410014886, 'weight_decay': 0.000247082500026181, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 19:13:48,805] Trial 1 finished with value: 0.5334517409672329 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.38636882799915206, 'ffn_dropout': 0.18085902479509552, 'lr': 9.024334489666502e-05, 'weight_decay': 0.000652210057637454, 'batch_size': 64}. Best is trial 1 with value: 0.5334517409672329.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.533452
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.38636882799915206, 'ffn_dropout': 0.18085902479509552, 'lr': 9.024334489666502e-05, 'weight_decay': 0.000652210057637454, 'batch_size': 64}
+  Best Value (CSI): 0.533452
+  Best Trial: 1
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.38636882799915206, 'ffn_dropout': 0.18085902479509552, 'lr': 9.024334489666502e-05, 'weight_decay': 0.000652210057637454, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 19:17:03,923] Trial 2 finished with value: 0.5458125039748464 and parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1587538990323335, 'ffn_dropout': 0.2904165146005051, 'lr': 0.00018365747922966267, 'weight_decay': 0.08900255730292767, 'batch_size': 64}. Best is trial 2 with value: 0.5458125039748464.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.545813
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1587538990323335, 'ffn_dropout': 0.2904165146005051, 'lr': 0.00018365747922966267, 'weight_decay': 0.08900255730292767, 'batch_size': 64}
+  Best Value (CSI): 0.545813
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1587538990323335, 'ffn_dropout': 0.2904165146005051, 'lr': 0.00018365747922966267, 'weight_decay': 0.08900255730292767, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 19:25:04,291] Trial 3 finished with value: 0.5466284706855625 and parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2535804581397322, 'ffn_dropout': 0.38751684839588385, 'lr': 0.00042041686603716034, 'weight_decay': 0.001336629427296846, 'batch_size': 32}. Best is trial 3 with value: 0.5466284706855625.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.546628
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2535804581397322, 'ffn_dropout': 0.38751684839588385, 'lr': 0.00042041686603716034, 'weight_decay': 0.001336629427296846, 'batch_size': 32}
+  Best Value (CSI): 0.546628
+  Best Trial: 3
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2535804581397322, 'ffn_dropout': 0.38751684839588385, 'lr': 0.00042041686603716034, 'weight_decay': 0.001336629427296846, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 19:28:21,226] Trial 4 finished with value: 0.4870939358844726 and parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.15939959485476937, 'ffn_dropout': 0.203050088780319, 'lr': 7.605432624495413e-05, 'weight_decay': 0.02114229127092804, 'batch_size': 128}. Best is trial 3 with value: 0.5466284706855625.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.487094
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.15939959485476937, 'ffn_dropout': 0.203050088780319, 'lr': 7.605432624495413e-05, 'weight_decay': 0.02114229127092804, 'batch_size': 128}
+  Best Value (CSI): 0.546628
+  Best Trial: 3
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2535804581397322, 'ffn_dropout': 0.38751684839588385, 'lr': 0.00042041686603716034, 'weight_decay': 0.001336629427296846, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 19:29:00,669] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.000683
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.262498055493321, 'ffn_dropout': 0.1268891467327522, 'lr': 0.001222995804781187, 'weight_decay': 0.004985200375250647, 'batch_size': 64}
+  Best Value (CSI): 0.546628
+  Best Trial: 3
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2535804581397322, 'ffn_dropout': 0.38751684839588385, 'lr': 0.00042041686603716034, 'weight_decay': 0.001336629427296846, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 19:29:17,201] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.309722
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.31360651123852146, 'ffn_dropout': 0.1406902470759515, 'lr': 2.031552446004212e-05, 'weight_decay': 0.0011992935202302245, 'batch_size': 128}
+  Best Value (CSI): 0.546628
+  Best Trial: 3
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2535804581397322, 'ffn_dropout': 0.38751684839588385, 'lr': 0.00042041686603716034, 'weight_decay': 0.001336629427296846, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 19:29:45,485] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.032541
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.23404914807934393, 'ffn_dropout': 0.13211437991876396, 'lr': 0.0015971818363471882, 'weight_decay': 0.06034816396335996, 'batch_size': 128}
+  Best Value (CSI): 0.546628
+  Best Trial: 3
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2535804581397322, 'ffn_dropout': 0.38751684839588385, 'lr': 0.00042041686603716034, 'weight_decay': 0.001336629427296846, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 19:38:13,012] Trial 8 finished with value: 0.5520605008418618 and parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.29828015124936436, 'ffn_dropout': 0.1615812663662866, 'lr': 0.00016062851372004058, 'weight_decay': 0.004155620940999291, 'batch_size': 32}. Best is trial 8 with value: 0.5520605008418618.
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.552061
+  Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.29828015124936436, 'ffn_dropout': 0.1615812663662866, 'lr': 0.00016062851372004058, 'weight_decay': 0.004155620940999291, 'batch_size': 32}
+  Best Value (CSI): 0.552061
+  Best Trial: 8
+  Best Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.29828015124936436, 'ffn_dropout': 0.1615812663662866, 'lr': 0.00016062851372004058, 'weight_decay': 0.004155620940999291, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 19:38:26,693] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.391198
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.22765643731941584, 'ffn_dropout': 0.3690250354489788, 'lr': 0.00033555491864971154, 'weight_decay': 0.06628616563114029, 'batch_size': 128}
+  Best Value (CSI): 0.552061
+  Best Trial: 8
+  Best Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.29828015124936436, 'ffn_dropout': 0.1615812663662866, 'lr': 0.00016062851372004058, 'weight_decay': 0.004155620940999291, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 19:39:55,053] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.000683
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.12024134460854768, 'ffn_dropout': 0.10266841542391442, 'lr': 0.006982061895271842, 'weight_decay': 0.004822198063230748, 'batch_size': 32}
+  Best Value (CSI): 0.552061
+  Best Trial: 8
+  Best Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.29828015124936436, 'ffn_dropout': 0.1615812663662866, 'lr': 0.00016062851372004058, 'weight_decay': 0.004155620940999291, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 19:47:10,509] Trial 11 finished with value: 0.5578292783958204 and parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2941938157012187, 'ffn_dropout': 0.36773295501771575, 'lr': 0.0005697089836996394, 'weight_decay': 0.0019057445511098665, 'batch_size': 32}. Best is trial 11 with value: 0.5578292783958204.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.557829
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2941938157012187, 'ffn_dropout': 0.36773295501771575, 'lr': 0.0005697089836996394, 'weight_decay': 0.0019057445511098665, 'batch_size': 32}
+  Best Value (CSI): 0.557829
+  Best Trial: 11
+  Best Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2941938157012187, 'ffn_dropout': 0.36773295501771575, 'lr': 0.0005697089836996394, 'weight_decay': 0.0019057445511098665, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 19:50:22,509] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.425654
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.31116992471832144, 'ffn_dropout': 0.31739936341585406, 'lr': 3.1044287702737566e-05, 'weight_decay': 0.007721485317587983, 'batch_size': 32}
+  Best Value (CSI): 0.557829
+  Best Trial: 11
+  Best Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2941938157012187, 'ffn_dropout': 0.36773295501771575, 'lr': 0.0005697089836996394, 'weight_decay': 0.0019057445511098665, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 19:50:54,888] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.410714
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3178344933477064, 'ffn_dropout': 0.20990090127857483, 'lr': 0.0006153736612946382, 'weight_decay': 0.002137043115536503, 'batch_size': 256}
+  Best Value (CSI): 0.557829
+  Best Trial: 11
+  Best Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2941938157012187, 'ffn_dropout': 0.36773295501771575, 'lr': 0.0005697089836996394, 'weight_decay': 0.0019057445511098665, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 19:52:15,517] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.383484
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.37407718474989915, 'ffn_dropout': 0.32749870977431106, 'lr': 1.1396160360865782e-05, 'weight_decay': 0.012312475883367872, 'batch_size': 32}
+  Best Value (CSI): 0.557829
+  Best Trial: 11
+  Best Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2941938157012187, 'ffn_dropout': 0.36773295501771575, 'lr': 0.0005697089836996394, 'weight_decay': 0.0019057445511098665, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 19:53:45,577] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.404919
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.28950542083211667, 'ffn_dropout': 0.2383486643665559, 'lr': 4.644565767203515e-05, 'weight_decay': 0.0034145033007323463, 'batch_size': 32}
+  Best Value (CSI): 0.557829
+  Best Trial: 11
+  Best Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2941938157012187, 'ffn_dropout': 0.36773295501771575, 'lr': 0.0005697089836996394, 'weight_decay': 0.0019057445511098665, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 19:54:06,748] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.400738
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3604632485086947, 'ffn_dropout': 0.39723730302900606, 'lr': 0.00018422805038315588, 'weight_decay': 0.00011345073719032854, 'batch_size': 256}
+  Best Value (CSI): 0.557829
+  Best Trial: 11
+  Best Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2941938157012187, 'ffn_dropout': 0.36773295501771575, 'lr': 0.0005697089836996394, 'weight_decay': 0.0019057445511098665, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 19:55:33,341] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.000368
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.341304612288705, 'ffn_dropout': 0.35709624930828915, 'lr': 0.000833954206267635, 'weight_decay': 0.0006363398003855473, 'batch_size': 32}
+  Best Value (CSI): 0.557829
+  Best Trial: 11
+  Best Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2941938157012187, 'ffn_dropout': 0.36773295501771575, 'lr': 0.0005697089836996394, 'weight_decay': 0.0019057445511098665, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 20:00:39,536] Trial 18 finished with value: 0.5559576299465977 and parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.28535402731157145, 'ffn_dropout': 0.27419232643813984, 'lr': 0.00028408291083977357, 'weight_decay': 0.0028877204175900384, 'batch_size': 32}. Best is trial 11 with value: 0.5578292783958204.
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.555958
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.28535402731157145, 'ffn_dropout': 0.27419232643813984, 'lr': 0.00028408291083977357, 'weight_decay': 0.0028877204175900384, 'batch_size': 32}
+  Best Value (CSI): 0.557829
+  Best Trial: 11
+  Best Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2941938157012187, 'ffn_dropout': 0.36773295501771575, 'lr': 0.0005697089836996394, 'weight_decay': 0.0019057445511098665, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 20:01:17,538] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.114776
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.34124673680762085, 'ffn_dropout': 0.28326270713550894, 'lr': 0.002492136015271463, 'weight_decay': 0.0017882738162503095, 'batch_size': 32}
+  Best Value (CSI): 0.557829
+  Best Trial: 11
+  Best Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2941938157012187, 'ffn_dropout': 0.36773295501771575, 'lr': 0.0005697089836996394, 'weight_decay': 0.0019057445511098665, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 20:05:51,496] Trial 20 finished with value: 0.5599809245798847 and parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39733800524948154, 'ffn_dropout': 0.3456021599313298, 'lr': 0.0005197977356768663, 'weight_decay': 0.012755783722561802, 'batch_size': 32}. Best is trial 20 with value: 0.5599809245798847.
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.559981
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39733800524948154, 'ffn_dropout': 0.3456021599313298, 'lr': 0.0005197977356768663, 'weight_decay': 0.012755783722561802, 'batch_size': 32}
+  Best Value (CSI): 0.559981
+  Best Trial: 20
+  Best Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39733800524948154, 'ffn_dropout': 0.3456021599313298, 'lr': 0.0005197977356768663, 'weight_decay': 0.012755783722561802, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 20:09:42,340] Trial 21 finished with value: 0.5469949923023917 and parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3779328300507695, 'ffn_dropout': 0.34379482204582573, 'lr': 0.00041523837539485757, 'weight_decay': 0.014842552481655419, 'batch_size': 32}. Best is trial 20 with value: 0.5599809245798847.
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.546995
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3779328300507695, 'ffn_dropout': 0.34379482204582573, 'lr': 0.00041523837539485757, 'weight_decay': 0.014842552481655419, 'batch_size': 32}
+  Best Value (CSI): 0.559981
+  Best Trial: 20
+  Best Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39733800524948154, 'ffn_dropout': 0.3456021599313298, 'lr': 0.0005197977356768663, 'weight_decay': 0.012755783722561802, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 20:14:35,815] Trial 22 finished with value: 0.5633420079946059 and parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39130884741380056, 'ffn_dropout': 0.31747304984650526, 'lr': 0.0006307339704492065, 'weight_decay': 0.029346384417081962, 'batch_size': 32}. Best is trial 22 with value: 0.5633420079946059.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.563342
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39130884741380056, 'ffn_dropout': 0.31747304984650526, 'lr': 0.0006307339704492065, 'weight_decay': 0.029346384417081962, 'batch_size': 32}
+  Best Value (CSI): 0.563342
+  Best Trial: 22
+  Best Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39130884741380056, 'ffn_dropout': 0.31747304984650526, 'lr': 0.0006307339704492065, 'weight_decay': 0.029346384417081962, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 20:14:49,339] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.386310
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3957397916283861, 'ffn_dropout': 0.31632777870510437, 'lr': 0.0006669706504333678, 'weight_decay': 0.03335292708832044, 'batch_size': 256}
+  Best Value (CSI): 0.563342
+  Best Trial: 22
+  Best Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39130884741380056, 'ffn_dropout': 0.31747304984650526, 'lr': 0.0006307339704492065, 'weight_decay': 0.029346384417081962, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 20:15:39,869] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.410579
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.35714104155378446, 'ffn_dropout': 0.37079507967854647, 'lr': 0.0018517855077351795, 'weight_decay': 0.031637738635873484, 'batch_size': 32}
+  Best Value (CSI): 0.563342
+  Best Trial: 22
+  Best Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39130884741380056, 'ffn_dropout': 0.31747304984650526, 'lr': 0.0006307339704492065, 'weight_decay': 0.029346384417081962, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 20:25:15,503] Trial 25 finished with value: 0.5550903654901403 and parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.39942489442906143, 'ffn_dropout': 0.3404917526953313, 'lr': 0.0010769319867698513, 'weight_decay': 0.011690555123057335, 'batch_size': 32}. Best is trial 22 with value: 0.5633420079946059.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.555090
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.39942489442906143, 'ffn_dropout': 0.3404917526953313, 'lr': 0.0010769319867698513, 'weight_decay': 0.011690555123057335, 'batch_size': 32}
+  Best Value (CSI): 0.563342
+  Best Trial: 22
+  Best Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39130884741380056, 'ffn_dropout': 0.31747304984650526, 'lr': 0.0006307339704492065, 'weight_decay': 0.029346384417081962, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 20:29:54,479] Trial 26 finished with value: 0.55939181263975 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3411040979072826, 'ffn_dropout': 0.311035543851922, 'lr': 0.0005896875676686477, 'weight_decay': 0.008178262611152964, 'batch_size': 32}. Best is trial 22 with value: 0.5633420079946059.
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.559392
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3411040979072826, 'ffn_dropout': 0.311035543851922, 'lr': 0.0005896875676686477, 'weight_decay': 0.008178262611152964, 'batch_size': 32}
+  Best Value (CSI): 0.563342
+  Best Trial: 22
+  Best Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39130884741380056, 'ffn_dropout': 0.31747304984650526, 'lr': 0.0006307339704492065, 'weight_decay': 0.029346384417081962, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 20:33:50,512] Trial 27 finished with value: 0.5504349933152191 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39883351702844466, 'ffn_dropout': 0.3003911590355156, 'lr': 0.00028910644866873944, 'weight_decay': 0.008674763492451389, 'batch_size': 32}. Best is trial 22 with value: 0.5633420079946059.
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.550435
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39883351702844466, 'ffn_dropout': 0.3003911590355156, 'lr': 0.00028910644866873944, 'weight_decay': 0.008674763492451389, 'batch_size': 32}
+  Best Value (CSI): 0.563342
+  Best Trial: 22
+  Best Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39130884741380056, 'ffn_dropout': 0.31747304984650526, 'lr': 0.0006307339704492065, 'weight_decay': 0.029346384417081962, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 20:36:21,390] Trial 28 finished with value: 0.5617702504367125 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3603219989247117, 'ffn_dropout': 0.3070740159727858, 'lr': 0.001147442549345772, 'weight_decay': 0.02153662133095635, 'batch_size': 64}. Best is trial 22 with value: 0.5633420079946059.
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.561770
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3603219989247117, 'ffn_dropout': 0.3070740159727858, 'lr': 0.001147442549345772, 'weight_decay': 0.02153662133095635, 'batch_size': 64}
+  Best Value (CSI): 0.563342
+  Best Trial: 22
+  Best Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39130884741380056, 'ffn_dropout': 0.31747304984650526, 'lr': 0.0006307339704492065, 'weight_decay': 0.029346384417081962, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 20:37:11,579] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.404421
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3662330169848604, 'ffn_dropout': 0.25999209531996054, 'lr': 0.0029250720613883483, 'weight_decay': 0.02361092421963226, 'batch_size': 64}
+  Best Value (CSI): 0.563342
+  Best Trial: 22
+  Best Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39130884741380056, 'ffn_dropout': 0.31747304984650526, 'lr': 0.0006307339704492065, 'weight_decay': 0.029346384417081962, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 20:41:13,641] Trial 30 finished with value: 0.5739423798669575 and parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}. Best is trial 30 with value: 0.5739423798669575.
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.573942
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 20:42:18,059] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.436872
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3807953366579965, 'ffn_dropout': 0.3008574669872796, 'lr': 0.0010160619594718244, 'weight_decay': 0.04025909225038649, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 20:44:59,167] Trial 32 finished with value: 0.5618651622550636 and parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3795712151186757, 'ffn_dropout': 0.33217869025520586, 'lr': 0.0014229096680863002, 'weight_decay': 0.019542774890304003, 'batch_size': 64}. Best is trial 30 with value: 0.5739423798669575.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.561865
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3795712151186757, 'ffn_dropout': 0.33217869025520586, 'lr': 0.0014229096680863002, 'weight_decay': 0.019542774890304003, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 20:45:37,878] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.000683
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.35241173755029787, 'ffn_dropout': 0.3313686956858255, 'lr': 0.003361011632909906, 'weight_decay': 0.05373029045269388, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 20:50:03,065] Trial 34 finished with value: 0.5655181937406549 and parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3784401077937454, 'ffn_dropout': 0.28918021102980446, 'lr': 0.0017761212737014292, 'weight_decay': 0.022494434992569402, 'batch_size': 64}. Best is trial 30 with value: 0.5739423798669575.
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.565518
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3784401077937454, 'ffn_dropout': 0.28918021102980446, 'lr': 0.0017761212737014292, 'weight_decay': 0.022494434992569402, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 20:50:53,597] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.418049
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38035456691282044, 'ffn_dropout': 0.2834701479495254, 'lr': 0.0019484666245800318, 'weight_decay': 0.08536436271893352, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 20:51:26,112] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.000683
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.37379356704923183, 'ffn_dropout': 0.2663007491816, 'lr': 0.004631635199035477, 'weight_decay': 0.09939003399688277, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 20:52:17,610] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.423899
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3358011605492438, 'ffn_dropout': 0.2963393646635246, 'lr': 0.0015371744859914594, 'weight_decay': 0.04212961482789545, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 20:53:47,335] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.425016
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.382997367539803, 'ffn_dropout': 0.245232254370993, 'lr': 0.0008922586476283271, 'weight_decay': 0.026196118872176506, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 20:56:17,866] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.564516
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3316417065930028, 'ffn_dropout': 0.2836670232286214, 'lr': 0.0014672149515341092, 'weight_decay': 0.01679567471713732, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 20:57:00,385] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.439860
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.36357255020557244, 'ffn_dropout': 0.32883820088416715, 'lr': 0.002134183616985511, 'weight_decay': 0.04332567059798113, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 20:58:34,364] Trial 41 finished with value: 0.5491478271415667 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.35876914791226844, 'ffn_dropout': 0.30793630053418053, 'lr': 0.0013509663384419625, 'weight_decay': 0.02044411099592554, 'batch_size': 64}. Best is trial 30 with value: 0.5739423798669575.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.549148
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.35876914791226844, 'ffn_dropout': 0.30793630053418053, 'lr': 0.0013509663384419625, 'weight_decay': 0.02044411099592554, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 20:59:10,950] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.408374
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3875825095683591, 'ffn_dropout': 0.294353465450384, 'lr': 0.0008751682569871705, 'weight_decay': 0.0247182553603873, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 20:59:51,194] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.419002
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.35419582407430966, 'ffn_dropout': 0.31968769225963617, 'lr': 0.0013903669067727087, 'weight_decay': 0.017577671620451166, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:00:02,886] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.419536
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3704721780843632, 'ffn_dropout': 0.30744347023590446, 'lr': 0.003082452093230661, 'weight_decay': 0.06486041198727516, 'batch_size': 128}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:00:49,905] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.420447
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3834418233071221, 'ffn_dropout': 0.27733559881672915, 'lr': 0.001124694051144312, 'weight_decay': 0.03426761351175438, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:01:41,349] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.430225
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.3258115245749298, 'ffn_dropout': 0.2951572468132474, 'lr': 0.0019387029087251449, 'weight_decay': 0.05288197809421397, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 21:03:48,231] Trial 47 finished with value: 0.5410692749755706 and parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3504195800312222, 'ffn_dropout': 0.32527961828877716, 'lr': 0.000796585984433297, 'weight_decay': 0.025957611499380303, 'batch_size': 64}. Best is trial 30 with value: 0.5739423798669575.
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.541069
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3504195800312222, 'ffn_dropout': 0.32527961828877716, 'lr': 0.000796585984433297, 'weight_decay': 0.025957611499380303, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:04:02,215] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.231242
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.369074683718013, 'ffn_dropout': 0.26812377653928404, 'lr': 0.004275900330718431, 'weight_decay': 0.019566827768521807, 'batch_size': 128}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:04:13,681] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.393647
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3885117876636148, 'ffn_dropout': 0.3352230174160327, 'lr': 0.0004294465780109999, 'weight_decay': 0.006388699820100441, 'batch_size': 256}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:05:25,813] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.413793
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.31736360489497384, 'ffn_dropout': 0.35303618272460857, 'lr': 0.0007740167856545144, 'weight_decay': 0.010110776428871121, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:06:16,566] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.446682
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.395950660694602, 'ffn_dropout': 0.3189649390773042, 'lr': 0.001177566240481648, 'weight_decay': 0.01324506264326737, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:07:04,192] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.422251
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.38844284320069034, 'ffn_dropout': 0.34502670840653643, 'lr': 0.0005157095053941474, 'weight_decay': 0.016343983249055213, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:07:31,907] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.391226
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.39947863322369054, 'ffn_dropout': 0.35406762659448393, 'lr': 0.000718756721326717, 'weight_decay': 0.01162435729199829, 'batch_size': 128}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:07:42,919] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.358849
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3695801375224326, 'ffn_dropout': 0.30998095451090996, 'lr': 0.0005063445099882185, 'weight_decay': 0.02918110238099175, 'batch_size': 256}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+[I 2025-12-25 21:11:11,194] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.599469
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3490298737939594, 'ffn_dropout': 0.33255119904366465, 'lr': 0.0010667989277785895, 'weight_decay': 0.01893896510278957, 'batch_size': 32}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:11:54,393] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.413815
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.37557123205809045, 'ffn_dropout': 0.28672399896244044, 'lr': 0.0003634711928204699, 'weight_decay': 0.03777674258929632, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:13:02,618] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.397428
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3627313732525173, 'ffn_dropout': 0.36909467727156414, 'lr': 0.000640128266456393, 'weight_decay': 0.01490957664012032, 'batch_size': 32}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 21:17:18,616] Trial 58 finished with value: 0.5511796622846928 and parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38964197351487956, 'ffn_dropout': 0.3409667148688909, 'lr': 0.0016419398503787022, 'weight_decay': 0.023309905237820377, 'batch_size': 32}. Best is trial 30 with value: 0.5739423798669575.
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.551180
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38964197351487956, 'ffn_dropout': 0.3409667148688909, 'lr': 0.0016419398503787022, 'weight_decay': 0.023309905237820377, 'batch_size': 32}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:17:53,538] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.438783
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.35032840066018256, 'ffn_dropout': 0.31829784814184925, 'lr': 0.00023739997889007713, 'weight_decay': 0.010554268173336765, 'batch_size': 64}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:18:08,642] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.389649
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.39999843716481176, 'ffn_dropout': 0.38295990649014866, 'lr': 0.002421069392654888, 'weight_decay': 0.052802037227199776, 'batch_size': 256}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 21:24:40,828] Trial 61 finished with value: 0.5602019085442339 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.37654335691627466, 'ffn_dropout': 0.3102323471459902, 'lr': 0.0005754783713231304, 'weight_decay': 0.006638880733046025, 'batch_size': 32}. Best is trial 30 with value: 0.5739423798669575.
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.560202
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.37654335691627466, 'ffn_dropout': 0.3102323471459902, 'lr': 0.0005754783713231304, 'weight_decay': 0.006638880733046025, 'batch_size': 32}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 21:26:29,696] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.434753
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3773865501948357, 'ffn_dropout': 0.3040078477870456, 'lr': 0.00046089664767572504, 'weight_decay': 0.006234310058972083, 'batch_size': 32}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 21:31:04,410] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.559061
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.36422260626178843, 'ffn_dropout': 0.32455065181670145, 'lr': 0.0005968915440851955, 'weight_decay': 0.014024791992890034, 'batch_size': 32}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 21:34:48,410] Trial 64 finished with value: 0.5665880163727613 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3890792732898677, 'ffn_dropout': 0.29268256927689495, 'lr': 0.0007189259567489787, 'weight_decay': 0.03136458170589396, 'batch_size': 32}. Best is trial 30 with value: 0.5739423798669575.
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.566588
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3890792732898677, 'ffn_dropout': 0.29268256927689495, 'lr': 0.0007189259567489787, 'weight_decay': 0.03136458170589396, 'batch_size': 32}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 21:38:51,737] Trial 65 finished with value: 0.5706486791923684 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.38737025053500174, 'ffn_dropout': 0.2885530256036787, 'lr': 0.0009615470402995749, 'weight_decay': 0.0298947595557374, 'batch_size': 32}. Best is trial 30 with value: 0.5739423798669575.
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.570649
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.38737025053500174, 'ffn_dropout': 0.2885530256036787, 'lr': 0.0009615470402995749, 'weight_decay': 0.0298947595557374, 'batch_size': 32}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 21:42:55,975] Trial 66 finished with value: 0.5615172280155561 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3880516617886017, 'ffn_dropout': 0.29071859501263, 'lr': 0.0009684755365473027, 'weight_decay': 0.029378966445853867, 'batch_size': 32}. Best is trial 30 with value: 0.5739423798669575.
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.561517
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3880516617886017, 'ffn_dropout': 0.29071859501263, 'lr': 0.0009684755365473027, 'weight_decay': 0.029378966445853867, 'batch_size': 32}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 21:46:58,418] Trial 67 finished with value: 0.5629685429875654 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.34425218202151553, 'ffn_dropout': 0.2769073742107657, 'lr': 0.0012387424376063703, 'weight_decay': 0.07546857055825353, 'batch_size': 32}. Best is trial 30 with value: 0.5739423798669575.
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.562969
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.34425218202151553, 'ffn_dropout': 0.2769073742107657, 'lr': 0.0012387424376063703, 'weight_decay': 0.07546857055825353, 'batch_size': 32}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 21:51:06,738] Trial 68 finished with value: 0.5692813708774116 and parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3406548464622327, 'ffn_dropout': 0.25644132572381045, 'lr': 0.0016262866532545932, 'weight_decay': 0.07725577996196337, 'batch_size': 32}. Best is trial 30 with value: 0.5739423798669575.
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.569281
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3406548464622327, 'ffn_dropout': 0.25644132572381045, 'lr': 0.0016262866532545932, 'weight_decay': 0.07725577996196337, 'batch_size': 32}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 21:58:59,748] Trial 69 finished with value: 0.5628392979329541 and parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.33767114719270636, 'ffn_dropout': 0.2563228033177759, 'lr': 0.0017363667797323876, 'weight_decay': 0.07371002706547067, 'batch_size': 32}. Best is trial 30 with value: 0.5739423798669575.
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.562839
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.33767114719270636, 'ffn_dropout': 0.2563228033177759, 'lr': 0.0017363667797323876, 'weight_decay': 0.07371002706547067, 'batch_size': 32}
+  Best Value (CSI): 0.573942
+  Best Trial: 30
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.38148005983524574, 'ffn_dropout': 0.3010843219695151, 'lr': 0.0010835171323778457, 'weight_decay': 0.04092948451329147, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 22:09:00,517] Trial 70 finished with value: 0.5788930444782093 and parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}. Best is trial 70 with value: 0.5788930444782093.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.578893
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+[I 2025-12-25 22:13:10,261] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.595218
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.30008365770585377, 'ffn_dropout': 0.27491644995920816, 'lr': 0.000723688838853777, 'weight_decay': 0.08323835540876641, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 22:15:28,179] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.410857
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34253885679161217, 'ffn_dropout': 0.26632190993694255, 'lr': 0.0012053422335897349, 'weight_decay': 0.06931988179559491, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 22:23:14,343] Trial 73 finished with value: 0.5551125267882084 and parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.32248581721297853, 'ffn_dropout': 0.2785784584565224, 'lr': 0.0009249269917747778, 'weight_decay': 0.04427446957378213, 'batch_size': 32}. Best is trial 70 with value: 0.5788930444782093.
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.555113
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.32248581721297853, 'ffn_dropout': 0.2785784584565224, 'lr': 0.0009249269917747778, 'weight_decay': 0.04427446957378213, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 22:32:24,659] Trial 74 finished with value: 0.5670259390135723 and parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.30996466621320123, 'ffn_dropout': 0.2517994263582584, 'lr': 0.0012537725979048621, 'weight_decay': 0.0996051466868357, 'batch_size': 32}. Best is trial 70 with value: 0.5788930444782093.
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.567026
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.30996466621320123, 'ffn_dropout': 0.2517994263582584, 'lr': 0.0012537725979048621, 'weight_decay': 0.0996051466868357, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 22:39:12,999] Trial 75 finished with value: 0.5581589134457916 and parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.33100274641091665, 'ffn_dropout': 0.2370402114907833, 'lr': 0.0008142032158604621, 'weight_decay': 0.0917125256812803, 'batch_size': 32}. Best is trial 70 with value: 0.5788930444782093.
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.558159
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.33100274641091665, 'ffn_dropout': 0.2370402114907833, 'lr': 0.0008142032158604621, 'weight_decay': 0.0917125256812803, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 22:40:18,580] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.001480
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.31352276934889156, 'ffn_dropout': 0.29051072975461034, 'lr': 0.001650775700828699, 'weight_decay': 0.05764269884567653, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 22:41:23,567] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.000000
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2824221278748904, 'ffn_dropout': 0.2545318467527504, 'lr': 0.0020954414274899497, 'weight_decay': 0.09733286345391665, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 22:43:11,854] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.398859
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3602818586257841, 'ffn_dropout': 0.29864795802031036, 'lr': 0.0009562108701837911, 'weight_decay': 0.059737355894209414, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 22:44:32,567] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.125343
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.30724566298703504, 'ffn_dropout': 0.2876518207185582, 'lr': 0.0006957193701722695, 'weight_decay': 0.03642485402655179, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 22:45:41,143] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.000683
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.37008560158513737, 'ffn_dropout': 0.26339907489289943, 'lr': 0.0013314078993539626, 'weight_decay': 0.045164283946253894, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 22:53:15,239] Trial 81 finished with value: 0.5651597176208346 and parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3480240975324377, 'ffn_dropout': 0.2715471757619841, 'lr': 0.001323723068581014, 'weight_decay': 0.0707799273774972, 'batch_size': 32}. Best is trial 70 with value: 0.5788930444782093.
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.565160
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3480240975324377, 'ffn_dropout': 0.2715471757619841, 'lr': 0.001323723068581014, 'weight_decay': 0.0707799273774972, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 23:03:48,629] Trial 82 finished with value: 0.5628704264715482 and parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3573703706958984, 'ffn_dropout': 0.2504938218419798, 'lr': 0.001542618187645844, 'weight_decay': 0.07362777361146328, 'batch_size': 32}. Best is trial 70 with value: 0.5788930444782093.
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.562870
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3573703706958984, 'ffn_dropout': 0.2504938218419798, 'lr': 0.001542618187645844, 'weight_decay': 0.07362777361146328, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 23:12:39,633] Trial 83 finished with value: 0.565812261770987 and parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3268795235299381, 'ffn_dropout': 0.2705959622998229, 'lr': 0.0010420768151247234, 'weight_decay': 0.04918193311558426, 'batch_size': 32}. Best is trial 70 with value: 0.5788930444782093.
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.565812
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3268795235299381, 'ffn_dropout': 0.2705959622998229, 'lr': 0.0010420768151247234, 'weight_decay': 0.04918193311558426, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 23:14:01,430] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.428181
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3271590122321988, 'ffn_dropout': 0.2705307329833598, 'lr': 0.0022634644955080965, 'weight_decay': 0.048764632133182925, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+[I 2025-12-25 23:17:52,081] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.583204
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.33014281590307154, 'ffn_dropout': 0.2599902383267461, 'lr': 0.0010102992357125479, 'weight_decay': 0.06143589759794122, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 23:19:33,360] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.419783
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3221892430452531, 'ffn_dropout': 0.28202546254283495, 'lr': 0.0018791466531770835, 'weight_decay': 0.0796201590672731, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 23:20:08,918] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.000683
+  Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.3355768427548912, 'ffn_dropout': 0.2739572592202757, 'lr': 0.0013587601011859163, 'weight_decay': 0.06292861151506204, 'batch_size': 128}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 23:21:09,545] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.000683
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.3449721072590436, 'ffn_dropout': 0.24506762797136117, 'lr': 0.0026752949720094572, 'weight_decay': 0.03492776211596092, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 23:28:41,662] Trial 89 finished with value: 0.5565324832208595 and parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.35430658446143476, 'ffn_dropout': 0.26881177234303066, 'lr': 0.0008413636189470239, 'weight_decay': 0.05061578234544091, 'batch_size': 32}. Best is trial 70 with value: 0.5788930444782093.
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.556532
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.35430658446143476, 'ffn_dropout': 0.26881177234303066, 'lr': 0.0008413636189470239, 'weight_decay': 0.05061578234544091, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 23:36:40,182] Trial 90 finished with value: 0.5614517821634438 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.38258481486342033, 'ffn_dropout': 0.2998804506065136, 'lr': 0.0010949431786518207, 'weight_decay': 0.09851811155962549, 'batch_size': 32}. Best is trial 70 with value: 0.5788930444782093.
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.561452
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.38258481486342033, 'ffn_dropout': 0.2998804506065136, 'lr': 0.0010949431786518207, 'weight_decay': 0.09851811155962549, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+[I 2025-12-25 23:40:02,762] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.605410
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3932423152092808, 'ffn_dropout': 0.29301872108818416, 'lr': 0.0006998506199632206, 'weight_decay': 0.03970538329055356, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 23:47:21,754] Trial 92 finished with value: 0.5599567437283716 and parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.37251809274549397, 'ffn_dropout': 0.28150476079501385, 'lr': 0.0012287252121993462, 'weight_decay': 0.03022829917433228, 'batch_size': 32}. Best is trial 70 with value: 0.5788930444782093.
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.559957
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.37251809274549397, 'ffn_dropout': 0.28150476079501385, 'lr': 0.0012287252121993462, 'weight_decay': 0.03022829917433228, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 23:48:24,023] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.000683
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.38377935609886926, 'ffn_dropout': 0.2879655871230607, 'lr': 0.0016194408896374286, 'weight_decay': 0.048061556827654915, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 23:48:34,937] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.395203
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.39240721936065365, 'ffn_dropout': 0.2632843898405157, 'lr': 0.0007847351498047383, 'weight_decay': 0.06391170731527436, 'batch_size': 256}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 23:49:21,095] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.062007
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3674599917982618, 'ffn_dropout': 0.30333793777215273, 'lr': 0.0010032512033497254, 'weight_decay': 0.08173363107303909, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+[I 2025-12-25 23:53:17,024] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.601802
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3806036905422855, 'ffn_dropout': 0.31432255157989114, 'lr': 0.0006033798131889798, 'weight_decay': 0.041648225988786286, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+[I 2025-12-25 23:53:41,012] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.411919
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.34952380464959953, 'ffn_dropout': 0.27217217230790786, 'lr': 0.0014220974799586887, 'weight_decay': 0.032451004333123, 'batch_size': 128}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-25 23:59:57,894] Trial 98 finished with value: 0.5566019310765739 and parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.39412281191838444, 'ffn_dropout': 0.2956749054464063, 'lr': 0.0008934989219919747, 'weight_decay': 0.054029038695790246, 'batch_size': 32}. Best is trial 70 with value: 0.5788930444782093.
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.556602
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.39412281191838444, 'ffn_dropout': 0.2956749054464063, 'lr': 0.0008934989219919747, 'weight_decay': 0.054029038695790246, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.3198333333333334, 1: 1.3198333333333334, 2: 0.6735561792974398} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.3259583333333333, 1: 1.3259583333333333, 2: 0.6703954159556763} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.3280416666666666, 1: 1.3280416666666666, 2: 0.6693336693336693} (클래스별 샘플 수: {0: 8000, 1: 8000, 2: 15873})
+[I 2025-12-26 00:03:11,808] Trial 99 finished with value: 0.553663457901731 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.36414671164460966, 'ffn_dropout': 0.2813245471284871, 'lr': 0.0019368781689484509, 'weight_decay': 0.026749424188425228, 'batch_size': 32}. Best is trial 70 with value: 0.5788930444782093.
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.553663
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.36414671164460966, 'ffn_dropout': 0.2813245471284871, 'lr': 0.0019368781689484509, 'weight_decay': 0.026749424188425228, 'batch_size': 32}
+  Best Value (CSI): 0.578893
+  Best Trial: 70
+  Best Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5789
+Best Hyperparameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3074211243640513, 'ffn_dropout': 0.27614330827417055, 'lr': 0.0008013502604362781, 'weight_decay': 0.07731586815781724, 'batch_size': 32}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4938
+  - 최종 CSI: 0.5537
+  - 최고 CSI: 0.6054
+  - 최저 CSI: 0.0000
+  - 평균 CSI: 0.4202
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/ft_transformer_smote_seoul_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 20, Best CSI: 0.4754
+  Fold 1 학습 완료 (검증 CSI: 0.4754)
+Fold 2 학습 중...
+  Early stopping at epoch 23, Best CSI: 0.6354
+  Fold 2 학습 완료 (검증 CSI: 0.6354)
+Fold 3 학습 중...
+  Early stopping at epoch 30, Best CSI: 0.5902
+  Fold 3 학습 완료 (검증 CSI: 0.5902)
+
+모든 모델 저장 완료: ../save_model/ft_transformer_optima/ft_transformer_smote_seoul.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/ft_transformer_optima/scaler/ft_transformer_smote_seoul_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/ft_transformer_optima/ft_transformer_smote_seoul.pkl
+
+✓ 완료: ft_transformer_smote/ft_transformer_smote_seoul.py (소요 시간: 18329초)
+
+==========================================
+FT-Transformer SMOTE 파일 실행 완료
+종료 시간: 2025-12-26 00:10:08
+총 소요 시간: 27시간 4분 14초
+성공: 6개
+실패: 0개
+==========================================
diff --git a/Analysis_code/5.optima/run_bash/ft_transformer/ft_transformer_smotenc_ctgan20000.log b/Analysis_code/5.optima/run_bash/ft_transformer/ft_transformer_smotenc_ctgan20000.log
index e7360ffc197c50a58db6d5f6fc637ea31f868a04..e98b40db1dd16fba699cd18e3a5c00b0f9e85ffe 100644
--- a/Analysis_code/5.optima/run_bash/ft_transformer/ft_transformer_smotenc_ctgan20000.log
+++ b/Analysis_code/5.optima/run_bash/ft_transformer/ft_transformer_smotenc_ctgan20000.log
@@ -2591,3 +2591,5239 @@ Trial 95 완료
 ================================================================================
 
   Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 17:41:04,175] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.364322
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2494223529450214, 'ffn_dropout': 0.3389806213065006, 'lr': 0.0013763857095719582, 'weight_decay': 0.0001960642114127266, 'batch_size': 32}
+  Best Value (CSI): 0.448883
+  Best Trial: 88
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.270864990314075, 'ffn_dropout': 0.3735925199999471, 'lr': 0.0016588396308948813, 'weight_decay': 0.00014669112268617685, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 17:41:47,136] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.413889
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.24304923066931705, 'ffn_dropout': 0.34695489252702016, 'lr': 0.0009668084444298899, 'weight_decay': 0.00011915297867117992, 'batch_size': 128}
+  Best Value (CSI): 0.448883
+  Best Trial: 88
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.270864990314075, 'ffn_dropout': 0.3735925199999471, 'lr': 0.0016588396308948813, 'weight_decay': 0.00014669112268617685, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 17:42:09,972] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.147604
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2909166353820615, 'ffn_dropout': 0.3092605775801698, 'lr': 0.000444323116299147, 'weight_decay': 0.0004129425167692841, 'batch_size': 128}
+  Best Value (CSI): 0.448883
+  Best Trial: 88
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.270864990314075, 'ffn_dropout': 0.3735925199999471, 'lr': 0.0016588396308948813, 'weight_decay': 0.00014669112268617685, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 17:42:48,076] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.120859
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.28090400843827085, 'ffn_dropout': 0.32705154264897696, 'lr': 0.0011679069021254063, 'weight_decay': 0.00027939919309894525, 'batch_size': 128}
+  Best Value (CSI): 0.448883
+  Best Trial: 88
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.270864990314075, 'ffn_dropout': 0.3735925199999471, 'lr': 0.0016588396308948813, 'weight_decay': 0.00014669112268617685, 'batch_size': 128}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4489
+Best Hyperparameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.270864990314075, 'ffn_dropout': 0.3735925199999471, 'lr': 0.0016588396308948813, 'weight_decay': 0.00014669112268617685, 'batch_size': 128}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.0421
+  - 최종 CSI: 0.1209
+  - 최고 CSI: 0.4489
+  - 최저 CSI: 0.0000
+  - 평균 CSI: 0.3269
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/ft_transformer_smotenc_ctgan20000_daegu_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 26, Best CSI: 0.4518
+  Fold 1 학습 완료 (검증 CSI: 0.4518)
+Fold 2 학습 중...
+  Early stopping at epoch 27, Best CSI: 0.4355
+  Fold 2 학습 완료 (검증 CSI: 0.4355)
+Fold 3 학습 중...
+  Early stopping at epoch 38, Best CSI: 0.3924
+  Fold 3 학습 완료 (검증 CSI: 0.3924)
+
+모든 모델 저장 완료: ../save_model/ft_transformer_optima/ft_transformer_smotenc_ctgan20000_daegu.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/ft_transformer_optima/scaler/ft_transformer_smotenc_ctgan20000_daegu_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/ft_transformer_optima/ft_transformer_smotenc_ctgan20000_daegu.pkl
+
+✓ 완료: ft_transformer_smotenc_ctgan20000/ft_transformer_smotenc_ctgan20000_daegu.py (소요 시간: 14385초)
+
+----------------------------------------
+실행 중: ft_transformer_smotenc_ctgan20000/ft_transformer_smotenc_ctgan20000_daejeon.py
+시작 시간: 2025-12-25 17:46:35
+----------------------------------------
+[I 2025-12-25 17:46:36,437] A new study created in memory with name: no-name-f760f634-d259-461a-b45c-6aa451ac1793
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 17:53:10,752] Trial 0 finished with value: 0.10114217631060095 and parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2831606899487012, 'ffn_dropout': 0.24865706134225704, 'lr': 0.0037188515388611276, 'weight_decay': 0.0002672168816784975, 'batch_size': 64}. Best is trial 0 with value: 0.10114217631060095.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.101142
+  Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2831606899487012, 'ffn_dropout': 0.24865706134225704, 'lr': 0.0037188515388611276, 'weight_decay': 0.0002672168816784975, 'batch_size': 64}
+  Best Value (CSI): 0.101142
+  Best Trial: 0
+  Best Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2831606899487012, 'ffn_dropout': 0.24865706134225704, 'lr': 0.0037188515388611276, 'weight_decay': 0.0002672168816784975, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 18:00:24,681] Trial 1 finished with value: 0.10114217631060095 and parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.3089381546308435, 'ffn_dropout': 0.14980007078493468, 'lr': 0.0037053730305581577, 'weight_decay': 0.001682732357942515, 'batch_size': 32}. Best is trial 0 with value: 0.10114217631060095.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.101142
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.3089381546308435, 'ffn_dropout': 0.14980007078493468, 'lr': 0.0037053730305581577, 'weight_decay': 0.001682732357942515, 'batch_size': 32}
+  Best Value (CSI): 0.101142
+  Best Trial: 0
+  Best Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2831606899487012, 'ffn_dropout': 0.24865706134225704, 'lr': 0.0037188515388611276, 'weight_decay': 0.0002672168816784975, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 18:06:34,660] Trial 2 finished with value: 0.47203183874296967 and parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2896406488248088, 'ffn_dropout': 0.18477055022949218, 'lr': 0.0003707417380713788, 'weight_decay': 0.004126913652613803, 'batch_size': 64}. Best is trial 2 with value: 0.47203183874296967.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.472032
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2896406488248088, 'ffn_dropout': 0.18477055022949218, 'lr': 0.0003707417380713788, 'weight_decay': 0.004126913652613803, 'batch_size': 64}
+  Best Value (CSI): 0.472032
+  Best Trial: 2
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2896406488248088, 'ffn_dropout': 0.18477055022949218, 'lr': 0.0003707417380713788, 'weight_decay': 0.004126913652613803, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 18:13:41,848] Trial 3 finished with value: 0.45890343496941116 and parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.32543210817110235, 'ffn_dropout': 0.1848348738583612, 'lr': 0.0009013655283200891, 'weight_decay': 0.07820717354315297, 'batch_size': 64}. Best is trial 2 with value: 0.47203183874296967.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.458903
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.32543210817110235, 'ffn_dropout': 0.1848348738583612, 'lr': 0.0009013655283200891, 'weight_decay': 0.07820717354315297, 'batch_size': 64}
+  Best Value (CSI): 0.472032
+  Best Trial: 2
+  Best Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2896406488248088, 'ffn_dropout': 0.18477055022949218, 'lr': 0.0003707417380713788, 'weight_decay': 0.004126913652613803, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 18:23:53,938] Trial 4 finished with value: 0.4794390839030278 and parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}. Best is trial 4 with value: 0.4794390839030278.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.479439
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:24:56,307] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.246758
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1653151790239713, 'ffn_dropout': 0.1665962154626497, 'lr': 3.062385100457289e-05, 'weight_decay': 0.00012134567069726468, 'batch_size': 256}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 18:28:05,132] Trial 6 finished with value: 0.4756308234375424 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.11627819455002496, 'ffn_dropout': 0.36123197269066665, 'lr': 0.0006178552667773511, 'weight_decay': 0.04320635278916393, 'batch_size': 64}. Best is trial 4 with value: 0.4794390839030278.
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.475631
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.11627819455002496, 'ffn_dropout': 0.36123197269066665, 'lr': 0.0006178552667773511, 'weight_decay': 0.04320635278916393, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:30:04,504] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.404642
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1745596229714939, 'ffn_dropout': 0.33919471385910616, 'lr': 0.00024774475543946206, 'weight_decay': 0.0042919879350441685, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:31:55,311] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.389121
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.22074840807641544, 'ffn_dropout': 0.12837975154990003, 'lr': 0.00010095750891989077, 'weight_decay': 0.0035331011895194703, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:32:50,608] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.258306
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3123260979035447, 'ffn_dropout': 0.20622984347989892, 'lr': 1.0294198202353091e-05, 'weight_decay': 0.004187917980314604, 'batch_size': 256}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:33:53,241] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.061059
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.37706189293207976, 'ffn_dropout': 0.10645776655943859, 'lr': 0.009105845738232036, 'weight_decay': 0.0005535862355039157, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:34:26,402] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.389302
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.1073531277781145, 'ffn_dropout': 0.37761622896988717, 'lr': 0.0009131767066424821, 'weight_decay': 0.07147770763740706, 'batch_size': 128}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:36:56,898] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.423117
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.10166373554107444, 'ffn_dropout': 0.30681506472886777, 'lr': 0.0008859270832480185, 'weight_decay': 0.021805314234993285, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:37:29,267] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.284981
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.13931220309807268, 'ffn_dropout': 0.27919494326809063, 'lr': 0.00019428694512364493, 'weight_decay': 0.015664044000754614, 'batch_size': 128}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:40:30,497] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.412008
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.19401266018089117, 'ffn_dropout': 0.39390438465530014, 'lr': 0.0005025656825689997, 'weight_decay': 0.000492079911359133, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:42:42,876] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.393416
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1389064501384776, 'ffn_dropout': 0.10619944901071887, 'lr': 9.546364128811673e-05, 'weight_decay': 0.0012449962569268554, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:43:19,473] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.392379
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.2226214097093998, 'ffn_dropout': 0.23434476653157024, 'lr': 0.001699248303276072, 'weight_decay': 0.012521468719373421, 'batch_size': 128}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:43:59,026] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.054401
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13794992294854577, 'ffn_dropout': 0.29071346385367247, 'lr': 0.001959642994819157, 'weight_decay': 0.03128963010573255, 'batch_size': 256}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:45:20,316] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.405242
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.20629649427096433, 'ffn_dropout': 0.23097383987731035, 'lr': 0.0004925861540373041, 'weight_decay': 0.009297892584514023, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:47:05,805] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.356349
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.2476364237496872, 'ffn_dropout': 0.34944546508001484, 'lr': 0.00018136227405473536, 'weight_decay': 0.04160864730792858, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:48:43,274] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.413823
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.16936384287934592, 'ffn_dropout': 0.26865541062398524, 'lr': 0.0005776795732574341, 'weight_decay': 0.0073564225063171935, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:50:03,339] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.402462
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.25325318573029115, 'ffn_dropout': 0.20794175728955364, 'lr': 0.0003401966799381887, 'weight_decay': 0.0019387629287274028, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:52:11,663] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.427046
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.11521018712447549, 'ffn_dropout': 0.14942265348307068, 'lr': 0.00033418021746793646, 'weight_decay': 0.09983442308921685, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 18:53:15,795] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.400810
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.1258699970708025, 'ffn_dropout': 0.18210640875400272, 'lr': 0.001135196552593845, 'weight_decay': 0.0064765679299096275, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 18:57:00,865] Trial 24 finished with value: 0.4677657623496904 and parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1584452402432232, 'ffn_dropout': 0.1292130079096412, 'lr': 0.000516193359242554, 'weight_decay': 0.023680904366061867, 'batch_size': 64}. Best is trial 4 with value: 0.4794390839030278.
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.467766
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1584452402432232, 'ffn_dropout': 0.1292130079096412, 'lr': 0.000516193359242554, 'weight_decay': 0.023680904366061867, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:00:57,445] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.433699
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.10288384573925327, 'ffn_dropout': 0.2040925817344531, 'lr': 0.00012359477864719908, 'weight_decay': 0.0025848651206527304, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:01:23,328] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.272674
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.1374488612610574, 'ffn_dropout': 0.30864906939774184, 'lr': 0.0003005750477526622, 'weight_decay': 0.0009779403918960246, 'batch_size': 128}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:01:54,985] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.376180
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.18920369637341222, 'ffn_dropout': 0.25953876791943514, 'lr': 0.001338173629224841, 'weight_decay': 0.050694037281934966, 'batch_size': 256}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:03:17,769] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.400822
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.15817475626527164, 'ffn_dropout': 0.10155769587048034, 'lr': 0.0020451271552376325, 'weight_decay': 0.014471872941430068, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:04:06,405] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.082650
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.27290433537992126, 'ffn_dropout': 0.24758403876072382, 'lr': 0.0028432268893370536, 'weight_decay': 0.005446082742481839, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:06:19,674] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.409465
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10060880282629787, 'ffn_dropout': 0.225589635561244, 'lr': 0.0007539056605546439, 'weight_decay': 0.009369354194789096, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:08:10,120] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.405248
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.14921461850365655, 'ffn_dropout': 0.13514691252719796, 'lr': 0.0005662527815879305, 'weight_decay': 0.03295886913021135, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:09:17,568] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.301535
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13007051544416964, 'ffn_dropout': 0.12617017680792592, 'lr': 0.0013762058715205867, 'weight_decay': 0.026050961633456503, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:11:27,790] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.412138
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12546465984997884, 'ffn_dropout': 0.16308183815788133, 'lr': 0.0004197171631293649, 'weight_decay': 0.01936302063023497, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:13:45,450] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.409135
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.16282088985500462, 'ffn_dropout': 0.15313936109300752, 'lr': 0.0007714598715442097, 'weight_decay': 0.04521226855018762, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:15:14,611] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.398014
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.14747465289345918, 'ffn_dropout': 0.13772034165602898, 'lr': 0.0006285713070553023, 'weight_decay': 0.002953862859363485, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:17:03,640] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.420849
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.17846884806635216, 'ffn_dropout': 0.17705562376457767, 'lr': 0.00041148374666929457, 'weight_decay': 0.00017617977610049414, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:17:23,451] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.275663
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.15543373306910985, 'ffn_dropout': 0.16731376811386386, 'lr': 0.000237876987870936, 'weight_decay': 0.059860490810864836, 'batch_size': 256}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:19:19,984] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.355509
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.12358355965225964, 'ffn_dropout': 0.1295793088814544, 'lr': 0.0009554123880235912, 'weight_decay': 0.02370552192220478, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:20:20,200] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.082650
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11694997112012975, 'ffn_dropout': 0.11960793764174257, 'lr': 0.0035095500195400876, 'weight_decay': 0.004544944261338899, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:21:40,228] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.426148
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.292961776544581, 'ffn_dropout': 0.147980816724813, 'lr': 0.0007109321843460932, 'weight_decay': 0.07045732762669117, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:24:10,584] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.363248
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.3292501944580009, 'ffn_dropout': 0.18843079365418286, 'lr': 0.0008522031751486062, 'weight_decay': 0.041684913486344705, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:25:57,419] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.395349
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.34466674646638334, 'ffn_dropout': 0.11902370832870246, 'lr': 0.001048005681887154, 'weight_decay': 0.08381392619114733, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:27:20,975] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.407244
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.30188602265450204, 'ffn_dropout': 0.11406282219488198, 'lr': 0.0004960511995101767, 'weight_decay': 0.06553174307739905, 'batch_size': 128}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:29:14,950] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.202658
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.2865504361525316, 'ffn_dropout': 0.14003699720467983, 'lr': 0.001355617156989053, 'weight_decay': 0.09071203719000302, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:30:57,205] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.397045
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.31480448481654544, 'ffn_dropout': 0.1658969307013161, 'lr': 0.0002742831439835242, 'weight_decay': 0.01864650115014211, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:32:15,061] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.373989
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.1141601379785131, 'ffn_dropout': 0.10062672818502483, 'lr': 0.0004061678313692881, 'weight_decay': 0.03036175602510023, 'batch_size': 256}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:36:51,788] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.415254
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.14548045921647523, 'ffn_dropout': 0.15554616074537697, 'lr': 0.0006621093694505821, 'weight_decay': 0.012333887153231204, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:38:28,966] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.407470
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.17545953091534366, 'ffn_dropout': 0.19484751242465392, 'lr': 0.001778759658774027, 'weight_decay': 0.055505023751457556, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:38:57,753] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.032212
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.27370134697034115, 'ffn_dropout': 0.11617320669249372, 'lr': 0.007229461392893837, 'weight_decay': 0.0004235117381729491, 'batch_size': 128}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:40:09,315] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.003188
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1612280144770974, 'ffn_dropout': 0.17290455146496678, 'lr': 0.0010280872436310574, 'weight_decay': 0.0009357016846392856, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:41:33,937] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.082650
+  Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.23518690871609854, 'ffn_dropout': 0.2222788375296622, 'lr': 0.004722472801902956, 'weight_decay': 0.003573172692283876, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:42:52,245] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.082650
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.32339680871922655, 'ffn_dropout': 0.24676223595818417, 'lr': 0.002522087020224437, 'weight_decay': 0.03711396732470164, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:45:00,152] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.423391
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.21135007142431328, 'ffn_dropout': 0.2086257012595508, 'lr': 0.00020468701078824812, 'weight_decay': 0.0022724437645208857, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:47:17,060] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.082650
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2657835395508201, 'ffn_dropout': 0.19312542201783683, 'lr': 0.0014874934903712975, 'weight_decay': 0.0016796977183618632, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:49:36,696] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.423110
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.11170933092699961, 'ffn_dropout': 0.274396481837994, 'lr': 0.0005139578822059886, 'weight_decay': 0.02640626923356078, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:49:55,763] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.280748
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.13532033468671903, 'ffn_dropout': 0.17997657323154861, 'lr': 0.00032768529749598686, 'weight_decay': 0.04668784700965264, 'batch_size': 256}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:51:42,952] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.396504
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2886045714592196, 'ffn_dropout': 0.3630055912369151, 'lr': 0.0011518289090853555, 'weight_decay': 0.09962982372399778, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:53:07,292] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.311475
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.24858273118604002, 'ffn_dropout': 0.322842570854772, 'lr': 0.002132507790924421, 'weight_decay': 0.03579267899422179, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 19:54:28,146] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.424755
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.19215905543399633, 'ffn_dropout': 0.2846413100096474, 'lr': 0.0008065144690646186, 'weight_decay': 0.0710565194893732, 'batch_size': 128}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:00:20,038] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.449287
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.12264865753939296, 'ffn_dropout': 0.24079961316377688, 'lr': 0.0006024962282526384, 'weight_decay': 0.017169356369336183, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:01:39,909] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.197459
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2992065689410639, 'ffn_dropout': 0.39995176286322554, 'lr': 0.004134011707966688, 'weight_decay': 0.0014969343967866642, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:02:58,129] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.003188
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.33841761320614483, 'ffn_dropout': 0.14522852259834734, 'lr': 0.006127787841994441, 'weight_decay': 0.003254926791999708, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:04:17,698] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.104179
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.30933347359625135, 'ffn_dropout': 0.25907247236742115, 'lr': 0.0016515004066333357, 'weight_decay': 0.002395468478823066, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:05:26,698] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.036662
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.13315599600645578, 'ffn_dropout': 0.12972339544693665, 'lr': 0.0028329453685656558, 'weight_decay': 0.0010431889757109675, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:06:57,928] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.405431
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.10719642701745037, 'ffn_dropout': 0.16049610165175582, 'lr': 0.0003852295794037518, 'weight_decay': 0.0005554246893080671, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:07:39,723] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.352941
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.10059901900531157, 'ffn_dropout': 0.2998273280116266, 'lr': 0.002305614903516311, 'weight_decay': 0.053855444737070046, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:08:42,298] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.012556
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.14595860207567957, 'ffn_dropout': 0.14420879496953956, 'lr': 0.0034280375656768358, 'weight_decay': 0.006944972110591257, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:09:23,333] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.082650
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.27899825992146077, 'ffn_dropout': 0.21813400245749803, 'lr': 0.00966755020662718, 'weight_decay': 0.004544103971695636, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:10:13,472] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.399592
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2570911299643366, 'ffn_dropout': 0.15570247721535913, 'lr': 0.0012002406363328035, 'weight_decay': 0.020939722992630912, 'batch_size': 256}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:11:58,780] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.386214
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.28216790995107677, 'ffn_dropout': 0.2363839130722406, 'lr': 0.0004704997818598726, 'weight_decay': 0.030931665299920617, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:13:10,163] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.418944
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.11371479101016399, 'ffn_dropout': 0.1376807971117434, 'lr': 0.0003105693077327873, 'weight_decay': 0.08291829224335837, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:14:48,857] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.405767
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.12051503578072004, 'ffn_dropout': 0.10968455758342585, 'lr': 0.0006730880969401189, 'weight_decay': 0.06473198955673239, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:16:05,095] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.406635
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13483113013071477, 'ffn_dropout': 0.1278215893123774, 'lr': 0.0008956325649525063, 'weight_decay': 0.05014545227134472, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:17:05,813] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.380591
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.12837974942219846, 'ffn_dropout': 0.1521228143947659, 'lr': 0.00024522257199356397, 'weight_decay': 0.08106946489169191, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:22:03,037] Trial 75 finished with value: 0.4773165864934213 and parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.29563392885065054, 'ffn_dropout': 0.17095574759777818, 'lr': 0.00034033123094662194, 'weight_decay': 0.04015055968468594, 'batch_size': 64}. Best is trial 4 with value: 0.4794390839030278.
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.477317
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.29563392885065054, 'ffn_dropout': 0.17095574759777818, 'lr': 0.00034033123094662194, 'weight_decay': 0.04015055968468594, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:24:22,081] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.395325
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2960171562771131, 'ffn_dropout': 0.18591150151111346, 'lr': 0.0005476244398874502, 'weight_decay': 0.0437533436074344, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:25:00,157] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.348929
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.29432980925398594, 'ffn_dropout': 0.17426045851921362, 'lr': 0.00034540181468466425, 'weight_decay': 0.022267182456778684, 'batch_size': 128}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:33:02,154] Trial 78 finished with value: 0.4478985477356674 and parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3025037976818863, 'ffn_dropout': 0.164336674096795, 'lr': 0.00015785653431335504, 'weight_decay': 0.027369715520844218, 'batch_size': 32}. Best is trial 4 with value: 0.4794390839030278.
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.447899
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3025037976818863, 'ffn_dropout': 0.164336674096795, 'lr': 0.00015785653431335504, 'weight_decay': 0.027369715520844218, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:34:40,102] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.376268
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.30928922647348045, 'ffn_dropout': 0.1684020371909276, 'lr': 0.00013394683004977983, 'weight_decay': 0.0287445276166224, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:39:00,125] Trial 80 finished with value: 0.4742279530628539 and parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.31881358314770847, 'ffn_dropout': 0.18114516366382494, 'lr': 0.0001596395229069402, 'weight_decay': 0.0382848539558849, 'batch_size': 64}. Best is trial 4 with value: 0.4794390839030278.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.474228
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.31881358314770847, 'ffn_dropout': 0.18114516366382494, 'lr': 0.0001596395229069402, 'weight_decay': 0.0382848539558849, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:40:33,979] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.397273
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.31969652633807716, 'ffn_dropout': 0.2004922949872361, 'lr': 0.0001658870789949671, 'weight_decay': 0.024961224212949204, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:42:06,129] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.413333
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.30566118516918206, 'ffn_dropout': 0.18045230170051724, 'lr': 0.00027281813846264416, 'weight_decay': 0.038799985361987276, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:43:30,036] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.423903
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.30333087492976024, 'ffn_dropout': 0.1916818169812485, 'lr': 0.00038890730221628983, 'weight_decay': 0.03461308318929088, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:44:47,702] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.397345
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.31653642765808443, 'ffn_dropout': 0.1601265001101481, 'lr': 9.490417572145297e-05, 'weight_decay': 0.059518118095511836, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:46:34,925] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.414070
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2899714211697524, 'ffn_dropout': 0.21487409503851498, 'lr': 0.0004430036484603725, 'weight_decay': 0.014041664050612496, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:48:01,907] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.392644
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3280103497270644, 'ffn_dropout': 0.19834812408967606, 'lr': 0.0007843068835696079, 'weight_decay': 0.041618608986185746, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:49:19,766] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.390198
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.27895794420115694, 'ffn_dropout': 0.18487559651889107, 'lr': 0.0006274832859211233, 'weight_decay': 0.029987020112709966, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:52:43,014] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.412910
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2692521022237785, 'ffn_dropout': 0.17086749713092725, 'lr': 0.00021391070211560395, 'weight_decay': 0.05189751607881994, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:53:43,308] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.393548
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.31028806393352665, 'ffn_dropout': 0.2099972860252744, 'lr': 0.0009073257738796153, 'weight_decay': 0.019200806302852667, 'batch_size': 256}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:55:16,484] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.416298
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.29731367912144385, 'ffn_dropout': 0.202084809410184, 'lr': 0.0004962018152795354, 'weight_decay': 0.02477358178714799, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:57:31,680] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.400822
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2861227426532186, 'ffn_dropout': 0.17701686236937117, 'lr': 0.00028591162940486733, 'weight_decay': 0.035369503117015984, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:59:06,944] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.082650
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.30288823609327237, 'ffn_dropout': 0.164262795369077, 'lr': 0.0017279496869866112, 'weight_decay': 0.011902433111238696, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:01:17,513] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.339683
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.3144773612555949, 'ffn_dropout': 0.22800817381972965, 'lr': 0.00036372302064352805, 'weight_decay': 0.005761892405685662, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:03:30,052] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.386680
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3249967477542412, 'ffn_dropout': 0.12223068443015965, 'lr': 0.000571239963582788, 'weight_decay': 0.016487883676002034, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:04:16,116] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.243590
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.335356679112468, 'ffn_dropout': 0.1468402412289941, 'lr': 0.0011158648895417571, 'weight_decay': 0.008354986946457607, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:05:16,155] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.434365
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.353710192212825, 'ffn_dropout': 0.18443457693172816, 'lr': 0.0013940875661769393, 'weight_decay': 0.06472545372275262, 'batch_size': 128}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:07:26,217] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.003188
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.29126991275036723, 'ffn_dropout': 0.15679500623530732, 'lr': 0.0007257018965523121, 'weight_decay': 0.0026881448423374038, 'batch_size': 32}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:08:56,807] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.428713
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.3197359148582833, 'ffn_dropout': 0.1322304232782615, 'lr': 0.00042528323713952713, 'weight_decay': 0.04859494333518805, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:10:08,489] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.082650
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.15265276804699845, 'ffn_dropout': 0.10810151959425085, 'lr': 0.0009859949042432773, 'weight_decay': 0.0019720817063937405, 'batch_size': 64}
+  Best Value (CSI): 0.479439
+  Best Trial: 4
+  Best Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4794
+Best Hyperparameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.1011
+  - 최종 CSI: 0.0827
+  - 최고 CSI: 0.4794
+  - 최저 CSI: 0.0032
+  - 평균 CSI: 0.3266
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/ft_transformer_smotenc_ctgan20000_daejeon_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 37, Best CSI: 0.4054
+  Fold 1 학습 완료 (검증 CSI: 0.4054)
+Fold 2 학습 중...
+  Early stopping at epoch 21, Best CSI: 0.4771
+  Fold 2 학습 완료 (검증 CSI: 0.4771)
+Fold 3 학습 중...
+  Early stopping at epoch 25, Best CSI: 0.4971
+  Fold 3 학습 완료 (검증 CSI: 0.4971)
+
+모든 모델 저장 완료: ../save_model/ft_transformer_optima/ft_transformer_smotenc_ctgan20000_daejeon.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/ft_transformer_optima/scaler/ft_transformer_smotenc_ctgan20000_daejeon_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/ft_transformer_optima/ft_transformer_smotenc_ctgan20000_daejeon.pkl
+
+✓ 완료: ft_transformer_smotenc_ctgan20000/ft_transformer_smotenc_ctgan20000_daejeon.py (소요 시간: 12810초)
+
+----------------------------------------
+실행 중: ft_transformer_smotenc_ctgan20000/ft_transformer_smotenc_ctgan20000_gwangju.py
+시작 시간: 2025-12-25 21:20:05
+----------------------------------------
+[I 2025-12-25 21:20:06,390] A new study created in memory with name: no-name-2ee8af21-624d-478a-bc09-265641d0fda7
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 21:25:23,147] Trial 0 finished with value: 0.48372288996217055 and parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21346320964398893, 'ffn_dropout': 0.3725640866886927, 'lr': 0.0001820770604878082, 'weight_decay': 0.00118470509914376, 'batch_size': 128}. Best is trial 0 with value: 0.48372288996217055.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.483723
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21346320964398893, 'ffn_dropout': 0.3725640866886927, 'lr': 0.0001820770604878082, 'weight_decay': 0.00118470509914376, 'batch_size': 128}
+  Best Value (CSI): 0.483723
+  Best Trial: 0
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21346320964398893, 'ffn_dropout': 0.3725640866886927, 'lr': 0.0001820770604878082, 'weight_decay': 0.00118470509914376, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 21:32:41,980] Trial 1 finished with value: 0.4647832727791206 and parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2668608884600342, 'ffn_dropout': 0.3217467248145599, 'lr': 0.00024876380934079186, 'weight_decay': 0.0008122595007764616, 'batch_size': 64}. Best is trial 0 with value: 0.48372288996217055.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.464783
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2668608884600342, 'ffn_dropout': 0.3217467248145599, 'lr': 0.00024876380934079186, 'weight_decay': 0.0008122595007764616, 'batch_size': 64}
+  Best Value (CSI): 0.483723
+  Best Trial: 0
+  Best Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.21346320964398893, 'ffn_dropout': 0.3725640866886927, 'lr': 0.0001820770604878082, 'weight_decay': 0.00118470509914376, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 21:39:00,243] Trial 2 finished with value: 0.4848504186984368 and parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2084981846598, 'ffn_dropout': 0.11820171074339306, 'lr': 0.0001859499149751709, 'weight_decay': 0.02231654929905672, 'batch_size': 32}. Best is trial 2 with value: 0.4848504186984368.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.484850
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2084981846598, 'ffn_dropout': 0.11820171074339306, 'lr': 0.0001859499149751709, 'weight_decay': 0.02231654929905672, 'batch_size': 32}
+  Best Value (CSI): 0.484850
+  Best Trial: 2
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2084981846598, 'ffn_dropout': 0.11820171074339306, 'lr': 0.0001859499149751709, 'weight_decay': 0.02231654929905672, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 21:53:14,742] Trial 3 finished with value: 0.4861335251119714 and parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1158420998787305, 'ffn_dropout': 0.2569632294108326, 'lr': 0.00012778869825819076, 'weight_decay': 0.0002891406493568458, 'batch_size': 32}. Best is trial 3 with value: 0.4861335251119714.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.486134
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1158420998787305, 'ffn_dropout': 0.2569632294108326, 'lr': 0.00012778869825819076, 'weight_decay': 0.0002891406493568458, 'batch_size': 32}
+  Best Value (CSI): 0.486134
+  Best Trial: 3
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1158420998787305, 'ffn_dropout': 0.2569632294108326, 'lr': 0.00012778869825819076, 'weight_decay': 0.0002891406493568458, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:01:49,008] Trial 4 finished with value: 0.4737587078036308 and parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2910083355043665, 'ffn_dropout': 0.3142987264987416, 'lr': 0.0003037958177521694, 'weight_decay': 0.019150555613368023, 'batch_size': 64}. Best is trial 3 with value: 0.4861335251119714.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.473759
+  Parameters: {'d_token': 256, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2910083355043665, 'ffn_dropout': 0.3142987264987416, 'lr': 0.0003037958177521694, 'weight_decay': 0.019150555613368023, 'batch_size': 64}
+  Best Value (CSI): 0.486134
+  Best Trial: 3
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1158420998787305, 'ffn_dropout': 0.2569632294108326, 'lr': 0.00012778869825819076, 'weight_decay': 0.0002891406493568458, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:02:04,561] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.349873
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.36928433154536466, 'ffn_dropout': 0.28726241199716074, 'lr': 0.005183204060432148, 'weight_decay': 0.0001835316742210229, 'batch_size': 256}
+  Best Value (CSI): 0.486134
+  Best Trial: 3
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1158420998787305, 'ffn_dropout': 0.2569632294108326, 'lr': 0.00012778869825819076, 'weight_decay': 0.0002891406493568458, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:03:22,223] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.414433
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3989143284975655, 'ffn_dropout': 0.3374225566938547, 'lr': 0.0013105743424169658, 'weight_decay': 0.005332348842462533, 'batch_size': 256}
+  Best Value (CSI): 0.486134
+  Best Trial: 3
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1158420998787305, 'ffn_dropout': 0.2569632294108326, 'lr': 0.00012778869825819076, 'weight_decay': 0.0002891406493568458, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:04:03,069] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.357095
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.10036107003792832, 'ffn_dropout': 0.18536539823371878, 'lr': 0.00011631102748288045, 'weight_decay': 0.0001691767062242687, 'batch_size': 64}
+  Best Value (CSI): 0.486134
+  Best Trial: 3
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1158420998787305, 'ffn_dropout': 0.2569632294108326, 'lr': 0.00012778869825819076, 'weight_decay': 0.0002891406493568458, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:05:18,154] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.003985
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.22910923445557219, 'ffn_dropout': 0.367468113285742, 'lr': 0.0014420011531592653, 'weight_decay': 0.00041874464555023564, 'batch_size': 64}
+  Best Value (CSI): 0.486134
+  Best Trial: 3
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1158420998787305, 'ffn_dropout': 0.2569632294108326, 'lr': 0.00012778869825819076, 'weight_decay': 0.0002891406493568458, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:07:39,461] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.003985
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2534596619484337, 'ffn_dropout': 0.30891008169786094, 'lr': 0.0013534476758457214, 'weight_decay': 0.000277652105103073, 'batch_size': 32}
+  Best Value (CSI): 0.486134
+  Best Trial: 3
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1158420998787305, 'ffn_dropout': 0.2569632294108326, 'lr': 0.00012778869825819076, 'weight_decay': 0.0002891406493568458, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:08:57,771] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.303637
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.10561022099260317, 'ffn_dropout': 0.2415968754556495, 'lr': 2.0576233693315415e-05, 'weight_decay': 0.00010591559131705319, 'batch_size': 32}
+  Best Value (CSI): 0.486134
+  Best Trial: 3
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1158420998787305, 'ffn_dropout': 0.2569632294108326, 'lr': 0.00012778869825819076, 'weight_decay': 0.0002891406493568458, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:10:58,486] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.372064
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.16491776483594744, 'ffn_dropout': 0.11156706892985568, 'lr': 5.320417136828853e-05, 'weight_decay': 0.034185522828103075, 'batch_size': 32}
+  Best Value (CSI): 0.486134
+  Best Trial: 3
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1158420998787305, 'ffn_dropout': 0.2569632294108326, 'lr': 0.00012778869825819076, 'weight_decay': 0.0002891406493568458, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:12:19,424] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.337257
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1638700319307705, 'ffn_dropout': 0.2116835405822817, 'lr': 4.172195885248482e-05, 'weight_decay': 0.00598491877836218, 'batch_size': 32}
+  Best Value (CSI): 0.486134
+  Best Trial: 3
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1158420998787305, 'ffn_dropout': 0.2569632294108326, 'lr': 0.00012778869825819076, 'weight_decay': 0.0002891406493568458, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:13:24,383] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.349175
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.16268262741554726, 'ffn_dropout': 0.10207407217855827, 'lr': 1.10970849901695e-05, 'weight_decay': 0.0958546095301826, 'batch_size': 32}
+  Best Value (CSI): 0.486134
+  Best Trial: 3
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1158420998787305, 'ffn_dropout': 0.2569632294108326, 'lr': 0.00012778869825819076, 'weight_decay': 0.0002891406493568458, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:14:24,868] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.438557
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.2001412865708697, 'ffn_dropout': 0.15646809855569482, 'lr': 8.389734663664799e-05, 'weight_decay': 0.0020424981324430264, 'batch_size': 128}
+  Best Value (CSI): 0.486134
+  Best Trial: 3
+  Best Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1158420998787305, 'ffn_dropout': 0.2569632294108326, 'lr': 0.00012778869825819076, 'weight_decay': 0.0002891406493568458, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:23:04,220] Trial 15 finished with value: 0.49840606228991896 and parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.498406
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:24:43,460] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.388514
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1345538345329726, 'ffn_dropout': 0.27139784167526165, 'lr': 0.0005088221407454512, 'weight_decay': 0.0005163965817761533, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:27:21,176] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.367718
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.12374580584307766, 'ffn_dropout': 0.24495773412177177, 'lr': 0.0004003683799215408, 'weight_decay': 0.0008330297840615228, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:27:59,229] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.345178
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1253283154703022, 'ffn_dropout': 0.21995748770090606, 'lr': 0.0005959844151283836, 'weight_decay': 0.0003693720940468745, 'batch_size': 256}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:28:32,619] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.374412
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.15013928857235548, 'ffn_dropout': 0.27512249744191375, 'lr': 9.893426694289239e-05, 'weight_decay': 0.0017356165834729361, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:29:58,702] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.399332
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1846727807103707, 'ffn_dropout': 0.2630914045820804, 'lr': 3.692796855756947e-05, 'weight_decay': 0.0006692920901051421, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:32:05,822] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.397180
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.18662242308563035, 'ffn_dropout': 0.17226796755895085, 'lr': 0.00011614853214262054, 'weight_decay': 0.003566901011920947, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:33:47,825] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.390300
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.13113935977572894, 'ffn_dropout': 0.22170190360714037, 'lr': 0.00017863103271983206, 'weight_decay': 0.00024806460126083354, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:36:42,644] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.312977
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1471682791665036, 'ffn_dropout': 0.13782715469011264, 'lr': 0.000181884372640476, 'weight_decay': 0.00010529123424150662, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:43:26,586] Trial 24 finished with value: 0.4764436806404675 and parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.10525906977006419, 'ffn_dropout': 0.20255555931103614, 'lr': 0.0005360630268797583, 'weight_decay': 0.0013394903534300244, 'batch_size': 32}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.476444
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.10525906977006419, 'ffn_dropout': 0.20255555931103614, 'lr': 0.0005360630268797583, 'weight_decay': 0.0013394903534300244, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:45:47,702] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.364583
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.17932355887090837, 'ffn_dropout': 0.25083694085087843, 'lr': 5.759886786602365e-05, 'weight_decay': 0.00040627701515435854, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:46:50,778] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.370930
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.21742816645076501, 'ffn_dropout': 0.19250484949230043, 'lr': 0.0002776139063729696, 'weight_decay': 0.0025460699063219387, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:47:17,857] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.358189
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.14601369050456345, 'ffn_dropout': 0.2251443650150529, 'lr': 7.226301910039722e-05, 'weight_decay': 0.0009680928395234776, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:48:01,703] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.364596
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.12215940993954488, 'ffn_dropout': 0.28797316769320874, 'lr': 0.0008845190239282522, 'weight_decay': 0.00964270351675896, 'batch_size': 256}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:48:53,102] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.412924
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.20775014114500034, 'ffn_dropout': 0.17009076042431676, 'lr': 0.00014732278211788751, 'weight_decay': 0.0012656990317928849, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:55:18,851] Trial 30 finished with value: 0.4794767051181757 and parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.22765300895402485, 'ffn_dropout': 0.23533338983618107, 'lr': 0.00023698867844648312, 'weight_decay': 0.0031319339049720334, 'batch_size': 32}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.479477
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.22765300895402485, 'ffn_dropout': 0.23533338983618107, 'lr': 0.00023698867844648312, 'weight_decay': 0.0031319339049720334, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:56:30,559] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.371084
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.19743742679889356, 'ffn_dropout': 0.39344071648568013, 'lr': 0.00018779852688157267, 'weight_decay': 0.0007569336738398527, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:57:26,059] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.388209
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.27502270201561896, 'ffn_dropout': 0.3485991752416561, 'lr': 0.00022373961088201802, 'weight_decay': 0.0005989696291618829, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:03:51,938] Trial 33 finished with value: 0.4793027385233059 and parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2292849883394241, 'ffn_dropout': 0.3019156760644556, 'lr': 0.00038461768440998026, 'weight_decay': 0.0012044984960641071, 'batch_size': 64}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.479303
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2292849883394241, 'ffn_dropout': 0.3019156760644556, 'lr': 0.00038461768440998026, 'weight_decay': 0.0012044984960641071, 'batch_size': 64}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:07:17,742] Trial 34 finished with value: 0.4733494127719579 and parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.24963627845141828, 'ffn_dropout': 0.3244614374723655, 'lr': 0.00031911080108400853, 'weight_decay': 0.001710596215202563, 'batch_size': 128}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.473349
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.24963627845141828, 'ffn_dropout': 0.3244614374723655, 'lr': 0.00031911080108400853, 'weight_decay': 0.001710596215202563, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:08:43,018] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.397129
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.1703643058212566, 'ffn_dropout': 0.29405151691864834, 'lr': 0.00012937867056012845, 'weight_decay': 0.0009236400754227986, 'batch_size': 256}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:09:28,821] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.394253
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.30620669243371457, 'ffn_dropout': 0.3208939682041325, 'lr': 0.00027121555858332385, 'weight_decay': 0.0005366237368653677, 'batch_size': 64}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:09:56,854] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.388935
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1891470886541938, 'ffn_dropout': 0.25472778042717115, 'lr': 9.179925425745212e-05, 'weight_decay': 0.0002918659038130922, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:10:58,147] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.385965
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.11233985153813573, 'ffn_dropout': 0.2869525863561352, 'lr': 0.0001501468922693348, 'weight_decay': 0.00018799518076128812, 'batch_size': 64}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:13:02,069] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.003985
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.1419027472128915, 'ffn_dropout': 0.33772077965792113, 'lr': 0.0029429976482076756, 'weight_decay': 0.00046425546188629067, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:14:06,976] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.364269
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.15736988450283235, 'ffn_dropout': 0.267147624353145, 'lr': 0.000745933053051001, 'weight_decay': 0.015101682543261271, 'batch_size': 256}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:20:49,815] Trial 41 finished with value: 0.4840420164606394 and parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.22250268522197666, 'ffn_dropout': 0.24025013432134437, 'lr': 0.00023618381355069466, 'weight_decay': 0.00372158304962893, 'batch_size': 32}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.484042
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.22250268522197666, 'ffn_dropout': 0.24025013432134437, 'lr': 0.00023618381355069466, 'weight_decay': 0.00372158304962893, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:22:34,617] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.363966
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.20824440080044282, 'ffn_dropout': 0.20056978746002252, 'lr': 0.00035376847697482646, 'weight_decay': 0.004126338818967217, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:24:29,932] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.321644
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.17232848895142716, 'ffn_dropout': 0.23721590443495988, 'lr': 0.00023815410540797924, 'weight_decay': 0.005868285694195187, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:26:50,346] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.330402
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.10186356641277763, 'ffn_dropout': 0.36314294045827233, 'lr': 0.00014245837726324005, 'weight_decay': 0.002573738386246996, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 23:34:04,201] Trial 45 finished with value: 0.48126775000682104 and parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.15671686359834947, 'ffn_dropout': 0.3041481780014057, 'lr': 0.0004733778108757363, 'weight_decay': 0.04243617742110823, 'batch_size': 32}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.481268
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.15671686359834947, 'ffn_dropout': 0.3041481780014057, 'lr': 0.0004733778108757363, 'weight_decay': 0.04243617742110823, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:36:06,763] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.374684
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2461632862898201, 'ffn_dropout': 0.13362127910286659, 'lr': 0.00033247129312286694, 'weight_decay': 0.011751152579042997, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:37:42,961] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.364350
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.1373207466783949, 'ffn_dropout': 0.2777889012670552, 'lr': 0.0001173590164683733, 'weight_decay': 0.02229856124815205, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:38:29,675] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.400605
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.17615878073448846, 'ffn_dropout': 0.3171659734077421, 'lr': 7.508491176820663e-05, 'weight_decay': 0.0006347971539466756, 'batch_size': 64}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:40:01,997] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.405183
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.19487913689755446, 'ffn_dropout': 0.2590131615752733, 'lr': 0.00020367442077653028, 'weight_decay': 0.004849485149429755, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:40:48,189] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.392857
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.11243643055013398, 'ffn_dropout': 0.209191947355023, 'lr': 0.0007559400253317549, 'weight_decay': 0.007289460502867801, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:42:33,856] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.371910
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.1612915395208359, 'ffn_dropout': 0.3058586604484493, 'lr': 0.00046405792620229583, 'weight_decay': 0.047419535173522875, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:44:23,338] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.357809
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.15530088570029416, 'ffn_dropout': 0.2769846463459473, 'lr': 0.0004498876497406829, 'weight_decay': 0.043145651101506394, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:46:08,640] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.369089
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.13267993228721928, 'ffn_dropout': 0.23788551262357863, 'lr': 0.00024905903420021584, 'weight_decay': 0.024711872785764352, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:48:15,180] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.364882
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1684088734647707, 'ffn_dropout': 0.25687243386155856, 'lr': 0.0003162688105841667, 'weight_decay': 0.08066343726445575, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:50:02,538] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.400239
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.18414487034172705, 'ffn_dropout': 0.29840066587945235, 'lr': 0.00018187137314994116, 'weight_decay': 0.00034135487229599757, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:51:28,823] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.364084
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1199982070967209, 'ffn_dropout': 0.31137989280061507, 'lr': 0.0005845148928921555, 'weight_decay': 0.014571603616598783, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:53:22,840] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.400877
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.14365207076992628, 'ffn_dropout': 0.22701431640008574, 'lr': 0.0001554764291016946, 'weight_decay': 0.02923202475484064, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:53:35,814] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.200366
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.13023617943613297, 'ffn_dropout': 0.25213452527318975, 'lr': 0.00011114170457518931, 'weight_decay': 0.01861131042369739, 'batch_size': 256}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:55:59,434] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.392576
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1534191524766948, 'ffn_dropout': 0.26730969064263477, 'lr': 0.0004391029284786918, 'weight_decay': 0.0004963848679784123, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:56:24,387] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.238242
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1772132767961795, 'ffn_dropout': 0.18672271161535092, 'lr': 0.00010030994600401824, 'weight_decay': 0.008003966625878442, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 23:57:58,125] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.349941
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.21825459811141232, 'ffn_dropout': 0.24512643902822306, 'lr': 0.00027498098024077145, 'weight_decay': 0.0020962939323636138, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-26 00:03:16,301] Trial 62 finished with value: 0.4718399687681063 and parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.23042266663893285, 'ffn_dropout': 0.23436062896301588, 'lr': 0.0002230692069938929, 'weight_decay': 0.0039796509597071405, 'batch_size': 32}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.471840
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.23042266663893285, 'ffn_dropout': 0.23436062896301588, 'lr': 0.0002230692069938929, 'weight_decay': 0.0039796509597071405, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 00:04:56,558] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.371859
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.23909958883614, 'ffn_dropout': 0.21532198212059442, 'lr': 0.00020499871619712303, 'weight_decay': 0.002976737471477156, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-26 00:11:21,309] Trial 64 finished with value: 0.48181230905597205 and parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.20079879517764765, 'ffn_dropout': 0.24661332840907887, 'lr': 0.00037292442852595356, 'weight_decay': 0.0007646956101587916, 'batch_size': 32}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.481812
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.20079879517764765, 'ffn_dropout': 0.24661332840907887, 'lr': 0.00037292442852595356, 'weight_decay': 0.0007646956101587916, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-26 00:15:24,915] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.428249
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2045930628883914, 'ffn_dropout': 0.2838112011876339, 'lr': 0.00037283474999213833, 'weight_decay': 0.0008266932202585447, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 00:16:58,140] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.411236
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.21561365545612102, 'ffn_dropout': 0.2715999812294845, 'lr': 0.0004985470667659301, 'weight_decay': 0.00040676032786569487, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 00:18:13,381] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.361461
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.1943555674672662, 'ffn_dropout': 0.24559586237932152, 'lr': 0.00017296542457151265, 'weight_decay': 0.0010439206925488033, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 00:19:09,701] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.422149
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.1833908972936007, 'ffn_dropout': 0.2616105082481707, 'lr': 0.0003014540526410793, 'weight_decay': 0.0005677941629566493, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 00:20:59,593] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.003985
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.20138962194582646, 'ffn_dropout': 0.2298291742015669, 'lr': 0.0006769220801408388, 'weight_decay': 0.001386574105603196, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 00:21:48,924] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.364532
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.19034136159535947, 'ffn_dropout': 0.2964249909006615, 'lr': 0.000534030213298193, 'weight_decay': 0.0002279146259718452, 'batch_size': 64}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-26 00:25:46,291] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.442563
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.22239433841977418, 'ffn_dropout': 0.22147789480978314, 'lr': 0.00025016340377996316, 'weight_decay': 0.0032638902083724484, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-26 00:31:00,777] Trial 72 finished with value: 0.4823952479730737 and parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2093950634104731, 'ffn_dropout': 0.23177092967447208, 'lr': 0.0004042750866433493, 'weight_decay': 0.0007810132677050465, 'batch_size': 32}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.482395
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2093950634104731, 'ffn_dropout': 0.23177092967447208, 'lr': 0.0004042750866433493, 'weight_decay': 0.0007810132677050465, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 00:32:11,334] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.375144
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.20855637979164285, 'ffn_dropout': 0.24421242977966104, 'lr': 0.00038930074915543634, 'weight_decay': 0.0007938370979948668, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 00:33:26,621] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.383897
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.16424863453097832, 'ffn_dropout': 0.2652225582064566, 'lr': 0.00013453555328428205, 'weight_decay': 0.0007434815518792257, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 00:33:38,657] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.382725
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2133946895874894, 'ffn_dropout': 0.20966121608580546, 'lr': 0.00032830372994374255, 'weight_decay': 0.0010222855239881133, 'batch_size': 256}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 00:34:54,639] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.334214
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2001280951771686, 'ffn_dropout': 0.25121392242751894, 'lr': 0.0009254964638918736, 'weight_decay': 0.00035987075241262015, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 00:35:30,662] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.354610
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.23480092176962009, 'ffn_dropout': 0.2823491296560678, 'lr': 0.00039326269144103064, 'weight_decay': 0.0006055053620312649, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-26 00:48:15,544] Trial 78 finished with value: 0.4814932578525175 and parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.2217962504149459, 'ffn_dropout': 0.22980442371103418, 'lr': 0.0001652848481714356, 'weight_decay': 0.000447714763969572, 'batch_size': 32}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.481493
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.2217962504149459, 'ffn_dropout': 0.22980442371103418, 'lr': 0.0001652848481714356, 'weight_decay': 0.000447714763969572, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-26 00:59:16,957] Trial 79 finished with value: 0.4686812399457628 and parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.22423083791449172, 'ffn_dropout': 0.21921961800738346, 'lr': 0.0001552345637589303, 'weight_decay': 0.00046261429580583667, 'batch_size': 32}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.468681
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.22423083791449172, 'ffn_dropout': 0.21921961800738346, 'lr': 0.0001552345637589303, 'weight_decay': 0.00046261429580583667, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-26 01:09:52,548] Trial 80 finished with value: 0.4722945104732808 and parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.23727503974846959, 'ffn_dropout': 0.19920833057507337, 'lr': 0.00018826300515392566, 'weight_decay': 0.0006685746353840874, 'batch_size': 32}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.472295
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.23727503974846959, 'ffn_dropout': 0.19920833057507337, 'lr': 0.00018826300515392566, 'weight_decay': 0.0006685746353840874, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:11:24,970] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.390387
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.2114466654278321, 'ffn_dropout': 0.23210198229714407, 'lr': 0.0002622823751044674, 'weight_decay': 0.0004884166804740916, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:13:58,279] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.406024
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.22292019845024202, 'ffn_dropout': 0.24304915323325194, 'lr': 0.00011612270077331507, 'weight_decay': 0.0009666362607698635, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:15:13,376] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.370234
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.20199258856633617, 'ffn_dropout': 0.27411901212952505, 'lr': 0.00021419603016494137, 'weight_decay': 0.0003188956129505065, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-26 01:22:39,190] Trial 84 finished with value: 0.467950232346922 and parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.2145978875499715, 'ffn_dropout': 0.25972453067361007, 'lr': 0.0002794421961033291, 'weight_decay': 0.0012083715972301568, 'batch_size': 32}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.467950
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.2145978875499715, 'ffn_dropout': 0.25972453067361007, 'lr': 0.0002794421961033291, 'weight_decay': 0.0012083715972301568, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:24:08,027] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.391414
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.19320473540751415, 'ffn_dropout': 0.22647039949025233, 'lr': 0.00044225869853118147, 'weight_decay': 0.0003900274543658891, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:24:46,093] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.393782
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.1498431471072864, 'ffn_dropout': 0.23820110940713038, 'lr': 8.929024312875033e-05, 'weight_decay': 0.0002631785448196263, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:25:25,667] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.396752
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.18155429302831538, 'ffn_dropout': 0.2900744341178435, 'lr': 0.00016698247295297746, 'weight_decay': 0.0014815568644873638, 'batch_size': 64}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:26:52,944] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.348259
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2572422299994559, 'ffn_dropout': 0.33079585053669136, 'lr': 0.00013761407775061133, 'weight_decay': 0.0006998092188927516, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:27:40,450] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.390387
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.17175370475871432, 'ffn_dropout': 0.17379694395494105, 'lr': 0.00033959809938205785, 'weight_decay': 0.0005827683213173623, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:28:18,191] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.414751
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.14020711510622844, 'ffn_dropout': 0.3112556144434017, 'lr': 0.00020847321366042757, 'weight_decay': 0.0016544886887722172, 'batch_size': 256}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:29:05,385] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.395577
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2306639261644685, 'ffn_dropout': 0.21475596788541843, 'lr': 0.00024860368117781795, 'weight_decay': 0.0011404590259588762, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:30:18,565] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.383815
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.22380127864261556, 'ffn_dropout': 0.23270778074437465, 'lr': 0.0005767066682694046, 'weight_decay': 0.0009184452913840088, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:31:31,379] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.367844
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.20468385731444175, 'ffn_dropout': 0.25135711390281634, 'lr': 0.00030244725826305673, 'weight_decay': 0.0007793149186865472, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:32:40,559] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.392567
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.24334665174251408, 'ffn_dropout': 0.30334761006116434, 'lr': 0.0001760323027388513, 'weight_decay': 0.004993294428686409, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:33:53,578] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.368481
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2090896605534632, 'ffn_dropout': 0.24013058161112336, 'lr': 0.00022001922851678924, 'weight_decay': 0.0004177782467367, 'batch_size': 32}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-26 01:35:40,921] Trial 96 finished with value: 0.48073714088047953 and parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.21807894416859547, 'ffn_dropout': 0.25967167581415096, 'lr': 0.0004003183185930734, 'weight_decay': 0.0018834733866838996, 'batch_size': 128}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.480737
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.21807894416859547, 'ffn_dropout': 0.25967167581415096, 'lr': 0.0004003183185930734, 'weight_decay': 0.0018834733866838996, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-26 01:36:08,163] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.369512
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.19782007782479602, 'ffn_dropout': 0.2583659718400824, 'lr': 0.0004837659770634386, 'weight_decay': 0.0018369626149392479, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-26 01:37:49,286] Trial 98 finished with value: 0.4768151330715296 and parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.23317845175778534, 'ffn_dropout': 0.2478400727936002, 'lr': 0.00042188261827584106, 'weight_decay': 0.0022596816955623455, 'batch_size': 128}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.476815
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.23317845175778534, 'ffn_dropout': 0.2478400727936002, 'lr': 0.00042188261827584106, 'weight_decay': 0.0022596816955623455, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-26 01:39:34,059] Trial 99 finished with value: 0.48425770398094525 and parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.18795685699999037, 'ffn_dropout': 0.27011524870091175, 'lr': 0.00035455567649456284, 'weight_decay': 0.0014623785738583832, 'batch_size': 128}. Best is trial 15 with value: 0.49840606228991896.
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.484258
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.18795685699999037, 'ffn_dropout': 0.27011524870091175, 'lr': 0.00035455567649456284, 'weight_decay': 0.0014623785738583832, 'batch_size': 128}
+  Best Value (CSI): 0.498406
+  Best Trial: 15
+  Best Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4984
+Best Hyperparameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13369638195384545, 'ffn_dropout': 0.25693944180246137, 'lr': 0.00046073450640759476, 'weight_decay': 0.0005511169366746759, 'batch_size': 32}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4837
+  - 최종 CSI: 0.4843
+  - 최고 CSI: 0.4984
+  - 최저 CSI: 0.0040
+  - 평균 CSI: 0.3818
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/ft_transformer_smotenc_ctgan20000_gwangju_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 16, Best CSI: 0.4030
+  Fold 1 학습 완료 (검증 CSI: 0.4030)
+Fold 2 학습 중...
+  Early stopping at epoch 36, Best CSI: 0.5059
+  Fold 2 학습 완료 (검증 CSI: 0.5059)
+Fold 3 학습 중...
+  Early stopping at epoch 25, Best CSI: 0.5004
+  Fold 3 학습 완료 (검증 CSI: 0.5004)
+
+모든 모델 저장 완료: ../save_model/ft_transformer_optima/ft_transformer_smotenc_ctgan20000_gwangju.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/ft_transformer_optima/scaler/ft_transformer_smotenc_ctgan20000_gwangju_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/ft_transformer_optima/ft_transformer_smotenc_ctgan20000_gwangju.pkl
+
+✓ 완료: ft_transformer_smotenc_ctgan20000/ft_transformer_smotenc_ctgan20000_gwangju.py (소요 시간: 15992초)
+
+----------------------------------------
+실행 중: ft_transformer_smotenc_ctgan20000/ft_transformer_smotenc_ctgan20000_incheon.py
+시작 시간: 2025-12-26 01:46:37
+----------------------------------------
+[I 2025-12-26 01:46:38,340] A new study created in memory with name: no-name-dab50be9-0d0e-44ba-aab7-5fc722c6a634
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 01:53:52,792] Trial 0 finished with value: 0.5587702310628659 and parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}. Best is trial 0 with value: 0.5587702310628659.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.558770
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 01:55:55,961] Trial 1 finished with value: 0.5444175090297078 and parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.37366052951658524, 'ffn_dropout': 0.1554614841862499, 'lr': 0.0007456957121307549, 'weight_decay': 0.05910777817785268, 'batch_size': 128}. Best is trial 0 with value: 0.5587702310628659.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.544418
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.37366052951658524, 'ffn_dropout': 0.1554614841862499, 'lr': 0.0007456957121307549, 'weight_decay': 0.05910777817785268, 'batch_size': 128}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 01:58:32,854] Trial 2 finished with value: 0.1479627758232053 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.3567165989348816, 'ffn_dropout': 0.21003687602439788, 'lr': 0.003600001169654458, 'weight_decay': 0.020976315827953736, 'batch_size': 128}. Best is trial 0 with value: 0.5587702310628659.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.147963
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.3567165989348816, 'ffn_dropout': 0.21003687602439788, 'lr': 0.003600001169654458, 'weight_decay': 0.020976315827953736, 'batch_size': 128}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 02:15:33,157] Trial 3 finished with value: 0.5486416156141098 and parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3394516106937555, 'ffn_dropout': 0.37332923639965376, 'lr': 0.005758780377306426, 'weight_decay': 0.07858084935356081, 'batch_size': 32}. Best is trial 0 with value: 0.5587702310628659.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.548642
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3394516106937555, 'ffn_dropout': 0.37332923639965376, 'lr': 0.005758780377306426, 'weight_decay': 0.07858084935356081, 'batch_size': 32}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 02:23:02,830] Trial 4 finished with value: 0.5419096499150071 and parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.3394681276879379, 'ffn_dropout': 0.12466375992691184, 'lr': 3.969256123556905e-05, 'weight_decay': 0.0012067172469772617, 'batch_size': 64}. Best is trial 0 with value: 0.5587702310628659.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.541910
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.3394681276879379, 'ffn_dropout': 0.12466375992691184, 'lr': 3.969256123556905e-05, 'weight_decay': 0.0012067172469772617, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 02:24:26,995] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.448430
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.3252319864546302, 'ffn_dropout': 0.1570061045687427, 'lr': 0.0005346131581778342, 'weight_decay': 0.00034303429824904, 'batch_size': 128}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 02:26:35,797] Trial 6 finished with value: 0.5456357389894592 and parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.15583526489723185, 'ffn_dropout': 0.36201990892015046, 'lr': 0.0016324128364924048, 'weight_decay': 0.00018271164419523874, 'batch_size': 256}. Best is trial 0 with value: 0.5587702310628659.
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.545636
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.15583526489723185, 'ffn_dropout': 0.36201990892015046, 'lr': 0.0016324128364924048, 'weight_decay': 0.00018271164419523874, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 02:27:07,698] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.426996
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13097386149286785, 'ffn_dropout': 0.23653013079710455, 'lr': 8.100850454221868e-05, 'weight_decay': 0.010234899113584356, 'batch_size': 128}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 02:36:51,692] Trial 8 finished with value: 0.5376998025464391 and parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2735731488225443, 'ffn_dropout': 0.24290212406639408, 'lr': 0.0009644959938096378, 'weight_decay': 0.0007570896459389863, 'batch_size': 32}. Best is trial 0 with value: 0.5587702310628659.
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.537700
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.2735731488225443, 'ffn_dropout': 0.24290212406639408, 'lr': 0.0009644959938096378, 'weight_decay': 0.0007570896459389863, 'batch_size': 32}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 02:38:21,864] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.457962
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.21704772551348434, 'ffn_dropout': 0.14245530009532142, 'lr': 0.0003383735656637399, 'weight_decay': 0.011812341381667908, 'batch_size': 128}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 02:40:06,823] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.492893
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.3944818682627955, 'ffn_dropout': 0.2996352306142834, 'lr': 1.0625684684013198e-05, 'weight_decay': 0.003454209599392831, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 02:42:21,021] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2929995180767028, 'ffn_dropout': 0.3912987345273517, 'lr': 0.008881396088303772, 'weight_decay': 0.08303942560348745, 'batch_size': 32}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 02:47:29,209] Trial 12 finished with value: 0.5476982047207368 and parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.31433864553697427, 'ffn_dropout': 0.3070001735791416, 'lr': 0.00014192351623897297, 'weight_decay': 0.09688561746838062, 'batch_size': 64}. Best is trial 0 with value: 0.5587702310628659.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.547698
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.31433864553697427, 'ffn_dropout': 0.3070001735791416, 'lr': 0.00014192351623897297, 'weight_decay': 0.09688561746838062, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 02:49:18,025] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.25467441879554586, 'ffn_dropout': 0.29057477857230096, 'lr': 0.0025575166805039073, 'weight_decay': 0.03318639166926802, 'batch_size': 32}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 02:49:33,719] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.008523
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.39575561092416006, 'ffn_dropout': 0.2002158944438344, 'lr': 0.008208789078701212, 'weight_decay': 0.03652214710280682, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 02:52:33,646] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.491514
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34021313031687933, 'ffn_dropout': 0.34021402537487067, 'lr': 0.00028902231299442974, 'weight_decay': 0.0160124234830787, 'batch_size': 32}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 02:53:20,726] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.496463
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.2978192449270944, 'ffn_dropout': 0.26653291844792887, 'lr': 0.0014956485849632298, 'weight_decay': 0.005641381119051794, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 02:55:16,738] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.497781
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.22993481104432578, 'ffn_dropout': 0.3947220489683638, 'lr': 0.0003213161506453874, 'weight_decay': 0.0423051819085012, 'batch_size': 32}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 02:56:11,836] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.033309
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.3686743811524074, 'ffn_dropout': 0.33343385003388093, 'lr': 0.004180150220150227, 'weight_decay': 0.02870673903038044, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 02:59:52,850] Trial 19 finished with value: 0.5449075911553761 and parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.31785641706736023, 'ffn_dropout': 0.27501601274473847, 'lr': 0.0009220421338596502, 'weight_decay': 0.09642272226486212, 'batch_size': 256}. Best is trial 0 with value: 0.5587702310628659.
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.544908
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.31785641706736023, 'ffn_dropout': 0.27501601274473847, 'lr': 0.0009220421338596502, 'weight_decay': 0.09642272226486212, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:00:41,135] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.474555
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3432513002912038, 'ffn_dropout': 0.10481777939669683, 'lr': 0.0017911989310436922, 'weight_decay': 0.05032855661357391, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:01:28,414] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.529281
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3050055821243847, 'ffn_dropout': 0.31779605843547565, 'lr': 0.0001997396087260459, 'weight_decay': 0.09095883872273372, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:02:15,706] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.518937
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3158459077590512, 'ffn_dropout': 0.36148891305190595, 'lr': 0.0001428175243801761, 'weight_decay': 0.09876551013677735, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 03:07:20,696] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.543694
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3641285046766046, 'ffn_dropout': 0.30606415776811735, 'lr': 0.00011671993373643644, 'weight_decay': 0.021809483760609667, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:08:52,346] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.481095
+  Parameters: {'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2884063257585626, 'ffn_dropout': 0.27836583872121223, 'lr': 0.000498232444212991, 'weight_decay': 0.056471684271283795, 'batch_size': 32}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:09:50,229] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.484888
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.328496960499703, 'ffn_dropout': 0.3187558117501914, 'lr': 6.86758512561796e-05, 'weight_decay': 0.04936710655798688, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:10:58,263] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.452290
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.2754073613459495, 'ffn_dropout': 0.25962007579682406, 'lr': 0.000195050352315866, 'weight_decay': 0.02601444706683067, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:12:34,352] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.446244
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3526311214388148, 'ffn_dropout': 0.35604873132457343, 'lr': 0.0005297621205886926, 'weight_decay': 0.010833300048001615, 'batch_size': 32}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:12:53,491] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.31364187064330856, 'ffn_dropout': 0.3766912911113559, 'lr': 0.005488369928528254, 'weight_decay': 0.06386073227943485, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:13:37,562] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.331518
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3701353585774312, 'ffn_dropout': 0.2912238953259189, 'lr': 0.001104776368291479, 'weight_decay': 0.057957601813492604, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:15:31,229] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.460399
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3944521832218051, 'ffn_dropout': 0.33921658457660214, 'lr': 0.0006810037123950648, 'weight_decay': 0.0349236381556746, 'batch_size': 32}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:16:02,898] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.417437
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.10873825653860342, 'ffn_dropout': 0.35731703576127, 'lr': 0.0024873349391287634, 'weight_decay': 0.00017124193805569858, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:16:21,364] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.121998
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.16962947186775582, 'ffn_dropout': 0.37234590435134973, 'lr': 0.006105326805699287, 'weight_decay': 0.00015146783729774158, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:16:46,727] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.039922
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.35047810526270873, 'ffn_dropout': 0.39422586195497084, 'lr': 0.0033174968854580967, 'weight_decay': 0.0013272529038535967, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:17:08,859] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.474187
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.33955333971263807, 'ffn_dropout': 0.3256558953276691, 'lr': 0.0014642331794051905, 'weight_decay': 0.018148307737328173, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 03:19:54,136] Trial 35 finished with value: 0.5339611530702174 and parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.3264977953457961, 'ffn_dropout': 0.346697371154064, 'lr': 0.002198410666590786, 'weight_decay': 0.06612535476528124, 'batch_size': 128}. Best is trial 0 with value: 0.5587702310628659.
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.533961
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.3264977953457961, 'ffn_dropout': 0.346697371154064, 'lr': 0.002198410666590786, 'weight_decay': 0.06612535476528124, 'batch_size': 128}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:20:40,748] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.18442273932969286, 'ffn_dropout': 0.36916684939564665, 'lr': 0.004358860413575937, 'weight_decay': 0.0001162605104800144, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:21:23,815] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.2787377892218927, 'ffn_dropout': 0.21519853151373214, 'lr': 0.0034286887654630446, 'weight_decay': 0.007271204842256108, 'batch_size': 128}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:21:40,692] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.496145
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25894394321263703, 'ffn_dropout': 0.31267292228130494, 'lr': 0.0007306386624073975, 'weight_decay': 0.0003806805495906629, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:22:54,049] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.481433
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.37853357123343684, 'ffn_dropout': 0.3483419847618282, 'lr': 0.0011192372991976915, 'weight_decay': 0.0020118526860901566, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 03:26:48,916] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.548163
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.30562889376323454, 'ffn_dropout': 0.3268429956354424, 'lr': 0.00044967570977719656, 'weight_decay': 0.014638646464557276, 'batch_size': 128}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:27:45,391] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.504750
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3251327484903447, 'ffn_dropout': 0.2787120311528673, 'lr': 0.0009496097869045288, 'weight_decay': 0.09941542717293726, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:28:40,645] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.458627
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.35487590341736064, 'ffn_dropout': 0.23232113523139056, 'lr': 0.0008305761272990338, 'weight_decay': 0.0696939484952942, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:29:23,458] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2911110213303823, 'ffn_dropout': 0.3796300237966227, 'lr': 0.009219372701657994, 'weight_decay': 0.08068782584322115, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:30:18,718] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.431258
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.31875395800769496, 'ffn_dropout': 0.1820822764065428, 'lr': 0.0017546932169052812, 'weight_decay': 0.04055312976291704, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:32:59,793] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.473786
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.26466471962921145, 'ffn_dropout': 0.30258494186221385, 'lr': 0.00034787724738879, 'weight_decay': 0.04422716130326381, 'batch_size': 32}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 03:34:54,759] Trial 46 finished with value: 0.5546426748172475 and parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.2437694108716114, 'ffn_dropout': 0.2614000041894434, 'lr': 0.0013158290173757813, 'weight_decay': 0.02774449986183839, 'batch_size': 256}. Best is trial 0 with value: 0.5587702310628659.
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.554643
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.2437694108716114, 'ffn_dropout': 0.2614000041894434, 'lr': 0.0013158290173757813, 'weight_decay': 0.02774449986183839, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:36:40,031] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.24049035138845282, 'ffn_dropout': 0.25044777289841696, 'lr': 0.0032829686310000875, 'weight_decay': 0.02307993350820701, 'batch_size': 32}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:37:59,627] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.448208
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.217709849550182, 'ffn_dropout': 0.2925094483114275, 'lr': 0.0006224024651950728, 'weight_decay': 0.02957828264801411, 'batch_size': 64}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:38:25,030] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.477848
+  Parameters: {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2448678863692198, 'ffn_dropout': 0.3335781172614827, 'lr': 0.0013564132389009965, 'weight_decay': 0.07031037439933467, 'batch_size': 256}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:40:12,106] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.2676447502080628, 'ffn_dropout': 0.39910408848492335, 'lr': 0.006233443737029244, 'weight_decay': 0.03549223531914368, 'batch_size': 32}
+  Best Value (CSI): 0.558770
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.34182762434629993, 'ffn_dropout': 0.20516720144542075, 'lr': 0.0002648398993978442, 'weight_decay': 0.09055429230610533, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 03:42:25,766] Trial 51 finished with value: 0.5658689261675397 and parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}. Best is trial 51 with value: 0.5658689261675397.
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.565869
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:42:54,796] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.448525
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3369916147028472, 'ffn_dropout': 0.2384710142361171, 'lr': 0.001983867570345685, 'weight_decay': 0.07421274401986226, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:43:23,962] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.461736
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3011333661299567, 'ffn_dropout': 0.2192878500373079, 'lr': 0.00221228430500615, 'weight_decay': 0.0542532735466507, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:43:58,241] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.481577
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.28564160114361326, 'ffn_dropout': 0.2535238144758982, 'lr': 0.0030702604305415464, 'weight_decay': 0.04652265726168276, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 03:46:23,982] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.289520
+  Parameters: {'d_token': 64, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.31025309991562644, 'ffn_dropout': 0.2005509724476892, 'lr': 0.0025946828722431974, 'weight_decay': 0.09993578178655149, 'batch_size': 64}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:46:48,666] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.505168
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.30150819719947775, 'ffn_dropout': 0.26263038823774343, 'lr': 0.004976801571747986, 'weight_decay': 0.07781628477953773, 'batch_size': 128}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:47:48,142] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.33270081613368596, 'ffn_dropout': 0.2320750162754175, 'lr': 0.006807582895674544, 'weight_decay': 0.027160347978731854, 'batch_size': 64}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 03:49:58,236] Trial 58 finished with value: 0.5322014325820453 and parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3486084492616679, 'ffn_dropout': 0.270327731231985, 'lr': 0.0012402631709711417, 'weight_decay': 0.05320979909211251, 'batch_size': 256}. Best is trial 51 with value: 0.5658689261675397.
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.532201
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.3486084492616679, 'ffn_dropout': 0.270327731231985, 'lr': 0.0012402631709711417, 'weight_decay': 0.05320979909211251, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:51:24,173] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.473552
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.28447165057221024, 'ffn_dropout': 0.2490009127855201, 'lr': 0.00026868782595524667, 'weight_decay': 0.0398268912584212, 'batch_size': 32}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:52:24,912] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2968437790003496, 'ffn_dropout': 0.38482004514285456, 'lr': 0.004359944425711276, 'weight_decay': 0.02006440278686655, 'batch_size': 64}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 03:55:31,023] Trial 61 finished with value: 0.5450508140944997 and parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3198731416179766, 'ffn_dropout': 0.27527101224591716, 'lr': 0.0016462273550450141, 'weight_decay': 0.08317967616738514, 'batch_size': 256}. Best is trial 51 with value: 0.5658689261675397.
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.545051
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3198731416179766, 'ffn_dropout': 0.27527101224591716, 'lr': 0.0016462273550450141, 'weight_decay': 0.08317967616738514, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:56:13,226] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.461883
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.33261824111498284, 'ffn_dropout': 0.28423526554144224, 'lr': 0.0015569422963970742, 'weight_decay': 0.08508354783995578, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:56:55,492] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3212619423613564, 'ffn_dropout': 0.26886544373879984, 'lr': 0.002567915025224402, 'weight_decay': 0.06011487982790014, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:57:25,940] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.448163
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.36344780860775383, 'ffn_dropout': 0.296932701929482, 'lr': 0.001576536821155656, 'weight_decay': 0.07750017039682405, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:58:08,067] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.474286
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3107608246222477, 'ffn_dropout': 0.36656817431058897, 'lr': 0.0010761913793068286, 'weight_decay': 0.032329491706285976, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 03:58:55,338] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.482211
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.34750741173424293, 'ffn_dropout': 0.2608319512182219, 'lr': 0.0019808723197397085, 'weight_decay': 0.04758611157509047, 'batch_size': 64}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:01:46,993] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.375590
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.33913572374527795, 'ffn_dropout': 0.28596577997945843, 'lr': 0.0008570337378510212, 'weight_decay': 0.06369129680426544, 'batch_size': 32}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:02:15,197] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.490966
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2744632096023234, 'ffn_dropout': 0.30213475097103315, 'lr': 0.003859904706032665, 'weight_decay': 0.09814784969942256, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:03:06,529] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.486554
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.31527667458553676, 'ffn_dropout': 0.3858930644586859, 'lr': 0.0028821445485241242, 'weight_decay': 0.06013200840206882, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:03:55,080] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3249956645113395, 'ffn_dropout': 0.3544276371417385, 'lr': 0.007418846761471996, 'weight_decay': 0.08339003455991001, 'batch_size': 64}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 04:07:18,389] Trial 71 finished with value: 0.5501107093998284 and parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.29859079796797616, 'ffn_dropout': 0.27523091934211247, 'lr': 0.0014003363724514725, 'weight_decay': 0.08501374495474277, 'batch_size': 256}. Best is trial 51 with value: 0.5658689261675397.
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.550111
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.29859079796797616, 'ffn_dropout': 0.27523091934211247, 'lr': 0.0014003363724514725, 'weight_decay': 0.08501374495474277, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:08:15,566] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.514042
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.29262886047835307, 'ffn_dropout': 0.2732673810668396, 'lr': 0.0012709731614241459, 'weight_decay': 0.04114192462631701, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 04:10:41,022] Trial 73 finished with value: 0.5513811044783637 and parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3043996281161073, 'ffn_dropout': 0.2823773607327542, 'lr': 0.0019422549755146704, 'weight_decay': 0.06940157866788899, 'batch_size': 256}. Best is trial 51 with value: 0.5658689261675397.
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.551381
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3043996281161073, 'ffn_dropout': 0.2823773607327542, 'lr': 0.0019422549755146704, 'weight_decay': 0.06940157866788899, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:11:23,772] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3037769532916815, 'ffn_dropout': 0.30672189866491917, 'lr': 0.004902494281930458, 'weight_decay': 0.05770459961710986, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 04:13:28,618] Trial 75 finished with value: 0.5577779866662523 and parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2569368000656096, 'ffn_dropout': 0.24513264783114322, 'lr': 0.004057601993852997, 'weight_decay': 0.0682571195021235, 'batch_size': 256}. Best is trial 51 with value: 0.5658689261675397.
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.557778
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2569368000656096, 'ffn_dropout': 0.24513264783114322, 'lr': 0.004057601993852997, 'weight_decay': 0.0682571195021235, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:13:57,132] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.491206
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2825481611165924, 'ffn_dropout': 0.2557235316091041, 'lr': 0.003718000209654103, 'weight_decay': 0.070561157276761, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:14:28,543] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2679529710371297, 'ffn_dropout': 0.24458189304250455, 'lr': 0.005823602362200398, 'weight_decay': 0.04979759137222155, 'batch_size': 128}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:16:23,518] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2970944117666119, 'ffn_dropout': 0.2855175565872237, 'lr': 0.0099405692942946, 'weight_decay': 0.06664365240325425, 'batch_size': 32}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:16:53,040] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.460492
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.25641819019827256, 'ffn_dropout': 0.2587634995617249, 'lr': 0.002262554808755784, 'weight_decay': 0.03494627620990865, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:17:51,119] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.017077
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.310888641840111, 'ffn_dropout': 0.2646628416948956, 'lr': 0.007763323517462201, 'weight_decay': 0.09088488914713594, 'batch_size': 64}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:18:28,264] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.485911
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2816668644969869, 'ffn_dropout': 0.2244908855479126, 'lr': 0.002835804076381769, 'weight_decay': 0.09987968487707476, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 04:21:00,239] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.142694
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2517016982937829, 'ffn_dropout': 0.24309179393491268, 'lr': 0.002112750446263649, 'weight_decay': 0.0515581867806668, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:21:29,939] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.501784
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.32955545499661293, 'ffn_dropout': 0.28112703363639135, 'lr': 0.003710932988746407, 'weight_decay': 0.07189749432607226, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:21:45,175] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.515596
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.23179822628753727, 'ffn_dropout': 0.2958671165060939, 'lr': 0.001813504711427063, 'weight_decay': 0.04196535933913575, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 04:24:02,288] Trial 85 finished with value: 0.5411055162243366 and parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.19931091141522436, 'ffn_dropout': 0.2510033275950906, 'lr': 0.001058756680273705, 'weight_decay': 0.08418023518317508, 'batch_size': 256}. Best is trial 51 with value: 0.5658689261675397.
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.541106
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.19931091141522436, 'ffn_dropout': 0.2510033275950906, 'lr': 0.001058756680273705, 'weight_decay': 0.08418023518317508, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:25:07,558] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.462783
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.14919229222854158, 'ffn_dropout': 0.3164730567145671, 'lr': 0.0013390269673914, 'weight_decay': 0.06363543567790979, 'batch_size': 64}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:26:49,499] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.037403
+  Parameters: {'d_token': 96, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.2893620687025783, 'ffn_dropout': 0.24129197125595728, 'lr': 0.0030624009447195507, 'weight_decay': 0.024039799499175667, 'batch_size': 32}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:27:35,148] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.454078
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.27766665525715245, 'ffn_dropout': 0.26941244791913194, 'lr': 0.0007966558212129106, 'weight_decay': 0.030573935998079005, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:27:53,604] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.116741
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3437990125426006, 'ffn_dropout': 0.23145135546840231, 'lr': 0.00450481413047273, 'weight_decay': 0.00421564411463884, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 04:30:47,646] Trial 90 finished with value: 0.4448669709848068 and parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2621606639275094, 'ffn_dropout': 0.3650302990297463, 'lr': 0.0017090975482176654, 'weight_decay': 0.05235108387429086, 'batch_size': 128}. Best is trial 51 with value: 0.5658689261675397.
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.444867
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.2621606639275094, 'ffn_dropout': 0.3650302990297463, 'lr': 0.0017090975482176654, 'weight_decay': 0.05235108387429086, 'batch_size': 128}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:31:27,168] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.514845
+  Parameters: {'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.31885940339009533, 'ffn_dropout': 0.2755639001090285, 'lr': 0.001620038326626115, 'weight_decay': 0.08176756491184979, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:31:55,884] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.510638
+  Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.30642937754215466, 'ffn_dropout': 0.2906986493269853, 'lr': 0.002278808501888205, 'weight_decay': 0.07619065376875979, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:32:30,337] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.535737
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3221586900537614, 'ffn_dropout': 0.27492374370082184, 'lr': 0.001968199861289408, 'weight_decay': 0.08811661729639704, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:33:18,167] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.469726
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.2966394760046305, 'ffn_dropout': 0.2661165967785283, 'lr': 0.0012301327989768663, 'weight_decay': 0.05913679450390294, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-26 04:35:55,327] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.545614
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.33632093826913656, 'ffn_dropout': 0.3743898976242319, 'lr': 0.000961230058076095, 'weight_decay': 0.07115713580941216, 'batch_size': 64}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 04:37:02,142] Trial 96 finished with value: 0.5510847527121311 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3134473238026919, 'ffn_dropout': 0.256487201536107, 'lr': 0.002575371332372382, 'weight_decay': 0.038711800127264356, 'batch_size': 256}. Best is trial 51 with value: 0.5658689261675397.
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.551085
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3134473238026919, 'ffn_dropout': 0.256487201536107, 'lr': 0.002575371332372382, 'weight_decay': 0.038711800127264356, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 04:37:59,200] Trial 97 finished with value: 0.5460650987185982 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.27114183677806286, 'ffn_dropout': 0.2551734544682139, 'lr': 0.002605850363629184, 'weight_decay': 0.045336571392682525, 'batch_size': 256}. Best is trial 51 with value: 0.5658689261675397.
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.546065
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.27114183677806286, 'ffn_dropout': 0.2551734544682139, 'lr': 0.002605850363629184, 'weight_decay': 0.045336571392682525, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-26 04:39:09,590] Trial 98 finished with value: 0.5533642404853625 and parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.27069786190748896, 'ffn_dropout': 0.24753378048750502, 'lr': 0.0026305097846398957, 'weight_decay': 0.044367807795891306, 'batch_size': 256}. Best is trial 51 with value: 0.5658689261675397.
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.553364
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.27069786190748896, 'ffn_dropout': 0.24753378048750502, 'lr': 0.0026305097846398957, 'weight_decay': 0.044367807795891306, 'batch_size': 256}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-26 04:40:34,855] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.431462
+  Parameters: {'d_token': 64, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25993940851357394, 'ffn_dropout': 0.24676514789840534, 'lr': 0.0038300438906361638, 'weight_decay': 0.037463970644995855, 'batch_size': 32}
+  Best Value (CSI): 0.565869
+  Best Trial: 51
+  Best Parameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5659
+Best Hyperparameters: {'d_token': 64, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.3014135618352386, 'ffn_dropout': 0.2377846194971104, 'lr': 0.0023493513093616647, 'weight_decay': 0.0983836135411264, 'batch_size': 256}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5588
+  - 최종 CSI: 0.4315
+  - 최고 CSI: 0.5659
+  - 최저 CSI: 0.0085
+  - 평균 CSI: 0.3834
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/ft_transformer_smotenc_ctgan20000_incheon_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 16, Best CSI: 0.5238
+  Fold 1 학습 완료 (검증 CSI: 0.5238)
+Fold 2 학습 중...
+  Early stopping at epoch 23, Best CSI: 0.5849
+  Fold 2 학습 완료 (검증 CSI: 0.5849)
+Fold 3 학습 중...
+  Early stopping at epoch 40, Best CSI: 0.5523
+  Fold 3 학습 완료 (검증 CSI: 0.5523)
+
+모든 모델 저장 완료: ../save_model/ft_transformer_optima/ft_transformer_smotenc_ctgan20000_incheon.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/ft_transformer_optima/scaler/ft_transformer_smotenc_ctgan20000_incheon_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/ft_transformer_optima/ft_transformer_smotenc_ctgan20000_incheon.pkl
+
+✓ 완료: ft_transformer_smotenc_ctgan20000/ft_transformer_smotenc_ctgan20000_incheon.py (소요 시간: 10576초)
+
+----------------------------------------
+실행 중: ft_transformer_smotenc_ctgan20000/ft_transformer_smotenc_ctgan20000_seoul.py
+시작 시간: 2025-12-26 04:42:53
+----------------------------------------
+[I 2025-12-26 04:42:54,995] A new study created in memory with name: no-name-f385d293-c956-432c-9643-52ef34236b1a
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 04:46:31,065] Trial 0 finished with value: 0.09805561793547309 and parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1688610819529528, 'ffn_dropout': 0.3226200636504205, 'lr': 0.006607342610437163, 'weight_decay': 0.0004396458346270925, 'batch_size': 64}. Best is trial 0 with value: 0.09805561793547309.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.098056
+  Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1688610819529528, 'ffn_dropout': 0.3226200636504205, 'lr': 0.006607342610437163, 'weight_decay': 0.0004396458346270925, 'batch_size': 64}
+  Best Value (CSI): 0.098056
+  Best Trial: 0
+  Best Parameters: {'d_token': 96, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1688610819529528, 'ffn_dropout': 0.3226200636504205, 'lr': 0.006607342610437163, 'weight_decay': 0.0004396458346270925, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 04:48:09,391] Trial 1 finished with value: 0.42844022011491356 and parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2971145798427403, 'ffn_dropout': 0.294642450983742, 'lr': 2.7743800867557626e-05, 'weight_decay': 0.004230079098902941, 'batch_size': 256}. Best is trial 1 with value: 0.42844022011491356.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.428440
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2971145798427403, 'ffn_dropout': 0.294642450983742, 'lr': 2.7743800867557626e-05, 'weight_decay': 0.004230079098902941, 'batch_size': 256}
+  Best Value (CSI): 0.428440
+  Best Trial: 1
+  Best Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2971145798427403, 'ffn_dropout': 0.294642450983742, 'lr': 2.7743800867557626e-05, 'weight_decay': 0.004230079098902941, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 04:51:34,541] Trial 2 finished with value: 0.5225703811194782 and parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.21625816762576203, 'ffn_dropout': 0.11978634458609289, 'lr': 0.0015697537035869965, 'weight_decay': 0.00021316541587130725, 'batch_size': 128}. Best is trial 2 with value: 0.5225703811194782.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.522570
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.21625816762576203, 'ffn_dropout': 0.11978634458609289, 'lr': 0.0015697537035869965, 'weight_decay': 0.00021316541587130725, 'batch_size': 128}
+  Best Value (CSI): 0.522570
+  Best Trial: 2
+  Best Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.21625816762576203, 'ffn_dropout': 0.11978634458609289, 'lr': 0.0015697537035869965, 'weight_decay': 0.00021316541587130725, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 04:55:53,949] Trial 3 finished with value: 0.5559722627339405 and parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11853427090474081, 'ffn_dropout': 0.311703173134966, 'lr': 0.0007608332786345335, 'weight_decay': 0.0069341697758805, 'batch_size': 128}. Best is trial 3 with value: 0.5559722627339405.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.555972
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11853427090474081, 'ffn_dropout': 0.311703173134966, 'lr': 0.0007608332786345335, 'weight_decay': 0.0069341697758805, 'batch_size': 128}
+  Best Value (CSI): 0.555972
+  Best Trial: 3
+  Best Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11853427090474081, 'ffn_dropout': 0.311703173134966, 'lr': 0.0007608332786345335, 'weight_decay': 0.0069341697758805, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 05:10:21,196] Trial 4 finished with value: 0.5582312292430999 and parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}. Best is trial 4 with value: 0.5582312292430999.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.558231
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+  Best Value (CSI): 0.558231
+  Best Trial: 4
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 05:11:12,491] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.386555
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.34476617335932724, 'ffn_dropout': 0.2649695188597106, 'lr': 0.00022833581461653455, 'weight_decay': 0.0013868451340478152, 'batch_size': 256}
+  Best Value (CSI): 0.558231
+  Best Trial: 4
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 05:11:57,224] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.475424
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3113443424202794, 'ffn_dropout': 0.34065669725379666, 'lr': 0.0026520458769820135, 'weight_decay': 0.002169850273465297, 'batch_size': 256}
+  Best Value (CSI): 0.558231
+  Best Trial: 4
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 05:14:52,952] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.457215
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2949855905950483, 'ffn_dropout': 0.25396522173604835, 'lr': 0.0004563801961685499, 'weight_decay': 0.0003354640785081885, 'batch_size': 32}
+  Best Value (CSI): 0.558231
+  Best Trial: 4
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 05:15:43,944] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.353226
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.32672083229329896, 'ffn_dropout': 0.3396227511637848, 'lr': 6.525871176676631e-05, 'weight_decay': 0.004323520998985153, 'batch_size': 128}
+  Best Value (CSI): 0.558231
+  Best Trial: 4
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 05:17:21,179] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.332024
+  Parameters: {'d_token': 256, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.1537125440702668, 'ffn_dropout': 0.17552289360256784, 'lr': 3.254287192729668e-05, 'weight_decay': 0.022299898199725738, 'batch_size': 64}
+  Best Value (CSI): 0.558231
+  Best Trial: 4
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 05:26:16,505] Trial 10 finished with value: 0.43981404481401754 and parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.10049678476923007, 'ffn_dropout': 0.38806735190491165, 'lr': 1.0790110964686756e-05, 'weight_decay': 0.07546346225011095, 'batch_size': 32}. Best is trial 4 with value: 0.5582312292430999.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.439814
+  Parameters: {'d_token': 192, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.10049678476923007, 'ffn_dropout': 0.38806735190491165, 'lr': 1.0790110964686756e-05, 'weight_decay': 0.07546346225011095, 'batch_size': 32}
+  Best Value (CSI): 0.558231
+  Best Trial: 4
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 05:27:14,276] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.418780
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.10276663406539158, 'ffn_dropout': 0.20485456849692446, 'lr': 0.00028733321051008925, 'weight_decay': 0.02006106103797667, 'batch_size': 128}
+  Best Value (CSI): 0.558231
+  Best Trial: 4
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 05:29:59,088] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.360478
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.22532988450482666, 'ffn_dropout': 0.21518871928307048, 'lr': 0.0006956104807009871, 'weight_decay': 0.08837824376237668, 'batch_size': 32}
+  Best Value (CSI): 0.558231
+  Best Trial: 4
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 05:36:20,676] Trial 13 finished with value: 0.5572770116485796 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.15916677593841122, 'ffn_dropout': 0.2862085192021495, 'lr': 0.00011383966413904215, 'weight_decay': 0.017884308977802375, 'batch_size': 32}. Best is trial 4 with value: 0.5582312292430999.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.557277
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.15916677593841122, 'ffn_dropout': 0.2862085192021495, 'lr': 0.00011383966413904215, 'weight_decay': 0.017884308977802375, 'batch_size': 32}
+  Best Value (CSI): 0.558231
+  Best Trial: 4
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 05:38:35,923] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.439297
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.17488396418605945, 'ffn_dropout': 0.23344036973866344, 'lr': 0.00013997797799289745, 'weight_decay': 0.031538423369213524, 'batch_size': 32}
+  Best Value (CSI): 0.558231
+  Best Trial: 4
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 05:40:46,063] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.457429
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.3762048670393106, 'ffn_dropout': 0.2737106098691968, 'lr': 0.00011903351118449938, 'weight_decay': 0.012557219928246585, 'batch_size': 32}
+  Best Value (CSI): 0.558231
+  Best Trial: 4
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 05:45:45,699] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.452043
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.15111017828776924, 'ffn_dropout': 0.1697342529930378, 'lr': 0.00014090463438264832, 'weight_decay': 0.05199525683196024, 'batch_size': 32}
+  Best Value (CSI): 0.558231
+  Best Trial: 4
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 05:47:31,073] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.388784
+  Parameters: {'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.2002270386208052, 'ffn_dropout': 0.24122020788608434, 'lr': 5.7279962000947914e-05, 'weight_decay': 0.03973594312933733, 'batch_size': 32}
+  Best Value (CSI): 0.558231
+  Best Trial: 4
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1252900892246469, 'ffn_dropout': 0.21889680181862703, 'lr': 0.00016422484766125022, 'weight_decay': 0.06486652525850926, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 05:53:35,616] Trial 18 finished with value: 0.5589080541781382 and parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}. Best is trial 18 with value: 0.5589080541781382.
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.558908
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 05:54:59,629] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.465531
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2571091210032936, 'ffn_dropout': 0.19812013260351238, 'lr': 0.000352155428022985, 'weight_decay': 0.010376275243431897, 'batch_size': 64}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 05:58:50,734] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.482940
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.24822767460525183, 'ffn_dropout': 0.2414696722808476, 'lr': 0.0008206330613921693, 'weight_decay': 0.04448666931918938, 'batch_size': 32}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+[I 2025-12-26 06:03:52,696] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.560547
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.13425432487457994, 'ffn_dropout': 0.2832458024514293, 'lr': 0.00020192621601288505, 'weight_decay': 0.017475385751380256, 'batch_size': 32}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 06:13:10,295] Trial 22 finished with value: 0.5538997909516382 and parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.19031336847949304, 'ffn_dropout': 0.29914122716822283, 'lr': 8.386528876957199e-05, 'weight_decay': 0.09844156790506807, 'batch_size': 32}. Best is trial 18 with value: 0.5589080541781382.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.553900
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.19031336847949304, 'ffn_dropout': 0.29914122716822283, 'lr': 8.386528876957199e-05, 'weight_decay': 0.09844156790506807, 'batch_size': 32}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 06:20:51,000] Trial 23 finished with value: 0.557984425765537 and parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.13742681475275587, 'ffn_dropout': 0.26550617685610817, 'lr': 0.00021333202559997727, 'weight_decay': 0.008847371863804887, 'batch_size': 32}. Best is trial 18 with value: 0.5589080541781382.
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.557984
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.13742681475275587, 'ffn_dropout': 0.26550617685610817, 'lr': 0.00021333202559997727, 'weight_decay': 0.008847371863804887, 'batch_size': 32}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 06:22:46,013] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.443532
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13202885647435936, 'ffn_dropout': 0.26332091310253297, 'lr': 0.0004367296225453023, 'weight_decay': 0.008044223907887267, 'batch_size': 32}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 06:30:58,007] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.526638
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.18380191217429342, 'ffn_dropout': 0.22761013022093174, 'lr': 0.00019651143582082896, 'weight_decay': 0.029402480972531838, 'batch_size': 32}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 06:42:19,312] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.551038
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13165908296294712, 'ffn_dropout': 0.2538067928396913, 'lr': 0.0003568638720323078, 'weight_decay': 0.007247399355216826, 'batch_size': 32}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 06:43:12,982] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.449367
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.20552347302812257, 'ffn_dropout': 0.267683438782757, 'lr': 0.0002494887672954075, 'weight_decay': 0.013866475990470587, 'batch_size': 64}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 06:43:52,876] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.467606
+  Parameters: {'d_token': 96, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.24412332057344857, 'ffn_dropout': 0.22015027754255817, 'lr': 0.000516707175702527, 'weight_decay': 0.053400000726829946, 'batch_size': 256}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 06:45:10,285] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.444147
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.16892274801099114, 'ffn_dropout': 0.3216132848063601, 'lr': 0.001216375764828957, 'weight_decay': 0.02707157529795893, 'batch_size': 64}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 06:47:02,172] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.000683
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.18465705902587348, 'ffn_dropout': 0.3005424780022715, 'lr': 0.007844134828955313, 'weight_decay': 0.0012886620128377576, 'batch_size': 32}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 06:48:39,330] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.463706
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.151050957762616, 'ffn_dropout': 0.28545951413312814, 'lr': 0.00010606242532533244, 'weight_decay': 0.017911699241124474, 'batch_size': 32}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 06:50:24,385] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.474235
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.1591410219927958, 'ffn_dropout': 0.2806235555673639, 'lr': 0.0001888707395731759, 'weight_decay': 0.0111874920866003, 'batch_size': 32}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 06:52:05,591] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.400859
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.12129565836950536, 'ffn_dropout': 0.24059813536774002, 'lr': 7.615684819995783e-05, 'weight_decay': 0.004697494362548353, 'batch_size': 32}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 06:53:24,247] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.426067
+  Parameters: {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.14272431632481877, 'ffn_dropout': 0.30668416163627715, 'lr': 0.0001478563857527373, 'weight_decay': 0.02620245567780444, 'batch_size': 32}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 06:56:20,320] Trial 35 finished with value: 0.556433945616672 and parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.11748441041502432, 'ffn_dropout': 0.2564727048241845, 'lr': 0.000292120524391473, 'weight_decay': 0.01448291496661086, 'batch_size': 128}. Best is trial 18 with value: 0.5589080541781382.
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.556434
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.11748441041502432, 'ffn_dropout': 0.2564727048241845, 'lr': 0.000292120524391473, 'weight_decay': 0.01448291496661086, 'batch_size': 128}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 06:56:49,147] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.242852
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.16381823375028498, 'ffn_dropout': 0.2918241258101949, 'lr': 4.782270775092767e-05, 'weight_decay': 0.006649588737678475, 'batch_size': 256}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:00:02,228] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.480903
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.1706847005555509, 'ffn_dropout': 0.3206472396380816, 'lr': 0.00012196971099112249, 'weight_decay': 0.059300169437347276, 'batch_size': 32}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:00:20,957] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.161017
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.27547454679715894, 'ffn_dropout': 0.26888049963285454, 'lr': 9.626509664513916e-05, 'weight_decay': 0.038174256214686945, 'batch_size': 256}
+  Best Value (CSI): 0.558908
+  Best Trial: 18
+  Best Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.25491350496954723, 'ffn_dropout': 0.2840802831515487, 'lr': 0.0002681546741118081, 'weight_decay': 0.012340150271613571, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 07:03:55,801] Trial 39 finished with value: 0.5624926645546813 and parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.11580183737987138, 'ffn_dropout': 0.33625819783295235, 'lr': 0.00017970360665618638, 'weight_decay': 0.0033452951102042527, 'batch_size': 128}. Best is trial 39 with value: 0.5624926645546813.
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.562493
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.11580183737987138, 'ffn_dropout': 0.33625819783295235, 'lr': 0.00017970360665618638, 'weight_decay': 0.0033452951102042527, 'batch_size': 128}
+  Best Value (CSI): 0.562493
+  Best Trial: 39
+  Best Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.11580183737987138, 'ffn_dropout': 0.33625819783295235, 'lr': 0.00017970360665618638, 'weight_decay': 0.0033452951102042527, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 07:06:52,144] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.538526
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.11206939474131507, 'ffn_dropout': 0.3348633073716394, 'lr': 0.00023164557010490794, 'weight_decay': 0.0027719583469081794, 'batch_size': 128}
+  Best Value (CSI): 0.562493
+  Best Trial: 39
+  Best Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.11580183737987138, 'ffn_dropout': 0.33625819783295235, 'lr': 0.00017970360665618638, 'weight_decay': 0.0033452951102042527, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+[I 2025-12-26 07:08:58,558] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.568144
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1368240492713909, 'ffn_dropout': 0.3501170442480443, 'lr': 0.00017674197331864227, 'weight_decay': 0.005941344609698836, 'batch_size': 128}
+  Best Value (CSI): 0.562493
+  Best Trial: 39
+  Best Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.11580183737987138, 'ffn_dropout': 0.33625819783295235, 'lr': 0.00017970360665618638, 'weight_decay': 0.0033452951102042527, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 07:12:35,656] Trial 42 finished with value: 0.560158118123303 and parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.12225070497470589, 'ffn_dropout': 0.2935806986131965, 'lr': 0.00026754166060578263, 'weight_decay': 0.00891119192369517, 'batch_size': 128}. Best is trial 39 with value: 0.5624926645546813.
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.560158
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.12225070497470589, 'ffn_dropout': 0.2935806986131965, 'lr': 0.00026754166060578263, 'weight_decay': 0.00891119192369517, 'batch_size': 128}
+  Best Value (CSI): 0.562493
+  Best Trial: 39
+  Best Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.11580183737987138, 'ffn_dropout': 0.33625819783295235, 'lr': 0.00017970360665618638, 'weight_decay': 0.0033452951102042527, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+[I 2025-12-26 07:15:05,348] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.573230
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10092530700498725, 'ffn_dropout': 0.31049522346380426, 'lr': 0.0003022926173915944, 'weight_decay': 0.0030088351972531635, 'batch_size': 128}
+  Best Value (CSI): 0.562493
+  Best Trial: 39
+  Best Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.11580183737987138, 'ffn_dropout': 0.33625819783295235, 'lr': 0.00017970360665618638, 'weight_decay': 0.0033452951102042527, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 07:18:45,401] Trial 44 finished with value: 0.5636322643892201 and parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.12076140929675035, 'ffn_dropout': 0.3557009918748616, 'lr': 0.0005330325142258028, 'weight_decay': 0.009069279144723356, 'batch_size': 128}. Best is trial 44 with value: 0.5636322643892201.
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.563632
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.12076140929675035, 'ffn_dropout': 0.3557009918748616, 'lr': 0.0005330325142258028, 'weight_decay': 0.009069279144723356, 'batch_size': 128}
+  Best Value (CSI): 0.563632
+  Best Trial: 44
+  Best Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.12076140929675035, 'ffn_dropout': 0.3557009918748616, 'lr': 0.0005330325142258028, 'weight_decay': 0.009069279144723356, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+[I 2025-12-26 07:21:43,514] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.542636
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.11420743517754864, 'ffn_dropout': 0.35433243608831055, 'lr': 0.000492263785766198, 'weight_decay': 0.004864294628969225, 'batch_size': 128}
+  Best Value (CSI): 0.563632
+  Best Trial: 44
+  Best Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.12076140929675035, 'ffn_dropout': 0.3557009918748616, 'lr': 0.0005330325142258028, 'weight_decay': 0.009069279144723356, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+[I 2025-12-26 07:24:02,084] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.572744
+  Parameters: {'d_token': 128, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.12176424161074156, 'ffn_dropout': 0.3349948354892894, 'lr': 0.0005987764164801227, 'weight_decay': 0.009977173987129443, 'batch_size': 128}
+  Best Value (CSI): 0.563632
+  Best Trial: 44
+  Best Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.12076140929675035, 'ffn_dropout': 0.3557009918748616, 'lr': 0.0005330325142258028, 'weight_decay': 0.009069279144723356, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 07:27:59,680] Trial 47 finished with value: 0.5675273679868712 and parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}. Best is trial 47 with value: 0.5675273679868712.
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.567527
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:28:35,742] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.486755
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10941350113365345, 'ffn_dropout': 0.3808076529026461, 'lr': 0.0007604109787544506, 'weight_decay': 0.001701281949741284, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:29:11,649] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.484493
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.14627870356211692, 'ffn_dropout': 0.3714998960011087, 'lr': 0.0003832688166203088, 'weight_decay': 0.003447000258758838, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:29:37,085] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.468826
+  Parameters: {'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10118385023446878, 'ffn_dropout': 0.3954874744013941, 'lr': 0.0010002363818656897, 'weight_decay': 0.0010993168808721542, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:30:13,070] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.482548
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.12183508328105874, 'ffn_dropout': 0.3663671855918084, 'lr': 0.0002984286118670387, 'weight_decay': 0.0023561418986706335, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+[I 2025-12-26 07:32:39,068] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.579782
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.12644771953654546, 'ffn_dropout': 0.36094501550076546, 'lr': 0.0005626318008587873, 'weight_decay': 0.0018864563881669058, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:33:09,476] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.473810
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.11366893308503163, 'ffn_dropout': 0.381757678203216, 'lr': 0.00040388583517611114, 'weight_decay': 0.004171213303272343, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:33:51,690] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.483564
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.14968603549384013, 'ffn_dropout': 0.3432098399134266, 'lr': 0.00015444921185456037, 'weight_decay': 0.005895574974935588, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:34:33,421] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.488196
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.1292216294046759, 'ffn_dropout': 0.39953983687651645, 'lr': 0.0002650551462800435, 'weight_decay': 0.021293800021610607, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+[I 2025-12-26 07:36:59,527] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.587440
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1074654847497368, 'ffn_dropout': 0.3288152921530378, 'lr': 0.0006408413481010924, 'weight_decay': 0.0728327640504423, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:37:47,447] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.462520
+  Parameters: {'d_token': 224, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.14521588615982284, 'ffn_dropout': 0.3466339361900702, 'lr': 0.0004297717687119151, 'weight_decay': 0.008349229687656761, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:38:31,482] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.419536
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10057563806540154, 'ffn_dropout': 0.35797777641088185, 'lr': 0.00017343902243899137, 'weight_decay': 0.013106306937740347, 'batch_size': 64}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:39:07,640] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.463082
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.13738710246242358, 'ffn_dropout': 0.32066398241938077, 'lr': 0.0002295729421291729, 'weight_decay': 0.009702153817306878, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:39:56,274] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.432210
+  Parameters: {'d_token': 224, 'n_blocks': 6, 'n_heads': 8, 'attention_dropout': 0.1608341074130449, 'ffn_dropout': 0.3677433359732003, 'lr': 0.0003430394139685012, 'weight_decay': 0.015785700042893286, 'batch_size': 256}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:41:15,606] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.474124
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.1295022528512118, 'ffn_dropout': 0.2941687185217535, 'lr': 0.0002236611066128808, 'weight_decay': 0.008243595567930868, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:41:45,950] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.465116
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.13895636898704083, 'ffn_dropout': 0.31258770765164035, 'lr': 0.00013324458779655348, 'weight_decay': 0.02263897632421655, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:43:20,540] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.482385
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.12139289946939415, 'ffn_dropout': 0.2775841183205525, 'lr': 0.0004738619088724872, 'weight_decay': 0.011951245976885452, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+[I 2025-12-26 07:46:41,047] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.580739
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.11670677641944645, 'ffn_dropout': 0.25875051072396443, 'lr': 0.00026468618708351066, 'weight_decay': 0.003788388646117577, 'batch_size': 64}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:48:02,709] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.464371
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.10913134089304281, 'ffn_dropout': 0.2498584969839813, 'lr': 0.00032579146952854145, 'weight_decay': 0.004920741140730303, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:48:39,880] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.455453
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15427971524313006, 'ffn_dropout': 0.2750827790264202, 'lr': 0.00017236268013326317, 'weight_decay': 0.006843578392433049, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:51:25,934] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.472527
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.1298547141802462, 'ffn_dropout': 0.3008547844613563, 'lr': 0.0002077361565892292, 'weight_decay': 0.03579486068983126, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:54:10,512] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.470978
+  Parameters: {'d_token': 128, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13973883205017848, 'ffn_dropout': 0.28870008467242264, 'lr': 0.00012151475777189404, 'weight_decay': 0.018662297821748716, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:54:43,538] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.295637
+  Parameters: {'d_token': 160, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.17643713606874145, 'ffn_dropout': 0.32855082772183686, 'lr': 8.773615744512145e-05, 'weight_decay': 0.009250613795033088, 'batch_size': 256}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:55:15,232] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.459574
+  Parameters: {'d_token': 160, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.22475552638191593, 'ffn_dropout': 0.346357955926274, 'lr': 0.00014843641853427324, 'weight_decay': 0.012442137145564296, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:56:28,292] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.461184
+  Parameters: {'d_token': 256, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.1592986627065734, 'ffn_dropout': 0.2672817037357714, 'lr': 0.00011660028864257202, 'weight_decay': 0.014752096814575046, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:57:36,369] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.475652
+  Parameters: {'d_token': 192, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.12343086569819173, 'ffn_dropout': 0.2881291466114159, 'lr': 0.00010219634546836681, 'weight_decay': 0.02475505677609892, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 07:58:53,138] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.479012
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.10978514015914548, 'ffn_dropout': 0.2788031029878565, 'lr': 0.00019485540663805902, 'weight_decay': 0.016182724230456212, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:00:11,270] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.483165
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.1354826176520879, 'ffn_dropout': 0.2970464048664643, 'lr': 0.00036081955349028655, 'weight_decay': 0.010784526690023074, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:01:27,480] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.483401
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.14373450788002587, 'ffn_dropout': 0.24636087967638234, 'lr': 7.457079193808878e-05, 'weight_decay': 0.0320392540892686, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:02:09,134] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.483348
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.15142900083514055, 'ffn_dropout': 0.2601919596512191, 'lr': 0.0002538956207809392, 'weight_decay': 0.007305447302753266, 'batch_size': 64}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:04:01,417] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.469847
+  Parameters: {'d_token': 192, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.11663081428185883, 'ffn_dropout': 0.30684883250866046, 'lr': 0.000533929428108255, 'weight_decay': 0.018490539990745226, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:04:48,066] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.470425
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1294428590631095, 'ffn_dropout': 0.2718004224247295, 'lr': 0.00016366359026890654, 'weight_decay': 0.008465626364797856, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 08:13:19,067] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.535273
+  Parameters: {'d_token': 192, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.10050562050678905, 'ffn_dropout': 0.31619062612848553, 'lr': 0.0003017450455988339, 'weight_decay': 0.01096008383042621, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:13:39,221] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.434454
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.10898248441404615, 'ffn_dropout': 0.23331505811337236, 'lr': 0.00040519827033918697, 'weight_decay': 0.005458751538759343, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+[I 2025-12-26 08:15:53,172] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.574634
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.11887761881971026, 'ffn_dropout': 0.25142969473855525, 'lr': 0.00026907457916991977, 'weight_decay': 0.014899676172530808, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:16:16,897] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.417317
+  Parameters: {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.12267563718825111, 'ffn_dropout': 0.2628816906490939, 'lr': 0.00021258598498135573, 'weight_decay': 0.02210986744212482, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:16:58,471] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.464542
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.11518124465767132, 'ffn_dropout': 0.2821071734086055, 'lr': 0.0003109915716140392, 'weight_decay': 0.01346092959768805, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:17:35,127] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.442582
+  Parameters: {'d_token': 256, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.13993413543377442, 'ffn_dropout': 0.30167069305455413, 'lr': 0.00014124497872665889, 'weight_decay': 0.007158660797733339, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:18:13,072] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.484202
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10735460626230944, 'ffn_dropout': 0.2704669426176276, 'lr': 0.0004600109000244126, 'weight_decay': 0.042007378096406006, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:19:21,065] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.470992
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.13167354559922217, 'ffn_dropout': 0.2548108702644709, 'lr': 0.0006790463414972025, 'weight_decay': 0.005926029009911852, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:19:49,032] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.425532
+  Parameters: {'d_token': 224, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.14648776369909616, 'ffn_dropout': 0.2858057231847173, 'lr': 0.0002467562327513418, 'weight_decay': 0.025948258357794303, 'batch_size': 256}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:20:16,267] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.452672
+  Parameters: {'d_token': 128, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.126085073914152, 'ffn_dropout': 0.3504710632226909, 'lr': 0.00018768425963584974, 'weight_decay': 0.009282718208764397, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:20:37,730] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.458636
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.11522942412483318, 'ffn_dropout': 0.33818437061165557, 'lr': 0.00036335541898907563, 'weight_decay': 0.019710593946545188, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:22:13,139] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.479557
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.16674634146579193, 'ffn_dropout': 0.2434636668206483, 'lr': 0.0005552460087136023, 'weight_decay': 0.0038956875477894354, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:23:40,852] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.472344
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.13565023427066697, 'ffn_dropout': 0.2927328934981082, 'lr': 0.0008648529537750946, 'weight_decay': 0.01093563246951345, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+[I 2025-12-26 08:26:32,743] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.584834
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.1052943089473327, 'ffn_dropout': 0.31461114611148727, 'lr': 0.00043928883736237933, 'weight_decay': 0.004581947922618611, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+[I 2025-12-26 08:29:24,877] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.567729
+  Parameters: {'d_token': 256, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.12544451588071365, 'ffn_dropout': 0.3291986425955673, 'lr': 0.00022361828593185916, 'weight_decay': 0.012581210622794905, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+[I 2025-12-26 08:31:43,224] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.559454
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 4, 'attention_dropout': 0.11423719304064564, 'ffn_dropout': 0.2791250004543338, 'lr': 0.0002904797604294979, 'weight_decay': 0.016595865885934657, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:32:38,759] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.484333
+  Parameters: {'d_token': 160, 'n_blocks': 5, 'n_heads': 4, 'attention_dropout': 0.13344642131481305, 'ffn_dropout': 0.305892682163834, 'lr': 0.00016199629000824292, 'weight_decay': 0.006240636413650678, 'batch_size': 64}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:33:20,122] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.483871
+  Parameters: {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10009757682561016, 'ffn_dropout': 0.293280782423687, 'lr': 0.0006232383137033992, 'weight_decay': 0.008123225318447719, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:36:18,967] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.448931
+  Parameters: {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.15549397359785894, 'ffn_dropout': 0.2654014265014536, 'lr': 0.00034453476810476666, 'weight_decay': 0.003215402726501743, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:36:56,503] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.475083
+  Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1198965059408194, 'ffn_dropout': 0.27411460182273667, 'lr': 0.00013211272281279872, 'weight_decay': 0.03134100954934849, 'batch_size': 128}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 08:38:03,826] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.481513
+  Parameters: {'d_token': 160, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.14186306208737332, 'ffn_dropout': 0.21984845218479535, 'lr': 0.0007287434441488795, 'weight_decay': 0.009587787472770745, 'batch_size': 32}
+  Best Value (CSI): 0.567527
+  Best Trial: 47
+  Best Parameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5675
+Best Hyperparameters: {'d_token': 192, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.10255871141670557, 'ffn_dropout': 0.3735851591876593, 'lr': 0.00038893486056538687, 'weight_decay': 0.0020864133600508325, 'batch_size': 128}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.0981
+  - 최종 CSI: 0.4815
+  - 최고 CSI: 0.5874
+  - 최저 CSI: 0.0007
+  - 평균 CSI: 0.4694
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/ft_transformer_smotenc_ctgan20000_seoul_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 24, Best CSI: 0.4959
+  Fold 1 학습 완료 (검증 CSI: 0.4959)
+Fold 2 학습 중...
+  Early stopping at epoch 27, Best CSI: 0.5828
+  Fold 2 학습 완료 (검증 CSI: 0.5828)
+Fold 3 학습 중...
+  Early stopping at epoch 27, Best CSI: 0.5595
+  Fold 3 학습 완료 (검증 CSI: 0.5595)
+
+모든 모델 저장 완료: ../save_model/ft_transformer_optima/ft_transformer_smotenc_ctgan20000_seoul.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/ft_transformer_optima/scaler/ft_transformer_smotenc_ctgan20000_seoul_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/ft_transformer_optima/ft_transformer_smotenc_ctgan20000_seoul.pkl
+
+✓ 완료: ft_transformer_smotenc_ctgan20000/ft_transformer_smotenc_ctgan20000_seoul.py (소요 시간: 14369초)
+
+==========================================
+FT-Transformer SMOTENC CTGAN20000 파일 실행 완료
+종료 시간: 2025-12-26 08:42:22
+총 소요 시간: 22시간 4분 7초
+성공: 6개
+실패: 0개
+==========================================
diff --git a/Analysis_code/5.optima/run_bash/ft_transformer/run_ft_transformer_ctgan10000.sh b/Analysis_code/5.optima/run_bash/ft_transformer/run_ft_transformer_ctgan10000.sh
new file mode 100755
index 0000000000000000000000000000000000000000..77c18800d9c1d987af15d0c616b28c458757f795
--- /dev/null
+++ b/Analysis_code/5.optima/run_bash/ft_transformer/run_ft_transformer_ctgan10000.sh
@@ -0,0 +1,80 @@
+#!/bin/bash
+
+# 스크립트 디렉토리로 이동 (상위 디렉토리인 5.optima로 이동)
+cd "$(dirname "$0")/../.."
+
+# 시작 시간 기록
+START_TIME=$(date +%s)
+echo "=========================================="
+echo "FT-Transformer CTGAN10000 파일 실행 시작"
+echo "시작 시간: $(date '+%Y-%m-%d %H:%M:%S')"
+echo "GPU: 1번 (CUDA_VISIBLE_DEVICES=1)"
+echo "=========================================="
+echo ""
+
+# 실행할 파일 목록
+FILES=(
+    "ft_transformer_ctgan10000_busan.py"
+    "ft_transformer_ctgan10000_daegu.py"
+    "ft_transformer_ctgan10000_daejeon.py"
+    "ft_transformer_ctgan10000_gwangju.py"
+    "ft_transformer_ctgan10000_incheon.py"
+    "ft_transformer_ctgan10000_seoul.py"
+)
+
+# 에러 발생 시 중단 여부 (set -e를 사용하면 에러 발생 시 즉시 중단)
+set -e
+
+# 각 파일 실행
+SUCCESS_COUNT=0
+FAIL_COUNT=0
+
+for file in "${FILES[@]}"; do
+    filepath="ft_transformer_ctgan10000/$file"
+    if [ ! -f "$filepath" ]; then
+        echo "⚠️  경고: $filepath 파일을 찾을 수 없습니다. 건너뜁니다."
+        FAIL_COUNT=$((FAIL_COUNT + 1))
+        continue
+    fi
+    
+    echo "----------------------------------------"
+    echo "실행 중: $filepath"
+    echo "시작 시간: $(date '+%Y-%m-%d %H:%M:%S')"
+    echo "----------------------------------------"
+    
+    FILE_START=$(date +%s)
+    
+    # Python 스크립트 실행 (GPU 1번 설정, 색상 코드 비활성화)
+    if NO_COLOR=1 TERM=dumb CUDA_VISIBLE_DEVICES=1 python3 -u "$filepath"; then
+        FILE_END=$(date +%s)
+        FILE_DURATION=$((FILE_END - FILE_START))
+        echo ""
+        echo "✓ 완료: $filepath (소요 시간: ${FILE_DURATION}초)"
+        SUCCESS_COUNT=$((SUCCESS_COUNT + 1))
+    else
+        FILE_END=$(date +%s)
+        FILE_DURATION=$((FILE_END - FILE_START))
+        echo ""
+        echo "✗ 실패: $filepath (소요 시간: ${FILE_DURATION}초)"
+        FAIL_COUNT=$((FAIL_COUNT + 1))
+        echo "에러 발생으로 인해 스크립트를 중단합니다."
+        exit 1
+    fi
+    echo ""
+done
+
+# 종료 시간 기록
+END_TIME=$(date +%s)
+TOTAL_DURATION=$((END_TIME - START_TIME))
+HOURS=$((TOTAL_DURATION / 3600))
+MINUTES=$(((TOTAL_DURATION % 3600) / 60))
+SECONDS=$((TOTAL_DURATION % 60))
+
+echo "=========================================="
+echo "FT-Transformer CTGAN10000 파일 실행 완료"
+echo "종료 시간: $(date '+%Y-%m-%d %H:%M:%S')"
+echo "총 소요 시간: ${HOURS}시간 ${MINUTES}분 ${SECONDS}초"
+echo "성공: ${SUCCESS_COUNT}개"
+echo "실패: ${FAIL_COUNT}개"
+echo "=========================================="
+
diff --git a/Analysis_code/5.optima/run_bash/lgb/lgb_ctgan10000.log b/Analysis_code/5.optima/run_bash/lgb/lgb_ctgan10000.log
new file mode 100644
index 0000000000000000000000000000000000000000..926dbb169d81b9a297b4255db8cb4453e6f11d0c
--- /dev/null
+++ b/Analysis_code/5.optima/run_bash/lgb/lgb_ctgan10000.log
@@ -0,0 +1,219 @@
+nohup: ignoring input
+/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)
+==========================================
+LGB CTGAN10000 파일 실행 시작
+시작 시간: 2025-12-28 04:29:39
+GPU: 1번 (CUDA_VISIBLE_DEVICES=1)
+==========================================
+
+----------------------------------------
+실행 중: lgb_ctgan10000/LGB_ctgan10000_busan.py
+시작 시간: 2025-12-28 04:29:39
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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100%|██████████| 100/100 [1:01:40<00:00, 37.00s/trial, best loss: -0.4836]
+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.7243835590014314, 'learning_rate': 0.052472053724070156, 'max_depth': 15.0, 'min_child_weight': 9.0, 'num_leaves': 120.0, 'reg_alpha': 0.566895668532905, 'reg_lambda': 0.9659771198744264, 'subsample': 0.8425484904296862}
+최적 CSI 점수: 0.4836 (loss: -0.4836)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4238
+  - 최종 CSI: 0.4676
+  - 최고 CSI: 0.4836
+  - 최저 CSI: 0.4200
+  - 평균 CSI: 0.4700
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_ctgan10000_busan_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.3905
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.5044
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.4985
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_ctgan10000_busan.pkl에 저장되었습니다.
+
+✓ 완료: lgb_ctgan10000/LGB_ctgan10000_busan.py (소요 시간: 3720초)
+
+----------------------------------------
+실행 중: lgb_ctgan10000/LGB_ctgan10000_daegu.py
+시작 시간: 2025-12-28 05:31:39
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.885595994008888, 'learning_rate': 0.010106842270671093, 'max_depth': 14.0, 'min_child_weight': 1.0, 'num_leaves': 122.0, 'reg_alpha': 0.12648188723964116, 'reg_lambda': 0.873682924904797, 'subsample': 0.7806598236159817}
+최적 CSI 점수: 0.4339 (loss: -0.4339)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.3977
+  - 최종 CSI: 0.3974
+  - 최고 CSI: 0.4339
+  - 최저 CSI: 0.3802
+  - 평균 CSI: 0.4106
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_ctgan10000_daegu_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4293
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.4656
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.3270
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_ctgan10000_daegu.pkl에 저장되었습니다.
+
+✓ 완료: lgb_ctgan10000/LGB_ctgan10000_daegu.py (소요 시간: 3538초)
+
+----------------------------------------
+실행 중: lgb_ctgan10000/LGB_ctgan10000_daejeon.py
+시작 시간: 2025-12-28 06:30:37
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.7077604272501928, 'learning_rate': 0.10351387699107398, 'max_depth': 6.0, 'min_child_weight': 4.0, 'num_leaves': 51.0, 'reg_alpha': 0.06973941883143871, 'reg_lambda': 0.8477821589656351, 'subsample': 0.8664583588640111}
+최적 CSI 점수: 0.4908 (loss: -0.4908)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4792
+  - 최종 CSI: 0.4809
+  - 최고 CSI: 0.4908
+  - 최저 CSI: 0.4682
+  - 평균 CSI: 0.4815
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_ctgan10000_daejeon_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4324
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.4714
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.5557
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_ctgan10000_daejeon.pkl에 저장되었습니다.
+
+✓ 완료: lgb_ctgan10000/LGB_ctgan10000_daejeon.py (소요 시간: 3642초)
+
+----------------------------------------
+실행 중: lgb_ctgan10000/LGB_ctgan10000_gwangju.py
+시작 시간: 2025-12-28 07:31:19
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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100%|██████████| 100/100 [1:06:19<00:00, 32.53s/trial, best loss: -0.4994]
100%|██████████| 100/100 [1:06:19<00:00, 39.79s/trial, best loss: -0.4994]
+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.9974290644555891, 'learning_rate': 0.03488079849604818, 'max_depth': 15.0, 'min_child_weight': 13.0, 'num_leaves': 47.0, 'reg_alpha': 0.22968749134640387, 'reg_lambda': 0.42745540115072206, 'subsample': 0.9875948361694687}
+최적 CSI 점수: 0.4994 (loss: -0.4994)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4807
+  - 최종 CSI: 0.4865
+  - 최고 CSI: 0.4994
+  - 최저 CSI: 0.4678
+  - 평균 CSI: 0.4866
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_ctgan10000_gwangju_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4045
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.5536
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.5277
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_ctgan10000_gwangju.pkl에 저장되었습니다.
+
+✓ 완료: lgb_ctgan10000/LGB_ctgan10000_gwangju.py (소요 시간: 4001초)
+
+----------------------------------------
+실행 중: lgb_ctgan10000/LGB_ctgan10000_incheon.py
+시작 시간: 2025-12-28 08:38:00
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.7615743363801121, 'learning_rate': 0.032013705340192794, 'max_depth': 12.0, 'min_child_weight': 4.0, 'num_leaves': 135.0, 'reg_alpha': 0.07355917150019742, 'reg_lambda': 0.7693270890686972, 'subsample': 0.8491133431153928}
+최적 CSI 점수: 0.5592 (loss: -0.5592)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5469
+  - 최종 CSI: 0.5567
+  - 최고 CSI: 0.5592
+  - 최저 CSI: 0.5428
+  - 평균 CSI: 0.5520
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_ctgan10000_incheon_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4967
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.6068
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.5748
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_ctgan10000_incheon.pkl에 저장되었습니다.
+
+✓ 완료: lgb_ctgan10000/LGB_ctgan10000_incheon.py (소요 시간: 4299초)
+
+----------------------------------------
+실행 중: lgb_ctgan10000/LGB_ctgan10000_seoul.py
+시작 시간: 2025-12-28 09:49:39
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.661683034534645, 'learning_rate': 0.02117850018558707, 'max_depth': 15.0, 'min_child_weight': 16.0, 'num_leaves': 83.0, 'reg_alpha': 0.6438462886327296, 'reg_lambda': 0.8929153518940249, 'subsample': 0.9547922722157274}
+최적 CSI 점수: 0.5671 (loss: -0.5671)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5479
+  - 최종 CSI: 0.5505
+  - 최고 CSI: 0.5671
+  - 최저 CSI: 0.5432
+  - 평균 CSI: 0.5579
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_ctgan10000_seoul_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4967
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.5994
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.5777
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_ctgan10000_seoul.pkl에 저장되었습니다.
+
+✓ 완료: lgb_ctgan10000/LGB_ctgan10000_seoul.py (소요 시간: 3571초)
+
+==========================================
+LGB CTGAN10000 파일 실행 완료
+종료 시간: 2025-12-28 10:49:10
+총 소요 시간: 6시간 19분 31초
+성공: 6개
+실패: 0개
+==========================================
diff --git a/Analysis_code/5.optima/run_bash/lgb/lgb_pure_busan.log b/Analysis_code/5.optima/run_bash/lgb/lgb_pure_busan.log
new file mode 100644
index 0000000000000000000000000000000000000000..0e02d59aaaea8f45759769cef317081b8d821c8e
--- /dev/null
+++ b/Analysis_code/5.optima/run_bash/lgb/lgb_pure_busan.log
@@ -0,0 +1,83 @@
+nohup: ignoring input
+/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)
+==========================================
+LGB Pure 파일 실행 시작
+시작 시간: 2025-12-27 16:01:58
+GPU: 1번 (os.environ['CUDA_VISIBLE_DEVICES'] = '1')
+==========================================
+
+----------------------------------------
+실행 중: lgb_pure/LGB_pure_busan.py
+시작 시간: 2025-12-27 16:01:58
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
+  0%|          | 0/100 [00:00<?, ?trial/s, best loss=?]1 warning generated.
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+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.9406061312055983, 'learning_rate': 0.17468151642796886, 'max_depth': 6.0, 'min_child_weight': 17.0, 'num_leaves': 138.0, 'reg_alpha': 0.4593420461637059, 'reg_lambda': 0.5948987302333338, 'subsample': 0.6557839186758097}
+최적 CSI 점수: 0.4849 (loss: -0.4849)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4676
+  - 최종 CSI: 0.4718
+  - 최고 CSI: 0.4849
+  - 최저 CSI: 0.4314
+  - 평균 CSI: 0.4661
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_pure_busan_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.3911
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.5193
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.5177
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_pure_busan.pkl에 저장되었습니다.
+
+✓ 완료: lgb_pure/LGB_pure_busan.py (소요 시간: 25719초)
+
+==========================================
+LGB Pure 파일 실행 완료
+종료 시간: 2025-12-27 23:10:37
+총 소요 시간: 7시간 8분 39초
+성공: 1개
+실패: 0개
+==========================================
diff --git a/Analysis_code/5.optima/run_bash/lgb/lgb_smote.log b/Analysis_code/5.optima/run_bash/lgb/lgb_smote.log
index 172299a6d0868638a9468419bf09f3cc61142217..2a013c4f77eaa6d0aa50848db530263a3d1a2435 100644
--- a/Analysis_code/5.optima/run_bash/lgb/lgb_smote.log
+++ b/Analysis_code/5.optima/run_bash/lgb/lgb_smote.log
@@ -2,161 +2,483 @@ nohup: ignoring input
 /bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)
 ==========================================
 LGB SMOTE 파일 실행 시작
-시작 시간: 2025-12-23 18:56:36
+시작 시간: 2025-12-27 13:17:48
 GPU: 1번 (os.environ['CUDA_VISIBLE_DEVICES'] = '1')
 ==========================================
 
 ----------------------------------------
 실행 중: lgb_smote/LGB_smote_busan.py
-시작 시간: 2025-12-23 18:56:36
+시작 시간: 2025-12-27 13:17:48
 ----------------------------------------
 데이터 로딩 중...
 데이터 전처리 중...
 하이퍼파라미터 최적화 시작...
 
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-최적화 완료. 최적 파라미터: {'colsample_bytree': 0.8045832132317227, 'learning_rate': 0.024638617632624038, 'max_depth': 14.0, 'min_child_weight': 12.0, 'num_leaves': 96.0, 'reg_alpha': 0.35408999190539164, 'reg_lambda': 0.1238056933488465, 'subsample': 0.7670016443132639}
-최종 모델 학습 시작...
-Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
-Fold 1 검증 CSI: 0.4532
-Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
-Fold 2 검증 CSI: 0.4741
-Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
-Fold 3 검증 CSI: 0.4615
-모델 저장 중...
-모델이 ../save_model/lgb_optima/lgb_smote_busan.pkl에 저장되었습니다.
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+ 97%|█████████▋| 97/100 [1:02:06<01:48, 36.13s/trial, best loss: -0.4798]
+ 98%|█████████▊| 98/100 [1:02:49<01:16, 38.32s/trial, best loss: -0.4798]
+ 99%|█████████▉| 99/100 [1:03:55<00:46, 46.54s/trial, best loss: -0.4798]
+100%|██████████| 100/100 [1:04:19<00:00, 39.91s/trial, best loss: -0.4798]
+100%|██████████| 100/100 [1:04:19<00:00, 38.60s/trial, best loss: -0.4798]
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.8798108126736107, 'learning_rate': 0.018973645502331604, 'max_depth': 15.0, 'min_child_weight': 12.0, 'num_leaves': 147.0, 'reg_alpha': 0.03444822116314217, 'reg_lambda': 0.19407225134838502, 'subsample': 0.7009485341014761}
+최적 CSI 점수: 0.4798 (loss: -0.4798)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4627
+  - 최종 CSI: 0.4698
+  - 최고 CSI: 0.4798
+  - 최저 CSI: 0.4550
+  - 평균 CSI: 0.4683
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_smote_busan_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4574
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.4773
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.4583
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_smote_busan.pkl에 저장되었습니다.
+
+✓ 완료: lgb_smote/LGB_smote_busan.py (소요 시간: 3892초)
+
+----------------------------------------
+실행 중: lgb_smote/LGB_smote_daegu.py
+시작 시간: 2025-12-27 14:22:40
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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+ 96%|█████████▌| 96/100 [46:11<01:36, 24.18s/trial, best loss: -0.4671]
+ 97%|█████████▋| 97/100 [46:38<01:14, 24.82s/trial, best loss: -0.4671]
+ 98%|█████████▊| 98/100 [47:00<00:48, 24.04s/trial, best loss: -0.4671]
+ 99%|█████████▉| 99/100 [47:19<00:22, 22.44s/trial, best loss: -0.4671]
+100%|██████████| 100/100 [47:41<00:00, 22.56s/trial, best loss: -0.4671]
+100%|██████████| 100/100 [47:41<00:00, 28.62s/trial, best loss: -0.4671]
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.999946333457191, 'learning_rate': 0.07031680296643952, 'max_depth': 4.0, 'min_child_weight': 17.0, 'num_leaves': 32.0, 'reg_alpha': 0.055815317687804816, 'reg_lambda': 0.2293760134119255, 'subsample': 0.6363907923464539}
+최적 CSI 점수: 0.4671 (loss: -0.4671)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4483
+  - 최종 CSI: 0.4491
+  - 최고 CSI: 0.4671
+  - 최저 CSI: 0.4238
+  - 평균 CSI: 0.4511
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_smote_daegu_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.3771
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.4659
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.5062
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_smote_daegu.pkl에 저장되었습니다.
+
+✓ 완료: lgb_smote/LGB_smote_daegu.py (소요 시간: 2870초)
+
+----------------------------------------
+실행 중: lgb_smote/LGB_smote_daejeon.py
+시작 시간: 2025-12-27 15:10:30
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
+  0%|          | 0/100 [00:00<?, ?trial/s, best loss=?]
+  1%|          | 1/100 [00:31<51:57, 31.49s/trial, best loss: -0.5211]
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100%|██████████| 100/100 [3:39:09<00:00, 385.25s/trial, best loss: -0.5317]
100%|██████████| 100/100 [3:39:09<00:00, 131.50s/trial, best loss: -0.5317]
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.7585295616897205, 'learning_rate': 0.012807299958074884, 'max_depth': 8.0, 'min_child_weight': 2.0, 'num_leaves': 149.0, 'reg_alpha': 0.8175154308532824, 'reg_lambda': 0.7481509687757377, 'subsample': 0.8155067304500027}
+최적 CSI 점수: 0.5317 (loss: -0.5317)
 
-✓ 완료: lgb_smote/LGB_smote_busan.py (소요 시간: 4678초)
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5211
+  - 최종 CSI: 0.5200
+  - 최고 CSI: 0.5317
+  - 최저 CSI: 0.5140
+  - 평균 CSI: 0.5239
 
-----------------------------------------
-실행 중: lgb_smote/LGB_smote_daegu.py
-시작 시간: 2025-12-23 20:14:34
-----------------------------------------
-데이터 로딩 중...
-데이터 전처리 중...
-하이퍼파라미터 최적화 시작...
-
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100%|██████████| 100/100 [55:33<00:00, 33.33s/trial, best loss: -0.467]
-최적화 완료. 최적 파라미터: {'colsample_bytree': 0.8060303705817004, 'learning_rate': 0.0304803685904912, 'max_depth': 7.0, 'min_child_weight': 12.0, 'num_leaves': 21.0, 'reg_alpha': 0.6134626726173433, 'reg_lambda': 0.4048896871422857, 'subsample': 0.794962334741688}
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_smote_daejeon_trials.pkl에 저장되었습니다.
 최종 모델 학습 시작...
 Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
-Fold 1 검증 CSI: 0.3778
+Fold 1 검증 CSI: 0.4660
 Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
-Fold 2 검증 CSI: 0.4815
+Fold 2 검증 CSI: 0.5365
 Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
-Fold 3 검증 CSI: 0.4936
+Fold 3 검증 CSI: 0.5802
 모델 저장 중...
-모델이 ../save_model/lgb_optima/lgb_smote_daegu.pkl에 저장되었습니다.
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_smote_daejeon.pkl에 저장되었습니다.
 
-✓ 완료: lgb_smote/LGB_smote_daegu.py (소요 시간: 3345초)
+✓ 완료: lgb_smote/LGB_smote_daejeon.py (소요 시간: 13709초)
 
 ----------------------------------------
-실행 중: lgb_smote/LGB_smote_daejeon.py
-시작 시간: 2025-12-23 21:10:19
+실행 중: lgb_smote/LGB_smote_gwangju.py
+시작 시간: 2025-12-27 18:58:59
 ----------------------------------------
 데이터 로딩 중...
 데이터 전처리 중...
 하이퍼파라미터 최적화 시작...
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-최적화 완료. 최적 파라미터: {'colsample_bytree': 0.6803179957156765, 'learning_rate': 0.040860391070553635, 'max_depth': 9.0, 'min_child_weight': 8.0, 'num_leaves': 143.0, 'reg_alpha': 0.303814589536064, 'reg_lambda': 0.7249407645233729, 'subsample': 0.776464876412028}
-최종 모델 학습 시작...
-Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
-Fold 1 검증 CSI: 0.4661
-Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
-Fold 2 검증 CSI: 0.5374
-Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
-Fold 3 검증 CSI: 0.5832
-모델 저장 중...
-모델이 ../save_model/lgb_optima/lgb_smote_daejeon.pkl에 저장되었습니다.
+
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100%|██████████| 100/100 [4:43:11<00:00, 28.84s/trial, best loss: -0.5297]
100%|██████████| 100/100 [4:43:11<00:00, 169.92s/trial, best loss: -0.5297]
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.9919060649789312, 'learning_rate': 0.054631157314326724, 'max_depth': 15.0, 'min_child_weight': 3.0, 'num_leaves': 47.0, 'reg_alpha': 0.9190252546800255, 'reg_lambda': 0.8800706832709921, 'subsample': 0.7859941375783913}
+최적 CSI 점수: 0.5297 (loss: -0.5297)
 
-✓ 완료: lgb_smote/LGB_smote_daejeon.py (소요 시간: 4203초)
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5140
+  - 최종 CSI: 0.5162
+  - 최고 CSI: 0.5297
+  - 최저 CSI: 0.5076
+  - 평균 CSI: 0.5214
 
-----------------------------------------
-실행 중: lgb_smote/LGB_smote_gwangju.py
-시작 시간: 2025-12-23 22:20:22
-----------------------------------------
-데이터 로딩 중...
-데이터 전처리 중...
-하이퍼파라미터 최적화 시작...
-
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-최적화 완료. 최적 파라미터: {'colsample_bytree': 0.9899674815271164, 'learning_rate': 0.0184851042732691, 'max_depth': 11.0, 'min_child_weight': 14.0, 'num_leaves': 141.0, 'reg_alpha': 0.6980428859928156, 'reg_lambda': 0.32290654243745576, 'subsample': 0.8587523994860693}
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_smote_gwangju_trials.pkl에 저장되었습니다.
 최종 모델 학습 시작...
 Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
-Fold 1 검증 CSI: 0.4699
+Fold 1 검증 CSI: 0.4658
 Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
-Fold 2 검증 CSI: 0.5395
+Fold 2 검증 CSI: 0.5445
 Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
-Fold 3 검증 CSI: 0.5353
+Fold 3 검증 CSI: 0.5548
 모델 저장 중...
-모델이 ../save_model/lgb_optima/lgb_smote_gwangju.pkl에 저장되었습니다.
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_smote_gwangju.pkl에 저장되었습니다.
 
-✓ 완료: lgb_smote/LGB_smote_gwangju.py (소요 시간: 3923초)
+✓ 완료: lgb_smote/LGB_smote_gwangju.py (소요 시간: 17011초)
 
 ----------------------------------------
 실행 중: lgb_smote/LGB_smote_incheon.py
-시작 시간: 2025-12-23 23:25:45
+시작 시간: 2025-12-27 23:42:30
 ----------------------------------------
 데이터 로딩 중...
 데이터 전처리 중...
 하이퍼파라미터 최적화 시작...
-
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-최적화 완료. 최적 파라미터: {'colsample_bytree': 0.9929155579300695, 'learning_rate': 0.1869601734321118, 'max_depth': 4.0, 'min_child_weight': 4.0, 'num_leaves': 32.0, 'reg_alpha': 0.6946998211046669, 'reg_lambda': 0.04135126448440768, 'subsample': 0.6740139329061862}
+
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100%|██████████| 100/100 [47:09<00:00, 28.30s/trial, best loss: -0.5986]
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.7149911519913482, 'learning_rate': 0.14061649313221522, 'max_depth': 4.0, 'min_child_weight': 4.0, 'num_leaves': 46.0, 'reg_alpha': 0.3323739596170201, 'reg_lambda': 0.3615804769440283, 'subsample': 0.7361106038020775}
+최적 CSI 점수: 0.5986 (loss: -0.5986)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5809
+  - 최종 CSI: 0.5894
+  - 최고 CSI: 0.5986
+  - 최저 CSI: 0.5696
+  - 평균 CSI: 0.5862
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_smote_incheon_trials.pkl에 저장되었습니다.
 최종 모델 학습 시작...
 Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
-Fold 1 검증 CSI: 0.5798
+Fold 1 검증 CSI: 0.5834
 Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
-Fold 2 검증 CSI: 0.6239
+Fold 2 검증 CSI: 0.6287
 Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
-Fold 3 검증 CSI: 0.5693
+Fold 3 검증 CSI: 0.5714
 모델 저장 중...
-모델이 ../save_model/lgb_optima/lgb_smote_incheon.pkl에 저장되었습니다.
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_smote_incheon.pkl에 저장되었습니다.
 
-✓ 완료: lgb_smote/LGB_smote_incheon.py (소요 시간: 3240초)
+✓ 완료: lgb_smote/LGB_smote_incheon.py (소요 시간: 2836초)
 
 ----------------------------------------
 실행 중: lgb_smote/LGB_smote_seoul.py
-시작 시간: 2025-12-24 00:19:45
+시작 시간: 2025-12-28 00:29:46
 ----------------------------------------
 데이터 로딩 중...
 데이터 전처리 중...
 하이퍼파라미터 최적화 시작...
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-최적화 완료. 최적 파라미터: {'colsample_bytree': 0.7830111482901303, 'learning_rate': 0.056905885530147686, 'max_depth': 14.0, 'min_child_weight': 3.0, 'num_leaves': 133.0, 'reg_alpha': 0.3488689455774506, 'reg_lambda': 0.37609809520904414, 'subsample': 0.7244470059632984}
+
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+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.7712993813350446, 'learning_rate': 0.014764448164722338, 'max_depth': 13.0, 'min_child_weight': 2.0, 'num_leaves': 144.0, 'reg_alpha': 0.9375760221934153, 'reg_lambda': 0.6881109653195318, 'subsample': 0.6932106255794361}
+최적 CSI 점수: 0.5895 (loss: -0.5895)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5774
+  - 최종 CSI: 0.5837
+  - 최고 CSI: 0.5895
+  - 최저 CSI: 0.5609
+  - 평균 CSI: 0.5779
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_smote_seoul_trials.pkl에 저장되었습니다.
 최종 모델 학습 시작...
 Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
-Fold 1 검증 CSI: 0.4668
+Fold 1 검증 CSI: 0.4598
 Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
-Fold 2 검증 CSI: 0.6564
+Fold 2 검증 CSI: 0.6692
 Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
-Fold 3 검증 CSI: 0.6120
+Fold 3 검증 CSI: 0.6130
 모델 저장 중...
-모델이 ../save_model/lgb_optima/lgb_smote_seoul.pkl에 저장되었습니다.
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_smote_seoul.pkl에 저장되었습니다.
 
-✓ 완료: lgb_smote/LGB_smote_seoul.py (소요 시간: 3444초)
+✓ 완료: lgb_smote/LGB_smote_seoul.py (소요 시간: 3932초)
 
 ==========================================
 LGB SMOTE 파일 실행 완료
-종료 시간: 2025-12-24 01:17:09
-총 소요 시간: 6시간 20분 33초
+종료 시간: 2025-12-28 01:35:18
+총 소요 시간: 12시간 17분 30초
 성공: 6개
 실패: 0개
 ==========================================
diff --git a/Analysis_code/5.optima/run_bash/lgb/lgb_smotenc_ctgan20000.log b/Analysis_code/5.optima/run_bash/lgb/lgb_smotenc_ctgan20000.log
index 7c999de81e7a9e19d21a8a31ee675b64827ce333..15a4fa6dbb2ad44e06abb1487320193abbf20b90 100644
--- a/Analysis_code/5.optima/run_bash/lgb/lgb_smotenc_ctgan20000.log
+++ b/Analysis_code/5.optima/run_bash/lgb/lgb_smotenc_ctgan20000.log
@@ -2,77 +2,212 @@ nohup: ignoring input
 /bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)
 ==========================================
 LGB SMOTENC CTGAN20000 파일 실행 시작
-시작 시간: 2025-12-24 14:12:06
+시작 시간: 2025-12-27 02:05:14
 GPU: 1번 (CUDA_VISIBLE_DEVICES=1)
 ==========================================
 
+----------------------------------------
+실행 중: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_busan.py
+시작 시간: 2025-12-27 02:05:14
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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100%|██████████| 100/100 [55:29<00:00, 33.29s/trial, best loss: -0.4447]
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.6317882901026403, 'learning_rate': 0.16009804087232835, 'max_depth': 12.0, 'min_child_weight': 7.0, 'num_leaves': 79.0, 'reg_alpha': 0.7559497285087418, 'reg_lambda': 0.36818298791457293, 'subsample': 0.6759358549390172}
+최적 CSI 점수: 0.4447 (loss: -0.4447)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4396
+  - 최종 CSI: 0.4311
+  - 최고 CSI: 0.4447
+  - 최저 CSI: 0.4100
+  - 평균 CSI: 0.4298
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_smotenc_ctgan20000_busan_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.3694
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.4901
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.4746
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_smotenc_ctgan20000_busan.pkl에 저장되었습니다.
+
+✓ 완료: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_busan.py (소요 시간: 3341초)
+
+----------------------------------------
+실행 중: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daegu.py
+시작 시간: 2025-12-27 03:00:55
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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100%|██████████| 100/100 [50:41<00:00, 30.42s/trial, best loss: -0.4455]
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.9935566129264934, 'learning_rate': 0.011803766157843702, 'max_depth': 10.0, 'min_child_weight': 18.0, 'num_leaves': 78.0, 'reg_alpha': 0.5555179443217245, 'reg_lambda': 0.23478947295729824, 'subsample': 0.7059612447576378}
+최적 CSI 점수: 0.4455 (loss: -0.4455)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.3900
+  - 최종 CSI: 0.3762
+  - 최고 CSI: 0.4455
+  - 최저 CSI: 0.3511
+  - 평균 CSI: 0.4169
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_smotenc_ctgan20000_daegu_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4194
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.4300
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.2989
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_smotenc_ctgan20000_daegu.pkl에 저장되었습니다.
+
+✓ 완료: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daegu.py (소요 시간: 3061초)
+
+----------------------------------------
+실행 중: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daejeon.py
+시작 시간: 2025-12-27 03:51:56
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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100%|██████████| 100/100 [1:11:44<00:00, 43.05s/trial, best loss: -0.5056]
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.7827606965781482, 'learning_rate': 0.013695421561409111, 'max_depth': 13.0, 'min_child_weight': 6.0, 'num_leaves': 147.0, 'reg_alpha': 0.11677800966310199, 'reg_lambda': 0.7741495746536297, 'subsample': 0.7509016581783318}
+최적 CSI 점수: 0.5056 (loss: -0.5056)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4795
+  - 최종 CSI: 0.5014
+  - 최고 CSI: 0.5056
+  - 최저 CSI: 0.3527
+  - 평균 CSI: 0.4891
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_smotenc_ctgan20000_daejeon_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4406
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.4619
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.5329
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_smotenc_ctgan20000_daejeon.pkl에 저장되었습니다.
+
+✓ 완료: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daejeon.py (소요 시간: 4345초)
+
 ----------------------------------------
 실행 중: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_gwangju.py
-시작 시간: 2025-12-24 14:12:06
+시작 시간: 2025-12-27 05:04:21
 ----------------------------------------
 데이터 로딩 중...
 데이터 전처리 중...
 하이퍼파라미터 최적화 시작...
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-최적화 완료. 최적 파라미터: {'colsample_bytree': 0.8512280002778762, 'learning_rate': 0.010771032680479646, 'max_depth': 15.0, 'min_child_weight': 6.0, 'num_leaves': 133.0, 'reg_alpha': 0.5278434766177793, 'reg_lambda': 0.35329997167147315, 'subsample': 0.6320079603632285}
+
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+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.9823131365674758, 'learning_rate': 0.09913135171477433, 'max_depth': 12.0, 'min_child_weight': 9.0, 'num_leaves': 123.0, 'reg_alpha': 0.7300219334294252, 'reg_lambda': 0.1069375287034794, 'subsample': 0.6733134215038767}
+최적 CSI 점수: 0.5098 (loss: -0.5098)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4737
+  - 최종 CSI: 0.4957
+  - 최고 CSI: 0.5098
+  - 최저 CSI: 0.4292
+  - 평균 CSI: 0.4859
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_smotenc_ctgan20000_gwangju_trials.pkl에 저장되었습니다.
 최종 모델 학습 시작...
 Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
-Fold 1 검증 CSI: 0.3886
+Fold 1 검증 CSI: 0.3952
 Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
-Fold 2 검증 CSI: 0.5681
+Fold 2 검증 CSI: 0.5517
 Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
-Fold 3 검증 CSI: 0.4872
+Fold 3 검증 CSI: 0.4933
 모델 저장 중...
 모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_smotenc_ctgan20000_gwangju.pkl에 저장되었습니다.
 
-✓ 완료: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_gwangju.py (소요 시간: 4533초)
+✓ 완료: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_gwangju.py (소요 시간: 2516초)
 
 ----------------------------------------
 실행 중: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_incheon.py
-시작 시간: 2025-12-24 15:27:39
+시작 시간: 2025-12-27 05:46:17
 ----------------------------------------
 데이터 로딩 중...
 데이터 전처리 중...
 하이퍼파라미터 최적화 시작...
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-최적화 완료. 최적 파라미터: {'colsample_bytree': 0.7079111290056588, 'learning_rate': 0.029571496444768038, 'max_depth': 14.0, 'min_child_weight': 10.0, 'num_leaves': 139.0, 'reg_alpha': 0.6308609261525304, 'reg_lambda': 0.6327593216573336, 'subsample': 0.7699737725675289}
+
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100%|██████████| 100/100 [52:58<00:00, 31.79s/trial, best loss: -0.5827]
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.8256738533611337, 'learning_rate': 0.03224794137518454, 'max_depth': 12.0, 'min_child_weight': 6.0, 'num_leaves': 128.0, 'reg_alpha': 0.1310437036184594, 'reg_lambda': 0.6161059673451368, 'subsample': 0.8412610992284884}
+최적 CSI 점수: 0.5827 (loss: -0.5827)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5733
+  - 최종 CSI: 0.5623
+  - 최고 CSI: 0.5827
+  - 최저 CSI: 0.5287
+  - 평균 CSI: 0.5710
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_smotenc_ctgan20000_incheon_trials.pkl에 저장되었습니다.
 최종 모델 학습 시작...
 Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
-Fold 1 검증 CSI: 0.4833
+Fold 1 검증 CSI: 0.4886
 Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
-Fold 2 검증 CSI: 0.6038
+Fold 2 검증 CSI: 0.6085
 Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
-Fold 3 검증 CSI: 0.5673
+Fold 3 검증 CSI: 0.5648
 모델 저장 중...
 모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_smotenc_ctgan20000_incheon.pkl에 저장되었습니다.
 
-✓ 완료: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_incheon.py (소요 시간: 4638초)
+✓ 완료: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_incheon.py (소요 시간: 3201초)
 
 ----------------------------------------
 실행 중: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_seoul.py
-시작 시간: 2025-12-24 16:44:57
+시작 시간: 2025-12-27 06:39:38
 ----------------------------------------
 데이터 로딩 중...
 데이터 전처리 중...
 하이퍼파라미터 최적화 시작...
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-최적화 완료. 최적 파라미터: {'colsample_bytree': 0.6596044939069141, 'learning_rate': 0.011159426256562091, 'max_depth': 14.0, 'min_child_weight': 8.0, 'num_leaves': 74.0, 'reg_alpha': 0.32554321631876115, 'reg_lambda': 0.21038765329899758, 'subsample': 0.9189984742418659}
+
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100%|██████████| 100/100 [53:27<00:00, 32.07s/trial, best loss: -0.5622]
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.6345219883667235, 'learning_rate': 0.010708867004743688, 'max_depth': 14.0, 'min_child_weight': 14.0, 'num_leaves': 111.0, 'reg_alpha': 0.12632598375404414, 'reg_lambda': 0.632120993668035, 'subsample': 0.6705295797223814}
+최적 CSI 점수: 0.5622 (loss: -0.5622)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5425
+  - 최종 CSI: 0.5327
+  - 최고 CSI: 0.5622
+  - 최저 CSI: 0.4334
+  - 평균 CSI: 0.5449
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/lgb_smotenc_ctgan20000_seoul_trials.pkl에 저장되었습니다.
 최종 모델 학습 시작...
 Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
-Fold 1 검증 CSI: 0.4744
+Fold 1 검증 CSI: 0.4788
 Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
-Fold 2 검증 CSI: 0.5864
+Fold 2 검증 CSI: 0.5856
 Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
-Fold 3 검증 CSI: 0.5839
+Fold 3 검증 CSI: 0.5705
 모델 저장 중...
 모델이 /workspace/visibility_prediction/Analysis_code/save_model/lgb_optima/lgb_smotenc_ctgan20000_seoul.pkl에 저장되었습니다.
 
-✓ 완료: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_seoul.py (소요 시간: 4228초)
+✓ 완료: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_seoul.py (소요 시간: 3236초)
 
 ==========================================
 LGB SMOTENC CTGAN20000 파일 실행 완료
-종료 시간: 2025-12-24 17:55:25
-총 소요 시간: 3시간 43분 19초
-성공: 3개
+종료 시간: 2025-12-27 07:33:34
+총 소요 시간: 5시간 28분 20초
+성공: 6개
 실패: 0개
 ==========================================
diff --git a/Analysis_code/5.optima/run_bash/lgb/lgb_smotenc_ctgan20000_1.log b/Analysis_code/5.optima/run_bash/lgb/lgb_smotenc_ctgan20000_1.log
deleted file mode 100644
index 82a3bf5d0e6a5ecda3228c59588ad6b23ea3387e..0000000000000000000000000000000000000000
--- a/Analysis_code/5.optima/run_bash/lgb/lgb_smotenc_ctgan20000_1.log
+++ /dev/null
@@ -1,97 +0,0 @@
-nohup: ignoring input
-/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)
-==========================================
-LGB SMOTENC CTGAN20000 파일 실행 시작
-시작 시간: 2025-12-24 01:30:53
-GPU: 1번 (CUDA_VISIBLE_DEVICES=1)
-==========================================
-
-----------------------------------------
-실행 중: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_busan.py
-시작 시간: 2025-12-24 01:30:53
-----------------------------------------
-데이터 로딩 중...
-데이터 전처리 중...
-하이퍼파라미터 최적화 시작...
-
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-최적화 완료. 최적 파라미터: {'colsample_bytree': 0.722254060384934, 'learning_rate': 0.010456441386956757, 'max_depth': 13.0, 'min_child_weight': 15.0, 'num_leaves': 106.0, 'reg_alpha': 0.24597772797554324, 'reg_lambda': 0.47168540903448464, 'subsample': 0.6819497853968971}
-최종 모델 학습 시작...
-Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
-Fold 1 검증 CSI: 0.3416
-Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
-Fold 2 검증 CSI: 0.5094
-Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
-Fold 3 검증 CSI: 0.4627
-모델 저장 중...
-모델이 ../save_model/lgb_optima/lgb_smotenc_ctgan20000_busan.pkl에 저장되었습니다.
-
-✓ 완료: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_busan.py (소요 시간: 5457초)
-
-----------------------------------------
-실행 중: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daegu.py
-시작 시간: 2025-12-24 03:01:50
-----------------------------------------
-데이터 로딩 중...
-데이터 전처리 중...
-하이퍼파라미터 최적화 시작...
-
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-최적화 완료. 최적 파라미터: {'colsample_bytree': 0.9563288207018893, 'learning_rate': 0.013348533318915456, 'max_depth': 11.0, 'min_child_weight': 19.0, 'num_leaves': 68.0, 'reg_alpha': 0.20781716747112422, 'reg_lambda': 0.04161214084483722, 'subsample': 0.9157525056412512}
-최종 모델 학습 시작...
-Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
-Fold 1 검증 CSI: 0.4109
-Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
-Fold 2 검증 CSI: 0.4300
-Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
-Fold 3 검증 CSI: 0.2970
-모델 저장 중...
-모델이 ../save_model/lgb_optima/lgb_smotenc_ctgan20000_daegu.pkl에 저장되었습니다.
-
-✓ 완료: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daegu.py (소요 시간: 3263초)
-
-----------------------------------------
-실행 중: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daejeon.py
-시작 시간: 2025-12-24 03:56:13
-----------------------------------------
-데이터 로딩 중...
-데이터 전처리 중...
-하이퍼파라미터 최적화 시작...
-
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100%|██████████| 100/100 [1:22:38<00:00, 49.59s/trial, best loss: -0.5088]
-최적화 완료. 최적 파라미터: {'colsample_bytree': 0.7747208651250155, 'learning_rate': 0.013083291438003773, 'max_depth': 12.0, 'min_child_weight': 7.0, 'num_leaves': 131.0, 'reg_alpha': 0.6070332630483457, 'reg_lambda': 0.6559957550054907, 'subsample': 0.7361558020225378}
-최종 모델 학습 시작...
-Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
-Fold 1 검증 CSI: 0.4324
-Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
-Fold 2 검증 CSI: 0.4697
-Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
-Fold 3 검증 CSI: 0.5375
-모델 저장 중...
-모델이 ../save_model/lgb_optima/lgb_smotenc_ctgan20000_daejeon.pkl에 저장되었습니다.
-
-✓ 완료: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_daejeon.py (소요 시간: 5020초)
-
-----------------------------------------
-실행 중: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_gwangju.py
-시작 시간: 2025-12-24 05:19:53
-----------------------------------------
-데이터 로딩 중...
-Traceback (most recent call last):
-  File "/workspace/visibility_prediction/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_gwangju.py", line 192, in <module>
-    df_gwangju = pd.read_csv(os.path.join(data_base_dir, "data_for_modeling/gwangju_train.csv"))
-  File "/opt/conda/envs/py39/lib/python3.9/site-packages/pandas/util/_decorators.py", line 211, in wrapper
-    return func(*args, **kwargs)
-  File "/opt/conda/envs/py39/lib/python3.9/site-packages/pandas/util/_decorators.py", line 331, in wrapper
-    return func(*args, **kwargs)
-  File "/opt/conda/envs/py39/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 950, in read_csv
-    return _read(filepath_or_buffer, kwds)
-  File "/opt/conda/envs/py39/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 605, in _read
-    parser = TextFileReader(filepath_or_buffer, **kwds)
-  File "/opt/conda/envs/py39/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1442, in __init__
-    self._engine = self._make_engine(f, self.engine)
-  File "/opt/conda/envs/py39/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1735, in _make_engine
-    self.handles = get_handle(
-  File "/opt/conda/envs/py39/lib/python3.9/site-packages/pandas/io/common.py", line 856, in get_handle
-    handle = open(
-FileNotFoundError: [Errno 2] No such file or directory: '/workspace/visibility_prediction/Analysis_code/data/data_for_modeling/gwangju_train.csv'
-
-✗ 실패: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_gwangju.py (소요 시간: 1초)
-에러 발생으로 인해 스크립트를 중단합니다.
diff --git a/Analysis_code/5.optima/run_bash/lgb/lgb_smotenc_ctgan20000_2.log b/Analysis_code/5.optima/run_bash/lgb/lgb_smotenc_ctgan20000_2.log
deleted file mode 100644
index 4b2f05156696286a31235d145f54646ba7e82f04..0000000000000000000000000000000000000000
--- a/Analysis_code/5.optima/run_bash/lgb/lgb_smotenc_ctgan20000_2.log
+++ /dev/null
@@ -1,32 +0,0 @@
-nohup: ignoring input
-/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)
-==========================================
-LGB SMOTENC CTGAN20000 파일 실행 시작
-시작 시간: 2025-12-24 05:28:28
-GPU: 1번 (CUDA_VISIBLE_DEVICES=1)
-==========================================
-
-----------------------------------------
-실행 중: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_gwangju.py
-시작 시간: 2025-12-24 05:28:28
-----------------------------------------
-데이터 로딩 중...
-데이터 전처리 중...
-하이퍼파라미터 최적화 시작...
-
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100%|██████████| 100/100 [1:07:33<00:00, 40.54s/trial, best loss: -0.5132]
-최적화 완료. 최적 파라미터: {'colsample_bytree': 0.8162865140038518, 'learning_rate': 0.012535385881369373, 'max_depth': 15.0, 'min_child_weight': 7.0, 'num_leaves': 143.0, 'reg_alpha': 0.29159003327103133, 'reg_lambda': 0.38610627379641516, 'subsample': 0.8759153129042118}
-최종 모델 학습 시작...
-Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
-Fold 1 검증 CSI: 0.3799
-Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
-Fold 2 검증 CSI: 0.5654
-Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
-Fold 3 검증 CSI: 0.4932
-모델 저장 중...
-Traceback (most recent call last):
-  File "/workspace/visibility_prediction/Analysis_code/5.optima/lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_gwangju.py", line 287, in <module>
-    model_save_path = os.path.join(base_dir, "save_model/lgb_optima/lgb_smotenc_ctgan20000_gwangju.pkl")
-NameError: name 'base_dir' is not defined
-
-✗ 실패: lgb_smotenc_ctgan20000/LGB_smotenc_ctgan20000_gwangju.py (소요 시간: 4113초)
-에러 발생으로 인해 스크립트를 중단합니다.
diff --git a/Analysis_code/5.optima/run_bash/lgb/run_lgb_pure.sh b/Analysis_code/5.optima/run_bash/lgb/run_lgb_pure.sh
index efc54abc0149e1c760a232d95c37631e5cb4a4d5..723b4d86bfa5c0eb4c8c198188f4e54e753ad6e8 100755
--- a/Analysis_code/5.optima/run_bash/lgb/run_lgb_pure.sh
+++ b/Analysis_code/5.optima/run_bash/lgb/run_lgb_pure.sh
@@ -45,7 +45,7 @@ for file in "${FILES[@]}"; do
     FILE_START=$(date +%s)
     
     # Python 스크립트 실행 (os.environ으로 GPU 1번 설정)
-    if CUDA_VISIBLE_DEVICES=1 python3 -u "$filepath"; then
+    if CUDA_VISIBLE_DEVICES=0 python3 -u "$filepath"; then
         FILE_END=$(date +%s)
         FILE_DURATION=$((FILE_END - FILE_START))
         echo ""
diff --git a/Analysis_code/5.optima/run_bash/lgb/run_lgb_smotenc_ctgan20000.sh b/Analysis_code/5.optima/run_bash/lgb/run_lgb_smotenc_ctgan20000.sh
index e4b46e6205446ecdd169b38db1e2ca1aae8205de..a28b882a15e2fd8e4b171e6d7a0dab1ef8eb3c3c 100755
--- a/Analysis_code/5.optima/run_bash/lgb/run_lgb_smotenc_ctgan20000.sh
+++ b/Analysis_code/5.optima/run_bash/lgb/run_lgb_smotenc_ctgan20000.sh
@@ -14,9 +14,9 @@ echo ""
 
 # 실행할 파일 목록
 FILES=(
-    # "LGB_smotenc_ctgan20000_busan.py"
-    # "LGB_smotenc_ctgan20000_daegu.py"
-    # "LGB_smotenc_ctgan20000_daejeon.py"
+    "LGB_smotenc_ctgan20000_busan.py"
+    "LGB_smotenc_ctgan20000_daegu.py"
+    "LGB_smotenc_ctgan20000_daejeon.py"
     "LGB_smotenc_ctgan20000_gwangju.py"
     "LGB_smotenc_ctgan20000_incheon.py"
     "LGB_smotenc_ctgan20000_seoul.py"
@@ -45,7 +45,7 @@ for file in "${FILES[@]}"; do
     FILE_START=$(date +%s)
     
     # Python 스크립트 실행 (GPU 1번 설정)
-    if CUDA_VISIBLE_DEVICES=1 python3 -u "$filepath"; then
+    if CUDA_VISIBLE_DEVICES=0 python3 -u "$filepath"; then
         FILE_END=$(date +%s)
         FILE_DURATION=$((FILE_END - FILE_START))
         echo ""
diff --git a/Analysis_code/5.optima/run_bash/resnet_like/resnet_like_ctgan10000.log b/Analysis_code/5.optima/run_bash/resnet_like/resnet_like_ctgan10000.log
new file mode 100644
index 0000000000000000000000000000000000000000..ec3ce585cebde544a319ecd78acc8e170d428c12
--- /dev/null
+++ b/Analysis_code/5.optima/run_bash/resnet_like/resnet_like_ctgan10000.log
@@ -0,0 +1,8185 @@
+nohup: ignoring input
+/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)
+==========================================
+ResNet-Like CTGAN10000 파일 실행 시작
+시작 시간: 2025-12-28 12:36:12
+GPU: 0번 (CUDA_VISIBLE_DEVICES=0)
+==========================================
+
+----------------------------------------
+실행 중: resnet_like_ctgan10000/resnet_like_ctgan10000_busan.py
+시작 시간: 2025-12-28 12:36:12
+----------------------------------------
+[I 2025-12-28 12:36:13,366] A new study created in memory with name: no-name-1f24db1f-078f-4f45-8c0d-b754824a68a0
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:37:07,064] Trial 0 finished with value: 0.46002231957555956 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3271063544470221, 'dropout_second': 0.10613919805291927, 'lr': 0.0013021745164756519, 'weight_decay': 0.008875347346606789, 'batch_size': 128}. Best is trial 0 with value: 0.46002231957555956.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.460022
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3271063544470221, 'dropout_second': 0.10613919805291927, 'lr': 0.0013021745164756519, 'weight_decay': 0.008875347346606789, 'batch_size': 128}
+  Best Value (CSI): 0.460022
+  Best Trial: 0
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3271063544470221, 'dropout_second': 0.10613919805291927, 'lr': 0.0013021745164756519, 'weight_decay': 0.008875347346606789, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:38:48,883] Trial 1 finished with value: 0.47554701890151535 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.39939444068736674, 'dropout_second': 0.011774526054862245, 'lr': 0.0013407873458120595, 'weight_decay': 0.0006392156031611434, 'batch_size': 32}. Best is trial 1 with value: 0.47554701890151535.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.475547
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.39939444068736674, 'dropout_second': 0.011774526054862245, 'lr': 0.0013407873458120595, 'weight_decay': 0.0006392156031611434, 'batch_size': 32}
+  Best Value (CSI): 0.475547
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.39939444068736674, 'dropout_second': 0.011774526054862245, 'lr': 0.0013407873458120595, 'weight_decay': 0.0006392156031611434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:42:28,490] Trial 2 finished with value: 0.44138455912886587 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2144686157281775, 'dropout_second': 0.10485989851120243, 'lr': 0.0001420539990227173, 'weight_decay': 0.0001955645435230074, 'batch_size': 32}. Best is trial 1 with value: 0.47554701890151535.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.441385
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2144686157281775, 'dropout_second': 0.10485989851120243, 'lr': 0.0001420539990227173, 'weight_decay': 0.0001955645435230074, 'batch_size': 32}
+  Best Value (CSI): 0.475547
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.39939444068736674, 'dropout_second': 0.011774526054862245, 'lr': 0.0013407873458120595, 'weight_decay': 0.0006392156031611434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:44:00,443] Trial 3 finished with value: 0.45741670799414075 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.11617741942008643, 'dropout_second': 0.03157540490774646, 'lr': 0.002105624277671913, 'weight_decay': 0.008685446012691318, 'batch_size': 64}. Best is trial 1 with value: 0.47554701890151535.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.457417
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.11617741942008643, 'dropout_second': 0.03157540490774646, 'lr': 0.002105624277671913, 'weight_decay': 0.008685446012691318, 'batch_size': 64}
+  Best Value (CSI): 0.475547
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.39939444068736674, 'dropout_second': 0.011774526054862245, 'lr': 0.0013407873458120595, 'weight_decay': 0.0006392156031611434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:44:48,677] Trial 4 finished with value: 0.44325754497571873 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.30287788336622884, 'dropout_second': 0.01094578962914139, 'lr': 0.00032669670762066325, 'weight_decay': 0.0006277577546334518, 'batch_size': 256}. Best is trial 1 with value: 0.47554701890151535.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.443258
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.30287788336622884, 'dropout_second': 0.01094578962914139, 'lr': 0.00032669670762066325, 'weight_decay': 0.0006277577546334518, 'batch_size': 256}
+  Best Value (CSI): 0.475547
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.39939444068736674, 'dropout_second': 0.011774526054862245, 'lr': 0.0013407873458120595, 'weight_decay': 0.0006392156031611434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 12:44:56,441] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.415671
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.25104729808983084, 'dropout_second': 0.1644417686955974, 'lr': 0.0002325837361710971, 'weight_decay': 0.02426030440928542, 'batch_size': 128}
+  Best Value (CSI): 0.475547
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.39939444068736674, 'dropout_second': 0.011774526054862245, 'lr': 0.0013407873458120595, 'weight_decay': 0.0006392156031611434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 12:45:08,620] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.445532
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.21163638255562897, 'dropout_second': 0.19983230729777623, 'lr': 0.0001531430471865302, 'weight_decay': 0.08841463218314632, 'batch_size': 64}
+  Best Value (CSI): 0.475547
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.39939444068736674, 'dropout_second': 0.011774526054862245, 'lr': 0.0013407873458120595, 'weight_decay': 0.0006392156031611434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 12:45:16,808] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.408369
+  Parameters: {'d_main': 64, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.2956043499341538, 'dropout_second': 0.15928635256600499, 'lr': 0.0002734329691626549, 'weight_decay': 0.0026761412002030095, 'batch_size': 128}
+  Best Value (CSI): 0.475547
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.39939444068736674, 'dropout_second': 0.011774526054862245, 'lr': 0.0013407873458120595, 'weight_decay': 0.0006392156031611434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 12:45:35,019] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.262679
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.1839700322163606, 'dropout_second': 0.005469487709982169, 'lr': 4.100938685450976e-05, 'weight_decay': 0.02604124160569234, 'batch_size': 32}
+  Best Value (CSI): 0.475547
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.39939444068736674, 'dropout_second': 0.011774526054862245, 'lr': 0.0013407873458120595, 'weight_decay': 0.0006392156031611434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:47:37,214] Trial 9 finished with value: 0.4565275844254404 and parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.16301200660504395, 'dropout_second': 0.17659997737991281, 'lr': 0.000684069306804488, 'weight_decay': 0.022401513113451448, 'batch_size': 32}. Best is trial 1 with value: 0.47554701890151535.
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.456528
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.16301200660504395, 'dropout_second': 0.17659997737991281, 'lr': 0.000684069306804488, 'weight_decay': 0.022401513113451448, 'batch_size': 32}
+  Best Value (CSI): 0.475547
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.39939444068736674, 'dropout_second': 0.011774526054862245, 'lr': 0.0013407873458120595, 'weight_decay': 0.0006392156031611434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:48:06,958] Trial 10 finished with value: 0.47209652669158936 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3889712188587918, 'dropout_second': 0.052441820995019504, 'lr': 0.006895529821659723, 'weight_decay': 0.00013247629707886256, 'batch_size': 256}. Best is trial 1 with value: 0.47554701890151535.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.472097
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3889712188587918, 'dropout_second': 0.052441820995019504, 'lr': 0.006895529821659723, 'weight_decay': 0.00013247629707886256, 'batch_size': 256}
+  Best Value (CSI): 0.475547
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.39939444068736674, 'dropout_second': 0.011774526054862245, 'lr': 0.0013407873458120595, 'weight_decay': 0.0006392156031611434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:48:44,115] Trial 11 finished with value: 0.48897806727261894 and parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.488978
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:49:20,696] Trial 12 finished with value: 0.4779397198323713 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.39282030654417405, 'dropout_second': 0.05277554682398976, 'lr': 0.008104586041246652, 'weight_decay': 0.0004544597163928747, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.477940
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.39282030654417405, 'dropout_second': 0.05277554682398976, 'lr': 0.008104586041246652, 'weight_decay': 0.0004544597163928747, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:49:50,202] Trial 13 finished with value: 0.47904284514776546 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.3559068717492953, 'dropout_second': 0.05256702854550449, 'lr': 0.009481009305620736, 'weight_decay': 0.00010233245107552098, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.479043
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.3559068717492953, 'dropout_second': 0.05256702854550449, 'lr': 0.009481009305620736, 'weight_decay': 0.00010233245107552098, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:50:22,185] Trial 14 finished with value: 0.4649957087554517 and parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.34863202837133156, 'dropout_second': 0.07380653316814655, 'lr': 0.003953822842504932, 'weight_decay': 0.0001327302519222985, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.464996
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.34863202837133156, 'dropout_second': 0.07380653316814655, 'lr': 0.003953822842504932, 'weight_decay': 0.0001327302519222985, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:50:47,857] Trial 15 finished with value: 0.48404274349198545 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.3617274682083289, 'dropout_second': 0.07835447560219608, 'lr': 0.009330089881618975, 'weight_decay': 0.00031588329102706186, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.484043
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.3617274682083289, 'dropout_second': 0.07835447560219608, 'lr': 0.009330089881618975, 'weight_decay': 0.00031588329102706186, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:51:20,088] Trial 16 finished with value: 0.4687007780704187 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3543041355145342, 'dropout_second': 0.08506892154313718, 'lr': 0.0033852010564198523, 'weight_decay': 0.00034571922536029333, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.468701
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3543041355145342, 'dropout_second': 0.08506892154313718, 'lr': 0.0033852010564198523, 'weight_decay': 0.00034571922536029333, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 12:51:26,209] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.451985
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.28168236460568774, 'dropout_second': 0.11656024890143346, 'lr': 0.00406942089339321, 'weight_decay': 0.0011766878108548271, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:51:51,745] Trial 18 finished with value: 0.470187186822804 and parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.32600659029460394, 'dropout_second': 0.1262817607231636, 'lr': 0.008964860125709599, 'weight_decay': 0.00026313615797898175, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.470187
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.32600659029460394, 'dropout_second': 0.1262817607231636, 'lr': 0.008964860125709599, 'weight_decay': 0.00026313615797898175, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:52:54,263] Trial 19 finished with value: 0.46625481677037345 and parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.26983430800019464, 'dropout_second': 0.08160908295500749, 'lr': 0.0020724268273662376, 'weight_decay': 0.00023080573261783594, 'batch_size': 64}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.466255
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.26983430800019464, 'dropout_second': 0.08160908295500749, 'lr': 0.0020724268273662376, 'weight_decay': 0.00023080573261783594, 'batch_size': 64}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:53:30,102] Trial 20 finished with value: 0.4698985087653655 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3252283849340092, 'dropout_second': 0.04205552671404267, 'lr': 0.004835154368266673, 'weight_decay': 0.0011009481094552051, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.469899
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3252283849340092, 'dropout_second': 0.04205552671404267, 'lr': 0.004835154368266673, 'weight_decay': 0.0011009481094552051, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:53:50,713] Trial 21 finished with value: 0.45733798604181614 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.36367073861945826, 'dropout_second': 0.06458626755095526, 'lr': 0.009751671523775018, 'weight_decay': 0.00010694505090223855, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.457338
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.36367073861945826, 'dropout_second': 0.06458626755095526, 'lr': 0.009751671523775018, 'weight_decay': 0.00010694505090223855, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:54:13,198] Trial 22 finished with value: 0.4772741892197458 and parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.36492928090656335, 'dropout_second': 0.03284091933828292, 'lr': 0.009953983605496014, 'weight_decay': 0.00010263220377486303, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.477274
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.36492928090656335, 'dropout_second': 0.03284091933828292, 'lr': 0.009953983605496014, 'weight_decay': 0.00010263220377486303, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:54:38,002] Trial 23 finished with value: 0.4756356842768039 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.36568420322484374, 'dropout_second': 0.06255063606323341, 'lr': 0.005028120828482917, 'weight_decay': 0.00021577021598687994, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.475636
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.36568420322484374, 'dropout_second': 0.06255063606323341, 'lr': 0.005028120828482917, 'weight_decay': 0.00021577021598687994, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:55:04,843] Trial 24 finished with value: 0.4781396852289192 and parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.37892087149027615, 'dropout_second': 0.08674478962409181, 'lr': 0.0031259599491846256, 'weight_decay': 0.00038080222677246915, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.478140
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.37892087149027615, 'dropout_second': 0.08674478962409181, 'lr': 0.0031259599491846256, 'weight_decay': 0.00038080222677246915, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:55:36,492] Trial 25 finished with value: 0.46110878185835297 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3971865818632332, 'dropout_second': 0.023446037083554486, 'lr': 0.005438297820589799, 'weight_decay': 0.0001564861797453144, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.461109
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3971865818632332, 'dropout_second': 0.023446037083554486, 'lr': 0.005438297820589799, 'weight_decay': 0.0001564861797453144, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 12:55:43,133] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.452446
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3413680470758985, 'dropout_second': 0.04639987809842338, 'lr': 0.002364899799514675, 'weight_decay': 0.000231776487622433, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:56:34,688] Trial 27 finished with value: 0.48213763709688484 and parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3732609257469741, 'dropout_second': 0.06555920930000027, 'lr': 0.005393569104858457, 'weight_decay': 0.00010859804965510137, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.482138
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3732609257469741, 'dropout_second': 0.06555920930000027, 'lr': 0.005393569104858457, 'weight_decay': 0.00010859804965510137, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:57:18,892] Trial 28 finished with value: 0.46165298003293936 and parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.3786220715472516, 'dropout_second': 0.06781382987292689, 'lr': 0.005695889886953736, 'weight_decay': 0.0003326056767399221, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.461653
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.3786220715472516, 'dropout_second': 0.06781382987292689, 'lr': 0.005695889886953736, 'weight_decay': 0.0003326056767399221, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 12:57:28,082] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.430152
+  Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3321534140640021, 'dropout_second': 0.0972674272864395, 'lr': 0.001158522093197676, 'weight_decay': 0.00015883840216152336, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:58:04,558] Trial 30 finished with value: 0.4693292379572445 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.31619631006000787, 'dropout_second': 0.07106579691000477, 'lr': 0.002894750135886721, 'weight_decay': 0.0005675162512951304, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.469329
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.31619631006000787, 'dropout_second': 0.07106579691000477, 'lr': 0.002894750135886721, 'weight_decay': 0.0005675162512951304, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:58:50,635] Trial 31 finished with value: 0.47891875449415844 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3553019180532253, 'dropout_second': 0.05638677077529178, 'lr': 0.006660112898963236, 'weight_decay': 0.00011131067781498786, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.478919
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3553019180532253, 'dropout_second': 0.05638677077529178, 'lr': 0.006660112898963236, 'weight_decay': 0.00011131067781498786, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 12:59:41,835] Trial 32 finished with value: 0.4650540019772395 and parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.34192674489747943, 'dropout_second': 0.040413296408583266, 'lr': 0.006552267900895644, 'weight_decay': 0.00017378981532849093, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.465054
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.34192674489747943, 'dropout_second': 0.040413296408583266, 'lr': 0.006552267900895644, 'weight_decay': 0.00017378981532849093, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:00:42,885] Trial 33 finished with value: 0.4746371492948282 and parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.3730305161694133, 'dropout_second': 0.023818323181427417, 'lr': 0.009344733504553818, 'weight_decay': 0.00010138574853037129, 'batch_size': 64}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.474637
+  Parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.3730305161694133, 'dropout_second': 0.023818323181427417, 'lr': 0.009344733504553818, 'weight_decay': 0.00010138574853037129, 'batch_size': 64}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:02:25,593] Trial 34 finished with value: 0.4676240382857557 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3965795929933733, 'dropout_second': 0.055983352713613016, 'lr': 0.0016393800123355499, 'weight_decay': 0.00027392629672837345, 'batch_size': 32}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.467624
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3965795929933733, 'dropout_second': 0.055983352713613016, 'lr': 0.0016393800123355499, 'weight_decay': 0.00027392629672837345, 'batch_size': 32}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:02:53,777] Trial 35 finished with value: 0.47461016058220024 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.3799938602269157, 'dropout_second': 0.04340348225527961, 'lr': 0.003959844798674193, 'weight_decay': 0.00016912332908883762, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.474610
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.3799938602269157, 'dropout_second': 0.04340348225527961, 'lr': 0.003959844798674193, 'weight_decay': 0.00016912332908883762, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:03:43,354] Trial 36 finished with value: 0.45918828235895665 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.31143749715707275, 'dropout_second': 0.01800504077313715, 'lr': 0.001119789353474089, 'weight_decay': 0.0001966148638423486, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.459188
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.31143749715707275, 'dropout_second': 0.01800504077313715, 'lr': 0.001119789353474089, 'weight_decay': 0.0001966148638423486, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+[I 2025-12-28 13:04:19,938] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.461283
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3998231158323985, 'dropout_second': 0.03441990714709686, 'lr': 0.002738266551216714, 'weight_decay': 0.00047809593454671096, 'batch_size': 64}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:06:36,322] Trial 38 finished with value: 0.46772331909067894 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.34036385660835905, 'dropout_second': 0.004450286901761136, 'lr': 0.006369284154862937, 'weight_decay': 0.0007949588774452658, 'batch_size': 32}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.467723
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.34036385660835905, 'dropout_second': 0.004450286901761136, 'lr': 0.006369284154862937, 'weight_decay': 0.0007949588774452658, 'batch_size': 32}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:07:08,391] Trial 39 finished with value: 0.47735776614304437 and parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.35779097695881357, 'dropout_second': 0.09398474674442545, 'lr': 0.00458033053391424, 'weight_decay': 0.00025601314823952355, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.477358
+  Parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.35779097695881357, 'dropout_second': 0.09398474674442545, 'lr': 0.00458033053391424, 'weight_decay': 0.00025601314823952355, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:07:44,500] Trial 40 finished with value: 0.43982405180769946 and parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.30124444117094307, 'dropout_second': 0.08090113088230479, 'lr': 0.009896330391993708, 'weight_decay': 0.0001534568752954874, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.439824
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.30124444117094307, 'dropout_second': 0.08090113088230479, 'lr': 0.009896330391993708, 'weight_decay': 0.0001534568752954874, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:08:28,216] Trial 41 finished with value: 0.47702750861851145 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3560424601635095, 'dropout_second': 0.05783591972074225, 'lr': 0.006297032919191331, 'weight_decay': 0.00011197373213137517, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.477028
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3560424601635095, 'dropout_second': 0.05783591972074225, 'lr': 0.006297032919191331, 'weight_decay': 0.00011197373213137517, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:09:06,380] Trial 42 finished with value: 0.47558126563787245 and parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.38045542804451915, 'dropout_second': 0.05988200244247882, 'lr': 0.006490539264779014, 'weight_decay': 0.00010205696123605501, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.475581
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.38045542804451915, 'dropout_second': 0.05988200244247882, 'lr': 0.006490539264779014, 'weight_decay': 0.00010205696123605501, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:09:41,934] Trial 43 finished with value: 0.46969032193773347 and parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.34891198875208496, 'dropout_second': 0.07413653123422684, 'lr': 0.007343055487479717, 'weight_decay': 0.00015632312906643613, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.469690
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.34891198875208496, 'dropout_second': 0.07413653123422684, 'lr': 0.007343055487479717, 'weight_decay': 0.00015632312906643613, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:10:16,112] Trial 44 finished with value: 0.4704100690839877 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.37386518791464973, 'dropout_second': 0.048023815008248046, 'lr': 0.0034515617804377016, 'weight_decay': 0.00013029117890764796, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.470410
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.37386518791464973, 'dropout_second': 0.048023815008248046, 'lr': 0.0034515617804377016, 'weight_decay': 0.00013029117890764796, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:10:41,053] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.424198
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3309181119818605, 'dropout_second': 0.05372229949595641, 'lr': 0.004724069847329303, 'weight_decay': 0.0003483179219944935, 'batch_size': 32}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:11:12,854] Trial 46 finished with value: 0.4574084365516337 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3890601495116524, 'dropout_second': 0.036185716185668376, 'lr': 0.0018733856662709566, 'weight_decay': 0.00019454561187039424, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.457408
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3890601495116524, 'dropout_second': 0.036185716185668376, 'lr': 0.0018733856662709566, 'weight_decay': 0.00019454561187039424, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:11:49,577] Trial 47 finished with value: 0.4704385339850797 and parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3655136055927436, 'dropout_second': 0.06669060533383227, 'lr': 0.007451792594298789, 'weight_decay': 0.000308141812498485, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.470439
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3655136055927436, 'dropout_second': 0.06669060533383227, 'lr': 0.007451792594298789, 'weight_decay': 0.000308141812498485, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:12:47,877] Trial 48 finished with value: 0.4728470437447105 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.35485844667866495, 'dropout_second': 0.049405258788729756, 'lr': 0.0025421669606738883, 'weight_decay': 0.00012772082086075242, 'batch_size': 64}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.472847
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.35485844667866495, 'dropout_second': 0.049405258788729756, 'lr': 0.0025421669606738883, 'weight_decay': 0.00012772082086075242, 'batch_size': 64}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:13:22,156] Trial 49 finished with value: 0.4669316407094297 and parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3185378593240947, 'dropout_second': 0.07690220060058889, 'lr': 0.0035604751153008444, 'weight_decay': 0.0002147620967260985, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.466932
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3185378593240947, 'dropout_second': 0.07690220060058889, 'lr': 0.0035604751153008444, 'weight_decay': 0.0002147620967260985, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:13:49,099] Trial 50 finished with value: 0.48484937884896057 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3864101825372547, 'dropout_second': 0.02713771698002103, 'lr': 0.007890124972050203, 'weight_decay': 0.00047207688930424644, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.484849
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3864101825372547, 'dropout_second': 0.02713771698002103, 'lr': 0.007890124972050203, 'weight_decay': 0.00047207688930424644, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:14:20,216] Trial 51 finished with value: 0.46840105600101606 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.38718801598838865, 'dropout_second': 0.027786539572093122, 'lr': 0.007877892489572316, 'weight_decay': 0.0004327903378815623, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.468401
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.38718801598838865, 'dropout_second': 0.027786539572093122, 'lr': 0.007877892489572316, 'weight_decay': 0.0004327903378815623, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:14:43,580] Trial 52 finished with value: 0.4730334277706561 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.37249739196066545, 'dropout_second': 0.012395648717412444, 'lr': 0.005443932500390804, 'weight_decay': 0.00012751255000404516, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.473033
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.37249739196066545, 'dropout_second': 0.012395648717412444, 'lr': 0.005443932500390804, 'weight_decay': 0.00012751255000404516, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:15:08,887] Trial 53 finished with value: 0.4723094377163252 and parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.34894138403178027, 'dropout_second': 0.041064981351191396, 'lr': 0.007704238240581142, 'weight_decay': 0.00029766011183873336, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.472309
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.34894138403178027, 'dropout_second': 0.041064981351191396, 'lr': 0.007704238240581142, 'weight_decay': 0.00029766011183873336, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:15:14,687] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.432773
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3895150488271053, 'dropout_second': 0.05720428111366467, 'lr': 0.004040662542039585, 'weight_decay': 0.0001945566356083365, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:15:19,097] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.427338
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.365739920000039, 'dropout_second': 0.029874659864396073, 'lr': 0.00781110422047825, 'weight_decay': 0.00010061823765946468, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:15:49,596] Trial 56 finished with value: 0.47182766389266284 and parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.398361168165777, 'dropout_second': 0.06686254636567136, 'lr': 0.005030615916983558, 'weight_decay': 0.00013877797629676013, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.471828
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.398361168165777, 'dropout_second': 0.06686254636567136, 'lr': 0.005030615916983558, 'weight_decay': 0.00013877797629676013, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:15:55,674] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.450340
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.38410002521768505, 'dropout_second': 0.05009282940119406, 'lr': 0.009312174508754913, 'weight_decay': 0.0002635772357910126, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:16:42,982] Trial 58 finished with value: 0.47280352473568127 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.3366483160232008, 'dropout_second': 0.06298958756785102, 'lr': 0.009997229735874373, 'weight_decay': 0.0004026777453591807, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.472804
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.3366483160232008, 'dropout_second': 0.06298958756785102, 'lr': 0.009997229735874373, 'weight_decay': 0.0004026777453591807, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:17:09,514] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.361446
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.35906694620744023, 'dropout_second': 0.03694531503052429, 'lr': 0.005517086779741481, 'weight_decay': 0.0007618482405082665, 'batch_size': 32}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:17:54,036] Trial 60 finished with value: 0.47562992702139306 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.34860669835492814, 'dropout_second': 0.07653702134309241, 'lr': 0.0033552941568929266, 'weight_decay': 0.00021978679694194478, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.475630
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.34860669835492814, 'dropout_second': 0.07653702134309241, 'lr': 0.0033552941568929266, 'weight_decay': 0.00021978679694194478, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:18:24,659] Trial 61 finished with value: 0.47496462095617575 and parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.3757255785430811, 'dropout_second': 0.08556086323794306, 'lr': 0.006273332142496085, 'weight_decay': 0.00037979294492322804, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.474965
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.3757255785430811, 'dropout_second': 0.08556086323794306, 'lr': 0.006273332142496085, 'weight_decay': 0.00037979294492322804, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:18:53,832] Trial 62 finished with value: 0.47103023690152596 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.3847231826553181, 'dropout_second': 0.07131692562661286, 'lr': 0.004343837421951514, 'weight_decay': 0.0005197123249537659, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.471030
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.3847231826553181, 'dropout_second': 0.07131692562661286, 'lr': 0.004343837421951514, 'weight_decay': 0.0005197123249537659, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:19:23,493] Trial 63 finished with value: 0.47174390641747754 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3711201255976197, 'dropout_second': 0.08987709406862213, 'lr': 0.002905359979235921, 'weight_decay': 0.0001548656057460447, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.471744
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3711201255976197, 'dropout_second': 0.08987709406862213, 'lr': 0.002905359979235921, 'weight_decay': 0.0001548656057460447, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:19:47,969] Trial 64 finished with value: 0.47567608745410106 and parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.3900479464628991, 'dropout_second': 0.10584764801205512, 'lr': 0.00745014621983708, 'weight_decay': 0.00018148112682772223, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.475676
+  Parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.3900479464628991, 'dropout_second': 0.10584764801205512, 'lr': 0.00745014621983708, 'weight_decay': 0.00018148112682772223, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:21:13,639] Trial 65 finished with value: 0.46910771490658587 and parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.3998745121344208, 'dropout_second': 0.06215746030654685, 'lr': 0.005440665789090981, 'weight_decay': 0.0003089944247935613, 'batch_size': 64}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.469108
+  Parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.3998745121344208, 'dropout_second': 0.06215746030654685, 'lr': 0.005440665789090981, 'weight_decay': 0.0003089944247935613, 'batch_size': 64}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:21:42,535] Trial 66 finished with value: 0.4782133704098556 and parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.36395566113898364, 'dropout_second': 0.04590000661713958, 'lr': 0.008234661629541125, 'weight_decay': 0.0001187841612437313, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.478213
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.36395566113898364, 'dropout_second': 0.04590000661713958, 'lr': 0.008234661629541125, 'weight_decay': 0.0001187841612437313, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:22:10,261] Trial 67 finished with value: 0.469578328427771 and parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.36451433628410257, 'dropout_second': 0.04609905267034453, 'lr': 0.007934294539866084, 'weight_decay': 0.00013533538149834758, 'batch_size': 256}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.469578
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.36451433628410257, 'dropout_second': 0.04609905267034453, 'lr': 0.007934294539866084, 'weight_decay': 0.00013533538149834758, 'batch_size': 256}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:22:47,334] Trial 68 finished with value: 0.47872058413059887 and parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3510724912016995, 'dropout_second': 0.040982371739200794, 'lr': 0.008393034260356485, 'weight_decay': 0.0001215510702703694, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.478721
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3510724912016995, 'dropout_second': 0.040982371739200794, 'lr': 0.008393034260356485, 'weight_decay': 0.0001215510702703694, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:23:24,051] Trial 69 finished with value: 0.4378190830308098 and parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.32842744332551077, 'dropout_second': 0.05353741767105186, 'lr': 0.00984273843331358, 'weight_decay': 0.00025167105308483923, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.437819
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.32842744332551077, 'dropout_second': 0.05353741767105186, 'lr': 0.00984273843331358, 'weight_decay': 0.00025167105308483923, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:24:12,400] Trial 70 finished with value: 0.4720108376859364 and parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3409129891274837, 'dropout_second': 0.03884237174733469, 'lr': 0.004276521422632242, 'weight_decay': 0.00012195939815143145, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.472011
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3409129891274837, 'dropout_second': 0.03884237174733469, 'lr': 0.004276521422632242, 'weight_decay': 0.00012195939815143145, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:24:41,735] Trial 71 finished with value: 0.47788931356483516 and parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.35871302408501876, 'dropout_second': 0.044716370360847114, 'lr': 0.006451191221248614, 'weight_decay': 0.00011889131214845788, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.477889
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.35871302408501876, 'dropout_second': 0.044716370360847114, 'lr': 0.006451191221248614, 'weight_decay': 0.00011889131214845788, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:25:19,467] Trial 72 finished with value: 0.4824113330673525 and parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.36998626844641175, 'dropout_second': 0.029822395924738104, 'lr': 0.008205570192017007, 'weight_decay': 0.0001590792533994896, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.482411
+  Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.36998626844641175, 'dropout_second': 0.029822395924738104, 'lr': 0.008205570192017007, 'weight_decay': 0.0001590792533994896, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:25:54,273] Trial 73 finished with value: 0.47268567341661166 and parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.37217423336041117, 'dropout_second': 0.028467150759676162, 'lr': 0.005824608516155443, 'weight_decay': 0.0001669642049150376, 'batch_size': 128}. Best is trial 11 with value: 0.48897806727261894.
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.472686
+  Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.37217423336041117, 'dropout_second': 0.028467150759676162, 'lr': 0.005824608516155443, 'weight_decay': 0.0001669642049150376, 'batch_size': 128}
+  Best Value (CSI): 0.488978
+  Best Trial: 11
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3964805396457519, 'dropout_second': 0.04338626283497668, 'lr': 0.009555721447115261, 'weight_decay': 0.00012022274222709934, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:26:39,080] Trial 74 finished with value: 0.4928843318546061 and parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3812651418544208, 'dropout_second': 0.01853217850830045, 'lr': 0.008371352554225246, 'weight_decay': 0.00010054403898045303, 'batch_size': 128}. Best is trial 74 with value: 0.4928843318546061.
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.492884
+  Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3812651418544208, 'dropout_second': 0.01853217850830045, 'lr': 0.008371352554225246, 'weight_decay': 0.00010054403898045303, 'batch_size': 128}
+  Best Value (CSI): 0.492884
+  Best Trial: 74
+  Best Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3812651418544208, 'dropout_second': 0.01853217850830045, 'lr': 0.008371352554225246, 'weight_decay': 0.00010054403898045303, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:27:19,570] Trial 75 finished with value: 0.47667740387038315 and parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3801368423035239, 'dropout_second': 0.018577684050570017, 'lr': 0.004695801812192229, 'weight_decay': 0.00018300118688190228, 'batch_size': 128}. Best is trial 74 with value: 0.4928843318546061.
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.476677
+  Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3801368423035239, 'dropout_second': 0.018577684050570017, 'lr': 0.004695801812192229, 'weight_decay': 0.00018300118688190228, 'batch_size': 128}
+  Best Value (CSI): 0.492884
+  Best Trial: 74
+  Best Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3812651418544208, 'dropout_second': 0.01853217850830045, 'lr': 0.008371352554225246, 'weight_decay': 0.00010054403898045303, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:27:54,745] Trial 76 finished with value: 0.4467072075154231 and parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3880659135883525, 'dropout_second': 0.009954624741619851, 'lr': 0.006699243015919014, 'weight_decay': 0.00010146921113755008, 'batch_size': 128}. Best is trial 74 with value: 0.4928843318546061.
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.446707
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3880659135883525, 'dropout_second': 0.009954624741619851, 'lr': 0.006699243015919014, 'weight_decay': 0.00010146921113755008, 'batch_size': 128}
+  Best Value (CSI): 0.492884
+  Best Trial: 74
+  Best Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3812651418544208, 'dropout_second': 0.01853217850830045, 'lr': 0.008371352554225246, 'weight_decay': 0.00010054403898045303, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:28:32,525] Trial 77 finished with value: 0.4722048805537014 and parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.3834201904888761, 'dropout_second': 0.022527166324155933, 'lr': 0.003598146731910028, 'weight_decay': 0.00015146428046095944, 'batch_size': 128}. Best is trial 74 with value: 0.4928843318546061.
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.472205
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.3834201904888761, 'dropout_second': 0.022527166324155933, 'lr': 0.003598146731910028, 'weight_decay': 0.00015146428046095944, 'batch_size': 128}
+  Best Value (CSI): 0.492884
+  Best Trial: 74
+  Best Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3812651418544208, 'dropout_second': 0.01853217850830045, 'lr': 0.008371352554225246, 'weight_decay': 0.00010054403898045303, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:29:15,138] Trial 78 finished with value: 0.46488806013798273 and parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3931997792363941, 'dropout_second': 0.057750996324229205, 'lr': 0.009888414273351265, 'weight_decay': 0.00023054915105001346, 'batch_size': 128}. Best is trial 74 with value: 0.4928843318546061.
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.464888
+  Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3931997792363941, 'dropout_second': 0.057750996324229205, 'lr': 0.009888414273351265, 'weight_decay': 0.00023054915105001346, 'batch_size': 128}
+  Best Value (CSI): 0.492884
+  Best Trial: 74
+  Best Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3812651418544208, 'dropout_second': 0.01853217850830045, 'lr': 0.008371352554225246, 'weight_decay': 0.00010054403898045303, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:30:03,360] Trial 79 finished with value: 0.4768026738597479 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.3712440806523047, 'dropout_second': 0.03444969201043473, 'lr': 0.006639676247360521, 'weight_decay': 0.00019968107154492483, 'batch_size': 128}. Best is trial 74 with value: 0.4928843318546061.
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.476803
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.3712440806523047, 'dropout_second': 0.03444969201043473, 'lr': 0.006639676247360521, 'weight_decay': 0.00019968107154492483, 'batch_size': 128}
+  Best Value (CSI): 0.492884
+  Best Trial: 74
+  Best Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3812651418544208, 'dropout_second': 0.01853217850830045, 'lr': 0.008371352554225246, 'weight_decay': 0.00010054403898045303, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:32:09,605] Trial 80 finished with value: 0.47646666202637117 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.34569841662411427, 'dropout_second': 0.05233027888348091, 'lr': 0.005346765162220376, 'weight_decay': 0.00014354311534853933, 'batch_size': 32}. Best is trial 74 with value: 0.4928843318546061.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.476467
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.34569841662411427, 'dropout_second': 0.05233027888348091, 'lr': 0.005346765162220376, 'weight_decay': 0.00014354311534853933, 'batch_size': 32}
+  Best Value (CSI): 0.492884
+  Best Trial: 74
+  Best Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3812651418544208, 'dropout_second': 0.01853217850830045, 'lr': 0.008371352554225246, 'weight_decay': 0.00010054403898045303, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:32:50,446] Trial 81 finished with value: 0.46898760082970264 and parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3525510488333868, 'dropout_second': 0.032475936407071526, 'lr': 0.0078085284724838, 'weight_decay': 0.00011428186964192401, 'batch_size': 128}. Best is trial 74 with value: 0.4928843318546061.
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.468988
+  Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3525510488333868, 'dropout_second': 0.032475936407071526, 'lr': 0.0078085284724838, 'weight_decay': 0.00011428186964192401, 'batch_size': 128}
+  Best Value (CSI): 0.492884
+  Best Trial: 74
+  Best Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3812651418544208, 'dropout_second': 0.01853217850830045, 'lr': 0.008371352554225246, 'weight_decay': 0.00010054403898045303, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:33:25,062] Trial 82 finished with value: 0.484311547089058 and parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.35233944063655875, 'dropout_second': 0.028195259862292738, 'lr': 0.008576605538174756, 'weight_decay': 0.00010156969279491822, 'batch_size': 128}. Best is trial 74 with value: 0.4928843318546061.
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.484312
+  Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.35233944063655875, 'dropout_second': 0.028195259862292738, 'lr': 0.008576605538174756, 'weight_decay': 0.00010156969279491822, 'batch_size': 128}
+  Best Value (CSI): 0.492884
+  Best Trial: 74
+  Best Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3812651418544208, 'dropout_second': 0.01853217850830045, 'lr': 0.008371352554225246, 'weight_decay': 0.00010054403898045303, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:34:07,584] Trial 83 finished with value: 0.49363300915248276 and parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.493633
+  Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:34:50,293] Trial 84 finished with value: 0.471647200509314 and parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3771694443790554, 'dropout_second': 0.02504229813703642, 'lr': 0.008420656453415283, 'weight_decay': 0.00016209692752110184, 'batch_size': 128}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.471647
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3771694443790554, 'dropout_second': 0.02504229813703642, 'lr': 0.008420656453415283, 'weight_decay': 0.00016209692752110184, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:35:17,745] Trial 85 finished with value: 0.4575974040518476 and parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3906064679276962, 'dropout_second': 0.018070583090029665, 'lr': 0.008648150827906879, 'weight_decay': 0.00024332185861955226, 'batch_size': 128}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.457597
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3906064679276962, 'dropout_second': 0.018070583090029665, 'lr': 0.008648150827906879, 'weight_decay': 0.00024332185861955226, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:36:37,130] Trial 86 finished with value: 0.47655440752968126 and parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.36731788313759134, 'dropout_second': 0.030734153121400702, 'lr': 0.004812919263380794, 'weight_decay': 0.0001794939492568784, 'batch_size': 64}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.476554
+  Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.36731788313759134, 'dropout_second': 0.030734153121400702, 'lr': 0.004812919263380794, 'weight_decay': 0.0001794939492568784, 'batch_size': 64}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:37:29,094] Trial 87 finished with value: 0.4856579772632555 and parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.38005581284434053, 'dropout_second': 0.0022831070963547744, 'lr': 0.005916551696843995, 'weight_decay': 0.0001455535294356836, 'batch_size': 128}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.485658
+  Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.38005581284434053, 'dropout_second': 0.0022831070963547744, 'lr': 0.005916551696843995, 'weight_decay': 0.0001455535294356836, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:38:08,950] Trial 88 finished with value: 0.47985442073163337 and parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.38017480002792475, 'dropout_second': 0.0012013584560443338, 'lr': 0.005696202361249735, 'weight_decay': 0.00028173315558039307, 'batch_size': 128}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.479854
+  Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.38017480002792475, 'dropout_second': 0.0012013584560443338, 'lr': 0.005696202361249735, 'weight_decay': 0.00028173315558039307, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:38:55,003] Trial 89 finished with value: 0.4804541403664612 and parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.39464603563878037, 'dropout_second': 0.012710517734852104, 'lr': 0.003963057157316482, 'weight_decay': 0.0001382141497177989, 'batch_size': 128}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.480454
+  Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.39464603563878037, 'dropout_second': 0.012710517734852104, 'lr': 0.003963057157316482, 'weight_decay': 0.0001382141497177989, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:39:04,277] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.424588
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.36003885425785637, 'dropout_second': 0.007873222796529238, 'lr': 0.0069411446589980365, 'weight_decay': 0.00020543482921293218, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:39:41,807] Trial 91 finished with value: 0.4718544389908644 and parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.39240973704584614, 'dropout_second': 0.015971225841246973, 'lr': 0.004143173436062753, 'weight_decay': 0.0001431180694058482, 'batch_size': 128}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.471854
+  Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.39240973704584614, 'dropout_second': 0.015971225841246973, 'lr': 0.004143173436062753, 'weight_decay': 0.0001431180694058482, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:40:21,509] Trial 92 finished with value: 0.46989031877603127 and parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3990293093003577, 'dropout_second': 0.012720121373018936, 'lr': 0.00596698248278344, 'weight_decay': 0.00010018037805303039, 'batch_size': 128}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.469890
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3990293093003577, 'dropout_second': 0.012720121373018936, 'lr': 0.00596698248278344, 'weight_decay': 0.00010018037805303039, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:40:51,860] Trial 93 finished with value: 0.4709278533252264 and parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.38438892212180165, 'dropout_second': 0.005665905910673519, 'lr': 0.008605786239792317, 'weight_decay': 0.0001354389024890776, 'batch_size': 128}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.470928
+  Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.38438892212180165, 'dropout_second': 0.005665905910673519, 'lr': 0.008605786239792317, 'weight_decay': 0.0001354389024890776, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:41:39,201] Trial 94 finished with value: 0.4819297449311883 and parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3766840020172695, 'dropout_second': 0.0233480739307389, 'lr': 0.0072795871629069454, 'weight_decay': 0.00017042451132574324, 'batch_size': 128}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.481930
+  Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3766840020172695, 'dropout_second': 0.0233480739307389, 'lr': 0.0072795871629069454, 'weight_decay': 0.00017042451132574324, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:42:14,664] Trial 95 finished with value: 0.4675851896722307 and parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3760628752682178, 'dropout_second': 0.022567149063539862, 'lr': 0.007683350983542716, 'weight_decay': 0.00017713164125068222, 'batch_size': 128}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.467585
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3760628752682178, 'dropout_second': 0.022567149063539862, 'lr': 0.007683350983542716, 'weight_decay': 0.00017713164125068222, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:43:03,746] Trial 96 finished with value: 0.4742564238994647 and parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3678723342758362, 'dropout_second': 0.028368212477737274, 'lr': 0.007060573545363519, 'weight_decay': 0.0002087399174268203, 'batch_size': 128}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.474256
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3678723342758362, 'dropout_second': 0.028368212477737274, 'lr': 0.007060573545363519, 'weight_decay': 0.0002087399174268203, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+[I 2025-12-28 13:43:12,035] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.409742
+  Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3607395180121176, 'dropout_second': 0.01852262617838953, 'lr': 0.009826736824468274, 'weight_decay': 0.00034376629900222187, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:43:46,401] Trial 98 finished with value: 0.4720999084953399 and parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3768058353192507, 'dropout_second': 0.024713227915373122, 'lr': 0.00604716901040986, 'weight_decay': 0.0002586500895477237, 'batch_size': 128}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.472100
+  Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3768058353192507, 'dropout_second': 0.024713227915373122, 'lr': 0.00604716901040986, 'weight_decay': 0.0002586500895477237, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.244073079346132, 1: 1.1395596051632497, 2: 0.758347481607244} (클래스별 샘플 수: {0: 10053, 1: 10975, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 1.2421505376344086, 1: 1.1364571982080156, 2: 0.7604464159121752} (클래스별 샘플 수: {0: 10075, 1: 11012, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 1.244002650762094, 1: 1.1330617172174438, 2: 0.7612790721252306} (클래스별 샘플 수: {0: 10060, 1: 11045, 2: 16439})
+[I 2025-12-28 13:44:31,062] Trial 99 finished with value: 0.4719346662109231 and parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 5, 'dropout_first': 0.38419988196920496, 'dropout_second': 0.0005462859666002726, 'lr': 0.004871982816344849, 'weight_decay': 0.0001624430718262584, 'batch_size': 128}. Best is trial 83 with value: 0.49363300915248276.
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.471935
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 5, 'dropout_first': 0.38419988196920496, 'dropout_second': 0.0005462859666002726, 'lr': 0.004871982816344849, 'weight_decay': 0.0001624430718262584, 'batch_size': 128}
+  Best Value (CSI): 0.493633
+  Best Trial: 83
+  Best Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4936
+Best Hyperparameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4600
+  - 최종 CSI: 0.4719
+  - 최고 CSI: 0.4936
+  - 최저 CSI: 0.2627
+  - 평균 CSI: 0.4630
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/resnet_like_ctgan10000_busan_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 36, Best CSI: 0.4918
+  Fold 1 학습 완료 (검증 CSI: 0.4918)
+Fold 2 학습 중...
+  Early stopping at epoch 26, Best CSI: 0.4921
+  Fold 2 학습 완료 (검증 CSI: 0.4921)
+Fold 3 학습 중...
+  Early stopping at epoch 29, Best CSI: 0.4378
+  Fold 3 학습 완료 (검증 CSI: 0.4378)
+
+모든 모델 저장 완료: ../save_model/resnet_like_optima/resnet_like_ctgan10000_busan.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/resnet_like_optima/scaler/resnet_like_ctgan10000_busan_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/resnet_like_optima/resnet_like_ctgan10000_busan.pkl
+
+✓ 완료: resnet_like_ctgan10000/resnet_like_ctgan10000_busan.py (소요 시간: 4147초)
+
+----------------------------------------
+실행 중: resnet_like_ctgan10000/resnet_like_ctgan10000_daegu.py
+시작 시간: 2025-12-28 13:45:19
+----------------------------------------
+[I 2025-12-28 13:45:21,093] A new study created in memory with name: no-name-538c8c34-08e2-449f-a55d-f2f7fcdf0174
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 13:46:16,064] Trial 0 finished with value: 0.3370227832046575 and parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.21109282413446437, 'dropout_second': 0.14617367049899407, 'lr': 0.00010159178137387685, 'weight_decay': 0.004838043988336698, 'batch_size': 128}. Best is trial 0 with value: 0.3370227832046575.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.337023
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.21109282413446437, 'dropout_second': 0.14617367049899407, 'lr': 0.00010159178137387685, 'weight_decay': 0.004838043988336698, 'batch_size': 128}
+  Best Value (CSI): 0.337023
+  Best Trial: 0
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.21109282413446437, 'dropout_second': 0.14617367049899407, 'lr': 0.00010159178137387685, 'weight_decay': 0.004838043988336698, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 13:47:51,443] Trial 1 finished with value: 0.3432752972832341 and parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.20066121464453457, 'dropout_second': 0.10659348943107477, 'lr': 0.00016273413508459332, 'weight_decay': 0.0005598821415249937, 'batch_size': 32}. Best is trial 1 with value: 0.3432752972832341.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.343275
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.20066121464453457, 'dropout_second': 0.10659348943107477, 'lr': 0.00016273413508459332, 'weight_decay': 0.0005598821415249937, 'batch_size': 32}
+  Best Value (CSI): 0.343275
+  Best Trial: 1
+  Best Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.20066121464453457, 'dropout_second': 0.10659348943107477, 'lr': 0.00016273413508459332, 'weight_decay': 0.0005598821415249937, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 13:50:38,166] Trial 2 finished with value: 0.4295794064438582 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29571181083296716, 'dropout_second': 0.09995421041271604, 'lr': 0.003152141177826013, 'weight_decay': 0.03163286570979001, 'batch_size': 32}. Best is trial 2 with value: 0.4295794064438582.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.429579
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29571181083296716, 'dropout_second': 0.09995421041271604, 'lr': 0.003152141177826013, 'weight_decay': 0.03163286570979001, 'batch_size': 32}
+  Best Value (CSI): 0.429579
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29571181083296716, 'dropout_second': 0.09995421041271604, 'lr': 0.003152141177826013, 'weight_decay': 0.03163286570979001, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 13:52:41,504] Trial 3 finished with value: 0.40888661891586037 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.3147119841735042, 'dropout_second': 0.18920306258715727, 'lr': 0.0020030096630479525, 'weight_decay': 0.0003059057261279675, 'batch_size': 32}. Best is trial 2 with value: 0.4295794064438582.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.408887
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.3147119841735042, 'dropout_second': 0.18920306258715727, 'lr': 0.0020030096630479525, 'weight_decay': 0.0003059057261279675, 'batch_size': 32}
+  Best Value (CSI): 0.429579
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29571181083296716, 'dropout_second': 0.09995421041271604, 'lr': 0.003152141177826013, 'weight_decay': 0.03163286570979001, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 13:54:44,455] Trial 4 finished with value: 0.4142339212279655 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.37416604035550327, 'dropout_second': 0.12705920221810338, 'lr': 0.0049690473763496544, 'weight_decay': 0.00015795637023727167, 'batch_size': 32}. Best is trial 2 with value: 0.4295794064438582.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.414234
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.37416604035550327, 'dropout_second': 0.12705920221810338, 'lr': 0.0049690473763496544, 'weight_decay': 0.00015795637023727167, 'batch_size': 32}
+  Best Value (CSI): 0.429579
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29571181083296716, 'dropout_second': 0.09995421041271604, 'lr': 0.003152141177826013, 'weight_decay': 0.03163286570979001, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 13:54:49,359] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.237430
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.30336121243339176, 'dropout_second': 0.04554455822971475, 'lr': 3.455490855951619e-05, 'weight_decay': 0.002361179044780619, 'batch_size': 256}
+  Best Value (CSI): 0.429579
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29571181083296716, 'dropout_second': 0.09995421041271604, 'lr': 0.003152141177826013, 'weight_decay': 0.03163286570979001, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 13:54:58,711] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.301158
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3428189558670304, 'dropout_second': 0.13095196297121378, 'lr': 0.00016662647338260662, 'weight_decay': 0.004116693567758415, 'batch_size': 64}
+  Best Value (CSI): 0.429579
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29571181083296716, 'dropout_second': 0.09995421041271604, 'lr': 0.003152141177826013, 'weight_decay': 0.03163286570979001, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+[I 2025-12-28 13:56:10,976] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.401349
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.13177994514235686, 'dropout_second': 0.14106216937061006, 'lr': 0.0006674192085065437, 'weight_decay': 0.07354588622374399, 'batch_size': 32}
+  Best Value (CSI): 0.429579
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29571181083296716, 'dropout_second': 0.09995421041271604, 'lr': 0.003152141177826013, 'weight_decay': 0.03163286570979001, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 13:56:48,302] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.340040
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.15218605260840784, 'dropout_second': 0.03725095053649465, 'lr': 0.00011506179935280335, 'weight_decay': 0.028541264446138536, 'batch_size': 32}
+  Best Value (CSI): 0.429579
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29571181083296716, 'dropout_second': 0.09995421041271604, 'lr': 0.003152141177826013, 'weight_decay': 0.03163286570979001, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 13:57:22,619] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.345756
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.3963341140333666, 'dropout_second': 0.1158234810046174, 'lr': 0.00033044992237741194, 'weight_decay': 0.00018143242358204613, 'batch_size': 32}
+  Best Value (CSI): 0.429579
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29571181083296716, 'dropout_second': 0.09995421041271604, 'lr': 0.003152141177826013, 'weight_decay': 0.03163286570979001, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 13:58:15,732] Trial 10 finished with value: 0.4252487880355467 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.261789630429515, 'dropout_second': 0.07941796847785985, 'lr': 0.005932645341470903, 'weight_decay': 0.01861789227079304, 'batch_size': 128}. Best is trial 2 with value: 0.4295794064438582.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.425249
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.261789630429515, 'dropout_second': 0.07941796847785985, 'lr': 0.005932645341470903, 'weight_decay': 0.01861789227079304, 'batch_size': 128}
+  Best Value (CSI): 0.429579
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29571181083296716, 'dropout_second': 0.09995421041271604, 'lr': 0.003152141177826013, 'weight_decay': 0.03163286570979001, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 13:59:04,772] Trial 11 finished with value: 0.413985109042078 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.27434798425960577, 'dropout_second': 0.0708159167898394, 'lr': 0.00837692996645315, 'weight_decay': 0.020419131736491725, 'batch_size': 128}. Best is trial 2 with value: 0.4295794064438582.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.413985
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.27434798425960577, 'dropout_second': 0.0708159167898394, 'lr': 0.00837692996645315, 'weight_decay': 0.020419131736491725, 'batch_size': 128}
+  Best Value (CSI): 0.429579
+  Best Trial: 2
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29571181083296716, 'dropout_second': 0.09995421041271604, 'lr': 0.003152141177826013, 'weight_decay': 0.03163286570979001, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 13:59:54,421] Trial 12 finished with value: 0.4319821105045898 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2533038211940113, 'dropout_second': 0.0024996078004908134, 'lr': 0.002435040523380477, 'weight_decay': 0.09599976366548699, 'batch_size': 128}. Best is trial 12 with value: 0.4319821105045898.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.431982
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2533038211940113, 'dropout_second': 0.0024996078004908134, 'lr': 0.002435040523380477, 'weight_decay': 0.09599976366548699, 'batch_size': 128}
+  Best Value (CSI): 0.431982
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2533038211940113, 'dropout_second': 0.0024996078004908134, 'lr': 0.002435040523380477, 'weight_decay': 0.09599976366548699, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:01:37,150] Trial 13 finished with value: 0.4197590343289748 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.22843523883382655, 'dropout_second': 0.003079776644502291, 'lr': 0.0011559495580954165, 'weight_decay': 0.09685440565734596, 'batch_size': 64}. Best is trial 12 with value: 0.4319821105045898.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.419759
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.22843523883382655, 'dropout_second': 0.003079776644502291, 'lr': 0.0011559495580954165, 'weight_decay': 0.09685440565734596, 'batch_size': 64}
+  Best Value (CSI): 0.431982
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2533038211940113, 'dropout_second': 0.0024996078004908134, 'lr': 0.002435040523380477, 'weight_decay': 0.09599976366548699, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:02:08,114] Trial 14 finished with value: 0.4040476231483985 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2782834546012202, 'dropout_second': 0.012445889529512635, 'lr': 0.0024381176497818968, 'weight_decay': 0.06682021072363388, 'batch_size': 256}. Best is trial 12 with value: 0.4319821105045898.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.404048
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2782834546012202, 'dropout_second': 0.012445889529512635, 'lr': 0.0024381176497818968, 'weight_decay': 0.06682021072363388, 'batch_size': 256}
+  Best Value (CSI): 0.431982
+  Best Trial: 12
+  Best Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2533038211940113, 'dropout_second': 0.0024996078004908134, 'lr': 0.002435040523380477, 'weight_decay': 0.09599976366548699, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:02:56,084] Trial 15 finished with value: 0.43508186407639654 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.24089812895219873, 'dropout_second': 0.07966995525122542, 'lr': 0.002488368221768655, 'weight_decay': 0.03674428444216707, 'batch_size': 128}. Best is trial 15 with value: 0.43508186407639654.
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.435082
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.24089812895219873, 'dropout_second': 0.07966995525122542, 'lr': 0.002488368221768655, 'weight_decay': 0.03674428444216707, 'batch_size': 128}
+  Best Value (CSI): 0.435082
+  Best Trial: 15
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.24089812895219873, 'dropout_second': 0.07966995525122542, 'lr': 0.002488368221768655, 'weight_decay': 0.03674428444216707, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:03:07,569] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.378323
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.1812920098610963, 'dropout_second': 0.027668017668869643, 'lr': 0.009946332409446604, 'weight_decay': 0.011671367769224497, 'batch_size': 128}
+  Best Value (CSI): 0.435082
+  Best Trial: 15
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.24089812895219873, 'dropout_second': 0.07966995525122542, 'lr': 0.002488368221768655, 'weight_decay': 0.03674428444216707, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:03:13,743] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.301920
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.2573870738114872, 'dropout_second': 0.0010224708729921345, 'lr': 0.0007613303336536931, 'weight_decay': 0.09850505474695291, 'batch_size': 128}
+  Best Value (CSI): 0.435082
+  Best Trial: 15
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.24089812895219873, 'dropout_second': 0.07966995525122542, 'lr': 0.002488368221768655, 'weight_decay': 0.03674428444216707, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:03:26,672] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.359833
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.10010131909682807, 'dropout_second': 0.05276745534220664, 'lr': 0.0017196261509663943, 'weight_decay': 0.041449122362615096, 'batch_size': 128}
+  Best Value (CSI): 0.435082
+  Best Trial: 15
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.24089812895219873, 'dropout_second': 0.07966995525122542, 'lr': 0.002488368221768655, 'weight_decay': 0.03674428444216707, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:04:09,001] Trial 19 finished with value: 0.4153406911673947 and parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.2361430868794701, 'dropout_second': 0.021860776611282523, 'lr': 0.003712770875731798, 'weight_decay': 0.010417492399226258, 'batch_size': 128}. Best is trial 15 with value: 0.43508186407639654.
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.415341
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.2361430868794701, 'dropout_second': 0.021860776611282523, 'lr': 0.003712770875731798, 'weight_decay': 0.010417492399226258, 'batch_size': 128}
+  Best Value (CSI): 0.435082
+  Best Trial: 15
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.24089812895219873, 'dropout_second': 0.07966995525122542, 'lr': 0.002488368221768655, 'weight_decay': 0.03674428444216707, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:04:58,889] Trial 20 finished with value: 0.43748722925933387 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.437487
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:05:11,696] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.374165
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.23961721741155292, 'dropout_second': 0.05925749176768667, 'lr': 0.0011454311719214212, 'weight_decay': 0.04735308622732924, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:05:19,103] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.356502
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.2457944923490147, 'dropout_second': 0.02881413340692611, 'lr': 0.003851573726435059, 'weight_decay': 0.04982674346162384, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:05:31,750] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.380090
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.20088719749317074, 'dropout_second': 0.08156400229887663, 'lr': 0.0017081980951157562, 'weight_decay': 0.046870161671785183, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:05:43,288] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.352688
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.22377322738870561, 'dropout_second': 0.042534259797953534, 'lr': 0.0005473970856367887, 'weight_decay': 0.02477156533089326, 'batch_size': 64}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:06:11,006] Trial 25 finished with value: 0.3899408351339609 and parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.25737222623366895, 'dropout_second': 0.0590246309841823, 'lr': 0.0011365241969334228, 'weight_decay': 0.09717859884325422, 'batch_size': 256}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.389941
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.25737222623366895, 'dropout_second': 0.0590246309841823, 'lr': 0.0011365241969334228, 'weight_decay': 0.09717859884325422, 'batch_size': 256}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:06:23,037] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.368530
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2859352480477644, 'dropout_second': 0.023295934156104006, 'lr': 0.0026572527243931264, 'weight_decay': 0.0483953389874057, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:07:06,188] Trial 27 finished with value: 0.42425216912753455 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.17783866065881115, 'dropout_second': 0.06529617004905293, 'lr': 0.00583574022600149, 'weight_decay': 0.014962487047768576, 'batch_size': 128}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.424252
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.17783866065881115, 'dropout_second': 0.06529617004905293, 'lr': 0.00583574022600149, 'weight_decay': 0.014962487047768576, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:07:41,790] Trial 28 finished with value: 0.4080665303385605 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.2234272761519179, 'dropout_second': 0.0412137575539642, 'lr': 0.0017068959665271254, 'weight_decay': 0.032019795241946335, 'batch_size': 128}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.408067
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.2234272761519179, 'dropout_second': 0.0412137575539642, 'lr': 0.0017068959665271254, 'weight_decay': 0.032019795241946335, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:07:49,097] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.336152
+  Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 5, 'dropout_first': 0.21029912574433346, 'dropout_second': 0.09189075927712965, 'lr': 0.0004650284062387191, 'weight_decay': 0.008383985322920073, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:08:01,140] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.354639
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2697295145874507, 'dropout_second': 0.07455654444890417, 'lr': 0.0009126818631924093, 'weight_decay': 0.06463094688471252, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:10:27,530] Trial 31 finished with value: 0.42238059023729874 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29368147014230345, 'dropout_second': 0.09251904934154133, 'lr': 0.0027314891923512193, 'weight_decay': 0.02991647847457028, 'batch_size': 32}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.422381
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29368147014230345, 'dropout_second': 0.09251904934154133, 'lr': 0.0027314891923512193, 'weight_decay': 0.02991647847457028, 'batch_size': 32}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:11:02,891] Trial 32 finished with value: 0.41618880119803187 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.314439429723033, 'dropout_second': 0.10440783526629153, 'lr': 0.0036840473888863495, 'weight_decay': 0.035523902706750016, 'batch_size': 256}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.416189
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.314439429723033, 'dropout_second': 0.10440783526629153, 'lr': 0.0036840473888863495, 'weight_decay': 0.035523902706750016, 'batch_size': 256}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:13:06,795] Trial 33 finished with value: 0.42370615716864285 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.24965566149574941, 'dropout_second': 0.09205706219548765, 'lr': 0.0016935676130801721, 'weight_decay': 0.020804551586452752, 'batch_size': 32}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.423706
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.24965566149574941, 'dropout_second': 0.09205706219548765, 'lr': 0.0016935676130801721, 'weight_decay': 0.020804551586452752, 'batch_size': 32}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:16:04,646] Trial 34 finished with value: 0.42044806805431606 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.2902680346720225, 'dropout_second': 0.11427580677418202, 'lr': 0.0029816331102501216, 'weight_decay': 0.06355476613735178, 'batch_size': 32}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.420448
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.2902680346720225, 'dropout_second': 0.11427580677418202, 'lr': 0.0029816331102501216, 'weight_decay': 0.06355476613735178, 'batch_size': 32}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:16:29,595] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.373522
+  Parameters: {'d_main': 256, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.3343017363041155, 'dropout_second': 0.05500565735532233, 'lr': 0.005022203434767776, 'weight_decay': 0.00737052300845962, 'batch_size': 64}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:16:36,248] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.335893
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.27037779964029557, 'dropout_second': 0.06808430633261275, 'lr': 0.0023111616214406667, 'weight_decay': 0.016106446086058074, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:17:12,746] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.365957
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.3032643157430049, 'dropout_second': 0.08249069849356504, 'lr': 0.001201500955456668, 'weight_decay': 0.0016202467010117052, 'batch_size': 32}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:17:16,822] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.109405
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.24116726961828425, 'dropout_second': 0.15561604679932534, 'lr': 1.1982068538756003e-05, 'weight_decay': 0.031191653252290716, 'batch_size': 256}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:19:39,249] Trial 39 finished with value: 0.4133267393818237 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.22074269603392588, 'dropout_second': 0.013150744449260635, 'lr': 0.0004645042620106774, 'weight_decay': 0.06520712637375296, 'batch_size': 32}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.413327
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.22074269603392588, 'dropout_second': 0.013150744449260635, 'lr': 0.0004645042620106774, 'weight_decay': 0.06520712637375296, 'batch_size': 32}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:20:42,149] Trial 40 finished with value: 0.4065494703794655 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.2567567889507051, 'dropout_second': 0.04863374445767936, 'lr': 0.006892182641814923, 'weight_decay': 0.025109189517228663, 'batch_size': 64}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.406549
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.2567567889507051, 'dropout_second': 0.04863374445767936, 'lr': 0.006892182641814923, 'weight_decay': 0.025109189517228663, 'batch_size': 64}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:20:48,958] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.341053
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.26454930632736195, 'dropout_second': 0.08336682059213603, 'lr': 0.006010723433627047, 'weight_decay': 0.017313080875990584, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:21:08,719] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.370130
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.279866087743989, 'dropout_second': 0.10196146510533526, 'lr': 0.005189864314943928, 'weight_decay': 0.03687321707992403, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:21:57,214] Trial 43 finished with value: 0.40919578107558063 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.25299150796870107, 'dropout_second': 0.07515336707346668, 'lr': 0.003833721410969937, 'weight_decay': 0.024120870659718442, 'batch_size': 128}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.409196
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.25299150796870107, 'dropout_second': 0.07515336707346668, 'lr': 0.003833721410969937, 'weight_decay': 0.024120870659718442, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:22:47,723] Trial 44 finished with value: 0.42568378677734 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.2979831739914553, 'dropout_second': 0.11072645507117972, 'lr': 0.007516273875353587, 'weight_decay': 0.07653389325605259, 'batch_size': 128}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.425684
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.2979831739914553, 'dropout_second': 0.11072645507117972, 'lr': 0.007516273875353587, 'weight_decay': 0.07653389325605259, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:23:43,780] Trial 45 finished with value: 0.41954083365952805 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3231582722923267, 'dropout_second': 0.12039109632990438, 'lr': 0.007945926042919646, 'weight_decay': 0.07250061352340932, 'batch_size': 128}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.419541
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3231582722923267, 'dropout_second': 0.12039109632990438, 'lr': 0.007945926042919646, 'weight_decay': 0.07250061352340932, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:26:17,225] Trial 46 finished with value: 0.41451182262764674 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.30578322010174264, 'dropout_second': 0.11000931411863661, 'lr': 0.0022393419256625563, 'weight_decay': 0.09676534990857281, 'batch_size': 32}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.414512
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.30578322010174264, 'dropout_second': 0.11000931411863661, 'lr': 0.0022393419256625563, 'weight_decay': 0.09676534990857281, 'batch_size': 32}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:26:29,583] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.373102
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2976545778440691, 'dropout_second': 0.09854604255569362, 'lr': 0.00815897996923328, 'weight_decay': 0.05574275337903514, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:26:58,620] Trial 48 finished with value: 0.4133348957526035 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.34788080524619774, 'dropout_second': 0.0354582256127467, 'lr': 0.009974235528405817, 'weight_decay': 0.03986490661133994, 'batch_size': 128}. Best is trial 20 with value: 0.43748722925933387.
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.413335
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.34788080524619774, 'dropout_second': 0.0354582256127467, 'lr': 0.009974235528405817, 'weight_decay': 0.03986490661133994, 'batch_size': 128}
+  Best Value (CSI): 0.437487
+  Best Trial: 20
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2439890426858556, 'dropout_second': 0.05680327041661658, 'lr': 0.0011920192014038022, 'weight_decay': 0.060737914534755494, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:29:21,363] Trial 49 finished with value: 0.43962567325641694 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.276258384877647, 'dropout_second': 0.13052052526320873, 'lr': 0.003251868387383338, 'weight_decay': 0.07457988689972424, 'batch_size': 32}. Best is trial 49 with value: 0.43962567325641694.
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.439626
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.276258384877647, 'dropout_second': 0.13052052526320873, 'lr': 0.003251868387383338, 'weight_decay': 0.07457988689972424, 'batch_size': 32}
+  Best Value (CSI): 0.439626
+  Best Trial: 49
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.276258384877647, 'dropout_second': 0.13052052526320873, 'lr': 0.003251868387383338, 'weight_decay': 0.07457988689972424, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:31:54,215] Trial 50 finished with value: 0.43282096186893854 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.2790163777526576, 'dropout_second': 0.13567740567926492, 'lr': 0.003521434293104508, 'weight_decay': 0.05230409657603545, 'batch_size': 32}. Best is trial 49 with value: 0.43962567325641694.
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.432821
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.2790163777526576, 'dropout_second': 0.13567740567926492, 'lr': 0.003521434293104508, 'weight_decay': 0.05230409657603545, 'batch_size': 32}
+  Best Value (CSI): 0.439626
+  Best Trial: 49
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.276258384877647, 'dropout_second': 0.13052052526320873, 'lr': 0.003251868387383338, 'weight_decay': 0.07457988689972424, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:34:56,689] Trial 51 finished with value: 0.46086300130604796 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.460863
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:37:36,240] Trial 52 finished with value: 0.4238183905570823 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.28219070700957194, 'dropout_second': 0.13486514690288134, 'lr': 0.004177368019117101, 'weight_decay': 0.04864727313656566, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.423818
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.28219070700957194, 'dropout_second': 0.13486514690288134, 'lr': 0.004177368019117101, 'weight_decay': 0.04864727313656566, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:39:52,824] Trial 53 finished with value: 0.4159966549535025 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.26717492443396146, 'dropout_second': 0.14740671243145215, 'lr': 0.002906233358410579, 'weight_decay': 0.08309861908943458, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.415997
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.26717492443396146, 'dropout_second': 0.14740671243145215, 'lr': 0.002906233358410579, 'weight_decay': 0.08309861908943458, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:42:15,691] Trial 54 finished with value: 0.42549830700285646 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.23673455002308155, 'dropout_second': 0.12025124474468382, 'lr': 0.004548031869033561, 'weight_decay': 0.056595064944475144, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.425498
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.23673455002308155, 'dropout_second': 0.12025124474468382, 'lr': 0.004548031869033561, 'weight_decay': 0.056595064944475144, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:44:29,282] Trial 55 finished with value: 0.431713124991507 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.27343827415351635, 'dropout_second': 0.13152639072384067, 'lr': 0.0020034370492493874, 'weight_decay': 0.03905051690903401, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.431713
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.27343827415351635, 'dropout_second': 0.13152639072384067, 'lr': 0.0020034370492493874, 'weight_decay': 0.03905051690903401, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:47:00,483] Trial 56 finished with value: 0.42454323264847244 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.24464237773620834, 'dropout_second': 0.1515620882931492, 'lr': 0.0034311430201767155, 'weight_decay': 0.08147403351052267, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.424543
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.24464237773620834, 'dropout_second': 0.1515620882931492, 'lr': 0.0034311430201767155, 'weight_decay': 0.08147403351052267, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:48:54,496] Trial 57 finished with value: 0.42759061554895456 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.28258814027834817, 'dropout_second': 0.12649854053491932, 'lr': 0.0013908434774981366, 'weight_decay': 0.0595127325873782, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.427591
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.28258814027834817, 'dropout_second': 0.12649854053491932, 'lr': 0.0013908434774981366, 'weight_decay': 0.0595127325873782, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:51:34,217] Trial 58 finished with value: 0.43719017038747937 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.2578875510596925, 'dropout_second': 0.14102384744711194, 'lr': 0.002124134514600572, 'weight_decay': 0.09941529544511475, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.437190
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.2578875510596925, 'dropout_second': 0.14102384744711194, 'lr': 0.002124134514600572, 'weight_decay': 0.09941529544511475, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:53:56,244] Trial 59 finished with value: 0.4240452795790282 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.2317099596390013, 'dropout_second': 0.1623958140449442, 'lr': 0.0020231466055132875, 'weight_decay': 0.04302992976282335, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.424045
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.2317099596390013, 'dropout_second': 0.1623958140449442, 'lr': 0.0020231466055132875, 'weight_decay': 0.04302992976282335, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:56:03,095] Trial 60 finished with value: 0.4358342428278832 and parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.26390608672831384, 'dropout_second': 0.14262075171585945, 'lr': 0.0015215419880071292, 'weight_decay': 0.05400930977513991, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.435834
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.26390608672831384, 'dropout_second': 0.14262075171585945, 'lr': 0.0015215419880071292, 'weight_decay': 0.05400930977513991, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 14:56:41,731] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.388759
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.2603832142661876, 'dropout_second': 0.1399336230237433, 'lr': 0.003027155039110075, 'weight_decay': 0.07703123409497285, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 14:58:41,600] Trial 62 finished with value: 0.4242265311707965 and parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.24642940818082756, 'dropout_second': 0.14173371867038934, 'lr': 0.0008760726201512049, 'weight_decay': 0.05443315058244469, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.424227
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.24642940818082756, 'dropout_second': 0.14173371867038934, 'lr': 0.0008760726201512049, 'weight_decay': 0.05443315058244469, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:00:40,480] Trial 63 finished with value: 0.43047477413059915 and parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.27284797887786916, 'dropout_second': 0.16059783615764034, 'lr': 0.0014663448625270169, 'weight_decay': 0.09984228652299194, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.430475
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.27284797887786916, 'dropout_second': 0.16059783615764034, 'lr': 0.0014663448625270169, 'weight_decay': 0.09984228652299194, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:02:54,377] Trial 64 finished with value: 0.3994379494354143 and parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.2649041014616503, 'dropout_second': 0.124474254090258, 'lr': 0.002388676292413663, 'weight_decay': 0.03377556541112246, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.399438
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.2649041014616503, 'dropout_second': 0.124474254090258, 'lr': 0.002388676292413663, 'weight_decay': 0.03377556541112246, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:05:01,148] Trial 65 finished with value: 0.41417224961339855 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.2878101759400844, 'dropout_second': 0.13491424876162078, 'lr': 0.004131950918554453, 'weight_decay': 0.044901914908037056, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.414172
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.2878101759400844, 'dropout_second': 0.13491424876162078, 'lr': 0.004131950918554453, 'weight_decay': 0.044901914908037056, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:07:28,003] Trial 66 finished with value: 0.4259886079254016 and parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.25537680582853456, 'dropout_second': 0.14358588950438872, 'lr': 0.003213224841721534, 'weight_decay': 0.06436092031876865, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.425989
+  Parameters: {'d_main': 128, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.25537680582853456, 'dropout_second': 0.14358588950438872, 'lr': 0.003213224841721534, 'weight_decay': 0.06436092031876865, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:07:58,086] Trial 67 finished with value: 0.4132935210980772 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22898429019352057, 'dropout_second': 0.1284837877222496, 'lr': 0.0019517837683462225, 'weight_decay': 0.083336443230296, 'batch_size': 256}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.413294
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22898429019352057, 'dropout_second': 0.1284837877222496, 'lr': 0.0019517837683462225, 'weight_decay': 0.083336443230296, 'batch_size': 256}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:08:55,548] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.365152
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.24649025836540236, 'dropout_second': 0.11769021249892192, 'lr': 0.0013676735718664021, 'weight_decay': 0.02715719349481549, 'batch_size': 64}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:11:29,162] Trial 69 finished with value: 0.4269004897751387 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.274707221804167, 'dropout_second': 0.16714329303928782, 'lr': 0.0009705767849080497, 'weight_decay': 0.05338543858436262, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.426900
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.274707221804167, 'dropout_second': 0.16714329303928782, 'lr': 0.0009705767849080497, 'weight_decay': 0.05338543858436262, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:14:19,128] Trial 70 finished with value: 0.423750373320136 and parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.28822731057966877, 'dropout_second': 0.13573373837826996, 'lr': 0.002700826526788786, 'weight_decay': 0.037365734130973854, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.423750
+  Parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.28822731057966877, 'dropout_second': 0.13573373837826996, 'lr': 0.002700826526788786, 'weight_decay': 0.037365734130973854, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:17:02,632] Trial 71 finished with value: 0.42114985078112094 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.25634600083062964, 'dropout_second': 0.061411273395649967, 'lr': 0.0016681207121217098, 'weight_decay': 0.07205501051497937, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.421150
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.25634600083062964, 'dropout_second': 0.061411273395649967, 'lr': 0.0016681207121217098, 'weight_decay': 0.07205501051497937, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:19:27,372] Trial 72 finished with value: 0.4306331984748016 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.23811194065068303, 'dropout_second': 0.12413266651706692, 'lr': 0.0023210184788187154, 'weight_decay': 0.09902133446398335, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.430633
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.23811194065068303, 'dropout_second': 0.12413266651706692, 'lr': 0.0023210184788187154, 'weight_decay': 0.09902133446398335, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:19:40,324] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.372632
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.26505276153763396, 'dropout_second': 0.14708964747748804, 'lr': 0.0049549745846910066, 'weight_decay': 0.04848274157097736, 'batch_size': 128}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:20:50,903] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.324675
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.24936336791358873, 'dropout_second': 0.00016665536533369595, 'lr': 0.003328283800332454, 'weight_decay': 0.06186818863760167, 'batch_size': 64}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:21:26,079] Trial 75 finished with value: 0.4141120745403715 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.2762736827811607, 'dropout_second': 0.04906517879590806, 'lr': 0.0026185103521846175, 'weight_decay': 0.08174741625424192, 'batch_size': 256}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.414112
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.2762736827811607, 'dropout_second': 0.04906517879590806, 'lr': 0.0026185103521846175, 'weight_decay': 0.08174741625424192, 'batch_size': 256}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:22:10,054] Trial 76 finished with value: 0.4097278278826631 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.21680184747815376, 'dropout_second': 0.11519043747598502, 'lr': 0.005733334225970647, 'weight_decay': 0.03305161314670154, 'batch_size': 128}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.409728
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.21680184747815376, 'dropout_second': 0.11519043747598502, 'lr': 0.005733334225970647, 'weight_decay': 0.03305161314670154, 'batch_size': 128}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:24:19,244] Trial 77 finished with value: 0.4209402746904937 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.26295139308558013, 'dropout_second': 0.01269600781737798, 'lr': 0.00428771796883548, 'weight_decay': 0.044951599617812074, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.420940
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.26295139308558013, 'dropout_second': 0.01269600781737798, 'lr': 0.00428771796883548, 'weight_decay': 0.044951599617812074, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:24:25,334] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.328897
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.23855087599769773, 'dropout_second': 0.005255902882373466, 'lr': 0.0015613735840116238, 'weight_decay': 0.02134854705824673, 'batch_size': 128}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:27:22,715] Trial 79 finished with value: 0.4101081178433113 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.22899182762474457, 'dropout_second': 0.03414871272952938, 'lr': 0.00208953033773157, 'weight_decay': 0.02802043987706483, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.410108
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.22899182762474457, 'dropout_second': 0.03414871272952938, 'lr': 0.00208953033773157, 'weight_decay': 0.02802043987706483, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:27:29,436] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.311864
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.2808594537573417, 'dropout_second': 0.06982233090558276, 'lr': 0.0011789833851281112, 'weight_decay': 0.06639740328487558, 'batch_size': 128}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:29:47,208] Trial 81 finished with value: 0.42862014417150635 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2760691906354678, 'dropout_second': 0.13433796666983402, 'lr': 0.001909295972386863, 'weight_decay': 0.042451982801717705, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.428620
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2760691906354678, 'dropout_second': 0.13433796666983402, 'lr': 0.001909295972386863, 'weight_decay': 0.042451982801717705, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:32:32,897] Trial 82 finished with value: 0.43612222462654665 and parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.2677316776638785, 'dropout_second': 0.10790323357157286, 'lr': 0.003344931839184307, 'weight_decay': 0.037064107505975656, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.436122
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.2677316776638785, 'dropout_second': 0.10790323357157286, 'lr': 0.003344931839184307, 'weight_decay': 0.037064107505975656, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:34:10,609] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.368000
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.2505984209558294, 'dropout_second': 0.10816104893428496, 'lr': 0.0034796999736907814, 'weight_decay': 0.054491776106536924, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:37:09,777] Trial 84 finished with value: 0.41559021907620536 and parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.2601615180134029, 'dropout_second': 0.09824042830490243, 'lr': 0.0027267549590104575, 'weight_decay': 0.0718502657043432, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.415590
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.2601615180134029, 'dropout_second': 0.09824042830490243, 'lr': 0.0027267549590104575, 'weight_decay': 0.0718502657043432, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:39:38,506] Trial 85 finished with value: 0.4183169039794743 and parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.29226299228713226, 'dropout_second': 0.1302734647919979, 'lr': 0.004654899135819482, 'weight_decay': 0.08599570835061895, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.418317
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.29226299228713226, 'dropout_second': 0.1302734647919979, 'lr': 0.004654899135819482, 'weight_decay': 0.08599570835061895, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:39:51,269] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.359140
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.2685656463853551, 'dropout_second': 0.08800064791423728, 'lr': 0.0037048667743306406, 'weight_decay': 0.035046533963042914, 'batch_size': 128}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 15:40:34,916] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.394180
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.2543547613647302, 'dropout_second': 0.10685274745059609, 'lr': 0.0024609542328722896, 'weight_decay': 0.057942211551812166, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:40:56,875] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.311615
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.24357581141217133, 'dropout_second': 0.0781724375742957, 'lr': 0.0031553563962246254, 'weight_decay': 0.02965783033343166, 'batch_size': 256}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:41:53,121] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.301435
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.26976152815872, 'dropout_second': 0.12366894813500762, 'lr': 0.006847394624884505, 'weight_decay': 0.06625910754074525, 'batch_size': 64}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:44:27,240] Trial 90 finished with value: 0.43033316401205474 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2831601926566732, 'dropout_second': 0.06338845915577956, 'lr': 0.0019588875783444416, 'weight_decay': 0.05054324517054057, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.430333
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2831601926566732, 'dropout_second': 0.06338845915577956, 'lr': 0.0019588875783444416, 'weight_decay': 0.05054324517054057, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:46:33,476] Trial 91 finished with value: 0.4237156465393366 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2726055202797912, 'dropout_second': 0.13967738270008292, 'lr': 0.0017670798487651514, 'weight_decay': 0.03873876703503302, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.423716
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2726055202797912, 'dropout_second': 0.13967738270008292, 'lr': 0.0017670798487651514, 'weight_decay': 0.03873876703503302, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:48:41,345] Trial 92 finished with value: 0.4355873001020898 and parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.259461156363008, 'dropout_second': 0.13027700206313245, 'lr': 0.001372818256589927, 'weight_decay': 0.04043567530686107, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.435587
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.259461156363008, 'dropout_second': 0.13027700206313245, 'lr': 0.001372818256589927, 'weight_decay': 0.04043567530686107, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:50:56,999] Trial 93 finished with value: 0.42361826996995705 and parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.25116222865183413, 'dropout_second': 0.1156918009369281, 'lr': 0.002927632839913041, 'weight_decay': 0.08605097971625117, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.423618
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.25116222865183413, 'dropout_second': 0.1156918009369281, 'lr': 0.002927632839913041, 'weight_decay': 0.08605097971625117, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:53:09,337] Trial 94 finished with value: 0.4341440147355105 and parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.2610351160957948, 'dropout_second': 0.054370074324468526, 'lr': 0.0013292056768448473, 'weight_decay': 0.04516060349176726, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.434144
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.2610351160957948, 'dropout_second': 0.054370074324468526, 'lr': 0.0013292056768448473, 'weight_decay': 0.04516060349176726, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:55:09,183] Trial 95 finished with value: 0.44950991541859664 and parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.2606207908860985, 'dropout_second': 0.1202687884436883, 'lr': 0.0014338220506373526, 'weight_decay': 0.04273065668341658, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.449510
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.2606207908860985, 'dropout_second': 0.1202687884436883, 'lr': 0.0014338220506373526, 'weight_decay': 0.04273065668341658, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 15:57:29,097] Trial 96 finished with value: 0.41230978230619514 and parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.2645873892695231, 'dropout_second': 0.05444756725719985, 'lr': 0.0013351007584598178, 'weight_decay': 0.02470577050669325, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.412310
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.2645873892695231, 'dropout_second': 0.05444756725719985, 'lr': 0.0013351007584598178, 'weight_decay': 0.02470577050669325, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 1.247350410312635, 1: 1.1808517330313895, 2: 0.7399436331027415} (클래스별 샘플 수: {0: 10033, 1: 10598, 2: 16913})
+[I 2025-12-28 16:00:24,344] Trial 97 finished with value: 0.42895230071881013 and parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.25737182023392713, 'dropout_second': 0.12656000434577785, 'lr': 0.0010329105416356787, 'weight_decay': 0.041962518140907526, 'batch_size': 32}. Best is trial 51 with value: 0.46086300130604796.
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.428952
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.25737182023392713, 'dropout_second': 0.12656000434577785, 'lr': 0.0010329105416356787, 'weight_decay': 0.041962518140907526, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.168284789644013, 2: 0.7447876371282905} (클래스별 샘플 수: {0: 10029, 1: 10712, 2: 16803})
+[I 2025-12-28 16:01:39,267] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.420035
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.24224327119716768, 'dropout_second': 0.1194876719851468, 'lr': 0.0008706244121032396, 'weight_decay': 0.0314807587922305, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2464288087170288, 1: 1.1469797016385423, 2: 0.7542314959996783} (클래스별 샘플 수: {0: 10034, 1: 10904, 2: 16582})
+[I 2025-12-28 16:02:12,668] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.380744
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.23455220057872506, 'dropout_second': 0.11243665123083846, 'lr': 0.0007786392307167591, 'weight_decay': 0.02267791758945375, 'batch_size': 32}
+  Best Value (CSI): 0.460863
+  Best Trial: 51
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4609
+Best Hyperparameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.3370
+  - 최종 CSI: 0.3807
+  - 최고 CSI: 0.4609
+  - 최저 CSI: 0.1094
+  - 평균 CSI: 0.3939
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/resnet_like_ctgan10000_daegu_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 30, Best CSI: 0.4553
+  Fold 1 학습 완료 (검증 CSI: 0.4553)
+Fold 2 학습 중...
+  Early stopping at epoch 33, Best CSI: 0.4670
+  Fold 2 학습 완료 (검증 CSI: 0.4670)
+Fold 3 학습 중...
+  Early stopping at epoch 16, Best CSI: 0.3981
+  Fold 3 학습 완료 (검증 CSI: 0.3981)
+
+모든 모델 저장 완료: ../save_model/resnet_like_optima/resnet_like_ctgan10000_daegu.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/resnet_like_optima/scaler/resnet_like_ctgan10000_daegu_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/resnet_like_optima/resnet_like_ctgan10000_daegu.pkl
+
+✓ 완료: resnet_like_ctgan10000/resnet_like_ctgan10000_daegu.py (소요 시간: 8346초)
+
+----------------------------------------
+실행 중: resnet_like_ctgan10000/resnet_like_ctgan10000_daejeon.py
+시작 시간: 2025-12-28 16:04:25
+----------------------------------------
+[I 2025-12-28 16:04:26,430] A new study created in memory with name: no-name-40446e72-7c84-4e90-9ee1-4da84b4ae658
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:06:58,655] Trial 0 finished with value: 0.4352997904650637 and parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.27557601312582714, 'dropout_second': 0.17183919233227582, 'lr': 3.305655162566025e-05, 'weight_decay': 0.017734968515894907, 'batch_size': 64}. Best is trial 0 with value: 0.4352997904650637.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.435300
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.27557601312582714, 'dropout_second': 0.17183919233227582, 'lr': 3.305655162566025e-05, 'weight_decay': 0.017734968515894907, 'batch_size': 64}
+  Best Value (CSI): 0.435300
+  Best Trial: 0
+  Best Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.27557601312582714, 'dropout_second': 0.17183919233227582, 'lr': 3.305655162566025e-05, 'weight_decay': 0.017734968515894907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:09:25,961] Trial 1 finished with value: 0.4218999862095832 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.11030635585301277, 'dropout_second': 0.1775778520282819, 'lr': 2.0978430876564278e-05, 'weight_decay': 0.0006946995884057872, 'batch_size': 64}. Best is trial 0 with value: 0.4352997904650637.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.421900
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.11030635585301277, 'dropout_second': 0.1775778520282819, 'lr': 2.0978430876564278e-05, 'weight_decay': 0.0006946995884057872, 'batch_size': 64}
+  Best Value (CSI): 0.435300
+  Best Trial: 0
+  Best Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.27557601312582714, 'dropout_second': 0.17183919233227582, 'lr': 3.305655162566025e-05, 'weight_decay': 0.017734968515894907, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:10:55,202] Trial 2 finished with value: 0.447312093936253 and parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.279384527323956, 'dropout_second': 0.049623187443222076, 'lr': 6.397074458687223e-05, 'weight_decay': 0.00047280986207773215, 'batch_size': 128}. Best is trial 2 with value: 0.447312093936253.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.447312
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.279384527323956, 'dropout_second': 0.049623187443222076, 'lr': 6.397074458687223e-05, 'weight_decay': 0.00047280986207773215, 'batch_size': 128}
+  Best Value (CSI): 0.447312
+  Best Trial: 2
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.279384527323956, 'dropout_second': 0.049623187443222076, 'lr': 6.397074458687223e-05, 'weight_decay': 0.00047280986207773215, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:12:58,263] Trial 3 finished with value: 0.47899903002695393 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.33409851093502857, 'dropout_second': 0.07020994349967867, 'lr': 0.00098122744864316, 'weight_decay': 0.00123357526102964, 'batch_size': 32}. Best is trial 3 with value: 0.47899903002695393.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.478999
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.33409851093502857, 'dropout_second': 0.07020994349967867, 'lr': 0.00098122744864316, 'weight_decay': 0.00123357526102964, 'batch_size': 32}
+  Best Value (CSI): 0.478999
+  Best Trial: 3
+  Best Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.33409851093502857, 'dropout_second': 0.07020994349967867, 'lr': 0.00098122744864316, 'weight_decay': 0.00123357526102964, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:13:33,818] Trial 4 finished with value: 0.4758694970530977 and parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.27104868816113403, 'dropout_second': 0.08846482260593887, 'lr': 0.0024650800962241187, 'weight_decay': 0.00712684143156514, 'batch_size': 256}. Best is trial 3 with value: 0.47899903002695393.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.475869
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.27104868816113403, 'dropout_second': 0.08846482260593887, 'lr': 0.0024650800962241187, 'weight_decay': 0.00712684143156514, 'batch_size': 256}
+  Best Value (CSI): 0.478999
+  Best Trial: 3
+  Best Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.33409851093502857, 'dropout_second': 0.07020994349967867, 'lr': 0.00098122744864316, 'weight_decay': 0.00123357526102964, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:16:57,892] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.433896
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.24820052309561583, 'dropout_second': 0.011060429671752848, 'lr': 3.981642283871909e-05, 'weight_decay': 0.029139245460298217, 'batch_size': 32}
+  Best Value (CSI): 0.478999
+  Best Trial: 3
+  Best Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.33409851093502857, 'dropout_second': 0.07020994349967867, 'lr': 0.00098122744864316, 'weight_decay': 0.00123357526102964, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:19:52,748] Trial 6 finished with value: 0.4983708710480454 and parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2274520551367763, 'dropout_second': 0.13356917647186076, 'lr': 0.004479135894707135, 'weight_decay': 0.0008808608187577898, 'batch_size': 32}. Best is trial 6 with value: 0.4983708710480454.
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.498371
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2274520551367763, 'dropout_second': 0.13356917647186076, 'lr': 0.004479135894707135, 'weight_decay': 0.0008808608187577898, 'batch_size': 32}
+  Best Value (CSI): 0.498371
+  Best Trial: 6
+  Best Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2274520551367763, 'dropout_second': 0.13356917647186076, 'lr': 0.004479135894707135, 'weight_decay': 0.0008808608187577898, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:22:23,797] Trial 7 finished with value: 0.48136934632515366 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.3796577410766486, 'dropout_second': 0.11197682007496457, 'lr': 0.00740388601639193, 'weight_decay': 0.00020654823761584708, 'batch_size': 32}. Best is trial 6 with value: 0.4983708710480454.
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.481369
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.3796577410766486, 'dropout_second': 0.11197682007496457, 'lr': 0.00740388601639193, 'weight_decay': 0.00020654823761584708, 'batch_size': 32}
+  Best Value (CSI): 0.498371
+  Best Trial: 6
+  Best Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2274520551367763, 'dropout_second': 0.13356917647186076, 'lr': 0.004479135894707135, 'weight_decay': 0.0008808608187577898, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:23:27,913] Trial 8 finished with value: 0.4941037100435706 and parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.1677229054093598, 'dropout_second': 0.1186158977679054, 'lr': 0.0031590987111073665, 'weight_decay': 0.0023285053148008987, 'batch_size': 64}. Best is trial 6 with value: 0.4983708710480454.
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.494104
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.1677229054093598, 'dropout_second': 0.1186158977679054, 'lr': 0.0031590987111073665, 'weight_decay': 0.0023285053148008987, 'batch_size': 64}
+  Best Value (CSI): 0.498371
+  Best Trial: 6
+  Best Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2274520551367763, 'dropout_second': 0.13356917647186076, 'lr': 0.004479135894707135, 'weight_decay': 0.0008808608187577898, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:24:13,802] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.436218
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.18293835826351199, 'dropout_second': 0.06751636150106433, 'lr': 4.47221222346668e-05, 'weight_decay': 0.00013606405161901934, 'batch_size': 64}
+  Best Value (CSI): 0.498371
+  Best Trial: 6
+  Best Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2274520551367763, 'dropout_second': 0.13356917647186076, 'lr': 0.004479135894707135, 'weight_decay': 0.0008808608187577898, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:24:58,861] Trial 10 finished with value: 0.47227753241672854 and parameters: {'d_main': 64, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.21207105085583594, 'dropout_second': 0.14261060829347877, 'lr': 0.00036948932963480136, 'weight_decay': 0.09414926280210963, 'batch_size': 128}. Best is trial 6 with value: 0.4983708710480454.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.472278
+  Parameters: {'d_main': 64, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.21207105085583594, 'dropout_second': 0.14261060829347877, 'lr': 0.00036948932963480136, 'weight_decay': 0.09414926280210963, 'batch_size': 128}
+  Best Value (CSI): 0.498371
+  Best Trial: 6
+  Best Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2274520551367763, 'dropout_second': 0.13356917647186076, 'lr': 0.004479135894707135, 'weight_decay': 0.0008808608187577898, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:25:29,246] Trial 11 finished with value: 0.49234116052294147 and parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.15757374324955048, 'dropout_second': 0.12656633390654176, 'lr': 0.006574123909964238, 'weight_decay': 0.0024888439741274724, 'batch_size': 256}. Best is trial 6 with value: 0.4983708710480454.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.492341
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.15757374324955048, 'dropout_second': 0.12656633390654176, 'lr': 0.006574123909964238, 'weight_decay': 0.0024888439741274724, 'batch_size': 256}
+  Best Value (CSI): 0.498371
+  Best Trial: 6
+  Best Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2274520551367763, 'dropout_second': 0.13356917647186076, 'lr': 0.004479135894707135, 'weight_decay': 0.0008808608187577898, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:26:40,902] Trial 12 finished with value: 0.4832678346699078 and parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.1560921354337186, 'dropout_second': 0.14501955196549568, 'lr': 0.0017558760859252142, 'weight_decay': 0.0024735095809248473, 'batch_size': 64}. Best is trial 6 with value: 0.4983708710480454.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.483268
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.1560921354337186, 'dropout_second': 0.14501955196549568, 'lr': 0.0017558760859252142, 'weight_decay': 0.0024735095809248473, 'batch_size': 64}
+  Best Value (CSI): 0.498371
+  Best Trial: 6
+  Best Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2274520551367763, 'dropout_second': 0.13356917647186076, 'lr': 0.004479135894707135, 'weight_decay': 0.0008808608187577898, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:29:28,576] Trial 13 finished with value: 0.49026276135983754 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.22564436241250496, 'dropout_second': 0.11140256026521468, 'lr': 0.008594494336374605, 'weight_decay': 0.0012203104477035507, 'batch_size': 32}. Best is trial 6 with value: 0.4983708710480454.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.490263
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.22564436241250496, 'dropout_second': 0.11140256026521468, 'lr': 0.008594494336374605, 'weight_decay': 0.0012203104477035507, 'batch_size': 32}
+  Best Value (CSI): 0.498371
+  Best Trial: 6
+  Best Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2274520551367763, 'dropout_second': 0.13356917647186076, 'lr': 0.004479135894707135, 'weight_decay': 0.0008808608187577898, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:30:50,601] Trial 14 finished with value: 0.5001021950987296 and parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.10550202571916634, 'dropout_second': 0.15344786045106396, 'lr': 0.002968059555306397, 'weight_decay': 0.005143106045048729, 'batch_size': 64}. Best is trial 14 with value: 0.5001021950987296.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.500102
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.10550202571916634, 'dropout_second': 0.15344786045106396, 'lr': 0.002968059555306397, 'weight_decay': 0.005143106045048729, 'batch_size': 64}
+  Best Value (CSI): 0.500102
+  Best Trial: 14
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.10550202571916634, 'dropout_second': 0.15344786045106396, 'lr': 0.002968059555306397, 'weight_decay': 0.005143106045048729, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:31:46,774] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.419776
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 5, 'dropout_first': 0.11309620617808475, 'dropout_second': 0.19962641273033632, 'lr': 0.0006664072773567396, 'weight_decay': 0.00634122073476959, 'batch_size': 32}
+  Best Value (CSI): 0.500102
+  Best Trial: 14
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.10550202571916634, 'dropout_second': 0.15344786045106396, 'lr': 0.002968059555306397, 'weight_decay': 0.005143106045048729, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:32:11,298] Trial 16 finished with value: 0.4796980031923785 and parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.1085251849549378, 'dropout_second': 0.1461514529587268, 'lr': 0.0032114127974015487, 'weight_decay': 0.000353585616840619, 'batch_size': 256}. Best is trial 14 with value: 0.5001021950987296.
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.479698
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.1085251849549378, 'dropout_second': 0.1461514529587268, 'lr': 0.0032114127974015487, 'weight_decay': 0.000353585616840619, 'batch_size': 256}
+  Best Value (CSI): 0.500102
+  Best Trial: 14
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.10550202571916634, 'dropout_second': 0.15344786045106396, 'lr': 0.002968059555306397, 'weight_decay': 0.005143106045048729, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:32:19,072] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.392612
+  Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.205201110675377, 'dropout_second': 0.16449185547295808, 'lr': 0.00016060363320174814, 'weight_decay': 0.0008993480112051674, 'batch_size': 128}
+  Best Value (CSI): 0.500102
+  Best Trial: 14
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.10550202571916634, 'dropout_second': 0.15344786045106396, 'lr': 0.002968059555306397, 'weight_decay': 0.005143106045048729, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:34:13,546] Trial 18 finished with value: 0.48074279601533165 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.13587200443028058, 'dropout_second': 0.13589766823488894, 'lr': 0.0010903753494643077, 'weight_decay': 0.0003096121329921245, 'batch_size': 32}. Best is trial 14 with value: 0.5001021950987296.
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.480743
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.13587200443028058, 'dropout_second': 0.13589766823488894, 'lr': 0.0010903753494643077, 'weight_decay': 0.0003096121329921245, 'batch_size': 32}
+  Best Value (CSI): 0.500102
+  Best Trial: 14
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.10550202571916634, 'dropout_second': 0.15344786045106396, 'lr': 0.002968059555306397, 'weight_decay': 0.005143106045048729, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:35:47,804] Trial 19 finished with value: 0.48472945263451184 and parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.13386593314129905, 'dropout_second': 0.09345176625718862, 'lr': 0.004261959888092946, 'weight_decay': 0.006472720429716249, 'batch_size': 64}. Best is trial 14 with value: 0.5001021950987296.
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.484729
+  Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.13386593314129905, 'dropout_second': 0.09345176625718862, 'lr': 0.004261959888092946, 'weight_decay': 0.006472720429716249, 'batch_size': 64}
+  Best Value (CSI): 0.500102
+  Best Trial: 14
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.10550202571916634, 'dropout_second': 0.15344786045106396, 'lr': 0.002968059555306397, 'weight_decay': 0.005143106045048729, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:35:58,390] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.394943
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.19156771396691435, 'dropout_second': 0.15565689431699303, 'lr': 0.001506167756637931, 'weight_decay': 0.00010083635625790831, 'batch_size': 64}
+  Best Value (CSI): 0.500102
+  Best Trial: 14
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.10550202571916634, 'dropout_second': 0.15344786045106396, 'lr': 0.002968059555306397, 'weight_decay': 0.005143106045048729, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:37:34,736] Trial 21 finished with value: 0.500318917633677 and parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.17207707022600813, 'dropout_second': 0.1325325251903285, 'lr': 0.004192204505674924, 'weight_decay': 0.0016432693068091778, 'batch_size': 64}. Best is trial 21 with value: 0.500318917633677.
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.500319
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.17207707022600813, 'dropout_second': 0.1325325251903285, 'lr': 0.004192204505674924, 'weight_decay': 0.0016432693068091778, 'batch_size': 64}
+  Best Value (CSI): 0.500319
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.17207707022600813, 'dropout_second': 0.1325325251903285, 'lr': 0.004192204505674924, 'weight_decay': 0.0016432693068091778, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:38:41,863] Trial 22 finished with value: 0.4908102488511197 and parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.14121606659915759, 'dropout_second': 0.12949379639748784, 'lr': 0.004378808822383254, 'weight_decay': 0.001333094345673052, 'batch_size': 64}. Best is trial 21 with value: 0.500318917633677.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.490810
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.14121606659915759, 'dropout_second': 0.12949379639748784, 'lr': 0.004378808822383254, 'weight_decay': 0.001333094345673052, 'batch_size': 64}
+  Best Value (CSI): 0.500319
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.17207707022600813, 'dropout_second': 0.1325325251903285, 'lr': 0.004192204505674924, 'weight_decay': 0.0016432693068091778, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:39:52,578] Trial 23 finished with value: 0.4769500215150198 and parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.17678078168392428, 'dropout_second': 0.15778417283616314, 'lr': 0.002085790008046795, 'weight_decay': 0.0005926911153884723, 'batch_size': 64}. Best is trial 21 with value: 0.500318917633677.
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.476950
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.17678078168392428, 'dropout_second': 0.15778417283616314, 'lr': 0.002085790008046795, 'weight_decay': 0.0005926911153884723, 'batch_size': 64}
+  Best Value (CSI): 0.500319
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.17207707022600813, 'dropout_second': 0.1325325251903285, 'lr': 0.004192204505674924, 'weight_decay': 0.0016432693068091778, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:40:04,017] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.385554
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.22901606405475217, 'dropout_second': 0.13059204709143366, 'lr': 0.005466890571194488, 'weight_decay': 0.0036819621693368887, 'batch_size': 64}
+  Best Value (CSI): 0.500319
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.17207707022600813, 'dropout_second': 0.1325325251903285, 'lr': 0.004192204505674924, 'weight_decay': 0.0016432693068091778, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:42:25,870] Trial 25 finished with value: 0.4861908462682491 and parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.18677058430594146, 'dropout_second': 0.18213738670597648, 'lr': 0.007778542336448684, 'weight_decay': 0.0016153959743968067, 'batch_size': 32}. Best is trial 21 with value: 0.500318917633677.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.486191
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.18677058430594146, 'dropout_second': 0.18213738670597648, 'lr': 0.007778542336448684, 'weight_decay': 0.0016153959743968067, 'batch_size': 32}
+  Best Value (CSI): 0.500319
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.17207707022600813, 'dropout_second': 0.1325325251903285, 'lr': 0.004192204505674924, 'weight_decay': 0.0016432693068091778, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:43:02,392] Trial 26 finished with value: 0.48964344008508015 and parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.102350282498709, 'dropout_second': 0.15248142904069334, 'lr': 0.003328209733320682, 'weight_decay': 0.0007895308865161863, 'batch_size': 128}. Best is trial 21 with value: 0.500318917633677.
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.489643
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.102350282498709, 'dropout_second': 0.15248142904069334, 'lr': 0.003328209733320682, 'weight_decay': 0.0007895308865161863, 'batch_size': 128}
+  Best Value (CSI): 0.500319
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.17207707022600813, 'dropout_second': 0.1325325251903285, 'lr': 0.004192204505674924, 'weight_decay': 0.0016432693068091778, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:43:33,750] Trial 27 finished with value: 0.49830348568735 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.15114953868470454, 'dropout_second': 0.10353141433262913, 'lr': 0.009608098034503083, 'weight_decay': 0.0037039369368525375, 'batch_size': 256}. Best is trial 21 with value: 0.500318917633677.
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.498303
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.15114953868470454, 'dropout_second': 0.10353141433262913, 'lr': 0.009608098034503083, 'weight_decay': 0.0037039369368525375, 'batch_size': 256}
+  Best Value (CSI): 0.500319
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.17207707022600813, 'dropout_second': 0.1325325251903285, 'lr': 0.004192204505674924, 'weight_decay': 0.0016432693068091778, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:43:51,182] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.417254
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.12395949436942921, 'dropout_second': 0.12240391522734403, 'lr': 0.0020275472808971165, 'weight_decay': 0.00039728787805012693, 'batch_size': 64}
+  Best Value (CSI): 0.500319
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.17207707022600813, 'dropout_second': 0.1325325251903285, 'lr': 0.004192204505674924, 'weight_decay': 0.0016432693068091778, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:44:11,020] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.423860
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.16903821332646202, 'dropout_second': 0.16899892743349026, 'lr': 0.0048729350771543525, 'weight_decay': 0.010062492950254997, 'batch_size': 64}
+  Best Value (CSI): 0.500319
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.17207707022600813, 'dropout_second': 0.1325325251903285, 'lr': 0.004192204505674924, 'weight_decay': 0.0016432693068091778, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:44:33,839] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.410922
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1484052965685374, 'dropout_second': 0.13823365042150682, 'lr': 0.0006720888890320883, 'weight_decay': 0.0017443962820349903, 'batch_size': 32}
+  Best Value (CSI): 0.500319
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.17207707022600813, 'dropout_second': 0.1325325251903285, 'lr': 0.004192204505674924, 'weight_decay': 0.0016432693068091778, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:45:09,866] Trial 31 finished with value: 0.5018401196247365 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.501840
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:45:54,631] Trial 32 finished with value: 0.49892506296826555 and parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.12526402689481908, 'dropout_second': 0.10749907498536435, 'lr': 0.009685956065050126, 'weight_decay': 0.003791770380826292, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.498925
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.12526402689481908, 'dropout_second': 0.10749907498536435, 'lr': 0.009685956065050126, 'weight_decay': 0.003791770380826292, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:46:27,169] Trial 33 finished with value: 0.4946499341661352 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.12117216753169535, 'dropout_second': 0.10033468863178417, 'lr': 0.008719507851994922, 'weight_decay': 0.004034236559680596, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.494650
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.12117216753169535, 'dropout_second': 0.10033468863178417, 'lr': 0.008719507851994922, 'weight_decay': 0.004034236559680596, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:47:00,772] Trial 34 finished with value: 0.4937935821588633 and parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.12559444846525733, 'dropout_second': 0.08686656498423588, 'lr': 0.005847654436640894, 'weight_decay': 0.004442509462489041, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.493794
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.12559444846525733, 'dropout_second': 0.08686656498423588, 'lr': 0.005847654436640894, 'weight_decay': 0.004442509462489041, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:47:26,380] Trial 35 finished with value: 0.48090848618050935 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.10411408605612561, 'dropout_second': 0.11526844320570809, 'lr': 0.0024653615960766907, 'weight_decay': 0.0025767598055517617, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.480908
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.10411408605612561, 'dropout_second': 0.11526844320570809, 'lr': 0.0024653615960766907, 'weight_decay': 0.0025767598055517617, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:47:38,933] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.429236
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.13605278283258085, 'dropout_second': 0.0796792908284849, 'lr': 0.009526010906324855, 'weight_decay': 0.012038093800039328, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:48:12,187] Trial 37 finished with value: 0.4893951054936824 and parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.12143984498020544, 'dropout_second': 0.10660519480885865, 'lr': 0.005897499969215511, 'weight_decay': 0.005738838942235801, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.489395
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.12143984498020544, 'dropout_second': 0.10660519480885865, 'lr': 0.005897499969215511, 'weight_decay': 0.005738838942235801, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:48:46,568] Trial 38 finished with value: 0.4837964919721989 and parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.16007164112588873, 'dropout_second': 0.12259035379969105, 'lr': 0.003439526945965823, 'weight_decay': 0.00967223814676994, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.483796
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.16007164112588873, 'dropout_second': 0.12259035379969105, 'lr': 0.003439526945965823, 'weight_decay': 0.00967223814676994, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:49:14,531] Trial 39 finished with value: 0.48931739614461955 and parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.1443002355636802, 'dropout_second': 0.09445682093522526, 'lr': 0.00633390512925609, 'weight_decay': 0.0019250556434934583, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.489317
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.1443002355636802, 'dropout_second': 0.09445682093522526, 'lr': 0.00633390512925609, 'weight_decay': 0.0019250556434934583, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:49:34,173] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.413580
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.10267391022834667, 'dropout_second': 0.10937257220627174, 'lr': 0.003787149883930079, 'weight_decay': 0.0030565322797995002, 'batch_size': 64}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:50:27,698] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.427592
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.1690331395879545, 'dropout_second': 0.13731187983079118, 'lr': 0.004436854571386125, 'weight_decay': 0.0011085819977763216, 'batch_size': 32}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:50:35,089] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.398985
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2684634366860789, 'dropout_second': 0.11819527856449258, 'lr': 0.0026063790433880143, 'weight_decay': 0.0018258667952315549, 'batch_size': 128}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:51:02,585] Trial 43 finished with value: 0.49469073197343144 and parameters: {'d_main': 64, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.12477840481750918, 'dropout_second': 0.13038442524515764, 'lr': 0.009803639392197082, 'weight_decay': 0.0009462020574056608, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.494691
+  Parameters: {'d_main': 64, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.12477840481750918, 'dropout_second': 0.13038442524515764, 'lr': 0.009803639392197082, 'weight_decay': 0.0009462020574056608, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:52:34,095] Trial 44 finished with value: 0.4969463825756752 and parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.19251946862876396, 'dropout_second': 0.145404294826239, 'lr': 0.005652117963215124, 'weight_decay': 0.005090622483439675, 'batch_size': 64}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.496946
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.19251946862876396, 'dropout_second': 0.145404294826239, 'lr': 0.005652117963215124, 'weight_decay': 0.005090622483439675, 'batch_size': 64}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:53:28,923] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.429550
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.1537234412074191, 'dropout_second': 0.10325092807881275, 'lr': 0.006912432497238321, 'weight_decay': 0.0030544802007734816, 'batch_size': 32}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:53:41,142] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.392428
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.11360824387874222, 'dropout_second': 0.11665479627675532, 'lr': 0.0027501349522590338, 'weight_decay': 0.0013147278901500079, 'batch_size': 64}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:53:48,151] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.379899
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.17878982590236953, 'dropout_second': 0.1279348648606341, 'lr': 0.0015398922818319657, 'weight_decay': 0.0005486591682726807, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:54:30,969] Trial 48 finished with value: 0.4931486263083591 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.1378337434587043, 'dropout_second': 0.07430537791004709, 'lr': 0.003993528330025108, 'weight_decay': 0.0022684069225244277, 'batch_size': 128}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.493149
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.1378337434587043, 'dropout_second': 0.07430537791004709, 'lr': 0.003993528330025108, 'weight_decay': 0.0022684069225244277, 'batch_size': 128}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:54:52,368] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.390582
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.1620194329756845, 'dropout_second': 0.0627296474275086, 'lr': 0.0069606470767078, 'weight_decay': 0.0006507723651433688, 'batch_size': 32}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:55:22,103] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.437143
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2038929040890564, 'dropout_second': 0.1111050786423782, 'lr': 0.0029236433656658683, 'weight_decay': 0.005115982578438752, 'batch_size': 64}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:55:49,242] Trial 51 finished with value: 0.49625625972587467 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1461620934205122, 'dropout_second': 0.0977239389652564, 'lr': 0.008494632922353938, 'weight_decay': 0.004250231344904889, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.496256
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1461620934205122, 'dropout_second': 0.0977239389652564, 'lr': 0.008494632922353938, 'weight_decay': 0.004250231344904889, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:55:57,601] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.429825
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.15168063058303022, 'dropout_second': 0.10492370880143309, 'lr': 0.009483731181946967, 'weight_decay': 0.0034313324964505027, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:56:35,476] Trial 53 finished with value: 0.4940663241323175 and parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.11532109848938576, 'dropout_second': 0.08613978646206887, 'lr': 0.005114490774396992, 'weight_decay': 0.007614561643807118, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.494066
+  Parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.11532109848938576, 'dropout_second': 0.08613978646206887, 'lr': 0.005114490774396992, 'weight_decay': 0.007614561643807118, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:57:03,408] Trial 54 finished with value: 0.5001853870073812 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1308294120142467, 'dropout_second': 0.12152346109180678, 'lr': 0.006923288411319498, 'weight_decay': 0.002869123929036591, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.500185
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1308294120142467, 'dropout_second': 0.12152346109180678, 'lr': 0.006923288411319498, 'weight_decay': 0.002869123929036591, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:57:34,320] Trial 55 finished with value: 0.4940856047864319 and parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.13095328213242857, 'dropout_second': 0.12186061853357058, 'lr': 0.0070223312545909375, 'weight_decay': 0.0025016790823727364, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.494086
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.13095328213242857, 'dropout_second': 0.12186061853357058, 'lr': 0.0070223312545909375, 'weight_decay': 0.0025016790823727364, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:57:54,766] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.406703
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.11524636391008512, 'dropout_second': 0.1495374835400458, 'lr': 0.004105823595104714, 'weight_decay': 0.0010098096138517142, 'batch_size': 32}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 16:58:26,874] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.437092
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.10149771492054023, 'dropout_second': 0.1421746545212068, 'lr': 0.004674865801363971, 'weight_decay': 0.0013181870042940117, 'batch_size': 64}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 16:58:54,492] Trial 58 finished with value: 0.48764841746333104 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.1323608415229153, 'dropout_second': 0.13802316448791413, 'lr': 0.0020671752883511404, 'weight_decay': 0.0015666902231828727, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.487648
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.1323608415229153, 'dropout_second': 0.13802316448791413, 'lr': 0.0020671752883511404, 'weight_decay': 0.0015666902231828727, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:00:12,920] Trial 59 finished with value: 0.4960667439690963 and parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.17433916125146007, 'dropout_second': 0.13218595054509963, 'lr': 0.0033739822076673696, 'weight_decay': 0.0007863208394339452, 'batch_size': 64}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.496067
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.17433916125146007, 'dropout_second': 0.13218595054509963, 'lr': 0.0033739822076673696, 'weight_decay': 0.0007863208394339452, 'batch_size': 64}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:00:54,537] Trial 60 finished with value: 0.4978111592680379 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.16428302399654154, 'dropout_second': 0.15546288713092515, 'lr': 0.007338187960734008, 'weight_decay': 0.0021010596548502796, 'batch_size': 128}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.497811
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.16428302399654154, 'dropout_second': 0.15546288713092515, 'lr': 0.007338187960734008, 'weight_decay': 0.0021010596548502796, 'batch_size': 128}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:01:08,310] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.428433
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.14799343770389908, 'dropout_second': 0.11088347683920384, 'lr': 0.009564310486856359, 'weight_decay': 0.0030955618982584374, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:01:41,851] Trial 62 finished with value: 0.4995360131678453 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.14061743580636596, 'dropout_second': 0.12468033927470049, 'lr': 0.00591892910548161, 'weight_decay': 0.004147924325098708, 'batch_size': 256}. Best is trial 31 with value: 0.5018401196247365.
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.499536
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.14061743580636596, 'dropout_second': 0.12468033927470049, 'lr': 0.00591892910548161, 'weight_decay': 0.004147924325098708, 'batch_size': 256}
+  Best Value (CSI): 0.501840
+  Best Trial: 31
+  Best Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1321333553695069, 'dropout_second': 0.10080420884169183, 'lr': 0.00949822797545903, 'weight_decay': 0.00379353821287717, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:02:16,008] Trial 63 finished with value: 0.5021007490382424 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.1389587658671789, 'dropout_second': 0.12281957437672734, 'lr': 0.005282525693440644, 'weight_decay': 0.004510656609564175, 'batch_size': 256}. Best is trial 63 with value: 0.5021007490382424.
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.502101
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.1389587658671789, 'dropout_second': 0.12281957437672734, 'lr': 0.005282525693440644, 'weight_decay': 0.004510656609564175, 'batch_size': 256}
+  Best Value (CSI): 0.502101
+  Best Trial: 63
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.1389587658671789, 'dropout_second': 0.12281957437672734, 'lr': 0.005282525693440644, 'weight_decay': 0.004510656609564175, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:02:24,029] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.413850
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.11141192647137624, 'dropout_second': 0.12482091499876444, 'lr': 0.005152856471664194, 'weight_decay': 0.006516545862610339, 'batch_size': 256}
+  Best Value (CSI): 0.502101
+  Best Trial: 63
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.1389587658671789, 'dropout_second': 0.12281957437672734, 'lr': 0.005282525693440644, 'weight_decay': 0.004510656609564175, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:02:52,705] Trial 65 finished with value: 0.5101768615009369 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.510177
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:03:24,471] Trial 66 finished with value: 0.5006172439860487 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.14019011620358682, 'dropout_second': 0.11719204802163674, 'lr': 0.006911019708880101, 'weight_decay': 0.004706441199734947, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.500617
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.14019011620358682, 'dropout_second': 0.11719204802163674, 'lr': 0.006911019708880101, 'weight_decay': 0.004706441199734947, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:03:50,680] Trial 67 finished with value: 0.4906478139164386 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.15798445401321312, 'dropout_second': 0.11651890701869634, 'lr': 0.006960683441363903, 'weight_decay': 0.004915136596291544, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.490648
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.15798445401321312, 'dropout_second': 0.11651890701869634, 'lr': 0.006960683441363903, 'weight_decay': 0.004915136596291544, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:04:22,946] Trial 68 finished with value: 0.49056557722157623 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.13233037949919443, 'dropout_second': 0.13205834350235618, 'lr': 0.003695624651953711, 'weight_decay': 0.007525353108892978, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.490566
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.13233037949919443, 'dropout_second': 0.13205834350235618, 'lr': 0.003695624651953711, 'weight_decay': 0.007525353108892978, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:04:59,899] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.471047
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.1170038830498527, 'dropout_second': 0.14306901829621738, 'lr': 0.0047490504512179175, 'weight_decay': 0.0028344446071706573, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:05:05,418] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.412671
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.1282883393687717, 'dropout_second': 0.1169540638861697, 'lr': 0.002849659654658714, 'weight_decay': 0.006111577836671327, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:05:19,221] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.443934
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.14180678067550362, 'dropout_second': 0.1249128789714779, 'lr': 0.005970813405824369, 'weight_decay': 0.004100748936034952, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:05:54,411] Trial 72 finished with value: 0.48924483272074104 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.14090498259327677, 'dropout_second': 0.1259296485175171, 'lr': 0.008104237931619396, 'weight_decay': 0.003459239370669703, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.489245
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.14090498259327677, 'dropout_second': 0.1259296485175171, 'lr': 0.008104237931619396, 'weight_decay': 0.003459239370669703, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:06:08,907] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.427619
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.13919477372676994, 'dropout_second': 0.13587741261064198, 'lr': 0.005758540942682887, 'weight_decay': 0.004803965306311616, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:06:38,655] Trial 74 finished with value: 0.491418820569881 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.10990553773276077, 'dropout_second': 0.10064932794617057, 'lr': 0.003833636616384449, 'weight_decay': 0.0022007272814582746, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.491419
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.10990553773276077, 'dropout_second': 0.10064932794617057, 'lr': 0.003833636616384449, 'weight_decay': 0.0022007272814582746, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:07:14,530] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.435922
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1541932303767295, 'dropout_second': 0.14957787354312455, 'lr': 0.0049799137419889, 'weight_decay': 0.0027209679628175647, 'batch_size': 64}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:07:41,378] Trial 76 finished with value: 0.49952354413292577 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.12210804618269319, 'dropout_second': 0.11249554248557901, 'lr': 0.0076257580443588055, 'weight_decay': 0.00380088052199155, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.499524
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.12210804618269319, 'dropout_second': 0.11249554248557901, 'lr': 0.0076257580443588055, 'weight_decay': 0.00380088052199155, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:08:12,718] Trial 77 finished with value: 0.4918096022549016 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1662201762563913, 'dropout_second': 0.12056393249938326, 'lr': 0.00640383802777463, 'weight_decay': 0.005789108819272811, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.491810
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1662201762563913, 'dropout_second': 0.12056393249938326, 'lr': 0.00640383802777463, 'weight_decay': 0.005789108819272811, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:08:23,568] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.411009
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.12962903865307113, 'dropout_second': 0.13306003634081692, 'lr': 0.0030770137587067216, 'weight_decay': 0.007684980192312645, 'batch_size': 64}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:08:55,995] Trial 79 finished with value: 0.5053945259265531 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.10617628218497391, 'dropout_second': 0.09384052361625367, 'lr': 0.008225087194006248, 'weight_decay': 0.004488612692677221, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.505395
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.10617628218497391, 'dropout_second': 0.09384052361625367, 'lr': 0.008225087194006248, 'weight_decay': 0.004488612692677221, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:09:25,647] Trial 80 finished with value: 0.49453934929086163 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.1058861679920271, 'dropout_second': 0.0934704325170345, 'lr': 0.007628702512129177, 'weight_decay': 0.0033903411907894713, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.494539
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.1058861679920271, 'dropout_second': 0.0934704325170345, 'lr': 0.007628702512129177, 'weight_decay': 0.0033903411907894713, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:09:54,357] Trial 81 finished with value: 0.4800389196789179 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.10047035171630961, 'dropout_second': 0.10820689039897004, 'lr': 0.005966067226634982, 'weight_decay': 0.004870233018925763, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.480039
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.10047035171630961, 'dropout_second': 0.10820689039897004, 'lr': 0.005966067226634982, 'weight_decay': 0.004870233018925763, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:10:22,560] Trial 82 finished with value: 0.49565717190367026 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.11946717545741337, 'dropout_second': 0.12794836420498998, 'lr': 0.004210012168458966, 'weight_decay': 0.004301093429264487, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.495657
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.11946717545741337, 'dropout_second': 0.12794836420498998, 'lr': 0.004210012168458966, 'weight_decay': 0.004301093429264487, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:10:52,812] Trial 83 finished with value: 0.49667073356937647 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.14573402292377755, 'dropout_second': 0.11362962460641411, 'lr': 0.00802658001843935, 'weight_decay': 0.0027953720873128657, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.496671
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.14573402292377755, 'dropout_second': 0.11362962460641411, 'lr': 0.00802658001843935, 'weight_decay': 0.0027953720873128657, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:11:06,319] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.438224
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.13482803188512352, 'dropout_second': 0.12008158878085234, 'lr': 0.005107445173045242, 'weight_decay': 0.005586382387852912, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:11:25,314] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.406308
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1101181538909009, 'dropout_second': 0.09924791765654126, 'lr': 0.006380723947539066, 'weight_decay': 0.002084458887079864, 'batch_size': 64}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:12:00,703] Trial 86 finished with value: 0.4890079139496198 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.12278493766842338, 'dropout_second': 0.10569494812636444, 'lr': 0.00241042932296332, 'weight_decay': 0.004107145684765343, 'batch_size': 128}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.489008
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.12278493766842338, 'dropout_second': 0.10569494812636444, 'lr': 0.00241042932296332, 'weight_decay': 0.004107145684765343, 'batch_size': 128}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:12:14,028] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.434698
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.13807773242283, 'dropout_second': 0.1149605980899084, 'lr': 0.0086649028662491, 'weight_decay': 0.002451055042700613, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:12:20,098] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.414751
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.1553626630156391, 'dropout_second': 0.1386800187053295, 'lr': 0.003448716057834926, 'weight_decay': 0.0032400661442076955, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:12:34,428] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.428296
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.12853449510108006, 'dropout_second': 0.08976646499023232, 'lr': 0.009949443448824015, 'weight_decay': 0.008679478737043674, 'batch_size': 64}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:13:01,606] Trial 90 finished with value: 0.4892954951729864 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.11822667787655435, 'dropout_second': 0.12276809938840762, 'lr': 0.004352459129681158, 'weight_decay': 0.006082833025758841, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.489295
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.11822667787655435, 'dropout_second': 0.12276809938840762, 'lr': 0.004352459129681158, 'weight_decay': 0.006082833025758841, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:13:14,466] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.440860
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.12199958468816874, 'dropout_second': 0.11097301098953635, 'lr': 0.007965884852278992, 'weight_decay': 0.003783961625370588, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:13:41,911] Trial 92 finished with value: 0.49914390961832344 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.10680252737954653, 'dropout_second': 0.10495681344933702, 'lr': 0.007089384593297118, 'weight_decay': 0.0045914333986291685, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.499144
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.10680252737954653, 'dropout_second': 0.10495681344933702, 'lr': 0.007089384593297118, 'weight_decay': 0.0045914333986291685, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:14:07,715] Trial 93 finished with value: 0.4984473731865371 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.14599737764967657, 'dropout_second': 0.12807448959584639, 'lr': 0.005301006526701929, 'weight_decay': 0.003548462320703793, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.498447
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.14599737764967657, 'dropout_second': 0.12807448959584639, 'lr': 0.005301006526701929, 'weight_decay': 0.003548462320703793, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:14:35,352] Trial 94 finished with value: 0.49419243957066095 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.1271426960454167, 'dropout_second': 0.11898673338958386, 'lr': 0.0063388590274632214, 'weight_decay': 0.005406262490734777, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.494192
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.1271426960454167, 'dropout_second': 0.11898673338958386, 'lr': 0.0063388590274632214, 'weight_decay': 0.005406262490734777, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:15:13,326] Trial 95 finished with value: 0.5024841318471255 and parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.11497029515640043, 'dropout_second': 0.11204904313556939, 'lr': 0.008321113906000327, 'weight_decay': 0.007032815561781923, 'batch_size': 256}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.502484
+  Parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.11497029515640043, 'dropout_second': 0.11204904313556939, 'lr': 0.008321113906000327, 'weight_decay': 0.007032815561781923, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:15:24,178] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.406542
+  Parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.13548261891906635, 'dropout_second': 0.09653298298446945, 'lr': 0.004520426085923558, 'weight_decay': 0.01162722103370241, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:15:36,092] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.416438
+  Parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.11334420623798827, 'dropout_second': 0.10300456007052364, 'lr': 0.008305609610624159, 'weight_decay': 0.007221091039606599, 'batch_size': 64}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+[I 2025-12-28 17:15:41,315] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.408526
+  Parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.15113519354018048, 'dropout_second': 0.10885737840121176, 'lr': 0.005606945818706788, 'weight_decay': 0.0017538396470460767, 'batch_size': 256}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2327911943486118, 1: 1.0479861460253617, 2: 0.8099648122962675} (클래스별 샘플 수: {0: 10145, 1: 11934, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 1.2371161196783973, 1: 1.0686249395155552, 2: 0.7962503446374414} (클래스별 샘플 수: {0: 10116, 1: 11711, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 1.240918856387374, 1: 1.07192005710207, 2: 0.7928704173002197} (클래스별 샘플 수: {0: 10085, 1: 11675, 2: 15784})
+[I 2025-12-28 17:16:24,212] Trial 99 finished with value: 0.4948067369690085 and parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.10766770287827106, 'dropout_second': 0.13207105659629897, 'lr': 0.003780104242570227, 'weight_decay': 0.0029872587328690545, 'batch_size': 128}. Best is trial 65 with value: 0.5101768615009369.
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.494807
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.10766770287827106, 'dropout_second': 0.13207105659629897, 'lr': 0.003780104242570227, 'weight_decay': 0.0029872587328690545, 'batch_size': 128}
+  Best Value (CSI): 0.510177
+  Best Trial: 65
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5102
+Best Hyperparameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4353
+  - 최종 CSI: 0.4948
+  - 최고 CSI: 0.5102
+  - 최저 CSI: 0.3799
+  - 평균 CSI: 0.4627
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/resnet_like_ctgan10000_daejeon_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 32, Best CSI: 0.4461
+  Fold 1 학습 완료 (검증 CSI: 0.4461)
+Fold 2 학습 중...
+  Early stopping at epoch 25, Best CSI: 0.5150
+  Fold 2 학습 완료 (검증 CSI: 0.5150)
+Fold 3 학습 중...
+  Early stopping at epoch 40, Best CSI: 0.5364
+  Fold 3 학습 완료 (검증 CSI: 0.5364)
+
+모든 모델 저장 완료: ../save_model/resnet_like_optima/resnet_like_ctgan10000_daejeon.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/resnet_like_optima/scaler/resnet_like_ctgan10000_daejeon_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/resnet_like_optima/resnet_like_ctgan10000_daejeon.pkl
+
+✓ 완료: resnet_like_ctgan10000/resnet_like_ctgan10000_daejeon.py (소요 시간: 4361초)
+
+----------------------------------------
+실행 중: resnet_like_ctgan10000/resnet_like_ctgan10000_gwangju.py
+시작 시간: 2025-12-28 17:17:06
+----------------------------------------
+[I 2025-12-28 17:17:07,424] A new study created in memory with name: no-name-d07b0613-9a52-4445-b10f-d32027d9626b
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:18:02,939] Trial 0 finished with value: 0.47908039892265514 and parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.10602911606292667, 'dropout_second': 0.034151397572362144, 'lr': 0.00020189638025302264, 'weight_decay': 0.002811360136959552, 'batch_size': 128}. Best is trial 0 with value: 0.47908039892265514.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.479080
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.10602911606292667, 'dropout_second': 0.034151397572362144, 'lr': 0.00020189638025302264, 'weight_decay': 0.002811360136959552, 'batch_size': 128}
+  Best Value (CSI): 0.479080
+  Best Trial: 0
+  Best Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.10602911606292667, 'dropout_second': 0.034151397572362144, 'lr': 0.00020189638025302264, 'weight_decay': 0.002811360136959552, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:19:14,226] Trial 1 finished with value: 0.4903023092386456 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.18153142875190895, 'dropout_second': 0.1021517769493917, 'lr': 0.002191484329720406, 'weight_decay': 0.0010422629334642967, 'batch_size': 64}. Best is trial 1 with value: 0.4903023092386456.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.490302
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.18153142875190895, 'dropout_second': 0.1021517769493917, 'lr': 0.002191484329720406, 'weight_decay': 0.0010422629334642967, 'batch_size': 64}
+  Best Value (CSI): 0.490302
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.18153142875190895, 'dropout_second': 0.1021517769493917, 'lr': 0.002191484329720406, 'weight_decay': 0.0010422629334642967, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:22:57,474] Trial 2 finished with value: 0.42703949206436304 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.15823014322238482, 'dropout_second': 0.16790037492173832, 'lr': 1.8004513188550736e-05, 'weight_decay': 0.024191659614961163, 'batch_size': 32}. Best is trial 1 with value: 0.4903023092386456.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.427039
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.15823014322238482, 'dropout_second': 0.16790037492173832, 'lr': 1.8004513188550736e-05, 'weight_decay': 0.024191659614961163, 'batch_size': 32}
+  Best Value (CSI): 0.490302
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.18153142875190895, 'dropout_second': 0.1021517769493917, 'lr': 0.002191484329720406, 'weight_decay': 0.0010422629334642967, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:27:58,631] Trial 3 finished with value: 0.4274741370522624 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.22169536932088896, 'dropout_second': 0.19828570633354248, 'lr': 1.1765671027283218e-05, 'weight_decay': 0.021320293112797067, 'batch_size': 32}. Best is trial 1 with value: 0.4903023092386456.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.427474
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.22169536932088896, 'dropout_second': 0.19828570633354248, 'lr': 1.1765671027283218e-05, 'weight_decay': 0.021320293112797067, 'batch_size': 32}
+  Best Value (CSI): 0.490302
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.18153142875190895, 'dropout_second': 0.1021517769493917, 'lr': 0.002191484329720406, 'weight_decay': 0.0010422629334642967, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:32:19,997] Trial 4 finished with value: 0.4617770283050578 and parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.16318708973039087, 'dropout_second': 0.1891881740499994, 'lr': 5.049412614546352e-05, 'weight_decay': 0.0001289695704527429, 'batch_size': 32}. Best is trial 1 with value: 0.4903023092386456.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.461777
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.16318708973039087, 'dropout_second': 0.1891881740499994, 'lr': 5.049412614546352e-05, 'weight_decay': 0.0001289695704527429, 'batch_size': 32}
+  Best Value (CSI): 0.490302
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.18153142875190895, 'dropout_second': 0.1021517769493917, 'lr': 0.002191484329720406, 'weight_decay': 0.0010422629334642967, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:34:47,794] Trial 5 finished with value: 0.4924025439842607 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.25702891231404534, 'dropout_second': 0.19502399001539705, 'lr': 0.00640257113990963, 'weight_decay': 0.00010715595694358642, 'batch_size': 32}. Best is trial 5 with value: 0.4924025439842607.
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.492403
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.25702891231404534, 'dropout_second': 0.19502399001539705, 'lr': 0.00640257113990963, 'weight_decay': 0.00010715595694358642, 'batch_size': 32}
+  Best Value (CSI): 0.492403
+  Best Trial: 5
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.25702891231404534, 'dropout_second': 0.19502399001539705, 'lr': 0.00640257113990963, 'weight_decay': 0.00010715595694358642, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:36:01,519] Trial 6 finished with value: 0.48981692505168245 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.3852133291312224, 'dropout_second': 0.08681156169821964, 'lr': 0.003866182214940672, 'weight_decay': 0.0001615638366970564, 'batch_size': 64}. Best is trial 5 with value: 0.4924025439842607.
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.489817
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.3852133291312224, 'dropout_second': 0.08681156169821964, 'lr': 0.003866182214940672, 'weight_decay': 0.0001615638366970564, 'batch_size': 64}
+  Best Value (CSI): 0.492403
+  Best Trial: 5
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.25702891231404534, 'dropout_second': 0.19502399001539705, 'lr': 0.00640257113990963, 'weight_decay': 0.00010715595694358642, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:36:07,321] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.218228
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3802272128040992, 'dropout_second': 0.09945228142581226, 'lr': 1.0567014714778762e-05, 'weight_decay': 0.0004130710703314996, 'batch_size': 128}
+  Best Value (CSI): 0.492403
+  Best Trial: 5
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.25702891231404534, 'dropout_second': 0.19502399001539705, 'lr': 0.00640257113990963, 'weight_decay': 0.00010715595694358642, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:36:48,598] Trial 8 finished with value: 0.49756683019274 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.28127513911509844, 'dropout_second': 0.1721614309242186, 'lr': 0.004253442830491379, 'weight_decay': 0.006353152174001674, 'batch_size': 256}. Best is trial 8 with value: 0.49756683019274.
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.497567
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.28127513911509844, 'dropout_second': 0.1721614309242186, 'lr': 0.004253442830491379, 'weight_decay': 0.006353152174001674, 'batch_size': 256}
+  Best Value (CSI): 0.497567
+  Best Trial: 8
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.28127513911509844, 'dropout_second': 0.1721614309242186, 'lr': 0.004253442830491379, 'weight_decay': 0.006353152174001674, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:37:06,306] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.362021
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.25388639193371354, 'dropout_second': 0.12809055575058823, 'lr': 4.515298138519859e-05, 'weight_decay': 0.0007429051100912337, 'batch_size': 32}
+  Best Value (CSI): 0.497567
+  Best Trial: 8
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.28127513911509844, 'dropout_second': 0.1721614309242186, 'lr': 0.004253442830491379, 'weight_decay': 0.006353152174001674, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:37:16,104] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.405671
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.30142535021191336, 'dropout_second': 0.14916538785843145, 'lr': 0.0011628971177890406, 'weight_decay': 0.09028195951137336, 'batch_size': 256}
+  Best Value (CSI): 0.497567
+  Best Trial: 8
+  Best Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.28127513911509844, 'dropout_second': 0.1721614309242186, 'lr': 0.004253442830491379, 'weight_decay': 0.006353152174001674, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:37:56,050] Trial 11 finished with value: 0.5039791986323177 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.29365002435583076, 'dropout_second': 0.1993495411958012, 'lr': 0.007740958984701579, 'weight_decay': 0.003428627442014238, 'batch_size': 256}. Best is trial 11 with value: 0.5039791986323177.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.503979
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.29365002435583076, 'dropout_second': 0.1993495411958012, 'lr': 0.007740958984701579, 'weight_decay': 0.003428627442014238, 'batch_size': 256}
+  Best Value (CSI): 0.503979
+  Best Trial: 11
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.29365002435583076, 'dropout_second': 0.1993495411958012, 'lr': 0.007740958984701579, 'weight_decay': 0.003428627442014238, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:38:09,365] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.444444
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.312872870659854, 'dropout_second': 0.1625378823134742, 'lr': 0.007058321347167949, 'weight_decay': 0.004354328993426728, 'batch_size': 256}
+  Best Value (CSI): 0.503979
+  Best Trial: 11
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.29365002435583076, 'dropout_second': 0.1993495411958012, 'lr': 0.007740958984701579, 'weight_decay': 0.003428627442014238, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:38:40,897] Trial 13 finished with value: 0.5019529379612727 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.31398561198532815, 'dropout_second': 0.15067696091553065, 'lr': 0.008702792737363492, 'weight_decay': 0.004395263304218242, 'batch_size': 256}. Best is trial 11 with value: 0.5039791986323177.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.501953
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.31398561198532815, 'dropout_second': 0.15067696091553065, 'lr': 0.008702792737363492, 'weight_decay': 0.004395263304218242, 'batch_size': 256}
+  Best Value (CSI): 0.503979
+  Best Trial: 11
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.29365002435583076, 'dropout_second': 0.1993495411958012, 'lr': 0.007740958984701579, 'weight_decay': 0.003428627442014238, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:39:09,979] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.434343
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.3380120850245447, 'dropout_second': 0.1374121024184854, 'lr': 0.001264322230945237, 'weight_decay': 0.0018386740748969083, 'batch_size': 256}
+  Best Value (CSI): 0.503979
+  Best Trial: 11
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.29365002435583076, 'dropout_second': 0.1993495411958012, 'lr': 0.007740958984701579, 'weight_decay': 0.003428627442014238, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:39:20,870] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.411022
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.35390499733058284, 'dropout_second': 0.17491848341263605, 'lr': 0.007055453643697042, 'weight_decay': 0.0059684876644793446, 'batch_size': 256}
+  Best Value (CSI): 0.503979
+  Best Trial: 11
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.29365002435583076, 'dropout_second': 0.1993495411958012, 'lr': 0.007740958984701579, 'weight_decay': 0.003428627442014238, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:39:31,294] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.441813
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.32697118147437976, 'dropout_second': 0.14696063358993994, 'lr': 0.009106551194715748, 'weight_decay': 0.0017070747521523897, 'batch_size': 256}
+  Best Value (CSI): 0.503979
+  Best Trial: 11
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.29365002435583076, 'dropout_second': 0.1993495411958012, 'lr': 0.007740958984701579, 'weight_decay': 0.003428627442014238, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:39:40,942] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.424897
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.2834054710376041, 'dropout_second': 0.1944783526589015, 'lr': 0.0006430446538979031, 'weight_decay': 0.009959076399985261, 'batch_size': 256}
+  Best Value (CSI): 0.503979
+  Best Trial: 11
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.29365002435583076, 'dropout_second': 0.1993495411958012, 'lr': 0.007740958984701579, 'weight_decay': 0.003428627442014238, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:39:51,907] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.424508
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.34341624272305565, 'dropout_second': 0.12181145739025818, 'lr': 0.0026721995979963435, 'weight_decay': 0.002900322200706964, 'batch_size': 256}
+  Best Value (CSI): 0.503979
+  Best Trial: 11
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.29365002435583076, 'dropout_second': 0.1993495411958012, 'lr': 0.007740958984701579, 'weight_decay': 0.003428627442014238, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:40:38,279] Trial 19 finished with value: 0.5007606980043126 and parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2952327196853076, 'dropout_second': 0.15681099033565293, 'lr': 0.009447501269865124, 'weight_decay': 0.013026099826222574, 'batch_size': 128}. Best is trial 11 with value: 0.5039791986323177.
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.500761
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2952327196853076, 'dropout_second': 0.15681099033565293, 'lr': 0.009447501269865124, 'weight_decay': 0.013026099826222574, 'batch_size': 128}
+  Best Value (CSI): 0.503979
+  Best Trial: 11
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.29365002435583076, 'dropout_second': 0.1993495411958012, 'lr': 0.007740958984701579, 'weight_decay': 0.003428627442014238, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:41:01,641] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.417219
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.23742583468680872, 'dropout_second': 0.1777954874456383, 'lr': 0.002140616028403285, 'weight_decay': 0.0005220983852515797, 'batch_size': 64}
+  Best Value (CSI): 0.503979
+  Best Trial: 11
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.29365002435583076, 'dropout_second': 0.1993495411958012, 'lr': 0.007740958984701579, 'weight_decay': 0.003428627442014238, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:41:55,108] Trial 21 finished with value: 0.510302842874457 and parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.510303
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:42:39,946] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.493160
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3174146480979304, 'dropout_second': 0.17620264898803042, 'lr': 0.0046058949791762584, 'weight_decay': 0.007704440281369281, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:43:18,471] Trial 23 finished with value: 0.4976394491650236 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2728337344278036, 'dropout_second': 0.15074882125525377, 'lr': 0.0039137017791977405, 'weight_decay': 0.004009765694405672, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.497639
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2728337344278036, 'dropout_second': 0.15074882125525377, 'lr': 0.0039137017791977405, 'weight_decay': 0.004009765694405672, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:43:34,562] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.445279
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.36242324422852545, 'dropout_second': 0.19960417072652956, 'lr': 0.009025052793902755, 'weight_decay': 0.012774651687572459, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:43:40,146] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.405470
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3094966689929888, 'dropout_second': 0.1649374948152798, 'lr': 0.0027152763253729248, 'weight_decay': 0.0018608334235288506, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:43:45,346] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.386503
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.3281312368629343, 'dropout_second': 0.18247102650498656, 'lr': 0.009922696389261312, 'weight_decay': 0.004785074931104206, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:44:21,661] Trial 27 finished with value: 0.5007240926726806 and parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.29457706426071056, 'dropout_second': 0.13573340587405755, 'lr': 0.004978004943327873, 'weight_decay': 0.02181045607322877, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.500724
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.29457706426071056, 'dropout_second': 0.13573340587405755, 'lr': 0.004978004943327873, 'weight_decay': 0.02181045607322877, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:44:34,937] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.424065
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.39781524893152925, 'dropout_second': 0.16021621597193814, 'lr': 0.001346943026082523, 'weight_decay': 0.0030226827030575437, 'batch_size': 64}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:44:43,520] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.427988
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2705121164637923, 'dropout_second': 0.18390282753630527, 'lr': 0.0004106287436112111, 'weight_decay': 0.002857622423510375, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:44:48,604] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.408876
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3569373572787546, 'dropout_second': 0.0046512099341085855, 'lr': 0.00022065522607621232, 'weight_decay': 0.00692023937971954, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:45:31,125] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.498643
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3048520876622298, 'dropout_second': 0.15280431520829857, 'lr': 0.009865739105057943, 'weight_decay': 0.009975454743930573, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:46:26,321] Trial 32 finished with value: 0.5003494014891183 and parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29273975840250793, 'dropout_second': 0.11714308902569062, 'lr': 0.005730224570359354, 'weight_decay': 0.013608009957046606, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.500349
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29273975840250793, 'dropout_second': 0.11714308902569062, 'lr': 0.005730224570359354, 'weight_decay': 0.013608009957046606, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:46:41,075] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.438923
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3199122449635839, 'dropout_second': 0.15808597267713614, 'lr': 0.0031786610859806854, 'weight_decay': 0.034946666454078304, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:47:22,861] Trial 34 finished with value: 0.5000414031039098 and parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.26751507011702164, 'dropout_second': 0.13993950582802445, 'lr': 0.0062446679010066996, 'weight_decay': 0.00472656727098646, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.500041
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.26751507011702164, 'dropout_second': 0.13993950582802445, 'lr': 0.0062446679010066996, 'weight_decay': 0.00472656727098646, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:47:36,417] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.445590
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.29337987604523313, 'dropout_second': 0.1703326674406914, 'lr': 0.001997419192495723, 'weight_decay': 0.015080235107852762, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:47:50,719] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.415718
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.22919937357736947, 'dropout_second': 0.19036122753818746, 'lr': 0.005586094473165576, 'weight_decay': 0.0082499313016532, 'batch_size': 64}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:48:17,417] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.359274
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.20249742391929187, 'dropout_second': 0.1628250868905841, 'lr': 0.003875944039690514, 'weight_decay': 0.03323827872311066, 'batch_size': 32}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:48:26,809] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.297368
+  Parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.25236907655177604, 'dropout_second': 0.18459190532190572, 'lr': 0.009948663568689802, 'weight_decay': 0.016208450787701584, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:48:37,473] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.434077
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.2848206465443869, 'dropout_second': 0.19886035407457267, 'lr': 0.0031145842466909503, 'weight_decay': 0.0013770755008218859, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:50:28,010] Trial 40 finished with value: 0.5006836717029609 and parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.2623644496690097, 'dropout_second': 0.1722237970413506, 'lr': 0.006150806286398, 'weight_decay': 0.002223507312718383, 'batch_size': 32}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.500684
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.2623644496690097, 'dropout_second': 0.1722237970413506, 'lr': 0.006150806286398, 'weight_decay': 0.002223507312718383, 'batch_size': 32}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:51:12,862] Trial 41 finished with value: 0.5058053821906029 and parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.2964757434829088, 'dropout_second': 0.13353399239572095, 'lr': 0.004960070544255159, 'weight_decay': 0.01974549228041944, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.505805
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.2964757434829088, 'dropout_second': 0.13353399239572095, 'lr': 0.004960070544255159, 'weight_decay': 0.01974549228041944, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:51:21,699] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.389541
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 5, 'dropout_first': 0.304737029647376, 'dropout_second': 0.12851742339057912, 'lr': 0.007311704156999013, 'weight_decay': 0.010081375894196304, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:51:29,470] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.339547
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.2781319014911187, 'dropout_second': 0.14454305405129803, 'lr': 0.004705219050768364, 'weight_decay': 0.019621475638256287, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:52:16,909] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.473316
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.32803610797015487, 'dropout_second': 0.10975376722096741, 'lr': 0.006878534281261387, 'weight_decay': 0.030297265827729476, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:52:34,461] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.433775
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.24763609940562392, 'dropout_second': 0.1558844979101491, 'lr': 0.0036971587817119372, 'weight_decay': 0.006067517616194901, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:52:40,740] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.420616
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2965511637773238, 'dropout_second': 0.13190461634299588, 'lr': 0.004782478763805951, 'weight_decay': 0.0036170335534497747, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:52:53,972] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.302510
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3130811612579376, 'dropout_second': 0.14270023681788357, 'lr': 0.007973918321610034, 'weight_decay': 0.005213597216613398, 'batch_size': 64}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:53:17,585] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.374843
+  Parameters: {'d_main': 64, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.3384675475254012, 'dropout_second': 0.1677293458479845, 'lr': 0.0017979768080890533, 'weight_decay': 0.044319136470019234, 'batch_size': 32}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:53:47,929] Trial 49 finished with value: 0.5003232375700751 and parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.2783078953309076, 'dropout_second': 0.0902548627805245, 'lr': 0.007336504075290727, 'weight_decay': 0.018918179594865678, 'batch_size': 256}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.500323
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.2783078953309076, 'dropout_second': 0.0902548627805245, 'lr': 0.007336504075290727, 'weight_decay': 0.018918179594865678, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:53:53,494] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.401478
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.29198890508934844, 'dropout_second': 0.15056872898717638, 'lr': 0.002645227421599404, 'weight_decay': 0.011987795808147444, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:54:36,387] Trial 51 finished with value: 0.5076060929224347 and parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2948756410571978, 'dropout_second': 0.134824839211299, 'lr': 0.004787519375240329, 'weight_decay': 0.022083401682002585, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.507606
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2948756410571978, 'dropout_second': 0.134824839211299, 'lr': 0.004787519375240329, 'weight_decay': 0.022083401682002585, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:54:45,022] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.407621
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 5, 'dropout_first': 0.2596563112049807, 'dropout_second': 0.13958171813263437, 'lr': 0.005338641930726007, 'weight_decay': 0.00791514180383454, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:55:22,901] Trial 53 finished with value: 0.5062149250697119 and parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.30906673393400713, 'dropout_second': 0.1284255198930067, 'lr': 0.0037429615962198603, 'weight_decay': 0.023859608400174863, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.506215
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.30906673393400713, 'dropout_second': 0.1284255198930067, 'lr': 0.0037429615962198603, 'weight_decay': 0.023859608400174863, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:55:37,337] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.415704
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.3154978528012223, 'dropout_second': 0.12639280700440395, 'lr': 0.004162158575930581, 'weight_decay': 0.01951607073317257, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:56:20,889] Trial 55 finished with value: 0.49725007471801264 and parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 4, 'dropout_first': 0.304598688772384, 'dropout_second': 0.11621957884206355, 'lr': 0.003147121930640778, 'weight_decay': 0.025823125803257126, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.497250
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 4, 'dropout_first': 0.304598688772384, 'dropout_second': 0.11621957884206355, 'lr': 0.003147121930640778, 'weight_decay': 0.025823125803257126, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:56:26,483] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.389706
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3257365768184553, 'dropout_second': 0.13697619470656125, 'lr': 0.0056843311453431185, 'weight_decay': 0.043984270471178054, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:57:14,996] Trial 57 finished with value: 0.49591325004610914 and parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.33720288352442107, 'dropout_second': 0.12192676780953887, 'lr': 0.008123229750603781, 'weight_decay': 0.025560764199912378, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.495913
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.33720288352442107, 'dropout_second': 0.12192676780953887, 'lr': 0.008123229750603781, 'weight_decay': 0.025560764199912378, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:58:00,867] Trial 58 finished with value: 0.4915858755115341 and parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 5, 'dropout_first': 0.28703749795284045, 'dropout_second': 0.1490970692977629, 'lr': 0.0023302107499433345, 'weight_decay': 0.016111286056030596, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.491586
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 5, 'dropout_first': 0.28703749795284045, 'dropout_second': 0.1490970692977629, 'lr': 0.0023302107499433345, 'weight_decay': 0.016111286056030596, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:58:10,370] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.411339
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2722986018101947, 'dropout_second': 0.1311749389214824, 'lr': 0.0035315642792651003, 'weight_decay': 0.0115801176732221, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 17:59:25,884] Trial 60 finished with value: 0.4949774716653894 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3062678966437176, 'dropout_second': 0.10682436796455916, 'lr': 0.0015484717456097632, 'weight_decay': 0.0037546007857156734, 'batch_size': 64}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.494977
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3062678966437176, 'dropout_second': 0.10682436796455916, 'lr': 0.0015484717456097632, 'weight_decay': 0.0037546007857156734, 'batch_size': 64}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:59:38,131] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.397343
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2808074162937736, 'dropout_second': 0.1458584167866854, 'lr': 0.007925063415413967, 'weight_decay': 0.008432733288625071, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 17:59:50,447] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.437267
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.317588734462684, 'dropout_second': 0.15663475832784066, 'lr': 0.0047325976325106895, 'weight_decay': 0.014144662006934426, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:00:02,973] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.414404
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29969556919410445, 'dropout_second': 0.17773023793413822, 'lr': 0.006502762578223434, 'weight_decay': 0.006484287429787849, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 18:00:40,367] Trial 64 finished with value: 0.5057225091439846 and parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.28716231357589594, 'dropout_second': 0.16378794694922091, 'lr': 0.00985999689599744, 'weight_decay': 0.023755578407491193, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.505723
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.28716231357589594, 'dropout_second': 0.16378794694922091, 'lr': 0.00985999689599744, 'weight_decay': 0.023755578407491193, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:00:48,910] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.392133
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2668433639983964, 'dropout_second': 0.16585641175606577, 'lr': 0.008467486459737194, 'weight_decay': 0.055771446424436426, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:00:58,590] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.387179
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 4, 'dropout_first': 0.28493999615325033, 'dropout_second': 0.19294173044949844, 'lr': 0.003979098936175429, 'weight_decay': 0.023747560083171155, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:01:07,171] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.425993
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2993248542239547, 'dropout_second': 0.161372088367294, 'lr': 0.006011517912918606, 'weight_decay': 0.019965531838013112, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:01:27,445] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.271691
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3106510951105382, 'dropout_second': 0.13481204510516834, 'lr': 0.009923591959747626, 'weight_decay': 0.027805579873242142, 'batch_size': 32}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:01:35,277] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.384615
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.3222736014635103, 'dropout_second': 0.14599169730766526, 'lr': 0.005051266778622565, 'weight_decay': 0.010591437589396237, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:01:41,471] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.426738
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.34648820729233815, 'dropout_second': 0.17140047968265307, 'lr': 0.006648333230863709, 'weight_decay': 0.004894665757864686, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:01:54,668] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.375318
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29424662889836356, 'dropout_second': 0.15050492637705912, 'lr': 0.008332902452381571, 'weight_decay': 0.016912976253519108, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:02:02,829] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.420238
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.2887460516867691, 'dropout_second': 0.15686515113786376, 'lr': 0.007809429002745246, 'weight_decay': 0.023995657677518385, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:02:15,750] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.431349
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27408974422452753, 'dropout_second': 0.1847695079607154, 'lr': 0.009573584974551245, 'weight_decay': 0.014805088043423752, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 18:02:51,521] Trial 74 finished with value: 0.5055428458137142 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.3034489736215915, 'dropout_second': 0.14035150058606677, 'lr': 0.005497085404893161, 'weight_decay': 0.009688421646675582, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.505543
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.3034489736215915, 'dropout_second': 0.14035150058606677, 'lr': 0.005497085404893161, 'weight_decay': 0.009688421646675582, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:03:04,836] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.429594
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.3108516487117027, 'dropout_second': 0.13814107440895265, 'lr': 0.004281191066553051, 'weight_decay': 0.013011099544518035, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:03:18,511] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.408516
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.29958645493794694, 'dropout_second': 0.14147944472843127, 'lr': 0.002895666141962163, 'weight_decay': 0.009836432730925606, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 18:04:03,402] Trial 77 finished with value: 0.4994740503817032 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.33451419443439223, 'dropout_second': 0.1251719675959626, 'lr': 0.0053979117451423714, 'weight_decay': 0.0054866729827092, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.499474
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.33451419443439223, 'dropout_second': 0.1251719675959626, 'lr': 0.0053979117451423714, 'weight_decay': 0.0054866729827092, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 18:05:17,431] Trial 78 finished with value: 0.493206142357802 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.3198108341221096, 'dropout_second': 0.1331624698983957, 'lr': 0.003493396560068426, 'weight_decay': 0.002516930816145849, 'batch_size': 64}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.493206
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.3198108341221096, 'dropout_second': 0.1331624698983957, 'lr': 0.003493396560068426, 'weight_decay': 0.002516930816145849, 'batch_size': 64}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:05:23,429] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.402676
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.2832747743561931, 'dropout_second': 0.16412792211372026, 'lr': 0.00682694442184334, 'weight_decay': 0.01743801781512719, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:05:38,260] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.399513
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.32943733633481975, 'dropout_second': 0.1530670394375932, 'lr': 0.002371399551467901, 'weight_decay': 0.022126384723674224, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:05:51,076] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.396319
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.3086674248839149, 'dropout_second': 0.16113768170208584, 'lr': 0.005902946686587314, 'weight_decay': 0.013360236502718992, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:06:04,229] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.425401
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2901049837870145, 'dropout_second': 0.17474694156045925, 'lr': 0.00843328009395522, 'weight_decay': 0.008876370066834803, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:06:18,219] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.411980
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.3034701395558957, 'dropout_second': 0.14471103723172385, 'lr': 0.0042672862786121745, 'weight_decay': 0.007127135200185152, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:06:30,255] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.402597
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.27451456961886106, 'dropout_second': 0.16829848031951095, 'lr': 0.009927504448994498, 'weight_decay': 0.01172720951215931, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:06:43,059] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.421615
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.315593945987005, 'dropout_second': 0.15438130996447358, 'lr': 0.006821661192274079, 'weight_decay': 0.0042171558308139265, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:07:28,294] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.431507
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.2963512448624852, 'dropout_second': 0.18043727237895557, 'lr': 0.005281436696276016, 'weight_decay': 0.02038840716651517, 'batch_size': 32}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:07:36,976] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.395859
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.26396466068920393, 'dropout_second': 0.14055010313709548, 'lr': 0.007064398513828445, 'weight_decay': 0.0033956449215794384, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 1.241657571849059, 1: 1.0691727182115904, 2: 0.7940778341793571} (클래스별 샘플 수: {0: 10079, 1: 11705, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 1.2451165721487083, 1: 1.1027109583810615, 2: 0.7751899570531879} (클래스별 샘플 수: {0: 10051, 1: 11349, 2: 16144})
+[I 2025-12-28 18:08:15,471] Trial 88 finished with value: 0.4950647989183758 and parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2857684552942365, 'dropout_second': 0.15870151288125717, 'lr': 0.004608774644680192, 'weight_decay': 0.031637221069014364, 'batch_size': 128}. Best is trial 21 with value: 0.510302842874457.
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.495065
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2857684552942365, 'dropout_second': 0.15870151288125717, 'lr': 0.004608774644680192, 'weight_decay': 0.031637221069014364, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:08:30,887] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.416076
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.2784722378102269, 'dropout_second': 0.12995359946901192, 'lr': 0.0032575723471490676, 'weight_decay': 0.017406404661973948, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:08:39,651] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.344782
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.32093608153024417, 'dropout_second': 0.1511486709415848, 'lr': 0.00834637159663019, 'weight_decay': 0.006054219032227768, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:08:53,611] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.394541
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.29212426387212426, 'dropout_second': 0.13371856227236004, 'lr': 0.0059733650020363915, 'weight_decay': 0.026637377352920023, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:09:07,353] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.387931
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.30245147372027764, 'dropout_second': 0.14696981462537037, 'lr': 0.005296084615830935, 'weight_decay': 0.02189541305579326, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:09:15,024] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.400483
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.31121473170687886, 'dropout_second': 0.13709169957335546, 'lr': 0.007395780919948352, 'weight_decay': 0.0154254294767853, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:09:28,762] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.366005
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2966363037890217, 'dropout_second': 0.14167708485261885, 'lr': 0.0037704910953657036, 'weight_decay': 0.012320510076334836, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:09:42,549] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.421115
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.2694270333532506, 'dropout_second': 0.12449349985261553, 'lr': 0.004744246570176726, 'weight_decay': 0.017821996117125746, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:10:05,131] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.437500
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.30465028734836347, 'dropout_second': 0.19670799625810945, 'lr': 0.008649741555637788, 'weight_decay': 0.035403539277958154, 'batch_size': 64}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:10:20,139] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.376284
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.2803102800074561, 'dropout_second': 0.11895605617932714, 'lr': 0.006238569061276298, 'weight_decay': 0.022410966029930174, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:10:25,284] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.378691
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.2899012318249355, 'dropout_second': 0.13055377638033103, 'lr': 0.004149061737358675, 'weight_decay': 0.009257490447494505, 'batch_size': 256}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2432074221338636, 1: 1.0627690913210968, 2: 0.7970090916815362} (클래스별 샘플 수: {0: 10060, 1: 11768, 2: 15692})
+[I 2025-12-28 18:10:33,370] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.396204
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.31312882599979136, 'dropout_second': 0.15359937967727308, 'lr': 0.0027421549941395455, 'weight_decay': 0.007361002619848907, 'batch_size': 128}
+  Best Value (CSI): 0.510303
+  Best Trial: 21
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5103
+Best Hyperparameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4791
+  - 최종 CSI: 0.3962
+  - 최고 CSI: 0.5103
+  - 최저 CSI: 0.2182
+  - 평균 CSI: 0.4302
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/resnet_like_ctgan10000_gwangju_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 26, Best CSI: 0.4469
+  Fold 1 학습 완료 (검증 CSI: 0.4469)
+Fold 2 학습 중...
+  Early stopping at epoch 27, Best CSI: 0.5365
+  Fold 2 학습 완료 (검증 CSI: 0.5365)
+Fold 3 학습 중...
+  Early stopping at epoch 34, Best CSI: 0.5106
+  Fold 3 학습 완료 (검증 CSI: 0.5106)
+
+모든 모델 저장 완료: ../save_model/resnet_like_optima/resnet_like_ctgan10000_gwangju.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/resnet_like_optima/scaler/resnet_like_ctgan10000_gwangju_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/resnet_like_optima/resnet_like_ctgan10000_gwangju.pkl
+
+✓ 완료: resnet_like_ctgan10000/resnet_like_ctgan10000_gwangju.py (소요 시간: 3257초)
+
+----------------------------------------
+실행 중: resnet_like_ctgan10000/resnet_like_ctgan10000_incheon.py
+시작 시간: 2025-12-28 18:11:23
+----------------------------------------
+[I 2025-12-28 18:11:24,319] A new study created in memory with name: no-name-1b26c438-53de-4d5c-ad5b-c0ee106c8c29
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:13:24,218] Trial 0 finished with value: 0.5127661226947663 and parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.15514961665040872, 'dropout_second': 0.000613238639276359, 'lr': 1.5821472990869903e-05, 'weight_decay': 0.0003053676971687908, 'batch_size': 64}. Best is trial 0 with value: 0.5127661226947663.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.512766
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.15514961665040872, 'dropout_second': 0.000613238639276359, 'lr': 1.5821472990869903e-05, 'weight_decay': 0.0003053676971687908, 'batch_size': 64}
+  Best Value (CSI): 0.512766
+  Best Trial: 0
+  Best Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.15514961665040872, 'dropout_second': 0.000613238639276359, 'lr': 1.5821472990869903e-05, 'weight_decay': 0.0003053676971687908, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:14:13,681] Trial 1 finished with value: 0.5629095276515831 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.12328693838381151, 'dropout_second': 0.13763527906670803, 'lr': 0.0023336919155917094, 'weight_decay': 0.0275093802728357, 'batch_size': 128}. Best is trial 1 with value: 0.5629095276515831.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.562910
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.12328693838381151, 'dropout_second': 0.13763527906670803, 'lr': 0.0023336919155917094, 'weight_decay': 0.0275093802728357, 'batch_size': 128}
+  Best Value (CSI): 0.562910
+  Best Trial: 1
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.12328693838381151, 'dropout_second': 0.13763527906670803, 'lr': 0.0023336919155917094, 'weight_decay': 0.0275093802728357, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:15:46,204] Trial 2 finished with value: 0.46667315306886353 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.36832385644799626, 'dropout_second': 0.12709071932924612, 'lr': 1.3745485284966359e-05, 'weight_decay': 0.0058418232345344165, 'batch_size': 256}. Best is trial 1 with value: 0.5629095276515831.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.466673
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.36832385644799626, 'dropout_second': 0.12709071932924612, 'lr': 1.3745485284966359e-05, 'weight_decay': 0.0058418232345344165, 'batch_size': 256}
+  Best Value (CSI): 0.562910
+  Best Trial: 1
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.12328693838381151, 'dropout_second': 0.13763527906670803, 'lr': 0.0023336919155917094, 'weight_decay': 0.0275093802728357, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:17:18,460] Trial 3 finished with value: 0.5558695490117472 and parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 4, 'dropout_first': 0.12014892487823453, 'dropout_second': 0.10713644631438746, 'lr': 0.007602271080152687, 'weight_decay': 0.0006556310568313393, 'batch_size': 64}. Best is trial 1 with value: 0.5629095276515831.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.555870
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 4, 'dropout_first': 0.12014892487823453, 'dropout_second': 0.10713644631438746, 'lr': 0.007602271080152687, 'weight_decay': 0.0006556310568313393, 'batch_size': 64}
+  Best Value (CSI): 0.562910
+  Best Trial: 1
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.12328693838381151, 'dropout_second': 0.13763527906670803, 'lr': 0.0023336919155917094, 'weight_decay': 0.0275093802728357, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:17:48,896] Trial 4 finished with value: 0.5703778305720454 and parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.267704315042923, 'dropout_second': 0.08629011630270625, 'lr': 0.00396076641912095, 'weight_decay': 0.006112203504299177, 'batch_size': 256}. Best is trial 4 with value: 0.5703778305720454.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.570378
+  Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.267704315042923, 'dropout_second': 0.08629011630270625, 'lr': 0.00396076641912095, 'weight_decay': 0.006112203504299177, 'batch_size': 256}
+  Best Value (CSI): 0.570378
+  Best Trial: 4
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.267704315042923, 'dropout_second': 0.08629011630270625, 'lr': 0.00396076641912095, 'weight_decay': 0.006112203504299177, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:18:09,052] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.423077
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.24619701595654525, 'dropout_second': 0.17368996181282173, 'lr': 0.0001419986504266512, 'weight_decay': 0.006403129570868395, 'batch_size': 32}
+  Best Value (CSI): 0.570378
+  Best Trial: 4
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.267704315042923, 'dropout_second': 0.08629011630270625, 'lr': 0.00396076641912095, 'weight_decay': 0.006112203504299177, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:18:24,541] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.517113
+  Parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.3136981089769826, 'dropout_second': 0.17047399836067348, 'lr': 0.00046250261266420176, 'weight_decay': 0.005167747699805527, 'batch_size': 64}
+  Best Value (CSI): 0.570378
+  Best Trial: 4
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.267704315042923, 'dropout_second': 0.08629011630270625, 'lr': 0.00396076641912095, 'weight_decay': 0.006112203504299177, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:18:32,703] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.450696
+  Parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.3671584030632167, 'dropout_second': 0.09885201181848013, 'lr': 7.865415409354117e-05, 'weight_decay': 0.002703216967950092, 'batch_size': 128}
+  Best Value (CSI): 0.570378
+  Best Trial: 4
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.267704315042923, 'dropout_second': 0.08629011630270625, 'lr': 0.00396076641912095, 'weight_decay': 0.006112203504299177, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:18:45,644] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.448316
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.23007343443361625, 'dropout_second': 0.11248523539331992, 'lr': 6.735384365562118e-05, 'weight_decay': 0.0021226884293248697, 'batch_size': 64}
+  Best Value (CSI): 0.570378
+  Best Trial: 4
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.267704315042923, 'dropout_second': 0.08629011630270625, 'lr': 0.00396076641912095, 'weight_decay': 0.006112203504299177, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:18:50,556] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.348866
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.33180462884827433, 'dropout_second': 0.1312296166947021, 'lr': 1.545323408866729e-05, 'weight_decay': 0.0006526124862010638, 'batch_size': 256}
+  Best Value (CSI): 0.570378
+  Best Trial: 4
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.267704315042923, 'dropout_second': 0.08629011630270625, 'lr': 0.00396076641912095, 'weight_decay': 0.006112203504299177, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:19:31,348] Trial 10 finished with value: 0.5792550794483187 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20514058868054122, 'dropout_second': 0.06328193653505537, 'lr': 0.009122778755365069, 'weight_decay': 0.06553734089678324, 'batch_size': 256}. Best is trial 10 with value: 0.5792550794483187.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.579255
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20514058868054122, 'dropout_second': 0.06328193653505537, 'lr': 0.009122778755365069, 'weight_decay': 0.06553734089678324, 'batch_size': 256}
+  Best Value (CSI): 0.579255
+  Best Trial: 10
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20514058868054122, 'dropout_second': 0.06328193653505537, 'lr': 0.009122778755365069, 'weight_decay': 0.06553734089678324, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:20:04,017] Trial 11 finished with value: 0.5841411648886256 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.584141
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:20:33,948] Trial 12 finished with value: 0.5781831072328564 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20092079391403417, 'dropout_second': 0.057762946563539, 'lr': 0.009493367540823347, 'weight_decay': 0.09418663144957193, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.578183
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20092079391403417, 'dropout_second': 0.057762946563539, 'lr': 0.009493367540823347, 'weight_decay': 0.09418663144957193, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:21:07,453] Trial 13 finished with value: 0.5620294822518478 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.18442334925812717, 'dropout_second': 0.05283188314516293, 'lr': 0.001908741754112009, 'weight_decay': 0.06782094128544719, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.562029
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.18442334925812717, 'dropout_second': 0.05283188314516293, 'lr': 0.001908741754112009, 'weight_decay': 0.06782094128544719, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:21:33,219] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.513174
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.2067207424505364, 'dropout_second': 0.06331008492066134, 'lr': 0.0010737757194817928, 'weight_decay': 0.02772939349822741, 'batch_size': 32}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:21:39,057] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.515675
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.16297031616420765, 'dropout_second': 0.027613256778879047, 'lr': 0.005038556205390101, 'weight_decay': 0.030942534282666533, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:22:08,882] Trial 16 finished with value: 0.5715487273386929 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.2712541624719464, 'dropout_second': 0.07815226040221292, 'lr': 0.008864489447557182, 'weight_decay': 0.08577680590561193, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.571549
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.2712541624719464, 'dropout_second': 0.07815226040221292, 'lr': 0.008864489447557182, 'weight_decay': 0.08577680590561193, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:22:14,845] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.519924
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.23362881285240863, 'dropout_second': 0.03207359066696559, 'lr': 0.0029164813711412187, 'weight_decay': 0.014833185321109336, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:22:43,192] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.458251
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.10095347933942699, 'dropout_second': 0.07625215015565893, 'lr': 0.0011086300170707602, 'weight_decay': 0.04899917681366304, 'batch_size': 32}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:23:17,912] Trial 19 finished with value: 0.5761339414387233 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.16497729377099346, 'dropout_second': 0.045642467111388973, 'lr': 0.0043388841067753614, 'weight_decay': 0.014494825334565377, 'batch_size': 128}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.576134
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.16497729377099346, 'dropout_second': 0.045642467111388973, 'lr': 0.0043388841067753614, 'weight_decay': 0.014494825334565377, 'batch_size': 128}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:23:24,025] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.523663
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.21156980641416379, 'dropout_second': 0.08922842236072806, 'lr': 0.001298295543448349, 'weight_decay': 0.09267497586713777, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:23:55,100] Trial 21 finished with value: 0.5795042308697116 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.19380355597973178, 'dropout_second': 0.06504216836725377, 'lr': 0.008336806278935694, 'weight_decay': 0.08976723060234196, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.579504
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.19380355597973178, 'dropout_second': 0.06504216836725377, 'lr': 0.008336806278935694, 'weight_decay': 0.08976723060234196, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:24:27,212] Trial 22 finished with value: 0.5694630312944008 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.18717419027438012, 'dropout_second': 0.07309246626538969, 'lr': 0.0060615755504717615, 'weight_decay': 0.04817107706128867, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.569463
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.18717419027438012, 'dropout_second': 0.07309246626538969, 'lr': 0.0060615755504717615, 'weight_decay': 0.04817107706128867, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:24:33,083] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.540816
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.2195483171718605, 'dropout_second': 0.06750864667108161, 'lr': 0.00894670936798468, 'weight_decay': 0.04763213923646278, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:24:39,389] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.520270
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.26101162456813326, 'dropout_second': 0.05212384101511036, 'lr': 0.0035004245385519867, 'weight_decay': 0.09925280940252175, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:25:04,145] Trial 25 finished with value: 0.5726608037277362 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.17890549550810436, 'dropout_second': 0.031812981996910725, 'lr': 0.005909693034005845, 'weight_decay': 0.016326620399197186, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.572661
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.17890549550810436, 'dropout_second': 0.031812981996910725, 'lr': 0.005909693034005845, 'weight_decay': 0.016326620399197186, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:25:09,597] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.528185
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.22903732267706922, 'dropout_second': 0.09664714053105078, 'lr': 0.0018496947381671652, 'weight_decay': 0.04828219616544741, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:25:18,051] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.535956
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.1977584200676612, 'dropout_second': 0.08584585215009646, 'lr': 0.0036647181195482693, 'weight_decay': 0.027149621695417694, 'batch_size': 128}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:25:45,716] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.492401
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.24344001841763205, 'dropout_second': 0.04431143766372592, 'lr': 0.005750889018163509, 'weight_decay': 0.07014324854233657, 'batch_size': 32}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:25:57,571] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.447249
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.14656063317578608, 'dropout_second': 0.02797260739658463, 'lr': 0.009676437368618842, 'weight_decay': 0.0518694733732098, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:26:03,375] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.521362
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.15742045627808615, 'dropout_second': 0.0060656022009708335, 'lr': 0.0024376703296282464, 'weight_decay': 0.019653682450768612, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:26:28,434] Trial 31 finished with value: 0.573825167188399 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20709969668379158, 'dropout_second': 0.06451209717837376, 'lr': 0.008941547330659352, 'weight_decay': 0.07570488947847241, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.573825
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20709969668379158, 'dropout_second': 0.06451209717837376, 'lr': 0.008941547330659352, 'weight_decay': 0.07570488947847241, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:26:34,640] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.538989
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.19211882040035305, 'dropout_second': 0.05826610481191407, 'lr': 0.00545326279056444, 'weight_decay': 0.09782435990537601, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:27:03,480] Trial 33 finished with value: 0.5824348428205952 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.1771642388218515, 'dropout_second': 0.06973383997674173, 'lr': 0.009625301301540205, 'weight_decay': 0.03817173399402105, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.582435
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.1771642388218515, 'dropout_second': 0.06973383997674173, 'lr': 0.009625301301540205, 'weight_decay': 0.03817173399402105, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:27:10,205] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.490805
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.13578254435742895, 'dropout_second': 0.07429512258185395, 'lr': 0.0029144302122968845, 'weight_decay': 0.03694928193975725, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:27:16,679] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.504861
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.17488321693742956, 'dropout_second': 0.08533710542487648, 'lr': 0.006944104482640321, 'weight_decay': 0.03469351338720787, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:27:24,829] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.518632
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.14416231475596097, 'dropout_second': 0.11441560895337657, 'lr': 0.003953365067352113, 'weight_decay': 0.06099526833682712, 'batch_size': 128}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:28:15,798] Trial 37 finished with value: 0.5785184709620761 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.1654165769878374, 'dropout_second': 0.06976181062795113, 'lr': 0.006263815360524537, 'weight_decay': 0.021214921557048974, 'batch_size': 64}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.578518
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.1654165769878374, 'dropout_second': 0.06976181062795113, 'lr': 0.006263815360524537, 'weight_decay': 0.021214921557048974, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:28:22,081] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.489664
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.21885063254889353, 'dropout_second': 0.09786709243508959, 'lr': 0.004244937910270902, 'weight_decay': 0.009849843594617822, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:31:02,493] Trial 39 finished with value: 0.5699968590413954 and parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.18568420519956508, 'dropout_second': 0.04283654328456428, 'lr': 0.00712046733390142, 'weight_decay': 0.03350140375953505, 'batch_size': 32}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.569997
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.18568420519956508, 'dropout_second': 0.04283654328456428, 'lr': 0.00712046733390142, 'weight_decay': 0.03350140375953505, 'batch_size': 32}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:31:08,387] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.533453
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.12885597764305146, 'dropout_second': 0.10987207607579583, 'lr': 0.0023742164377937133, 'weight_decay': 0.05893688805259361, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:32:10,578] Trial 41 finished with value: 0.5796729239985915 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.16069185498781358, 'dropout_second': 0.06888572635720953, 'lr': 0.0057659248428165106, 'weight_decay': 0.02279854881128079, 'batch_size': 64}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.579673
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.16069185498781358, 'dropout_second': 0.06888572635720953, 'lr': 0.0057659248428165106, 'weight_decay': 0.02279854881128079, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:32:35,492] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.464449
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.17536669283895295, 'dropout_second': 0.08061834970978504, 'lr': 0.009382600324046975, 'weight_decay': 0.04047559765381361, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:32:48,498] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.533669
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.1980222857967801, 'dropout_second': 0.06376022551375757, 'lr': 0.004951916973792163, 'weight_decay': 0.06860971703858974, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:33:12,707] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.504768
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.15114465142741984, 'dropout_second': 0.08734067521743386, 'lr': 0.006703950622131667, 'weight_decay': 0.022988442425454208, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:34:03,636] Trial 45 finished with value: 0.5670605971229765 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.16907717924065324, 'dropout_second': 0.055875931043323335, 'lr': 0.0032904094317543478, 'weight_decay': 0.03851865673225894, 'batch_size': 64}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.567061
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.16907717924065324, 'dropout_second': 0.055875931043323335, 'lr': 0.0032904094317543478, 'weight_decay': 0.03851865673225894, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:35:41,572] Trial 46 finished with value: 0.5795340761183531 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.19551491084631153, 'dropout_second': 0.06987205545510657, 'lr': 0.007551144998075716, 'weight_decay': 0.06329576301077798, 'batch_size': 64}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.579534
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.19551491084631153, 'dropout_second': 0.06987205545510657, 'lr': 0.007551144998075716, 'weight_decay': 0.06329576301077798, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:35:54,596] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.513531
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.18939965701583925, 'dropout_second': 0.07638194135621212, 'lr': 0.004812966276903438, 'weight_decay': 0.03061778536995492, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:37:08,792] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.548221
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.15316196031421753, 'dropout_second': 0.09216541264613413, 'lr': 0.0072146549366478, 'weight_decay': 0.06620115555147038, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:37:20,954] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.480583
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.11728386134693568, 'dropout_second': 0.10294614848875791, 'lr': 0.0006446250043965927, 'weight_decay': 0.025888416614657662, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:37:33,872] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.522009
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.173968676734222, 'dropout_second': 0.06987763916328175, 'lr': 0.004446337914205332, 'weight_decay': 0.010391671942000679, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:37:39,880] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.512330
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20400948528846796, 'dropout_second': 0.059787090104223235, 'lr': 0.0072640757170855386, 'weight_decay': 0.074054000428946, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:37:55,803] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.514668
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.21697537658858937, 'dropout_second': 0.08077814600946097, 'lr': 0.009576333290640348, 'weight_decay': 0.05599914233284889, 'batch_size': 128}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:38:01,892] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.507517
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.18444902297735388, 'dropout_second': 0.0673193536965183, 'lr': 0.0051290685494335695, 'weight_decay': 0.04160109914923373, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:38:22,140] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.439666
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.1992393757243582, 'dropout_second': 0.051702321713511246, 'lr': 0.007148323775485466, 'weight_decay': 0.08471119991626057, 'batch_size': 32}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:38:57,593] Trial 55 finished with value: 0.5656780197372284 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.1586068675567526, 'dropout_second': 0.062427162813311315, 'lr': 0.0032486875988082266, 'weight_decay': 0.09833490283980313, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.565678
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.1586068675567526, 'dropout_second': 0.062427162813311315, 'lr': 0.0032486875988082266, 'weight_decay': 0.09833490283980313, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:39:11,486] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.487964
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.21080409425696198, 'dropout_second': 0.04904021862125679, 'lr': 0.008126792020105633, 'weight_decay': 0.05592209360375116, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:39:39,422] Trial 57 finished with value: 0.5640445012376928 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2270950724491404, 'dropout_second': 0.08042823747869264, 'lr': 0.009590606517170724, 'weight_decay': 0.043673392916093574, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.564045
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2270950724491404, 'dropout_second': 0.08042823747869264, 'lr': 0.009590606517170724, 'weight_decay': 0.043673392916093574, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:39:45,386] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.519097
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.23718339235768862, 'dropout_second': 0.03962891210108836, 'lr': 0.0018178367231600247, 'weight_decay': 0.073815779456359, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:40:36,566] Trial 59 finished with value: 0.5766848496286282 and parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.250893135684583, 'dropout_second': 0.056181180141915116, 'lr': 0.005508851430838662, 'weight_decay': 0.029703606364904413, 'batch_size': 128}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.576685
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.250893135684583, 'dropout_second': 0.056181180141915116, 'lr': 0.005508851430838662, 'weight_decay': 0.029703606364904413, 'batch_size': 128}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:41:08,657] Trial 60 finished with value: 0.5674876733082393 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.18195235421344125, 'dropout_second': 0.0723878608102759, 'lr': 0.003938528683527668, 'weight_decay': 0.05023199114859893, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.567488
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.18195235421344125, 'dropout_second': 0.0723878608102759, 'lr': 0.003938528683527668, 'weight_decay': 0.05023199114859893, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:42:12,861] Trial 61 finished with value: 0.5766036826955375 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.16539051771231672, 'dropout_second': 0.06991395652959782, 'lr': 0.006207000388321043, 'weight_decay': 0.03888023076048073, 'batch_size': 64}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.576604
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.16539051771231672, 'dropout_second': 0.06991395652959782, 'lr': 0.006207000388321043, 'weight_decay': 0.03888023076048073, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:43:50,615] Trial 62 finished with value: 0.5777054297532142 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.16812065218354194, 'dropout_second': 0.06611300898803958, 'lr': 0.009925032948325077, 'weight_decay': 0.01986535512002112, 'batch_size': 64}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.577705
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.16812065218354194, 'dropout_second': 0.06611300898803958, 'lr': 0.009925032948325077, 'weight_decay': 0.01986535512002112, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:44:02,906] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.507330
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.1965318143011828, 'dropout_second': 0.050903444009789384, 'lr': 0.00765788121019602, 'weight_decay': 0.022653441331525224, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:44:15,302] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.482781
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.16149067819581386, 'dropout_second': 0.05976673924628368, 'lr': 0.006555656150833353, 'weight_decay': 0.07097644380653012, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:45:18,463] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.547459
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.17955499647848555, 'dropout_second': 0.07530191970133732, 'lr': 0.004868130751557983, 'weight_decay': 0.08320577810097078, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:45:47,801] Trial 66 finished with value: 0.5778366461039998 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.19272296498200525, 'dropout_second': 0.037982982331472806, 'lr': 0.005809613295597369, 'weight_decay': 0.05543679265737278, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.577837
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.19272296498200525, 'dropout_second': 0.037982982331472806, 'lr': 0.005809613295597369, 'weight_decay': 0.05543679265737278, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:46:13,146] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.482028
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20870761266646679, 'dropout_second': 0.047343511013752795, 'lr': 0.003892845224576451, 'weight_decay': 0.03161341866175025, 'batch_size': 32}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:46:18,705] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.528926
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.2233598620295389, 'dropout_second': 0.09257451801815646, 'lr': 0.002763497042904115, 'weight_decay': 0.04446300176362779, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:47:45,070] Trial 69 finished with value: 0.5748896457765348 and parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.14148299268876874, 'dropout_second': 0.08277020018801182, 'lr': 0.007703643110923673, 'weight_decay': 0.08404452627189278, 'batch_size': 64}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.574890
+  Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.14148299268876874, 'dropout_second': 0.08277020018801182, 'lr': 0.007703643110923673, 'weight_decay': 0.08404452627189278, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:48:07,201] Trial 70 finished with value: 0.5689938045211088 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.21317063068078307, 'dropout_second': 0.06901965108261425, 'lr': 0.005870280247338706, 'weight_decay': 0.025976573098505495, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.568994
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.21317063068078307, 'dropout_second': 0.06901965108261425, 'lr': 0.005870280247338706, 'weight_decay': 0.025976573098505495, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:48:36,469] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.554081
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20345178000380962, 'dropout_second': 0.06038171351575333, 'lr': 0.00995773565337858, 'weight_decay': 0.09721888537911309, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:49:07,624] Trial 72 finished with value: 0.5803993921328127 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.1909188911968277, 'dropout_second': 0.07460690219389719, 'lr': 0.00745746937153316, 'weight_decay': 0.06301268639986142, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.580399
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.1909188911968277, 'dropout_second': 0.07460690219389719, 'lr': 0.00745746937153316, 'weight_decay': 0.06301268639986142, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:49:37,840] Trial 73 finished with value: 0.5758118780663465 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.18658204340010756, 'dropout_second': 0.07586919887619394, 'lr': 0.00814406585341076, 'weight_decay': 0.05967655793751578, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.575812
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.18658204340010756, 'dropout_second': 0.07586919887619394, 'lr': 0.00814406585341076, 'weight_decay': 0.05967655793751578, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:50:06,042] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.538756
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.19173301196448103, 'dropout_second': 0.08569584421679786, 'lr': 0.004768177328561972, 'weight_decay': 0.06389560308838842, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:50:11,817] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.530902
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.170920772599851, 'dropout_second': 0.05341544953536054, 'lr': 0.00798184647512136, 'weight_decay': 0.045250007690448964, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:50:37,197] Trial 76 finished with value: 0.576591450187533 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.15492754056220923, 'dropout_second': 0.06352446025139942, 'lr': 0.006239817121625923, 'weight_decay': 0.03357879641811616, 'batch_size': 256}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.576591
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.15492754056220923, 'dropout_second': 0.06352446025139942, 'lr': 0.006239817121625923, 'weight_decay': 0.03357879641811616, 'batch_size': 256}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:50:51,337] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.500317
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.17621174208567136, 'dropout_second': 0.07203383290234223, 'lr': 0.003374983751276673, 'weight_decay': 0.050998910617856906, 'batch_size': 64}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:51:35,346] Trial 78 finished with value: 0.5840032775970784 and parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.20229345808050572, 'dropout_second': 0.07853155111538822, 'lr': 0.00398499901136722, 'weight_decay': 0.0782709827248594, 'batch_size': 128}. Best is trial 11 with value: 0.5841411648886256.
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.584003
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.20229345808050572, 'dropout_second': 0.07853155111538822, 'lr': 0.00398499901136722, 'weight_decay': 0.0782709827248594, 'batch_size': 128}
+  Best Value (CSI): 0.584141
+  Best Trial: 11
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.20822264231701257, 'dropout_second': 0.06245159580272064, 'lr': 0.008524623500597047, 'weight_decay': 0.0856435873305381, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:52:19,180] Trial 79 finished with value: 0.5847579866844873 and parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}. Best is trial 79 with value: 0.5847579866844873.
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.584758
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:52:59,968] Trial 80 finished with value: 0.5723876417432404 and parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22448001728075268, 'dropout_second': 0.09255548755567987, 'lr': 0.0037578346892317817, 'weight_decay': 0.08263817382141983, 'batch_size': 128}. Best is trial 79 with value: 0.5847579866844873.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.572388
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22448001728075268, 'dropout_second': 0.09255548755567987, 'lr': 0.0037578346892317817, 'weight_decay': 0.08263817382141983, 'batch_size': 128}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:53:15,686] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.472492
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.20180936230908528, 'dropout_second': 0.07976095790683052, 'lr': 0.004691984743405313, 'weight_decay': 0.06590530698990661, 'batch_size': 128}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:53:24,181] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.536465
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2163819493450892, 'dropout_second': 0.07571519241862276, 'lr': 0.008123787741300782, 'weight_decay': 0.07962057413266443, 'batch_size': 128}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:54:02,888] Trial 83 finished with value: 0.578119131518498 and parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.1943007239982849, 'dropout_second': 0.06600197330589654, 'lr': 0.005687142453894287, 'weight_decay': 0.09771053496761592, 'batch_size': 128}. Best is trial 79 with value: 0.5847579866844873.
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.578119
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.1943007239982849, 'dropout_second': 0.06600197330589654, 'lr': 0.005687142453894287, 'weight_decay': 0.09771053496761592, 'batch_size': 128}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:54:46,002] Trial 84 finished with value: 0.5770986193357213 and parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.21015460362469826, 'dropout_second': 0.05888946934242371, 'lr': 0.004116171497514842, 'weight_decay': 0.06209877995049221, 'batch_size': 128}. Best is trial 79 with value: 0.5847579866844873.
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.577099
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.21015460362469826, 'dropout_second': 0.05888946934242371, 'lr': 0.004116171497514842, 'weight_decay': 0.06209877995049221, 'batch_size': 128}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:55:41,162] Trial 85 finished with value: 0.5810532763989003 and parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.23375405851439734, 'dropout_second': 0.08536373503521381, 'lr': 0.0065410638310150985, 'weight_decay': 0.07510992519146122, 'batch_size': 128}. Best is trial 79 with value: 0.5847579866844873.
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.581053
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.23375405851439734, 'dropout_second': 0.08536373503521381, 'lr': 0.0065410638310150985, 'weight_decay': 0.07510992519146122, 'batch_size': 128}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:56:26,328] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.551070
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.23517151270554082, 'dropout_second': 0.08915224187232738, 'lr': 0.0027585214318577374, 'weight_decay': 0.0775479833333343, 'batch_size': 128}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:56:34,703] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.534161
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.22374134817635621, 'dropout_second': 0.08487667563127198, 'lr': 0.006632342191535044, 'weight_decay': 0.03608578775007095, 'batch_size': 128}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:57:16,447] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.548946
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.18103205591642293, 'dropout_second': 0.07778384630261787, 'lr': 0.008095661992038138, 'weight_decay': 0.051418647902448315, 'batch_size': 128}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:58:00,802] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.548690
+  Parameters: {'d_main': 64, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.20415118528120704, 'dropout_second': 0.08250538442918574, 'lr': 0.005250432468441987, 'weight_decay': 0.04360449583361173, 'batch_size': 128}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:58:08,562] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.414248
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.2171578369072941, 'dropout_second': 0.07256262988378882, 'lr': 0.004432937474943794, 'weight_decay': 0.06986725168995579, 'batch_size': 128}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:58:17,075] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.518565
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.1895251455633306, 'dropout_second': 0.06552523270410161, 'lr': 0.008576656310035584, 'weight_decay': 0.08759637553803917, 'batch_size': 128}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:58:43,888] Trial 92 finished with value: 0.5774905714384017 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.23151106542796313, 'dropout_second': 0.07021100871603264, 'lr': 0.007188049224669478, 'weight_decay': 0.05584121274019998, 'batch_size': 256}. Best is trial 79 with value: 0.5847579866844873.
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.577491
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.23151106542796313, 'dropout_second': 0.07021100871603264, 'lr': 0.007188049224669478, 'weight_decay': 0.05584121274019998, 'batch_size': 256}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:59:07,279] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.495101
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.19744285023796654, 'dropout_second': 0.05493033662648922, 'lr': 0.009960886731473682, 'weight_decay': 0.07046823387208702, 'batch_size': 32}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:59:16,005] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.510463
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.20611542300892216, 'dropout_second': 0.07852440065359567, 'lr': 0.006632903534172637, 'weight_decay': 0.09969042905199386, 'batch_size': 128}
+  Best Value (CSI): 0.584758
+  Best Trial: 79
+  Best Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22130071005923063, 'dropout_second': 0.07884344024161535, 'lr': 0.003921848486349116, 'weight_decay': 0.081406016145236, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 18:59:48,075] Trial 95 finished with value: 0.5876200434398301 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.213366405042877, 'dropout_second': 0.0616930432275245, 'lr': 0.005092968501532562, 'weight_decay': 0.06153947659623341, 'batch_size': 256}. Best is trial 95 with value: 0.5876200434398301.
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.587620
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.213366405042877, 'dropout_second': 0.0616930432275245, 'lr': 0.005092968501532562, 'weight_decay': 0.06153947659623341, 'batch_size': 256}
+  Best Value (CSI): 0.587620
+  Best Trial: 95
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.213366405042877, 'dropout_second': 0.0616930432275245, 'lr': 0.005092968501532562, 'weight_decay': 0.06153947659623341, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:59:54,012] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.522210
+  Parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2140894907846063, 'dropout_second': 0.062115610677817686, 'lr': 0.005287126760419305, 'weight_decay': 0.05772222037632493, 'batch_size': 256}
+  Best Value (CSI): 0.587620
+  Best Trial: 95
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.213366405042877, 'dropout_second': 0.0616930432275245, 'lr': 0.005092968501532562, 'weight_decay': 0.06153947659623341, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 18:59:59,733] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.503364
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.23966693776749085, 'dropout_second': 0.08853331985655667, 'lr': 0.0030555506076344057, 'weight_decay': 0.03859809498203023, 'batch_size': 256}
+  Best Value (CSI): 0.587620
+  Best Trial: 95
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.213366405042877, 'dropout_second': 0.0616930432275245, 'lr': 0.005092968501532562, 'weight_decay': 0.06153947659623341, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 1.2077462523322395, 1: 0.9975820379965458, 2: 0.8550021634670129} (클래스별 샘플 수: {0: 10362, 1: 12545, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 1.2141910028783027, 1: 0.9899277540473553, 2: 0.8574626013474934} (클래스별 샘플 수: {0: 10307, 1: 12642, 2: 14595})
+[I 2025-12-28 19:00:54,476] Trial 98 finished with value: 0.5723554201291846 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.18481797297827424, 'dropout_second': 0.0735548043893911, 'lr': 0.004314985213919695, 'weight_decay': 0.04697185766292859, 'batch_size': 128}. Best is trial 95 with value: 0.5876200434398301.
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.572355
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.18481797297827424, 'dropout_second': 0.0735548043893911, 'lr': 0.004314985213919695, 'weight_decay': 0.04697185766292859, 'batch_size': 128}
+  Best Value (CSI): 0.587620
+  Best Trial: 95
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.213366405042877, 'dropout_second': 0.0616930432275245, 'lr': 0.005092968501532562, 'weight_decay': 0.06153947659623341, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.2061593853473496, 1: 0.9928289804450795, 2: 0.8593284778525949} (클래스별 샘플 수: {0: 10369, 1: 12597, 2: 14554})
+[I 2025-12-28 19:01:00,372] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.507228
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.22011759183163876, 'dropout_second': 0.056647918094933455, 'lr': 0.003372613305490138, 'weight_decay': 0.07445140455956582, 'batch_size': 256}
+  Best Value (CSI): 0.587620
+  Best Trial: 95
+  Best Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.213366405042877, 'dropout_second': 0.0616930432275245, 'lr': 0.005092968501532562, 'weight_decay': 0.06153947659623341, 'batch_size': 256}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5876
+Best Hyperparameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.213366405042877, 'dropout_second': 0.0616930432275245, 'lr': 0.005092968501532562, 'weight_decay': 0.06153947659623341, 'batch_size': 256}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5128
+  - 최종 CSI: 0.5072
+  - 최고 CSI: 0.5876
+  - 최저 CSI: 0.3489
+  - 평균 CSI: 0.5324
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/resnet_like_ctgan10000_incheon_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 27, Best CSI: 0.5670
+  Fold 1 학습 완료 (검증 CSI: 0.5670)
+Fold 2 학습 중...
+  Early stopping at epoch 24, Best CSI: 0.6140
+  Fold 2 학습 완료 (검증 CSI: 0.6140)
+Fold 3 학습 중...
+  Early stopping at epoch 27, Best CSI: 0.5602
+  Fold 3 학습 완료 (검증 CSI: 0.5602)
+
+모든 모델 저장 완료: ../save_model/resnet_like_optima/resnet_like_ctgan10000_incheon.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/resnet_like_optima/scaler/resnet_like_ctgan10000_incheon_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/resnet_like_optima/resnet_like_ctgan10000_incheon.pkl
+
+✓ 완료: resnet_like_ctgan10000/resnet_like_ctgan10000_incheon.py (소요 시간: 3007초)
+
+----------------------------------------
+실행 중: resnet_like_ctgan10000/resnet_like_ctgan10000_seoul.py
+시작 시간: 2025-12-28 19:01:30
+----------------------------------------
+[I 2025-12-28 19:01:31,678] A new study created in memory with name: no-name-20209b53-7c22-4a0d-81d6-b5144788683e
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:04:54,754] Trial 0 finished with value: 0.47985360135529004 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.25963877110494227, 'dropout_second': 0.16105581067838967, 'lr': 1.87946392440541e-05, 'weight_decay': 0.00033945875790719463, 'batch_size': 32}. Best is trial 0 with value: 0.47985360135529004.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.479854
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.25963877110494227, 'dropout_second': 0.16105581067838967, 'lr': 1.87946392440541e-05, 'weight_decay': 0.00033945875790719463, 'batch_size': 32}
+  Best Value (CSI): 0.479854
+  Best Trial: 0
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.25963877110494227, 'dropout_second': 0.16105581067838967, 'lr': 1.87946392440541e-05, 'weight_decay': 0.00033945875790719463, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:05:22,146] Trial 1 finished with value: 0.5611295173727503 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3759458735688056, 'dropout_second': 0.11444973879750078, 'lr': 0.0018526685201217517, 'weight_decay': 0.0002707998567947091, 'batch_size': 256}. Best is trial 1 with value: 0.5611295173727503.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.561130
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3759458735688056, 'dropout_second': 0.11444973879750078, 'lr': 0.0018526685201217517, 'weight_decay': 0.0002707998567947091, 'batch_size': 256}
+  Best Value (CSI): 0.561130
+  Best Trial: 1
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3759458735688056, 'dropout_second': 0.11444973879750078, 'lr': 0.0018526685201217517, 'weight_decay': 0.0002707998567947091, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:06:29,637] Trial 2 finished with value: 0.581564899591627 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}. Best is trial 2 with value: 0.581564899591627.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.581565
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:07:23,430] Trial 3 finished with value: 0.48836415108745657 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.2595607819244277, 'dropout_second': 0.1928586564775638, 'lr': 5.3732836270165346e-05, 'weight_decay': 0.05523318453071954, 'batch_size': 256}. Best is trial 2 with value: 0.581564899591627.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.488364
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.2595607819244277, 'dropout_second': 0.1928586564775638, 'lr': 5.3732836270165346e-05, 'weight_decay': 0.05523318453071954, 'batch_size': 256}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:10:33,789] Trial 4 finished with value: 0.44228124484354 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.11491028089009911, 'dropout_second': 0.19165309242904827, 'lr': 1.0576680234683087e-05, 'weight_decay': 0.00013557202720823033, 'batch_size': 64}. Best is trial 2 with value: 0.581564899591627.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.442281
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.11491028089009911, 'dropout_second': 0.19165309242904827, 'lr': 1.0576680234683087e-05, 'weight_decay': 0.00013557202720823033, 'batch_size': 64}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:11:17,092] Trial 5 finished with value: 0.4981061550037082 and parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.14477248572814372, 'dropout_second': 0.17423554396655283, 'lr': 3.024241846172442e-05, 'weight_decay': 0.03464124083808809, 'batch_size': 256}. Best is trial 2 with value: 0.581564899591627.
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.498106
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.14477248572814372, 'dropout_second': 0.17423554396655283, 'lr': 3.024241846172442e-05, 'weight_decay': 0.03464124083808809, 'batch_size': 256}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:12:52,208] Trial 6 finished with value: 0.565982129572946 and parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.19200863283066327, 'dropout_second': 0.12108623107195636, 'lr': 0.0005189579789637183, 'weight_decay': 0.00014853818601327822, 'batch_size': 32}. Best is trial 2 with value: 0.581564899591627.
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.565982
+  Parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.19200863283066327, 'dropout_second': 0.12108623107195636, 'lr': 0.0005189579789637183, 'weight_decay': 0.00014853818601327822, 'batch_size': 32}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:13:24,470] Trial 7 finished with value: 0.5483365154936504 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.16912987945681845, 'dropout_second': 0.13342132341315766, 'lr': 0.00047211358979749364, 'weight_decay': 0.04588833642844226, 'batch_size': 256}. Best is trial 2 with value: 0.581564899591627.
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.548337
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.16912987945681845, 'dropout_second': 0.13342132341315766, 'lr': 0.00047211358979749364, 'weight_decay': 0.04588833642844226, 'batch_size': 256}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:14:07,753] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.429306
+  Parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.35718274196052346, 'dropout_second': 0.16939880123661993, 'lr': 5.676255693966712e-05, 'weight_decay': 0.0005212097607009329, 'batch_size': 32}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:14:38,351] Trial 9 finished with value: 0.5471784574308184 and parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.19452329372500032, 'dropout_second': 0.12834927908154073, 'lr': 0.0004259567358516489, 'weight_decay': 0.00014372636058650298, 'batch_size': 256}. Best is trial 2 with value: 0.581564899591627.
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.547178
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.19452329372500032, 'dropout_second': 0.12834927908154073, 'lr': 0.0004259567358516489, 'weight_decay': 0.00014372636058650298, 'batch_size': 256}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:15:43,522] Trial 10 finished with value: 0.5660021752349561 and parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.3106881904525484, 'dropout_second': 0.06223417105149689, 'lr': 0.009347058236747356, 'weight_decay': 0.001884441377436021, 'batch_size': 64}. Best is trial 2 with value: 0.581564899591627.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.566002
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.3106881904525484, 'dropout_second': 0.06223417105149689, 'lr': 0.009347058236747356, 'weight_decay': 0.001884441377436021, 'batch_size': 64}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:16:14,404] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.465015
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.31151549856908567, 'dropout_second': 0.04992017714099183, 'lr': 0.009963202084764018, 'weight_decay': 0.0016706637593940938, 'batch_size': 64}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:16:46,482] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.474359
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.308816828357394, 'dropout_second': 0.06460820594796883, 'lr': 0.007383815021278748, 'weight_decay': 0.002418938168784523, 'batch_size': 64}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:17:42,073] Trial 13 finished with value: 0.5718408630221726 and parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.22454364337972715, 'dropout_second': 0.015567918028581804, 'lr': 0.0037532121503247055, 'weight_decay': 0.006300653982662737, 'batch_size': 128}. Best is trial 2 with value: 0.581564899591627.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.571841
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.22454364337972715, 'dropout_second': 0.015567918028581804, 'lr': 0.0037532121503247055, 'weight_decay': 0.006300653982662737, 'batch_size': 128}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:18:26,333] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.551510
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 4, 'dropout_first': 0.22733465361420402, 'dropout_second': 0.0053419385834071464, 'lr': 0.0027477972192838152, 'weight_decay': 0.010557089161404281, 'batch_size': 128}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:19:13,393] Trial 15 finished with value: 0.5641733360378229 and parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.23607585633808867, 'dropout_second': 0.08977746564335164, 'lr': 0.0023131829857839575, 'weight_decay': 0.006569234792919093, 'batch_size': 128}. Best is trial 2 with value: 0.581564899591627.
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.564173
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.23607585633808867, 'dropout_second': 0.08977746564335164, 'lr': 0.0023131829857839575, 'weight_decay': 0.006569234792919093, 'batch_size': 128}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:19:29,104] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.461654
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.21171560643118917, 'dropout_second': 0.0012308827904578268, 'lr': 0.0010626389612866625, 'weight_decay': 0.0008770761290269027, 'batch_size': 128}
+  Best Value (CSI): 0.581565
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24356994284057773, 'dropout_second': 0.09454027136712742, 'lr': 0.004883426899099399, 'weight_decay': 0.0009444569993068374, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:20:19,153] Trial 17 finished with value: 0.5877804152156907 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2698842155940411, 'dropout_second': 0.030590652881771097, 'lr': 0.004146144821548479, 'weight_decay': 0.0053502995905921604, 'batch_size': 128}. Best is trial 17 with value: 0.5877804152156907.
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.587780
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2698842155940411, 'dropout_second': 0.030590652881771097, 'lr': 0.004146144821548479, 'weight_decay': 0.0053502995905921604, 'batch_size': 128}
+  Best Value (CSI): 0.587780
+  Best Trial: 17
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2698842155940411, 'dropout_second': 0.030590652881771097, 'lr': 0.004146144821548479, 'weight_decay': 0.0053502995905921604, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:21:31,133] Trial 18 finished with value: 0.5639535674049941 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.27776959792825867, 'dropout_second': 0.030931333393582117, 'lr': 0.0011266868747925988, 'weight_decay': 0.001072644503162475, 'batch_size': 64}. Best is trial 17 with value: 0.5877804152156907.
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.563954
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.27776959792825867, 'dropout_second': 0.030931333393582117, 'lr': 0.0011266868747925988, 'weight_decay': 0.001072644503162475, 'batch_size': 64}
+  Best Value (CSI): 0.587780
+  Best Trial: 17
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2698842155940411, 'dropout_second': 0.030590652881771097, 'lr': 0.004146144821548479, 'weight_decay': 0.0053502995905921604, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:22:15,350] Trial 19 finished with value: 0.5435908966077766 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.2626243280871337, 'dropout_second': 0.08394249577633747, 'lr': 0.0001805347810175134, 'weight_decay': 0.004098529837227666, 'batch_size': 128}. Best is trial 17 with value: 0.5877804152156907.
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.543591
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.2626243280871337, 'dropout_second': 0.08394249577633747, 'lr': 0.0001805347810175134, 'weight_decay': 0.004098529837227666, 'batch_size': 128}
+  Best Value (CSI): 0.587780
+  Best Trial: 17
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2698842155940411, 'dropout_second': 0.030590652881771097, 'lr': 0.004146144821548479, 'weight_decay': 0.0053502995905921604, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:22:37,437] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.457326
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.287904909525981, 'dropout_second': 0.04706246657991901, 'lr': 0.00464225337250244, 'weight_decay': 0.015073034727815697, 'batch_size': 64}
+  Best Value (CSI): 0.587780
+  Best Trial: 17
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2698842155940411, 'dropout_second': 0.030590652881771097, 'lr': 0.004146144821548479, 'weight_decay': 0.0053502995905921604, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:23:19,945] Trial 21 finished with value: 0.5667645111828707 and parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.23738955565074368, 'dropout_second': 0.029727190795918262, 'lr': 0.006350942844576396, 'weight_decay': 0.003968768254472755, 'batch_size': 128}. Best is trial 17 with value: 0.5877804152156907.
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.566765
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.23738955565074368, 'dropout_second': 0.029727190795918262, 'lr': 0.006350942844576396, 'weight_decay': 0.003968768254472755, 'batch_size': 128}
+  Best Value (CSI): 0.587780
+  Best Trial: 17
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2698842155940411, 'dropout_second': 0.030590652881771097, 'lr': 0.004146144821548479, 'weight_decay': 0.0053502995905921604, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:23:32,868] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.463670
+  Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.2133204292587146, 'dropout_second': 0.019551583118935685, 'lr': 0.0040907281978436955, 'weight_decay': 0.006282321579295619, 'batch_size': 128}
+  Best Value (CSI): 0.587780
+  Best Trial: 17
+  Best Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2698842155940411, 'dropout_second': 0.030590652881771097, 'lr': 0.004146144821548479, 'weight_decay': 0.0053502995905921604, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:24:27,226] Trial 23 finished with value: 0.5889744558864985 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.588974
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:24:39,859] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.473643
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.28612408088567054, 'dropout_second': 0.07721279209206522, 'lr': 0.005239843694120498, 'weight_decay': 0.08873052604141042, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:25:25,681] Trial 25 finished with value: 0.5613330317958751 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.24765056027270393, 'dropout_second': 0.03784968100973721, 'lr': 0.0013725987758180516, 'weight_decay': 0.030641384863491265, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.561333
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.24765056027270393, 'dropout_second': 0.03784968100973721, 'lr': 0.0013725987758180516, 'weight_decay': 0.030641384863491265, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:25:59,807] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.568643
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.19483470358254545, 'dropout_second': 0.10134647828025327, 'lr': 0.0032252518824221195, 'weight_decay': 0.015769627589936573, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:26:21,254] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.439513
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.33564805081608745, 'dropout_second': 0.01803833651226353, 'lr': 0.0021935367672792474, 'weight_decay': 0.090781243609659, 'batch_size': 64}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:26:57,102] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.469888
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2714388704084465, 'dropout_second': 0.05708586472029406, 'lr': 0.006427905017163249, 'weight_decay': 0.002894932905353898, 'batch_size': 32}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:27:25,338] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.472411
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.25161587668179863, 'dropout_second': 0.043146386983312794, 'lr': 0.0035385951264350784, 'weight_decay': 0.0011961755070259534, 'batch_size': 32}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:28:10,639] Trial 30 finished with value: 0.5652580471744071 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.24745251682885624, 'dropout_second': 0.06995888776563136, 'lr': 0.0008510985423765403, 'weight_decay': 0.0005606770789215859, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.565258
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.24745251682885624, 'dropout_second': 0.06995888776563136, 'lr': 0.0008510985423765403, 'weight_decay': 0.0005606770789215859, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:29:01,099] Trial 31 finished with value: 0.5804185274414206 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.23540963920896305, 'dropout_second': 0.016001224834631515, 'lr': 0.004308515983382599, 'weight_decay': 0.01739889397903351, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.580419
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.23540963920896305, 'dropout_second': 0.016001224834631515, 'lr': 0.004308515983382599, 'weight_decay': 0.01739889397903351, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:29:46,452] Trial 32 finished with value: 0.5800729161580348 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.27407235856426143, 'dropout_second': 0.03092040183061749, 'lr': 0.0021590936260804513, 'weight_decay': 0.022635827463879488, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.580073
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.27407235856426143, 'dropout_second': 0.03092040183061749, 'lr': 0.0021590936260804513, 'weight_decay': 0.022635827463879488, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:30:39,483] Trial 33 finished with value: 0.5873880630905682 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.25692985524464035, 'dropout_second': 0.0007072769483469632, 'lr': 0.0052216228713881045, 'weight_decay': 0.010107820713592832, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.587388
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.25692985524464035, 'dropout_second': 0.0007072769483469632, 'lr': 0.0052216228713881045, 'weight_decay': 0.010107820713592832, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:30:51,977] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.465133
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.2592297069779813, 'dropout_second': 0.004982973661855737, 'lr': 0.001674994617209199, 'weight_decay': 0.010415766900035687, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:31:14,830] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.488446
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.25933114102846944, 'dropout_second': 0.04458698919197284, 'lr': 0.006040632213522359, 'weight_decay': 0.056086349638768816, 'batch_size': 64}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-28 19:31:29,812] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.593718
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.29265899175389765, 'dropout_second': 0.00010429128540035597, 'lr': 0.0029351252649881168, 'weight_decay': 0.03467312776134367, 'batch_size': 256}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:32:10,889] Trial 37 finished with value: 0.5669315493353567 and parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.21497526309688517, 'dropout_second': 0.054028841042257976, 'lr': 0.0018647132003256532, 'weight_decay': 0.0037404574288456443, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.566932
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.21497526309688517, 'dropout_second': 0.054028841042257976, 'lr': 0.0018647132003256532, 'weight_decay': 0.0037404574288456443, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:32:42,948] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.475705
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.2702358380140141, 'dropout_second': 0.02869486131821202, 'lr': 0.008669435281323988, 'weight_decay': 0.052865259593512876, 'batch_size': 32}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:33:09,641] Trial 39 finished with value: 0.5679832880351924 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.1721681598017775, 'dropout_second': 0.14504401917103685, 'lr': 0.005915305705127664, 'weight_decay': 0.0022974403388179875, 'batch_size': 256}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.567983
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.1721681598017775, 'dropout_second': 0.14504401917103685, 'lr': 0.005915305705127664, 'weight_decay': 0.0022974403388179875, 'batch_size': 256}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:33:32,335] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.461140
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.24569177605797896, 'dropout_second': 0.10035596362037044, 'lr': 0.00985935156653583, 'weight_decay': 0.022984253005712735, 'batch_size': 64}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:34:10,495] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.588870
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.23266066003134558, 'dropout_second': 0.012082648712296438, 'lr': 0.0044946875092122165, 'weight_decay': 0.012017984403265904, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:34:24,186] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.465605
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.24169287150367288, 'dropout_second': 0.02098747439517825, 'lr': 0.0029864266791005687, 'weight_decay': 0.0056389965526562, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:35:02,552] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.568499
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.2595797594839017, 'dropout_second': 0.013476598593570949, 'lr': 0.004340597256575382, 'weight_decay': 0.007494051853443603, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:35:50,525] Trial 44 finished with value: 0.5833632386260418 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.22441046851727198, 'dropout_second': 0.03823108251879885, 'lr': 0.007224505863623864, 'weight_decay': 0.008431126643289962, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.583363
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.22441046851727198, 'dropout_second': 0.03823108251879885, 'lr': 0.007224505863623864, 'weight_decay': 0.008431126643289962, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:36:26,675] Trial 45 finished with value: 0.5687664498985421 and parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.3955367640261208, 'dropout_second': 0.058448115660016356, 'lr': 0.007295941851674221, 'weight_decay': 0.00023026471564280827, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.568766
+  Parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.3955367640261208, 'dropout_second': 0.058448115660016356, 'lr': 0.007295941851674221, 'weight_decay': 0.00023026471564280827, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:36:35,156] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.470935
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.22658913967035121, 'dropout_second': 0.04222129846311738, 'lr': 0.008345186751627449, 'weight_decay': 0.008146426814681322, 'batch_size': 256}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-28 19:37:20,245] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.588889
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1992688158595646, 'dropout_second': 0.02532872917664796, 'lr': 0.005345759953997748, 'weight_decay': 0.004858832340518529, 'batch_size': 64}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:37:31,782] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.471727
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.2211795831900942, 'dropout_second': 0.0371784450085408, 'lr': 0.0026372380723341422, 'weight_decay': 0.009200322529271878, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:38:10,222] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.467255
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.30273105646331916, 'dropout_second': 0.010339881056235978, 'lr': 0.0033411424782264615, 'weight_decay': 0.003087300959464219, 'batch_size': 32}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:38:17,536] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.445265
+  Parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.20328633976910176, 'dropout_second': 0.06671158754365784, 'lr': 0.00689627983621809, 'weight_decay': 0.0016913475603741908, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:38:30,649] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.448034
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.23344550434873057, 'dropout_second': 0.021741649868406807, 'lr': 0.005004335791596452, 'weight_decay': 0.005212961375783321, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:38:39,166] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.468421
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.25162319215756396, 'dropout_second': 0.009826106071864118, 'lr': 0.003489090124860373, 'weight_decay': 0.014956437349776839, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:38:52,140] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.443106
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.22092034196194274, 'dropout_second': 0.0002485175225543234, 'lr': 0.009753435667617771, 'weight_decay': 0.008340731358075741, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:39:33,422] Trial 54 finished with value: 0.5812351720741423 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.23942989799285108, 'dropout_second': 0.05113516904478227, 'lr': 0.004158161611381146, 'weight_decay': 0.012223948873932177, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.581235
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.23942989799285108, 'dropout_second': 0.05113516904478227, 'lr': 0.004158161611381146, 'weight_decay': 0.012223948873932177, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:40:43,944] Trial 55 finished with value: 0.5687552167829032 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.20885358121048944, 'dropout_second': 0.05303792439969866, 'lr': 0.002390856417040472, 'weight_decay': 0.004678861776723935, 'batch_size': 64}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.568755
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.20885358121048944, 'dropout_second': 0.05303792439969866, 'lr': 0.002390856417040472, 'weight_decay': 0.004678861776723935, 'batch_size': 64}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:40:56,107] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.444075
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.2771315075096183, 'dropout_second': 0.0348377513057753, 'lr': 0.006747512694391059, 'weight_decay': 0.012340928104909773, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-28 19:41:21,381] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.586641
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.18438269578564276, 'dropout_second': 0.024551389528315842, 'lr': 0.0016325690748345922, 'weight_decay': 0.0060595120529116035, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:41:42,233] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.462709
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.2636866176526311, 'dropout_second': 0.04926274205087426, 'lr': 0.004174768516166255, 'weight_decay': 0.00758524239664166, 'batch_size': 64}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:42:23,550] Trial 59 finished with value: 0.572185531605249 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.2394165938084202, 'dropout_second': 0.03862314465473585, 'lr': 0.007715037262583217, 'weight_decay': 0.010567745001272675, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.572186
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.2394165938084202, 'dropout_second': 0.03862314465473585, 'lr': 0.007715037262583217, 'weight_decay': 0.010567745001272675, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-28 19:42:40,719] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.581147
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.22849844955941145, 'dropout_second': 0.03213205356516469, 'lr': 0.005370915469016247, 'weight_decay': 0.0023033409503906708, 'batch_size': 256}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:42:49,455] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.458741
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.23919863394528956, 'dropout_second': 0.014283665318877141, 'lr': 0.003824150230784343, 'weight_decay': 0.015296722241870616, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:43:35,980] Trial 62 finished with value: 0.5695708919135364 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.21962991456828748, 'dropout_second': 0.020035649189056745, 'lr': 0.0027031936966690254, 'weight_decay': 0.020958427761605034, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.569571
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.21962991456828748, 'dropout_second': 0.020035649189056745, 'lr': 0.0027031936966690254, 'weight_decay': 0.020958427761605034, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-28 19:44:01,682] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.592809
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2521103002228231, 'dropout_second': 0.029470566075448165, 'lr': 0.004821007714946662, 'weight_decay': 0.006706451740796464, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-28 19:44:26,952] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.596321
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.23171736077316699, 'dropout_second': 0.008274245826342879, 'lr': 0.0036911375940863345, 'weight_decay': 0.01923133708732632, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:45:02,888] Trial 65 finished with value: 0.578234797317669 and parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.28466045667192835, 'dropout_second': 0.017385468513417258, 'lr': 0.007770992002845293, 'weight_decay': 0.009390675066297762, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.578235
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.28466045667192835, 'dropout_second': 0.017385468513417258, 'lr': 0.007770992002845293, 'weight_decay': 0.009390675066297762, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:45:13,699] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.449664
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.2672833269492486, 'dropout_second': 0.04874312648568069, 'lr': 0.005598945220258048, 'weight_decay': 0.0035306064381188584, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:45:26,433] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.475262
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.20767847160446085, 'dropout_second': 0.00734683283456548, 'lr': 0.002320148053190869, 'weight_decay': 0.02738730132118725, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:46:22,801] Trial 68 finished with value: 0.5731165589027537 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.25447851859750653, 'dropout_second': 0.025835398073945345, 'lr': 0.003071044905484706, 'weight_decay': 0.01294877796345228, 'batch_size': 64}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.573117
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.25447851859750653, 'dropout_second': 0.025835398073945345, 'lr': 0.003071044905484706, 'weight_decay': 0.01294877796345228, 'batch_size': 64}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:47:14,445] Trial 69 finished with value: 0.5872787116048499 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.24496218703263636, 'dropout_second': 0.042555905657956514, 'lr': 0.004282460083099589, 'weight_decay': 0.004348466273034763, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.587279
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.24496218703263636, 'dropout_second': 0.042555905657956514, 'lr': 0.004282460083099589, 'weight_decay': 0.004348466273034763, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:49:02,519] Trial 70 finished with value: 0.5773959262437091 and parameters: {'d_main': 256, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.24559698253022028, 'dropout_second': 0.04068377737973084, 'lr': 0.006538818321181696, 'weight_decay': 0.004609609255919771, 'batch_size': 32}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.577396
+  Parameters: {'d_main': 256, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.24559698253022028, 'dropout_second': 0.04068377737973084, 'lr': 0.006538818321181696, 'weight_decay': 0.004609609255919771, 'batch_size': 32}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:49:48,103] Trial 71 finished with value: 0.5722852751369066 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.263664317565509, 'dropout_second': 0.04529633453549355, 'lr': 0.004528811093218795, 'weight_decay': 0.017793679828656726, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.572285
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.263664317565509, 'dropout_second': 0.04529633453549355, 'lr': 0.004528811093218795, 'weight_decay': 0.017793679828656726, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:50:00,823] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.469080
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.24013956643907536, 'dropout_second': 0.034724948934701294, 'lr': 0.0040324042701225075, 'weight_decay': 0.006177341156711956, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:50:13,677] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.471366
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.2266514234575177, 'dropout_second': 0.0731932521968575, 'lr': 0.005692379816871172, 'weight_decay': 0.011010893888324255, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:51:03,150] Trial 74 finished with value: 0.574413284176372 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2541957860073597, 'dropout_second': 0.06142818278213237, 'lr': 0.0020776397166952206, 'weight_decay': 0.004206974165731799, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.574413
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2541957860073597, 'dropout_second': 0.06142818278213237, 'lr': 0.0020776397166952206, 'weight_decay': 0.004206974165731799, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:51:54,747] Trial 75 finished with value: 0.579229133534776 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2683019819986191, 'dropout_second': 0.016417751705904468, 'lr': 0.008167023253389388, 'weight_decay': 0.04200604116859201, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.579229
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2683019819986191, 'dropout_second': 0.016417751705904468, 'lr': 0.008167023253389388, 'weight_decay': 0.04200604116859201, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-28 19:52:20,819] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.596078
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.21840631009566008, 'dropout_second': 0.026705286517151622, 'lr': 0.0032484447723357675, 'weight_decay': 0.0029059405741852103, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:52:43,525] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.448250
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.24425004959660612, 'dropout_second': 0.05331455078079595, 'lr': 0.0026513173773926994, 'weight_decay': 0.07124504329270816, 'batch_size': 64}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-28 19:53:09,338] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.594887
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.23445096898583148, 'dropout_second': 0.004421135213700134, 'lr': 0.0046755752064165275, 'weight_decay': 0.02881926837284418, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:53:39,593] Trial 79 finished with value: 0.5848373087933203 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.27844188893612326, 'dropout_second': 0.035402570930814274, 'lr': 0.006359595839439241, 'weight_decay': 0.009684001187151783, 'batch_size': 256}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.584837
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.27844188893612326, 'dropout_second': 0.035402570930814274, 'lr': 0.006359595839439241, 'weight_decay': 0.009684001187151783, 'batch_size': 256}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:53:47,906] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.448994
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2731335636604891, 'dropout_second': 0.04442838977974186, 'lr': 0.009883177187922029, 'weight_decay': 0.007049125623291681, 'batch_size': 256}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:53:56,058] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.458689
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.28113153897477833, 'dropout_second': 0.03831023900934766, 'lr': 0.006406753117177443, 'weight_decay': 0.00925301192581643, 'batch_size': 256}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:54:04,308] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.442281
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.2580505258011643, 'dropout_second': 0.03169885663829936, 'lr': 0.0037141132953407343, 'weight_decay': 0.01349817856669448, 'batch_size': 256}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:54:12,677] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.460295
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.24890386997593936, 'dropout_second': 0.020722716619402982, 'lr': 0.005073878455467339, 'weight_decay': 0.010716173478444245, 'batch_size': 256}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:54:20,656] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.461155
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.29137295082425685, 'dropout_second': 0.013981510597986342, 'lr': 0.007847698545276845, 'weight_decay': 0.01775148974972123, 'batch_size': 256}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:55:08,175] Trial 85 finished with value: 0.5859988973170748 and parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2772908490750507, 'dropout_second': 0.06022551438587856, 'lr': 0.005843946281276804, 'weight_decay': 0.005522731107048339, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.585999
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2772908490750507, 'dropout_second': 0.06022551438587856, 'lr': 0.005843946281276804, 'weight_decay': 0.005522731107048339, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:55:45,281] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.472561
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.27783212131686863, 'dropout_second': 0.08121210786825102, 'lr': 0.007268317617395799, 'weight_decay': 0.005415506802577591, 'batch_size': 32}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:55:58,843] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.421658
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 5, 'dropout_first': 0.26646561968951005, 'dropout_second': 0.04960148828610549, 'lr': 0.006297898577101857, 'weight_decay': 0.003572302304577157, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:56:12,217] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.360383
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2608603090371255, 'dropout_second': 0.09265428242138099, 'lr': 0.008657907395469703, 'weight_decay': 0.008075459098286749, 'batch_size': 64}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:57:06,561] Trial 89 finished with value: 0.5657538292715275 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.29642106383489847, 'dropout_second': 0.05733819698759185, 'lr': 0.003113664377203404, 'weight_decay': 0.005339171828541645, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.565754
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.29642106383489847, 'dropout_second': 0.05733819698759185, 'lr': 0.003113664377203404, 'weight_decay': 0.005339171828541645, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:57:20,054] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.463636
+  Parameters: {'d_main': 256, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.28617365441443254, 'dropout_second': 0.06548644689713787, 'lr': 0.003961038126554364, 'weight_decay': 0.006904194021330622, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:57:33,319] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.465385
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2476456543537777, 'dropout_second': 0.04117196417746079, 'lr': 0.00543862472355998, 'weight_decay': 0.009731260663879386, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:57:46,600] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.471858
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.23657932941900853, 'dropout_second': 0.03304109110250297, 'lr': 0.0044596533960558835, 'weight_decay': 0.0043713971237254205, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:58:29,657] Trial 93 finished with value: 0.5672990910068108 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.2569849599690749, 'dropout_second': 0.023644321576151872, 'lr': 0.005989519857847225, 'weight_decay': 0.008467457079181173, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.567299
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.2569849599690749, 'dropout_second': 0.023644321576151872, 'lr': 0.005989519857847225, 'weight_decay': 0.008467457079181173, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+[I 2025-12-28 19:58:55,787] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.599602
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.22516199000907983, 'dropout_second': 0.03567231081981993, 'lr': 0.0034644830115898124, 'weight_decay': 0.013225142217846264, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 19:59:08,866] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.421019
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.27527905084557047, 'dropout_second': 0.047501490672032895, 'lr': 0.0049830363212334024, 'weight_decay': 0.005536238789923172, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 19:59:44,541] Trial 96 finished with value: 0.5759649787224383 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2141185373732069, 'dropout_second': 0.028729611067052775, 'lr': 0.007096721118850226, 'weight_decay': 0.02517091566342067, 'batch_size': 128}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.575965
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2141185373732069, 'dropout_second': 0.028729611067052775, 'lr': 0.007096721118850226, 'weight_decay': 0.02517091566342067, 'batch_size': 128}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+[I 2025-12-28 20:00:07,464] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.443526
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.23190489750392107, 'dropout_second': 0.062070398281102876, 'lr': 0.004098990029623451, 'weight_decay': 0.00747591026630959, 'batch_size': 64}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 20:00:40,022] Trial 98 finished with value: 0.5833858551221528 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.24276044134742905, 'dropout_second': 0.05298689741086391, 'lr': 0.009086765230671156, 'weight_decay': 0.011956789338259773, 'batch_size': 256}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.583386
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.24276044134742905, 'dropout_second': 0.05298689741086391, 'lr': 0.009086765230671156, 'weight_decay': 0.011956789338259773, 'batch_size': 256}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 1.24655304162929, 1: 1.058899895577569, 2: 0.7978225737858298} (클래스별 샘플 수: {0: 10033, 1: 11811, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 1.2478479077342375, 1: 1.0703615007412475, 2: 0.7909161768733278} (클래스별 샘플 수: {0: 10029, 1: 11692, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 1.2494675186368478, 1: 1.0737594737594738, 2: 0.7884247884247885} (클래스별 샘플 수: {0: 10016, 1: 11655, 2: 15873})
+[I 2025-12-28 20:01:14,692] Trial 99 finished with value: 0.5750296725059525 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24399561293156177, 'dropout_second': 0.054997793021790156, 'lr': 0.008941860231917356, 'weight_decay': 0.003908310370411755, 'batch_size': 256}. Best is trial 23 with value: 0.5889744558864985.
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.575030
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24399561293156177, 'dropout_second': 0.054997793021790156, 'lr': 0.008941860231917356, 'weight_decay': 0.003908310370411755, 'batch_size': 256}
+  Best Value (CSI): 0.588974
+  Best Trial: 23
+  Best Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5890
+Best Hyperparameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4799
+  - 최종 CSI: 0.5750
+  - 최고 CSI: 0.5996
+  - 최저 CSI: 0.3604
+  - 평균 CSI: 0.5206
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/resnet_like_ctgan10000_seoul_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 22, Best CSI: 0.5017
+  Fold 1 학습 완료 (검증 CSI: 0.5017)
+Fold 2 학습 중...
+  Early stopping at epoch 31, Best CSI: 0.6438
+  Fold 2 학습 완료 (검증 CSI: 0.6438)
+Fold 3 학습 중...
+  Early stopping at epoch 39, Best CSI: 0.6016
+  Fold 3 학습 완료 (검증 CSI: 0.6016)
+
+모든 모델 저장 완료: ../save_model/resnet_like_optima/resnet_like_ctgan10000_seoul.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/resnet_like_optima/scaler/resnet_like_ctgan10000_seoul_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/resnet_like_optima/resnet_like_ctgan10000_seoul.pkl
+
+✓ 완료: resnet_like_ctgan10000/resnet_like_ctgan10000_seoul.py (소요 시간: 3640초)
+
+==========================================
+ResNet-Like CTGAN10000 파일 실행 완료
+종료 시간: 2025-12-28 20:02:10
+총 소요 시간: 7시간 25분 58초
+성공: 6개
+실패: 0개
+==========================================
diff --git a/Analysis_code/5.optima/run_bash/resnet_like/resnet_like_smotenc_ctgan20000.log b/Analysis_code/5.optima/run_bash/resnet_like/resnet_like_smotenc_ctgan20000.log
index e210e13b6025e56755520d29d85b8185c1786018..318dfdb81347cb9e2fb5c7db003db76f4af717bf 100644
--- a/Analysis_code/5.optima/run_bash/resnet_like/resnet_like_smotenc_ctgan20000.log
+++ b/Analysis_code/5.optima/run_bash/resnet_like/resnet_like_smotenc_ctgan20000.log
@@ -341,3 +341,7547 @@ Trial 24 완료
 
   Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
   Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 17:40:30,583] Trial 25 finished with value: 0.41977357135705945 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1590595991363142, 'dropout_second': 0.043640159902482815, 'lr': 0.0012312777152577734, 'weight_decay': 0.0022882681068870456, 'batch_size': 64}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.419774
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1590595991363142, 'dropout_second': 0.043640159902482815, 'lr': 0.0012312777152577734, 'weight_decay': 0.0022882681068870456, 'batch_size': 64}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 17:42:37,897] Trial 26 finished with value: 0.4364561241282346 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.12116804340961462, 'dropout_second': 0.07893571291399146, 'lr': 0.003218290640804975, 'weight_decay': 0.0050599512049778, 'batch_size': 64}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.436456
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.12116804340961462, 'dropout_second': 0.07893571291399146, 'lr': 0.003218290640804975, 'weight_decay': 0.0050599512049778, 'batch_size': 64}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 17:43:46,877] Trial 27 finished with value: 0.43077544717701394 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.1260221647031624, 'dropout_second': 0.06739467266299842, 'lr': 0.007270419073242312, 'weight_decay': 0.0008199297235874438, 'batch_size': 128}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.430775
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.1260221647031624, 'dropout_second': 0.06739467266299842, 'lr': 0.007270419073242312, 'weight_decay': 0.0008199297235874438, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 17:44:41,391] Trial 28 finished with value: 0.4291695992917257 and parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.10039892679672777, 'dropout_second': 0.0009178140639602567, 'lr': 0.0035781163788604913, 'weight_decay': 0.000373788329453503, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.429170
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.10039892679672777, 'dropout_second': 0.0009178140639602567, 'lr': 0.0035781163788604913, 'weight_decay': 0.000373788329453503, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:45:02,062] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.349415
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.15358074803054367, 'dropout_second': 0.032382340227208005, 'lr': 0.0034635100794032925, 'weight_decay': 0.0018927235177384886, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:45:23,906] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.246400
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.10162190017533464, 'dropout_second': 0.054875491710907165, 'lr': 0.006596474381373522, 'weight_decay': 0.0004094833927234412, 'batch_size': 64}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:46:02,572] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.336296
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.11826371293086199, 'dropout_second': 0.08976170460389932, 'lr': 0.0036425231129955564, 'weight_decay': 0.005990053452014816, 'batch_size': 64}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 17:47:41,403] Trial 32 finished with value: 0.437109401224975 and parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.12426837527669528, 'dropout_second': 0.07134354406791624, 'lr': 0.0024279433272129257, 'weight_decay': 0.003511244603810391, 'batch_size': 64}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.437109
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.12426837527669528, 'dropout_second': 0.07134354406791624, 'lr': 0.0024279433272129257, 'weight_decay': 0.003511244603810391, 'batch_size': 64}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+[I 2025-12-25 17:48:50,441] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.413882
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.13378033949629217, 'dropout_second': 0.06801028105374285, 'lr': 0.0018201690354449093, 'weight_decay': 0.0026666282173935193, 'batch_size': 64}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:49:23,148] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.371681
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.16198153641297047, 'dropout_second': 0.020886973702375147, 'lr': 0.0012387179097252824, 'weight_decay': 0.001017744615792777, 'batch_size': 64}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:50:09,388] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.385075
+  Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.11755664087458852, 'dropout_second': 0.04636111449939405, 'lr': 0.002339952009841199, 'weight_decay': 0.0017885894231821047, 'batch_size': 64}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 17:51:01,241] Trial 36 finished with value: 0.4266306232083597 and parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.10028008324418825, 'dropout_second': 0.027195568329612736, 'lr': 0.006614655516666298, 'weight_decay': 0.003003328268727232, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.426631
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.10028008324418825, 'dropout_second': 0.027195568329612736, 'lr': 0.006614655516666298, 'weight_decay': 0.003003328268727232, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:51:36,810] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.360534
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1411859097485109, 'dropout_second': 0.01024960350413797, 'lr': 0.003961528941644301, 'weight_decay': 0.004208717261031123, 'batch_size': 32}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:51:49,197] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.343179
+  Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.17222877364609646, 'dropout_second': 0.07825007220036592, 'lr': 0.0007083615793569464, 'weight_decay': 0.0016194057926184687, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:52:06,716] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.339820
+  Parameters: {'d_main': 256, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.14685844872564532, 'dropout_second': 0.037319836190158925, 'lr': 0.00978756830759386, 'weight_decay': 0.003560353718463398, 'batch_size': 64}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:52:37,243] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.336811
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.12067748832895207, 'dropout_second': 0.09297001993299314, 'lr': 0.000374340285011335, 'weight_decay': 0.00790130961618234, 'batch_size': 32}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:53:10,685] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.358434
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.11939316577139243, 'dropout_second': 0.07765510057615492, 'lr': 0.0030891889740048677, 'weight_decay': 0.004418032420446754, 'batch_size': 64}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 17:54:59,827] Trial 42 finished with value: 0.4335566757676794 and parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.13135051091790867, 'dropout_second': 0.0847365137741097, 'lr': 0.002516771738165402, 'weight_decay': 0.0009490307509880011, 'batch_size': 64}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.433557
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.13135051091790867, 'dropout_second': 0.0847365137741097, 'lr': 0.002516771738165402, 'weight_decay': 0.0009490307509880011, 'batch_size': 64}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:55:33,414] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.379728
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.11546351643967871, 'dropout_second': 0.06177365130135308, 'lr': 0.004410793660279042, 'weight_decay': 0.0055021662151719195, 'batch_size': 64}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:55:49,474] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.377874
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.14597287394915245, 'dropout_second': 0.0484630824196535, 'lr': 0.001516355785438279, 'weight_decay': 0.010421748096932006, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:56:22,975] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.307573
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.16602223354120108, 'dropout_second': 0.07029015227807012, 'lr': 0.007112870263297798, 'weight_decay': 0.0029707456217530934, 'batch_size': 64}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 17:57:38,594] Trial 46 finished with value: 0.4397182276204426 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.11265056796642833, 'dropout_second': 0.05856570120600936, 'lr': 0.002848506394611552, 'weight_decay': 0.001375427742908066, 'batch_size': 128}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.439718
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.11265056796642833, 'dropout_second': 0.05856570120600936, 'lr': 0.002848506394611552, 'weight_decay': 0.001375427742908066, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:57:50,386] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.375714
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.11176294853738658, 'dropout_second': 0.05781526915398148, 'lr': 0.0010002023746414975, 'weight_decay': 0.0013436189084290783, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 17:58:52,299] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.391927
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.1848780157695079, 'dropout_second': 0.04933656940127184, 'lr': 0.0025976876834225607, 'weight_decay': 0.0007282492451211234, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 17:59:02,216] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.377158
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.12972868353533717, 'dropout_second': 0.014790336139428616, 'lr': 0.0005426087470823765, 'weight_decay': 0.0020403052208599884, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:00:16,146] Trial 50 finished with value: 0.42877603699997374 and parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.1448249277442711, 'dropout_second': 0.041288425286436, 'lr': 0.0017834218186803196, 'weight_decay': 0.0011074207519753704, 'batch_size': 128}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.428776
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.1448249277442711, 'dropout_second': 0.041288425286436, 'lr': 0.0017834218186803196, 'weight_decay': 0.0011074207519753704, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:01:17,090] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.374242
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.11130091674528161, 'dropout_second': 0.08311897106899616, 'lr': 0.0031655803881439773, 'weight_decay': 0.002112318457521122, 'batch_size': 32}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:02:13,417] Trial 52 finished with value: 0.4466423908520986 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.13144017789301193, 'dropout_second': 0.060251229312278325, 'lr': 0.004133356157768121, 'weight_decay': 0.003253291062897583, 'batch_size': 128}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.446642
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.13144017789301193, 'dropout_second': 0.060251229312278325, 'lr': 0.004133356157768121, 'weight_decay': 0.003253291062897583, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:03:23,318] Trial 53 finished with value: 0.4375816165228641 and parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.13605029699990884, 'dropout_second': 0.05712281322587135, 'lr': 0.004548796753521619, 'weight_decay': 0.001415276651710775, 'batch_size': 128}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.437582
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.13605029699990884, 'dropout_second': 0.05712281322587135, 'lr': 0.004548796753521619, 'weight_decay': 0.001415276651710775, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:03:42,956] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.381232
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.13229032661879844, 'dropout_second': 0.05610234242679097, 'lr': 0.004974256314759303, 'weight_decay': 0.0014543867953180965, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:03:54,054] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.243326
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 4, 'dropout_first': 0.17244863745435407, 'dropout_second': 0.030734330864243736, 'lr': 0.007901425304499365, 'weight_decay': 0.0008491650165754073, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:05:04,305] Trial 56 finished with value: 0.4528900654809945 and parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.15160141312645525, 'dropout_second': 0.06210697644157499, 'lr': 0.005571626002492793, 'weight_decay': 0.0005797263601651527, 'batch_size': 128}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.452890
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.15160141312645525, 'dropout_second': 0.06210697644157499, 'lr': 0.005571626002492793, 'weight_decay': 0.0005797263601651527, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:05:13,554] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.356204
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.15367088271371054, 'dropout_second': 0.04410409400372592, 'lr': 0.005724896108515615, 'weight_decay': 0.0005610460646759618, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:06:24,504] Trial 58 finished with value: 0.445526114317426 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.11012453739700595, 'dropout_second': 0.026957257799443912, 'lr': 0.008121330615453814, 'weight_decay': 0.000617279568285567, 'batch_size': 128}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.445526
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.11012453739700595, 'dropout_second': 0.026957257799443912, 'lr': 0.008121330615453814, 'weight_decay': 0.000617279568285567, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:06:34,231] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.322254
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.10533173448670506, 'dropout_second': 0.021410806289783334, 'lr': 0.00857794449915248, 'weight_decay': 0.0003320069697973699, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:06:44,590] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.276827
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.11143992161770494, 'dropout_second': 0.0301048343624686, 'lr': 0.009952074209612709, 'weight_decay': 0.0006108926382155476, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:06:54,672] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.349112
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.11005494740703047, 'dropout_second': 0.03988300581251624, 'lr': 0.006347120491797792, 'weight_decay': 0.0004629340030285864, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:07:44,210] Trial 62 finished with value: 0.4327893815001172 and parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.13861902076435265, 'dropout_second': 0.04930459469398957, 'lr': 0.004102204506217774, 'weight_decay': 0.0008673777103015042, 'batch_size': 128}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.432789
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.13861902076435265, 'dropout_second': 0.04930459469398957, 'lr': 0.004102204506217774, 'weight_decay': 0.0008673777103015042, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:07:54,084] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.360483
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.1000928017420343, 'dropout_second': 0.05988788810689341, 'lr': 0.00570232266743583, 'weight_decay': 0.000647461033360737, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:08:03,437] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.306028
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1251781443753177, 'dropout_second': 0.06470798426206599, 'lr': 0.008294145724347283, 'weight_decay': 0.001187686705253999, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:08:21,563] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.387816
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.15450224162439052, 'dropout_second': 0.03562882466972926, 'lr': 0.00475595664904716, 'weight_decay': 0.0006971454469294469, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:08:31,410] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.372635
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.11020984583006961, 'dropout_second': 0.05336864028469914, 'lr': 0.007475546273587002, 'weight_decay': 0.0005087048296630865, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:08:38,783] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.371429
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.12845818240358933, 'dropout_second': 0.023975012676642173, 'lr': 0.005600918794726769, 'weight_decay': 0.0009912430677162778, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:08:48,553] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.304012
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.1438093301214794, 'dropout_second': 0.0716956677552912, 'lr': 0.0036759764233295954, 'weight_decay': 0.0012184352447648088, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:08:58,069] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.318113
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.19705192652015327, 'dropout_second': 0.06322736992266689, 'lr': 0.003076657043824711, 'weight_decay': 0.0025182197307389107, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:09:27,037] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.360000
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.11942321336294068, 'dropout_second': 0.04155921171617232, 'lr': 0.004203429976283357, 'weight_decay': 0.0003177662367319092, 'batch_size': 32}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:09:36,345] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.381285
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.13637113461463052, 'dropout_second': 0.048696319732100264, 'lr': 0.007606924029482, 'weight_decay': 0.0016482035559100354, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:10:39,318] Trial 72 finished with value: 0.4298646351077273 and parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.10824443649212888, 'dropout_second': 0.05268782137476267, 'lr': 0.0048976117921103925, 'weight_decay': 0.0016316096600420657, 'batch_size': 128}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.429865
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.10824443649212888, 'dropout_second': 0.05268782137476267, 'lr': 0.0048976117921103925, 'weight_decay': 0.0016316096600420657, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:11:39,410] Trial 73 finished with value: 0.4388381633405048 and parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.13685991121665037, 'dropout_second': 0.05871936583381992, 'lr': 0.006511744421628746, 'weight_decay': 0.0007983511039949962, 'batch_size': 128}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.438838
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.13685991121665037, 'dropout_second': 0.05871936583381992, 'lr': 0.006511744421628746, 'weight_decay': 0.0007983511039949962, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:12:47,117] Trial 74 finished with value: 0.4313279382835209 and parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.16119674550350463, 'dropout_second': 0.06609911857083739, 'lr': 0.0065808250331651575, 'weight_decay': 0.000821415410492985, 'batch_size': 128}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.431328
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.16119674550350463, 'dropout_second': 0.06609911857083739, 'lr': 0.0065808250331651575, 'weight_decay': 0.000821415410492985, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:12:56,211] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.366573
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.12578654453292093, 'dropout_second': 0.03559634922287895, 'lr': 0.0028096045433196794, 'weight_decay': 0.0006674165537457793, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:13:49,360] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.413279
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.1510414408186439, 'dropout_second': 0.044621344119286185, 'lr': 0.002071836758772361, 'weight_decay': 0.001105034034478499, 'batch_size': 128}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:14:30,424] Trial 77 finished with value: 0.45311641613256953 and parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.12072565685386229, 'dropout_second': 0.06181027361673415, 'lr': 0.00892919783886571, 'weight_decay': 0.0005059284410795805, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.453116
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.12072565685386229, 'dropout_second': 0.06181027361673415, 'lr': 0.00892919783886571, 'weight_decay': 0.0005059284410795805, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:15:14,822] Trial 78 finished with value: 0.44411606340268306 and parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.11753968483659906, 'dropout_second': 0.07506722696254377, 'lr': 0.008879551738159033, 'weight_decay': 0.0004409416525167959, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.444116
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.11753968483659906, 'dropout_second': 0.07506722696254377, 'lr': 0.008879551738159033, 'weight_decay': 0.0004409416525167959, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:15:57,162] Trial 79 finished with value: 0.44302800809846093 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.10062075463912226, 'dropout_second': 0.06985411854588458, 'lr': 0.009910984789075377, 'weight_decay': 0.00043748227786154453, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.443028
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.10062075463912226, 'dropout_second': 0.06985411854588458, 'lr': 0.009910984789075377, 'weight_decay': 0.00043748227786154453, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:16:41,828] Trial 80 finished with value: 0.45129457308983256 and parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.10099844439340852, 'dropout_second': 0.07451892531222282, 'lr': 0.008797492618067107, 'weight_decay': 0.0004368562392977642, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.451295
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.10099844439340852, 'dropout_second': 0.07451892531222282, 'lr': 0.008797492618067107, 'weight_decay': 0.0004368562392977642, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:17:23,550] Trial 81 finished with value: 0.4438333625682788 and parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.10006809118658906, 'dropout_second': 0.07435727144305146, 'lr': 0.008945694368619356, 'weight_decay': 0.0004210249989728685, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.443833
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.10006809118658906, 'dropout_second': 0.07435727144305146, 'lr': 0.008945694368619356, 'weight_decay': 0.0004210249989728685, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:18:03,668] Trial 82 finished with value: 0.4416985178080013 and parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.11855104200670058, 'dropout_second': 0.07434099683942198, 'lr': 0.00905053063966739, 'weight_decay': 0.00028106841930734496, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.441699
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.11855104200670058, 'dropout_second': 0.07434099683942198, 'lr': 0.00905053063966739, 'weight_decay': 0.00028106841930734496, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:18:42,955] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.375171
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.10638566341393586, 'dropout_second': 0.08458843524821569, 'lr': 0.008334336892125966, 'weight_decay': 0.00042643736074705294, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:18:49,392] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.354412
+  Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.11850781959213859, 'dropout_second': 0.07406162074959444, 'lr': 0.007874456511681378, 'weight_decay': 0.0005388319160975436, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:19:29,128] Trial 85 finished with value: 0.4300308161383312 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.12649690201150035, 'dropout_second': 0.06366209350427449, 'lr': 0.0058676422384680745, 'weight_decay': 0.00022202313253046808, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.430031
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.12649690201150035, 'dropout_second': 0.06366209350427449, 'lr': 0.0058676422384680745, 'weight_decay': 0.00022202313253046808, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:20:32,622] Trial 86 finished with value: 0.42548212065154045 and parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 4, 'dropout_first': 0.10638208863450563, 'dropout_second': 0.07820482816059239, 'lr': 0.006744419466421171, 'weight_decay': 0.0004734086994087792, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.425482
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 4, 'dropout_first': 0.10638208863450563, 'dropout_second': 0.07820482816059239, 'lr': 0.006744419466421171, 'weight_decay': 0.0004734086994087792, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:21:15,573] Trial 87 finished with value: 0.43624160157683506 and parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.11619660870660505, 'dropout_second': 0.06644803564601681, 'lr': 0.008677258500555379, 'weight_decay': 0.00035844498628261474, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.436242
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.11619660870660505, 'dropout_second': 0.06644803564601681, 'lr': 0.008677258500555379, 'weight_decay': 0.00035844498628261474, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:21:22,366] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.384726
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.1000689932933059, 'dropout_second': 0.053280033386827165, 'lr': 0.009989012004435574, 'weight_decay': 0.000594093247767888, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:22:06,937] Trial 89 finished with value: 0.43693336362440566 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.12366221323086919, 'dropout_second': 0.09092142143354219, 'lr': 0.005576552325196165, 'weight_decay': 0.00039919679635568413, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.436933
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.12366221323086919, 'dropout_second': 0.09092142143354219, 'lr': 0.005576552325196165, 'weight_decay': 0.00039919679635568413, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:22:54,408] Trial 90 finished with value: 0.42766209719560866 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.11485972912487638, 'dropout_second': 0.06200149492473589, 'lr': 0.007491106070498205, 'weight_decay': 0.0002720314444408559, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.427662
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.11485972912487638, 'dropout_second': 0.06200149492473589, 'lr': 0.007491106070498205, 'weight_decay': 0.0002720314444408559, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:23:01,044] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.189406
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.10457105291252099, 'dropout_second': 0.07309668463748846, 'lr': 0.009280582415366383, 'weight_decay': 0.0005430935401380864, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:23:07,763] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.361644
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.1004062114491782, 'dropout_second': 0.06774854895148243, 'lr': 0.006695179229093736, 'weight_decay': 0.0004300255739149238, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:23:58,209] Trial 93 finished with value: 0.44916851451621714 and parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.13018300330948623, 'dropout_second': 0.08165927127720109, 'lr': 0.00517704559850302, 'weight_decay': 0.000462915329772585, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.449169
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.13018300330948623, 'dropout_second': 0.08165927127720109, 'lr': 0.00517704559850302, 'weight_decay': 0.000462915329772585, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:24:43,951] Trial 94 finished with value: 0.4491230078477138 and parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.13217560165674125, 'dropout_second': 0.08378332685504078, 'lr': 0.004831249806554082, 'weight_decay': 0.0007546643266744159, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.449123
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.13217560165674125, 'dropout_second': 0.08378332685504078, 'lr': 0.004831249806554082, 'weight_decay': 0.0007546643266744159, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:25:30,473] Trial 95 finished with value: 0.42873991057279826 and parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.12911658950868882, 'dropout_second': 0.08425390617583343, 'lr': 0.003876443915794142, 'weight_decay': 0.0007188500360140441, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.428740
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.12911658950868882, 'dropout_second': 0.08425390617583343, 'lr': 0.003876443915794142, 'weight_decay': 0.0007188500360140441, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:26:14,980] Trial 96 finished with value: 0.45002043254640683 and parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.1445068850503021, 'dropout_second': 0.07957047396054039, 'lr': 0.005017540405143429, 'weight_decay': 0.0008959348818974407, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.450020
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.1445068850503021, 'dropout_second': 0.07957047396054039, 'lr': 0.005017540405143429, 'weight_decay': 0.0008959348818974407, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:27:09,353] Trial 97 finished with value: 0.44235412862408824 and parameters: {'d_main': 256, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.1502472566003928, 'dropout_second': 0.10117282738341518, 'lr': 0.0051692460895483025, 'weight_decay': 0.000947939272584084, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.442354
+  Parameters: {'d_main': 256, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.1502472566003928, 'dropout_second': 0.10117282738341518, 'lr': 0.0051692460895483025, 'weight_decay': 0.000947939272584084, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+  Fold 2 - 클래스별 가중치: {0: 0.9440162601626017, 1: 0.917172195892575, 2: 1.1759332401612284} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16457})
+  Fold 3 - 클래스별 가중치: {0: 0.9437235772357724, 1: 0.9168878357030016, 2: 1.1768558509236167} (클래스별 샘플 수: {0: 20500, 1: 21100, 2: 16439})
+[I 2025-12-25 18:27:49,261] Trial 98 finished with value: 0.43264803455529827 and parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.14047599640108413, 'dropout_second': 0.07985849453151411, 'lr': 0.0041803927794113035, 'weight_decay': 0.000614762527186565, 'batch_size': 256}. Best is trial 10 with value: 0.45568873098715307.
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.432648
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.14047599640108413, 'dropout_second': 0.07985849453151411, 'lr': 0.0041803927794113035, 'weight_decay': 0.000614762527186565, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.9205079365079365, 2: 1.1721238580321771} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16492})
+[I 2025-12-25 18:27:55,839] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.377874
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.13546402119537257, 'dropout_second': 0.0550928111033197, 'lr': 0.0047156076391025046, 'weight_decay': 0.0007859440267674708, 'batch_size': 256}
+  Best Value (CSI): 0.455689
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4557
+Best Hyperparameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4301
+  - 최종 CSI: 0.3779
+  - 최고 CSI: 0.4557
+  - 최저 CSI: 0.1894
+  - 평균 CSI: 0.3849
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/resnet_like_smotenc_ctgan20000_busan_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 20, Best CSI: 0.4115
+  Fold 1 학습 완료 (검증 CSI: 0.4115)
+Fold 2 학습 중...
+  Early stopping at epoch 14, Best CSI: 0.4200
+  Fold 2 학습 완료 (검증 CSI: 0.4200)
+Fold 3 학습 중...
+  Early stopping at epoch 17, Best CSI: 0.4025
+  Fold 3 학습 완료 (검증 CSI: 0.4025)
+
+모든 모델 저장 완료: ../save_model/resnet_like_optima/resnet_like_smotenc_ctgan20000_busan.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/resnet_like_optima/scaler/resnet_like_smotenc_ctgan20000_busan_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/resnet_like_optima/resnet_like_smotenc_ctgan20000_busan.pkl
+
+✓ 완료: resnet_like_smotenc_ctgan20000/resnet_like_smotenc_ctgan20000_busan.py (소요 시간: 5372초)
+
+----------------------------------------
+실행 중: resnet_like_smotenc_ctgan20000/resnet_like_smotenc_ctgan20000_daegu.py
+시작 시간: 2025-12-25 18:28:41
+----------------------------------------
+[I 2025-12-25 18:28:43,145] A new study created in memory with name: no-name-3cc79e95-fe34-4865-bedc-85865abecb19
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:33:00,878] Trial 0 finished with value: 0.3939964343997837 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.20601264641478823, 'dropout_second': 0.08182658762912028, 'lr': 0.003289286790150662, 'weight_decay': 0.0012697327122946182, 'batch_size': 32}. Best is trial 0 with value: 0.3939964343997837.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.393996
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.20601264641478823, 'dropout_second': 0.08182658762912028, 'lr': 0.003289286790150662, 'weight_decay': 0.0012697327122946182, 'batch_size': 32}
+  Best Value (CSI): 0.393996
+  Best Trial: 0
+  Best Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.20601264641478823, 'dropout_second': 0.08182658762912028, 'lr': 0.003289286790150662, 'weight_decay': 0.0012697327122946182, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:34:17,182] Trial 1 finished with value: 0.36804372702125754 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.3945096446748433, 'dropout_second': 0.17470264798120663, 'lr': 0.0007432044036165738, 'weight_decay': 0.01875985335027074, 'batch_size': 256}. Best is trial 0 with value: 0.3939964343997837.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.368044
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.3945096446748433, 'dropout_second': 0.17470264798120663, 'lr': 0.0007432044036165738, 'weight_decay': 0.01875985335027074, 'batch_size': 256}
+  Best Value (CSI): 0.393996
+  Best Trial: 0
+  Best Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.20601264641478823, 'dropout_second': 0.08182658762912028, 'lr': 0.003289286790150662, 'weight_decay': 0.0012697327122946182, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:34:54,052] Trial 2 finished with value: 0.32428506436001103 and parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.27747856295418416, 'dropout_second': 0.13780993222061888, 'lr': 0.00019947260219741866, 'weight_decay': 0.040902123669088006, 'batch_size': 256}. Best is trial 0 with value: 0.3939964343997837.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.324285
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.27747856295418416, 'dropout_second': 0.13780993222061888, 'lr': 0.00019947260219741866, 'weight_decay': 0.040902123669088006, 'batch_size': 256}
+  Best Value (CSI): 0.393996
+  Best Trial: 0
+  Best Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.20601264641478823, 'dropout_second': 0.08182658762912028, 'lr': 0.003289286790150662, 'weight_decay': 0.0012697327122946182, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:37:41,352] Trial 3 finished with value: 0.30329890871287063 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.3726157260080062, 'dropout_second': 0.0514509509342618, 'lr': 3.710077066500457e-05, 'weight_decay': 0.008619236966443851, 'batch_size': 64}. Best is trial 0 with value: 0.3939964343997837.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.303299
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.3726157260080062, 'dropout_second': 0.0514509509342618, 'lr': 3.710077066500457e-05, 'weight_decay': 0.008619236966443851, 'batch_size': 64}
+  Best Value (CSI): 0.393996
+  Best Trial: 0
+  Best Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.20601264641478823, 'dropout_second': 0.08182658762912028, 'lr': 0.003289286790150662, 'weight_decay': 0.0012697327122946182, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:38:32,311] Trial 4 finished with value: 0.3741619938806599 and parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.19330306474122066, 'dropout_second': 0.13281844606726045, 'lr': 0.002729720516561729, 'weight_decay': 0.0019713280759122635, 'batch_size': 256}. Best is trial 0 with value: 0.3939964343997837.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.374162
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.19330306474122066, 'dropout_second': 0.13281844606726045, 'lr': 0.002729720516561729, 'weight_decay': 0.0019713280759122635, 'batch_size': 256}
+  Best Value (CSI): 0.393996
+  Best Trial: 0
+  Best Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.20601264641478823, 'dropout_second': 0.08182658762912028, 'lr': 0.003289286790150662, 'weight_decay': 0.0012697327122946182, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:41:39,002] Trial 5 finished with value: 0.3905166951492025 and parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.2977384252165462, 'dropout_second': 0.07108039769647499, 'lr': 0.00136001516775653, 'weight_decay': 0.0032018714726329283, 'batch_size': 32}. Best is trial 0 with value: 0.3939964343997837.
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.390517
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.2977384252165462, 'dropout_second': 0.07108039769647499, 'lr': 0.00136001516775653, 'weight_decay': 0.0032018714726329283, 'batch_size': 32}
+  Best Value (CSI): 0.393996
+  Best Trial: 0
+  Best Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.20601264641478823, 'dropout_second': 0.08182658762912028, 'lr': 0.003289286790150662, 'weight_decay': 0.0012697327122946182, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:45:03,467] Trial 6 finished with value: 0.37726306969498546 and parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2224351750645261, 'dropout_second': 0.17671320565515491, 'lr': 0.00760569132439978, 'weight_decay': 0.08085948850366646, 'batch_size': 32}. Best is trial 0 with value: 0.3939964343997837.
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.377263
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2224351750645261, 'dropout_second': 0.17671320565515491, 'lr': 0.00760569132439978, 'weight_decay': 0.08085948850366646, 'batch_size': 32}
+  Best Value (CSI): 0.393996
+  Best Trial: 0
+  Best Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.20601264641478823, 'dropout_second': 0.08182658762912028, 'lr': 0.003289286790150662, 'weight_decay': 0.0012697327122946182, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:45:53,254] Trial 7 finished with value: 0.4091396796609308 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.38917434514646054, 'dropout_second': 0.046022589252125504, 'lr': 0.0068388888212231325, 'weight_decay': 0.0024727147846186643, 'batch_size': 128}. Best is trial 7 with value: 0.4091396796609308.
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.409140
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.38917434514646054, 'dropout_second': 0.046022589252125504, 'lr': 0.0068388888212231325, 'weight_decay': 0.0024727147846186643, 'batch_size': 128}
+  Best Value (CSI): 0.409140
+  Best Trial: 7
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.38917434514646054, 'dropout_second': 0.046022589252125504, 'lr': 0.0068388888212231325, 'weight_decay': 0.0024727147846186643, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:48:31,292] Trial 8 finished with value: 0.40631162650210406 and parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.23603658137538905, 'dropout_second': 0.18968250216333252, 'lr': 0.004497914348405908, 'weight_decay': 0.0004608269282804217, 'batch_size': 32}. Best is trial 7 with value: 0.4091396796609308.
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.406312
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.23603658137538905, 'dropout_second': 0.18968250216333252, 'lr': 0.004497914348405908, 'weight_decay': 0.0004608269282804217, 'batch_size': 32}
+  Best Value (CSI): 0.409140
+  Best Trial: 7
+  Best Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.38917434514646054, 'dropout_second': 0.046022589252125504, 'lr': 0.0068388888212231325, 'weight_decay': 0.0024727147846186643, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:51:36,183] Trial 9 finished with value: 0.42616364968715104 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.38918413018419384, 'dropout_second': 0.035999698498353626, 'lr': 0.0054775146274615985, 'weight_decay': 0.00033050923245535026, 'batch_size': 32}. Best is trial 9 with value: 0.42616364968715104.
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.426164
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.38918413018419384, 'dropout_second': 0.035999698498353626, 'lr': 0.0054775146274615985, 'weight_decay': 0.00033050923245535026, 'batch_size': 32}
+  Best Value (CSI): 0.426164
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.38918413018419384, 'dropout_second': 0.035999698498353626, 'lr': 0.0054775146274615985, 'weight_decay': 0.00033050923245535026, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:51:46,992] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.326870
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.15445835656130746, 'dropout_second': 0.016729434118021885, 'lr': 0.00033667782584093, 'weight_decay': 0.00012566532996637888, 'batch_size': 128}
+  Best Value (CSI): 0.426164
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.38918413018419384, 'dropout_second': 0.035999698498353626, 'lr': 0.0054775146274615985, 'weight_decay': 0.00033050923245535026, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:52:50,325] Trial 11 finished with value: 0.42952590587239553 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.429526
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:53:58,960] Trial 12 finished with value: 0.3993676333883405 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3315674118712249, 'dropout_second': 0.0024629990777304575, 'lr': 0.008792739977286598, 'weight_decay': 0.0002357267866590408, 'batch_size': 128}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.399368
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3315674118712249, 'dropout_second': 0.0024629990777304575, 'lr': 0.008792739977286598, 'weight_decay': 0.0002357267866590408, 'batch_size': 128}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:55:55,523] Trial 13 finished with value: 0.39364587677567836 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3532579680002008, 'dropout_second': 0.022395458855486765, 'lr': 0.009968644599514421, 'weight_decay': 0.0005892738476157552, 'batch_size': 64}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.393646
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3532579680002008, 'dropout_second': 0.022395458855486765, 'lr': 0.009968644599514421, 'weight_decay': 0.0005892738476157552, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:57:09,839] Trial 14 finished with value: 0.4028137237215043 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3256720892605167, 'dropout_second': 0.0007350978560752425, 'lr': 0.0019861695385873864, 'weight_decay': 0.0001028493534392728, 'batch_size': 128}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.402814
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3256720892605167, 'dropout_second': 0.0007350978560752425, 'lr': 0.0019861695385873864, 'weight_decay': 0.0001028493534392728, 'batch_size': 128}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 18:57:40,061] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.366848
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.34630333413204706, 'dropout_second': 0.03802279804577504, 'lr': 0.0011347152835057706, 'weight_decay': 0.0003770287256967055, 'batch_size': 32}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 18:58:43,779] Trial 16 finished with value: 0.40140966711973364 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.29692931991787924, 'dropout_second': 0.029094931395479515, 'lr': 0.0034213773797878095, 'weight_decay': 0.0007544077821618761, 'batch_size': 128}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.401410
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.29692931991787924, 'dropout_second': 0.029094931395479515, 'lr': 0.0034213773797878095, 'weight_decay': 0.0007544077821618761, 'batch_size': 128}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:00:14,176] Trial 17 finished with value: 0.4185492929075765 and parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3996832481140233, 'dropout_second': 0.06183092752948925, 'lr': 0.00414675787328371, 'weight_decay': 0.00024199684178981542, 'batch_size': 64}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.418549
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3996832481140233, 'dropout_second': 0.06183092752948925, 'lr': 0.00414675787328371, 'weight_decay': 0.00024199684178981542, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:00:49,486] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.376000
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.3639861295441663, 'dropout_second': 0.08784040289939266, 'lr': 0.0007775995505414419, 'weight_decay': 0.0009589271745170793, 'batch_size': 32}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:02:22,362] Trial 19 finished with value: 0.4087139329238468 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.27336294462181204, 'dropout_second': 0.03945602195056304, 'lr': 0.001896027057117649, 'weight_decay': 0.00022392622293142508, 'batch_size': 128}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.408714
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.27336294462181204, 'dropout_second': 0.03945602195056304, 'lr': 0.001896027057117649, 'weight_decay': 0.00022392622293142508, 'batch_size': 128}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:02:54,102] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.385638
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.3226161979918253, 'dropout_second': 0.018219193781784004, 'lr': 0.004996178578033171, 'weight_decay': 0.0010879956099756095, 'batch_size': 32}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:03:11,113] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.351124
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3991862199770536, 'dropout_second': 0.05993715229996932, 'lr': 0.009537422463035457, 'weight_decay': 0.00023314564539748465, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:04:46,614] Trial 22 finished with value: 0.40369578736312045 and parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.37161009649650245, 'dropout_second': 0.05843524415582991, 'lr': 0.004205874519481081, 'weight_decay': 0.000379628605582934, 'batch_size': 64}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.403696
+  Parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.37161009649650245, 'dropout_second': 0.05843524415582991, 'lr': 0.004205874519481081, 'weight_decay': 0.000379628605582934, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:06:45,196] Trial 23 finished with value: 0.38921142512302387 and parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3964477664155173, 'dropout_second': 0.03093225619561913, 'lr': 0.004744946806171445, 'weight_decay': 0.0001762682108076178, 'batch_size': 64}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.389211
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3964477664155173, 'dropout_second': 0.03093225619561913, 'lr': 0.004744946806171445, 'weight_decay': 0.0001762682108076178, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:07:04,248] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.382920
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.3561298888718664, 'dropout_second': 0.065367962350454, 'lr': 0.002447874843426436, 'weight_decay': 0.0001030030214950146, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:09:16,058] Trial 25 finished with value: 0.41483715243725755 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.37560608028826975, 'dropout_second': 0.04609687151623845, 'lr': 0.005930759515210996, 'weight_decay': 0.0003934030064417952, 'batch_size': 64}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.414837
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.37560608028826975, 'dropout_second': 0.04609687151623845, 'lr': 0.005930759515210996, 'weight_decay': 0.0003934030064417952, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:10:56,385] Trial 26 finished with value: 0.40651748760291895 and parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3417030448926963, 'dropout_second': 0.013620411860057899, 'lr': 0.002322605131186044, 'weight_decay': 0.0006373346250808595, 'batch_size': 128}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.406517
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3417030448926963, 'dropout_second': 0.013620411860057899, 'lr': 0.002322605131186044, 'weight_decay': 0.0006373346250808595, 'batch_size': 128}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:11:46,232] Trial 27 finished with value: 0.4184162649923551 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.37518037078641175, 'dropout_second': 0.03319714635668343, 'lr': 0.009920302176609703, 'weight_decay': 0.00029949183261133695, 'batch_size': 256}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.418416
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.37518037078641175, 'dropout_second': 0.03319714635668343, 'lr': 0.009920302176609703, 'weight_decay': 0.00029949183261133695, 'batch_size': 256}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:12:18,718] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.301498
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.31448405105254634, 'dropout_second': 0.013614633944353858, 'lr': 0.005509301627049867, 'weight_decay': 0.00016781000390015955, 'batch_size': 32}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:13:49,558] Trial 29 finished with value: 0.4146451463078498 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.341763133321381, 'dropout_second': 0.08197420331609495, 'lr': 0.0029003494607068255, 'weight_decay': 0.0013765039159473035, 'batch_size': 128}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.414645
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.341763133321381, 'dropout_second': 0.08197420331609495, 'lr': 0.0029003494607068255, 'weight_decay': 0.0013765039159473035, 'batch_size': 128}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:14:06,707] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.387500
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.39949465274211804, 'dropout_second': 0.07512065915881015, 'lr': 0.003522001850899107, 'weight_decay': 0.0005566908475817466, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:14:14,825] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.353503
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.3656164332313902, 'dropout_second': 0.03477670735151923, 'lr': 0.008970634935128459, 'weight_decay': 0.00029629714508384365, 'batch_size': 256}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:14:22,635] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.341667
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.3817099115787488, 'dropout_second': 0.05430714983355489, 'lr': 0.0059815945967570065, 'weight_decay': 0.0001629005755379469, 'batch_size': 256}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:14:30,493] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.325221
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.3836082886720787, 'dropout_second': 0.02798043534886273, 'lr': 0.003670258120390884, 'weight_decay': 0.00024852472603406216, 'batch_size': 256}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:14:38,483] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.362353
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.360972998055712, 'dropout_second': 0.04625431028302556, 'lr': 0.006666639918666677, 'weight_decay': 0.0003279587035110191, 'batch_size': 256}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:15:24,434] Trial 35 finished with value: 0.41494138778588036 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.3788296309338904, 'dropout_second': 0.0945873967253352, 'lr': 0.00992437171191109, 'weight_decay': 0.000637663872704433, 'batch_size': 256}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.414941
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.3788296309338904, 'dropout_second': 0.0945873967253352, 'lr': 0.00992437171191109, 'weight_decay': 0.000637663872704433, 'batch_size': 256}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:15:32,631] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.373762
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.35386508679612083, 'dropout_second': 0.06990492182627248, 'lr': 0.0017382672117461405, 'weight_decay': 0.00014388806841212376, 'batch_size': 256}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:16:05,409] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.381471
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3847352963423894, 'dropout_second': 0.006681312568127096, 'lr': 0.0032385873365451553, 'weight_decay': 0.0008742645635743035, 'batch_size': 32}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:16:11,577] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.273764
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.37137386195489186, 'dropout_second': 0.10446985571857624, 'lr': 0.0013179526895413286, 'weight_decay': 0.001652588127042675, 'batch_size': 256}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:16:45,682] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.377892
+  Parameters: {'d_main': 256, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.39930087908216444, 'dropout_second': 0.0398566815860098, 'lr': 0.005989712429390396, 'weight_decay': 0.0037905365858862017, 'batch_size': 32}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:17:03,123] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.310345
+  Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.3370809174462637, 'dropout_second': 0.024354968268326324, 'lr': 0.004603887297652333, 'weight_decay': 0.0004511758648008615, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:17:10,856] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.345566
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.3784586588212116, 'dropout_second': 0.0540513228254986, 'lr': 0.009987379323236735, 'weight_decay': 0.0006273857368516566, 'batch_size': 256}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:17:18,647] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.393064
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.3806822022725349, 'dropout_second': 0.09154433989531686, 'lr': 0.007124751832686859, 'weight_decay': 0.00028495208155990113, 'batch_size': 256}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:17:26,340] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.352041
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.3608117860477676, 'dropout_second': 0.11062410377666212, 'lr': 0.008074661371179202, 'weight_decay': 0.0005353094807023332, 'batch_size': 256}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:17:34,148] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.365625
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.3837040348333065, 'dropout_second': 0.009207283718376869, 'lr': 0.006990515161986272, 'weight_decay': 0.0008299303574284062, 'batch_size': 256}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:18:44,907] Trial 45 finished with value: 0.40675583354038364 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3530689060374576, 'dropout_second': 0.020059215926553284, 'lr': 0.002893006538194615, 'weight_decay': 0.0004470783866702497, 'batch_size': 128}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.406756
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3530689060374576, 'dropout_second': 0.020059215926553284, 'lr': 0.002893006538194615, 'weight_decay': 0.0004470783866702497, 'batch_size': 128}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:18:52,050] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.385757
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.368787588179617, 'dropout_second': 0.07066826655166054, 'lr': 0.004428686300261907, 'weight_decay': 0.00019543483238819578, 'batch_size': 256}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:19:20,596] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.387960
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.3904170039835067, 'dropout_second': 0.03389291530906513, 'lr': 0.00980475396115228, 'weight_decay': 0.00029130106846709966, 'batch_size': 32}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:19:30,748] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.304348
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.31299680287073717, 'dropout_second': 0.0016148959037286317, 'lr': 0.00037657167581995845, 'weight_decay': 0.00012784477619295163, 'batch_size': 128}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:19:39,366] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.343195
+  Parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.3442715146431222, 'dropout_second': 0.048040788305658805, 'lr': 0.006413587878371808, 'weight_decay': 0.0003525145450745043, 'batch_size': 256}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:22:50,021] Trial 50 finished with value: 0.41373606404820085 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.33161161694088354, 'dropout_second': 0.02334707006655981, 'lr': 0.003804570158641735, 'weight_decay': 0.0011656393921240088, 'batch_size': 32}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.413736
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.33161161694088354, 'dropout_second': 0.02334707006655981, 'lr': 0.003804570158641735, 'weight_decay': 0.0011656393921240088, 'batch_size': 32}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:25:01,513] Trial 51 finished with value: 0.397702370483698 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3751482957354323, 'dropout_second': 0.06160994372761622, 'lr': 0.007265057037072539, 'weight_decay': 0.00043658981348482966, 'batch_size': 64}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.397702
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3751482957354323, 'dropout_second': 0.06160994372761622, 'lr': 0.007265057037072539, 'weight_decay': 0.00043658981348482966, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:25:19,151] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.383041
+  Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.38879374320284127, 'dropout_second': 0.04253759402769811, 'lr': 0.0048671660267207584, 'weight_decay': 0.0002076477134146635, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:26:45,276] Trial 53 finished with value: 0.42506913868451396 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.37181583123860057, 'dropout_second': 0.051774835098875226, 'lr': 0.005938944537445116, 'weight_decay': 0.0003649670545760817, 'batch_size': 64}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.425069
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.37181583123860057, 'dropout_second': 0.051774835098875226, 'lr': 0.005938944537445116, 'weight_decay': 0.0003649670545760817, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:27:01,749] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.378125
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.364283769083499, 'dropout_second': 0.05195907105639713, 'lr': 0.008148524039430631, 'weight_decay': 0.00025191442022301773, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:28:18,878] Trial 55 finished with value: 0.39826171780746816 and parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.35193871680956046, 'dropout_second': 0.03304519550834999, 'lr': 0.0024956485743013995, 'weight_decay': 0.0006011460511815478, 'batch_size': 64}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.398262
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.35193871680956046, 'dropout_second': 0.03304519550834999, 'lr': 0.0024956485743013995, 'weight_decay': 0.0006011460511815478, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:28:30,052] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.279630
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.39254762394600423, 'dropout_second': 0.06498296717835518, 'lr': 0.009992668866170566, 'weight_decay': 0.00033749534309815735, 'batch_size': 128}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:28:47,777] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.370262
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.3704912939402116, 'dropout_second': 0.010078165082684568, 'lr': 0.004803708939172912, 'weight_decay': 0.0007233022693913928, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:32:16,496] Trial 58 finished with value: 0.41296589566840697 and parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.35038297233743104, 'dropout_second': 0.01778130067211215, 'lr': 0.0056322864428029655, 'weight_decay': 0.00017232513299756365, 'batch_size': 32}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.412966
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.35038297233743104, 'dropout_second': 0.01778130067211215, 'lr': 0.0056322864428029655, 'weight_decay': 0.00017232513299756365, 'batch_size': 32}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:32:27,685] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.364943
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.38925977866007505, 'dropout_second': 0.042753871292304145, 'lr': 0.003603297938036042, 'weight_decay': 0.0004852545574096804, 'batch_size': 128}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:32:44,150] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.210859
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.3308026032978626, 'dropout_second': 0.027201769701534782, 'lr': 0.007860106783398373, 'weight_decay': 0.00022145537152851375, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:34:08,414] Trial 61 finished with value: 0.41421358273269854 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.37547060836144147, 'dropout_second': 0.04886614098653043, 'lr': 0.005290289899441864, 'weight_decay': 0.0004312386607855752, 'batch_size': 64}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.414214
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.37547060836144147, 'dropout_second': 0.04886614098653043, 'lr': 0.005290289899441864, 'weight_decay': 0.0004312386607855752, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:36:03,731] Trial 62 finished with value: 0.4242513106221706 and parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3630642611103453, 'dropout_second': 0.03793032614577148, 'lr': 0.006159935416082503, 'weight_decay': 0.00034010175098097965, 'batch_size': 64}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.424251
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3630642611103453, 'dropout_second': 0.03793032614577148, 'lr': 0.006159935416082503, 'weight_decay': 0.00034010175098097965, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:37:35,773] Trial 63 finished with value: 0.4098980082133255 and parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.35899910892329223, 'dropout_second': 0.03756336557984744, 'lr': 0.007597541988692201, 'weight_decay': 0.00029149147792447414, 'batch_size': 64}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.409898
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.35899910892329223, 'dropout_second': 0.03756336557984744, 'lr': 0.007597541988692201, 'weight_decay': 0.00029149147792447414, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:37:52,637] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.380665
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3984040103629772, 'dropout_second': 0.029985335313449463, 'lr': 0.0030867047870508633, 'weight_decay': 0.0007311538650347637, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:39:50,640] Trial 65 finished with value: 0.4254143313517374 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.3420229692600856, 'dropout_second': 0.05649555728597207, 'lr': 0.004168973329579618, 'weight_decay': 0.00036930121467669124, 'batch_size': 64}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.425414
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.3420229692600856, 'dropout_second': 0.05649555728597207, 'lr': 0.004168973329579618, 'weight_decay': 0.00036930121467669124, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:41:14,425] Trial 66 finished with value: 0.40029268041614935 and parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3449624226971654, 'dropout_second': 0.058563558256799174, 'lr': 0.0040247527792832095, 'weight_decay': 0.00036382628286919837, 'batch_size': 64}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.400293
+  Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3449624226971654, 'dropout_second': 0.058563558256799174, 'lr': 0.0040247527792832095, 'weight_decay': 0.00036382628286919837, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:41:30,296] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.362500
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.32133773017976575, 'dropout_second': 0.05385261211655408, 'lr': 0.0021706476824903055, 'weight_decay': 0.00011936755035537653, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:41:52,170] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.354571
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.33985696147839245, 'dropout_second': 0.039988433398147595, 'lr': 0.0018965132691780525, 'weight_decay': 0.00020079008348179093, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:44:44,136] Trial 69 finished with value: 0.4117798989977776 and parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3646132310246848, 'dropout_second': 0.013256105346857387, 'lr': 0.0056160663753379554, 'weight_decay': 0.00014214581201230244, 'batch_size': 64}. Best is trial 11 with value: 0.42952590587239553.
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.411780
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3646132310246848, 'dropout_second': 0.013256105346857387, 'lr': 0.0056160663753379554, 'weight_decay': 0.00014214581201230244, 'batch_size': 64}
+  Best Value (CSI): 0.429526
+  Best Trial: 11
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3428909444946434, 'dropout_second': 0.0016442831035911182, 'lr': 0.009170435474815164, 'weight_decay': 0.00039281544241607597, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:46:34,713] Trial 70 finished with value: 0.433154124546205 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}. Best is trial 70 with value: 0.433154124546205.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.433154
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}
+  Best Value (CSI): 0.433154
+  Best Trial: 70
+  Best Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:47:58,776] Trial 71 finished with value: 0.4046279119864567 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38838411270858786, 'dropout_second': 0.02149083299822678, 'lr': 0.004547144137257335, 'weight_decay': 0.0002756909806349532, 'batch_size': 64}. Best is trial 70 with value: 0.433154124546205.
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.404628
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38838411270858786, 'dropout_second': 0.02149083299822678, 'lr': 0.004547144137257335, 'weight_decay': 0.0002756909806349532, 'batch_size': 64}
+  Best Value (CSI): 0.433154
+  Best Trial: 70
+  Best Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:48:15,205] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.380531
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.3699298426341249, 'dropout_second': 0.034952082365524335, 'lr': 0.0027954879386758225, 'weight_decay': 0.00035333710190750284, 'batch_size': 64}
+  Best Value (CSI): 0.433154
+  Best Trial: 70
+  Best Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:49:39,563] Trial 73 finished with value: 0.41862945803909607 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.35798024610773127, 'dropout_second': 0.027921623635588307, 'lr': 0.006481624455950349, 'weight_decay': 0.0002456400565066701, 'batch_size': 64}. Best is trial 70 with value: 0.433154124546205.
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.418629
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.35798024610773127, 'dropout_second': 0.027921623635588307, 'lr': 0.006481624455950349, 'weight_decay': 0.0002456400565066701, 'batch_size': 64}
+  Best Value (CSI): 0.433154
+  Best Trial: 70
+  Best Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:51:36,297] Trial 74 finished with value: 0.427301191710811 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.35757098449078556, 'dropout_second': 0.0048671328520605295, 'lr': 0.003997363546731763, 'weight_decay': 0.00024131585994915762, 'batch_size': 64}. Best is trial 70 with value: 0.433154124546205.
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.427301
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.35757098449078556, 'dropout_second': 0.0048671328520605295, 'lr': 0.003997363546731763, 'weight_decay': 0.00024131585994915762, 'batch_size': 64}
+  Best Value (CSI): 0.433154
+  Best Trial: 70
+  Best Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:53:32,975] Trial 75 finished with value: 0.4121076125818477 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.3569508302355794, 'dropout_second': 0.006911423159101093, 'lr': 0.0034056673453083464, 'weight_decay': 0.00023520574154156925, 'batch_size': 64}. Best is trial 70 with value: 0.433154124546205.
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.412108
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.3569508302355794, 'dropout_second': 0.006911423159101093, 'lr': 0.0034056673453083464, 'weight_decay': 0.00023520574154156925, 'batch_size': 64}
+  Best Value (CSI): 0.433154
+  Best Trial: 70
+  Best Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:55:02,698] Trial 76 finished with value: 0.41713091746293046 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.34537319496074725, 'dropout_second': 0.0008354286959643478, 'lr': 0.006633936354393195, 'weight_decay': 0.00017589915778806967, 'batch_size': 64}. Best is trial 70 with value: 0.433154124546205.
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.417131
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.34537319496074725, 'dropout_second': 0.0008354286959643478, 'lr': 0.006633936354393195, 'weight_decay': 0.00017589915778806967, 'batch_size': 64}
+  Best Value (CSI): 0.433154
+  Best Trial: 70
+  Best Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:56:56,068] Trial 77 finished with value: 0.4296257107051682 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.3334612870410075, 'dropout_second': 0.016910267683234212, 'lr': 0.002370857002522695, 'weight_decay': 0.0003816060516782646, 'batch_size': 64}. Best is trial 70 with value: 0.433154124546205.
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.429626
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.3334612870410075, 'dropout_second': 0.016910267683234212, 'lr': 0.002370857002522695, 'weight_decay': 0.0003816060516782646, 'batch_size': 64}
+  Best Value (CSI): 0.433154
+  Best Trial: 70
+  Best Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 19:57:14,520] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.386628
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.33551172346924746, 'dropout_second': 0.015929584030357137, 'lr': 0.0015938088476076956, 'weight_decay': 0.0005172076997781757, 'batch_size': 64}
+  Best Value (CSI): 0.433154
+  Best Trial: 70
+  Best Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 19:59:39,685] Trial 79 finished with value: 0.4003830381126166 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.2987656904458632, 'dropout_second': 0.00659752489164116, 'lr': 0.0025732807652395543, 'weight_decay': 0.0009732611304976795, 'batch_size': 64}. Best is trial 70 with value: 0.433154124546205.
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.400383
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.2987656904458632, 'dropout_second': 0.00659752489164116, 'lr': 0.0025732807652395543, 'weight_decay': 0.0009732611304976795, 'batch_size': 64}
+  Best Value (CSI): 0.433154
+  Best Trial: 70
+  Best Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:00:54,122] Trial 80 finished with value: 0.4135038538697769 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.34810141025155794, 'dropout_second': 0.021937644264235834, 'lr': 0.002181308411349351, 'weight_decay': 0.0003963185835741022, 'batch_size': 128}. Best is trial 70 with value: 0.433154124546205.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.413504
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.34810141025155794, 'dropout_second': 0.021937644264235834, 'lr': 0.002181308411349351, 'weight_decay': 0.0003963185835741022, 'batch_size': 128}
+  Best Value (CSI): 0.433154
+  Best Trial: 70
+  Best Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:02:46,177] Trial 81 finished with value: 0.41516728126545743 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.3581562783875449, 'dropout_second': 0.017042948349547598, 'lr': 0.004152941994286823, 'weight_decay': 0.0003209350233899447, 'batch_size': 64}. Best is trial 70 with value: 0.433154124546205.
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.415167
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.3581562783875449, 'dropout_second': 0.017042948349547598, 'lr': 0.004152941994286823, 'weight_decay': 0.0003209350233899447, 'batch_size': 64}
+  Best Value (CSI): 0.433154
+  Best Trial: 70
+  Best Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:03:03,188] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.396923
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.3620436141706983, 'dropout_second': 0.025582978983128397, 'lr': 0.005521650047798155, 'weight_decay': 0.00024368123548041903, 'batch_size': 64}
+  Best Value (CSI): 0.433154
+  Best Trial: 70
+  Best Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.390855567284905, 'dropout_second': 0.020544147718223566, 'lr': 0.0029694889519661124, 'weight_decay': 0.00025163150278058257, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:04:55,714] Trial 83 finished with value: 0.43482923124906 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}. Best is trial 83 with value: 0.43482923124906.
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.434829
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:05:25,997] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.391549
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.38094552799857595, 'dropout_second': 0.01198687552475529, 'lr': 0.00314407759058543, 'weight_decay': 0.00039821076650387336, 'batch_size': 32}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:07:30,680] Trial 85 finished with value: 0.4093094519323124 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.36881586380471115, 'dropout_second': 0.00503388640341197, 'lr': 0.0038828333521537706, 'weight_decay': 0.0005292569894036065, 'batch_size': 64}. Best is trial 83 with value: 0.43482923124906.
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.409309
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.36881586380471115, 'dropout_second': 0.00503388640341197, 'lr': 0.0038828333521537706, 'weight_decay': 0.0005292569894036065, 'batch_size': 64}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:07:48,112] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.383929
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.37696666850414545, 'dropout_second': 0.011980982316686026, 'lr': 0.0026395002102694736, 'weight_decay': 0.00049860765297678, 'batch_size': 64}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:09:41,871] Trial 87 finished with value: 0.4173086994030158 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3850185911861737, 'dropout_second': 0.0039755874660120614, 'lr': 0.005243797030727918, 'weight_decay': 0.0003273884381742583, 'batch_size': 64}. Best is trial 83 with value: 0.43482923124906.
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.417309
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3850185911861737, 'dropout_second': 0.0039755874660120614, 'lr': 0.005243797030727918, 'weight_decay': 0.0003273884381742583, 'batch_size': 64}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:10:12,179] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.290493
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.3930987414537563, 'dropout_second': 0.019538249469949375, 'lr': 0.008104475198449047, 'weight_decay': 0.0004089569899448772, 'batch_size': 32}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:10:22,130] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.340974
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.35014498943718736, 'dropout_second': 0.0011860987827194728, 'lr': 0.0031969016863459387, 'weight_decay': 0.0002782865692846938, 'batch_size': 128}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:12:23,505] Trial 90 finished with value: 0.426423476979462 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.33968845696676647, 'dropout_second': 0.009982733471468738, 'lr': 0.004052446370169728, 'weight_decay': 0.00019266339436573599, 'batch_size': 64}. Best is trial 83 with value: 0.43482923124906.
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.426423
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.33968845696676647, 'dropout_second': 0.009982733471468738, 'lr': 0.004052446370169728, 'weight_decay': 0.00019266339436573599, 'batch_size': 64}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:12:40,990] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.394322
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.33046572903238003, 'dropout_second': 0.008262689094835483, 'lr': 0.0042194687699795945, 'weight_decay': 0.0001877688773441143, 'batch_size': 64}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:12:58,365] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.392749
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.33949952257578847, 'dropout_second': 0.01494096916863108, 'lr': 0.002217310601611761, 'weight_decay': 0.0002114613047537733, 'batch_size': 64}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:13:16,295] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.401198
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.3671187672908151, 'dropout_second': 0.02392975404130416, 'lr': 0.0061339442990537224, 'weight_decay': 0.00016368408077797013, 'batch_size': 64}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:14:42,255] Trial 94 finished with value: 0.4142665504231111 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.35212114288804625, 'dropout_second': 0.030402757661890684, 'lr': 0.004799146703293545, 'weight_decay': 0.000325664252743194, 'batch_size': 64}. Best is trial 83 with value: 0.43482923124906.
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.414267
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.35212114288804625, 'dropout_second': 0.030402757661890684, 'lr': 0.004799146703293545, 'weight_decay': 0.000325664252743194, 'batch_size': 64}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:14:59,759] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.388060
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.37439994786260966, 'dropout_second': 0.010688593372623886, 'lr': 0.003847699128955492, 'weight_decay': 0.0005878553381567543, 'batch_size': 64}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:15:16,253] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.390313
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.32456666980844584, 'dropout_second': 0.020203374928404672, 'lr': 0.008511926146428683, 'weight_decay': 0.0002594910982592967, 'batch_size': 64}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+  Fold 2 - 클래스별 가중치: {0: 0.9447642276422764, 1: 0.9311378205128205, 2: 1.1526314745382769} (클래스별 샘플 수: {0: 20500, 1: 20800, 2: 16803})
+  Fold 3 - 클래스별 가중치: {0: 0.9433008130081301, 1: 0.9387216828478965, 2: 1.143361122607856} (클래스별 샘플 수: {0: 20500, 1: 20600, 2: 16913})
+[I 2025-12-25 20:18:43,535] Trial 97 finished with value: 0.42219716338924357 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.3929358015559298, 'dropout_second': 0.004879305613133689, 'lr': 0.0028713449755017545, 'weight_decay': 0.00020545321819022075, 'batch_size': 32}. Best is trial 83 with value: 0.43482923124906.
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.422197
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.3929358015559298, 'dropout_second': 0.004879305613133689, 'lr': 0.0028713449755017545, 'weight_decay': 0.00020545321819022075, 'batch_size': 32}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:18:53,105] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.376855
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.36457063247684846, 'dropout_second': 0.016111324321954196, 'lr': 0.003447523176123867, 'weight_decay': 0.00048595273894122814, 'batch_size': 128}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9444227642276423, 1: 0.9219365079365079, 2: 1.167571262011016} (클래스별 샘플 수: {0: 20500, 1: 21000, 2: 16582})
+[I 2025-12-25 20:19:11,843] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.396450
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.33607866790703556, 'dropout_second': 1.0974063845488324e-05, 'lr': 0.006933779389116374, 'weight_decay': 0.00014767168909722727, 'batch_size': 64}
+  Best Value (CSI): 0.434829
+  Best Trial: 83
+  Best Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4348
+Best Hyperparameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38064877864842067, 'dropout_second': 0.011258432283909839, 'lr': 0.003421315789279206, 'weight_decay': 0.0004979175699002674, 'batch_size': 64}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.3940
+  - 최종 CSI: 0.3964
+  - 최고 CSI: 0.4348
+  - 최저 CSI: 0.2109
+  - 평균 CSI: 0.3838
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/resnet_like_smotenc_ctgan20000_daegu_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 24, Best CSI: 0.4116
+  Fold 1 학습 완료 (검증 CSI: 0.4116)
+Fold 2 학습 중...
+  Early stopping at epoch 23, Best CSI: 0.4686
+  Fold 2 학습 완료 (검증 CSI: 0.4686)
+Fold 3 학습 중...
+  Early stopping at epoch 33, Best CSI: 0.3870
+  Fold 3 학습 완료 (검증 CSI: 0.3870)
+
+모든 모델 저장 완료: ../save_model/resnet_like_optima/resnet_like_smotenc_ctgan20000_daegu.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/resnet_like_optima/scaler/resnet_like_smotenc_ctgan20000_daegu_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/resnet_like_optima/resnet_like_smotenc_ctgan20000_daegu.pkl
+
+✓ 완료: resnet_like_smotenc_ctgan20000/resnet_like_smotenc_ctgan20000_daegu.py (소요 시간: 6741초)
+
+----------------------------------------
+실행 중: resnet_like_smotenc_ctgan20000/resnet_like_smotenc_ctgan20000_daejeon.py
+시작 시간: 2025-12-25 20:21:02
+----------------------------------------
+[I 2025-12-25 20:21:03,811] A new study created in memory with name: no-name-52a46490-77d4-4786-a19f-12352a2a0cd1
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:23:15,779] Trial 0 finished with value: 0.4171579810151793 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.34519938029496977, 'dropout_second': 0.19638840840415211, 'lr': 0.00012675161426092387, 'weight_decay': 0.006884268070416952, 'batch_size': 64}. Best is trial 0 with value: 0.4171579810151793.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.417158
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.34519938029496977, 'dropout_second': 0.19638840840415211, 'lr': 0.00012675161426092387, 'weight_decay': 0.006884268070416952, 'batch_size': 64}
+  Best Value (CSI): 0.417158
+  Best Trial: 0
+  Best Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.34519938029496977, 'dropout_second': 0.19638840840415211, 'lr': 0.00012675161426092387, 'weight_decay': 0.006884268070416952, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:28:15,369] Trial 1 finished with value: 0.45175245824716925 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.3971882650200258, 'dropout_second': 0.09767818706103999, 'lr': 0.0002485884572818274, 'weight_decay': 0.01878686248642255, 'batch_size': 32}. Best is trial 1 with value: 0.45175245824716925.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.451752
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.3971882650200258, 'dropout_second': 0.09767818706103999, 'lr': 0.0002485884572818274, 'weight_decay': 0.01878686248642255, 'batch_size': 32}
+  Best Value (CSI): 0.451752
+  Best Trial: 1
+  Best Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.3971882650200258, 'dropout_second': 0.09767818706103999, 'lr': 0.0002485884572818274, 'weight_decay': 0.01878686248642255, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:29:20,636] Trial 2 finished with value: 0.3842280867369867 and parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.37965379988338965, 'dropout_second': 0.16467839956851482, 'lr': 6.10472468513813e-05, 'weight_decay': 0.010029061457139013, 'batch_size': 128}. Best is trial 1 with value: 0.45175245824716925.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.384228
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.37965379988338965, 'dropout_second': 0.16467839956851482, 'lr': 6.10472468513813e-05, 'weight_decay': 0.010029061457139013, 'batch_size': 128}
+  Best Value (CSI): 0.451752
+  Best Trial: 1
+  Best Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.3971882650200258, 'dropout_second': 0.09767818706103999, 'lr': 0.0002485884572818274, 'weight_decay': 0.01878686248642255, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:32:12,162] Trial 3 finished with value: 0.43065743169472426 and parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.14338650021441735, 'dropout_second': 0.04559569743502104, 'lr': 0.00012518125340489844, 'weight_decay': 0.001183736504555252, 'batch_size': 32}. Best is trial 1 with value: 0.45175245824716925.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.430657
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.14338650021441735, 'dropout_second': 0.04559569743502104, 'lr': 0.00012518125340489844, 'weight_decay': 0.001183736504555252, 'batch_size': 32}
+  Best Value (CSI): 0.451752
+  Best Trial: 1
+  Best Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.3971882650200258, 'dropout_second': 0.09767818706103999, 'lr': 0.0002485884572818274, 'weight_decay': 0.01878686248642255, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:33:16,067] Trial 4 finished with value: 0.4199818008837329 and parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.15859775686303856, 'dropout_second': 0.032252057874508425, 'lr': 0.00020710711331600832, 'weight_decay': 0.0014415887027355602, 'batch_size': 256}. Best is trial 1 with value: 0.45175245824716925.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.419982
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.15859775686303856, 'dropout_second': 0.032252057874508425, 'lr': 0.00020710711331600832, 'weight_decay': 0.0014415887027355602, 'batch_size': 256}
+  Best Value (CSI): 0.451752
+  Best Trial: 1
+  Best Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.3971882650200258, 'dropout_second': 0.09767818706103999, 'lr': 0.0002485884572818274, 'weight_decay': 0.01878686248642255, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:38:09,465] Trial 5 finished with value: 0.40765488284261364 and parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.1618179380624083, 'dropout_second': 0.14988799269645695, 'lr': 5.726172278398382e-05, 'weight_decay': 0.050930536187326216, 'batch_size': 32}. Best is trial 1 with value: 0.45175245824716925.
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.407655
+  Parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.1618179380624083, 'dropout_second': 0.14988799269645695, 'lr': 5.726172278398382e-05, 'weight_decay': 0.050930536187326216, 'batch_size': 32}
+  Best Value (CSI): 0.451752
+  Best Trial: 1
+  Best Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.3971882650200258, 'dropout_second': 0.09767818706103999, 'lr': 0.0002485884572818274, 'weight_decay': 0.01878686248642255, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:38:41,989] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.323016
+  Parameters: {'d_main': 64, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.304645186890081, 'dropout_second': 0.02843725910286552, 'lr': 2.538044461315757e-05, 'weight_decay': 0.000389647928297864, 'batch_size': 32}
+  Best Value (CSI): 0.451752
+  Best Trial: 1
+  Best Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.3971882650200258, 'dropout_second': 0.09767818706103999, 'lr': 0.0002485884572818274, 'weight_decay': 0.01878686248642255, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:43:12,211] Trial 7 finished with value: 0.42661445323860386 and parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.38938913340090375, 'dropout_second': 0.1873523099631733, 'lr': 0.00014717013970619658, 'weight_decay': 0.0314114646960732, 'batch_size': 32}. Best is trial 1 with value: 0.45175245824716925.
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.426614
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.38938913340090375, 'dropout_second': 0.1873523099631733, 'lr': 0.00014717013970619658, 'weight_decay': 0.0314114646960732, 'batch_size': 32}
+  Best Value (CSI): 0.451752
+  Best Trial: 1
+  Best Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.3971882650200258, 'dropout_second': 0.09767818706103999, 'lr': 0.0002485884572818274, 'weight_decay': 0.01878686248642255, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:43:21,646] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.328478
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.13444681884707133, 'dropout_second': 0.14004011573334155, 'lr': 2.3704958052541514e-05, 'weight_decay': 0.06965758760811602, 'batch_size': 128}
+  Best Value (CSI): 0.451752
+  Best Trial: 1
+  Best Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.3971882650200258, 'dropout_second': 0.09767818706103999, 'lr': 0.0002485884572818274, 'weight_decay': 0.01878686248642255, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:46:02,442] Trial 9 finished with value: 0.47679683388563765 and parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.11513364209330681, 'dropout_second': 0.1121903040392963, 'lr': 0.0031374145628053195, 'weight_decay': 0.00013971884435376602, 'batch_size': 32}. Best is trial 9 with value: 0.47679683388563765.
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.476797
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.11513364209330681, 'dropout_second': 0.1121903040392963, 'lr': 0.0031374145628053195, 'weight_decay': 0.00013971884435376602, 'batch_size': 32}
+  Best Value (CSI): 0.476797
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.11513364209330681, 'dropout_second': 0.1121903040392963, 'lr': 0.0031374145628053195, 'weight_decay': 0.00013971884435376602, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:46:41,764] Trial 10 finished with value: 0.4812895517315032 and parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}. Best is trial 10 with value: 0.4812895517315032.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.481290
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:47:17,256] Trial 11 finished with value: 0.4616551790720564 and parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.2114820203896654, 'dropout_second': 0.09708055270662086, 'lr': 0.005246313400326751, 'weight_decay': 0.000113724613178346, 'batch_size': 256}. Best is trial 10 with value: 0.4812895517315032.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.461655
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.2114820203896654, 'dropout_second': 0.09708055270662086, 'lr': 0.005246313400326751, 'weight_decay': 0.000113724613178346, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:47:28,889] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.349946
+  Parameters: {'d_main': 256, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.22686060928848628, 'dropout_second': 0.07309717286154836, 'lr': 0.003334970350506378, 'weight_decay': 0.00010773221102787222, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:47:57,226] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.384384
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.10642636948999944, 'dropout_second': 0.12364635124578441, 'lr': 0.0016825653794445932, 'weight_decay': 0.0003277659328202936, 'batch_size': 64}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:48:38,091] Trial 14 finished with value: 0.4438198518820697 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.26182024498409234, 'dropout_second': 0.06974980079296077, 'lr': 0.001006562318444359, 'weight_decay': 0.0003088930581433416, 'batch_size': 256}. Best is trial 10 with value: 0.4812895517315032.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.443820
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.26182024498409234, 'dropout_second': 0.06974980079296077, 'lr': 0.001006562318444359, 'weight_decay': 0.0003088930581433416, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:48:49,893] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.406897
+  Parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.19161783732478635, 'dropout_second': 0.11885693496862698, 'lr': 0.008687245962591543, 'weight_decay': 0.0025870485218602787, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:51:07,931] Trial 16 finished with value: 0.450857745973464 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.26893318802566424, 'dropout_second': 0.007357152001277734, 'lr': 0.0006466356221426607, 'weight_decay': 0.0005916354239432825, 'batch_size': 64}. Best is trial 10 with value: 0.4812895517315032.
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.450858
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.26893318802566424, 'dropout_second': 0.007357152001277734, 'lr': 0.0006466356221426607, 'weight_decay': 0.0005916354239432825, 'batch_size': 64}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:51:50,993] Trial 17 finished with value: 0.4780769413015525 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.10014199789860002, 'dropout_second': 0.08424834482322183, 'lr': 0.002620823592125427, 'weight_decay': 0.0001659644054198485, 'batch_size': 128}. Best is trial 10 with value: 0.4812895517315032.
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.478077
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.10014199789860002, 'dropout_second': 0.08424834482322183, 'lr': 0.002620823592125427, 'weight_decay': 0.0001659644054198485, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:52:08,572] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.397410
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.10172567325508101, 'dropout_second': 0.08234969957585836, 'lr': 0.00906897544042422, 'weight_decay': 0.0002150914319062789, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:52:30,932] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.400576
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.18545670022766594, 'dropout_second': 0.08555437666010851, 'lr': 0.0006227545063894202, 'weight_decay': 0.0005044230939670221, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:52:55,628] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.374251
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.24001506516951715, 'dropout_second': 0.062108839315677164, 'lr': 0.0017732820387908147, 'weight_decay': 0.00019758740994648043, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:53:12,216] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.384471
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.1261124923321118, 'dropout_second': 0.11267724060122375, 'lr': 0.0029117398863013253, 'weight_decay': 0.00012082692446714904, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:54:00,612] Trial 22 finished with value: 0.46512675237689666 and parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.10009296201544333, 'dropout_second': 0.10377720673010041, 'lr': 0.003849843603828173, 'weight_decay': 0.000195016233595085, 'batch_size': 128}. Best is trial 10 with value: 0.4812895517315032.
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.465127
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.10009296201544333, 'dropout_second': 0.10377720673010041, 'lr': 0.003849843603828173, 'weight_decay': 0.000195016233595085, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:54:53,448] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.390609
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.17291123157998126, 'dropout_second': 0.12821092319307822, 'lr': 0.001980325423702053, 'weight_decay': 0.0007000284856661414, 'batch_size': 32}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:55:10,334] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.380583
+  Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.13383786324443936, 'dropout_second': 0.09476957710605695, 'lr': 0.004974211655141533, 'weight_decay': 0.0001938863564266765, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:55:19,380] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.377758
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.19240687206736445, 'dropout_second': 0.11085137789803226, 'lr': 0.001108030152014992, 'weight_decay': 0.00011884490569147785, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:55:59,489] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.379518
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.1543037635143577, 'dropout_second': 0.05910593257941571, 'lr': 0.005854526875010888, 'weight_decay': 0.0008454429412796111, 'batch_size': 32}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:56:20,231] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.394340
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.12086209609446767, 'dropout_second': 0.08147484122287525, 'lr': 0.0021460104642312726, 'weight_decay': 0.0003377893580763442, 'batch_size': 64}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:57:09,312] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.366702
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.14768923924852367, 'dropout_second': 0.10629863459804466, 'lr': 0.0030832262871305764, 'weight_decay': 0.0004512536347743485, 'batch_size': 32}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:57:30,013] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.402214
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.11765250558983018, 'dropout_second': 0.13360801161557512, 'lr': 0.007068575683971438, 'weight_decay': 0.002682588375150562, 'batch_size': 64}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:57:40,130] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.406593
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.21219113546214402, 'dropout_second': 0.08759103330517524, 'lr': 0.005080607920727761, 'weight_decay': 0.00018213658246669646, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:57:53,688] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.395756
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.10812444648108493, 'dropout_second': 0.10708633174458222, 'lr': 0.009992865610714613, 'weight_decay': 0.0002322433018363716, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:58:07,261] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.401802
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.10295605058088815, 'dropout_second': 0.09919099656977677, 'lr': 0.003602615166305156, 'weight_decay': 0.00015786110518040426, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 20:58:56,764] Trial 33 finished with value: 0.463588420850724 and parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.12504394972687657, 'dropout_second': 0.12722415657731148, 'lr': 0.004303969913200091, 'weight_decay': 0.00010151114916005758, 'batch_size': 128}. Best is trial 10 with value: 0.4812895517315032.
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.463588
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.12504394972687657, 'dropout_second': 0.12722415657731148, 'lr': 0.004303969913200091, 'weight_decay': 0.00010151114916005758, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:59:10,317] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.387097
+  Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.14290393019757797, 'dropout_second': 0.11550862609918164, 'lr': 0.0024711328726565672, 'weight_decay': 0.0002816270522151964, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 20:59:25,653] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.405380
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.16617568276135497, 'dropout_second': 0.09581632524485176, 'lr': 0.0063853697048019175, 'weight_decay': 0.00016050069088517502, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:01:27,339] Trial 36 finished with value: 0.4607917967209913 and parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.14243279024560193, 'dropout_second': 0.1415846447727461, 'lr': 0.003767136602626745, 'weight_decay': 0.0004576281402274139, 'batch_size': 32}. Best is trial 10 with value: 0.4812895517315032.
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.460792
+  Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.14243279024560193, 'dropout_second': 0.1415846447727461, 'lr': 0.003767136602626745, 'weight_decay': 0.0004576281402274139, 'batch_size': 32}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:01:41,954] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.395392
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.12218517610375383, 'dropout_second': 0.1052743668688425, 'lr': 0.001304693330471736, 'weight_decay': 0.0002576720233775647, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:02:40,466] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.386340
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.1721584006662303, 'dropout_second': 0.1528853264995033, 'lr': 0.002602060090658878, 'weight_decay': 0.0011682053508329522, 'batch_size': 32}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:03:01,847] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.408759
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.149850835784209, 'dropout_second': 0.11920625082669181, 'lr': 0.00042963785478994176, 'weight_decay': 0.007640109947113743, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:03:59,032] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.413761
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.11219659238861576, 'dropout_second': 0.09099862791140222, 'lr': 0.0014238875707399666, 'weight_decay': 0.00016060862542893682, 'batch_size': 32}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:04:18,624] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.406043
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.12514906881452206, 'dropout_second': 0.1318668146755657, 'lr': 0.0037331719176980417, 'weight_decay': 0.00011188840704051121, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:04:38,294] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.405204
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.10090685679801392, 'dropout_second': 0.12301339019903473, 'lr': 0.00463087059673173, 'weight_decay': 0.00013813813826980386, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:04:53,477] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.409894
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.13407705394086492, 'dropout_second': 0.10167152096863578, 'lr': 0.002273367816191834, 'weight_decay': 0.00010487661977013227, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:05:04,209] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.382996
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.12934216844176422, 'dropout_second': 0.07629119252609111, 'lr': 0.007262918226822558, 'weight_decay': 0.00025394130639910486, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:05:17,646] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.383993
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.11704118335340001, 'dropout_second': 0.11288015489514056, 'lr': 0.004378500469247172, 'weight_decay': 0.00010241499750730568, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:05:27,560] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.417137
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.139508056700756, 'dropout_second': 0.08786065514759585, 'lr': 0.0028929419048300815, 'weight_decay': 0.00017418552328267496, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:05:44,827] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.411328
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.15684803172244421, 'dropout_second': 0.10057864604795054, 'lr': 0.0017160678262870938, 'weight_decay': 0.0003606673759055554, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:06:09,988] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.365828
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.11001358647090465, 'dropout_second': 0.12257254133652087, 'lr': 0.006872476608753687, 'weight_decay': 0.00024277921513218156, 'batch_size': 64}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:06:44,808] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.386213
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.10128035193960572, 'dropout_second': 0.07497993223187072, 'lr': 0.0009180822058307543, 'weight_decay': 0.00014703689975410086, 'batch_size': 32}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:07:00,412] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.390901
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.15770960211405724, 'dropout_second': 0.17081738062873547, 'lr': 0.004287145856656371, 'weight_decay': 0.00014082349384506826, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:07:12,040] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.417057
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.21784003348223988, 'dropout_second': 0.09523715960943133, 'lr': 0.005385987560196585, 'weight_decay': 0.00010290228670412909, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:07:21,680] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.399243
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.24879783955186063, 'dropout_second': 0.10898249745312322, 'lr': 0.002680069915031668, 'weight_decay': 0.00021274225252122288, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:07:31,349] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.394128
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.18359751507278374, 'dropout_second': 0.09531836686595464, 'lr': 0.009973876029170957, 'weight_decay': 0.0003230671525919009, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:07:40,770] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.403571
+  Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.20480929403596834, 'dropout_second': 0.0670436213636534, 'lr': 0.0038146115802066573, 'weight_decay': 0.00013522513586751892, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:07:49,950] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.401670
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.2307334110081891, 'dropout_second': 0.08065391588170544, 'lr': 0.001986447135420753, 'weight_decay': 0.0002133996542763052, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:07:59,012] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.415876
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.1284282415847643, 'dropout_second': 0.11701816472616577, 'lr': 0.007612988574959053, 'weight_decay': 0.0003904694107431637, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:08:40,845] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.369501
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.28893208865111086, 'dropout_second': 0.0906052169447869, 'lr': 0.005299158909433549, 'weight_decay': 0.000131113976471554, 'batch_size': 32}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:08:54,197] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.382896
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.1156755537457687, 'dropout_second': 0.10226785713622513, 'lr': 0.0031100660675898234, 'weight_decay': 0.0001885754205726464, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:09:14,634] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.389356
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.13737484330654656, 'dropout_second': 0.12808017815903408, 'lr': 0.007625343418716569, 'weight_decay': 0.00010024313801349718, 'batch_size': 64}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:09:24,897] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.393995
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 4, 'dropout_first': 0.17542725119895686, 'dropout_second': 0.08442087628451835, 'lr': 0.0015189153092996603, 'weight_decay': 0.00028152548416306974, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:10:02,389] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.374867
+  Parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.14684458735037031, 'dropout_second': 0.13761161768045707, 'lr': 0.003811976284356098, 'weight_decay': 0.00044984424076056104, 'batch_size': 32}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:10:39,475] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.399798
+  Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.11366678401009545, 'dropout_second': 0.14112805584608373, 'lr': 0.005314778437268221, 'weight_decay': 0.00016823813281950276, 'batch_size': 32}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:13:20,245] Trial 63 finished with value: 0.4788271888584353 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.1254408421303477, 'dropout_second': 0.11248854691697958, 'lr': 0.002064341264247902, 'weight_decay': 0.00013229257271025961, 'batch_size': 32}. Best is trial 10 with value: 0.4812895517315032.
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.478827
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.1254408421303477, 'dropout_second': 0.11248854691697958, 'lr': 0.002064341264247902, 'weight_decay': 0.00013229257271025961, 'batch_size': 32}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:13:55,147] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.403882
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.12420271384261347, 'dropout_second': 0.10747635000301259, 'lr': 0.0020753536996224306, 'weight_decay': 0.0001364642140323463, 'batch_size': 32}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:14:31,519] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.386819
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.11028307083296748, 'dropout_second': 0.11593164920516122, 'lr': 0.002510488940297496, 'weight_decay': 0.000194172534472747, 'batch_size': 32}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:14:45,357] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.372753
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.135411550365514, 'dropout_second': 0.12578478870743134, 'lr': 0.003033587497210008, 'weight_decay': 0.00012320413052782853, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:15:26,116] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.385455
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.16022634925927487, 'dropout_second': 0.10093212801823954, 'lr': 0.005998956771483733, 'weight_decay': 0.0002970540880615701, 'batch_size': 32}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:15:39,412] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.372568
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.10138245274236568, 'dropout_second': 0.11173094876115668, 'lr': 0.003251736899874832, 'weight_decay': 0.0002363738847629621, 'batch_size': 128}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:16:15,049] Trial 69 finished with value: 0.4682252489808047 and parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.20028175350209038, 'dropout_second': 0.09386436649424061, 'lr': 0.0044255965156544804, 'weight_decay': 0.00012388030711331033, 'batch_size': 256}. Best is trial 10 with value: 0.4812895517315032.
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.468225
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.20028175350209038, 'dropout_second': 0.09386436649424061, 'lr': 0.0044255965156544804, 'weight_decay': 0.00012388030711331033, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:17:39,813] Trial 70 finished with value: 0.4590724735109064 and parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.15135354355983344, 'dropout_second': 0.10635564729933737, 'lr': 0.001889225262789362, 'weight_decay': 0.00017005690707336458, 'batch_size': 64}. Best is trial 10 with value: 0.4812895517315032.
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.459072
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.15135354355983344, 'dropout_second': 0.10635564729933737, 'lr': 0.001889225262789362, 'weight_decay': 0.00017005690707336458, 'batch_size': 64}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:18:11,887] Trial 71 finished with value: 0.4667643533949703 and parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.1204960007530409, 'dropout_second': 0.09111329009380953, 'lr': 0.004108892253965015, 'weight_decay': 0.00012457938831883565, 'batch_size': 256}. Best is trial 10 with value: 0.4812895517315032.
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.466764
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.1204960007530409, 'dropout_second': 0.09111329009380953, 'lr': 0.004108892253965015, 'weight_decay': 0.00012457938831883565, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:18:21,462] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.390671
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.12375134956768936, 'dropout_second': 0.09143868037791948, 'lr': 0.004286379435056208, 'weight_decay': 0.00013620820631779683, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:18:32,670] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.393792
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.11690197897946882, 'dropout_second': 0.08673387383214701, 'lr': 0.0024450100618601174, 'weight_decay': 0.00012386105663986965, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:18:42,088] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.407654
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.10855034173705316, 'dropout_second': 0.08166053033452426, 'lr': 0.004428338229888454, 'weight_decay': 0.0002040011180635526, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:19:36,904] Trial 75 finished with value: 0.4671655970967559 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1306044863686996, 'dropout_second': 0.09780038736040267, 'lr': 0.006093635879783906, 'weight_decay': 0.0001660013001497977, 'batch_size': 256}. Best is trial 10 with value: 0.4812895517315032.
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.467166
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1306044863686996, 'dropout_second': 0.09780038736040267, 'lr': 0.006093635879783906, 'weight_decay': 0.0001660013001497977, 'batch_size': 256}
+  Best Value (CSI): 0.481290
+  Best Trial: 10
+  Best Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22058820449894415, 'dropout_second': 0.09429887075667182, 'lr': 0.004215541933844984, 'weight_decay': 0.00012429561370799972, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:20:12,915] Trial 76 finished with value: 0.48223852799636385 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}. Best is trial 76 with value: 0.48223852799636385.
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.482239
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:20:25,416] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.366762
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1425726016430413, 'dropout_second': 0.0969799126464651, 'lr': 0.008520464824817062, 'weight_decay': 0.0002674851073705089, 'batch_size': 256}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:20:38,227] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.405063
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.16413293142720062, 'dropout_second': 0.07931928002795391, 'lr': 0.005986738001626342, 'weight_decay': 0.00014681609753166598, 'batch_size': 256}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+[I 2025-12-25 21:21:03,597] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.422406
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.13345336537522495, 'dropout_second': 0.07183963456710629, 'lr': 0.0083437271925024, 'weight_decay': 0.00016346123932040472, 'batch_size': 256}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:22:09,206] Trial 80 finished with value: 0.4625369195460605 and parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.14871103362381421, 'dropout_second': 0.092553563613209, 'lr': 0.00643892201833138, 'weight_decay': 0.000573919567398768, 'batch_size': 256}. Best is trial 76 with value: 0.48223852799636385.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.462537
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.14871103362381421, 'dropout_second': 0.092553563613209, 'lr': 0.00643892201833138, 'weight_decay': 0.000573919567398768, 'batch_size': 256}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:22:18,780] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.416078
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.11903772532571008, 'dropout_second': 0.10319375585181563, 'lr': 0.003386216885716774, 'weight_decay': 0.00022456446535527012, 'batch_size': 256}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:22:28,055] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.380802
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.13083874481445532, 'dropout_second': 0.08617205571701038, 'lr': 0.005333934369474712, 'weight_decay': 0.00011786503789531678, 'batch_size': 256}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:22:37,647] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.397697
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.1063296961925419, 'dropout_second': 0.09662785618700992, 'lr': 0.006764156115086967, 'weight_decay': 0.0001842234467600182, 'batch_size': 256}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:22:47,159] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.402622
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.10944183763038141, 'dropout_second': 0.1106060466629037, 'lr': 0.0035641470674477914, 'weight_decay': 0.00015897642440036472, 'batch_size': 256}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:23:32,164] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.395508
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.12061694636191027, 'dropout_second': 0.10417076581691109, 'lr': 0.004793908473880703, 'weight_decay': 0.00011911981864808134, 'batch_size': 32}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+  Fold 2 - 클래스별 가중치: {0: 0.9433658536585365, 1: 0.8871100917431193, 2: 1.2304511039002355} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15717})
+  Fold 3 - 클래스별 가중치: {0: 0.9428292682926829, 1: 0.8906912442396313, 2: 1.2245311708058795} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15784})
+[I 2025-12-25 21:24:38,277] Trial 86 finished with value: 0.462895305889669 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.13967289693614265, 'dropout_second': 0.09824157845993287, 'lr': 0.002905816501405441, 'weight_decay': 0.0003617639941366446, 'batch_size': 256}. Best is trial 76 with value: 0.48223852799636385.
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.462895
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.13967289693614265, 'dropout_second': 0.09824157845993287, 'lr': 0.002905816501405441, 'weight_decay': 0.0003617639941366446, 'batch_size': 256}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:24:51,681] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.406934
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.1003452119164588, 'dropout_second': 0.11890064698439959, 'lr': 0.008612908567035372, 'weight_decay': 0.000218158089751448, 'batch_size': 256}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:25:45,979] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.385811
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.12868482333996262, 'dropout_second': 0.09024589995706128, 'lr': 0.002181786802809574, 'weight_decay': 0.00030301862883709465, 'batch_size': 32}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:25:58,635] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.392789
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.11909358849860655, 'dropout_second': 0.08594883495860553, 'lr': 0.004126334664443037, 'weight_decay': 0.00018113106411379976, 'batch_size': 256}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:26:14,048] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.407638
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.15380397179764005, 'dropout_second': 0.11194440875288747, 'lr': 0.006192152519216096, 'weight_decay': 0.0002466465785469313, 'batch_size': 256}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:26:30,870] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.387192
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.11279094332160429, 'dropout_second': 0.07789073370621125, 'lr': 0.004791303639320636, 'weight_decay': 0.00015007846700175717, 'batch_size': 128}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:26:44,711] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.400000
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.12641637197781178, 'dropout_second': 0.120208058095357, 'lr': 0.003701810249618473, 'weight_decay': 0.00010775093606229868, 'batch_size': 128}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:26:57,961] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.388566
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.14321556554750525, 'dropout_second': 0.09245618544190588, 'lr': 0.0024182615827467485, 'weight_decay': 0.00010128881580121344, 'batch_size': 128}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:27:11,230] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.390877
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.1063922624069795, 'dropout_second': 0.09989042479552887, 'lr': 0.0042595270710461975, 'weight_decay': 0.00012361320639839242, 'batch_size': 128}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:27:24,704] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.394140
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.13819107781933837, 'dropout_second': 0.10703895816382851, 'lr': 0.0028834642357719684, 'weight_decay': 0.00014863260603910128, 'batch_size': 128}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:27:54,859] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.393822
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.12892963801021964, 'dropout_second': 0.12377135626445405, 'lr': 0.007400137550800749, 'weight_decay': 0.00019062839939649304, 'batch_size': 64}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:28:51,900] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.383080
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.11524487522937887, 'dropout_second': 0.10325381501592534, 'lr': 0.0033540865718028135, 'weight_decay': 0.00011727019982611082, 'batch_size': 32}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:29:04,830] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.387187
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.12144239472839867, 'dropout_second': 0.1146339773228893, 'lr': 0.005751021723332198, 'weight_decay': 0.00013844966797082746, 'batch_size': 256}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.942130081300813, 1: 0.8778939393939394, 2: 1.2508041361742548} (클래스별 샘플 수: {0: 20500, 1: 22000, 2: 15441})
+[I 2025-12-25 21:29:22,913] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.392062
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.13358840418466894, 'dropout_second': 0.09495260449445203, 'lr': 0.0016943376025532779, 'weight_decay': 0.00017219719141703464, 'batch_size': 128}
+  Best Value (CSI): 0.482239
+  Best Trial: 76
+  Best Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4822
+Best Hyperparameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4172
+  - 최종 CSI: 0.3921
+  - 최고 CSI: 0.4822
+  - 최저 CSI: 0.3230
+  - 평균 CSI: 0.4062
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/resnet_like_smotenc_ctgan20000_daejeon_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 36, Best CSI: 0.4247
+  Fold 1 학습 완료 (검증 CSI: 0.4247)
+Fold 2 학습 중...
+  Early stopping at epoch 18, Best CSI: 0.4930
+  Fold 2 학습 완료 (검증 CSI: 0.4930)
+Fold 3 학습 중...
+  Early stopping at epoch 18, Best CSI: 0.5127
+  Fold 3 학습 완료 (검증 CSI: 0.5127)
+
+모든 모델 저장 완료: ../save_model/resnet_like_optima/resnet_like_smotenc_ctgan20000_daejeon.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/resnet_like_optima/scaler/resnet_like_smotenc_ctgan20000_daejeon_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/resnet_like_optima/resnet_like_smotenc_ctgan20000_daejeon.pkl
+
+✓ 완료: resnet_like_smotenc_ctgan20000/resnet_like_smotenc_ctgan20000_daejeon.py (소요 시간: 4155초)
+
+----------------------------------------
+실행 중: resnet_like_smotenc_ctgan20000/resnet_like_smotenc_ctgan20000_gwangju.py
+시작 시간: 2025-12-25 21:30:17
+----------------------------------------
+[I 2025-12-25 21:30:19,020] A new study created in memory with name: no-name-2aaa50fb-66af-487a-857a-89bb3cf04d5b
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 21:35:33,358] Trial 0 finished with value: 0.44150409100031557 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3339594502484202, 'dropout_second': 0.054867688015894193, 'lr': 2.673611966479501e-05, 'weight_decay': 0.0033018739882934725, 'batch_size': 32}. Best is trial 0 with value: 0.44150409100031557.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.441504
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3339594502484202, 'dropout_second': 0.054867688015894193, 'lr': 2.673611966479501e-05, 'weight_decay': 0.0033018739882934725, 'batch_size': 32}
+  Best Value (CSI): 0.441504
+  Best Trial: 0
+  Best Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3339594502484202, 'dropout_second': 0.054867688015894193, 'lr': 2.673611966479501e-05, 'weight_decay': 0.0033018739882934725, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 21:36:31,067] Trial 1 finished with value: 0.444117078107001 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3068969320817143, 'dropout_second': 0.12281297615702831, 'lr': 0.00024911473258486846, 'weight_decay': 0.00011430879036754191, 'batch_size': 256}. Best is trial 1 with value: 0.444117078107001.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.444117
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3068969320817143, 'dropout_second': 0.12281297615702831, 'lr': 0.00024911473258486846, 'weight_decay': 0.00011430879036754191, 'batch_size': 256}
+  Best Value (CSI): 0.444117
+  Best Trial: 1
+  Best Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3068969320817143, 'dropout_second': 0.12281297615702831, 'lr': 0.00024911473258486846, 'weight_decay': 0.00011430879036754191, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 21:40:55,234] Trial 2 finished with value: 0.4763848183165416 and parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3951984004077267, 'dropout_second': 0.1613610053388922, 'lr': 0.00663186048898409, 'weight_decay': 0.06358344239179757, 'batch_size': 32}. Best is trial 2 with value: 0.4763848183165416.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.476385
+  Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3951984004077267, 'dropout_second': 0.1613610053388922, 'lr': 0.00663186048898409, 'weight_decay': 0.06358344239179757, 'batch_size': 32}
+  Best Value (CSI): 0.476385
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3951984004077267, 'dropout_second': 0.1613610053388922, 'lr': 0.00663186048898409, 'weight_decay': 0.06358344239179757, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 21:43:32,955] Trial 3 finished with value: 0.4477879994139408 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.1425526323938709, 'dropout_second': 0.15184611884843013, 'lr': 0.00019215208841266593, 'weight_decay': 0.02828171080887571, 'batch_size': 32}. Best is trial 2 with value: 0.4763848183165416.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.447788
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.1425526323938709, 'dropout_second': 0.15184611884843013, 'lr': 0.00019215208841266593, 'weight_decay': 0.02828171080887571, 'batch_size': 32}
+  Best Value (CSI): 0.476385
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3951984004077267, 'dropout_second': 0.1613610053388922, 'lr': 0.00663186048898409, 'weight_decay': 0.06358344239179757, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 21:45:40,475] Trial 4 finished with value: 0.44277295433274827 and parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.23476328618938408, 'dropout_second': 0.015731463240677535, 'lr': 8.591134717694903e-05, 'weight_decay': 0.04600441148016691, 'batch_size': 64}. Best is trial 2 with value: 0.4763848183165416.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.442773
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.23476328618938408, 'dropout_second': 0.015731463240677535, 'lr': 8.591134717694903e-05, 'weight_decay': 0.04600441148016691, 'batch_size': 64}
+  Best Value (CSI): 0.476385
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3951984004077267, 'dropout_second': 0.1613610053388922, 'lr': 0.00663186048898409, 'weight_decay': 0.06358344239179757, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 21:46:33,273] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.356696
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.33443287609176076, 'dropout_second': 0.19501474121485907, 'lr': 8.190059502270449e-05, 'weight_decay': 0.00010306097301507801, 'batch_size': 32}
+  Best Value (CSI): 0.476385
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3951984004077267, 'dropout_second': 0.1613610053388922, 'lr': 0.00663186048898409, 'weight_decay': 0.06358344239179757, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 21:46:39,985] Trial 6 pruned. 
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.334320
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.37198486589691804, 'dropout_second': 0.1125917008230461, 'lr': 2.6611670321720033e-05, 'weight_decay': 0.07646620120207714, 'batch_size': 256}
+  Best Value (CSI): 0.476385
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3951984004077267, 'dropout_second': 0.1613610053388922, 'lr': 0.00663186048898409, 'weight_decay': 0.06358344239179757, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 21:47:29,294] Trial 7 finished with value: 0.4738398223511602 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.3030929215831236, 'dropout_second': 0.09740063267711326, 'lr': 0.0014334190140877971, 'weight_decay': 0.00019356930999583387, 'batch_size': 256}. Best is trial 2 with value: 0.4763848183165416.
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.473840
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.3030929215831236, 'dropout_second': 0.09740063267711326, 'lr': 0.0014334190140877971, 'weight_decay': 0.00019356930999583387, 'batch_size': 256}
+  Best Value (CSI): 0.476385
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3951984004077267, 'dropout_second': 0.1613610053388922, 'lr': 0.00663186048898409, 'weight_decay': 0.06358344239179757, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 21:47:36,692] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.233775
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.2263445964145291, 'dropout_second': 0.027454290918997006, 'lr': 1.2977388097043883e-05, 'weight_decay': 0.003017364883158644, 'batch_size': 256}
+  Best Value (CSI): 0.476385
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3951984004077267, 'dropout_second': 0.1613610053388922, 'lr': 0.00663186048898409, 'weight_decay': 0.06358344239179757, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 21:48:21,830] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.321196
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.2835841644721191, 'dropout_second': 0.15986961580142767, 'lr': 0.0011256965818739473, 'weight_decay': 0.0011055829111312476, 'batch_size': 32}
+  Best Value (CSI): 0.476385
+  Best Trial: 2
+  Best Parameters: {'d_main': 160, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3951984004077267, 'dropout_second': 0.1613610053388922, 'lr': 0.00663186048898409, 'weight_decay': 0.06358344239179757, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 21:49:49,171] Trial 10 finished with value: 0.48253831725434565 and parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}. Best is trial 10 with value: 0.48253831725434565.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.482538
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 21:50:03,257] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.293622
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39970405623792093, 'dropout_second': 0.1961864627267188, 'lr': 0.006685575343414883, 'weight_decay': 0.021694006358201196, 'batch_size': 128}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 21:50:16,033] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.371429
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.39886127926104625, 'dropout_second': 0.16791947244329042, 'lr': 0.008213828857870406, 'weight_decay': 0.014820319965762153, 'batch_size': 128}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 21:50:33,401] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.375628
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.35931297206532054, 'dropout_second': 0.19416697393113014, 'lr': 0.0030952135970016754, 'weight_decay': 0.07249704446790152, 'batch_size': 128}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 21:50:53,936] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.312500
+  Parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.3646137551432981, 'dropout_second': 0.14060114602561685, 'lr': 0.002762315752051285, 'weight_decay': 0.09348147526160615, 'batch_size': 64}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 21:51:17,180] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.342929
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39536916908945163, 'dropout_second': 0.16652015742571322, 'lr': 0.009940578571737508, 'weight_decay': 0.010766257918493055, 'batch_size': 128}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 21:54:44,830] Trial 16 finished with value: 0.4654592800527375 and parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.3431833576415626, 'dropout_second': 0.18019371239846177, 'lr': 0.0007810280404598189, 'weight_decay': 0.03346468102033731, 'batch_size': 32}. Best is trial 10 with value: 0.48253831725434565.
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.465459
+  Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.3431833576415626, 'dropout_second': 0.18019371239846177, 'lr': 0.0007810280404598189, 'weight_decay': 0.03346468102033731, 'batch_size': 32}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+[I 2025-12-25 21:55:15,112] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.485374
+  Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.2805968436247812, 'dropout_second': 0.14294813101578066, 'lr': 0.002848140987035245, 'weight_decay': 0.010513072224944865, 'batch_size': 128}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 21:57:17,296] Trial 18 finished with value: 0.4680293018576099 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3666654907147938, 'dropout_second': 0.17988182295545999, 'lr': 0.0006759173240744813, 'weight_decay': 0.04530261962602015, 'batch_size': 64}. Best is trial 10 with value: 0.48253831725434565.
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.468029
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3666654907147938, 'dropout_second': 0.17988182295545999, 'lr': 0.0006759173240744813, 'weight_decay': 0.04530261962602015, 'batch_size': 64}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 21:57:31,846] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.343188
+  Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 4, 'dropout_first': 0.3279126133860154, 'dropout_second': 0.08916913116704984, 'lr': 0.004806703788676701, 'weight_decay': 0.01601057210625248, 'batch_size': 128}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 21:58:26,656] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.274105
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.3795006452455455, 'dropout_second': 0.13486137296131376, 'lr': 0.009756767438779913, 'weight_decay': 0.006816267455822301, 'batch_size': 32}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 21:59:19,504] Trial 21 finished with value: 0.45831979233863346 and parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.3030141147445474, 'dropout_second': 0.09291302779450016, 'lr': 0.001671010160677852, 'weight_decay': 0.0003210088020718001, 'batch_size': 256}. Best is trial 10 with value: 0.48253831725434565.
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.458320
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.3030141147445474, 'dropout_second': 0.09291302779450016, 'lr': 0.001671010160677852, 'weight_decay': 0.0003210088020718001, 'batch_size': 256}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 21:59:31,999] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.361111
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.3581420214728926, 'dropout_second': 0.07457929125092415, 'lr': 0.0042824491049448105, 'weight_decay': 0.0007081077401501397, 'batch_size': 256}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:00:11,596] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.438011
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38303606269332297, 'dropout_second': 0.11672926085952294, 'lr': 0.0018261056363029148, 'weight_decay': 0.027734337670193326, 'batch_size': 256}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:00:56,649] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.354217
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.3488218489636109, 'dropout_second': 0.13282023916831853, 'lr': 0.005131112752426191, 'weight_decay': 0.05509234275419351, 'batch_size': 32}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:01:05,937] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.385870
+  Parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.31794491701332583, 'dropout_second': 0.15592340039057567, 'lr': 0.0045342119161321, 'weight_decay': 0.09496546326000237, 'batch_size': 256}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+[I 2025-12-25 22:01:35,267] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.515464
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.37989724240662326, 'dropout_second': 0.10232542704367156, 'lr': 0.0015115286611411163, 'weight_decay': 0.005609303511728669, 'batch_size': 128}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:01:57,795] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.364190
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3476250028244451, 'dropout_second': 0.17942263736893238, 'lr': 0.0026613781154271442, 'weight_decay': 0.036880656538107744, 'batch_size': 64}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:02:07,576] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.365079
+  Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2635893372330154, 'dropout_second': 0.127890669791929, 'lr': 0.0061390254217909175, 'weight_decay': 0.021106611945250594, 'batch_size': 256}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:02:43,275] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.355072
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3346770555276191, 'dropout_second': 0.06901249616791552, 'lr': 0.0006289953720168333, 'weight_decay': 0.002803695595705376, 'batch_size': 32}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:02:54,248] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.379826
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.3161369967104517, 'dropout_second': 0.15076832799295206, 'lr': 0.0037458516350484653, 'weight_decay': 0.001737368880785711, 'batch_size': 128}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:04:35,495] Trial 31 finished with value: 0.4694767696721726 and parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3751859964279145, 'dropout_second': 0.17968520600640886, 'lr': 0.0006961866327818883, 'weight_decay': 0.04982761366861502, 'batch_size': 64}. Best is trial 10 with value: 0.48253831725434565.
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.469477
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3751859964279145, 'dropout_second': 0.17968520600640886, 'lr': 0.0006961866327818883, 'weight_decay': 0.04982761366861502, 'batch_size': 64}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:06:58,852] Trial 32 finished with value: 0.46239119188980277 and parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.38415308155439926, 'dropout_second': 0.1695663217503081, 'lr': 0.0004144694419561557, 'weight_decay': 0.05445585485574957, 'batch_size': 64}. Best is trial 10 with value: 0.48253831725434565.
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.462391
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.38415308155439926, 'dropout_second': 0.1695663217503081, 'lr': 0.0004144694419561557, 'weight_decay': 0.05445585485574957, 'batch_size': 64}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+[I 2025-12-25 22:08:25,621] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.504886
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3601680288168716, 'dropout_second': 0.18578836138001373, 'lr': 0.001260553805141261, 'weight_decay': 0.00019249991827920992, 'batch_size': 64}
+  Best Value (CSI): 0.482538
+  Best Trial: 10
+  Best Parameters: {'d_main': 192, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.39696778264831234, 'dropout_second': 0.19616898293341842, 'lr': 0.008341531066873966, 'weight_decay': 0.01761884540305212, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:10:42,043] Trial 34 finished with value: 0.4856574549540508 and parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}. Best is trial 34 with value: 0.4856574549540508.
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.485657
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:11:38,307] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.334630
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.39838758814836217, 'dropout_second': 0.19982815422472605, 'lr': 0.006499761982714003, 'weight_decay': 0.027233806593719236, 'batch_size': 32}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:12:13,698] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.322251
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3462768061424193, 'dropout_second': 0.19018937076848458, 'lr': 0.002191597248000612, 'weight_decay': 0.005038463482752244, 'batch_size': 64}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:12:25,899] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.359102
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.331253656968581, 'dropout_second': 0.19828722733490503, 'lr': 0.0036584696799472063, 'weight_decay': 0.036592256979683095, 'batch_size': 256}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:12:56,841] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.291885
+  Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3773774002041789, 'dropout_second': 0.1178027275778347, 'lr': 0.00211476692300048, 'weight_decay': 0.06806308408959175, 'batch_size': 32}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:15:27,679] Trial 39 finished with value: 0.48054730119705574 and parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.30167743802781877, 'dropout_second': 0.14822982547606958, 'lr': 0.006497647295060044, 'weight_decay': 0.00011168905589814407, 'batch_size': 64}. Best is trial 34 with value: 0.4856574549540508.
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.480547
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.30167743802781877, 'dropout_second': 0.14822982547606958, 'lr': 0.006497647295060044, 'weight_decay': 0.00011168905589814407, 'batch_size': 64}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:15:55,489] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.350064
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.2193667296046455, 'dropout_second': 0.14785665876715454, 'lr': 0.006477828215197002, 'weight_decay': 0.0001107305389638896, 'batch_size': 64}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:16:24,150] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.349186
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.3042036618063083, 'dropout_second': 0.16580794581144728, 'lr': 0.007637409590036947, 'weight_decay': 0.00015962104015972037, 'batch_size': 64}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:16:55,907] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.349068
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.38649684876951, 'dropout_second': 0.15532919147163293, 'lr': 0.005066143917649329, 'weight_decay': 0.00034841165547035526, 'batch_size': 64}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:18:29,470] Trial 43 finished with value: 0.4842364159911546 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.35480128405990297, 'dropout_second': 0.18801999852370352, 'lr': 0.0035997445134709465, 'weight_decay': 0.00013668153152111148, 'batch_size': 64}. Best is trial 34 with value: 0.4856574549540508.
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.484236
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.35480128405990297, 'dropout_second': 0.18801999852370352, 'lr': 0.0035997445134709465, 'weight_decay': 0.00013668153152111148, 'batch_size': 64}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:18:50,749] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.422319
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.36632551258928614, 'dropout_second': 0.1894482935137596, 'lr': 0.008162466481920014, 'weight_decay': 0.0001032746319691634, 'batch_size': 64}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+[I 2025-12-25 22:19:25,459] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.511803
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3997594582333919, 'dropout_second': 0.1720058324189456, 'lr': 0.0035627938410284655, 'weight_decay': 0.000418547731696534, 'batch_size': 64}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:19:57,615] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.389330
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.3581814222241306, 'dropout_second': 0.1880537697341027, 'lr': 0.00965603405193361, 'weight_decay': 0.00015027731043374602, 'batch_size': 64}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:20:30,663] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.351077
+  Parameters: {'d_main': 64, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.36920341686090885, 'dropout_second': 0.1997110226300727, 'lr': 0.005534008196288557, 'weight_decay': 0.00012632674295352797, 'batch_size': 64}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:21:52,399] Trial 48 finished with value: 0.4771943022891369 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3889667558638951, 'dropout_second': 0.1743826098204652, 'lr': 0.0030625235819963037, 'weight_decay': 0.00022905725319566047, 'batch_size': 128}. Best is trial 34 with value: 0.4856574549540508.
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.477194
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3889667558638951, 'dropout_second': 0.1743826098204652, 'lr': 0.0030625235819963037, 'weight_decay': 0.00022905725319566047, 'batch_size': 128}
+  Best Value (CSI): 0.485657
+  Best Trial: 34
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3847648479805404, 'dropout_second': 0.19983124462710494, 'lr': 0.0021113115821017694, 'weight_decay': 0.037475074649325785, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:23:19,968] Trial 49 finished with value: 0.48900706469069605 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3882074375425333, 'dropout_second': 0.17295041332883132, 'lr': 0.0024893975464295977, 'weight_decay': 0.00022035389888789628, 'batch_size': 128}. Best is trial 49 with value: 0.48900706469069605.
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.489007
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3882074375425333, 'dropout_second': 0.17295041332883132, 'lr': 0.0024893975464295977, 'weight_decay': 0.00022035389888789628, 'batch_size': 128}
+  Best Value (CSI): 0.489007
+  Best Trial: 49
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3882074375425333, 'dropout_second': 0.17295041332883132, 'lr': 0.0024893975464295977, 'weight_decay': 0.00022035389888789628, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:23:38,104] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.348158
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38628052696878074, 'dropout_second': 0.16018941264912576, 'lr': 0.002222611442393155, 'weight_decay': 0.0002715594388170333, 'batch_size': 128}
+  Best Value (CSI): 0.489007
+  Best Trial: 49
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3882074375425333, 'dropout_second': 0.17295041332883132, 'lr': 0.0024893975464295977, 'weight_decay': 0.00022035389888789628, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:23:54,842] Trial 51 pruned. 
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.363178
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.38974434067304736, 'dropout_second': 0.17530952866874808, 'lr': 0.0033164828500025735, 'weight_decay': 0.00021045987593928283, 'batch_size': 128}
+  Best Value (CSI): 0.489007
+  Best Trial: 49
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3882074375425333, 'dropout_second': 0.17295041332883132, 'lr': 0.0024893975464295977, 'weight_decay': 0.00022035389888789628, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:25:01,351] Trial 52 finished with value: 0.46595790514009355 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.371349984627527, 'dropout_second': 0.18855136575550885, 'lr': 0.0010751004667758578, 'weight_decay': 0.00013327315207290166, 'batch_size': 128}. Best is trial 49 with value: 0.48900706469069605.
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.465958
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.371349984627527, 'dropout_second': 0.18855136575550885, 'lr': 0.0010751004667758578, 'weight_decay': 0.00013327315207290166, 'batch_size': 128}
+  Best Value (CSI): 0.489007
+  Best Trial: 49
+  Best Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3882074375425333, 'dropout_second': 0.17295041332883132, 'lr': 0.0024893975464295977, 'weight_decay': 0.00022035389888789628, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:25:55,188] Trial 53 finished with value: 0.492660748799503 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}. Best is trial 53 with value: 0.492660748799503.
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.492661
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}
+  Best Value (CSI): 0.492661
+  Best Trial: 53
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+[I 2025-12-25 22:26:20,011] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.510784
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.3520267982471964, 'dropout_second': 0.1662491362883777, 'lr': 0.0022589378552944238, 'weight_decay': 0.00017892959610567787, 'batch_size': 128}
+  Best Value (CSI): 0.492661
+  Best Trial: 53
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+[I 2025-12-25 22:26:45,846] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.510288
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.37301592242079995, 'dropout_second': 0.18017289440422882, 'lr': 0.004351263726660742, 'weight_decay': 0.00014081385694522905, 'batch_size': 128}
+  Best Value (CSI): 0.492661
+  Best Trial: 53
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:26:58,858] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.323871
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.34010399328146934, 'dropout_second': 0.18478017169646704, 'lr': 0.0016735012083193914, 'weight_decay': 0.0001063018578520518, 'batch_size': 128}
+  Best Value (CSI): 0.492661
+  Best Trial: 53
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:27:14,056] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.337629
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.39797514353317115, 'dropout_second': 0.19325231126802125, 'lr': 0.0026781153802049556, 'weight_decay': 0.00025726393494305985, 'batch_size': 128}
+  Best Value (CSI): 0.492661
+  Best Trial: 53
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:27:25,252] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.373153
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3614771535299806, 'dropout_second': 0.16385046154048533, 'lr': 0.007338767826190229, 'weight_decay': 0.00015635820355657358, 'batch_size': 128}
+  Best Value (CSI): 0.492661
+  Best Trial: 53
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:27:47,968] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.402050
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3539048666578976, 'dropout_second': 0.17572145456052507, 'lr': 0.005333643728184392, 'weight_decay': 0.00020177461786378574, 'batch_size': 64}
+  Best Value (CSI): 0.492661
+  Best Trial: 53
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:29:02,324] Trial 60 finished with value: 0.4833133627827735 and parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3727934761336972, 'dropout_second': 0.15985083424983498, 'lr': 0.003517170015235952, 'weight_decay': 0.00013013552251311567, 'batch_size': 128}. Best is trial 53 with value: 0.492660748799503.
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.483313
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3727934761336972, 'dropout_second': 0.15985083424983498, 'lr': 0.003517170015235952, 'weight_decay': 0.00013013552251311567, 'batch_size': 128}
+  Best Value (CSI): 0.492661
+  Best Trial: 53
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:29:22,038] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.356711
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3791422369567712, 'dropout_second': 0.15880825407682883, 'lr': 0.00406569587755456, 'weight_decay': 0.00010044364372646441, 'batch_size': 128}
+  Best Value (CSI): 0.492661
+  Best Trial: 53
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:30:44,250] Trial 62 finished with value: 0.4755358672678372 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.39031628550844405, 'dropout_second': 0.14591979891759932, 'lr': 0.0026075213508100735, 'weight_decay': 0.0001353476571286172, 'batch_size': 128}. Best is trial 53 with value: 0.492660748799503.
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.475536
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.39031628550844405, 'dropout_second': 0.14591979891759932, 'lr': 0.0026075213508100735, 'weight_decay': 0.0001353476571286172, 'batch_size': 128}
+  Best Value (CSI): 0.492661
+  Best Trial: 53
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:31:03,622] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.383526
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.3694062260029485, 'dropout_second': 0.18397872250138225, 'lr': 0.003460087833045032, 'weight_decay': 0.000176761117643317, 'batch_size': 128}
+  Best Value (CSI): 0.492661
+  Best Trial: 53
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:32:54,084] Trial 64 finished with value: 0.48989934237814753 and parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.39062786951427864, 'dropout_second': 0.1932139714199726, 'lr': 0.005867914286975952, 'weight_decay': 0.00044474069195980277, 'batch_size': 128}. Best is trial 53 with value: 0.492660748799503.
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.489899
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.39062786951427864, 'dropout_second': 0.1932139714199726, 'lr': 0.005867914286975952, 'weight_decay': 0.00044474069195980277, 'batch_size': 128}
+  Best Value (CSI): 0.492661
+  Best Trial: 53
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+[I 2025-12-25 22:33:35,680] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.509901
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.39018132038869996, 'dropout_second': 0.194803180072969, 'lr': 0.0018753906645275747, 'weight_decay': 0.0004562252898378012, 'batch_size': 128}
+  Best Value (CSI): 0.492661
+  Best Trial: 53
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3911413303501954, 'dropout_second': 0.17122890607248628, 'lr': 0.0025362178890723924, 'weight_decay': 0.00010005532547006419, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:35:18,292] Trial 66 finished with value: 0.4937343167803819 and parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}. Best is trial 66 with value: 0.4937343167803819.
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.493734
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:35:32,400] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.377614
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.37802235333197487, 'dropout_second': 0.19193585143454472, 'lr': 0.0014109701758142441, 'weight_decay': 0.0006333002686982491, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:35:45,273] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.332484
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.36396513067487607, 'dropout_second': 0.1718986005863099, 'lr': 0.002977676174518071, 'weight_decay': 0.0002377916785777813, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:36:00,432] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.361858
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3541905709670148, 'dropout_second': 0.1838040302256139, 'lr': 0.004342101051333249, 'weight_decay': 0.0002748506316366242, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:36:14,294] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.360825
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.34177679820281903, 'dropout_second': 0.19375036330646686, 'lr': 0.0017980197330190874, 'weight_decay': 0.0003079643376500091, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:36:28,368] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.375000
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3928361396847232, 'dropout_second': 0.18005250135205486, 'lr': 0.005391643106476634, 'weight_decay': 0.00017771637617070238, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:36:42,446] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.410880
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3996454886781101, 'dropout_second': 0.19843518264061955, 'lr': 0.004452872933150267, 'weight_decay': 0.00022436621693732142, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:36:57,022] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.393643
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.38174755921553793, 'dropout_second': 0.18767669792102507, 'lr': 0.00792902668309717, 'weight_decay': 0.0001284901177921455, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:37:11,634] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.377070
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.373688175550885, 'dropout_second': 0.16932702149053128, 'lr': 0.003594061080083501, 'weight_decay': 0.0003702083551148463, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:37:26,189] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.405603
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.38397577303971886, 'dropout_second': 0.1937099230932496, 'lr': 0.006029820570974566, 'weight_decay': 0.0009572393583208202, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:38:38,394] Trial 76 finished with value: 0.48140426539259046 and parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3921461008582265, 'dropout_second': 0.17556497249997366, 'lr': 0.0025545981060076713, 'weight_decay': 0.0005244015975153105, 'batch_size': 128}. Best is trial 66 with value: 0.4937343167803819.
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.481404
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3921461008582265, 'dropout_second': 0.17556497249997366, 'lr': 0.0025545981060076713, 'weight_decay': 0.0005244015975153105, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:38:55,943] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.357494
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3648371461931397, 'dropout_second': 0.18393911525207685, 'lr': 0.004872280229608737, 'weight_decay': 0.0002959859535498517, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:39:17,851] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.383473
+  Parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.3759084363145406, 'dropout_second': 0.15419130010955687, 'lr': 0.009981348011238489, 'weight_decay': 0.0003603975948644452, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+[I 2025-12-25 22:39:43,020] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.513369
+  Parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.3829628893925923, 'dropout_second': 0.16323897053717387, 'lr': 0.00309718753062625, 'weight_decay': 0.0001609282893597351, 'batch_size': 256}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:43:14,495] Trial 80 finished with value: 0.47122513844265645 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.39987215332874754, 'dropout_second': 0.19888226500251935, 'lr': 0.003952336491531946, 'weight_decay': 0.00019795680550290708, 'batch_size': 32}. Best is trial 66 with value: 0.4937343167803819.
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.471225
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.39987215332874754, 'dropout_second': 0.19888226500251935, 'lr': 0.003952336491531946, 'weight_decay': 0.00019795680550290708, 'batch_size': 32}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:43:30,064] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.342932
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3930065333631529, 'dropout_second': 0.1755637805046569, 'lr': 0.002536997436337725, 'weight_decay': 0.0005317947374850497, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:44:38,868] Trial 82 finished with value: 0.48255042684155397 and parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.39309723866385404, 'dropout_second': 0.18983686413942055, 'lr': 0.002076173215582142, 'weight_decay': 0.00043704421863406494, 'batch_size': 128}. Best is trial 66 with value: 0.4937343167803819.
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.482550
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.39309723866385404, 'dropout_second': 0.18983686413942055, 'lr': 0.002076173215582142, 'weight_decay': 0.00043704421863406494, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:44:49,293] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.314171
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.3701245332150079, 'dropout_second': 0.1907755913336097, 'lr': 0.005944678070304644, 'weight_decay': 0.00025572397956794994, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:46:00,265] Trial 84 finished with value: 0.4809152235669963 and parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.38416144451706585, 'dropout_second': 0.18320920421172368, 'lr': 0.001201797326244388, 'weight_decay': 0.00032416892551155683, 'batch_size': 128}. Best is trial 66 with value: 0.4937343167803819.
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.480915
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.38416144451706585, 'dropout_second': 0.18320920421172368, 'lr': 0.001201797326244388, 'weight_decay': 0.00032416892551155683, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+[I 2025-12-25 22:46:43,332] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.516162
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.36125931443849624, 'dropout_second': 0.1948976733960188, 'lr': 0.0020267483262613587, 'weight_decay': 0.00012095066653074233, 'batch_size': 64}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:46:56,995] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.336646
+  Parameters: {'d_main': 64, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.37475906628813593, 'dropout_second': 0.18783598172270888, 'lr': 0.0032758798845590386, 'weight_decay': 0.000850266342076615, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:47:11,136] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.353717
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.38862202333312656, 'dropout_second': 0.1999633799066133, 'lr': 0.0015209222592148465, 'weight_decay': 0.0004156036664064367, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:47:30,710] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.348894
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.35663854219510316, 'dropout_second': 0.179214900756009, 'lr': 0.00690372971766834, 'weight_decay': 0.042762815858652964, 'batch_size': 64}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:47:44,023] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.362195
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.3929224565077347, 'dropout_second': 0.16725223720352325, 'lr': 0.002174122990615027, 'weight_decay': 0.000153231394850402, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:49:04,096] Trial 90 finished with value: 0.47161907198560565 and parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.37850956380531103, 'dropout_second': 0.18813258614108583, 'lr': 0.00468038382768651, 'weight_decay': 0.000230412629194878, 'batch_size': 128}. Best is trial 66 with value: 0.4937343167803819.
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.471619
+  Parameters: {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.37850956380531103, 'dropout_second': 0.18813258614108583, 'lr': 0.00468038382768651, 'weight_decay': 0.000230412629194878, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+[I 2025-12-25 22:49:30,858] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.512658
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3928852240492017, 'dropout_second': 0.1760131298394748, 'lr': 0.0028251605720347626, 'weight_decay': 0.0005099762185488563, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:49:44,810] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.293963
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.38266281687836534, 'dropout_second': 0.1720253882039247, 'lr': 0.0022946225938787895, 'weight_decay': 0.0013348859917248707, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:49:58,759] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.327411
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3674077416881407, 'dropout_second': 0.19126607167775558, 'lr': 0.003939010255415014, 'weight_decay': 0.0006019303670333386, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:51:16,867] Trial 94 finished with value: 0.48683543203630597 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.39311566811141874, 'dropout_second': 0.1784650021682551, 'lr': 0.003088666591854556, 'weight_decay': 0.0007324967619842592, 'batch_size': 128}. Best is trial 66 with value: 0.4937343167803819.
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.486835
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.39311566811141874, 'dropout_second': 0.1784650021682551, 'lr': 0.003088666591854556, 'weight_decay': 0.0007324967619842592, 'batch_size': 128}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:52:58,742] Trial 95 finished with value: 0.48568757775818655 and parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.34855038861384124, 'dropout_second': 0.18208116980983147, 'lr': 0.0031062900347831393, 'weight_decay': 0.0007976980722849215, 'batch_size': 64}. Best is trial 66 with value: 0.4937343167803819.
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.485688
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.34855038861384124, 'dropout_second': 0.18208116980983147, 'lr': 0.0031062900347831393, 'weight_decay': 0.0007976980722849215, 'batch_size': 64}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+[I 2025-12-25 22:53:40,890] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.519190
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3500890471454225, 'dropout_second': 0.1811856898313913, 'lr': 0.001824453156994052, 'weight_decay': 0.0007247135860465048, 'batch_size': 64}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+[I 2025-12-25 22:54:22,950] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.516507
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.3710627094872352, 'dropout_second': 0.1600283498363995, 'lr': 0.0031671794002708174, 'weight_decay': 0.0006727474371682838, 'batch_size': 64}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+[I 2025-12-25 22:54:44,599] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.385572
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.3618332346667801, 'dropout_second': 0.17108062059877824, 'lr': 0.0036153741383310495, 'weight_decay': 0.0007715367044765667, 'batch_size': 64}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9429593495934959, 1: 0.8867278287461774, 2: 1.2318803636672615} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15692})
+  Fold 2 - 클래스별 가중치: {0: 0.9440650406504065, 1: 0.8877675840978593, 2: 1.2280033840947546} (클래스별 샘플 수: {0: 20500, 1: 21800, 2: 15760})
+  Fold 3 - 클래스별 가중치: {0: 0.9438048780487804, 1: 0.9041121495327102, 2: 1.1984638255698712} (클래스별 샘플 수: {0: 20500, 1: 21400, 2: 16144})
+[I 2025-12-25 22:57:07,645] Trial 99 finished with value: 0.47629307999002596 and parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.3460083761768918, 'dropout_second': 0.18581360884000084, 'lr': 0.0025504835276786834, 'weight_decay': 0.0004117274713581657, 'batch_size': 64}. Best is trial 66 with value: 0.4937343167803819.
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.476293
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.3460083761768918, 'dropout_second': 0.18581360884000084, 'lr': 0.0025504835276786834, 'weight_decay': 0.0004117274713581657, 'batch_size': 64}
+  Best Value (CSI): 0.493734
+  Best Trial: 66
+  Best Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.4937
+Best Hyperparameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4415
+  - 최종 CSI: 0.4763
+  - 최고 CSI: 0.5192
+  - 최저 CSI: 0.2338
+  - 평균 CSI: 0.4098
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/resnet_like_smotenc_ctgan20000_gwangju_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 15, Best CSI: 0.4195
+  Fold 1 학습 완료 (검증 CSI: 0.4195)
+Fold 2 학습 중...
+  Early stopping at epoch 29, Best CSI: 0.5446
+  Fold 2 학습 완료 (검증 CSI: 0.5446)
+Fold 3 학습 중...
+  Early stopping at epoch 28, Best CSI: 0.4986
+  Fold 3 학습 완료 (검증 CSI: 0.4986)
+
+모든 모델 저장 완료: ../save_model/resnet_like_optima/resnet_like_smotenc_ctgan20000_gwangju.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/resnet_like_optima/scaler/resnet_like_smotenc_ctgan20000_gwangju_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/resnet_like_optima/resnet_like_smotenc_ctgan20000_gwangju.pkl
+
+✓ 완료: resnet_like_smotenc_ctgan20000/resnet_like_smotenc_ctgan20000_gwangju.py (소요 시간: 5272초)
+
+----------------------------------------
+실행 중: resnet_like_smotenc_ctgan20000/resnet_like_smotenc_ctgan20000_incheon.py
+시작 시간: 2025-12-25 22:58:09
+----------------------------------------
+[I 2025-12-25 22:58:11,161] A new study created in memory with name: no-name-8d649d8a-a59e-484f-bb9b-08ba9cb082cf
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 22:59:24,711] Trial 0 finished with value: 0.5224359410864087 and parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.10384746305103044, 'dropout_second': 0.03954280069126057, 'lr': 0.0001389876447664599, 'weight_decay': 0.0012256704827169492, 'batch_size': 128}. Best is trial 0 with value: 0.5224359410864087.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.522436
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.10384746305103044, 'dropout_second': 0.03954280069126057, 'lr': 0.0001389876447664599, 'weight_decay': 0.0012256704827169492, 'batch_size': 128}
+  Best Value (CSI): 0.522436
+  Best Trial: 0
+  Best Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.10384746305103044, 'dropout_second': 0.03954280069126057, 'lr': 0.0001389876447664599, 'weight_decay': 0.0012256704827169492, 'batch_size': 128}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:01:57,827] Trial 1 finished with value: 0.555828938870092 and parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.22670070516289725, 'dropout_second': 0.14967007925873396, 'lr': 0.00467104469084359, 'weight_decay': 0.0008491996017770434, 'batch_size': 32}. Best is trial 1 with value: 0.555828938870092.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.555829
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.22670070516289725, 'dropout_second': 0.14967007925873396, 'lr': 0.00467104469084359, 'weight_decay': 0.0008491996017770434, 'batch_size': 32}
+  Best Value (CSI): 0.555829
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.22670070516289725, 'dropout_second': 0.14967007925873396, 'lr': 0.00467104469084359, 'weight_decay': 0.0008491996017770434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:05:08,400] Trial 2 finished with value: 0.5228257367885555 and parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.3227865036854465, 'dropout_second': 0.17350419335672762, 'lr': 0.00034771870268298006, 'weight_decay': 0.007501880854798937, 'batch_size': 32}. Best is trial 1 with value: 0.555828938870092.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.522826
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.3227865036854465, 'dropout_second': 0.17350419335672762, 'lr': 0.00034771870268298006, 'weight_decay': 0.007501880854798937, 'batch_size': 32}
+  Best Value (CSI): 0.555829
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.22670070516289725, 'dropout_second': 0.14967007925873396, 'lr': 0.00467104469084359, 'weight_decay': 0.0008491996017770434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:05:53,730] Trial 3 finished with value: 0.5186398554013997 and parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.16816329669265617, 'dropout_second': 0.06034535650437267, 'lr': 0.0006196918747667301, 'weight_decay': 0.00010557535339719517, 'batch_size': 256}. Best is trial 1 with value: 0.555828938870092.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.518640
+  Parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.16816329669265617, 'dropout_second': 0.06034535650437267, 'lr': 0.0006196918747667301, 'weight_decay': 0.00010557535339719517, 'batch_size': 256}
+  Best Value (CSI): 0.555829
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.22670070516289725, 'dropout_second': 0.14967007925873396, 'lr': 0.00467104469084359, 'weight_decay': 0.0008491996017770434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:07:36,599] Trial 4 finished with value: 0.5239761637768204 and parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.11250482159730571, 'dropout_second': 0.02292022390630426, 'lr': 7.404926213628803e-05, 'weight_decay': 0.021343856420260838, 'batch_size': 64}. Best is trial 1 with value: 0.555828938870092.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.523976
+  Parameters: {'d_main': 224, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.11250482159730571, 'dropout_second': 0.02292022390630426, 'lr': 7.404926213628803e-05, 'weight_decay': 0.021343856420260838, 'batch_size': 64}
+  Best Value (CSI): 0.555829
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.22670070516289725, 'dropout_second': 0.14967007925873396, 'lr': 0.00467104469084359, 'weight_decay': 0.0008491996017770434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:07:44,923] Trial 5 pruned. 
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.481850
+  Parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.285700278093994, 'dropout_second': 0.07534203263569701, 'lr': 0.00024511039273058133, 'weight_decay': 0.018453972779604637, 'batch_size': 256}
+  Best Value (CSI): 0.555829
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.22670070516289725, 'dropout_second': 0.14967007925873396, 'lr': 0.00467104469084359, 'weight_decay': 0.0008491996017770434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:09:16,023] Trial 6 finished with value: 0.5336679424942646 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.3330402872728965, 'dropout_second': 0.050631331691968144, 'lr': 0.00029578393007247974, 'weight_decay': 0.00019893319185338, 'batch_size': 64}. Best is trial 1 with value: 0.555828938870092.
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.533668
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.3330402872728965, 'dropout_second': 0.050631331691968144, 'lr': 0.00029578393007247974, 'weight_decay': 0.00019893319185338, 'batch_size': 64}
+  Best Value (CSI): 0.555829
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.22670070516289725, 'dropout_second': 0.14967007925873396, 'lr': 0.00467104469084359, 'weight_decay': 0.0008491996017770434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-25 23:09:33,009] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.547480
+  Parameters: {'d_main': 64, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.14712472916857028, 'dropout_second': 0.06972890949219383, 'lr': 0.00015466338977003358, 'weight_decay': 0.023192307420915402, 'batch_size': 256}
+  Best Value (CSI): 0.555829
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.22670070516289725, 'dropout_second': 0.14967007925873396, 'lr': 0.00467104469084359, 'weight_decay': 0.0008491996017770434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:10:33,317] Trial 8 finished with value: 0.5243106390233812 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.2184210575627552, 'dropout_second': 0.1972861028441859, 'lr': 0.0002784415276254519, 'weight_decay': 0.002765995257845053, 'batch_size': 128}. Best is trial 1 with value: 0.555828938870092.
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.524311
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.2184210575627552, 'dropout_second': 0.1972861028441859, 'lr': 0.0002784415276254519, 'weight_decay': 0.002765995257845053, 'batch_size': 128}
+  Best Value (CSI): 0.555829
+  Best Trial: 1
+  Best Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.22670070516289725, 'dropout_second': 0.14967007925873396, 'lr': 0.00467104469084359, 'weight_decay': 0.0008491996017770434, 'batch_size': 32}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:11:13,880] Trial 9 finished with value: 0.5588602554914753 and parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}. Best is trial 9 with value: 0.5588602554914753.
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.558860
+  Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:11:21,702] Trial 10 pruned. 
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.400302
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.37870163110979643, 'dropout_second': 0.12802139068657506, 'lr': 1.1204825168857261e-05, 'weight_decay': 0.06886277503103222, 'batch_size': 256}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:12:01,099] Trial 11 pruned. 
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.304845
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.2337784831589282, 'dropout_second': 0.12093459328699596, 'lr': 0.009606333086862475, 'weight_decay': 0.0005926417371308098, 'batch_size': 32}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:12:42,297] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.379840
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.20043034390400943, 'dropout_second': 0.14211855316915373, 'lr': 0.006969856420648141, 'weight_decay': 0.0005124281097334341, 'batch_size': 32}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:13:15,774] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.381684
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.26094705460032713, 'dropout_second': 0.09691276831011592, 'lr': 0.0021611599020483234, 'weight_decay': 0.0014111649539111852, 'batch_size': 32}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:13:25,035] Trial 14 pruned. 
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.477011
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.2019657336804727, 'dropout_second': 0.157641527683253, 'lr': 0.003047077909353131, 'weight_decay': 0.00031574802231918705, 'batch_size': 256}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:13:59,763] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.349196
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.258707225823847, 'dropout_second': 0.10540356578230109, 'lr': 0.0018350650115200688, 'weight_decay': 0.0008764409135871526, 'batch_size': 32}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:14:26,129] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.358940
+  Parameters: {'d_main': 256, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.1874611297774434, 'dropout_second': 0.14969169756013873, 'lr': 0.0034390196074695254, 'weight_decay': 0.0021621201289067924, 'batch_size': 64}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:14:40,788] Trial 17 pruned. 
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.413727
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.24218958217397762, 'dropout_second': 0.11250463834341916, 'lr': 0.0011893262645684458, 'weight_decay': 0.00025192130618801933, 'batch_size': 128}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:15:14,751] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.360688
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.1556455198238161, 'dropout_second': 0.0038131933529987477, 'lr': 0.005889223855819439, 'weight_decay': 0.0006265102175513165, 'batch_size': 32}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:15:24,253] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.448098
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.22656523291691805, 'dropout_second': 0.08775025821456323, 'lr': 0.00447687363956233, 'weight_decay': 0.003921454033542806, 'batch_size': 256}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:16:00,928] Trial 20 finished with value: 0.5385736214632512 and parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.2790033826074093, 'dropout_second': 0.12854003565290825, 'lr': 0.0011562007449123404, 'weight_decay': 0.0013061186523123443, 'batch_size': 256}. Best is trial 9 with value: 0.5588602554914753.
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.538574
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.2790033826074093, 'dropout_second': 0.12854003565290825, 'lr': 0.0011562007449123404, 'weight_decay': 0.0013061186523123443, 'batch_size': 256}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:16:09,426] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.392787
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.27598504328267187, 'dropout_second': 0.13024350672677656, 'lr': 0.009900317920076585, 'weight_decay': 0.0013015853488160384, 'batch_size': 256}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:16:17,690] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.407574
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.29127138555380205, 'dropout_second': 0.11068881847311403, 'lr': 0.0011305164433176352, 'weight_decay': 0.00045370883576981435, 'batch_size': 256}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:16:25,874] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.432915
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22578754002496354, 'dropout_second': 0.13703042034956103, 'lr': 0.004442828020073193, 'weight_decay': 0.0009424962572737787, 'batch_size': 256}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:16:34,506] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.405617
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.24823165039802272, 'dropout_second': 0.16330656835241408, 'lr': 0.0022494779136446375, 'weight_decay': 0.002066357465697157, 'batch_size': 256}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:17:06,207] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.419608
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.20797025022270915, 'dropout_second': 0.08976877358825117, 'lr': 0.0049252318944887526, 'weight_decay': 0.0003706176664541153, 'batch_size': 32}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:17:19,648] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.417154
+  Parameters: {'d_main': 160, 'd_hidden': 448, 'n_blocks': 3, 'dropout_first': 0.18297200282421544, 'dropout_second': 0.12292010825539913, 'lr': 0.0013696820873515165, 'weight_decay': 0.0007904224599527155, 'batch_size': 128}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:17:38,803] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.381266
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.2591157400924291, 'dropout_second': 0.13977455433872263, 'lr': 0.002485406417117675, 'weight_decay': 0.0039779947569999075, 'batch_size': 64}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:17:47,857] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.414286
+  Parameters: {'d_main': 256, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.305019100431161, 'dropout_second': 0.11519510570032383, 'lr': 0.0008173145230022452, 'weight_decay': 0.0001763732474195749, 'batch_size': 256}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:17:59,911] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.398271
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.2381461288989195, 'dropout_second': 0.10018365728082694, 'lr': 0.003432645888169016, 'weight_decay': 0.0011025694530821118, 'batch_size': 128}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:18:37,459] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.380013
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.2188984766547715, 'dropout_second': 0.14490033498157143, 'lr': 0.006201656690435642, 'weight_decay': 0.0003910883489313019, 'batch_size': 32}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:18:55,612] Trial 31 pruned. 
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.414174
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.33403731126952857, 'dropout_second': 0.05162230432936377, 'lr': 0.001606985185659973, 'weight_decay': 0.00018603821339554009, 'batch_size': 64}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:19:14,902] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.442940
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.32750519612358714, 'dropout_second': 0.08359170965588054, 'lr': 0.0006108793772040534, 'weight_decay': 0.00025521062169725245, 'batch_size': 64}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:19:42,228] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.430052
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.3085945961083504, 'dropout_second': 0.04273817173211725, 'lr': 0.0004383742281042417, 'weight_decay': 0.00010096277239438557, 'batch_size': 64}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:20:00,888] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.420882
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.35488173114095256, 'dropout_second': 0.12983947065009763, 'lr': 0.0009489071317632075, 'weight_decay': 0.000676089061457214, 'batch_size': 64}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:20:19,076] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.448784
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.2764055663065649, 'dropout_second': 0.058922674477374946, 'lr': 0.0006160754193436823, 'weight_decay': 0.00017237475046627657, 'batch_size': 64}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:20:27,383] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.415918
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.39753611069350997, 'dropout_second': 0.10250719141248248, 'lr': 0.0016816144967003368, 'weight_decay': 0.00047997116942961985, 'batch_size': 256}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-25 23:20:46,181] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.569658
+  Parameters: {'d_main': 256, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.30079859664606556, 'dropout_second': 0.17569151176987133, 'lr': 0.00039248575168566617, 'weight_decay': 0.0014233022809797126, 'batch_size': 256}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:21:18,150] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.383430
+  Parameters: {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.2785023575142934, 'dropout_second': 0.0713157961316541, 'lr': 0.00020287145099363608, 'weight_decay': 0.00015435911545857482, 'batch_size': 32}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:21:52,653] Trial 39 finished with value: 0.5336591720575274 and parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.31952008261846715, 'dropout_second': 0.030158599703318273, 'lr': 0.0028609108203279197, 'weight_decay': 0.00029082293126663525, 'batch_size': 256}. Best is trial 9 with value: 0.5588602554914753.
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.533659
+  Parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.31952008261846715, 'dropout_second': 0.030158599703318273, 'lr': 0.0028609108203279197, 'weight_decay': 0.00029082293126663525, 'batch_size': 256}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:22:11,727] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.386744
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.2670107698667258, 'dropout_second': 0.07922082334175687, 'lr': 0.0001383488327129012, 'weight_decay': 0.0007297855513409189, 'batch_size': 64}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:22:21,177] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.437661
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.318810753149683, 'dropout_second': 0.02542466308438076, 'lr': 0.003021497534801721, 'weight_decay': 0.0002974323713634535, 'batch_size': 256}
+  Best Value (CSI): 0.558860
+  Best Trial: 9
+  Best Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2205939671208104, 'dropout_second': 0.11346779379716013, 'lr': 0.004805652260664701, 'weight_decay': 0.00042747631522639933, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:23:05,521] Trial 42 finished with value: 0.5600438569228808 and parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3467771723380215, 'dropout_second': 0.029471813552598352, 'lr': 0.006939714087804261, 'weight_decay': 0.0004702160091348632, 'batch_size': 256}. Best is trial 42 with value: 0.5600438569228808.
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.560044
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3467771723380215, 'dropout_second': 0.029471813552598352, 'lr': 0.006939714087804261, 'weight_decay': 0.0004702160091348632, 'batch_size': 256}
+  Best Value (CSI): 0.560044
+  Best Trial: 42
+  Best Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3467771723380215, 'dropout_second': 0.029471813552598352, 'lr': 0.006939714087804261, 'weight_decay': 0.0004702160091348632, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:23:14,995] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.400527
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3448558863740794, 'dropout_second': 0.03872427772088023, 'lr': 0.0043556842705346835, 'weight_decay': 0.0005399970768066204, 'batch_size': 256}
+  Best Value (CSI): 0.560044
+  Best Trial: 42
+  Best Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3467771723380215, 'dropout_second': 0.029471813552598352, 'lr': 0.006939714087804261, 'weight_decay': 0.0004702160091348632, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:23:24,477] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.387742
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 4, 'dropout_first': 0.35838217539044426, 'dropout_second': 0.013118091202450763, 'lr': 0.007373880044469156, 'weight_decay': 0.0004027712356841905, 'batch_size': 256}
+  Best Value (CSI): 0.560044
+  Best Trial: 42
+  Best Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3467771723380215, 'dropout_second': 0.029471813552598352, 'lr': 0.006939714087804261, 'weight_decay': 0.0004702160091348632, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:24:07,723] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.462366
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.29407172320479136, 'dropout_second': 0.06638684118209648, 'lr': 0.007824869986619647, 'weight_decay': 0.0009252583719865802, 'batch_size': 32}
+  Best Value (CSI): 0.560044
+  Best Trial: 42
+  Best Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3467771723380215, 'dropout_second': 0.029471813552598352, 'lr': 0.006939714087804261, 'weight_decay': 0.0004702160091348632, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:24:48,021] Trial 46 finished with value: 0.5423620823065319 and parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.2507310906472483, 'dropout_second': 0.1211137391720508, 'lr': 0.002127870223202996, 'weight_decay': 0.0005855028480361441, 'batch_size': 256}. Best is trial 42 with value: 0.5600438569228808.
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.542362
+  Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.2507310906472483, 'dropout_second': 0.1211137391720508, 'lr': 0.002127870223202996, 'weight_decay': 0.0005855028480361441, 'batch_size': 256}
+  Best Value (CSI): 0.560044
+  Best Trial: 42
+  Best Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3467771723380215, 'dropout_second': 0.029471813552598352, 'lr': 0.006939714087804261, 'weight_decay': 0.0004702160091348632, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:24:58,349] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.442010
+  Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.24460992803026685, 'dropout_second': 0.1158647852002458, 'lr': 0.0021701118298548434, 'weight_decay': 0.0005342961213606322, 'batch_size': 256}
+  Best Value (CSI): 0.560044
+  Best Trial: 42
+  Best Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3467771723380215, 'dropout_second': 0.029471813552598352, 'lr': 0.006939714087804261, 'weight_decay': 0.0004702160091348632, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:25:07,929] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.469981
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.25854068919552675, 'dropout_second': 0.12604426486940074, 'lr': 0.005810520005739945, 'weight_decay': 0.0014490703094126862, 'batch_size': 256}
+  Best Value (CSI): 0.560044
+  Best Trial: 42
+  Best Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3467771723380215, 'dropout_second': 0.029471813552598352, 'lr': 0.006939714087804261, 'weight_decay': 0.0004702160091348632, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:25:17,332] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.389764
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.21556730149147652, 'dropout_second': 0.09595988948295218, 'lr': 0.003748265405734225, 'weight_decay': 0.0007955186913609087, 'batch_size': 256}
+  Best Value (CSI): 0.560044
+  Best Trial: 42
+  Best Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3467771723380215, 'dropout_second': 0.029471813552598352, 'lr': 0.006939714087804261, 'weight_decay': 0.0004702160091348632, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:26:08,027] Trial 50 finished with value: 0.5581070864364377 and parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.23598504514984095, 'dropout_second': 0.10870751067467437, 'lr': 0.009302660464994918, 'weight_decay': 0.0010617808298271714, 'batch_size': 256}. Best is trial 42 with value: 0.5600438569228808.
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.558107
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.23598504514984095, 'dropout_second': 0.10870751067467437, 'lr': 0.009302660464994918, 'weight_decay': 0.0010617808298271714, 'batch_size': 256}
+  Best Value (CSI): 0.560044
+  Best Trial: 42
+  Best Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.3467771723380215, 'dropout_second': 0.029471813552598352, 'lr': 0.006939714087804261, 'weight_decay': 0.0004702160091348632, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:26:49,513] Trial 51 finished with value: 0.5608422873538353 and parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}. Best is trial 51 with value: 0.5608422873538353.
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.560842
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-25 23:27:08,132] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.535245
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2365307265326616, 'dropout_second': 0.10708837357062319, 'lr': 0.008947131265622833, 'weight_decay': 0.0006423945703904284, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:27:17,653] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.381588
+  Parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.23292721420300944, 'dropout_second': 0.11505971578654631, 'lr': 0.00687447000139071, 'weight_decay': 0.0010095307032789793, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-25 23:27:36,025] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.473878
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.1917314599353037, 'dropout_second': 0.09345989776068198, 'lr': 0.008897055207917863, 'weight_decay': 0.0003816502683545021, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:27:45,488] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.400788
+  Parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.24892275338644138, 'dropout_second': 0.10545231776342005, 'lr': 0.005576768014657569, 'weight_decay': 0.0006124632413215199, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:28:00,696] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.431111
+  Parameters: {'d_main': 128, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.21123458905361076, 'dropout_second': 0.12021869801885805, 'lr': 0.009902019757920558, 'weight_decay': 0.001756861081958321, 'batch_size': 128}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:28:41,775] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.476744
+  Parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.22455965135399908, 'dropout_second': 0.08365159580804049, 'lr': 0.004979477516361539, 'weight_decay': 0.0011071163616633612, 'batch_size': 32}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:28:51,364] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.379102
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.20161963768596314, 'dropout_second': 0.13371586145128062, 'lr': 0.007124478422468613, 'weight_decay': 0.0008535455216667618, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:29:00,914] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.384767
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 4, 'dropout_first': 0.1773377935792973, 'dropout_second': 0.09672117148958517, 'lr': 0.003711760568768328, 'weight_decay': 0.0004696323627919663, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:29:41,079] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.409179
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.22737671243591878, 'dropout_second': 0.10969595096505333, 'lr': 0.005409979872840098, 'weight_decay': 0.002392818667113918, 'batch_size': 32}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-25 23:29:58,981] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.547450
+  Parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.25399114337812734, 'dropout_second': 0.12565962542153325, 'lr': 0.004265676753035848, 'weight_decay': 0.0012320255514159319, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:30:08,345] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.421977
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24290128154073293, 'dropout_second': 0.13377037399305727, 'lr': 0.0029010098459565405, 'weight_decay': 0.0016434123950806751, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-25 23:30:26,134] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.545288
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2702611988989374, 'dropout_second': 0.1185122660370813, 'lr': 0.007615156544120954, 'weight_decay': 0.0010782979171802445, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-25 23:30:45,661] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.577513
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 5, 'dropout_first': 0.28740097750142884, 'dropout_second': 0.10224206509318208, 'lr': 0.0023451073790688166, 'weight_decay': 0.000753345089865849, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:31:00,271] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.385743
+  Parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.26245489168294117, 'dropout_second': 0.14466778180812934, 'lr': 0.005974214439060911, 'weight_decay': 0.0019054735363837377, 'batch_size': 128}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-25 23:31:17,366] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.478370
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.2331003255966331, 'dropout_second': 0.1242715949360223, 'lr': 0.009955346676774343, 'weight_decay': 0.0013229166292725186, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:31:26,794] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.355246
+  Parameters: {'d_main': 224, 'd_hidden': 512, 'n_blocks': 4, 'dropout_first': 0.25208406724637666, 'dropout_second': 0.13407566449373987, 'lr': 0.0041542768682116754, 'weight_decay': 0.0005618699089034217, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:32:01,067] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.385175
+  Parameters: {'d_main': 256, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2182506492092093, 'dropout_second': 0.11190571060941422, 'lr': 0.004939944860199436, 'weight_decay': 0.002790127092930282, 'batch_size': 32}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:32:10,233] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.415752
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.20817582679810656, 'dropout_second': 0.09062504612360954, 'lr': 0.0033365038532872557, 'weight_decay': 0.0008906828787954452, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:32:18,439] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.442518
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.19434231325669626, 'dropout_second': 0.11879391986965206, 'lr': 0.0017637849041623277, 'weight_decay': 0.00035603580444032624, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:32:36,281] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.402503
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.2465144836777804, 'dropout_second': 0.11079173022905008, 'lr': 0.007598061038429338, 'weight_decay': 0.0002143427894201753, 'batch_size': 64}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:32:55,186] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.416831
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.2693004605382418, 'dropout_second': 0.09920157112279239, 'lr': 0.0061411211157240235, 'weight_decay': 0.00044218960465902614, 'batch_size': 64}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:33:03,318] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.424051
+  Parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.2832855211890755, 'dropout_second': 0.1403305593568798, 'lr': 0.0027016008286991086, 'weight_decay': 0.0006779936294704955, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:33:15,493] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.407383
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.2392680257059239, 'dropout_second': 0.12835456594645148, 'lr': 0.003635069556281527, 'weight_decay': 0.0003502890080648877, 'batch_size': 128}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:33:46,832] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.388305
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 2, 'dropout_first': 0.21787806776076654, 'dropout_second': 0.1506203517353086, 'lr': 0.001353149909915792, 'weight_decay': 0.00025231401500731674, 'batch_size': 32}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:34:08,352] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.412467
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.22824025163941056, 'dropout_second': 0.10822719996578595, 'lr': 0.002068558409785349, 'weight_decay': 0.0004887724074863938, 'batch_size': 64}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:34:16,505] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.427165
+  Parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.25530451494476003, 'dropout_second': 0.10452700787420775, 'lr': 0.004790374469184287, 'weight_decay': 0.0003095235125869626, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:34:25,258] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.376323
+  Parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.20174566182226703, 'dropout_second': 0.07713452213001926, 'lr': 0.008344404352300898, 'weight_decay': 0.0010341783177099605, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:35:39,586] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.498855
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.3116466866831781, 'dropout_second': 0.12254815793439196, 'lr': 0.0063523040619092915, 'weight_decay': 0.0006668985601424397, 'batch_size': 64}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:36:11,048] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.375668
+  Parameters: {'d_main': 128, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.335861029953231, 'dropout_second': 0.11322133182819083, 'lr': 0.0024444697438653407, 'weight_decay': 0.0001204618848523106, 'batch_size': 32}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:36:19,722] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.405617
+  Parameters: {'d_main': 160, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3310834362891305, 'dropout_second': 0.022669000917670573, 'lr': 0.004014145400056281, 'weight_decay': 0.0002828808275188715, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:36:58,258] Trial 82 finished with value: 0.5492922594867475 and parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3197381005165854, 'dropout_second': 0.03447648982019932, 'lr': 0.0030670143154618765, 'weight_decay': 0.00044216162157769784, 'batch_size': 256}. Best is trial 51 with value: 0.5608422873538353.
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.549292
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.3197381005165854, 'dropout_second': 0.03447648982019932, 'lr': 0.0030670143154618765, 'weight_decay': 0.00044216162157769784, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:37:07,109] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.420366
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 3, 'dropout_first': 0.29439578183142845, 'dropout_second': 0.04416685182823954, 'lr': 0.0032408840154795238, 'weight_decay': 0.0004372046084384003, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:37:15,112] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.403555
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.31649604059386804, 'dropout_second': 0.05161204518031829, 'lr': 0.005257590750442561, 'weight_decay': 0.0005661288792326778, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:37:24,325] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.428759
+  Parameters: {'d_main': 224, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.30263656551201434, 'dropout_second': 0.03249903726971549, 'lr': 0.008404779124510053, 'weight_decay': 0.000776713118783943, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:37:32,601] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.397368
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.23661151115090395, 'dropout_second': 0.1299709042332637, 'lr': 0.006867741895659899, 'weight_decay': 0.0009520493693076766, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:38:18,552] Trial 87 finished with value: 0.5440300031651121 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.32474189757869965, 'dropout_second': 0.11736870460392801, 'lr': 0.0026107643600196207, 'weight_decay': 0.00022045210203268663, 'batch_size': 256}. Best is trial 51 with value: 0.5608422873538353.
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.544030
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.32474189757869965, 'dropout_second': 0.11736870460392801, 'lr': 0.0026107643600196207, 'weight_decay': 0.00022045210203268663, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:38:27,718] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.410458
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.32510443033466163, 'dropout_second': 0.11591136664219584, 'lr': 0.0028094252939439403, 'weight_decay': 0.0002115030891768373, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-25 23:38:45,918] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.549582
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.24773903659410768, 'dropout_second': 0.1201827118492818, 'lr': 0.001850309698615121, 'weight_decay': 0.0015567581802496137, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:38:55,402] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.455671
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.2656878761885808, 'dropout_second': 0.10087150723793166, 'lr': 0.004396506038603112, 'weight_decay': 0.0012207210494845557, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:39:37,082] Trial 91 finished with value: 0.5497159053488089 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.34440781883617205, 'dropout_second': 0.10669732250621328, 'lr': 0.003233570255040347, 'weight_decay': 0.00034068972888931455, 'batch_size': 256}. Best is trial 51 with value: 0.5608422873538353.
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.549716
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.34440781883617205, 'dropout_second': 0.10669732250621328, 'lr': 0.003233570255040347, 'weight_decay': 0.00034068972888931455, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:40:19,101] Trial 92 finished with value: 0.5584825025016332 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.3458586722643802, 'dropout_second': 0.1066628308265106, 'lr': 0.0033660690637526804, 'weight_decay': 0.0003520070952341027, 'batch_size': 256}. Best is trial 51 with value: 0.5608422873538353.
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.558483
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.3458586722643802, 'dropout_second': 0.1066628308265106, 'lr': 0.0033660690637526804, 'weight_decay': 0.0003520070952341027, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:40:28,361] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.395847
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.3579621976679143, 'dropout_second': 0.09408016710778155, 'lr': 0.0035274569619353977, 'weight_decay': 0.000331327252777835, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:40:37,591] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.431310
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.34716246179540483, 'dropout_second': 0.10803467635916153, 'lr': 0.0025757208819591434, 'weight_decay': 0.00039856025505067926, 'batch_size': 256}
+  Best Value (CSI): 0.560842
+  Best Trial: 51
+  Best Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.2348916107140658, 'dropout_second': 0.10686844446349712, 'lr': 0.009604608099061072, 'weight_decay': 0.0011259481514055976, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:41:17,284] Trial 95 finished with value: 0.5707798446437679 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.3626907864360457, 'dropout_second': 0.08738106548329602, 'lr': 0.005205322934309404, 'weight_decay': 0.0002577881849067971, 'batch_size': 256}. Best is trial 95 with value: 0.5707798446437679.
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.570780
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.3626907864360457, 'dropout_second': 0.08738106548329602, 'lr': 0.005205322934309404, 'weight_decay': 0.0002577881849067971, 'batch_size': 256}
+  Best Value (CSI): 0.570780
+  Best Trial: 95
+  Best Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.3626907864360457, 'dropout_second': 0.08738106548329602, 'lr': 0.005205322934309404, 'weight_decay': 0.0002577881849067971, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+[I 2025-12-25 23:41:35,636] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.532967
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.36776080710489023, 'dropout_second': 0.09018026459786346, 'lr': 0.006333309885903695, 'weight_decay': 0.0002389375248451174, 'batch_size': 256}
+  Best Value (CSI): 0.570780
+  Best Trial: 95
+  Best Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.3626907864360457, 'dropout_second': 0.08738106548329602, 'lr': 0.005205322934309404, 'weight_decay': 0.0002577881849067971, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+  Fold 2 - 클래스별 가중치: {0: 0.9388130081300813, 1: 0.8515781710914454, 2: 1.3148641570449318} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14637})
+  Fold 3 - 클래스별 가중치: {0: 0.9397560975609756, 1: 0.8486784140969162, 2: 1.3199725933538884} (클래스별 샘플 수: {0: 20500, 1: 22700, 2: 14595})
+[I 2025-12-25 23:42:19,292] Trial 97 finished with value: 0.5498759557700766 and parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.33852975474780317, 'dropout_second': 0.1043648744595291, 'lr': 0.005255219169300538, 'weight_decay': 0.00029795369407975515, 'batch_size': 256}. Best is trial 95 with value: 0.5707798446437679.
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.549876
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.33852975474780317, 'dropout_second': 0.1043648744595291, 'lr': 0.005255219169300538, 'weight_decay': 0.00029795369407975515, 'batch_size': 256}
+  Best Value (CSI): 0.570780
+  Best Trial: 95
+  Best Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.3626907864360457, 'dropout_second': 0.08738106548329602, 'lr': 0.005205322934309404, 'weight_decay': 0.0002577881849067971, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:42:28,744] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.394841
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.3415590852669094, 'dropout_second': 0.08229275278087955, 'lr': 0.008198240249649774, 'weight_decay': 0.000285092816963203, 'batch_size': 256}
+  Best Value (CSI): 0.570780
+  Best Trial: 95
+  Best Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.3626907864360457, 'dropout_second': 0.08738106548329602, 'lr': 0.005205322934309404, 'weight_decay': 0.0002577881849067971, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9374634146341464, 1: 0.850353982300885, 2: 1.3204617287343685} (클래스별 샘플 수: {0: 20500, 1: 22600, 2: 14554})
+[I 2025-12-25 23:42:38,105] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.444156
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.3519757754029289, 'dropout_second': 0.0978865926336878, 'lr': 0.004961235284154937, 'weight_decay': 0.0003980320503554098, 'batch_size': 256}
+  Best Value (CSI): 0.570780
+  Best Trial: 95
+  Best Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.3626907864360457, 'dropout_second': 0.08738106548329602, 'lr': 0.005205322934309404, 'weight_decay': 0.0002577881849067971, 'batch_size': 256}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5708
+Best Hyperparameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.3626907864360457, 'dropout_second': 0.08738106548329602, 'lr': 0.005205322934309404, 'weight_decay': 0.0002577881849067971, 'batch_size': 256}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5224
+  - 최종 CSI: 0.4442
+  - 최고 CSI: 0.5775
+  - 최저 CSI: 0.3048
+  - 평균 CSI: 0.4496
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/resnet_like_smotenc_ctgan20000_incheon_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 21, Best CSI: 0.4828
+  Fold 1 학습 완료 (검증 CSI: 0.4828)
+Fold 2 학습 중...
+  Early stopping at epoch 22, Best CSI: 0.5887
+  Fold 2 학습 완료 (검증 CSI: 0.5887)
+Fold 3 학습 중...
+  Early stopping at epoch 29, Best CSI: 0.5732
+  Fold 3 학습 완료 (검증 CSI: 0.5732)
+
+모든 모델 저장 완료: ../save_model/resnet_like_optima/resnet_like_smotenc_ctgan20000_incheon.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/resnet_like_optima/scaler/resnet_like_smotenc_ctgan20000_incheon_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/resnet_like_optima/resnet_like_smotenc_ctgan20000_incheon.pkl
+
+✓ 완료: resnet_like_smotenc_ctgan20000/resnet_like_smotenc_ctgan20000_incheon.py (소요 시간: 2714초)
+
+----------------------------------------
+실행 중: resnet_like_smotenc_ctgan20000/resnet_like_smotenc_ctgan20000_seoul.py
+시작 시간: 2025-12-25 23:43:23
+----------------------------------------
+[I 2025-12-25 23:43:24,403] A new study created in memory with name: no-name-e3aa17bc-dc8e-4248-9392-0b49cf82be09
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-25 23:44:38,846] Trial 0 finished with value: 0.46853264863089 and parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.339177920518485, 'dropout_second': 0.004994138843200924, 'lr': 4.1393458195034955e-05, 'weight_decay': 0.05482991880947211, 'batch_size': 256}. Best is trial 0 with value: 0.46853264863089.
+
+================================================================================
+Trial 0 완료
+  Value (CSI): 0.468533
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.339177920518485, 'dropout_second': 0.004994138843200924, 'lr': 4.1393458195034955e-05, 'weight_decay': 0.05482991880947211, 'batch_size': 256}
+  Best Value (CSI): 0.468533
+  Best Trial: 0
+  Best Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.339177920518485, 'dropout_second': 0.004994138843200924, 'lr': 4.1393458195034955e-05, 'weight_decay': 0.05482991880947211, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-25 23:45:35,205] Trial 1 finished with value: 0.45933659587853937 and parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.3522803949183383, 'dropout_second': 0.06585666057156536, 'lr': 6.960944478778582e-05, 'weight_decay': 0.02020442011221413, 'batch_size': 256}. Best is trial 0 with value: 0.46853264863089.
+
+================================================================================
+Trial 1 완료
+  Value (CSI): 0.459337
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 4, 'dropout_first': 0.3522803949183383, 'dropout_second': 0.06585666057156536, 'lr': 6.960944478778582e-05, 'weight_decay': 0.02020442011221413, 'batch_size': 256}
+  Best Value (CSI): 0.468533
+  Best Trial: 0
+  Best Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.339177920518485, 'dropout_second': 0.004994138843200924, 'lr': 4.1393458195034955e-05, 'weight_decay': 0.05482991880947211, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-25 23:47:16,398] Trial 2 finished with value: 0.45649986432226414 and parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2510651018293846, 'dropout_second': 0.02017546571932878, 'lr': 1.1579773390628263e-05, 'weight_decay': 0.0008364605027728892, 'batch_size': 128}. Best is trial 0 with value: 0.46853264863089.
+
+================================================================================
+Trial 2 완료
+  Value (CSI): 0.456500
+  Parameters: {'d_main': 256, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.2510651018293846, 'dropout_second': 0.02017546571932878, 'lr': 1.1579773390628263e-05, 'weight_decay': 0.0008364605027728892, 'batch_size': 128}
+  Best Value (CSI): 0.468533
+  Best Trial: 0
+  Best Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.339177920518485, 'dropout_second': 0.004994138843200924, 'lr': 4.1393458195034955e-05, 'weight_decay': 0.05482991880947211, 'batch_size': 256}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-25 23:48:56,645] Trial 3 finished with value: 0.47853687368383224 and parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.28393332255475656, 'dropout_second': 0.10333504463687677, 'lr': 7.203584891579507e-05, 'weight_decay': 0.06937633991753853, 'batch_size': 64}. Best is trial 3 with value: 0.47853687368383224.
+
+================================================================================
+Trial 3 완료
+  Value (CSI): 0.478537
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.28393332255475656, 'dropout_second': 0.10333504463687677, 'lr': 7.203584891579507e-05, 'weight_decay': 0.06937633991753853, 'batch_size': 64}
+  Best Value (CSI): 0.478537
+  Best Trial: 3
+  Best Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.28393332255475656, 'dropout_second': 0.10333504463687677, 'lr': 7.203584891579507e-05, 'weight_decay': 0.06937633991753853, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-25 23:51:28,605] Trial 4 finished with value: 0.5571199836340152 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}. Best is trial 4 with value: 0.5571199836340152.
+
+================================================================================
+Trial 4 완료
+  Value (CSI): 0.557120
+  Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-25 23:54:40,006] Trial 5 finished with value: 0.5563466479121164 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3842506615494192, 'dropout_second': 0.15744555840459062, 'lr': 0.0017280908217099248, 'weight_decay': 0.017047388106556986, 'batch_size': 32}. Best is trial 4 with value: 0.5571199836340152.
+
+================================================================================
+Trial 5 완료
+  Value (CSI): 0.556347
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3842506615494192, 'dropout_second': 0.15744555840459062, 'lr': 0.0017280908217099248, 'weight_decay': 0.017047388106556986, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-25 23:55:57,840] Trial 6 finished with value: 0.5549681029887404 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.33558709072883774, 'dropout_second': 0.0432597653482201, 'lr': 0.003143017451786458, 'weight_decay': 0.008220294623575444, 'batch_size': 128}. Best is trial 4 with value: 0.5571199836340152.
+
+================================================================================
+Trial 6 완료
+  Value (CSI): 0.554968
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.33558709072883774, 'dropout_second': 0.0432597653482201, 'lr': 0.003143017451786458, 'weight_decay': 0.008220294623575444, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-25 23:56:15,049] Trial 7 pruned. 
+
+================================================================================
+Trial 7 완료
+  Value (CSI): 0.465272
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.35598639499172124, 'dropout_second': 0.021030249547184465, 'lr': 0.000375203737586533, 'weight_decay': 0.00040990488658376907, 'batch_size': 256}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-25 23:56:59,184] Trial 8 pruned. 
+
+================================================================================
+Trial 8 완료
+  Value (CSI): 0.494819
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.37834353189045467, 'dropout_second': 0.12422296734726389, 'lr': 0.004467083245713576, 'weight_decay': 0.030186880101814494, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-25 23:57:05,913] Trial 9 pruned. 
+
+================================================================================
+Trial 9 완료
+  Value (CSI): 0.345400
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.2546842800285539, 'dropout_second': 0.02209784317774668, 'lr': 3.871389391179669e-05, 'weight_decay': 0.0005898990067735424, 'batch_size': 256}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-25 23:58:53,069] Trial 10 finished with value: 0.4985730721099057 and parameters: {'d_main': 64, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.16004555103197882, 'dropout_second': 0.19527378694430442, 'lr': 0.008526400490745979, 'weight_decay': 0.0032898612116598687, 'batch_size': 64}. Best is trial 4 with value: 0.5571199836340152.
+
+================================================================================
+Trial 10 완료
+  Value (CSI): 0.498573
+  Parameters: {'d_main': 64, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.16004555103197882, 'dropout_second': 0.19527378694430442, 'lr': 0.008526400490745979, 'weight_decay': 0.0032898612116598687, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 00:02:07,166] Trial 11 finished with value: 0.5513487502691617 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.16770422133734728, 'dropout_second': 0.15145920812693306, 'lr': 0.0012088108914799058, 'weight_decay': 0.008682243897753333, 'batch_size': 32}. Best is trial 4 with value: 0.5571199836340152.
+
+================================================================================
+Trial 11 완료
+  Value (CSI): 0.551349
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.16770422133734728, 'dropout_second': 0.15145920812693306, 'lr': 0.0012088108914799058, 'weight_decay': 0.008682243897753333, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:03:14,117] Trial 12 pruned. 
+
+================================================================================
+Trial 12 완료
+  Value (CSI): 0.476516
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.39820312668219104, 'dropout_second': 0.15480141171977865, 'lr': 0.0016692851130591072, 'weight_decay': 0.00011984279789460534, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:03:59,886] Trial 13 pruned. 
+
+================================================================================
+Trial 13 완료
+  Value (CSI): 0.463500
+  Parameters: {'d_main': 96, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.19875377055983698, 'dropout_second': 0.08613049147103384, 'lr': 0.009526515468719823, 'weight_decay': 0.013675608184459882, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 00:07:57,205] Trial 14 finished with value: 0.5316003091072362 and parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.2982962740060076, 'dropout_second': 0.13560245662475806, 'lr': 0.0008106194017350491, 'weight_decay': 0.003550224226397828, 'batch_size': 32}. Best is trial 4 with value: 0.5571199836340152.
+
+================================================================================
+Trial 14 완료
+  Value (CSI): 0.531600
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.2982962740060076, 'dropout_second': 0.13560245662475806, 'lr': 0.0008106194017350491, 'weight_decay': 0.003550224226397828, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:08:33,735] Trial 15 pruned. 
+
+================================================================================
+Trial 15 완료
+  Value (CSI): 0.479695
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.11910761444276385, 'dropout_second': 0.17702718291561725, 'lr': 0.0029158588671825032, 'weight_decay': 0.02694552784672563, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:09:05,671] Trial 16 pruned. 
+
+================================================================================
+Trial 16 완료
+  Value (CSI): 0.459617
+  Parameters: {'d_main': 96, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.30100206636651167, 'dropout_second': 0.12062814183038506, 'lr': 0.0005549946457763892, 'weight_decay': 0.07152683391703589, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 00:12:37,865] Trial 17 finished with value: 0.547770986558486 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.21245808858602772, 'dropout_second': 0.1623766307935326, 'lr': 0.0015681106486891687, 'weight_decay': 0.006784462934471387, 'batch_size': 32}. Best is trial 4 with value: 0.5571199836340152.
+
+================================================================================
+Trial 17 완료
+  Value (CSI): 0.547771
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.21245808858602772, 'dropout_second': 0.1623766307935326, 'lr': 0.0015681106486891687, 'weight_decay': 0.006784462934471387, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:12:47,216] Trial 18 pruned. 
+
+================================================================================
+Trial 18 완료
+  Value (CSI): 0.421875
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.39617882115059355, 'dropout_second': 0.19874418049119724, 'lr': 0.00026642692672290924, 'weight_decay': 0.031408314322785215, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:13:20,391] Trial 19 pruned. 
+
+================================================================================
+Trial 19 완료
+  Value (CSI): 0.480442
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3185619078127263, 'dropout_second': 0.14147361503791994, 'lr': 0.005294634301866872, 'weight_decay': 0.012566608598992803, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:14:19,099] Trial 20 pruned. 
+
+================================================================================
+Trial 20 완료
+  Value (CSI): 0.467116
+  Parameters: {'d_main': 224, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.2725672555103972, 'dropout_second': 0.1721258636516901, 'lr': 0.0021787082170953583, 'weight_decay': 0.005017664117944093, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:14:43,883] Trial 21 pruned. 
+
+================================================================================
+Trial 21 완료
+  Value (CSI): 0.464195
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.3159132619249883, 'dropout_second': 0.05544432803103884, 'lr': 0.0035971444001848237, 'weight_decay': 0.011530537114611604, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:15:08,855] Trial 22 pruned. 
+
+================================================================================
+Trial 22 완료
+  Value (CSI): 0.468068
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.36333262687948864, 'dropout_second': 0.11293221057314123, 'lr': 0.003079305873640964, 'weight_decay': 0.0018681702782209495, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:15:31,860] Trial 23 pruned. 
+
+================================================================================
+Trial 23 완료
+  Value (CSI): 0.471503
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.33697663525866195, 'dropout_second': 0.09116550162451238, 'lr': 0.0055469949131973775, 'weight_decay': 0.006776612530614833, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:15:56,778] Trial 24 pruned. 
+
+================================================================================
+Trial 24 완료
+  Value (CSI): 0.475820
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.37435648246936254, 'dropout_second': 0.1357938787408721, 'lr': 0.0010473338537155394, 'weight_decay': 0.019974794732452165, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:16:21,123] Trial 25 pruned. 
+
+================================================================================
+Trial 25 완료
+  Value (CSI): 0.474870
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.3316037599224588, 'dropout_second': 0.10406330870077932, 'lr': 0.0022221988388164294, 'weight_decay': 0.00948910752323048, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:16:46,380] Trial 26 pruned. 
+
+================================================================================
+Trial 26 완료
+  Value (CSI): 0.401685
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.2726852361341167, 'dropout_second': 0.07171340086065832, 'lr': 0.005564452541796679, 'weight_decay': 0.042455281178289124, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:17:28,368] Trial 27 pruned. 
+
+================================================================================
+Trial 27 완료
+  Value (CSI): 0.466953
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.3809678752895238, 'dropout_second': 0.12765359617241817, 'lr': 0.0021583751198039898, 'weight_decay': 0.09710384424507085, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:17:38,169] Trial 28 pruned. 
+
+================================================================================
+Trial 28 완료
+  Value (CSI): 0.443556
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.3501386840683215, 'dropout_second': 0.14555319823281823, 'lr': 0.008516696860468504, 'weight_decay': 0.018493008849181886, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:17:45,223] Trial 29 pruned. 
+
+================================================================================
+Trial 29 완료
+  Value (CSI): 0.419800
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.3263477598387323, 'dropout_second': 0.11209684523230556, 'lr': 0.0009073632917144997, 'weight_decay': 0.004618111789546119, 'batch_size': 256}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:18:22,452] Trial 30 pruned. 
+
+================================================================================
+Trial 30 완료
+  Value (CSI): 0.468777
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.30533452473529027, 'dropout_second': 0.002080544812490523, 'lr': 0.003217116052817351, 'weight_decay': 0.046709521275335474, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 00:20:34,196] Trial 31 finished with value: 0.55395284776608 and parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22346194827887664, 'dropout_second': 0.15465026334125415, 'lr': 0.0012709063360262134, 'weight_decay': 0.009780031804941903, 'batch_size': 32}. Best is trial 4 with value: 0.5571199836340152.
+
+================================================================================
+Trial 31 완료
+  Value (CSI): 0.553953
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.22346194827887664, 'dropout_second': 0.15465026334125415, 'lr': 0.0012709063360262134, 'weight_decay': 0.009780031804941903, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:21:30,175] Trial 32 pruned. 
+
+================================================================================
+Trial 32 완료
+  Value (CSI): 0.475786
+  Parameters: {'d_main': 96, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.2351547681854742, 'dropout_second': 0.16019323108608213, 'lr': 0.001405603305042605, 'weight_decay': 0.01565697079898335, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:22:25,886] Trial 33 pruned. 
+
+================================================================================
+Trial 33 완료
+  Value (CSI): 0.455013
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 2, 'dropout_first': 0.34311419293565154, 'dropout_second': 0.1341558712141348, 'lr': 0.002382640861496219, 'weight_decay': 0.008272929205831557, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:23:16,754] Trial 34 pruned. 
+
+================================================================================
+Trial 34 완료
+  Value (CSI): 0.472293
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.22954649906618713, 'dropout_second': 0.1461556573024163, 'lr': 0.003976480828395441, 'weight_decay': 0.020286371809207417, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:24:12,922] Trial 35 pruned. 
+
+================================================================================
+Trial 35 완료
+  Value (CSI): 0.459150
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.28756481582552795, 'dropout_second': 0.17719889919563742, 'lr': 0.0006355470417270689, 'weight_decay': 0.0023378632805758403, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:24:24,265] Trial 36 pruned. 
+
+================================================================================
+Trial 36 완료
+  Value (CSI): 0.456943
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.26208009001512506, 'dropout_second': 0.1267233339874632, 'lr': 0.0016938638571591526, 'weight_decay': 0.011876415850892426, 'batch_size': 256}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:25:01,634] Trial 37 pruned. 
+
+================================================================================
+Trial 37 완료
+  Value (CSI): 0.493311
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.36246723745133946, 'dropout_second': 0.05005034976020886, 'lr': 0.006345425546525922, 'weight_decay': 0.006828282388724745, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:25:08,451] Trial 38 pruned. 
+
+================================================================================
+Trial 38 완료
+  Value (CSI): 0.410348
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 4, 'dropout_first': 0.24242364555874757, 'dropout_second': 0.03434746366211385, 'lr': 0.003967625044289481, 'weight_decay': 0.02567007334443799, 'batch_size': 256}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:25:59,397] Trial 39 pruned. 
+
+================================================================================
+Trial 39 완료
+  Value (CSI): 0.460841
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3491421977358039, 'dropout_second': 0.09302866040433326, 'lr': 0.0012554579322103974, 'weight_decay': 0.01824615373114865, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:26:10,038] Trial 40 pruned. 
+
+================================================================================
+Trial 40 완료
+  Value (CSI): 0.435194
+  Parameters: {'d_main': 160, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2530854229863786, 'dropout_second': 0.16669298498172816, 'lr': 0.00041420438042957455, 'weight_decay': 0.03559967347881568, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:27:01,185] Trial 41 pruned. 
+
+================================================================================
+Trial 41 완료
+  Value (CSI): 0.472393
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.19234300167403626, 'dropout_second': 0.15119225990546808, 'lr': 0.0013716982774727097, 'weight_decay': 0.01069771091950075, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:27:55,198] Trial 42 pruned. 
+
+================================================================================
+Trial 42 완료
+  Value (CSI): 0.449612
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.18376802532139458, 'dropout_second': 0.15316463301859057, 'lr': 0.0010176801288268224, 'weight_decay': 0.009046462626037297, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:28:46,559] Trial 43 pruned. 
+
+================================================================================
+Trial 43 완료
+  Value (CSI): 0.485345
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.2211170615155601, 'dropout_second': 0.18434293829583273, 'lr': 0.0020944704954660317, 'weight_decay': 0.005196447884456882, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:29:11,494] Trial 44 pruned. 
+
+================================================================================
+Trial 44 완료
+  Value (CSI): 0.446662
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.24875664269253076, 'dropout_second': 0.15967124069680097, 'lr': 0.0028586944075510174, 'weight_decay': 0.013315775155847758, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:29:35,603] Trial 45 pruned. 
+
+================================================================================
+Trial 45 완료
+  Value (CSI): 0.463158
+  Parameters: {'d_main': 96, 'd_hidden': 448, 'n_blocks': 2, 'dropout_first': 0.17033958399193727, 'dropout_second': 0.1434886594927571, 'lr': 0.0006763249418762951, 'weight_decay': 0.02299507856959901, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 00:32:31,611] Trial 46 pruned. 
+
+================================================================================
+Trial 46 완료
+  Value (CSI): 0.539613
+  Parameters: {'d_main': 128, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.20617346931764197, 'dropout_second': 0.1523916368375038, 'lr': 0.001123205929792329, 'weight_decay': 0.01591703922205374, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:33:23,093] Trial 47 pruned. 
+
+================================================================================
+Trial 47 완료
+  Value (CSI): 0.469072
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.14653844333897154, 'dropout_second': 0.009009084395734862, 'lr': 0.004445757808778326, 'weight_decay': 0.008064497841479808, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:33:57,008] Trial 48 pruned. 
+
+================================================================================
+Trial 48 완료
+  Value (CSI): 0.484375
+  Parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.2129823162259949, 'dropout_second': 0.1873260026918995, 'lr': 0.0017120200444484615, 'weight_decay': 0.025050204189931802, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:34:10,780] Trial 49 pruned. 
+
+================================================================================
+Trial 49 완료
+  Value (CSI): 0.470796
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.2886627227868058, 'dropout_second': 0.16857385597122126, 'lr': 0.007288236058355363, 'weight_decay': 0.014463605767653582, 'batch_size': 256}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:35:06,472] Trial 50 pruned. 
+
+================================================================================
+Trial 50 완료
+  Value (CSI): 0.467014
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.2373164605132956, 'dropout_second': 0.11899865368203352, 'lr': 0.0008381902532242939, 'weight_decay': 0.0035401027235256147, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 00:38:51,995] Trial 51 finished with value: 0.5428492389895513 and parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2223689098687325, 'dropout_second': 0.162135864842605, 'lr': 0.0016119517566883794, 'weight_decay': 0.0065043528133630355, 'batch_size': 32}. Best is trial 4 with value: 0.5571199836340152.
+
+================================================================================
+Trial 51 완료
+  Value (CSI): 0.542849
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2223689098687325, 'dropout_second': 0.162135864842605, 'lr': 0.0016119517566883794, 'weight_decay': 0.0065043528133630355, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:40:04,665] Trial 52 pruned. 
+
+================================================================================
+Trial 52 완료
+  Value (CSI): 0.469804
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.20690695334196385, 'dropout_second': 0.13891156975651703, 'lr': 0.002773018084798209, 'weight_decay': 0.01051200484820843, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:40:37,574] Trial 53 pruned. 
+
+================================================================================
+Trial 53 완료
+  Value (CSI): 0.445111
+  Parameters: {'d_main': 256, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1927452376334894, 'dropout_second': 0.16330349865027188, 'lr': 0.004363906150126819, 'weight_decay': 0.00579571839649661, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:40:51,594] Trial 54 pruned. 
+
+================================================================================
+Trial 54 완료
+  Value (CSI): 0.455157
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.39816003750017154, 'dropout_second': 0.1321424188336339, 'lr': 0.0018757554514596108, 'weight_decay': 0.008047459520274605, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:42:10,292] Trial 55 pruned. 
+
+================================================================================
+Trial 55 완료
+  Value (CSI): 0.471264
+  Parameters: {'d_main': 192, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.22304184462353607, 'dropout_second': 0.14843526740918744, 'lr': 0.0012923511932189742, 'weight_decay': 0.004244701078117462, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:42:51,461] Trial 56 pruned. 
+
+================================================================================
+Trial 56 완료
+  Value (CSI): 0.458716
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.26038810689240255, 'dropout_second': 0.15886240515471822, 'lr': 0.0027393558869274713, 'weight_decay': 0.006023849025493638, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:43:13,966] Trial 57 pruned. 
+
+================================================================================
+Trial 57 완료
+  Value (CSI): 0.455836
+  Parameters: {'d_main': 96, 'd_hidden': 320, 'n_blocks': 4, 'dropout_first': 0.317287196548087, 'dropout_second': 0.17437254511828987, 'lr': 0.0036358788784401357, 'weight_decay': 0.011217146217692351, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:44:02,605] Trial 58 pruned. 
+
+================================================================================
+Trial 58 완료
+  Value (CSI): 0.474087
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.3865416056648628, 'dropout_second': 0.1404371309338193, 'lr': 0.005442282589895252, 'weight_decay': 0.002736438457173257, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:44:27,508] Trial 59 pruned. 
+
+================================================================================
+Trial 59 완료
+  Value (CSI): 0.377680
+  Parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.274271805591002, 'dropout_second': 0.1553503221666948, 'lr': 0.009841812684075132, 'weight_decay': 0.004246578406750539, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:45:09,371] Trial 60 pruned. 
+
+================================================================================
+Trial 60 완료
+  Value (CSI): 0.478998
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.36096187527379175, 'dropout_second': 0.16930922587229458, 'lr': 0.0023807253064513268, 'weight_decay': 0.015092333824548344, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:46:26,404] Trial 61 pruned. 
+
+================================================================================
+Trial 61 완료
+  Value (CSI): 0.462920
+  Parameters: {'d_main': 224, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2186574930877121, 'dropout_second': 0.16103000028285205, 'lr': 0.0015688329575275159, 'weight_decay': 0.006624702621455245, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:47:43,437] Trial 62 pruned. 
+
+================================================================================
+Trial 62 완료
+  Value (CSI): 0.445956
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.22799423510637845, 'dropout_second': 0.14804087667652416, 'lr': 0.0016147750340893788, 'weight_decay': 0.00897755484120123, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:48:44,030] Trial 63 pruned. 
+
+================================================================================
+Trial 63 완료
+  Value (CSI): 0.482353
+  Parameters: {'d_main': 256, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2405045597610501, 'dropout_second': 0.16666739563676924, 'lr': 0.0010998877114796377, 'weight_decay': 0.006665916064898236, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:49:58,272] Trial 64 pruned. 
+
+================================================================================
+Trial 64 완료
+  Value (CSI): 0.474051
+  Parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.37091654664142987, 'dropout_second': 0.13380996238528703, 'lr': 0.0019260707283553788, 'weight_decay': 0.012287282918392038, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+  Fold 2 - 클래스별 가중치: {0: 0.9434634146341463, 1: 0.8912903225806451, 2: 1.222334576249763} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15823})
+  Fold 3 - 클래스별 가중치: {0: 0.9442764227642276, 1: 0.8920583717357911, 2: 1.2195342195342196} (클래스별 샘플 수: {0: 20500, 1: 21700, 2: 15873})
+[I 2025-12-26 00:51:05,046] Trial 65 pruned. 
+
+================================================================================
+Trial 65 완료
+  Value (CSI): 0.526360
+  Parameters: {'d_main': 224, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.21425069440001432, 'dropout_second': 0.1408385475394855, 'lr': 0.003163933742625223, 'weight_decay': 0.0046899614267134, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:52:13,436] Trial 66 pruned. 
+
+================================================================================
+Trial 66 완료
+  Value (CSI): 0.454697
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 4, 'dropout_first': 0.23006774918504497, 'dropout_second': 0.15386998033364863, 'lr': 0.0007969804833427745, 'weight_decay': 0.007305970120573552, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:53:29,474] Trial 67 pruned. 
+
+================================================================================
+Trial 67 완료
+  Value (CSI): 0.449808
+  Parameters: {'d_main': 192, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.33824145297550945, 'dropout_second': 0.17922825865869582, 'lr': 0.0014350665360823725, 'weight_decay': 0.009881521856104712, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:53:35,220] Trial 68 pruned. 
+
+================================================================================
+Trial 68 완료
+  Value (CSI): 0.429231
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.2036194865033594, 'dropout_second': 0.17252899796001253, 'lr': 0.0004805856813078222, 'weight_decay': 0.018072161526914852, 'batch_size': 256}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:53:45,667] Trial 69 pruned. 
+
+================================================================================
+Trial 69 완료
+  Value (CSI): 0.416667
+  Parameters: {'d_main': 256, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.24486624513643973, 'dropout_second': 0.1254910740711958, 'lr': 0.002444342120276572, 'weight_decay': 0.005549807900295851, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:53:59,198] Trial 70 pruned. 
+
+================================================================================
+Trial 70 완료
+  Value (CSI): 0.420866
+  Parameters: {'d_main': 96, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.30731024620452047, 'dropout_second': 0.16219855033926958, 'lr': 0.0032973284416208904, 'weight_decay': 0.03356734816844712, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:55:19,946] Trial 71 pruned. 
+
+================================================================================
+Trial 71 완료
+  Value (CSI): 0.443816
+  Parameters: {'d_main': 160, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.32501121882860573, 'dropout_second': 0.14823306029294878, 'lr': 0.00098333458521357, 'weight_decay': 0.0036650258971861506, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:56:15,672] Trial 72 pruned. 
+
+================================================================================
+Trial 72 완료
+  Value (CSI): 0.468137
+  Parameters: {'d_main': 192, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.29763994507851466, 'dropout_second': 0.1316032572563878, 'lr': 0.0007025281623065209, 'weight_decay': 0.008983836374512841, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:57:31,775] Trial 73 pruned. 
+
+================================================================================
+Trial 73 완료
+  Value (CSI): 0.467615
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.2612061952513628, 'dropout_second': 0.14236765086945696, 'lr': 0.0019138125029205745, 'weight_decay': 0.01266291832192076, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 00:58:52,098] Trial 74 pruned. 
+
+================================================================================
+Trial 74 완료
+  Value (CSI): 0.493484
+  Parameters: {'d_main': 64, 'd_hidden': 384, 'n_blocks': 5, 'dropout_first': 0.34688291318394554, 'dropout_second': 0.15422399346518, 'lr': 0.0012018739448401493, 'weight_decay': 0.00738692851122474, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:00:14,492] Trial 75 pruned. 
+
+================================================================================
+Trial 75 완료
+  Value (CSI): 0.454338
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 5, 'dropout_first': 0.33195245516832317, 'dropout_second': 0.11904623118633711, 'lr': 0.0008987148586568046, 'weight_decay': 0.005067453621402613, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:01:19,737] Trial 76 pruned. 
+
+================================================================================
+Trial 76 완료
+  Value (CSI): 0.476556
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.24874078432478441, 'dropout_second': 0.13696569349480262, 'lr': 0.0015234316604311696, 'weight_decay': 0.0217230281612398, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:01:29,840] Trial 77 pruned. 
+
+================================================================================
+Trial 77 완료
+  Value (CSI): 0.436019
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.23288521046637967, 'dropout_second': 0.14541877779480444, 'lr': 0.0005336670326123394, 'weight_decay': 0.0031941104414877587, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:02:01,724] Trial 78 pruned. 
+
+================================================================================
+Trial 78 완료
+  Value (CSI): 0.461416
+  Parameters: {'d_main': 192, 'd_hidden': 320, 'n_blocks': 2, 'dropout_first': 0.2754500169913486, 'dropout_second': 0.1573514660168549, 'lr': 0.0021723406471382896, 'weight_decay': 0.010604163649337788, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:03:13,704] Trial 79 pruned. 
+
+================================================================================
+Trial 79 완료
+  Value (CSI): 0.474186
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.35506564779645255, 'dropout_second': 0.16557957283699867, 'lr': 0.0011649747592188718, 'weight_decay': 0.015891524618200297, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:03:20,125] Trial 80 pruned. 
+
+================================================================================
+Trial 80 완료
+  Value (CSI): 0.429188
+  Parameters: {'d_main': 160, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.25633053673372247, 'dropout_second': 0.10832298534074984, 'lr': 0.00031047765729098697, 'weight_decay': 0.005769417413598116, 'batch_size': 256}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:03:37,686] Trial 81 pruned. 
+
+================================================================================
+Trial 81 완료
+  Value (CSI): 0.401869
+  Parameters: {'d_main': 64, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.16178914043026388, 'dropout_second': 0.19958781725792263, 'lr': 0.007031335276481616, 'weight_decay': 0.00777183873643267, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:04:17,071] Trial 82 pruned. 
+
+================================================================================
+Trial 82 완료
+  Value (CSI): 0.459289
+  Parameters: {'d_main': 64, 'd_hidden': 448, 'n_blocks': 5, 'dropout_first': 0.26803309469264686, 'dropout_second': 0.18417253838985945, 'lr': 0.004852949397811465, 'weight_decay': 0.001876856821006989, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:04:34,642] Trial 83 pruned. 
+
+================================================================================
+Trial 83 완료
+  Value (CSI): 0.351792
+  Parameters: {'d_main': 96, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.1971402289402861, 'dropout_second': 0.19181061513242187, 'lr': 0.006642751101672581, 'weight_decay': 0.003934773260204525, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:04:56,103] Trial 84 pruned. 
+
+================================================================================
+Trial 84 완료
+  Value (CSI): 0.453006
+  Parameters: {'d_main': 224, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.18459369953420562, 'dropout_second': 0.17270774710263428, 'lr': 0.0039071848656701425, 'weight_decay': 0.002885284825935228, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:05:51,781] Trial 85 pruned. 
+
+================================================================================
+Trial 85 완료
+  Value (CSI): 0.472653
+  Parameters: {'d_main': 256, 'd_hidden': 512, 'n_blocks': 5, 'dropout_first': 0.22292800500649032, 'dropout_second': 0.17916087800347757, 'lr': 0.002555633122661011, 'weight_decay': 0.009221010601073928, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:05:59,792] Trial 86 pruned. 
+
+================================================================================
+Trial 86 완료
+  Value (CSI): 0.394716
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.23885695825617592, 'dropout_second': 0.1638036051926608, 'lr': 0.001787922183969038, 'weight_decay': 0.005235531344032543, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:07:19,533] Trial 87 pruned. 
+
+================================================================================
+Trial 87 완료
+  Value (CSI): 0.456126
+  Parameters: {'d_main': 192, 'd_hidden': 448, 'n_blocks': 5, 'dropout_first': 0.34289879950741936, 'dropout_second': 0.15766743610845416, 'lr': 0.0013125630967190732, 'weight_decay': 0.013608529934945005, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:07:42,397] Trial 88 pruned. 
+
+================================================================================
+Trial 88 완료
+  Value (CSI): 0.463813
+  Parameters: {'d_main': 160, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.21136222123662907, 'dropout_second': 0.15185980682340522, 'lr': 0.008267368766854483, 'weight_decay': 0.006311638635356557, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:08:21,900] Trial 89 pruned. 
+
+================================================================================
+Trial 89 완료
+  Value (CSI): 0.450952
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.13774649522273585, 'dropout_second': 0.19233116629179367, 'lr': 0.005793213525974647, 'weight_decay': 0.004510896696212847, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:09:09,663] Trial 90 pruned. 
+
+================================================================================
+Trial 90 완료
+  Value (CSI): 0.460113
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.2827633117272858, 'dropout_second': 0.12913065342353602, 'lr': 0.0035429763858320336, 'weight_decay': 0.011086091989876834, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:09:22,029] Trial 91 pruned. 
+
+================================================================================
+Trial 91 완료
+  Value (CSI): 0.384365
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.2528036965376745, 'dropout_second': 0.11364880261873286, 'lr': 4.724944097923664e-05, 'weight_decay': 0.02448451593780744, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:09:34,805] Trial 92 pruned. 
+
+================================================================================
+Trial 92 완료
+  Value (CSI): 0.439212
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.3139552595102868, 'dropout_second': 0.13744162490421857, 'lr': 0.00016171315414596567, 'weight_decay': 0.058419823706047994, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:10:04,051] Trial 93 pruned. 
+
+================================================================================
+Trial 93 완료
+  Value (CSI): 0.473684
+  Parameters: {'d_main': 128, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.2922388076989338, 'dropout_second': 0.10046655865650853, 'lr': 0.0021711975653795146, 'weight_decay': 0.09259997408638895, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:10:19,863] Trial 94 pruned. 
+
+================================================================================
+Trial 94 완료
+  Value (CSI): 0.457841
+  Parameters: {'d_main': 96, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.28287932622662876, 'dropout_second': 0.08139173512178297, 'lr': 0.0009192113523230281, 'weight_decay': 0.017554518831974956, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:10:35,507] Trial 95 pruned. 
+
+================================================================================
+Trial 95 완료
+  Value (CSI): 0.451420
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 2, 'dropout_first': 0.3006528509203219, 'dropout_second': 0.12160059021885006, 'lr': 0.002886959220596539, 'weight_decay': 0.028834366511596675, 'batch_size': 64}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:10:44,365] Trial 96 pruned. 
+
+================================================================================
+Trial 96 완료
+  Value (CSI): 0.409805
+  Parameters: {'d_main': 224, 'd_hidden': 256, 'n_blocks': 2, 'dropout_first': 0.24502860546029173, 'dropout_second': 0.1433488057490408, 'lr': 0.004716489497399456, 'weight_decay': 0.041285989178419844, 'batch_size': 128}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:11:46,622] Trial 97 pruned. 
+
+================================================================================
+Trial 97 완료
+  Value (CSI): 0.474400
+  Parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.39239505870222663, 'dropout_second': 0.14876434951549222, 'lr': 0.0015106633951424788, 'weight_decay': 0.007697911522573135, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:12:02,943] Trial 98 pruned. 
+
+================================================================================
+Trial 98 완료
+  Value (CSI): 0.463687
+  Parameters: {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.3816820311626427, 'dropout_second': 0.16866090146298435, 'lr': 0.0010757108386162599, 'weight_decay': 0.006571729972674285, 'batch_size': 256}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+  Fold 1 - 클래스별 가중치: {0: 0.9443252032520325, 1: 0.8839573820395739, 2: 1.2349238751382154} (클래스별 샘플 수: {0: 20500, 1: 21900, 2: 15676})
+[I 2025-12-26 01:13:22,097] Trial 99 pruned. 
+
+================================================================================
+Trial 99 완료
+  Value (CSI): 0.460305
+  Parameters: {'d_main': 160, 'd_hidden': 192, 'n_blocks': 5, 'dropout_first': 0.2665283382964606, 'dropout_second': 0.13621221659810137, 'lr': 0.0007650382948165907, 'weight_decay': 0.020049527528299513, 'batch_size': 32}
+  Best Value (CSI): 0.557120
+  Best Trial: 4
+  Best Parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+================================================================================
+
+
+최적화 완료.
+Best CSI Score: 0.5571
+Best Hyperparameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2550924108208247, 'dropout_second': 0.1294554884955162, 'lr': 0.0046329086177801316, 'weight_decay': 0.012820236692231602, 'batch_size': 64}
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4685
+  - 최종 CSI: 0.4603
+  - 최고 CSI: 0.5571
+  - 최저 CSI: 0.3454
+  - 평균 CSI: 0.4637
+
+최적화 Study 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/resnet_like_smotenc_ctgan20000_seoul_trials.pkl에 저장되었습니다.
+
+==================================================
+최적화된 하이퍼파라미터로 최종 모델 학습 시작
+==================================================
+최종 모델 학습 시작...
+Fold 1 학습 중...
+  Early stopping at epoch 31, Best CSI: 0.4878
+  Fold 1 학습 완료 (검증 CSI: 0.4878)
+Fold 2 학습 중...
+  Early stopping at epoch 19, Best CSI: 0.5802
+  Fold 2 학습 완료 (검증 CSI: 0.5802)
+Fold 3 학습 중...
+  Early stopping at epoch 19, Best CSI: 0.5730
+  Fold 3 학습 완료 (검증 CSI: 0.5730)
+
+모든 모델 저장 완료: ../save_model/resnet_like_optima/resnet_like_smotenc_ctgan20000_seoul.pkl (총 3개 fold)
+Scaler 저장 완료: ../save_model/resnet_like_optima/scaler/resnet_like_smotenc_ctgan20000_seoul_scaler.pkl (총 3개 fold)
+
+최종 모델 학습 및 저장 완료!
+저장된 모델 경로: ../save_model/resnet_like_optima/resnet_like_smotenc_ctgan20000_seoul.pkl
+
+✓ 완료: resnet_like_smotenc_ctgan20000/resnet_like_smotenc_ctgan20000_seoul.py (소요 시간: 5512초)
+
+==========================================
+ResNet-Like SMOTENC CTGAN20000 파일 실행 완료
+종료 시간: 2025-12-26 01:15:15
+총 소요 시간: 8시간 16분 6초
+성공: 6개
+실패: 0개
+==========================================
diff --git a/Analysis_code/5.optima/run_bash/resnet_like/run_resnet_like_ctgan10000.sh b/Analysis_code/5.optima/run_bash/resnet_like/run_resnet_like_ctgan10000.sh
new file mode 100755
index 0000000000000000000000000000000000000000..7a3ddd4ed17bcce08e84a71454436600b9f9d502
--- /dev/null
+++ b/Analysis_code/5.optima/run_bash/resnet_like/run_resnet_like_ctgan10000.sh
@@ -0,0 +1,80 @@
+#!/bin/bash
+
+# 스크립트 디렉토리로 이동 (상위 디렉토리인 5.optima로 이동)
+cd "$(dirname "$0")/../.."
+
+# 시작 시간 기록
+START_TIME=$(date +%s)
+echo "=========================================="
+echo "ResNet-Like CTGAN10000 파일 실행 시작"
+echo "시작 시간: $(date '+%Y-%m-%d %H:%M:%S')"
+echo "GPU: 0번 (CUDA_VISIBLE_DEVICES=0)"
+echo "=========================================="
+echo ""
+
+# 실행할 파일 목록
+FILES=(
+    "resnet_like_ctgan10000_busan.py"
+    "resnet_like_ctgan10000_daegu.py"
+    "resnet_like_ctgan10000_daejeon.py"
+    "resnet_like_ctgan10000_gwangju.py"
+    "resnet_like_ctgan10000_incheon.py"
+    "resnet_like_ctgan10000_seoul.py"
+)
+
+# 에러 발생 시 중단 여부 (set -e를 사용하면 에러 발생 시 즉시 중단)
+set -e
+
+# 각 파일 실행
+SUCCESS_COUNT=0
+FAIL_COUNT=0
+
+for file in "${FILES[@]}"; do
+    filepath="resnet_like_ctgan10000/$file"
+    if [ ! -f "$filepath" ]; then
+        echo "⚠️  경고: $filepath 파일을 찾을 수 없습니다. 건너뜁니다."
+        FAIL_COUNT=$((FAIL_COUNT + 1))
+        continue
+    fi
+    
+    echo "----------------------------------------"
+    echo "실행 중: $filepath"
+    echo "시작 시간: $(date '+%Y-%m-%d %H:%M:%S')"
+    echo "----------------------------------------"
+    
+    FILE_START=$(date +%s)
+    
+    # Python 스크립트 실행 (GPU 0번 설정)
+    if CUDA_VISIBLE_DEVICES=0 python3 -u "$filepath"; then
+        FILE_END=$(date +%s)
+        FILE_DURATION=$((FILE_END - FILE_START))
+        echo ""
+        echo "✓ 완료: $filepath (소요 시간: ${FILE_DURATION}초)"
+        SUCCESS_COUNT=$((SUCCESS_COUNT + 1))
+    else
+        FILE_END=$(date +%s)
+        FILE_DURATION=$((FILE_END - FILE_START))
+        echo ""
+        echo "✗ 실패: $filepath (소요 시간: ${FILE_DURATION}초)"
+        FAIL_COUNT=$((FAIL_COUNT + 1))
+        echo "에러 발생으로 인해 스크립트를 중단합니다."
+        exit 1
+    fi
+    echo ""
+done
+
+# 종료 시간 기록
+END_TIME=$(date +%s)
+TOTAL_DURATION=$((END_TIME - START_TIME))
+HOURS=$((TOTAL_DURATION / 3600))
+MINUTES=$(((TOTAL_DURATION % 3600) / 60))
+SECONDS=$((TOTAL_DURATION % 60))
+
+echo "=========================================="
+echo "ResNet-Like CTGAN10000 파일 실행 완료"
+echo "종료 시간: $(date '+%Y-%m-%d %H:%M:%S')"
+echo "총 소요 시간: ${HOURS}시간 ${MINUTES}분 ${SECONDS}초"
+echo "성공: ${SUCCESS_COUNT}개"
+echo "실패: ${FAIL_COUNT}개"
+echo "=========================================="
+
diff --git a/Analysis_code/5.optima/run_bash/xgb/run_xgb_pure.sh b/Analysis_code/5.optima/run_bash/xgb/run_xgb_pure.sh
index a943bb8a2ace095282f63707862f0b8dd328ab14..d215d3f34a9a55510c26fd41edd0df01012904e8 100755
--- a/Analysis_code/5.optima/run_bash/xgb/run_xgb_pure.sh
+++ b/Analysis_code/5.optima/run_bash/xgb/run_xgb_pure.sh
@@ -14,8 +14,8 @@ echo ""
 
 # 실행할 파일 목록
 FILES=(
-    # "XGB_pure_busan.py"
-    # "XGB_pure_daegu.py"
+    "XGB_pure_busan.py"
+    "XGB_pure_daegu.py"
     "XGB_pure_daejeon.py"
     "XGB_pure_gwangju.py"
     "XGB_pure_incheon.py"
@@ -45,7 +45,7 @@ for file in "${FILES[@]}"; do
     FILE_START=$(date +%s)
     
     # Python 스크립트 실행 (os.environ으로 GPU 0번 설정)
-    if CUDA_VISIBLE_DEVICES=0 python3 -u "$filepath"; then
+    if CUDA_VISIBLE_DEVICES=1 python3 -u "$filepath"; then
         FILE_END=$(date +%s)
         FILE_DURATION=$((FILE_END - FILE_START))
         echo ""
diff --git a/Analysis_code/5.optima/run_bash/xgb/run_xgb_smote.sh b/Analysis_code/5.optima/run_bash/xgb/run_xgb_smote.sh
index 194439690f12ccb7cd32dabf42314d3362bae433..845e122c9731a1b8dc2e56ff5cc9fed012c1ecb0 100755
--- a/Analysis_code/5.optima/run_bash/xgb/run_xgb_smote.sh
+++ b/Analysis_code/5.optima/run_bash/xgb/run_xgb_smote.sh
@@ -14,8 +14,8 @@ echo ""
 
 # 실행할 파일 목록
 FILES=(
-    # "XGB_smote_busan.py"
-    # "XGB_smote_daegu.py"
+    "XGB_smote_busan.py"
+    "XGB_smote_daegu.py"
     "XGB_smote_daejeon.py"
     "XGB_smote_gwangju.py"
     "XGB_smote_incheon.py"
@@ -45,7 +45,7 @@ for file in "${FILES[@]}"; do
     FILE_START=$(date +%s)
     
     # Python 스크립트 실행 (os.environ으로 GPU 0번 설정)
-    if CUDA_VISIBLE_DEVICES=0 python3 -u "$filepath"; then
+    if CUDA_VISIBLE_DEVICES=1 python3 -u "$filepath"; then
         FILE_END=$(date +%s)
         FILE_DURATION=$((FILE_END - FILE_START))
         echo ""
diff --git a/Analysis_code/5.optima/run_bash/xgb/xgb_ctgan10000.log b/Analysis_code/5.optima/run_bash/xgb/xgb_ctgan10000.log
index 5e46a1f8c4c9b10ca1aa136080173712f2e256fd..bb586d3808cc833c50204948cdecbc8e803fe4eb 100644
--- a/Analysis_code/5.optima/run_bash/xgb/xgb_ctgan10000.log
+++ b/Analysis_code/5.optima/run_bash/xgb/xgb_ctgan10000.log
@@ -1,156 +1,219 @@
-nohup: ignoring input
-/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)
-==========================================
-XGB CTGAN10000 파일 실행 시작
-시작 시간: 2025-12-23 19:08:25
-GPU: 0번 (CUDA_VISIBLE_DEVICES=0)
-==========================================
-
-----------------------------------------
-실행 중: xgb_ctgan10000/XGB_ctgan10000_busan.py
-시작 시간: 2025-12-23 19:08:25
-----------------------------------------
-데이터 로딩 중...
-데이터 전처리 중...
-하이퍼파라미터 최적화 시작...
-
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-100%|██████████| 100/100 [1:18:46<00:00, 47.26s/trial, best loss: -0.4861]
-
-최적화 완료. 최적 파라미터: {'colsample_bytree': 0.791441621112874, 'gamma': 0.42439975661558715, 'learning_rate': 0.011136150784699721, 'max_depth': 12.0, 'min_child_weight': 12.0, 'reg_alpha': 0.5297190743505086, 'reg_lambda': 0.8682538180888419, 'subsample': 0.736720141444502}
-최적 CSI 점수: 0.4861 (loss: -0.4861)
-
-최적화 과정 요약:
-  - 총 시도 횟수: 100
-  - 성공한 시도: 100
-  - 최초 CSI: 0.4742
-  - 최종 CSI: 0.4829
-  - 최고 CSI: 0.4861
-  - 최저 CSI: 0.4146
-  - 평균 CSI: 0.4732
-
-최적화 Trials 객체가 ../optimization_history/xgb_ctgan10000_busan_trials.pkl에 저장되었습니다.
-최종 모델 학습 시작...
-Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
-Fold 1 검증 CSI: 0.4306
-Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
-Fold 2 검증 CSI: 0.5131
-Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
-Fold 3 검증 CSI: 0.5145
-모델 저장 중...
-모델이 ../save_model/xgb_optima/xgb_ctgan10000_busan.pkl에 저장되었습니다.
-
-✓ 완료: xgb_ctgan10000/XGB_ctgan10000_busan.py (소요 시간: 4785초)
-
-⚠️  경고: xgb_ctgan10000/XGB_ctgan10000_daegu.py 파일을 찾을 수 없습니다. 건너뜁니다.
-⚠️  경고: xgb_ctgan10000/XGB_ctgan10000_daejeon.py 파일을 찾을 수 없습니다. 건너뜁니다.
-⚠️  경고: xgb_ctgan10000/XGB_ctgan10000_gwangju.py 파일을 찾을 수 없습니다. 건너뜁니다.
-⚠️  경고: xgb_ctgan10000/XGB_ctgan10000_incheon.py 파일을 찾을 수 없습니다. 건너뜁니다.
-⚠️  경고: xgb_ctgan10000/XGB_ctgan10000_seoul.py 파일을 찾을 수 없습니다. 건너뜁니다.
-==========================================
-XGB CTGAN10000 파일 실행 완료
-종료 시간: 2025-12-23 20:28:10
-총 소요 시간: 1시간 19분 45초
-성공: 1개
-실패: 5개
-==========================================
+nohup: ignoring input
+/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)
+==========================================
+XGB CTGAN10000 파일 실행 시작
+시작 시간: 2025-12-28 04:31:17
+GPU: 0번 (CUDA_VISIBLE_DEVICES=0)
+==========================================
+
+----------------------------------------
+실행 중: xgb_ctgan10000/XGB_ctgan10000_busan.py
+시작 시간: 2025-12-28 04:31:17
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.6875780545091519, 'gamma': 2.658982028747467, 'learning_rate': 0.11595019019771602, 'max_depth': 12.0, 'min_child_weight': 18.0, 'reg_alpha': 0.9071038284958559, 'reg_lambda': 0.2880553949579837, 'subsample': 0.7100915343806586}
+최적 CSI 점수: 0.4881 (loss: -0.4881)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4522
+  - 최종 CSI: 0.4780
+  - 최고 CSI: 0.4881
+  - 최저 CSI: 0.4459
+  - 평균 CSI: 0.4737
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/xgb_ctgan10000_busan_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4329
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.5007
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.5306
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/xgb_optima/xgb_ctgan10000_busan.pkl에 저장되었습니다.
+
+✓ 완료: xgb_ctgan10000/XGB_ctgan10000_busan.py (소요 시간: 3501초)
+
+----------------------------------------
+실행 중: xgb_ctgan10000/XGB_ctgan10000_daegu.py
+시작 시간: 2025-12-28 05:29:38
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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100%|██████████| 100/100 [58:02<00:00, 34.83s/trial, best loss: -0.4392]
+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.8557253370653987, 'gamma': 1.1563957561197729, 'learning_rate': 0.19232018363683898, 'max_depth': 10.0, 'min_child_weight': 8.0, 'reg_alpha': 0.21149066093980878, 'reg_lambda': 0.2690618200166374, 'subsample': 0.714526167464626}
+최적 CSI 점수: 0.4392 (loss: -0.4392)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4037
+  - 최종 CSI: 0.4027
+  - 최고 CSI: 0.4392
+  - 최저 CSI: 0.3123
+  - 평균 CSI: 0.4163
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/xgb_ctgan10000_daegu_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4690
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.4544
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.3943
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/xgb_optima/xgb_ctgan10000_daegu.pkl에 저장되었습니다.
+
+✓ 완료: xgb_ctgan10000/XGB_ctgan10000_daegu.py (소요 시간: 3515초)
+
+----------------------------------------
+실행 중: xgb_ctgan10000/XGB_ctgan10000_daejeon.py
+시작 시간: 2025-12-28 06:28:13
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.7816067762051363, 'gamma': 0.5799629095615334, 'learning_rate': 0.06287412401679013, 'max_depth': 3.0, 'min_child_weight': 5.0, 'reg_alpha': 0.36731169488772253, 'reg_lambda': 0.03192020366519255, 'subsample': 0.6004605601176048}
+최적 CSI 점수: 0.5029 (loss: -0.5029)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4839
+  - 최종 CSI: 0.4873
+  - 최고 CSI: 0.5029
+  - 최저 CSI: 0.4796
+  - 평균 CSI: 0.4918
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/xgb_ctgan10000_daejeon_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4534
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.4712
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.5842
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/xgb_optima/xgb_ctgan10000_daejeon.pkl에 저장되었습니다.
+
+✓ 완료: xgb_ctgan10000/XGB_ctgan10000_daejeon.py (소요 시간: 3996초)
+
+----------------------------------------
+실행 중: xgb_ctgan10000/XGB_ctgan10000_gwangju.py
+시작 시간: 2025-12-28 07:34:49
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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100%|██████████| 100/100 [59:02<00:00, 35.43s/trial, best loss: -0.4999]
+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.9825715751506052, 'gamma': 0.029399796024525915, 'learning_rate': 0.08575762330828458, 'max_depth': 11.0, 'min_child_weight': 8.0, 'reg_alpha': 0.8534545581720909, 'reg_lambda': 0.43478744549007325, 'subsample': 0.7629247503643918}
+최적 CSI 점수: 0.4999 (loss: -0.4999)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4941
+  - 최종 CSI: 0.4935
+  - 최고 CSI: 0.4999
+  - 최저 CSI: 0.4364
+  - 평균 CSI: 0.4867
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/xgb_ctgan10000_gwangju_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4102
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.5658
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.5239
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/xgb_optima/xgb_ctgan10000_gwangju.pkl에 저장되었습니다.
+
+✓ 완료: xgb_ctgan10000/XGB_ctgan10000_gwangju.py (소요 시간: 3571초)
+
+----------------------------------------
+실행 중: xgb_ctgan10000/XGB_ctgan10000_incheon.py
+시작 시간: 2025-12-28 08:34:20
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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100%|██████████| 100/100 [1:00:50<00:00, 36.51s/trial, best loss: -0.5752]
+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.9876604099689714, 'gamma': 2.7259563165720655, 'learning_rate': 0.014353110732979967, 'max_depth': 5.0, 'min_child_weight': 8.0, 'reg_alpha': 0.43412935826888077, 'reg_lambda': 0.45790677460553664, 'subsample': 0.6390967315026312}
+최적 CSI 점수: 0.5752 (loss: -0.5752)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5632
+  - 최종 CSI: 0.5595
+  - 최고 CSI: 0.5752
+  - 최저 CSI: 0.5385
+  - 평균 CSI: 0.5582
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/xgb_ctgan10000_incheon_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.5292
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.6124
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.5840
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/xgb_optima/xgb_ctgan10000_incheon.pkl에 저장되었습니다.
+
+✓ 완료: xgb_ctgan10000/XGB_ctgan10000_incheon.py (소요 시간: 3691초)
+
+----------------------------------------
+실행 중: xgb_ctgan10000/XGB_ctgan10000_seoul.py
+시작 시간: 2025-12-28 09:35:51
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.8837225390968168, 'gamma': 0.1115044781500254, 'learning_rate': 0.10805110293567466, 'max_depth': 12.0, 'min_child_weight': 4.0, 'reg_alpha': 0.02183712172236562, 'reg_lambda': 0.6932207560084631, 'subsample': 0.6767341133785678}
+최적 CSI 점수: 0.5824 (loss: -0.5824)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.5553
+  - 최종 CSI: 0.5700
+  - 최고 CSI: 0.5824
+  - 최저 CSI: 0.5435
+  - 평균 CSI: 0.5687
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/xgb_ctgan10000_seoul_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.5165
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.6230
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.6077
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/xgb_optima/xgb_ctgan10000_seoul.pkl에 저장되었습니다.
+
+✓ 완료: xgb_ctgan10000/XGB_ctgan10000_seoul.py (소요 시간: 3488초)
+
+==========================================
+XGB CTGAN10000 파일 실행 완료
+종료 시간: 2025-12-28 10:33:59
+총 소요 시간: 6시간 2분 42초
+성공: 6개
+실패: 0개
+==========================================
diff --git a/Analysis_code/5.optima/run_bash/xgb/xgb_pure_busan_dagu.log b/Analysis_code/5.optima/run_bash/xgb/xgb_pure_busan_dagu.log
new file mode 100644
index 0000000000000000000000000000000000000000..2912ebc219b4fc40f375a941b82c20d90e018d28
--- /dev/null
+++ b/Analysis_code/5.optima/run_bash/xgb/xgb_pure_busan_dagu.log
@@ -0,0 +1,81 @@
+nohup: ignoring input
+/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)
+==========================================
+XGB Pure 파일 실행 시작
+시작 시간: 2025-12-27 02:09:40
+GPU: 0번 (os.environ['CUDA_VISIBLE_DEVICES'] = '0')
+==========================================
+
+----------------------------------------
+실행 중: xgb_pure/XGB_pure_busan.py
+시작 시간: 2025-12-27 02:09:40
+----------------------------------------
+데이터 로딩 중...
+하이퍼파라미터 최적화 시작...
+
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+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.8651175745135303, 'gamma': 2.0220518303820976, 'learning_rate': 0.04196437449161767, 'max_depth': 7.0, 'min_child_weight': 17.0, 'reg_alpha': 0.9213159636887744, 'reg_lambda': 0.9407811453878014, 'subsample': 0.7200034080497129}
+최적 CSI 점수: 0.4949 (loss: -0.4949)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4829
+  - 최종 CSI: 0.4915
+  - 최고 CSI: 0.4949
+  - 최저 CSI: 0.4639
+  - 평균 CSI: 0.4820
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/xgb_pure_busan_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4090
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.5284
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.5474
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/xgb_optima/xgb_pure_busan.pkl에 저장되었습니다.
+
+✓ 완료: xgb_pure/XGB_pure_busan.py (소요 시간: 3823초)
+
+----------------------------------------
+실행 중: xgb_pure/XGB_pure_daegu.py
+시작 시간: 2025-12-27 03:13:23
+----------------------------------------
+데이터 로딩 중...
+하이퍼파라미터 최적화 시작...
+
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+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.8800494992202731, 'gamma': 0.28651615767316957, 'learning_rate': 0.025526450870185433, 'max_depth': 3.0, 'min_child_weight': 5.0, 'reg_alpha': 0.6787273055071508, 'reg_lambda': 0.6641153401816423, 'subsample': 0.7222783789369407}
+최적 CSI 점수: 0.4409 (loss: -0.4409)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.3762
+  - 최종 CSI: 0.4260
+  - 최고 CSI: 0.4409
+  - 최저 CSI: 0.3762
+  - 평균 CSI: 0.4143
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/xgb_pure_daegu_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4967
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.4603
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.3656
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/xgb_optima/xgb_pure_daegu.pkl에 저장되었습니다.
+
+✓ 완료: xgb_pure/XGB_pure_daegu.py (소요 시간: 5024초)
+
+==========================================
+XGB Pure 파일 실행 완료
+종료 시간: 2025-12-27 04:37:07
+총 소요 시간: 2시간 27분 27초
+성공: 2개
+실패: 0개
+==========================================
diff --git a/Analysis_code/5.optima/run_bash/xgb/xgb_smote_busan_dagu.log b/Analysis_code/5.optima/run_bash/xgb/xgb_smote_busan_dagu.log
new file mode 100644
index 0000000000000000000000000000000000000000..21274eaedb584ca8116f2f21501bbbf86d8f1c81
--- /dev/null
+++ b/Analysis_code/5.optima/run_bash/xgb/xgb_smote_busan_dagu.log
@@ -0,0 +1,87 @@
+nohup: ignoring input
+/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)
+==========================================
+XGB SMOTE 파일 실행 시작
+시작 시간: 2025-12-27 02:09:06
+GPU: 0번 (os.environ['CUDA_VISIBLE_DEVICES'] = '0')
+==========================================
+
+----------------------------------------
+실행 중: xgb_smote/XGB_smote_busan.py
+시작 시간: 2025-12-27 02:09:06
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.7955102167770075, 'gamma': 0.16947237102826285, 'learning_rate': 0.04201247161970075, 'max_depth': 11.0, 'min_child_weight': 4.0, 'reg_alpha': 0.9136392250501164, 'reg_lambda': 0.4323511178052387, 'subsample': 0.6554957061124282}
+최적 CSI 점수: 0.4773 (loss: -0.4773)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4609
+  - 최종 CSI: 0.4728
+  - 최고 CSI: 0.4773
+  - 최저 CSI: 0.4510
+  - 평균 CSI: 0.4668
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/xgb_smote_busan_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.4773
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.4851
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.4696
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/xgb_optima/xgb_smote_busan.pkl에 저장되었습니다.
+
+✓ 완료: xgb_smote/XGB_smote_busan.py (소요 시간: 5032초)
+
+----------------------------------------
+실행 중: xgb_smote/XGB_smote_daegu.py
+시작 시간: 2025-12-27 03:32:58
+----------------------------------------
+데이터 로딩 중...
+데이터 전처리 중...
+하이퍼파라미터 최적화 시작...
+
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+
+최적화 완료. 최적 파라미터: {'colsample_bytree': 0.8132816721507904, 'gamma': 0.9002659162503241, 'learning_rate': 0.04046864452016672, 'max_depth': 4.0, 'min_child_weight': 17.0, 'reg_alpha': 0.4681545450085154, 'reg_lambda': 0.531313515098387, 'subsample': 0.827198506312037}
+최적 CSI 점수: 0.4672 (loss: -0.4672)
+
+최적화 과정 요약:
+  - 총 시도 횟수: 100
+  - 성공한 시도: 100
+  - 최초 CSI: 0.4577
+  - 최종 CSI: 0.4534
+  - 최고 CSI: 0.4672
+  - 최저 CSI: 0.4275
+  - 평균 CSI: 0.4521
+
+최적화 Trials 객체가 /workspace/visibility_prediction/Analysis_code/optimization_history/xgb_smote_daegu_trials.pkl에 저장되었습니다.
+최종 모델 학습 시작...
+Fold 1 학습 중... (학습 연도: [2018, 2019], 검증 연도: 2020)
+Fold 1 검증 CSI: 0.3865
+Fold 2 학습 중... (학습 연도: [2018, 2020], 검증 연도: 2019)
+Fold 2 검증 CSI: 0.4828
+Fold 3 학습 중... (학습 연도: [2019, 2020], 검증 연도: 2018)
+Fold 3 검증 CSI: 0.5322
+모델 저장 중...
+모델이 /workspace/visibility_prediction/Analysis_code/save_model/xgb_optima/xgb_smote_daegu.pkl에 저장되었습니다.
+
+✓ 완료: xgb_smote/XGB_smote_daegu.py (소요 시간: 4763초)
+
+==========================================
+XGB SMOTE 파일 실행 완료
+종료 시간: 2025-12-27 04:52:21
+총 소요 시간: 2시간 43분 15초
+성공: 2개
+실패: 0개
+==========================================
diff --git a/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_busan.py b/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_busan.py
index 0f7517aaab09979e1b552d0d6d9a74e9e28f60c8..42f51e25f162537c8a7caca375a3510118748d72 100644
--- a/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_busan.py
+++ b/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_busan.py
@@ -265,6 +265,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/xgb_ctgan10000_busan_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_daegu.py b/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_daegu.py
new file mode 100644
index 0000000000000000000000000000000000000000..31d6f5cd3d080d960dad7114cc0a649c78082697
--- /dev/null
+++ b/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_daegu.py
@@ -0,0 +1,305 @@
+import pandas as pd
+import os
+import numpy as np
+import joblib
+from xgboost import XGBClassifier
+from warnings import filterwarnings
+filterwarnings('ignore')
+from sklearn.metrics import confusion_matrix
+from hyperopt import fmin, tpe, Trials, hp
+
+# 상수 정의
+RANDOM_STATE = 42
+N_ESTIMATORS = 4000
+EARLY_STOPPING_ROUNDS = 400
+MAX_EVALS = 100
+
+# Fold 설정: (train_years, val_year)
+FOLD_CONFIGS = [
+    ([2018, 2019], 2020),  # Fold 1
+    ([2018, 2020], 2019),  # Fold 2
+    ([2019, 2020], 2018),  # Fold 3
+]
+
+def calculate_csi(Y_test, pred):
+    """CSI(Critical Success Index) 점수를 계산합니다.
+    
+    Args:
+        Y_test: 실제 레이블
+        pred: 예측 레이블
+        
+    Returns:
+        CSI 점수 (0~1 사이의 값)
+    """
+    cm = confusion_matrix(Y_test, pred)
+    H = (cm[0, 0] + cm[1, 1])
+    F = (cm[1, 0] + cm[2, 0] + cm[0, 1] + cm[2, 1])
+    M = (cm[0, 2] + cm[1, 2])
+    CSI = H / (H + F + M + 1e-10)
+    return CSI
+
+def eval_metric_csi(y_true, pred_prob):
+    """XGBoost용 CSI 메트릭 함수.
+    
+    Args:
+        y_true: 실제 레이블
+        pred_prob: 예측 확률
+        
+    Returns:
+        CSI 점수의 음수값
+    """
+    pred = np.argmax(pred_prob, axis=1)
+    csi = calculate_csi(y_true, pred)
+    return -1*csi
+
+def add_derived_features(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    제거했던 파생 변수들을 복구
+    
+    Args:
+        df: 데이터프레임
+    
+    Returns:
+        파생 변수가 추가된 데이터프레임
+    """
+    df = df.copy()
+    df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
+    df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
+    df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
+    df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)
+    df['ground_temp - temp_C'] = df['groundtemp'] - df['temp_C']
+    return df
+
+def preprocessing(df):
+    """데이터 전처리 함수.
+    
+    Args:
+        df: 원본 데이터프레임
+        
+    Returns:
+        전처리된 데이터프레임
+    """
+    df = df[df.columns].copy()
+    df['year'] = df['year'].astype('int')
+    df['month'] = df['month'].astype('int')
+    df['hour'] = df['hour'].astype('int')
+    df = add_derived_features(df).copy()
+    df['multi_class'] = df['multi_class'].astype('int')
+    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
+    df['wind_dir'] = df['wind_dir'].astype('int')
+    df = df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
+       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
+       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
+       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
+       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
+       'month_sin', 'month_cos','multi_class']].copy()
+    return df
+
+def create_xgb_model(search_space=None, best_params=None):
+    """XGBoost 모델을 생성합니다.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간 (objective_func에서 사용)
+        best_params: 최적화된 하이퍼파라미터 (최종 모델 학습에서 사용)
+        
+    Returns:
+        XGBClassifier 인스턴스
+    """
+    base_params = {
+        'n_estimators': N_ESTIMATORS,
+        'tree_method': 'hist',
+        'device': 'cuda',
+        'enable_categorical': True,
+        'eval_metric': eval_metric_csi,
+        'objective': 'multi:softprob',
+        'random_state': RANDOM_STATE,
+        'early_stopping_rounds': EARLY_STOPPING_ROUNDS,
+    }
+    
+    if search_space is not None:
+        # 하이퍼파라미터 최적화 중
+        params = {
+            **base_params,
+            'learning_rate': search_space['learning_rate'],
+            'max_depth': int(search_space['max_depth']),
+            'min_child_weight': int(search_space['min_child_weight']),
+            'gamma': search_space['gamma'],
+            'subsample': search_space['subsample'],
+            'colsample_bytree': search_space['colsample_bytree'],
+            'reg_alpha': search_space['reg_alpha'],
+            'reg_lambda': search_space['reg_lambda'],
+        }
+    elif best_params is not None:
+        # 최적화된 파라미터로 최종 모델 생성
+        params = {
+            **base_params,
+            'learning_rate': best_params['learning_rate'],
+            'max_depth': int(best_params['max_depth']),
+            'min_child_weight': int(best_params['min_child_weight']),
+            'gamma': best_params['gamma'],
+            'subsample': best_params['subsample'],
+            'colsample_bytree': best_params['colsample_bytree'],
+            'reg_alpha': best_params['reg_alpha'],
+            'reg_lambda': best_params['reg_lambda'],
+        }
+    else:
+        params = base_params
+    
+    return XGBClassifier(**params)
+
+
+# 데이터 로딩 및 전처리
+print("데이터 로딩 중...")
+# 파일 위치 기반으로 데이터 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+data_base_dir = os.path.abspath(os.path.join(current_file_dir, '../../../data'))
+df_daegu = pd.read_csv(os.path.join(data_base_dir, "data_for_modeling/daegu_train.csv"))
+df_ctgan_daegu_1 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_1_daegu.csv"))
+df_ctgan_daegu_2 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_2_daegu.csv"))
+df_ctgan_daegu_3 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_3_daegu.csv"))
+
+print("데이터 전처리 중...")
+df_ctgan_daegu_1 = preprocessing(df_ctgan_daegu_1)
+df_ctgan_daegu_2 = preprocessing(df_ctgan_daegu_2)
+df_ctgan_daegu_3 = preprocessing(df_ctgan_daegu_3)
+df_daegu = preprocessing(df_daegu)
+
+# CTGAN 데이터 리스트 (fold 순서와 일치)
+df_ctgan_list = [df_ctgan_daegu_1, df_ctgan_daegu_2, df_ctgan_daegu_3]
+
+def split_data(df_sampled, df_original, train_years, val_year):
+    """데이터를 학습용과 검증용으로 분할합니다.
+    
+    Args:
+        df_sampled: 샘플링된 데이터프레임
+        df_original: 원본 데이터프레임
+        train_years: 학습에 사용할 연도 리스트
+        val_year: 검증에 사용할 연도
+        
+    Returns:
+        (X_train, X_val, y_train, y_val) 튜플
+    """
+    # 학습 데이터: 샘플링된 데이터에서 train_years에 해당하는 데이터
+    train_mask = df_sampled['year'].isin(train_years)
+    X_train = df_sampled.loc[train_mask, df_sampled.columns != 'multi_class'].copy()
+    y_train = df_sampled.loc[train_mask, 'multi_class']
+    
+    # 검증 데이터: 원본 데이터에서 val_year에 해당하는 데이터
+    val_mask = df_original['year'] == val_year
+    X_val = df_original.loc[val_mask, df_original.columns != 'multi_class'].copy()
+    y_val = df_original.loc[val_mask, 'multi_class']
+    
+    X_train.drop(columns=['year'], inplace=True)
+    X_val.drop(columns=['year'], inplace=True)
+    
+    return X_train, X_val, y_train, y_val
+
+
+# 하이퍼파라미터 검색 공간 정의
+xgb_search_space = {
+    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.2)),
+    'max_depth': hp.quniform('max_depth', 3, 12, 1),
+    'min_child_weight': hp.quniform('min_child_weight', 1, 20, 1),
+    'gamma': hp.uniform('gamma', 0, 5),  # 트리 분할을 위한 최소 손실 감소 값
+    'subsample': hp.uniform('subsample', 0.6, 1.0),
+    'colsample_bytree': hp.uniform('colsample_bytree', 0.6, 1.0),
+    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),
+    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0)
+}
+
+def objective_func(search_space):
+    """하이퍼파라미터 최적화를 위한 목적 함수.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간
+        
+    Returns:
+        평균 CSI 점수의 음수값 (hyperopt는 최소화를 수행하므로)
+    """
+    xgb_model = create_xgb_model(search_space=search_space)
+    csi_scores = []
+
+    # 각 fold에 대해 교차 검증 수행
+    for df_sampled, (train_years, val_year) in zip(df_ctgan_list, FOLD_CONFIGS):
+        X_train, X_val, y_train, y_val = split_data(
+            df_sampled, df_daegu, train_years, val_year
+        )
+        
+        xgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)], verbose=False)
+        csi = calculate_csi(y_val, xgb_model.predict(X_val))
+        csi_scores.append(csi)
+
+    return -1*round(np.mean(csi_scores), 4)
+
+
+# 하이퍼파라미터 최적화
+print("하이퍼파라미터 최적화 시작...")
+trials = Trials()
+xgb_best = fmin(
+    fn=objective_func,
+    space=xgb_search_space,
+    algo=tpe.suggest,
+    max_evals=MAX_EVALS, 
+    trials=trials
+)
+
+# 최적화 결과 분석 및 출력
+print(f"\n최적화 완료. 최적 파라미터: {xgb_best}")
+
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+# 파일 위치 기반으로 base 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/xgb_ctgan10000_daegu_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
+# 최적화된 하이퍼파라미터로 최종 모델 학습
+print("최종 모델 학습 시작...")
+models = []
+
+for fold_idx, (df_sampled, (train_years, val_year)) in enumerate(
+    zip(df_ctgan_list, FOLD_CONFIGS), start=1
+):
+    print(f"Fold {fold_idx} 학습 중... (학습 연도: {train_years}, 검증 연도: {val_year})")
+    
+    X_train, X_val, y_train, y_val = split_data(
+        df_sampled, df_daegu, train_years, val_year
+    )
+    
+    xgb_model = create_xgb_model(best_params=xgb_best)
+    xgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)], verbose=False)
+    
+    # 검증 성능 출력
+    val_csi = calculate_csi(y_val, xgb_model.predict(X_val))
+    print(f"Fold {fold_idx} 검증 CSI: {val_csi:.4f}")
+    
+    models.append(xgb_model)
+
+
+# 모델 저장
+print("모델 저장 중...")
+os.makedirs(os.path.join(base_dir, "save_model/xgb_optima"), exist_ok=True)
+model_save_path = os.path.join(base_dir, "save_model/xgb_optima/xgb_ctgan10000_daegu.pkl")
+joblib.dump(models, model_save_path)
+print(f"모델이 {model_save_path}에 저장되었습니다.")
+
diff --git a/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_daejeon.py b/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_daejeon.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac858e0ec1435484a07a08dca02a70f3379f4d17
--- /dev/null
+++ b/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_daejeon.py
@@ -0,0 +1,305 @@
+import pandas as pd
+import os
+import numpy as np
+import joblib
+from xgboost import XGBClassifier
+from warnings import filterwarnings
+filterwarnings('ignore')
+from sklearn.metrics import confusion_matrix
+from hyperopt import fmin, tpe, Trials, hp
+
+# 상수 정의
+RANDOM_STATE = 42
+N_ESTIMATORS = 4000
+EARLY_STOPPING_ROUNDS = 400
+MAX_EVALS = 100
+
+# Fold 설정: (train_years, val_year)
+FOLD_CONFIGS = [
+    ([2018, 2019], 2020),  # Fold 1
+    ([2018, 2020], 2019),  # Fold 2
+    ([2019, 2020], 2018),  # Fold 3
+]
+
+def calculate_csi(Y_test, pred):
+    """CSI(Critical Success Index) 점수를 계산합니다.
+    
+    Args:
+        Y_test: 실제 레이블
+        pred: 예측 레이블
+        
+    Returns:
+        CSI 점수 (0~1 사이의 값)
+    """
+    cm = confusion_matrix(Y_test, pred)
+    H = (cm[0, 0] + cm[1, 1])
+    F = (cm[1, 0] + cm[2, 0] + cm[0, 1] + cm[2, 1])
+    M = (cm[0, 2] + cm[1, 2])
+    CSI = H / (H + F + M + 1e-10)
+    return CSI
+
+def eval_metric_csi(y_true, pred_prob):
+    """XGBoost용 CSI 메트릭 함수.
+    
+    Args:
+        y_true: 실제 레이블
+        pred_prob: 예측 확률
+        
+    Returns:
+        CSI 점수의 음수값
+    """
+    pred = np.argmax(pred_prob, axis=1)
+    csi = calculate_csi(y_true, pred)
+    return -1*csi
+
+def add_derived_features(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    제거했던 파생 변수들을 복구
+    
+    Args:
+        df: 데이터프레임
+    
+    Returns:
+        파생 변수가 추가된 데이터프레임
+    """
+    df = df.copy()
+    df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
+    df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
+    df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
+    df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)
+    df['ground_temp - temp_C'] = df['groundtemp'] - df['temp_C']
+    return df
+
+def preprocessing(df):
+    """데이터 전처리 함수.
+    
+    Args:
+        df: 원본 데이터프레임
+        
+    Returns:
+        전처리된 데이터프레임
+    """
+    df = df[df.columns].copy()
+    df['year'] = df['year'].astype('int')
+    df['month'] = df['month'].astype('int')
+    df['hour'] = df['hour'].astype('int')
+    df = add_derived_features(df).copy()
+    df['multi_class'] = df['multi_class'].astype('int')
+    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
+    df['wind_dir'] = df['wind_dir'].astype('int')
+    df = df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
+       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
+       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
+       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
+       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
+       'month_sin', 'month_cos','multi_class']].copy()
+    return df
+
+def create_xgb_model(search_space=None, best_params=None):
+    """XGBoost 모델을 생성합니다.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간 (objective_func에서 사용)
+        best_params: 최적화된 하이퍼파라미터 (최종 모델 학습에서 사용)
+        
+    Returns:
+        XGBClassifier 인스턴스
+    """
+    base_params = {
+        'n_estimators': N_ESTIMATORS,
+        'tree_method': 'hist',
+        'device': 'cuda',
+        'enable_categorical': True,
+        'eval_metric': eval_metric_csi,
+        'objective': 'multi:softprob',
+        'random_state': RANDOM_STATE,
+        'early_stopping_rounds': EARLY_STOPPING_ROUNDS,
+    }
+    
+    if search_space is not None:
+        # 하이퍼파라미터 최적화 중
+        params = {
+            **base_params,
+            'learning_rate': search_space['learning_rate'],
+            'max_depth': int(search_space['max_depth']),
+            'min_child_weight': int(search_space['min_child_weight']),
+            'gamma': search_space['gamma'],
+            'subsample': search_space['subsample'],
+            'colsample_bytree': search_space['colsample_bytree'],
+            'reg_alpha': search_space['reg_alpha'],
+            'reg_lambda': search_space['reg_lambda'],
+        }
+    elif best_params is not None:
+        # 최적화된 파라미터로 최종 모델 생성
+        params = {
+            **base_params,
+            'learning_rate': best_params['learning_rate'],
+            'max_depth': int(best_params['max_depth']),
+            'min_child_weight': int(best_params['min_child_weight']),
+            'gamma': best_params['gamma'],
+            'subsample': best_params['subsample'],
+            'colsample_bytree': best_params['colsample_bytree'],
+            'reg_alpha': best_params['reg_alpha'],
+            'reg_lambda': best_params['reg_lambda'],
+        }
+    else:
+        params = base_params
+    
+    return XGBClassifier(**params)
+
+
+# 데이터 로딩 및 전처리
+print("데이터 로딩 중...")
+# 파일 위치 기반으로 데이터 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+data_base_dir = os.path.abspath(os.path.join(current_file_dir, '../../../data'))
+df_daejeon = pd.read_csv(os.path.join(data_base_dir, "data_for_modeling/daejeon_train.csv"))
+df_ctgan_daejeon_1 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_1_daejeon.csv"))
+df_ctgan_daejeon_2 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_2_daejeon.csv"))
+df_ctgan_daejeon_3 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_3_daejeon.csv"))
+
+print("데이터 전처리 중...")
+df_ctgan_daejeon_1 = preprocessing(df_ctgan_daejeon_1)
+df_ctgan_daejeon_2 = preprocessing(df_ctgan_daejeon_2)
+df_ctgan_daejeon_3 = preprocessing(df_ctgan_daejeon_3)
+df_daejeon = preprocessing(df_daejeon)
+
+# CTGAN 데이터 리스트 (fold 순서와 일치)
+df_ctgan_list = [df_ctgan_daejeon_1, df_ctgan_daejeon_2, df_ctgan_daejeon_3]
+
+def split_data(df_sampled, df_original, train_years, val_year):
+    """데이터를 학습용과 검증용으로 분할합니다.
+    
+    Args:
+        df_sampled: 샘플링된 데이터프레임
+        df_original: 원본 데이터프레임
+        train_years: 학습에 사용할 연도 리스트
+        val_year: 검증에 사용할 연도
+        
+    Returns:
+        (X_train, X_val, y_train, y_val) 튜플
+    """
+    # 학습 데이터: 샘플링된 데이터에서 train_years에 해당하는 데이터
+    train_mask = df_sampled['year'].isin(train_years)
+    X_train = df_sampled.loc[train_mask, df_sampled.columns != 'multi_class'].copy()
+    y_train = df_sampled.loc[train_mask, 'multi_class']
+    
+    # 검증 데이터: 원본 데이터에서 val_year에 해당하는 데이터
+    val_mask = df_original['year'] == val_year
+    X_val = df_original.loc[val_mask, df_original.columns != 'multi_class'].copy()
+    y_val = df_original.loc[val_mask, 'multi_class']
+    
+    X_train.drop(columns=['year'], inplace=True)
+    X_val.drop(columns=['year'], inplace=True)
+    
+    return X_train, X_val, y_train, y_val
+
+
+# 하이퍼파라미터 검색 공간 정의
+xgb_search_space = {
+    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.2)),
+    'max_depth': hp.quniform('max_depth', 3, 12, 1),
+    'min_child_weight': hp.quniform('min_child_weight', 1, 20, 1),
+    'gamma': hp.uniform('gamma', 0, 5),  # 트리 분할을 위한 최소 손실 감소 값
+    'subsample': hp.uniform('subsample', 0.6, 1.0),
+    'colsample_bytree': hp.uniform('colsample_bytree', 0.6, 1.0),
+    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),
+    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0)
+}
+
+def objective_func(search_space):
+    """하이퍼파라미터 최적화를 위한 목적 함수.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간
+        
+    Returns:
+        평균 CSI 점수의 음수값 (hyperopt는 최소화를 수행하므로)
+    """
+    xgb_model = create_xgb_model(search_space=search_space)
+    csi_scores = []
+
+    # 각 fold에 대해 교차 검증 수행
+    for df_sampled, (train_years, val_year) in zip(df_ctgan_list, FOLD_CONFIGS):
+        X_train, X_val, y_train, y_val = split_data(
+            df_sampled, df_daejeon, train_years, val_year
+        )
+        
+        xgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)], verbose=False)
+        csi = calculate_csi(y_val, xgb_model.predict(X_val))
+        csi_scores.append(csi)
+
+    return -1*round(np.mean(csi_scores), 4)
+
+
+# 하이퍼파라미터 최적화
+print("하이퍼파라미터 최적화 시작...")
+trials = Trials()
+xgb_best = fmin(
+    fn=objective_func,
+    space=xgb_search_space,
+    algo=tpe.suggest,
+    max_evals=MAX_EVALS, 
+    trials=trials
+)
+
+# 최적화 결과 분석 및 출력
+print(f"\n최적화 완료. 최적 파라미터: {xgb_best}")
+
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+# 파일 위치 기반으로 base 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/xgb_ctgan10000_daejeon_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
+# 최적화된 하이퍼파라미터로 최종 모델 학습
+print("최종 모델 학습 시작...")
+models = []
+
+for fold_idx, (df_sampled, (train_years, val_year)) in enumerate(
+    zip(df_ctgan_list, FOLD_CONFIGS), start=1
+):
+    print(f"Fold {fold_idx} 학습 중... (학습 연도: {train_years}, 검증 연도: {val_year})")
+    
+    X_train, X_val, y_train, y_val = split_data(
+        df_sampled, df_daejeon, train_years, val_year
+    )
+    
+    xgb_model = create_xgb_model(best_params=xgb_best)
+    xgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)], verbose=False)
+    
+    # 검증 성능 출력
+    val_csi = calculate_csi(y_val, xgb_model.predict(X_val))
+    print(f"Fold {fold_idx} 검증 CSI: {val_csi:.4f}")
+    
+    models.append(xgb_model)
+
+
+# 모델 저장
+print("모델 저장 중...")
+os.makedirs(os.path.join(base_dir, "save_model/xgb_optima"), exist_ok=True)
+model_save_path = os.path.join(base_dir, "save_model/xgb_optima/xgb_ctgan10000_daejeon.pkl")
+joblib.dump(models, model_save_path)
+print(f"모델이 {model_save_path}에 저장되었습니다.")
+
diff --git a/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_gwangju.py b/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_gwangju.py
new file mode 100644
index 0000000000000000000000000000000000000000..6217b2b9fcb4d82e5b6023ca91d1d52e6dffafb5
--- /dev/null
+++ b/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_gwangju.py
@@ -0,0 +1,305 @@
+import pandas as pd
+import os
+import numpy as np
+import joblib
+from xgboost import XGBClassifier
+from warnings import filterwarnings
+filterwarnings('ignore')
+from sklearn.metrics import confusion_matrix
+from hyperopt import fmin, tpe, Trials, hp
+
+# 상수 정의
+RANDOM_STATE = 42
+N_ESTIMATORS = 4000
+EARLY_STOPPING_ROUNDS = 400
+MAX_EVALS = 100
+
+# Fold 설정: (train_years, val_year)
+FOLD_CONFIGS = [
+    ([2018, 2019], 2020),  # Fold 1
+    ([2018, 2020], 2019),  # Fold 2
+    ([2019, 2020], 2018),  # Fold 3
+]
+
+def calculate_csi(Y_test, pred):
+    """CSI(Critical Success Index) 점수를 계산합니다.
+    
+    Args:
+        Y_test: 실제 레이블
+        pred: 예측 레이블
+        
+    Returns:
+        CSI 점수 (0~1 사이의 값)
+    """
+    cm = confusion_matrix(Y_test, pred)
+    H = (cm[0, 0] + cm[1, 1])
+    F = (cm[1, 0] + cm[2, 0] + cm[0, 1] + cm[2, 1])
+    M = (cm[0, 2] + cm[1, 2])
+    CSI = H / (H + F + M + 1e-10)
+    return CSI
+
+def eval_metric_csi(y_true, pred_prob):
+    """XGBoost용 CSI 메트릭 함수.
+    
+    Args:
+        y_true: 실제 레이블
+        pred_prob: 예측 확률
+        
+    Returns:
+        CSI 점수의 음수값
+    """
+    pred = np.argmax(pred_prob, axis=1)
+    csi = calculate_csi(y_true, pred)
+    return -1*csi
+
+def add_derived_features(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    제거했던 파생 변수들을 복구
+    
+    Args:
+        df: 데이터프레임
+    
+    Returns:
+        파생 변수가 추가된 데이터프레임
+    """
+    df = df.copy()
+    df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
+    df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
+    df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
+    df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)
+    df['ground_temp - temp_C'] = df['groundtemp'] - df['temp_C']
+    return df
+
+def preprocessing(df):
+    """데이터 전처리 함수.
+    
+    Args:
+        df: 원본 데이터프레임
+        
+    Returns:
+        전처리된 데이터프레임
+    """
+    df = df[df.columns].copy()
+    df['year'] = df['year'].astype('int')
+    df['month'] = df['month'].astype('int')
+    df['hour'] = df['hour'].astype('int')
+    df = add_derived_features(df).copy()
+    df['multi_class'] = df['multi_class'].astype('int')
+    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
+    df['wind_dir'] = df['wind_dir'].astype('int')
+    df = df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
+       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
+       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
+       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
+       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
+       'month_sin', 'month_cos','multi_class']].copy()
+    return df
+
+def create_xgb_model(search_space=None, best_params=None):
+    """XGBoost 모델을 생성합니다.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간 (objective_func에서 사용)
+        best_params: 최적화된 하이퍼파라미터 (최종 모델 학습에서 사용)
+        
+    Returns:
+        XGBClassifier 인스턴스
+    """
+    base_params = {
+        'n_estimators': N_ESTIMATORS,
+        'tree_method': 'hist',
+        'device': 'cuda',
+        'enable_categorical': True,
+        'eval_metric': eval_metric_csi,
+        'objective': 'multi:softprob',
+        'random_state': RANDOM_STATE,
+        'early_stopping_rounds': EARLY_STOPPING_ROUNDS,
+    }
+    
+    if search_space is not None:
+        # 하이퍼파라미터 최적화 중
+        params = {
+            **base_params,
+            'learning_rate': search_space['learning_rate'],
+            'max_depth': int(search_space['max_depth']),
+            'min_child_weight': int(search_space['min_child_weight']),
+            'gamma': search_space['gamma'],
+            'subsample': search_space['subsample'],
+            'colsample_bytree': search_space['colsample_bytree'],
+            'reg_alpha': search_space['reg_alpha'],
+            'reg_lambda': search_space['reg_lambda'],
+        }
+    elif best_params is not None:
+        # 최적화된 파라미터로 최종 모델 생성
+        params = {
+            **base_params,
+            'learning_rate': best_params['learning_rate'],
+            'max_depth': int(best_params['max_depth']),
+            'min_child_weight': int(best_params['min_child_weight']),
+            'gamma': best_params['gamma'],
+            'subsample': best_params['subsample'],
+            'colsample_bytree': best_params['colsample_bytree'],
+            'reg_alpha': best_params['reg_alpha'],
+            'reg_lambda': best_params['reg_lambda'],
+        }
+    else:
+        params = base_params
+    
+    return XGBClassifier(**params)
+
+
+# 데이터 로딩 및 전처리
+print("데이터 로딩 중...")
+# 파일 위치 기반으로 데이터 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+data_base_dir = os.path.abspath(os.path.join(current_file_dir, '../../../data'))
+df_gwangju = pd.read_csv(os.path.join(data_base_dir, "data_for_modeling/gwangju_train.csv"))
+df_ctgan_gwangju_1 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_1_gwangju.csv"))
+df_ctgan_gwangju_2 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_2_gwangju.csv"))
+df_ctgan_gwangju_3 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_3_gwangju.csv"))
+
+print("데이터 전처리 중...")
+df_ctgan_gwangju_1 = preprocessing(df_ctgan_gwangju_1)
+df_ctgan_gwangju_2 = preprocessing(df_ctgan_gwangju_2)
+df_ctgan_gwangju_3 = preprocessing(df_ctgan_gwangju_3)
+df_gwangju = preprocessing(df_gwangju)
+
+# CTGAN 데이터 리스트 (fold 순서와 일치)
+df_ctgan_list = [df_ctgan_gwangju_1, df_ctgan_gwangju_2, df_ctgan_gwangju_3]
+
+def split_data(df_sampled, df_original, train_years, val_year):
+    """데이터를 학습용과 검증용으로 분할합니다.
+    
+    Args:
+        df_sampled: 샘플링된 데이터프레임
+        df_original: 원본 데이터프레임
+        train_years: 학습에 사용할 연도 리스트
+        val_year: 검증에 사용할 연도
+        
+    Returns:
+        (X_train, X_val, y_train, y_val) 튜플
+    """
+    # 학습 데이터: 샘플링된 데이터에서 train_years에 해당하는 데이터
+    train_mask = df_sampled['year'].isin(train_years)
+    X_train = df_sampled.loc[train_mask, df_sampled.columns != 'multi_class'].copy()
+    y_train = df_sampled.loc[train_mask, 'multi_class']
+    
+    # 검증 데이터: 원본 데이터에서 val_year에 해당하는 데이터
+    val_mask = df_original['year'] == val_year
+    X_val = df_original.loc[val_mask, df_original.columns != 'multi_class'].copy()
+    y_val = df_original.loc[val_mask, 'multi_class']
+    
+    X_train.drop(columns=['year'], inplace=True)
+    X_val.drop(columns=['year'], inplace=True)
+    
+    return X_train, X_val, y_train, y_val
+
+
+# 하이퍼파라미터 검색 공간 정의
+xgb_search_space = {
+    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.2)),
+    'max_depth': hp.quniform('max_depth', 3, 12, 1),
+    'min_child_weight': hp.quniform('min_child_weight', 1, 20, 1),
+    'gamma': hp.uniform('gamma', 0, 5),  # 트리 분할을 위한 최소 손실 감소 값
+    'subsample': hp.uniform('subsample', 0.6, 1.0),
+    'colsample_bytree': hp.uniform('colsample_bytree', 0.6, 1.0),
+    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),
+    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0)
+}
+
+def objective_func(search_space):
+    """하이퍼파라미터 최적화를 위한 목적 함수.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간
+        
+    Returns:
+        평균 CSI 점수의 음수값 (hyperopt는 최소화를 수행하므로)
+    """
+    xgb_model = create_xgb_model(search_space=search_space)
+    csi_scores = []
+
+    # 각 fold에 대해 교차 검증 수행
+    for df_sampled, (train_years, val_year) in zip(df_ctgan_list, FOLD_CONFIGS):
+        X_train, X_val, y_train, y_val = split_data(
+            df_sampled, df_gwangju, train_years, val_year
+        )
+        
+        xgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)], verbose=False)
+        csi = calculate_csi(y_val, xgb_model.predict(X_val))
+        csi_scores.append(csi)
+
+    return -1*round(np.mean(csi_scores), 4)
+
+
+# 하이퍼파라미터 최적화
+print("하이퍼파라미터 최적화 시작...")
+trials = Trials()
+xgb_best = fmin(
+    fn=objective_func,
+    space=xgb_search_space,
+    algo=tpe.suggest,
+    max_evals=MAX_EVALS, 
+    trials=trials
+)
+
+# 최적화 결과 분석 및 출력
+print(f"\n최적화 완료. 최적 파라미터: {xgb_best}")
+
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+# 파일 위치 기반으로 base 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/xgb_ctgan10000_gwangju_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
+# 최적화된 하이퍼파라미터로 최종 모델 학습
+print("최종 모델 학습 시작...")
+models = []
+
+for fold_idx, (df_sampled, (train_years, val_year)) in enumerate(
+    zip(df_ctgan_list, FOLD_CONFIGS), start=1
+):
+    print(f"Fold {fold_idx} 학습 중... (학습 연도: {train_years}, 검증 연도: {val_year})")
+    
+    X_train, X_val, y_train, y_val = split_data(
+        df_sampled, df_gwangju, train_years, val_year
+    )
+    
+    xgb_model = create_xgb_model(best_params=xgb_best)
+    xgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)], verbose=False)
+    
+    # 검증 성능 출력
+    val_csi = calculate_csi(y_val, xgb_model.predict(X_val))
+    print(f"Fold {fold_idx} 검증 CSI: {val_csi:.4f}")
+    
+    models.append(xgb_model)
+
+
+# 모델 저장
+print("모델 저장 중...")
+os.makedirs(os.path.join(base_dir, "save_model/xgb_optima"), exist_ok=True)
+model_save_path = os.path.join(base_dir, "save_model/xgb_optima/xgb_ctgan10000_gwangju.pkl")
+joblib.dump(models, model_save_path)
+print(f"모델이 {model_save_path}에 저장되었습니다.")
+
diff --git a/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_incheon.py b/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_incheon.py
new file mode 100644
index 0000000000000000000000000000000000000000..22426b878fd4d36fbc62413868f66bc26c5cafef
--- /dev/null
+++ b/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_incheon.py
@@ -0,0 +1,305 @@
+import pandas as pd
+import os
+import numpy as np
+import joblib
+from xgboost import XGBClassifier
+from warnings import filterwarnings
+filterwarnings('ignore')
+from sklearn.metrics import confusion_matrix
+from hyperopt import fmin, tpe, Trials, hp
+
+# 상수 정의
+RANDOM_STATE = 42
+N_ESTIMATORS = 4000
+EARLY_STOPPING_ROUNDS = 400
+MAX_EVALS = 100
+
+# Fold 설정: (train_years, val_year)
+FOLD_CONFIGS = [
+    ([2018, 2019], 2020),  # Fold 1
+    ([2018, 2020], 2019),  # Fold 2
+    ([2019, 2020], 2018),  # Fold 3
+]
+
+def calculate_csi(Y_test, pred):
+    """CSI(Critical Success Index) 점수를 계산합니다.
+    
+    Args:
+        Y_test: 실제 레이블
+        pred: 예측 레이블
+        
+    Returns:
+        CSI 점수 (0~1 사이의 값)
+    """
+    cm = confusion_matrix(Y_test, pred)
+    H = (cm[0, 0] + cm[1, 1])
+    F = (cm[1, 0] + cm[2, 0] + cm[0, 1] + cm[2, 1])
+    M = (cm[0, 2] + cm[1, 2])
+    CSI = H / (H + F + M + 1e-10)
+    return CSI
+
+def eval_metric_csi(y_true, pred_prob):
+    """XGBoost용 CSI 메트릭 함수.
+    
+    Args:
+        y_true: 실제 레이블
+        pred_prob: 예측 확률
+        
+    Returns:
+        CSI 점수의 음수값
+    """
+    pred = np.argmax(pred_prob, axis=1)
+    csi = calculate_csi(y_true, pred)
+    return -1*csi
+
+def add_derived_features(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    제거했던 파생 변수들을 복구
+    
+    Args:
+        df: 데이터프레임
+    
+    Returns:
+        파생 변수가 추가된 데이터프레임
+    """
+    df = df.copy()
+    df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
+    df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
+    df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
+    df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)
+    df['ground_temp - temp_C'] = df['groundtemp'] - df['temp_C']
+    return df
+
+def preprocessing(df):
+    """데이터 전처리 함수.
+    
+    Args:
+        df: 원본 데이터프레임
+        
+    Returns:
+        전처리된 데이터프레임
+    """
+    df = df[df.columns].copy()
+    df['year'] = df['year'].astype('int')
+    df['month'] = df['month'].astype('int')
+    df['hour'] = df['hour'].astype('int')
+    df = add_derived_features(df).copy()
+    df['multi_class'] = df['multi_class'].astype('int')
+    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
+    df['wind_dir'] = df['wind_dir'].astype('int')
+    df = df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
+       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
+       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
+       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
+       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
+       'month_sin', 'month_cos','multi_class']].copy()
+    return df
+
+def create_xgb_model(search_space=None, best_params=None):
+    """XGBoost 모델을 생성합니다.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간 (objective_func에서 사용)
+        best_params: 최적화된 하이퍼파라미터 (최종 모델 학습에서 사용)
+        
+    Returns:
+        XGBClassifier 인스턴스
+    """
+    base_params = {
+        'n_estimators': N_ESTIMATORS,
+        'tree_method': 'hist',
+        'device': 'cuda',
+        'enable_categorical': True,
+        'eval_metric': eval_metric_csi,
+        'objective': 'multi:softprob',
+        'random_state': RANDOM_STATE,
+        'early_stopping_rounds': EARLY_STOPPING_ROUNDS,
+    }
+    
+    if search_space is not None:
+        # 하이퍼파라미터 최적화 중
+        params = {
+            **base_params,
+            'learning_rate': search_space['learning_rate'],
+            'max_depth': int(search_space['max_depth']),
+            'min_child_weight': int(search_space['min_child_weight']),
+            'gamma': search_space['gamma'],
+            'subsample': search_space['subsample'],
+            'colsample_bytree': search_space['colsample_bytree'],
+            'reg_alpha': search_space['reg_alpha'],
+            'reg_lambda': search_space['reg_lambda'],
+        }
+    elif best_params is not None:
+        # 최적화된 파라미터로 최종 모델 생성
+        params = {
+            **base_params,
+            'learning_rate': best_params['learning_rate'],
+            'max_depth': int(best_params['max_depth']),
+            'min_child_weight': int(best_params['min_child_weight']),
+            'gamma': best_params['gamma'],
+            'subsample': best_params['subsample'],
+            'colsample_bytree': best_params['colsample_bytree'],
+            'reg_alpha': best_params['reg_alpha'],
+            'reg_lambda': best_params['reg_lambda'],
+        }
+    else:
+        params = base_params
+    
+    return XGBClassifier(**params)
+
+
+# 데이터 로딩 및 전처리
+print("데이터 로딩 중...")
+# 파일 위치 기반으로 데이터 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+data_base_dir = os.path.abspath(os.path.join(current_file_dir, '../../../data'))
+df_incheon = pd.read_csv(os.path.join(data_base_dir, "data_for_modeling/incheon_train.csv"))
+df_ctgan_incheon_1 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_1_incheon.csv"))
+df_ctgan_incheon_2 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_2_incheon.csv"))
+df_ctgan_incheon_3 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_3_incheon.csv"))
+
+print("데이터 전처리 중...")
+df_ctgan_incheon_1 = preprocessing(df_ctgan_incheon_1)
+df_ctgan_incheon_2 = preprocessing(df_ctgan_incheon_2)
+df_ctgan_incheon_3 = preprocessing(df_ctgan_incheon_3)
+df_incheon = preprocessing(df_incheon)
+
+# CTGAN 데이터 리스트 (fold 순서와 일치)
+df_ctgan_list = [df_ctgan_incheon_1, df_ctgan_incheon_2, df_ctgan_incheon_3]
+
+def split_data(df_sampled, df_original, train_years, val_year):
+    """데이터를 학습용과 검증용으로 분할합니다.
+    
+    Args:
+        df_sampled: 샘플링된 데이터프레임
+        df_original: 원본 데이터프레임
+        train_years: 학습에 사용할 연도 리스트
+        val_year: 검증에 사용할 연도
+        
+    Returns:
+        (X_train, X_val, y_train, y_val) 튜플
+    """
+    # 학습 데이터: 샘플링된 데이터에서 train_years에 해당하는 데이터
+    train_mask = df_sampled['year'].isin(train_years)
+    X_train = df_sampled.loc[train_mask, df_sampled.columns != 'multi_class'].copy()
+    y_train = df_sampled.loc[train_mask, 'multi_class']
+    
+    # 검증 데이터: 원본 데이터에서 val_year에 해당하는 데이터
+    val_mask = df_original['year'] == val_year
+    X_val = df_original.loc[val_mask, df_original.columns != 'multi_class'].copy()
+    y_val = df_original.loc[val_mask, 'multi_class']
+    
+    X_train.drop(columns=['year'], inplace=True)
+    X_val.drop(columns=['year'], inplace=True)
+    
+    return X_train, X_val, y_train, y_val
+
+
+# 하이퍼파라미터 검색 공간 정의
+xgb_search_space = {
+    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.2)),
+    'max_depth': hp.quniform('max_depth', 3, 12, 1),
+    'min_child_weight': hp.quniform('min_child_weight', 1, 20, 1),
+    'gamma': hp.uniform('gamma', 0, 5),  # 트리 분할을 위한 최소 손실 감소 값
+    'subsample': hp.uniform('subsample', 0.6, 1.0),
+    'colsample_bytree': hp.uniform('colsample_bytree', 0.6, 1.0),
+    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),
+    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0)
+}
+
+def objective_func(search_space):
+    """하이퍼파라미터 최적화를 위한 목적 함수.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간
+        
+    Returns:
+        평균 CSI 점수의 음수값 (hyperopt는 최소화를 수행하므로)
+    """
+    xgb_model = create_xgb_model(search_space=search_space)
+    csi_scores = []
+
+    # 각 fold에 대해 교차 검증 수행
+    for df_sampled, (train_years, val_year) in zip(df_ctgan_list, FOLD_CONFIGS):
+        X_train, X_val, y_train, y_val = split_data(
+            df_sampled, df_incheon, train_years, val_year
+        )
+        
+        xgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)], verbose=False)
+        csi = calculate_csi(y_val, xgb_model.predict(X_val))
+        csi_scores.append(csi)
+
+    return -1*round(np.mean(csi_scores), 4)
+
+
+# 하이퍼파라미터 최적화
+print("하이퍼파라미터 최적화 시작...")
+trials = Trials()
+xgb_best = fmin(
+    fn=objective_func,
+    space=xgb_search_space,
+    algo=tpe.suggest,
+    max_evals=MAX_EVALS, 
+    trials=trials
+)
+
+# 최적화 결과 분석 및 출력
+print(f"\n최적화 완료. 최적 파라미터: {xgb_best}")
+
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+# 파일 위치 기반으로 base 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/xgb_ctgan10000_incheon_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
+# 최적화된 하이퍼파라미터로 최종 모델 학습
+print("최종 모델 학습 시작...")
+models = []
+
+for fold_idx, (df_sampled, (train_years, val_year)) in enumerate(
+    zip(df_ctgan_list, FOLD_CONFIGS), start=1
+):
+    print(f"Fold {fold_idx} 학습 중... (학습 연도: {train_years}, 검증 연도: {val_year})")
+    
+    X_train, X_val, y_train, y_val = split_data(
+        df_sampled, df_incheon, train_years, val_year
+    )
+    
+    xgb_model = create_xgb_model(best_params=xgb_best)
+    xgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)], verbose=False)
+    
+    # 검증 성능 출력
+    val_csi = calculate_csi(y_val, xgb_model.predict(X_val))
+    print(f"Fold {fold_idx} 검증 CSI: {val_csi:.4f}")
+    
+    models.append(xgb_model)
+
+
+# 모델 저장
+print("모델 저장 중...")
+os.makedirs(os.path.join(base_dir, "save_model/xgb_optima"), exist_ok=True)
+model_save_path = os.path.join(base_dir, "save_model/xgb_optima/xgb_ctgan10000_incheon.pkl")
+joblib.dump(models, model_save_path)
+print(f"모델이 {model_save_path}에 저장되었습니다.")
+
diff --git a/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_seoul.py b/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_seoul.py
new file mode 100644
index 0000000000000000000000000000000000000000..733713bb198b6c4300557945a4186ee7cbb7d650
--- /dev/null
+++ b/Analysis_code/5.optima/xgb_ctgan10000/XGB_ctgan10000_seoul.py
@@ -0,0 +1,305 @@
+import pandas as pd
+import os
+import numpy as np
+import joblib
+from xgboost import XGBClassifier
+from warnings import filterwarnings
+filterwarnings('ignore')
+from sklearn.metrics import confusion_matrix
+from hyperopt import fmin, tpe, Trials, hp
+
+# 상수 정의
+RANDOM_STATE = 42
+N_ESTIMATORS = 4000
+EARLY_STOPPING_ROUNDS = 400
+MAX_EVALS = 100
+
+# Fold 설정: (train_years, val_year)
+FOLD_CONFIGS = [
+    ([2018, 2019], 2020),  # Fold 1
+    ([2018, 2020], 2019),  # Fold 2
+    ([2019, 2020], 2018),  # Fold 3
+]
+
+def calculate_csi(Y_test, pred):
+    """CSI(Critical Success Index) 점수를 계산합니다.
+    
+    Args:
+        Y_test: 실제 레이블
+        pred: 예측 레이블
+        
+    Returns:
+        CSI 점수 (0~1 사이의 값)
+    """
+    cm = confusion_matrix(Y_test, pred)
+    H = (cm[0, 0] + cm[1, 1])
+    F = (cm[1, 0] + cm[2, 0] + cm[0, 1] + cm[2, 1])
+    M = (cm[0, 2] + cm[1, 2])
+    CSI = H / (H + F + M + 1e-10)
+    return CSI
+
+def eval_metric_csi(y_true, pred_prob):
+    """XGBoost용 CSI 메트릭 함수.
+    
+    Args:
+        y_true: 실제 레이블
+        pred_prob: 예측 확률
+        
+    Returns:
+        CSI 점수의 음수값
+    """
+    pred = np.argmax(pred_prob, axis=1)
+    csi = calculate_csi(y_true, pred)
+    return -1*csi
+
+def add_derived_features(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    제거했던 파생 변수들을 복구
+    
+    Args:
+        df: 데이터프레임
+    
+    Returns:
+        파생 변수가 추가된 데이터프레임
+    """
+    df = df.copy()
+    df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
+    df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
+    df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
+    df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)
+    df['ground_temp - temp_C'] = df['groundtemp'] - df['temp_C']
+    return df
+
+def preprocessing(df):
+    """데이터 전처리 함수.
+    
+    Args:
+        df: 원본 데이터프레임
+        
+    Returns:
+        전처리된 데이터프레임
+    """
+    df = df[df.columns].copy()
+    df['year'] = df['year'].astype('int')
+    df['month'] = df['month'].astype('int')
+    df['hour'] = df['hour'].astype('int')
+    df = add_derived_features(df).copy()
+    df['multi_class'] = df['multi_class'].astype('int')
+    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
+    df['wind_dir'] = df['wind_dir'].astype('int')
+    df = df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
+       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
+       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
+       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
+       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
+       'month_sin', 'month_cos','multi_class']].copy()
+    return df
+
+def create_xgb_model(search_space=None, best_params=None):
+    """XGBoost 모델을 생성합니다.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간 (objective_func에서 사용)
+        best_params: 최적화된 하이퍼파라미터 (최종 모델 학습에서 사용)
+        
+    Returns:
+        XGBClassifier 인스턴스
+    """
+    base_params = {
+        'n_estimators': N_ESTIMATORS,
+        'tree_method': 'hist',
+        'device': 'cuda',
+        'enable_categorical': True,
+        'eval_metric': eval_metric_csi,
+        'objective': 'multi:softprob',
+        'random_state': RANDOM_STATE,
+        'early_stopping_rounds': EARLY_STOPPING_ROUNDS,
+    }
+    
+    if search_space is not None:
+        # 하이퍼파라미터 최적화 중
+        params = {
+            **base_params,
+            'learning_rate': search_space['learning_rate'],
+            'max_depth': int(search_space['max_depth']),
+            'min_child_weight': int(search_space['min_child_weight']),
+            'gamma': search_space['gamma'],
+            'subsample': search_space['subsample'],
+            'colsample_bytree': search_space['colsample_bytree'],
+            'reg_alpha': search_space['reg_alpha'],
+            'reg_lambda': search_space['reg_lambda'],
+        }
+    elif best_params is not None:
+        # 최적화된 파라미터로 최종 모델 생성
+        params = {
+            **base_params,
+            'learning_rate': best_params['learning_rate'],
+            'max_depth': int(best_params['max_depth']),
+            'min_child_weight': int(best_params['min_child_weight']),
+            'gamma': best_params['gamma'],
+            'subsample': best_params['subsample'],
+            'colsample_bytree': best_params['colsample_bytree'],
+            'reg_alpha': best_params['reg_alpha'],
+            'reg_lambda': best_params['reg_lambda'],
+        }
+    else:
+        params = base_params
+    
+    return XGBClassifier(**params)
+
+
+# 데이터 로딩 및 전처리
+print("데이터 로딩 중...")
+# 파일 위치 기반으로 데이터 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+data_base_dir = os.path.abspath(os.path.join(current_file_dir, '../../../data'))
+df_seoul = pd.read_csv(os.path.join(data_base_dir, "data_for_modeling/seoul_train.csv"))
+df_ctgan_seoul_1 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_1_seoul.csv"))
+df_ctgan_seoul_2 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_2_seoul.csv"))
+df_ctgan_seoul_3 = pd.read_csv(os.path.join(data_base_dir, "data_oversampled/ctgan10000/ctgan10000_3_seoul.csv"))
+
+print("데이터 전처리 중...")
+df_ctgan_seoul_1 = preprocessing(df_ctgan_seoul_1)
+df_ctgan_seoul_2 = preprocessing(df_ctgan_seoul_2)
+df_ctgan_seoul_3 = preprocessing(df_ctgan_seoul_3)
+df_seoul = preprocessing(df_seoul)
+
+# CTGAN 데이터 리스트 (fold 순서와 일치)
+df_ctgan_list = [df_ctgan_seoul_1, df_ctgan_seoul_2, df_ctgan_seoul_3]
+
+def split_data(df_sampled, df_original, train_years, val_year):
+    """데이터를 학습용과 검증용으로 분할합니다.
+    
+    Args:
+        df_sampled: 샘플링된 데이터프레임
+        df_original: 원본 데이터프레임
+        train_years: 학습에 사용할 연도 리스트
+        val_year: 검증에 사용할 연도
+        
+    Returns:
+        (X_train, X_val, y_train, y_val) 튜플
+    """
+    # 학습 데이터: 샘플링된 데이터에서 train_years에 해당하는 데이터
+    train_mask = df_sampled['year'].isin(train_years)
+    X_train = df_sampled.loc[train_mask, df_sampled.columns != 'multi_class'].copy()
+    y_train = df_sampled.loc[train_mask, 'multi_class']
+    
+    # 검증 데이터: 원본 데이터에서 val_year에 해당하는 데이터
+    val_mask = df_original['year'] == val_year
+    X_val = df_original.loc[val_mask, df_original.columns != 'multi_class'].copy()
+    y_val = df_original.loc[val_mask, 'multi_class']
+    
+    X_train.drop(columns=['year'], inplace=True)
+    X_val.drop(columns=['year'], inplace=True)
+    
+    return X_train, X_val, y_train, y_val
+
+
+# 하이퍼파라미터 검색 공간 정의
+xgb_search_space = {
+    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.2)),
+    'max_depth': hp.quniform('max_depth', 3, 12, 1),
+    'min_child_weight': hp.quniform('min_child_weight', 1, 20, 1),
+    'gamma': hp.uniform('gamma', 0, 5),  # 트리 분할을 위한 최소 손실 감소 값
+    'subsample': hp.uniform('subsample', 0.6, 1.0),
+    'colsample_bytree': hp.uniform('colsample_bytree', 0.6, 1.0),
+    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),
+    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0)
+}
+
+def objective_func(search_space):
+    """하이퍼파라미터 최적화를 위한 목적 함수.
+    
+    Args:
+        search_space: 하이퍼파라미터 검색 공간
+        
+    Returns:
+        평균 CSI 점수의 음수값 (hyperopt는 최소화를 수행하므로)
+    """
+    xgb_model = create_xgb_model(search_space=search_space)
+    csi_scores = []
+
+    # 각 fold에 대해 교차 검증 수행
+    for df_sampled, (train_years, val_year) in zip(df_ctgan_list, FOLD_CONFIGS):
+        X_train, X_val, y_train, y_val = split_data(
+            df_sampled, df_seoul, train_years, val_year
+        )
+        
+        xgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)], verbose=False)
+        csi = calculate_csi(y_val, xgb_model.predict(X_val))
+        csi_scores.append(csi)
+
+    return -1*round(np.mean(csi_scores), 4)
+
+
+# 하이퍼파라미터 최적화
+print("하이퍼파라미터 최적화 시작...")
+trials = Trials()
+xgb_best = fmin(
+    fn=objective_func,
+    space=xgb_search_space,
+    algo=tpe.suggest,
+    max_evals=MAX_EVALS, 
+    trials=trials
+)
+
+# 최적화 결과 분석 및 출력
+print(f"\n최적화 완료. 최적 파라미터: {xgb_best}")
+
+# Best loss (CSI 점수의 음수값이므로, 실제 CSI는 -loss)
+best_loss = trials.best_trial['result']['loss']
+best_csi = -best_loss
+print(f"최적 CSI 점수: {best_csi:.4f} (loss: {best_loss:.4f})")
+
+# 모든 trial의 loss 값 추출
+losses = [trial['result']['loss'] for trial in trials.trials if trial['result']['status'] == 'ok']
+csi_scores = [-loss for loss in losses]
+
+print(f"\n최적화 과정 요약:")
+print(f"  - 총 시도 횟수: {len(trials.trials)}")
+print(f"  - 성공한 시도: {len(losses)}")
+print(f"  - 최초 CSI: {csi_scores[0]:.4f}")
+print(f"  - 최종 CSI: {csi_scores[-1]:.4f}")
+print(f"  - 최고 CSI: {max(csi_scores):.4f}")
+print(f"  - 최저 CSI: {min(csi_scores):.4f}")
+print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
+
+# Trials 객체 저장
+# 파일 위치 기반으로 base 디렉토리 경로 설정
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
+os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
+trials_path = os.path.join(base_dir, "optimization_history/xgb_ctgan10000_seoul_trials.pkl")
+joblib.dump(trials, trials_path)
+print(f"\n최적화 Trials 객체가 {trials_path}에 저장되었습니다.")
+
+# 최적화된 하이퍼파라미터로 최종 모델 학습
+print("최종 모델 학습 시작...")
+models = []
+
+for fold_idx, (df_sampled, (train_years, val_year)) in enumerate(
+    zip(df_ctgan_list, FOLD_CONFIGS), start=1
+):
+    print(f"Fold {fold_idx} 학습 중... (학습 연도: {train_years}, 검증 연도: {val_year})")
+    
+    X_train, X_val, y_train, y_val = split_data(
+        df_sampled, df_seoul, train_years, val_year
+    )
+    
+    xgb_model = create_xgb_model(best_params=xgb_best)
+    xgb_model.fit(X_train, y_train, eval_set=[(X_val, y_val)], verbose=False)
+    
+    # 검증 성능 출력
+    val_csi = calculate_csi(y_val, xgb_model.predict(X_val))
+    print(f"Fold {fold_idx} 검증 CSI: {val_csi:.4f}")
+    
+    models.append(xgb_model)
+
+
+# 모델 저장
+print("모델 저장 중...")
+os.makedirs(os.path.join(base_dir, "save_model/xgb_optima"), exist_ok=True)
+model_save_path = os.path.join(base_dir, "save_model/xgb_optima/xgb_ctgan10000_seoul.pkl")
+joblib.dump(models, model_save_path)
+print(f"모델이 {model_save_path}에 저장되었습니다.")
+
diff --git a/Analysis_code/5.optima/xgb_pure/XGB_pure_daegu.py b/Analysis_code/5.optima/xgb_pure/XGB_pure_daegu.py
index 37683b276714578dcd2b6c632bf6c8440c079d21..2006c338af3fd690c3f3e327afa673c12395a0db 100644
--- a/Analysis_code/5.optima/xgb_pure/XGB_pure_daegu.py
+++ b/Analysis_code/5.optima/xgb_pure/XGB_pure_daegu.py
@@ -248,6 +248,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/xgb_pure_daegu_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/xgb_pure/XGB_pure_daejeon.py b/Analysis_code/5.optima/xgb_pure/XGB_pure_daejeon.py
index 3c9553d7951fdf25d8b4cecf693f0475c2618fef..c540eef60bb7cc98e68bc2df60750f9cf1fa978a 100644
--- a/Analysis_code/5.optima/xgb_pure/XGB_pure_daejeon.py
+++ b/Analysis_code/5.optima/xgb_pure/XGB_pure_daejeon.py
@@ -248,6 +248,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/xgb_pure_daejeon_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/xgb_pure/XGB_pure_gwangju.py b/Analysis_code/5.optima/xgb_pure/XGB_pure_gwangju.py
index ab3982591cb070ac28889b49b8d25db5ef040a1f..6f41b8ad9471eed5822c6c7a5e6646ec787e1dbd 100644
--- a/Analysis_code/5.optima/xgb_pure/XGB_pure_gwangju.py
+++ b/Analysis_code/5.optima/xgb_pure/XGB_pure_gwangju.py
@@ -248,6 +248,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/xgb_pure_gwangju_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/xgb_pure/XGB_pure_incheon.py b/Analysis_code/5.optima/xgb_pure/XGB_pure_incheon.py
index a1a76867352730db7d5dcf99269849270d675069..1267e7c241e70ef4f3ecbc3bac02da6f89e83f15 100644
--- a/Analysis_code/5.optima/xgb_pure/XGB_pure_incheon.py
+++ b/Analysis_code/5.optima/xgb_pure/XGB_pure_incheon.py
@@ -248,6 +248,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/xgb_pure_incheon_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/xgb_pure/XGB_pure_seoul.py b/Analysis_code/5.optima/xgb_pure/XGB_pure_seoul.py
index 6abe84dd0335de5a1818c5da8fdc5216558d2f84..d6f68238970813459dca99d85e0e1ed467aa3737 100644
--- a/Analysis_code/5.optima/xgb_pure/XGB_pure_seoul.py
+++ b/Analysis_code/5.optima/xgb_pure/XGB_pure_seoul.py
@@ -248,6 +248,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/xgb_pure_seoul_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/xgb_smote/XGB_smote_busan.py b/Analysis_code/5.optima/xgb_smote/XGB_smote_busan.py
index dc3f181104ba8b05e78cdcc852e9ba856c67dd5c..7355ddb02871a63b54e4de6aa1cc66a780ab27ff 100644
--- a/Analysis_code/5.optima/xgb_smote/XGB_smote_busan.py
+++ b/Analysis_code/5.optima/xgb_smote/XGB_smote_busan.py
@@ -265,6 +265,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/xgb_smote_busan_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/xgb_smote/XGB_smote_daegu.py b/Analysis_code/5.optima/xgb_smote/XGB_smote_daegu.py
index d5760070e0fbfd5180abf9daf65a6c4a6f3d2f71..4c17d3b5ae9a0fb4bfbf1371f70691b5cf299847 100644
--- a/Analysis_code/5.optima/xgb_smote/XGB_smote_daegu.py
+++ b/Analysis_code/5.optima/xgb_smote/XGB_smote_daegu.py
@@ -265,6 +265,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/xgb_smote_daegu_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/xgb_smote/XGB_smote_daejeon.py b/Analysis_code/5.optima/xgb_smote/XGB_smote_daejeon.py
index f5125779a10333817a4454646747869360595d1a..203a082591da88e69fb63f135cd22d72aa2279e7 100644
--- a/Analysis_code/5.optima/xgb_smote/XGB_smote_daejeon.py
+++ b/Analysis_code/5.optima/xgb_smote/XGB_smote_daejeon.py
@@ -265,6 +265,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/xgb_smote_daejeon_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/xgb_smote/XGB_smote_gwangju.py b/Analysis_code/5.optima/xgb_smote/XGB_smote_gwangju.py
index 8e349926a7fb6df4d4a9e79de1f5471df95632b6..84044646011ad663e617076a65aaeb06904b6a28 100644
--- a/Analysis_code/5.optima/xgb_smote/XGB_smote_gwangju.py
+++ b/Analysis_code/5.optima/xgb_smote/XGB_smote_gwangju.py
@@ -265,6 +265,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/xgb_smote_gwangju_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/xgb_smote/XGB_smote_incheon.py b/Analysis_code/5.optima/xgb_smote/XGB_smote_incheon.py
index ec2d1c8db59c2913b4328b59d7a8511b27ad3a24..6e16e7476aad6ebc4bfcb537bae352d391985d5a 100644
--- a/Analysis_code/5.optima/xgb_smote/XGB_smote_incheon.py
+++ b/Analysis_code/5.optima/xgb_smote/XGB_smote_incheon.py
@@ -265,6 +265,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/xgb_smote_incheon_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/5.optima/xgb_smote/XGB_smote_seoul.py b/Analysis_code/5.optima/xgb_smote/XGB_smote_seoul.py
index b0878029827cd0331deef10a80683dc2f3694e9e..6225566b6c0c26d3b5dcb7b4a989e402faa5eedc 100644
--- a/Analysis_code/5.optima/xgb_smote/XGB_smote_seoul.py
+++ b/Analysis_code/5.optima/xgb_smote/XGB_smote_seoul.py
@@ -265,6 +265,7 @@ print(f"  - 최저 CSI: {min(csi_scores):.4f}")
 print(f"  - 평균 CSI: {np.mean(csi_scores):.4f}")
 
 # Trials 객체 저장
+base_dir = os.path.dirname(os.path.dirname(current_file_dir))  # 5.optima 디렉토리
 os.makedirs(os.path.join(base_dir, "optimization_history"), exist_ok=True)
 trials_path = os.path.join(base_dir, "optimization_history/xgb_smote_seoul_trials.pkl")
 joblib.dump(trials, trials_path)
diff --git a/Analysis_code/6.optima_models_analysis/best_samples_best_datasample_per_model_per_region.csv b/Analysis_code/6.optima_models_analysis/best_samples_best_datasample_per_model_per_region.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e2ab3d7fc8551175abd67b1c38137d24566a888a
--- /dev/null
+++ b/Analysis_code/6.optima_models_analysis/best_samples_best_datasample_per_model_per_region.csv
@@ -0,0 +1,31 @@
+model,data_sample,region,optimization_library,best_csi,n_trials,best_params
+ft_transformer,ctgan10000,seoul,optuna,0.5936576575682742,100,"{'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}"
+xgb,smote,seoul,hyperopt,0.5914,100,"{'colsample_bytree': 0.9204650350059359, 'gamma': 0.006794691671452219, 'learning_rate': 0.08811009219503893, 'max_depth': 12.0, 'min_child_weight': 3.0, 'reg_alpha': 0.6690476963880017, 'reg_lambda': 0.5388102924254845, 'subsample': 0.8320339664880525}"
+lgb,smote,seoul,hyperopt,0.5895,100,"{'colsample_bytree': 0.7712993813350446, 'learning_rate': 0.014764448164722338, 'max_depth': 13.0, 'min_child_weight': 2.0, 'num_leaves': 144.0, 'reg_alpha': 0.9375760221934153, 'reg_lambda': 0.6881109653195318, 'subsample': 0.6932106255794361}"
+resnet_like,ctgan10000,seoul,optuna,0.5889744558864985,100,"{'d_main': 160, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.24169683403631037, 'dropout_second': 0.018646743579449815, 'lr': 0.003891520111503718, 'weight_decay': 0.09736563298725749, 'batch_size': 128}"
+deepgbm,smotenc_ctgan20000,seoul,optuna,0.5657974901513136,100,"{'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.1024240157205574, 'lr': 0.009019177625524915, 'weight_decay': 0.08874117499066106, 'batch_size': 256}"
+xgb,smote,incheon,hyperopt,0.6,100,"{'colsample_bytree': 0.8863531635625073, 'gamma': 1.4432252696586687, 'learning_rate': 0.14431831840673584, 'max_depth': 4.0, 'min_child_weight': 4.0, 'reg_alpha': 0.7656890601027424, 'reg_lambda': 0.5796745106013773, 'subsample': 0.8862819830666011}"
+lgb,smote,incheon,hyperopt,0.5986,100,"{'colsample_bytree': 0.7149911519913482, 'learning_rate': 0.14061649313221522, 'max_depth': 4.0, 'min_child_weight': 4.0, 'num_leaves': 46.0, 'reg_alpha': 0.3323739596170201, 'reg_lambda': 0.3615804769440283, 'subsample': 0.7361106038020775}"
+resnet_like,ctgan10000,incheon,optuna,0.5876200434398301,100,"{'d_main': 160, 'd_hidden': 192, 'n_blocks': 3, 'dropout_first': 0.213366405042877, 'dropout_second': 0.0616930432275245, 'lr': 0.005092968501532562, 'weight_decay': 0.06153947659623341, 'batch_size': 256}"
+ft_transformer,smote,incheon,optuna,0.5674050423289939,100,"{'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.3637871224837107, 'ffn_dropout': 0.11479322703553738, 'lr': 0.0003009840584939789, 'weight_decay': 0.0003336039035587163, 'batch_size': 32}"
+deepgbm,ctgan10000,incheon,optuna,0.5644485264432356,100,"{'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.16846849111235224, 'lr': 0.007871644587352598, 'weight_decay': 0.0005399258093557023, 'batch_size': 128}"
+xgb,smote,gwangju,hyperopt,0.53,100,"{'colsample_bytree': 0.7658195937298418, 'gamma': 1.040884657831581, 'learning_rate': 0.04553328563585195, 'max_depth': 7.0, 'min_child_weight': 12.0, 'reg_alpha': 0.8031012977426317, 'reg_lambda': 0.6205464163959697, 'subsample': 0.6524796151581305}"
+lgb,smote,gwangju,hyperopt,0.5297,100,"{'colsample_bytree': 0.9919060649789312, 'learning_rate': 0.054631157314326724, 'max_depth': 15.0, 'min_child_weight': 3.0, 'num_leaves': 47.0, 'reg_alpha': 0.9190252546800255, 'reg_lambda': 0.8800706832709921, 'subsample': 0.7859941375783913}"
+deepgbm,ctgan10000,gwangju,optuna,0.5204031176113428,100,"{'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}"
+resnet_like,ctgan10000,gwangju,optuna,0.510302842874457,100,"{'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}"
+ft_transformer,ctgan10000,gwangju,optuna,0.5052817328725289,100,"{'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}"
+xgb,smote,daejeon,hyperopt,0.5371,100,"{'colsample_bytree': 0.733236256331133, 'gamma': 0.7990977235867733, 'learning_rate': 0.17558281930946487, 'max_depth': 9.0, 'min_child_weight': 11.0, 'reg_alpha': 0.1596833778659402, 'reg_lambda': 0.9170555745286906, 'subsample': 0.6403574066792026}"
+lgb,smote,daejeon,hyperopt,0.5317,100,"{'colsample_bytree': 0.7585295616897205, 'learning_rate': 0.012807299958074884, 'max_depth': 8.0, 'min_child_weight': 2.0, 'num_leaves': 149.0, 'reg_alpha': 0.8175154308532824, 'reg_lambda': 0.7481509687757377, 'subsample': 0.8155067304500027}"
+resnet_like,ctgan10000,daejeon,optuna,0.5101768615009369,100,"{'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}"
+deepgbm,ctgan10000,daejeon,optuna,0.5101248146449113,100,"{'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}"
+ft_transformer,ctgan10000,daejeon,optuna,0.5026041056392309,100,"{'d_token': 64, 'n_blocks': 5, 'n_heads': 8, 'attention_dropout': 0.39639878146052787, 'ffn_dropout': 0.16243660840447227, 'lr': 0.0005258652715359098, 'weight_decay': 0.06319928258911829, 'batch_size': 128}"
+xgb,smote,daegu,hyperopt,0.4672,100,"{'colsample_bytree': 0.8132816721507904, 'gamma': 0.9002659162503241, 'learning_rate': 0.04046864452016672, 'max_depth': 4.0, 'min_child_weight': 17.0, 'reg_alpha': 0.4681545450085154, 'reg_lambda': 0.531313515098387, 'subsample': 0.827198506312037}"
+lgb,smote,daegu,hyperopt,0.4671,100,"{'colsample_bytree': 0.999946333457191, 'learning_rate': 0.07031680296643952, 'max_depth': 4.0, 'min_child_weight': 17.0, 'num_leaves': 32.0, 'reg_alpha': 0.055815317687804816, 'reg_lambda': 0.2293760134119255, 'subsample': 0.6363907923464539}"
+resnet_like,ctgan10000,daegu,optuna,0.46086300130604796,100,"{'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}"
+ft_transformer,ctgan10000,daegu,optuna,0.44918319157422554,100,"{'d_token': 128, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.1615322006432558, 'ffn_dropout': 0.14353691142809796, 'lr': 0.00025225999310114116, 'weight_decay': 0.002948085679186959, 'batch_size': 32}"
+deepgbm,ctgan10000,daegu,optuna,0.4390250453058284,100,"{'d_main': 64, 'd_hidden': 192, 'n_blocks': 2, 'dropout': 0.29112938728448373, 'lr': 0.002745246324742509, 'weight_decay': 0.07823286969698617, 'batch_size': 32}"
+ft_transformer,ctgan10000,busan,optuna,0.4960458104166546,100,"{'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}"
+xgb,pure,busan,hyperopt,0.4949,100,"{'colsample_bytree': 0.8651175745135303, 'gamma': 2.0220518303820976, 'learning_rate': 0.04196437449161767, 'max_depth': 7.0, 'min_child_weight': 17.0, 'reg_alpha': 0.9213159636887744, 'reg_lambda': 0.9407811453878014, 'subsample': 0.7200034080497129}"
+resnet_like,ctgan10000,busan,optuna,0.49363300915248276,100,"{'d_main': 128, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3784300740258752, 'dropout_second': 0.026029354045211155, 'lr': 0.008483242466300268, 'weight_decay': 0.00016367394584020504, 'batch_size': 128}"
+lgb,pure,busan,hyperopt,0.4849,100,"{'colsample_bytree': 0.9406061312055983, 'learning_rate': 0.17468151642796886, 'max_depth': 6.0, 'min_child_weight': 17.0, 'num_leaves': 138.0, 'reg_alpha': 0.4593420461637059, 'reg_lambda': 0.5948987302333338, 'subsample': 0.6557839186758097}"
+deepgbm,ctgan10000,busan,optuna,0.475435864212341,100,"{'d_main': 128, 'd_hidden': 128, 'n_blocks': 2, 'dropout': 0.1477619274404685, 'lr': 0.00606559205480389, 'weight_decay': 0.08582651929034574, 'batch_size': 64}"
diff --git a/Analysis_code/6.optima_models_analysis/extract_result_from_omptimized_models.ipynb b/Analysis_code/6.optima_models_analysis/extract_result_from_omptimized_models.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e6bd608f78da5fef25dbbaa237843bc654de8d6a
--- /dev/null
+++ b/Analysis_code/6.optima_models_analysis/extract_result_from_omptimized_models.ipynb
@@ -0,0 +1,1073 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a30803c4",
+   "metadata": {},
+   "source": [
+    "# Hyperparameter Optimization Results\n",
+    "\n",
+    "### This notebook provides a clear summary and visualizations of the hyperparameter optimization outcomes \n",
+    "### for various machine learning models and sampling techniques, organized by region. \n",
+    "### Key performance metrics are extracted and compared for easier interpretation and insights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "70effd7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import joblib\n",
+    "import os\n",
+    "import re\n",
+    "from glob import glob\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2942ba86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimization_history_dir = \"../optimization_history/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d55af59c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODELS = ['ft_transformer', 'resnet_like', 'deepgbm', 'lgb', 'xgb']\n",
+    "REGIONS = ['seoul', 'busan', 'incheon', 'daegu', 'daejeon', 'gwangju']\n",
+    "\n",
+    "pkl_files = glob(os.path.join(optimization_history_dir, \"*_trials.pkl\"))\n",
+    "\n",
+    "def parse_filename(filename):\n",
+    "    basename = os.path.basename(filename)\n",
+    "    name_without_ext = basename.replace('_trials.pkl', '')\n",
+    "    model = None\n",
+    "    for m in MODELS:\n",
+    "        if name_without_ext.startswith(m + '_'):\n",
+    "            model = m\n",
+    "            break\n",
+    "    region = None\n",
+    "    for r in REGIONS:\n",
+    "        if name_without_ext.endswith('_' + r):\n",
+    "            region = r\n",
+    "            data_sample = name_without_ext[len(model) + 1:-(len(region) + 1)]\n",
+    "            break\n",
+    "    return model, data_sample, region\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "832eb50a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_optimization_info(obj):\n",
+    "    if hasattr(obj, 'best_value') and hasattr(obj, 'best_params') and hasattr(obj, 'trials'):\n",
+    "        return extract_optuna_info(obj)\n",
+    "    elif hasattr(obj, 'best_trial') and hasattr(obj, 'trials'):\n",
+    "        return extract_hyperopt_info(obj)\n",
+    "    return None\n",
+    "\n",
+    "def extract_optuna_info(study):\n",
+    "    best_csi = study.best_value\n",
+    "    best_params = study.best_params\n",
+    "    n_trials = len(study.trials)\n",
+    "    successful_trials = [t for t in study.trials if t.value is not None]\n",
+    "    n_successful = len(successful_trials)\n",
+    "    csi_scores = [t.value for t in successful_trials]\n",
+    "    \n",
+    "    if len(csi_scores) > 0:\n",
+    "        stats = {\n",
+    "            'first_csi': csi_scores[0],\n",
+    "            'last_csi': csi_scores[-1],\n",
+    "            'max_csi': max(csi_scores),\n",
+    "            'min_csi': min(csi_scores),\n",
+    "            'mean_csi': np.mean(csi_scores),\n",
+    "            'std_csi': np.std(csi_scores) if len(csi_scores) > 1 else 0.0\n",
+    "        }\n",
+    "    else:\n",
+    "        stats = {\n",
+    "            'first_csi': None, 'last_csi': None, 'max_csi': None,\n",
+    "            'min_csi': None, 'mean_csi': None, 'std_csi': None\n",
+    "        }\n",
+    "    \n",
+    "    return {\n",
+    "        'best_csi': best_csi,\n",
+    "        'best_loss': None,\n",
+    "        'n_trials': n_trials,\n",
+    "        'n_successful': n_successful,\n",
+    "        'best_params': best_params,\n",
+    "        'optimization_library': 'optuna',\n",
+    "        **stats\n",
+    "    }\n",
+    "\n",
+    "def extract_hyperopt_info(trials):\n",
+    "    best_trial = trials.best_trial\n",
+    "    best_loss = best_trial['result']['loss']\n",
+    "    best_csi = -best_loss\n",
+    "    n_trials = len(trials.trials)\n",
+    "    successful_trials = [t for t in trials.trials if t['result']['status'] == 'ok']\n",
+    "    n_successful = len(successful_trials)\n",
+    "    losses = [t['result']['loss'] for t in successful_trials]\n",
+    "    csi_scores = [-loss for loss in losses]\n",
+    "    \n",
+    "    if len(csi_scores) > 0:\n",
+    "        stats = {\n",
+    "            'first_csi': csi_scores[0],\n",
+    "            'last_csi': csi_scores[-1],\n",
+    "            'max_csi': max(csi_scores),\n",
+    "            'min_csi': min(csi_scores),\n",
+    "            'mean_csi': np.mean(csi_scores),\n",
+    "            'std_csi': np.std(csi_scores) if len(csi_scores) > 1 else 0.0\n",
+    "        }\n",
+    "    else:\n",
+    "        stats = {\n",
+    "            'first_csi': None, 'last_csi': None, 'max_csi': None,\n",
+    "            'min_csi': None, 'mean_csi': None, 'std_csi': None\n",
+    "        }\n",
+    "    \n",
+    "    best_params = {}\n",
+    "    if 'misc' in best_trial and 'vals' in best_trial['misc']:\n",
+    "        for key, value_list in best_trial['misc']['vals'].items():\n",
+    "            if len(value_list) > 0:\n",
+    "                best_params[key] = value_list[0]\n",
+    "    \n",
+    "    return {\n",
+    "        'best_csi': best_csi,\n",
+    "        'best_loss': best_loss,\n",
+    "        'n_trials': n_trials,\n",
+    "        'n_successful': n_successful,\n",
+    "        'best_params': best_params,\n",
+    "        'optimization_library': 'hyperopt',\n",
+    "        **stats\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2b22a08b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = []\n",
+    "\n",
+    "for pkl_file in pkl_files:\n",
+    "    model, data_sample, region = parse_filename(pkl_file)\n",
+    "    if model is None or data_sample is None or region is None:\n",
+    "        continue\n",
+    "    \n",
+    "    obj = joblib.load(pkl_file)\n",
+    "    info = extract_optimization_info(obj)\n",
+    "    if info is None:\n",
+    "        continue\n",
+    "    \n",
+    "    result = {\n",
+    "        'model': model,\n",
+    "        'data_sample': data_sample,\n",
+    "        'region': region,\n",
+    "        'filename': os.path.basename(pkl_file),\n",
+    "        **info\n",
+    "    }\n",
+    "    results.append(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "095f2637",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_optimization = pd.DataFrame(results)\n",
+    "column_order = [\n",
+    "    'model', 'data_sample', 'region', 'optimization_library',\n",
+    "    'best_csi', 'n_trials','best_params'\n",
+    "]\n",
+    "available_columns = [col for col in column_order if col in df_optimization.columns]\n",
+    "df_optimization = df_optimization[available_columns + [col for col in df_optimization.columns if col not in available_columns]]\n",
+    "df_optimization = df_optimization.loc[:, column_order]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b6245ef4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>data_sample</th>\n",
+       "      <th>region</th>\n",
+       "      <th>optimization_library</th>\n",
+       "      <th>best_csi</th>\n",
+       "      <th>n_trials</th>\n",
+       "      <th>best_params</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ft_transformer</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>busan</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.452471</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_token': 96, 'n_blocks': 2, 'n_heads': 4, '...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ft_transformer</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>daegu</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.384659</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_token': 224, 'n_blocks': 2, 'n_heads': 8, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smotenc_ctgan20000</td>\n",
+       "      <td>daegu</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.427700</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.7114033147242765, 'gamm...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            model         data_sample region optimization_library  best_csi  \\\n",
+       "0  ft_transformer               smote  busan               optuna  0.452471   \n",
+       "1  ft_transformer                pure  daegu               optuna  0.384659   \n",
+       "2             xgb  smotenc_ctgan20000  daegu             hyperopt  0.427700   \n",
+       "\n",
+       "   n_trials                                        best_params  \n",
+       "0       100  {'d_token': 96, 'n_blocks': 2, 'n_heads': 4, '...  \n",
+       "1       100  {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, ...  \n",
+       "2       100  {'colsample_bytree': 0.7114033147242765, 'gamm...  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_optimization.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a0c4b0dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_optimization.to_csv(\"optimization_result.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "580e5ae9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1200 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 데이터 복사 및 모델명 매핑 (표시용)\n",
+    "df_plot = df_optimization.copy()\n",
+    "# 모델명 매핑: 'lgb' -> 'lightgbm', 'xgb' -> 'xgboost'\n",
+    "model_mapping = {\n",
+    "    'lgb': 'lightgbm',\n",
+    "    'xgb': 'xgboost',\n",
+    "    'ft_transformer': 'ft_transformer',\n",
+    "    'resnet_like': 'resnet_like',\n",
+    "    'deepgbm': 'deepgbm'\n",
+    "}\n",
+    "df_plot['model_display'] = df_plot['model'].map(model_mapping)\n",
+    "\n",
+    "# region을 category로 변환하여 정렬\n",
+    "df_plot['region'] = df_plot['region'].astype('category')\n",
+    "df_plot['region'] = df_plot['region'].cat.reorder_categories(sorted(df_plot['region'].cat.categories))\n",
+    "\n",
+    "# data_sample 색상 고정: pure(검정), smote(파랑), smotenc_ctgan20000(빨강)\n",
+    "hue_order = ['pure', 'smote', 'smotenc_ctgan20000', 'ctgan10000']\n",
+    "palette_dict = {\n",
+    "    'pure': 'black',\n",
+    "    'smote': 'blue',\n",
+    "    'smotenc_ctgan20000': 'red',\n",
+    "    'ctgan10000': 'green'\n",
+    "}\n",
+    "\n",
+    "# 모델 목록 (표시 순서)\n",
+    "models_display = ['xgboost', 'lightgbm', 'resnet_like', 'ft_transformer', 'deepgbm']\n",
+    "\n",
+    "# 2행 3열 subplot 생성 (5개 모델)\n",
+    "fig, axes = plt.subplots(2, 3, figsize=(20, 12))\n",
+    "fig.suptitle('Best CSI Score by Region (Grouped by Data Sample)', \n",
+    "             fontsize=16, fontweight='bold', y=0.995)\n",
+    "\n",
+    "# axes를 1차원 배열로 변환\n",
+    "axes_flat = axes.flatten()\n",
+    "\n",
+    "# 각 모델별로 plot 생성\n",
+    "for model_idx, model_display in enumerate(models_display):\n",
+    "    ax = axes_flat[model_idx]\n",
+    "    \n",
+    "    # 해당 모델의 데이터만 필터링\n",
+    "    model_df = df_plot[df_plot['model_display'] == model_display]\n",
+    "    \n",
+    "    # 데이터가 있는 경우에만 plot\n",
+    "    if len(model_df) > 0:\n",
+    "        # Seaborn lineplot 사용 (색상 고정)\n",
+    "        sns.lineplot(\n",
+    "            data=model_df,\n",
+    "            x='region',\n",
+    "            y='best_csi',\n",
+    "            hue='data_sample',\n",
+    "            hue_order=hue_order,\n",
+    "            palette=palette_dict,\n",
+    "            marker='o',\n",
+    "            markersize=6,\n",
+    "            linewidth=2.5,\n",
+    "            ax=ax\n",
+    "        )\n",
+    "    \n",
+    "    ax.set_title(f'{model_display}', fontsize=14, fontweight='bold', pad=15)\n",
+    "    ax.set_xlabel('Region', fontsize=12)\n",
+    "    ax.set_ylabel('Best CSI Score', fontsize=12)\n",
+    "    ax.tick_params(axis='x', rotation=45)\n",
+    "    ax.grid(True, alpha=0.3, linestyle='--')\n",
+    "    ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=9, framealpha=0.9)\n",
+    "\n",
+    "# 마지막 subplot (6번째)는 사용하지 않으므로 숨김\n",
+    "axes_flat[5].axis('off')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0469a132",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 각 지역(region)과 모델(model) 별로 best_csi가 가장 높은 data_sample 행만 필터링\n",
+    "df_best_samples = df_optimization.loc[\n",
+    "    df_optimization.groupby(['region', 'model'])['best_csi'].idxmax()\n",
+    "].reset_index(drop=True)\n",
+    "df_best_samples.shape\n",
+    "df_best_samples.sort_values(by=['region','best_csi'], ascending=False, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2a58dc99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_best_samples.to_csv(\"best_samples_best_datasample_per_model_per_region.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3545f217",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>data_sample</th>\n",
+       "      <th>region</th>\n",
+       "      <th>optimization_library</th>\n",
+       "      <th>best_csi</th>\n",
+       "      <th>n_trials</th>\n",
+       "      <th>best_params</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>ft_transformer</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>seoul</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.593658</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_token': 224, 'n_blocks': 4, 'n_heads': 8, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>seoul</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.591400</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.9204650350059359, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>lgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>seoul</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.589500</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.7712993813350446, 'lear...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>resnet_like</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>seoul</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.588974</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 160, 'd_hidden': 128, 'n_blocks': 4...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>deepgbm</td>\n",
+       "      <td>smotenc_ctgan20000</td>\n",
+       "      <td>seoul</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.565797</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 128, 'd_hidden': 64, 'n_blocks': 5,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>incheon</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.8863531635625073, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>lgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>incheon</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.598600</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.7149911519913482, 'lear...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>resnet_like</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>incheon</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.587620</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 160, 'd_hidden': 192, 'n_blocks': 3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>ft_transformer</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>incheon</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.567405</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_token': 96, 'n_blocks': 3, 'n_heads': 8, '...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>deepgbm</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>incheon</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.564449</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 64, 'd_hidden': 192, 'n_blocks': 5,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.530000</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.7658195937298418, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>lgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.529700</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.9919060649789312, 'lear...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>deepgbm</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.520403</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 128, 'd_hidden': 192, 'n_blocks': 6...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>resnet_like</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.510303</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 64, 'd_hidden': 320, 'n_blocks': 3,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>ft_transformer</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.505282</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_token': 160, 'n_blocks': 3, 'n_heads': 4, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.537100</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.733236256331133, 'gamma...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>lgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.531700</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.7585295616897205, 'lear...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>resnet_like</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.510177</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 128, 'd_hidden': 256, 'n_blocks': 5...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>deepgbm</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.510125</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 64, 'd_hidden': 256, 'n_blocks': 2,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>ft_transformer</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.502604</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_token': 64, 'n_blocks': 5, 'n_heads': 8, '...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>daegu</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.467200</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.8132816721507904, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>lgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>daegu</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.467100</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.999946333457191, 'learn...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>resnet_like</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>daegu</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.460863</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 96, 'd_hidden': 256, 'n_blocks': 3,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>ft_transformer</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>daegu</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.449183</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_token': 128, 'n_blocks': 4, 'n_heads': 8, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>deepgbm</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>daegu</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.439025</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 64, 'd_hidden': 192, 'n_blocks': 2,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ft_transformer</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>busan</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.496046</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_token': 224, 'n_blocks': 2, 'n_heads': 8, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>busan</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.494900</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.8651175745135303, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>resnet_like</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>busan</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.493633</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 128, 'd_hidden': 512, 'n_blocks': 2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>lgb</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>busan</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.484900</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.9406061312055983, 'lear...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>deepgbm</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>busan</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.475436</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 128, 'd_hidden': 128, 'n_blocks': 2...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             model         data_sample   region optimization_library  \\\n",
+       "26  ft_transformer          ctgan10000    seoul               optuna   \n",
+       "29             xgb               smote    seoul             hyperopt   \n",
+       "27             lgb               smote    seoul             hyperopt   \n",
+       "28     resnet_like          ctgan10000    seoul               optuna   \n",
+       "25         deepgbm  smotenc_ctgan20000    seoul               optuna   \n",
+       "24             xgb               smote  incheon             hyperopt   \n",
+       "22             lgb               smote  incheon             hyperopt   \n",
+       "23     resnet_like          ctgan10000  incheon               optuna   \n",
+       "21  ft_transformer               smote  incheon               optuna   \n",
+       "20         deepgbm          ctgan10000  incheon               optuna   \n",
+       "19             xgb               smote  gwangju             hyperopt   \n",
+       "17             lgb               smote  gwangju             hyperopt   \n",
+       "15         deepgbm          ctgan10000  gwangju               optuna   \n",
+       "18     resnet_like          ctgan10000  gwangju               optuna   \n",
+       "16  ft_transformer          ctgan10000  gwangju               optuna   \n",
+       "14             xgb               smote  daejeon             hyperopt   \n",
+       "12             lgb               smote  daejeon             hyperopt   \n",
+       "13     resnet_like          ctgan10000  daejeon               optuna   \n",
+       "10         deepgbm          ctgan10000  daejeon               optuna   \n",
+       "11  ft_transformer          ctgan10000  daejeon               optuna   \n",
+       "9              xgb               smote    daegu             hyperopt   \n",
+       "7              lgb               smote    daegu             hyperopt   \n",
+       "8      resnet_like          ctgan10000    daegu               optuna   \n",
+       "6   ft_transformer          ctgan10000    daegu               optuna   \n",
+       "5          deepgbm          ctgan10000    daegu               optuna   \n",
+       "1   ft_transformer          ctgan10000    busan               optuna   \n",
+       "4              xgb                pure    busan             hyperopt   \n",
+       "3      resnet_like          ctgan10000    busan               optuna   \n",
+       "2              lgb                pure    busan             hyperopt   \n",
+       "0          deepgbm          ctgan10000    busan               optuna   \n",
+       "\n",
+       "    best_csi  n_trials                                        best_params  \n",
+       "26  0.593658       100  {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, ...  \n",
+       "29  0.591400       100  {'colsample_bytree': 0.9204650350059359, 'gamm...  \n",
+       "27  0.589500       100  {'colsample_bytree': 0.7712993813350446, 'lear...  \n",
+       "28  0.588974       100  {'d_main': 160, 'd_hidden': 128, 'n_blocks': 4...  \n",
+       "25  0.565797       100  {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5,...  \n",
+       "24  0.600000       100  {'colsample_bytree': 0.8863531635625073, 'gamm...  \n",
+       "22  0.598600       100  {'colsample_bytree': 0.7149911519913482, 'lear...  \n",
+       "23  0.587620       100  {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3...  \n",
+       "21  0.567405       100  {'d_token': 96, 'n_blocks': 3, 'n_heads': 8, '...  \n",
+       "20  0.564449       100  {'d_main': 64, 'd_hidden': 192, 'n_blocks': 5,...  \n",
+       "19  0.530000       100  {'colsample_bytree': 0.7658195937298418, 'gamm...  \n",
+       "17  0.529700       100  {'colsample_bytree': 0.9919060649789312, 'lear...  \n",
+       "15  0.520403       100  {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6...  \n",
+       "18  0.510303       100  {'d_main': 64, 'd_hidden': 320, 'n_blocks': 3,...  \n",
+       "16  0.505282       100  {'d_token': 160, 'n_blocks': 3, 'n_heads': 4, ...  \n",
+       "14  0.537100       100  {'colsample_bytree': 0.733236256331133, 'gamma...  \n",
+       "12  0.531700       100  {'colsample_bytree': 0.7585295616897205, 'lear...  \n",
+       "13  0.510177       100  {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5...  \n",
+       "10  0.510125       100  {'d_main': 64, 'd_hidden': 256, 'n_blocks': 2,...  \n",
+       "11  0.502604       100  {'d_token': 64, 'n_blocks': 5, 'n_heads': 8, '...  \n",
+       "9   0.467200       100  {'colsample_bytree': 0.8132816721507904, 'gamm...  \n",
+       "7   0.467100       100  {'colsample_bytree': 0.999946333457191, 'learn...  \n",
+       "8   0.460863       100  {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3,...  \n",
+       "6   0.449183       100  {'d_token': 128, 'n_blocks': 4, 'n_heads': 8, ...  \n",
+       "5   0.439025       100  {'d_main': 64, 'd_hidden': 192, 'n_blocks': 2,...  \n",
+       "1   0.496046       100  {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, ...  \n",
+       "4   0.494900       100  {'colsample_bytree': 0.8651175745135303, 'gamm...  \n",
+       "3   0.493633       100  {'d_main': 128, 'd_hidden': 512, 'n_blocks': 2...  \n",
+       "2   0.484900       100  {'colsample_bytree': 0.9406061312055983, 'lear...  \n",
+       "0   0.475436       100  {'d_main': 128, 'd_hidden': 128, 'n_blocks': 2...  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_best_samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "676b7afb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df= df_best_samples.loc[df_best_samples.groupby(['region','optimization_library'])['best_csi'].idxmax(),:]\n",
+    "df.sort_values(by=['region','best_csi'], ascending=False, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0002db33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>data_sample</th>\n",
+       "      <th>region</th>\n",
+       "      <th>optimization_library</th>\n",
+       "      <th>best_csi</th>\n",
+       "      <th>n_trials</th>\n",
+       "      <th>best_params</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>ft_transformer</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>seoul</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.593658</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_token': 224, 'n_blocks': 4, 'n_heads': 8, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>seoul</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.591400</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.9204650350059359, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>incheon</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.8863531635625073, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>resnet_like</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>incheon</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.587620</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 160, 'd_hidden': 192, 'n_blocks': 3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.530000</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.7658195937298418, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>deepgbm</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.520403</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 128, 'd_hidden': 192, 'n_blocks': 6...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.537100</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.733236256331133, 'gamma...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>resnet_like</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.510177</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 128, 'd_hidden': 256, 'n_blocks': 5...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>daegu</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.467200</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.8132816721507904, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>resnet_like</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>daegu</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.460863</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 96, 'd_hidden': 256, 'n_blocks': 3,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ft_transformer</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>busan</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.496046</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_token': 224, 'n_blocks': 2, 'n_heads': 8, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>busan</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.494900</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.8651175745135303, 'gamm...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             model data_sample   region optimization_library  best_csi  \\\n",
+       "26  ft_transformer  ctgan10000    seoul               optuna  0.593658   \n",
+       "29             xgb       smote    seoul             hyperopt  0.591400   \n",
+       "24             xgb       smote  incheon             hyperopt  0.600000   \n",
+       "23     resnet_like  ctgan10000  incheon               optuna  0.587620   \n",
+       "19             xgb       smote  gwangju             hyperopt  0.530000   \n",
+       "15         deepgbm  ctgan10000  gwangju               optuna  0.520403   \n",
+       "14             xgb       smote  daejeon             hyperopt  0.537100   \n",
+       "13     resnet_like  ctgan10000  daejeon               optuna  0.510177   \n",
+       "9              xgb       smote    daegu             hyperopt  0.467200   \n",
+       "8      resnet_like  ctgan10000    daegu               optuna  0.460863   \n",
+       "1   ft_transformer  ctgan10000    busan               optuna  0.496046   \n",
+       "4              xgb        pure    busan             hyperopt  0.494900   \n",
+       "\n",
+       "    n_trials                                        best_params  \n",
+       "26       100  {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, ...  \n",
+       "29       100  {'colsample_bytree': 0.9204650350059359, 'gamm...  \n",
+       "24       100  {'colsample_bytree': 0.8863531635625073, 'gamm...  \n",
+       "23       100  {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3...  \n",
+       "19       100  {'colsample_bytree': 0.7658195937298418, 'gamm...  \n",
+       "15       100  {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6...  \n",
+       "14       100  {'colsample_bytree': 0.733236256331133, 'gamma...  \n",
+       "13       100  {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5...  \n",
+       "9        100  {'colsample_bytree': 0.8132816721507904, 'gamm...  \n",
+       "8        100  {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3,...  \n",
+       "1        100  {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, ...  \n",
+       "4        100  {'colsample_bytree': 0.8651175745135303, 'gamm...  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Analysis_code/6.optima_models_analysis/optimization_result.csv b/Analysis_code/6.optima_models_analysis/optimization_result.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1874b5fe32346cf8be3686b4191cab066625f3c0
--- /dev/null
+++ b/Analysis_code/6.optima_models_analysis/optimization_result.csv
@@ -0,0 +1,121 @@
+model,data_sample,region,optimization_library,best_csi,n_trials,best_params
+ft_transformer,smote,busan,optuna,0.4524714686603366,100,"{'d_token': 96, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.39058519125437824, 'ffn_dropout': 0.35781462874340314, 'lr': 0.0006705029713893173, 'weight_decay': 0.0001426209011329072, 'batch_size': 32}"
+ft_transformer,pure,daegu,optuna,0.3846594392002736,100,"{'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.2747124536980523, 'ffn_dropout': 0.1909754518903343, 'lr': 0.00016739997447268043, 'weight_decay': 0.027811856175591093, 'batch_size': 32}"
+xgb,smotenc_ctgan20000,daegu,hyperopt,0.4277,100,"{'colsample_bytree': 0.7114033147242765, 'gamma': 0.9625212091684073, 'learning_rate': 0.029642239831837253, 'max_depth': 12.0, 'min_child_weight': 10.0, 'reg_alpha': 0.1587708255877442, 'reg_lambda': 0.4648399906011745, 'subsample': 0.90006374997474}"
+resnet_like,pure,seoul,optuna,0.5566756722322981,100,"{'d_main': 96, 'd_hidden': 512, 'n_blocks': 2, 'dropout_first': 0.3834324386470386, 'dropout_second': 0.029813309707734794, 'lr': 0.0005002634113960448, 'weight_decay': 0.05408195750724482, 'batch_size': 32}"
+resnet_like,smote,gwangju,optuna,0.48671080174496345,100,"{'d_main': 96, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.2808602138036661, 'dropout_second': 0.14619910058769206, 'lr': 0.0055368090707776305, 'weight_decay': 0.001341561695377514, 'batch_size': 256}"
+resnet_like,smotenc_ctgan20000,busan,optuna,0.45568873098715307,100,"{'d_main': 256, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.1045169105881141, 'dropout_second': 0.05372596450552944, 'lr': 0.00943776593390798, 'weight_decay': 0.0010575459741554466, 'batch_size': 128}"
+ft_transformer,smote,daegu,optuna,0.40272068323222654,100,"{'d_token': 96, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.2530528162951749, 'ffn_dropout': 0.1899328578250674, 'lr': 0.0013866055842525612, 'weight_decay': 0.00020752948982868583, 'batch_size': 64}"
+xgb,ctgan10000,daejeon,hyperopt,0.5029,100,"{'colsample_bytree': 0.7816067762051363, 'gamma': 0.5799629095615334, 'learning_rate': 0.06287412401679013, 'max_depth': 3.0, 'min_child_weight': 5.0, 'reg_alpha': 0.36731169488772253, 'reg_lambda': 0.03192020366519255, 'subsample': 0.6004605601176048}"
+resnet_like,smotenc_ctgan20000,gwangju,optuna,0.4937343167803819,100,"{'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.3792559101674334, 'dropout_second': 0.19251437428644594, 'lr': 0.00405325576056642, 'weight_decay': 0.0005628495861261046, 'batch_size': 128}"
+lgb,smote,seoul,hyperopt,0.5895,100,"{'colsample_bytree': 0.7712993813350446, 'learning_rate': 0.014764448164722338, 'max_depth': 13.0, 'min_child_weight': 2.0, 'num_leaves': 144.0, 'reg_alpha': 0.9375760221934153, 'reg_lambda': 0.6881109653195318, 'subsample': 0.6932106255794361}"
+ft_transformer,pure,busan,optuna,0.44685896765010097,100,"{'d_token': 192, 'n_blocks': 3, 'n_heads': 8, 'attention_dropout': 0.11973480060658612, 'ffn_dropout': 0.15614454735278546, 'lr': 0.00016413327466283583, 'weight_decay': 0.002548468041818763, 'batch_size': 64}"
+ft_transformer,ctgan10000,gwangju,optuna,0.5052817328725289,100,"{'d_token': 160, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.21128609103276186, 'ffn_dropout': 0.18610731195171396, 'lr': 0.0019139767886005993, 'weight_decay': 0.03127877612669642, 'batch_size': 128}"
+xgb,ctgan10000,gwangju,hyperopt,0.4999,100,"{'colsample_bytree': 0.9825715751506052, 'gamma': 0.029399796024525915, 'learning_rate': 0.08575762330828458, 'max_depth': 11.0, 'min_child_weight': 8.0, 'reg_alpha': 0.8534545581720909, 'reg_lambda': 0.43478744549007325, 'subsample': 0.7629247503643918}"
+resnet_like,smote,daejeon,optuna,0.48175460460533026,100,"{'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3137027413848088, 'dropout_second': 0.18760941198412298, 'lr': 0.00565595693998524, 'weight_decay': 0.04866049178978196, 'batch_size': 128}"
+ft_transformer,smotenc_ctgan20000,busan,optuna,0.4767443843216419,100,"{'d_token': 128, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.1711218539171086, 'ffn_dropout': 0.2636519595986711, 'lr': 0.004358575253869887, 'weight_decay': 0.00046754184762985804, 'batch_size': 256}"
+xgb,ctgan10000,daegu,hyperopt,0.4392,100,"{'colsample_bytree': 0.8557253370653987, 'gamma': 1.1563957561197729, 'learning_rate': 0.19232018363683898, 'max_depth': 10.0, 'min_child_weight': 8.0, 'reg_alpha': 0.21149066093980878, 'reg_lambda': 0.2690618200166374, 'subsample': 0.714526167464626}"
+deepgbm,smote,gwangju,optuna,0.45804386918458945,100,"{'d_main': 160, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.1698480826585593, 'lr': 0.0003762961039889282, 'weight_decay': 0.0014343061148430866, 'batch_size': 64}"
+ft_transformer,ctgan10000,seoul,optuna,0.5936576575682742,100,"{'d_token': 224, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.15689079322421481, 'ffn_dropout': 0.27970845546044715, 'lr': 0.00011858823893766563, 'weight_decay': 0.02942637540327823, 'batch_size': 64}"
+lgb,smote,daejeon,hyperopt,0.5317,100,"{'colsample_bytree': 0.7585295616897205, 'learning_rate': 0.012807299958074884, 'max_depth': 8.0, 'min_child_weight': 2.0, 'num_leaves': 149.0, 'reg_alpha': 0.8175154308532824, 'reg_lambda': 0.7481509687757377, 'subsample': 0.8155067304500027}"
+resnet_like,pure,incheon,optuna,0.5717111423727251,100,"{'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.3502671083503836, 'dropout_second': 0.15938013319711236, 'lr': 0.0006801289543741389, 'weight_decay': 0.005292744372677132, 'batch_size': 128}"
+lgb,ctgan10000,daejeon,hyperopt,0.4908,100,"{'colsample_bytree': 0.7077604272501928, 'learning_rate': 0.10351387699107398, 'max_depth': 6.0, 'min_child_weight': 4.0, 'num_leaves': 51.0, 'reg_alpha': 0.06973941883143871, 'reg_lambda': 0.8477821589656351, 'subsample': 0.8664583588640111}"
+lgb,smotenc_ctgan20000,daegu,hyperopt,0.4455,100,"{'colsample_bytree': 0.9935566129264934, 'learning_rate': 0.011803766157843702, 'max_depth': 10.0, 'min_child_weight': 18.0, 'num_leaves': 78.0, 'reg_alpha': 0.5555179443217245, 'reg_lambda': 0.23478947295729824, 'subsample': 0.7059612447576378}"
+deepgbm,smotenc_ctgan20000,busan,optuna,0.40671898361820197,100,"{'d_main': 64, 'd_hidden': 256, 'n_blocks': 3, 'dropout': 0.2843714965028271, 'lr': 0.009315324608427497, 'weight_decay': 0.022174119634941862, 'batch_size': 128}"
+ft_transformer,smotenc_ctgan20000,daejeon,optuna,0.4794390839030278,100,"{'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.13096961306141697, 'ffn_dropout': 0.10368225384926379, 'lr': 0.0008212785631177437, 'weight_decay': 0.0007599672775598784, 'batch_size': 32}"
+lgb,ctgan10000,busan,hyperopt,0.4836,100,"{'colsample_bytree': 0.7243835590014314, 'learning_rate': 0.052472053724070156, 'max_depth': 15.0, 'min_child_weight': 9.0, 'num_leaves': 120.0, 'reg_alpha': 0.566895668532905, 'reg_lambda': 0.9659771198744264, 'subsample': 0.8425484904296862}"
+ft_transformer,pure,daejeon,optuna,0.4655886251588111,100,"{'d_token': 64, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.24759508454949322, 'ffn_dropout': 0.2013907953941948, 'lr': 0.0003711331440914647, 'weight_decay': 0.06954769328501528, 'batch_size': 32}"
+xgb,pure,gwangju,hyperopt,0.5016,100,"{'colsample_bytree': 0.9713207386536029, 'gamma': 2.2482753887012703, 'learning_rate': 0.16732973947259167, 'max_depth': 8.0, 'min_child_weight': 3.0, 'reg_alpha': 0.2664406256084806, 'reg_lambda': 0.7263114796775476, 'subsample': 0.6483131273031051}"
+ft_transformer,pure,seoul,optuna,0.562070126103511,100,"{'d_token': 256, 'n_blocks': 2, 'n_heads': 4, 'attention_dropout': 0.2855445640312001, 'ffn_dropout': 0.20563167448836292, 'lr': 7.430025637172839e-05, 'weight_decay': 0.012136192435211931, 'batch_size': 32}"
+resnet_like,smote,busan,optuna,0.4473603019416436,100,"{'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.37677375956516684, 'dropout_second': 0.18705700465884292, 'lr': 0.005004477317296484, 'weight_decay': 0.00012190453086381686, 'batch_size': 64}"
+deepgbm,pure,incheon,optuna,0.5622375492647842,100,"{'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.3553750103803738, 'lr': 0.0017038392317957017, 'weight_decay': 0.04010324241876258, 'batch_size': 32}"
+ft_transformer,smote,gwangju,optuna,0.48424711598330084,100,"{'d_token': 96, 'n_blocks': 4, 'n_heads': 8, 'attention_dropout': 0.19167241349972552, 'ffn_dropout': 0.10372384139481815, 'lr': 0.0003028578585093515, 'weight_decay': 0.021302792376896054, 'batch_size': 32}"
+lgb,pure,busan,hyperopt,0.4849,100,"{'colsample_bytree': 0.9406061312055983, 'learning_rate': 0.17468151642796886, 'max_depth': 6.0, 'min_child_weight': 17.0, 'num_leaves': 138.0, 'reg_alpha': 0.4593420461637059, 'reg_lambda': 0.5948987302333338, 'subsample': 0.6557839186758097}"
+xgb,ctgan10000,seoul,hyperopt,0.5824,100,"{'colsample_bytree': 0.8837225390968168, 'gamma': 0.1115044781500254, 'learning_rate': 0.10805110293567466, 'max_depth': 12.0, 'min_child_weight': 4.0, 'reg_alpha': 0.02183712172236562, 'reg_lambda': 0.6932207560084631, 'subsample': 0.6767341133785678}"
+resnet_like,ctgan10000,gwangju,optuna,0.510302842874457,100,"{'d_main': 64, 'd_hidden': 320, 'n_blocks': 3, 'dropout_first': 0.29522157561687634, 'dropout_second': 0.15684305218104422, 'lr': 0.008180657303015811, 'weight_decay': 0.01303718192830624, 'batch_size': 128}"
+deepgbm,pure,busan,optuna,0.4429286988285861,100,"{'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.2275228702990485, 'lr': 0.002636923316902024, 'weight_decay': 0.05132270066782015, 'batch_size': 32}"
+ft_transformer,ctgan10000,busan,optuna,0.4960458104166546,100,"{'d_token': 224, 'n_blocks': 2, 'n_heads': 8, 'attention_dropout': 0.3873943566967484, 'ffn_dropout': 0.14296280926606936, 'lr': 0.0007665967810279031, 'weight_decay': 0.00878158688959246, 'batch_size': 32}"
+resnet_like,ctgan10000,daejeon,optuna,0.5101768615009369,100,"{'d_main': 128, 'd_hidden': 256, 'n_blocks': 5, 'dropout_first': 0.1381181811099212, 'dropout_second': 0.11702484025760711, 'lr': 0.0064016726045039805, 'weight_decay': 0.004366638608686326, 'batch_size': 256}"
+resnet_like,smotenc_ctgan20000,incheon,optuna,0.5707798446437679,100,"{'d_main': 224, 'd_hidden': 256, 'n_blocks': 4, 'dropout_first': 0.3626907864360457, 'dropout_second': 0.08738106548329602, 'lr': 0.005205322934309404, 'weight_decay': 0.0002577881849067971, 'batch_size': 256}"
+xgb,pure,daegu,hyperopt,0.4409,100,"{'colsample_bytree': 0.8800494992202731, 'gamma': 0.28651615767316957, 'learning_rate': 0.025526450870185433, 'max_depth': 3.0, 'min_child_weight': 5.0, 'reg_alpha': 0.6787273055071508, 'reg_lambda': 0.6641153401816423, 'subsample': 0.7222783789369407}"
+ft_transformer,smotenc_ctgan20000,daegu,optuna,0.4488825045464337,100,"{'d_token': 64, 'n_blocks': 3, 'n_heads': 4, 'attention_dropout': 0.270864990314075, 'ffn_dropout': 0.3735925199999471, 'lr': 0.0016588396308948813, 'weight_decay': 0.00014669112268617685, 'batch_size': 128}"
+resnet_like,ctgan10000,daegu,optuna,0.46086300130604796,100,"{'d_main': 96, 'd_hidden': 256, 'n_blocks': 3, 'dropout_first': 0.27926914874893893, 'dropout_second': 0.13114004557533837, 'lr': 0.004133395383387492, 'weight_decay': 0.05462768451276688, 'batch_size': 32}"
+deepgbm,smote,daegu,optuna,0.3991148650568916,100,"{'d_main': 64, 'd_hidden': 192, 'n_blocks': 5, 'dropout': 0.15327234825163508, 'lr': 0.0021257323880618864, 'weight_decay': 0.06824750948093047, 'batch_size': 32}"
+deepgbm,smote,seoul,optuna,0.5551122990255606,100,"{'d_main': 64, 'd_hidden': 64, 'n_blocks': 2, 'dropout': 0.2869842021080176, 'lr': 0.002077449117732186, 'weight_decay': 0.00019614736051849963, 'batch_size': 32}"
+lgb,smote,incheon,hyperopt,0.5986,100,"{'colsample_bytree': 0.7149911519913482, 'learning_rate': 0.14061649313221522, 'max_depth': 4.0, 'min_child_weight': 4.0, 'num_leaves': 46.0, 'reg_alpha': 0.3323739596170201, 'reg_lambda': 0.3615804769440283, 'subsample': 0.7361106038020775}"
+deepgbm,ctgan10000,daejeon,optuna,0.5101248146449113,100,"{'d_main': 64, 'd_hidden': 256, 'n_blocks': 2, 'dropout': 0.2845653149911174, 'lr': 0.0030479748817488737, 'weight_decay': 0.08478209494184558, 'batch_size': 32}"
+resnet_like,smote,seoul,optuna,0.5589425055467122,100,"{'d_main': 128, 'd_hidden': 512, 'n_blocks': 3, 'dropout_first': 0.3304233130420639, 'dropout_second': 0.14346618645388706, 'lr': 0.006514721583103225, 'weight_decay': 0.0719094067694046, 'batch_size': 64}"
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+xgb,ctgan10000,busan,hyperopt,0.4881,100,"{'colsample_bytree': 0.6875780545091519, 'gamma': 2.658982028747467, 'learning_rate': 0.11595019019771602, 'max_depth': 12.0, 'min_child_weight': 18.0, 'reg_alpha': 0.9071038284958559, 'reg_lambda': 0.2880553949579837, 'subsample': 0.7100915343806586}"
+lgb,smotenc_ctgan20000,seoul,hyperopt,0.5622,100,"{'colsample_bytree': 0.6345219883667235, 'learning_rate': 0.010708867004743688, 'max_depth': 14.0, 'min_child_weight': 14.0, 'num_leaves': 111.0, 'reg_alpha': 0.12632598375404414, 'reg_lambda': 0.632120993668035, 'subsample': 0.6705295797223814}"
+lgb,pure,daejeon,hyperopt,0.4963,100,"{'colsample_bytree': 0.8561310986458904, 'learning_rate': 0.04190098150317689, 'max_depth': 13.0, 'min_child_weight': 10.0, 'num_leaves': 20.0, 'reg_alpha': 0.8699443310189154, 'reg_lambda': 0.6437824460768218, 'subsample': 0.7878286207681128}"
+resnet_like,pure,busan,optuna,0.4542970487265281,100,"{'d_main': 160, 'd_hidden': 128, 'n_blocks': 3, 'dropout_first': 0.3972607776862854, 'dropout_second': 0.13044016240028491, 'lr': 0.009478600883696408, 'weight_decay': 0.009976371611112884, 'batch_size': 64}"
+resnet_like,smote,daegu,optuna,0.39559298655454533,100,"{'d_main': 96, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.3420738859809193, 'dropout_second': 0.1494827111234321, 'lr': 0.008042182880401372, 'weight_decay': 0.00036799147801704795, 'batch_size': 32}"
+deepgbm,smotenc_ctgan20000,daejeon,optuna,0.4848928597361463,100,"{'d_main': 96, 'd_hidden': 64, 'n_blocks': 6, 'dropout': 0.2751230389403302, 'lr': 0.007765569142416956, 'weight_decay': 0.019237770834817113, 'batch_size': 128}"
+resnet_like,smotenc_ctgan20000,daejeon,optuna,0.48223852799636385,100,"{'d_main': 256, 'd_hidden': 128, 'n_blocks': 2, 'dropout_first': 0.1346841434587851, 'dropout_second': 0.09633438692418629, 'lr': 0.008312402405575084, 'weight_decay': 0.00017010818801516423, 'batch_size': 256}"
+deepgbm,pure,daegu,optuna,0.3570454117885781,100,"{'d_main': 160, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.39867335991930597, 'lr': 0.002428396450034936, 'weight_decay': 0.05750366129158036, 'batch_size': 32}"
+resnet_like,pure,daegu,optuna,0.4075222542316909,100,"{'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.10590946492415504, 'dropout_second': 0.10708545709499212, 'lr': 0.0012544175963123808, 'weight_decay': 0.08314171375285831, 'batch_size': 32}"
+deepgbm,ctgan10000,gwangju,optuna,0.5204031176113428,100,"{'d_main': 128, 'd_hidden': 192, 'n_blocks': 6, 'dropout': 0.3938212564993552, 'lr': 0.007164979269975063, 'weight_decay': 0.0923681134285374, 'batch_size': 64}"
+ft_transformer,ctgan10000,incheon,optuna,0.5643832267965512,100,"{'d_token': 128, 'n_blocks': 6, 'n_heads': 4, 'attention_dropout': 0.1369145590780379, 'ffn_dropout': 0.16996780951611673, 'lr': 0.0003662598943307626, 'weight_decay': 0.000729119254134465, 'batch_size': 256}"
diff --git a/Analysis_code/7.ensemble/analysis_of_shap.ipynb b/Analysis_code/7.ensemble/analysis_of_shap.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..484c119b625ea3f80a93d2f438b2a34ae41b7f50
--- /dev/null
+++ b/Analysis_code/7.ensemble/analysis_of_shap.ipynb
@@ -0,0 +1,824 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5fe1f4f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from joblib import dump, load\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import os\n",
+    "import re\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from xgboost import XGBClassifier\n",
+    "from lightgbm import LGBMClassifier\n",
+    "import shap\n",
+    "from shap_utils import *\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bcc0e411",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>data_sample</th>\n",
+       "      <th>region</th>\n",
+       "      <th>optimization_library</th>\n",
+       "      <th>best_csi</th>\n",
+       "      <th>n_trials</th>\n",
+       "      <th>best_params</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>ft_transformer</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>busan</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.496046</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_token': 224, 'n_blocks': 2, 'n_heads': 8, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>daegu</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.467200</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.8132816721507904, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.537100</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.733236256331133, 'gamma...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.530000</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.7658195937298418, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>incheon</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.8863531635625073, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ft_transformer</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>seoul</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.593658</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_token': 224, 'n_blocks': 4, 'n_heads': 8, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             model data_sample   region optimization_library  best_csi  \\\n",
+       "25  ft_transformer  ctgan10000    busan               optuna  0.496046   \n",
+       "20             xgb       smote    daegu             hyperopt  0.467200   \n",
+       "15             xgb       smote  daejeon             hyperopt  0.537100   \n",
+       "10             xgb       smote  gwangju             hyperopt  0.530000   \n",
+       "5              xgb       smote  incheon             hyperopt  0.600000   \n",
+       "0   ft_transformer  ctgan10000    seoul               optuna  0.593658   \n",
+       "\n",
+       "    n_trials                                        best_params  \n",
+       "25       100  {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, ...  \n",
+       "20       100  {'colsample_bytree': 0.8132816721507904, 'gamm...  \n",
+       "15       100  {'colsample_bytree': 0.733236256331133, 'gamma...  \n",
+       "10       100  {'colsample_bytree': 0.7658195937298418, 'gamm...  \n",
+       "5        100  {'colsample_bytree': 0.8863531635625073, 'gamm...  \n",
+       "0        100  {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, ...  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_samples = pd.read_csv(\"../6.optima_models_analysis/best_samples_best_datasample_per_model_per_region.csv\")\n",
+    "best_samples = best_samples.loc[best_samples.groupby(['region'])['best_csi'].idxmax(),:]\n",
+    "best_samples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ee4db72",
+   "metadata": {},
+   "source": [
+    "## **Busan**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "eba95c27",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[FT_TRANSFORMER] busan - ctgan10000 SHAP Analysis Start...\n",
+      "  Processing Fold 1...\n",
+      "  Processing Fold 2...\n",
+      "  Processing Fold 3...\n",
+      "Train Period - Top 5 Dominant Features: ['hm', 'temp_C', 'vap_pressure', 'PM25', 'PM10']\n",
+      "Train Period - Cumulative Importance Share (Top 5): 44.56%\n",
+      "----------------------------------------\n",
+      "Test Period - Top 5 Dominant Features: ['hm', 'temp_C', 'vap_pressure', 'PM10', 'low_cloudbase']\n",
+      "Test Period - Cumulative Importance Share (Top 5): 42.01%\n",
+      "----------------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  [FT_TRANSFORMER] SHAP Analysis Complete!\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 단일 모델 분석\n",
+    "result = analyze_dl_model_shap(\n",
+    "    model_name=\"ft_transformer\",\n",
+    "    region=\"busan\",\n",
+    "    data_sample=\"ctgan10000\",\n",
+    "    target_class=0,\n",
+    "    n_background_samples=200,\n",
+    "    n_test_samples=500,\n",
+    "    device='cuda',\n",
+    "    top_n=5\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f62c43f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "elbow_plot(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5ef482e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[BUSAN] Wasserstein Distance Analysis\n",
+      "Model Type: DL, Data Sample: ctgan10000\n",
+      "------------------------------------------------------------\n",
+      "Feature: hm                   | WD: 11.3078 | Train Mean: 76.2088 | Test Mean: 64.9089\n",
+      "Feature: temp_C               | WD: 1.1636 | Train Mean: 16.6938 | Test Mean: 15.7622\n",
+      "Feature: vap_pressure         | WD: 1.9898 | Train Mean: 15.9555 | Test Mean: 13.9694\n",
+      "Feature: PM25                 | WD: 7.0812 | Train Mean: 22.8545 | Test Mean: 16.3232\n",
+      "Feature: PM10                 | WD: 6.0916 | Train Mean: 38.0569 | Test Mean: 34.9512\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 방법 1: 직접 변수 리스트 지정\n",
+    "wd_df = calculate_wasserstein_distance(\n",
+    "    region='busan',\n",
+    "    data_sample='ctgan10000',\n",
+    "    top_features=result['top_n_features_train'],\n",
+    "    model_type='dl'  # 또는 'dl'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7c417a5",
+   "metadata": {},
+   "source": [
+    "## **Daegu**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e7d7b75b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Period - Top 5 Dominant Features: ['lm_cloudcover', 'hm', 'O3', 'low_cloudbase', 'hour_sin']\n",
+      "Train Period - Cumulative Importance Share (Top 5): 75.55%\n",
+      "----------------------------------------\n",
+      "Test Period - Top 5 Dominant Features: ['lm_cloudcover', 'hm', 'O3', 'low_cloudbase', 'hour_sin']\n",
+      "Test Period - Cumulative Importance Share (Top 5): 77.35%\n",
+      "----------------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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xMTEG4PBycXExypYtazzzzDNGamrqv3Y+2dnZxjPPPGPUrFnT8PX1Nby9vY2aNWsan3zyiUO9li1bGlWrVnVqX6pUKaNLly5O5YAxdOhQh7LNmzcbHTt2NHx8fAwvLy+jdevWxtq1a53aTpo0yShbtqxhsVgMwFixYoV924oVK4yOHTsa/v7+hoeHhxEdHW3cf//9xsaNG6/yCji78Pq7uLgYQUFBRsOGDY3nn3/eOHz4sEP96tWrGyVLlrzkPlu1amWEhoYaubm5BX6+578GDBjg1H7OnDlG586djZCQEMPFxcUICAgwmjVrZowZM8ZISkpyqn/s2DHjoYceMiIiIuzxd+3a1Vi/fv0/ujYiIiIiIiLnU47sSDmycmQRkZudyTCuYK4RERERERERERERERERERG5ZZmLOgAREREREREREREREREREbkxqINZREREREREREREREREREQKRR3MIiIiIiIiIiIiIiIiIiJSKOpgFhERERERERERERERERGRQlEHs4iIiIiIiIiIiIiIiIiIFIo6mEVEREREREREREREREREpFDUwSwiIgLcf//9lC5duqjDuCr/Rewmk4nXXnutUHV///133NzcOHz48L8aw81m5MiRNGzY8Joe87XXXsNkMl3TY4qIiIiIiNwolE9fvc8++4ySJUuSnZ19zY45ZcoUTCYThw4dumbHFBGRgqmDWURErmsmk6lQr5UrVxZ1qA5WrlzpEJ+rqytly5blvvvu4+DBg0Ud3r/qxRdfpF+/fpQqVQoAm83GlClT6N69O1FRUXh7e1OtWjXefPNNsrKyCtzH//3f/1G5cmU8PDwoX748EydOdKqzZ88ennjiCZo0aYKHh8clk8qsrCzeeecdqlSpgpeXFxEREfTu3ZsdO3YU6px++OEH+vTpQ9myZfHy8qJixYo89dRTJCUlFVh//vz51KlTBw8PD0qWLMmrr75KXl6eQ50RI0bw559/Mn/+/ELFcClZWVmMHz+ehg0b4u/vj4eHBxUqVOCxxx5j7969/3j/10JhrpmIiIiIiPw3rmWunZGRwWuvvVbofSmf/vfz6SvJcWfOnEn//v0pX748JpOJVq1aFXjc+++/n5ycHD7//POrPv+zrFYrkydPplWrVgQFBeHu7k7p0qUZOHAgGzdu/Mf7vxbWrl1Ls2bN8PLyokSJEgwfPpy0tLSiDktE5D/jUtQBiIiIXMq0adMc3n/99dcsXbrUqbxy5cr/6DiTJk3CZrP9o30UZPjw4dSvX5/c3Fw2b97MF198wYIFC9i2bRvh4eH/yjH+q9gLY+vWrSxbtoy1a9fayzIyMhg4cCCNGjXikUceITQ0lHXr1vHqq6/yyy+/sHz5coenaj///HMeeeQR7rzzTp588klWr17N8OHDycjI4LnnnrPXW7duHR9++CFVqlShcuXKbN269aJx3XPPPcyfP59BgwZRp04dTpw4wccff0zjxo3Ztm2bPXm/mMGDBxMeHk7//v0pWbIk27Zt46OPPmLhwoVs3rwZT09Pe91FixbRs2dPWrVqxcSJE9m2bRtvvvkmp0+f5tNPP7XXK1GiBD169GDs2LF07979Si6zg/j4eDp16sSmTZvo2rUrd999Nz4+PuzZs4dvv/2WL774gpycnKve/7VQ2GsmIiIiIiL/jWuVa0N+jjhq1CiAi3ZWFkT59L+XT19Jjvvpp5+yadMm6tevz5kzZy4av4eHBwMGDOD9999n2LBhVz17VmZmJnfccQc///wzLVq04IUXXiAoKIhDhw7x3XffMXXqVI4cOUJkZORV7f9a2Lp1K23btqVy5cq8//77HDt2jLFjx7Jv3z4WLVpU1OGJiPw3DBERkRvI0KFDjcL8+kpPT78G0VzcihUrDMCYNWuWQ/mHH35oAMbbb7/9j4+Rlpb2j/dxMYDx6quvXrbe8OHDjZIlSxo2m81elp2dbaxZs8ap7qhRowzAWLp0qb0sIyPDCA4ONrp06eJQ95577jG8vb2NhIQEe9mZM2eMlJQUwzAMY8yYMQZgxMTEOB3n2LFjBmA8/fTTDuXLly83AOP999+/7HmtWLHCqWzq1KkGYEyaNMmhvEqVKkbNmjWN3Nxce9mLL75omEwmY9euXQ51Z8+ebZhMJuPAgQOXjeFiunTpYpjNZmP27NlO27KysoynnnrK/v7VV18t1PfLtXYl10xERERERP57hc21r0ZcXFyhc0zDUD79X+TTV5LjHjlyxLBarYZhGEbVqlWNli1bXvQcNm7caADGL7/8cslzvZSzX3vjx4932paXl2eMGTPGOHr0qGEYhjF58uSL3gsoSrfddpsRFhZmJCcn28smTZpkAMbixYuLMDIRkf+OpsgWEZEbXqtWrahWrRqbNm2iRYsWeHl58cILLwAwb948unTpQnh4OO7u7kRHR/PGG29gtVod9nHhukuHDh3CZDIxduxYvvjiC6Kjo3F3d6d+/fr88ccfVx1rmzZtAIiJibGXLVq0iObNm+Pt7Y2vry9dunRxmsr5/vvvx8fHhwMHDtC5c2d8fX255557CowdID09naeeeoqoqCjc3d2pWLEiY8eOxTAMh3rZ2dk88cQThISE4OvrS/fu3Tl27Fihz2fu3Lm0adPGYaSym5sbTZo0cap7++23A7Br1y572YoVKzhz5gxDhgxxqDt06FDS09NZsGCBvSwoKAhfX9/LxpSamgpA8eLFHcrDwsIAHEZmX0xBo+oLin/nzp3s3LmTwYMH4+JybmKYIUOGYBgGs2fPdthHu3btgPyvy/PFxsaye/ducnNzLxnXhg0bWLBgAQ8++CB33nmn03Z3d3fGjh17yX1MnjyZNm3aEBoairu7O1WqVCnwqeGNGzfSsWNHihUrhqenJ2XKlOGBBx5wqPPtt99St25dfH198fPzo3r16nzwwQeXPP6VXjMRERERESkaNpuNCRMmULVqVTw8PChevDgPP/wwiYmJDvUulTscOnSIkJAQAEaNGmWf9rqwaxSfT/n01efThc1xAaKiojCbC9dtULduXYKCgpxy3Pj4eHbv3k1GRsYl2x87dozPP/+c9u3bM2LECKftFouFp59++pJPLxf2vs++ffu48847KVGiBB4eHkRGRtK3b1+Sk5PtdZYuXUqzZs0ICAjAx8eHihUr2u8vXUxKSgpLly6lf//++Pn52cvvu+8+fHx8+O677y7ZXkTkRqUpskVE5KZw5swZbrvtNvr27Uv//v3tnYtTpkzBx8eHJ598Eh8fH5YvX84rr7xCSkoKY8aMuex+Z8yYQWpqKg8//DAmk4n33nuPO+64g4MHD+Lq6nrFcR44cACA4OBgIH9asgEDBtCxY0dGjx5NRkYGn376Kc2aNWPLli0OiW5eXh4dO3akWbNmjB07Fi8vrwKPYRgG3bt3Z8WKFTz44IPUqlWLxYsX88wzz3D8+HHGjx9vr/vQQw/xzTffcPfdd9OkSROWL19Oly5dCnUux48f58iRI9SpU6dQ9U+ePAlAsWLF7GVbtmwBoF69eg5169ati9lsZsuWLfTv379Q+z8rOjqayMhIxo0bR8WKFalduzYnTpzg2WefpUyZMvTt2/eK9nc18YeHhxMZGWnffpa/vz/R0dGsWbOGJ554wl7+/PPPM3XqVGJiYpxubpzv7PrN995771WdA+RPd1a1alW6d++Oi4sLP/74I0OGDMFmszF06FAATp8+TYcOHQgJCWHkyJEEBARw6NAhfvjhB/t+li5dSr9+/Wjbti2jR48G8m9MrFmzhscff/yix7/SayYiIiIiIkXj4YcfZsqUKQwcOJDhw4cTExPDRx99xJYtW1izZg2urq6XzR1CQkL49NNPefTRR7n99tu54447AKhRo8YVx6N8+t/Npwva59WoU6cOa9ascSj76KOPGDVqFCtWrLjktOiLFi0iLy/vH+W4hbnvk5OTQ8eOHcnOzmbYsGGUKFGC48eP89NPP5GUlIS/vz87duyga9eu1KhRg9dffx13d3f279/vdG4X2rZtG3l5eU6fg5ubG7Vq1VKOKyI3r6J8fFpERORKFTRtV8uWLQ3A+Oyzz5zqZ2RkOJU9/PDDhpeXl5GVlWUvGzBggFGqVCn7+5iYGAMwgoODHaaVmjdvngEYP/744yXjPDul11dffWXExcUZJ06cMBYsWGCULl3aMJlMxh9//GGkpqYaAQEBxqBBgxzanjx50vD393coHzBggAEYI0eOdDrWhbHPnTvXAIw333zToV6vXr0Mk8lk7N+/3zAMw9i6dasBGEOGDHGod/fddxdqSq9ly5YV6lqc1a5dO8PPz89ITEy0lw0dOtSwWCwF1g8JCTH69u1b4LZLTZFtGIaxYcMGIzo62gDsr7p16xqxsbGFirUgDz74oGGxWIy9e/c6xXHkyBGn+vXr1zcaNWrkVN6hQwejcuXKDmVnP9/LTfN1++23G4DDNbyUgqbILuh7omPHjkbZsmXt7+fMmWMAxh9//HHRfT/++OOGn5+fkZeXV6hYzrqaayYiIiIiIv+tC3Pt1atXG4Axffp0h3o///yzQ3lhcoernSJb+fQ5/2Y+fVZBOe6FLjdFtmEYxuDBgw1PT0+HsrO5aEFTc5/viSeeMABjy5Ytl6x3VkFTZBfmvs+WLVsKnHb9fOPHjzcAIy4urlCxnDVr1iwDMFatWuW0rXfv3kaJEiWuaH8iIjcKTZEtIiI3BXd3dwYOHOhUfv50yKmpqcTHx9O8eXMyMjLYvXv3Zffbp08fAgMD7e+bN28OwMGDBwsV1wMPPEBISAjh4eF06dKF9PR0pk6dSr169Vi6dClJSUn069eP+Ph4+8tisdCwYUNWrFjhtL9HH330ssdcuHAhFouF4cOHO5Q/9dRTGIbBokWL7PUAp3oFTUtVkDNnzgA4XJ+Lefvtt1m2bBnvvvsuAQEB9vLMzEzc3NwKbOPh4UFmZmahYrlQYGAgtWrVYuTIkcydO5exY8dy6NAhevfuTVZW1hXvb8aMGfzf//0fTz31FOXLl3eIH/K//gobf2BgIPHx8Q5lU6ZMwTCMSz69DPlTbwGFmir8Ys7/nkhOTiY+Pp6WLVty8OBB+9RgZz+jn3766aLTdgcEBJCens7SpUuv6PhXc81EREREROTamjVrFv7+/rRv394hX61bty4+Pj72fLUwucPVUj6d77/Ipy+W416NwMBAMjMzHabDfu211zAM45JPL8O/n+Ne7L6Pv78/AIsXL77otN1nr+28efOw2WyFPr5yXBG5VWmKbBERuSlEREQUmFjt2LGDl156ieXLl9sTl7POX2fnYkqWLOnw/mzyd+GaUxfzyiuv0Lx5cywWC8WKFaNy5cr2dWf37dsHnFtH6kLnr90D4OLicsl1h846fPgw4eHhTgla5cqV7dvP/ms2m4mOjnaoV7FixUKc2TnGBetQXWjmzJm89NJLPPjgg04JvaenJzk5OQW2y8rKKtR6yRdKTk6mefPmPPPMMzz11FP28nr16tGqVSsmT57Mo48+SmZmptPXQIkSJZz2t3r1ah588EE6duzIW2+95RQ/5K+9Vdj4DcNwWGPrSpz9mkhNTXW4sXAl1qxZw6uvvsq6deucEuvk5GT8/f1p2bIld955J6NGjWL8+PG0atWKnj17cvfdd9uT5iFDhvDdd99x2223ERERQYcOHbjrrrvo1KnTJY9/NddMRERERESurX379pGcnExoaGiB20+fPg1QqNzhaimf/m/y6UvluFfj7DlcTZ57fo57tQpz36dMmTI8+eSTvP/++0yfPp3mzZvTvXt3+vfvb+987tOnD19++SUPPfQQI0eOpG3bttxxxx306tXrkmtSK8cVkVuVOphFROSmUNAf7ElJSbRs2RI/Pz9ef/11oqOj8fDwYPPmzTz33HOFGpFqsVgKLL9cEnhW9erVadeuXYHbzh5/2rRpBXZsnk2cz3J3d79kUnOtnV336lKd7UuXLuW+++6jS5cufPbZZ07bw8LCsFqtnD592uHGRU5ODmfOnCE8PPyK4/r+++85deoU3bt3dyg/+7WwZs0aHn30UWbOnOn01PuFn+uff/5J9+7dqVatGrNnz3b6TMLCwgCIjY0lKirKYVtsbCwNGjRwii8xMfGq17iqVKkSkL/G09mn6a/EgQMHaNu2LZUqVeL9998nKioKNzc3Fi5cyPjx4+1fkyaTidmzZ7N+/Xp+/PFHFi9ezAMPPMC4ceNYv349Pj4+hIaGsnXrVhYvXsyiRYtYtGgRkydP5r777mPq1KkXjeFqrpmIiIiIiFxbNpuN0NBQpk+fXuD2kJAQoHC5w9VSPv3v59OXy3GvRmJiIl5eXlfVkXp+jlurVq0rbn8l933GjRvH/fffz7x581iyZAnDhw/nnXfeYf369URGRuLp6cmqVatYsWIFCxYs4Oeff2bmzJm0adOGJUuWXPT+0Pk57oViY2Ov6r6GiMiNQB3MIiJy01q5ciVnzpzhhx9+oEWLFvbymJiYIozqnLMjnUNDQy+aNF+NUqVKsWzZMlJTUx1GXZ+dGqpUqVL2f202GwcOHHAYZb1nz55CHedsInix67lhwwZuv/126tWrx3fffVdg4no2gdy4cSOdO3e2l2/cuBGbzXZVCeapU6cAsFqtDuWGYWC1WsnLywOgY8eOl5ze+cCBA3Tq1InQ0FAWLlxY4I2R8+M/v2P0xIkTHDt2jMGDBzu1iYmJoWbNmld8XgDdunXjnXfe4ZtvvrmqDuYff/yR7Oxs5s+f7/B0fkHTxwE0atSIRo0a8dZbbzFjxgzuuecevv32Wx566CEA3Nzc6NatG926dcNmszFkyBA+//xzXn75ZcqVK1fgPq/mmomIiIiIyLUVHR3NsmXLaNq0aaE6Di+VO1ztDE6Xiw+UT0Ph8+nC5LhXIyYmxv6E95W67bbbsFgsfPPNN9x7771X3P5K7/tUr16d6tWr89JLL7F27VqaNm3KZ599xptvvgmA2Wymbdu2tG3blvfff5+3336bF198kRUrVlz066xatWq4uLiwceNG7rrrLnt5Tk4OW7dudSgTEbmZXD/DtkRERP5lZ0eXnv9Uak5ODp988klRheSgY8eO+Pn58fbbbxe4VlVcXNxV7bdz585YrVY++ugjh/Lx48djMpm47bbbAOz/fvjhhw71JkyYUKjjREREEBUVxcaNG5227dq1iy5dulC6dGl++umni96QaNOmDUFBQXz66acO5Z9++ileXl506dKlULGcr0KFCgB8++23DuXz588nPT2d2rVrA/mjjNu1a+fwOuvkyZN06NABs9nM4sWL7aPzL1S1alUqVarEF1984dCh/emnn2IymejVq5dD/eTkZA4cOECTJk0cymNjY9m9e/dl1yxr3LgxnTp14ssvv2Tu3LlO23Nycnj66acv2r6g74nk5GQmT57sUC8xMdHpae6zNyfOTvt1ds2ws8xmMzVq1HCoU5ArvWYiIiIiInLt3XXXXVitVt544w2nbXl5eSQlJQGFyx28vLwA7G3+DcqnryyfLmyOezU2b97slOPGx8eze/fui653fFZUVBSDBg1iyZIlTJw40Wm7zWZj3LhxHDt2rMD2hb3vk5KSYh9sflb16tUxm832r9OEhASn/V/4tVwQf39/2rVrxzfffOMw1fe0adNIS0ujd+/eF20rInIj0xPMIiJy02rSpAmBgYEMGDCA4cOHYzKZmDZtWqGnt/6v+fn58emnn3LvvfdSp04d+vbtS0hICEeOHGHBggU0bdrUKaktjG7dutG6dWtefPFFDh06RM2aNVmyZAnz5s1jxIgR9pHetWrVol+/fnzyySckJyfTpEkTfvnlF/bv31/oY/Xo0YM5c+Y4rCucmppKx44dSUxM5JlnnmHBggUObaKjo2ncuDGQP7X5G2+8wdChQ+nduzcdO3Zk9erVfPPNN7z11lsEBQXZ2yUnJ9sTzjVr1gDw0UcfERAQQEBAAI899pj9/KtWrcrrr7/O4cOHadSoEfv37+ejjz4iLCyMBx988LLn1alTJw4ePMizzz7Lb7/9xm+//WbfVrx4cdq3b29/P2bMGLp3706HDh3o27cv27dv56OPPuKhhx5yGsW9bNkyDMOgR48eDuXPP/88U6dOJSYmhtKlS18ytq+//poOHTpwxx130K1bN9q2bYu3tzf79u3j22+/JTY2lrFjxxbYtkOHDvanjh9++GHS0tKYNGkSoaGhDtN5TZ06lU8++YTbb7+d6OhoUlNTmTRpEn5+fvaR8Q899BAJCQm0adOGyMhIDh8+zMSJE6lVq9ZlR69fyTUTEREREZFrr2XLljz88MO88847bN26lQ4dOuDq6sq+ffuYNWsWH3zwAb169SpU7uDp6UmVKlWYOXMmFSpUICgoiGrVqlGtWrWrjk/59JXl01eS465atYpVq1YB+R316enp9id8W7Ro4fCk8KZNm0hISHDKcT/66CNGjRrFihUraNWq1SWvw7hx4zhw4ADDhw/nhx9+oGvXrgQGBnLkyBFmzZrF7t276du3b4FtC3vfZ/ny5Tz22GP07t2bChUqkJeXx7Rp07BYLNx5550AvP7666xatYouXbpQqlQpTp8+zSeffEJkZCTNmjW75Dm89dZbNGnShJYtWzJ48GCOHTvGuHHj6NChA506dbpkWxGRG5YhIiJyAxk6dKhx4a+vli1bGlWrVi2w/po1a4xGjRoZnp6eRnh4uPHss88aixcvNgBjxYoV9noDBgwwSpUqZX8fExNjAMaYMWOc9gkYr7766iXjXLFihQEYs2bNuuw5rVixwujYsaPh7+9veHh4GNHR0cb9999vbNy40SE+b2/vAttfGLthGEZqaqrxxBNPGOHh4Yarq6tRvnx5Y8yYMYbNZnOol5mZaQwfPtwIDg42vL29jW7duhlHjx4t1DkahmFs3rzZAIzVq1fby85eu4u9BgwY4LSfL774wqhYsaLh5uZmREdHG+PHj3eK9VL7vfD8ExISjCeeeMKoUKGC4e7ubhQrVszo27evcfDgwcuek2EYl4y/ZcuWTvXnzJlj1KpVy3B3dzciIyONl156ycjJyXGq16dPH6NZs2ZO5QMGDDAAIyYmplDxZWRkGGPHjjXq169v+Pj4GG5ubkb58uWNYcOGGfv377fXe/XVV52+X+bPn2/UqFHD8PDwMEqXLm2MHj3a+OqrrxyOv3nzZqNfv35GyZIlDXd3dyM0NNTo2rWrw9fk7NmzjQ4dOhihoaGGm5ubUbJkSePhhx82YmNjC3UOhb1mIiIiIiLy3yso1zaM/Fytbt26hqenp+Hr62tUr17dePbZZ40TJ04YhlG43MEwDGPt2rVG3bp1DTc3t8vmm8qn//18+kpy3LN5ZEGvC8/rueeeM0qWLOl0vLP7OP++y6Xk5eUZX375pdG8eXPD39/fcHV1NUqVKmUMHDjQ2LJli73e5MmTnXLnwtz3OXjwoPHAAw8Y0dHRhoeHhxEUFGS0bt3aWLZsmX0/v/zyi9GjRw8jPDzccHNzM8LDw41+/foZe/fuLdQ5rF692mjSpInh4eFhhISEGEOHDjVSUlIK1VZE5EZkMozr5DEuERERuSG1bduW8PBwpk2bVtShXNdOnjxJmTJl+Pbbb51Gd4uIiIiIiMit50bOp7OzsyldujQjR47k8ccfL+pwRETkGlMHs4iIiPwjGzZsoHnz5uzbt49SpUoVdTjXrZEjR7J8+XJ+//33og5FRERERERErgM3cj792Wef8fbbb7Nv3z7c3d2LOhwREbnG1MEsIiIiIiIiIiIiIiIiIiKFYi7qAERERERERERERERERERE5MagDmYRERERERERERERERERESkUdTCLiIiIiIiIiIiIiIiIiEihqINZREREREREREREREREREQKRR3MIiIi8q8ymUw89thjRR2GiIiIiIiISJFTjiwiIjcjdTCLiIj8h0wmU6FeK1euLLJY3n333UK137ZtG7169aJUqVJ4eHgQERFB+/btmThx4n8c+bXVqlWri14rV1dXh7qlS5cusN4jjzxS6OMdOHCAu+++m9DQUDw9PSlfvjwvvviiQ525c+dSqVIl/P396datGydOnHDaT/fu3Rk8ePDVnbSIiIiIiMg1oBz5xlPYHHnlypWX/EzfeuutKzru9OnTMZlM+Pj4OG1TjiwiUvRcijoAERGRm9m0adMc3n/99dcsXbrUqbxy5crXJJ727dtz3333OZTVrl37su3Wrl1L69atKVmyJIMGDaJEiRIcPXqU9evX88EHHzBs2LD/KuRr7sUXX+Shhx5yKEtPT+eRRx6hQ4cOTvVr1arFU0895VBWoUKFQh1r69attGrVioiICJ566imCg4M5cuQIR48etdc5ePAgffr0oU+fPjRu3JgJEyYwcOBAFi9ebK+zePFiVq1axb59+67kVEVERERERK4p5cg3nsLmyJUrV3b6HCH/M1+yZEmB+fTFpKWl8eyzz+Lt7e20TTmyiMj1QR3MIiIi/6H+/fs7vF+/fj1Lly51Kr9WKlSocFXHfuutt/D39+ePP/4gICDAYdvp06f/pegKLz09vcBE89/Qvn17p7JvvvkGgHvuucdpW0RExFVdU5vNxr333kulSpVYsWIFnp6eBdZbsmQJkZGRTJ06FZPJROXKlWnTpg1ZWVl4eHiQl5fHE088wSuvvEJISMgVxyEiIiIiInKtKEf+b1wPOXLx4sULvJajRo2ifPny1K9fv9DHfPPNN/H19aV169bMnTvXYZtyZBGR64OmyBYRESli6enpPPXUU0RFReHu7k7FihUZO3YshmE41Du7btP06dOpWLEiHh4e1K1bl1WrVl3R8TIzM8nKyrqiNgcOHKBq1apOiTNAaGhogW3mzp1LtWrVcHd3p2rVqvz8888O2w8fPsyQIUOoWLEinp6eBAcH07t3bw4dOuRQb8qUKZhMJn799VeGDBlCaGgokZGR9u2LFi2iefPmeHt74+vrS5cuXdixY4fDPnJzc9m9ezexsbFXdN5nzZgxA29vb3r06FHg9pycHNLT069on0uWLGH79u28+uqreHp6kpGRgdVqdaqXmZlJQEAAJpMJgKCgIAzDIDMzE4CPPvoIq9V6U42QFxERERGRW5dy5Bs/Rz7r999/Z//+/QUO1r6Yffv2MX78eN5//31cXJyfj1OOLCJyfVAHs4iISBEyDIPu3bszfvx4OnXqxPvvv0/FihV55plnePLJJ53q//rrr4wYMYL+/fvz+uuvc+bMGTp16sT27dsLdbwpU6bg7e2Np6cnVapUYcaMGYVqV6pUKTZt2lTo4/z2228MGTKEvn378t5775GVlcWdd97JmTNn7HX++OMP1q5dS9++ffnwww955JFH+OWXX2jVqhUZGRlO+xwyZAg7d+7klVdeYeTIkUD+VFtdunTBx8eH0aNH8/LLL7Nz506aNWvmkIQfP36cypUr8/zzzxcq/vPFxcWxdOlSevbsWeCI8OXLl+Pl5YWPjw+lS5fmgw8+KNR+ly1bBoC7uzv16tXD29sbLy8v+vbtS0JCgr1e/fr12bJlC//73/+IiYnhrbfeoly5cgQGBhIXF8eoUaN4//33ndaHFhERERERudEoR77xc+TzTZ8+HSh4NrCLGTFiBK1bt6Zz584FbleOLCJynTBERETkmhk6dKhx/q/fuXPnGoDx5ptvOtTr1auXYTKZjP3799vLAAMwNm7caC87fPiw4eHhYdx+++2XPXaTJk2MCRMmGPPmzTM+/fRTo1q1agZgfPLJJ5dtu2TJEsNisRgWi8Vo3Lix8eyzzxqLFy82cnJynOoChpubm0Psf/75pwEYEydOtJdlZGQ4tV23bp0BGF9//bW9bPLkyQZgNGvWzMjLy7OXp6amGgEBAcagQYMc9nHy5EnD39/foTwmJsYAjAEDBlz2XC80ceJEAzAWLlzotK1bt27G6NGjjblz5xr/93//ZzRv3twAjGefffay++3evbsBGMHBwcY999xjzJ4923j55ZcNFxcXo0mTJobNZrPXHT58uP3zDwoKMpYvX24YhmEMGjTI6NSp0xWfk4iIiIiIyPVAOfLNlSOfLy8vzyhevLjRoEGDQu/7p59+MlxcXIwdO3YYhmEYAwYMMLy9vZ3qKUcWESl66mAWERG5hi5MngcPHmxYLBYjJSXFod7ZJPL8ZBMwGjdu7LTPPn36GF5eXg6JZWFkZ2cb1apVMwICAgpMZC/0+++/G7fffrvh5eVlT+RCQkKMefPmOdQDjM6dOzu19/PzM5544okC952Tk2PEx8cbcXFxRkBAgDFixAj7trPJ89SpUx3a/PDDDwZgLF++3IiLi3N4dejQwShXrlxhLsNlNW7c2AgJCTFyc3MvW9dmsxkdO3Y0XFxcjKNHj16ybps2bQzAKfl95513DMBYunSpQ/nhw4eNDRs2GKmpqYZhGMaWLVsMd3d3Y9euXUZSUpJxzz33GOHh4UbLli2NnTt3XuFZioiIiIiIXHvKkW/eHHnx4sUGYHzwwQeF2m92drZRvnx547HHHrOXXayD2TCUI4uIFDVNkS0iIlKEDh8+THh4OL6+vg7llStXtm8/X/ny5Z32UaFCBTIyMoiLi7uiY7u5ufHYY4+RlJTEpk2bLlu/fv36/PDDDyQmJvL777/z/PPPk5qaSq9evdi5c6dD3ZIlSzq1DwwMJDEx0f4+MzOTV155xb6uVrFixQgJCSEpKYnk5GSn9mXKlHF4v2/fPgDatGlDSEiIw2vJkiWcPn26UNfhUg4ePMi6devo06dPgWs/XchkMvHEE0+Ql5fHypUrL1nX09MTgH79+jmU33333QCsXbvWobxkyZI0aNAAHx8fAIYPH84jjzxCpUqVGDp0KEePHmXevHlUr16dbt26kZeXV9jTFBERERERuS4oR755cuTp06djsVjo06dPofY9fvx44uPjGTVqVKHqK0cWESlal79TKiIiIjetqKgoAIc1fy/Hzc2N+vXrU79+fSpUqMDAgQOZNWsWr776qr2OxWIpsK1hGPb/Dxs2jMmTJzNixAgaN26Mv78/JpOJvn37YrPZnNqe7ZA962ydadOmUaJECaf6hekQvpyz629dyXpRhb2m4eHhABQvXtyhPDQ0FMDhRsOFZs6cya5du5g/fz5Wq5XvvvuOJUuWUK9ePapWrcqkSZNYv349zZo1K3TcIiIiIiIitzrlyJdW2Bw5MzOTOXPm0K5dO6ectyDJycm8+eabDBkyhJSUFFJSUgBIS0vDMAwOHTqEl5eXPV++kHJkEZFrTx3MIiIiRahUqVIsW7aM1NRUhxHau3fvtm8/39kRyefbu3cvXl5ehISEXPHxDx48CHBVbQHq1asHQGxs7BW3nT17NgMGDGDcuHH2sqysLJKSkgrVPjo6GsjvkG3Xrt0VH78wZsyYQXR0NI0aNSp0m8Je07p16zJp0iSOHz/uUH7ixIlLts/IyOCZZ57hjTfeICAggFOnTpGbm2vvsPb09CQwMNBpvyIiIiIiItc75cg3R448f/58UlNTCz1YOzExkbS0NN577z3ee+89p+1lypShR48ezJ0712mbcmQRkaKhKbJFRESKUOfOnbFarXz00UcO5ePHj8dkMnHbbbc5lK9bt47Nmzfb35+d8qlDhw4XHRENFDg1WGpqKhMmTKBYsWLUrVv3knGuWLHCYWT1WQsXLgSgYsWKl2xfEIvF4rTPiRMnYrVaC9W+Y8eO+Pn58fbbb5Obm+u0/fxzzs3NZffu3VeU5G/ZsoVdu3bZp6y+UEJCglOsubm5vPvuu7i5udG6dWt7eXJyMrt373aY1qxHjx64u7szefJkh9HoX375JQDt27cv8LijR48mMDCQQYMGARAcHIyLi4v9hkt8fDxxcXEFjlgXERERERG5nilHvnFz5PPNmDEDLy8vbr/99gK3X5gjh4aGMmfOHKdX69at8fDwYM6cOTz//PMF7ks5sohI0dATzCIiIkWoW7dutG7dmhdffJFDhw5Rs2ZNlixZwrx58xgxYoR9BPJZ1apVo2PHjgwfPhx3d3c++eQTgMuuUfTxxx8zd+5cunXrRsmSJYmNjeWrr77iyJEjTJs2DTc3t0u2HzZsGBkZGdx+++1UqlSJnJwc1q5dy8yZMyldujQDBw684nPv2rUr06ZNw9/fnypVqrBu3TqWLVtGcHBwodr7+fnx6aefcu+991KnTh369u1LSEgIR44cYcGCBTRt2tR+U+L48eNUrlyZAQMGMGXKlELtf/r06cDFp/6aP38+b775Jr169aJMmTIkJCQwY8YMtm/fzttvv+2QvM6ZM4eBAwcyefJk7r//fgBKlCjBiy++yCuvvEKnTp3o2bMnf/75J5MmTaJfv37Ur1/f6ZhHjhxhzJgxLFiwwH6zxMXFhR49ejBixAiOHDnCnDlzCA8Pp3HjxoU6TxERERERkeuFcuQbN0c+KyEhgUWLFnHnnXfa10e+0IU5speXFz179nSqN3fuXH7//fcCt4FyZBGRoqQOZhERkSJkNpuZP38+r7zyCjNnzmTy5MmULl2aMWPG8NRTTznVb9myJY0bN2bUqFEcOXKEKlWqMGXKFGrUqHHJ4zRt2pS1a9fy5ZdfcubMGby9vWnQoAFfffUVbdq0uWycY8eOZdasWSxcuJAvvviCnJwcSpYsyZAhQ3jppZcICAi44nP/4IMPsFgsTJ8+naysLJo2bcqyZcvo2LFjofdx9913Ex4ezrvvvsuYMWPIzs4mIiKC5s2bX1VCf5bNZuPbb7+lTp06Fx15Xr16dapUqcI333xDXFwcbm5u1KpVi++++47evXsX6jgvvfQSgYGBTJw4kREjRjh0Ohfk6aef5rbbbnN4Ohrgk08+4aGHHuKFF16gfPnyzJkz57I3RERERERERK43ypFv3Bz5rFmzZpGbm1uoJ53/KeXIIiJFx2QUNJeHiIiIXHdMJhNDhw51mipMRERERERE5FajHFlERKToaA1mEREREREREREREREREREpFHUwi4iIiIiIyBV57bXXLrqm3vnbDh06hMlkYvbs2Ve0/6ttJyIiIiIiInKt3Yo5stZgFhERERERkf9EWFgY69ato0KFCkUdioiIiIiIiEiRuplyZHUwi4iI3CAMwyjqEERERK6Iu7s7jRo1KuowRERE5CakHFlERG40N1OOrCmyRURERERE5D9R0DReOTk5DB8+nKCgIAICAnj44YeZMWMGJpOJQ4cOObTPysriscceIzAwkLCwMJ5++mny8vKu8VmIiIiIiIiI/HM3U46sDmYRERERERG5Knl5eU4vm812yTYjR47k888/57nnnmPmzJnYbDZGjhxZYN0XX3wRs9nMd999xyOPPMK4ceP48ssv/4tTEREREREREflHbqUcWVNki4iIyL8uNzeXyZMnAzBw4EBcXV2LOCIREbEz3VG4esYPl9ycnp5+0Z/v3t7eBZYnJCTw6aef8tJLL/Hcc88B0LFjR9q1a8fRo0ed6jds2JAPP/wQgPbt27NixQpmz57NI488UrhzEBEREblOKE8WEblOFTZHhkvmybdajqwOZhEREREREblinp6erFq1yqn8iy++YMaMGQW22bZtG1lZWXTv3t2hvEePHvzyyy9O9Tt06ODwvkqVKixfvvwfRC0iIiIiIiLy77vVcmR1MIuIiIiIiMgVM5vN1KtXz6n8p59+umib2NhYAEJCQhzKQ0NDC6wfEBDg8N7NzY2srKwrjFRERERERETkv3Wr5chag1lEREREROSWYirk698XFhYGQFxcnEP56dOn/5PjiYiIiIiIiFxaYXPkfz9PvpFzZHUwi4iIiIiIyDVRrVo1PDw8mDdvnkP53LlziyYgERERERERkSJyI+fImiJbRERERERErong4GAeffRR3nrrLTw8PKhVqxazZs1i7969QP6UYiIiIiIiIiK3ghs5R75+IxMREREREZGbzrvvvsvgwYN555136N27N7m5uYwcORIAf3//Io5ORERERERE5Nq5UXNkk2EYRlEHISIiIjeX3NxcJk+eDMDAgQNxdXUt4ohERMTO1Ktw9YzZ/20c57n33nv57bffiImJuWbHFBEREbmWlCeLiFynCpsjwzXLk2+EHFlTZIuIiIiIiMg18+uvv7JmzRrq1q2LzWbjp59+Yvr06bz//vtFHZqIiIiIiIjINXWj5sjqYBYREREREZFrxsfHh59++onRo0eTmZlJmTJleP/99xkxYkRRhyYiIiIiIiJyTd2oObI6mEVERERERG4ppiI9et26dVm7dm2RxiAiIiIiIiKSTzny1TAXdQAiIiIiIiIiIiIiIiIiInJjUAeziIiIiIiIiIiIiIiIiIgUiqbIFhERERERuaUU7fRfIiIiIiIiItcP5chXQ08wi4iIiIiIiIiIiIiIiIhIoaiDWURERERERERERERERERECkUdzCIiIiIiIiIiIiIiIiIiUijqYBYRERERERERERERERERkUJRB7OIiIiIiIiIiIiIiIiIiBSKOphFRERERERERERERERERKRQXIo6ABEREREREbmWTEUdgIiIiIiIiMh1Qjny1dATzCIiIiIiIiIiIiIiIiIiUijqYBYRERERERERERERERERkULRFNkiInJLePbhraw3/NgaFYpnThauebkYZgsBWRmUiT/N409WpX1N76IOU0RE5BrQ9F8iIiLXu5/+zOLz0bspFRdPjrs3jY4exDc3h58rVqVx62I8NLxsUYcoIiJyk1COfDX0BLOIiNz0nmq3grFlK7O6QklSPT047R/AicBieGVb2BsazoLqdXh0RiJLD+YVdagiIiIiIiJyixs3+QSvTDqOYVgxefuS42VlbflIQtPj+HDe16xdcYI7Bu8s6jBFRETkFqYOZhERuaklpeQws2EVDIvFodwwm0j09qDdziMAHAgpweOfnCiKEEVERERERETsfloTz+TZExmz7Bt6b17J6wvn0yTmAHcNeJAV5SvwwsqlbAsoTs7hxKIOVURERG5RmiJbRERuap/PSeCUjz8B6Tn4Z+aS5Wom3tcdq9lMgrcb2yNC8MzOIdPdjSRPL257cB+lk3LwSs/EKz2JN1a3K+pTEBERERERkVvI8PULqHHqGKmWMMrEHwbg7s0bKZaWRs8HHmbQujU0On6GuQts3DUksIijFRERkVuROphFROSmFlbclRK7c4hIzrKXhaZmsy3CH8Ns4ligF5jy19mI9Q/AanKj6d4/CUjP4kCJQO7vuZEpc+sVVfgiIiL/Aa0vJSIicj35/pVNrPg9Dc+0PIolZvPQwd3k4YGb1epQr8Pe3fhmZ/NZsxY0PBJHTrjlInsUERGRwlOOfDU0RbaIiNzUivvYiEjKdCjzyrHimfN3om5y/APitJ8X+0oW50DpMKrHnCTHpl+VIiIiIiIi8t9Y+MVuJse4kmv1oMnu01Q4lUye1Ytdxco71U13dSPLNf95oT/DAlnxf/v5pMYsxtWbw8Klcdc6dBEREbmF6a65iIjcdAzDYMHn+/hkyG/M+S7WqRM5zsedTLeCR3qbDANXqw3DZOJoeDFKxyWSnZpzLcIWERERERGRW8w3S7NYW7Y07bcetJf95V2Tr2u15XBAsEPdiS1akeXqBkCWi4WvWjbgqT5dCInPIXngEn4Ztvqaxi4iIiK3Lk2RLSIiN5UfF8bzyxfHwOLK3oAoTrh6YjEbuNrytyd6uXIw1Oei7SvFpeCTm/90c7abK9V2nyY7OQd3X7drEb6IiMg1oOm/RERErhd5Nis+WblYjPz3m8qWYEt0DQLTsnnojvuof+ogVWJP4JGby8+VqtrbuebZyHW1kOXuxts9W1D9yGnCdyawuO0iFlepQkSAKz+8GoaHi37vi4iIXJp+V14NPcEsIiI3lQX/d5IMd3c+rxfNpvBgTIaJI4GenPF2I89s4qSfR8ENDYPGh0/T7sBJe5F/Uiqhmcn4RHhfo+hFRERERETkVmEYBnWOHCPZ240jxfz4qm1NstwtvPHtrzz503o++Gop5mRPBvW7lyWVqrD8kwn02roZ1zwrtvPuhbu5uFAqz0qY4ULbA6dZ8PFU2i3eTK3nT2MYRtGdoIiIiNy09ASziIjcNPYsO4QHJn4oH06WqwsRmZnsLuGLxWoj3t/rou38snK4Y8dRYgK9MVmtuNgMbLm5jO5Ql/K1I3l18I+Yu1SnXffSuJg1ok1ERERERESuznc/nebQm+sITs9gTs0qxDSrS4qnB8/d24bg1ExenbXSoX7fNduZ0bw639Wuy/i5s3lu2WJ8z5jo99t2Bj3aFW/MtIk5TZ6rOycD3VlUNwi3vFy6/bmNTDcvvpll5d67wovmZEVEROSmpQ5mEZHrTFaujSdHH8V/0VbaHDnIMf8g5lesxtGoErQuA6OHhWE2awKKgryzwcDsYua0tzsAMaE+2M52CJ8dtW0y5f//73WZTYZB00NxWAyDcglpfNi0Mr5Z2bTefYAvvpnN4eBAHujQglP7/XB/N5PvernSvYJrUZyeiIjIv0SDpURERIpC3IFEKvT7mpUtGzKxTVPOeHuR6Z6/HFO2uyvhR+MwX/DAsdmAiIRUYoN8qf30C4z58Xsmt63N4pplOVHMnzt2HHE6zvaSpal0/Dg7w4NInn4I1MEsIiJyCcqRr4Y6mEVEriMHE208/+Bamh+PoUbsCXyzs2iyYx9Nj+6h2pDn2BTnwoTXMtk41IOaxS0ObY+dyOT07kSyg71J/CuJ5l2L4xt4kemgbxIL/spk2JRkEj3daX3wME0OHCWmeHFKJGdwuJjfuc7ls/7uVMZkonhKBqEZOYDBsvJh5FrM+GdmA5Dq6cH82lVZH12KhR9+RdXYU9z2+INku7lxxxwrmU8buFr0h4eIiIiIiIgU3jvD/yKtdWMmtWgIgMlmo/W+bdz151pqnTjMSZ9A0t2D8c7Os7dJ9nRne8lQAI4GBTGw3wAAThTzB2BniD/xXu5k50GmxUx0agaVrHnE+vmwtmwkd23Zfo3PUkRERG4FegTuP7Zx40bq1avHjz/+WNShXNRrr71GvXr1ijoMBg8eTLdu3Yo6DJEi9d7QDVg9XLhv0+80OHqYynGnSPLywjs3i0Gb1uFqGATk5tHy43TqPbKbki8nUPmpWKZUns6Jqp/x/idH6f4D3HMwiFpvJ3N/t3W0vn8v0c+cotqIw/i/mkrks3HEJuc5HHfPkSzm/HiK7TuTyUrOYc/6OOIT8jtbc3Ks5OVaAa6rtZtWbErlpY9PcjLYD1cXC/WOJeFi9qR8XApDV22hTHySva57TjZ3bN7Ohrc/4tDId/ji6+/xyc6mYnwK20oEEpKSymO/rKXb1p14Z+fY25328+HrxrVpcGwviz75gKD0dKwWC7sTiuCERURERG4CypELTzmyyM0hIymHaVNiaTP6NJWOHuWHOtXo8ftuOm3ZyV/vP8PyL97gkQ2/0Ojofnru+oNKuZs5GZC/xNOxIF+eu68dWW7nnhHKdHNz2P/uUH9WlSnOhrIh/BXkx5xSJdjm58ljd/ckMCOTnSX8r+n5ioiIyK1BTzCLiPzLtq5P4KH/JbGteBi+2Xl4ZWeT4OFGlpsLVrOZ6Z0t3F2j4CmWG2zfSWR6Em5Wq73MLzOb1SXLciwklFqJ6ZgBAzgZUIKjnh5Mmb2IqOR43u7Wgnk1agJgshmkeLgx7MxB6sUdYPfpcPYWjyDR2yDT05MG76SS5WoiNDWNgNQsAtJzaLj3GEuCfJnSqiY2kxdBPyXSLGYP1U8eo3hqOpGHcvm9dAVii/lRPiuOptnHSXL1ITUymHr9yxPdKfoaXN1zPnrrIDsaVCTXxYXOOw7hlXuu09zVZtBu9xEmNQsAoPu2vxg/c4l9qrGu23ZjMgw+btWCBgeP8O2k/+GRl99+2Io1dH3sfuL8fABI9rMSRAwdY2JY9+ERqj/zLpHeoF+hIiIiIiIicj7DMNgdbxDhCztOWPlj4h485+3lUPFAvEsVY2KbFnz4v++JPhPPmWK5VDt11GkfIbbTRGfuweKVxYKW7fgj2nl66xJJmcT7upNnOe/ZIYsZ3CyQY2VynVpE2bKpdCKWaokHgdr/4VmLiIjIrUh3x0VELmA9HIc5KhgbsHFPJvXLuGHLyGHLUYPXvogjJS0Xm2se9WIPsLBSdfaXiACbLX/6ZZMJ8IUoP7DZCI87iUdOLoGpnvhk5VAx4RgbVmcz38ebBVVq0CjmGCUTTnE4OJg0N0+eTLMSmp4GQJbJlb98K3HGNQBLah6lTuRy6u+BxyYgLDmLyqcPExvsRp/de7iz3EA8cqyUjUvDNzuPbBcz28JK8/EP0znm50/tZ14izSN/beJEH3de/GUBj679ib/CohjZ+W5G1WnF89+vpuKZVGqcTiE8+SivLPkCr9z8J3rPePnyZoOOrC9dmvqHDpC9I4V4H2/S0k3se30LdR+ex/7gYmyKLEeZxGSqnzlIeFwSZ9w88LdmgYsFizWXDZGl8c6GBjFH8crNwsOaTYbVjQwPD463qUS9sDwyy4Rx6K9MYhLM7A31JSg7m0ZLVxGQa8Oc6UWKmw+5PZqR65L/a8wvKwcD2FksgMN+vkSlphGQlR93VGI89WNindax6rhzL4PuvYMfPptu71zOr5/M/Ws3MqZTKwDu2L7Bfs0rJBzl8V9/InBk73/7y07k1nXemugX3Z6XB2nZ+d+IGVng4wkWS/4NNFdXSEiFrQchyBfyrFAnGmwG7DsOR85AagY0rAB7T0BxPziVDP5e8NFCaF0NujeAfSfBaoVcKwR4QXQYxMRBoBeYzODhCu4WsLjk37zLygVvd0jLBD9vyMoGqw28PfN/J9hskGcDN5f8cshvl5GVX2b5e5mDs/+mZeSfY4hffv3s3L+vi3Fuv2ZTfoyufw9SslrPtb/wesangGGD1Kz862Y2Q3F/8HDPjy0nF44ngKsLBHqDr9flP6uc3PyXxZK/TwPwcLv053exz/T8NjZbfnxyDWmZBxERubFYbQZWq4Gbq5k8m4FhQG6WFU9PCyazifQcAzeLQU66lcRj6XhZcwko7cuhubs5uDudMzVKE+pto9xf+/hzfTxpaWZcKhTDOzURc0oOE0rXJDAumbv++pNy8Sfxy0zHsNr4vGEzllWuRvddG7l386+c9gxgdOvuHA4OxiMrlxZ7D1Hn2AE2lSnD8rLlyTK74J1rkOLjS4qnC5leEcQNqQSGgTnPyq+fTKDesfw1k93OnLno+eZZXDjqX5FBv+0i1+TL5MZN7NsC07IplZDBab8ClsRyMYHNTKZhocWOYwSkJzJgzx/Anf/2RyIiN4uz+ZjNlp9Hm8j/Nzs3f7ubS35um5UNh+KgYgRYTPl5dalQyM2FzTEQ6gueHuDlBulZkJ4LpYrl57E2Iz/3vNKYLvz/tWKz5efhBcWcZ83P7a80D5brnD7Pq6EOZpH/iGEYZGZm4uVViBu2txDjeBK5j8zAtmQ3puhiuIy5HUvnajBqJrzzA+T83dHn5gI/vwStazi2T0rH1vRVzDtjwGzGWqYU1iMZGLkG5jAvXJeMwFQtyrHNruPktn8f2/EMcDXh8mgzTFv3Yl71FwAZrm4MHfQkEXtOcMeu36h74iDf1WjEI3cMItHblwqnT/D595+TY3HFp04n/ioTTYKPF0dDAzkaWCz/IBfe4Acwm9kWEYVHTjZeOXl03rmNKY0bY5jNmG02Om/7i58qVIMypfEkDw8jj2ORFtpsisGVLDb7NOaMawAAVpMLrbcdpHT6UX4vW45DQWFgMvHcL8upf+wAAJ65OUQm5OCdk//0s3uejS1h5fgjshTzq1W3dy4DGCYTb7XtQp45l3cW/Y/2+/7iznuf4pPODej/52HMJhP9Ny2xdy4DBGek8uDvS3DPacVbi+ZR99hRfo0ux3e16tLu8CYikxJpfvQAp4JC+KVyFWZ71cEnJ40H1qwlfN9J4kLzaHJ6F5XjDnLArSKhOSkEWlPy4wGsmRYiFqTyV7FotkcHAxZO+PmQmOLKPb/9xDeNa9Fj10YaHf4TW7YLby85ztHig/grMoxtEcXY5efH+sji9nhLpaThnZWL2bDhn5Hh9PFMatYAzGYiE5OdtpVKSCIkNZnnV8yl+85NDtuG/baYvL4ncfl2GDFr41n95p8knbFSMvUYbSun4jvpAQjyJScjj5Xj9rD3l1N4+LnSYGAZatwe6fx1InIrSs2Eps/DtiNFHQlMXQV8UtRRFD13V4j9P+j3PizeenX7CAuA9GxIybzytmYTuFjO/R1wvlIhsOw1eHs2TF5xrrxlFZjwINwxGmJO55dZzDCgNXw8KL/zW0RErhvKkeVidmxKZf7Xp4g/mUOlmt70fjicgGBXJv1l4411Nk6kGQSlpDJwwzpm1apDTLEQLFYrVosFky2XZvtieG/2z0QmJWMCktw9STBsmK2xhFtz8HH34oS/P3+FleCZFm04WrsWjfccpc+83XikpFAsM5XRxgl8SKM4p7CQPzgwycOTmMiKNDmZSXxQNf6vaRQfNaxBove5jt3AvEx+L1+a1VGlyHZ3BYz8BNftgnsEJhOtDu6zdy4D2HDDTK7T9cjDhY1la5DoHQhA/UOptNk/lU3hZVhZrjJu1vyOloCMHBJ83B0bu+UPwjRbLeRYzLTfsYc5Jatw7OszjO8fhItZN9BFbnn7Y+HBj2HVzqKLoURA/mDoIJ/8wd/bz7s3cLZD+kJuLtCxFnwyGCKL/fMYPl8Mb86GhDS4qwn0agxPT4Xdxx3rmU35+XJEEOw/6bjNYoJnesI79/7zeK4lw4BXv4VPfs6/1oPbw1t3F3x/W+QS9KjANXb+elOzZs3ijjvuoEmTJvTp04fVq1cDsH//foYNG0bLli1p27YtY8aMIS+vgJtthRAfH8+YMWPo0aMHjRs3pn379gwZMoT169dftu2+fft4+umnadu2LU2aNKF3795MnToV63lT98LF14U6ceIE9erV4/PPP3coT0lJ4c0336Rt27Y0a9aMwYMHs2vXrovGsXv3bp577jk6dOhA48aN6dKlCy+88ALHjh1zqDd37lzuuecemjZtSsuWLRk6dChbt261b7darXTq1Il77rmnwON8//331KtXj5UrV9rLcnJy+Oqrr7jrrrto0qQJrVq14oknnmD37t0Obc//XL/77jt69+5NkyZNmDZt2kXP61aVc/dX2H7aDjl5GLtOknvHJIz35sOo7xxvKufkQcc3nNrb2r+NZedBTBgYNsg7kI6RC2DCFptJbutxTm3yWr6H7XgmYIJcyPvwN1i1C1N+Cc93voelgWXovXMVdU8cJMXdk0G9HiHR2xeAvaHhDOwzlBQPD76rV49jwYFkuLtzNCjkvKeWLy7LzZ3Q1BT+V7c+xt8j7mwG/FSqMmRbIddGZq4Zc24eD/6xnGIZKVjIIc412GlfHXZs49uZrzNw40JcrXlUjDuBiy0/+R20dg2uVhtHgrxI9syl77afePHXyXzdqCbjWrdz2pfJMIhxK8/iqMbkmi10/2sHj//yGw337AAgMNO54zUoM4VfK1Sg2fCn+bFKNVoe2M/H38+k1oljFMtIx8UwsJlcyXHxwDsH8kw+jLirFwklcuhw+A9yDC9+9W9Lmqu/vXOZvz8HF6z4kUK9+J2Ens4/dnhKGpVOH6bLo49R78RBmhzeixlwIY9qSTvxsKVjM5vZGFmc9RGhDrEe9vMhw2LhhG8w7kkuHCgWZN/2Z2QYb3TLvybLKzlP7b0nNJw9Y0bw+OqFTttKpKXgMnMFaa/M46dnt5KQYGAzmTnkV5KFu4Nh8KcArPpwHzsXxJKXZSPtdDbLR+/m6CYt3iwCwF1jr4/OZTknOxdKP3L1ncsAsUlX17kM+b8YC+pcBjgcB/WfcexcBvh1JzQeea5zGfJHmX/1C7w4/eriEJFrTjmycmS5tSXG5fDVe0c5dSwba57Bjk1pTJtwjF+PGgxeYuNoKlgNE28tmMeEVm2JKRYCgPXvm+CG2czR4EBKJiVhwcCMQXB2GuVzjuBhzR8w7ZedQYXTJxlx+11sKlmK075+zKtXlY+71sU3I5dcw4M83EkimFgi7LHtDitL8QyrPedP8fSnbmyiQ/zrS5Vle2gk2T7u4GoGV4tz5/LfgjIzARsupOLGGcBKDp727TYsZBDAT6U72TuXAbJd3fHJdOWVnxdQ7nSS/Rmr0vHpBKZn53cUGI6dMTaLmb2hfnzRvCFd9+3hf/tcGLc6+4o/HxG5yRgGdH+naDuXAU4m5T/lfDTesXMZCu5chvx88ceN0Mf53u8VW7ENHvkcjp2BjGyYsgJ6vOPcuXw2nswc585lAKsB787J39+N5PMl8MYsOJMKiWkweg586HwPVORy9ARzEZk1axYpKSn07NkTNzc3Zs6cydNPP83o0aN588036dixIy1btmTDhg3MnDmTwMBAHnrooSs6xokTJ3jwwQdJSEigc+fOVKlShczMTLZt28bvv/9Oo0aNLtp2586dDB48GBcXF3r37k1wcDCrV69m4sSJ7Nu3jzfffPOqzjsvL4/HHnuMnTt30rlzZ6pXr87evXsZMmQI/v7+TvVXr17Ns88+i6enJz169CAqKoozZ86wbt069u/fT2Rk/tOAH374IV9//TVVq1ZlyJAhZGRkMGfOHB5++GHGjRtHs2bNsFgs3HbbbUybNo0DBw4QHe3YqbRgwQICAgJo1qyZPdZhw4bx119/0blzZ+666y7S0tKYM2cODz74IJMmTaJKlSoO+/jf//5HcnIyPXv2JDg4mOLFiyPnGGfSMFbtdyzMycM6bX3BP4xyrRBzCsqcu47mrefa23DnwukrbPHZGPGpmIrldw5zKglrXDYXjqex4YHl75HC31dvyMOrVlEr9hAAK8pVJcXTcVT9oaBQPm7SqbCn6sTNlmdPgAHItjnVOePizWeNOvDcynkAeFvTSXXxc6jjbU0H4MGNCwhPyiAoM/99ipsniypVZme4P67WHNZ/PIqSSfnTbd25/Xc8cnL4qHlnh3013XWEZ+etJdUjgOojPmR/ifzRf655VrrvOc72sMpEJJ9yaDOvar38/5hMPN+1J912bnc6jy67trC8fHUALAYEp+fgYct/gni/R3lsJgs+Vucnis/yIJtS8ac5HZr/MyHbzcrRwGJ037HRqW73nRtZX7rCuelWXUx/Pw6d/8eoYRhEnTiJX4aNo64h7KpQHC9rLnNqVrPv440ubSiekka7XfvIcXFhTq0aTGtcnVd/hRVlatLq0F9YDFv+bvFiS/GSNDi1m4wZ67EWa+sQT6xvCdJ/Woy3YXBg5WkudGBlHFF1g5zKRW45y2+w5OtWkXLxn81FLukisWU5P/UDwA8bYNzA/y6eG5xRyOm/9IyTXEvKkZUjy61px6Y0rHmOnQkHdmawf1suZ/N4t7xc8swWclwKvo15PMDxe8WFXEw47tOMQZVTsawpW85etrZ8KTLcXfHJPvf3RAp+2DBhxiCmmPPaxxEXDKZL8fDEcC/cb8ylFSuBORUXW+bfMeWSbvbloEctgjMyycODleXLkOjp/GTe5qhyfNWsMebsHPLMJlxsBq42gwqn0tgT6kPShU8yAycCfAnIzGR6vUb03LSH+SEVeK5lAdNqi8itY8cR2HXs8vWuZ2v3QGwChP2D+2tzNjiXWS/SsV0YExdC6+pX3/5a+6GAgZXfr4MnnAdI3ioKmyOD8uTzqYO5iMTFxTFr1ix8fHwAqF+/Pv369eOZZ55h9OjRtGnTBoBevXrRv39/Zs2adcXJ87vvvktcXBwTJ06kcePGDttsNufOrfONHTuW3NxcJk+eTPny5QHo06cPzz//PD///DPdu3enQYMGVxQPwPz589m5cyeDBg3i4YcftpeXKVOG999/n7CwMHtZVlYWo0aNwsfHh+nTpxMaeu7pxEGDBtnP4dChQ0ybNo2aNWvy2Wef4fr3uoQ9e/akd+/ejB49msaNG2OxWOjatSvTpk3jp59+4vHHH7fv79ixY/z111/06dMHl78TlpkzZ7Jp0yan69erVy/69OnDhAkT+OKLLxzO7+TJk8yePZugoOunAykhIQFvb2/c3fOTjbS0NAzDwNc3vwM2JyeH1NRUgoPPPS0bGxvr8Flc+P7kyZMUL14c09+jeAt7jCBvP/Bxz19j8jymEn6wwzl2AzCF+jocw9XTA1NqfvJnooCvYxPw9zpEsbGxhPkHYTIZFw7mdWhbIjWJA8HFSPLwIiArg8gk53WQLFYrgRlpzscrpJ3Fw3DPzSX77LqZFwYEYMDO4uemUK6a8RcbfJtgmPKTav+8RKKyDwPgYtjosG8LBvnTf57x8OePUqXBbKbf1vX2zuWz3ls4nc8adyDvvIR84IqtAMxpWNneuQyQ62JhbckQSiW2xCc7nfqHt5Dr4sLYFl35ul4re70z3t4FnqtT0m9Agmf+z7pc09/xugbkf74F7gEy3c5Na3rSN//76Zh/MNEJjh3eRwP+/rq1mMDPLX/aGshfGyY9F6w2Zn43lV2hVfgruhy5ri4Ui08mJPncTYF0D3cGDuyNZ04OGCYy3V1ptW83v5Srz13bV553NBNWPFheoSYNTu3GFOzrFLerNRdLkDsG4BXsRmaSY8eHV7BbkX4PXutjlChRgpMnT/6nx7hZrtUtdwxfz/yRsnJdudTP5RuNrbi/fWhZUX1/uLlpim6RK6EcWTnytXTD/y11Ex3DN8D51qS7hwkPj1wg/5i5Zgs+2Rd/+tZsszn8HWPD4vR3jQ04FOQ4S5hnbi5ueY4zEJix2Tuni6c4z0CV4OX4+71K7Cl2F3LghFdOFh42xw5qb1sqr/ZsxUnvEqS5ufPiknl4pfuR43nuOKd8vfiwdW0y3PK/l72yc6kTE48JE3vDAgrsXAaod/gYm0qXotrpw5zy8SIq/8drkX/mt/oxlCfrGEV6jGDf/Ac0CroveYMwPN0w+eU/GHTV16pEwL8aU2ao73nzUVxnn3lBxyjg/G3F/Uk8c0Z5slwRTZFdRLp27WpPnAHKly+Pt7c3ISEh9sT5rFq1anHmzBkyClhD9GKSk5NZt24dTZo0cUqcAczmi3/0CQkJ/PXXX7Ro0cKeOAOYTCYeeOABAFasWHGx5pe0cuVKLBaL0xRcvXr1wvuCzqp169aRlJTEPffc45A4X3gOv/76K4ZhcN9999kTZ4CQkBC6detGbGwse/bsASA6OprKlSvz888/O9xAWLBgAZD/uZy1aNEiSpcuTeXKlUlKSrK/8vLyaNiwIX/++SdZWVkOMXXp0uW6S5yDgoLsP9QBfHx87D/UAdzc3Bx+cQAOvygKel+iRAn7L44rOYbJwxWXFzo67MtUvxTm9+8GP08uZKpeEry9HI5hjLzDPg7ZTBamC9YrculZFdPfSVdYWBh4uePSuzqcP3rZzYSZc99Pryybzbf16vNrqfz1nusej+HOvxxHcg1b8zPP/vojZptj8mmfiuqCP8xMF9ygynNxITg9Lb8TE3A3FXADy81MqwPnetrDcmNpn7SIGulbqJ+6jlbJv+BC/vFzzS4YuGAzmTgYEMqrt3WGv78nPHKdn+ZytVlxt56bRrD+vuPUPJz/hO2xYD+n+snuLlgtLsyq3Z0Rd7xK9HOf8VrHuxzqtNy/L/+p3vO+FmzAD9XO3VizmSDB2xXfDDjhG0hUdv60N+kWL3Z4lyPX5Dx1WDJ+7I/M/5o74efL/oBIiqWl8OJtfR2OtT+oBNPqtMh/YzKd61wGcDETmZ1JxZQzeBpmNlWuTK6bK5hMxIcE4OrlyoVCkjLwzsqhZFwSGeZidN7r+DVgwsBMLvuLFccwuxD0eX9KVHbsZK5/YjMeo/pgMplo9GBZTOf9qPUt7k617hFF+j14rY9xftJ8I5+HjvEfHOPd/sj1x/RE18tXKio96jv+nD+rfQ3nMrMZ86t97G+L6vtDRK6McmTlyNfSDf+31E10jGr1fImKdnyqtt2dITzWyIuSfx/GMJv5tVx5Wu3bQ0F6bd7mcIPThgtZOM5KZjWb8fo7H7e327Ad8wW5fBBnMJGf26a45nHG89z3kA1wtdookZqJX1YOdY+cZPzc2ZRIduw0xjCwnO24thm45uYRHZtAtz/2YCtgOF+OiwurKpTljLcXPfatolhCErlWA9vfE3Stjo7AdF6cGe6unPZ1xSMrjVN+F3Qu/12vTFwSnTYf4bbtR7h96yY2VIliZJf83L+oP/Nb/RjKk3WMIj1GWBA83IEbmemZnuCd/3vjqq/VQ+0g6rzZIsxmqOA8a0WheLnhOeZ+52Nc4n2Rf1093QN8z7sX7+WO+fk7lSfLFdMTzEUkIiLCqczPz6/A6aLOfhMmJyfj5eXltL0gR48exTAMKlaseMWxnThxAoCyZcs6bStTpgxms5njxwtYj6AQjh8/TrFixRxuHED+D5eIiAhSU889zXTkSH5HVKVKlQoV74XTeZ1fdvz4cftUXV26dGHs2LH2KdAMw2DhwoWULVuWypUr29vGxMSQnZ1Nu3bOa9eelZSURIkSJezvS5YseclYBVye74SpYRlsi3diKheC5Z4GmLzcYMcH8MFP8L/VkGOFu1vAhAec2lte6IGtWiS2sQvA1wPXZ7phm/QrxsEzWPo3xDzU+fOyzHwMU+vlWL9eh6lUMJaxd4HNhrXeSMzxSXSLP8D6nrmMKf0oea+74mNN5cG1K9gREsne0DDa7/mTGicOMaNGU27ftI0dkSWI8/WmVGIcPbZvZl9QCCvKVeb42TWZyU+AsVoJS00hNC0Bk8lM+bhYeu74nfDkROJ8/NgYWIHxzdqRa7GAq5lyKWcoGZ/CYb/iRKWcxoyBty2D6Kz9ZJu8SfD0oVhmKid9/Hnwrkf5K6wMVpOZh1b/wUm/c53EP1RvwLuLpuOfdS7J/b56Q9Ld/07aDYMBfz+9DNBo7zG+b+w4lV37HXtpv+UvTvn5sDUsmB5bkphdtxoJPt5gGFQ5GcvUGVNJN3lz3DeQ0qknccGK1WSi0eG9HPcP4KSfL4GZyUz7ZjamTE/63/kUteL20en3Q+Tl+hPnGoS3WzrR2fvIw0IWAeThiisZVD21m0n1W3Iy0JcqscfxyU4mz92DHgNG0nPbRtrsPEx2pj8D12xmW0QJNpaKJM3TMbGOTEjlQGQAf0WU40I2N+cBDWleZh5ds4jTXsVIdSuOywVr6f198cgzW8gtHYpbrZL0+jSCPfOPkLh4L6Vck4ka3wsa5//cLd+mOP0me7Jv+Wk8/V2p3CUMT3/9ISUCwEPtoWxxGPJF/tpL/l756/7kWMHLHSqE5c9E8OchyCvoe/EyPN3yb65dbPrkf4ObJT/eC0UFwfHEc+tGFTQ63MS5cU+uFvB2g6QC1i4+v975ZRZz/vUBcLeAq1t+PJ5u+etYZWQXHNvFuJph7vPQuS70b5m/FtW2w/nlft7g7gruLnDg5MX3G+IH0x+HdXthyVY4cCr/vOuXy1/HKjYRKkbkf547j4HNBt7uUCY0f0mMltXyO5B/3wcuZkjPzp+yOzQAHukID7SFw6fhvg9hSwxEBsPMp6BqFExdAV/+Ainp0LACjOgK1UoV/vxF5LqgHFk5styaLC4mhr1Rhk2rkog7mUOlWj5UqJ7//bDlPgtTdxgcTrGx268exf48xMObNxATGMSfoWGkuroRmprOjmql+Sq1CS027cE1z8rGyHAS3LzpvGcn7m55xPn5YrFl8dm3M/ixenVi/QJovOsYVfcnk+NmIyArAavFhJc5EXdzHtZsOOEbxI+V6hHj40LLveuJTE5gdXglrF7laHDoDKe83Wm3609qHz/ENzMm8XbbLvxeKpIkTw/MuXnYsmxgNlH7+CnenfUrgen5gy+SKUYgcfbzjwkMYWHl2gC0O3AACzbqp23gm4hBbC0fycD1uxjy2xqiUo7xe8nSPN+1G7kuruwND+bD/1vEyUBftpXK/zlpMgweXLONYulZFE/NID3Amya7D/Fkl95sHuZFZLCeMxIR4JPB0LY6TFoKmw5ARg4EeENxfzh0GpL/HsB37gkfcHOF7Nxz014V9gHoyCCIT83PDcsWh2L++U/PtqsBB09BsG9+Tvfxovypu8uXAD+v/PyxmF9+fpiTB3XK5ud4HWpCpzr//BqEBsCWcfm55JlU6N0EKobD9FWwYFP+VOLHE8DHAzrWgugwqF0GvvkVFv+Zfy38PKBjHRg/EHwL9/fodaNGadg+Ab5emX/von8LKFviMo1EnKmDuYhYzl+L9TyXGjVtXKdTV5w/SuV81gI7Z4pep06dmDBhAgsWLKBRo0Zs3bqV48ePM2zYMKe65cqV44knnrjovgIDAx3ee3hoLZvCsLSpiKXNBTd2IovBmPvzX5dh7l4Xutc9975V5UvU/rvOI20wP+L45IPl1JdA/t9FtYEZVYC7z33et9n/1wBogPNKjoFM2FiWRUtzyMi14Z2aQZ6rKxg2QlIzMAPF01O5b+M6KiXGEJ6SwIroqvxZrBQd/4qh1/79tN4dy5guTQmJ8uSpgSXo6jKEyIREvvnuS8onHsWEjRw86XvHIH4uV4mOx/ezqFo18iz5P75d86x02r4X36wcVlfIv+F12jeANg+/ymtLvqNswmkWVazFq38/fVw2LoHhy9YwrXVNfLJzqX7kNPX3n6DTpn38UrMsuS4Wqh0/TfnkXA6GBNJ90xZu+9OG1WSiREISq8pF02nvDqrEHedQoA+7ioeSZ/JgbOWWvHp7IGUaBnOnpwe9bAbuEQEYNhuYqmEymfjFasO63MbJjxNYlBuANTMHz5QSBIcFEjLjAbzO+2Os+d+vfBUA+OvXY+x/cj5JNniuxx24ZsFDy7fS2uU4E3q4sKqKY2dCjcOn2FAhkh1hkRTEZDMwTNgHBST4+PF/jdvy3vdzabl7Hcd9IolOibHXt5rMWA1P3HJscOgUAC4eFqreVQbuKlPgMUIr+hFa0fkJcREB2tSA3R8VdRRyoTrR8Pt7V9++fW14pc/l612NUqHwawFrnA5sm/8SkRuacuSioxxZipqbu5nG7Z2fdA/yNPFEPRNghjY+QLUCWp/NI9v//YJzq6l3dqrdxqnEkWGzYbMalDFMPPZXOlt/jycqzp+MjhVo52Wm59GDeLQsiaWYOxWL18bk1YA6G48xJcKXjONH2ZzlyaQNWezyDSLB3Yu9JYvxf61qMHj5Vvwyc9hrrsGxcrkE2BLYWTyS34Nr0nTXcaqeSCQqK5sjVCDDw0z1pP3kHXej/tGNtDm8BoDmR9fT/PBmGg17HcNs5lgxf16etYphD97GqUAfKpxKoOpJx2m9zwR6094rl8hg51m8ROQWZTJBryb5r+tF57qXr/NvC/aFJ7s7lj3UPv91Md2vfDmU61bJEHipd1FHITc4dTDfpKKiojCZTPZpr65EeHj+dBAHDx502nbo0CFsNpvD6HI/Pz92797tVLegEdwRERFs2LCBtLQ0hxHaOTk5HD9+HL/znsIsVSq/s2jPnj00atTIaV/n7xPgwIEDREY6diSdPYfz4w0ICKBp06asWLGCjIwMFixYgNlspnNnx8QjKiqKxMRE6tevf8mbGnJrG1HPhRH1nH+UHk32YsH6VFLSXTnZqjuZRxJYUCWC26u5MrSkY/1O5/0/zyOF4wF+1H/yRR5cuRpXm5V5FauxPziEcvFneHn+r1hdYUW5Kvhm5dB/3UaSPD1IdXfDLTeXHBcXTIbBnqAwptZswfroyhwPyL/J45uZTfHkTOZVqEi1kyfY2zUac5kKuIYFkLY0mTJH46h56CTHiwXxY+lwmrlk4Fq5GH+UrMT28AiCU3NpGnOC/h2CiRh1JwA1/477/Mluz781aDrve8dkMePSvhqR7asx6CqudY2WkdTYNASAB4AH79/OlIbVuXPTAR5YuoXjQb4cKJF/U6LZzsN45OZimE1sLRFOuRTHJwMNExgFTLV695bf6LF7Pem2krim+JBKcTxIJsfszl/hlYlISCAoHaxtC5iSVURERESuW8qRcTgH5cgiBTOZzVj+/vJuXM+XxvV8YUjBA4rP8igbwtmhFCWBnved25aZayN6Uk0W1a1I9IkzxPj7kuDticnbBcNs5ouPf6JY6kGyvN2pcuQImypG0W/AA2T/Pb39qQCDpl9ssC951eDYXjrt2cqq0lWpdCwOz1wrP77zPyb2aU1wekEz4pg4lVhAuYiIiMg/pIzgJuXv70+TJk1Yu3YtGzZscNp+qZHeQUFB1KhRg1WrVrF//36HNpMnTwagdevW9vJSpUqRnp7O9u3b7WU2m40ZM2Y47btly5ZYrVamT5/uUD579mzS09Mdyho1akRAQADTp08nPj7+oufQokULTCYT06ZNIy/v3Bqz8fHx/Pjjj4SFhTlNg9a1a1eysrJYuHAhy5Yto2HDhoSEhDjU6dKlC2fOnHGK9awzZ84UWC4CEOVv5pGO/jx7RyBv3hXEM0+XY3xnT1qUvPS4nj8e88Jqhkw3Nw6FhnHYJxTDMHPnth3M+N9sjgYF4JtmIiI5kdgXvSn/VnNeuq8jnzavS1hSMt23/MnLC38kIiWRw8FhtN62ndr7D9Jqxz7a/7kfs5HN5HdK8uW8Zrz1cW3ufboGfe8pyeop1dnzcSTfLajHmqll2f1ROF/ObcEj2x7l/xa0ZsOkCiz8tipvrmpDxKhW1+QaXs6HH1fiePEAxnasTYZPHt+Pn8q0D75n9nsz6bVuJ591rA9And1HndomuDl/DuXjTjBmwTf42hJxIxUwk0YJ4qjIzJp3kIMHiys04WCxQDw/H/xfn56IiIiI/IuUIytHFikKnq5mTgxxJXGULxsnlWa++1+0PHYYy9/fr982qwpA9PGTBJHIC9162DuXAX4rW5mZtRyfMqwae5y3ZyzHMzd/VoQML3eCsvPA4uKwVjNAns3gri3L/8tTFBERkVuUnmC+iT377LM88MADDB8+nK5du1K5cmWysrLYsWMHYWFhDB8+/KJtn376aQYPHsygQYPo3bs3wcHB/Pbbb6xbt45OnTrRoMG56SBuv/12vvnmG5555hn69u2Lq6srv/zyS4HTf3Xv3p05c+YwadIkjh8/To0aNdizZw/Lli0jMjLSoY2Hhwcvv/wyzz33HH369KFHjx72EdPr16/n7rvvplWrVpQuXZp7772Xr7/+mkGDBtG+fXsyMjKYM2cOGRkZvPHGG07TrTVr1gx/f38mTpxIeno6Xbp0cYq1X79+bNiwgQ8++IA//viD+vXr4+3tzcmTJ/njjz9wc3Pj888/v5qPRuSiyga58HT1PCb9forV5cvw7JJVjFm4BBebjd/KlWZQ/zuwebqwsr8bZnczjzSARxqcPzV0CaAeo+zvy1/zc7hWvL1d+GVaZX5dn8zvW/2Y+dtx9lnNZLn4EpCdSI6rCxgGtfed4FhGDodLhZLnYiH4TApx/l545Fjxzs7ljG/+WPMmh/Zi/jsZD+Ig2fhixZ2PWvbC54yVLRFVMFmtlOoZAWWCi/LURURE/qGCp+8VudkpR1aOLFLUGneMZEzP7+nz0EBiQoNZXrMMvnnpvPrtb1iwsjck1KnN7pBzMw7YMFFxfyZ19iX9XWIQV9yLqkcOUfnYcYJSU1lQoyYmw0Smi4lx7Rqy8dNF1+bkREREbljKka+GOphvYhEREUybNo0vv/ySNWvWsGDBAvz8/Chfvjy33377JdtWqVKFr776is8//5zZs2eTmZlJREQEw4YNo3///g51IyIiGDt2LJ988gmfffYZ/v7+dO7cme7du9OrVy+Huq6urnz88cd88MEH/PrrryxfvpwqVarw8ccfM2HCBGJjYx3qt2zZki+//JLJkyczb948MjIyCAoKonbt2pQrV85eb/jw4URFRTFr1iw++ugjXF1dqVq1Km+++Sa1a9d2Oj9XV1c6duzId999h7e3N61atXKq4+LiwoQJE5g9ezYLFy60J8ohISFUrVqVrl27XvIailyt5/qF8Vw/yMm1kt65Nlm2eviW9KVXmBddcsHXXZNPnK9lI39aNvKHR6Lo9vphyv5xmPqH9/BNgwZgMnGkmB8lj8cTcTwew2TCbBj4lbLh83sKf4ZXxCsnj5pxSfhYS/BFk/5027aEsNTTeJDKGS+D19s25sPpS7HlZBPydE0e76fOZREREZEbkXJk5cgiRc3cojy2SE9a7t9HntmMb1YOtU4cwZMskgmg1f69LK9QyaFNg5hDAOTgzu+BdXnmnh74ZWQSkZBKq337GPbrOnvdXSVCeKl7M9rt3cvgdatI82zGsZLlKIeIiIjIv8tkXGoeKBERkRtIXHwW79+zkns3rqHGsy9htViodug0o2auxGTkj0QrnhNLo9Q1bIqowLDuI6iakIRv7rmpA/0yU3h58XhsJri/zxB+LVONo6OCsFjUsX8lcnNz7VNGDhw4ENfzpnkTEZGiZZjuu3wlwGR8/R9HIiIicusxcq2sGLeRrzfn8EvZ8vRat5snfltJhs2XvMA47rtvMFsiS+KZk83AlRt5cOkOTFjJtLjyfYvqTGlRiwQfd/v+Om7fQ8u9BzkcHMh3daoTnprEwi8+oc/9D+KSY+O97v407hpWhGcsZylPFhG5PhU2RwblyefTE8wiInLTCCnmQVRuCnkmMyN+Xc64Nu3ZXjoUa4njNNl7FA9bJsF5+WvD+WVn4JWb59C5DJDi6cfznYbybe3KxPr589ivq0hIbkVIkDqYRUTk5mAUcvovTRImIiLy7zO5WmgzsiFt/n7fp+txzrj5EBfkRYsTJ9n8wdMc9Q8mICsdn+xsFpVswpcNOrAvPIQP50xlfcVirPWJtu9vcbWKbI2IYMSC9bSzHCQq+yTNhz9JcEoG90Uk07hrpYIDEREREaDwOTIoTz6fOphvMBkZGWRkZFyyjsViITAw8BpFJCJyfRmy/C6mPbee2779jTLx8Xxfqzb/q92Y3tvzpw1Lsfixw6s6WwOjiTpxmlw3V1zNjn8abAmLplrsSabMmMbcyo0o5lkUZyIiIiIil6McWURudPvDAjnkHc6LvVoRkdGQRV+9TVRy/sDoXLOFd7u0Z0PJCrjl2Wgz/CkATIaBYcrPY002g6fmryXF0x2fzBzqPlWbJrk5tGhVihBvDZQWERGR/4Y6mG8w06ZNY9KkSZesExYWxo8//niNIhIRuf7cO7oRjG5EW+DUDym8caAiD98xiG7bNzG3TDv2hoXwW+WSGGYTrnlWWsecpnJ8CgApri4EZcTz2eypzK7bhmyTCyZPTVslIiIicj1SjiwiN7qe3mlYXFK4Z+1mXurTieaPvs5Dv/+Ci81gddn6WAimbEIau0P97W0Mk4nAtExqHjrFbZv2UvZkIine7rT4sCHtG/kU4dmIiIjIrUIdzDeYLl26UKtWrUvWcXd3v+R2EZFbSZg1E/Dki8bt+aJROzA5Pq2c62JhdclilD6VyN4SwewK8aXn7sO80e0hLNY89gZ5FU3gIiIiInJZypFF5Eb3wuiq9DuRyyc/fE5asQzea92D9aUrgGEQkZKJzWwi1sfDqV2muys99mxnR72y2KpXY8TwKDxcNHGniIiIXBvqYL7BREZGEhkZWdRhiIjcMGqlxgFB+W9MBSfbme6ulDpyHJecJJocsJDuF4Brbi7mtFTadQi4ZrGKiIhcG7r5LDcP5cgicqOzuFv47ru6zJz5Gg3fmccvH4/nzgGDSfDz47j/xQc8mwyDgXcG4P90jWsYrYiIyM1IOfLV0EIcIiJyUwtvEHbZOj6Z2TSIPcwpHy+OukGZg3swZ56mz4e1eHlIqWsQpYiIiIiIiNzK+vQJp+fWR2l1eCRhyWnnNhgGkN+hfJZ3Ti51DhzHvfbl810RERGR/4KeYBYRkZtaqWrBeM9NId3jgpHfhgEmEybD4JGVf2C45LGpclliRgUVTaAiIiIiIiIiwMR5c+h9/32c8fEFkwnvrGwaHDuDzWIhzssNt7Qs3LLzcG9doahDFRERkVuUOphFROSm91J4Oi+fciHPxTV/muy/O5eDU1MZtWABa0tG8+A9fdj2QkBRhyoiInINaPovERGR61mzH/vwf7dP53iQHx55ebTbuxv33Fwm127GIf8SzK9ZkSkPemIya3JKERGRf0458tVQB7OIiNz0Rj4QxlNWG2/MSCL+VBZ1K7gQvmAjhmHi+27Nua1nCabXdC/qMEVERERERERwrV6CHvsfI/nP0+w7nM7ChDZEVw2gSqJBrzLufFb+4mszi4iIiFwL6mAWEZFbgqvFzOv3njf9dY/OAHQuonhERERERERELsW/Zij1akK9og5ERERE5ALqYBYREREREbmFGJr+S0RERERERARQjny1tFCHiIiIiIiIiIiIiIiIiIgUijqYRURERERERERERERERESkUNTBLCIiIiIiIiIiIiIiIiIihaI1mEVE5Jby59gN7J2wm6jkZNLc3fitXBQWsjkSUZxJ3zct6vBERESuAa0vJSIiIoVjGAaf3b2ezKPpmA0ISk4kKdvEgY6Vef/DKljM+rtCRERudPpddjX0BLOIiNwylvWbx+GxO2lw/DhhaWmUP5PAgA1/siGiDKVOp9Hzrk1FHaKIiIiIiIjIdeO9FsuJjzOI9wvgeEAgByNKsq1iaSr+vJPX2/1a1OGJiIhIEVEHs4iI3DK+TSpGmcQEhzIz8OSq9SwoX5aSGbaiCUxERERERETkOvPd96fJcPMg19UNFwM8bQYJbq6cDgxkbvUK+B5LIivHWtRhioiISBFQB7OIiNwy0tw8CiwvG5/I1MlzcM3K5tTxtGsclYiIyLVlFPIlIiIit7ZfvzpErpu7Q1lAno1qJw5SLsfKurIRpMdlF1F0IiIi/47C5sjKkx1pDWYREbllJPp6FPiXgAlwxUrnLTu5+4liND6yl05HtpJnc2OfRyn2FC/OM6PLUbxVyYvuu8/A7Rw2eVMyIZ57f/8Dc1go0YOrUXFwRUwmreMhIiIiIiIiN5YkiwvBNhsGkOHqgmteDsEpKZQ5EcvselHU3bMfS2Dtog5TREREioA6mEVE5JbR8FQyXnk5ZFssuFsdp/EyTFAsNYMHtv/C3bs22MtrmY+yKqkxC/vHc//RqAI7i1v134eLhz9BJhOpxUrwbrcefD3jfXa+lsKS+WcY/F1DPLz1K1dERERERERuDHkH4rl99yb+LF2VRF9/kry9ADhWLITjIcW4d/Vqfq5QHh93SxFHKiIiIkVBd7tFROSWEZCWgc0MsQGBlD4T77At3seXVBcPeu1ei81kYmNkaYIy0il35jRR2ccxiGRypwUs9wzBajZTKjEdc66NhhUh0ChJ9t8dzyaTCX+bwUvdH2XM7Ekk7/BjyYcH6f58haI4ZRERkQJoZg0RERG5uON1PmD/aSvvt+nOjuAgupxJxgzYgCyLmZjAIM6E+BGanMWQXhup1dQf/8pBNK3uTakoD83iJSIiNxj93roa6mAWEZFbRp41h0x3gzQ3d/aHFqdM3GkshkGilxfbIiPxScogJrgYXR4cwcFioQD02bqBcd/+yBGPKHYme9Dm0AmyvTzwzUyn8rEYfk8qR04jd6dj+WVmMrbd7bRfvYNdS0+BOphFRERERETkOpezOgbTjiM8ce9QtoSEUiU5DZ+cXAwgwd2VZDdXAL5u2JSea7dS7ZddTM6KYtNhL9wXpFMqMYUXB5WkXRv/oj0RERER+U+ZizoAERGRa6Xm0UME2xJIN8ykursT5+PHcf9A/owqRaarO5Hp8f/P3l3H2VGfix//zMxx2T1n3SW7cRciJAQIDsWhUKSUttT9tvdWfveWyq17qd0KLS3SQlusQHEIEOLuyWbd5bjPzO+Ps1nJBgiBZBPyvF+vhZzR73fO2T3zzPMVPn3ZzUPJZW8syUuVU/n9nLNwEqVYUektySec46a9uIiV0+dy4Z715EZiY861bPtu5jW0MiHaR05X6HhXVQghhBBCCCGEeNO6b7qPtbXT2VxQSF0kxvK+IJppYjFNihIpvKk0AKaq8vL0ekpiMb759Cra7vg8T9/7AwwtzSf+GiSdMsa5JkIIIYQ4lqQHsxBCiFPG2slTMXYpzA3uxxJ3DC0vCQZYO6mKQI6LF+onAnDjC1u4cOM+LIZJyGnnuYVz8Cfio46XcthoySth0Y5d9Ob4KekPkLBZac/3ETNNzt21nwxQ09Z3PKsphBBCCCGEEEIclb4BlX5/Dq5Uivl9AVKqSthmxVDAlc6Qk0oTHuzFHPQ4cepJeqzFNDKD/EAn9//zV0z+8DfZsCHGosWeca6NEEIIIY4VSTALIYQ4KTU93cJvftjEuhwfZdEw5zXvZO5/LmTaTTPo3TnAnR97lefyikFVWdHcRNyhUdfZxZ7CcqZ39aCPOJaCQmlvkC9ffB5JRWX2gU7etX7v0PqceJLK1h4iBSOCY9PEHUmiGyqablDVnU0i2zI6nlgnf1qygH6vm4s37iFvqvc4XRUhhBDijZkyv5QQQgghgK2ruvjwnUkU3WR67wDFwRCzvBXU7+3mibY/8eKM+bwyYQLm4JzKCYsF52APZoBZTW1U0UJZWiVODn1UUhLbx8L+Djx5k8erWkIIIcSbIjHy0ZEEsxBCiJPOL894nNhAml9dvJyA044jnSEvE+C6936LZ//faaz3lXDd7s18PBHj2yvO4z+vvghTzc4KsWRfG5dt3wrm6GNGLXZWVZdCQmdKa8+Yc/oHwvSW+nCkMzhiKaZsa8eRSAM2KpNRuotctOR42FaUT348wdSuHh6cNY0ztx0gtKLqOFwVIYQQQgghhBDi8Lr+93nafvAqnc4cIpY84g4bf50/jaaKIj6yYRdW3aBuZye2lILTSFPf28Xz6EPJ5YMSFg3VMJh9oIUrVm0irLopMbpxkKSHMsL4aM/Jx+HWxqmmQgghhDgeJMEsTjmPPPIIX/va1/j1r3/NggULxrs4QohDPPvbvexY2UGuBdacO5PrTvewrCY7/FY6rrPj3n2U7Ovh4zdczPyBCBVtvaimSSivns9e/gXKevZx9dbNFCRi9LlcfP/cc4eSywCr6sp4YlYdipbLjqoKHOk0y7buYH1xIYaqgmHS4hk7jFfM5aDH78MXjjB5a9tgchlAwR9Ksrkin28uX4ArlSEvliBus1AE7KorwZKTexyunBBCCCGEEEdH4mQh3nnadwW455pVlPTF0BWDF2aUMLl6Opdt3gd0ALAjP4eayADOdAo9lqHT58CVzlDWYxBVbagZfcxxi8MB/veff2G9awZddh9ddh8F6ULmRneSQuHZ2qmkLBZ2NiSoK7cd51oLIYQQ4niRBLMQQohxEwum+MdNLxLbMUB5oJeU4kXVVcpUhYaKXHoiTdz2cgG7i/KY19LO9Ru3cs7e/XidHv730edpLiqmtaIYFIUGn5uVs6vJaDP5n0sv4//9+wnO3reXpNU6+qSKwn1Ll1MbzM6nHAL+uXQxuzQgowMKq6uKuaDQx6SewOBOJt1luRiqSm9uDu5QYkxdIpqFq7Y28O5N+7DrBt0eJ0/OqqKxqIji3swxvIpCCCHEmyXDfwkhhBAnu4F17fS0xtAaAvT+eSut/Saramt4etpELtq5jVl7guQoDl6YWklDvpf3bFnD0j19jLwPeN+rW6jiAF2uHJ4tnY8CVId7qI70A3Duzh2smjg52xh70Lm7N7PfUUVaG36s3GvNo8VWgpJS+Vf9DN6z4VU8H7jweF0KIYQQ4i2SGPloSIJZCPG2SXTFSfYlcBQ50RM6kYSBqaiYhonFroDNitdnQQH61nVhBFPk1PvwTs5FT+k0/n4XbQmFiolu8uu9xHuTHFjZS8hpp6JApX5FEcGYgtIcoM9mI6/Ww+7HGpk31U6gvpI4GvUlFl79Vycup0JtpUagL0lJVy+pabUkm0P07QridaTY1WhgSYSw7OiiYEou/Rk7tlWtmFOL2N8QxTa3jD6fl7jFQn6Fk4I8C9u2dFPdGaLPsFPe2oc9GMKWNNG7wmA3ybt0KqsbwNYdwZdnxZUMY/VasS4oI7UvRJGW4kCnQmfGihpO4jgwgC/XoOTKeiq7WlDzLegdSfp0O0+0OvC0BciPB8mf5mPWHRcRjhqEPU7qauzYrKO/9JKhFKHH9mPb14Vjmp9+nET7k+zweJliTVB3eiltTTG22nIJ7eli6Yub0SYWk161F57ZRX/KAyk7psNOyOIEi4GrSCNV5yed66FwdysFHp3wliDWWARrIkyHy0GurtPnyCOjW9hRXoepqLhiIfYUlhDwuDl9dzMJjx1Nz1Dd2UVeuovWnAK2ltSwqbKYyzZs57SeA1Tq7dw98V00FxXiC8Wobe2hpjXIHStOo7nMR1VvhJhm5bLNe0jpNnLDJrndPZR1R3mwtBBbPMGaaWVktGzQm9E0vnbhRVz8o+34YzEGXK6ha+WPxJnT2kPcbidlyyafTVVhdizGqxkTMNGBr164hB8/+AJlgSjtRbncsOkxLp7yMRpzfUwvzWNWa++o96DT6+Hm9XuGXhdF4lyxYS/VeiexjhRsuBuuXAw/eRhCcbBZIJOB68+AG8+Ce1bC4+shnoZbz4L/dx1YNHhsHXz8dxCNwwVz4L/fDX96DjYdgI5+mF8HH70I5tTCiKD/Heeh1XDFd4/NsTXA4wCfG6JJiCWgyAdVBdA5ADYbRBOg65CXC5V+qCyEfE/2vQwnIJWB7U1wxnSYWAqPb4BcF7T3Zbc9fQqs2Qv5XjhvNlQXQTCafZ3Ws5+Hx9Znj7doIhTkgM0KXifoBtQUvXE9ekODjSTIfnYKcrL/zujQ3AOVBWCVWz8hhBBCiHeqWDhDJmWQkz+212gmYzLQnyG/wEIokMFqU3F7NAzDpLcnTSSYxmpR8OZasdhUMhkTn9+C3hkBi4pWMBxTBbqSuHIs2JwaA1v7iOwPk7e8FHeenWhLmOhv12K6rORM9pJ+ZBfx0ycT/XcjRpWPoqk5NPabmDlO8ko0mgJW6s4uwdzbT9phpaoIwhYn1v2dWJfVsm99P2W2FG0DCo5YnEpnBmV2JWgKoY19pBv7UYtzUKw6qtVKql/HXe7A6rfRsjNCwVQfvhl5x/NteG2mCU09UJQLLnt2UWs/xsq9xKImnf/7Kn0RhQGHl4BhwxtPUhLqI63YMTMqffkeVIeBOxln0kAHOXqUMiCpKegOF4GccqJOK4tbOymPwsr6Kn5zyQIMVcWVSvLnh+8kSOkhhVJIY6XdWTj0aH1ioGtobXVfL59//FF+fcZySmMRljbs5NydW3k5Z9aY6rU7immqK2FqKMRjU6bz/p374KUucFqz9e0KwN5OKPTAQxvAqsGHzwWnDZ7dBrNrYMnEbAxUVZCNad6qRAo6BrLH097i8QYi2ZjtQA9saYJXdkMikY3h0oNxmKZCcS7MqIKCXFg2BdwOSOmwcT/kuVn21BYa5xbDew2wAqv3wDObIZUGrwcumgslPsjzZo/Z1J2NGz3ONy5jW1+2LFd/Fxq6IBA78vppCuR5IBiDHBecMxP2tGdjYq8zW4YSX/Yz7LLBjGqYUAIHusAwsq/ffw68uAPSmWw8/Mg66AvBZYugMAdqiyCWglgS1u2Dc2ZDdWH2/O39YFEhkoCK/Gw8LIQQ4oSmmKZpvvFmQrxzyNBfb790OM3a962k59lOTCBjVYnkOOksz8OwaJiAoYChqsQ9Tsobu8jvCqCaJopu4k/pbJ2Wz9aaelAU7MkUCzbvpbgtRDTHTn+xB1NV0AyDJXt2kx+P8FT9BL5w8Xn0u5y40hlu3r2dlD2fuWv2UdcdIKMoOB1R6gPZVrcGOmFyCHjcPHTGPEIeJzP2NeJMpVEMg7K+ECU9EXrcbtKWbAJmR2U+X7v+TFIWjS8/9S8+/dKzeJMJmj1FaGEPu3PLMBMKCpCyafxr6TT2VhVjS2eYdaCFac0duFMJqnr66XTl0VGZR8KdDSKnt+/ljOZNWEwT0FGJYyGDgYqOgzQ2FLJ5L9BJKiZfO+sW1k2sx6qYfOaGXC5Z7sY0TV75nw3svK8RgMUde9k2oY69xRXkh0Ocv2kD7mQSMHhySi3dHhcXrWslhRM3A9SynT6qiJANuG0kCdkc7HeUkrZrpK0qKhlKkgEm9PWjY+XRyXV8bcUy+lwuqoID/Pypx9ldM42w0w2AYhjU7m2juDtIXiaGRTHoKvDRVOinsrWdiCVn6LNjI8Xyrq3cP28pT86cN7TcF4zS6XESd9hIqQqN+W6++uBzTG7oIqlYcaXS2AcTadvqyihJtXHJp24b89n801/+hCeZ4sPXvYdej4czdzdzxeZ9qGZ2CuaOgjw68/0A5IbC/GzKhFH7f/b5tVy5bT8vzZpEW7mPsq4Gbj99BbU9Qb73t5X4Y8ns58ud4ZGp1Szf1z+mDEZ+I1fuX3dEv0tvid0KT30Vzph27M91BNLpNHfeeScAt956K9ZDe5K/GYXvhd7I21Syk9S8CfD3/zx8ojmdgQ/8Au5+EYzB2zpFgRuXw7VL4CO/yT5UKcqF33wErlh0fMsuhDjhpJUPHdF2VvP/jnFJhBAnComTT26GbvLoL5rY8GQfhm5SO9vLdV+uw5WTjW3Xro7wlzu7iYR0HKqJkTbRNJh1mpdde2Ike9NopolCNk4yAQN437bVlO5uBVXBddNM9K+ew9+/s5+exjhuPUNlQydKLIOuKvQW5VIaCzCprYnyeD8KkFbUbNyPgYVs/BbDQReFpN0K9y9fxqTmJs5r2Eh+PIyOSnNuAaWxflzpJH0ONx+8+BpWVtXwvysf4SMbV2IzdNJY6aeEGG5iuAAFE5NWt5+01QKYWE2dimAAExioLeDMvdejaOPYIHdTI1z3U9jTAV4n5jeuxfz3TszHt6CjMaAVsKeghindTVjNDBHVRdqwo2HiJoidBGncKICumDjM1NChdVQ0UoTddr5z3i20eNzk9nTw2Pzp3LJ2AwubWslYFEJuFws2deBID4+wpaJTRQMb8yey01cNwIUHNmM1jFHF/+lpC/nI5lV4U9n3cburml6bb9Q2v18yjd1VhZzd3YfT0Klu2c5nNjx6dNerOBf+8gk4d+bR7Q/wlxfh03dCfySbYP7Lp+CMqW/+OIYBt9wBf3mebA+3gz8Hf1NejzpiW0ZtbwKK0w7x5NjdFAWuWQI7W2FbczZB/5Wr4cvXHP40XQG45vvw0s43UbETRF1JNsn87NbhZYU58KsPw9VLxq9cQohTypHGyCBx8kjv4K5OQrw+0zT585//zOWXX86SJUu46qqrePTR0Te+CxYs4Pbbb2ft2rXceuutLF26lIsvvpg//vGPAIRCIb7+9a9z3nnnsXTpUj7zmc/Q09MzDrUZX7u/t5WeZzsB0C0KuqbQUZGPMdjaVAFUE1TTxBOMUtg5gDrYtsXUFIJ+na21E7M30IBqmJS2BDE0lb6SbHIZQFdVNtXUELVZ+fSlF9DvyrbejFkt/GHqTKp3tlDXHQCgOB2iPjAAKBioRMhBQeGRZXPpKPQzuakVZyo7h66pqrQV+ujweYeSywDTWvq4dO0eLtm5ha8+/S98iTiaaVIb7iJhN2AwuQxgS+mctWEfACmrhXWTaunJ8RC1OdhfXER/oXcoueyLhziraeNgchlAw8SRrTsGKkksmAy3rdWwmyr/+fz9AKRNhR/eE6I/pNPwWBs7/tqMqajUBzrpzfOzt6QSFIWztm0ZTC5nj3z+riauWLePFNnrVsVewhQRIZ+DAVIKBwWpMCWZAaxpE9UwMbDQ4cwnpdjocTn5/IXn0DfYI7g518/vF501lFwGsCczVDUNUB3tIy8ZJScRZ2JrBwt2N45KLgOksLEhr45nps0etTyQ6yYz2NPSZpgs3ttJcVuEoN1Nwmaj3+MmPrh+ansL8zqbyY3FRx1DMQyWtO7kXbvWs/mb3+PpH/+GqzbuRT2YfwNKe/uxptNouk63onDlun1cs2YvBeHssTpz3PjjMYr7B+j1+NhSN48bWrqpxeSvly3kheXT+Pd5s2me5uPW3S9xKI0UT9dOGbP8mEim4fJvD/dgfafY1izJZYANDfDR3xx+3R2Pw59fGE4uQ7Z3xF9egOt+mE0uA3QH4cafQCB6zIsrhDixmShH9COEOPVInHxy2vh0L+se78XQs/eDBzaHefIPrQCEwzq/+1UXkbCBZmSTy5AdnGfjq2HivWks5vBf/YOps3M3b84mlwEMk9hdW9hy69P0NGZjpcL93SixbKJSM0yKOwP4egeoGEwuA1jNbBPqg8llABcJ/ITQohrLt25jcftu8uPh7HHQmRDswJXOxrH5iSi/ffwBLm3YxqfWP4/NyB7HSpoc+oaSywD9Dvdgcjlbi7RiIWh3ogL5B3pZf9Nzb9v1Pio33pFNLgOE4yifuQse34QCxPDR5itkbtceXGYSKzp+I0wOEUw04njJDCaXAWzm4LMMIEguvRTRRSXRhJ9nqvL5x6wqHlo8i9/f8zf+86lnOGvPbs7dsYu6zn7+sXwhAXf2mUDCrqG6o7wyYTqpjIPK5hAl7VE6nb5RRc8oCvaEQSblIYIbA4UJiQ6c+nBidG1VEU9MrWZPrpcHK0rwJtPEvcVHf726gnD9z7I9kI9Gez/c+stschmguReu//HRxct/fjEbWwHDvyEH//16j9ZHbguHJqMVOHxyGbLx3P2vZONhyPb4/co9r51A/tydJ2dyGWB/5+jkMkBPCG7+abbXuBBCHAdHGiNLnDyajJMoTlm/+MUvSCaTXHXVVdhsNh544AFuv/12KioqmDNnztB2u3fvZuXKlVx55ZVccsklPPXUU9xxxx3Y7XYeffRRysrK+NCHPkRLSwt//etf+epXv8ovf/nL8avYOOh9MTt8kkl2qOGE04Z5SMvg1/vT21wxukdeYU8I1YSE2zqUdD4o4nCyqayUiN0+anlaUwm5HUOvXcbwTXoGDVBIWjTaivJQTJOc6OhkJEDUZcMVHT1X7qS2fio6msZsqxvqmDjCH4xng4DBMnfk+ygMRUhZLCQdw703qwJdY66HiQUTZfBr6mAL15FUvGaInFickMuJYcKWvSm0V4cf1BTFQmwsmwSAM5nEHx2bRNLJXjcFHSdReqkas00SO/npEF22fFQDTAUyqkbY5mBteSmpQ4apsh5S1tK2flxGCushwZMvFkP1GqPmbgLo8uShH2aoqjM37OO5efXomsrcfW1jrkjUbsOZzuDMJHGl0/z07//gY9ddS8xmw6Lr3P7vB6nv6wagQO+kOOpk+yGfJwUoGQgwq7EBe6OOI50t8w2rdvGF689g6YE2WvJy0YHq1k5ayoqwAHWx7BzMnSXZ3s/VezuZHm4k6cql1VYOgGrqvDStlAt3bRpTt2NmIAo7WmBWzfE757H2jb+NdwlOHIcG3W+0HCCRHv06lswOwXbB3LevXEIIIYR4x5A4+eTUsCk8ZtmBzSEA9u9JkBlMKquHGcRQUZRsHHuImu7uMcucOzpgbgmqruOMj038WYyxx1EZu8xBNp4q6g1Tk+gate7QuK8gHuWK3VvGHMNGYtTWccvYYcHjFiu+ZDb2j7zUMWb9cdMZgB2th1mRvTYaOp5YakzdbWSfa4xM0Gdle8TGcZFgeOhyRbdyw6ub+fK151PT38fixtHPMma3NPLIvIX84srzcSVS6KpB0j447LJpsmBjA7O3tzCg5NJW6qA8EqbH5UZPW7l2awMJnCSABHbyjX5OC+/im6efy/2zZ5KwWTizsZ1ul4OtpQUELBac6lt87NwXhs1N2SmE3qwXd45NJrcPwK627PDVb8az2xjugXzouzSyN/Nx8OxWWHaYXtjPvE5MeLKKp2DVbrh4/niXRAghxGuQHszilJVKpbjrrru45ZZbeM973sOvfvUrrFYrf/vb6GTGvn37+OlPf8onPvEJrrnmGu644w7y8/P50Y9+xJw5c/j+97/Ptddey+c+9zmuu+461qxZQ2Nj4/hU6jD6+/tJJoeTrZFIhHB4OPhMpVL09fWN2qejo+N1X3d2djJydH1b9XBiF9PElsqMCVCHbsUPE7jmDwRHvY4MJoqtybEtS22ZNBN6+9AOGa4pmzRODNdrRCCjDiY6rbqOO57AVBRi9rHBpyOZGbOsodhHk3/sfE1WNT1mWdRlG5UQ9w72qNV0I3tNBgUd7jH7Zluyjh0yaZhJAifREeWuKrHgm+Adeh2xOciPZB8iJKxW4taxdVQ52NJZI44LK4kx21jIkFBtYJoYB78lTBNHJk3tQGDM9s3O0cn+w73HQ+c/zLqyYD8VA6PnM7amM8zb3sy0/dnPnkUf+1kwlWwQl9QdGMCVW7ay8xvf4uFf/5Yd3/omX37uXwCE7G5MFXzhDBZ99HusGjqfee4BJu3tGEouA7hTGT75/FYWH2inJ8cDQH1LB8vWbWPq7gYyBz9/psmUA834mwy2chqOmIrhjNJQZePnF83jd6fNJ2wb+z4cM1YNqgqPye/50fwtKSkpecvniF44dm6vU5U5tWLo36Ou1ZTy195JPeQBiKrC5Oz2x+I9Px6fKzmHnONkOYcQQpyMJE7OOtm+jworHRyqoNJJKpXC7hxueGweruX3a8RvvTk5Y5YlinKB7PRXaevYRsKGMvYEhzt6imwD7LDbQZ977HlGSmoa60oqxyzPMHoKHpsxNp63jYgjzXLX+L3n+Z7skL9jDI7WhorlMOU3h9aPvtY6GgYKKcbGmosaWgDwvEbPX3s6DYpCzGkncTC5DKAobJhdS9xhRTMVZnW08uMz5rLLW0ht/+jG62lspLBhoHDjK7v5+uOv8JMHnuX9q7fzxefW88Vn1+HIZMiJvbWRk0ybBSYM94J+U+/H1LExkumyEfYP/64c8Xs+pZzhxPLhPtGv9QzkGCSdp1Yc9rObrjvMVEonO0Wh2z/69/xE+rsr55BzyDkkThaSYBansGuvvXbUnKBFRUVUVVXR0tIyaruZM2cyY8aModdWq5Xp06djmibXX3/9qG3nzs32CDv0GOMpLy8P+4jevh6PB693OClps9nIz88ftU9paenrvi4pKcm2ch4087/nY8uzZ+cMzphYUxnyekKj9jGVbHAUcTiIjuhpbALFe2P4I8NJ5v48D/35bpyxNO5gYtS209paKYtE+czLq0cd/10HGmivKSI2OCRWn+YlpmXfXwsGNpKoJpy1fieKYbK3qgx9ROLFF4pR1hcclcjcX+LjkUWT+O3iM9iXXzi0PK1q2NMmMe9wMKerCi/Oqxt6XTwQpLq7H80wqBrowzsQxZrMJnebfKW05Iy8+TfRSIyYwefQvr9gkuZv01YM9fQ9Z56dCeVWJl9bTV5VNijc7S9jYnsr+eEgpqry6uQpo47TmWsj5Iujkg1cm5lMLt1oDCfLs8OXpWi1FZDRFEwlO+R5Sbwfl55kam8/79m8fVTZLt2+nrlNu7MPJkyTiMdOEgvpQ4LgtM2gKDX6RkTXFMrSvXzqmYepaO9DMQzy+8Nc8sxmHKkM+aEYAEGvY0xolr2BUVAMlT4tl4RixZdIcFpjMxNCrfQ7c/n1kuv54dkf4PsrPsiuskpOP7ADWyZbX4ueYWHLDpyZFEnGzg9c1zVAjq0X64i5ray6Tk4kTp+Z5t5pFbxU4uWM9bvImHYMNBQUJnZEeK52MhuKiulTVXx6YMyxj5lv3gA+9zH5PT+avyWdnZ1v+RzuW88/fF1PNS47yg/fN/Ry1LX6j8ugvnTsPnUl2XUjfeHyoXmcj8V7fjw+V3IOOcfJcg4hhDgZSZycdbJ9Hy28tIiiEQ2/nR6Nc99bjs1mY+q0Is67MJsY1tXR/Yl9eRbSmjoqbjw4B/NzM2cSdQ4f01KfR/l3l6NZFVAUusryRh0r4nXQ73QTtDhHHUsfHK3roDQaA/hAMXlh9nRWl01GVw4+nlSIWEckPYEfnXYmv553BqvKaoeWGShE8A/18AXwJWKoIxqia4ZO7mDv5bDbwaK/njd+77nVAj+6GUaM9GZetgCzLHs+CykKkr2kDhloMoZ78JpZMEZc7ZRq5dmKWXQfJjm/tzh7zFUTqmnNHb2+PddPa152fUoZ2xfX0FRC3uz1zygadz32Fz6y7+nDjkiXwkInRTSVFaKZxqhtZnb24Yom2eZ8a8OIKrdfMyox/6bej9k18OHzRhxMQfn2jXjLh5/FHPF7/tHzoeZgonvMk4k3qMXIns+Hux6vc40q8rONyA86bzZcsfCwn13rj94PHudhDnISsKjgc41d/rlLKVoyusH5ifR3V84h55BzSJwsZIhscQorLx/bmjE3N3dMQuRw2+UMtuQtKysbtfzgH8xgMDhmn3cy78Qcztt8Ge0PNRPaHsBV5yWRNGjtyZDJdZCM6VicVvyTvJTVuaG9nL2/3QUdMUoWFDDtf+Yw8++NvPCbHbR6vNTZk9RelkfnDidaZ5qM3YrfbTLz7DzilLF/5QGW5Fv4RXQ3e3synGvpoX3FbAK+XCb8dzXP/2w/qpkhepqD/Od2U9/bjXXhZDpWRajrDPHe1c+zJb8K0wIlwS5y02EK+kJYNAukIuyYUEN3jo+A3c7la3fhsZv8x2U3ct6OTUzq6mNbbiUVwShF4V7Cfg8JqwNrJsOS3XvxxrMJ8aqBPoqjQTyJON35buKqSn1DEzvKSmjI9/P3ovmsMHdRkuolotnJTw/gQKdPK2BTbiVhJZe6gU5KjW7SikbD9WfjXzyRq+w2zjzdzewp2UDf5rFy5RPn0/R4C31ffQFr2M7VnZtoCBQSUi28PLeSAifMO7uUjfYK/pH0seLll7hyzRY0xaAv7ced6iaGH1BJorHLVkFaA5uaxJHJkFYsoJu0e+2oqsYHt27g6h3r2Z+fx2mdzUwMBOnXvHi64zSXFqOmdLqK3cTDFvzpGKpmELdDyp4Ea5IyYy+tnmpSipOm8mI2uibgTKdYsrGBku7RvzvteQ4W7WugIhqmr8iDNxgnpllwxDL4Iyl6vW4KwlEcug5oJNBQ9Wxr9gdnnktHbjZ4TFlsaKpBdaCH8i19hOwuvMk4QVf2xiaPIAFyR527JDmADTtlwRB7CoeDnYRVY+XUagbcDgbcJUQtdrypJDlEhkLD7/7rcc5/73VMTmTYVrmYxZ2N5CXDoMGY0c0ASn3QERh+neuEB78ESydD9YdHryvKyc5HdDBOtahwyXz41k0wbWzL/neE6L0w8aPQHhjvkgxTyPYO1s3se5AZ8VjOZYPYIa0yvY7sMosGC+ugLB+iSSjzQZEPDnTB01sglYHyPKgtgtpi8HuhpjD7HheO/owOKfHDtp/Av9bDYKMMclzZfexWeO9ZsHovLKiD2bWHP4YQ4hQj80YJIQ5P4uSTk8tr4aN3TGPvuhDJmM7kRbk43MOP/K6/uZDFy7y0NKUoK7fS25nC5dKYNsdNb0+ajWsjtO+LoyomVbUOrB4LNqdG5R+nwzP7URwWHBfXU2K38MklRexfFyC3yE5RkYUd39pCV1OcyosrmHNzLR3fW0Xznzajo+DygrUrTNDhwRUMY2gafYUl9NlcdFQUsqhvPwdqinj03Oup3d1Exmal5pw8ggciuJq6GDhvOrm9Tu5K7WLtjZcTWLuNZfFu1BXTsZbmY79nF5amAEaln/gEP0VBUAdSeAs0kuUeWrfn4a32cPr/nY7FNbZR8XF10xlwxhR4ZhtMLEE5YyqqYWD89kWsP3mWtn4resDEltFRTJO4aseuA5goSop+h4adNEGLj7055SRUJ0l3P8R1MLJJyJDDzg8vXAZA2mLh0+++mrvvvAtDUdlYXsn/W3EOAx4Pmgk9dguXdgewjMiP2pJp8vsjWElRZbaSsNhYVzUdT4eCMz4cyCZsVl6ZeBpTd7fSUppPYWf/mOpuzsvlmtYt2XjnYAOCeBKSGUilYHDYdvzO7PpQHPLccO3ibEPZC2bDtIoxx31Tfv0heP/ZsLUZzpgKk8reeJ/D8blhz8/g/lXwvYdgb/uIeO/1EswKuKyQn5uNHftCGIk0pq4TznPgfvS/sc6ZANf9MDt/ciKVjeOuWJid0uiS+dnY/8lNUFkAK2aOmUZuyKJJ0PwbuHclPLIOnt48OkY9FixqNgF+cCjy2mK4dUU2rh2Igl2DjQcgrUOeB2bWwOJJ2Vj31d2wZh+cNRO+fUO2rI+uy86ZbbfCafUwd8KxLb8QQowiMfLRkASzOGWp6uE78JuHDA+lHWZu2Ddad+gxTgUWj5WqG+tGLZv2WhtPcTFhxeihcytuncSNt046wrO9/pC58xYvGH7xuTnD5xixzUWvs/+i11wzHYBLXvfsr2/0eS8fs94L1Bxmv9e7MqpFpfbSamovfe/QsuLDbPf+wR+4YfDn8Ka8zrley9GGffs/+AiBe1/FG0vhJEKLUk7CdKOrCsECBwdKc/jz5CoWNbTx7vWbWdy8h/2Wcr723nMIO1140zr+QILzdu/i1tUvDx23yVFFU97oh165g8NzWQyDvHgEgG6Pl5hDo763hzh2OpSi7HDrxCkgO4TL3qJ8HpgzlWkdPfT4XDwzcwLduZ6h48bsNopSwVG3Ia5Mhi+/ug5/b4q22kIe+cQXuOUHMzgq7X84uv3eSVx2aBu8Do1d8P47IJ2GaBqsKvzqIzCv7vWP8U5nt8JViw+/bkZ19kcIIYQQ4g1InHzy0iwqUxb7XnN9Ta2DmtpsQ+W6icM9HYtLbFx46dhpoYa8Z3Qc4823MeeC4V6gp/1iyejzfGM5fGP5myj5QQvHLCnjYBR+0NxRr3yfPO11jzj7KEpxTFUXZhOegxRVRfvwWWgfPosjTaUV7++ndns3oVCSp3fNZu/ebpyvNqCrJn85bRbXrt/Euppaagb6uHrjahoK/Xzh8htIKwqtLhsDNguoKlfs24zV0LBpuSStdjzxGOes2USN3kgFbSiKwe+XXEdHbhGOCUnm7WyguD/AQI6HbRMqMA2TDTMqCTos5Ngs2FOjh/jeUlHIf7zvErji42/jBTwKCydmf94qqwVuOCP78xbo6TR33nknALfOrwOrFR760mvvUJYH71txZAf3e+BjF2V/DjJN2NqYTYjbrdmRr7zHuKfzF69+420+eN7YZTee+faXRQghxDElCWYhhBDjpu53l8LvLh167XrpAD/76gHaPDm05dhJaHDxtp385ZdTcfon8rsvb0X72wbKOvpJejJMDkZYVV7C/fPmsreogGX79xF0Ohlw+VF1HWPEw62mgmJqe0f3vHhs+jS+ff45zOzooNftxptI8rGnV6OZeSzen6YgGuWhWdPYX5TP/qJ82oq9JByjvzoPlOQwIdw1pm4TQmE21E3gvJ0bWFtymOGexNGpKYZnvzHepRBCCCGEEEKI485Wl4etLg83cDMAU4FsYu66Q7Z94RMam1c1cdmWjWyorCEv7uSA30uX28703na++cyDpFQLvR4fRZEB/jb7XKpbGrGYBrsKa4dGBEs47LwydyoAWipNSWfPUAPr/HCUsM+FEohhS2UwFIW24lzsikKL73UaLojjQ1FgloxiJYQQ4tiQBLMQQogTRv6yWr72zGsHPx/81kz41kz2nPE4Dxb6eaG4krKUQX5aZ1NFJZsqKrGmUhTFgsxt7EHxujEHe2F05XqxECeNE0NR+MfsmfzonLMwVZUtg0P8FQaj3HX6UgDuWTif/3n0iVHzheUF4nQUeTAH5+8ujMSZ3NNLV04u5aGBUWXdWVaGYjEoDgcItkfe1uskhBBCvBWmDP8lhBBCvOOdeccizmQResZk/TMdvPi/GzFTSUIOJxVdGTbkT2RSuBV7JsVf55zLr+av4PHqiXz5xQcZcHoPe0xXLD7mLkIxDLrL81EzOoaqErfC5O4B5uTIXJtCCCFODhIjHx1JMAshhDjpfPvFC7lywS+4wz+L8qCOxVRpKfJhljr44XdrufuKbQyoNjwDA9lezEaaSY2NPF5xGm1lJeyqLudvdeUktOGvQWtGpyCcHnqdtFq577R5vGv7dnaVZFtuO5MZqtoClBoZ8hNJJvaFeeq02ZT0BUg3a1T29WEoCgeKithWUU71QC9h1cXUSNtxv0ZCCCGEEEIIIYRmUVh4QRkLLxg9B3HTrhDf+MirrCorYWN5BQ5Tpcleh7LoPQSLfUzr7UMZ+cDdNFFfZ6h7w5IdQazF42HprgNE9bFztQshhBDinUMSzEIIIU4+isLC9Z/grtdY/emXDj/L9pdPf45AWTG6xcpNuw/QaVfYVFbGlJ5eooqNkNszanvTUDhrZyN+424emTqfbm8RpSmT4kRqaJuWPD85wQi3n7Ocnfk+8uNJLmxsJep0sNNRTofiZ0aO9GAWQgghhBBCCHHiqJ6Sw3efP5+d28O89GAnM+e7SZhWgp/cxm9Ll7Pf76c0EiEvFqU42M/Zu9exq6iGNnsejEg8B1xOrINLehx2+p1OZre3Y8Qyr3VqIYQQQrwDKKb5Ok3PhBBCiHeQ7y55ij5/PnM7d3DN5qexGAZJzcKd88/j/114GbU9w4ngy9Zs513rdw2+MiljH4onyh8W38iA2w9A0Gal3e2kL55gVdVwa3CbrrMiEMWdznDFUy9x5iVuKn935fGs6rhLp9PceeedANx6661YrdZxLpEQQoiDkspHj2g7u/mrY1wSIYQQQpxoXqz4HY/Mmsv+wnxSVguTOtv5yMp/Ux7qI6Fa2VBQz86KalTdAEVlU2khT1SXMTUQ4szuXhqsVq7bvI4LOj423lU54UicLIQQJ6YjjZFB4uSR1PEugBBCCHE8mKbJGS1rWbp5O9duegqLYQBg1zPctOlZ0laDDp8TS0anIBjh4qHkMoBCG/UMaD6mt26iy6mxx59LS66XyY2trK4oGXWulKbRbrfiCkVYXVdKi55/HGsqhBBCCCGEEEIcncm3TMCZSrK4oYmlexooDcZp8JXx0oSZ/GP+maRyHSzfs424ZqF+oIH6YICP7G7grK5eOqw2rtvwKsGvvmu8qyGEEEKIY0yGyBZCCHFKUBSFHrubmtYOtEMG7/CkkkztbmdPXhmff+AFclKxMS2wFBQeLV/Ak/On4ovrlHf0UNnXR3VXM8yfMeZ8vmCIv9WW8cnOJgqXVBzDmgkhhBBCCCGEEG+Poq+fxZxJf+LfsxaQk0iQVhUcFRXsqKgBYAMTqStrp7KhDaXHQUmshxdn1BIvdHHBdDuzf/VBSmpd41sJIYQQQhxzkmAWQghxyuiw1pIsCTKjeTsqw0nmiM3OrsJSPvj0OqymSVKzY5AclWSOWzQ+u/pKPu8ZO4TV9967l00lxUOvrbpOXfc+4obB+W3N1N16+DmhhRBCCCGEEEKIE4miqVy+7Sbqr/wHT/V6yQvFWTN3dKPq/cVleHsCoJvc3HId71OVwx9MCCGEEO9YMkS2EEKIU4YvHqO5vJznJyxCV7JfgUnNwr3TzuSBX/6J/37lfu47ewbffPcyfn7xIsJ2GwAxq4b1tqloh0kuA/zzo24u3rOf8nCY2V1dfOzVV9mdX8XXCrs5c8v7Ua3ydSuEEOLEYaIc0Y8QQgghTk2a08qsJ64jPxRFNw7fP+mhuTPo8zshljrOpRNCCCHeXkcaI0ucPJr0YBZCCHHKcBopDKuF9RUz2FU0gfxogG5PHhNbW6kNdBG1afzywhj3v9RPwK0TXZYDp9Vx+n/Pxup67a/MmiVl/OvBshFLJh77ygghhBBCCCGEEMeQ3amSyijYkklSdvvQctPQacz18/KkOt6fAec4llEIIYQQ40MSzEIIIU4ZNlNH1XUMTSNqcxG1ZeeF8qZC3DVzKdNiPVz/ocV86EPjXFAhhBBCCCGEEGKcVawo5aU1MS5e/xK/PetCctNpwhYr+z252HSdgkQSp8/+xgcSQgghxDuOjNkphBDilLF7UjUzDjSCOTz/siMS56mK6TwzZSLNdml3LYQQ4lSgHOGPEEIIIU5lS3+whB6ng4xVZdGezbya52Orz0tagcp4glyrPt5FFEIIId4GRxojS5w8kvRgFkIIccr48D+XcdeFSc7asYmIzUmXx8mvT19EUrNx7ep1fHrzNeNdRCGEEEIIIYQQ4oSgagrf+PFEvv7JFDYMPrvqBXaUVtKSX4Ru0fnag6ePdxGFEEIIMU4kwSyEEOKU4cy18eFVF5FOXkA6nKS/McSFhS68RU4szprxLp4QQgghhBBCCHFCcc8s5rvPFwMQTZmkEhlyPRZUVXpxCSGEEKcySTALIYQ45VjtKla7E1eBDIkthBDi1GPKsF5CCCGEOApum4LbZh3vYgghhBBvK4mRj47MwSyEEEIIIYQQQgghhBBCCCGEEOKISIJZCCGEEEIIIYQQQgghhBBCCCHEEZEEsxBCCCGEEEIIIYQQQgghhBBCiCMiczALIYQQQgghxClF5pcSQgghhBBCCCGyJEY+GpJgFkII8Y7SEzG47lP7mLetla5cL5V9Aea1dpOrB6gLNlK+6wvY64vGu5hCCCGEEEIIIcRJ5ZVNEXrOvZ/cTBzNyFAQDZNv9OB79iPYzp4y3sUTQgghxHEkCWYhhBAnHdMwiHbGMJw2bA6VRGuMnFoPaVPhXZ86QFlvnFdrKrhk637q+4KYqkZAzWd9fh6BBb9mXuB/xrsKx8TOp7r48e978faFqW/roSLSxcyBbeSdVkHOs58f7+IJIYQQQgghhDhJxVImLRf+k9+es5iZLd1cv3oHMSOPBB4iK/5CrfENFEV6gAkhhBCnCkkwCyGEOGmYpsl3L36Csqa99Hsc1HcnacmtJ2OxkBOKknSoTK0oY3JTL9Pbe8loKrpVHT6AotBkr2WuYaCo6muf6CS09sEO7rgnyNzdrczZ1oJmZJfvdcyhbcDGdbkfwRn89fgWUgghxAnBlOG/hBBCCPEm/fsb2/nvq85hTls3N63aMbTcwEZUyWXrB//JrN9fNY4lFEIIIY6OxMhH5531dF0IIcQ7Uuer3Wz60VZ+ueIRGjLgDVk4d32Aoi4TA5OEy0Z3iZ+4w0l+PM20jn5sZgZDHXtzkMFCsj8+DrU4tu75Sy+edJqpe7uGkssAjkSGnHaN7Z6C8SucEEIIIYQQQoiTWtvmfvJSKW5evYWuglw2zKhl94QyUhaNlOnB9o9XxruIQgghhDiOpAezEEKIE1a8P8nd5z7JvvIKwlYrq6umM7e1h4Ajzs7iFMXdEWZtbePfZ8zAphpY1TQl4TitxT4yXiuqYTKpcT8TYgewkma7ezoKKpZEEnCPd/XeVmk0DKuKM54EFIJOG45UBrtukNcX4cVF88l8dxsb7jzA8xMrCbkczG3vJu1U+I97F1Kabx/vKgghhBBCCCGEOEHlFaucv+sAaa+VF2bMGFq+r6aEC15ayzMT5vCP059FU+CcH8xlwRL/OJZWCCGEEMeaJJiFEEKcsB46+1F2TZiEAWx1u7l4dwMl8QRxt524205XYQ4htxtXKMqaGbX4YwkGPG7mtrTjSSSpaWolZPWzKcdHRbKVC/qeYEAt4S/LnHSVern+jtOonl883tV8yxpe6SGqaQzYrLT7PPz8nHlsLy/Akcpw9fo9XL92JznhIC//8QDPTK2jKBpjVrAbp57BiMI9F71I94U1fPfrE8e7KkIIIYQQQgghTkBOr4PFTbtYNWPOqOWhHBf3nLOU5jwPSw+0oJrwwuc2UHHvYkpq3lkNu4UQQggxTIbIFkIIccJqyvGjGCZ1u1tYsjObXD4oraoMOG3Y9TT4HNT1DWA1DcrDEdIOG55gDEO3ZjdWFFodlTS46vEZ3cTy8smLKKy9+ClWfW/dONXu7bP2kS5yEzEiCvzinLlsL88Oh52wWbh7yTTWVJbiGtD50ZmncaAwF4/bRizXS78/l4zVCppK8NWeca6FEEIIIYQQQogTVQEGE7q7SdisY1cqClG7g+frq+l3u0BR+MeXth7/QgohhBDiuJEEsxBCiBNWm9tNfm+Q8u5+bti5mvMaVzG3axdBUyERNVEiBpqioigKBYkktYEQAzYrMZsVZyw55ng91kIsZDht437mbGuhfCBK4w92j0PN3l5On5UZrU1M7uhhR2k+qmmSYxh4DQPFNHlubi0/OfMMJgeCvHt/E/5E9tqYqkrY48ZUVRRFI7ylY5xrIoQQQgghhBDiRLP+5QHWPN2OLxVhYmfbqHWKYeAPRzi9sYV5bd10+nLYUVaCc2vLOJVWCCGEEMeDJJiFEEKcsLo1ldxgkEXBzUyP7ac21EF5V4Azt7Zyxt5WVmxvZO76A9iSaQBSqsIBu5U/V5ezLy93zPFyM0EGrD5AAcCSNqnrC5JqCh7HWr39Kub4iVlcuDMZfLrBhIxOmW5QrhvUZnQ6SvPJs9oZcNr57eQJPFpUQNA0szsrCmmrBUsyzqOdtvGtiBBCCCGEEEKIE0oqZfCFO3pY3tmIw9R514ZVTGttxJpJUxTqZeG+9bhNHXcqjTeZYnJnD7qmsr2sfLyLLoQQQohjSBLMQgghTlg2Xaesr5PCTP/Qsk5KRm1jzegUdwYAuKe2ilcL8+m1Wvnt/KkEHMMJ09x0gPJkM+u8i0btrxrQta7v2FXiOCiscqIrKhaHnepEEsuIdTbAY5hs8bg4kOMlZrVywJdDsy1JbU8Dmp4ho6i8b9uz7NsbOy7l7YkazP9jGuW7aZTvpMn7cZoNHenjcm4hhBBgohzRjxBCCCHEg39o4czWTioHegFwpVNcte4lbnv2Ucp7mmnyVY3ZpzgUJmJ3Hu+iCiGEEEflSGNkiZNHs7zxJkIIIcT4WNrWQ8o++qvKOEzbqNqGbsxIij3zZgwt68xx8/Erz+K/Vm5gUl8vhtPFI7kXUtcSAsxR+/f0p6k8FhU4Tvqa4+iqgqIoKOrY62MoEBtc7kon+dvDP2NZ2x4AAo4c/j75Mub1NdC8Yx8c4yvx7VfSfHnliAUaDKRh/l1Ql5tm30cOM5+XEEIIIYQQQohx8dxzfRTYbPR5vJQHso2/t5eU8c2Lr8CaNjhvy240VSfqdgztYygKs3a3AHPHqdRCCCGEONakB7MQQogTTiZjYhgmcc1Cp7+Uvf6ioXV9Re5R2+qqQndhLtZ0Bsj2aFYNA4C0ptFYXkBvYT7thWVE87xY1cyIvU0UJYOpnNxfh6UT3XT4cijqDeJLje0J7MxkYHBI7Pdte3EouQzgS4S4YPdzbPNMZuuqKO/6S4JfrtdJ6eaY47xVv9uc5ssvjligAooCqgIq7A9Be0h/288rhBBCCCGEEOLo1HQMUBAO89C8RWQGGy7fvXApk5u7+co9T7B0834Wb9zHlH3ZuZkNBfRYEsNM8r272sez6EIIIYQ4hqQHsxBAJBLhvvvu47nnnqOlpQVd1ykrK2PZsmXcdNNN5Ofnj9q+v7+fn//85+zcuZPu7m4SiQRFRUXMmzePW2+9lcrKk7kvpBDjJxrJ8JMLVpLRNNJAeThGZSjMv6oXsVzbTl2wi40z6qls7qO4K0DSbqVhQjHecJyqxm5+9bdnmd3aTcJq4S8LpvD3+ZOZPhAkY88OlR3BSnelh/n799Kj+ojhIOKwUVKXQ8Yw0RRIpkzA5ImHunngH730OZzMLlK4fKGTqWcW4Cs88eYpbt4TwY6KO5pgZmsX8Zoy+gfrXBhPoKgqhRmdHquFWT3NY/Z/cUItd06/iLKBCO77dvDxpTP5zUaDzR8c3ZvYME1iKQOPXXvTZQwkTG57HEaNJGMA6ohEtgI/XWfw2UUKxa5sj2whhBDHgvx9FUK8PomRhRAA+9YN0OTycu3WDfzl/LP53sXXMLO1iWZ/Pl946hmsujG0bXnnAGGvzlntL5NWdfZ4Z7Hq58/zdM0FnLs8/7VPIoQQQow7iZGPhmKa5tvfRUmIk0hTUxOf/OQn6ejo4Oyzz2bBggVYLBa2bt3K448/jtvt5sc//jGzZs0a2qexsZGvf/3rzJo1i5KSEhwOB83NzTz88MOk02nuvPNOJkyYMI61EuLEZJomO57rZfV/rCNiqAzkeUnpOta0jlvVwQBNN/DHI+R3BOgqzCNty7aFUnQDRzRFZ2URcffouZyUeJryjn7m7mkZtfyZefVortFJUn8ojMU06cj3o+oGrmiChKawtqaCiNPB5L0deKIRnppWjUtV6HXaSRlQbkJ9NEa9V+cLv5/BieT7H99GT0sGeyzJ0jW7eeCChfR7nCiAPZ3BG2olktHQwhZO793KFS2v0JSTz9yuJv5nxbV8d9llQ8eq6AvRm+8lYbNw58XwwcdMdJPRY56YUGpXuPdqmFao4bLA11/U+cl6yABeCwQTJigmmCY3TFFZWK3ymWde45bj4LENhu/nTJjggwWlCqcVwwdmqfidR9bT/Kp/Zvjn/uy/Z+fBj1ZAgUtlVtHJ3VNdCCHeLlHlM0e0ndv8yTEthxDixCQxshDCME3+8HiAf9zTy7ztzVy8aR87Jlawbt5EAKK6zmcfeHbMflGLRoHWSmW6GacZ4/sL3s+fp0+h05+L1TBZWGLw6/f7KCs68RpuHw8H/x4C3HrrrVitRzZFlGEY5P7UIHLIgF8ldnjpJoU6/5tvBC6EEGLYkcbIIHHySJJgFqe0RCLBDTfcQHt7Oz/4wQ9YtmzZqPU7duzgYx/7GFarlfvuu29MK+1Dbd++nVtuuYVrrrmGL37xi8ey6CekDW06A3GTM2o0bJaja/WTypisbDLwOxXmlY1NBqUiaTo3D+ApdRLtTWHPsVI0JQeA/gMRQh1xdodUUqbCBef6sFlfO6GUjmXoXNuLu8SJZSBJz9YAB6IKXUGT5m0hLOk06UiajN9BAgtl4X7yS5wU5tkJ9adYE7OxJtdPSSrFxFCIhGIlmO8k6XWyJEcn3hrFORCmyAn2S+vwei1saEzTVuhlijvD3lcGKO7spnhVM/vLSyma6SO5O0i6PUrcbqe11EfUasM3EGRiSw8hpxXD6cCaMuhz2mjxeakLhEDRcEYTOIw0GJATTGD47WzN9XGgwEt1OIZVMelwOvGiUBiKUtbUiTuZJuR1EHc66PDn0uu0Y09FmdvRx8T2TvSMQkp1oJvQlOMkmadx/6zpuBQLJakMSVWhpD9KXmeQfqtCTTxCZTBCd66Hl6tLCdlsLD/QRm4iSdDlYFdpAVGnnRwDHLpBXDGZ29SGN53GAAJWGzabRn1/K7V97Wx31RLyeUa9Z4phkrJaGSjwYWjZAMqSSLG1uoyPPPA8OdHEqO13VBXRXTX699adSBD0jj7uQRng8eoyNhb4Sakqs7v7efeu/QQcVl4pLmRteRF5SZ2JiQSqqlIeDPOzX9ThLziy4HhDh8GLzQbxNKyohWcf7GJlk0He/k7O27aPwJn1dCputkY0HLE40/u7qQumCZnQ4bCxuaaU9hwPAy4nefEkhdE4hmmyqaIYr2GwpKMfRzRJ0O8ddd4uq4WVOR7mNXUzsTfA3+fXk9E0CqNBgn4fKcshQa0KqOpw4vfgr7OiZKevNhn8jzKYRB5cp5Aditskmyw+lDa43aEUssNkm+bo9QdvUZThdT472FVYUQk9MYjrcM1ksKjwqWcPnV37yGjAomIwVIimssfyWGFLT/b4mgIfnQ0/XDF64JeNXSb9CZOpebCxG6bmK0zwKezoNWmNmHis8FKbSUfYZHs/1HjhvTMU7tsFJiahJDgsMMkPNbkKC4oV/m+LSThlku+EYAKiGXh0P7gsEEwNXgYgnIISN9w2C66ZrLC6Q6E/AbfNUjgQNPndVoOWEMwogBn58K8G8NqhwKlwVqXCWVUq0ZTJk40G9+4yGYjDRRNgUp5ClVdh9wD0xg0SGQWLqnD1RNg9ALoBaSNb15rc7HvVGzP47VaTAgdUeBUWlCi0RWBPn8GWXhOnVeHKeoX2iMk/98G0fPjoHBX1LfRQzxgmL7WauKwKC0ullakQb5YkmIUQr0ViZCEgFkhx13/sYE9UwzXdh7fSyYRCjc3PdFLf0sekvh7sbo2gLwc1x0ntGYUkoxk27U4QjZosfXcp+8Ma2//cgG9HJ3mpBOm5JSTdTkqmemlOKCT8bp7fq9MaBk8iScBlx59M40kkmdnSydSmNraVlWDVTWJOJ4am4Y5E6fe7ibhdpKwaUw90UhiKYNitJC0qScWkNtZPU14+W0vLmNTTTmNOPrt9Pir7guSkMiT8HlKahpoxKAglSFhUXqktpDCWYnIwSoPDTo/NSllKp7Y/RE4qTlkoiqGpdBb46PX7APAMhLnklc3kRuKjrl2nN4e0pqGaOqdHVvLPBWdy96yzeMnnwZvWMRWoicZRTJM1Py/FchSjY70dWkMmW7tNFpQqvNRisLPP5LJ6k++sMumKwtQC2NwJrSFoCI3dXwVqc8GigWEq7A2+zslGhismg6N4mcz0qXQmoTcOBQ6o98MldZDUFdZ0mMwuhPllKssrVEp/feRTSVmU7Hl0wKFlQ/aEAS4N3Dbw2iDHDgf6YSDzRkfLFn9mQTZW7opDMgM+G3xgFjQE4ZXWbPgfTkMik61LmTe7XUc0e4zqXJjog2kFCh+erVLslhhOCHFikQTz0ZEEszil3XffffzgBz/g5ptv5tOf/vRht7n//vv57ne/y0033cRnPvOZ1z1ef38/559/Pueffz7f+ta3jkGJT0zJjMnldyX4997sDW95jsKT73cyrfjN9Rbc2W1w/h+TtIayf5bOr1d56EY7Dmv2xrPppW6e/MIG0rHseQxVxbBoVC7Kw5VnZ8uTXTw6pZ5uT3aO3lw9zR8/62fKJOeYc3Wt7+PJD71Cpj9JeUcYXdF45uyZeNNJbLqONRrH1xcaigMCPi87JtdiTaWYuq+RAYednyycxbLGdi7fvh+V7DxDnSWFbJ1QiW3wT6sjlaa+sY3+Ij+GpmECuz0uiuIJun25AJT0Bqhu66O6ow9UlfyeMIoJSZvGlvk1JN0OUBSckTjV+9vYUeDnjqWziFqt2AyDi1s6mB6JE/Jm6+2KxQloVu6ZUkXYbsOdSnPV3iYq4wliLie6ZsEVTTB30wHKOvr417IZbJtcyYzWdhbt2s05TWuxkcFAYbe3ip25tQRtVr580VJqdIOZ4SgmoKvqUP6x2WZlrcPGl59ZyyW7GonYrbTl5RLI9xDJzZYroajsqq0cjq1Mk0nNbRQEstFaxqJRHuvk4l3rGbB4eGzCIlKO4cRnSlVZV1xAe46Hab0DzO7oQ8voNJQVsXtCOe957FUquwdGvc+bplSxrbaIinA2qinpG6Ar30f6dVoJP15Zysqy4TmfK4Jhuu12UpqGWzfwatBrs3JjUydhi4aa0bn/7vrXPB5ASje5/G9pnmgYXmbN6KQ1lQd/dRdL2rM9rw3gx2eu4P9On0vA7UQxTc7c1cQtKzfy6Zsvwp9IU9ob4Np1u8hNJGnOy+GexdPZV5w/dE3Pa+ohNzM6+EwpCg/n5XLhzmaemFE9unCuwyTHFcCmjR0dZii5fMi2I/8/WA4OjX8Pzrf8WnGkduIHmAqQ+pyGbsAVDxo80WiOWT81H3b0jUvxhspwJDd20/KgOQyRsVN2H/F5vrJYoSpH4cNPGqPOeSRlcGqw7VaVCb4336v8QMDkvAd09geyr5eWw+NXa3htJ/5nSIgThSSYhRCvRWJkcarb+ngnP7wzSIvPA4qCAey1WfEGg/zfg09RMhhb9uV4uOv8MzE0DS2js3DdXjJOlX63i6enTSBt1Thz7wH88US2/a2mkrFYwDSxh+N86+yFdDmysZhimlzQ2cf0cBRMEy0Y5BVfHh95ZTueVAZdUdg5qYK91YVU9gdwZTKgKCi6QVFHP95QNsmrqwo2Z5L95X4++fI/GHA6uebST/D+V3fiHIwRB1wOXjptOhlrtiz+SJyyvZ20Ty2nS4HyWIxgTg6KolDZ3UteNDZ0bUygqaQIVzSBNxQj5bRxwUubcCdSGIpCyO4g7HAMbV+U7mTlwkn8u24ua30e0pqKO6OTk0gzPRyhwe1k6x3FqOrxvY//zis6/+95PTtS19tBYWxD6pFxssnh4+CDyw/XOFs5ZJ+D8fQ7xM/OVvjkfOl1LYQ4cUiC+ejIWJHilPbss9nhfK666qrX3ObSSy/FYrEMbTtSJpMhEAjQ29vLxo0b+cpXvgLA0qVLj02BT1B/WJcZSi4DtIVM/uOx5Js+zn88kRpKLgM8uc/gDxuyzSkN3eSFb2wdSi4DqIYBhkHL6n52P97BlpKioeQyQFCz8o3fHT7T88pXN5EKpvEFkziTOlunV2FRDGy6jmGY5PaHR93L+wJhcoNhivsCWA2Tx+qrsejGUHIZso1Qi7t6caaHm4AmrBZ6SguGetsqwJRIjLB7uJz1TV0U9wXJOCwUdGeTywD9hTkkPc6hICLucdJSVcwdp2eTy5BNuj5cWUanL2cweacQc7twaFCZSAEQtVn56+Qaoi4XupbtgRlzO1i1eDIJu41zV+8mLximMBjmzOaN2MiWX8VkargJXypMbirN2c2dlCZSWExzVHIZoCqVptQwuf2CxXR5nHiSaZypDDnB+FC52osLRsdUikJjeQnxwbmCe70eljdsA8BlJLGmhq+jrsBP503n7mn1PFdRwi/mTOXpimIciRS5kShpReEvZ8wkpQ2XKuR2cKC2mIaCfPI7u7h8zass3rkPd2LEZ9M0ye0LU9zUgzkQ5fmSAoLObHmshkFNPEmOZqFYN9BMk6imksoYFGUyvFDow5fR6bXbuP9v3Yf9nB109zZjVHIZIG3R8EdiLG4fHtZbVzQemD2VwOAQ4Kai8PzUGr53yVKK4xlSwK0vbyF3sA5V/SFuXLVt1DXtcI1NnoesGi6LQlfFYXqY6IdEs0O9hke8NgaXHS4AP1ywfLgA2eDwCeqR5zrBmcB7HtH543ZzTHL54PrxTC4fLMOR2NF/9Mnlg+f55qsmn3zGGHPOIylDXIf3Pna4D8ob+8pLxlByGeDlNrhjo7SXFOLNMFGO6EcIceqRGFmc6v7601ZaB5PLkH1wWp9K84WV64aSywD5oQj1bZ0A6BaNdfPqqTzQx4baUoIeJ/Ob2/HHsyNsKWSnglINA1SVVXUVQ8llyMZ9Kwt92ftoReHOqRN5/+pdeAZjYs00mbG7hequ3qHkMoCpqfSU5mEMvtYMk6jh5JOv/AOHkeZ3s5dx4Y6WoeQygD+WoKK7P/tCURjwurA4rOQGo8zr66fBl4uiKKiGgX9EcvlgPcq7+5i/aS8F/UE6ivzcdcWZ/PXCJWyoqxqVXAYIWnNYVTWdlYW5hO1WEhaNPoeNXpedFqcDt2Hyp2eiHE+NAZOvHOvk8tDyEf9+rX2PpBzvsOQywOeeN+mLSwwnhDhxHGmMLHHyaJJgFqe0/fv343a7qaysfM1tHA4HNTU1tLe3E4uNvrletWoV5557LhdeeCG33XYbDQ0NfOYzn+GSSy451kU/Yv39/SSTwwm1SCRCOBweep1KpejrG50R6ejoeN3XnZ2djBz84JWG0dcFYH3rcHLwSM+xvm1ssmFDe3ZZx4EuIp2JMeuVEeXoc43tqdwYGf4zd/CcpmHSvys7fpEjmcEEgj43joOJYdNEPczgDjmRGM7BALHT7aQyGB7zR1QzTFzx4XKq5uG/dhyZEdfHolHcF8aSHl3/QJ770N1ozM8lahudQDRUhQ77IUlFRaEwOZw9cuvGUJJ7aD9NpacwF3s6Q01vP+50Apc59hrnpbLXqqY/hAXzNRvfFmQyGKrK+opiIFt3WyqDNnhdzcMERKaiMODP9uQO221YjOw1sBtppvc2oaUyKBmdDoeDNo9r1L4PT6klpaqUdQ+w3ufmnzMncO0nruSn5y3gJxcu5P6LFpG02ygORynvjWIzUqjozGhozgb2QF53kMKuAN5ogokd/dz04hY63U7QFKZGExSnMnh1g7KMzsTB69lvsVAeTdLrsAMQ1DRe2Db8O3bo70d/fz/r2g4/7lTGoo26lgN2Fw1F/jHbBZ0O8hMpJnX2oR3y2awcCJMbG37fduTnMuPAcNLaF47yib+/wP/96XGW7W8cc+yy3j6u2rqV/GiU01paOH1fU3aMaBjuiWwwnHg+Eq+16cjW25qS/VE5aRLMAM+3wMoDx/chyIkseeQjtY2xe+DovqNWt43NjK/vHP7QHe538Fh/D8o55ByHO4cQQpyMToUYGU6c7wo5x4l3jgGHY0zsqgH+zNiYrrQ/MPTvtM1CwmHjYOayMBIZs/3BODRoG9soOGqxkFYUEqqClsqQFx/baD8vmhiTaDQ0lZR9eCqfomg/Dj17v9zj8lAeHFsO7yHDWvcWePHEkwRsVmK2EYnvMXtClz+HPbVlFPaHmLivFVU36PV7ibqcPDyvno/eegEffv8F/GPBJNpyC2lzOUmro5+chK0aCU0lrqk8uyFbz+P1nq9qjA21nz5mTqL4drxkTFjbEntH/y2Rc8g55BwSJ58KLG+8iRDvXJFIhIKCgjfczj3Y2zQSieByDSe5Zs6cyS9+8QuSySQNDQ08+eSThMNhMpkMFsuJ8euVl5c36rXHM3ruWZvNNmberNLS0td9XVJSMur1mRNd/GXr6OBncfVw/Y/0HIsrkzy8a3S2YlFFNilaNqGYnHInobbRgdDIwK84EuNAnm/U+one4aTtwXMqqkLhbD89mwdIOCx4Ymny+0J0l/uxZzKgKuiaijaiZ6cJ9Pu8+MIqObE4E/uDrC8pJKMoWEZ8kWY0lahruNWuoSiYpolySBAYH/n5UBT6ct1YDoly3OEEfaMvNSWhKLaMTsoyIllsmuSnRwe7aU2jd0TSOa5pY+e3BdzRBCmLRq/Xgz+RIK7YcJqjv+wD1ux8vmsqS8BqoSCVPmySuW+wTFUDYVKaRtKiYSgKxmCv4rxwhIhzdItme0bHGAw23YkkT02exaXb12erZcteD9UwqQ5G+Pyrm/nekjnog9snrBYa83Kw6zqtg8Nwd/i93L10BgBXNbTjCWW4aO0OurxeenOm4IolKUqEOW37bjbX15A7MDrYdmR0ztjTwtMz6nAe8n7kGgZ2w8hOl6SAN529Dn1WjWnLcoa2O/T3Iy8vj2XVOr/cODYTZ8no6Chog6F7TjpBVW+A5gLfqO2q+oJkvF4U29i/KzGbZajRgTee5OPPrufdqzZyzoYdBB1OcnvjVLGXEpq4sTPDRzZWcOYN/0nE4mJmRw//9eoznNXUCkBSs/DrJSt4ZXrd2HmUdRiq/EiHC55fqyX2wVbcI4dAO8laYp9XAyuq3dy97+h6376TKIDTArEjmLfrcGYWHN131LJKKw07Rn/AFpcNf44O9zv4Zs/xZr8H5RxyjsOdQwghTkanQowMJ853hZzjxDtHYayB3aY56lmDDgTVsX10Okc8f7Cl0jgSKaxq9j414HRSEh4dbxpK9hgTQtEx8XlZPIHNNDFNSNmsBBw2fInRsXnIYcs+fxixn6Ib2EaM/tXr8ZPu07AaOtfu2sDDZWcyt71n1HF6/d5Rrwu7QwyU5GCi0+y0UZVMYagqAx43+ZHhxrVpLbusL8eLPxihrqmT2uYudFXhsel13H3GjKFt71o+E08sRV44Ank5HMpuGITsNq5alu0ocLze8zMmmGhK+u3rwXy4hyOv1Sr/cI60F/M7jFWFxVUuvI7hC/VO+1si55BzyDkkTj4VSA9mcUrzeDxEDtOq9FDRaHRo+5F8Ph+LFi1i+fLlvO997+OnP/0pDzzwAN/97nePSXlPVLfMs3DNjOGE58R8hR9dbH/Tx/nRRVYm5g/fXF49XeN98waHllYVzv7abBy+4aSpoaqgqtStKGLmNRXM7O6mcnA+X4AiPcnXPnr4hyNLvzEXd4mTgVwHMaeFGTta0OIZElYLqqLQX+hHH0yMGopCR2kBUbeLrnwfGRUu3tdMeSTKPXMmkxzcTlcVOsoKSY5I/jrSaYrbulEHh6QygO05bnIiURQzm6DaV11MW4kfRyxB1DN83YrbBrCM6IWsZXTKO/u5adOeUT23Fw+EyEuOCDwNg6DFwi7v8IOepW2dWNOjg9OKll7yBsK8PLeO5sI8khYLL9TOJTPiq6HBXUafPZc1ZYXsynHTZLfT7LSj6Tojc0rNNisH7DbO3tdCTSBMh98LikLI58IcDMTzQmFCmQwW3cCiG7hTKWyGwXaPm6eqyzFtVtYUTeHuecvYXVDKpsK6UeWtDkWY3TXckm5WVxdfu2AJd86fweFMbG9hxrYDuKMxbBkdW1Ino1lod/vpcProLMh/zV7Vr0UxYWoiSbvDxuz+CJtcDorTGW492/Oa+wC8e6rKjdNHH9eWzhBwOnihupb04IMGQ1W4bOs+PCNaqy/Z18rX/vE8lngczWpnZ+nom71tlcUYqoKmG3zr/uc5fXe293J+OEJ9TwP9dRb+esYl/OSMj/PEpHOZPNDOK7/7Gff+8XG+8u/1WAK5NDqKSKsaq+qmYj/YsOJww1kf7MlsDq48dF4oyLbYP1yAfPBj9WbyyYf2mn4zvaiPARX4w4Ua752u8O7JwxU5mC+3abCo9PD7Hi9HOn3Z4lIoHDvowxGzKPDtM1R+f4E6ZvrsI5lO22eHey45utvQb52hMnPEn/YLaxQ+PufkaqggxHiTob+EEK9FYmRxqrv1u/VM6A0Mxdw6sNtm5XvLT6NrxKha/V43+8qyD9YtaZ0F6/fRVFfIgn1tVHQNsLGylMSIZwNDja91g7n7WlnUH8KhZ58TlMUSXNCZjXUV4KqmFn6/aOrQ/oYCO+vL2FZZjK4oQ3GRYpgUdA6gDDaOTts05vTu5fmqeegonNG6j+nhnbQNNsjWFYU9xfl0Fg6OmmWa5A9EWOf38mqhH4dhYk0mWZfjIqypNBTm0+92EbHb6PV62FdaMtRAvCc/OxKZapqsmVDOqkkVY67lc9OriCZSeFKjRyDyJ9M0+V1UJWNcudQ1Zr9jqSJH4SfnadgH35q35cH44eLU1wpdzSPYBkbHzW92RLETnAr85jwFn0PuNYUQJw4ZIvvoKKb5DvqGEuJN+tCHPsSGDRv45z//+ZpDgCUSCc466yyKiop4+OGH3/CYX/rSl3j22WdZuXLlKdcqZ2+vwUDcZEG5inqkWY5DGIbJ+nYDn0NhYsHYW/1MUqdnRxBvqZNoXwq714KvKhsshTvjRLqTtEQhlYFlZ/he/1wZg77tAZyFDpSBBJ0bBuiNQcSisX99EI+epq0tRVGFjR7DSnEoQOUEJ74yL32NUXYFFRpcXsy4Tm4gBlYLmSoXKY+LxcXQsS2AvTdCbb6K99o6rKrK7gMp2go81NtS7FwXwqeksT+6n0BdITlTculf2497ZzcJu42uslxa3U48oQRVPUGUHAUzkqEpL59t+bkErBpntHTh0XW0eArV4cCWMSjr7kOdlc9qxcsuh4uqYJSSRJIuqxWbqlKUjDGlpQ1/T4SVddV05rqJeNwE7Vbm9/SQF09Q091BJqPwalklm0oKcSRTXLF9P6ury+guyKU0lQSrBUOzsN3tpE9VWdjWzXkNbdhTaQzTpCk/l7SqYddUWgtzCSsKrkiMUEEexYaBCiQ1jaimss9lJT8YZkVbL/7uELsL/dQHBsa8Z89VFfP3KfWc1dTID595io9deAVf+fsaPvbhC2gsGX6/CyMxvv70Sxi6OtS62xZPkxNKDN0GbJ05gbK2HvL7h4dqiVktfP66FSQsFqaE4owcVNwwTWyGQa9Fw5VKE1MV8lT41ReLmF09doizwzkQMHm1VSeUgnNqVAYaIzy9PsrOlV0sW7+P6EWTqCty8OzqIMlgjEmJXsodPqLRFO2qhX/X1eJCobw/gJrK8GJ9BfsLfOiaws3r93Lpmp0A1PV2M0nfzM6KGp6euGJUGZY3vER4oICkOjq72FHpI+a2EyXD/1y2AjAPP5cyZLOYpjk66B3575G9nxVGRO3K8PDYb8Q85PwKFNoV6vKgwAnn1cLmbhhIwBUTYfeAwk/WmsTeZMdiBchzwJkV4LVBezi7sNwDzzZBTAdNhS+dBp8+bXSvm70DJgMJmJZvsrVXod4HhS6FpqBJexRKXCavtJt0Rkx290OdHy6rV/jnnuznqTueTbTOLoJ8h8r0Arh3p0kgaVLlAU1VGIib/HwTFLsgmMomdq0qNAShKgc+MRcuqFXZ2A2BJFw/RaE3ZnL3TpO9AyYzCmBKnsLKVpNiJ2gWlaXlMKNAJWOYrOkweGivSV8Czq+BAqdKrQ+agzCQNElmsjfZF9TA/qCCiklcV5jogyL34PzwaYO/7IRSt4nPrjK7CLpj0Bgw2N5n4rAonF+jEk3DEwcMJucpvKvurT3KMU2TDV3gssLUfLm5F+LNCiufO6LtvOaPjnFJhBAnGomRhch66PadbG41cM/zk1/jZmqxxtp/dVDX2k1pPIa9wk1EtZM2NWqWFxDrSbC/PUMUjTOvKaH5QIKNv91HpjmMxwneJcXoVivlk1x0BE3S+U4atsVZfSDNACpBlwN3Oo0jncGmqtTvbSRod1AbiNLvtBN22kmbOsWJNJFcN1bDoGgggjOZIup0EHNYOeB2cnpHK5qqEjWhPNjNC3WTeWZiPZZEijSwuL0Hm6bQ4svFqpugmEwcCNDmchJyuihNxGnVLHQ6bBREE8yOJkg7HUTdo0cjm7GnkSkNLSQsNoIOGz87byE7KgpHbVPaE6RDs/CJHXtYXVlGl8tJcTRGo9fJBDXBqz+qOo7v6Gh9MZOdfSazixS29Zjs7jO5uM7gO6tMAglYUApP7IdQAla1QeqQp+cODc6sBJtFocQD92yD6OFi0cOO+GUAJjUeDY8d9g9AZQ6cXg6XT1ToisHqDpOZhTCzUGN2IRT96sgCXQ3wWrMxY9oAhxUK7LAvCDk2KHaD3wZOK/TEYUPPGx4SjwVOL8s+WhmIw95AdiSrzy2Azlh2GqkCBzSFoDMKU/wwwZcN57f0QDIDk/xQ44M5xQrXTlIluSyEOOEcaYwMEiePJAlmcUq79957+eEPf8gtt9zCJz/5ycNu88ADD/Cd73yHG2+8kc9+9rNveMzPfe5zvPjiizz11FP4/WPnUhXiVJdKGvzq123sf7aXmG5QOtDPvLYWBpw+wk4X9QMtTOrs5JWKiSQsw0lQxTS4sGEL7nS2d+++glJWVUzCEtG5c+4E8jHYX+SnpjfIxVv24dJMEi4nSacdEwVbMkXV7i4UDUxNYevMWryRKJlIlIxqpzvHxcNzJ9Kc62FyRx9pmw2rquLRDTyZDKphEFBUyi0GD/2y8qgbURwLq54LsPHDKykMZXuSlMbbWBZ6mXtnX01jXs2obUuDnfgbxs7n1VPkYVNVIVuqcllZV8/3V6j88FWDzkOnGx6s9q0z4b+XKdgtGi1haA5k+OtOhe19MLvA5K87AQxQTM6r1bhlpspNjw72brYwemjsg8O8GQd7RWcT2BdWQXkOfPsMhULPkQ+p+MDODNf9C6zAM9dDWxgm58Hs4hNnWEYhhBhPkmAWQrwWiZGFEAetuq+FXf/zKn35uWyZUDc0/VVFXzdffeK3WI0MMc1Bg7uSRyYs4GuXLx8aCU4xDMxICnSDHAzmdPaRo6S54PpyPnZJ7gkVTx9P6XSaO++8E4Bbb70Vq/XIGqtv7c4w666xy2+bDt88U6XIJYOUCiHEWyEJ5qMjT1rFKe2KK67gb3/7G3fffTfz58/n9NNPH7V+165d/OIXv8Dv93PzzTcPLe/r6xszJwFAQ0MDa9eupaKiQgJnIV6Dza7y6U9XwqcP3yPi8du38u8Hd+NKxlBMO5gKrnSKqT2tuAaTy9tKq1hTNxWAV2r8XLK3mSm9gVHHCbsdJNzDCeqU005vmY+i1hBtNflg6Jy2q4GE3cbEn59B9VU1/HyoZ62bh9dEee6FINVGkla7A9Pq4AMX5zCt9sTrdWFVTTo8TnLjCWxpHa+eHdYwNxEes21OMoSiWDBNbdTy75y3kEu37mBAKaPx4xrVuRqfX3xk5y/zwqIyG9dOG17253SaO+/MRsC3XnMrvQkVzMF5qA2y8zkfHN7t4GsTNt6iMK1Aw3YkvZxfwzVTLehTj3p3IYQQQohTlsTIQoiDllxfSbAvifM7L2ONmGgJKEr2cXnLM1gHJ83y6HFmhfaQbLKQ/Led3y+eQbfTjpHWQTeZ0B/krg/nsXTJpHGuzcltZpEF8/PjXQohhBBiNEkwi1Oa0+nkRz/6EZ/85Cf5zGc+w4oVK5g/fz6aprF9+3Yee+wxXC4XP/jBDygoGJ7w8Y9//COrV69m6dKllJWVYZom+/fv57HHHiOTyfBf//Vf41grIU5uF90+E26fCcC/yu9kans3AClV49nyGbgySfZUVw9tbzHMMXM6QXYuqEPFPQ68wW7iLjt5sSglgSAJq42kw4LlkITmZQvdXLbQ/XZW7ZhRnBqdpQU4rDZqW7rwJXRMYFHzWvYU1BO3ZRPtmpGh3VWMpUKhuC2AamTnU350bj37i/3sbfbwj2+XUZ2rvf4Jj0KpV2VJhc6qVrIJZYPspNYmo4YNmyO9jIUQQgghxo3EyEKIkS78eD2P3LsZW3uaugN9VKgtWNDHbFcfbedjm5/kE5ueYHPpBL60eDkVA0Fu+1Q9S5fkjEPJhRBCCHGsyVNcccqrra3lvvvu49577+W5557j5ZdfxjAMSkpKuO6667jppptGBc4Ay5Yto6uri6effpr+/n4Mw6CoqIhzzz2Xm266ibq6unGqjRDvLEWh0NC/rYaOgUq/w4sxYnjl6YEgW0oKqAiNHss56HGNOZ4tkSFl0zBVhbjDSWNBHt5kmvKasdueTJIm2FGJuB240nGcOoSowBfv5ENr7mRr0QwemXga/pRKyuIg5YHG+kLyu8KUNwfYWl6IK5XmI5OCTCw8siG6jsYrN1uZ/ds0W3oHFxxsA6ACBjz57mN2aiGEEEIIcYQkRhZCjKR0G/gTcXRNoU/NI4WFtMXC/XPPY3tpPafv38olW1fjIjuC1vIDW/llPEFUsXLORYvGufRCCCGEOFYkwSwE4PF4uO2227jtttuOaPtFixaxaJHcJAtxrDUVFpEfaQaynVwnDPSyq6AYRyxJ3JvtlWszTGr0NC/UVTKntYuMRWPzhAocqQzVyRgZa/arTjFMSpoGaKsrAEVB0Q0aSgqZtrsdu/3k/jqcUWvDcFjpsin49GxQn8RHklyUtEFtW5xnz6nE1Oyc39BCTipNcSBESVsQQ1Foz/cyv62XZJHnmJd1821WEmmDM+/WWdMDKHD9JLjnUguKcmrOwyWEEEIIcaKRGFkIcZC1Ioea5xvZUVNIj7uYz88/n0ZfDr4MLBwIUdXXPWafkkgv64onjENphRBCCHG8nNxP1IUQQryj9Th8xLV2nHp2fidvMk5XuZ/WXD82Q8edSmMBTKcDm9PBP+sqCNmzcyRf/tJm5u5up7skF11Vye8O01/gob/EC4A7GCHpdJDRrFgPM5z2yaS5OYVhUXnJ62NFVTE4rUxrbwUUTDS2lJawqqoSFAU9o/OLvz6OJ5WmQ8nhj2fM5Nad+7OjVCcGjkt5HVaV1e9Tj8u5hBBCjGUiDXqEEEIIcWSW/uNsNhT/GW80zucvP5MBhx2AbqDfZuGDNvuYfVIWKzZ97FDaQgghxIlIYuSjIwlmIYQQJ6zS9iC/WjCfznw39ozOltJC7vnn3fxj8elsL66jIhzhYJrSbhhMDoTY7M9hckc3/nQcZypFZXN2PGYVk1TKQmlPL/02J5Z0hrjXhYmJc7Jv3Or4djBTBpgmF/b2s72yjq1V9bw0bQZVPd2kNIXnp0zjm4+9xPTWDiZ39wOwJ9/P+6+5EKwqn9ndAEBkWvl4VkMIIYQQQgghxAnG47NReNfZPPLT1qHk8kG9dhsPT5vP7JZ92AcbhuuKQqO/jExQG4/iCiGEEOI4kQSzEEKIE5ZqGDT7/DwyKztnW22gm6nhLXz1yU2sqZrD3+ZdOWp7u2Hwrm27cegGe8oK+dekarSMwhWb9jC9vY/yzgBndD/PH0+7loDXR21DJ57PzByPqr2tpi3MoSCwC91uJ61poCgEPF4CHi9Bu42cUIKcVAbDZqMtx8OaylJ+uHwB/S4n72rrBMCRjHHBJ08b55oIIYQQQgghhDjRTL1uAhNe6D/sunZ/IT8+790s37MFzTTYUlbHrP178XpzjnMphRBCCHE8SYJZCCHECcvIVVi8r4NH5mYTzBc2bMFiGgCUBbvGbK8YBlbDYG9eLr+cPwNDzQ5v8uisOn5z96Nc1vIy+UY3dR0txL0eTn/1UqrKHMevQsdI6+YgzoxOyKHAIfMY+2IJznl6E2ZhJ0/NOZ/9BXk0WK1Uh2Jc0tbFxFgMSybNmhVT+EKutDAXQohTgwz/JYQQQog3p9KeZmJ3P3uL8oaW1QUjTOzswZbO8FLNbEKaxtJ9u4iH7Zy19ZpxLK0QQgjxZkiMfDQkwSyEEOKEddbL7yJ/xt28/+Uc7lswlWZv/tC6imAHSw6sZVXtYK9b0yQvGEAz4YWqsqHkMoCuqTy2sIQPtLSRUqyc9uBFlM8tPt7VOWaCHQms6QyHm0laAdYurOLWNRtgtoXS5xpYDMQzaVyZDO1l+bznR4v40sSx82YJIYQQQgghhBAA3ZqLb/3tJR6aW8e+kjymtvVx6ab9mFaV9qIcBlwqF7UdQEdj6bbrcRY4x7vIQgghhDiGJMEshBDihOWr8nJG6CNMfLGdL3b0sumJJlo9pVREOgC4evOjOBJJWnMrqWlvwUhBQ3kNSU0dc6yoNdtT2WrqOI30ca3HsVa7MNuCXEul0Z3aqDZ3YYedwkgKTyrFRd+cyUXjU0QhhBBCCCGEECcx55o2XGmd96zZM2q5bppkvFYWJ/dxbs/Hxql0QgghhDjeJMEshBDihFeyvIwSYOJ1E3l0Soad0SAuM8GusnoGrPnMat5GTXeALmcBYaeDxS0d7CnwjzrGe7euBMBExT+7ZBxqcex4i+ws+GAdq/5wgA6XE1QVzTRJWCwkNJULt69i77wZyAzLQgghAEwZ/ksIIYQQb1LZLC+p1Z2kFevQMkMBM99kVutO8q+dOI6lE0IIIY6exMhHRxLMQgghTirv2vW+oX8vSRsoFoV0Yik2pxWlz6DjutVMDce4dvs+1pYW4EsF+dj6p7hh+ypMIIkVp+Wd9/W34pYKTr+mlG/dfoC2HWGK0jGmBjqZ27Qb2y0LmPm9d413EYUQQgghhBBCnKSW/+h0/vpEO8XNYeKKDbuZoVAP0plxMLtnC7nfl97LQgghxKnknfeEXQghxClDtWaHwrY5sy2oJ+arXP2jOfz785tYvr+N8/c0YiHFnJ5eAhYfpqmR0/Xj8SzyMeVwa3z9+/XjXQwhhBBCCCGEEO8wik3j4r+czl8+tY26rg4M3YDoAIu6O0k88UUsTusbH0QIIYQQ7xiSYBZCCPGOsnyWk+VPLjlk6TXjUhYhhBBCCCGEEOKdImdxJR9bUzlmee44lEUIIYQQ40sSzEIIIYQQQghxCpH5pYQQQgghhBBCiCyJkY+OOt4FEEIIIYQQQgghhBBCCCGEEEIIcXKQBLMQQgghhBBCCCGEEEIIIYQQQogjIkNkCyGEEEIIIcQpRYb/EkIIIYQQQgghsiRGPhqSYBZCCCHE20Lf10nw5T08tD7GczPmMCel4rEZ410sIYQQQgghhBAnMdM0aehMs60jxdxqG1X5tvEukhBCCHHKkwSzEEIIId6adJqk7Tp0zUqenuYGzULotHP5/Pk3kDHg1vEunxBCCCGEEEKIk9I/10Xou+VOHp48k4pIkL86HDxTM52Ob+ejup3jXTwhhBDilCUJZiGEEEK8JSH3e7GrVlx6GgC7nuHTrz7Bmoo67plxOrZvJjG/Zh3nUgohhDjIHO8CCCGEEEIcAd0w2fbpB/jrwhVszy8HwILJx7a8xOTPT2Pvr+rGuYRCCCHeCSRGPjrqeBdACCGEECc7BbuRTS7risI905bwifNvxpZMZacw0ce3dEIIIYQQQgghTj6bX+xgX04h333hb/zntqcotqXQvFZ+tvgcJgaC4108IYQQ4pQmPZiFEEII8ZY4Mumhf7//ktu4a+ay4ZVpA1RlHEolhBBCCCGEEOJktuWFJr666iHunXsG3zvrilHrVtfUYkQTqG7H+BROCCGEOMVJD2YhhBBCvCW6mr2daMwt4M8zTj9kpQlWud0QQgghhBBCCPHm9PfGmBDs5d7ZS8esizlsNIfHoVBCCCGEAKQHsxBCCCHeIpueAaDP6cFUDpdMlh7MQghxIjHl77IQQgghTgI+NRtr+hLRMeuqe4MUuQuOd5GEEEK8A0mMfHSkS5EQQggh3hIVE4C5nU3kxSOj1nkTSRhcL4QQQgghhBBCHClPiZd7py7iv154CNUwhpbb0ym+8fcn6UsYr7O3EEIIIY4l6cEshBBCiLckoWmoJlx3zafoz82FjAGGCSoknDZIpgHneBdTCCGEEEIIIcRJpGdHN5+++Da+vOpRXv35l2jLyeOniy9kRkczizr3kmuTHmdCCCHEeJEEsxBCCCHekqcmzOLhKfN5aNpp3LxlJTdtfomQ3cmPll7CqsqJFPWGgJzxLqYQQogh8jBWCCGEECe4SJzqV7ZwxXQ3t7/yICpwWmcjl+3ZwPriGpoLCtAypkSaQggh3gYSIx8NSTALIYQQ4i1JqxoHfIV8fM2/ueOxPw0tv2z3ehZ96BvsyikZx9IJIYQQQgghhDiZ/Lvuvzmt7QCB2ln8/Jm7R83xqALzuxqZ9dFPcO/2XsqXV4xXMYUQQohTmiSYhRBCiNcx50M7WNK+j5JYmMndbawur8OTTnBO0w7O2v+/Q9vt70lz77deZuK/VhKx2MDt5PInP0yB3z6OpT8+FrQd4JwD27l89/pRy226zgfXP8cnzrt5nEomhBBCCCGEEOJEZZomL1/wKypfWUujv4gNJTXkxGME88o4r2EH31hyKTftenXMfhGbm71FxeRM0cah1EIIIYQASTALIYQQYBhsLfgEmgIvVk8lZbVS198FJjjPuZpfL7toeFvdABP+PNDNp5b+gc+9/H5+cvofSOgptlRN5vb3fA5dszC78wCzp/4X9y1ZxCf++Z7xq9txsM0/mbr+Tvzx6Jh1imHCqPbmQgghxpspw38JIYQQYhx1BjJ84sOruGXT87xrz0YUoCoapC4YpsObz8ydq1Ex2Z9bSK/DTUFidKz5z0lz+daTf+eu/gr++2crxqcSQggh3jEkRj468sT3GFm3bh0LFizgkUceGe+iHLVHHnmEBQsWsG7dunEtx29+8xsWLFhAe3v70LLbb7+dBQsWjGOphBDvGKZJY+5t7CytZGXVJN6/+UU+ueZJzj2wjReqp/BqSR3EM5A2yIuG+OKLD3H3fT/FFY3xt0nzebH0i9wzdQH3LFjB/TNPR7dZwKKwubyWm6/7FJvTfq66cg3/97Pt413TYyL2y6focuZTEonQ4C+i253Df1x4E+ff8iW+evbV/Gb+CrnbEEIIIQQgcfLbSeJkIcTJxIwm2J7/Me6c9kN25H2Mdaf9LxftXEdJt8Kf667h4+d+lLNv/W9uvPY2NpYV4zAy3H72Veh2G585+waMEQ/+D+QW8KGLbqUi0Mnspzby3HdeHMeaCSGEEKcu6cEshBDipGGaJoZhsO9AiIHF38UZC9Cf76Mjt4j9/iJ2+osJOHO4efNq/jx/EYZi8IHkHrbbC6E9RFq1cPW2l5jT1YwBbCuu5JEp83n4hs9TO9DJ7x/6P3rcOdx65YdZWTqRhNUO5uDJ0wbLd21jk6+c7825AEPJZk0/cOkH0OwqGdUCGqAMBr6Kwq7iCvpzvPS6c3g4ZPDMTzv466dLx+PSHRP9d72C9tk/EVt4DdtYQE4sxIeuuI2dRdk5sJ6qnwUKYBjjW1AhhBBCCCGEEMdE+uENbHt8D649jQT60uzKOFEzToqiAfJjXfS4Pfxu4XnMnrqUcxo3M3Wgk2kDnRjAfn8N6yaez6/mT8NUFLBpvFg7ja8tv5J2fwEYJnfPWsqG4iouadhCa14hz1ZMJmWxktQ03rV7PT9/ooazvwi6YXLlAxkeaRgsmAKY2f9tuhWmFmhYtben9bNhmmQME9vbdDwhhBDiZCQJZiGEEEP2bI/y0F2d+Dft4KwNT7GhbAb78+oY8OYQt1io7d5Pb24RIUcOuWaS6jI7TQMq7cRJxtOgKjS5nCRVC5P7Orhh8xqsqsmusknU97ayL7eEVZW1bCgr5l17N/GeratIqBaerJ/JovYGvIk4hqKw319EUSzE1N52YpqGO52hNBpAAVZWT+Y3i86javEiPrD2BSpjCc5ufYl+h5v/m7eCff5i7p0zl5u3raQgFmJDSQ0XND3PkpZ9ADw7YTofuPwTXNi4j9N3bGPBujSrCwz+Mfd0/j7ndIrCAdIZSFhsw8niQQ9OOS07RDaAaYIJ+/JK+Pzqx7CqJt8+56ox19SayWBYNLBo3B/28/I3+kj7cuhJDueuMUf8DAbBMOLf5pjDvnmHjvQy+FoFFpXC1ZMU3lWvsq4TwimTKyaqlHgOPzzMP37SwKpng9yy6iE6a+byvjX340nFebJuJjuLrhu9sQlYVJQfZI6qyLl2mFEAGR3SBkzLh2K3wpIyhUvrFBQFHtlv0hKG86thVz80h+HiWoV6f7b8AwmT/33VoDUMt81SOKf6zT0E+M0mne+tNWkKgj64LNcCwcNUSQXy7BBIwpuv8bGhAnYN4vobbgqASwWXBfpS2dfFDqj1Q2MIUmmIZLJvq5p9/sOsAshzwPZe6E1ANA0OK1xaC60RCCbBocGmHkiNaGvg0MBthXlF4LWB36EwKQ8urlV5ud3k0X0G+a5s+Xf1QzgFXmv2/U0Z2V/FhA6LS+HxqxVsFo2UbvLt1Sar2gwcFsi1K7xnKlxQo6IoMtyREEIIIYQ4ha3aDX97GZ7dCo3dxFIm7VY3CauNpMWGPZPCn4zR6S3EHwsSsTtQFOh1efHFY7xUPZEHpi8mabFy5t6dvG/z89w7fRHfWXoZ6bxJ2BfrfOuZ+yno9mDqGvGomza9hmRU4cZN6zm3dSOeZHwoNFWBiQON7LcmMWNpACxhg4zfRYcvb3AjBTx2drrr2Fk7gZx4HHckCgr8cd6ZXLNrLVWBFj58xb/4/Wkr0DWNoWDXHP7f7DsBRQd0UKDUCcE0lHngikkKdT6FlKFQ4ICOqMnaDpMH90LyDWPx4QDHqcLFdfDnixT+P3v3HR9VlTZw/Hfv9Jn0QioBEiD0ohQbiAh27L2jIrZVV93V3X13dXfdXXV33bWLigXLKlZEREVpYqGD9JaEhBTSy/R23z8mmWRIgBBKAjzfzycwc+fce86dSWbmuc8pOxtUvi7QyI4LxaY69eiJRXY1aPxmYYBZW8F1KK5F7MGogtUANj1UucCgglkPDT5AC8WGNiM4/aGY8cQUqHFDhQO21IRi8uxY+PgilXqvwvKyIKDwS0WQwnoY213h96MUjHpJ/gshxOEmCWYhhBAAzHqvnG8/rSLWVc/kJZ/y5oirUJwqxQmpuMwmAPJT+pPc0IDF5wZgS0GAl/pn4TIYQ5kmpxeCoQhkZXIWdfHdyXYp2BxONE88HlXHt30H8sS8Gdy6aiHrkjNY0aMXjy3+FFUL7efS6TmtcPNeP6DO2rGep8dM4r+nXcD7Q05l/X8ewGEwcdItf2ZbYmh0cIKzgd8tmc3JxdvpWVNBn6rd4f3H520g2u3kq+yhpGqDmD1yAHNG9A0/XhabwPjNa5mf2X/fT5jSmP3VoCQ6nme+fYd/nz4Jr94QUcwTVEPPiarQs76agaX5fDF09B7HavzRtFZJbWjc3s7k4N7b2/bmIPBTKfxUqvHQouZKHloYZN6VOk7OiAzK/nzFGhocQbo11FMdncSw4o1EeV0ApNjrGFy2k3WpPZp3UBXQdyyY1gglapcUN29bWd70iMaIlFAw+lNp630fUODd81WGJisMmxHA03hqH2zRuGuYxgsTdO1qwynv+ts8flvJZQg9n5Wedh36iAnS/uQygDMY+lNuUuaGsjaeAwgleJeUtN5u98L/tuy7Hncg9DOvsGlL6D3g4cUH9ss+vwgs/9WouCtIvzeCVLhaPqoxYyNc3S/A/y6Qr71CNJH1pYQQQojjzG9nwD8/i9hkBXq73eH7Tf2dMxx1rXb/3YSreOL0iwEw+wOkea1Mj83Fo6oMq7azPCkODzoePPtaJq3bxK/n/UCDmghAH2c+w6tX7rVpg6sLWeHozt9+/JSTS3awrlt37r74FqptMc2FGuPkQWWFEAxS2i2ZBX0GszM+iWvXLua5UyYS0LXj+37jV6DSxphhey38a1ljr26FtuPxdnIF4eNt8Ol2jaDWHNNM6KHw1WXqUZFk/m5nkIkfBg9JH/e98QbB6wnF+gDuIDS0iK/dbqho/rWksKH1MXbUwbAZLWdKa27xgiKNF1drFE1VMHXwWoQQ4vgjMXLHyJW2I8jlcjF9+nTmzZtHeXk5MTExjB49mjvvvJO0tFBSxOv1csYZZzBhwgT+/Oc/h/f929/+xqeffsrVV1/NQw89FN7+u9/9jh9//JHvvvsOvb79L6fP5+O9997j66+/ZufOnej1erKysrjgggu46qqr9rlvbW0t06ZNY/HixVRVVZGYmMjYsWOZOnUqcXFx4XLTpk3j1Vdf5fPPPyc9PT3iGJMmTSItLY1XXnklvC0YDPLWW2/x6aefUllZSWZmJpMnT95nW2pqavjPf/7DDz/8gMfjYfDgwdx3333069cvotyHH37IwoULycvLo6amhtjYWEaNGsWdd97Zqm1LlixhxowZ7NixA7fbTVxcHAMGDOCee+6hR4/mpEllZSWvvvoqS5Ysoaqqiri4OMaMGcOdd95JQkLCPtstRFc0//MqFOCMbT9QFtMNX9CCK8YQTi4DJDgcER+3cV4ffescrO1mAl8gnFwGMASC9HQBmkZyVQ0qsDwrFXPAyeTViwDYkpTOkN1F4eQygCWw/3Gf161Zwte5wyiLiWdB78H8lJoTTi6jaXzz7hOcWFYAEJFcbjKyJJ+RJflUksbPfc5p9Xh5bFzoXJTIhG+io55nvnmbcTs3sSUhjakX3ML21AzeG3U6P/Xux4UblrEqI4cnv3mPE4vzWZqRw8OnXQY6K8SbGVZYxrrMjL2f2N6CWUUJteVwRnl7cPjgj0uCfHtVc4J59bwKGrwK3WrqwKijyhpPoqMm/PjQ3YUsnfZHLrzqQb7tNSjUbqvuoIL0fVnR+qUNC2jw20VBBicTTi43eXmtxp9O1kix7btd+XXBNpPLousJAqd/sGdyudn7m+HhURrDuknAIIQQXY3Eyc0kThZCHBa7KuHfs/ZbbG/flBuMZv5zynnh++MLy+lTawfAEgwyrryaCrORgigrBDQ2ZiVSq0tE1xi/pvr3HVStS8rgi1nPMnJ3AQADq0vIfbOM0VMfx9f0Hq5pENAYXraTR36czei7/kZJbAIn3vMEr34yjZ+z+u69giYtZwpr8wk4NLFCcI+4/dudGnPyNC7s3fVjkUcWH97k8pFS4YKnVwT53Unt61guhBCiY2SuiCPE7/dzzz338Oabb9KvXz8eeOABzjrrLL799ltuvvlmdu8OXSU3Go0MGTKEFStWROy/fPlyVFVl+fLl4W2aprFy5UqGDRt2wEHzPffcw3PPPUdCQgJ33HEHd911F/369WPBggX73Ndut3PLLbfw0UcfcdJJJ/Hggw9y8skn89FHH3HbbbfhcDgO4FmJ9J///IcXXniB1NRU7r33XsaNG8eTTz7J4sWL97rPr371KyorK5kyZQrXXHMNGzdu5Pbbb2f79u0R5d555x3i4uK46qqrePjhh5k4cSILFizglltuoba2Nlxu5cqVPPDAAzQ0NDB58mR+85vfcMkll1BXV0dRUVG4XFlZGTfccAPfffcd55xzDg8//DDnnXce33zzDbfeeit2u73Dz8OhVl1djcfTPJzObrfT0NDc/c/r9VJVVRWxT2lp6T7vl5WVobVICEodx0YdwcYkXJK9ivLoJAw+P15ji9G4moaujbV0Ezze0CjVQORjlkAQFQVF0zD4QwevsZhIctSHE8pxbgdWn7fVMfen1mIN386PTeLZ0WeH748u3h5OLu9PIqUMLypstX1EcR5TVi8Avz+0fnBQA03jh7f+wnUbfiLDXsv4wk0seOfvmPyhqbzyE1KY13soS175E5dvWEav2gqu3vAzcz/6D/gC6Gpc3L5oBfWWqAM+X2DvQfBhtKM28vdqx5o6AqqK2hhyaprSqlm3XHA73/YcFAregxo4/K0j7COksAE2l7fusBDUoLA+dHtffx/rK45IM8UhsrN+3483/T53pfddqePYrkMIsX8SJ++fxMmHR1f5rJA6pI4jUUflik0HFZM1mCx4DMbw/Zy61n/L2XZn6IbSGAaamq8luFTLXo/9bY8BjCzLDyeXmwwv28mg0p2o3kCo97BfI9HewP1LvyKzvpobVofeA11GEy+edFaHz+1I2VHbfDs1NTXisa70e9WynUe7Xyq1LvM3KHVIHVJHcx3i2CIjmI+Q2bNns3btWm644Qbuu+++8PbRo0dz//338/zzz/PXv/4VgBEjRrB8+XIKCwvJysqirKyMXbt2ce655zJ37txwb+gdO3ZQXV3NyJEjD6gt7733HitXrmTy5MncfffdEY8F20getfTWW29RWFjIww8/zBVXXBHe3rdvX5566ilmzJjBnXfeeUDtASgoKOD9999n5MiRPP/88+h0oR5m48eP54YbbtjrfmlpaTz11FPhdRXHjx/PjTfeyDPPPMNzzz0XLvf+++9jsUR+oR07dix33XUXs2bN4qabbgJg0aJFBINBXnjhhYje1bfddlvEvk899RR+v593332XlJSU8PYJEyYwefJk3n33XaZOnXrAz8PhsGcv8aioyOSW0WgkMTExYlvTSIG93d/zy7DUcWzUYY1pwFUfYHNKH8bs+JnvM08h2uGiIiEuVEhRcBsMWHy+iH23xUaHkssGFfzN7yH1eh0NOoVoVFwmIxaPl0FlVTx72lC2JaTSp7qMoKLyY/c+DCvbSXs1GM3hANLqdWPxevHr9eGlj1qOht4fBbjmp7V8euIQaqJC7xHdGmr5/cLP6FNVxsM/z6HvA/8h6NPIrSwht7osYv/M+hom7ljHF/1OBKDOYqPWGkWaoznTNaCqhBFl+axM7YWfAINKdzI/Nq7dbexMZ/ds7oeWkJDAuKvMLF+0BY/egAGNhBajlwHqTBY+GHBS5EE0Qosnm458z+EzuiuMSjPw5LLI34loIwzrFrq9r7+Ps3oqEUtii65t8iB4dnXbjxlUOD0z9F2hK73vSh3Hdh1CiP2TOHnfJE4+fLrKZ4XUIXUciTqSzh4FidFQ1cZcw+2Q3lDDyYVb+alxlHC90YDZFbkuUL2h8RKzTqVnVTmWoB2/akYf1Mgz9iHTW4SRyMTCd70GMCF/IxN2bmxVZ1lULPUWK0GdwtSl35BTW8H1634ABU698y/82DMXNRgkqKiUR8VwyfplfDpoVIfOL2xvS1YdJIVQbNmkrCzyukJX+r06p1eA/20+NiLgWwapXeZvUOqQOqSO5jrEsUVGMB8hCxYsQFXVVlNZnXbaafTt25fFixeHg9amQLipF/by5cvR6XRMnToVRVHC25t6b48YMeKA2vLVV18RExPTKhgEUNV9/0osXLiQ+Ph4Lrnkkojtl156KfHx8fvt2b03ixYtQtM0rrvuunDQDNCvXz9Gjx691/1uvPHGcNAM0L9/f0aPHs2yZctwOp3h7U1BczAYxG63U1tbS9++fYmKimL9+vXhck1vevPnz8fvb3uaXrvdzpIlSxg7diwmk4na2trwT3p6OpmZmSxdurRDz4MQnenu/+uO3qjwY/ZItif1YlTxMhQ0UiqqURvfn6psNlyGUE9kn6qw22wi0VkHGqhBDUXf/B6i6lVU9y50PiflyYn49Dpyquq4aP0OLrzuQeZlD2LQ7kIG7N7F20NPY3NiGhuSM/hfvwlUmuMikno1JgtbElLYGZPIY2dchqppnLtlNR+89zyZDfWhIFAFRQuyLSGFoujW0+9tjUmi2mSN2JYXm8SIutX8+O8neP7jGbz28cssmvYofapCAV9ObTnxPiegYNrLe4K7xZrLNo+bzLrqVmUajKH3oFdGD6XB3NiGlonwptuaFrm9aVtQCyfQO6SdMXJWDJh1oeIX9VZ4clzkZ0JSlpUTx8RQmRALvgDR3sie65sT0tD2tob0IRZlgH+drvLMeJW4xlncs2MhpvH2Kenwxjkqj52iMr57836xJvjiEhWDbv9Pikmv8MpZrUdpi8MrzQrteHkiDO8Gz5ypZ/Kg1jvGGuGjC1WSrPJKCiFEVyNx8r5JnCyEOCQsJvj4t5AZmTjQgMAe95vCTp+q8lXvwXydM5idMQl0a6gNl/sxLT4iPPUQYH1CdLjTeUVULGfWLCI/ycLnQ7J5a/RgtIiaQs7Mb51YBliRnk2f+//DjqQ00Kl8MPxUrtnwE2n2Wh644EZ+7JkLQFBVQYEetZXc9dNXpNdVtXm8MIW9xsZN8YfxEPSLHpYMIxr72CRaYNpZKgOTjo5Y5JnxKqem779cV2DRQ7otdLvls6sAtw9RmNhT0h5CCHG4yQjmI6SkpITk5GRiYmJaPZaTvmUHcQABAABJREFUk8PWrVupra0lISGBAQMGYLPZWLFiBZdddhnLly+nf//+ZGZm0rt3b1asWME555zD8uXLiY2NJTc394DaUlhYSG5uLiaTaf+F2ziP/v37t5pqrGltqs2bNx/wMQGKi4sB6NmzZ6vHevXqxc8//9zmfr169dpr+dLSUnJycoDQxYdXX32VDRs2REzTAERM03DllVeyaNEinnjiCZ577jmGDh3KKaecwtlnn018fDwQ6kUeDAaZNWsWs2a1vYZNRsY+1lgVoovK6mXh6Xf7UV7qwV93B1GVZQwsdVOamILFZiBKF6SmuIGUHnGU1il00wdJG9GNgg12rGk6ti0opkixYHTVULu6mJFpOlynJ2B+Zibv9xrNTouB7Lo6TsmvJqdc49PeJ7LNnEySB8oMcaxK9DO6ZDsnFa3l654nYPM5sZs0FmQPZWNaL3Irijh7y2qcmpHBW7eS6qzHpQU5b8dazt72C1/3GYKm1xHQ6ymzxZDREEr0Vlhi2B6fzJbYbPJjY5mw6xeGlRexNC2bl0eO5z/z3qNf3Wb6rdjMgl4DGH3H4zz+3Yf86uevAfj3nLe5+aI7+CW1B3UmC7Ge5kVei2ISmJ89CAAlGOQf894n2uuOeF6/yB7KlsQ01ECQs3etovdfb6Z3JqwqU/i5RKNHjMLiIlhaqpFhhVoPVDo1av1gAExqKNBPi4NLc2BJMcRZoE88GFX4bDvsqoV9TTiYoIc+MbC0xYDjTCvcPAjOzYFBySoBVOLNCi6fhjcIsaa2A+ArH87hEk+Ahe8XUfCnbZzAL+FgLi8+pc19cPvBHPrcSDVB0A/oQkG8H3B5wEnoS8nZmXBWb9jVAKdkQFYs5MSpFNs1zDqNGJMORQGbAYyNVwHuGKpg90KCRcEb0MK3m3x3lZ56T5BaN3SPUSIuuO7PbUN03DpYY83uIA1ejZWlYDPBpX0VPt6i8Zcl0C8BhqZCTiz0ToAUm4JB1ZixDpLM8EV+KPAdnQH94mBTDdR6Ic0Cy8pCM7GXuWFbJVR5ITcOGjyQGg0GXWiCgFgzROmgeyyk2aCscabNVBukR8Mrv8DmSugdBw+Ngu93gTMQ+h05NQP6xcN3hZBXB4lGWFUOG6vgpLTQseMt0CcB6twwIBGSbQrLSjQ0BUalQpJFYUetRrwZ8mrBqocGH6TYICtaQUOhyhVar2tbNSRZYGS6SqldwxfQSLCoLNgZRK+ATg+1LuibGDq3gUkqdR5IMIM7oJBsVQhqGttrNNJtGj5NodIJCkHizQrbazRQwKaH7bUwoYdCVOMI+dfP0fHCmUFK7BBvBncAUm0K6mFaB1wIIcTBkTh53yROFkIcMqcPhMJXQqOYvT4oKEdxuNn2fSHlCzYT5/eRWVvJT+YU0n0enDEm/FUeEhscLEvP4S9fv8cNaxbj1hmYuGUD5THxrM0cjF1nYPqgYUQ77cQ5NZLddq7Z8BN1Rj33bn8X544oTJoHUxsJ5rYszcjhkbOuwW5qnl2h1hLFf08+l399/S7zcwa22mdrQgoXXfcAToutXXXc1B/+OlZhRanGSRkqJr1KlBHsXogzQ7ULYk0a63YHmZ0H/gAk26BvPDy1DBYUtz7mzPMgIwYGJinEmkOxSZVLI8ZIuzo3dxXJVoUl1+qpdWv4/AH+8D3M2g5uLwQUuKo/6DT4phDO7QW58VBmhyIHlNphVx3ssIc6KkQrkBIFNS5wBeGK3nDVQLAZIdYAUWaFLVUaOXHgC4Riu3gzZEaHJsfzBEJjCXrE6Gjwaiho/FKpUeWEM3oopEfpCGpa+PWq9yrUezTSo8Ckl+SyEEIcCZJg7oL0ej3Dhg1jxYoVaJrGihUrOP/884FQL+ymXtyrVq1ixIgRB3Sh/EjaV7sCgfZ9sTwUNmzYwD333ENmZib33HMP6enpmEwmFEXh97//fcR0Z3FxccyYMYPVq1ezdOlSVq9ezdNPP820adN45plnGDJkSLjsueeeywUXXNBmnR25KCFEV9EtzQRpJiCWGCCr5YMjkgHIbLGp7wmxoW3X9m7ckgEMai7w6xM4sPEj0PKS2PXhW7nABK5pWdAfoKjfQ/z3yxl8MnA02xK60be8iOgJA2j4+x+JyYwjRVFIAQZ9vpLl/9vAUl8f4twOxhRt4eSdmzApGjWGKNakDOHS66ZQb7Fw3/k34TQYuf/Hudy0ajE9qyu45rK7GX/97/ny/X+S4qjHo9Pzc2YOi197jG+zB3Hl+p/oXxU51dWbA0/hjrNuBk2jl7uSpJ7xnD04NAIkJx6u6B8qd++BzeAY4R8TOr5vWywGhb2vkBViMOmYeFNPPl03kpLnV5LuqUABRhVvRwkG0VqM8jH4A/h0oD10cF85+u3jbdWoU0iwtL7dUoxJDY9uPlCKojA8NXSRYGyLP4ipw0M/e/Pk+ND/vzklcvs5LW7f0bEmtXJZv8j75/ZuXSY3+cCO2S9ygAPJjddrchNblwVIjQq97oNa1NMrrvn2Ff33HuTHmUP/RzfeVxWFvgnN3yPizdA08U5ii4kIBnVrfSyLQSUnfq9VCSEATeZmEEchiZMPLYmThTgOKQokNXboSQ99qe83cTj9uChc5Px97D6kxe1uNEf9e058v2S2gS9/HsSFF6Tjrqxm2apqKj9by5CKXYwt3o4CFFtjSXXVo2sx21VhbCKrE0dRbottVfeOhBRqSaFPeQXl0XERj43fsZ43P26Osn4338cTy5of33gL9O9mYE/d96imKY5MsgIonJCucsIeo3nPyWl1mL1KtHTNz6H2iDMrgJ5XzoVXDmM9OXHNt9uK7ZrENHaAT4+O3K4qSvj1SrQc3c+5EKJzSYzcMZJgPkIyMjL46aefaGhoIDo68tMwLy8Pm81GXFxceNvIkSP54Ycf+O677ygvLw9PBzZq1Cj+97//MX/+fBoaGg54XSmAHj16UFBQgNfrPeB57zMyMti5cyd+vz+id7bf76ewsDCiR3JTL/T6+nrS05u/kXk8HiorK8nMzIw4LoR6PbfcDpCfn7/X9uTn5zN48OBW23Q6XXhNgK+++opAIMCzzz4b0T6XyxXRK7uJTqdjxIgR4SnVtm3bxvXXX8/06dN55plnyMzMRFEU/H7/PqclE0IcAXod3bf/B4Df76do7IUnMuHCE2krHzvzovdw55m4YvUGXj1lBJqq8sg51zFt5JmseOF3nF6wiZJ/38N7Q07htotvBw2+6zuYaZ+8StlF49i+U8Pg81ESFUe6vRavquPloeN44MxryfDUcdbmtUy+ojunTL7ukD8FnemSfw3H/vtnqU6/FVPATzeHk+vWbeCDQQPw6XRYfD6CBuk5LIQQQoi2SZwscbIQ4thz2qQcTpvUlInNIGcS8OjpaKvyePH2OeRUl/NNzmACqp5Hv/+UBJeDvPhuPDfyMrIrg4wu2snGlMj3vBFbaykhl3vmrmXtbVnYzaEOKwNKizAOjugWzz/GG/jH+CNwokIIIcRxTq76HiHjxo0jGAzy5ptvRmz/4Ycf2LJlC2PHjo1Y16kpaJs2bRpGo5GhQ4cCMHz4cHQ6Ha+8Euo/1pHA+ZxzzqG+vp7p06e3ekzbzzqZp59+OjU1NXz22WcR2z/77DNqamo444wzwtt69OgB0Gqdpffeey+iN3TTcRVF4d13343otb1582aWLVvG3syYMSOizU3lR44cidUaGuLUtFbVnuf2+uuvt2pHbW1tqzp69uyJ2Wymvr4eCPXePvXUU5k/fz7r1q1rVV7TNGpqalptF0J0XdfPupYR18UxuHg7D333HZetWcoNyxZx58I5VJtj2Bkdz+qULOJdDhI9Dr4YeCLXr16C65RcLnvuXH5/axYzB55EtMfJT+nZTD3nZpZ1z+GaNT+z7XeJvPrlRZwy+YTOPs3DIirBxLuDTyHK7yXa72DGF0+y5pVHmffOP7njl+/wmfWgk68bQgghhGhN4uRmEicLIY51ygnZ3L3iV5yT91eennclf396NG8OPIc/jLuWuT3HcMaOfCYUzOcP82dyzdpl6AIBzD4vt//wAxet2g7AkKLdfPPE2/z2i+/47I2nuMFXwMuP9u/kMxNCCCGOTzKC+QiZNGkSX3zxBW+99RYlJSWccMIJFBUV8dFHH5GYmMjdd98dUT43N5fY2Fjy8/M58cQTw1NJRUVF0b9/f9avX09SUlKbayvtzzXXXMP333/P9OnT2bhxI6NHj8ZkMpGXl8fOnTt58cUX97rvTTfdxHfffcdTTz3Fli1byM3NZcuWLcyaNYsePXpw4403hsuOGjWKHj16MG3aNOrq6khPT2ft2rWsW7cuohc6hILTK664gpkzZ3LnnXcyfvx4qqurmTlzJn369GHLli1ttqe0tJR77rmHsWPHUllZycyZMzGZTNx3333hMuPGjeO9997jvvvu45JLLsFgMLB06VK2b9/eqh2PP/445eXljB49mrS0NDweD/PmzcPhcISnXwN45JFHuO2225gyZQrnn38+ubm5BINBiouLWbx4Meeddx5Tp+45SZAQoisb9siZDHtkz60TWbeshOf+sILBFTvZmpRGjN3PtA/fIuOB0zj/hgEA9L9oEP0vGgTcysnAyUe47Z2t2hrN8rQcRpbuQAEGVBfQr0bhwUnXElRU0IL7PYYQQgghjj8SJ0ucLIQ4ftkGZvDA8utoWFnA139exAJ3LHOsZ5DpruNfcz7hlVlvowsG2R3IxElceL9Yl4cTdlRycvVWhj7ya9QuuiSCEEIIcayTBPMRotfref7555k+fTrz5s1jwYIFREdHc+aZZ3LXXXeRmpoaUV5RFE444QQWLFjQqvf1yJEjWb9+fbj39oEyGAw8//zzvPPOO3z99de8+OKLGI1GsrKymDRp0j73jYqKYvr06UybNo3Fixfz+eefk5iYyGWXXcbUqVOx2Wzhsjqdjqeffpp//etffPDBBxgMBk466SReeeUVbr311lbHfuihh0hMTOTTTz/lmWeeoXv37jz88MMUFhbuNXB+7rnnePrpp3nllVdwu90MHjyY++67jz59+oTLDBs2jKeeeorXXnuNl19+GZPJxKhRo3jllVeYMmVKxPHOO+88Zs+ezZw5c6ipqcFms5Gdnc2TTz7JmWeeGS6XmprKO++8w1tvvcWiRYuYO3cuRqORlJQUxowZw8SJE9v1Wgghur7Bo9J5Zt6Fnd2MLu3q9UtZnnwK3ewOejSUUWmN5g8Tr+KXtB6oWpBgQBLMQgjRlcj6UqKrkDhZ4mQhhIg+sSeXf96Ty1ts+3jmSIyPfExUwEO11o3+Rc6IfRbm9uJ78yX8O8V8ZBsrhBDimCQxcsco2v7mehJCCCGE2IdN8b9lYfexAKzJiOej4QOojrJh9nmJdjjwoqP2qaRObqUQQogm5cof21Wum/bXw9wSIYQQQoi9+8N9P/MffW/+/tUSzthciKJpfDm4N3+6cBz9K4pY8M/eJEQfPeOnfD4fb7zxBgCTJ0/GYDB0couEEEJA+2NkkDi5paPnE1gIIYQQXZJea14TsH9ZHafuKCYvMY4YTzU/ZfQBvfRlE0IIIYQQQghxYIYn+HEFbKzpa+XZCVcR5/GyOSken15HTvVufJ4ecBQlmIUQQohjiXwCHyN8Ph91dXX7LRcfH49OpzsCLRJCCHG86FlfiM3bgMMYzaunDGVjWtNo5WQIaihBmSJbCCG6Fpn+SxwfJE4WQoijW+zuSozxfnJr/eQ48gHwFpSyLkFP/6oSUpIsndxCIYQQxwaJkTtCEszHiLVr13LHHXfst9znn39Oenr6EWiREEKI48WWhG5ctn02s/qOZWPaHmvrqQoaauc0TAghhBDHNYmThRDi6GZOsXHdytX4DQnhbcZgkD71QdYlZ6JpGooiSQEhhBCiM0iC+RjRt29fXnjhhf2WS0xMPAKtEUIIcTypskYzqKqUQdWb2i6gSsAvhBBCiCNP4mQhhDi61SbG0auhGgzGiO0xXj/nb/uF8vqTSImVdYyFEEKIziAJ5mNETEwMo0eP7uxmCCGEOA71qd4NwJDdRQwrLWBNWs8Wj2ogM2QLIYQQohNInCyEEEe3t3dGM9hfD3vkkCssJs4oL8RV1gCxCW3vLIQQQojDSuasFEIIIcRBiXM7w7e/fusfXLBlFQnOBgaWF2Fyu0HVOrF1Qggh9qS180cIIYQQojONPTUegw92RJvxN06FXWc0EO3cyfLUbNJ6xnRyC4UQQhwL2hsjS5wcSRLMQgghhDgolhtOw6Po0YBujnpmv/NPqv5xO1es/hG/Ts9Ngzq7hUIIIYQQQgghjjb3XJzCl7mZDC0sp1bzsdPixWbfht7TgN6gw2SSyTmFEEKIziKfwkIIIYQ4KMobv0J3ygAWP7aIGK+DGpuZOX2G8eKoiWQou3j1/JzObqIQQgghhBBCiKPQ+//M4rzHFLpX27H6HES7G8jMTuTq927o7KYJIYQQxzVJMAshhBDioOmnnMnpU84M3x/j85H7xhuN9yTBLIQQXYmG0tlNEEIIIYRol/RuFta82LfFljGd1hYhhBDHJomRO0amyBZCCCGEEEIIIYQQQgghhBBCCNEukmAWQgghhBBCCCGEEEIIIYQQQgjRLjJFthBCCCGEEEIcR2T6LyGEEEIIIYQQIkRi5I6REcxCCCGEEEIIIYQQQgghhBBCCCHaRRLMQgghxDGgdOEu7h+3kLvO/InXX93V2c0RQgghhBBCCCEOCU3TeP6zSv58y0p2zC3u7OYIIYQQApkiWwghhDgqnXfdFox6Hd0bXKBpWCvrUI1mTIpC/bNrefSN7fxh/hiMZl1nN1UIIYQQQgghhDhgBVvqufG5WoIejd71TjIcKk+/Uk3dy+W8+f5g9Ba5tC2EEEJ0FhnBLIQQQhxlLrxlK7GKgqZT+XRgFnNyM1md24t1OVmsz+7OV6OGYPF4mXzdus5uqhBCiC5JaeePEEIIIUTnOPP2fEa/EUDxQEBV+bpnKusTo4nz+UnRVO64e3NnN1EIIcQxo70xssTJLUk3LyGEEOIoU2G2UhhrYG1GIgAmX4DMOi+mQBCAgE7H5qx0eheVd2YzhRBCCCGEEEKIA/bN6wVsjYnl/B1lbEyKpVdJNVfM/4WdyTHUJMcQHwiyXIvp7GYKIYQQxzUZwSyEEEIcZfwKrE1PCN/3GHSs6B4XUcZtNJBWUXeEWyaEEEIIIYQQQhyc17520qvWQVF8LNEBqExJYOUJOUxZsZyLFq4ATaPMZu7sZgohhBDHNUkwCyGEEEeZeqMelMgpWVwGHb4Wm1SvD4M/eIRbJoQQ4migobTrRwghhBCiMwRVHUYl8rJ1eWwMK3v3YEB9GbkFuzh/3Taef2B1J7VQCCHEsaS9MbLEyZEkwSyEEEIcZfSBPRLHmgYOPz/ERvF9nI21cVZmDevJzjSZMkwIIYQQQgghxNHFaTKjKa0v4tdaLQD0rChnZZ8e5ExfdaSbJoQQQohGkmAWQgghjjLmOkcoqdzE6Qe3H7+q4FFVKlQdTk3B5nZ2XiOFEEIIIYQQQogOMPn92NEitimaRnydHYBdcVFsTopF0WQkmRBCCNFZ9J3dACGEEEIcmLzUZBRPgFi/D4fZiN/t3yP0BjxBPMbYzmieEEKILq7VZ4YQQgghRBfiCgbIKCknQVUpTkrA4vUyKG8XwYCCT1VZ3S2FM9YXUBtt6OymCiGEOAZIjNwxkmAWQgghjjK96u2stpqo94C13oFe1XDpjZGFVPAZdJ3TQCGEEEIIIYQQooNqbCbweDh/7TZgR3i7x6Bn6vWX0Keoipvmr2XWuH5c3XnNFEIIIY5rMkW2EEIIsReBoEZhhYfvXt5EXbW7s5sTVqvTgctPUAO70dA6uayATa9RbZF+ZEIIIYQQQgghji5pDS5+6NudGospYvt7pw7m2/7ZnJSfz+bsWHaabbjW7e6kVgohhBDHN7nyLIQQQrThmjcc9Hz1R74Z1JtVWdkk/NPJqB1b+fL9wShq567zVJAQA95g6wcsOlAUMOkJ+j2URZuPfOOEEEIIIYQQQoiDMCS/jNk5mTw/dji9GuyUxkRRGmtD8QSIK63j5isvZNKmrbhMCrc9uJV3vkpGUWUclRBCCHEkySev6JDZs2czYsQIVqxY0dlNOeymTZvGiBEjKCkp6eymCCGOkAVbvUT9bw2zhvZnVVY6yU4P6a4ABRkZXHDZLwS9fgDq8mt4t8frvNf7LT7s9Toze02nZHXVYW+ftrcEt9UQ+tEpaEH4clA2K2KeQAu0kYw+Gr30JSiXNv+c9+f27adpoR8hhBAAaCjt+hFCtJ/EyEIIcej8khKHLhDk5G3FLO7dnS2pidRbzNTF2bh4cx5Tlq5jTr8+jN+Wz8aUFGovmdHZTT467CgF0+WRcfWZfwKPr7NbJoQQnaq9MbLEyZEkwSyEEELs4c//3sWI/HI2ZSST0eBmaKWdVKeXTIcHT1IS95/+I5df9CNDX/byfW5/HERTr4tjXUpPLn7JzpDb8zn1zk34A4HD0j412MZxzY2jlwGr10OG3Y3JqOOtk0ZQpP8//Lsd+z2uY9FmHMp9kT+3v7fPfTRNQyut7tB5HJBeU+Gu1yK3zV0bCoj3ZuG60OPqZaEf5VJ46PXD204hhBBCCCGEEB22fmkV8Q1ubl6+gZGl5Tz6+feM2VoYfnzeoGxG7drN6Xm7WJOWwr++/Bbl89XUq/fhOPEpgt69J0u179ahpd+DptwQ+jn5UbR1hXstf0xZvxN63916NrT568F8Vee0SQghxFFNpsgWYj9uvfVWbr75ZoxG4/4LCyGOakGXj19d+jOj6v3kVO8mpb6BLLs/oky0z4c1xkq8wcxla3bSJ78Mv6qyPrs7OzK7cdvKpYzO20GFzcoff9nJjvMGMvMP3dvdhkAgiPMvn2I9eyBbC/0seHojvSvLGFqyibpnrueN/3mIHjEUl6JDH9Rw6nWMLirmxl/WsyonnSi/g9N2FvBtn0F8MGwQdy9ahQ4TpalPAyox/7uI2KsHtT73QADGvdS6Qa8uxTEwDdt9Z0Rs1hpcEHN78/2mG5eNQPnovnafb7sVVOz9sdV5MDw7ctvFf4dZbYwg+vcXMOUsyM1s3ubzwcod4PfDJz/Df75sfqxbDOx+86CaLoQQQghxLJEYWQhxKPnqvLz0bAEf5xsw+AOcsm4HJ7jcWP2hjtWxLg83/biOBrORNVmpePShy9l9K2qI89jpV12BnQRitBpYVYzT9BBKhg3Tmt+hT4oGQKtzQtzU1pX/nAdD/hCKZ2NMsO0plG4JR+jMj6A/vguPf7zvMsql8NgV8MerQKYbF0II0Q6SYBbHFLfbjV6vR68/dL/ah/p4QogDo2ka9a+spezVzWhmI/pMG/afdlHv89KrZhcGf4C18ekYXQb8io5l/Xrx5YCeDN+Vz6DKMkpiYtmRmELfWjsrembxS1oyiqaRXNOA2RsgzuOnf2UNPUoriaurZ6LLj9XnoXdtBb/7ehEfn3wyuYUlZFbU4DPoCERZ8ZlNJLt9LMpM4fkhufhVhVOKC3np49dJr3XgIoqEWj+5pUtZXFrKzDcCuIkiweGgNh6eH3c6W5MSuXr1cv7x5acoQT0+9MwaNohvc/qQH9+fk/+ynTt/WkjliRMJ6BX0eMj43SxyewxlTHEG/ewujJqGzuMhubqOaIfGdd9vILumgijqOXfDBh6f/Ql1ShxB9ECol3L9NbOIuWogitI8pYt3znp8F7y69xfh/s9gjwRzy+RyhI9XoCk3oFxyIoZxRnzmI/D+ecJDzbdP6QM/btt3+X73tv/Y5fX7HiW9L2Y93DIRAgE4ORcGdIdXvoFNuyAnFe44O7RdCCGOOJnWS4jjhcTIQoiO8HsCbPt4J9Wb60g5IZHkbiaqPtiBLtZI6pR+mLpH4fdrfPuzi80FXtLTDGzVGymuCnDqxp0kb69hh8FGuVMjUOagx84Kdkdb+L5/FooGVRY9u5PjMQY04u0eBpS5ydAc9KquoWdtA2Xx0VjrI2fhGp1XwpqsVMZvKCCruA7FGWBUZQFG3Bjx4MWMBVfoW06xA0/yH/Bhx0RoRPN+v/3UeyDlPiIWV3roXJR/Xnvontgj7b+fw6/fbH/5xz4M/bT0m4vgqZugsAJe+xbqnXDNGBjd95A2VQghOpfEyB0hEYE4KJqm8fbbb/PRRx9RXl5OWloat9xyCxdccEFEuc8++4wPP/yQgoIC9Ho9gwYNYsqUKQwbNixcpqSkhAsvvJApU6YwdWpkr8Jp06bx6quv8vnnn5Oeng7AY489xhdffMG8efN49tln+eGHH6ipqWHWrFnhMvuzZMkSZsyYwY4dO3C73cTFxTFgwADuueceevTosde6m7Z99NFHzJkzhzlz5lBTU0PPnj25++67Oe200zr6lAoh9lA59Su2v7qTIDoAXOZ6fBkKYws3h8ucWrGDnWRSSxwLMtO5/4d5nLd9CwBBFN447QLePWEgy7onh/cpiY1idP5uhpTXkVDdgNnhJrncjc0dGrFcQjeu+GU1alCH1dU8hVSwuo5dvTJYkxTPj8nNPZuXZPbg5RNG8+T8uTRYdOyMSmBAhR29qhFbYiDD1YDLoOehWy5hd3wUAC+dPh4MGi98+g5uxcQTYyeyLTkFgJ975PBjjxxe/vx/DC3LD9eT6HFSmJyNgobbYCZgMlGc3o2fe2eRXlJON0cWhelJjMnfwNkb16JXNEJfkpp7IO9KeYru5Q+Hzqe8Yd/J5Q7SfbqSK78z8N4Tow75sfdpf8nlI8nthxfnhm5P+yY0hXnTWtA/bIYZC+GDB+HKUzutiUIIIYQ4tCRGlhhZiGPBN7f+SOnPoVmkdr2ymYxyO0pjKFP2wkaGrryEp77y8f0qd3ifCouBtckx5OWbGFFpAvxE1dkZtWwbP/brzqPXjENTQxfw1WCQYOMo2Xi7k6c//ASLx8efx49hcb9czl29qVWbgsD1i39hyrdr0WkauaW1OIjCRDE6NPS49thDIUgUAezo6eAaw/+ai2bQofz9KJxC+r1FB5Zc3pt/zoJAEN5cANX20Lbn5sJHD8ElJx388YUQQhy1ZL4LcVBeeOEFvvzySy699FLuvfdeFEXhscceY82aNeEyzz77LI8//jh6vZ677rqL66+/nvz8fKZOncqSJUsOug133303lZWV3Hrrrdx9991YrdZ27bdy5UoeeOABGhoamDx5Mr/5zW+45JJLqKuro6ioqF3HeOyxx1i9ejXXX389d9xxBzU1NTz00EOUlJQczCkJIRoFa93sfn1rOLkMUNijGyN3bowopwApVBJUoDLWEE4uA+Qnp1Mal8jq9MSIfXx6FWNj57SY2gZi7N5wchlAQ6WUFKIdoUB0R3o3Zp0ynI/GjCQ/JpodUbZW7Z3dZyB1BitJLidPjDmdX9ITOb1gK1EuLwCLB2eFk8tNpo8ai91o4odevcLJ5SZLu2dj8kcGyQFF5XffTSOtoRIUhWqTgZeG9ea9wdn86+yTeHHscNZnZPDqqRPJi85E1dr4qK9oPk/3+dNaP74fWjC4/0KAtd5Hj18OwfrMhg6OIO5qNK31tn98cuTbIYQQQojDRmJkiZGFONrtXlUVTi4DJNa6wsllAH+1h63/Wh+RXAZIdvlIdHoYvrsmvC1tVyVmb4C3xw8JJ5cVTQsnlwFqoqxMGzeabg4nV2/egVlR2NwjjWCLwWRuvY6lad24Y94adC3iKj96GmiOsVtHXAo+2vceuFdPzDm4/TvLrS8eumM9Pbs5uQwQDEosK4QQQhLM4uB4vV5mzJjBTTfdxDXXXMNLL72EwWBg5syZABQUFPD2228zdOhQXnvtNa677jqmTJnCW2+9hcVi4cknnyQQCBxUG3Jycnjuuee46qqruPnmm4mLi2vXfosWLSIYDPLCCy9w/fXXc/HFF3PbbbfxyiuvtLt3dVxcHK+++irXXnstN910E//+97/x+/188knX+ZJVXV2Nx+MJ37fb7TQ0NITve71eqqqqIvYpLS3d5/2ysjK0Fl/opQ6p43DV4a6yE9zjLcJr1GPYcyOgI0BQUYh3OyPL6/RoEBGcNtFabDN5/a0ed2NC1TTKY6NZ3i8bl9lEUKdSGh9LUqB1kjXKE+Cr7iMoiOqGy2DgxREnEx1wYSSUYPbqda328as6AqoaEbC3pOwRIndz1KAPBqg3h4LohVkp1Jua178rSIqjKNoEQJwjMuBvqen51+r27OXdtojX3B9sI3Bvm8nR/Lx2+Peq9Utz7KgK/d101b9BqUPqkDo6XkdXpqG060cIceAkRu76MTJ0nc8KqUPq6Ip1VBZF7q8LtI7+GoobWm0DsPkCtIx6Le5Qh+06qym8ra1YsighFoCq6FCcW5oYz7cnDmJrRgqbuqfx7GnDKY6NZn9dndv69nLQ32laPDeH4vVITU2NuH/YXnPfoQuk23rN/OW1rets4Vj9+5A6pA6p4+Dq6KraGyNLnBxJEszioFxxxRUYDIbw/W7dupGVlRXu3bxo0SI0TePGG2+MKJecnMykSZMoLS1ly5YtrY57IK6//voO7RcVFfrSOn/+fPz+jn3puvrqqyPWMR04cCBWq5XCwsIOHe9wSEhIwGRq/iIfFRVFdHR0+L7RaCQxMXJkZ1pa2j7vp6amRpy31CF1HK46rDlJxA6JoWU4k1ZSzY7kDPZUQyz6oEaJLZ7SqOa2ZFcUE+VxM7isNqK8LhBE72tclzg+BqfFwJ6icJBTX0Jxi6mww+eIQrSveZotoz/AhRt3oCkKK5N7szgrG7vRFLHP2PWFRDk9EdsuWb+SWLeLMQVbGVAeObJjXN4msmraHgHcv2wHALutplaPVZkMaEBZbGyb+0Lz82967Zq9lmkp4jXXq+36OhVUoHBI83PX4d8r7wftauNR6arQ9Nhd9W9Q6pA6pI6O1yGEOD5JjNz1Y2ToOp8VUofU0RXryD0rG3N883eZhqjW32v63zGA1MTIDtRunUpRjIWSFjFqRWIMABPWNi/7hNI6mjxjUx4Ag3YVh7dVx0SxKrcX+d1TubysivN3V7G4X/eI/fwGiKF5ZG1biVA9nja2HoBhWeGbh+L1KCsri7h/2F7za8e06/TaQ+mb1mqb/rrTW9fZwrH69yF1SB1Sx8HVIY4tkmAWByUjo3WSJzY2lrq6OoDwNFg5OTmtyjVtKy4ubvXYgWhaB+pAXXnlleTm5vLEE09w5plncu+99/L+++9TU1Oz/50bZWZmttrW8vyFEAcv86vLSTspGoMugE4PGY46ygzdyE9IJaAoBFCoVqKpIAG/TmHqkg3ceMl1LOiZQ43ZzE9ZPUiryOO2n1cwNq+UZLubbg0u0mqc/NwthiWp8exOiKYhwYI9Sk9TSGrGRXdC72HmtnrYBQMMrGlg0pYCrlqzhce//oHeVaG/fY8aulh49YY1bIxPZV2PdHw6lSi3lxfe+JzTt20n0e7gnsXzmP7BG3gxYSeehxfO474f53F6/hZ+t2gOn773AnkJadSYba0C5TO3/siJhevo3uBkT7WqyrIYK6+MOrGNHt4aqZX3h+/px/aGS4cc0GuiqPv/+qBZDHx53yBcMYfgy6OhdfL/qBFtgXgbnDsc7j43dNugC21/YBL87drObqEQQgghDiGJkSVGFuJop7foOfvN00g7KRlTnBHLpb1Iur0/hlQLltxYer8+loSzM/nbvQmMHGgi2qbQvYcRZ/8YEvVBHBYd8fUOFDRKeqawrUcSN81bw1WL1pNQ7ySjog6b3Q2ahj4Q4OYlK7npx1UAFFstpJaWghaKZKM9XnrV1gOQ6XKz9IQcXj91IKu7J/NLrwR6+EoJoEdDwd84dro5dtZQ8WBotTbzAYgyo6z+W8f370wz7oek6P0W2y+zHra8ANPvhtwMSIuHhy6Cx47CdamFEEIcUvrOboA4uql7STK0nBqhvZQ2ejA22dcUYWaz+YDrgtDUXTNmzGD16tUsXbqU1atX8/TTTzNt2jSeeeYZhgzZf8LlUJ6/EKJturRosn+6hux9lIkBmvoxnw48AEB/AJoucWmaxtV2H/7dTjweP8u2eUmrrSN4eiyvf6gns9JLYYqBzUP7MKSkiKLUZMzGIZhqXGSXVLA9IwWHJfR+owSDnLCrmAs2r2BbYneCDZFp3J3x0Tz5zVf0L6rl6dPP5H9fjMJdWo9W5aS7yYS7wsSIHjrSXCeh29wdX1ElMcYA1/TvQdpVH/C3rz8hGAzy6FmXEe/2cOUvP6D3efAGQG8wEetzYAj6Gb9tCVUOE3+98AyKEhtHK/uD4AvSoNfh1uCeKy7gyY+/JiroDY061isYEiPXj7Z9fCuOBVtg/F7WaBqQ0nrbtw/BhH+1XX793wn0TaX0jTf2+podUl/+Hs4d0Xz/osfh81V7L//D3+Hk3NDtQAAMV+77+Crw9E3w27fAC8RaIO9F2FEOAR+M6As6XZs98Vt5fsr+ywghhBDiqCUxssTIQhwLkgbGcd47e4yAnRY5VX5WmoG/3xc52i0kt/GnyXAAJgaCKLrm94iSej/byzU+2GDltVNHURwfw664OLSgwuLEaG7YUUiqK3I2hXSXh2ljh2BTNd58eRYl+njyLUn0bdhNNPVoKKhoaGgoKKhv3gQWFT5dDu8vbd/JV72IknAIErNdQcVboSm+o64CZztnpnjyerj3gtAU21GW5jj3ljNDP0IIIUQjSTCLw6qp9/aOHTta9WTOy8uLKBMTE5o2p76+vtVxDrYH997odDpGjBjBiBGhxMS2bdu4/vrrmT59Os8888xhqVMI0TkURSEx2gjRoRG1WQMB0gEYc3Y6cEK47DU3b2CbaiQdPcNKqkgqreCs5evIT++G1e8lxl7P3JxedEtJYcqKheTFpLIuvicevZHKaCtmP1SZU/hyWBzvzR4JgDktBtJi6AXc3qepJhP0jo9o58SiP4RvPx2+dXn41s5PNvDGv7exJjWG2YOH8eS7C3l09hJ+feHpNJiMEXOCLeuRyeDKSqrMOqJdkLLrXozpMW0+P7YzcnG09YAKtg2/b/18njkULTgD1BtDG5IsKBWvNBdoMX34YdcyuQww6/9C/yuXtl3+lH7Nt/V60Nq5JuB9F0XeT9j7FORCCNGVSZpHiM4jMbIQ4njVMrkMkB6jJz1Gz9gZg4HB4e3zFlRT+qGTPGsUqa7aiH10QR9EG3HoFW688yKu/nE1j81fjBkHAcCABkYF87Lfoh/aYkaJK0ej3XcOnPLn1l+Eok2w+P9QhvU8lKfbdSgKOGZC6s2wu/XnSdgPf4NT+jffN8tUtkKI44fEyB0jCWZxWI0dO5bnnnuOt99+m1NPPRW9PvQrV1lZyezZs0lLSyM3N9Sr0WazkZiYyPLly9E0Ldxbe9euXSxcuPCQt622tpa4uLiIbT179sRsNrcZwAshjh9PP5PLr69ZwZyRA/HqFHItRtRAkCi7nfVpiWSa45n35kDumuJh1m4frgQDu+IS8fmNlJpUps87ea+jNw5Wj0sHUviGl3eH92Po7vrwN6ABpVUs7RG5FkrApGdNUiL9Xjqb5BsH7ffYNu0Z/F4vnin/g+Hdsdw5BtW09+mpFUUB7e2DOp92C34M6mWtt+94ae/7aJ/Aii0w8neh+0v+Aqfu/3kQQgghhDhcJEYWQoh9m3hGAsb3CiixGXHUKNiCoaDXr4Dmd3HOui18NaAvqS4Hf57/PTp8+FFJ0fYyw1Yj5aTeEDxC8WtXVPYmpNwE5Q2tH2t4NzRaWQghhDgAkmAWh1XPnj254YYbmDFjBlOmTGHixIk4nU4+/fRTnE4nf/3rX9HpdOHyV155JS+99BL33nsvp59+OpWVlXz88cfk5OSwcePGQ9q2xx9/nPLyckaPHk1aWhoej4d58+bhcDg4//zzD2ldQoijS1qsnn4DrIz/5DPm9hvI7F7pnJi/k3GVBVzx1wkMOTUVgJdeOxE4Eb/bS0NdgPiUIxOQbYy3cPbW7WzplsG2tCRySyu58petVNos7EiKQw0GOXVnMXabmQVTzcSObn9SVW80on/rpsPY+g5SlPaPNG5pRG7H9hNCCCGEOAwkRhZCiP3zqDqCfo2RmzeTl5rI26ecSF58FAGdSrTLDUBSvQMNKDNGMdz1aOc2+Gix+63OboEQQohjiCSYxWF377330r17dz788EOef/55DAYDAwcO5PHHH2f48OERZW+66SbsdjtffvklK1eupFevXvzxj39k06ZNhzx4Pu+885g9ezZz5syhpqYGm81GdnY2Tz75JGeeKWuKCHG8e+xfQ/A8PgCGv8fl61ezLS6J21Zeg8mga1VWbzYS37Gl7jokwVHGJ4NH4dXreOn04UxZsJIBu6v53cLlVFrNmPwBlmalEFdWRezoMfs/oBBCiOOKRjvWjBdCHDYSIwshxL41WM0kOTzoNXj11OEUJTYv9dRgMYMvyMaUZH5JSWHIxZkoh2kGMSGEEMcHiZE7RtE0TaYXF0IIIY4ip0xZz089+jRvcPnIrGlgXP4ujIEAP3ZPw6WqPPbDfG7e2DmjkX0+H2+88QYAkydPxmDY+1TbQgghjqydyt/bVa6H9vvD3BIhhBBCiNayf1PB6TvKMPh9fHxCb6qjrJEF/EHwa1y0ZiuffTS47YN0QRInCyFE19TeGBkkTm5JRjALIYQQR5mdsUmRG4w6dvVM4p3s5NB9TWPCT1s4beeOI984IYQQQgghhBDiIJy6o5DsGiebU7vRq7I+MsGsaRAIjZeqMZkJBjVUVUaeCSGEEEeaJJjFMaempoZAILDPMlarFavVus8yQgjRVZkCe0zTbdJBy4BaUXDE2agMJtL7yDZNCCHEUUCm/xLi+CIxshDiaBPldPNlr0zGl9eSaXdhUlQ2Jcdi9fnpV17Ld91TATgxrwRV7bOfowkhhBD7JjFyx0iCWRxzbrzxRkpLS/dZZsqUKUydOvUItUgIIQ6tfpX1DCosY2G/Hvh1KvqAQoPeFFGmzmaiRo3tpBYKIYQQQoiuQmJkIcTRplJvoiA6Cspr0Wsa4/J3My5/d/jxNakJnLCtlDHbdgCnd15DhRBCiOOYJJjFMeevf/0rHo9nn2UyMjKOUGuEEOLQ61+4iwHFVVy0dht2s4lpZ5/EJltkgvmULTspiYvupBYKIYQQQoiuQmJkIcTRxuALUm00UGo2kub2tno8oFM5aVs+5WnSqVoIIYToLJJgFsecYcOGdXYThBDisOpZWQmNU7fYLWYyGjx4DHZ2xVnRFEivcxHfUM8HYwdya+c2VQghhBBCdDKJkYUQR5vhJbv5ZkA27/bM4Ndb8tFpkY+nN7hoiNZx+7JLO6eBQgghhJAEsxBCCHG0cRsMWD1+ABLrGzD4/WRXO8mudgJg9biY3b8Pl63NA/p1YkuFEEJ0TbK+lBBCCCG6rpxMEzavl161NSha68dT7C6uXLkBRZlw5BsnhBDiGCQxckeond0AIYQQQhyYtd0zCaihLz6GQJCTNmwjyuUAIMbVQJE+SKLiJndXdWc2UwghhBBCCCGEOGBnv34a524q4PQdu/DQOsNsdrr5akDvTmiZEEIIIZpIglkIIYQ4ykz9b3++GN6f9T0y2NgjnU3dU3B46slT/NT4nIws3sXFPxbgf2REZzdVCCGEEEIIIYQ4ILY0Ky/PPwX7wERMVXU0rcIcUGBXlJmpC3/ktrlnd2obhRBCiOOdTJEthBBCHGXG5FgY80E/LvpDCaUVAYw6P9FlAfrXVRLAhGo3MPDpQZx7bnJnN1UIIUQX1MZMk0IIIYQQXc7L04cAUPh1EfdNryXR4aJIb2DwsmvpHieXtYUQQhwaEiN3jHwSCyGEEEepWX9L7+wmCCGEEEIIIYQQh1XW2d359Ozund0MIYQQQrQgU2QLIYQQQgghhBBCCCGEEEIIIYRoFxnBLIQQQgghhBDHEQ2ls5sghBBCCCGEEEJ0CRIjd4yMYBZCCCGEEEIIIYQQQgghhBBCCNEukmAWQgghhBBCCCGEEEIIIYQQQgjRLjJFthBCCCEOmZLvS3nxD+soj46nzDiSMUPWd3aThBBCCCGEEEKIA/bjwir++1wxxTYLU29O5cbx0Z3dJCGEEKLLkASzEEIIcRya+UoR7y/3YHW6uD7ZxfjHhmKMMx3UMT+eOIeBizZwu8+HT1X5sUd33oobQ6/rFnD5zLMOUcuFEEIcLFlfSgghhBBi3x68bCVLU9NYdUJfXCY9q3708ZfPStj+bHpnN00IIcQhJjFyx0iCWQghhDiOeFx+LrujkKW9utHT1sCAeg8f5xv58tr1jE7ycd2Mkzp03PVbXKSuzOObgb154vxxlMVEc9r2AmIra3jZ1otLGjzoog8ugS2EEEIIIYQQQhxOPl+Qp2/5hVUxSfzYIxlNDSUd3GYD+WmJfDW/hnPGx3dyK4UQQojOJ2swCyGEEMeRa2/PZ0tKHGMLSpm0qZCcWjvd3R6619kp2uymYqejQ8dd+14eTqOBX19zAaVxMWiqwvd9e7E5Jx2PwYh3RekhPhMhhBBCCCGEEOLQcbv8PHjRGmIW7+DMtVu47vtfiG9whh8PGnT848O6TmyhEEII0XXICGYhhBDiGJT7YDnl6OlZ28DpBaUMKSglZVwqUQ3R2HvqGLarAr0/AIDPaMAVZabHlhI+vHcNd8069YDrSy6u4qMRgwmqkX3XtqckEldfRp03iOWQnJkQQoiDJdN/CSGEEEK09sRlK7l4wTpi3R6c5dU44mOZsLMal76Wtalx7IqxEPQGO7uZQgghDjGJkTtGEsxCCCHEMSb3/jK2GiygV1mTksia1ERiR/bnjdfnoB/Sm/PW5RFtb+6FHVVnx2mzgKqwu8Lbrjp+OOljvGuriPN58BtU7KlJbBt/YqtyBn8Qvd+Lu0E7ZOcnhBBCCCGEEEIcap5aD2a/n6CiMGvscHamJwNgDmiM3lVNVKyFapss/SSEEEKATJEthBBCHHN2KgbQqaCqoCiAQp2q47fXnInfrGNQWVVE+YBRT0x1HXqXj9Vpifs9ft5/VuFaV0uq24E54CfK7aV/QQnX/7yRWFdkgjq7yk43h5eMiRmH8hSFEEIIIYQQQohDSvH7qTebyU+MZ2da0h4PKpy8qwKbJiOYhRBCCJARzEIIIcQxx6PXgaqEksu6xv8DQapVHZZAsPWkL4oCqsrOzATiAr79Hn/jH9eR7HO32m5xesgtqafeZsCj1xHr8mHyBggY9fi21GAYlXZoTlAIIcRBkTklhBBCCCFas5vNfHrqEOotJpSgRpTLTbeqOuqjLFQkxrI8qxu9yis6u5lCCCEOMYmRO0YSzEIIIcRR7p1freCDEjOpDS5O2lbCwLFD2ZCZDCYdGBonK9FUfF49FWYLiVYvW5ITcRv0ZNXW06eiCjUQwBUfRbpj/1Nk+3UqtLE2SZTThQLEOv2AP7RRUVBUlQqrjR6A75cSfC8vQM3thvm+iYfqKRBCCCGEEEIIITps5eNrWNc3C68hdLl8xPYiTl23Ixz57shI4pMrx3LyxoJOa6MQQgjRlUiCWQghhDiKvXzrcv5h60FZthFV01jbLY4Ltu8kLyMJl6HFShiKgiPGQrTdyVe5OQTV0GPFsTE49XoGeEqptllIbGjYb51BRaPWZCPK5wmvtaEBRrdGkttDpcUcLhvr8ZLQ4MS3tZL6wY8QxEQ98UA++vsXYx4YTcL633bo3AO1dvyvLEE9ZwD65FiUtNgOHUcIIYQQQgghxPEr6Akw5+MqvCelAqD3Bxi5qSCiW3VOcSV9i6sItu5rLYQQQhyXJMEshBBCHKX+M+QLPhnSj56+Bs6ps5Pk9hDt8eC2GOltd9C9tAqDplEUY2VVejxBnco3I3JJqfdEHGdTajIXLN5AeVIM+TbrfutVNA2vqqMoKoFYrwsFjTqjBbvZjMPnp5e3HpfBgNnnx45GgwIJlz2HDwt1JJBAMSbsuInGtSGIc8YqrDeecEDn7lImA0b8mNAe/hIj9biMycRu/C1qTvIBHUsIIYQQQgghxPFrseU1Ss8aGb5v9Xgx+QOtyqXVNLC5W9wRbNmB8V7/Mrz7Awp+yIzHsOMZMBo6u1lCCCGOUer+iwghhBCiq3l6/HzsmInW6YhSQ2sux3g8KEC1yUxunYvooIZZgz51Ts7eVsrpW4oYnVfW6lhKUMMRY2HolmJUdd/dsbWghl4Bh1VHAJVyawzl5hgUP5R2T2BYTQ2/RNnIMxrIM5vwW41sSk9Ahx8XUWSymTjKsGAnnlKSycN189sHdO72nN+hYcFDDH4sBLDgohsmbxWO8S8c0LGEEOL4pLTzRwghhBDi2Bew6ciurEEfCCWV661mamyWyDKKQl63eAJmU2c0cZ+8P+XhUO7A/+5KdHgw4EG/azcB07Wd3TQhhDhKtDdGlji5JUkwHwVWrFjBiBEjmD17dmc3RQghRBdQXe2HYi9fnTKQOquFWouZ9andyIuPRecL4NHp0e2xT6wvwHnbCunpcKILBCMeG55XjKZqmNx+RmwtYskfVrRZrz2vloVJr2E3G4h1ebAGfHgtelwxJjYPzsRpNbKhWzJDXG5OcLrJ9vmoNJqYvHg5fgxEUYcRR8Qx9Xgwabvbfe6e7/NR86rxYUEBzDRgowYzdnzEYi7cjOb2tft4mtuPc84mnH/8GO+kfxMsqmj3vkIIIYToHBIjCyGEOFSqy914Ajr0OhiTV0j3qloyyusoTIylyhpKJtebjLx8ymAUl59KqwnfHjF1Z3K+vQL3Kf9Gh4IOPX4S8GNDQUMlgG/wbzq7iUIIIY5RMkW2EEIIcZTJ/yCfzT1SQInsNVcYF0vvyhr8bfSmUxSFgKrSq6iS3F+KWDKoF/VWI7klFYzI20VAr6O4VyLFMdEkvpfPwoDKuCcip63+adwsZg/oy+nbi4lvcFMeH4XXqFIbb8OvVymMiiLZH8CgaQCcvWUp/9i+jMyaMlwk4CQKjci+fqEksbtd5+3dUo5r7NOY8aGhYKUWHaEe5jr86PChx0ew2o4uPX6vxwk4PNQM/A/sLMNCDWBFQ48fH+oXDwBewI+GjgBGiDFgqJ6OopN+eUIIIYQQQghxLPnfiXMZ6NKIqncT5XDTo7CSWYNyeOrCk/DpVeKr7NQHVQKN8eC5JWVc+g+F2f+X0sktB9+2Srw3vokFf4utCgGiUPGg4kddX4B/eQH6kT07q5lCCCGOUZJgFkIIIY4SK7/azbtfVBNcWUF9ckKrx1VNY4vZwuysFM5ocEQkci0uD/EVDWQWVKNqcO38NQBogDNGT4PNjF+vI7uiirU90qmfW8bp/9BQGpPYq8bNYkl8CkOLy+lRVY3dZsJt1JFaV8OJRQWoQQgqCj/nZvPViKGM37GKycs+A4JAAD8eTNioozfgx4kJPT5iKEPBHfGFJFDjwjlzPe68apwba4m9bQhxF/Wnqt+zxFNLA5mYcYaTy010BAhgJLAoH901kQlmTdOovmAGpi+XoqFgwIiCgkYMbqIBBRu70UeMsPaj4idQb8Knvxaj9n6HXjchhOhqNJnWSwghhBDHCU3T2LCqlhXflvPtSg9xATjlonii+8cz+7/buaKkFh1BBm3ehQK8Pn4oc0b3Ce9fkxQNdi94QvGn1R+k1zcbqTDuoOHT7fiL7MQ9MhLPl3l45+YDkPDmucTfNLTtBgU1Rv+hnOKpT4Q3dfv5Jqyjsw743Br6/hkbvohvdhpa4/8GNPwoqHhP+zt6zysHfHwhhDheSIzcMZJgFkeUw+HAZrN1djM6LBAI4PP5MJvNnd0UIQ5K7fR1NMzcii7RTMKDIzCf2Pk9b7uq7Z/sZPV/N+J1+EgdEEvP6hqql1QRcAVRzQo70xOo1RuJcrjJLKnG6vXg1JlAU9BpQTR9EL8K29OTifH6sXn9VMVGsTE9kTm9e6AGNE7ML+XMDQVoRj1fjRmMw2Qk1uUi4HDz6bDe2I0GjP4AN/yQx8jiSmxagNJaB4UZSRFff4buKOazrBQe+n4FfStrKE5OYOmAHEyBAN0cdjRFh6pFnp8C+HU61men8VNaIg1GA2l2JwN3lfK3k+Yz4pcCbEE/Np+Pi9GwaqHpp6MbvCQ77FiCHgKNXyd0msapm3dQFB/HvF792G2+juvWfE33mlo8JLZoqwEVA/Uk0EAiiezGrfwaO9F4sBL6Wtf01U6h9os8avgMBTNeYvBjBuxtvl5+LNRf+zHatZ9gxIGVKjQ0XHQDjLiJajyqRhAjoQS4ggk7BpwEMaA0Jq6VxhS0Hg9BVALKDQQw4CAeL2b0+LFShYKGgwT8mNDQ0BPAgBcFDQ2FYOOE5SoB/LYojB4Hes0DagC9rwEArxoLmg600DRmetwoBj1aVgKawYSquNEPT4WHLiKoNxCY8DRauT30XJlVdLmJqEkmuGAE/Opc0O05SboQQgghuiKJkYUQRzNvUQPljy+jdl4xGir6jCgCQfBVudEK69G8ARSLHmNODBabgql3HHE39qPukx3ULSnl44yezMvqTkzQz1hvHT3jQEuPodBvIjnZwLmT4khJNe6zDcX/9zOL3i8iraiGGK8HFIWAolDULZ7y+FiWDeqNoiqYPF62d0tkfUI0C1e6OX/GRk4pKEMxgt4dCMerS/q3keg16sATwOr3k93gYMr3y6j7PrSHCtT/an7jlNQhtTfPpebmuehHpJC1fDLuNWWUDX8d0Di7sZwCNIXnlSe91SIGDlHYI3iP2KZhxoWJphnCgqh4GuNcPUFMKI1zm/kx4/Pa8Cv3Q2OEqjWuI6oQCK3XjAMINB4lGhQjpisGYZhxK5gM+3z+hRBCHL8kwXyUcrlcTJ8+nXnz5lFeXk5MTAyjR4/mzjvvJC0tLaKspml89tlnfPbZZ+Tl5QGQnp7OGWecwR133NHuOlesWMEdd9zBo48+isPhYObMmZSVlZGamsqVV17J1VdfHVH+9ttvp7S0lJdeeolnn32WFStWUF9fz4oVobU9KysrefXVV1myZAlVVVXExcUxZswY7rzzThISmkfm1dXV8dprr7F48WIqKiqwWCykpaVx1llnceONN4bLffHFF8ycOZPCwkL8fj+JiYkMHjyYBx98kPj40Ei2SZMmkZaWxiuvRPbaa3lukyZNAmD27Nn8+c9/5oUXXmDdunXMnj2bsrIy/u///o9JkyahaRoff/wxn332Gfn5+aiqyoABA5gyZQojRoxo9/MqxJFW9eQyKh9ZEr5v/2wHPVdfjzG39YjY413BV7tY/NDy8FTUO3+spMztJ7vej4Egm3ukUhEfA4A92kJ1fBTDVxeiDwCNk0GrPpWqRDO9KupIsLsASK+oJa2ylkW9e1AQY2Pu0N4EVB3ubrFUxYYSoA1mE9uyLNgNBtDAq9ORancxJL8Uoz9Af6Cbw8nSgT2oio0mq7qWZK+Lf365mJ619QD0KSknp6yCn4b3A6AqJYasvAqcJj31FhNptQ40BX4Z0J1Xh/XFaQwFjptSErG4PFy4MQ+dXiHW7sGl6IjRvBHPjz6o4cWAbo/At1fZbr4a2p+SmCQ2JfbimY/fRq9BIJxoDWIkdCwNPU5iMeBrTC6HAt2mPtdNAXYo+NY1Joi1vUyrHcCECxMNOElEAwzYqaUPoKIAVqoxU4OChg8z9WRiwIURNxoWNHwouFsE9qH2qATQcFJDbzyELgL7MeHFhIaK0mKPID5UvNC4VSGAHyN+9OgdduxEE4MLa2B343kpeIPx0Hg5IogOPwoGnwdlR2VjXUbYVIL66TL8nli0IOFz0tzgX1uNnjrU79ZB/m545tY2nh8hhBDi2CUxssTIQogjK+j2s2PMRzh3OsOpVU9+Q2MkrKE2xXN2L961FWgEcP1YSu3bm9A0eHP8aD7LySHB6eKCLTvRaRpFANRQlhjP5ugo1qxy8Pg/s4iKarsDbdFvf2LBO3n0K63Dgg8/Kmih2b2yy6rZmp2JzxxKUJdEW1maEk/vBhfXLdvMWT9vAEKRu5/m5ZBS6hxs6p4MgKJpGAJB/IEgmXYnE8oqUBQVn96A0e/fI26ksYtx8xb/it2UT/kS52trGmtq8bzAXv9vvhe5yFTTc2vAhxFP0yuBHjsqoXWhdXjxEWgsqUdDDcfsQXQEaZkw1qHhRm2MgXWAAQ92LQn/zLUo5f9Gv+CRNp97IYQQQhLMRyG/388999zD2rVrOfPMM7n++uspLCzk448/ZunSpcyYMYOUlObRiH/605+YO3cugwYN4pZbbiE6OpqCggK+++67Awqem3zwwQdUVVVx6aWXYrVa+frrr/nXv/5FfX09t99+e0RZp9PJ1KlTGTJkCHfddRfV1dUAlJWVMXnyZHw+HxdddBGZmZkUFRXx8ccfs2LFCt5++22iokJJlkceeYRVq1Zx2WWX0adPHzweD/n5+axcuTIcPM+ZM4fHHnuM4cOHc8cdd2Aymdi9ezc//PAD1dXV4eC5I5555hn8fj+XXHIJNpuNHj16hJ/Xr7/+mjPPPJNJkybh8/mYO3cud999N0899RSnn356h+sU4nCqfXFtxH3N5afuzQ0k/2NMJ7Wo61r34uZW6xx7zHqCevChUpEYHfGYou0ZEIaShwHUcHK5SVKtnXEFJbw5sDcAP+VmkhWMTNRW7tFT+OS8Eoz+5mmhB+yqoCY1hmBtXeiYdQ3h5HKTzN1VmN0e3GYTPrOBaeefyNf9e+A16OheUceQkkrmnpBD0BdZ98I+WVyzbDNRHi8aoNeCbU4W4zLoifL5IrbtjrIQ5fXhUeC3X80joJkIQov+3KFRui2eOTyY2fPZa71es4ION6ER0ME2WhOaGiyKKpwk4iEWF4k0JW4N2LFQHS5twE00JWjYGkN3feNo7CA6WibTm8ZSa61qDaJv1bNcI3KtZoXQ6OUABoLoMOLGjy58bn6sQOQFkyB6NDwt0u0afqwYXbWNlyNavx4BLKj44NVv4V83gUG+5gkhhDg+SIwsMbIQ4shrmFOAd2cD2h6Xl5tSreyxNYgSSnRqoTjyh95Z+BWFgZU16LTI8gn1dmqjo2ioD7BiqZ1xZ8a22Ybq19ZTmZCMiarGCCkyShq+uYANOd0BKLKaQFHo4XBx8i879jhSc/R52zer2JyWSJLPx7lbi4j1+PCoCpUmEzqjkX5FxRj9flprOSa5mf21tY0jlpvjyrZojTNotT5m65L68LTYKhBoFR/r8aLgB1T0uPEREzFPWPOR1DZrsVJDHT1RF+ahr6iH5Jg22yyEEOL4Jlcej0KzZ89m7dq13HDDDdx3333h7aNHj+b+++/n+eef569//SsA8+bNY+7cuZx77rn8+c9/RlWbL3oHg21dnN+/wsJCPvzww3CAfuWVV3Lrrbcyffp0LrrooojAva6ujssuu4y77ror4hhPPfUUfr+fd999N6L8hAkTmDx5Mu+++y5Tp07FbrezfPlyLr/8cn7729/utU0LFy7EZrPx0ksvodc3/1p35OLAntxuN++9917ElF8LFixg7ty5/P73v+fSSy8Nb7/66quZPHky//73vxk7dmx47dLOVF1djc1mw2QyAWC329E0jejoUGLM6/XS0NBAYmJieJ/S0tKIXv573i8rKyMlJSV8flLH0VWHFmwj4Km3k9zi/tFwHkeijmAbz1WTttbm0A7wb15tcfyg0joY3TO4NHojg1ifXkdQVbA2uPAbdKha6/YqEJ4WOy/Gxux+vSAQBG+AorgodsdHtVl3sPFcAmqoFWpjolzfInDVAIfBQJTPS3NIqqEGNWJ9PoYX7CTJ7iCAbo9RzkpjIje0zYQbF9a9P1Etaoyiiobw1NZqxGPNSeumuiIvMOhxtjqi2kbSOIB5jwRzZNsPTtPllsP4+RDUQn8f9XVH/d+g1CF1HM11GI37ns6xM8n6UuJYIzFyaxIj71tX+ayQOqSOo7kOXbBl3NVxe0u4NmkKc9s6j8i1h9s4dosY2RwIvcd7VRWLuzneC43c1fA2xqmZ5Q7+98/PKO6eQEHf0PuxKaiR6PMzLyuVUzZuPaDzizgXWnek7oggavg4bX+v0wiioIbHZjeVbqtsG9c2aHo+NQgEj7nfXalD6pA6JE7ek8TIHaPuv4joahYsWICqqkyePDli+2mnnUbfvn1ZvHhxODCeO3cuAPfff39E4Ay0ut9e55xzTkTAazAYuPbaawkEAnz//fetyt9www0R9+12O0uWLGHs2LGYTCZqa2vDP+np6WRmZrJ06VIATCYTRqOR9evXU1JSstc2RUVF4Xa7WbJkCVobCZaDcfnll7daT+rLL7/EZrMxbty4iPbb7XbGjBlDSUkJhYWFh7QdHZWQkBB+U4fQc9X0pg5gNBojPjiAVlPI7Xk/NTU14sKA1HF01RE/dUjE/opJR/rdo4668zgSdQy5s19zNNvI5PGj+sHoD5BYHbkOsKa03U9b00GdNfJ9pC7KwvxeGeH7I3aUkFzbEFEm2ROZUJ6fG7kWlMnnJ6rOidnpxmcyYDea8CuR7+3FyfHYLSbqTUaWpSaBP9jcSA28GkS1GBXdZGRJBS6rgVqzJVzciw4fKgEUfKgU2uJwWYzNB2ssGe31siPGilvfNCq39Zc0BQ0rdcRThY5gi+m9WpbZk4aGhoVaVJwQTg1rKI1rHgO4CPVuN9KAhapwuwKYWh0xcnqwvdXcFFqrrR4LJbX37BgQeXFag8Z1mDVU/HgwoScQ3kuPE4XI10DF3yJlH7oQoMcJJkANrRu95++a2jRt+C3jMdqsx8TfoNQhdRzNdQghjhyJkVuTGHnfuspnhdQhdRzNdcSc3xNjhq3N+KetmEptimAUsPj8nLqtEL2msSEpoVWn35ro0LJEtiiVEaOi9noe8bcNIsHpwIeu8fiR73ebe6aHb2c3OElucLIp1saWnqkR5RQ0XAZjOMGgAJlF1SRUNMfoZr+f0gD8b0i/vSZ127pnvbIfLae7Du7lcvzexjW3phAMd+HW8GNpVcqHJfy6hLpUN42f3vOZ3nNxqhAniZhxoBuVBalxx9zvrtQhdUgdEieLQ0NGMB+FSkpKSE5OJiam9fQkOTk5bN26ldraWhISEigqKiIpKanVm8PB6NWrV6tt2dnZABQXF0dsj4+Pj3gTASgoKCAYDDJr1ixmzZrVZh0ZGaGki8Fg4IEHHuDf//43F154IdnZ2YwYMYJx48YxalRzQmzy5MmsWrWKhx56iNjYWE444QROPfVUJk6ciM1mO6jzzcrKarWtoKAAh8PBWWedtdf9qqurw1OFCdGVJPxhNGq8mYaZW9AlWkj4zQhMAw7de8SxpNf53fHWeVnz/GZ8rgApfaPpUVFNbY0Ov0ehb0klBSrUGY3YnG6676rChAeH3oRf0WEMBtD0CnF2J/nJ8SQ6Tdg8fmpirGxKT6RGpxLt8jCkqJwJ6/OxBAJ8ffoQnEYDMW43GcWl1A/MpsZiomdFLauyU0lwuzl9Y+PFOU3D6nDj16mgaXTfXk2tZsWGh4YYE1u7p/Lt8AF4GtdWbmv0OhpEef3YY8zgDRLT4GJYTQPjinbj16sQCFAaFUM3hwOdBr7GMcweDEybeBKDykroVVUTcci5A/uwqlsc6xJO5KLlK+hmt7NnP20FjViqMdOAkwSMBLFRh5Oo8NRgSosUa9NqVn6s6PEAPsCOh3iCKJjw4MOChoqdRBT8KGi4icVEOR4S8RKFDwsGQtOVB1FxEYO5cSrq5rYFGgPsQONlAA0NHRoW4thNA4n4MKPHh5VKVDQcJODH1HiWfoIooc4FjcF/6N8APqMVW8CBTlNw65LR+xyhtiihPRQtSGiCbDfodWjp8QT1BvR40A3rgfLbSzDojQTO+S9aZWhfxaig9opBl5QEF5wID17Y0V95IYQQ4qgkMbLEyEKII0+1GshefDm7H1tK3fwSNBR0aTYCmoK/xoNW0gDeABh1GLKiMEepmHrHEndDP+o/y+OWJduIMwT4umcWi/r14DRPLVkxClpaNN6Amdxues6/MIHomLbXXwbo/tTJTNArLPywiF4FVVj9PkAjqCiUxUeTXVBGQFHx63Vk7yqnf5/ufJebxeunD+MmBfoXlKFoGvpgEIMvlIhtKabWSXVy6D3braqUGA0UpyRzX0wUPeob9kjPantEsKDrHUfK+xfjum8X5ae+TXNcqzZOGg5NnYdD8WOz5i7FLbeGbvswoGEjgWJCS07FoMeNSgAf5nA83HSkYPiWv3HBJV3jUk7exsjXioYfDR0eolHQYzgnG/3Mg5/1QgghxLFLEszisNqzV3NL5557LhdccEGbj7Xs6XL55Zczbtw4lixZwsqVK/nuu++YOXMmEydO5B//+AcQCnA//PBDli1bxvLly1m1ahWPP/4406ZN49VXXyUzMxNgr9NxBQKtR+/t6xw0TSM+Pp7HH398r/vl5OTs9TEhOpOiKMTfPYz4u4d1dlOOCrnX5pB77d7/nk86iGP/X/hWBjAcgGv3UjbgM/PNKw7WFOl569T+9Kx1cvKGIvT+IAGdSlSdC7PbTwAd9VjZHpfMnFFDwmtIa5pGv4IStg3pG3FcRdM4eVcF21NiwO3lzMJKVFXBazRwx4bmRGXeo8vIf2otZn8Ah8FInNfJ1owktqclEO32MmHTNvyqyusnD+OT4f2B0BTe599+Pd8//wrR3mCL9Z20xjWiQqsaqzQQ5Z+OomucvMvrw/3WMurml+LeVIn1qgHEXj6Q2sHPYPQ48WPBSxR2UgkS6v3oIhk9bmzU0i3vftReSRHnaXb5qOn7T5y77BgxoKGjjnSC6PHjwIodlQAqbnTYw8G4RmjkcxA9Rv8b6HQ62rocvfdPu/Zpaxz13qiAWvHfg6xRCHE8O7RjGYUQB0JiZCHEscSUHUvWjL13LNmbmAuyyQQGAn88yDZk/v0krv/73iPzgNNHwcf5/LTQRIndTO+6OrL7WDj77dH86/liTvpuMzm7axuXT9pjJK+t+b33u26J+BtnuvjtpAl8omzCt6KMYLEd640D8X65HS2/AU2FuGfPJPHukeF9rad0p6f2e3w+HzNenM7Jv6/E6myOOpOX34ptRDoHQgsEqdY/RCw1GHDjx4wPBSN16PA1l0OHYWQ85mWPteu4Mr5QCHE8khi5YyTBfBTKyMjgp59+oqGhoVXP57y8PGw2G3FxcUAoqFy0aBFVVVWHrId2fn5+q215eXnhtu1PZmYmiqLg9/sZPXp0u+pMSkri4osv5uKLLyYQCPCnP/2Jr7/+muuvv56BAwcCoSkWTjvtNE477TQAlixZwv3338+7777Lww8/DEBMTAz19fWtjr9nr/L96d69O4WFhQwePBirtT3rhgohxMHRGVTOvbsP597dhzv/UsDqZdVk7K4lsc5NWbKtcequ5lHCvQsruPSrVXw2fjCxbg9XLlrJF32y0AVCCekmsR4fPe1OetmdRNXWY4+Nxm4wMKmnN6L+7D+PonThLlhcgceix6foSaupozQ+lrdOHcFbp5yIV6dSHGvhrMIK0pxuakwGfkqN5y/njeNfn32JDq1FC4OYsDdOW+0PJ5cBFKMBy5RTsUyJfA4S192Lv+9v8GPCjzWcXG7ixxR6DjYVwx4JZp3FQFLR7wHwbquk9oxXUYurAAserPgxEEMpBhwoje3UUNAwoKk6DI7poNt7z3khhBBCdB6JkSVGFkKIvdFZDeTc0JecG+D6PR57+uRUPkjZQgAFEz6cmMIp5sLEKFb16sbm2Gi2REdRamnuXBOINdPjhT1njjq7Xe0JWFSW/KcbkydPxmA4kK7GkRSdSuyOP+DM+RN6mmJVLRTDNiaYNUz4AfPSRztcjxBCCLE3sgbzUWjcuHEEg0HefPPNiO0//PADW7ZsYezYseG1o84991wAnn322fCaU006ug7TV199xe7du8P3fT4f7733HjqdLhy47ktcXBynnnoq8+fPZ926da0e1zSNmprQdKtutxu32x3xuE6no0+fPgDhQLi2trbVcfr16wdAXV1deFtWVhYFBQWUl5eHt3m9Xj788MP9trul888/n2AwyPPPP9/m41VVVQd0PCGEOBDXDLPgMxsI6nQEgMySehJqPOw5/sTscvPnd7/kgY+/I7OylrO27ASvD8Xnx+TzY/D6yLA7w/sVJSVQZLMwvqeXC/4zolW9J8+/mIY4PSavh12xsdy0ZCVGf+M60YrC0KISzt1ZTnaDE0sgSLrTwwUF5Szs3Q89Xow4MODCiINYStHjQ4dnr2tQ7UnfJwlD8X8xYEeHey+lVJSs+H0ex9gniW67fkeK9i+Sdj2I9bp+RE9KxvD7s1A876Non6Bon6BqH6PT3scQeAfFLP24hRBCiK5KYmSJkYUQoqMuzrsCHypu9AQMGpXxZqoTzGhmhXx0LEpOjEgumwIBZv0lbR9HPHL02YnoX7yWAKGlljQUvMThIQUPKXiJQxucHp7ZTAghhDiUZATzUWjSpEl88cUXvPXWW5SUlHDCCSdQVFTERx99RGJiInfffXe47IQJE5g4cSJz5syhqKiIsWPHEh0dTWFhIT/99BMzZ8484PqzsrK4+eabueyyy7BarXz11Vds3LiR2267jdTU1HYd45FHHuG2225jypQpnH/++eTm5hIMBikuLmbx4sWcd955TJ06lZ07d3L77bdzxhlnkJOTQ3R0NAUFBXz00UdkZGQwfHhoStm7776b6Ohohg8fTkpKCg0NDcyePRtFUTjvvPPC9V555ZV888033HXXXVx22WX4fD6+/PLLfU5T1pYJEyYwadIkZs6cyebNmxkzZgxxcXGUl5fzyy+/sGvXrr2unSWEEAfrlImJ/Oo7jfn9HFz/4wbMvgC+xo90rcUo4c9G98Ww2k/v8tAFyfR6O//9dCEvjhnOroQYchwuxlTXho8b7/GSUlvHVe+0Pb2YqlM5p+ZWPot5EyWgYAv6ee6dT9iQmUaCw4kzysaPQwZG7GMKBhlSXo8OD15s2KjGhANQcBKDGQc6ffsv5urSY/FjxkINTpJo2VfOiB1ddwvKoNbrAu6NISOOuHeubnd5IYQ4FmituiQJcXSTGFliZCGE6CiTzYBPr6L4FWrjrAR0KgHAHAhy86otrElNIsPvw6eqRPl8FHaLoUdi17mkbr3zVHzZsfjPeQYDgcZZwlQ0QMWOaVXbHX+EEEI0kxi5Y7rOp6FoN71ez/PPP8/06dOZN28eCxYsIDo6mjPPPJO77rqrVQD7t7/9jeHDhzNr1ixeffVVdDod6enpTJgwoUP1X3XVVTgcDj744APKyspITU3lwQcf5Jprrmn3MVJTU3nnnXd46623WLRoEXPnzsVoNJKSksKYMWOYOHEiACkpKVx44YWsXLmShQsX4vP5SE5O5pJLLuGmm24KB72XX3458+bN45NPPqGuro7Y2Fhyc3P57W9/y4gRzaPwhg0bxmOPPcbrr7/OM888Q7du3bjssssYMGAAd9555wE9D48++igjRozg008/5c0338Tn85GYmEi/fv0iLmAIIcShprfoeWGiws36bPqUVTNu867GaZ0VFELJZQ34YUAWn5zSn3GbCohxeShMjOVXXy7j1qXrKRrYC1VrXmFKUxR2W430ajGiZW+s/iAxFU6cNgO7jfFkFdZjdAVZN7YXaFqr3tG6YBA/Ogx4cRGPk0RU/NioIUgQ48qnDuj8o7VpuJUbiGUnTpIJYMCAA3OaHt2avx3QsYQQQghx9JMYWWJkIYQ4GAFVRdETsZwUhLoz96urx28xcV55FauS4ynVdb2VOg1nD8KgvYr7ov8S/HwzCn6UJCPG0tdBL0s9CSGEODwUraNzQInjzooVK7jjjjt49NFHmTRpUmc3RwghjnuaprHgt6vwv7Qem8NNAD0aCh6jnl+GZPH1CTms7haHV6fSr7KeK79ZzeNXj6EuqHLT1kK6ebzhZLSmaSxJj2dkYRnPfrnvtf++jHkLa0MgYptCkDkn98GdmoTP0Nx/TR8IUGLQMePaANqFz6BDRUPFgBsVN95BPbCs+3uHzj/gdOO76x3UgWkYHjoPRab9EkKIdtmk/Kdd5fprvz7MLRHi6CYxshBCHBv8DR7+l/MZOr2Cfo9L5Y9ccwZj83eT5fawIy6adYk2fnk+s0P1+Hw+3njjDYCDXoNZCCHEodPeGBkkTm5JRjALIYQQRylFURj/zxNZZYaFn5aTZnfiMepZ3b8nAZOR3Bo7uTX2UGFNY3j+bs5ZtYMPhvTGqDVP/qI0Hmvi9hK2JEXvt97yOCvZDbUEae4J7Tcr1ETbMAUD2NwB7AY9+mCQaK8PTDbMo7LQadNwvLcC5a3vUYenovu/i7FEWTp8/jqrGd2bt3V4fyGEEEIIIYQQQh9tIvfBfnz0WS3DCnejD4aSzN8MzSZoNpLm8QAQ4/WRGvR3ZlOFEEKILkMSzMe5QCBATU3NfsvFxsYegdYIIYToiBP+eiIn/LX5/vxrthDjD0RMVZ1eWoPBH2RgQTlRA3sS5fNTZjUR7/ZhCgYB0PQ6sl3O/dYXW+NmV1oU3Wqd6P0aDoueapuVz0fkcMGWUmamJlFhNqFoGsNq64n3e1GTbQDYrh0B147YTw1CCCEOJ1lfSoi9kxhZCCGOT6MeHspzS1by2tknkFtchctsZHR5HZdu3oXHbMYbDGIJBok3SYJZCCGONRIjd4wkmI9zu3fv5sILL9xvuZdffvkItEYIIcShUBZlwVzvJLHOic+gI7WshmFrCwBwmQ04DXpeGtIbt16HIRDkjF27GVZZx664GHpXVO73+P1+m8uKaTupibKFe3av7JOOTqdjQXICFWYTEFrXeXV8LMNr61FU+aImhBBCiK5PYmQhhDh+pdY6GFNQxnd9Mjl3Wwk2X/PSUJqqYjfoMNR4O7GFQgghRNchCebjXGJiIi+88MJ+y/Xt25eYmBhWrFhxBFolhBDiYFTFmEi3u6k3GzlrwTri60OjkvOTYsjLSkJVVdz60PTWPp3Kt1mpDCippCI5kayysv0ev/8fR1H4fhHOnW4CRj3oNGribdRYjAQtgVbld0Rb8e6oxpiTcGhPVAghhBDiEJMYWQghjl+xTg8nFFUxvLiK+ihbxKxgAKXRFnbEmDupdUIIIUTXIgnm45zJZGL06NGd3QwhhBCH0Mp/pzDhV17cHgtbLhlNTKWdVLuTXidGs7tcj1+viygfVBR8bg9+BZKT2vfV4OwNl0Xcf+BJO+6ggi2o4VUjy3qNehSf76DOSQghxKGjdXYDhOjCJEYWQojjlzvaSCAQQLNaUTQNbY8E8/bEaFyBYCe1TgghxOEiMXLHSIJZCCGEOAZ9+1z3NrdvvnApSwIB/LrmJLOiaUT5AhRHmbjhN4M6VN8lJYW8aUil3qiDgBbR0zu7uhZDvx4dOq4QQgghhBBCCHEk/H7OKbwyeh5VisamrBRS3X5svgAasCk5hi2J0Ywsq+rsZgohhBBdgrr/IkIIIYQ4Vrzy4QjG7ChCDYZ6XSuaxlkb8vipTyZPVW4itm9ch457/8VxnLd9RyixrFNC3zAaf7q5XIes/UIIIYQQQgghxOFgtRm4f/15+LsZmdc7g1dG9ObdIT15/YRsvu6dhqHew5M3x3V2M4UQQoguQRLMQgghxHFEZ9Ix/51cZkTt4uyN27n9xzX8Jq2Ob5/N5Ny3x3b4uL3OSGd8oIaRNXXoABQFg6YxyO7g9eH2Q9Z+IYQQB09DadePEEIIIcTx6B8fjOLkglIUb4Byq4l6gx5jnZsMj5txQyyd3TwhhBCHWHtjZImTI8kU2UIIIcRx6LqH+nDdIT7mlNlncd1PxZw7rQKn2YIW8HNf+hwy751yiGsSQgghhBBCCCEOn++m57Di51p+Pa2KerOROy6I5s7zkzu7WUIIIUSXIQlmIYQQQhwy1pMzWHQy+Hw+3njjDVzyVUMIIYQQQgghxFFoxElxfH9SXGc3QwghhOiSZIpsIYQQQgghhBBCCCGEEEIIIYQQ7SLDioQQQgghhBDiOCLrRgkhhBBCCCGEECESI3eMjGAWQgghhBBCCCGEEEIIIYQQQgjRLpJgFkIIIYQQQgghhBBCCCGEEEII0S4yRbYQQgghDlpNmZOHbvyFKqsVVVXI3F3FoJs6u1VCCCHaonV2A4QQQgghuhCvy0fDTieJ/WIBcDkD9PlTLbt1evwmA1/dqOfs3sZObqUQQojDRWLkjpEEsxBCCCEOyht/3s5/tyus698Hk9dPv4pqRjgbWD5zCCOv/KWzmyeEEEIIIYQQQrTpxcGz0ftCqQWHzYLdquetwf3QoWLQadgcXq5+XuXv19q4c5S5k1srhBBCdB0yRbYQQgghOqxmt4eZG3z8khhPr+p63CYDazJTeGHYMIbuKsHtUDq7iUIIIYQQQgghRCvTx3xFXI2L+Fon8bVOMoqrsHk0VJ+fQosJl8VInc1IraJw30xXZzdXCCGE6FIkwSyEEEKIDls7q5Rik5H+5TUEVIXL1u9g/I5dVNlMvN8vl52z0jq7iUIIIfagobTrRwghhBDiWBYsc2LwB8P3FSCltJrBDU5UqxEsBjAbUaJM6H2BzmuoEEKIw6q9MbLEyZFkimwhhBBCdFj1wl30sNuIrfcwaXNBuOfaeVt38tyowZxa6e/U9gkhhBBCCCGEEG3RtZEzNvj8JPj8BNXmJIKmqmhmwxFsmRBCCNH1SYJZCCGEEAfsrasXkr/JQ2F0NIYojTGFZRHTomTVOThneyFpte5Oa6MQQgghhBBCCLE3HoNK9B7bGmKs7IyxtSprOTJNEkIIIY4akmAWQgghxAF54+YlLC018sbYgXh1OgCuWrO1Vbk+DQ3ofJ4j3TwhhBBCCCGEEGK/FJ0Ot1GHyRtAATTAbjOzIX7PtDNkONxA7JFuohBCCNFlyRrMQgghhGi36vfXsa7EwgeD+4STywArMrq1KjtyZyHV8dYj2TwhhBDtIGtLCSGEEEKA4vKFk8sQWoM5urqBMpu5VVm9pvH3WbVHsnlCCCGOEFmDuWMkwSyEEEKIdnHcP5tvH9hAcVoidbrIrxD/PWUwpTYTaBqGgJ+sumpSHA5+6pbWSa0VQgghhBBCCCH2zuLzt0oVVEZZCaqtL5mf98MGcm+ew0/KC0emcUIIIUQXJwlmIYQQQuxXIL8azzM/onoDAOi1yMf9Oh0bUmMZVr6LQZWlJLod+FUVM82jnH3XP0tAuZSAchlOZQp1yv00KHfguOudI3kqQgghhBBCCCEEPmPr1SMza+rRB4IR2wz+ACdvKya11omGnp9jXjlSTRRCCCG6LFmDWQghhOhkvxS4eOiv5WzpFku11YDdqAd/kC13GOib2DU+qoP5NQD4VYXM0ioCca3XpAqouoje32syU/DbogDQ6hzo311IEAN2uhPAENoHC8pLq7D+92qUNoJ7IYQQh562/yJCCCGEEMe8oK712CuPxUT3hgbyTRbQwBYI8JtvVhDv9ACgoaJvcO37uIEA3nnbUM99OnKE9OrfHcLWCyGEOFQkRu4YuZIrhBBCHAJrn1nLyv9sJaBX8Rn0BAx6+p2TzMSnRu1zv0p7kAf+Vs6GhGh03iBBVcPs8+Oz6Ml9NciS6/yc2r3zP671J3VnU2IiHrORsxev46XcHrh0kZOJOa3G8O2yaBt/PXss/T0BVi+t4YQxd6ACGjr02AkQS9NEKhpGaky/IkF76QiekRBCCCGEEEKI45mmD8W09TFW6uKisEdbKY2xUazpoXH2Lgfg3GMibQ8qm5V/oovSkb3rLnSxoTWb/XmV2HP+RhADEMSKgtqYtvCjQzf8CZh24hE7vwhPfwoPvt18/9fnwdO3dU5bhBBCHBNkimwhOmDatGmMGDGCkpKSzm6KEKIL+PT/2bvv+Kiq9PHjn3unl/SQkELvRQRFASkiFgREEUHRZRVUFFd0Lai77rJiWZVVf7qWFRfXhq4iyteCFVHEsogoIhZAOoQESM/0mXvv749Jm0yAJARCed6vV14yd84959ybmMxzn1NGf8yqf24kZLWg2W1gMlFktrDuo0L+c8aS/Z479vYd/OB2scdkIc/twGc2EVBUtIAOCgx5KXzI+l28vpAdOXexOHcOn7V+kGLLTIw6S4FVUZxW3u/YBa/LhsdtB6PO2D6HmVfPPJnbxp9FntvF5+1yCVotFFlU1t7wLiaiHzrMBHCxBzf5tWs/VJcohBBCCCEOA4mRhRBHo3KXg6DVzM62mRRlpBB02Pgh0U2ozh7Mr/frEvNawRydteXR2Jj8BBUfbYrW1+kBdGwoKDjwoGKgEI14zWiEMdPlvW2H5dpiOzw+NrkM8Oj7cOpth78vQgghjhktPyVKiCPUqlWr+O6777jssstISIhfClYIIQACXo2CrQEMpw3Nbqk+nmbobExwUeBM4PN7d/LiX3NQlNhE6urPCtkVUCiyKGA11a0aIgagUPJJHh9d/TVdt+3EgU4EE37MGBk2Buye1qR+rz31adR1BVxw4WQ2paQB0Ku4kDtOWkixbsfaO53rXh0cc06bgIe9ydn82rktoaqlxFIdYFHBEu3/WwN7M2PZt1zy03q6Fpfx4AXDGf/Ll3EpZCseVELoWAEDhUiTrkMIIUTjGTKwRwjRBBIjCyGOJbpuELDb+KlDNt0352MLhSlPcmFKiv/9FrDUE68TTRyrwK5zF9Hx20tRCePAQxgbfpIABRNh7JXJZgthTn6nmN/GtDuk1xbjrLv2/d63mw5fP4QQ4ggmMXLTSIJZiH347rvvmDdvHmPHjpXgWYjjWGFZhJ9XFKJtLsUSCNPmtNZ4kt10yjLh2+3h0yc243E6sBta7ImKQteyCqxuJwvyFMZe/DPdu9g59dQkioqCbFtTzjx/Ir39AXZlJBNRlOisYAWwqmBWIWJgC4T5+60/M2XbDuxoaCjYCGNBo2iPhVXKQ5zw01RCqsHGs9/FlWmQZvXja5VM+bv5VKW8dcDcM5HOP1+Ld2spbb/9jbsHjeC2b/5Ha6+XxZ278J8+/fjZaaOVJYH8UjtXnbWCzWluRlcUMeSekzg/xceiPWX83KUDWeEIO102cFrirvvdk7oz45Nv6LergHPW/kxCqP6dTBSqZktrmAg143ftGDPoDljx277f194AVRalEUIIIcShJTGyEKKl6LqBrzBI2U4PtlYO0tu49llW84f5ctIyCrZ5aXtrT35YHcK7vpxe/e28uyKEyVDYabWyLied89xuTl6zEVPlCl2pReVcsnoDX589AKPWAPHRv9SedRxNFFdgw0kIEwYqOiUDniSJIDpmQtT0T8NCCAf2yoW2Veo8OzjUlq5tWDldl7hSCCFEo0iCWQghxCHx7YeFvP/EFpILSvC6nQQcNiyhCGGPn4dP7knAZsWFjjUQ5rTdRbTGQAWCEY2hP27kjdN7MXJMBv8pdLGzRMfvjWA3DAzAazGjGJASClKY6sZQFdAMrIEwp5SUk+4NY4Q1FnfMQLeZwaRCRMcZCeOzWUFVUA2DPjsKOXtjPs5QmC+z07GHQ5y6cw+ZxR6cviARVWVbZhKpoTAmXcfmC7FiQRFbcjPQdei9aQdOXxBzgh1UBQMwlGjdAAGXHavZzNW/bqXDlnzU/+nsfdvN1g5ZlDksTPcU8F7rVrQPhrF6A+xy2ii1WyFkRPd7cpsJqjY+GNidaT/+SDFuwpgBAychLGiEsfND79dJxI8bHSUPyojgIo82eFEAD4lUkITySzF7lTtxUYQZCzevWokrHF2Ce+jO7WR6vazNSOP6Je/zv5QTiZhNDHDbCLptrJv6MZ5CD+WpmSR4/ZxsUgmYVQp1A9Rao/wMg7KkFNZ0aEvvrTs487efMLBgoNZKKIOOGQ0bAA68aCRS7LqTlJJ7UKwH+Hji8cOk/wcfrwEMcNqhzBt9z2yCuy6BPm1h2tNQWAGtEmDeH2DsKfutVl/wLdqDH0KpD/XSU1HvHotiaaaPSpsL4PLH4at1scetZrhxDNx3GQTDcNqf4ecdjavbNAGcZnjkKpg+Mva9h96CuxeANxh7vEtr2FYIugHZqVBUDr5QdIBDkjN6H5Nd4AlE77eqwum94OEroEt2Y68+lqbBPQth/ufgtsPM8+HyMw6uTiGEEEIIIcQxwTAMFjyTzwdf+Qhiisb7gNsfoMOeQlx+P5qiUmyzkZlXhDsc4YfWafzn5B4M3bSLv368CjOQC+iXL8PcrTUkOvjlAy+FGWlsSkxgj9OGKxjB7gtWJ5erZBaXc0H+Xn5y2fGZzfQoq+CCnzZB5V7KdkIkESCImUISSKGcTEpRdAUfCdTeAspEqHr2MpXvWIDJ138HSjeYdnrz3rzdpXDTc/D6V9FYryGSLoXyOvGiqsCfx8N9v6s55gvACTfD5t3RCxnYDb66HxSZ9SeEEMcrSTCLFvXuu+9y9913869//Ys1a9bw9ttvU1JSQufOnZk5cyYnnHAC3333Hf/6179Yv349LpeLiRMncvXVV8fUs2zZMl566SU2bNiAoih06dKFyy+/nOHDh8eUGzt2LFlZWdx55508+uijrF69GkVRGDBgALfffjvp6ekAzJ49m8WLFwNw/vnnV58/bdo0rr322urXoVCIp556ivfee4+SkhLat2/P9ddfz5AhQw7RHRPi6PDLilLeemI7mfnFlKYmEbRHE4ma2YxitzJy007e6tkRDyqdIxH6hGP3Gf76hI6cs+I3bu7QnmgQp4DVQtAwaoIXBfYmJdS8VhVCioVdFTZytBBfZCWju6w171tN+JRoORQFXVH4oV0G+akJXPXdRkbsKCDB48XhCZJU6gOgPMlBWjgMCugmFX+CncyCIgJOG223FOD0R4Mwt8fHpq45eF1ODEXBFgyRVFaOoum4Knx0Xr+jOsTMKConwRtgfe8OhE0m2hlgL/EAkKDprLRZakZK+zVwmgk4LHhwVCaXoxfvw4aJCAZmEvBhqZW8tRIihaLqNu0EUDEoJRkDBQUTOqbq5HKVK39czasndCEzVIRVD6NEIKXUR4lJIS8xhRnDhnDNNz/SdUseFb07c0HeHt5NMLMn2R29z4ZBz70V+NyJvD5sIN+2zWbg9jWAQoQkTHhRKvus4aocuW1gJoKZCJrPgnfaItwvXrz/H7BJ/w/e+67mdVVyGSCiwaz/VvcHgN1lMG4ObHka2raqt0r9i9/QLn22+hz9gQ8AMN1/4f770hC6DufeC7/lx78XisDDb4Omw//WNz65XMUXgeuegbQEmHha9NiLn8HtL9Vf/reCmn9v31vzbwMoqbyfe8tjz3nnW/hpO2x4Ekz1LxHXIH9/E+55veb1FU9A6xQ4p2/T6xRCCCGOERIjCyGOd0vf3Mtb/wthqOaY5GWC30+SJxqrmNHJikRI1DWcwTDDthVgD2uc/8Nmas/BNVAIOiyYDINH+/RgR0LNzOLUQIBgPYObw2YTiYbBuC07+CYjHUeJjzRviKrEcQAbViI4CGMlQirl1bG3QewKXyYi1cnl6PuVSeaIAde8gp6dgjqmz8HcrlgTHoIvf23cOXWTyxBNTv/9TRjaE0b2ix7rd2s0uQzRC/nfepj4ELxx+0F1WQghxNFL1r0QR4Qnn3ySZcuWMWnSJKZNm0ZeXh4zZsxg2bJl3H777fTr14+bbrqJ9u3bM3fuXN5///3qcxcuXMjMmTMpLy/n6quv5qqrrqK8vJyZM2eyaNGiuLb27t3LtddeS+vWrbnxxhs599xz+eyzz7jrrpo9ScaPH88ZZ0RnU91yyy3cc8893HPPPYwYMSKmrtmzZ7N69WomT57M9OnTKSkpYebMmezatesQ3Skhjg4/fl6CJRzBGoxUJ5erGKqK4aw51rfcE3e+xWwmPVjPksm1R8aa1fiRsiYlumwzUOCwxb9fzzm7Exzscdlw+P0AOHw1wVXQUWf558pj6XtKq5PLAOWJLjwJbgw1Wn/QbqM80Y0KZOYXxe3i4QiEcPgCBGxW7JXHIgp8n5YQswwXmgG6weacdIL1jAnTKhfjstZKLgO4agW4tY+ZAA0w72O/Y7Ouc/0PX1N3nLPNH+aH9BR8Fgs2LYJiNzN98cf8adH7fDbnOW5/7wval3gZs2E3vfdUVJ+3vm0ue6xpRBfoNqGRSIRUNBIBExb8OKlJYprQCL35U719q7loDT74fv9loCa5XEXXYcFX+y7+6sq4c/RXvjlwOw2xalP9yeXaXlkOKzYcfFuvLK//381l827430H2879fNOyYEOIQUhr4JYRoKRIjCyGOV198Xh6Ni+vE7ullFXFlA05r9b+HbM5HrRMGKoA1rLE2NTkmuQxQYbHid1ipcNpjjv/SMYe3O+XwcN9efJGdyRkbdsa3S7RdBwGs+4ivAcLE1l37igxAf2XlPs9ttB2FjU8uH8gj79T8u76Y9v0GxOZCCHFUaGiMLHFybZJgFkcETdN44YUXmDx5MlOmTGHWrFl4vV7uuOMOnnjiCWbMmMGECRN48sknSUtLY+HChQCUl5fz+OOPk5ubywsvvMCUKVOYMmUKL7zwAjk5OTz22GNUVMR+AN2xYwczZ87kz3/+MxMmTKj+7zfffMPWrVsB6NOnD507dwZg+PDhjB49mtGjR9OlS5eYupKTk5k3bx6XXXYZV1xxBY888giRSKTeoL2lFBcXEwzWJMI8Hk/MPQmFQhQVFcWck5+fv9/XBQUFGLUSMdKGtFG3DatTQVcVwIhP9AGqXpMQDSvxf4oMQDvQH+x66gUwVS4Dpdb3/j5WiLJqenVit3aCV9X0uLImzSBijp29WZ7kBsDhC5C7rYDcrQWYwzorMtL31U0U3SBca38jj9kc87qaFp21rVJPX9BRIO4do557ZxBdwrsquWwhSN0bkhouxxUJU252E1Zrkuu6qWbZ7+f69eKkjZvJLSkDovdu2uffMfzXLbjCsXtJWQzY6XQD4bi2wMBKKKanGmp0afBK9f7slpSALT7x3yCprn3/7CY54ssnO4GD///D24D1YiJuG5ia4UNqcvShSSgUIug8RAvVVN6XJv8uSXLus85j9XeitHF8tiGEEAdDYuRD60j5WyFtSBvSRnwbDkdlvF0nmI7Us4pS7Zjdb4l/3wAiJpVya3wMGTaptMvfw5rubfilUw47s9JY3rcLT53Wm7JazwX89WybpFTGtzYi+3rMUN3+/ijJjub7frhs0a2OmlNaQnUbRt0B/BDd8qnSkf5zJW1IG9LGkdGGOLZIglkcESZMmIDFUvNhr1+/6PIrvXv3pmfPntXHLRYLvXr1Yvv27QB88803+P1+Jk2ahNvtri7ndruZNGkSPp+Pb76JnYHWqlUrzj777Jhj/fv3B6KBdWNMmjQJpdYHrF69euF0Oqv7dyRITU3FZquZLep2u0lISKh+bbVaSUtLizknKytrv69bt24dc93ShrRRt40hF2SiOCx4E5y4K7wxdVlCYfbWSip/k5JIpPYWvkBSaQWbkxOwarUTlgaWSK3XESOafK0tpNOrcrnknqUeqJPwJKLHBand9paRHAhT4o4m5nxue3UQ6C4PxJRXNR1FN9iek47PXjNS2hoK4/T46fLrdtL3lpFeWEanDTvZ7HDydcf4/WpDFhM+lx1XKFxdvzsSwVJPQhuTgqLrJOKvDmIBzGjYCWEniJfYWeIeEitTzzUqSCaCggmVCBZUdBTCmAliIowNL1aiS4Ovd7arPi9ojvZ1YMFuUv1+9ia6yS0sjutm7t4itDrxZlogQOeKPYAJiEDlktg1C4PVXE8YKzoRLKO6Vh+r92c3PT26Z++BJNZJGKe64ZIh+/zZVa8dBimxiU/T7dH9jA/2/w/XSV1g3Kn77a75LxNhxugDX9f+2K1wY7QOq9WK7U8Tmv8Bw5iToXe76jaa9Lvk9nGxsxESHPCHUcCx+ztR2jg+2xBCiIMhMfKhdaT8rZA2pA1pI76Niy5Lx6JpKHX2EN7ZKhU9ZgqwgctTk8h4/cTOrG2dGnOOAegK9C4uxaTHxtsJoRBWwyDDHyCU4qYwJ51Es8rEDdtRvDX1ft0xs06i2MBJ9H0FlWCdeLy26LZQ8QxAsaio15/RfN+P1ASYdtY++9JoigIPTq5uQ5kwKL7MrInV/zzSf66kDWlD2jgy2hDHFtmDWRwRcnJyYl4nJiYCkJ0dn5hJTEykrCw6cy4vLw+Ajh07xpWrOlZVZl9tASQlJQFU19tQubm59dbV2HqEONZktHVww5PdWfzEVgq+KCClsISAw4Y5FOHr9GS+7ZxDmwToZtXxbQ3iKfcRcTtBUcjeU4waCtN2cme+u8jMC7+Z2LQ7wo5tQXZpYPIE8ZlMOMNhTt1dxrfdctgVNGExdAak67RLsBDYDW3y/Wy3mSl3OUBVsGg6TkOj1OqoTuoOWbeDC9ZuQ1Hgu6wUfkl202VvCZl2K05fCN1kYmtSEkEFMsu82AzYnpXODqeDvD6dGfHrFmyhCI5yL3ZfMGbWtAKcuWknr53ak5RQhD6bdmKJ6JQmOFl5QgfW5KSi6Dpdi/0oJhNmRWHwnhK+ykghbKpMwKsKKgb3vLgUFZ10ygliRsHARgQvViBCGAclqNgJYyGCCx8lJGGunNtcQSI+3CRTTASFAOAEFBQMDBy1ltQuJwHN48RhhChMdvBJz/YkBwIM3vEjr73zI//qO4w9SQm0Lo3dn3dtbiu+bpdOj70VtCv1khAMkhQIsjcxFZUgOlaiieaqGepmFAKESMBAQUfFGN4V1/MTOaB7LoMOmfD4e9HXvdvCx2ug3AcdM+GN2yA9EW58Fr7fDCd3gsevBnc9s5Srvl/t0zF/91f0uZ9DqQ/l0lNRh3c7cF8aasGtMPcjeGBRdE9ow4gu2T6oG/ztYjjrxGi5NukwewF4AmBSIdkBRd791w0w9QyYOQ56tqk5NrAbfP8w/PllWPJDzexwqxn+NQ2e/hiC4WjSeM1WWLsteo965ESTvmkJUO6Hrbsh0QlnnwhXNcMDi4sGwfL7okt4u+1w7TnQOevA5wkhmk19K10IIY4sEiMLIY5XXfomcPefVV59uZANW0OEzNHI1q+o5Kel4giGCKoqW2022mAhu8zDD9npLOuSy5Jubbntk1WcuWEnCgZWDNKLKoioCpf+tpWP22RRZLfRyh8gKxBADYbxOWPjxIxQmI7+ILstZqxAl7ISXCYfhhaNZx2EsNRKHPtwomHgrLNCF4COBS9JWPBjJTpzzwDyu7rJWXQLSq/43+kH5clp0RjzgTejS1qbVAjuewlvAP44Gv75fuyxLlnw1p+gXUbNsQUzocN8mLckOpD5nklw7cjm7b8QQrQQiZGbRhLM4oig1rcsLGCqZ/mbQ9UWELOkw8HU1dh6hDgWpefYmfJgd6D7gUoC7ff57sMdASxAfcnBVvUcq2f53X3qWPnVVH3wFQVZ2H8xtor4JKAjHCHZ62PBiZ34vG8OK+bkYLaq/AEIlYeYNH0zIUOhe+VeUp2BkwqKWJ/s5r0O2WBVcPiCtHdqlOEkFS8OwkB0WWwdnaz7TqX17aeh1FkOzIjobEx6GIsviIKBiRDZ2r0oqoJhGBSqt+EkQJAEvFgwEUZHRcHM/7rbsYYSSM+x8dgLJ5De2g4M5cJnyzn7gc/YktGK9HIP5soR4F91bsP7J3YjbDbRN7+INmXR5LMBfJfVpnK0d/T3pYYJPwkYmIAkFCIYmDCzh8TPpjf81k89M/q1P6/e2vD6AKVDOqY5FzXqnAazWuDG86Jf+3PrBdGv2rYUQMc/1F/+xevh8v3chxPaweK/1P/eVWfXf/xwGNIj+iWEEEKIekmMLIQ4nnXq6eKv97sOXLBeYwAwdANFVRgAhIMaFpuJH/+zgU9+LaHnmWn06ZLO4gsicXs9A9h1Da/FjkdR+Oegk3mne2fefO1tEkLhuLIGCl7cpJ5jI/jxLuruz6ljJoSrOsHsBz6+tTtTu2Y28fr2Q1Xh98OjX1VsE2O2oorz2NXRr4Z48PfRLyGEEAJJMIujXNXo6M2bN3PqqbHLj27ZsgWofzR2Qyj1fMAUQoi6HClWdEMn6IjOeq5tT5KLszdu5a5nR+Cyxz5ssyZaWfTf7ky8dENM+GnXdU4sLuejdq0xDBWv08bvvj2Pih8LWX3Rx7g27kFBwY9C/6KrsaXWn1BXzCpdvLfX/56ikPjuVQTHPoWVCkK4iGADdDYmJnFKn9YMemgQzlx3zHmXnmyj2KxSmmBn2QndSS/3UOKw8b/ObZjyyQ+Q5KJVsCbg9gfDFLVuxc7ELHLLd2PgIIizMrkcZVR+FDngntvHsw6twTiy9i0UQgghxJFJYmQhhIhS1JrfWRZbNAbtc1VX+tQqYw1rBCIRvmjdilKLmY5ePz08XjY5HaRoOh1CYXRgc6Kb/+vRhcvX/BLTRnR1LlBMYHt8EsHu/yD6uD3292XtpbL1fS+sdWj89Bh0veEwNyqEEOJ4IAlmcVQbMGAADoeDBQsWMHbsWFyu6OhGr9fLggULcDqdDBw4sEl1O53RpE15eXm9y5AJIQREg9a+f+vHmtmr8TvMmMM6BuC3mum+Zy8jHj8lLrlcm6m1DerMft5jtxExqzhCGmFr9NyEPukM++2yZuu37bwe2Iwn0cp9aC+tQg9qJN4wlFOt+/5ocNGJVuZVzkcOm83kpyYD0C+vkG6/5FPhslKQlUrAYSOx3Eu5bpCXk8knuT244pd8FILopNRTsw7IrBYhhDhc5DeuEMcuiZGFEKLhypxW/tOhDWW26L6gv7mcfJmaTJJuMNTrpyqS7xAKszUjujepAQSBqp1EVaDj7hmY0xyg6PV80DKw4qs+98PbDrTKWzPrkgMXnwavfx3/3qL6B6ULIcTxRmLkppEEsziqJSQkcOONNzJnzhymTJnCeedFlx5dvHgxO3bs4M4778Ttdh+glvr17t0bgMcff5xRo0ZhtVrp1KkTnTt3brb+CyGODf2u7EzPCe1Y/KeVFCwtRMWg9ZAMzn9mICbzvpPLAK892o7xlwboXuFDAbxmE0vbZZAYjlDutKDfajukfTclOkmaMaxhZVUFlyeAx2YiUpmIDqkqPh2+6JLDKdt303NdHqpu4LdZ+N8J7TD5vQydPw4GfYoa8qMSQsMeU69CGHWYLJcshBBCCHGwJEYWQoiG+y47ozq5XMVrUjnFU5NchugD9HSl5kihOZGu13Um+Zo+OHvXbN2Vqv8/ikf+Cz7eDoBKCDteTJWpCwPw5DTtd/BBWTATFgA3/Bv+/Qn0aw9L7wbX4Z5OLYQQ4lgiCWZx1Js4cSLp6enMnz+fefPmAdC1a1cefvhhhg8f3uR6+/btyw033MCiRYu477770DSNadOmSfAshKiXLdHCRf8a3KRzF73ajdvm7WHRd2EKEuwEXTYeH2PlD6dYD3zyYWbVdFILPXgS7HzZtjULunbAbzHTfXcx56zdilo55C/BFyLRG8K2excdT+iC2fMiEevvcbILL23QsQCgEMRMKQmf/78WvCohhBBCiGOHxMhCCNEwuh5/TAHStHr2LDZFE8whzJQ6HWQ/fma9daZ+9AcADE0nfPLdGGs8GIAxsD3q8tvg+eebqfdN8MQ10S8hhBCiGSiGYcjsbyGEEEI0yOutXwOgzG7lurHD0CqD7Ou+XMvvvtsQU9Zrt/DyoLb856OhWCwWjI27CHe5mQguwjhQ0DAIkhD8N6rVctivRQghjlerlKcbVK6/cd0h7okQQgghRMu5v/f73De8P35zzRysE4pL6aGa4sp6gn6uXr6SpL1hOoxKpsPbFzW6vXA4zPOVCeapU6disUgcLIQQR4KGxsggcXJtMoNZCCGEEA22N9mBYrGy2WarTi4D+CzxHylMms4Ju3dXv1Y6Z2M1FnDkzcsWQojji4HS0l0QQgghhGhxxckubly7jg/bZFNot9GjpJwTPT6+6phLrj+ECmjAuiQn65LSGb/8J7r0DDUpuSyEEOLIJTFy0+x/Y0ghhBBCiFqmrTiXtL2lbMlKg1qLoLzXqz1l9tjUccii4nPIRw0hhBBCCCGEEEeetekpOCIaaYEQrX0B2ni8lLochEwm3slN58OsVN5s24qfk91oZpVuK8bR+4fLWrrbQgghxBFBZjALIYQQosGsyTYuKbgUcl5mdUoCW1KTASh02bnq0hGMX7OJZH+I5Z2yuXjNb2xNSWzZDgshhBBCCCGEEPUJa8zp2wt/5Ypc32WkcWpxKanhIDk+lW1uZ7ScSQEzDGkjj9KFEEKIKvJXUQghhBCNdubaCUw581O2JbrZmpbCr0lJ5Ce5+dfQPtVlbLpGcri8BXsphBCiPsaBiwghhBBCHPPcwXB1crnKVpeTK/IKOFU3CKoqugIr26TzVW6rFuqlEEKIQ01i5KaRdSuFEEII0WjpqXb+tno0V12UydU//IJd0+LKhCwmXN5wC/ROCCGEEEIIIYTYP81sijvW0+PFokdTDTZdx6HpnLSrWLIPQgghRB2SYBZCCCFEk3Ufm0Oqp4ILf94Yc1wxDM7cuJPyVtZ9nCmEEEIIIYQQQrScPkUlmPTYzHGSpseVUwA1EDlMvRJCCCGODrJEthBCCCGaLLWDG2+HdM77cStBi4Ulndti1TRGr99O//x8Wl0XBga0dDeFEELUoqO0dBeEEEIIIVrcmMtz2fn+Ft7r0Aa/yUROOIwLhYiiYDZqEs/fpyfTZW8Z4Gy5zgohhDhkJEZuGkkwCyGEEOKgjP9mHO+lz2XCT+v53Y+/EDKZAZ3X+nenn2VDS3dPCCGEEEIIIYSIM+CG7pRs/o6O32+gMCkRRdfp3d3Cx7ucWDUNBdiY6ELVdNb9O6uluyuEEEIcUSTBLIQQQoiDNqZwOpu+3ctzN/yIYVY47y8d6bfj45bulhBCCCGEEEIIsU/nPnoy59Y5dhXw7AdlvPapj8vPdDH93MSW6JoQQghxRJMEsxBCCCGaRadTWvH3FWcCEA6H+en5Fu6QEEIIIYQQQgjRBFePSuLqUUkt3Q0hhBDiiCUJZiGEEEIIIYQ4jhiyv5QQQgghhBBCCAFIjNxUakt3QAghhBBCCCGEEEIIIYQQQgghxNFBEsxCCCGEEEIIIYQQQgghhBBCCCEaRJbIFkIIIYQQQojjiNHSHRBCCCGEEEIIIY4QEiM3jSSYhRBCCHFE+eDbMq5/3k92OIzHZKLMClsez27pbgkhhBBCCCGEEIfUa/0W0G3rFsyaxoaMHM7/+fdYbKaW7pYQQggRR5bIFkIIIcQRY+k3Hu56ppwRpRV09wboX+5lQImfXrfsaOmuCSGEEEIIIYQQh8z/9XyB09asp1VpmJQKnVM27+DL9k+1dLeEEEKIekmCWQghhBBHjMcf2ERvXyDmmFvTUb3ykUUIIYQQQgghxLGr6/Y8VEOpfGWgGpBRGmDdZ3kt2i8hhBCiPvK0VgghhBCH1cI1Puz3eblsoRfDiN3lZGtKAko957T3Bw9P54QQ4jhgoDToSwghhBBCHD6miIoNP8kUkUIhLspJDnhYdtP/WrprQghxTGtojCxxcixJMAshhBDisFH+5uXiDy0EbVZe3WJFvT/Isq1hAErz/TgMg1KLOeYcHdAw6qlNCCGEEEIIIYQ4NjgjHlx4UNFRABtBEijDCEo8LIQQ4sgjCWYhhBBC7NeW/+3liVPe45Hff0Mw1PTA1n2PB9zW6KcPHTAAq4kzXooAULjJS8iA1ckuNiY4CaoKRTYLS7PS2OSwNcu1CCGEEEIIIYQQRyKX4cWnmiiz6ngsIfZabRiqglXVWrprQgghRBzzgYsIIYQQ4nh1d9+P2dExC2+XrnjNNj7+Qx7Zuofnn+ve6Lq8YcCsQ0iPJphNgFUFs4nyoIG/IsKgbfk83T6bLWrskjPpSvR1RDe4/h0fX+yEVdfYcVpNB3+RQghxnJFlvYQQQgghjjwVFgs5oW1YQpHKI3v4zdGFgMXdov0SQohjncTITSMJZiGEEELU67mp31LcJoOAy4VJUUjUNOwVXnYkJ/D5ijJOH5jUuAoNIKDXvNaIJputKhFNo/NpaTiCv2DU85nObzbx7voA58+PgBpNKrvuD4FhYNztbPI1CiGEEEIIIYQQR4KIamAxIjHHMrQCrAF/C/VICCGE2DdZIlsIIYQQ9fpyl0JxUgIoNRnfgNNOTrmHOU8XNb5CUz2zjTXAMCgLqVjtKlhMWMIRMoo9pBR5yCysIMnjxxoMcf4LoWgdugaaBoYBKKTM8Tb5GoUQQgghhBBCiCOB1xk/eNpQwaaHW6A3QgghxP5JglkIIYQQ9WpT7sFQ4z8qlDtsmLQm7MVs7GvfKAOLYqBaVTSTmS57ygiioCsKAVVFCWp0LPeB2QT+EIQ0CGvgD4OuURqWjzNCCNEYRgO/hBBCCCHE4bM2owcRJXZg9or2/dFN8slMCCEOpYbGyPLbOJY8kRVCCCFEvZSwhqLrsQcNAx0FRxM+UpnDOnFbmlgUCOlEdAVFUdiR4GSXwx7bpKIQVlXQ6vQFILivpLUQQgghhBBCCHFoeP0RnvjER0lF880uzqrII6zGJph9qg2nJjOYhRBCHHkkwSyEEEKIepW7HXTM24NamWRWdIMUn5/c8gqUUOMTu6oC7go/qgpYFFQLOCsCoOns8UTr+6VVCra6SW3AhFF/ghkgEKn/uBBCCCGEEEII0YwMw6D9jN10+FsFf1sS5oS7yml1895mqTu7Yi8OLRRz7Kx1X7PHbG2W+oUQQojmZG7pDgghhBDiyJO3uYJWpR78GcnkFJcRspgwGwYKoEQ0cooKufbcEjSziZy9e/ktJZVtrTLwWU18Mzc3bqIyQNhqwu4N4yr1ETSb0BWFiKqAyUSvVuAvDdG+rJx2gQj/S0+pPi8lFCKiAvuaxazX15oQQgghmtPs2bNZvHgxq1ataumuCCGEEC2m04wC8lMSCCnReVulVgtpwTAn3LmbtfdnHlTd5kh8vGvVQ5h0STALIYQ48kiCWRwzVq1axfTp0wH4y1/+woUXXhhXpn///gwZMoTHHnss5vj333/PggULWLNmDaWlpSQkJNCjRw/Gjx/P8OHD4+r5/PPPWbZsGT/++CO7d+/G7XbTsWNHJk+ezGmnnXYoLk8IIRos/H/fUjp+PhoazstOJvGVK6vfi/iD5CXMxqMl48SPm3L8mMkN3o9qjX4sWHnbV6z8sJhMT4jEbSW4vCHCZpVfu7Ti145ZFDsd/NAmi5OLSuha4cGTnkGSw0EPTQNvmOF/yOey861UhcDWO8tBMYNJwZPuQPdGwKSCXrnMtq6zfKvGkBQDXVHp6veTtSPAFpcTdyRCz/IKDEUhLymR4roXazXVPSKEEOIAjHqHAQlxaCxbtoz169dz7bXXtnRXWsyqVav47rvvuOyyy0hISGjp7hwykUiE9957j48++ogNGzbg8XhwuVx07tyZM844g3HjxmG32w9ckRBCHKEGzi5kp8tBWIldFLTIZiahtOHbSAWDKu++Xcw556bhdtc8no+E3RjE7iy1mxwshu0gey6EEGJ/JEZuGkkwi2PSv//9b0aNGtWg4PWpp57i+eefJysriwsuuIDs7GyKior48MMPmTlzJqNHj+auu+7CZKpJYtx///24XC5OP/102rVrR1lZGe+++y433ngj1113HVddddWhvDwhxH4YgTBGOIJis6BYzRj+EJhNKJbo/8PhzcUYqopiNWFKsaM6LNHzdIPA9wWYcl0ofg1z+2TCK7YSDkYwAhq2zmkYW/diHtwFJRJB31NO4b9/xLCbSBmRRWBnAMw6xsqtWJQIpotOAU8I0twY3jCW9kmoiXbUJCcAWpmfwNJ1BHaWYfjDGO//gOOCE9C7tcNIdEH3VkRWbEZdsxNdNxNau5Pizlnkfb0H3WnilA8/xIKKlRAKYMJPBAt+knDgxUI6Oql4/7uViv/+GTclmPFjJUAC6ZTTir0ksJtWJFLGbsetfJk5kNzifN4YcTp7+vRk7Aff4/JGl+fKT02gJD2Bk3bmAzDA5aj5EBEEtcxLsdnO3uxUepV7ePqDZE7I6MCCkl5YVQWbHl3G2q+DnmSLJpgNI7q8dVBjxWVLCY1IIb1MxeRykahF2JHqZlOimy+DYc7cuZtz8ovYY7PyTYoLr8kUXXPbpKLqOr2u3sIul5PSBCeWSISRSUH+NdlNZmsHazYE6JRtoTwASXawWU2oGFjtJlSTgt8T7ZvDLR+LhBBCiOa2bNkyFi9efFwnmL/77jvmzZvH2LFjj9kEc0lJCbfccgtr166ld+/eXHrppaSnp1NRUcHq1at59NFH+eGHH3jwwQdbuqtCiONcIKyTX6aT7FBYWxDG54+wJS8YTexGYFupzjOrDXyaSkRVUMwqrYNhIoZK0GoinOyAQN0toxSshsKky7egAO1z4IEHOtTb/tefD0dF5XsqePf/KjB0HROAbjArrPELJ5Fh2k5+SiblpBIqSqJz6Vb+dMZyeg9LYdDYHFLbunAnWbDYZPdLIYQQLUeepIpjTs+ePfnll1949dVXmTp16n7LvvXWWzz//POceuqp/L//9/9iEtKXX3459957L++99x7Z2dnVs6MB7rvvPk455ZSYui655BIuu+wy5s2bx8SJE0lMTGzeCxNC7JcRCBO69jW0l1eBrmNSw6gdUjG2loDTij6qJwUL96AZ0Xm1BmAhhGlEV5RUK+obq1EBHQUdgwQ81XWHseDBigI4KMeHQhHtcFNBJrsw3xPGjYaCFwUNBTAeWUCEJAzMmNAJYcVDCo5MKK5wovo0dMyksRkn5SiAvnwVCgYaJvaQSxmtSaYMAzNmQqRb1uKwqGT78rEQoIIsSkjATIBECrCiYaWQIEmY8aNjQsOOGR2DJMIkoVNOPp2owEWk8mNAIXY8egID93zNY2ddye70ZBLLfCR4AtX34IfeuexJcvJhuxxSDYORW7bH3H/drLKiTTolTge9vH7sBvxUeCIpqoKu14zk1hKt0eQygKKAwwJhndzSAIWL8umf6CA/18prnduyPdEFQKndyv91zGVkfhHJus7wUg8fdMxENwzwhtETrfySkQYWM6gqYYuFxREHi5/TMZkjaGYrzlCEtFI/xWaV0/aW0bXCS4oRwu42U1QW7V9WtoUbH+mCzS6zooUQQhw+Xq8Xl8vV0t0QoskMw+COO+5g7dq1zJw5k0mTJsW8P3nyZLZv384nn3zSQj0UQggIawajnihn+6YgNgMCioLPpJITDKMDGrDNYaXE5QS3CiGteoumPIcJdCDNEa3MH4nGs1V0gzbBMCais4+358Hky7fw8kuxSeYZ12xDIRoPV5+tKCiGgQr80L4DHfybSCsoolVhIZtS2vPJiZ0pTOmC5nKzaq3C92t2klTuIWS30fGUZC69rR1OGSwthBCiBcgwJ3HMOeuss+jRowcvvvgipaWl+ywXDod5+umncTqd3HfffXGznc1mM3feeSetW7dm/vz5lJSUVL9XN7kMYLfbGTp0KJFIhG3btjXb9QghGiZ8/8doL31bueyygqErGJuKogFhRYDC1/PRjWiSWCH6BzCCBe3TjfDGmuo/iCo6CXiqyymAlTAWwoCClySKaYsJjWy2Vx4HMGFQ++GwCRUVE9GA1EoIN6WU7zaDD3TMOCipTC5HW1IrF2Qxo9GaHSRRgVGZBI5gxQg7aeMrxI6PEtpRTiZhnPhJZS9dMYigAg7KcFNICltxsydmkRcPrSi3OCh2uyhNtONzWDCAAE6sukZRmhuAgN2CpipoZoVv+7ZlbbvWfJqZTrnTDmYT9S3+VWE2sdVu4+skN2E1elV6rdZ1VcEw15O8tZpY3jGXoNOK1TBIKiyuTi7XPnez24HPpGLTDJwRLZqodpijM8VNpugezdUnABYTmjl6zGc1syfFSWpI55OsNMotZkpUG3vLiT4YUBTy8yO89MDWeq5MCCGOLQZKg75Ew+zatYvbbruN008/ndNPP51bbrmFvLw8xo4dyzXXXBNTtn///syePZuVK1dy1VVXMXToUG6++ebq95ctW8aVV17JkCFDGDp0KFdeeSXLli2La7Oqnrreffdd+vfvH7NP8jPPPEP//v3ZunUrTz31FKNHj2bQoEFceumlfPnll3F1BINB/vnPf3LuuecyePBgLr/8clasWNGke3PNNdewePHi6j5Xfb377rvVZQoLC3nggQcYM2YMAwcO5Nxzz+Xvf/87xcWxG2NUXcfmzZt55JFHGDlyJIMHD+a6665j69atAHz66af87ne/Y/DgwYwdO5ZFixbF9anq3n3zzTdMmTKFwYMHM3LkSB5++GF8Pl+TrnN/Zs+ezbx58wA4//zzq+/BM888U13G4/Hw+OOPM27cOAYNGsRZZ53FnXfeyc6dO2Pqqvr+rly5knnz5nHeeecxePBgrrjiCtauXQtEZ0tfddVVDBkyhJEjR/Lss8/G9anqZ3PdunVMnz6doUOHMmLECO666664+94QX3zxBd9//z1nn312XHK5Stu2bbnyyivrfU8IIQ6H+z4KkLcxmlwGsBsGqeEIWmU8mGe3UuKwRgcua3p1chmIxpe2ylW0dAMCYdAr39d0CIQIoRCpTDpXfYoqKw9TWygUfRZR+3lD1WvVMMAV5MSCX1ArI+5OJVs5ec9qeuRvwx6JYNZ1dJOJskQ3zkCA374vZ/G8vENyv4QQ4njS0BhZ4uRYMrxJHHMURWHGjBlcf/31PPfcc9xyyy31lluzZg1FRUWMGjWK1NTUesvYbDZGjRrF888/z1dffcV5552337b37NkDsM/6hBCHjvbezzGvFfSY1yHi9yzSUTERIrogVbS8WjkDuS4TESJYiWDGQMVJeXXQV7sUlfOgDSxxdVgIVn4QibbgpqgyuRwvhJO648B0zGjYULHgI7me96yYqJl1rAB2igmRjBFddAuvycYeZ2L1aOugzYwBuPxhLEYERzhIhclMyGbh127ZtM/fzffd27HHZMJQFByGQdBsJi8xgdzyiuq2Cm1WNidEk9MlZjPuUBhMSszmUYpuoGg6hqnO+DYD3u/diUG79wKQEAjhDIXxWWPv4fokF+sTnbQOhPBUJo4xqRAx6t+Luc63J2gx4TR0DEVhu8tB7zIPuqqg1pphvXWDP74eIYQQYh9KS0uZNm0aRUVFXHTRRXTo0IHVq1czffp0/P76/6b88ssvfPrpp4wbNy4mvli4cCFz5syhffv2XH311QAsXryYmTNncueddzJ+/PiD6uvs2bMxm81MnjyZcDjMq6++ysyZM1m0aBHZ2dnV5f7yl7+wbNkyhg4dyqBBg9i5cye33XZbTJmGuvLKKzEMg9WrV3PPPfdUH+/Tpw8ABQUFTJ06lXA4zAUXXEBubi47duzgzTffZNWqVcyfPx+32x13HQ6Hg6lTp1JaWsrLL7/MDTfcwPTp03n88ceZMGECiYmJvP3229x///107NiRvn37xtSxbt06li5dyrhx4xgzZgyrVq3itddeY9OmTTz11FOoavONxR8/fjxer5fPPvuMW265heTkZAC6dOkCRJPLV155JQUFBZx//vl07NiRwsJC3njjDaZMmcL8+fPJysqKqfPJJ59E0zQmTZpEJBLh5ZdfZsaMGdx9993ce++9XHjhhYwaNYolS5Ywd+5csrOzGT16dEwde/bs4brrrmPEiBGceeaZrFu3jnfeeYdff/2Vl156qVF7JS9durT6WoUQ4ki1aFUg/kG4omAY0Si9zKzWDFrW6hlSHdGj2zwZRjSp7AvFvK0pCpqiYDFqzn3+uT3cdFMOALq+j7RErT50Ktoe93absp3sdHfAGQqgqSoRkwm9Mj43axrrvytv6C0QQgghmpXMYBbHpAEDBjBgwADeeOMN8vPz6y2zceNGALp167bfunr06BFTfl82bNjAp59+Sr9+/cjJyWlCrw+N4uJigsFg9WuPx0NFRU1SKBQKUVRUFHNO3XtW93VBQQFGrQ/M0oa0cSS0obZPI1Zs6KbWSThHSxiVCd+ado19/GmsCgWjM5INwljrLVVTV3x7OqaYhHKk3jqobCdcz1EDhUhl3fEBr0LdfaConBFdMxunzOKKXcoLCFlNOPDgT1AJmW0old+HwoxEDFUlaDFX305/5bmrszP5KTOdfLeLtempvNy5PXrtepW4f0RHZXtD0YC8SlgDHWyaVn1NJsPg9M2xM3ZqKlEocNiiSWWIjh43KzWjx2tT6/wM6AZVueTEcHTfZaXObXS51WPy/w9pQ9qQNg5/G+L48OKLL7J7927uuusubrvtNiZMmMDf//53zjrrrH2uprR582YeeughbrnlFi688EIuvPBCysvLefzxx8nNzeWFF15gypQpTJkyhRdeeIGcnBwee+yxmJ+xpkhOTmbevHlcdtllXHHFFTzyyCNEIpGYWb4rVqxg2bJlnHfeeTz66KNcfPHF3HLLLdx7771s2rSp0W0OHDiwOjYaPXp09Vdubi4A//jHP4hEIrzyyivccMMNXHjhhdx44408/fTT7Nq1i1deeSWuzrS0NObOncukSZOYPn06M2bMID8/n3/84x+8+OKLTJs2jUsuuYSnnnoKq9XK66+/HlfHxo0buffee7n11luZOHEic+bMYdKkSXz77bcsWbKk0de5P3369KFz584ADB8+vPoeVCWY586dS15eHs8++ywzZ85k/PjxXHPNNbz44ouEw+GYmc5VNE3jhRdeYPLkyUyZMoVZs2bh9Xq54447eOKJJ5gxYwYTJkzgySefJC0tjYULF8bVsXPnTqZNm8asWbOYOHEis2bN4qabbmLz5s289tprjbrGqp+Nrl27Nvb2tJgj5W+FtCFtSBuHr43sFCU+ijZqkr5W3aA6YFTrSQWb1Whi2WICS+xzA4eukxbRUI3YuLR/f3v1daj11VlHQWKruGNFzlTalO6JxttVMXdlv3VFJSndclR+P6QNaUPaOD7bEMcWSTCLY9YNN9xQvQx2fbxeL0DciPi6qvZD83g8+yxTUlLCbbfdht1u569//WsTe3xopKamYrPVzNx0u90kJCRUv7ZaraSlxSbm6o6Qr/u6devWKLUSSdKGtHEktGH56zngrknYanX+xCVbS2KCSQOwEiRktmFYzLWOq4TqjGs2oDqhbCGEmxICOCknKaacQoCqkFUhBLUSvgbgJQkLfpTK5HEp2TFLSMcmuolLFzuoQEdHR8VN7PKFJkL7SEpTa/kWHadRFve+xQhzAv/jpf4To0uCaxoWTcMWDKEaBu0KimgV0cAwKFFVKhQFQ1HYnJrCytwslme2wm+uuWfJES06CpvYNHhEVaKrjJUHwRuCiiB4w2DoXPzdOghr6LpBmd3KF93aRBPH+/qkEtCgPBB9322FiFbzMAAq1xmLfXyQXeZnl8NKW6+fNr4AqqZjqpWYVjAYd3XOMfn/h7QhbUgbh7+NI5nRwC9xYF988QXp6emMHDky5vjvf//7fZ7TtWtXBgwYEHPsm2++we/3M2nSpJj4xO12M2nSJHw+H998881B9XXSpEkxP8e9evXC6XSyfXvNbKmq5bjr9n/48OG0a9fuoNqvy+Px8OWXXzJs2DBsNhulpaXVX9nZ2eTm5tZ7zZdccknMdVTNTh42bBitW7euPp6SkkK7du3YsWNHXB3t2rVj+PDhMcemTJkCUO+S5IeKYRh88MEH9OvXj4yMjJh74HA46N27d73Lk0+YMAGLpWall379+gHQu3dvevbsWX3cYrHQq1evmO9xFZfLxcSJE2OOTZw4EZfLxWeffdao66iKrY+mvcSPlL8V0oa0IW0cvjYeuzghOku5Fl2heoB1digC4UhNErl2QlhVwGVB9UcHKpPmBKclmnR2mDnJH8KrgrXyA1TV56ghQ9JirqPeoeKVyWJDUVjRth973DXlK6wutiZ1ImxW8VvtBCvjbnsoRERVwaIy8vfZR+X3Q9qQNqSN47ONI1VDY2SJk2PJEtnimNW9e3dGjhzJhx9+yO9///vqEeJVGpI4hgMnosvKyrj++uspLCzksccea/YHL0KIhlH7tcHx29+I/Od/sHY7pv45cHZvjE83QKKdpIknY/3PCgof/B4tpGBu5cA6qC3Z/xiOqZWT/HFvon2+CSIGocQ0TK0iKD/no+sQxoxJDWE1ApjauklyR9j18x5205pyXNgJoBBBwUEiZZgJEcFGKWlo2DCrBrjtuM/IJunGIWiJbvae+wKWojI8JGGnAgMVLy50LJTbEvglvQsdC3aTqlVgIYKm6mx1pRCyppDhLyPNV0QCEUI4MRMkgT1EcKISxByTmlawU0yAIBFsZIVK2WZrQ1CtWfawa2AdhWRz+rdr4CQI2024y8KcvGoTBVkpDF3zG2ZNQ83JoMBiothsQtcNkozotgRZmkZuRTnFdjtOzeCkvUV8nZ1BudlMCLASnb2sVY+2BsK1RnZbFE7M241iwK50Ow+cOQCPrfJDp6pAqJ7ZyUTrsBV6OT9/D7usFla3SsHntoNZRdF0+qzLJzHLxW+qg+RAmGQ9wkAjzEm2CK372unX301mVxefvbEXXTc4e1ImmW0dzfdDKYQQ4pi3a9cuevXqFbekcmpqaszDlNratm0bdywvL7p/YseOHePeqzpWVaapqmYN15aUlERZWc3gs7y8PFRVrTem6dChA9u2bTuoPtS2detWdF3n7bff5u233663TH0rQ9W9jsTERIB6l/BOSEigoKAg7niHDh3ijqWnp5OQkHDA++zz+eL2ak5KSopJ+DZUSUkJZWVlrFixgrPOOqveMvUt1133vuzvHiQmJsZ8j2vXUbfPVquVnJycRv+sVcXWPp+vui9CCHGk6ZFp4vv7Url5gYeNOyPs1RV2BFXa+IIkhXWcEY1ungC7gmE0qxmTquC1m9ErZyyrYR09rEcHN5tNkBKNHRM8QQqsZnICWnUkbgL+dEf89nkPPZbN7TftwoDqtdSqlseOKDB4/bfMOfMP9M7fQFpFKYneMD67i5XtexFSVTAMHBUeXG4zvc/LZti4TFJb2+LaEUIIIQ4HSTCLY9p1113H0qVLeeKJJ3j88cdj3qtapmz9+vX7rWPdunUx5WsrKyvjD3/4A1u3buWRRx7hlFNOaaaeCyGaQmmdiOUvsTOIOLHmIa7z5jNoe/MZ9Z6b/c6ERrXV/gDvm4DMfbynAtmFs+KOV4WF6UD842XIqPM6/Mh7hJ5cAlvz0W1W7J7nCSz9jcC5D+DAj4JKGCuBnm1RJ5xM0p/GsCP1XvpVrGanLZdyNQlXOIAedhPMNHFawS303VjI131fQ3FayTHvpmvxRj7JHUS/LTvpk7eLvsXr+E/f4fzUtubhrllRGLRzL2l+Pz6XA7+iYrOa+Fvvt4hocP3mCZgNlYi5nunICmBWOefHi0gIhLn0D7/hcdQd0WhEI+9ay4FVLbMdVFXeyc4g8HB88A6d6jkW77KZMjBICCHE4dOYvW0bS9Pit8uosq99hWsva9cSRo0aFbMXdW21Z0BU2dd1HK7rmz9/PvPmzYs5NnfuXPr379/ouqr6duqpp3LFFVc0+Lx9XavJZGp0H5pDp06dWLduHevXr5eYWAhxRGvlVnn5qsYNhCkPaJgVg5y/eSBiYCmJELaaCVlMOAIRkiqiy8EawKsvxQ9gqi052UK/Uz/n+5VDqVqu67Gn25GcHP1793anFIZs/pZlXYdUn5PkK6P7tq1c9vOljeq3EEIIcahJglkc03JycpgwYQKvvvoqq1atinmvT58+pKWl8fnnn1NaWkpycnLc+cFgkA8++ACbzcZpp50W815VcnnLli089NBDDBo06FBeihBCxLHcOgbLrWNijtlH9gDjperXNmoS1wBt/fcBkFYRYPfk/8N53qmkTDup+n1n53TO8szgnbb/YZW7A+dv/5pLN7xPmc2NMxzAbGh06tCHvUUudqcmY9F0Miu8JAVCBC02ShQzq7pms/KRVJ5/PjqwO3R/Am3u97BbN0cz7/7Kh98KkGCFsI5FNQgHdUptFlzBMF6bpSahrBBd/jrmAXHlvxWFoOPQPagXQggh9icrK4sdO3ag63pM0q+4uLhReyZXzcrdvHkzp556asx7W7ZsAWJnrdadeVzlYGc55+TkoOs627Zto1On2IFaVf1orNrL5tWWm5uLoihEIpG4JcMPtfqupbCwkIqKinpnTdc2ZsyY6mW5qxxo7+F93YOUlBQSEhLwer2H/R7k5eURDodjZjGHQiHy8vJo3759o+oaMWIE7733Hm+99ZYkmIUQx5xEe3TwTsmc6KBm58wiuhV6qKi1TVRGKIKnAXssA9gdGqedvoypU6fGrSSxMbUj/UtWcem3b7K+dRdcQS+/pXdgfaYMihZCCHHkkT2YxTHvqquuwuVyxc1gtlqtXHvttfh8PmbNmkUgEIh5X9M0HnzwQfLz8/n9739PamrN7Ljy8nKuv/56Nm/ezD/+8Q8GDx58WK5FCCGaiznBTs7bl8Ykl2s7f/tVfN29J0u6DCSimkkOelANnWUdTyFsdtB/w2bm/k7h/SeyWPBaV2Z9fTqzlw/mySWnsOKp+AezBXelgE0BuwUSLNHEsrty8WwDftmrU+A3SIxoeK3maEJZM0A3sBgG5lp7JWMY0X2xAKwm+TQjhBCNZKA06Esc2LBhwygsLOSjjz6KOT5//vxG1TNgwAAcDgcLFiyo3qIHotv1LFiwAKfTycCBA6uPt23blrVr18bEMOXl5bzzzjtNvJKo008/vd7+L1u2rMnLYzsc0SVE6ybEk5OTGTx4MJ9++ilr166NO88wDEpKSprU5oFs27Ytbq/lF198Eai5B/uSm5vLgAEDYr4OtCy00+kEot+j2lRV5dxzz+Xnn3/mk08+qffc4uLi/dbdVF6vl4ULF8YcW7hwIV6vN25/6gMZNmwYJ510Eh999FFcnVV27NjB888/39TuCiHEEWPmYDO/JThwGDqtgyEyQyE0YOFfWh103e3Lt/J/J47hg15nsdeVylcdT2WvK53cbQc3gEwIIcT+NTRGljg5lsxgFse85ORkfv/73zN37ty498aPH8+OHTuYP38+F198MWPGjCErK4uioiI++ugjNm7cyKhRo5g2bVrMeddffz3r1q1j5MiRlJeX8/7778e836dPn3r3OBNCiKOJJazzS0ZHNqe1IcNTTJEzGZ/VTkFSIhXJyZwwtHXjKjSp4ItAKBJ9bQAmEzhVfi3U2ZvvZ1taMmhgwkBTFUyajqFDO6+fXQ4bfqslmnw2R/fBwqSCvo89moUQQohD7IorruDDDz/k7rvv5ueff6Z9+/asXr2aH3/8keTk5H3OXK0rISGBG2+8kTlz5jBlypTqJaMXL17Mjh07uPPOO3G73dXlL774YmbNmsX06dMZPXo0FRUVvPXWW9WxTFMNGjSIoUOHsnjxYsrKyjjttNPYuXMnixYtolOnTmzatKnRdZ5wwgm8/vrrPPjggwwZMgSz2Uzv3r3JycnhT3/6E1dffTXTpk1jzJgxdOvWDV3XycvLY/ny5YwePZprr722ydezL507d2bWrFmMGzeOtm3bsmrVKpYuXcpJJ53EOeec0+zt9e7dG4DHH3+cUaNGYbVa6dSpE507d+b6669nzZo1/PnPf2bp0qWccMIJWCwW8vPz+eqrr+jRowezZ89u9j7l5uYyb948Nm3aRI8ePfj111955513aN++PZMmTWpUXYqiMGfOHG6++WbmzJnD+++/z7Bhw0hLS6OiooIffviB5cuXM2LEiGa/DiGEONzuuTCJ64eH6PH3aFzrN+DDPyfQKffg90IesWUZP3TqRZkjkTJHdPDSgK3fklNeeNB1CyGEEM1NEsziuDB58mTeeOMNCgvjP5D98Y9/ZPDgwSxYsIBFixZRVlaG2+2mZ8+eXHvttZxxRvx+rb/++isAH330UdxsBYC77rpLEsxCiKNeUoWX39pn035PIaGkTAxgt9tNudtFSQMfmMeI6KBpkGSrTAwb4A1B2KBNArQtjQB6ZXI5Oi1ZU1VUxaDUaokml1HAYoKq5cc0o+bfQgghxGGWnJzMs88+y2OPPcY777yDoiicfPLJzJ07l8svv7zePYT3ZeLEiaSnp8fs8du1a1cefvjhuBmlo0aNYu/evbz++us8+uij5OTkcPXVV6OqKj/99NNBXdMDDzzA008/zYcffsjKlSvp1KkTDz30EB9++GGTEswjR45k/fr1fPzxxyxduhRd17nrrrvIycmhdevWvPzyy7z44ot8/vnnfPDBB1itVjIzMxk6dChnn332QV3LvnTv3p2bb76Zf/3rXyxatAiXy8XFF1/M9ddfv8/9jQ9G3759ueGGG1i0aBH33XcfmqYxbdo0OnfujNvt5rnnnuPll19myZIlLF++HJPJREZGBn379mXcuHHN3h+AjIwMHnzwQR577DE++ugjLBYL5557LjfddFP1rPPGSElJ4dlnn2Xx4sV8/PHHvPzyy3g8HtxuN126dGHmzJmMHTv2EFyJEEIcfpkpVoofTm/2esuciXQo2M0pG77CHgqxPjeHdv4N+COWA58shBBCHGaKYcRsaCiEEEIIAcAdw78kLzsTi6KgVn5c0BSFiKpSZOh88HLnfZ4bDoerl0Gs2ltKmeWJLottqvXg1jDAE2LOCBM39FWZMO033u/VKa6+3HIvO912MOo+9DUgEsG4f/9LUwohhKjxqdKwZWpHGFMPcU+OXaWlpZx11lmMHz+eO++8s6W7I2rp378/55133iGZFXy0GDt2LFlZWfz73/9u6a4IIcRxpb44uba3uz/Hyetjl8PelN0Kj+FnzK6bD1s/hRDieNPQGBkkTq5Ndi0UQgghRL1Us4YrFCKkqgRNJoImExGTCRSFjklNWJbarMQmlwEUBSwqOuDZE6T33uLqZHZt3Us9NUtrx3QSPrjC2vi+CCGEEM2k9j7IVar28x0wYMDh7o4QQgghjlIJHi3uWHZhKcXOg9/fWQghhGhuskS2EEIIIep13wdDmDZhLSabDd1kqj4eMAzmP9ml8RWqlcti113S2lBw2RSS2zhxaTonFpazulVStKwC7T1+sjw+jP+044T/V8ZPpeZoXeEIq661c3KOLBcmhBCi5fzxj38kKyuL7t27o+s63377LV988QV9+vSJW9r6WOHz+fD5fPstYzKZSElJOUw9Es1N0zRKSkoOWC4pKSluBp4QQoimUYz4QdUmI4w1sZ7B1kIIIUQLkwSzEEIIIeplsph49OlOXH3zNlDsqChoWpiXnu2C0pQ9mDUDNB0UNTpzGaL7MqsKo7pasNhV1qcm0a60nM2qSpnZjGoYZJRVUGCP7mG59pakZrxCIYQ4PhnI3vXNaejQobz33nt89tlnBINBMjMzmTx5MtOmTcNUa4DWsaT2PtH7kpWVxbvvvnuYeiSa2+7duzn//PMPWG7u3Ln079//MPRICCGOfe08v6HiQqdqlS6DjPA2MktlSyghhDiUJEZuGkkwCyGEEGKfkrITWLigd7PUtXa6mROeNUDXowlmXQcFTLpO51QbWkTHCAZ5v2NbQpUf7HRFYWVyIrmajNgWQghxZJo8eTKTJ09u6W4cVmPGjKFv3777LWOz2Q5PZxpp1apVLd2FFteQxH9aWhpPPfXUAct17dq1ObokhBACSPZ4SWInXtLQMeGkFDMBCgwZaC2EEOLIIwlmIYQQQhwWvVtb+WRSkLNe0cCsggI9kyKsvd4FwO6fy2jtC1Ynl2urOEZngAkhhBBHo9zcXHJzc1u6G+IQstlssoe4EEIcZn49lRT2ksju6mMVpKHragv2SgghhKifJJiFEEIIcdic2dmGcVf97znTbOSWe1EUMIzY9xJC4UPfOSGEEEIIIYQQooWEsbObzqSyExNhvKTiIZ2MU9NbumtCCCFEHEkwCyGEEOKIkJzrxOS0cPbOfD7Oyao+nqBpqBFJMAshRHOR/aWEEEIIIY48xWYHGRGdbfQFFGz4MRPgzNdHtXTXhBDimCYxctNIglkIIYQQR4yr3xlC+dj/0ba4nJ8y00GF/AQnW57OaemuCSGEEEIIIYQQh8yJvlvYYJtDqlGCCZ0AFtae0I1sRRIfQgghjjySYBZCCCHEEcOVZmfW12fgKQ8y7pFSLhlsYdo5qS3dLSGEEEIIIYQQ4pAyWcz00P/Cpjc3UvDlbgY/Opjclu6UEEIIsQ+SYBZCCCHEEcedaOOTuzNbuhtCCHFM0lu6A0IIIYQQYp86XdSZThd1buluCCHEcUNi5KZRW7oDQgghhBBCCCGEEEIIIYQQQgghjg6SYBZCCCGEEEIIIYQQQgghhBBCCNEgskS2EEIIIYQQQhxHDFVp6S4IIYQQQgghhBBHBImRm0ZmMAshhBBCCCGEEEIIIYQQQgghhGgQmcEshBBCiMPi+/5P0G7NWlRDY3NSG04umh1X5pWT/gO6wp7EDNrv3Y7DauLcNdce/s4KIYQQQgghhBC13DRsORm+IJaIRoXDxhUvnUynLokt3S0hhBCiRcgMZiGEEEIccitb30uf774kNVJIilbCScU/ssl5U0yZD65azI9ZvSmxtaXjdg8hvTXr3bl8f9unLdNpIYQQQgghhBAC+N25K8ku8bAmK52vO2RjDwT5z9TVLd0tIYQQosXIDGYhhBBCHHK9dm/CTLj6tQK09+9k8wsr+b//FFCQnIUllEDPXbsZ9NNv1eU8221s32XhpIdGtECvhRDi2GTI9lJCCCGEEI2i2Szcd94QKhw2ABb36cx1y77np41+end2tHDvhBBCHAyJkZtGZjALIYQQ4pBTlVDcMRM67zy7h6LkNlgwgzWRiMmMDoRMJgDcgSCOkHzKE0IIIYQQQgjRcn7IyaxOLgNETCY+7N2RN7+uaMFeCSGEEC1HZjALIYQQ4pArtqWSE9gVcyyMhb3J2THHNuW25suuHSl3O+mYv4eJX6wETcbDCSGEEEIIIYRoOflJ7vqPVZS3QG+EEEKIlicJZiGEEEIctFOeKGfVXhMYMKGnzn8n2GPeL0hoHZdgji4/YxBdMLuSoqCo0debszJYOPRUOuzMO7SdF0KI44yhysoQQgghhBCN4Yho1E0le21WnDIeWgghjnoSIzeN/AkUQgghxEFR7ixnVamVaKJY4Y3fzFjv8ceUKUhJjjvPaoRJ9McuJ2YNB8koLmPQz7+RVVTC5qwMihLiR4oLIYQQQgghhBCHS265N+6YOxSh0GO0QG+EEEKIliczmIUQQojj3KPLKrjlMxWsJtANpp+g8fS4hiV1DcMAmxkCWjS/bABBA2yWmHJ7relx55bZ3Iz55WMW9RlNuSORVG8JE1e/g1GUiV75EeXjk3qxITP+XCGEEEIIIYQQ4nBpV+ZjQ3qICpsVAMUwODmvkJBD5m8JIYQ4PkmCWQghhDiO3fdxObO+VKPJYV8EFIO562y0WurlnjNdjP93OV/9phFWFZZeb6NfW0fM+R//4gNdgQQLmFUwjGiyOazHlOuUt5slnYcxfPPXWPQIpfZEimwp7E5MYUdqGhGTBY/NRKE7ibSimvNGrP6FDaOHHoY7IYQQxw9DnoMKIYQQQjRK5/w9nLFmHR/36ECR28FZv24mKaihjjulpbsmhBDiIEmM3DSSYBZCCCGOY7M+0cBhhopgzcGgzr3LTTzxUQmazUKFy4yCwsinQwzuHub/rkisLrrHo4PNGk0uAyhKtL5IKKYdnzmRH3I6sy6pG45IgKKkNE7f+DWvnnw+mmqKNmux8eKAi7gu7xNsEQ0As2GQWVh3pyshhBBCCCGEEOLwySgqZf6A3qzsnAMKrOiQzbk/b+Y8rWn7dirWa4gZlv3TPai92jRLX4UQQojDQfLyQgghxPHMbgNPMPaYpkNIJ2IxU6ErEDYwwjp7TWa++iV2fymHWQdLPR8nzCqaURNoB00WbEVB1rVtx4qevSlISOLLTiejKSrWiIYjHMEa0QiarRQlJdR0BQVrMNCslyyEEEIIIYQQQjTGe306s7JnG7CbwGZCd1t4/6SuzP668XVdee3/4h/K9/5bc3RTCCGEOGxkBrMQQghxXNNBVSDBFt2DOaxBWRBTIILfaY0takC5OTYMXvGbDroBpjqjtnUDk1IrGe03+LlXWww1Wq4i0cl2NRNnOIKpspgZA7Omk1TuBSCCig8TWaG9zXrFQgghhBBCCCFEYyzv2R6bptF3UwEG8EP71oSsZkoTHGxT7q8uZ/vLqbS+76z91rWvOc96YSlqenKz9VkIIYQ4lCTBvA+zZ89m8eLFrFq1qqW7IoQQQhw6mgFpzpolrs0qWM1oJT6suoGmxoa+tXPG5b4Ij/1kAasGVoPcsgAZniARRaFYUZmXfxrTTooO597RulV1crmKz2nHqWkxxzIKy/g5IwddVUgp9aMnafhs9ua/biGEOI4ZdQcFCSGOCs888wzz5s3jnXfeITs7u6W7I4QQx5VUr5/b3/8fyd7oClteq5mHxg6m/5btMeWCf1/Jts2ltPvvhHrrmXjD//bdSKcbIN0JG+dGt58SQghxWEiM3DSSYG5my5YtY/369Vx77bUt3ZUWs2rVKr777jsuu+wyEhISDnzCUSoSifDee+/x0UcfsWHDBjweDy6Xi86dO3PGGWcwbtw47HZJigghWk6re8spDKiYVJ3A7ARUBf6+1Ms2D8wb50K9sxxslurkcqsyP61LA5h0gxK7mbCusctpi6nTrets2h2mU6aFlFnl6IkO0AC/zk6LhdIElZ4lXtqg4SONVZs64Xi/BAwjrn9GnWNJRRXscTn48Zz+ALTeU0rPHTspdLkOzQ0SQgghhDjCHC/xtBBCHE0u+tsOxn+XV51cVgB3KMKf3/6S3rvz4k94dQPF5/5A6uV9495KDO1rBrOBUu6Fci+oF8EfR8NjVzfjVQghhBDNSxLMzWzZsmUsXrz4uE4wf/fdd8ybN4+xY8ceswFxSUkJt9xyC2vXrqV3795ceumlpKenU1FRwerVq3n00Uf54YcfePDBB1u6q0KIo5wvZHDPJwFW79JQDJ2CIp2sVBOd0lW274yQmazQo62Z19eG+WaHjqIoGApgsYDFCmYDTTFhme0HdHBbwTD4z/1BFBQMAMMg0R+mbZGvut10f5gykwKqDYIR0HTMCiTqOr+/qxAfYE914dNrdVZR8FjMlNjMZATCKAps2duVzYvDqMP6YNM0kkNhbLoOhoE1EkFT1OrVtQMmlYKMlOrqCjKScQZDZJSUMmvolwRMKnvTkvHbrdj0CFk5VlLaOGnb3s64cxJxOurZC1oIIYQQ4ihyPMTTQgjREl7/yse/PgvQIezBEo7w004NXQVXRKfMbgMDQqqKFYO8tARuO1lj7YubKTbbKU9006vUw29tWqOpKrl7iknwB7BHNPwmS73tVVzxPhVXvE87484G9jAcm3j+5/vw6FUyk1kIIcQR67AnmL1eLy6ZiSSOYoZhcMcdd7B27VpmzpzJpEmTYt6fPHky27dv55NPPmmhHgohjhX/74sQt77tB8PAFNFoZRhYgd27wqxRVBI1nd+Apd+G2OmygsuOoSjRhLBJhVCt7K+ug6HDHm90WWzAcFqiwao3gjcQ4ReHjeRIhMywxl67hQKHNbq/MoABEQN2GAopigKqQlA1RWcm1wl48102MgJhnAYYZrU6kR0wm9mrqmR7fJTYrIQcDpL9fnKKPZQnO/Elxn8+KE50UZiRhKrrRFQTAUs0eA8YZn4uNaGXRbCsKeOVd8qZMiGZiaMSD8F3Qgghji26Kg8qhRBCCHF8CEQM2tzlodBmBcNJYanGTreDss4W7BGNgbtLyNANUKDIFo2DexV7efu9MKN3lZFenocObG+dztpObUBRWNcum8Fr1jH8tw1kVZTvt31d01FNBxoMraPgjz+sXgQr58ApXZp8/UIIIQ5MYuSmafRUn127dnHbbbdx+umnc/rpp3PLLbeQl5fH2LFjueaaa2LK9u/fn9mzZ7Ny5Uquuuoqhg4dys0331z9/rJly7jyyisZMmQIQ4cO5corr2TZsmVxbVbVU9e7775L//79Y/ZJfuaZZ+jfvz9bt27lqaeeYvTo0QwaNIhLL72UL7/8Mq6OYDDIP//5T84991wGDx7M5ZdfzooVKxp7WwC45pprWLx4cXWfq77efffd6jKFhYU88MADjBkzhoEDB3Luuefy97//neLi4pi6qq5j8+bNPPLII4wcOZLBgwdz3XXXsXXrVgA+/fRTfve73zF48GDGjh3LokWL4vpUde+++eYbpkyZwuDBgxk5ciQPP/wwPp8vrvzBmj17NvPmzQPg/PPPr74HzzzzTHUZj8fD448/zrhx4xg0aBBnnXUWd955Jzt37oypq+r7u3LlSubNm8d5553H4MGDueKKK1i7di0QHd191VVXMWTIEEaOHMmzzz4b16eqn81169Yxffp0hg4dyogRI7jrrrvi7ntDfPHFF3z//fecffbZccnlKm3btuXKK69sdN1CCFHFGzK49Z1ochlNJ6UyuQwQVk0kaXr16GabYZARikQTvYYRXfI6Umv5ad2AQBiCGhiAqkTX5PJVHvOF0TWDtEiEVE1nr81CnsuOpqqAAmYTWEygQFhR2Gs28WOSqypPHSekqOx02VABo87iX5qqssvtxGOzEjKb2JPgZk2bDApcLlRDj69MVUBR0FQTVl1H1aNlFEXBpkX/HTaZMBsGLywqIxzeR6eEEEIIISo1V6zZ0GcaVTHp1q1b+eMf/8iwYcM4/fTTuf322yksLKwu15B4GiAUCjXoeUdDrVq1ij/+8Y+ceeaZnHbaaVxwwQXcc889lJaWVpeJRCK88MILTJw4kdNOO40zzzyTmTNnsnHjxrj6Fi9ezOWXX87w4cMZMmQIF1xwAX/9618pKSlpch+FEKKpLptbEU0uAxn+EH6LmTJbdOBylzIvDr0mhkwLhjEbBmVWM8M27iC93ANEH6C3LyikfX70d7ZuUjFZILuifB/LXdfYMf6/Byiho+DZdz2j7j3A+UIIIUTLaNQM5tLSUqZNm0ZRUREXXXQRHTp0YPXq1UyfPh2/v55RVsAvv/zCp59+yrhx4zjvvPOqjy9cuJA5c+bQvn17rr46up/E4sWLmTlzJnfeeSfjx48/iMuKBmZms5nJkycTDod59dVXmTlzJosWLSI7O7u63F/+8heWLVvG0KFDGTRoEDt37uS2226LKdNQV155JYZhsHr1au65557q43369AGgoKCAqVOnEg6HueCCC8jNzWXHjh28+eabrFq1ivnz5+N2u+Ouw+FwMHXqVEpLS3n55Ze54YYbmD59Oo8//jgTJkwgMTGRt99+m/vvv5+OHTvSt2/fmDrWrVvH0qVLGTduHGPGjGHVqlW89tprbNq0iaeeegpVbb4lRcePH4/X6+Wzzz7jlltuITk5GYAuXaIj7TweD1deeSUFBQWcf/75dOzYkcLCQt544w2mTJnC/PnzycrKiqnzySefRNM0Jk2aRCQS4eWXX2bGjBncfffd3HvvvVx44YWMGjWKJUuWMHfuXLKzsxk9enRMHXv27OG6665jxIgRnHnmmaxbt4533nmHX3/9lZdeeqlReyUvXbq0+lqFEOJQ+WWvEU0GV6q9E7Klnv2MnWGt5oWiEHOypsWVrxaKgKrSKhwhuTJhW2ar/HigGzUzmFUVLAqEI5RaTNGZ0lV1m0zVs5hNEY3MigARRUGrp59AZeK6hq6q6LpGmteL327FZ49erSUcIWIzR0fDKaADZsMgVHmeqVb1BgqGAVt2hunawYoQQgghxIEcTKzZ2Gcae/fu5dprr2X48OHceOON/PbbbyxatAiv18tTTz0FHDiertLQ5x0N8eabb/Lggw+SkZHBRRddRFZWFgUFBXzxxRfs3r27ug+zZs1iyZIlDBgwgIsuuoiioiIWLlzI1KlTmTdvHt27dwfgvffeY/bs2fTr14/p06djs9nYvXs3X331FcXFxaSkpOynN0II0fxW7QYqV7FOCYUptNfEi8mhSFz5pFAEv8lMq9L4mcmtSsqwhMOklXvptjO/YR34ePsBCujExO91FXka1o4QQghxmDUqs/jiiy+ye/du7rrrLm677TYmTJjA3//+d84666yYka21bd68mYceeohbbrmFCy+8kAsvvJDy8nIef/xxcnNzeeGFF5gyZQpTpkzhhRdeICcnh8cee4yKioqDurDk5GTmzZvHZZddxhVXXMEjjzxCJBKJmeW7YsUKli1bxnnnncejjz7KxRdfzC233MK9997Lpk2bGt3mwIEDycnJAWD06NHVX7m5uQD84x//IBKJ8Morr3DDDTdw4YUXcuONN/L000+za9cuXnnllbg609LSmDt3LpMmTWL69OnMmDGD/Px8/vGPf/Diiy8ybdo0LrnkEp566imsViuvv/56XB0bN27k3nvv5dZbb2XixInMmTOHSZMm8e2337JkyZJGX+f+9OnTh86dOwMwfPjw6ntQFRDPnTuXvLw8nn32WWbOnMn48eO55pprePHFFwmHw3EjswE0TeOFF15g8uTJTJkyhVmzZuH1ernjjjt44oknmDFjBhMmTODJJ58kLS2NhQsXxtWxc+dOpk2bxqxZs5g4cSKzZs3ipptuYvPmzbz22muNusaqn42uXbs29va0iOLiYoLBYPVrj8cT8/9XKBSiqKgo5pz8/Pz9vi4oKMColTiSNqQNaaP52+iSGjt+OVzr35F6hjYHzKboP6pmMTdUZbLXrdfMHlarztfr1KMooKqoVcllw6icAR2KzpD2h9ACYTTAZBhUmEwxo7CziksZsCF+lgtEJ1SHzWb6bM+j4+69OMNh7IZe80HFiPZLq7Uct1arcsUwUBSwqoW1qz2qvufShrQhbRxbbQghjnxNjTWb8kxjx44dzJw5kz//+c9MmDCh+r/ffPNN9SplB4qnqzTkeUdD7N69m4cffpj27dvz2muv8Yc//IELL7yQ6667jv/+97/V7a5YsYIlS5Zw9tln8+STTzJp0iSuv/56/v3vfxMKhXj44Yer61y2bBkul4unn36aSZMmceGFFzJ9+nTmz59Pp06dGtW/Q+lI+VshbUgb0sahb6NzrV2Uyi1mkmollUut8XOvyqwWnJEIFU5H3Huti8o4YUse2UWlmCINjLtzbAcoYMZg32V0l+2Y+n5IG9KGtHF8tyGOLY1KMH/xxRekp6czcuTImOO///3v93lO165dGTBgQMyxb775Br/fz6RJk2Jm7LrdbiZNmoTP5+Obb75pTNfiTJo0CaXWQ+hevXrhdDrZvr1m1FjV0lV1+z98+HDatWt3UO3X5fF4+PLLLxk2bBg2m43S0tLqr+zsbHJzc+u95ksuuSTmOqpmJw8bNozWrVtXH09JSaFdu3bs2LEjro527doxfPjwmGNTpkwBqHf5rkPFMAw++OAD+vXrR0ZGRsw9cDgc9O7du97lySdMmIClcs9NgH79+gHQu3dvevbsWX3cYrHQq1evmO9xFZfLxcSJE2OOTZw4EZfLxWeffdao6/B6vdV1Hg1SU1Ox2Wo+qLrdbhISEqpfW61W0tLSYs6pO4u87uvWrVvH/FxKG9KGtNH8bSQ7FK4bYo0mdU0qJYpC1Txks27gq7U3SATYYzPXJJZ1A6y1sq8mE/VSFXBaQIFQrT6nB8Io+0hSu3Wd9HBlQF67jKZH21UUIpV1eVQFHQOrpjNg8zoG/raF7JIK0spjR2AnBQLYIho7U5MJmUxkllWQW1iCqXJGtQGYdJ2wyVQ9+9nAIFi5j5VZ09FUhfOGu2jfLnbWztH0PZc2pA1p49hq40hmqA37EuJY19RYsynPNFq1asXZZ58dc6x///4A9cbx+9OQ5x0N8cknnxAOh5k2bVrM77AqVaudVT03uPLKK2Pa7dq1K0OHDuWHH36oXv7a7XYTCAT48ssvYx44HmmOlL8V0oa0IW0c+jYW3ZCAqzKGzXfaSPMFSA5Gh3D/luTCXyu2LrJZ0IHUUITv22cRMtfE0l6LGbNeszrYbnciu13xvzvryvrhDwcsA/v+7KguvO2Y+n5IG9KGtHF8t3GkamiMLHFyrEYtkb1r1y569eoVt6RyampqvcEIRPeirSsvLw+Ajh07xr1XdayqTFNVzRquLSkpibKysph+qKpabzK5Q4cObNu27aD6UNvWrVvRdZ23336bt99+u94yVbOfa6t7HYmJ0WF39S17lZCQQEFBQdzxDh06xB1LT08nISHhgPfZ5/PF7dWclJQUE4Q3VElJCWVlZaxYsYKzzjqr3jL1Lddd977s7x4kJibGfI9r11G3z1arlZycnEb/rFUlln0+X3VfhBDiUPjXOAc3DrZw1QI/vxUqhDQdI2RgtZtIS1bwF0ZIdChkZZvYvsmIziRWK5fH1s1gqtxrGcBugapZygrR93SiiWGLiT2GBbcviBlwajptPX62O+2xk6ENA8NhJt0bIOLxs81pi1vISzEMrJUn6UQTw0FVoeeWfFqHiulZvIlRW5fxSt+z+S0jF7Oi0KrCQ/stBdhDYc4q+IYSVxIAZj3EV51OZEdiKypUOy5fgNJkN2V2K4bZhMuqYEu1k9XezlWXJJObdeR+UBVCCCHEkaepsWZTnmnUF+8nJUU/89QXw+5PQ553NERVYrtbt277Lbdr1y5UVa332ULHjh1ZtmwZeXl5pKSkMHXqVL7//ntmzpxJUlISJ510EoMHD+bss88+agZpCyGOLcl2hYp7nFz37zKWbNYx1DAn7d5NUpEPZyCArqqUJrooctopwUSrci8/uywMTbDzeVlrcvwhSt0OsveW0K60ZrC0oSj8mJVLqd3ERT/9tM9ZXFZ3/Ezo+hgQvw9zxSvQwPOFEEKIw61RCeamaMzeto2l7WdPyX3tK9zSI2hHjRoVsxd1bbVHd1TZ13UcruubP38+8+bNizk2d+7c6pHWjVHVt1NPPZUrrriiweft61pN+5qRd4h16tSJdevWsX79ek455ZQW6YMQ4vjRvZWZr2YceFR0XcGQhn2WB8wKaLD0ahtDO1hpN8fDXo/OV1NVBsxXAAUUhZDJwganlcRQGCwq5UENQ9Mqs8RqNCHtsmIqC/DcX5PpketA+XM5uKzgjS5xo2CQWjnruFxVyA5ppDu2k5TZgQ5l+QwsWlPdv78sn89nmaeyOqsr23Nbs65rG0b8/D2Z/gJy/BtRMNAx0644n1f7nM1VayY0x+0UQgghhKh2OGPNfbUFjY/jj9TnHRCdZLBw4UJWrlzJt99+y/fff899993HM888w7x58+pNjgshxKGmKApzr02ufJXaiDOjg4aGTttEqi/IBquFrntKqt/1Ws08O+QkJv70U/ypp2fTbtmUBrajYpAAVNQkmYf3kOSyEEKII1qjEsxZWVns2LEDXddjApri4uJG7ZlcFVBs3ryZU089Nea9LVu2ALGje/c1EvdgZznn5OSg6zrbtm2L2wuoqh+NVXtJgNpyc3NRFIVIJBK3ZPihVt+1FBYWUlFRUe8o6trGjBlTvSx3lQPtPbyve5CSkkJCQgJer/ew34O8vDzC4XDMLOZQKEReXh7t27dvVF0jRozgvffe46233pIEsxDiiGWzmjDmJMUd33VnTbLamA2WuzxE3HYwDHRFoZTK35PmEHZvBLNDxe+youoGyeUBSkwKPXKjQe76Gyx0e8YAc9Wy1VBsNlXPas7xl3HGCT8zdeqplN29tarVyv8q9CrayBZ3Dq4t2zFUhcyKPVio+TyhEsFGKVZPoNnuixBCCDDU+j+vCyEaprHPNBpjX/H0oVC14tyGDRv2u01Y1bOTLVu2xO0HXd/1Wq1WhgwZwpAhQwD48ssvuemmm3jllVe44447mvsyhBDikPtiXid6/NGJ31AZ9tsOeuYXUpDk4v/6dqNDYUn8Cbn2fSaXQ0QXxI7/ba9GZzGrwJ7nIC25+S5ACCHEfkmM3DSNWjF82LBhFBYW8tFHH8Ucnz9/fqMaHTBgAA6HgwULFlTvZwvRvW0XLFiA0+lk4MCB1cfbtm3L2rVrCQRqHjCXl5fzzjvvNKrduk4//fR6+79s2bImL4/tcEQfutdNiCcnJzN48GA+/fRT1q5dG3eeYRjVexY1t23btsXttfziiy8CNfdgX3JzcxkwYEDM14GWhXY6nUD0e1Sbqqqce+65/Pzzz3zyySf1nltcXLzfupvK6/WycOHCmGMLFy7E6/XG7U99IMOGDeOkk07io48+iquzyo4dO3j++eeb2l0hhDhsIv5wdC/l2g8zDQPCGgGbiUhQIzW/gsQ9HkoMhYi1Zmxa12wHXRU/qCpYzGC3YNgs0RnPisKNJ9f8rg/ZzIAGhFAIASEsRoiITcWk6VgDYUL1zMYxEaZ1cM+huwFCCCGEEI3U2GcajbGvePpQOPPMM7FYLMybNw+PxxP3ftWM6KrnBs8//3zMLOmNGzeyfPly+vbtS0pKCgClpaVx9XTv3h1o/FLgQghxJAnoBt33lDB/0An8efwZ/PPMU9mV7KZDcZ1JVx0SaLfjln3WM/+ZQftupGw+aIskuSyEEOKo0KgZzFdccQUffvghd999Nz///DPt27dn9erV/PjjjyQnJzd4pG1CQgI33ngjc+bMYcqUKdVLRi9evJgdO3Zw55134na7q8tffPHFzJo1i+nTpzN69GgqKip46623yMrKoqioqDGXEGPQoEEMHTqUxYsXU1ZWxmmnncbOnTtZtGgRnTp1YtOmTY2u84QTTuD111/nwQcfZMiQIZjNZnr37k1OTg5/+tOfuPrqq5k2bRpjxoyhW7du6LpOXl4ey5cvZ/To0Vx77bVNvp596dy5M7NmzWLcuHG0bduWVatWsXTpUk466STOOeecZm+vd+/eADz++OOMGjUKq9VKp06d6Ny5M9dffz1r1qzhz3/+M0uXLuWEE07AYrGQn5/PV199RY8ePZg9e3az9yk3N5d58+axadMmevTowa+//so777xD+/btmTRpUqPqUhSFOXPmcPPNNzNnzhzef/99hg0bRlpaGhUVFfzwww8sX76cESNGNPt1CCFEc1PNZvS6f78VBRUV3Wwi4FQJ2CzR4dWqUrnHc43bzrAw7VM1mmSGaDmLCSIamqFgUqIPIXfbU8n25hFdc9tAQcWu+bD7NSzh6JYXOyztWe8uopvnt+r6w4qZvNSMQ3PxQgghhBBN0NhnGo2xv3i6uWVmZnLrrbcyZ84cJk2axJgxY8jKymLPnj18/vnn/O1vf6Nbt24MHDiQs88+m48//piKigqGDBlCUVERCxcuxGq1MnPmzOo6r7/+ehISEujXrx+ZmZlUVFTw7rvvoigKo0ePbvZrEEKIw2XgtgLO+mUrnXcX8U2HHBICQQZszmNB/560M+5sVF317rcMqImyV70QQoijR6MSzMnJyTz77LM89thjvPPOOyiKwsknn8zcuXO5/PLL691DeF8mTpxIenp6zB6/Xbt25eGHH46bUTpq1Cj27t3L66+/zqOPPkpOTg5XX301qqryU317XDTCAw88wNNPP82HH37IypUr6dSpEw899BAffvhhkxLMI0eOZP369Xz88ccsXboUXde56667yMnJoXXr1rz88su8+OKLfP7553zwwQdYrVYyMzMZOnQoZ5999kFdy750796dm2++mX/9618sWrQIl8vFxRdfzPXXX7/ffaCaqm/fvtxwww0sWrSI++67D03TmDZtGp07d8btdvPcc8/x8ssvs2TJEpYvX47JZCIjI4O+ffsybty4Zu8PQEZGBg8++CCPPfYYH330ERaLhXPPPZebbrqpetZ5Y6SkpPDss8+yePFiPv74Y15++WU8Hg9ut5suXbowc+ZMxo4dewiuRAghmpcpoqHXO4M5AroOVkt0H2cABVL8PsBeXdQXNOKSzigKmNXq5DKAGtGJzmCuouPEgzvgI2iq+fzwQ9KJdPJuxmxoGIBhOCmyxS/1LYQQoumMY3j1r6rBu3v27OGiiy4iNzcXTdMoKysjKSnpkOytK45PjXmm0Rj7i6cPhQkTJpCbm8tLL73Ea6+9RjgcplWrVpxyyilkZmZWl7v33nvp1q0bixcv5rHHHsPhcHDSSSdx3XXXxfRtwoQJLFmyhEWLFlX/f9etWzduv/12+vfvf0iuQQghDoeQyUTAYsaFwunbCsjdU4QpojG0faTRdT3/5ACunPFNbJL5wz82W1+FEEI0jsTITaMYtdc3aqLS0lLOOussxo8fz513Nm7Elji0+vfvz3nnnXdIZgUfLcaOHUtWVhb//ve/W7orQghxxBnxVDlf7oJwkiOaKNYNLJ4Q0/vrPH5+As7ZZfixgK5zYUeDRVMSYs5/+4cKxr1tqadmg7mt56MoMHXqVLa2uoOOZbsIkwAYWPCgEuKNrAsJm2IHqPUoWYc74kUxVLJDxbzd5TQu3XDFobsJQghxnHkr9b8NKjeu+LJD3JPmYxgGt956K08++SSRSARFUViyZAkjRoygrKyMNm3acM8993DTTTe1dFeFEEIIcRRqf8se+hZ5CVmj8a+q6djKyml/RhqPzMhqUB3hcLh6S72pU6disdQXSwshhDjcGhojw9ETJx+OGLnR01dr74NcpWo/3wEDBjS5I0IIIYQ4/D69PpEOKRqOIi+OIi/mEh8DcyM8fn40keybnYQx24lxjzsuuQwwprcrfm0vIzqrufak6JDiwkcWYRIIk4iPLCLYMOqcrGoa9kgAt1ZBsSWJX53tSC6V/fqEEELs30MPPcQ///lPZs6cyZIlS2L2iU1KSmL8+PG8+eabLdhDIYQQQhzNsv3h6uQygG5SKWidhqnM34K9EkIIIep3OGLkRi2RDfDHP/6RrKwsunfvjq7rfPvtt3zxxRf06dPnoJaBOpL5fD58Pt9+y5hMJlJSUg5Tj0Rz0zSNkpKSA5ZLSkqS0YVCiGPO+plN//tlNqvgD4DTAXrlBxWLCp5gTDlbwEJsJlqhwNWO9r5CtjtbETJZsEVCnL3nYxJ1DwBZkQJ2WnKwBGSJbCGEEPs3b948Lr/8cu6//36Kiori3u/Tpw8ffPBBC/RMiMOvpKQETdP2W8bpdOJ0Og9Tj4QQ4uhn0eMXAbVqOop2DK+rKoQQ4qh1OGLkRieYhw4dynvvvcdnn31GMBgkMzOTyZMnM23atGN2P6vaeyrtS1ZWFu++++5h6pFobrt37+b8888/YLm5c+fKvlFCCFGH8UAy9jtKCNqiSWRnOEjpPQlUrvwFgFmPf8hZbk6iV9l6OpTlE1LtKGpZdXK5Sk44j92JqYf4CoQQ4vhiqMfeg9AdO3Zw2mmn7fN9l8tFeXn5YeyREC3n8ssvJz8/f79lpk2bxrXXXnuYeiSEEEe/cquKK6DHHCu1mcnM0fdxhhBCiKOFxMhN0+gE8+TJk5k8efJBNXq0GTNmDH379t1vGZvNtt/3W8qqVataugstriGJ/7S0NJ566qkDluvatWtzdEkIIY45gTm1Z0G7CIfDMe9vapVJWl5F7LGkLDZnZzJk0ypSQ7sptFogEluvAhQ4ZYUQIYQQ+5eRkcGOHTv2+f53331H27ZtD2OPhGg59957L8FgcL9lcnJyDlNvhBDi2LApwUWFS6d9iQ+TYZCX6CDPZiK/IH47SSGEEKKlHY4YudEJ5uNRbm4uubm5Ld0NcQjZbDbZQ1wIIQ4hn8nM0s59OXn7bwB8l9uFbcmZdC7cycqs/nicDpK8eWRs/zpmIW0NBSUYrr9SIYQQotL48eOZO3cuU6ZMISkpurWCokT/onz88ce88MIL3H777S3ZRSEOmwMNkBdCCNF4HQrL+bFtBluSnCi6gYZKZnEF1vZH5qQjIYQQx7fDESOrB91LIYQQQogDyCzzstPditf7ns7CE4exIzmTxIAP+ykOMooLyN6zh53ubH5q1YOqna00ReXjjiNYl9mhRfsuhBDHGl1p2NfR5O677yYrK4u+ffty+eWXoygKc+bMYciQIYwaNYo+ffpw5513tnQ3hRBCCHGUOm/NBpz+EHoYNE1BDWtcuvJnLhie3NJdE0IIcZAaGiMfTXHy4YiRZQazEEIIIQ65CqtGvx2bWZeZS8BqJd1Tzu5EG2e+cVl1mQ2nvM7SjsP5qu1AUvyl7HG1whXykDmycwv2XAghxNEgKSmJFStW8Mgjj/DGG29gt9v5/PPP6dSpE3fddRe33XYbDoejpbsphBBCiKPU1mQ39779OSs65hCwmDllaz4+q5lTu9hbumtCCCFEnMMRIyuGYRgHLiaEEEII0XDhcJjnn38egKlTp2KxWHijx3+whnQ8dgceq5nLv7oIu9MSc96LPf6L15VA2GTFFvbjN8PNK8e1wBUIIcSx681Wrzao3EV7Lz3EPRFCCCGEODoYhsEVo1dx0vYCLJrO+qw0znngBM4bmNTgOuqLk4UQQrS8hsbIIHFybTKDWQghhBCHxYRfrzpgmSt+jc5oNgyjel8QIYQQzctQ5ferEEIIIURjKIrCSx+c0tLdEEIIcQhIjNw0kmAWQgghxBFHkstCCCEa48orrzxgGUVR+M9//nMYeiOEEEIIIYQQQrScwxEjS4JZCCGEEEIIIcRR7dNPP40bnKRpGvn5+WiaRqtWrXC5XC3UOyGEEEIIIYQQ4vA5HDGyJJiFEEIIIYQQQhzVtm7dWu/xcDjMM888w2OPPcaSJUsOb6eEEEIIIYQQQogWcDhiZPWgzhZCCCGEEEIIcVQxlIZ9HQssFgszZszgnHPOYcaMGS3dHSGEEEIIIYQQR5iGxsjHQpzcnDGyJJiFEEIIcUhEykN4f5KPGkIIIVreiSeeyPLly1u6G0IIIYQQTRLRdfLLIy3dDSGEEMeI5oiRZYlsIYQQQjS7b9s8QDc9hTKryvevPMJPqW6u+k1mjgkhhGgZS5Yswel0tnQ3hBBCCCEaTZnlBXPltLlIkG45KuumO1q2U0IIIY5qzREjS4JZCCGEEM3qzXaPsjbnVIrcLhTFwBLuSP9dW1u6W0IIISoZyjGwrlcd99xzT73HS0tLWb58Od9//z1/+tOfDnOvhBBCCCEOjjLLCzYTGJUHzAbr8wz+80OYq/paWrRvQghxrJAYuWkkwSyEEEKIZlVuS8GmRPjjl19h0XX2ul28dFJfdnT+N7dvvKaluyeEEOIYNHv27HqPp6Sk0KlTJ+bOncu0adMOb6eEEEIIIQ6W3RxNLlflPkwKGDpXvyMJZiGEEPt2OGJkSTALIYQQolmtyu3A7Z99Vf26lcfLxLU/8026fOwQQghxaOi63tJdEEIIIYRofkY9x0wKhOp7QwghhIg6HDGyeshbEEIIIcRxpW25J/5YSSnrMjJaoDdCCCHq0pWGfQkhhBBCiCOURR7rCyFEc2lojCxxciyZSiSEEEKIZpVTURh/UNFoVRI8/J0RQghxTNq+fXuTzmvbtm0z90QIIYQQ4hAyDKhvb1BVZjALIYSo0RIxsiSYhRBCCNGsHJThpBwfiZVHdNKNAqxauxbtlxBCiGNH+/btUep72HoAmqYdgt4IIYQQQhwiuhFdEjvuuGwPIoQQokZLxMiSYBZCCCFEs6qw2NmakkCGfy+OcJg9zgTyHVnsSk1u6a4JIYQ4Rjz33HNNCp6FEEIIIY4qBmACqp7/K5XHZA9mIYQQtbREjCwJZiGEEEI0qyS/nxNLf8RqhAHIqIAtehtyS4tauGdCCCEADPXoT8xOmTKlpbsghBBCCLFfgaDGn/+6nVYJKjfdkoPF1oRKbCpYTGCuTCgrCmgG/H/27ju+iir94/hnZm5NL4Tei4AURVAsFAsqCGIB14orNlSsa/nt2vvqWlZdOyo2dsWuKIoNRCwgoiC9txAI6fXWmd8fNwRCggQMCeX7fr3yMnfmzJxnriGZ5z5zzglG6jRWEZEDmXLk3aMCs4iIiPxpfS5aTTDOjeF3eLasoLK4vEWb0vUUu/s2UHQiIiIiIiIi9eeSCxdilUJSNEKuYXDzRaU0bu2iWZddPNGW0WjbjkozqHnabBERkXqkArOIiIjstk2bSjjnhkwGrN9EammAX1s1odxT/bFs2zBJiAQbIEIRETmQfP/998yZM4fCwkLs7dYmNAyDO+64o4EiExERkQNFMBTBXWqTHtz64LUrEiV7LSS18BOfWF77k0Wd6p/gOw4YZt0EKyIi+7U9mSOrwCwiIrIPOevVQhbMLqfMYxEXDJNWFuSoxACPPNW1QeK57MpVHLdhMytaNmWDx03bnHwWJXSiR/xSmpYWVLZbkdSSi1b+im18julMbJBYRUQkxtkPB7zk5eUxdOhQZs2aheM4GIaB48SmktzyvQrMIiIiUh9uvHQxyaGqU1i7HAdXKMQnc47k7IFTa38y24l9bbl/27L0sqE1mEVE6opy5N2jR51kr/XCCy/Qp08fNmzY0NChiIjsFRzHYdrCKMcvWMFtH3/H36b8xMhZ85lZ5GHt2tJ6j+f3N1fgtW1+PrgjuSlJlMT5Wdq6OT917cphVzzAv44ZxvtdDmdho3Z0KsyndaAY8GMbF9R7rCIisn+7+eabmTdvHv/9739ZuXIljuMwZcoUli5dyhVXXMGhhx6qvEIOSLNnz6ZPnz706dOHDz74oMY2ffr04frrr6+2fc6cOfzf//0fgwcP5sgjj+TEE0/k2muvZdq0aTWe59tvv+Wee+5hxIgR9OvXj8GDB3PVVVfxww8/1OEViYjs/fLLTSynegHYBQzclMNr351Ayo35GDds5tq3i3Z4Htu2wW1CMAoFQcgPQiBasVcFZhER2bH6yJFVYJYGNXv2bF544QWKi4sbOhQRkb1e3H2ldMvcTMuCYn7s1oEZPTpRlBjPsLnLuOvS3zn99Ln1Gs8/38wlwTFwzKq3E0VJCWQlpfF/J53PFx370CUnj62PWxuAC/vk+7CN0bGvQ/5er3GLiMj+Z/LkyYwZM4azzz6bxMREAEzTpGPHjjzzzDO0bdu2xgKayIHkxRdfJBAI1KrtM888w+WXX87ChQs57bTT+Mc//sG5557Lxo0buemmm7jzzjuJRqNVjnnwwQeZO3cuAwYM4KabbuLcc88lOzuba6+9lpdffnlPXJKIyC6LFpZR9ux0St+dTdFPmWxOv4Vi42KKjUvIueB11v31I/InzmVd3O0sNe5gVduHCWcWVo76qo2gAYVeT+Vrw7bBtgmaFhZwZE4+btsBy81/ZtkYNxfy7HfVHxq37gtCKAplEbCJ1ZTLI7GCs2Pw6pza/U4XEZEDT33kyJoiWxrUL7/8wrhx4zj11FMrf8hFRPakkpIIFz66ieIN5Thd0hnS3sV7MwMESoIctXwdCRk+kkZ0YObsEn4tcxPwuEgmykp/HI5lQTgKhgEG+CM2HcrLScwvIa2ghBKPm7wUP9keL60LSin3uAm63TQpK8UVb2F6LcbkrCW1fzP+lptBZsjEHYqQWBwiPRAgxzTIT0ykXUExzQpKwOUiYhnkWCYLm6aSaDvEuz1M69OdAo+LgGWyqVFqrGQbiXDIkpVccfJPxNkObbOySSwJkJfgZ3NqPCNn/UajUJDazvjSxrmVH+eW8NTtS0gvKsOM2nTIK2Rm03S+7NMN2zDwd2nLceuyqx0bHwpVft9j0/qaO/hiBZVF53mbsI3R0LcdPD0K3vgBnnqvSqwOVUvUGBUb0xNh5kPQoVktr0xERBxj/5v/q6CggG7dugGQkJAAQElJSeX+k046iVtvvbVBYhPZGxx88MEsXLiQ//3vf4wePfoP23744YeMHz+eI444gscffxyfz1e578ILL+S+++7j008/pXnz5lxxxRWV++6//34OP/zwKuc6++yzOe+88xg3bhxnnXUWSUlJdXthIrJHOcUBog99AoVlGIe0xYiEYVMhtEjHOLM3ge9WEnz8K8z0eBL+eSpml+Y7P2lmLpH7PyQ/34fnyLYklGzCnrYSs0sTrH+fC3NXw9INFP1SSPGMTViuKK5QGWwspjwcj7t9HCl5KzFaplCwOEo4YBDu1g771ywSnQL8VjFmJIplBLEtE6M4RMD2Y2NiY+KnDC+lRHHjEI9NEmvpjkkI14Q1mNjkvr4AFwl4cAivcVjb8klMoixMaU3AisOwHSIugzKvRbnHQ8hrkZ0Sj8vnAzvKIaEIC9q1JC4YpGlOPv6ychwDUn0+gglxpIXDWDZ0LSpjeaIf07YZ+5HD2I8LATjvEIOnRySAZWwzYnkbodjI5tGTHEZ/FoSoTZskm8JyKHEszuxsMHGEt8ohv2ywWZJjM7CtRYuk/e9eUETkz1COvHtUYBYRkQPGrIVlPP1/Cxm6aC0AkWkGE/r1YEWLRtw0/WfSSgOwDMaZycxs3wKSY8fleM1YYgfgMiAYe3K53GUyPzEREhOhdWy3YTsMWrSSr7u2w64Y2Rss9tGqoITh386jMBDgP75WzO/ow+tEOGpzKf6oDY5BqgGu3NiMDnZiPBgGqxL9LE2OfR+KRGm2KZdumdmEXC7mt2nGj11aYQMtCgPM6doJo+KGaGmbVqQVFXHmjJ85c8H8XZ6yZIXxIP8bMIBBmdmxibcMg8ykeCYO6I1txvqId1vkpCZjQpVi8ODffqfziuW8e2g3NnvSsDEwq03fVcON28xVcPi9QCHGdi22b+1sWYIqtxg6joV7z4Y7zt7FqxQRkf1F8+bN2bhxIwBer5fGjRszd+5cTjvtNAAyMzMr/0aKHIgGDRqE4zi89tprnHHGGaSkpNTYLhwO89xzzxEXF8f9999fpbgM4HK5uPXWW/nll1944403OPvss0lNTQWoVlwG8Pl89O/fnwkTJrBmzRp69OhR59cmInuG/esa6Pt3XOEgAA4ejIrM0gHsMeB3Avgr2jsfTiF05wV47jlzxyf973QKz3+NRfQhig0TF5JACR1Yh/n1Wqxnv8ftZLOaQyklpfKwNLJxY+ElDJvyiVBI3JKV5HIY+TSG6eUYJBPPRuIoZssTyQaFBEjDJAETMAkSRyEmNm5CFOMimxaAgY2LbVdN9hLCQwQ3kEcKhitMGD+uaCy3tcIOhhOlPNHA4zJoWVKGU1yGGY5QHu+j54ZNxJWW4Q3EHsA2HIgvD4BlsiAjjQKXRZ7HHXszXVU/ov/vXId3F5RAvCf2WUR0u3zadsA0Kh5+N8DrYk1JxXTawNvL4d2HgkT/HisyX/JhiFd+jRWqXWaY8ae7ueAQlQVERPZn9ZEja4rsA8CkSZPo06cPs2bNYty4cQwbNoxjjjmGv/71r/z+++9AbCTxJZdcQr9+/Tj55JN56aWXqp1n2rRpXHzxxfTr14/+/ftz8cUX17j20qmnnsrll1/O6tWrue666xgwYAADBw7klltuIScnp7Ld3Xffzbhx4wAYPnx45bpQL7zwQpXzhUIhnnnmGU455RSOOuoozj33XGbMmLHb78fs2bO57rrrOOGEEzj66KM57bTTuPfeeykoKKhsE4lEePXVVznrrLM4+uijOeGEE7jppptYvnx5tfN98sknXHjhhRx77LH069eP0047jdtvv538/PzdjlFE9oyrXs5nYEVxGcBlO4ycuYjTZi6OFZeBdamJseJyZSNja3EZIPzH02I5plGluAywNtGP43axsFVjchPj+bF9U3CgXX5ZrLi8pauKU28p6AYsk2UVxWWAv/yyiKOWrSWpLECjohKOWbCCUtNhVXoCP7RNJz/Ow7bykpJY3yR9t/7YW0CvdRurlIVf6dersrgMkBEIYxgGTkXMDrFZu45euoxLfprDZ8+/wejvF7CBdmzzsQM7Wytq++LyjtpUcefEnV2SiIjsxwYMGMCXX35Z+frss8/mX//6Fw888AD33XcfTzzxBMcdd1wDRijSsAzD4Oqrr6akpIRXXnllh+3mzp1Lbm4uAwcOJC0trcY2Xq+XIUOGEAwG+f7773fad3Z2bMabHZ1PRPZO9sUvYFYUl8GsLC5DrBxrOVWnZzYA1wNvQXH5jk967cusoivRbcY8lZBAfkUxOep4KKUxpVue9q6QR1Psyv4NbDzk0iRWXK7gYJBJa7ZkiwYhbFyE2TpzQhzZmGzNwRMowsXWGbi2FdomRi9RMt2Nq+WhnoiDJ7h1hLFhECv8uixcto0rHGF769xu3mjTcutnBrYTe4J6W4ZByDDAMsFfQyHYMmMV6y3HQ+yzi23Yhsnt34b5YW20srgMELHh+s/DBCNaw1lEZH9WHzmyCswHkKeffppp06ZxzjnncNlll5GZmcnVV1/NtGnTuOWWW+jVqxfXX389bdu25fnnn2fy5MmVx77zzjvcdNNNFBUVcemll3LJJZdQVFTETTfdxPvvv1+tr82bNzNmzBiaNm3Ktddey+DBg5k6dSp33XVXZZszzzyz8gf4b3/7G/feey/33nsvxx9/fJVz3X333fz6669ccMEFXHHFFeTn53PTTTft1gLk7733HldeeSXLly9nxIgR3HzzzQwePJjFixezadOmynZ33HEHTz/9NI0bN+baa6/lzDPPZPbs2YwePZrFixdXtvv000+5++678Xq9XHHFFdx4440MGTKENWvWkJeXt8vx7Ql5eXkEg8HK1yUlJVXWvA6FQuTm5lY5Jisr6w9fb9y4scraM+pDfewrfcTnV09044NhWuUVVb7ekLLddP3b/6W02SnbrP7ntcRtkZ2SRNjlImqZYDskRKonm1WOcVlVpmg5emVmlf1u2+aI5ZkVfRqsSI+vdo7VjTN2HnANDMAfrki0K2LIj686giXgMisbOxVfvlAI93ZJtINFHo0rysoGe/L2Y3/92VUf6kN97Ht9SP3629/+xvDhwyv/v9x9990ceeSR3HHHHdx111307t2b//znPw0cpUjD6tu3L3379uXdd9+t9ntuiy0PVXfu3PkPz9W1a9cq7Xdk6dKlfPPNN/Tq1YsWLVr8Ydv6tLf8rVAf6mNv7sNYu+1ySNvncDUXJ81oBNZk77iP/BLKSKh2XDlbp3MOEk/1R4oNIliVryyClFF9qb0AcdtEZmOzzTrIhLG2KyYbOCSy88/vQrgq67nbcgDD2e5DAsOAigfJa/psICs+jmgN26upWJoLywC3EStcm0ZslLJpQISqT2fXMArtq9WwILt64LllkFW8//7sqg/1oT723j6k/tRHjmw42/4EyH5p0qRJ3HPPPXTu3JlXX30Vt9sNwLfffsuNN96IZVmMHz+egw8+GIhNiTVs2DCaN2/O+PHjKSoqYujQoaSnp/Pmm29Wma/9/PPPJz8/n08//bRyDeVTTz2VrKws/vnPf3LiiSdWxvHwww/zzjvv8O6779K2bVsAXnjhBcaNG8fHH39M8+ZV12nZsq9fv378+9//rhyuv2DBAv76179y0UUXcfXVV9f6fdi0aROnn346LVu25JVXXqm25rNt25imyU8//cTVV1/NiSeeyIMPPrh1utmlSxk1ahQ9evSoHOF98803M2vWLL7++mtcLk0tI7K3O+r2jVz81g9VUuPcBD+/tWnCCQtWx17H+/jHGcdtTQQtA7zbHBGMQg1LIG3LHYkQ3vZ3guMwICuf7ktX0S6vkNvPPp6gZdI+u4TOhaUVbbam7LExvg4hy2Rqs/TKUcOPvf0VyYFglb7e7duVz3t1AiClPMQxa6omx8N+nsOQefN38s5U5wBft++8tcBtGDx77GH82qppZRvDcRi0NhvXNslxtsvgqf++R4uiErbXnBVY2FRdUbk6o2KK7F1imRB5d1ePEhE5IL3R5p1atRu15qw9HMmeV1BQgGVZ1e79RQ4Us2fP5oorruC6665j1KhRLF68mFGjRjFkyBDuvfdeAPr06UO/fv144oknePnll3nuuee4/fbbOf3003d43pkzZzJ27FhOP/10br/99hrb5Ofnc9FFF1FQUMDrr79OmzZt9sQlisgeEhn0EK6vZ1W8MoFtZ8xycAhWy9siPj+uotfAvYPPyNqOYcGaNhRQ9UHo9qwmmVgRwqCY5RzKtjmjgU0GuZVbPBQC5SzgiCrnSaSQbiyseBUGQpRsWc8KmyRWY2xXHF/JIZSSWi1UDyG8RAhhkUk6ESyK09xY29STA16LoiQ3Ed82hexIlIhlUZqWhBWN4i8uw6z4+D3f6+HR3j3I91VdHxnLAMuimhRvbITyjmZS85hbC8+OU7XI7Di8OcyiT1ODrk8HqwySbpdqsPxaL6a5y5m3iMh+qbY5Muz7eXJd5sgawXwAGTlyZGVxGaBXr14AdO/evbK4DOB2u+nWrRtr18amkZ05cybl5eWcc845lcVliC0Mfs4551BWVsbMmTOr9JWRkVGluAyxpBVg3bp1uxT3OeecU2Uu+G7duhEXF1cZX2199dVXhMNhLrvsshr/8ZgVBZIt035ffPHFVfo96KCD6N+/P7/99lvl9NcJCQkEAgFmzJiBntUQ2ftNvqkRbx/djYArlrjlx/v43zHd+eiILixs0QiAtNIA/Zet3TpFVdSBsL31tVU1Yav8b8X37kiU039dQmLFOkuW7dCloARfSQmNi0tILSlnxOwluByH1elxbPK5K6bEhlLLrCy9Gg74IlG65xdj2bEM9suD21a5nmKfhx8PalX5ullR1SnKmubkkZRXSrm16w/ARIBpB7Xemno7Dpd9+yvppeWV19u8oIQ8w2ZuWiLLkuL4oXEKvzRrxLVnDa12PhehKlOR/ZGdTaJd4/6p99Tq3CIisn9auHBhjdtTUlJUXBbZRpcuXTj55JP5/PPPWbZsWbX98fGxGXFKSqo/LLit0tLYQ5LbfkawrcLCQsaOHUtOTg6PPvqoissi+yDrnWuINt/ygLGNg12ZhzmYRJMysLcpAkdMD7xx3Y6LywAf/Z0OccuJIzaLmIFNY7IrissOrrgwfn85jViPUZE/GkRpykrMihWSDaJ4KSGRAlqwvLKdiwhN2ciWacccXNi48JJTsc0kuN3U2xEsyomvaB9rFcEghEkZHrJIYS0ZRLFIpBTDjlDmswh6TEriXJT5TCLbPlxu2xi2gyccISs5ibDbTXFyIgsbp/NGlw7c27dX9eLyFtt9vnB0S2KfRYRtsGvIpQ22rsVsO1Ae2TpdtuOQ4XM4v7tF50YmTwx2E19RA2+VbDBhhFvFZRGR/Vx95MgacnkA2X46qqSk2Bok248c3rKvsLAQiC32DdC+fftq7bZs29JmR30BJCfHbuK2nLe2WrZsWeO5dvU8WwrbO5vqa8OGDZimSbt27arta9++PdOmTSMzM5PU1FRGjx7NnDlzuOmmm0hOTuawww7jmGOO4cQTT6xMzEVk75Ga4uLL1zvz/vRmfDc/wKFHpvBRS4P3vi5i/qmdKQ01p2WXRJ45phGrFxTyxnxwx5sclOzwZY7FrwVeGhMi6jUoiRp08EQ4thmEcspZMr+ExukmzVt6+a1fSzos3UwIk7hGXno1itLu2CYkFidxbGqI07qn0muNzZcrbA45OIF35oRJzyrEX1TEb43TaVFYSvuN2ZSlJNA5P5+4XD/zMlJZlpJA0DLxVEy3NadtU1y2TWJ5kCalYU6ZvYhil0FSOEybDdmklwZZkxzPmrQunLBwMU12MiX3tjo6t/IGcM9dqWTOycPVzE//QCEPz5vFrV174YlEOWfmAib0P5Ss7abO/qV1S35vmkH3jZsrJsSOksam7Z5ujyX4VSy8D6NtY5wZy3D+9RbGVwu2+QAjlj9veW2e0ReWZMKZR8J959X6ukREZP/UvXt3unfvzjnnnMNf/vIXOnbs2NAhiey1rrzySr7++mv+85//8NRTT1XZt+XfzpIlS/7wHFuWjqrp31phYSFXXXUVq1ev5rHHHuPwww+vo8hFpD4ZqfFYmc9i/7IaJxLBTI4Hr4WTXQwp8VgHNcEpKKP0vXlYqX58p/UAVw2jcLd1SDt8pa/Q69sFlJdauDpnYAVKcWavxTioKeZRHaGojGaZeWQYFoGpazFT3NjB/jhZ+ZRng29AS9wrlmP2bkv6zM2kbizAHNaLwBu/4vE2IpRsEM0qwd3IxPb5MX5fh7U4m0hRmGBOmCg+ovgoIAMTg87MIo/GZFes89yS1RTQghLScBHFR5CQZVFuGiQG84kYGUTdJrZjE8FNntdH1IKI3yLHn0BCIEhqcSnZfh9xtkOpZfJ0u1Y7fk8M8DoOwW0eXF/+93g6NHJj3B+ILZocccC97QhlJ5YcB6MQjWLg8NJpbhZl20xf7/D6aS46Z2wdVX3tkS5G97JYX+RwULqBpeKyiMh+rz5yZBWYDyDmDtb3sGqagmUP9QXs8kjfHZ1rbxgx3Lp1a9555x1mzZrFzz//zJw5c7j//vsrp/euqTguIg3vzAFJnDkgqfL1DeemV3zXpHJbtyZpDN1mSfg7Kr+r6Wnj5Bq2Na5h29Y+b2oDNw2Iff+v0wBStmtb/aGeng8UUJgQT0bF9NMDlqxjwJJ1LG+WQbnbRY7Lxatf9q2hX4DhO9j+x+66p/rNxyUV/3260490zcomK6n6yJX1iWkUeJPwhcN4CNA64tA9e1VFSdmGDAuS0yGvBDKfxNzmKW7jxG5w4n2x77ds2+6/IiKy+5wa1ujb1z333HO8/fbb3Hnnndxxxx0ceuihlYm0Rk6KVNWiRQtGjhzJ//73P2bPnl1lX8+ePUlPT+fbb7+loKCAlJSUascHg0E+++wzvF4vRx99dJV9W4rLq1at4pFHHuGoo47ak5ciIvXA7N226oZ2W3NdIzWe+Et349/5wG74K1+kQ7fWW/clxUFSHC4goUuzKodtHW8VWwc+8bht9g2qPlDkj2zJzgNvzsIzdTmtT+lB4undMCyTDCC8MpvQ2iIC7gRSj2r8h591bu+8Eb9TEOdnodtNh6ISWpUHWOff5sFsx8EwDLw4OIZB0DDIvD2B5ilVP6pv7HPIDhmxqbA9Ff2H7dhMa7ZD6Z1e4rxuaiPRa9A1Y/+7BxQRqQvKkXePpsiWndpSJF25cmW1fatWrQJqHrFcG0Y9/sNt3Tp2s7p06dI/bNeiRQts2668tm3VdL0ej4d+/fpxww038MYbb/DEE0+wefNmJkyYUIfRi4jAvNtS+Ojg9hTGxRJTB9iQnsyipmn866PD/qC4vGdc8vMptN+Yh2VUfeCncUkpbtsg6HJT6I9jsz+NH1IPpcjjI7aAtY35/QOYyx7FzH2+SnFZRERkd4wZM4avv/6azMxMnnzySeLj4/n73/9O+/btOeqoo3jyySfZsGFDQ4cpste45JJLiI+PrzaC2ePxMGbMGMrKyrjjjjsIBKouwRKNRnnooYfIyspi1KhRpKWlVe4rKipi7NixrFy5kn/9618cc8wx9XItIiJ/hu+CI0h5+TySRvTAsLZ+VO5u35j4YzuSfkzTXSouA/ijNl7HIeR1M71ZBiHLrDIFdnw4zMheJo+NsCh5JBXnsdRqxWWATTf5wWWAz9q61rLXAo8FJrUuLouIyIGnPnJkFZhlp/r27Yvf72fixImV6yxBbM2liRMnEhcXx5FHHrlb546LiwNiieiedsIJJ+B2uxk3blyN60ltGRE9cOBAAMaPH19llPTy5cuZPn06hx56KKmpqUBsQfTtdenSBdj1qcBFRGrj/adb8u8Bvfiwd1c+69WFbw5qzUM3N8Gb4Nn5wXXMn+Llu65t6Lohl+aFRXijEcDhpi9+qvgfZ1QAAQAASURBVNbWG4mw1kgCghjR16FT9eUZRERE/qwmTZpw9dVXM336dNauXctjjz2GYRjceOONGsksso2UlBRGjRpV49psZ555JqNGjeLHH3/kL3/5Cy+88AIff/wx48eP5/zzz2fSpEkMGTKEyy67rMpxY8eOZfHixRx//PEUFRUxefLkKl/r16+vr8sTEWlQ7brHkVFaRrOScjqWltE7GGJIQRFDNuUyyj2Pxw/9mAnnxXNV/xRc1k4G39Q0nfWWYrOIiMhO7MkcWVNky04lJiZy7bXX8vDDD3PRRRcxbNgwAD755BPWrVvHrbfeSkJC9elRa6N79+4APPXUUwwZMgSPx0OHDh32yHzwTZo04cYbb+Thhx/mnHPOYejQoTRr1ozs7Gy+/fZb7rzzTjp37syRRx7JiSeeyBdffEFxcTH9+vUjNzeXd955B4/Hw0033VR5zrFjx5KYmEivXr1o0qQJxcXFTJo0CcMwOOWUU+r8GkREmid7Wf+fpkDThg4FgMVPt6Tx3/Px29CyJEjYbWIY5biiUVoX5uMPh8mNi+f7tq2JK0ji0MDEhg5ZROSA5xwgn0c2a9aMbt260bVrV+bPn1/lYVkRgQsuuIB3332XnJycavuuu+46jjnmGCZOnMj7779PYWEhCQkJHHzwwYwZM4bjjjuu2jGLFi0CYMqUKUyZMqXa/rvuukvLSInIAeG2+zpy4+m/AxFsPKSWB0kIhggA3Q9atmsnsx3YfnVDxwFD48ZEROqKcuTdowKz1MpZZ51Fo0aNeOONNxg3bhwABx10EI8++ijHHnvsbp/30EMP5ZprruH999/n/vvvJxqNctlll+2RAjPAyJEjadmyJa+//jpvvfUW4XCYjIwMDj/8cJo02br26n333Ufnzp355JNPeOKJJ/D7/Rx22GFceeWVVWIbOXIkX375ZWXCnZycTOfOnbnlllvo06fPHrkGEZG9TfZDqXy/rJTrJpTz/tVx/HdqlC45WTQtid2kNCspYla7pmxIbtTAkYqIyP7OcRymTZvGxIkT+eCDD8jJySE1NZVzzjmHs88+u6HDE6l3ffr0qbbO8hY+n4/PP//8D4/dlbx2R/2IiBxoDMOgeb8U1v5QSHowRNQwKLcM/vl6F/478eddO1nIBpcJW5YZdBwIRjWCWUREamVP5siGs+0cwCIiIiJ/0sz0Byh1NWZS70PJSUyg2/oNDPn1N75P93HVurENHZ6IyAHvtXbv1qrdX1eN3MOR1J3vvvuOt99+m3fffZfs7GySkpI4/fTTOfvssxk0aBAul56tFhERkfoXCEbxekwMwyAcDjN+/HgARo8ejdu98zWUjdtLwTLAV3EvE45C0AbLwbl392aUFBGRqmqbI8O+kyfXR46sLFtERETqVLE7gWcGDyJYkSxPP7gzRX4f/rXLGzgyERHZXw0cOJCEhAROPfVUzj77bAYPHozH42nosEREROQA5/NuP7/1LnIZkLDNPY3bBDMCYfvPnVdERPZr9ZEjq8As+7z8/Hyi0egftomLiyMuLq6eIhIRObDNbt+psri8xdw2rRi0dmEDRSQiItuyjf1vSsV33nmHoUOH4vP5GjoUERERkbrjq+Hje68VG8ksIiJ1Qjny7lGBWfZ5F154IVlZWX/Y5rLLLmPMmDH1FJGIyIHNFa3+JLXpOBi2VuUQEZE9Y8SIEQ0dgoiIiEjd21HRwzLrNw4REdmn1EeOrAKz7PPuu+8+gsHgH7Zp0aJFPUUjIiJN8gpJKAtQErf1CblDV6whKyGxAaMSERERERER2cfYNljbTbOth7dFRGQvoAKz7PMOPfTQhg5BRES28WOrVtz48WQ+O7QneQkJdFu/gcNWrGLSQY0aOjQREQGc/W/2LxEREZH9k+2A42wdyew4sW221mAWEakrypF3jwrMIiIiUqfmt2zM+iWpjJz1C/5wmNy4eN47pDvG5pUNHZqIiIiIiIjIvsMwIOKA4YAB2AAGGX5VQ0REpGGpwCwiIiJ1qt+c2dw17FhO/20pGcVlzG+RwdTOrVn+9AkNHZqIiIiIiIjIviMaBZcLHGJfFVbe4G+wkEREREAFZhEREaljD86/AKfvh7x29NEU+bx0yc7m2qw5QPOGDk1ERADH0IgXERERkX2Bc0ccxn1l4HbFRjBHHd4d7pDg0f2ciEhdUY68e1RgFhERkTp374yhtBs/HoDR947G7e7UwBGJiMj+rqioiGeffZapU6eSnZ3NCy+8wBFHHEFeXh6vvvoqw4cPp2PHjg0dpoiIiMguce6Ia+gQRERkH7Snc2QVmEVERERERGSftn79egYOHMi6devo1KkTixcvpqSkBIC0tDReeOEF1qxZw5NPPtnAkYqIiIiIiIjsWfWRI6vALCIiIiIiIvu0m2++meLiYn777TcaN25M48aNq+w//fTT+eSTTxooOhEREREREZH6Ux85svmnjhYREREREZF9imMYtfral3zxxRdce+21HHzwwRg1xN6+fXvWrVvXAJGJiIiIiIjI3qy2OfK+lCfXR46sArOIiIiIiIjs08rLy8nIyNjh/uLi4nqMRkRERERERKTh1EeOrAKziIiI7BEffdGZ9z/py+c/5jR0KCIisp87+OCDmT59+g73f/jhh/Tq1aseIxIREZEDUeHGUqZe8A2bvlrT0KGIiMgBrD5yZK3BLCIiInWrqIxhV65gffN2bExM5G8flPHwUwuY8W63ho5MREQAZ9+Z1avWrr/+ev7617/Ss2dPzjrrLABs22b58uXcc889/Pjjj7z33nsNHKWIiIjsz95p+ToJ5TbJpUHmfbKaoNtg2ObRDR2WiIjshHLk3aMCs4iIiNSpM0f9zrTePQi5YrcZefHxbExM5LEH5nPjbd0bODoREdkfXXDBBaxZs4bbb7+d2267DYDBgwfjOA6mafLggw9y+umnN2yQIiIist/66blFNM8N4AtEAfAGg9iWw/QWzzMg84oGjk5ERA409ZEjq8AsIiIidcrjsgm5XKSVltEqv4BFTRtT4vPx5iI3NzZ0cCIist+67bbbGDVqFO+99x7Lly/Htm06dOjAmWeeSfv27Rs6PBEREdmPrfzXHDoHooCDjwBh3BB14c0va+jQRETkALWnc2QVmEVERKROZSanc8M3Mzj/5zlkpSbRJreAO4YNIduyGjo0ERHZD5WVldG/f38uu+wyrrjiCm644YaGDklEREQOMC4zzObWFnijeAMuOuashvI4NpkpDR2aiIgcYOorR1aBWUREROrU4CWLyI9P4MhbryFiWaSWlvHoO58yqWOXhg5NREQAx9y/FpiKi4tj1apVGMb+dV0iIiKybygtCBBM8XHkgtkkBsuxDYMl6a0pSkjAGw02dHgiIrITypF3j7lHzy4iIiIHnIRQOY+dNJBIxYjl/Pg47hp+Is2CoQaOTERE9leDBw9mypQpDR2GiIiIHIDeG/gexy//hcRgOQCm49A1Zw2JhVESIwUNG5yIiByQ6iNHVoFZRERE6tTkzl2rbVuflsKy1MQGiEZERA4Ed9xxB0uXLmXUqFHMmDGDzMxM8vLyqn2JiIiI1DUjEqJpUfX7DJ9RQsD0NUBEIiJyoKuPHFlTZIuIiEidCjtecBwI2xB1wG2SHA5RbDsNHZqIiADOfjiVdLdu3QBYuHAh//3vf3fYLhqN1ldIIiIicoBwU052QgqNSwpwMHAwAAcrDH7NkC0istdTjrx7VGAWERGROlXmduHPCxAFEkMR8nwemuYVsiY9paFDExGR/dSdd96pNZhFRESkQRycs5JN/gySy0pxbfNgdRoFLKUzK3q/TIdfLmnACEVE5EBTHzmyCswiIiJSpyKmzSHZuZy0diNe2ybf6+Z/ndpCNNzQoYmIyH7q7rvvbugQRERE5ACVHZ9Ou82FWNvN2hVnB0gnj8I5Wi5KRETqV33kyCowi4iICAChkM2RV6whEB9HoddNEzvAnMebV2lTtjyHFYc+Rmq0mGx/OsuatuPshRdVadO0pIyjc3MqX6cGw5yzbA2fNU7inFNmkRAMMadZY354tAW+pvH1cWkiIrINx9RIXxEREZG68luz7vRb9V8y6UwByfgJ0JJMvITxESTLldbQIYqIyB9Qjrx7VGAWERERFqwq4YFb1rK4c2sM0yApGGZuQirpN+aS+1g6ACVLCoh0u4ke0SIAWgY20KlwPS91Mrl02YWV50qwzWrnTwuGuP+LH+ietRmAgMtiZOFAPn73EEyvbkdEROTPuffee3faxjAM7rjjjnqIRkRERA4kSSUhFps9ybG3PqCdSxqH8RsB00fEVT1H3pGCsghri6FnE+XJIiKy++ojR9ZfKtnrzZ49myuuuKLKNr/fT5s2bRg6dCh/+ctfsCyLSZMmcc899wBw3XXXMWrUqGrnWrx4MRdccAEAw4YNq5wmIBgMMnnyZL777juWLVtGXl4ejRo1olu3blx22WW0a9euynk2bNjA8OHDa4y3ffv2vP3223/2skVE6k1ZIMI1t2VSmJbI0Rtz6Z2dh+VAsdvFBx1asCKzlA4t4sns9i86R4tYl9iO+KCN4TiEXVGO2riiyvmCLne1PkpcLpoWllS+9kWinPv7Yj4+q5DTPz5+j1+jiIjs3/5o+i/DMHAcRwVmaVBLlixh2rRpnHrqqTRv3nznB+yDHMdh6tSpTJo0iYULF1JYWIjP56N9+/b079+fM888k+Tk5IYOU0Skzh26bBWb7eZsO/4tgI9NNCPgdeML1W65KOOfQTANcAAjzCktI3x6vn+PxCwiIvu3+siRVWCWfcbJJ5/MMcccg+M4bN68mU8++YTHHnuMlStXctttt1W283q9TJo0qcYC88cff4zX6yUYDFbZnpWVxQMPPMChhx7KaaedRqNGjcjMzOS9995j6tSp/Oc//6FPnz7Vznfcccdx3HHHVdmWmKh1VURk3zLkouX0LC6lceYmypO2/g5LDEcYsjqLW280KXAc/uWKsjCxBx0KsrceHIacVJN32r/EEevnU+aNx33iWSxITaJbfmykc9SAxckJnB8MEbJMDAfctk3zwmLmLNXvTBER+fNs265x25o1a3jmmWeYPn06n332WQNEJhKzdOlSxo0bR+/evffLAnMgEOAf//gH3333He3bt+fMM8+kadOmlJeX8/vvv/PSSy8xdepUXn/99YYOVUSkTjmOw28dO9Du96Jq+yK4yfUkkxQq2+l5jAfLwbIAA2wbAmEmrzAJhSN43PoIX0REdk195Mj66yT7jC5dunDKKadUvh45ciRnnXUWH374YZURzsceeyxTpkxh/vz5dO/evXJ7KBRiypQpHHfccXz++edVzp2SksKECRPo3Llzle1Dhgzh/PPP58knn+SNN96oFlPHjh2rxCQi8kcWLQkwf0E5zZq6OfKIeH79Ppf3nlqHuzyCP8XN8Ze1Yc5vpeT8nke2109TQuQWRvimcRNWpiWQFIjQubiYLhtyiCuPsiY5gZ9aZJAf7yMasQm7TByPhc+2ufPDT5jWtguReDdtCvJJLQyxOT6RVa0aU+5zQzRCu+xC1iUlUZqSSPvV6yn3+SpjzXe7+DU1mUK3m5SyMvquyuTrDsdw7PpFlW1sDIK4sKI2b/U7gWk5h9F3wQoGzVvMitatmNEkHds0iQuGaJWfz8jRI1ncOB0D6Lsmk0ErVjF03nK+aPQKQdtkZUoi37dvxec9O1DqdZMUCfPZpE9pumY5jQJrcQNm7FFubExswrhwcDCxsbEABxcRXARIIIoLAxsXAUzDwsEkarkhauOzivDGh6BpKoFgPOb6PGzbImy7COPBIkwEP27KiScbI8FH6JSjCU5ahhNxMI7ugOX3QCiCr70bT1420e+XY2QXgOMQxU+IeJxuzYk7sTXWZcfDwa3q+SdORGQHjANjfSnTNGnXrh2PPvoo559/Ptdccw3//e9/Gzoskf3Sgw8+yHfffceoUaO45pprMM2t08Gec8455OTkMHHixAaMUESkunvuXMOqFUHMcJQUO0C0W2Pimvi5pFkB7z+5gnx/Kq5IhN6LF9FlUybp4SLCbpNPehyBJxJiU0ojDNuikWHgtUKEop7Kc7uIEEiwWJ7emK5lq2l0VxEbbtuac/+eFWXsR0F+32hT6neDDYQi4DLBa4HlhuIw3gdCQCi233Y4pAkM6+zixw0236w3MAyDv/YwGD/cU+36RESklpQj7xYVmGWflZCQQI8ePfjmm2/IzMys3N6/f39mzpzJpEmTqhSYv/32WwoLCzn11FNrLDCnpKRU66N9+/Z06NCBFStWVNu3RTAYxHEcfNsUZkREtvfhpALeereg8vU7E3NIWLqZpC1TZW0KM+WfSyiyYepB7ei9Yj0FlsO4ft0odbvAMCiJg40p8eQaPobOX8LUwzqRmxIPURsCUYiPTU1dBtw68jSOXZ6FL+zQa2kOJlDcswkdS4JQEsQGvmvXnPiSMH02bsZ0HFyRCAAhw+DzZo0JWhaNSsr464/z8EZjT73NTe+I3w6SXFaKl1LS7Vziigq588MJOMXJGEDYMpncrQudN+cRdVl80aEVH3RpX3ntDvBT25b83LY5F/40j/Sy2NPcLfKLabO5jG+6tqPYa1JgeThx+DA2P3IdFm4MokDsGi3Awo2JXXlWg1IAwjSimAwcLCBWjk51cnATwYxECQP+SD5GoQOFJfgxKaY1TsW5TQxCxGERJJlMTKJQEsD19mSCtCZKHL5vl1ROf1b+jYXNJnyUYGABbiyieCgisqCcwIK1xP1nEsZ7t8BpR9TFj5OIiOyiAQMG8H//938NHYYcoF544QXGjRsHUOXh6C3LNoVCId58800+//xz1q9fj8fjoVevXowZM4YuXbpUtt+yfNRdd91FIBDgf//7Hxs3bqRVq1ZcffXV9O/fn+XLl/Pkk08yb948XC4XgwcP5oYbbsDl2vrxz+WXX05WVhbPPfccjz/+OL/88gsAhx9+ONdffz0tW7bcpetbtmwZkydPpkePHlx77bUYNXxA16hRI8aOHbtL5xUR2ZPGjF5OKAyGYeJ4TLIND5lZUL45zKavy0hLaQKGQRgvv3U7mD55S0kKF0MYRs35kkcGXUDY7eOU6T/TujSbKAY5JFOKj6DfxbpWSVAcIattY0zDIXFdHt7b03ihI/xe2owxT4digRhASRi2PJgTsiEYhSQPeCywHYjYseKHA3M3wtxNEYhzg2XgAK/Od5idHeT3S70N9XaKiMg+pi5yZHPnTUT2To7jsH79eoAqxWGXy8WQIUP44osvqkyF/fHHH9O5c+dqo5T/iG3b5OTkkJaWVuP+CRMm0K9fP/r168fQoUN5/vnnCYVCu3dBIrLfCgRtPphUWGVbXk6EuO3WYXJHbaJxPkzboUdWNj92bEGpx13lKTrbNMhMi+Pb9m3ITY6LbSyPgr/qM2O222JdejLd12wgLhRmY9NGhN1b10Y2gUOyC7Ech0Jf7ElnTzgMkSjr4/wErVhx9vA1GyqLy1ssSmqDiyDpdn7sOCIcUrwIP8WV13HtF7M4aGk273Rqz5LUlBrfl+h2Hz7GE6RxSRlnzlka22AY9Fm/HBMTkxBbistbxcYzx8QKuwZQSkplcRnAwaSMhIpWUfwUYWxzpImNl8JtXjuAQxw5seJyBQNIIgsPoSpra1lEcROo2OaqaEnFqzBR/BB14M7/1fg+iIjInjd79uwqIypF6tPxxx/PGWecAcDo0aO59957uffeeznzzDOJRCJcc801jBs3jh49evC3v/2Niy66iJUrV3LJJZewcOHCaud75513mDBhAsOHD+fqq68mEAhw0003MW3aNK644gpat27NNddcw2GHHcbEiRN59dVXq52jvLycMWPG4Ha7ufrqqxk+fDjff/89l1xyCTk5Obt0fd988w0Ap59+eo3FZRGRvc2m7BChkFNlm9txSAlHMByHjFjluXJfiT+OGQcdUvk6MyWDsNtHan4JGaUFAFg4NKGA9mzEkxDkt84dOahgNWd9/x2bM1K59rufAVhXnszEzb2qBrT9786oEys0b9lnAc428bqtqscYBvOz9ftXRERqry5yZGXYss8IBAIUFBSQn5/PsmXLeOCBB1i6dCk9evSgdevWVdqedtppFBcXM3XqVAA2bdrEzJkzGT58+C71+d5775GTk8OwYcOqbDdNk8MPP5yrrrqKRx99lNtvv5127drx0ksvcf311xONRndwxvqXl5dXpdBeUlJCcXFx5etQKERubm6VY7Kysv7w9caNG3G2ubFVH+pDffxxH/n5AYLB7ZLXqE1N6Z/btvFEImBZFHtqnmgk5DKJuFxbE0rHoaaThSyThPJA7Psa1mzyVsSwOjmB9Smx0ccJJaVkx8dVtvFEq6/XUWr5aWRX/+DRiIv1FcWg1PKzsF1TNibH13gNsQOqBr2l6NuouLxyW7OSAmp7u+JUtItWK0RDdJuC87Zl6a3bItvEEYvF2mbb1nbRbUZNV92+9ejt99mx2NbHfpb2pZ9d9aE+1Mfu97E3c0yjVl/7ktdff73Gr6eeeoqRI0fy8ssvM3LkyIYOUw5QnTp1omfPngD07duXU045hVNOOYWePXsyceJEfvnlFx5//HHuuOMORo4cyUUXXcSECRNISUnhiSeeqHa+zZs38+abb3LRRRdx3nnn8eijjxKNRrn55pu59dZbufnmmxk5ciSPPPIIXbp04Z133ql2joKCAo4//ngefvhhzjrrLG688UYefPBBcnNzefHFF3fp+pYvXw6wSw9zN7S95W+F+lAf6qNh+tiwPlTjdKiW42AATg37cuNTKr8Puipm1oralXnoFiZhyjxu8uLi+N8RJ+I2QjQvyKVpUQkAgfhWFEe3mQXRoeapWSM2lanr9vdlO7lP29f+f6gP9aE+Dpw+9la1zZH3pTy5PnJkw9n2J0BkL7RlGrDtmaZJv379uO2220hPT2fSpEncc889PPTQQwwaNIgLL7yQhIQEnn32WV5++WVefvllPv/8c2zbZtCgQZXTke3I3LlzufLKK2nbti3jx4/H6935NDMPPPAAH3zwAffddx9Dhgz5M5ctIvuZW+/awMrVW2+kDNvm4KxNWNv8GXaAzPg4fmzVnLN/+I1Zh3bgg25tqp2rbU4ph63awAeHdcCxzNj0WRBbp6nyZA691+bTZ/l6+i5ZRV5KIgu7tq9yniUp8ZQ6sWQ4ORIlo6SUxPJyMpOTWJoQR9A0aZ1XyGU//FYlZfYFI5ya9RUJTlmV8/3QuDfx2W4K3V5mHNmNmW2a8HGb5jt+U0xYet8zlS+DuCginv87cwAzDopNzdiovIgNT96IgZvY6OBtI3EwiFaWdA1KMYiST0sKqdpvAgXEUVaRnweJZ3OV/aU0JUxixVkhiBc/+SSTWaVdOUmU0wQ3240+ZzM+SqEyzq0KSSeZ9TD6eHjl6h2/HyIi9eS5np/Uqt2V84btvNFe4o+evG7UqBGXXnopd955p5a1kQazJV99/vnn6dOnT+X2Cy64gEAgwEsvvVTtmKeeeopPP/2Ub7/9Fp/PV5kbjx49utp00wMHDiQ+Pp7JkydX2f7oo4/y1ltvMX36dOLiYg8RXn755cyZM4fPP/+cRo0aVWk/YsQISkpKmDJlSq2v7aqrrmLWrFl88MEHtGrVqtbHiYg0pIvPW1alUOAAm71ecr0e+q/JxPFUfXD5xAU/cuyyXytf33/yRRiYDJn6C03sPBIiRaSyDjdBijzxvNl7GCsadyGjOJ9GG3P5LTmZZ/r14dn2b/H65r78VNxu68kto3qR2W3FCslOxRTZW4KE2DrNvu0fIndw/qF1mEVEdkdtc2TYd/Lk+siRtQaz7DPOOOMMBg0ahGEY+P1+WrduTXJy8g7bn3rqqTzyyCNkZWXxySefMHDgQJKSkigoKNhpX4sWLeL6668nIyODJ554olbFZYCLL76YDz74gBkzZqjALCJVXDc2gxdfyWXBogBNm7j4y6lJfPREEXFFAdy2TdQwCCb48AQitMvO5fOenRm4ZA2FHhfT2zXFrrgpaFJUTscNuRy1bgPlPjdfdGlF1GtBaUXB02PiikQZPns+a9LS2Jgez5KWjemwYTNtV2WyunVTgm4XK9ISyIzz0jyvnJAZS2Y3JyawOTE2lXSrUIQ808AEVqan0GlzPrZhYFgmv/fqRJpRwImZ31WOBs7xpLLJ05T25JKdkUJJchwdi0rxRaIEXFZNbwnYUOjzkRgIEMJFnpXAG0cezIxOLWJJtGlwzMosiskgnuKKEcUWsSKzg0G4Ys3jWEnYIEoUD/EUEcVDKakA+CnBRxk2BgH82MTjpgQ3sZHSEfxEif2eN4hQRjwmUE4qLsqJI6+inYdCWlQUtG1cRHEqxj7HUY6NURFTbPyzjUk5CcQb2Tgjj8b49+i6+4ESEZEqVq1aVW2bYRikpqaSmJjYABGJ1M6qVasIBoMMGjRoh20KCgpo2rRp5esWLVpUa5OUlESTJk2qbd/y819YWFhZYN6yffviMkC7du2YNm0a5eXl+P3+Wl1DfHxsxprS0tJatRcR2RucNaoRE9/MAcMgahgEgCLLxHQcHCOIFYZoxfr1Xdev4YjlS3Aqsr11KY04af6P/NDhUKYf0Y2NSX7u/upp3KHYyL2kUCljfnqHewbfwIa0DLw5Rbx5WDeuOgKsPDgvYzZ2SntmrauoGG87/sswYgXnLcXlaBRcVmzabJxY+huxIRQFd0XxwIHfL913RtWJiMieVx85sgrMss9o3bo1ffv2rXX7wYMH88QTT3D//fezbt06brnlllodt3jxYsaOHUtCQgLPP/88jRs3rnWfTZo0wbKsWhWxReTA0qSxmzv+3hTbdjArnpI+uv+hRCNRgqEoXq8LyzKx7Vhi6dgOhtkaHIdg1CYajj3A7HYnELUTcOzWXBmO4kRtVuaEiUtwsW59GV8tjdClpZfe53Zl8VMz+dCdQaizzcpW6XyW0ooVrjiaBoMclLmWVMvFCn8SKS43WFWfzg5ZJjl+L+sT/BiOzdrGaSTlF5GR6APD4NteR7CiRSu6Zy4jPz6Rzf504kvLWNK+MfNaNgPAa9uct2Id3zZrxKrEOGxjuyfnTPjvYQdz5aNdaX9QMolFUZ5u7GXA/DBzNhs8fJIHt9kLwzyMwKpNlHS8BQuHIC7AwVex9nE5XjwPj8KVk4vZrTXmSYeQ2iiRFNvBCJSD243h9eCUBEhIjidaUo7pc2M4DrjduGyb+EgUIxLB8HnxFxRh+fw4lokdDOJ4PRiWhdtlkRGKYDsObq8bx7ah4gMGTBNsGzsciX0QUFSKmZJIvGnG+tG6nyKyF6lp2sd9nWEYZGRk7LAgVl5ezubNm6strSOyN+jYsSM33HDDDvenpqZWeW1ZNT+890ejFPbk5HUdO3Zk6tSpLFmyhC5duuyxfkRE6tKQIakMGZJKcXEYx3FISvJU/q40jKY4ts307wpJKi2nxwkHE7QHY5SFCCzLo1W3DAJLS2h05TSyQ3G0NkpIDpVUOb/l2HTKWcVs/yEcs2oFBXOHEQ6HGT8evGaUGVf4sKzYR/PnTyzjrRVmrLjssmLF43AUfC7MUIRV11tkxFk4DsR5rcrPFfLLI4Si0CRBH/GLiPwZypF3j/76yH4rMTGRY489lilTptCkSZNaFacXL17MVVddRVxcHM8//zzNmjXbpT4zMzOJRqOkpaXtbtgisp8zt1urw3JZxG0zwrdyv7l14me/ZcI2M12ZFoCJxxv7M96zYjKHjk18HNd7a7uDHz+eM7fp659Vet469fbdT6/n27kRHDN2vgiwJN5HgdtF20CAgGNz9ZMdcTo/w7cDjiHkiQWzvnEz1jduRnpBHv0HuWlzTBe+enYta+xkWkZjU1c3CQT5y6pM3j64NSu8cVBRQMcAfBYHRQN0Oio2nXV8emzXhX29XLjd++bv0BS/83r1NxSIq3FrBe82hfOK9aCthKo3VoZpYnlMqJgCzZOWUvPxgOVxVa7mbFR+iLvl/5mJ6a34H5W+zTn2w5tUEZG9Tbt27XjjjTc477zzatz/8ccfc9555xGNRus5MpEYYwf3A61atSI/P5/DDz/8DwvEda24uJicnJxqo5hXrVpFWlparUcvAxx33HGMGzeOjz76iOHDh+/wWkVE9kaJiVtzvm1/fxmmycCBqVAxM5YLwO/Gmx7LK7v39tF9Vmztysf7fUrQcpPrSaXQk0SLso0khUvIiU+n69p1GEakxr635P//Ozee6LvlvLPcjE3Y5bXAZ0EgSvTu6qPMthyX6tdH+yIiUrP6yJE1nEb2axdddBGXXXYZt9xyy06T9S0jl/1+P88//3yN045tUdMIZdu2efbZZwEYMGDAn4pbRKQ+3X11SxJLC8jzQL4JvyX6CZgGHcrK8QZD/PLfzvTtmMhh80eRVphfZfouKxrFCdkccdcAmgzqzPnvn0jI46Ygzk/YjE2gHXRZdMkrhQQL4l0QZ0GiC6I2f7m1a8NduIiI7Dd2NjozHA7Xa/FOZHtbCrZFRUVVtg8dOpTc3FwmTJhQ43G5ubl7LKbXXnutyuupU6eyZs0aBg4cuEvnOeiggzjllFOYN28eTz/9dI3/HnNycnjmmWf+VLwiInur9PwsJnYczietT+K7pkcysd1wPm87gEIS6Dt7CfmenU9F+vZIP48OtCEQhlCUeDuMc8fur4spIiIHtvrIkfWYk+zXOnXqRKdOnXbaLisri7Fjx1JUVMTZZ5/NvHnzmDdvXpU2xx13XOWHAg888AClpaX07NmTJk2aUFBQwDfffMOiRYsYOHAgJ5xwwh65HhGRPeXj//bghHMWMbdVE3xug+RAGEJhrjl16xr0nq4t6DzvRcrdXgyXgW0YhCIGYz49usq5NnncJDiQt800Xc2KyjhkZRZz2zQF08AKRIjflEfaHe3r7RpFRGT/UlRUVOXBz9zcXNauXVutXUFBAW+99dYuz04kUpe6deuGaZq88sorFBUV4ff7adGiBeeeey4zZ87kySef5Oeff+bwww8nPj6ejRs38vPPP+PxeHjhhRfqPJ6UlBS++eYbNm/eTO/evVm7di3vvvsu6enpjBkzZpfPd+utt1JcXMxrr73GjBkzOP7442nWrBllZWUsWLCAqVOn0rFjxzq/DhGRvcG6pGY0zg9XvnYMkzW+1hy8fA0GJv5AqFbnufEoHzcetaeiFBGR/V1958gqMIsQm9q6sLAQgBdffLHGNh9//HFlgfmYY45h8uTJfPDBBxQWFuLxeGjfvj3/93//x4gRIzQ6QkT2SV+/tfPRxAMi9zAAWPLMLyQd1YRmh7Ws1ubg7EJWNEmhVSSKY5oY0Si/xfs5bEU2q3x+PI7Nj9fE07Fj9WNFRGTPc4z941713//+N/feey8Qm9Ly+uuv5/rrr6+xreM43H///fUYnUhVTZs25c477+S1117joYceIhKJMGzYMO6++26eeOIJ3n33XSZPnlxZTM7IyKBbt24MGzZsj8Tj9/t57rnnePzxxytHHR911FHccMMN1abNrg2fz8fjjz/ON998w6RJk3j//fcpKCjA7/fTvn17Lr30UkaMGLEHrkREpOGllpUBVZdXMhwwbIOoZYPpqflAERHZKyhH3j2Gs7Nx0iIiIiK74Krhv7OyURK/xfuxgTjH4ajCUoKBct5/p1tDhycicsB75tDPatVu7G9D9nAkf86PP/7IDz/8gOM43HLLLZx77rkcdthhVdoYhkF8fDy9e/emT58+DRSpyN7l8ssvJysri0mTJjV0KCIi+4W7B3xDs+witl2BPuKyiIvAQStX44kE6WPfRDgcZvz48QCMHj0at9td8wlFRKRe1TZHhr07T67vHFkjmEVERKRO2aEALct9tAhunSIspaiE3Gj4D44SERHZNUcddRRHHRWbR7K0tJQRI0bQvXv3Bo5KREREDjRpgRCG28COgOk4RFwWRekplDhwyIoQB6+7rKFDFBGRA0B958gqMIuIiEidapu1gdUuh7KUNNwOlJgG/ZYv5vsWrRs6NBERARzT2Hmjfcxdd93V0CGI7HdycnJ22iYhIQGfz1cP0YiI7L3sUJBm5QXMbXsQZtTGdlmV+6z4IrwtkhswOhER2RnlyLtHBWYRERGpUwU+H8ubNadRMEwYwDB4sf/RNF2/saFDExGR/dz333/PnDlzKCwsxLbtKvsMw+COO+5ooMhE9j2DBw/eaZu77rqLU089tR6iERHZe6XllxEXDoBhVCku245DwBXXgJGJiMiBbk/myCowi4iISJ1a3LY1jUIRMAwcwABaBW189s6OFBER2T15eXkMHTqUWbNm4TgOhmHgOA5A5fcqMIvEvPjii7Vq98wzz+y0TYcOHf5sOCIi+7wudxzC7P8sZlVyIu0KiwGIGgY/N21My+xWHNHA8YmIyIGnPnJkFZhFRESkTjleb7VtJlDo8dR/MCIiUo1j7H/Tf918883MmzeP//73v/Tt25f27dszZcoU2rVrx7///W9+/PFHPvvss4YOU2Sf0rdv34YOQURkn3DEpQfz/vhs5jTJYHlKEomhMDl+P0GXRZ5X02OLiOztlCPvHrOOYhUREREBIGBUv71wgC6Zm+s/GBEROSBMnjyZMWPGcPbZZ5OYmAiAaZp07NiRZ555hrZt23L99dc3bJAiIiKy3zpi9TISyoIUeb1kJiYQdFmkFpXRaaPyYBERqX/1kSOrwCwiIiJ1KiO3gNA2D/45QDnghAINFZKIiOznCgoK6NatGwAJCQkAlJSUVO4/6aSTmDJlSoPEJiIiIvu/dnYuF0z7lbYb87CiUTpk5nLOjLm0y8ps6NBEROQAVB85sqbIFhERkTrlTSrBlWeRmZGK23GIRqMM+H05Z4zTNIsiIrJnNG/enI0bNwLg9Xpp3Lgxc+fO5bTTTgMgMzMTYz+c9kxERET2Dj0WXkth51do9n0hnnKbqA9SIoV0mPnXhg5NREQOQPWRI6vALCIiInXq5f/24y+nfM9R83MwMIgrK8f91/YcfGTjhg5NREQA9sM664ABA/jyyy+57bbbADj77LP517/+hWVZ2LbNE088wcknn9zAUYqIiMj+ypUaR6/3h5Mz4GVMl4FRYpPw0qnE927Z0KGJiMjOKEfeLSowi4iISJ2b8NERjB8/HoDRo0fjdrsbOCIREdmf/e1vf+PLL78kGAzi9Xq5++67WbBgAXfccQcQS67/85//NHCUIiIisj9L7teaZPuehg5DRESkXnJkFZhFRERERERkn9ajRw969OhR+To1NZWvvvqKgoICLMsiMTGxAaMTERERERERqT/1kSOrwCwiIiIiInIAcQ6gtYhTUlIaOgQRERERERHZiylH3j1mnZ1JREREREREpIGsXbuWK664gs6dO5OWlsb06dMByMnJ4dprr+XXX39t4AhFRERERERE6seezpE1gllERERERET2aQsXLqR///7Ytk3fvn1Zvnw5kUgEgEaNGjFjxgxKS0t5+eWXGzhSERERERERkT2rPnJkFZhFRESkzi353wqa3mqTHCjnP699wN++/0tDhyQiIhUcc/+b/uuWW24hJSWFn376CcMwaNy4cZX9Q4cOZeLEiQ0UnYiIiBxo5q8PcOxzARK8sPgfifjcVkOHJCIiO6AcefdoimwRERGpU+NavUb+jZ/TNTeP5qUBTpy7gs9Sn23osEREZD82ffp0rrzySjIyMjBqWD+rdevWZGZmNkBkIiIicqA56JaN9HgySG7YYE2Jgf+2Eh78srShwxIRkQNIfeTIKjCLiIhIncleWUKTaJAmOcUkWxtIdK8nvTSP5mXFvPvikoYOT0RE9lO2bRMXF7fD/Zs3b8br9dZjRCIiInIgCkdsluEH24GIHfuK2tz2RbihQxMRkQNIfeTIKjCLiIhInZl01wI6Zm1mY1MvHx40iA87ncyP7bqSYueS88i8hg5PRET2U4cddhiffvppjfsikQhvvfUWRx55ZD1HJSIiIgeau17PBgfiolEaR6JkRKJ4bAecho5MREQOJPWRI6vALCIiInWmOD9CIAkWpnXBNmK3GZv9jVjYtDWeSKiBoxMREQDHMGr1tS/5xz/+weeff86VV17J/PnzAdi0aRNfffUVJ510EosWLeLvf/97A0cpIiIi+7ufMx0SwlGaRm1MnwvT56JZNIonajd0aCIisgO1zZH3pTy5PnJkV10EKiIiIgJgey02JaRV274xrgnZ8TuelkVEROTPGDJkCK+++irXXXcdL774IgAXXHABjuOQlJTE66+/zoABAxo4ShEREdnfJXhMErFZ1zyJsNsCwIrYJGWXNHBkIiJyIKmPHFkFZhEREakzJRGIWtVvL3zRECVWUgNEJCIiB4pRo0Zx5pln8sUXX7B8+XJs26ZDhw6cfPLJJCYmNnR4IiIicgBI9BsUJ/kIB6NQEAAgmuChLNnXwJGJiMiBZk/nyCowi4iISJ1pVFbKYVnLyEpMJ1pxm2E6UdwuG1xWA0cnIiLAPjWt1x+59dZbOeecc+jZs2fltvj4eM4444wGjEpEREQOZHF2iFLbDyXBrRvzA4SSPDi2FmIWEdkbKUfePVqDWUREROpMuNzBa0cYsWw6zcJ5xFtBehauYsiSnyjxeBo6PBER2Y889NBDlWtJAeTm5mJZFt98800DRiUiIiIHmiUzc7iv/7fcM2A6xqpinFC0WhszEOHVNzc2QHQiInKgqO8cWSOYRUREpM74oiE2xyXQpiSHwctmAuAAIcONy6meZIuIiNQlx9HIIBEREak/j5zwLU4IfI6DYdu0mJMNRzUnlglvFTVNXljqYkyHholTREQOTHsyR9YIZhEREakzJXjYFJeAh2IsAlgE8VBKbqIb134y3YyIyL7OMYxafYmIiIjIH4uGDRzHwQxHMaIOBg6Jjg3bf6DvdZHndTdMkCIi8odqmyMrT65KI5hFRESkzpT4PYTcBiH85NGUMB7iKCYpWEgwZDd0eCIiIiIiIiJ1orQwiDsQovGmfAwHDCDksuixMZ9VqUkUmwYGkBi1yY7YeILKiUVEZP+hArOIiIjUmQ9bt6TL5iVsyGyPgwVAEV68IS+WT7cdIiJSt1avXs2cOXMAKCwsBGDZsmWkpKTU2P6www6rr9BERERkP5e3IUCjzYWY2wxW9kSiDJ+7lHEDexMX3brDFwqzuTzSAFGKiMiBpD5zZH3SK3VuyZIlTJs2jVNPPZXmzZs3dDh7hOM4TJ06lUmTJrFw4UIKCwvx+Xy0b9+e/v37c+aZZ5KcnNzQYYqI7FB2QZgrL5hL+/Jy0ktKSMzdREqkmENuPgzv1ZMx8BDATXb3Nhz/+/m1OmeZcRFPtj2EUMhbWVzeIuAkkFRxUyMiIlJX7rjjDu64444q26666qpq7RzHwTAMotFofYUmssfMnj2bK664oso2v99PmzZtGDp0KH/5y1+wLItJkyZxzz33AHDdddcxatSoaudavHgxF1xwAQDDhg3j7rvvBiAYDDJ58mS+++47li1bRl5eHo0aNaJbt25cdtlltGvXrsp5NmzYwPDhw2uMt3379rz99tt/9rJFRPY6gfIoUdPEFa06MrllQTGb/G5CLgvbNIgLRUgORchMrf5ZoVMWgJ+WQt+DMOJ99RW6iIjsp+ozR1aBWerc0qVLGTduHL17994vC8yBQIB//OMffPfdd7Rv354zzzyTpk2bUl5ezu+//85LL73E1KlTef311xs6VBGRGuUWhLji0qUkp6SQm5REuSeRVFcCx/38I/YNMyilMX6CgIdm8zP50vUcJ0au/MNzFhkXEIeLQzcs4ssWxwOlVfYbOMxu0WLPXVQF27ZZkRuhU4Znj/clIrKv2l/WjRo/fnxDhyDSoE4++WSOOeYYHMdh8+bNfPLJJzz22GOsXLmS2267rbKd1+tl0qRJNRaYP/74Y7xeL8FgsMr2rKwsHnjgAQ499FBOO+00GjVqRGZmJu+99x5Tp07lP//5D3369Kl2vuOOO47jjjuuyrbExMQ6umIRkb1LEBebM1JouSGnyvaf2jSjxLc1Jy3yeSj1uEgORTnqb2v54eEWOOEIYc85WDgYgANEMLHWPovZqnH9XoiIyAFOOfLuUYFZZBc9+OCDfPfdd4waNYprrrkG0zQr951zzjnk5OQwceLEBoxQRPY23f4vm2hekO6FJfgjUZpk53PmuhVkjR/Bp59k41pbgGF6iMb5SW/mZpbpYUNWgGbBMKPmLaDzxk2EHYOUYDkzz+7HT02bkjR3E6f/MofE8jKWNG3BikaNWJ2UQFa8nzyfh15rMmkTCFHWLJ3yiEFCOIxjGuQnxTPF66Wfx4MVDFMScVjStinQlB8PasPNkz+lb/5vmGaUuGCIDbSlVdTmB/MZHjvpcL46uD0lbjfxoSArnngcE/hvt56U9z6Rm3/5nDXJLdmQnkrHDZuID4Yq3wOXWYbvD+7Vckodbvk8yDu/RykJ19DAZ4HLBNMAs4b9tgMRJ5aVO4AZBNvGZZhgQIIHHAdKgzaRilnJmibAJb0sPlxss6kMmiXAQ4PcnNzR4OLPbD5aGIGoQ2HAgYqH+dqlwcob4lie79Dr+XJKyh3wWGCZWIbN3/pYrC6Ew5sbjO1jEefeP25QRUT2Rn/9618bOgSRBtWlSxdOOeWUytcjR47krLPO4sMPP6wywvnYY49lypQpzJ8/n+7du1duD4VCTJkyheOOO47PP/+8yrlTUlKYMGECnTt3rrJ9yJAhnH/++Tz55JO88cYb1WLq2LFjlZhERPZmjuOw4qN1rJ26kcQWcRz81w7EN/ED8NEza/ju60KitkGTjTn4wmF8gRCuSBQjGiXitihMjCMY5yfstnCHY0ljwGXxv8O7VOvLsm1mPfkUaYEIp1+9Fq6+AFdFcbmyDWC3vpZoxZxgDhAmHnADBqbXxtUtCXPZRiiPQNNkGHclDD50z75RIiKyT6jvHFkFZqlTL7zwAuPGjQOoktBumWorFArx5ptv8vnnn7N+/Xo8Hg+9evVizJgxdOmy9eZry5Rfd911F4FAgP/9739s3LiRVq1acfXVV9O/f3+WL1/Ok08+ybx583C5XAwePJgbbrgBl2vrj/Xll19OVlYWzz33HI8//ji//PILAIcffjjXX389LVu23KXrW7ZsGZMnT6ZHjx5ce+21GDU82dKoUSPGjh27S+cVkf3XwbfmUl4Y4YzN+ZV/dJ1GyTzeqA+/fRAAkqBZEt5olGPyCnm7KIHVfh+k+VkKlNoRPpg/H4BNiUk8b2eQXeTjuTm/0jFrI18c2ouC+AR8psnMlk1ZlBabcmtjchIXLl+HGXSIx8E2LUo9bubHJdCntAyXadB9xXIm9j28MtZyr5txJx7PU96h2IZB37WLuGnaWwTDHVnUKYn3e3WFit97JT4/vzZrRq4/nuuGDOWu7ycBQRJDxUQti++7HUTbTTnEBYLkJCXSZfVaeq1ex7sLejCym7vKexSMOBz+XBmr83fwJrpNcFmxbNusoWBrOxABMMCI/QfHAcskEgUcKAgAURu2mblsYzE8MD0CVqxinVMOQ/8XokMTixVZkVjB2mXANutmrcqFjv8uY0UJEHQgyVMZUxSTR2bbEHF4ZxF8vsLm6ws0klpERETqR0JCAj169OCbb74hMzOzcnv//v2ZOXMmkyZNqlJg/vbbbyksLOTUU0+tscBc0zpt7du3p0OHDqxYsWKHcQSDQRzHwefTVK8isnf75fGFzH1uSeXrFR+v48zPBvHxS5lM/74MLDeWE8VlQlpuEZbt4AAliV4cyyQhEMSyLDa0bkI0arMsKZ73unWgzOuu1lfrgkLiAgYh/Hgpwb1dcTn2vY2BiYGBSTkAJgUEycDBTTQIxpwcTCqeyl5fAEP+Cd/eCQO67aF3SUREpGY1jQES2W3HH388Z5xxBgCjR4/m3nvv5d577+XMM88kEolwzTXXMG7cOHr06MHf/vY3LrroIlauXMkll1zCwoULq53vnXfeYcKECQwfPpyrr76aQCDATTfdxLRp07jiiito3bo111xzDYcddhgTJ07k1VdfrXaO8vJyxowZg9vt5uqrr2b48OF8//33XHLJJeTk5FRr/0e++eYbAE4//fQai8siIttbHzDpWlJW5YkuA/B7q65RHLQsVsb5YsXlbcxu0ZxfmjUFoElxEWfPW4QViXDYhiw2paRSEJ+AA2yO81UWlwGO2Jxf5Y+8AViOQ2E0is+yOH7+XIrjvFX6cjsOJf4EoqaFY5j81KYbEw47EccVZNwRfSqLyxCbOub+Y4/nhd6H4xgGDjYG0KZwHWlFhYTcbpa2bMZvHdtihhxCjh/b7+fi90Ns75PF0R0XlyFW5DWoubgMVYrG1bZv+ybUePz22wxWbIzGissQK15vZ0WeA2Fihe/tz2kZlaf8ZrXDbxt3FJyISMNxDKNWXyKyb3Ech/Xr1wNUKQ67XC6GDBnCF198UWUq7I8//pjOnTtXG6X8R2zbJicnh7S0tBr3T5gwgX79+tGvXz+GDh3K888/TyhU/f5PRKSh2WGbha9XfVimdGM5Kz9dz08ziivz34TychKKyrAqcsOwx8KpeEg5EOcnEO/HsUxMj4vOgSAdi0oAsLZbl/mq73+O9YuFQ/VMFLY8Lx3bv+021zZLUFnUMOXXrW/V+rpFRKS62ubIypOrUoFZ6lSnTp3o2bMnAH379uWUU07hlFNOoWfPnkycOJFffvmFxx9/nDvuuIORI0dy0UUXMWHCBFJSUnjiiSeqnW/z5s28+eabXHTRRZx33nk8+uijRKNRbr75Zm699VZuvvlmRo4cySOPPEKXLl145513qp2joKCA448/nocffpizzjqLG2+8kQcffJDc3FxefPHFXbq+5cuXA+xSAt7Q8vLyqnyIUFJSQnFxceXrUChEbm5ulWOysrL+8PXGjRtxnK23u+pDfaiPHffhGOCqoUhZ07bIDm5Sytxbn36OC4dx27FENWJt/TMeNqv+SXfXcH7TgfSojTsS5aCsLA7euKHKfsupfsyvLTpR7PZXiWHbuEJWrFA+p2kbIJb8zk9P5rsWTfiheWOmt2jM2kbJuIjgRKOEoluP3/JelYar91uFs5Obtz/avTv3fTsJp3L/Dm8qt56gNLzv/uyqD/WhPv5cHyIie1ogEKCgoID8/HyWLVvGAw88wNKlS+nRowetW7eu0va0006juLiYqVOnArBp0yZmzpzJ8OHDd6nP9957j5ycHIYNG1Zlu2maHH744Vx11VU8+uij3H777bRr146XXnqJ66+/nmg0uoMzNoy95W+F+lAf6qPh+ggGgkQC1X83RcqjFY9PxxiOg2lvLRZvW1wIeavPWHVYdh7/mfg5k595i8tn/Mr5v8zlrTfe4ax5WwfWRPnjma6Mak9ROzv4viLmorIqr/fF/x/qQ32ojwOjD9m/GM62PwEidWDSpEncc889PP/88/Tp06dy+wUXXEAgEOCll16qdsxTTz3Fp59+yrfffovP56ucInv06NHVppseOHAg8fHxTJ48ucr2Rx99lLfeeovp06cTFxcHxKbInjNnDp9//jmNGjWq0n7EiBGUlJQwZcqUWl/bVVddxaxZs/jggw9o1apVrY8TkQNXyxtySCwJcOrmvCrbF3s9LGycXvnachyOzi1gUkYquZ6txdyWhUVMf+UN3LZNkc/PlWcMZ1mjNN5/7U2alpQyqffhBD0eIi6Lp3p2Jis+tl5Ut/wizlxT9cavxOvhu7Rkjs3M4aQlc+m5ZiUv9D+WL7sejG2apJeWEfJWHdV81Krf6fP7Jn5rncztJw6qsu/l99+lzOXmmlNOxbRtvn77YbK9qVx62mUUb5Nod8nJ56ofZ5JjxWE9dhx3Hl91lHZBuUOrf5VRsqP7TI8V+3JRc1HXcSDsbJdzb3lhg1NRfN9uimyg+shoxyEpwaCo0I6NXraMaqOY47wGZVFio5yTPVVjitiVo587pRksvtKNqacbRWQv82i/abVqd9OMY/doHCKye7bky9szTZN+/fpx2223kZ6eXpmbP/TQQwwaNIgLL7yQhIQEnn32WV5++WVefvllPv/8c2zbZtCgQZVLW+3I3LlzufLKK2nbti3jx4/Hu919Y00eeOABPvjgA+677z6GDBnyZy5bRKTOTfvbz6z4eF3la5ffYuQXJ/LaY+tYvCaWPLrDYVplbiJjUwEAUdOgNNELhkFxciLh7YrMi1OSGP7jb3TbsBmAOMpwbZeIugmQzlJcsbWeKjkY2PiAKBahim0QIr1iO7gowk2gylG8cTVcMOBPvhsiIgeu2ubIoDx5W1qDWerNqlWrCAaDDBo0aIdtCgoKaNq0aeXrFi1aVGuTlJREkyZNqm1PTEwEoLCwsLLAvGX79sVlgHbt2jFt2jTKy8vx+/21uob4+HgASktLd9JSRCRm+cOptL0xh29Sk+hRXIbLsXHKglwy4xd+uWsI0+YHiSsuJ9Ux8TgOQ0oKmZ6QQJFt0DgY4u/fz6TU56PY4yOAxSHF+ZQnx/FI/2O5afq39Fu0gHlt25GVnMyIpav5oXkGmQlxNM7NxZ+TT1lqEkGXi7holMRAgN5FLrI9bn7q2IX2m7K48rtpnP3LLDYlJhPxlPPKUUPISUoBICFQRt856/G7Iwz7LZv3uhzM782bYBsGHTZu5LQliwEIWC7e6XEwdx99Brn+pCrFZYDFjVIZN6Avp/26mHuOr74WX4rfYPplPi54O8jCzTU89xaKxqbJjphgOVVHJdtOrMBrECvuGgaVlWaDyuKyQWw9ZSfiVBaZTROObenw40aHkG3gdxlc29fiqiNcDJoYZUlmBCdsxwrQTqyAbVoGRbd6+XodnDw+CCVh8Llic8LYDodkQGnQoE8zg38e71JxWUT2SprWS2T/cMYZZzBo0CAMw8Dv99O6dWuSk5N32P7UU0/lkUceISsri08++YSBAweSlJREQUHBTvtatGgR119/PRkZGTzxxBO1Ki4DXHzxxXzwwQfMmDFDBWYR2escc38vfOle1k/bSHzzOA67rivxzeK46tHOPDpmIetyHCKWRanPhyclnriSAKZt4wpGCflceMqDhD3uyoeOg6bJgrQUsvr34ehlq1nctBFHrV7LsCVL8EdjxWSTCDYQJB6DUsyKIrODReyj+lhh2SY2W1gYPzZuwMYkjOULQ9COzfTltuD2ESoui4j8ScqRd48KzFKvOnbsyA033LDD/ampqVVeW5ZVYzvT3PHs7ntyUH7Hjh2ZOnUqS5YsoUuXLnusHxHZf/g8Fhv/U/2hGOjNabU6Q9XZEo6r/K4lcCgAvWo8rqY+Y775Kosfbs3nhUGn0HFTFhgQxOSUWTM5r+wn1qY2xjJs2mfmEko22RwwGBC8iTlV1htOglcfAuAm4FLjSuIJ0emKO6v1ZzgO81tm0H1FVrV9W/RqbrHg+rgd7t9eaSDC079E+PvUraOSm1sOGwKA26CJz2HjLfG1Pt/2Fl1qAtWnBd/ipLbg3FP7eEVERETqWuvWrenbt2+t2w8ePJgnnniC+++/n3Xr1nHLLbfU6rjFixczduxYEhISeP7552ncuHGt+2zSpAmWZdWqiC0iUt/ccS6OvK0n3Naz2r6bXjj4D4+1bZu3//4ra6cXs7ZZGovSktjo8RJ0uVjRpilfH9QKDHinT1f+ET6BPms3kBQNcePkz3COD9H/X/dhHHQ9EfyYRDAJAzb2lSfhenZMZT+1e5xHRESk/qnALHXO2MHTHq1atSI/P5/DDz/8DwvEda24uJicnJxqo5hXrVpFWlparUcvAxx33HGMGzeOjz76iOHDh+/wWkVE9mbHD2rG3GkFbJ6eRa7hJaG0mL4r5oNRRlzuQkblziDPl4ZlR1icm8RJpfdgmH/8+y4x/B9K3ddzyy8zufr4ITjbrA/txoGoQ0589dHLuyve5+L/jnHxf8fU2SlFRERE9muJiYkce+yxTJkyhSZNmtSqOL148WKuuuoq4uLieP7552nWrNku9ZmZmUk0GiUtLW13wxYR2SuZpsnhozqw9OfFxDkQ5zgUJvlwIjZFHhf4XbGZuAyDYMTF9+1bMfqn31nxSGxwzcC2TTCdd4ne+BrOk58RvuAEPK+O1Yf1IiKyz9DfLKlzWwq2RUVFVbYPHTqUJ598kgkTJjBq1Khqx+Xm5pKenl5te1147bXXuPHGGytfT506lTVr1nDGGWfs0nkOOuggTjnlFCZPnszTTz/N1VdfXa3InJOTw8SJE6utHS0isje54f6uQNdttpwOQIeKV1vG/jav5fksl4sk52nmDvuZXjm5LMxII2BZ4DGxPS4OX5fLIZsL6iJ0EREREdlNF110Ea1bt6ZLly47ffB7y8hlv9/P888/X+MSVlsUFBSQkpJSZZtt2zz77LMADBig6VtFZP8TH+/CikQB6JpXTNe8YvI9bl7q2Q7cZmxJJ8cBlwleF0YkVO0c7sf+Co/9tb5DFxER+dNUYJY6161bN0zT5JVXXqGoqAi/30+LFi0499xzmTlzJk8++SQ///wzhx9+OPHx8WzcuJGff/4Zj8fDCy+8UOfxpKSk8M0337B582Z69+7N2rVreffdd0lPT2fMmDE7P8F2br31VoqLi3nttdeYMWMGxx9/PM2aNaOsrIwFCxYwdepUOnbsWOfXISKyL4j6TNqFQvRcl02O30OZ26JpSQDDAcuONnR4IiICODuZlUJE9l+dOnWiU6dOO22XlZXF2LFjKSoq4uyzz2bevHnMmzevSpvjjjuu8gHzBx54gNLSUnr27EmTJk0oKCjgm2++YdGiRQwcOJATTjhhj1yPiEhDKjHd+EsCFKYnVm5LDYXBBAJRiFYs42cZYMHCI9rTl8UNE6yIiOyQcuTdowKz1LmmTZty55138tprr/HQQw8RiUQYNmwYd999N0888QTvvvsukydPriwmZ2Rk0K1bN4YNG7ZH4vH7/Tz33HM8/vjjPP300ziOw1FHHcUNN9xQbdrs2vD5fDz++ON88803TJo0iffff5+CggL8fj/t27fn0ksvZcSIEXvgSkRE9n5dykpZErZoESzhoiW/4Y2EmdWuC18fdDC+ouKGDk9EREREaiEzM5PCwkIAXnzxxRrbfPzxx5UF5mOOOYbJkyfzwQcfUFhYiMfjoX379vzf//0fI0aMqNdlskRE6kuzpm7cwQBRwNpme1x5hDLXNh+7R2PLRhUU6KFrERHZfxiO4zgNHYTInnL55ZeTlZXFpEmTGjoUEZEDwuMDvqLTwrUMzJ+He5sRyxN7HsN3GS14+av+DRidiIgA/Gvg9Fq1u+VbTWkrIiIi8kce6/EppSnJ5GekYDgOjmnybNc2hNzbjeuybToWFnHzwR8DMHr0aNxudwNELCIi26ttjgzKk7elR0hFRESkzkQNaBbNqVJcBjhu+XwygpEGikpERLblGEatvkRERETkj934+1ASW5o0Xr+JxPwigsEyQjXdR0UdhjYL13+AIiKyU7XNkZUnV6UpskWAnJycnbZJSEjA5/PVQzQiIvsuX5ybqNug3HBR6nFhEcUbsfGHQiSUljV0eCIiIiIiIiJ16ob/HV35/bkv58Ga6gUIw7Z56JpGvP5mfUYmIiKy56jALAIMHjx4p23uuusuTj311HqIRkRk32VFHRalNefgvCW0CpYCYGMwuc0RFMbHNXB0IiIiIiIiInvOhrwomNULzI7LxNp+2mwREZF9mP6qyX7txRdfrFW7Z555ZqdtOnTo8GfDERHZ75WXhGlXtpnEaGnlNhOHw7OX8FWbXg0YmYiIiIiIiMie1cYPFNngsqruiNgNEo+IiMieogKzCNC3b9+GDkFEZL/gmBYZZUXVtqcES3GiWm9KRGRvoHWjRERERPYMIy8I0Yol9iwz9t9wFGyn4YISEZE/pBx595gNHYCIiIjsP9LSLJamtq62fX5aWwoSEhogIhEREREREZH6kdrGD/FuCEagLBT7CkfBa+38YBERkX2ICswiIiJSZ4bc15OFic35tnFvykwvNgbLEltze/8zaO6PNHR4IiIiIiIiInvMP89NjY1cTvHFisoeK/a9BseJiMh+RlNki4iISJ1pdkgqPdev5LWDj+bO5sMwcCgxPFw68yeuWH5+Q4cnIiJo+i8RERGRPcXvMYkrKaMsOR48FVNlR2wGuEqAtAaNTUREaqYcefeowCwiIiJ16i/ZV0DTf+Ndkgy2g2kVcvLKaxs6LBEREREREZE9rvT+FP46sYT/LTZw2zb3nODmpgFNCIfDDR2aiIhInVGBWUREROrcGeuuZvz48QCMHn0NbrduOUREREREROTA8NrZCbzW0EGIiIjsQfq0V0RERERE5ACi6b9EREREREREYpQj7x6zoQMQEREREREREREREREREZF9gwrMIiIiIiIiIiIiIiIiIiJSKyowi4iIiIiIiIiIiIiIiIhIrWgNZhEREdll1zy1gam/BskoDtC1lcmz/+7c0CGJiEgtaX0pERERkfrxwIub+Pi3KLZp8Mo1iQ0djoiI1EA58u5RgVlERER2SZfLVrMsPhE71Q+p8HMwjNP/e5777piGDk1ERERERERkrzDg8jX8mpFOSZPYR/ADXgsxzN2R/i2WN3BkIiIif54KzCIiIrJLNrl9nDVvBX+ZtxzbMHj9sIOYm5FGaXY58Y39Ozyu/d+y2ZCeiCvq0DqvgIVPNK/HqEVERERERETqh+M4rE5OosRtkR4IETEMCrwephcdQn9UYBYRkX2fCswiIiKyS/4ybzmXfj8fp+L17V/8zMTDO/PR4xHOe6hHtfaO45B6WxEFKUkQdghaJouaN6LjtVksf6pZ/QYvIiKa/ktERERkD9uQVUKpy+L01ZvwR21Mx2Gzz8PPjZIaOjQREdmOcuTdYzZ0ACIiIrLvcByH4QtWEcWo+DKJYjBgRRbrPVaNx4x6s5QClxuiDjhAxIHyKJmNU+o1dhEREREREZH6YJdH6ZVTSNQ0KfG4KfJ6iI/atCsqbejQRERE6oQKzCIiIlJr0WgUd8TGwQC2PN1nkFJUxueLHAZfshTHcaoc89HSbZ4C3HLnYYM/GKmPkEVERERERETqlc8LcVEHthkVFzFNmgbCDRiViIhI3dEU2SIiIlJrlmURpfq0MSuapjKtdye8kQinXLCIHhsL6Xh2bF/UNMEywGfFkmvHwQhE6L8qG0is3wsQEREczf4lIiIiskeFDReOYVTLnkvc+jheRGRvoxx592gEs4iIiNTanUd8hRmxq2z7sXNLLrvmVByXScDn4fNDO5Ef7yFzQhcAbAsMr7X1yW3DwPG5SAlrBLOIiIiIiIjsfxwX5Lhjy0hFjdhqUQBrfSowi4jI/kF/0URERKTWWmYW4wtEKHW7+PHgVli2zcT+3bDNbZ5ZMww+7daWa6b+HnvtGDhmbOQyETtWaHaZrE2Jp8QYG2tChETnhQa4IhEREREREZG6EQlFmNz9Q3KLI8y9YDBmspdSrwvLdkgoCRFXWtLQIYqIiNQJFZhFRESkVrJXFNK4sIyN6Qlcf/lgUqI2jQtKyU+Iq9a23OPFqBjonB4IkOf3EC0OxQrMAF4LXyBIhHjAwCRCiXEFCc7z9XdBIiIiIiIiInWkcEkenxw3hdLkeIKN47GSPJT43ABELYPCZB+tCzY1cJQiIiJ1Q1Nki4iISK38/OJKkoNB3jjxUI7LzOPo9Xl0LAkyaG0uKeWhKm2PXJVFxsYCAA7KKyE+v2xrcRkgGCUfBypWpLJx4eAjvGpzPV2NiMiByzGMWn2JiIiISO1NPuYzspukk1QQZr3XT5nXXa3NppSMyu9DT08lbF1IMOMabNuu1lZEROpHbXNk5clVqcAsIiIitbKuGKKGAX4f/m2KxYZpcMbcVTQtKKJddh5XfvETI374HcOMrTIVsUxKnOo3YIlFhVVeR3FTfudHe/YiRERERERERPaAjU1T6fFbJhkbCznp51U0Kiqt1saKxvJkx3Mxrmuew7DBnZML1tmEl2+o75BFRER2m6bIFhERkVpJbeICw6BRaQBcVW8hWhcVMfuB9wBwgFnN2+GYYU4a8y2PXDMc221CxKlyTIeSTLzkESIZBxMTm+jajfV1OSIiIiIiIiJ1psW6QgJ+i7x0H+VeF4PmLeW//XtBxYg3bzBMXH4hZtMwBg5lNMEiio2FQRhPpxvAmdjAVyEiIlI7GsEsIiJyINqN6bfylxbhDtu0y8qptu+gzEwipsnS5s1Z3LIVTUsLSYwGcQMF8V7MeBeYW0cxWyaUJfvxk4uHYjyUYVGGE4j+masSEZFa0NRfIiIiInWr4Jcs4kIhNjWNIzlUToe8PAosLxSHoTwCZRGC5Q7x5SG6frSWEEmxWbxIJIIbA4Movoa+DBGRA5KmyN49GsEsIiJyAPjllhkUjF/KITkz8BEHOBhEMSnCFxiP4fXu9BxOWZh4AsxtlMLGBD+dSsoxgOXxfjp53Kw67ngKExIA8IWC9F0+mw1xrTlj4QpePqI7pHk59vdlnLxwKRf9NpfVjRIJkYSBjYOBCVi1iENERERERESkoXzaagLRvCiGy+C4308nvlUCK279joI0H902bSK9rAyAJz78go45eTw1sC++cJiA28261GRavJNHkFRsYms027iI4saHHrgWEZF9hwrMUu9KS0uJj49v6DB2274ev4gcOBakPEWo2MYAPLZBYxzcJOFjIwZg4BDFTdh3GeVD+pA8+VoAVt41i+UPzwcg5HFTHOfj966tKE5Jx9OhGZ+3b0m5y2JmSiIGsXWZS3r15KS84sq+A24PK5q0pdiXyqkLFrEgI515TdOZ1qUT07p04t3DD+HNN96miCb4KceJRYn13VqCxijcq5/AbJNeJ+/DD1Py+HlqPi6PycBh6XQ/IqlOzisiIiJSF/b1HHNfj19EZGeWD/+E0kkrKcchYPloHg1hY+AAa9u8SBwB4vES37hxZXF5i7EzZjNm1mxSQmUsyWjE60f0Jq4sDRsXBhH8FGIQxsGLgcUPrf7N4uZtOeKRAXQfUDc5sYiIyJ6gAvNeJhgM8uqrrzJlyhQ2bdqE2+2mSZMmHH300Vx33XWV7WbOnMnrr7/OggULCIVCtG7dmpEjRzJy5Mgq5/vpp5/46KOPWLhwITk5Objdbrp168bFF19M7969dzm+u+++m08++YQvv/ySf//733z//fcEg0F69OjBddddR5cuXSrbbtiwgeHDh3PZZZfRrl07Xn/9dVatWsWJJ57I3XffvUvXMXfuXF5++WWWLFlCcXExycnJdOrUicsuu4wePXoAUFhYyEsvvcT06dPZvHkzfr+fZs2acdJJJ3HhhRcCMHv2bK644gruuusuTj311Bqvbfbs2ZXbLr/8crKysnjuued46qmnmD17NkVFRZVtcnJyGDduHDNmzCA3N5eUlBT69+/PlVdeSVpa2i6/vyIidSVz0FsECh382DiYGDh4COIjnwjJgAWAQYgIJnGf/UxwyjKcns1Y/8BvuFwuiuJi03P5Q2EOm7+aX/ocxAvH9Krsw95mWphSrxfYWmB2RyLkJGSQk5ABwBGb8vihTfPK/b+0askz/Y7k7q+m4FTEEuMhQjJm2+sx7Ncx/uTUM99/nst7L2ZVvl65oJQr7m5Lpx4Jf+q8IiL7MlvTesk+RDmycmQRkYa04Y6fCExagQVETC/p0WDFHgcbKMGPgUEEC2/UqXa8ASxs3oJyn5sjVq/kzi++IYobF2H8lABuwFX5EPhh65eTH5/K5Lvn0/GTfvjirGrnFBGRuqUcefeowLyXefjhh/n4448ZOnQo559/PtFolHXr1vHzzz9Xtnn//ff55z//SY8ePbj44ovx+/3MnDmThx56iMzMzCpJ9qRJkygsLOSUU06hSZMmZGdn89FHH3HVVVfx/PPP06tXr5rC2KlrrrmGpKQkLrvsMnJzc3n77be5/PLLeeWVV+jYsWOVtt9++y0TJ05kxIgRjBgxovLJ5tpex+rVqxk7dizp6emcc845pKWlkZeXx2+//cbSpUsrk+e///3vzJkzhxEjRtCpUyeCwSCrVq3il19+qUyed0dZWRljxoyhZ8+eXHXVVeTl5QGwceNGRo8eTTgc5rTTTqNly5asW7eO9957j9mzZ/PGG2+QkKAChog0jMKv12K5PBAx2HqL5BD7029ts8WDRYgQiYQe/JaVkXS8UZvCeE+V87miNk2z8tjUoSXJ4Qjllglbbr4cB3ObPNqwHSy7amKdGHFoWlLOxuS42Iaow9wWzXAT2i5yo2JktQ/zp9VYR7X7U+/DzK/zq7x2nNg2FZhFRET2DcqRlSOLiDSknMd/xQRCmCTYVfNXk9jiUxFMopjkJ8YRzTOwnK35cHZSAs+dOBAMg7f79uG2SZ/QoiBMsl1QcQagImt3Kub2OmzdIma3PZjPXljLGTf8uZxYRERkTzF33kTq07Rp0zj66KO55557GDFiBH/5y1+48cYbefPNN4HY08CPPvooJ510Eq+88goXXnghZ511Fo8++ijnnHMOEyZMYP369ZXnu/3223n22WcZM2YMp59+Opdffjmvv/46CQkJjB8/frfjbNasGc888wznnHMOY8eO5fnnn6e8vJwnn3yyWtsVK1bw8ssvM3bsWM444wxOOumkXbqOn376iUAgwAMPPMDo0aM57bTTGD16NE8++SQjRowAoKSkhJ9//pkzzjiDW265hTPOOINzzjmHf/zjHzXGtCsKCwsZOnQo//znPxk5ciSXX345AP/617+IRCJMmDCBa665hjPOOINrr72W5557jg0bNjBhwoQ/1W9dycvLIxgMVr4uKSmhuHjrKMNQKERubm6VY7Kysv7w9caNG3G2uVlWH+pDfex9fViAU+3pOwOnYo2nbdlbnjfzujDjY/udGh7cMxwHXzhMUtSmbUk5CeEIrmgUojaUBmizZgOmHcVw7OoHA17LwXTBicuXc9msnzlq1eqKScWqczAoj4T+9Htl25Hq12Fs7XN/+n+uPtSH+ti7+hCRuqEcWTnynrC3/K1QH+pDfez9fRhWLDk2gJrGtxnEHuU2sYkrC7EiLZ2IGWuZmxDHcycfW/lwdpnXy4e9D8Oyoxg1ni0mYsYeCvf4zH3qvVIf6kN9qA/lyQcWjWDeyyQkJLBy5UqWL19e7SlngK+++opQKMRpp51GQUFBlX39+/fnrbfeYtasWbRs2RIAv99fub+srIxQKIRlWXTv3p358+fvdpwXXnhhlWlLu3btSt++fZk1axZlZWXExcVV7uvXrx/t2lV92m5XrmPLE87ffvstnTp1wuv1VovH6/Xi8XiYP38+GzZsoHnz5tXa/BmjRo2q8rqkpIQZM/6fvfsOj6rK/zj+vtPSewKE0AkdFDUoHRREBEQBURRF0UVWsaxtd3+76uLqrroCigqIiAgoFhRRwLqugI1uAaVDaAklhfTJtPv7IzAwJGIIIUP5vJ5nnmXOPfee7836JPOd7z3nfMNVV11FSEhIwD3UrVuXevXqsXz5ckaPHl2tcVTFscuQHfvEuMPhICEhcE+X5OTk476vU6eOxtAYGuM0HyNmRCsOztqOnSOJsAsHXqxYjynqugkjlAM4/tWbNqlJLEuaSXipm8KwI79vvYbB3jpxdMzI4uf6dQg3DGKdbjBN2m5Ip2Gxi4/Pb43PAu0ytxBd4iM/LNZ/foHdys6ocO7/+Cu67tpIv+0/g2nFRSiBz7uZ+LBgs3uJ7NbipH9WvQbVYfaEI18qW20G3folBvysTnaM0+X/c42hMTTG6TWGiFQP5cjKkU+F0+VvhcbQGBrj9B/D9nRn9o75Chs+Cg0Hkabbf9wEvBiE4sGLhdj8InYlxVEUEsIFGbt4t0samXExAdfclpiIHeeh2coVF5mXN2pHuKuYK/5QD5s9cIns0/lnpTE0hsbQGL83hpxdVGA+zTzwwAP84x//YNiwYaSkpJCWlka3bt3o3r07FouF9PR0AO66667fvMbh5akAdu/ezaRJk1i2bFnA0yLASe1reWwyfLht2bJlZGZm0rRpU397gwYNyvU9kfvo06cPH3/8MTNmzGDOnDm0a9eOjh07csUVV/h/qdntdh544AHGjx/PwIEDadKkCWlpafTs2ZOLL764yvcJEBcXR1RUVLn4fT4fH374IR9++GGF56WkpJzUuCIiJ6POzKso3vQ6B1bmYcMgxOsDDLKJI5Ecf0nXi4VQcuCVO7F3KPvitdl7l/Hz9Uuwuj14rVZKQuxsblqHghAbWRERR5bGBjAMimNjWJ4a7W/6oX5LamXn0W33dgrCIrB4TWZd0gZbfikTunRlAl05f99uvnzjRWJKCykhAgMDC268WLBZSgnJmlQtP4cLusYSEmpl5Ve52B0WulwZT4Nm4b9/oojIWcw8zowZkdONcuQjlCOLiNS8pLvOw7WrkKz/rMHwuSnCRgheTAxKsRFJCSE4KSIUw2uAxaDY6uDH5BTq78/hp4b1A67nsVkxscKh9byO3tLKxOC7xm0oig7jxqkdyhWXRUTk1FCOXDUqMJ9mevbsyUcffcS3337LmjVrWLFiBR9++CEXXHABkydP9i858Pjjj5OYmFjhNQ4nbcXFxYwaNYqSkhJuuOEGUlNTiYiIwDAMXn/99YA9q06l0NDQcm0nch8Oh4PJkyezbt06li1bxpo1a5g6dSrTpk3jySef5NJLLwXg2muvpWfPnnzzzTesXr2aL7/8knfffZfLL7+cp556Cjj+FwZer7fS8R925ZVXMmDAgAqPVfQUuYhITWry/a00OfTvUmcp2bPWE/vYK9j3ZWNixcCLCYSY7wScl3xNKsml5WcIfXL3Ml7IOebpQ7eHpKJCGucfZG9cDPsOPZ3tstkYtPprSkJgaaMO7AwNwzxq3e2fatdjykVd+b/vvsBBCRaK8BKF+48dCJsyslp/Dq3TomidFvX7HUVEROS0oxz5COXIIiLBkfJUZ1Ke6ux/v+7fa9g+bi3hqVF0XD4IwzBYPvBDjIX7iXCV0uDAQUI8XurmFrItKZH1DetiAjGlLhxeH5kN8mi2s6x4fGR9MQs+fPTadmdN356IiEiVqMB8GoqJiaFfv37069cP0zR58cUXmTVrFkuWLKF+/bKn3mJjY7nkkkuOe50VK1Zw4MABHnvsMQYOHBhwbMqUKScV4/bt22nXrl25NqvVWm6phIqcyH0c1rZtW9q2bQuUre8/fPhwpkyZ4k+eARITE7nmmmu45ppr8Hq9PPbYY3z22WfcdNNNtGnThpiYssJHXl5euevv2bOnUnEA1KtXD8Mw8Hg8lY5fRCSYQkJDqHtHe7hjckB7+R2Zf5t9dzbxRhR748reR5Y46f/zBmJLnACkZu5nbcMUNtVLJqbASbE3jPjiPSxPisM0y19vfWLZUjo2nBjYARdm46QTvzkRERE5qylHrphyZBGR4Gj7twtp+7cLA9raPNeNL79eSNO92VgO5b9RThfDl6zitasvA8PA6vOxJTaC3Jax2Hbuw02Ef86cDwM7+TV7IyIiIifB8vtdpKZ4vd4Kl+hq0aJsD8q8vDwuv/xyHA4HU6dOxel0lrtGYWGhf7N0q/XQk3DHfKu/bNmyk9pbCmDWrFkB192wYQMrVqygQ4cOAXtL/ZYTuY9j958CqF27NnFxcf4k2Ol0lruO1WqlWbNmAOTnl31Aq1u3LlarlRUrVgT0/emnn1i7du3vxn1YbGwsXbp04X//+1+F55mmSW5ubqWvJyJyRqgbR7wBnTanc8uXq7jzs2X+4vJhLXbvxe7y0Hn1VnaE1saLi6+aNaCi+So9dmzBPGpRMAs+LOt2nPr7EBE5x5mGUamXSLApRz5CObKIyOktsmk8Fhv+4vJh4aVuYguLsQA+i8GBUAebhjTCh0EI+VhxYqWYcHIw8QQldhGRc11lc2TlyYE0g/k0UlxcTN++fenevTstWrQgLi6OjIwM3nvvPaKjo+nevTtJSUn89a9/5cknn2To0KH069eP5ORkcnNz2bJlC4sXL2bu3LnUrVuX9u3bk5CQwPPPP09mZia1atVi06ZNfPzxx6SmprJly5Yqx5qZmcndd99N9+7dycrK4t133yUkJIT77ruvUufXrl270vcxffp0li1bRteuXUlJScE0Tb7++mvS09MZMWIEADt27OCOO+7g0ksvpWnTpkRFRZGens57771HSkoKF1xwAQDh4eFcddVVzJ8/n7/97W9cdNFF7Nq1iwULFtCsWTM2bdpU6Z/BX//6V/7whz8watQo+vfvT4sWLfD5fOzZs4elS5fSr18/Ro8efeI/XBGR01SttBii5xTSaOd+krIKKIooP//Z7vUy8v3viC4qZXOTFDb9uRMFP4ZghFvBZYLHBNPk4r072Vq7DgZejKP2ObHExtbgHYmIiMjpTDmycmQRkTOJx1J+z2SPxUJJ6OFHrg1qF5UA4Au3UlrswEEJPiw4ceAofLEGoxURETk5KjCfRkJDQ7nhhhtYsWIFK1asoLi4mMTERLp3787IkSNJSipbNnTgwIE0aNCAN954g3nz5lFQUEBsbCwNGzbkzjvvJCEhAYCoqCheeuklXnjhBd555x28Xi8tW7Zk4sSJfPjhhyeVPL/44otMmDCBV155BafTSbt27bjvvvv8T0NXRmXvo0ePHmRlZfHf//6XnJwcQkJCqF+/Po888ghXX301UJaMDxw4kNWrV7N48WLcbjdJSUkMGjSIW265JWCPqAceeADTNFm8eDFLliyhVatWTJgwgQ8++OCEkuc6derwxhtvMHPmTJYsWcInn3yCw+Ggdu3adOvWjcsvv7zS1xIRORP88n0BDXfsJbS47KnqEKeHgigHHPX0Xky+k+hiFx67hT0pcdQlg6LwUEK8Js4oB3hNMGBFfHPyE5J46vO5/nO92HA82LvG70tEREROT8qRlSOLiJxJzBIv+eGhRBcfWUFiTavGuO1HvoKPKy3Lp+0HX8LYX4Cn478wujYh/K0xNR6viIjIyTDMY9eGEjmOsWPHsnDhQlatWhXsUEREpIZ988gP7Hh9E5nR0aTuLVvisDTESkGUA6fDRu3cQhL3FlEcEsLX3dvgs0DqNat4ePt1nJdXzLeNk/xLydg9Pq7YuJsPXn8CAxMvVpyEEm1OCuYtioicEx7pv6ZS/Z5cdOHvdxI5xylHFhGRw9yFThY2mkeU24nD7WFG3444HEce6DGBjTGhDL/oU0aOHIndXn5VMBERqXmVzZFBefLRtAeziIiIVEqnx8+nONTBhxemUnLoCeyQUi8uj5W327ciPyyS7zu2ZNGANApiwvHYD+9zCCn5JVz7yy4uyMzh4t3ZDFubjgcrRURSRBQlhKm4LCIiIiIiImcse2Qog7JuJObaphxoFM+sjq1YWzsap81CocPK9/XjWVM7NthhioiIVAstkS0UFxdTXFx83D5Wq5W4uLgaikhERE5HVqsFZ6SFgd9v5vFrutAgJ58Sh5119RP566crWXNR2RKQhxfMDisu21vK7jWx+nwkFnuoVVzqv16ezUuMqT2mRERE5PSiHFlERE5Gh+k9aJZXgu9lk+/rJ/J9/UT/MUuJO4iRiYiIVB8VmIXZs2czbdq04/ZJTk5mwYIFNRSRiIicru7+YSAfRLzNhNf+x/ctUihx2Lj/g5W4w21sblgbZ2QYNq+POlnZNLxuHQAhbjcRXm+5a8WVKrEWERGR049yZBEROVlmkRPcYWAYYDn0GLbbh8NdPjcWERE5E6nALPTv35/27dsft09ISAhQtr/U2LFjT31QIiJyGjNxeHz0+GWXv8XjcuFzl9BxYAJ3X5+C2+1mxoyyArPDAItplruKtYI2ERE59UzD+P1OIucw5cgiInKyLBGhWDwmPq+3rMBsmmCC3RfsyERE5FjKkatGBWahXr161KtXL9hhiIjIGaIwwk6k043lUH3YZ4DHZtC5uYM/XJ9Srv/AGCdZmRYSPF7MQ5/XDBOKQ/QxRERERE4/ypFFRORkOd0moV4fxTYb+I48XB3j1EpeIiJydrAEOwARERE5c5imSanDisdu4D708jgsgEGb1NAKz/nPX+vgtBkYponVV/byAdsTK+4vIiIiIiIiciYLtZm0yCkIbDRN6hUVBycgERGRaqYCs4iIiFSaxWLhy3aNwTAwLWUvgN2JUXS4tWmF51itBjOnNGZzdCiFNgtZIXb2XhzPson1azJ0ERE5xDQq9xIRERGRqomJjSCp1EWXzGySSkqpW1hC7137Aa2RLSJyuqlsjqw8OZDWphQREZETYo228l3zFDpv2gPAwfAQVraozR8jHb95TlSUnU9mVFyAFhERERERETnbhIW4qXPQRe1SF3avD5fVwqVJS4IdloiISLVQgVlEREROyJvvnscvq3L4yzN1CHe6+L87ErjxKs1GFhERERERETls/qRU9m3J44/P7iXS6mHyf5rw+hxnsMMSERGpFiowi4iIyAlrkxbPwrnxwQ5DRESqwGdoXS8RERGRmlA7NYYPpsYA4Ha7gxyNiIhURDly1WgPZhERERERERERERERERERqRQVmEVEREREREREREREREREpFJUYBYRERERERERERERERERkUrRHswiIiIiIiLnEFP7S4mIiIiIiIgAypGrSgVmERERqVZr4h4lI7ke9XxeamXugZHBjkhERERERESk5n3yyja+np8DpklIqEHylcGOSEREpHpoiWwRERGpNt/V+RcvXnotG0KasjaiBS9efj2rk/8d7LBEREREREREasz+iyeQb7md9OnriS5xEu0sxX7Qybb3zw92aCIiItVCM5hFRESk2ixs0ZVBn6zD4fYC0Hzjfj66uDsdgxyXiIgcoeW/RERERE4dd7GT0JXb+C6+CwlbiogMd7G/XhzOcDvhbg8+X7AjFBGRoylHrhrNYBYREZFq03pztr+4DBDmdJO6PS+IEYmIiIiIiIjUnKyI/2OjJRXybETnOUnKLKDZzxnYPF4shsFPn7cOdogiIiInTQVmERERqTa2Uk+5NmupHs8WERERERGRc4MbG9nEBbQ5Sj1E55Rg8/mwF1mDFJmIiEj1UYFZREREqk1JePlicna0lpkRERERERGRc0MYRVBhGmziNQxcSpFFROQsoD2YRUREpNr0z1xGnjeRCEowMSgmhLQt+1hQdwdXZfwp2OGJiAjg0/5SIiIiIqeMFQ/xZg45RoK/zRViJS8unLzIcDze8it/iYhI8ChHrhoVmEVERKTahHl9hJHjfx9NMRavSa/MnzFNE0Mf2EREREREROQslu1IoiDRRrYZjuExKI4MZWeTRPJiIjBMwF0a7BBFREROmpbIFhERkVPMAMPG5BuWBDsQERERERERkVOqxGFjY2ITrFElOOLz2NoqiZz4aBxuN7GFhdjK7ywlIiJyxtEMZhEREak2BiZmuVYTt8VOwfaiIEQkIiLHMrWYhIiIiMgpU0IUvXYuocXBLQBctfkzPmnUm411m3EgPhavRzOYRUROJ8qRq0YFZhEREak2Vjx4sAFln8w8hoXpPQbTYN9eTK2bIiIiIiIiIme52s69NPDsoJBaFNmi2ZaUjDMiDK/dQpHDgbXUGewQRURETpoKzCIiIlJtvNjYFRtDdlQSHquV5U1bsTu+FjsS6rDFWcj/BTtAERERERERkVMoyZNNLk3wEoLhMWmamUuxNZr0BAumzwdm+XW/REREzjSaSyQ1burUqaSlpZGRkVHt1x47dixpaWnVdr2MjAzS0tKYOnXqcdtERM4FB99bQ75xJ7nGA+QYD5Jl3A3A3f1WMHrwWu7pv5rNsQ1Y2LI773S8jPc79GB3fC0ATIsFIzYumOGLiMghJkalXiJSM5Qji4icXbyE4MNGCMWEUUQIxbTdsx2r10ud/AK2xUfjmAjGOA+1XvIEO1wRkXNeZXNk5cmBVGAWERGRSrEPnYKNUmJJJ5Z0wihlRtu3yKxdm6zYGDJqJfJDSmPsLne5fZh9QF6IPRhhi4iIiIiIiNQYAwsOnFjwAWDBR5hZTKirFEz4on0qh7eVOuAsKzSLiIicabREttS422+/nVtvvRWHwxHsUKokOTmZb7/9FqvVGuxQRERqROHyLTg7/psIPIRQjAGYWAgljwEbvmNZi7Y4vAA+Qjyl2G1W0sPDaFxcAoAJbI0IZ3t0OMbTpWAr+/1pBQ7eA5Eh+jgiIiIi5y7lyCIiZxcPIdiOeezaAJIKD/Je2+bgM8BignFkJpzH68Nm1VwwERE5c+gbXalxNpsNm+3M/U/PMAxCQkIq1beoqIiIiIhTHJGISHlFfSZT/N/dYEIIuYSRj42SQ0cPP0cNHouVb+p2ZGtMY+xeN50yV1Irz4UFN0VEk8gewnESiQcT8Bg2lte6gAJ7FBft/5FNSXWpn5uPYZrsiI5iQree/HnJp2yLjGBfaAiRHg/5NjslNivZCZFwVMLsBaJeBDjytPYDF8H4S8/cvxEiIiIiJ0o5sojImcl0eym99lXMj5ZTQjQOXDgoJt9Wn3hPIRyzlOovKXVZ2qgO2MsXkiOf8+F8SAVmERE5c+ivllRJZmZmhXss3X333aSlpfHmm28GtN9yyy1ce+21QMX7Sx1uS09PZ9KkSfTr149OnTpxww038M0335Qbv7S0lIkTJ9K3b1+6dOnCiBEjWLZs2Und048//shtt91Gly5d6NOnD8888wzFxcXl+v3enlOff/45N910E126dOHZZ589qZhERKqi5I63KPpiL6Zpw8SGg1LslGDAoZcPC2DFx7d1O7Ixvjkeq50SRzi7w5rhoJhckqlNBjZKsRwqAJdaHcxtfjU/1G7PlvimvNXqWr5O7YLNNLECTfILaHkwj9ScA7TZt4cim419oaGU2Ky0PLAXV6g94AntikxYDV/u8B23j4iInByfYVTqJSKVpxxZObKInHtcYz/F+9GPuIgmjCLCKWJ7bH18tvI57cba9ciNSSDBU/Fy2KXAr1nKhUVEgqGyObLy5EBn7iOyElTJycmkpKSwcuVKRo8eDYDb7ebHH3/EYrGwatUqhg8fDkBhYSEbNmxg8ODBv3vdsWPHYrPZuOmmm3C73bz11ls89NBDzJs3j7p16/r7/f3vf2fx4sV069aNTp06sXv3bh5++OGAPidi3bp13HXXXYSHhzNixAiioqL4/PPP+cc//nFC11myZAnvvPMOQ4YMYciQIXoyW0SCovSjjQHvHRRW0Ktsua6tMY0CWuvl5WBiYsdzqBDt9h/7Nb4lB0Ni/e9doQ4wAp9Va557kMWpzblu0498XZxPgd3GBRnpeEPi+dBsiVmJD2IvrPHRq6GegRMREZEzh3LkiilHFpGzmWfWCmyUUEQMUeQCkBMaTR1nPsfOXi45tA1CdKmb7MjQCq/37EofM65ULiwiImcG/cWSKuvQoQPr1q3D6XQCsHbtWpxOJ1dccQVr1qzBc+iJvDVr1uD1eklLS/vda8bGxjJt2jRuvPFGbrnlFsaPH4/H42HevHn+PsuWLWPx4sUMGDCA5557juuuu44HHniAJ554gq1bt1bpXiZMmIDP52P69OmMHj2aG2+8kWnTpp3wHlJbt25l+vTpjBkzhkGDBtGnT58qxVOdcnJyKC0t9b8vLCykoKDA/97lcpGdnR1wTmZm5nHf7927F9M8speMxtAYGuP0GoOYwP37yuYXH6ss2Q33lAS0Ou12LPjwYcXE5OiPCgWOyMArBG4p5bejXnOWtrgEMyKJJMK4bsNKmufuwur7jROOUS+qLLaz5f8PjaExNMa5OYaInHuUI5d3OubIcPr8rdAYGkNjnNljeBPDMbFgHMqeAeoU7SfSVcqx2mZsBdOk0PHb872axp69PyuNoTE0hsZQnnz2Mcyj/wsQOQGffvopjzzyCC+++CKdOnVi6tSpvP/++zz//POMGDGC1157jfPOO48JEybw1ltv8d///peYmBimTp3KtGnT+Oijj/xPUx9ue+mll+jYsWPAON27d6djx4785z//AeDpp5/mvffe45133qFp06YBfYcMGcKOHTtYtWpVpe8jJyeHPn360KtXL5555pkK73HUqFH+p9AzMjIYOHBghW09evRg/PjxJ/aDFBGpZp6Fa8m96h3MQ8XhEA4SzW7/89OH//AbwMbYVL6s392/dHVK3gF67lhDMeFEk0sIRVgpxgDSo+rzceMjXwr6LAaF0VFgOfJkdmZUJN7QwD34Om/9gcZZO+l8z0O/G7sFyLrbQlyonoETETlV7rv210r1m/he61McicjZRTmycmQRObd4/rcZZ68XsVBCCZHEUbYi2EFLIo5jVru2k8/kS69l5gUX8XNyXIXXK/mTlVCbll8VEalplc2RQXny0fTtrVRZhw4dAFi5ciUAq1at4qKLLqJly5ZER0cHtDdr1oyYmJjfvWa9evXKtcXExJCXl+d/v2fPHiwWCw0bNizXt3Hjxid8H3v27AGgUaNG5Y41adLkhK7VoEGDEx5fRKS62Qa0I/772wnvnkBYLRcOSy5OonARjptQPDjwEMJBI47Eohw67V5BvdxMWu/fTO89X2AjlzAKKCGEPGJxE4aLcGIKIK64AA49m2bzeGmTvg0oK1rvDwvFPDSpxer1EltQSOLBfPaHJ7A1sR52r89/bkVaxsGeP6q4LCIiImcm5cjlKUcWkbOZ7bJmhP38V8zUJMLIJYd4DpKA3VeClSN71ltwEsJBQlyl9N64E8NXfq/lJYNRcVlERM4o2oNZqiwhIYEmTZqwatUqnE4n69at4+GHH8ZisXDhhReycuVKhgwZwubNm7nxxhsrdU2LpeKiwpky0T40tOI9VEREapq1Y2Mil9x33D6xh/43Abjgd653oNXfYINJry2/ku8IocTuIKmokNWNG2JSNhs6scRJwoE8fmnYiFq5eVgP/e522iLYmBRHkwP5bKwTi/mQPn6IiIjI2Uc5cnnKkUXkbGdtV5eIzf8GIPxQW5ExijByMbFgYmDFTW5oDIXWOBKLSojIL6EwJty/khhA9ybKk0VE5MyiKUJyUtLS0tiwYQNLly7F7XZz8cUXA2VPbv/888989913mKbpf5K7OqSkpODz+dixY0e5Y9u3bz/h6x1egiw9Pb3csW3btp3w9UREzkZJ6/+NQQEGbqJdTmoX5WOjhPXx0exIjGdPXCzpSQm02rOLyJISf3H5sPBSFxft2K/isojIacA0jEq9ROTEKUcWERErJZiE4TOsHAyLZF3tVrx9/rWYFgtui4UL9mTBUR+1OtYKXqwiIlL5HFl5ciAVmOWkdOjQAZ/Px7Rp06hTp45/+a4OHTrgcrl4/fXXsVqtXHDB782Nq7wePXoAMHv27ID2xYsXV5hQ/56EhATatWvHkiVLAs53u93MmTPn5IIVETmLJJov4Ytx4SAHBwcI/2gYD626lpfntubFeW155d1WfNOqDV6LtcLzS0xPDUcsIiIiUrOUI4uIiIkBeLCaLiyY7IpJIcaZj8Xrwe7zkbo/i/f6wb0XgvcBC9+P0IPYIiJy5tFfLzkpF110ERaLhe3bt3PVVVf525s0aUJCQgLbtm2jXbt2REREVNuYnTp1olu3bixcuJC8vDw6d+7M7t27mTdvHk2bNmXr1q0nfM3777+f0aNHc/vttzN06FCioqL4/PPP8Xq91Ra3iMjZoPbB8cc9vi82DtMwiCkqxnLULGYTk1oZmcB5pzhCERERkeBRjiwiIj7CMMjGwCSu5CBXbvovPgymXnIbB8NiiPS4GdgMhrTWV/MiInLm0gxmOSnR0dE0b94cKFsK7GiHl/w6tr06PPXUUwwfPpxff/2V559/nh9++IFnn32WVq1aVel65513HpMmTaJBgwbMnDmT119/nVatWvH4449Xc+QiIme3MFcxPouF/bHRFIc4KLXZwPDRZs92Ghm+YIcnIiIickopRxYRkcOP4riIooREXERhYJKSn4EJuA0VlkVE5MxnmOYxmySKiIiIVNHnzcfx0fn94NCeJBafj7v++wGlthA+admWv35zZZAjFBGRu69bX6l+L71btcKUiIiIyLlsn/UeonwevIT522wU8WLPa8kNjyfbajLp/fOx2+1BjFJERA6rbI4MypOPpselREREpNocDIvgT1/M4dumF2L3eei05VcSC/PJjI6lw031gx2eiIiIiIiIyCll93kDissAHiIoDo2kIDwMR/7B4AQmIiJSjVRglrNWcXExxcXFx+1jtVqJi4uroYhERM5+2+Ib0GJ/JjeuWQQYuAklJzSG/FA7vf7YNtjhiYiIiJyzlCOLiNQMg8CZySbgstkJdbvBMDAPrfglIiJyJlOBWc5as2fPZtq0acftk5yczIIFC2ooIhGRs9/NK78guSjX/96CG6tpwfPcbUGMSkREjqYvNUXOTcqRRURqhhvT/6X7r8kNWHBBZw5GRMGhnSpdaMdKEZHTiXLkqlGBWc5a/fv3p3379sftExISUjPBiIicI5KKCgLeG0BsaQnJNzYNTkAiIiIiAihHFhGpKSWEE4qL/ZGxzOnUG6/VWnbAMDAB2+H3IiIiZzAVmOWsVa9ePerVqxfsMEREzjEVPfFnqfEoRERERCSQcmQRkZphYFBIJJlxCUeKy/5jYJq+4AQmIiJSjfSNr4iIiFSbUsID3puAk4jgBCMiIhXyYVTqJSIiIiInLqJNOCYGnlJHuWMmEJmaWfNBiYjIb6psjqw8OZAKzCIiIlJttkc2wEkUHhx4cFBCNBviGwU7LBEREREREZEakbDuUQzDTaO83eDxBh70eGneNis4gYmIiFQjFZhFRESk2qxIimJzVCPySOQgifwa24wNF9UJdlgiIiIiIiIiNSbZ9U9iOcj1X/+X0OJivD4fIcXFxFr3Bzs0ERGRaqE9mEVERKTa/GHbaLxeL3ObT8djMSh+2GTkyEHBDktERERERESkxhg2G3HOiXQDoj7fz7af8ul3dyqzZs8MdmgiIiLVQjOYRUREpFpZrVaGbBhJ8cNmsEMREZEKmIZRqZeIiIiInLz2fWox+OFUrDZ9FS8icjqqbI6sPDmQ/qqJiIiIiIiIiIiIiIiIiEilqMAsIiIiIiIiIiIiIiIiIiKVoj2YRUREREREziE+reolIiIiIiIiAihHrirNYBYRERERERERERERERERkUrRDGYRERGpdg/2XkyznCRsHpPxr3xG7V5RjHymR7DDEhEREREREQmKXQdKefqXgcQAk+8/wP7wEDL+kxDssERERKpEBWYRERGpVnd2+IAL8iC2tIAodzH7S+PJ/9gLzwQ7MhERAfAZWv9LREREpKZd8Wwx6QkxmKYJQLzXS/LD+8l8tlaQIxMRObcpR64aFZhFRESkWjXz2OiybyWt87cDUGqx82WdSxjd9g2mrrspyNGJiIiIiIiI1KySklK2R0dghtjAWlbIyHF6qOd0BTkyERGRqtEezCIiIlKtUopzaZ2/HRNwWW2E+Nx03f8DYVFxwQ5NREREREREpMYtWpGNN9TuLy4DEGrjQFho8IISERE5CZrBLCIiItUqsSSHsb2HML7nFRQ7bDTMOcgHrz9Pyr7MYIcmIiIiIiIiUuMiAGzll2AtdVhrPBYREZHqoBnMIiIiUq3ePu9CnujTn6IQO6ZhkJ4QR99RD5MdFhHs0EREBDANo1IvEREREakesRF2LD6zXLtRQZuIiNSsyubIypMDqcAsIiIi1eqb5i3KfeDaFx3JjqTEIEUkIiIiIiIiEjy/ZHuoqCyR7Cyt8VhERESqgwrMIiIiUq2iKkiQ7R4vRmh4EKIRERERERERCa6UaC82n69ce6TLHYRoRERETp72YBYREZGTsqX9U/h+KSsqh3RLoGnkJeyPDGdHrVh/nz4/bSU3oTaN7tnL9hdqY2hJGRGRoPHpV7CIiIhIjSouAJel/Fyv3aEhQYhGRESOphy5alRgFhERkSrbYn2Qg5Ex1CIP04D9a2y0aJ1Lt4++ZXlqClnREbTdtZ9me3P4pE0TClISqfNANvue03LZIiIiIiIicnYrKihiXqsFmIaBcfc19NycQesDeTitVr5rmMT6xOhghygiIlIlKjCLiIhIlbkcIUTle4ghHxMDZ140tZzFOLzQbeOugL71cvLIiQ4j0umi3h3p7H6lUXCCFhERERERETmFzMJivFE3sTq6M3WLYgj1wq3fbaCOt2yZ7Bg8XLNhNwVtGgKaxSwiImce7cEsIiIiVRbnLKAZvxBDLrHk0Ix1pOZsxKygb0moA4DCUDuuECXQIiLBYmJU6iUiIiIiJ863eRe+qNuwEMLF+WuJ8xYAEHNMomwA7fYdrPH4REQkUGVzZOXJgVRglkpZsGABaWlprFq1KtihiIjIaSTCkoflqHKyBZNQCgn1ltBl32ou27uM83I24jVgd2zUoV4GURVVoEVERETOEMqRRUTkt3iaP4AFDxZKCaWA9nxLLPspdJRfTNRj1dfzIiJyZtIS2SIiIlIlrtq3Y1oM8AW2RzuLaWbfwweXXElRSDj1czOwOPfxyXmph3qYFFn1xJ+IiIiIiIicfaxYMXD531swacwGei7fyC/nN/a3F9usbIqPqugSIiIipz0VmEVERM4wpmnCrmyIj4ADBfj2FWDEh2McLISMXLwrtmPGRGH9aQvGX6+CxFh8X/2K0a0F5u6DGLEhWBomQURo2QX35mLuzwOrDVqnYP6agbllH64/voZvXz5W04UPE1ujOnjSd2EFLFiwY2KrYGkYm8fOwot6YRplx3bF1SXloEGYq5QSRwiY4LJaa/AnJiIiIiIiIlI9zMJSzIPFGKs24dt5ENNuxdI0ESMxGufw53FUsGlUKMWUOCB1/Q5+aF6Pn+vX4ofacTTJzg/CHYiIiJw8FZjljOZ0OrHZbNhs+k9ZRM4N5oqtcONk2Lof0wDDNDFwH3qBDxseInDgwsACb60AXJjYKSUOEysGPhzWAmwP94bF6zCXbQMcgIFpGJSakfiw4aAAGz7ADpiY6fuwY2BgYuI7VFo2j0mdbeyJaewvLh+2JzaZEI+XEgdgGES4PKf+hyUiIhXyGVpFQuRspRxZROTUcj3+Cd4nFxLi2Y+BFwv4H7v2YMNGaIV7dO4JT8a0GHgxuXDddl7u0oZmucU4fFoiW0Qk2JQjV43+gskJMU2T2bNnc/XVV9OpUycGDx7MwoULy/WbP38+w4cPp0uXLvTo0YMxY8bw448/BvTJyMggLS2NqVOnljt/6tSppKWlkZGR4W8bO3YsaWlp5Obm8vjjj9OnTx+6devG/v37T+geVq1axX333UevXr3o3LkzV199Nf/85z85ePCgv4/H4+H1119n6NChdO7cmV69evHQQw+xZcuWctdbuHAhI0aMoGfPnnTt2pWrr76aRx55hNzc3BOKS0Tk95g+HwybBFvLfu8ZJoAXy6HiMoAFDyEUlhWXy3ph4qCUeEzKZg2bWCj1RuF7+kPMZZs5XFwuu6aJgyLAjQ0vR1Jl41AR2zz0Dv//GkfOBhzUKs4pF7vF6yYvLKzs3z6T83ILq+NHIiIiIhJUypGVI4vIucP73424x35CiOcAFrzAkdzYh4FBKAZeSknCRYy/0LwvNIlvkjr6r1MQEYI71M7+yBAivT7umlk+hxYRETnd6ZFWOSGTJk2itLSUwYMH43A4eO+99xg7diz16tWjffv2ALzwwgvMmjWLNm3acNddd1FcXMwHH3zA6NGjGT9+PF27dj2pGMaMGUNCQgK33347JSUlhIeHV/rc999/n6effppatWoxZMgQkpOT2bt3L19//TX79u0jNjYWgEcffZQvvviCSy65hCFDhpCdnc3cuXMZOXIk06ZNo2XLlgAsWrSIsWPHcsEFF/DHP/6RkJAQ9u3bx7fffktOTg5xcXEnda8iIgG2HYDtB45p9FbQ0XfMOzvlnymz4CMUK0445ulqCz4clBzTbvqLy7+lbGazl4bZu4jIL2Zzcjwb46OJcHuIKCgum9VsmqQeLCbc9B33WiIiIiJnAuXIypFF5Nzh/XLToVnLFa3IZWDBxH1oUyk30biJ5PvEVqTHJgf03FQ3AYCsqFC2xbvJWu2EW059/CIiItVJM5jlhLhcLmbNmsUtt9zCDTfcwJQpU7Db7bz77rsApKenM3v2bM4//3xeffVVhg8fzqhRo5g5cyZhYWE888wzeL0VFUMqr2nTprz44otcf/313Hrrrf6E9/fs27ePcePG0ahRI95++23uuusuBg0axJ133smcOXNo1qwZAMuWLeOLL77g8ssv56WXXmLYsGGMGTOGV155BZfLxbhx4/zXXLx4MREREUyZMoVhw4YxaNAg/vjHPzJ79myaNm16UvdZXXJycigtLfW/LywspKCgwP/e5XKRnZ0dcE5mZuZx3+/du7dsD1iNoTE0Rs2OkRwDUaEEqmgJF+OYdz4oVxw2MXBV0F7W4iak3LGjl/nyHecjxLbIOpTklzCzbWO+T0nkv43q8H39WjTOKaBz5kFCMHHabWf+/x8aQ2NoDI1xnDFOZz7DqNRLRH6fcuQzL0eG0+dvhcbQGBrjzBojv04IJtYKl8A2Dm0kdfSD2SYWUvIKsR31e77IYWdu5zb+96UOCzFRNXsfGkNjaAyNoTw5UGVzZOXJgQzz6P8CRH7DggULePzxx/n73//OoEGDAo5df/31OBwOZs+ezcyZM3nxxRcZP348PXr0COg3fvx43nrrLWbNmkXr1q3JyMhg4MCBjBo1itGjRwf0nTp1KtOmTeOjjz6ibt26QNnyXwsXLuTNN9+kRYsWJ3wPb775Js899xz//ve/6dOnz2/2e/rpp3nvvfd46623/An1YQ899BCLFy/miy++IC4ujscff5yPP/6YZ555hh49emDoF4yInGLm5P/C3bPA/+fbBEr8SayJgZcwrAEJrwcXYXg4krXaKMJxfjTGpgzMEijbZ/lwcTkcDyGEko3lqAWwTf9ez6Z/oezA5NnAxMJ2SzN63ns/uxOiA2JPzCsm2mqQHh9BSvp+dk5Oqb4fjIiIVNpNI7ZXqt8bsxqf4khEzlzKkcsoRxaRc4npdOO8fDKWb37GQe4xa36Bl1AMLJQSiUkIJga5JOKy2NgbGc2r3dJ475I2FIaF+M9rvyuL1c/XxqLflyIiQVPZHBmUJx9NS2TLCUlJKV8MiImJYe/evQD+/aAqejL5cNuePXto3bp1lWNo2LBhlc7btWsXwO8m3hkZGVgsFho3Lv+LokmTJixevJg9e/YQFxfHyJEjWbNmDQ899BAxMTFceOGFdOnShcsvv5yIiIgqxSkicjzGXb0xe7eBxevBYmBu2It3w34suLF4SjHX7MKb48NrerCZpVgaxePrfT7WxesxmtXDCAvDEmZguTEN44oLIKcQY85SzKUbISkWBl+MMXsVzFlOqTcCKyXY8WBi4sGOcajgbKUUy6FC85H9mMvK24m+vWRHhpWLPSs6jKywso8ennBHDf3ERERERE4d5cjKkUXk3GGE2gldcg/ezzbg+XAV1i9WYu4twHRYsSSGY4RY8fyyFwvFeAnBwMSOC3wGDfJzKQ21BBSXLT4Tp9dUcVlERM5IKjDLCbFYKl4StSoT4Y/3JPPxlggLDT12edjgadCgAXPnzmXFihWsXLmSNWvW8OSTT/qfLq9Xr16wQxSRs5DRPBmal+3hZBC434UBhBzT33q8iyVGw70DMO4d4D/fcXk7HLNGlut69HVMlwtfyDDKis2BfwNCcXPlLxuZd2HbI3GZJob10O7QponPowVURESCxafvMEWqjXLkQMqRReRsZ1gs2K5sDVe2BkaUO24FXMbNHP6tHUE+JUTiwsHH57UM6OuzGOyMjyp3DRERqVnKkatGezBLtTr89PbWrVvLHdu2bVtAn+josqVT8/Pzy/Xds2dPtcfWoEEDADZt2nTcfikpKfh8PrZvL78swuG2o59SdzgcdO3alfvvv5/Zs2fz/PPPc+DAAd58881qjF5E5PRiOBxYzXlkWWqXO1ZsRPPSx+9ww5rVhLtcNMrJ5uV57xLldfv72Hwnt9egiIiIyJlAObJyZBE59xiUYKFsr1ELJhEUAG4qevYozPTVbHAiIiLVRAVmqVbdu3fHMAxmz56Nx+Pxt2dlZbFgwQKSk5P9y29FRESQkJDAypUrA57u3r17N4sXL6722Hr16oXdbmfatGkUFhaWO344hsP7Ys2YMSMgri1btrB06VLat29PXFwcAAcPHix3nZYty55GzMvLq+5bEBE57ezwtSGXBEzK9pzKI44dZhMiS1386ftvefrzL/nXf/9HcmE+eWGHls02DEpDtUS2iIiInP2UIytHFpFzj+vKi3CQhZ08SrCzmzrspB7XfL8xoJ/V66P9vtwgRSkiInJytES2VKtGjRpx8803M2vWLEaNGsXll19OcXExH3zwAcXFxTzxxBNYrUcWWb3uuuuYMmUK9957Lz169CArK4v333+fpk2b8uuvv1ZrbLVr1+bBBx/kmWeeYdiwYfTv35/k5GT279/PkiVLeOyxx2jRogUdO3bk8ssv5/PPP6egoICuXbuSnZ3N3LlzcTgcPPTQQ/5rjhkzhqioKC644AJq165NQUEBCxYswDAM+vXrV63xi4icjmz4+JWLA9oKoiz8e0BvciOT/G0uS1ny7LVawDSJzi+p6VBFREREapxyZOXIInLuifj4/yhu8QChm9LZHN0MT0EiFhP+8PkaYkpKWdi5FeEeDx13ZrEz8vTZ5kBEROREqMAs1e7ee++lfv36zJ07l5deegm73U6bNm148sknueCCCwL63nLLLRQWFvLxxx+zevVqGjduzKOPPsr69eurPXkGuPbaa6lXrx6zZs3i7bffxu12k5SURIcOHahd+8gyr0888QQtWrRg4cKFPP/884SFhXHhhRdy5513kpqaGnC9L774gnnz5pGXl0dMTAwtWrTgz3/+M2lpadUev4jI6Saa/ZRQn7Ldm8HAZHrvizkQnYjdd2SGi8MHdQpK2RMTCm4f3z1bfmltERGpGT60wZRITVKOrBxZRM494RsnAFBw3Wfs//YgVp+Pr9s1I85u4Ya16QC4LQbfx8cEMUoREQHlyFVlmGZFuz+IiIiIVM5m42+UEo+BiZ1sBo/5Eyklbprv3E9sQTHb6iWxPz6a3eF2tkSF0o2D/O+J+sEOW0TknDXslvRK9Xt7ZqNTGoeIiIjIucA0TSb0+JgPW7bH7vVQr6iEEquVzPAwDobYWfuSHsAWEQmmyubIoDz5aJrBLCIiIielmfnvgPcthq5i6NKt1N+fD4BvxQY+6NaOiDgb6+enAVFBiFJERERERESk5hmGQeozF7Ll3RAyQ+1waOsoPD5SvJ5ghyciIlIlKjDLGS83Nxev13vcPuHh4YSHh9dQRCIi57ZL1+/wF5cBLCb0WbmRn+q7AS2NKCISbKah5b9EzmbKkUVETj+1Q00ywxwQZoPDn8W8PrKcwY1LRESUI1eVCsxyxhsxYgSZmZnH7TNq1ChGjx5dQxGJiJzbskOjgZyAtqgSF9mhicEJSEREROQcohxZROT0k11oQOhRxWUom8msooaIiJyhVGCWM94TTzxBaWnpcfukpKTUUDQiIrKrVgxew8Bqmv62dQ2S8FmLghiViIiIyLlBObKIyOnH4rCCpXwx2W2zBCEaERGRk6cCs5zx2rdvH+wQRETkKInFhUy8piO3fPEjCYUl/NyoFnN7nseV33wZ7NBEREREznrKkUVETj/5xR7w+OCYgrLhM3/jDBERkdObCswiIiJSrTxArMfLI7f3xYdJtMtD39W/ctCqJ7NFRE4HPq3EKCIiIlKjerSJgS88YNjKlsY2TXB5ifB6gx2aiMg5Tzly1ajALCIiItUqvDCHpDwnrXdkUBgWSq2D+WyqG8dzy28LdmgiIiIiIiIiNa5OrQjifHnklnjAAEzAAhHO429pICIicrrSVCIRERGpVv9cdS2F4T52JUaQF2ZlY70YoosOBDssERERERERkaA5P7qEel4PYRaIspg0Li4h47lawQ5LRESkSjSDWURERKrdY4uv4KW/vUrhL1H89cPrsdvtwQ5JREQO8Rla/0tERESkpn3+cAIzZsxga14899/enzrx0cEOSUREUI5cVSowi4iIyCkR0cxKRLPiYIchIiIiIiIictpoGpNDQpS+lhcRkTOblsgWEREREREREREREREREZFK0aNSIiIiIiIi5xAfWv5LREREREREBJQjV5VmMIuIiIiIiIiIiIiIiIiISKWowCwiIiIiIiIiIiIiIiIiIpWiJbJFRESkWgy/8juuWbaTiGI3bofBL22b02bkpmCHJSIiIiIiIhIUb9//He/sjGR1al2ScwoZvKw2cffsC3ZYIiIiJ00FZhEREakW136/g6g8FwYmDhf0Xb2Ld+0NGDky2JGJiMjRvNpeSkRERKRGTCysw7JODQDYlRTLLw2T+L9JC0F5sojIaUM5ctWowCwiIiIn7eaeX3NbnpNIu5MIdylui4UCTxiX78gOdmgiIiIiIiIiNe65qxby0yWXcfvHP9BhQwZ74yN5o3c7dtRJCXZoIiIiJ00FZhERETlptX1uHOFuIopLAbD7fMRRxHZbdJAjExEREREREal563NM7n9/BZf+uAOAppkHab91Hx/1qhfkyERERE6eJdgBiIiIyJmv1OIhrrg4oM0ASu3Qc+TG4AQlIiIV8hlGpV4iIiIiUnXO0BC6/7QjoC3C6aZJRvFvnCEiIsFQ2RxZeXIgFZhFRETkpNnsDopCQ8q1f92kKcml+UGISERERERERCR4uq/PxGcp//W726dFRUVE5MynArOIiIictPVx8Sxt3wLzqLaMxDiWNm/I9ymN8Hg8QYtNREREREREpKZlNEpkadsGAW25EaHsSYoMUkQiIiLVR49LiYiIyMkzvbx7SRtW1KtDw8wDFIWHsaNuLXZEhRPuduH1mtj0qUNE5LTg06peIiIiIqfcqpS6bElKZm9cJG12HCArOowv2zfmsl+2YnVcD+a8YIcoIiIoR64qfdUrIiIiJ+2Kn3/lgXYtWREdBa0alTWaJhgGXocNm02LpoiIiIiIiMi5oyAklAv2HeCbNvX5um0D2uzfwd1rFnLhzi0YAMZgFZlFROSMpQKziIiInLSfm9Qj8WARg3/axPa68WyrFUee3UFWWCjO0BAMQ48CioiIiIiIyLmj5YF9xBrhtFq7kRa7NzNw22eE4A52WCIiItVCBWYRERE5aRnR4Yz+5kde73UhexKi/e319udS5HPgeW0Zjj9cEcQIRURERERERGqQaeHCrTtpvXU3MezHoeKyiIicRbRepbBgwQLS0tJYtWpVUOOYOnUqaWlpZGRkBDUOERE5cedv2YczzBFQXHa4XKSUHOCt1yZjGTU1iNGJiMjRfBiVeomcq5Qji4hIdbh4825abd2NASSRrk9XIiKnqcrmyMqTA2kGs4iIiJy0b9s0pf+6dM7bsZefG9Tm7m8+4alP3yLSVYoPtL+UiIiIiIiInFOiDzr9s7vsuIIai4iISHVTgVlEREROypWDl/OXbzeQuj+Xa37YyPq6cfQ98CkR7lJAy6WIiIiIiIjIuafYHgL4SOQAPhxYcAY7JBERkWqjArPIKVRUVERERESwwxARqRLfgp/wrsvENE1e/9zLZ0kpuKxw8Y5ddMjMpMXYiykc8ymj4mqTuj/ff96FGTuIoLTii3q8YLPW0B2IiEhFvIaW9RKR4FCOLCJnk/99lsnYDwq4ZH06JfZQ6hYUciA0BKdh5dc6CXRsnkhTXwYvduvN++dfSHxxAX//8n1uWvO1FlkVETmNKEeuGhWYz3Jut5s5c+bw2WefsWPHDmw2Gw0aNGDAgAFcf/31xz334MGDTJ06laVLl5KdnU1CQgLdu3dn9OjRxMbG+vtNnTqVadOm8dFHH1G3bt2Aa1x11VUkJyfzyiuv+Nt8Ph8zZ87kgw8+ICsri3r16jFy5MjfjCMrK4sZM2bwzTffsH//fiIjI2nWrBkjRoygY8eO/n5r1qzh1Vdf5ZdffsHj8dCoUSOGDh3KNddc4+/zf//3f3z11Vd8+umnAfcAkJ6ezrXXXssNN9zAgw8+6G///PPPeeedd9i8eTNer5fU1FRuvvlmevfuHXB+WloaAwYMoF+/fkydOpVNmzbRqlWrgHsXETkT+NwePM0egx05AAy9eQyfXNEMt73sY8PCtHY037uPxbdPJsJnIaQ4B89RHym+atacelvWYje95S9uHwrfPA5d2tXIvYiIiIgcTTmycmQRkerQ955NdPxhMy99vwWbz8RnwPI2zVjTuglfNUjAZ7WwNK0VWctCea1jFwAOREZxy/VjqJ1XQp+tq8ou5PWCVQ9hi4jImUcF5rOY2+3m7rvvZvXq1XTs2JErr7wSh8PBli1b+Oqrr46bPBcWFnLbbbexa9cuBg4cSMuWLdm4cSPvvfceK1euZObMmVV+6vi5557jrbfe4sILL+TGG28kJyeHZ555hpSUlHJ9MzIyuP3228nJyaFfv360bt2akpIS1q5dy4oVK/zJ89KlS3n44YdJSEjgpptuIjw8nM8//5wnn3ySPXv2MGbMGAD69+/PF198wWeffVbu/hctWuTvc9jkyZN57bXX6Ny5M3/84x+xWCx89dVX/PWvf+XPf/4z1113XcA1fv31V/73v/9xzTXXMGDAgCr9fEREgs33tw/9xeXdUQl80aKpv7h82IP/XYrVV7b4tRW3v8D81MCuvNGtPb8udvCfj9/EYprlB+j6D+3FLCIiIjVOObJyZBGR6rBxaSbLEmvz7PcfY/WVtVlM6LRuMzM7t8HlsFMYYgdgftvzA841LRamX9LjSIG5zX2w4aWaDF9ERKRaaFvEs9icOXNYvXo1I0eO5KWXXuLmm2/m+uuv5+9//zuTJ08+7rkzZ85k586d/PnPf+bRRx9l6NChPPLIIzz88MOkp6cza9asKsWUnp7O22+/TYcOHZgyZQrDhg3jrrvu4uWXX2bTpk3l+j/99NMcOHCA559/nn/84x8MHTqUESNG8Oyzz3L33XcD4PV6+c9//kNYWBgzZ85k1KhRDB8+nFdffZXzzjvPfy8AnTp1IiEhwZ8oH2aaJp988gmpqam0bNkSgA0bNvDaa68xcuRIXnjhBW688UaGDRvG1KlT6dGjB5MmTaKoqCjgOtu2bePZZ5/lgQceYNCgQQwaNKhKP6fqlJOTQ2npkaVqCwsLKSgo8L93uVxkZ2cHnJOZmXnc93v37sU8qmikMTSGxji7xvB9u9X/709bn0dJiINjXZy+y//vjKRw1qUkkR0Rxtudy2Ymj+85kBYPP89Nw+7mx+QG5c6vifvQGBpDY2iMYI5xOvMZlXuJnG2UIytHPux0+VuhMTSGxjgzx/j4yywu3r7bX1w+Wq3cfErsR2Ykhzor6OQ9EqdvY0bQ7kNjaAyNoTFqeozTVWVzZOXJgQzz6P8C5Kxyww03sG/fPj755BNCQkJ+s9+CBQt4/PHHefnll0lLSwNg6NChHDx4kE8++QSb7cisNY/Hw5VXXklcXBzvvvsucGLLf82cOZMXX3yR559/nq5duwb0vfvuu1m2bJn/Onl5efTu3ZtOnTrxwgsv/Gb869at49Zbb+XGG2/kgQceCDi2ePFiHnroIe677z5uvvlmAJ5//nneeOMN3nvvPRo1agTAqlWr+OMf/8if/vQnbrrpJqDsKfI5c+Ywd+7cckuFLVmyhCeeeIKXXnrJ/4R4WloazZs3Z86cOb8Zq4jImcDztw/wPfUZALuj4mn7l0fKFZlnzniLvr+WfemZEx7GxffdxVuz3mXAn24rd733Z45j8LoVgY2awSwiEjS9Rmf8fifgy6l1f7+TyBlEObJyZBGR6rBl+T4u+Cyc7x6fHlBkNoFRdw5mbUoShSF2wp1u7p23gs87N+CC3Rn8WD+F7XEJvDV5Gn2yvyk76fyG8ONzQbkPEREpU9kcGZQnH01LZJ/Fdu7cSYsWLY6bOP+WjIwMWrVqFZA4A/79qTZs2FClmPbs2QPgT1qP1rhxY5YtW+Z/v2vXLkzTpEWLFr8bK0CTJk3KHWvatGnAuAADBgzgjTfeYNGiRf5lwRYtWoTVaqVv377+ftu3b8c0Ta699trfHPvYp3oaNKh4lp6IyJnE8sRAfO//AJv2U68gh/7rN/FRmxa47HZ/n4mXdaXP+o1YTIP44hKa7Msibc8uUvdnsaVWor9fdEkJvbasDRzgx//U1K2IiIiI+ClHVo4sIlIdUi+pTc85m3mzRyuGLd2Iw+vDY7Hw7fktSCn2st7lJsr0UWS3c+26H7j/h//6z91pT6Ce+8iqYfwwIQh3ICIicvJUYJaTZhi/vS6A1+utwUgqJzU1lebNm/PJJ59w1113UVpayv/+9z8uueQSEhMTA/oahsELL7yAxVLxavKHk/PDQkNDT1ncIiI1xWK14tj4T3xLNuH7ZQ9zrAW8895/mRdbH6cNumxLp11eNrb/jSCn79t82KYtm1JqkRMWzszX3+bhIQNY3qgBrfbuo/aBbBYm92T49k/KLu59D37jd6qIiIjI2UA58hHKkUXkbLVgYjNWL4vl/jcac/76nXhsIdTPyaNu+nZu3L6d/7VoyHUbNpDiDnzwpr47G2+oC5yHGo7zN0NEROR0pgLzWaxhw4akp6fjcrlwOMrvn3k8KSkp7NixA4/HU275r507d5KSkuJvi46OBiA/Pz9g+a/S0lKysrKoV69ewHWhbJ+po9uh7Gnoo9WvXx/DMNi4cePvxgplezsd63Db0fFC2RPaEyZMYNWqVWRlZVFUVMSAAQPKjf/dd99Rp04dGjdufNwYRETORpYezbH0aA7ATaPhJv+R9v5/JTofY/bI9TTeX8CYa65i+nsf8MHLM/EBy1MaYCm10zSrCBMwQMVlEZHTgBd9kSnnJuXIypFFRKrTRR2T+KpjEtC8wuNfJn3MsRmwAbzWoS+PfP3mqQ5PREQqSTly1ehb3rNY3759yc/PZ/r06eWO/d7W2z169CA3N5f58+cHtM+fP5/c3FwuvfRSf1vDhg0BWL58eUDfOXPm4PP5Atp69OiBYRi8+eabAU9ub9iwgRUrAvfnjImJoXPnznz33Xflrn30PbRs2ZI6deqwYMECsrKy/Mc9Hg+zZ8/GMAx69OgRcG7fvn2xWq0sWrSIRYsWERkZWa5Pv379AJg0aVKFT5kfu/SXiMi56ucZrQgznDg8cM3tI1heqzE5xNJsTxFNsw4SzsFghygiIiKiHFk5sohIjcqx2nDa7AFtpTYboZ7iIEUkIiJSfTSD+Sx2ww038PXXXzN9+nR+/fVXLrnkEkJCQti2bRs7duxg8uTJv3nuLbfcwpdffsl//vMfNm7cSIsWLdi4cSMffvghDRs2ZMSIEf6+F198MQ0bNmTq1Knk5eVRt25dfvrpJ9auXUtsbGzAdRs1asTQoUN59913ufPOO7nsssvIycnh3XffpVmzZuWexP7zn//Mbbfdxr333suAAQNo1aoVTqeTX375heTkZO69916sVit//vOfefjhh7nlllsYNGgQ4eHhfPHFF6xdu5aRI0eW2/cpPj6ezp078+WXX+JyuRg4cGC5fbjatGnDHXfcwSuvvMKNN95I7969SUpKIisri/Xr1/Ptt98G7IclInIuu/CXrSzo0oG9CVH8t11z6i35gRCPF9PqYk8tk5RMsJvzgh2miIiInMOUIytHFhGpSd6oMAqzIrCTjxUfXizkEUXaTj2QIyIiZz4VmM9idrudl156iTfeeIPPPvuMyZMn43A4aNCgAVddddVxz42MjGT69OlMnTqVpUuX8tFHH5GQkMCQIUMYPXo0ERER/r5Wq5UJEyYwbtw43nnnHex2Ox07duSVV17h9ttvL3fthx56iISEBD744AMmTpxI/fr1+ctf/sLOnTvLJc8pKSnMnj2bV199lW+//ZZFixYRHR1Ns2bNGDRokL9f9+7dmTx5MtOnT2f27Nm43W4aNWrEI488wjXXXFPhPQ4YMICvv/4agP79+1fY54477qB169a8/fbbvPXWW5SUlBAfH0/Tpk156KGHjvszFBE5l+xOSmJvQhQAL/TtyOvd2xNb7KRZTjEel4v/3l4Y5AhFROQwr1b/knOUcmTlyCIiNcniNsFrJZdYDExMDCweg8SComCHJiIiR1GOXDWG+XvrQImIiIj8ju7X/cDyC1rhsgc+u9Z/UyaWooPMm9kqYL9CEREJnm5/zKxUv69fTj7FkYiIiIicvcZdMI8rf9wZsLOnCdRlE3HsPdSglb5ERIKtsjkyKE8+mvZgFhERkZMWYhikZOYFtDXOLaLFvn2sbFQXw9CjgCIiIiIiInLu2JyUQBglAW2hlLCw2floxpeIiJzpVGAWERGRkxafl0/dwlw4emEUn4/26T+zLzZaBWYRkdOIzzAq9RIRERGRqkvKL6Yk1EIMeURQRAx5uEINmuXsxQeavSwicpqobI6sPDmQ1qoUERGRkxbrLOX9Fk3gqA9a2xOiuG3YjWCaKjCLiIiIiIjIOcXqNZjQrTPFoXbiXU72h4exLqk2Dy5dykWZf8Ma7ABFREROggrMIiIictL2RsfitZZfGMXh9lLiMzEMRxCiEhEREREREQmOVfVrs7hVE4pjw8sexjZNcPtY0K49twQ7OBERkZOkJbJFRETkpNXLyaF+Tm5Am8Xn49rlPzHk+1VBikpEREREREQkOJrv2EZxdPiRlb4MAxxWVjeoG9zAREREqoEKzCIiInLS7IaFKXPmUT/nIADRJU7+8/4i2u3LYs4HHYMbnIiIBPAaRqVeIiIiIlJ1pWGRYC3/mcpr1+LYIiKnk8rmyMqTA2mJbBERETlpG+Nj6bbbyoqnJ7IjPo7a+QUU2+081atPsEMTERERERERqXGpyRYMn4lpCSxIXLwjE2gWnKBERESqiWYwi4iIyEn75MMOPNujJ7MvuACvabCkcWNGXHcdLmdhsEMTERERERERqXEPzL2cPut3lO29DGCaxBc66bD91+AGJiIiUg00g1lERESqRZO8bN5r354Xu3YhsaSU+MJCel27CegS7NBEROQonmAHICIiInKOGNPWSaePvuWbFo1plJVLy23pRN2VE+ywRETkKMqRq0YFZhEREakWcz64xP9vt9vNjBkzghiNiIiIiIiISHBd9WBbrnqw7N9ud11mzFgR3IBERESqiZbIFhERERERERERERERERGRStEMZhERERERkXOI1zCCHYKIiIiIiIjIaUE5ctVoBrOIiIiIiIiIiIiIiIiIiFSKCswiIiIiIiIiIiIiIiIiIlIpKjCLiIjIKeH1wvzNF/HTpuJghyIiIiIiIiISVHklPn4uTg52GCIiItVCezCLiIhItWt4fzY+YxAYBv1ecRPqzmTnZCXSIiKnA4+2lxIRERGpEdlfpbPtsgX8lFyHJwd3Y0ftPlBo8uQLbnY+aA92eCIignLkqtIMZhEREalWHo8Hl82OxTCwADYTXDY7de/MCHZoIiIiIiIiIjXm58u/wGkJJdQwuXbNRuKKnWBY2FVixTTNYIcnIiJSZSowi4iISLVqMCYThy8wUbYARWEhwQlIREREREREpIZ9b51IuM9LhM+NxedjWcNkQopd4PaCaWL7d1GwQxQREakyLZEtIiIi1ao41EGYCSE+H9FeH06LQaHVitduDXZoIiICeND6XyIiIiKnmstiJ8rjIz/UwR9v6kvB4YeufYBp4rPpM5mIyOlAOXLVqMAsIiIi1SrE6yPKaqV1can/49kBu5XtoVo4RURERERERM4Nh/PhJc0bHCkuH2YC7pqOSEREpPqowCwiIiLVKqW4lDoWb8Czf0luL5a8IiA+WGGJiIiIiIiI1JhQTwkFIdE4DNeRRoOy4rKIiMgZTlOJREREpFoZ+Ajxlc+YvaaWmxEROR24jcq9RERERKTqIoxSanlzuW7dWpKdxVgjrBBlxxpuxWZRlVlE5HRR2RxZeXIgzWAWERGRatNwzC56p2+k+55izt+xm4MR4Xx24Xmsr1+X9KiwYIcnIiIiIiIiUiOcNju13aUsb9yAvUnRmEZZZcJrsxDqsOBx+YIcoYiISNVpBrOIiIhUmy4ZG+i5LZfOm7YRUeoiJecgI/73DdmGl3yLhf0/7Ap2iCIiIiIiIiKnnMPnBeCNThf5i8uHOR02DI82YRYRkTOXZjCLiIhItXm/1cX8Y/4r5IeF8kXa+eyok0R8fiEtM/ax3WZnfaeF1HLeGewwRURERERERE4pj2kn32ojvshZ7pjV66PHvoNATI3HJSIiUh00g1lERESqjc9qw2ux8GHXi9lcvy5ew4LpMYi0hvL3b1axOzE82CGKiJzz3IZRqZeIiIiIVJ2Jm92OBPqt3EBSgRO7x0eou2xWc2yxi3i39mEWETkdVDZHVp4cSDOYRUREpFrcetUPXGWzsrlOAnuSEkjOzKX7N+sJcXvwGeCMtjPj0rbcNLYIc2xEsMMVEREROSstWLCAxx9/nJdffpm0tLRghyMicu6yGaSUZrErP4G+P29hd60kDAxMn4/I0mKwOIIdoYiISJVpBrOIiIicFJfbg+XvB6lTUEwzZzH/GdCdWS3qMeXi5iy6qCkAFhPC89wk5ZVAqB3jbzlBjlpERERERETk1Al3ubD6StjT3MKeWrUwKJv5ZlgsFNtDsXi9QY5QRESk6jSDWURERE5KyD+dJBc4+Ty1HrnhoaTXigXDICcqjBcGdMDu8bKyVQo7asdg9frAaoBFH0FERILFHewARERERM4BDrxEWXLJjL2o3DG3zUZoSWkQohIRkWMpR64afbsrNcrj8eD1egkJCQl2KFVWVFRERISWdhURAah173aITCQzIoLMw78bnW4IO7LU14uDLsblOOojh2mCw0ZuoZO4yNAajlhEREREgsHpdGKz2bDZ9FWUiJx9PCUeMqf/iOtfn7LvQDw+n0G8zU6Kp4CGufvL9Y/weLF4fUGIVEREpHpoiezTwLfffktaWhpvv/12hcdHjhxJ79698Xg8rFu3jrFjxzJ48GC6dOlC9+7due222/jqq6/KnTd27FjS0tLIzc3lscceo1evXnTt2pU777yTDRs2VCnWtLQ0xo4dy/Lly7n11lvp0qULV1xxBePGjaO4uDig79SpU0lLS2Pr1q1MmDCBfv360blzZ9auXQuAy+Xitdde47rrrqNz58707NmT+++/v1xsPp+POXPmMGzYMLp3706PHj0YPHgw//znP/F4PP5+P/30E/feey9XXHEFnTt35sorr+Tee+/1j3f0z+R493ZYRkYGaWlpTJ06lc8//5ybbrqJLl268Oyzz/r7LF++nDFjxtCzZ086d+7MsGHDeO+996r0sxUROePYr6XEEQHmMe1eEw4nyhYCi8sAhgFeH/HP10SQIiIiIucm0zSZPXs2V199NZ06dWLw4MEsXLiwXL/58+czfPhwunTpQo8ePRgzZgw//vhjQJ+j8+NjHc79MzIy/G1Hfx/x+OOP06dPH7p168b+/eWLLCIiZzpnlpMf2k/Fd89c9uxNxu0NpbZ5gIO2aExs9PllPeGuI7OVLaZJjNuDYTGCGLWIiMjJ0WOjp4GOHTuSkJDAokWLGDZsWMCxnTt3snbtWoYNG4bNZmPx4sWkp6fTu3dvkpOTycvLY+HChTz88MM8+eST9O3bt9z177nnHqKjoxk1ahTZ2dm8++673HHHHbz22mukpqaecLwbNmzgyy+/5JprrqF///6sWrWKt99+m61btzJp0iQslsDnFh599FFCQkIYPnw4hmGQmJiIx+Phnnvu4eeff6Zfv35cd911FBYW8sEHH3D77bczbdo0WrduDcBrr73Gyy+/TLdu3RgyZAgWi4WMjAyWLl2Ky+XCZrORnp7OmDFjSEhIYNiwYcTHx5OTk8OPP/7Ipk2baNeu3Qnf52FLlizhnXfeYciQIQwZMsQ/e3nevHk89dRTtGvXjttuu42wsDCWL1/O008/zZ49e7jvvvuqPKaIyGnvuY/IskVRaA+r+LjPBIuJ3e3DbT/meTbTPHTcwBhbhDlWq0KIiIiIVLdJkyZRWlrK4MGDcTgcvPfee4wdO5Z69erRvn17AF544QVmzZpFmzZtuOuuuyguLuaDDz5g9OjRjB8/nq5du55UDIfz9Ntvv52SkhLCw8Or4c5ERE4vG2ZsoeXmFaylMyYGBj4akk6BJwwPoSS49jB/5sPkhUbyzgV9+bbJxVgA0zAoKfYSFm4N9i2IiIicMM1gPg1YrVb69evH+vXr2bZtW8CxRYsWATBgwAAAbr/9dl5//XXuuusuBg0axK233sobb7xBgwYNmD59eoXXT05OZtKkSQwbNowxY8bw8ssvU1JSwsSJE6sU75YtW3jiiSd48MEHGTp0KM888wzDhg1j5cqVfPHFF+X6R0ZGMm3aNIYPH86NN95Io0aNeOedd1i9ejUTJkzg0Ucf5dprr+XWW2/lzTffJDY2lueff95//ldffUXjxo157rnnGDp0KEOGDOGee+5h7ty5/uR02bJlOJ1O/vWvfzFy5EiuvvpqRo4cycSJExkyZEiV7vOwrVu3Mn36dMaMGcOgQYPo06cPWVlZjBs3jj59+vDaa68xYsQIhg4dyrhx4xg2bBhvvvkmu3fvPqlxq0tOTg6lpUeekiwsLKSgoMD/3uVykZ2dHXBOZmbmcd/v3bsX0zwyZVFjaAyNce6N4f5qLbGlBWXF4qP6YYLd7cUodmM/WILjQCHJWQUEcPvKXoZR9grifWgMjaExNMapGuN0VmwYlXqJyJnN5XIxa9YsbrnlFm644QamTJmC3W7n3XffBSA9PZ3Zs2dz/vnn8+qrrzJ8+HBGjRrFzJkzCQsL45lnnsHr9Z5UDE2bNuXFF1/k+uuv59ZbbyU2NrYa7uzknS5/KzSGxtAYZ8cYhemF2EwPvkNftdvwYsFHbc9BQsnBRglW00d8ST53fvcuqVk7AbD4fPy6/vS5D42hMTSGxjjVY5yuKpsjK08OZJhH/xcgQbNlyxaGDRvGLbfcwj333AOULWd19dVXExYWxjvvvFPuHKfTidPpBGDKlCm8//77LF68mMjISKBsSaqFCxcyY8aMcjN47777blauXMlXX311Qk8Qp6Wl0bBhQ95///2A9qysLPr27cvll1/OU089BZQtkzVt2jTGjRtHz549A/rfdNNNOJ1OXn311XJjvPDCCyxatIglS5YQGhrK6NGj2bBhAxMnTvQ/ZX2shQsXMnbsWEaOHMkf/vCH39zj+fDPZNWqVRXe24ABA/zLZGdkZDBw4EB69OjB+PHjA/q+/fbbjBs3jsmTJ9O8efOAYxs3bmTMmDH87W9/Y/DgwRXGISJyxlu+if09n6LO/RMxD33vGOb1ElVcyv6owBnJF+89wIp2DY40OD1Q7IZIBy2ivWy4WzNZRERqUux92b/fCTg4MeEURyIip8KCBQt4/PHH+fvf/86gQYMCjl1//fU4HA5mz57NzJkzefHFFxk/fjw9evQI6Dd+/HjeeustZs2aRevWrf358ahRoxg9enRA38O5/0cffUTdunWBI7n3m2++SYsWLU7tDYuIBNnWd7YTffMzbHOfh5NQANJYzl7q04Q1HFuO+LBNL15P60eks5RZb7XAUMFCRCSoKpsjg/Lko2mJ7NNEamoqLVu25NNPP2XMmDFYLBbWrFlDRkYG9957r79fTk4OU6ZMYcmSJeTk5JS7TmFhob/AfFjjxo3L9WvcuDHLli0jMzOTpk2bnlCsFV0vMTGRqKgo9uzZU+5YgwYNyrVt376d0tJSevfu/ZvjHDx4kDp16jBmzBgeeugh/vCHP5CUlMRFF11E165d6dWrF3a7HYA+ffrw8ccfM2PGDObMmUO7du3o2LEjV1xxBcnJySd0f5WJPz09HYC77rrrN8+r6P8fEZGzxiXNqXVBbTpkbGNFSlPwmkxc+BWPXNY5sJ/FYEXb+oFtIVbwesGj4rKIiIjIqZKSklKuLSYmhr179wL490yu6DuBw2179uzxb19VFQ0bNqzyuSIiZ4omQxvx04rhNJrwFltojwcr6ziPOArxYseGO6D/ttgEfomNoltGiYrLIiJyxlKB+TTSv39/xo8fz8qVK7nkkktYtGiRf/lsKJvRfPfdd7N9+3aGDRtG69atiYyMxGKxsGDBAj799FN8Pl+Q76K80NDQCttTU1O5//77f/O8uLg4AM477zzmz5/P999/z6pVq1i9ejWffvop06dP59VXXyUmJgaHw8HkyZNZt24dy5YtY82aNf6nqJ988kkuvfRSgN/80ObxeE4o/sMT/x9//HESExMrPK+iZF5E5Kzy3dPkP50HxVYalBZRp+AgKfkFgTOYbYHLYANl770m5j+197KISDCU6HtMkXOCxVLxrmhVWcjueAWQ4y2j/VvfB4iInE0Mi0H78d1w/7MTF23ey8H/7sJjcVD64OfkUIdEdvn3qUyPq8OXzS6mltNN6aGJMyIiElzKkatGBebTSN++fZk4cSKLFi3i/PPP58svv+SSSy7xFzA3b97Mpk2bKlySav78+b953e3bt5dbInv79u1YrdYqze7dvn17ubasrCwKCgoqXVStX78+ubm5dOjQ4TeT3qOFh4fTq1cvevXqBcDcuXN55pln+PDDDxkxYoS/X9u2bWnbti1QtgfA8OHDmTJlir/AHB0dDUBeXh4xMTH+8yqaef178QPExsZyySWXnNC5IiJnk/V/jcF4rIADpoXs+Eju/WYlo4dcictW9hEjPq+Y/Eg7Husxv+tPcj8/ERERETk5h/P3rVu3Uq9evYBj27ZtC+hzOJfOz88vd50TzadFRM5W9ggb9vb1qNO+7Hfquoc+pdRM4fE+g+iQvZXsiBj+1/QinPYQbF4vxVZ9NS8iImeu36/sSY2Ji4ujc+fOfPXVV3z66acUFRXRv39///HDhdhjnzbesmULixcv/s3rzpo1K+CcDRs2sGLFCjp06HBC+y8ftmPHjnLjzZw5E6Dcvk2/pX///mRnZ/Pmm29WePzoDeUPHjxY7njLli2BI8ltRX1q165NXFwceXl5/rbDy12vWLEioO8bb7xRqbgPu/zyy3E4HEydOtW/D/bRCgsLT+tN60VEqpP5zygW3hnOT8n1eLvDeby08H/8cfmP/OXzZdz31Y/cM38ZcQUlAIS63ODxsvo2PaktIiIiEkzdu3fHMAxmz54dsKpXVlYWCxYsIDk52b9/ckREBAkJCaxcuTLg+4Xdu3cf9/sIEZFzWZEZjpNILAVWXr/oSj5u2RmnPQQTaFRYTK2S8t8pioiInCn0mNRpZsCAASxdupTnnnuOyMhIevbs6T/WuHFjmjRpwqxZs3A6nTRs2JCdO3cyb948UlNTWb9+fYXXzMzM5O6776Z79+5kZWXx7rvvEhISwn333VelGFNTU3n00Ue55ppraNCgAatWreLLL7/kwgsvpE+fPpW6xg033MDy5cuZOHEiK1eupEOHDkRERLB3715WrlzpL94CXHvttbRr1442bdqQlJREVlYWH3zwAXa73T/e9OnTWbZsGV27diUlJQXTNPn6669JT08PmOF8xRVXMHnyZP71r3+Rnp5OdHQ033//fYUF6uOpXbs2f/3rX3nyyScZOnQo/fr1Izk5mdzcXH/Bf+7cudStW/eErisicqa6LDWMtrPO44p/5PB/V/eid0YW1323FIe7bKbywJWb2R8TzvzOzXmrXVMubBob3IBFRM5hLrT+l4hAo0aNuPnmm5k1axajRo3i8ssvp7i4mA8++IDi4mKeeOIJrFarv/91113HlClTuPfee+nRowdZWVm8//77NG3alF9//TWIdyIicnqKogQnDob9+BOzL2tPpMeHzWeyPzyEHdGh9NuWGewQRUQE5chVpQLzaaZbt27ExMSQl5fHNddcQ0hIiP+Y1Wpl4sSJPP/88yxcuJCSkhKaNm3K2LFj2bRp028WmF988UUmTJjAK6+8gtPppF27dtx33300a9asSjG2bNmS+++/n8mTJzNv3jwiIiK47rrrGDNmTKWWuwaw2Ww8//zzvPfee3z88cf+YnJSUhJt2rRhwIAB/r433XQT3377Le+88w6FhYXEx8fTtm1bRo4cSfPmzQH8ye1///tfcnJyCAkJoX79+jzyyCNcffXV/mtFRkYyceJEJkyYwIwZMwgLC+Oyyy7jiSee8C+jXVkDBw6kQYMGvPHGG8ybN4+CggJiY2Np2LAhd955JwkJCSd0PRGRM12tOpFctPcnkopq826jZBzdz2P0lz8AYPf68Nis3PzdD8xZcFGQIxURERERgHvvvZf69eszd+5cXnrpJex2O23atOHJJ5/kggsuCOh7yy23UFhYyMcff8zq1atp3Lgxjz76KOvXr1eBWUSkAjZKSSCH2eefT15YCHlHHdsWG0lOiFb2EhGRM5dhHrvespw1xo4dy8KFC1m1alW1XTMtLY0BAwYwduzYarumiIicPYYPXUmUGcb8RnXZFxZK6r5cLtq+l30x4Vyxczcdfl1Hr8L7gx2miMg5zfhTTqX6mc/Hn+JIRERERM5eK0Im0NiVzc1Dh/LZ+a3LHe+5NYOvXmtU84GJiEiAyubIoDz5aNqDWURERKpNrsVNVqiNophQmufmkxsVyromtTgv7yCrG9dVcVlERERERETOCWG+skVXG2blYnh9AcfCnS4Wp8QFJzAREZFqoCWyhdzcXLxe73H7hIeHEx4eXkMRiYjImSqxTQy5a3MwaMCmRokAuEpCmdy4Fi13Zgc5OhERAdD2UiIiIiKnnuHx4rZYuWHdz7zbti2F8RF4bRZspR7chgGGI9ghiogIKEeuIhWYhREjRpCZmXncPqNGjWL06NE1FJGIiJypZj3WhnpjMum0I5tfosPItttxeLy0LsmjwKqPHSIiIiIiInJuKCaccJ+X+JISmnoLWB1ZNmPZHWoDpxfcvt+5goiIyOlL3/SexcaOHVupvZKfeOIJSktLj9snJSUFoFr3cxYRkbOTxfSyNiqMzNBQAJxWK/l2G+0OFgQ5MhEREREREZGaYbF62eNLZuJlF7K6cYPAgyEW8HqCE5iIiEg1UIFZaN++fbBDEBGRs4jTsHAgLDSgzW2xUGixBCkiEREJYGj9LxEREZFTLcznJjMslHUpyeUP6vOYiMjpQ7+Tq0Tf9IqIiEi1CvF4sfjM8gcqahMRERERERE5C5WaYaR49mMCmMfkwz4TPMqRRUTkzKUCs4iIiFQrr8VLg8LigLb4UhcOn/aXEhERERERkXNDaMdYrC4bQ374sayYfLjIfKi43KupNajxiYiInAwVmEVERKRaZUxpRJTHTcvsPBoUFJOaW0DjgwW0bK7kWURERERERM4Nrb+/FS8+hixLJ/lAPrhMKPWB2wdeH/+9NSzYIYqIiFSZCswiIiJS7W4fHIYr1EG8z4PVbiEnwsHchyrYd0pERERERETkLHWheT8x19bjldc+Ij6/oKzRMPn5D9rvU0REzmy2YAcgIiIiZ5+7Lo0mLH0GACNHjsRutwc5IhEREREREZGalzp3IKnAXrebGTPK8uSWiSODG5SIiMhJ0gxmERERERERERERERERERGpFM1gFhEREREROZcYWpJRREREREREBFCOXEWawSwiIiIiIiIiIiIiIiIiIpWiArOIiIiIiIiIiIiIiIiIiFSKCswiIiJySrR9cB/nj8kj88n/BTsUERE5mlHJl4iIiIhUm/yrX6P7E1vB7Qt2KCIicrTK5sjKkwOowCwiIiLVbp/jESJKIzDt4ZQ8uYYNxiPBDklERERERESkxjmXbafIuInwT76m0e7d3Hz31+QMfjXYYYmIiJwUW7ADEBERkbPLWuNR9sU1ZV9kHAAWXwJNc/aRvWoLCWmpQY5OREREREREpOY4Oz1BDMUBE988C1cAdwYrJBERkZOmGcwiIiJSrTyOKH9xGcBnsbIzLp6MDtODGJWIiByhtb9EREREaooFA7DjIwIfkZg4sOEOdlgiIuKnNbKrQjOYRUREpFq5QgwM00e9ggM43F4OhoaTHRFLsSUs2KGJiIiIiIiI1CgDA5Nw/3uTMKx4gxiRiIjIyVOBWURERKqV4XPTMDubLEccOAwsXh+p2bvxGNZghyYiIiIiIiJSowxscExB2dDX8iIicobTEtkiIiJSrUJcVrJCYsEoWzbGZ1jItiUQ4nUGNzARERERERGRGlbxgqpaZlVERM5sKjCLiIhItTKNI09iR3iLaVaynfquPaTg4uBnPwcxMhERAbS1lIiIiEgNMjEwA96DVzOYRUROH9qCuUr0l0xERESql8UFQJQvn9buzcSXFgJlSXRR39fAfD54sYmIiIiIiIjUIBfhOIkhjCIMfDiJwIeF0GAHJiIichJUYBYREZFqszDxRXamtiYrKpafUy/B6RhA0wMZPPjVPOoU5BKGK9ghioiIiIiIiNSYPVFJ1CoophCHv213VBS1/q8A31NRQYxMRESk6rREtoiIiFSLzyOeZV9yPbLj4ljRuilORwgAW5Pq8lL3qwAw8AUzRBERAS39JSIiIlJDbhm+gVKHDQeFxLGTeHYQRi4FDgdhHm+wwxMREdAS2VWkGcwiIiJSLXKSkigKCacoPJQGB/MpsVnJDQvFY7Xya52GuKxWbF7NYBYREREREZFzQ6HFQaQ3Dysl7Lakkh8Szo+NEjkYCTaXCswiInLm0gzms9xVV13FHXfcEewwqtXUqVNJS0sjIyMj2KGIiMhRLIaDgsgIbFYbNtPEtFqJdXuILS0lsTAfq9eHmxC+Wnsw2KGKiIiIiIiInHJe0yQ7NI4DNMb0OYgq8dBt/V7SoxvijA4JdngiIiJVphnMcs4pKChgzpw5XHTRRaSlpZ3UtVasWMG8efNYu3YtOTk52O12GjRoQKdOnbj22mupXbt2NUUtInJ6q/NAFrOKCigJD6PUaiE/LNR/zGZCXEkpb17cl7Rt6xn9motNzwUxWBGRc57W9RIRERGpCaE+E58rgk31IsiKjaJO9kEaZ+5j1NJVTOzTmey8YhJiwoMdpojIOU45clWowCxnnNtvv51bb70Vh8NRpfMLCgqYNm0aQJULzD6fj3//+9/Mnz+f5ORkrrjiCho0aIDb7Wb9+vXMnTuX+fPn88UXX1Tp+iIiZ5La9+/H6jFZ0aIVsTl5FIQ4IDoqoE9uVDQ/hLTgp/rNSCoqDVKkIiIiIiIiIjXHjskPLZqyo86hSSjNoPX2nXRe9ysYsHiThyEdghujiIhIVajALGccm82GzRbc/3RfeeUV5s+fzxVXXMHYsWOx2+0Bx++//35eeeWVIEUnIlIDXv8S/jQDXB5CRj7P8PQMLBYLSftyID6mXPeYkgIwrHgtNmq5PHhcPmwO7dQhIiIi5zaPx4PX6yUk5MxdJrWoqIiIiIhghyEictr56n8HCHe5/cXlEquFn2Oj+LL2BbxyQQtweykp8gQ5ShERkarRN7tnib179/LXv/6VHj160KNHD+6//3527979m/2XL1/OmDFj6NmzJ507d2bYsGG89957AX0ef/xxOnfuTGnpkZlmP//8M2lpaVx22WX4fD5/+7fffktaWhqff/45ABkZGaSlpTF16lQ+/fRThg0bRufOnenfvz9Tp07F4yn/4Wnz5s089NBD9OrVi86dOzN06FBmzpyJ1+sN6FfRHsyH29LT05k0aRL9+vWjU6dO3HDDDXzzzTf+fqtWrWLgwIEATJs2jbS0NNLS0rjqqqsq82MGICcnh9mzZ5OcnMxjjz1WrrgMEBUVxYMPPljpa4qInFFe/hRGToK8Yihx0WNfNlYgvLAYgMScPOJz8vzdDzgsfFg/mU/qxJNtMfFaDPr+cVeQghcREZHT3eH88u23367w+MiRI+nduzcej4d169YxduxYBg8eTJcuXejevTu33XYbX331Vbnzxo4dS1paGrm5uTz22GP06tWLrl27cuedd7Jhw4YqxZqWlsbYsWNZvnw5t956K126dOGKK65g3LhxFBcXB/Q9nLdu3bqVCRMm0K9fPzp37szatWsBcLlcvPbaa1x33XV07tyZnj17cv/995eLzefzMWfOHIYNG0b37t3p0aMHgwcP5p///GdArv3TTz9x7733csUVV9C5c2euvPJK7r33Xv94R/9Mjndvhx2d53/++efcdNNNdOnShWeffdbfpzLfNYiInAt27SnlgxcyCD30vWapxWBOw7p8mxTPT3HRrKhTB0p93Pypvp4XEZEzk2YwnwUKCgq444472LdvH4MHD6ZJkyasWbOG0aNHBxSHD5s3bx5PPfUU7dq147bbbiMsLIzly5fz9NNPs2fPHu677z6gLJlcsGABP/30ExdffDFQtuewxWIhPz+fjRs30qpVK6CscGsYRrnEdOnSpezZs4ehQ4eSkJDA0qVLmTZtGnv37uUf//iHv9+vv/7KHXfcgc1m8/f9+uuvefHFF9m8eTNPPvlkpX4WY8eOxWazcdNNN+F2u3nrrbd46KGHmDdvHnXr1qVx48Y88MADTJgwgUsvvZRLL70UgPDwyu918s0331BaWkr//v3P6KfMRUSq7KFZAW9DPGUJs8de9rHCAM5fv43c6Ei+SU3m4/pN/X03R8XQprCQ1LwStqw8SGqH2JqKWkREDtP2UnKa69ixIwkJCSxatIhhw4YFHNu5cydr165l2LBh2Gw2Fi9eTHp6Or179yY5OZm8vDwWLlzIww8/zJNPPknfvn3LXf+ee+4hOjqaUaNGkZ2dzbvvvssdd9zBa6+9Rmpq6gnHu2HDBr788kuuueYa+vfvz6pVq3j77bfZunUrkyZNwmIJLB48+uijhISEMHz4cAzDIDExEY/Hwz333MPPP/9Mv379uO666ygsLOSDDz7g9ttvZ9q0abRu3RqA1157jZdffplu3boxZMgQLBYLGRkZLF26FJfLhc1mIz09nTFjxpCQkMCwYcOIj48nJyeHH3/8kU2bNtGuXbsTvs/DlixZwjvvvMOQIUMYMmSIf/ZyZb9rEBE5Fzz+13TqO0vZHR1FtNvFxugY8hzHTFJx+7CXeiu+gIiI1BzlyFWiR6TOArNmzSIjI4O///3v/OUvf2Ho0KE89dRTXHbZZRw8eDCgb1ZWFuPGjaNPnz689tprjBgxgqFDhzJu3DiGDRvGm2++6Z/53KFD2QYgK1eu9J+/atUqunbtSkREBCtWrPC3r1y5kiZNmhAfHx8w3ubNm3n55ZcZM2YMw4YNY9KkSfTs2ZMFCxYEPDU9btw43G4306ZN8/d96aWX6N27N59++mnAWMcTGxvLtGnTuPHGG7nlllsYP348Ho+HefPmAZCQkEDPnj0BSE1NpV+/fvTr18/fVhlbt24FoHnz5pU+J5hycnICHjQoLCykoKDA/97lcpGdnR1wTmZm5nHf7927F9M0NYbG0Bjn6hgud0Dfw+/zY6NwHZUwx+fl82lqI461IywMr8XCri0Hzv6flcbQGBrjnB1DRKrOarXSr18/1q9fz7Zt2wKOLVq0CIABAwYAcPvtt/P6669z1113MWjQIG699VbeeOMNGjRowPTp0yu8fnJyMpMmTWLYsGGMGTOGl19+mZKSEiZOnFileLds2cITTzzBgw8+yNChQ3nmmWcYNmwYK1eu5IsvvijXPzIykmnTpjF8+HBuvPFGGjVqxDvvvMPq1auZMGECjz76KNdeey233norb775JrGxsTz//PP+87/66isaN27Mc889x9ChQxkyZAj33HMPc+fO9T88vWzZMpxOJ//6178YOXIkV199NSNHjmTixIkMGTKkSvd52NatW5k+fTpjxoxh0KBB9OnT54S+azgdnC5/KzSGxtAYZ/EY7rKVHy0+H1lhYXiOWaHxt5x296ExNIbG0BjVOIacXQzz6P8C5Iw0dOhQ8vPz+fjjj7Farf72rKws+vbty4UXXujfD/jtt99m3LhxTJ48uVyBdOPGjYwZM4a//e1vDB48GIDBgwcTExPDjBkzKC0t5bLLLuNPf/oT3333HW63m5deeomCggJ69erFddddx0MPPQSULZ01cOBAOnXqxIsvvhgwzs8//8xtt93GiBEjuPfee8nJyaFPnz5ceumlAUtrAWzatIkbb7yRoUOH8pe//AUoW1Zs2rRpfPTRR9StWzeg7aWXXqJjx44B1+jevTsdO3bkP//5T0Bso0aNYvTo0Sf8837yySeZP38+kyZN4pJLLjnh80VEznh9/wmf/eh/+4erxxJitxNRWorF6yOkqITIghKu2LCUC//0Z7y+wNPtpo9+GVm892oj7cMsIhIExl/yK9XPfCb6FEci8tu2bNnCsGHDuOWWW7jnnnsAME2Tq6++mrCwMN55551y5zidTpxOJwBTpkzh/fffZ/HixURGRgJlK14tXLiQGTNmlJvBe/fdd7Ny5Uq++uqrE1rhKi0tjYYNG/L+++8HtB/Oxy+//HKeeuop4EjeOm7cuHIPOd900004nU5effXVcmO88MILLFq0iCVLlhAaGsro0aPZsGEDEydOpH379hXGtXDhQsaOHcvIkSP5wx/+8Jurbx3+maxatarCexswYIB/mezDuXSPHj0YP358QN8T/a5BRORsN/uNvWyYs5vcEAetd2TSdMdurv7DtbiP+u4Wrw8sPsynYoMWp4iIVD5HBuXJR9MS2WeBPXv20Lp164DiMkBiYiJRUVEBbenp6QDcddddv3m9nJwc/787dOjA/PnzKSoq4tdff6W0tJQOHTrgcrmYMmUKbreb1atX4/P5Kty3qVGjRuXamjRp4o8b8O+lfLj9aI0bN8Zisfj7/p569eqVa4uJiSEvL6+C3lVzePmvY/fTEhE5Z3z6GDS9E7btA2BXfAzJLg9FIY6y43ExJGTns2NrU9pk7eXn+DoBp9s9XmJSbSoui4gEjdb/ktNfamoqLVu25NNPP2XMmDFYLBbWrFlDRkYG9957r79fTk4OU6ZMYcmSJQG57GGFhYX+AvNhjRs3LtevcePGLFu2jMzMTJo2bVru+PFUdL3D+XhFuWyDBg3KtW3fvp3S0lJ69+79m+McPHiQOnXqMGbMGB566CH+8Ic/kJSUxEUXXUTXrl3p1asXdnvZajJ9+vTh448/ZsaMGcyZM4d27drRsWNHrrjiCpKTk0/o/ioT/4l+1yAicra7+aY6DP/4IImFThruy6JxTh4vvfc5L3a/iMzoSM7fvY//NazHwFZaIltEJPiUI1eFCsznmMMT1h9//HESExMr7JOSkuL/d4cOHXj//ff54Ycf+Pnnn0lKSqJRo0a4XC6ee+451q5dy8qVK7FarVx00UU1cg/Hc+zeVodV50T9w182bNy40b+Hs4jIOWfrFP8/D9yVQbLLE3C48/r1tM9P571Zu7jq1hFsjIsHE+oV5OPxGcwcV/6LSREREZGj9e/fn/Hjx7Ny5UouueQSFi1a5F8+G8ryvLvvvpvt27czbNgwWrduTWRkJBaLhQULFvDpp5/i8/l+Z5SaFxoaWmF7amoq999//2+eFxcXB8B5553H/Pnz+f7771m1ahWrV6/m008/Zfr06bz66qvExMTgcDiYPHky69atY9myZaxZs8Y/g/rJJ5/057KGUfGXaR6Pp8L234r/RL9rEBE5F7w5pyX3XvUDWTFRNMvYR/dtu+i+bRcA81qn8r+G9Xisp/V3riIiInJ6UoH5LJCSksKuXbvwer3llsg+eo17gPr16wNlexVXZnnntLQ0DMNgxYoVrF271j9LuVmzZsTGxrJy5UpWrVpFixYtys2WhiNPMR/t8B5ah5PLw8tcH7u31uHzfT5ftSaiv5VAV1bXrl0JCQnh448/5rbbbsPhcFRTZCIiZ6bO55ks+zmc87PycHh9tN28g6471mMAIU4PS1+eRoHdgccbSm6ElSvvuDXYIYuIiMgZoG/fvkycOJFFixZx/vnn8+WXX3LJJZf4C5ibN29m06ZNFW5/NH/+/N+87vbt28stkb19+3asVmuVZvdu3769XNvhfLyyuWz9+vXJzc2lQ4cOv/ng9NHCw8Pp1asXvXr1AmDu3Lk888wzfPjhh4wYMcLfr23btrRt2xYo2ydv+PDhTJkyxV9gjo4uW+IvLy+PmJgY/3mVXUXs6Pih8t81iIicK7bGRmNv2oBGGftIycsFYFd0LJM6tgfAEapZcyIicmbS2pRngR49epCdnc2iRYsC2mfOnFmu7+WXX47D4WDq1Kn+vamOVlhYGLDZemxsLKmpqXzzzTf8+uuvdOjw/+3dd3hUxf7H8c+m94SEFgg1dKQaiggJghRBEBAsiBQFLChYuBfrFS5Y8EqTonQMKCoYURD9US5FUOoFRBFEIIj0kJBed8/vD8zKsgFC2GRJ8n49Tx7dObNnvmeyS/a7c2amhaRLg7QRERFat26djh49mufy2JK0fft2HTx40PrYMAxFR0dLknXPqeDgYDVu3FibN2/W77//blN34cKFkuTQmcLe3t6SpKSk/K+rf7ng4GA9+uijOnXqlMaPH6/s7Gy7OikpKXZ7UgFASTXjycraNStUd2/Zpbu2/KTmR47aLSzjn50lD0uOQjLS5Zue6pQ4AQB/MeXzB3CyMmXKqE2bNtqwYYO+++47paamqnv37tbjuQOxV65Y9fvvv2vjxo1XPW90dLTNcw4ePKgdO3aoRYsWN7T/cq7jx4/btZebj0dFReXrHN27d9eFCxf08ccf53n8woUL1v+/ePGi3fF69epJ+jvPzatOhQoVVKZMGZstpHKXu96xY4dN3SVLluQr7lw3+l0DAJQWFdIzVefgMXkGnFFN0w6Fa7sSy53TRR8PydWkRlWCnB0iACC/OTJ5sg1mMJcAAwcO1Hfffac333xTv/76q8LDw7V792799NNPCgoKsqlboUIFvfTSS5owYYL69eunbt26KTQ0VAkJCdYkfNmyZdZZxdKlWcxLly6VJOsAc275unXr7MovV7t2bT355JPq16+fypYtq02bNmnHjh3q1q2bGjdubK03evRoDR8+XMOGDVO/fv0UEhKiLVu26Mcff1TXrl3VsmVLR3WXgoKCVKVKFa1Zs0ZhYWEKDg6Wt7e3IiMj832O4cOHKy4uTitWrNC+ffvUuXNnhYWFKScnR4cOHdL69evl7u6uF1980WFxA8CtzkuZSvb2UoLhJV2xTb0hySSzXLNN+nNmFafEBwAAip97771Xmzdv1pQpU+Tn52e9UVm6tPdxzZo1FR0drYyMDFWrVk1//PGHYmJiVKtWLf366695nvP06dN65plnFBkZqbi4OH3++efy9PTUqFGjChRjrVq19Prrr6tXr16qWrWqdu3apfXr16t58+bq3Llzvs7x8MMPa/v27Zo2bZp27typFi1ayNfXV2fOnNHOnTutg7eS1LdvXzVq1EgNGzZUuXLlFBcXpy+//FLu7u7W9ubPn69t27apbdu2qly5sgzD0Pfff6/Y2FibGc5dunTRrFmz9Oabbyo2NlYBAQH68ccf8xygvpaCfNcAAKWBd1am7jq5U3XS/56A0+fI/5SyZqkGDXzKiZEBAHBzGGAuAQICAjRv3jxNnjxZq1evliQ1b95cs2fP1lNP2X9Q6dmzp6pWraolS5YoJiZGycnJCgoKUrVq1fTUU08pJCTEpn6LFi20dOlSVa5c2Wa5sNxBXzc3NzVr1izP2CIjI1WtWjUtWrRIx48fV3BwsIYOHaqhQ4fa1GvQoIEWLFig2bNna/ny5UpPT1flypX17LPPasCAATfVP3kZP368Jk+erJkzZyojI0OhoaE3NMDs4uKi1157TZ06dVJMTIxWr16t+Ph4eXh4qGrVqurXr5/69evn8LgB4FYWnBGvZI8AxXkH6IKXn0IyUqzHslxMyra4yUW33j6IAADg1tWuXTsFBgYqMTFRvXr1kqenp/WYq6urpk2bpqlTp2rVqlVKT09XeHi4xo4dq99+++2qA8zTp0/X5MmTNWfOHGVkZKhRo0YaNWqUateuXaAY69Wrp+eff16zZs1STEyMfH199cADD2jEiBH5Wu5aupRXT506VcuXL9fq1autg8nlypVTw4YNde+991rrDhgwQFu3btVnn32mlJQUBQcH67bbbtOQIUNUp04dSZdmTsfFxWndunWKj4+Xp6enqlSpotdee0333Xef9Vx+fn6aNm2aJk+erIULF8rb21sdOnTQ+PHjb3glsRv9rgEASgOLYah85jm78r6//U+DzE4ICAAABzEZV64lBTjAqVOn1LNnzzz3wgIAlExms1nL6nyi7BxPSSaVS0uUtzlTcT4BynJxVdMTpxWsOFUw3nZ2qABQqpleSs5XPeMd/0KOBChaY8eO1apVq7Rr1y6HnTMiIkL33nuvxo4d67BzAgBKjoT4dCWEvaya6X/YlJ93K6cKL7wry8RAJ0UGAMiV3xxZIk++HHswAwAAh3B1ddWDhx9R3YuxynY16ZR/kI6UqahETx+lu3sq0PWCPJXu7DABAOwtBQAAUCTKBHvrw0ZtlSUPa1mO3PRuRCede9XbiZEBAKzYg7lAWCIb+EtCQoLM5muvTePj4yMfH58iiggAih+Ti4s8MnMkk+0nLo/sbHmY05SpTCdFBgAAkD/khgAAR7r9dIJqD5mg17d9Jy9ztv59xz167sedKhvgcf0nAwBwi2KAGfjLwIEDdfr06WvWYclvALi+nCxvVYhPlp+RIe/sbCV4ecslzaQz8tdtxlRnhwcAAHBN5IYAAEeqn3BBM1ev14ImEcpwc9OrG3eo/cljzg4LAICbwh7MwF/27t2rzMxrz6yrXLmywsLCiigiACietrm+r7KWdLnJYi1LlaeSlabWxmtOjAwAIEmml1PyVc9426+QIwFuTeSGAABH+iFosgISXWxWVs30Nqt52otOiwkA8Lf85sgSefLlmMEM/KVp06bODgEASgTD00Vu6RabMh9l6oKnu5MiAgAAyD9yQwCAI8W7llGgEm3KsszsvwwAKN5cnB0AAAAoWVxcs/MsD8rK/92AAAAAAACUBB7Z9guIulhYVBQAULwxgxkAADhUUFqCsl185W75exZzopeXPAKvvdQkAKCImK5fBQAAAI6R6u4qQ7Yfwc76ssQqANwyyJELhBnMAADAoQKntZe3LirVw03pbm7KcJcCsuPU4MxYZ4cGAAAAAECRCqjkquM+ZZRi8lSayUMnPQPlbk53dlgAANwUBpgBAIBDVXymg8x315SLJVvuRqrMhlnVMv/t7LAAAAAAAChyHfcPUPX7yinLzaQMNxcZIanqGP+4s8MCAOCmsEQ2AABwuEqrBmvhwoWSpCFDhsjV1dXJEQEAAAAA4BwNP+mm7I+y/8qTPZwdDgAAN40BZgAAAAAoTUxsMAUAAAAAgCRy5AJiiWwAAAAAAAAAAAAAQL4wwAwAAAAAAAAAAAAAyBcGmAEAAAAAAAAAAAAA+cIezAAAwOHS0zL19X8jJReTPv3vYf330wbODgkAAAAAAKc4fTJN7z+yS4a5roI7n3B2OAAA3DRmMAMAAIdKSUlXp3/Ea394BW1uWEVnw8rqroFHnB0WAAAAAABFbv74XxVz9zqF/xmnWqcvKGCJv97uu8PZYQEAcFMYYAYAAA5197Mn9UvVsqoRn6JOh0/LzWLoUNUQDXjuJ2eHBgAAAABAkbq47Jg8s3Osj93MZgUeSXBiRAAA3DyWyAYAAA6VEOinh/YfV5a7h2QyqdnZZF3wStORbG9nhwYAkCSTswMAAAAoPbwzs2QxSa7Gpcdmk+SVmeXcoAAAfyNHLhBmMAMAAIc55P6y6l+4qCwPT8l06dOZxcVFZdNzZJbh5OgAAAAAAChaGe5u1sFl6dJAc4aHh/MCAgDAARhgBgAADnHMNEaBOe4KSUu3O2Z2dZWbwccOAAAAAEDp4n3Z8tjWsqws7f8l0QnRAADgGHzTCwAAHMJPFvnonIZvXW93zDAM/eHn5YSoAAD2TPn8AQAAwM0y5bGYl8mQ+k6JL/pgAAB5yG+OTJ58OQaYAQCAQ7goS1kKUfjZBLX+7VfJuJRFGzK0IyRA5VPtZzYDAAAAAFCSXfTzsStL8/SQq4mBCgBA8cUAMwAAcAhDf+8h1W/HDxq88VuZs+MVU7W84r3dVCEz04nRAQAAAABQ9H6uHqoUT3flmEzyycrQqQBvHa4cohYnTjo7NAAACszN2QEAAICSwSJ3651rJkmNT55WUGqa0sp4q+6J87JYuDsbAG4J/HMMAABQJCxPzlPUCV9Fnt6nikkpyjQHqNWf/lrRuL6+rx7m7PAAABI5cgExwAwAABwiR67ykMWmbNCOfTI8yujLhlWVcTHVSZEBAAAAAOAEszfrcblaH/opVRbDRffvOyCPrGxJEc6LDQCAm8AS2QAAwCFclWVXluDrp3JpGep14A/5GmYnRAUAAAAAgLOY7B75KkmSVPdcnBPiAQDAMRhgRrG3cuVKRUREaNeuXc4OBQBKtdN+/sp0/XtxlIs+Pvq/eg1lNplUPjVDbgbrzQAAADjK7NmzFRERoVOnTpWKdgGgeDLsHhl/fSVvXHEMAIDihCWyAQCAQ5z1LqeNtauq9p/n9UdQgN7u1Fopfj7KNhm6/8ifSnd1vf5JAACFj/t9gGJj165d2r17t/r37y9/f39nhwMAuGGZknxklosSVEHp8pUk7alSUUYOq3wBwC2BHLlAmMEMAABuWuy/v1F2tqsa/XRGfudz1OBwvObOX6NHfj2uLmcStLlyBVlc+LQGAABwI3bv3q25c+cqOTnZ2aFYPf7449q6datCQ0OdHQoA3NIs3kMkucqQlKDySpefLo1imNTsxBn5ZDPADAAovpjBDBSSjIwMubm5yc2NtxmA4u23jkuU+n2cPC05SnX1VGqVENV8o5X++P64yi7ZqPLZF5Tl5icX14ZyM+dYn+eVmaPm+2K1IbKhmiSmyjUzRznZFrm5c38bAABAcUWeC6DUslhk1BwhHT8vyXRpqWs3H8nTQ6pVQfrphGSYdWnWcu4KXh4ySdaZy5fzz8gsutgBAHAwvuFFiWEYhhYvXqz77rtPd9xxh/r06aNVq1bZ1VuxYoUeeeQR3XnnnYqKitKIESO0d+9emzqnTp1SRESEZs+ebff8vPabGjt2rCIiIpSQkKBx48apc+fOateunc6dO+fw6wSAopR06ry08ZTKZacowJyh0KxElY89pX1Pb1Pwkk2qm35MgTkX9EuZ+nLNsd8/qurJCzJZLPLPMcvF1U3DH/rVCVcBALBlyucPUDqsXLlSERER2rFjh+bOnat7771Xd955pwYNGqT9+/dLujST+PHHH1fbtm3VpUsXzZs3z+48Gzdu1GOPPaa2bduqXbt2euyxx7Rx40a7ej169NDw4cMVGxurUaNGKTIyUlFRUfrnP/+puLg4a72xY8dq7ty5kqSePXsqIiIizzw1KytLM2fOVLdu3XTHHXfo4Ycf1pYtWwrUF1u2bNHw4cPVsWNH3Xnnnerevbv+8Y9/6Pjx49Y6eeXEuWWxsbEOiwUAbjVG1eEyHT8rkywyySyTsqUcs5SaJe3746/tlrMleUvK0qXPUxaZlKaz/n5258v0cFdKlqVIrwEAkJf85sjkyZfjllOUGDNnzlRmZqb69OkjDw8PLV++XGPHjlVYWJiaNm0qSXr//fcVHR2thg0b6umnn1ZaWpq+/PJLPfHEE5o0aZLatm17UzGMGDFCISEhevzxx5Weni4fHx8HXBkAOM+R8MUqZ8myKQswZ8jLnKFgXZQkZbq4Ks3NV64emfLKtF3iy81sUdipeB2qVl45Jsnk6qKMDIu8vLjHDQAA3FpmzJghs9mshx56SDk5OVqyZImeeeYZjRs3TuPHj1fv3r11zz33aO3atfrwww9VqVIldevWTZK0bNkyTZw4UdWrV9fQoUMlSatWrdLo0aP1yiuvqE+fPjZtnT9/Xk888YTat2+vkSNH6vDhw4qJiVFqaqpmzpwpSerTp49SU1O1YcMGvfDCCwoKCpIk1a5d2+ZcY8eOlZubmwYMGKDs7GwtXbpUo0ePVkxMjCpVqpTv69+9e7deeOEFhYeHa8iQIfLz81NcXJx27NihEydOqFq1atc9h6NiAYBbjtksnYy3Kbo0zJAtQ65/Pcq96dpi/TEpXRur19fUiPaa8sW3cjUu1TlcNljHA/z05qfZWjfQs6iuAgAAh+HbXZQYWVlZio6O1qBBg/Twww/rgw8+kLu7uz7//HNJUmxsrBYvXqwmTZpo3rx5euSRRzRs2DB99NFH8vb21sSJE2U239zeJ+Hh4Zo+fboefPBBDR482PoFgDPFx8crM/PvJXdSUlJs9u/KysrShQsXbJ5z+vTpaz4+c+aMDOPvmYq0QRu0UXLbsJ+TfIlJkv6KwdOSpcDsRKX55n3fmmuOWbsqlZFfjkWGIRkWo8ivgzZogzZoo6jbAFD8mM1mLVq0SAMGDNDgwYP1+uuvKzU1VWPGjNH06dP1zDPPqG/fvpoxY4ZCQkK0bNkySVJSUpLef/99hYWFadGiRRo8eLAGDx6sRYsWqXLlypo6dardHsonTpzQ6NGj9fLLL6tv377W/27fvl2xsbGSpMaNG6tWrVqSpPbt26tbt27q1q2b3QBzUFCQ5s6dq/79+2vQoEGaNGmScnJyFBMTc0PXv2nTJlksFs2cOVMDBgxQr169NHToUM2ZMyffN2M7KpbCdqv8raAN2qCN4tVG3q7Mmk2X/V+WTJLW1mysrxvVV/cnH9Xku9ropR6ddN/wR3S0fFklpWYU+XXQBm3QBm2QJ8MRTMblrwCgGFq5cqXGjRunV199Vb1797Y59uCDD8rDw0OLFy/WRx99pOnTp2vSpEmKioqyqTdp0iQtXbpU0dHRatCggU6dOqWePXtq2LBheuKJJ2zqzp49W3PnztXXX39tvQN77NixWrVqlT7++GPVrVu3cC8YAIpQ4r7TOtc8Wt6WbGtZiqunDntXUq2cw6qXcUQWZeuih582hkTJL95iM4s5ydtDg17oofoJaQpPTJNreoYWftHQGZcCAPiL6Y30fNUzxnkXciTAreFqOWVSUpI6dOigJk2aaP78+TbPeeGFF7Rv3z6tX79ea9eu1csvv6zRo0froYcesqm3dOlSTZo0Se+8847uvvtuSZeWyM7JydG3335rU3f9+vUaM2aMpkyZonbt2knKO//MlXtsxowZat26tc2xyMhItW7dWu+++26++2HOnDmaM2eOXnrpJfXq1euq+yznFZOjYwGAW5FRYYhM5xL/fizJkJ8u7bdsSH/ttix5SUqWy1+Dz2/eNUCvR3WTYbps8NkwtOW9D9Tg3CgFeTMHDACcKb85skSefDn+eqHEqFy5sl1ZYGCgEhMvffDL3R8qPDzcrl5u2cmTJ28qhvwsGQYAxUlgk1BlNQhWnLufUl08ddbDXyfLVVDDibfr3D0ROuhVXUluwTJluarRxX3ytGTKW2lyV5YyvQ0dqlZGj287pOYnzysnI0MffFzf2ZcEAACQpytzyoCAAEnKc2nngIAAa66Zm0fWrFnTrl5u2ZW55tXyV0nW8+ZXWFhYnue60fM88MADqlu3rt555x117NhRI0eO1KeffqqEhIQijwUAbkWmE3NklA+SIRcZcpUhD8nFVfJwlWqW/6uWh6RMSd5/DUBLR8vWUI2kNOsqYDIMhaakK9vNncFlAECxxR7MKDFcXPL+QFaQSfom09U3a7/WMtpeXl433BYA3Ooa7h+WZ3nNp+tJuleSdOiumTq6w1eJId6qfiZBhptJGxrV0E/VQ1UhPUNNj/+pPfVrsfcyAAC4ZV0tp3R1dS2ytqQbz2EdlQsHBQUpOjpae/bs0fbt27Vnzx5NnjxZs2fP1rRp09S4ceMiiwUAbkke7jKdXWB9ePVvDy+xeA6QsqQck6E//L2l3O8bTSaleLhrc3h1RV37FAAA3LIYYEapkXuH+JEjR+zuqj569KhNndw71ZOSkuzOc7OznAGgJKq7YYTOu8yWT5pFe2tV0r/vj9Rpfx/r8agKwSqTwMwVAABQ8uTml0ePHlXLli1tjh07dkxS3jOW8+NaNz8XBldXV0VERCgiIkKSdPjwYQ0YMEDz58/XtGnTijQWACjuXDKXyGIarD0VwpRzxQ04SZ7u+qGG/aoPAAAUF0wjQqkRGRkpk8mkxYsXKycnx1oeFxenlStXKjQ01Lp/sq+vr0JCQrRz506bO63//PNPbdy4sahDB4DiwcUsD+Xo99AQm8FlSfqxSnkluXJfGwAAKHlatWolb29vffbZZ0pNTbWWp6am6rPPPpOPj4/dvsT55eNz6TNVXjc/O9rFixftyqpXry4vL68iaR8ASiqvPFZDNBmG7tv7ixOiAQDAMfimF6VG9erV9eijjyo6OlrDhg1Tp06dlJaWpi+//FJpaWkaP368zdJnDzzwgD744AONHDlSUVFRiouL0xdffKHw8HAdOHDAiVcCALemEEuqsuWhiLO/66X/XtRnTZrrWEiIJCnL1VUWN+5rAwAAJY+/v79GjhypiRMnavDgwbr33ktbiKxatUonTpzQK6+8Ij8/vwKd+7bbbpMkvf/++7rnnnvk4eGh8PBw1apVy2Hx55owYYLOnTunVq1aKTQ0VJmZmVq7dq1SU1PVvXt3h7cHAKXF7adP6kBwgFLd//4qvnx6pvyyrr4NHwAAtzoGmFGqjBw5UlWqVNGyZcs0Y8YMubu7q2HDhpowYYKaNWtmU3fQoEFKSUnR6tWrtXv3btWoUUOvv/66fv31VwaYASAPJkOqruOq+Vus7v1Neu77Tbpn6JPaUzlMNeITJbPF2SECAAAUin79+qls2bJavHix5s6dK0mqU6eO3nvvPbVv377A523atKmeffZZxcTEaMKECTKbzRo2bFihDDB369ZNK1eu1DfffKOEhAT5+vqqZs2amjhxojp27Ojw9gCgdDCr9pmjalM2VMf9vJXi7qqyGVn609dLm2vX0ABnhwcAQAGZjMvX/wUAACigM6ZX5ac0m7IVDW/TWx27657f/tA35QJ1MLquk6IDAOQyjU3PVz1jrHchRwIAAFCyWfq8p/rlH1RiUKCqpmTIMEmxft6K8/bQ41t2aN6ads4OEQBKvfzmyBJ58uVYqxIAADiEu7LsylqeOK2H9/2u/4WWV7kc7mkDAAAAAJQeLjGjleXppjgvd2VmZchITVPSX9tHhSYkOjk6AAAKjiWyAQCAQ6TKR55Ksi3MkKIb11W7c/H6xZ2PHQAAAEUtISFBZvO19/n08fGRj49PEUUEAKVL7bPxGrJlvyonpkiSkrw8NL397Vp4x+0a7+TYAAAoKL7pBQAADpEkPxlyU6ASZZKhs27BWhneSG6eLvI05yjb3dXZIQIAJMlkcnYEAIrQwIEDdfr06WvWGTZsmJ544okiiggASpe7DxyXp2Ho1+qVJUlVz8Sp6y9HtaZOmJMjAwBIIkcuIAaYAQCAQ7grURdUURcUogV3NdWcTrdfGlS2GDrn7aGGv590dogAAAClzvjx45WZmXnNOpUrVy6iaACg9DHc3PTfJnVldr100/XhKhV1+8+/K/pRTydHBgBAwTHADAAAHKKuMUEHTK9pSucemtfp9r8PuJgU7+elMwEsuwgAAFDUmjZt6uwQAKBUO1i9snVwWZLMrq46Uamc2rSt6MSoAAC4OS7ODgAAAJQcDYwJWt66gf0BF5Oy3FgiGwAAAABQumR4uOerDACA4oQBZgAA4FDuaVn2hRZDVSwXij4YAAAAAACcKDA52a6s7MUkJ0QCAIDjMMAMAAAc6sdnXeWRnv13gWEo6GKavp/V2HlBAQAAAADgBM0rZir8xGl5ZGXLMytbdWJPqnwGA8wAgOKNPZgBAIBDhVcN0ubeF3R3tElmT3f5Jqbq/PvsLQUAAAAAKH2Gz2+jY5tO6aPRB+RiWBTS7KSGzxrg7LAAALgpDDADAACHa143QJNqLZQkDfnXECdHAwCwYXJ2AAAAAKVLjahKevWHclq4cKGzQwEAXIkcuUBYIhsAAAAAAAAAAAAAkC8MMAMAAAAAAAAAAAAA8oUlsgEAAACgVGH9LwAAAAAALiFHLghmMAMAAAAAAAAAAAAA8oUZzAAAwOH+2WODfi3TRi4W6Yfl27VoTVtnhwQAAAAAgFNYcszKCv6HHk4xy+JqlqVPqlQ2yNlhAQBQYMxgBgAADjX8rtXaEFZHf5YJ0pFywdpSt5Z69dnj7LAAAAAAAHCKVPcXlJXiqRz5yGL2V2a5sc4OCQCAm8IMZgAA4FB/lK+oI+WDleLlaS0LSUtTTkaW3Lw8nBgZAEAS20sBAAAUoeTWbylHnjZlZnkq2ecZ+afNcFJUAAArcuQCYQYzAABwKLO7p83gsiTtrVRBv87Y6aSIAAAAAABwjnM7UpTX6IUpPbvogwEAwEEYYAYAAA61t1JFu7IsN1eZZTghGgAAAAAAnOPLCh8r3iVEkuWKIwYZMgCgWGOAGQAAOMye70/KM9v+LmxXi0WeySlOiAgAAAAAAOcwpVvk5ZIhd+XIJIskQyZZ5KZsucns7PAAACgw9mAGAAAO88HYY+rs6aXYSmk6WCZI6W4uqpScqDqn4mVxu+js8AAAAAAAKDI5Hi5KzfaVKfucXJWtEyEV5J6To0qJF+zmNAMAUJwwwAwAABzGxcVNSWUD5WXyUNOLaZIk76xsvbZ+tUxPNXNydAAAAAAAFJ10TzeZkrJ13idAC6K665x/GUlSzXOn9Nj3n8rXyfEBAFBQLJENAAAcJtXHWxd8fPVLoLcqn/1ZDU/sVrK7uzY3qKMsHw9nhwcAkCRTPn8AAABwU1wsFqW7+ulX93CVPZeqComJKpeUpLOBIfqyWRdnhwcAkPKfI5Mn22AGMwAAcJgsc7bOuGZp//TRCsjKkCRluH6skfc+rS6mLCdHBwAAAABA0fHJssgjw0VHq5fThXIBki7N+PLLzNSp4ArODQ4AgJvADGYAAOAwZwN9NHvVbOvgsiR5mXP0+n8/lsoHODEyAAAAAACKlndGtiSTzpezz4c9cnKKPiAAAByEAWYAAOAwVS/Eq37cKbvy0JQE/ftHZjADAAAAAEoPs+nSeqre6Xnkw2ZyZABA8cUAM4qN2bNnKyIiQqdO2Q9clMR2AaA4qpCQrj/9Q+zKM109tLRWSydEBAAA8LcePXpo+PDhzg7DochZAeDWleFuksUkhf9+Wq45Zmt5uqurTvh6OzEyAABuDnsw45aya9cu7d69W/3795e/v7+zwwEA5JPF9IAkN7ULa6nFt92lBucXy9NyabkvQ9K4qPslV5NTYwQAAIC95ORkffLJJ7r99tsVERFxU+fasWOHYmJitH//fsXHx8vd3V1Vq1bVHXfcob59+6pCBfYbBVC6pPt4KDbYVVXOJav/ljX6o0J5yeyijVXCtat+NWeHBwBAgTGDGbeU3bt3a+7cuUpOTnZ2KFaPP/64tm7dqtDQUGeHAgC3FEtCktLfjFGiaYgkH0meqpF9Ub9Urqo+D/1DP4WE64hfdb3RdqC+aNhSzWJP6bjpLWeHDQAAUKLcbM6anJysuXPnavfu3QWOwWKxaMKECXr66af1yy+/qEuXLhozZoyeffZZ1alTR8uWLdOAAQMKfH4AKK4yvdx11L+sPFPMOptRTmHH49Tuzx16fvvXSvF0d3Z4AAAUGDOYgetwc3OTmxtvFQDFi6X1yzJtP3RZSe7sYTcZj90tl/nD8n7i+9/I8t7X0okUSbnJbrZMypRkkXRpRrLkJ8lFnpI8ZSj3nrVUN5NyPCzqduCAfC/6KcvFW0O2HlD/7Qf1e5kyynToVQIACsTEihJASXIr5Kxz5szRihUr1KVLF40dO1bu7raDJs8//7zmzJnjpOgAwDm2tFymMhezVO1okrY1qKvD4RXklmNR1IGfdN/xjep26LAkZjEDgNORIxcIM5hLoZUrVyoiIkI7duzQ3Llzde+99+rOO+/UoEGDtH//fkmXZhI//vjjatu2rbp06aJ58+bZnWfjxo167LHH1LZtW7Vr106PPfaYNm7caFcvd4+r2NhYjRo1SpGRkYqKitI///lPxcXFWeuNHTtWc+fOlST17NlTERERioiI0OzZs23Ol5WVpZkzZ6pbt26644479PDDD2vLli0F6ostW7Zo+PDh6tixo+688051795d//jHP3T8+HFrnbz2s8oti42NdVgsAOAwf8bJtP2QTNJlP8ZfP9kyLVgjyzd77J+3cL00ar50IlWSx2XPdpdkWB9JvpJcLzvuIilTJiWp1cmftG7eeN1/YLvczO7yyc6RiyF5Z1tUPTFR6dywAwAAisCZM2f00ksvKSoqSlFRUXr++ef1559/XrX+9u3bNWLECLVv315t2rTRQw89pOXLl9vUGTdunNq0aaPMzL9vmfvpp58UERGhDh06yGKxWMu3bt2qiIgIrVmzRpJ06tQpa3773Xff6aGHHlKbNm3UvXt3zZ49Wzk5OXYxHT58WKNHj1bHjh3Vpk0b9evXTx999JHMZrNNvZvJWXft2qWePXtKkubOnWvNw3v06JGfbpYkxcfHa/HixQoNDdW//vUvu8FlSfL399eLL76Y73MCQHGXdeaiTsTmyCPDrJ8bVNGOiHAllPHT+XIBWh7VVj+H1pCbYWjonAvODhUAgALhW95SbMaMGTKbzXrooYeUk5OjJUuW6JlnntG4ceM0fvx49e7dW/fcc4/Wrl2rDz/8UJUqVVK3bt0kScuWLdPEiRNVvXp1DR06VJK0atUqjR49Wq+88or69Olj09b58+f1xBNPqH379ho5cqQOHz6smJgYpaamaubMmZKkPn36KDU1VRs2bNALL7ygoKAgSVLt2rVtzjV27Fi5ublpwIABys7O1tKlSzV69GjFxMSoUqVK+b7+3bt364UXXlB4eLiGDBkiPz8/xcXFaceOHTpx4oSqVbv+HYSOigUAHMnSdPQ17yAzySzjH59K3ZvZHvj4+79mJ1/58SB3kDnrr8euVxzPkosybEpOeFdRpTTDpswzx6JED698XAEAAEDBJScna/jw4Tp79qz69OmjmjVr6n//+5+eeOIJm8HhXDExMXr77bfVqFEjPfbYY/L29tb27dv1zjvv6OTJkxo1apQkKSIiQitXrtS+ffvUsmVLSZf2HHZxcVFSUpIOHTqk+vXrS7o0cGsymez2NN68ebNOnjypfv36KSQkRJs3b9bcuXN15swZvfHGG9Z6Bw4c0PDhw+Xm5mat+/3332v69Ok6fPiwJkyYkK++uF7OWqNGDb3wwguaPHmy7rrrLt11112SJB8fn3z395YtW5SZmanu3bvL09Mz388DgJLshxZfKtvDV9lZFh0Ot99//r+1m2tvaFl5rz8rDQ9xQoQAANwcZjCXYmazWYsWLdKAAQM0ePBgvf7660pNTdWYMWM0ffp0PfPMM+rbt69mzJihkJAQLVu2TJKUlJSk999/X2FhYVq0aJEGDx6swYMHa9GiRapcubKmTp1qt4fyiRMnNHr0aL388svq27ev9b/bt29XbGysJKlx48aqVauWJKl9+/bq1q2bunXrZjfAHBQUpLlz56p///4aNGiQJk2apJycHMXExNzQ9W/atEkWi0UzZ87UgAED1KtXLw0dOlRz5sxR27Zt83UOR8VSmOLj422+RElJSbH5/WRlZenCBdu7JU+fPn3Nx2fOnJFh/D1wRBu0QRu3Vhvmsv66FkNSpq/tzJLTp09L/tca/DWuesRkHXj+W1BWkl1ZnK+vzJd99LgV+oo2aIM2aKOw2gDgPNHR0Tp16pReffVVjRkzRv369dPbb7+tDh066OLFizZ14+Li9N5776lz585asGCBBg4cqH79+um9997TQw89pI8//tg687lFixaSpJ07d1qfv2vXLrVt21a+vr7asWOHtXznzp2qWbOmgoODbdo7fPiwPvzwQ40YMUIPPfSQZs6cqfbt22vlypXWFcUk6b333lN2drbmzp1rrTtjxgzdfffd+u6772zaupbr5awhISFq3769JKlWrVrWPDy3LD+OHDkiSapTp06+n+Nst8rfCtqgDdoouW14VPSQDItS/N3larboStkurmp+6jdluNrewH2rXQdt0AZt0IYj20DJYjIufwWgVFi5cqXGjRunV199Vb1797aWJyUlqUOHDmrSpInmz59v85wXXnhB+/bt0/r167V27Vq9/PLLGj16tB566CGbekuXLtWkSZP0zjvv6O6775Z0aYnsnJwcffvttzZ1169frzFjxmjKlClq166dpEvLeM2dO1dff/213Qzg3GMzZsxQ69atbY5FRkaqdevWevfdd/PdD3PmzNGcOXP00ksvqVevXlfdsyqvmBwdCwA4VGqGDL/+utruIYY8ZPw+TS5X3kX9/QGp41hZst0kXT77xCKTkq3ns8hDkpdyF8w2KVUm2S7r+FtQqE663qaaFxIuPcckrbitnu79+ZBqW16+yQsEANwM01v5S+yNVzwKORKgcPTr109JSUlavXq1XC/74j4uLk5du3ZV8+bNrfsBf/rpp3rvvfc0a9YsuwHSQ4cOacSIETardPXp00eBgYFauHChMjMz1aFDBz333HP64YcflJ2drRkzZig5OVkdO3bUAw88oNGjR0u6tER2z549dccdd2j69Ok27fz000967LHHNHDgQI0cOVLx8fHq3Lmz7rrrLv3nP/+xqfvbb7+pf//+6tevn8aMGSPp5nPW3NiGDRumJ5544ob7e8KECVqxYoVmzpypVq1a3fDzAaBEyjHr64DFyvFx15HqZbWn4d9/YzxysvXG6gVaW/s2hczuo/ub5X/VCACA4+U3R5bIky/HEtmlWOXKlW0eBwQESFKeSzsHBAQoMTFRknTy5ElJUs2aNe3q5Zbl1rlaW5IUGBgoSdbz5ldYWFie57rR8zzwwAPatGmT3nnnHU2fPl1NmjRRmzZt1KVLF5UpU6ZIYwEAh/L1krH+XzI6/lsm/TX32MNbpmyzDF9f6dt/2g8uS1K7BtKPb0vz1kofbZPSDUkukp+HjBR3STmXHstFUpouDTJLhjwl5VgHoA1JNRPP6r1u9yrL0lQhqWnaWquaPvh4hcR9bQAAoJCdPHlSDRo0sBlclqSyZcvK3992pZfcFbWefvrpq54vPj7e+v8tWrTQihUrlJqaqgMHDigzM1MtWrRQVlaWPvjgA2VnZ2v37t2yWCx2y2NLUvXq1e3Krsyjc/dSzivnrlGjhlxcXOxy7qspipzV19dXkpSWluawcwJAsefmqju+ba9tPTbLw5KiMWuWaEt4Y3nmZKnjod2qcvG8LC4+DC4DAIotBphLMReXvFdIvzIJL8y2JOlGJ9Ff7Vw3ep6goCBFR0drz5492r59u/bs2aPJkydr9uzZmjZtmho3blxksQCAo7l0aCoZl5Y+vHwm89Vcju+mAAAufklEQVRmNVvdHi6X28OlD568apUrz2ExPSxDvpKylSOT3ou6Rx+1jNJp/zJK8vKWJIVdTNB/ukTqnTFBN3YhAAAAhSg3dxs3bpzKli2bZ53Lb5hu0aKFvvjiC+3Zs0c//fSTypUrp+rVqysrK0tTpkzR/v37tXPnTrm6uur2228vkmu4lqLIWcPDwyVdmvGdu4czAEAqF1VdiQE7tbVWVT2151s1PBNrPZbl4qocvj4EABRjDDDjhuXeAX306FG1bNnS5tixY8ck5T1jOT9MpusOfTiUq6urIiIirHeWHz58WAMGDND8+fM1bdq0Io0FAIorF2OpLK98IuPtLzXrtl5qdO6YDpW3/TvQ8eABfdSytT5pc619ngEAAG5e5cqVdeLECZnNZrslsi/fB06SqlSpIunSDcj5Wd45IiJCJpNJO3bs0P79+625ZO3atRUUFKSdO3dq165dqlu3rt1saenvGdOXO3r0qDVu6e9VxXLLr3y+xWIpcM6dl5vNw9u2bStPT0+tXr1ajz32mDw8WDYQAHJlermq2rlUra7aWlGn98o/O12n/AM14v5HlOThp2ecHSAAAAV09WmlwFW0atVK3t7e+uyzz5SammotT01N1WeffSYfHx+7PZ7yy8fn0rIwSUlJDon1Wi5evGhXVr16dXl5eRVJ+wBQkri81V8uxjKd9C+nlkePa9qKhap88YL8M9L01A//p17bDsuUlunsMAEAQCkQFRWlCxcu6JtvvrEp/+ijj+zqdurUSR4eHpo9e7YyMjLsjqekpCgr6+892YKCglSrVi1t2bJFBw4cUIsWLSRdGqSNiIjQunXrdPTo0TyXx5ak7du36+DBg9bHhmEoOjpaktS+fXtJUnBwsBo3bqzNmzfr999/t6m7cOFCSXLoTGFv70srzhQ0Dw4ODtajjz6qU6dOafz48crOzrark5KSokmTJt1UnABQLBmGGsWel1uCp7a5t9DX5dvrjciHtaphE/1WoZyzowMAoMCYwYwb5u/vr5EjR2rixIkaPHiw7r33XknSqlWrdOLECb3yyivy8/Mr0Llvu+02SdL777+ve+65Rx4eHgoPD1etWrUcFn+uCRMm6Ny5c2rVqpVCQ0OVmZmptWvXKjU1Vd27d3d4ewBQGhytWE6bKrRR/11bNXLrt8pw8dCukGb63a+q9htbJXVzdogAAKCEGzhwoL777ju9+eab+vXXXxUeHq7du3frp59+UlBQkE3dChUq6KWXXtKECRPUr18/devWTaGhoUpISNDvv/+ujRs3atmyZdZZxdKlWcxLly6VJOsAc275unXr7MovV7t2bT355JPq16+fypYtq02bNmnHjh3q1q2bzTZNo0eP1vDhwzVs2DD169dPISEh2rJli3788Ud17drVbjWxmxEUFKQqVapozZo1CgsLU3BwsLy9vRUZGZnvcwwfPlxxcXFasWKF9u3bp86dOyssLEw5OTk6dOiQ1q9fL3d3d7344osOixsAigOPLLOqnkySy1/LYfulSR33HNOi9s1U/8wZScFOjQ8AgIJigBkFkpsML168WHPnzpUk1alTR++99571ruuCaNq0qZ599lnFxMRowoQJMpvNGjZsWKEMMHfr1k0rV67UN998o4SEBPn6+qpmzZqaOHGiOnbs6PD2AKA0KJuUpkSPIK2o0l1eOenKcvWQxeQqd5cUyY/lEgHgllC0u9IARS4gIEDz5s3T5MmTtXr1aklS8+bNNXv2bD311FN29Xv27KmqVatqyZIliomJUXJysoKCglStWjU99dRTCgkJsanfokULLV26VJUrV1ZoaKi1PHfQ183NTc2aNcsztsjISFWrVk2LFi3S8ePHFRwcrKFDh2ro0KE29Ro0aKAFCxZo9uzZWr58udLT01W5cmU9++yzGjBgwE31T17Gjx+vyZMna+bMmcrIyFBoaOgNDTC7uLjotddeU6dOnRQTE6PVq1crPj5eHh4eqlq1qvr166d+/fo5PG4AuNUFJmVaB5dzlY1PV8WkRD24Y5ekBk6JCwBwGXLkAjEZhmFcvxoAAMD1Db37R7U7dkLZLu425UlBhro+GqYGIx032wYAUDCmt7OuX0mS8TI3BgGOcurUKfXs2VPDhg3TE0884exwAABFZJVftAJTbT97ZXqaVM/1oM67ealZ4ngnRQYAyJXfHFkiT74cezADAACHuejnLW9Tsk1ZhYw4xYaEypJjvx8fAAAAAAAlVaaHiyTb+V01M08qMC1LVVISnBMUAAAOwBLZKHESEhJkNpuvWcfHx0c+Pj5FFBEAlB5lk1MV3bKdBu76Xt7ZWcrwcNf2OrX1W1iYMv/Y5+zwAAAAkE/k1gBw8/xysuWjLGXLRa4yK1gX5ac0uSlTJku6s8MDAKDAGGBGiTNw4ECdPn36mnVYlgwACokpU3+UDZDF01VRx36SuyVHlZPjtS+silxcXZ0dHQBAEhtMAcgPcmsAuHnuFrPcZJGvUhWq03KTWWa5Klteclf+l2QFABQmcuSCYIAZJc748eOVmZl5zTqVK1cuomgAoHSZ/m2kprX9XA/8vMla1vr0r3r+v18po38zJ0YGAADgPJUqVdKuXbucHcYNIbcGgJsX7++jkNQ0helP616VbjLLkGThq3k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+      "text/plain": [
+       "<Figure size 2000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 함수 사용 예시\n",
+    "result = analyze_shap_values_across_folds(\n",
+    "    model_name=\"xgb\",\n",
+    "    region=\"daegu\",\n",
+    "    data_sample=\"smote\",\n",
+    "    target_class=0,\n",
+    "    n_folds=3,\n",
+    "    top_n=5\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "61222e69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "elbow_plot(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9bc3ba62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[DAEGU] Wasserstein Distance Analysis\n",
+      "Model Type: TREE, Data Sample: smote\n",
+      "------------------------------------------------------------\n",
+      "Feature: lm_cloudcover        | WD: 1.6903 | Train Mean: 4.7301 | Test Mean: 3.0397\n",
+      "Feature: hm                   | WD: 8.7135 | Train Mean: 72.8477 | Test Mean: 65.3895\n",
+      "Feature: O3                   | WD: 0.0094 | Train Mean: 0.0218 | Test Mean: 0.0311\n",
+      "Feature: low_cloudbase        | WD: 9.3813 | Train Mean: 35.6279 | Test Mean: 45.0092\n",
+      "Feature: hour_sin             | WD: 0.2615 | Train Mean: 0.2615 | Test Mean: -0.0000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 방법 1: 직접 변수 리스트 지정\n",
+    "wd_df = calculate_wasserstein_distance(\n",
+    "    region='daegu',\n",
+    "    data_sample='smote',\n",
+    "    top_features=result['top_n_features_train'],\n",
+    "    model_type='tree'  # 또는 'dl'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df04836c",
+   "metadata": {},
+   "source": [
+    "## **Daejeon**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "292c0fa5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Period - Top 5 Dominant Features: ['cloudcover', 'low_cloudbase', 'hm', 'lm_cloudcover', 'PM10']\n",
+      "Train Period - Cumulative Importance Share (Top 5): 59.97%\n",
+      "----------------------------------------\n",
+      "Test Period - Top 5 Dominant Features: ['cloudcover', 'low_cloudbase', 'hm', 'lm_cloudcover', 'PM10']\n",
+      "Test Period - Cumulative Importance Share (Top 5): 61.87%\n",
+      "----------------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 함수 사용 예시\n",
+    "result = analyze_shap_values_across_folds(\n",
+    "    model_name=\"xgb\",\n",
+    "    region=\"daejeon\",\n",
+    "    data_sample=\"smote\",\n",
+    "    target_class=0,\n",
+    "    n_folds=3,\n",
+    "    top_n=5\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e7a29b66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "elbow_plot(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "73c94bac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[DAEJEON] Wasserstein Distance Analysis\n",
+      "Model Type: TREE, Data Sample: smote\n",
+      "------------------------------------------------------------\n",
+      "Feature: cloudcover           | WD: 0.8070 | Train Mean: 6.1055 | Test Mean: 5.2985\n",
+      "Feature: low_cloudbase        | WD: 9.2311 | Train Mean: 35.0185 | Test Mean: 44.2495\n",
+      "Feature: hm                   | WD: 5.3263 | Train Mean: 77.2519 | Test Mean: 71.9280\n",
+      "Feature: lm_cloudcover        | WD: 1.3074 | Train Mean: 4.6232 | Test Mean: 3.3159\n",
+      "Feature: PM10                 | WD: 14.5504 | Train Mean: 45.3118 | Test Mean: 33.7221\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 방법 1: 직접 변수 리스트 지정\n",
+    "wd_df = calculate_wasserstein_distance(\n",
+    "    region='daejeon',\n",
+    "    data_sample='smote',\n",
+    "    top_features=result['top_n_features_train'],\n",
+    "    model_type='tree'  # 또는 'dl'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a5839c9",
+   "metadata": {},
+   "source": [
+    "## **Gwangjuo**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "09cc8747",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Period - Top 5 Dominant Features: ['lm_cloudcover', 'hm', 'low_cloudbase', 'cloudcover', 'precip_mm']\n",
+      "Train Period - Cumulative Importance Share (Top 5): 55.19%\n",
+      "----------------------------------------\n",
+      "Test Period - Top 5 Dominant Features: ['lm_cloudcover', 'low_cloudbase', 'hm', 'cloudcover', 'precip_mm']\n",
+      "Test Period - Cumulative Importance Share (Top 5): 56.95%\n",
+      "----------------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 함수 사용 예시\n",
+    "result = analyze_shap_values_across_folds(\n",
+    "    model_name=\"xgb\",\n",
+    "    region=\"gwangju\",\n",
+    "    data_sample=\"smote\",\n",
+    "    target_class=0,\n",
+    "    n_folds=3,\n",
+    "    top_n=5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "876bff64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "elbow_plot(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b5736084",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[GWANGJU] Wasserstein Distance Analysis\n",
+      "Model Type: TREE, Data Sample: smote\n",
+      "------------------------------------------------------------\n",
+      "Feature: lm_cloudcover        | WD: 1.3663 | Train Mean: 4.8012 | Test Mean: 3.4349\n",
+      "Feature: hm                   | WD: 6.1317 | Train Mean: 78.7261 | Test Mean: 72.6021\n",
+      "Feature: low_cloudbase        | WD: 10.0554 | Train Mean: 33.4755 | Test Mean: 43.5309\n",
+      "Feature: cloudcover           | WD: 0.7041 | Train Mean: 6.2908 | Test Mean: 5.5939\n",
+      "Feature: precip_mm            | WD: 0.9522 | Train Mean: 1.1005 | Test Mean: 0.1488\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 방법 1: 직접 변수 리스트 지정\n",
+    "wd_df = calculate_wasserstein_distance(\n",
+    "    region='gwangju',\n",
+    "    data_sample='smote',\n",
+    "    top_features=result['top_n_features_train'],\n",
+    "    model_type='tree'  # 또는 'dl'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28b52cf2",
+   "metadata": {},
+   "source": [
+    "## **Incheon**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ad93edc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Period - Top 5 Dominant Features: ['hm', 'cloudcover', 'low_cloudbase', 'lm_cloudcover', 'solarRad']\n",
+      "Train Period - Cumulative Importance Share (Top 5): 68.72%\n",
+      "----------------------------------------\n",
+      "Test Period - Top 5 Dominant Features: ['hm', 'cloudcover', 'low_cloudbase', 'lm_cloudcover', 'solarRad']\n",
+      "Test Period - Cumulative Importance Share (Top 5): 72.54%\n",
+      "----------------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 함수 사용 예시\n",
+    "result = analyze_shap_values_across_folds(\n",
+    "    model_name=\"xgb\",\n",
+    "    region=\"incheon\",\n",
+    "    data_sample=\"smote\",\n",
+    "    target_class=0,\n",
+    "    n_folds=3,\n",
+    "    top_n=5\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d87fc301",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "elbow_plot(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fb246362",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[INCHEON] Wasserstein Distance Analysis\n",
+      "Model Type: TREE, Data Sample: smote\n",
+      "------------------------------------------------------------\n",
+      "Feature: hm                   | WD: 12.0598 | Train Mean: 73.9842 | Test Mean: 61.9244\n",
+      "Feature: cloudcover           | WD: 0.9658 | Train Mean: 5.9903 | Test Mean: 5.0245\n",
+      "Feature: low_cloudbase        | WD: 8.7631 | Train Mean: 38.4618 | Test Mean: 47.2249\n",
+      "Feature: lm_cloudcover        | WD: 1.5583 | Train Mean: 4.4755 | Test Mean: 2.9172\n",
+      "Feature: solarRad             | WD: 0.1360 | Train Mean: 0.4515 | Test Mean: 0.5875\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 방법 1: 직접 변수 리스트 지정\n",
+    "wd_df = calculate_wasserstein_distance(\n",
+    "    region='incheon',\n",
+    "    data_sample='smote',\n",
+    "    top_features=result['top_n_features_train'],\n",
+    "    model_type='tree'  # 또는 'dl'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65b33028",
+   "metadata": {},
+   "source": [
+    "## **Seoul**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "023ab679",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[FT_TRANSFORMER] seoul - ctgan10000 SHAP Analysis Start...\n",
+      "  Processing Fold 1...\n",
+      "  Processing Fold 2...\n",
+      "  Processing Fold 3...\n",
+      "Train Period - Top 5 Dominant Features: ['PM25', 'PM10', 'solarRad', 'hm', 'dewpoint_C']\n",
+      "Train Period - Cumulative Importance Share (Top 5): 47.43%\n",
+      "----------------------------------------\n",
+      "Test Period - Top 5 Dominant Features: ['PM25', 'PM10', 'dewpoint_C', 'hm', 'temp_C']\n",
+      "Test Period - Cumulative Importance Share (Top 5): 47.84%\n",
+      "----------------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  [FT_TRANSFORMER] SHAP Analysis Complete!\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 단일 모델 분석\n",
+    "result = analyze_dl_model_shap(\n",
+    "    model_name=\"ft_transformer\",\n",
+    "    region=\"seoul\",\n",
+    "    data_sample=\"ctgan10000\",\n",
+    "    target_class=0,\n",
+    "    n_background_samples=200,\n",
+    "    n_test_samples=500,\n",
+    "    device='cuda',\n",
+    "    top_n=5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0ed53a6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "elbow_plot(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "cc6a5bfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[SEOUL] Wasserstein Distance Analysis\n",
+      "Model Type: DL, Data Sample: ctgan10000\n",
+      "------------------------------------------------------------\n",
+      "Feature: PM25                 | WD: 15.5837 | Train Mean: 36.2449 | Test Mean: 20.6925\n",
+      "Feature: PM10                 | WD: 5.3409 | Train Mean: 39.6609 | Test Mean: 41.7063\n",
+      "Feature: solarRad             | WD: 0.2072 | Train Mean: 0.3822 | Test Mean: 0.5894\n",
+      "Feature: hm                   | WD: 7.3173 | Train Mean: 71.8359 | Test Mean: 65.5893\n",
+      "Feature: dewpoint_C           | WD: 1.4986 | Train Mean: 5.7473 | Test Mean: 6.8398\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 방법 1: 직접 변수 리스트 지정\n",
+    "wd_df = calculate_wasserstein_distance(\n",
+    "    region='seoul',\n",
+    "    data_sample='ctgan10000',\n",
+    "    top_features=result['top_n_features_train'],\n",
+    "    model_type='dl'  # 또는 'dl'\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Analysis_code/7.ensemble/ensemble.ipynb b/Analysis_code/7.ensemble/ensemble.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..62c50dbad20d511343fa8f434da24c5151577a5c
--- /dev/null
+++ b/Analysis_code/7.ensemble/ensemble.ipynb
@@ -0,0 +1,789 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a19bb535",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# utils.py는 같은 디렉토리에 있으므로 직접 import 가능\n",
+    "from utils import predict_test_proba, calculate_csi\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1048a139",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df= pd.read_csv(\"../6.optima_models_analysis/best_samples_best_datasample_per_model_per_region.csv\")\n",
+    "df= df.loc[df.groupby(['region','optimization_library'])['best_csi'].idxmax(),:]\n",
+    "df.sort_values(by=['region','best_csi'], ascending=False, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "67d96046",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>data_sample</th>\n",
+       "      <th>region</th>\n",
+       "      <th>optimization_library</th>\n",
+       "      <th>best_csi</th>\n",
+       "      <th>n_trials</th>\n",
+       "      <th>best_params</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ft_transformer</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>seoul</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.593658</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_token': 224, 'n_blocks': 4, 'n_heads': 8, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>seoul</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.591400</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.9204650350059359, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>incheon</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.8863531635625073, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>resnet_like</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>incheon</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.587620</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 160, 'd_hidden': 192, 'n_blocks': 3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.530000</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.7658195937298418, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>deepgbm</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>gwangju</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.520403</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 128, 'd_hidden': 192, 'n_blocks': 6...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.537100</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.733236256331133, 'gamma...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>resnet_like</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>daejeon</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.510177</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 128, 'd_hidden': 256, 'n_blocks': 5...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>smote</td>\n",
+       "      <td>daegu</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.467200</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.8132816721507904, 'gamm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>resnet_like</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>daegu</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.460863</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_main': 96, 'd_hidden': 256, 'n_blocks': 3,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>ft_transformer</td>\n",
+       "      <td>ctgan10000</td>\n",
+       "      <td>busan</td>\n",
+       "      <td>optuna</td>\n",
+       "      <td>0.496046</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'d_token': 224, 'n_blocks': 2, 'n_heads': 8, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>xgb</td>\n",
+       "      <td>pure</td>\n",
+       "      <td>busan</td>\n",
+       "      <td>hyperopt</td>\n",
+       "      <td>0.494900</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'colsample_bytree': 0.8651175745135303, 'gamm...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             model data_sample   region optimization_library  best_csi  \\\n",
+       "0   ft_transformer  ctgan10000    seoul               optuna  0.593658   \n",
+       "1              xgb       smote    seoul             hyperopt  0.591400   \n",
+       "5              xgb       smote  incheon             hyperopt  0.600000   \n",
+       "7      resnet_like  ctgan10000  incheon               optuna  0.587620   \n",
+       "10             xgb       smote  gwangju             hyperopt  0.530000   \n",
+       "12         deepgbm  ctgan10000  gwangju               optuna  0.520403   \n",
+       "15             xgb       smote  daejeon             hyperopt  0.537100   \n",
+       "17     resnet_like  ctgan10000  daejeon               optuna  0.510177   \n",
+       "20             xgb       smote    daegu             hyperopt  0.467200   \n",
+       "22     resnet_like  ctgan10000    daegu               optuna  0.460863   \n",
+       "25  ft_transformer  ctgan10000    busan               optuna  0.496046   \n",
+       "26             xgb        pure    busan             hyperopt  0.494900   \n",
+       "\n",
+       "    n_trials                                        best_params  \n",
+       "0        100  {'d_token': 224, 'n_blocks': 4, 'n_heads': 8, ...  \n",
+       "1        100  {'colsample_bytree': 0.9204650350059359, 'gamm...  \n",
+       "5        100  {'colsample_bytree': 0.8863531635625073, 'gamm...  \n",
+       "7        100  {'d_main': 160, 'd_hidden': 192, 'n_blocks': 3...  \n",
+       "10       100  {'colsample_bytree': 0.7658195937298418, 'gamm...  \n",
+       "12       100  {'d_main': 128, 'd_hidden': 192, 'n_blocks': 6...  \n",
+       "15       100  {'colsample_bytree': 0.733236256331133, 'gamma...  \n",
+       "17       100  {'d_main': 128, 'd_hidden': 256, 'n_blocks': 5...  \n",
+       "20       100  {'colsample_bytree': 0.8132816721507904, 'gamm...  \n",
+       "22       100  {'d_main': 96, 'd_hidden': 256, 'n_blocks': 3,...  \n",
+       "25       100  {'d_token': 224, 'n_blocks': 2, 'n_heads': 8, ...  \n",
+       "26       100  {'colsample_bytree': 0.8651175745135303, 'gamm...  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "791fe38f",
+   "metadata": {},
+   "source": [
+    "## 1. Seoul\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "998634af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3371710526315512"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs, y_test = predict_test_proba(\n",
+    "    model_name='ft_transformer',\n",
+    "    region='seoul',\n",
+    "    data_sample='ctgan10000',\n",
+    "    device='cuda',  # Tree-based 모델은 device 파라미터가 사용되지 않음\n",
+    "    n_folds=3\n",
+    ")\n",
+    "probs_1 = np.mean(probs, axis=0)\n",
+    "calculate_csi(np.argmax(probs_1, axis=1), y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a4f94ce4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3431294678315851"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs, y_test = predict_test_proba(\n",
+    "    model_name='xgb',\n",
+    "    region='seoul',\n",
+    "    data_sample='smote',\n",
+    "    device='cuda',  # Tree-based 모델은 device 파라미터가 사용되지 않음\n",
+    "    n_folds=3\n",
+    ")\n",
+    "probs_2= np.mean(probs, axis=0)\n",
+    "calculate_csi(np.argmax(probs_2, axis=1), y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "69421641",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.35207823960877327"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_pred = np.array([probs_1, probs_2])\n",
+    "final_pred = np.mean(final_pred, axis=0)\n",
+    "final_pred = np.argmax(final_pred, axis=1)\n",
+    "calculate_csi(final_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "586f2131",
+   "metadata": {},
+   "source": [
+    "## 2. Incheon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8518d772",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5848329048842812"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs, y_test = predict_test_proba(\n",
+    "    model_name='xgb',\n",
+    "    region='incheon',\n",
+    "    data_sample='smote',\n",
+    "    device='cuda',  # Tree-based 모델은 device 파라미터가 사용되지 않음\n",
+    "    n_folds=3\n",
+    ")\n",
+    "probs_1 = np.mean(probs, axis=0)\n",
+    "calculate_csi(np.argmax(probs_1, axis=1), y_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5070785070784745"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs, y_test = predict_test_proba(\n",
+    "    model_name='resnet_like',\n",
+    "    region='incheon',\n",
+    "    data_sample='ctgan10000',\n",
+    "    device='cuda',  # Tree-based 모델은 device 파라미터가 사용되지 않음\n",
+    "    n_folds=3\n",
+    ")\n",
+    "probs_2= np.mean(probs, axis=0)\n",
+    "calculate_csi(np.argmax(probs_2, axis=1), y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1e156a8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.56152849740929"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_pred = np.array([probs_1, probs_2])\n",
+    "final_pred = np.mean(final_pred, axis=0)\n",
+    "final_pred = np.argmax(final_pred, axis=1)\n",
+    "calculate_csi(final_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41a294ed",
+   "metadata": {},
+   "source": [
+    "## 3. Gwangju"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e3d931c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.49895397489534526"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs, y_test = predict_test_proba(\n",
+    "    model_name='xgb',\n",
+    "    region='gwangju',\n",
+    "    data_sample='smote',\n",
+    "    device='cuda',  # Tree-based 모델은 device 파라미터가 사용되지 않음\n",
+    "    n_folds=3\n",
+    ")\n",
+    "probs_1 = np.mean(probs, axis=0)\n",
+    "calculate_csi(np.argmax(probs_1, axis=1), y_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "35e6a123",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4685314685314217"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs, y_test = predict_test_proba(\n",
+    "    model_name='deepgbm',\n",
+    "    region='gwangju',\n",
+    "    data_sample='ctgan10000',\n",
+    "    device='cuda',  # Tree-based 모델은 device 파라미터가 사용되지 않음\n",
+    "    n_folds=3\n",
+    ")\n",
+    "probs_2= np.mean(probs, axis=0)\n",
+    "calculate_csi(np.argmax(probs_2, axis=1), y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "281bec8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5052192066805318"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_pred = np.array([probs_1, probs_2])\n",
+    "final_pred = np.mean(final_pred, axis=0)\n",
+    "final_pred = np.argmax(final_pred, axis=1)\n",
+    "calculate_csi(final_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48178b79",
+   "metadata": {},
+   "source": [
+    "## 4. Daejeon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6531212c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.394344069128013"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs, y_test = predict_test_proba(\n",
+    "    model_name='xgb',\n",
+    "    region='daejeon',\n",
+    "    data_sample='smote',\n",
+    "    device='cuda',  # Tree-based 모델은 device 파라미터가 사용되지 않음\n",
+    "    n_folds=3\n",
+    ")\n",
+    "probs_1 = np.mean(probs, axis=0)\n",
+    "calculate_csi(np.argmax(probs_1, axis=1), y_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c7cdb38b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.37882352941173497"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs, y_test = predict_test_proba(\n",
+    "    model_name='resnet_like',\n",
+    "    region='daejeon',\n",
+    "    data_sample='ctgan10000',\n",
+    "    device='cuda',  # Tree-based 모델은 device 파라미터가 사용되지 않음\n",
+    "    n_folds=3\n",
+    ")\n",
+    "probs_2= np.mean(probs, axis=0)\n",
+    "calculate_csi(np.argmax(probs_2, axis=1), y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "be501df7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.40776699029122915"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_pred = np.array([probs_1, probs_2])\n",
+    "final_pred = np.mean(final_pred, axis=0)\n",
+    "final_pred = np.argmax(final_pred, axis=1)\n",
+    "calculate_csi(final_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2ccc35e",
+   "metadata": {},
+   "source": [
+    "## 5. Daegu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1f971cb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2303523035229728"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs, y_test = predict_test_proba(\n",
+    "    model_name='xgb',\n",
+    "    region='daegu',\n",
+    "    data_sample='smote',\n",
+    "    device='cuda',  # Tree-based 모델은 device 파라미터가 사용되지 않음\n",
+    "    n_folds=3\n",
+    ")\n",
+    "probs_1 = np.mean(probs, axis=0)\n",
+    "calculate_csi(np.argmax(probs_1, axis=1), y_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "da36de77",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.27622377622367966"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs, y_test = predict_test_proba(\n",
+    "    model_name='resnet_like',\n",
+    "    region='daegu',\n",
+    "    data_sample='ctgan10000',\n",
+    "    device='cuda',  # Tree-based 모델은 device 파라미터가 사용되지 않음\n",
+    "    n_folds=3\n",
+    ")\n",
+    "probs_2= np.mean(probs, axis=0)\n",
+    "calculate_csi(np.argmax(probs_2, axis=1), y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "500f73e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2738853503183841"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_pred = np.array([probs_1, probs_2])\n",
+    "final_pred = np.mean(final_pred, axis=0)\n",
+    "final_pred = np.argmax(final_pred, axis=1)\n",
+    "calculate_csi(final_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b22a8da",
+   "metadata": {},
+   "source": [
+    "## 6. Busan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "74bfd797",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.441005802707845"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs, y_test = predict_test_proba(\n",
+    "    model_name='ft_transformer',\n",
+    "    region='busan',\n",
+    "    data_sample='ctgan10000',\n",
+    "    device='cuda',  # Tree-based 모델은 device 파라미터가 사용되지 않음\n",
+    "    n_folds=3\n",
+    ")\n",
+    "probs_1 = np.mean(probs, axis=0)\n",
+    "calculate_csi(np.argmax(probs_1, axis=1), y_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "66fc12e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.49767441860453543"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs, y_test = predict_test_proba(\n",
+    "    model_name='xgb',\n",
+    "    region='busan',\n",
+    "    data_sample='pure',\n",
+    "    device='cuda',  # Tree-based 모델은 device 파라미터가 사용되지 않음\n",
+    "    n_folds=3\n",
+    ")\n",
+    "probs_2= np.mean(probs, axis=0)\n",
+    "calculate_csi(np.argmax(probs_2, axis=1), y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "78b1b5f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4711934156377631"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_pred = np.array([probs_1, probs_2])\n",
+    "final_pred = np.mean(final_pred, axis=0)\n",
+    "final_pred = np.argmax(final_pred, axis=1)\n",
+    "calculate_csi(final_pred, y_test)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Analysis_code/7.ensemble/shap_utils.py b/Analysis_code/7.ensemble/shap_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b86c28b76e62b1e708fa8b7b6c9b3fed6413cc35
--- /dev/null
+++ b/Analysis_code/7.ensemble/shap_utils.py
@@ -0,0 +1,1260 @@
+from joblib import load
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+import sys
+from contextlib import contextmanager
+from io import StringIO
+from typing import Optional, Tuple, List, Dict, Any
+from dataclasses import dataclass
+from abc import ABC, abstractmethod
+from sklearn.metrics import confusion_matrix
+from sklearn.preprocessing import LabelEncoder
+from scipy.stats import wasserstein_distance
+import shap
+import torch
+import warnings
+
+# 모든 경고 무시
+warnings.filterwarnings('ignore')
+warnings.filterwarnings('ignore', category=UserWarning, message='.*unrecognized nn.Module.*')
+warnings.filterwarnings('ignore', category=UserWarning, message='.*LayerNorm.*')
+warnings.filterwarnings('ignore', category=UserWarning, message='.*unrecognized.*')
+warnings.filterwarnings('ignore', module='shap.*')
+
+# 모델 클래스 import를 위한 경로 설정
+try:
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    models_path = os.path.abspath(os.path.join(current_file_dir, '../models'))
+except NameError:
+    cwd = os.getcwd()
+    if 'Analysis_code' in cwd:
+        analysis_code_dir = cwd[:cwd.index('Analysis_code') + len('Analysis_code')]
+        models_path = os.path.join(analysis_code_dir, 'models')
+    else:
+        models_path = '/workspace/visibility_prediction/Analysis_code/models'
+
+if models_path not in sys.path:
+    sys.path.insert(0, models_path)
+
+from ft_transformer import FTTransformer
+from resnet_like import ResNetLike
+from deepgbm import DeepGBM
+
+
+# ==========================================
+# 1. Config 클래스
+# ==========================================
+
+class SHAPConfig:
+    """SHAP 분석 설정을 관리하는 클래스."""
+    
+    # 상수 정의
+    FOLD_YEARS = [[2018, 2019], [2018, 2019], [2019, 2020]]
+    TEST_YEAR = 2021
+    DROP_COLUMNS = ['multi_class', 'year']
+    FEATURE_COLUMNS = [
+        'temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
+        'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
+        'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
+        'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
+        'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
+        'month_sin', 'month_cos', 'multi_class'
+    ]
+    
+    # 기본 설정값
+    DEFAULT_N_FOLDS = 3
+    DEFAULT_N_BACKGROUND_SAMPLES = 200
+    DEFAULT_N_TEST_SAMPLES = 500
+    DEFAULT_FIGSIZE = (20, 8)
+    DEFAULT_TOP_N = 3
+    DEFAULT_TARGET_CLASS = 0
+    DEFAULT_DEVICE = 'cpu'
+    DEFAULT_PLOT_MARGIN = 0.05
+    
+    def __init__(
+        self,
+        n_folds: int = DEFAULT_N_FOLDS,
+        n_background_samples: int = DEFAULT_N_BACKGROUND_SAMPLES,
+        n_test_samples: int = DEFAULT_N_TEST_SAMPLES,
+        figsize: Tuple[int, int] = DEFAULT_FIGSIZE,
+        top_n: int = DEFAULT_TOP_N,
+        target_class: int = DEFAULT_TARGET_CLASS,
+        device: str = DEFAULT_DEVICE
+    ):
+        self.n_folds = n_folds
+        self.n_background_samples = n_background_samples
+        self.n_test_samples = n_test_samples
+        self.figsize = figsize
+        self.top_n = top_n
+        self.target_class = target_class
+        self.device = device
+        self._validate()
+    
+    def _validate(self):
+        """설정값 검증."""
+        if self.n_folds <= 0:
+            raise ValueError("n_folds must be positive")
+        if self.n_background_samples <= 0:
+            raise ValueError("n_background_samples must be positive")
+        if self.n_test_samples <= 0:
+            raise ValueError("n_test_samples must be positive")
+        if self.top_n <= 0:
+            raise ValueError("top_n must be positive")
+
+
+# ==========================================
+# 2. 결과 데이터 클래스
+# ==========================================
+
+@dataclass
+class ImportanceData:
+    """Feature importance 데이터를 담는 클래스."""
+    df: pd.DataFrame
+    top_n_features: List[str]
+    top_n_share: float
+
+
+@dataclass
+class SHAPResult:
+    """SHAP 분석 결과를 담는 클래스."""
+    shap_train: Optional[np.ndarray]
+    X_train: Optional[pd.DataFrame]
+    shap_test: Optional[np.ndarray]
+    X_test: Optional[pd.DataFrame]
+    feature_names: List[str]
+    importance_train: Optional[ImportanceData]
+    importance_test: Optional[ImportanceData]
+    importance_df_combined: Optional[pd.DataFrame]
+    metadata: Dict[str, Any]
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """기존 dict 형태로 변환 (하위 호환성)."""
+        return {
+            'shap_train': self.shap_train,
+            'X_train': self.X_train,
+            'shap_test': self.shap_test,
+            'X_test': self.X_test,
+            'feature_names': self.feature_names,
+            'top_n_features_train': self.importance_train.top_n_features if self.importance_train else None,
+            'top_n_share_train': self.importance_train.top_n_share if self.importance_train else None,
+            'importance_df_train': self.importance_train.df if self.importance_train else None,
+            'top_n_features_test': self.importance_test.top_n_features if self.importance_test else None,
+            'top_n_share_test': self.importance_test.top_n_share if self.importance_test else None,
+            'importance_df_test': self.importance_test.df if self.importance_test else None,
+            'importance_df_combined': self.importance_df_combined,
+            **self.metadata
+        }
+
+
+# ==========================================
+# 3. 헬퍼 클래스들
+# ==========================================
+
+class PreprocessingUtils:
+    """전처리 유틸리티 클래스."""
+    
+    @staticmethod
+    def add_derived_features(df: pd.DataFrame) -> pd.DataFrame:
+        """파생 변수 추가."""
+        df = df.copy()
+        df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
+        df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
+        df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
+        df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)
+        df['ground_temp - temp_C'] = df['groundtemp'] - df['temp_C']
+        return df
+    
+    @staticmethod
+    def preprocessing(df: pd.DataFrame, config: SHAPConfig) -> pd.DataFrame:
+        """데이터 전처리."""
+        df = df[df.columns].copy()
+        df['year'] = df['year'].astype('int')
+        df['month'] = df['month'].astype('int')
+        df['hour'] = df['hour'].astype('int')
+        df = PreprocessingUtils.add_derived_features(df).copy()
+        df['multi_class'] = df['multi_class'].astype('int')
+        df.loc[df['wind_dir'] == '정온', 'wind_dir'] = "0"
+        df['wind_dir'] = df['wind_dir'].astype('int')
+        df = df[config.FEATURE_COLUMNS].copy()
+        return df
+
+
+class ShapValueExtractor:
+    """SHAP 값 추출 및 변환 유틸리티 클래스."""
+    
+    @staticmethod
+    def _to_numpy(value: Any) -> np.ndarray:
+        """다양한 타입을 numpy array로 변환."""
+        if isinstance(value, torch.Tensor):
+            return value.cpu().numpy()
+        elif isinstance(value, list):
+            return np.array(value)
+        elif isinstance(value, np.ndarray):
+            return value
+        else:
+            return np.array(value)
+    
+    @staticmethod
+    def _combine_num_cat_shap(shap_num: Any, shap_cat: Any) -> np.ndarray:
+        """Numerical과 categorical SHAP 값을 결합."""
+        shap_num = ShapValueExtractor._to_numpy(shap_num)
+        shap_cat = ShapValueExtractor._to_numpy(shap_cat)
+        return np.concatenate([shap_num, shap_cat], axis=-1)
+    
+    @staticmethod
+    def _extract_from_list_of_tuples(shap_list: List[Tuple]) -> np.ndarray:
+        """튜플 리스트에서 SHAP 값을 추출."""
+        combined = []
+        for shap_item in shap_list:
+            if isinstance(shap_item, tuple):
+                combined.append(ShapValueExtractor._combine_num_cat_shap(shap_item[0], shap_item[1]))
+            else:
+                combined.append(ShapValueExtractor._to_numpy(shap_item))
+        return np.array(combined)
+    
+    @staticmethod
+    def _extract_from_list(shap_list: List[Any]) -> np.ndarray:
+        """리스트에서 SHAP 값을 추출."""
+        if len(shap_list) == 0:
+            return np.array([])
+        
+        if isinstance(shap_list[0], tuple):
+            return ShapValueExtractor._extract_from_list_of_tuples(shap_list)
+        
+        if len(shap_list) == 2:
+            return ShapValueExtractor._combine_num_cat_shap(shap_list[0], shap_list[1])
+        
+        try:
+            return np.array([ShapValueExtractor._to_numpy(item) for item in shap_list])
+        except (ValueError, TypeError):
+            converted = [ShapValueExtractor._to_numpy(item) for item in shap_list]
+            if len(converted) == 2:
+                return np.concatenate(converted, axis=-1)
+            else:
+                return np.array(converted)
+    
+    @staticmethod
+    def extract(shap_values: Any, target_class: int = 0) -> np.ndarray:
+        """SHAP 값을 추출하여 numpy array로 변환."""
+        # 튜플인 경우
+        if isinstance(shap_values, tuple):
+            return ShapValueExtractor._combine_num_cat_shap(shap_values[0], shap_values[1])
+        
+        # 리스트인 경우
+        if isinstance(shap_values, list):
+            target_shap = shap_values[target_class]
+            
+            if isinstance(target_shap, tuple):
+                return ShapValueExtractor._combine_num_cat_shap(target_shap[0], target_shap[1])
+            
+            if isinstance(target_shap, list):
+                return ShapValueExtractor._extract_from_list(target_shap)
+            
+            return ShapValueExtractor._to_numpy(target_shap)
+        
+        # numpy array인 경우
+        if isinstance(shap_values, np.ndarray):
+            if len(shap_values.shape) == 3:
+                return shap_values[:, target_class, :]
+            return shap_values
+        
+        # 기타 타입
+        return ShapValueExtractor._to_numpy(shap_values)
+
+
+@contextmanager
+def suppress_shap_output():
+    """SHAP DeepExplainer의 출력을 억제하는 컨텍스트 매니저."""
+    old_stdout = sys.stdout
+    sys.stdout = StringIO()
+    try:
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')
+            yield
+    finally:
+        sys.stdout = old_stdout
+
+
+class ModelWrapper(torch.nn.Module):
+    """PyTorch 모델을 SHAP DeepExplainer에 맞게 래핑하는 클래스."""
+    
+    def __init__(self, base_model, numerical_cols):
+        super().__init__()
+        self.base_model = base_model
+        self.num_features = len(numerical_cols)
+    
+    def forward(self, *inputs):
+        """SHAP이 리스트로 전달하는 경우 처리"""
+        if len(inputs) == 1 and isinstance(inputs[0], (list, tuple)):
+            x_num, x_cat = inputs[0]
+        elif len(inputs) == 2:
+            x_num, x_cat = inputs
+        else:
+            x = inputs[0]
+            x_num = x[:, :self.num_features]
+            x_cat = x[:, self.num_features:].long()
+            return self.base_model(x_num, x_cat)
+        
+        if x_cat.dtype != torch.long:
+            x_cat = x_cat.long()
+        
+        return self.base_model(x_num, x_cat)
+
+
+# ==========================================
+# 4. DataLoader 클래스
+# ==========================================
+
+class DataLoader:
+    """데이터 로딩 및 전처리를 담당하는 클래스."""
+    
+    def __init__(self, config: SHAPConfig):
+        self.config = config
+    
+    def load_raw_data(
+        self,
+        region: str,
+        data_sample: str,
+        fold_idx: int
+    ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+        """원본 데이터 로드 (전처리만 적용)."""
+        if data_sample == 'pure':
+            dat_path = f"../../data/data_for_modeling/{region}_train.csv"
+            train_data = PreprocessingUtils.preprocessing(
+                pd.read_csv(dat_path, index_col=0), self.config
+            )
+            train_data = train_data.loc[
+                ~train_data['year'].isin([self.config.TEST_YEAR - (fold_idx + 1)]), :
+            ]
+        else:
+            train_data = pd.read_csv(
+                f"../../data/data_oversampled/{data_sample}/{data_sample}_{fold_idx+1}_{region}.csv"
+            )
+            train_data = PreprocessingUtils.preprocessing(train_data, self.config)
+        
+        val_data = pd.read_csv(f"../../data/data_for_modeling/{region}_train.csv")
+        val_data = PreprocessingUtils.preprocessing(val_data, self.config)
+        val_data = val_data.loc[
+            val_data['year'].isin([self.config.TEST_YEAR - (fold_idx + 1)]), :
+        ]
+        
+        test_data = pd.read_csv(f"../../data/data_for_modeling/{region}_test.csv")
+        test_data = PreprocessingUtils.preprocessing(test_data, self.config)
+        test_data = test_data.loc[test_data['year'].isin([self.config.TEST_YEAR]), :]
+        
+        # 컬럼 정렬
+        common_columns = train_data.columns.to_list()
+        train_data = train_data[common_columns]
+        val_data = val_data[common_columns]
+        test_data = test_data[common_columns]
+        
+        return train_data, val_data, test_data
+    
+    def prepare_for_tree_model(
+        self,
+        region: str,
+        data_sample: str,
+        fold_idx: int
+    ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+        """Tree 모델용 데이터 준비."""
+        train_data = pd.read_csv(
+            f"../../data/data_oversampled/{data_sample}/{data_sample}_{fold_idx+1}_{region}.csv"
+        )
+        val_data = pd.read_csv(f"../../data/data_for_modeling/{region}_train.csv")
+        test_data = pd.read_csv(f"../../data/data_for_modeling/{region}_test.csv")
+        
+        train_data = PreprocessingUtils.preprocessing(train_data, self.config).copy()
+        val_data = PreprocessingUtils.preprocessing(val_data, self.config).copy()
+        test_data = PreprocessingUtils.preprocessing(test_data, self.config).copy()
+        
+        train_data = train_data.loc[
+            train_data['year'].isin(self.config.FOLD_YEARS[fold_idx]), :
+        ]
+        val_data = val_data.loc[
+            ~val_data['year'].isin(self.config.FOLD_YEARS[fold_idx]), :
+        ]
+        test_data = test_data.loc[test_data['year'].isin([self.config.TEST_YEAR]), :]
+        
+        return train_data, val_data, test_data
+    
+    def prepare_for_dl_model(
+        self,
+        region: str,
+        data_sample: str,
+        fold_idx: int,
+        scaler: Any
+    ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.Index, pd.Index,
+               torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Deep Learning 모델용 데이터 준비 (scaler 적용, 텐서 변환)."""
+        train_data, val_data, test_data = self.load_raw_data(region, data_sample, fold_idx)
+        
+        X_train = train_data.drop(columns=self.config.DROP_COLUMNS)
+        X_val = val_data.drop(columns=self.config.DROP_COLUMNS)
+        X_test = test_data.drop(columns=self.config.DROP_COLUMNS)
+        
+        # 범주형 & 연속형 변수 분리
+        categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns
+        numerical_cols = X_train.select_dtypes(include=['float64']).columns
+        
+        # Label Encoding
+        label_encoders = {}
+        for col in categorical_cols:
+            le = LabelEncoder()
+            le.fit(X_train[col])
+            label_encoders[col] = le
+        
+        for col in categorical_cols:
+            X_train[col] = label_encoders[col].transform(X_train[col])
+            X_val[col] = label_encoders[col].transform(X_val[col])
+            X_test[col] = label_encoders[col].transform(X_test[col])
+        
+        # Scaler 변환
+        X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])
+        X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])
+        X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])
+        
+        # 텐서 변환
+        X_train_num = torch.tensor(X_train[numerical_cols].values, dtype=torch.float32)
+        X_train_cat = torch.tensor(X_train[categorical_cols].values, dtype=torch.long)
+        X_val_num = torch.tensor(X_val[numerical_cols].values, dtype=torch.float32)
+        X_val_cat = torch.tensor(X_val[categorical_cols].values, dtype=torch.long)
+        X_test_num = torch.tensor(X_test[numerical_cols].values, dtype=torch.float32)
+        X_test_cat = torch.tensor(X_test[categorical_cols].values, dtype=torch.long)
+        
+        return (X_train, X_val, X_test, categorical_cols, numerical_cols,
+                X_train_num, X_train_cat, X_val_num, X_val_cat, X_test_num, X_test_cat)
+
+
+# ==========================================
+# 5. SHAPAnalyzer 베이스 클래스
+# ==========================================
+
+class SHAPAnalyzer(ABC):
+    """SHAP 분석을 위한 추상 베이스 클래스."""
+    
+    def __init__(self, config: SHAPConfig, data_loader: DataLoader):
+        self.config = config
+        self.data_loader = data_loader
+    
+    @abstractmethod
+    def create_explainer(self, model: Any, background_data: Any) -> Any:
+        """SHAP Explainer 생성."""
+        pass
+    
+    @abstractmethod
+    def calculate_shap_values(self, explainer: Any, sample_data: Any) -> Any:
+        """SHAP 값 계산."""
+        pass
+    
+    def _calculate_importance(
+        self,
+        shap_values: np.ndarray,
+        feature_names: List[str],
+        period_name: str = ''
+    ) -> ImportanceData:
+        """누적 설명력(Cumulative Share) 계산."""
+        global_importances = np.abs(shap_values).mean(axis=0)
+        
+        importance_df = pd.DataFrame({
+            'feature': feature_names,
+            'importance': global_importances
+        }).sort_values('importance', ascending=False)
+        
+        total_importance = importance_df['importance'].sum()
+        top_n_df = importance_df.head(self.config.top_n)
+        top_n_importance = top_n_df['importance'].sum()
+        top_n_share = (top_n_importance / total_importance) * 100
+        top_n_features = top_n_df['feature'].tolist()
+        
+        if period_name:
+            print(f"{period_name} - Top {self.config.top_n} Dominant Features: {top_n_features}")
+            print(f"{period_name} - Cumulative Importance Share (Top {self.config.top_n}): {top_n_share:.2f}%")
+        else:
+            print(f"Top {self.config.top_n} Dominant Features: {top_n_features}")
+            print(f"Cumulative Importance Share (Top {self.config.top_n}): {top_n_share:.2f}%")
+        print(f"{'-'*40}")
+        
+        return ImportanceData(
+            df=importance_df,
+            top_n_features=top_n_features,
+            top_n_share=top_n_share
+        )
+    
+    def _create_combined_importance_df(
+        self,
+        importance_train: Optional[ImportanceData],
+        importance_test: Optional[ImportanceData],
+        feature_names: List[str]
+    ) -> Optional[pd.DataFrame]:
+        """Train과 Test 중요도 DataFrame 통합."""
+        if importance_train is None and importance_test is None:
+            return None
+        
+        if importance_train is not None:
+            train_df = importance_train.df[['feature', 'importance']].copy()
+            train_df.columns = ['feature', 'importance_train']
+            total_importance_train = train_df['importance_train'].sum()
+            train_df['share_train'] = (train_df['importance_train'] / total_importance_train) * 100
+        else:
+            train_df = pd.DataFrame({'feature': feature_names})
+            train_df['importance_train'] = np.nan
+            train_df['share_train'] = np.nan
+        
+        if importance_test is not None:
+            test_df = importance_test.df[['feature', 'importance']].copy()
+            test_df.columns = ['feature', 'importance_test']
+            total_importance_test = test_df['importance_test'].sum()
+            test_df['share_test'] = (test_df['importance_test'] / total_importance_test) * 100
+        else:
+            test_df = pd.DataFrame({'feature': feature_names})
+            test_df['importance_test'] = np.nan
+            test_df['share_test'] = np.nan
+        
+        importance_df_combined = pd.merge(train_df, test_df, on='feature', how='outer')
+        
+        if importance_train is not None:
+            importance_df_combined = importance_df_combined.sort_values(
+                'importance_train', ascending=False
+            ).reset_index(drop=True)
+        elif importance_test is not None:
+            importance_df_combined = importance_df_combined.sort_values(
+                'importance_test', ascending=False
+            ).reset_index(drop=True)
+        else:
+            importance_df_combined = importance_df_combined.sort_values('feature').reset_index(drop=True)
+        
+        return importance_df_combined
+
+
+# ==========================================
+# 6. TreeSHAPAnalyzer 구현
+# ==========================================
+
+class TreeSHAPAnalyzer(SHAPAnalyzer):
+    """Tree 기반 모델(XGBoost, LightGBM) SHAP 분석 클래스."""
+    
+    def create_explainer(self, model: Any, background_data: Any = None) -> shap.TreeExplainer:
+        """TreeExplainer 생성."""
+        with warnings.catch_warnings():
+            warnings.filterwarnings('ignore', category=UserWarning, message='.*deprecated binary model format.*')
+            return shap.TreeExplainer(model.get_booster())
+    
+    def calculate_shap_values(self, explainer: shap.TreeExplainer, sample_data: pd.DataFrame) -> np.ndarray:
+        """Tree SHAP 값 계산."""
+        shap_values = explainer.shap_values(sample_data)
+        if isinstance(shap_values, list):
+            return shap_values[self.config.target_class]
+        return shap_values
+    
+    def analyze(
+        self,
+        model_name: str,
+        region: str,
+        data_sample: str,
+        show_plot: bool = True,
+        show_train_plot: bool = True,
+        show_test_plot: bool = True,
+        suppress_warnings: bool = True
+    ) -> SHAPResult:
+        """Tree 모델 SHAP 분석 수행."""
+        # 모델 로드
+        model_path = f"../save_model/{model_name}_optima/{model_name}_{data_sample}_{region}.pkl"
+        models = load(model_path)
+        
+        if len(models) != self.config.n_folds:
+            raise ValueError(f"로드된 모델 개수({len(models)})가 n_folds({self.config.n_folds})와 일치하지 않습니다.")
+        
+        # 각 Fold에 대해 SHAP 값 계산
+        oof_shap_values = []
+        oof_X_data = []
+        test_shap_values = []
+        
+        for fold_idx in range(self.config.n_folds):
+            _, val_data, test_data = self.data_loader.prepare_for_tree_model(
+                region, data_sample, fold_idx
+            )
+            
+            X_val = val_data.drop(columns=self.config.DROP_COLUMNS)
+            if fold_idx == 0:
+                X_test = test_data.drop(columns=self.config.DROP_COLUMNS)
+            
+            explainer = self.create_explainer(models[fold_idx])
+            shap_val = self.calculate_shap_values(explainer, X_val)
+            oof_shap_values.append(shap_val)
+            oof_X_data.append(X_val)
+            
+            shap_test = self.calculate_shap_values(explainer, X_test)
+            test_shap_values.append(shap_test)
+        
+        # 데이터 통합
+        all_shap_train = np.concatenate(oof_shap_values, axis=0)
+        all_X_train = pd.concat(oof_X_data, axis=0)
+        all_shap_test = np.mean(test_shap_values, axis=0)
+        feature_names = list(all_X_train.columns)
+        
+        # 중요도 계산
+        importance_train = self._calculate_importance(
+            all_shap_train, feature_names, period_name='Train Period'
+        )
+        importance_test = self._calculate_importance(
+            all_shap_test, feature_names, period_name='Test Period'
+        )
+        print()
+        
+        importance_df_combined = self._create_combined_importance_df(
+            importance_train, importance_test, feature_names
+        )
+        
+        # 시각화
+        if show_plot:
+            visualizer = SHAPVisualizer(self.config)
+            visualizer.plot_summary(
+                all_shap_train, all_X_train, all_shap_test, X_test,
+                feature_names, importance_train, importance_test,
+                show_train_plot=show_train_plot, show_test_plot=show_test_plot,
+                model_name=model_name, data_sample=data_sample, region=region
+            )
+        
+        return SHAPResult(
+            shap_train=all_shap_train,
+            X_train=all_X_train,
+            shap_test=all_shap_test,
+            X_test=X_test,
+            feature_names=feature_names,
+            importance_train=importance_train,
+            importance_test=importance_test,
+            importance_df_combined=importance_df_combined,
+            metadata={'model_name': model_name, 'region': region, 'data_sample': data_sample}
+        )
+
+
+# ==========================================
+# 7. DeepSHAPAnalyzer 구현
+# ==========================================
+
+class DeepSHAPAnalyzer(SHAPAnalyzer):
+    """Deep Learning 모델 SHAP 분석 클래스."""
+    
+    def __init__(self, config: SHAPConfig, data_loader: DataLoader, device: str = 'cpu'):
+        super().__init__(config, data_loader)
+        self.device = device
+    
+    def create_explainer(
+        self,
+        model: torch.nn.Module,
+        background_data: Tuple[torch.Tensor, torch.Tensor, pd.Index],
+        model_name: str
+    ) -> shap.DeepExplainer:
+        """DeepExplainer 생성."""
+        X_bg_num, X_bg_cat, numerical_cols = background_data
+        wrapped_model = ModelWrapper(model, numerical_cols)
+        wrapped_model.to(self.device)
+        wrapped_model.eval()
+        
+        with suppress_shap_output():
+            if model_name in ('ft_transformer', 'deepgbm'):
+                X_bg_cat_float = X_bg_cat.float()
+                explainer = shap.DeepExplainer(wrapped_model, [X_bg_num, X_bg_cat_float])
+            elif model_name == 'resnet_like':
+                X_bg_combined = torch.cat([X_bg_num, X_bg_cat.float()], dim=1)
+                explainer = shap.DeepExplainer(wrapped_model, X_bg_combined)
+            else:
+                raise ValueError(f"Unknown model_name: {model_name}")
+        
+        return explainer
+    
+    def calculate_shap_values(
+        self,
+        explainer: shap.DeepExplainer,
+        sample_data: Tuple[torch.Tensor, torch.Tensor],
+        model_name: str
+    ) -> Any:
+        """Deep SHAP 값 계산."""
+        X_sample_num, X_sample_cat = sample_data
+        
+        with suppress_shap_output():
+            if model_name in ('ft_transformer', 'deepgbm'):
+                X_sample_cat_float = X_sample_cat.float()
+                shap_values = explainer.shap_values([X_sample_num, X_sample_cat_float])
+            elif model_name == 'resnet_like':
+                X_sample_combined = torch.cat([X_sample_num, X_sample_cat.float()], dim=1)
+                shap_values = explainer.shap_values(X_sample_combined)
+            else:
+                raise ValueError(f"Unknown model_name: {model_name}")
+        
+        return shap_values
+    
+    def analyze(
+        self,
+        model_name: str,
+        region: str,
+        data_sample: str,
+        show_plot: bool = True,
+        show_train_plot: bool = True,
+        show_test_plot: bool = True
+    ) -> Optional[SHAPResult]:
+        """Deep Learning 모델 SHAP 분석 수행."""
+        print(f"[{model_name.upper()}] {region} - {data_sample} SHAP Analysis Start...")
+        
+        # 모델 및 scaler 로드
+        try:
+            model_path = f"../save_model/{model_name}_optima/{model_name}_{data_sample}_{region}.pkl"
+            models = load(model_path)
+            
+            if data_sample == 'pure':
+                scaler_filename = f'{model_name}_pure_{region}_scaler.pkl'
+            else:
+                scaler_filename = f'{model_name}_{data_sample}_{region}_scaler.pkl'
+            scaler_path = f'../save_model/{model_name}_optima/scaler/{scaler_filename}'
+            scalers = load(scaler_path)
+        except Exception as e:
+            print(f"모델 또는 scaler 로드 실패: {e}")
+            return None
+        
+        if len(models) != self.config.n_folds or len(scalers) != self.config.n_folds:
+            raise ValueError(
+                f"모델 개수({len(models)}) 또는 scaler 개수({len(scalers)})가 "
+                f"n_folds({self.config.n_folds})와 일치하지 않습니다."
+            )
+        
+        # 각 fold의 SHAP 값 저장
+        all_shap_train = []
+        all_shap_test = []
+        all_train_data = []
+        all_test_data = []
+        feature_names = None
+        
+        for fold_idx in range(self.config.n_folds):
+            print(f"  Processing Fold {fold_idx + 1}...")
+            
+            try:
+                (X_train, X_val, X_test, categorical_cols, numerical_cols,
+                 X_train_num, X_train_cat, X_val_num, X_val_cat, X_test_num, X_test_cat) = \
+                    self.data_loader.prepare_for_dl_model(region, data_sample, fold_idx, scalers[fold_idx])
+                
+                if feature_names is None:
+                    feature_names = list(numerical_cols) + list(categorical_cols)
+                
+                model = models[fold_idx]
+                model.to(self.device)
+                model.eval()
+                
+                # 배경 데이터 샘플링
+                n_bg = min(self.config.n_background_samples, len(X_val_num))
+                bg_indices = np.random.choice(len(X_val_num), n_bg, replace=False)
+                X_bg_num = X_val_num[bg_indices].to(self.device)
+                X_bg_cat = X_val_cat[bg_indices].to(self.device)
+                
+                # Validation 데이터 샘플링
+                n_train = min(self.config.n_test_samples, len(X_val_num))
+                available_indices = np.setdiff1d(np.arange(len(X_val_num)), bg_indices)
+                if len(available_indices) < n_train:
+                    train_indices = available_indices
+                else:
+                    train_indices = np.random.choice(available_indices, n_train, replace=False)
+                X_train_sample_num = X_val_num[train_indices].to(self.device)
+                X_train_sample_cat = X_val_cat[train_indices].to(self.device)
+                
+                # 테스트 데이터 샘플링
+                n_test = min(self.config.n_test_samples, len(X_test_num))
+                test_indices = np.random.choice(len(X_test_num), n_test, replace=False)
+                X_test_sample_num = X_test_num[test_indices].to(self.device)
+                X_test_sample_cat = X_test_cat[test_indices].to(self.device)
+                
+                # SHAP Explainer 생성 및 계산
+                try:
+                    explainer = self.create_explainer(
+                        model, (X_bg_num, X_bg_cat, numerical_cols), model_name
+                    )
+                    
+                    shap_values_train = self.calculate_shap_values(
+                        explainer, (X_train_sample_num, X_train_sample_cat), model_name
+                    )
+                    shap_val_train = ShapValueExtractor.extract(shap_values_train, self.config.target_class)
+                    all_shap_train.append(shap_val_train)
+                    X_train_original = X_val.iloc[train_indices].copy()
+                    all_train_data.append(X_train_original)
+                    
+                    shap_values_test = self.calculate_shap_values(
+                        explainer, (X_test_sample_num, X_test_sample_cat), model_name
+                    )
+                    shap_val_test = ShapValueExtractor.extract(shap_values_test, self.config.target_class)
+                    all_shap_test.append(shap_val_test)
+                    X_test_original = X_test.iloc[test_indices].copy()
+                    all_test_data.append(X_test_original)
+                    
+                except Exception as e:
+                    print(f"  Warning: Fold {fold_idx + 1} SHAP 계산 실패: {e}")
+                    continue
+            except Exception as e:
+                print(f"  Warning: Fold {fold_idx + 1} 처리 실패: {e}")
+                continue
+        
+        if len(all_shap_train) == 0 and len(all_shap_test) == 0:
+            print("  모든 fold에서 SHAP 계산 실패")
+            return None
+        
+        # 데이터 통합
+        all_shap_train_combined = np.concatenate(all_shap_train, axis=0) if len(all_shap_train) > 0 else None
+        all_train_data_combined = pd.concat(all_train_data, axis=0).reset_index(drop=True) if len(all_train_data) > 0 else None
+        
+        if len(all_shap_test) > 0:
+            all_shap_test_combined = np.mean(all_shap_test, axis=0)
+            all_test_data_combined = all_test_data[0] if len(all_test_data) > 0 else None
+        else:
+            all_shap_test_combined = None
+            all_test_data_combined = None
+        
+        # 중요도 계산
+        if all_shap_train_combined is not None:
+            importance_train = self._calculate_importance(
+                all_shap_train_combined, feature_names, period_name='Train Period'
+            )
+        else:
+            importance_train = None
+        
+        if all_shap_test_combined is not None:
+            importance_test = self._calculate_importance(
+                all_shap_test_combined, feature_names, period_name='Test Period'
+            )
+            print()
+        else:
+            importance_test = None
+        
+        importance_df_combined = self._create_combined_importance_df(
+            importance_train, importance_test, feature_names
+        )
+        
+        # 시각화
+        if show_plot:
+            visualizer = SHAPVisualizer(self.config)
+            visualizer.plot_summary(
+                all_shap_train_combined, all_train_data_combined,
+                all_shap_test_combined, all_test_data_combined,
+                feature_names, importance_train, importance_test,
+                show_train_plot=show_train_plot, show_test_plot=show_test_plot,
+                model_name=model_name, data_sample=data_sample, region=region
+            )
+        
+        print(f"  [{model_name.upper()}] SHAP Analysis Complete!\n")
+        
+        return SHAPResult(
+            shap_train=all_shap_train_combined,
+            X_train=all_train_data_combined[feature_names] if all_train_data_combined is not None else None,
+            shap_test=all_shap_test_combined,
+            X_test=all_test_data_combined[feature_names] if all_test_data_combined is not None else None,
+            feature_names=feature_names,
+            importance_train=importance_train,
+            importance_test=importance_test,
+            importance_df_combined=importance_df_combined,
+            metadata={'model_name': model_name, 'region': region, 'data_sample': data_sample}
+        )
+
+
+# ==========================================
+# 8. SHAPVisualizer 클래스
+# ==========================================
+
+class SHAPVisualizer:
+    """SHAP 시각화를 담당하는 클래스."""
+    
+    def __init__(self, config: SHAPConfig):
+        self.config = config
+    
+    def _calculate_xlim(
+        self,
+        shap_train: Optional[np.ndarray],
+        shap_test: Optional[np.ndarray]
+    ) -> Tuple[Optional[float], Optional[float]]:
+        """x축 범위 계산."""
+        if shap_train is not None and shap_test is not None:
+            x_min = min(shap_train.min(), shap_test.min())
+            x_max = max(shap_train.max(), shap_test.max())
+        elif shap_train is not None:
+            x_min, x_max = shap_train.min(), shap_train.max()
+        elif shap_test is not None:
+            x_min, x_max = shap_test.min(), shap_test.max()
+        else:
+            return None, None
+        
+        margin = (x_max - x_min) * self.config.DEFAULT_PLOT_MARGIN
+        x_min -= margin
+        x_max += margin
+        return x_min, x_max
+    
+    def plot_summary(
+        self,
+        shap_train: Optional[np.ndarray],
+        X_train: Optional[pd.DataFrame],
+        shap_test: Optional[np.ndarray],
+        X_test: Optional[pd.DataFrame],
+        feature_names: List[str],
+        importance_train: Optional[ImportanceData],
+        importance_test: Optional[ImportanceData],
+        show_train_plot: bool = True,
+        show_test_plot: bool = True,
+        model_name: Optional[str] = None,
+        data_sample: Optional[str] = None,
+        region: Optional[str] = None
+    ) -> None:
+        """SHAP summary plot 생성."""
+        num_plots = int(show_train_plot and shap_train is not None) + int(show_test_plot and shap_test is not None)
+        if num_plots == 0:
+            return
+        
+        x_min, x_max = self._calculate_xlim(shap_train, shap_test)
+        
+        # Title prefix 생성
+        title_parts = []
+        if model_name is not None:
+            title_parts.append(model_name.upper())
+        if data_sample is not None:
+            title_parts.append(data_sample)
+        if region is not None:
+            title_parts.append(region.upper())
+        
+        title_prefix = " - ".join(title_parts) + "\n" if title_parts else ""
+        
+        if num_plots == 2:
+            fig, axes = plt.subplots(1, 2, figsize=self.config.figsize)
+            
+            if show_train_plot and shap_train is not None:
+                plt.sca(axes[0])
+                shap.summary_plot(
+                    shap_train,
+                    X_train[feature_names] if isinstance(X_train, pd.DataFrame) else X_train,
+                    show=False, plot_size=None
+                )
+                if x_min is not None and x_max is not None:
+                    plt.xlim(x_min, x_max)
+                share_text = (
+                    f"\nTop {self.config.top_n} Share: {importance_train.top_n_share:.1f}%"
+                    if importance_train else ""
+                )
+                plt.title(f"{title_prefix}Train Period (2018-20): Class {self.config.target_class}{share_text}")
+            
+            if show_test_plot and shap_test is not None:
+                plt.sca(axes[1])
+                shap.summary_plot(
+                    shap_test,
+                    X_test[feature_names] if isinstance(X_test, pd.DataFrame) else X_test,
+                    show=False, plot_size=None
+                )
+                if x_min is not None and x_max is not None:
+                    plt.xlim(x_min, x_max)
+                share_text = (
+                    f"\nTop {self.config.top_n} Share: {importance_test.top_n_share:.1f}%"
+                    if importance_test else ""
+                )
+                plt.title(f"{title_prefix}Test Period (2021): Class {self.config.target_class}{share_text}")
+        else:
+            plt.figure(figsize=(self.config.figsize[0]//2, self.config.figsize[1]))
+            if show_train_plot and shap_train is not None:
+                data = X_train[feature_names] if isinstance(X_train, pd.DataFrame) else X_train
+                shap_v = shap_train
+                share_text = (
+                    f"\nTop {self.config.top_n} Share: {importance_train.top_n_share:.1f}%"
+                    if importance_train else ""
+                )
+                title = f"{title_prefix}Train Period (2018-20): Class {self.config.target_class}{share_text}"
+            elif show_test_plot and shap_test is not None:
+                data = X_test[feature_names] if isinstance(X_test, pd.DataFrame) else X_test
+                shap_v = shap_test
+                share_text = (
+                    f"\nTop {self.config.top_n} Share: {importance_test.top_n_share:.1f}%"
+                    if importance_test else ""
+                )
+                title = f"{title_prefix}Test Period (2021): Class {self.config.target_class}{share_text}"
+            else:
+                return
+            
+            shap.summary_plot(shap_v, data, show=False)
+            if x_min is not None and x_max is not None:
+                plt.xlim(x_min, x_max)
+            plt.title(title)
+        
+        plt.tight_layout()
+        plt.show()
+    
+    def plot_elbow(self, importance_df_combined: pd.DataFrame) -> None:
+        """Elbow plot 생성."""
+        elbow = importance_df_combined.loc[:, ['feature', 'importance_train', 'share_train']]
+        elbow = elbow.sort_values('importance_train', ascending=False)
+        plt.figure(figsize=(8, 5))
+        plt.plot(range(1, len(elbow) + 1), elbow['share_train'].values, marker='o')
+        plt.xlabel('Feature Rank')
+        plt.ylabel('Share Train (%)')
+        plt.title('Elbow Plot for Feature Importance (share_train)')
+        plt.xticks(range(1, len(elbow) + 1))
+        plt.grid(True, linestyle='--', alpha=0.5)
+        plt.show()
+
+
+# ==========================================
+# 9. WassersteinCalculator 클래스
+# ==========================================
+
+class WassersteinCalculator:
+    """Wasserstein Distance 계산을 담당하는 클래스."""
+    
+    def __init__(self, config: SHAPConfig, data_loader: DataLoader):
+        self.config = config
+        self.data_loader = data_loader
+    
+    def _compute_wd_for_feature(
+        self,
+        train_dist: np.ndarray,
+        test_dist: np.ndarray
+    ) -> Dict[str, float]:
+        """단일 feature에 대한 WD 계산."""
+        wd_val = wasserstein_distance(train_dist, test_dist)
+        return {
+            'WD_Score': wd_val,
+            'Train_Mean': np.mean(train_dist),
+            'Test_Mean': np.mean(test_dist),
+            'Train_Std': np.std(train_dist),
+            'Test_Std': np.std(test_dist),
+            'Train_Count': len(train_dist),
+            'Test_Count': len(test_dist)
+        }
+    
+    def calculate(
+        self,
+        region: str,
+        data_sample: str,
+        top_features: List[str],
+        model_type: str = 'tree',
+        fold_idx: int = 0
+    ) -> pd.DataFrame:
+        """Wasserstein Distance 계산."""
+        # 데이터 로드
+        if model_type == 'tree':
+            train_df, val_df, test_df = self.data_loader.prepare_for_tree_model(
+                region, data_sample, fold_idx
+            )
+        elif model_type == 'dl':
+            train_df, val_df, test_df = self.data_loader.load_raw_data(
+                region, data_sample, fold_idx
+            )
+        else:
+            raise ValueError(f"Unknown model_type: {model_type}. Must be 'tree' or 'dl'")
+        
+        train_period = pd.concat([train_df, val_df], axis=0)
+        test_period = test_df
+        
+        results = []
+        
+        print(f"[{region.upper()}] Wasserstein Distance Analysis")
+        print(f"Model Type: {model_type.upper()}, Data Sample: {data_sample}")
+        print(f"{'-'*60}")
+        
+        for feature in top_features:
+            if feature in train_period.columns:
+                dist_train = train_period[feature].dropna().values
+                dist_test = test_period[feature].dropna().values
+                
+                if len(dist_train) == 0 or len(dist_test) == 0:
+                    print(f"Warning: {feature} 변수에 유효한 데이터가 없습니다.")
+                    continue
+                
+                wd_metrics = self._compute_wd_for_feature(dist_train, dist_test)
+                results.append({
+                    'Region': region,
+                    'Feature': feature,
+                    **wd_metrics
+                })
+                print(f"Feature: {feature:20} | WD: {wd_metrics['WD_Score']:.4f} | "
+                      f"Train Mean: {wd_metrics['Train_Mean']:.4f} | Test Mean: {wd_metrics['Test_Mean']:.4f}")
+            else:
+                print(f"Warning: {feature} 변수를 찾을 수 없습니다.")
+        
+        print()
+        return pd.DataFrame(results)
+    
+    def calculate_from_shap_result(
+        self,
+        shap_result: Dict[str, Any],
+        top_n: int = 3,
+        use_train_features: bool = True,
+        model_type: str = 'tree',
+        fold_idx: int = 0
+    ) -> pd.DataFrame:
+        """SHAP 분석 결과에서 상위 변수를 자동 추출하여 WD 계산."""
+        # 상위 변수 추출
+        if use_train_features:
+            if 'top_n_features_train' in shap_result and shap_result['top_n_features_train'] is not None:
+                top_features = shap_result['top_n_features_train'][:top_n]
+            elif 'importance_df_train' in shap_result and shap_result['importance_df_train'] is not None:
+                top_features = shap_result['importance_df_train'].head(top_n)['feature'].tolist()
+            else:
+                raise ValueError("SHAP 결과에서 Train 상위 변수를 찾을 수 없습니다.")
+        else:
+            if 'top_n_features_test' in shap_result and shap_result['top_n_features_test'] is not None:
+                top_features = shap_result['top_n_features_test'][:top_n]
+            elif 'importance_df_test' in shap_result and shap_result['importance_df_test'] is not None:
+                top_features = shap_result['importance_df_test'].head(top_n)['feature'].tolist()
+            else:
+                raise ValueError("SHAP 결과에서 Test 상위 변수를 찾을 수 없습니다.")
+        
+        region = shap_result.get('region', 'unknown')
+        data_sample = shap_result.get('data_sample', 'unknown')
+        
+        return self.calculate(region, data_sample, top_features, model_type, fold_idx)
+
+
+# ==========================================
+# 10. 하위 호환성 레이어
+# ==========================================
+
+def analyze_shap_values_across_folds(
+    model_name: str,
+    region: str,
+    data_sample: str,
+    target_class: int = 0,
+    n_folds: int = 3,
+    figsize: Tuple[int, int] = (20, 10),
+    show_plot: bool = True,
+    show_train_plot: bool = True,
+    show_test_plot: bool = True,
+    suppress_warnings: bool = True,
+    top_n: int = 3
+) -> Dict[str, Any]:
+    """Tree 모델 SHAP 분석 (하위 호환성 래퍼 함수).
+    
+    Deprecated: Use TreeSHAPAnalyzer instead.
+    """
+    config = SHAPConfig(
+        n_folds=n_folds,
+        figsize=figsize,
+        top_n=top_n,
+        target_class=target_class
+    )
+    data_loader = DataLoader(config)
+    analyzer = TreeSHAPAnalyzer(config, data_loader)
+    result = analyzer.analyze(
+        model_name, region, data_sample,
+        show_plot=show_plot,
+        show_train_plot=show_train_plot,
+        show_test_plot=show_test_plot
+    )
+    return result.to_dict()
+
+
+def analyze_dl_model_shap(
+    model_name: str,
+    region: str,
+    data_sample: str,
+    target_class: int = 0,
+    n_folds: int = 3,
+    n_background_samples: int = 200,
+    n_test_samples: int = 500,
+    device: str = 'cpu',
+    figsize: Tuple[int, int] = (20, 8),
+    show_plot: bool = True,
+    show_train_plot: bool = True,
+    show_test_plot: bool = True,
+    top_n: int = 3
+) -> Optional[Dict[str, Any]]:
+    """Deep Learning 모델 SHAP 분석 (하위 호환성 래퍼 함수).
+    
+    Deprecated: Use DeepSHAPAnalyzer instead.
+    """
+    config = SHAPConfig(
+        n_folds=n_folds,
+        n_background_samples=n_background_samples,
+        n_test_samples=n_test_samples,
+        figsize=figsize,
+        top_n=top_n,
+        target_class=target_class,
+        device=device
+    )
+    data_loader = DataLoader(config)
+    analyzer = DeepSHAPAnalyzer(config, data_loader, device=device)
+    result = analyzer.analyze(
+        model_name, region, data_sample,
+        show_plot=show_plot,
+        show_train_plot=show_train_plot,
+        show_test_plot=show_test_plot
+    )
+    return result.to_dict() if result else None
+
+
+def calculate_wasserstein_distance(
+    region: str,
+    data_sample: str,
+    top_features: List[str],
+    model_type: str = 'tree',
+    fold_idx: int = 0
+) -> pd.DataFrame:
+    """Wasserstein Distance 계산 (하위 호환성 래퍼 함수).
+    
+    Deprecated: Use WassersteinCalculator instead.
+    """
+    config = SHAPConfig()
+    data_loader = DataLoader(config)
+    calculator = WassersteinCalculator(config, data_loader)
+    return calculator.calculate(region, data_sample, top_features, model_type, fold_idx)
+
+
+def calculate_wd_from_shap_result(
+    shap_result: Dict[str, Any],
+    top_n: int = 3,
+    use_train_features: bool = True,
+    model_type: str = 'tree',
+    fold_idx: int = 0
+) -> pd.DataFrame:
+    """SHAP 결과에서 WD 계산 (하위 호환성 래퍼 함수).
+    
+    Deprecated: Use WassersteinCalculator instead.
+    """
+    config = SHAPConfig()
+    data_loader = DataLoader(config)
+    calculator = WassersteinCalculator(config, data_loader)
+    return calculator.calculate_from_shap_result(
+        shap_result, top_n, use_train_features, model_type, fold_idx
+    )
+
+
+def elbow_plot(result: Dict[str, Any]) -> None:
+    """Elbow plot 생성 (하위 호환성 래퍼 함수)."""
+    config = SHAPConfig()
+    visualizer = SHAPVisualizer(config)
+    if 'importance_df_combined' in result:
+        visualizer.plot_elbow(result['importance_df_combined'])
+
+
+# ==========================================
+# 유틸리티 함수 (기존 호환성 유지)
+# ==========================================
+
+def calculate_csi(y_true: np.ndarray, y_pred: np.ndarray) -> float:
+    """CSI(Critical Success Index) 점수를 계산합니다."""
+    cm = confusion_matrix(y_true, y_pred)
+    H = cm[0, 0] + cm[1, 1]
+    F = cm[1, 0] + cm[2, 0] + cm[0, 1] + cm[2, 1]
+    M = cm[0, 2] + cm[1, 2]
+    csi = H / (H + F + M + 1e-10)
+    return csi
+
+
+def csi_metric(y_true: np.ndarray, pred_prob: np.ndarray) -> Tuple[str, float, bool]:
+    """LightGBM용 CSI 메트릭 함수."""
+    y_pred_binary = np.argmax(pred_prob, axis=1)
+    score = calculate_csi(y_true, y_pred_binary)
+    return 'CSI', score, True
+
+
+def eval_metric_csi(y_true: np.ndarray, pred_prob: np.ndarray) -> float:
+    """XGBoost용 CSI 메트릭 함수."""
+    pred = np.argmax(pred_prob, axis=1)
+    csi = calculate_csi(y_true, pred)
+    return -1 * csi
+
+
+# 기존 상수들 (하위 호환성)
+FOLD_YEARS = SHAPConfig.FOLD_YEARS
+TEST_YEAR = SHAPConfig.TEST_YEAR
+DROP_COLUMNS = SHAPConfig.DROP_COLUMNS
+FEATURE_COLUMNS = SHAPConfig.FEATURE_COLUMNS
diff --git a/Analysis_code/7.ensemble/utils.py b/Analysis_code/7.ensemble/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..db28713cb180cbfd58fa9717746d53762878414c
--- /dev/null
+++ b/Analysis_code/7.ensemble/utils.py
@@ -0,0 +1,512 @@
+# 표준 라이브러리
+import os
+import sys
+import warnings
+from typing import List, Tuple, Any
+
+# 서드파티 라이브러리
+import numpy as np
+import pandas as pd
+import torch
+import torch.nn.functional as F
+from joblib import load
+from sklearn.metrics import confusion_matrix
+from sklearn.preprocessing import LabelEncoder
+
+# 경고 무시
+warnings.filterwarnings('ignore')
+
+# ==========================================
+# 상수 정의
+# ==========================================
+TEST_YEAR = 2021
+DROP_COLUMNS = ['multi_class', 'year']
+FEATURE_COLUMNS = [
+    'temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
+    'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
+    'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
+    'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
+    'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
+    'month_sin', 'month_cos', 'multi_class'
+]
+
+# 모델 타입 상수
+TREE_MODELS = ['xgb', 'lgb']
+DL_MODELS = ['ft_transformer', 'resnet_like', 'deepgbm']
+
+# ==========================================
+# 경로 설정
+# ==========================================
+def _setup_model_path() -> str:
+    """모델 클래스 import를 위한 경로를 설정하고 반환합니다.
+    
+    Returns:
+        models 디렉토리의 절대 경로
+    """
+    try:
+        current_file_dir = os.path.dirname(os.path.abspath(__file__))
+        models_path = os.path.abspath(os.path.join(current_file_dir, '../models'))
+    except NameError:
+        # 노트북 환경에서 __file__이 없는 경우
+        cwd = os.getcwd()
+        if 'Analysis_code' in cwd:
+            analysis_code_dir = cwd[:cwd.index('Analysis_code') + len('Analysis_code')]
+            models_path = os.path.join(analysis_code_dir, 'models')
+        else:
+            models_path = '/workspace/visibility_prediction/Analysis_code/models'
+    
+    if models_path not in sys.path:
+        sys.path.insert(0, models_path)
+    
+    return models_path
+
+# 모델 경로 설정 및 import
+_setup_model_path()
+from ft_transformer import FTTransformer
+from resnet_like import ResNetLike
+from deepgbm import DeepGBM
+
+# ==========================================
+# 메트릭 함수 (pickle 호환성)
+# ==========================================
+def calculate_csi(y_true: np.ndarray, y_pred: np.ndarray) -> float:
+    """CSI(Critical Success Index) 점수를 계산합니다.
+    
+    Args:
+        y_true: 실제 레이블
+        y_pred: 예측 레이블
+        
+    Returns:
+        CSI 점수 (0~1 사이의 값)
+    """
+    cm = confusion_matrix(y_true, y_pred)
+    
+    # 혼동 행렬에서 H(Hit), F(False alarm), M(Miss) 추출
+    H = cm[0, 0] + cm[1, 1]
+    F = cm[1, 0] + cm[2, 0] + cm[0, 1] + cm[2, 1]
+    M = cm[0, 2] + cm[1, 2]
+    
+    # CSI 계산
+    csi = H / (H + F + M + 1e-10)
+    return csi
+
+def eval_metric_csi(y_true: np.ndarray, pred_prob: np.ndarray) -> float:
+    """XGBoost용 CSI 메트릭 함수.
+    
+    Args:
+        y_true: 실제 레이블
+        pred_prob: 예측 확률
+        
+    Returns:
+        CSI 점수의 음수값
+    """
+    pred = np.argmax(pred_prob, axis=1)
+    csi = calculate_csi(y_true, pred)
+    return -1 * csi
+
+def csi_metric(y_true: np.ndarray, pred_prob: np.ndarray) -> Tuple[str, float, bool]:
+    """LightGBM용 CSI 메트릭 함수.
+    
+    Args:
+        y_true: 실제 레이블
+        pred_prob: 예측 확률 (shape: [n_samples, n_classes])
+        
+    Returns:
+        ('CSI', score, higher_better) 튜플
+    """
+    y_pred_binary = np.argmax(pred_prob, axis=1)
+    score = calculate_csi(y_true, y_pred_binary)
+    return 'CSI', score, True
+
+# ==========================================
+# Pickle 호환성 함수 등록
+# ==========================================
+def _register_pickle_functions(model_name: str) -> None:
+    """모델 로드를 위해 __main__ 모듈에 필요한 함수를 등록합니다.
+    
+    모델이 저장될 때 __main__ 모듈에서 정의된 함수를 참조했을 수 있으므로,
+    pickle이 모델을 로드할 때 함수를 찾을 수 있도록 등록합니다.
+    
+    Args:
+        model_name: 모델 이름 ('xgb' 또는 'lgb')
+    """
+    if '__main__' not in sys.modules:
+        return
+    
+    main_module = sys.modules['__main__']
+    main_module.calculate_csi = calculate_csi
+    
+    if model_name == 'xgb':
+        main_module.eval_metric_csi = eval_metric_csi
+    elif model_name == 'lgb':
+        main_module.csi_metric = csi_metric
+
+# ==========================================
+# 데이터 전처리 함수
+# ==========================================
+def add_derived_features(df: pd.DataFrame) -> pd.DataFrame:
+    """제거했던 파생 변수들을 복구합니다.
+    
+    Args:
+        df: 원본 데이터프레임
+        
+    Returns:
+        파생 변수가 추가된 데이터프레임
+    """
+    df = df.copy()
+    df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
+    df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
+    df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
+    df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)
+    df['ground_temp - temp_C'] = df['groundtemp'] - df['temp_C']
+    return df
+
+def preprocessing(df: pd.DataFrame) -> pd.DataFrame:
+    """데이터 전처리 함수.
+    
+    Args:
+        df: 원본 데이터프레임
+        
+    Returns:
+        전처리된 데이터프레임
+    """
+    df = df[df.columns].copy()
+    df['year'] = df['year'].astype('int')
+    df['month'] = df['month'].astype('int')
+    df['hour'] = df['hour'].astype('int')
+    df = add_derived_features(df).copy()
+    df['multi_class'] = df['multi_class'].astype('int')
+    df.loc[df['wind_dir'] == '정온', 'wind_dir'] = "0"
+    df['wind_dir'] = df['wind_dir'].astype('int')
+    df = df[FEATURE_COLUMNS].copy()
+    return df
+
+# ==========================================
+# 모델 로드 함수
+# ==========================================
+def load_model(model_name: str, region: str, data_sample: str) -> List[Any]:
+    """모델을 로드하는 함수.
+    
+    Args:
+        model_name: 모델 이름 ('xgb', 'lgb', 'ft_transformer', 'resnet_like', 'deepgbm')
+        region: 지역명 (예: 'seoul', 'busan')
+        data_sample: 데이터 샘플 타입 (예: 'pure', 'smote', 'ctgan10000')
+        
+    Returns:
+        로드된 모델 리스트 (각 fold별 모델)
+        
+    Raises:
+        FileNotFoundError: 모델 파일을 찾을 수 없는 경우
+        ValueError: 모델 이름이 유효하지 않은 경우
+    """
+    if model_name not in TREE_MODELS + DL_MODELS:
+        raise ValueError(
+            f"Unknown model_name: {model_name}. "
+            f"Must be one of {TREE_MODELS + DL_MODELS}"
+        )
+    
+    # Tree-based 모델의 경우 pickle 호환성 함수 등록
+    if model_name in TREE_MODELS:
+        _register_pickle_functions(model_name)
+    
+    model_path = f"../save_model/{model_name}_optima/{model_name}_{data_sample}_{region}.pkl"
+    
+    if not os.path.exists(model_path):
+        raise FileNotFoundError(
+            f"모델 파일을 찾을 수 없습니다: {model_path}"
+        )
+    
+    try:
+        models = load(model_path)
+    except Exception as e:
+        raise FileNotFoundError(f"모델 로드 실패 ({model_path}): {e}")
+    
+    return models
+
+def load_scaler(model_name: str, region: str, data_sample: str) -> List[Any]:
+    """각 fold의 scaler를 로드하는 함수.
+    
+    Args:
+        model_name: 모델 이름
+        region: 지역명
+        data_sample: 데이터 샘플 타입
+        
+    Returns:
+        로드된 scaler 리스트 (각 fold별 scaler)
+        
+    Raises:
+        FileNotFoundError: Scaler 파일을 찾을 수 없는 경우
+    """
+    if data_sample == 'pure':
+        scaler_filename = f'{model_name}_pure_{region}_scaler.pkl'
+    else:
+        scaler_filename = f'{model_name}_{data_sample}_{region}_scaler.pkl'
+    
+    scaler_path = f'../save_model/{model_name}_optima/scaler/{scaler_filename}'
+    
+    if not os.path.exists(scaler_path):
+        raise FileNotFoundError(
+            f"Scaler 파일을 찾을 수 없습니다: {scaler_path}"
+        )
+    
+    try:
+        scalers = load(scaler_path)
+    except Exception as e:
+        raise FileNotFoundError(f"Scaler 로드 실패 ({scaler_path}): {e}")
+    
+    return scalers
+
+# ==========================================
+# Tree-based 모델용 헬퍼 함수
+# ==========================================
+def _prepare_test_data_tree(region: str) -> Tuple[pd.DataFrame, np.ndarray]:
+    """Tree-based 모델용 test 데이터를 준비합니다.
+    
+    Args:
+        region: 지역명
+        
+    Returns:
+        tuple: (X_test, y_test)
+            - X_test: 전처리된 test 데이터 (설명변수만)
+            - y_test: test 데이터의 target 값 (multi_class)
+    """
+    test_data = pd.read_csv(f"../../data/data_for_modeling/{region}_test.csv")
+    test_data = preprocessing(test_data)
+    test_data = test_data.loc[test_data['year'].isin([TEST_YEAR]), :]
+    X_test = test_data.drop(columns=DROP_COLUMNS)
+    y_test = test_data['multi_class'].values
+    return X_test, y_test
+
+def _predict_tree_model(model: Any, X_test: pd.DataFrame) -> np.ndarray:
+    """Tree-based 모델로 예측을 수행합니다.
+    
+    Args:
+        model: 학습된 모델 (XGBoost 또는 LightGBM)
+        X_test: Test 데이터
+        
+    Returns:
+        예측 확률 (n_samples, n_classes)
+    """
+    return model.predict_proba(X_test)
+
+# ==========================================
+# Deep Learning 모델용 헬퍼 함수
+# ==========================================
+def _prepare_test_data_dl(
+    region: str,
+    data_sample: str,
+    fold_idx: int,
+    scaler: Any
+) -> Tuple[torch.Tensor, torch.Tensor, pd.Index, pd.Index, np.ndarray]:
+    """Deep learning 모델용 test 데이터를 준비합니다.
+    
+    Args:
+        region: 지역명
+        data_sample: 데이터 샘플 타입
+        fold_idx: Fold 인덱스 (0, 1, 2)
+        scaler: 해당 fold의 scaler (QuantileTransformer)
+        
+    Returns:
+        tuple: (X_test_num, X_test_cat, categorical_cols, numerical_cols, y_test)
+            - X_test_num: Numerical features tensor
+            - X_test_cat: Categorical features tensor
+            - categorical_cols: 범주형 변수 컬럼명
+            - numerical_cols: 연속형 변수 컬럼명
+            - y_test: test 데이터의 target 값 (multi_class)
+    """
+    # Train 데이터 로드 (Label Encoder 학습용)
+    if data_sample == 'pure':
+        train_path = f"../../data/data_for_modeling/{region}_train.csv"
+        train_data = preprocessing(pd.read_csv(train_path, index_col=0))
+        train_data = train_data.loc[
+            ~train_data['year'].isin([TEST_YEAR - (fold_idx + 1)]), :
+        ]
+    else:
+        train_data = pd.read_csv(
+            f"../../data/data_oversampled/{data_sample}/"
+            f"{data_sample}_{fold_idx+1}_{region}.csv"
+        )
+        train_data = preprocessing(train_data)
+    
+    # Test 데이터 로드
+    test_data = pd.read_csv(f"../../data/data_for_modeling/{region}_test.csv")
+    test_data = preprocessing(test_data)
+    test_data = test_data.loc[test_data['year'].isin([TEST_YEAR]), :]
+    
+    # 컬럼 정렬 (일관성 유지)
+    common_columns = train_data.columns.to_list()
+    train_data = train_data[common_columns]
+    test_data = test_data[common_columns]
+    
+    # 설명변수 분리
+    X_train = train_data.drop(columns=DROP_COLUMNS)
+    X_test = test_data.drop(columns=DROP_COLUMNS)
+    
+    # 범주형 & 연속형 변수 분리
+    categorical_cols = X_train.select_dtypes(
+        include=['object', 'category', 'int64']
+    ).columns
+    numerical_cols = X_train.select_dtypes(include=['float64']).columns
+    
+    # 범주형 변수 Label Encoding (train 데이터 기준으로 학습)
+    label_encoders = {}
+    for col in categorical_cols:
+        le = LabelEncoder()
+        le.fit(X_train[col])
+        label_encoders[col] = le
+    
+    # 변환 적용
+    for col in categorical_cols:
+        X_test[col] = label_encoders[col].transform(X_test[col])
+    
+    # 연속형 변수 Scaler 변환
+    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])
+    
+    # 텐서 변환
+    X_test_num = torch.tensor(
+        X_test[numerical_cols].values, dtype=torch.float32
+    )
+    X_test_cat = torch.tensor(
+        X_test[categorical_cols].values, dtype=torch.long
+    )
+    
+    # Target 값 추출
+    y_test = test_data['multi_class'].values
+    
+    return X_test_num, X_test_cat, categorical_cols, numerical_cols, y_test
+
+def _predict_dl_model(
+    model: torch.nn.Module,
+    X_test_num: torch.Tensor,
+    X_test_cat: torch.Tensor,
+    model_name: str,
+    device: str,
+    batch_size: int = 1024
+) -> np.ndarray:
+    """Deep learning 모델로 예측을 수행합니다.
+    
+    Args:
+        model: PyTorch 모델
+        X_test_num: Numerical features tensor
+        X_test_cat: Categorical features tensor
+        model_name: 모델 이름 ('ft_transformer', 'resnet_like', 'deepgbm')
+        device: 연산 장치 ('cpu' 또는 'cuda')
+        batch_size: 배치 크기 (메모리 효율을 위해, 기본값: 1024)
+    
+    Returns:
+        예측 확률 (n_samples, n_classes)
+        
+    Raises:
+        ValueError: 알 수 없는 모델 이름인 경우
+    """
+    model.eval()
+    model.to(device)
+    
+    n_samples = X_test_num.shape[0]
+    all_probs = []
+    
+    with torch.no_grad():
+        for i in range(0, n_samples, batch_size):
+            end_idx = min(i + batch_size, n_samples)
+            batch_num = X_test_num[i:end_idx].to(device)
+            batch_cat = X_test_cat[i:end_idx].to(device)
+            
+            # 모델별 입력 형식 처리
+            # 모든 딥러닝 모델이 (x_num, x_cat) 두 개의 텐서를 받음
+            # resnet_like는 내부에서 torch.cat으로 결합함
+            if model_name in DL_MODELS:
+                output = model(batch_num, batch_cat)
+            else:
+                raise ValueError(
+                    f"Unknown model_name: {model_name}. "
+                    f"Must be one of {DL_MODELS}"
+                )
+            
+            # Softmax 적용하여 확률 변환
+            probs = F.softmax(output, dim=1)
+            all_probs.append(probs.cpu().numpy())
+    
+    return np.vstack(all_probs)
+
+# ==========================================
+# 메인 함수
+# ==========================================
+def predict_test_proba(
+    model_name: str,
+    region: str,
+    data_sample: str,
+    device: str = 'cpu',
+    n_folds: int = 3
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Test 데이터에 대한 예측 확률과 target 값을 반환하는 함수.
+    
+    Args:
+        model_name: 모델 이름 ('xgb', 'lgb', 'ft_transformer', 'resnet_like', 'deepgbm')
+        region: 지역명 (예: 'seoul', 'busan')
+        data_sample: 데이터 샘플 타입 (예: 'pure', 'smote', 'ctgan10000')
+        device: 연산 장치 ('cpu' 또는 'cuda', 기본값: 'cpu')
+        n_folds: Fold 개수 (기본값: 3)
+    
+    Returns:
+        tuple: (probs, y_test)
+            - probs: shape (n_folds, n_samples, n_classes) - 각 fold별 예측 확률
+            - y_test: shape (n_samples,) - test 데이터의 target 값 (multi_class)
+        
+    Raises:
+        ValueError: 모델 이름이 유효하지 않거나 fold 개수가 일치하지 않는 경우
+        FileNotFoundError: 모델 또는 scaler 파일을 찾을 수 없는 경우
+    """
+    # 모델 타입 확인
+    if model_name not in TREE_MODELS + DL_MODELS:
+        raise ValueError(
+            f"Unknown model_name: {model_name}. "
+            f"Must be one of {TREE_MODELS + DL_MODELS}"
+        )
+    
+    # 모델 로드
+    models = load_model(model_name, region, data_sample)
+    
+    if len(models) != n_folds:
+        raise ValueError(
+            f"로드된 모델 개수({len(models)})가 "
+            f"n_folds({n_folds})와 일치하지 않습니다."
+        )
+    
+    # Tree-based 모델 처리
+    if model_name in TREE_MODELS:
+        X_test, y_test = _prepare_test_data_tree(region)
+        all_probs = []
+        
+        for fold_idx in range(n_folds):
+            prob = _predict_tree_model(models[fold_idx], X_test)
+            all_probs.append(prob)
+        
+        return np.array(all_probs), y_test
+    
+    # Deep learning 모델 처리
+    else:
+        # Scaler 로드
+        scalers = load_scaler(model_name, region, data_sample)
+        
+        if len(scalers) != n_folds:
+            raise ValueError(
+                f"로드된 scaler 개수({len(scalers)})가 "
+                f"n_folds({n_folds})와 일치하지 않습니다."
+            )
+        
+        all_probs = []
+        y_test = None
+        
+        for fold_idx in range(n_folds):
+            # Test 데이터 준비
+            X_test_num, X_test_cat, _, _, y_test = _prepare_test_data_dl(
+                region, data_sample, fold_idx, scalers[fold_idx]
+            )
+            
+            # 예측
+            prob = _predict_dl_model(
+                models[fold_idx], X_test_num, X_test_cat, model_name, device
+            )
+            all_probs.append(prob)
+        
+        return np.array(all_probs), y_test
diff --git a/Analysis_code/final_test/final.ipynb b/Analysis_code/final_test/final.ipynb
deleted file mode 100644
index 5c690bfbbdf6c13ab32eb8ad3bb33c130c6d3ac3..0000000000000000000000000000000000000000
--- a/Analysis_code/final_test/final.ipynb
+++ /dev/null
@@ -1,1143 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader, TensorDataset\n",
-    "import random\n",
-    "from collections import Counter\n",
-    "import sys\n",
-    "sys.path.append('../../../../../../../../mnt/workspace/LightGBM/python-package')\n",
-    "from lightgbm import LGBMClassifier\n",
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.inspection import permutation_importance\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "from xgboost import XGBClassifier\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "import sys\n",
-    "sys.path.append('../../')\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "import optuna\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import random\n",
-    "from models.ft_transformer import FTTransformer\n",
-    "from models.resnet_like import ResNetLike\n",
-    "from models.deepgbm import DeepGBM\n",
-    "from pytorch_tabnet.tab_model import TabNetClassifier\n",
-    "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Python 및 Numpy 시드 고정\n",
-    "seed = 42\n",
-    "random.seed(seed)\n",
-    "np.random.seed(seed)\n",
-    "\n",
-    "# PyTorch 시드 고정\n",
-    "torch.manual_seed(seed)\n",
-    "torch.cuda.manual_seed(seed)\n",
-    "torch.cuda.manual_seed_all(seed)  # Multi-GPU 환경에서 동일한 시드 적용\n",
-    "\n",
-    "# PyTorch 연산의 결정적 모드 설정\n",
-    "torch.backends.cudnn.deterministic = True  # 실행마다 동일한 결과를 보장\n",
-    "torch.backends.cudnn.benchmark = True  # 성능 최적화를 활성화 (가능한 한 빠른 연산 수행)\n",
-    "\n",
-    "# 전처리 함수\n",
-    "def preprocessing(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df['lm_cloudcover'] = df['lm_cloudcover'].astype('int')\n",
-    "    df['cloudcover'] = df['cloudcover'].astype('int')\n",
-    "    return df\n",
-    "\n",
-    "# 데이터셋 준비 함수\n",
-    "def prepare_dataset(region, data_sample='pure', target='multi', fold=3):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    return X_train, X_val, X_test, y_train, y_val, y_test, categorical_cols, numerical_cols\n",
-    "\n",
-    "# 데이터 변환 및 dataloader 생성 함수\n",
-    "def prepare_dataloader(region, data_sample='pure', target='multi', fold=3, random_state=None):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    # 연속형 변수와 범주형 변수 분리\n",
-    "    X_train_num = torch.tensor(X_train[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_train_cat = torch.tensor(X_train[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_val_num = torch.tensor(X_val[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_val_cat = torch.tensor(X_val[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_test_num = torch.tensor(X_test[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_test_cat = torch.tensor(X_test[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    # 레이블 변환\n",
-    "    if target == \"binary\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.float32)  # 이진 분류 → float32\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.float32)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32)\n",
-    "    elif target == \"multi\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.long)  # 다중 분류 → long\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.long)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.long)\n",
-    "    else:\n",
-    "        raise ValueError(\"target must be 'binary' or 'multi'\")\n",
-    "\n",
-    "    # TensorDataset 생성\n",
-    "    train_dataset = TensorDataset(X_train_num, X_train_cat, y_train_tensor)\n",
-    "    val_dataset = TensorDataset(X_val_num, X_val_cat, y_val_tensor)\n",
-    "    test_dataset = TensorDataset(X_test_num, X_test_cat, y_test_tensor)\n",
-    "\n",
-    "    # DataLoader 생성\n",
-    "    if random_state == None:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
-    "    else:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, generator=torch.Generator().manual_seed(random_state))\n",
-    "    val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
-    "    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
-    "    \n",
-    "    return X_train, categorical_cols, numerical_cols, train_loader, val_loader, test_loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "# 디바이스 설정 (CUDA 사용 가능하면 GPU로, 아니면 CPU로)\n",
-    "import glob\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
-    "def calculate_csi(Y_test, pred):\n",
-    "\n",
-    "    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경\n",
-    "    # 혼동 행렬에서 H, F, M 추출\n",
-    "    H = (cm[0, 0] + cm[1, 1])\n",
-    "    \n",
-    "    F = (cm[1, 0] + cm[2, 0] +\n",
-    "         cm[0, 1] + cm[2, 1])\n",
-    "\n",
-    "    M = (cm[0, 2] + cm[1, 2])\n",
-    "    \n",
-    "    # CSI 계산\n",
-    "    CSI = H / (H + F + M + 1e-10)\n",
-    "    return CSI\n",
-    "\n",
-    "def csi_metric(y_true, pred):\n",
-    "    y_pred_binary = np.argmax(pred, axis=1)\n",
-    "    score = calculate_csi(y_true, y_pred_binary)\n",
-    "    return 'CSI', score, True  # higher_better=True\n",
-    "\n",
-    "\n",
-    "def eval_metric_csi(y_true, pred_prob):\n",
-    "\n",
-    "    pred = np.argmax(pred_prob, axis=1)\n",
-    "    y_true = y_true\n",
-    "    y_pred = pred\n",
-    "    csi = calculate_csi(y_true, y_pred)\n",
-    "    return -1*csi\n",
-    "\n",
-    "\n",
-    "from sklearn.metrics import matthews_corrcoef, accuracy_score\n",
-    "\n",
-    "def multiclass_mcc(y_val, y_pred):\n",
-    "    \"\"\"\n",
-    "    다중 분류에서도 sklearn의 matthews_corrcoef를 그대로 사용할 수 있음.\n",
-    "    \"\"\"\n",
-    "    return matthews_corrcoef(y_val, y_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "# 디바이스 설정 (CUDA 사용 가능하면 GPU로, 아니면 CPU로)\n",
-    "import glob\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "\n",
-    "def calculate_csi(Y_test, pred):\n",
-    "\n",
-    "    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경\n",
-    "    # 혼동 행렬에서 H, F, M 추출\n",
-    "    H = (cm[0, 0] + cm[1, 1])\n",
-    "    \n",
-    "    F = (cm[1, 0] + cm[2, 0] +\n",
-    "         cm[0, 1] + cm[2, 1])\n",
-    "\n",
-    "    M = (cm[0, 2] + cm[1, 2])\n",
-    "    \n",
-    "    # CSI 계산\n",
-    "    CSI = H / (H + F + M + 1e-10)\n",
-    "    return CSI\n",
-    "\n",
-    "# Soft Voting 앙상블\n",
-    "def get_proba(region, model_choose, data_sample, fold, target='multi'):\n",
-    "    _, _, _, _,val_loader , test_loader = prepare_dataloader(region=region, data_sample=data_sample, target=target,fold=fold ,random_state=120)\n",
-    "\n",
-    "    folder_path = f'../save_model/{model_choose}/{data_sample}'\n",
-    "    model_paths = [path for path in glob.glob(f'{folder_path}/*.pth') if f'{region}' in path]\n",
-    "\n",
-    "    model = torch.load(model_paths[fold-1], weights_only=False).to(device)\n",
-    "    model.eval()\n",
-    "\n",
-    "    test_preds = []\n",
-    "\n",
-    "\n",
-    "    with torch.no_grad():\n",
-    "        for x_num_batch, x_cat_batch, _ in test_loader:\n",
-    "            output = model(x_num_batch.to(device), x_cat_batch.to(device))\n",
-    "            output = torch.softmax(output, dim=1)\n",
-    "            test_preds.extend(output.cpu().numpy())\n",
-    "\n",
-    "\n",
-    "    return test_preds\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_seoul_test = pd.read_csv(\"../../data/data_for_modeling/seoul_test.csv\")\n",
-    "\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "df_busan_test = pd.read_csv(\"../../data/data_for_modeling/busan_test.csv\")\n",
-    "\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "df_daegu_test = pd.read_csv(\"../../data/data_for_modeling/daegu_test.csv\")\n",
-    "\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "df_daejeon_test = pd.read_csv(\"../../data/data_for_modeling/daejeon_test.csv\")\n",
-    "\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "df_incheon_test = pd.read_csv(\"../../data/data_for_modeling/incheon_test.csv\")\n",
-    "\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")\n",
-    "df_gwangju_test = pd.read_csv(\"../../data/data_for_modeling/gwangju_test.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocessing_df(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df['year'] = df['year'].astype('int')\n",
-    "    df['month'] = df['month'].astype('int')\n",
-    "    df['hour'] = df['hour'].astype('int')\n",
-    "    df['binary_class'] = df['binary_class'].astype('int')\n",
-    "    df['multi_class'] = df['multi_class'].astype('int')\n",
-    "\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',\n",
-    "       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',\n",
-    "       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',\n",
-    "       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',\n",
-    "       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',\n",
-    "       'month_sin', 'month_cos','multi_class']]\n",
-    "    return df\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul_test= preprocessing_df(df_seoul_test).copy()\n",
-    "df_busan_test= preprocessing_df(df_busan_test).copy()\n",
-    "df_daegu_test= preprocessing_df(df_daegu_test).copy()\n",
-    "df_gwangju_test= preprocessing_df(df_gwangju_test).copy()\n",
-    "df_daejeon_test= preprocessing_df(df_daejeon_test).copy()\n",
-    "df_incheon_test= preprocessing_df(df_incheon_test).copy()\n",
-    "\n",
-    "df_seoul= preprocessing_df(df_seoul).copy()\n",
-    "df_busan= preprocessing_df(df_busan).copy()\n",
-    "df_daegu= preprocessing_df(df_daegu).copy()\n",
-    "df_gwangju= preprocessing_df(df_gwangju).copy()\n",
-    "df_daejeon= preprocessing_df(df_daejeon).copy()\n",
-    "df_incheon= preprocessing_df(df_incheon).copy()\n",
-    "\n",
-    "df_seoul_test.drop(columns=['year'], inplace=True)\n",
-    "df_busan_test.drop(columns=['year'], inplace=True)\n",
-    "df_daegu_test.drop(columns=['year'], inplace=True)\n",
-    "df_daejeon_test.drop(columns=['year'], inplace=True)\n",
-    "df_incheon_test.drop(columns=['year'], inplace=True)\n",
-    "df_gwangju_test.drop(columns=['year'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import joblib\n",
-    "\n",
-    "lgb_seoul= joblib.load('../save_model/LGB_optima/lgb_seoul_smote.pkl')\n",
-    "lgb_busan= joblib.load('../save_model/LGB_optima/lgb_busan_smote.pkl')\n",
-    "lgb_incheon= joblib.load('../save_model/LGB_optima/lgb_incheon_smote.pkl')\n",
-    "lgb_daegu= joblib.load('../save_model/LGB_optima/lgb_daegu_smote.pkl')\n",
-    "lgb_daejeon= joblib.load('../save_model/LGB_optima/lgb_daejeon_smote.pkl')\n",
-    "lgb_gwangju= joblib.load('../save_model/LGB_optima/lgb_gwangju_smote.pkl')\n",
-    "\n",
-    "xgb_seoul= joblib.load('../save_model/XGB_optima/xgb_seoul_smote.pkl')\n",
-    "xgb_busan= joblib.load('../save_model/XGB_optima/xgb_busan_ctgan20000.pkl')\n",
-    "xgb_incheon= joblib.load('../save_model/XGB_optima/xgb_incheon_smote.pkl')\n",
-    "xgb_daegu= joblib.load('../save_model/XGB_optima/xgb_daegu_smote.pkl')\n",
-    "xgb_daejeon= joblib.load('../save_model/XGB_optima/xgb_daejeon_smote.pkl')\n",
-    "xgb_gwangju= joblib.load('../save_model/XGB_optima/xgb_gwangju_smote.pkl')\n",
-    "\n",
-    "lgb_seoul_1= lgb_seoul[0]\n",
-    "lgb_seoul_2= lgb_seoul[1]\n",
-    "lgb_seoul_3= lgb_seoul[2]\n",
-    "\n",
-    "lgb_busan_1= lgb_busan[0]\n",
-    "lgb_busan_2= lgb_busan[1]\n",
-    "lgb_busan_3= lgb_busan[2]\n",
-    "\n",
-    "lgb_incheon_1= lgb_incheon[0]\n",
-    "lgb_incheon_2= lgb_incheon[1]\n",
-    "lgb_incheon_3= lgb_incheon[2]\n",
-    "\n",
-    "lgb_daegu_1= lgb_daegu[0]\n",
-    "lgb_daegu_2= lgb_daegu[1]\n",
-    "lgb_daegu_3= lgb_daegu[2]\n",
-    "\n",
-    "lgb_daejeon_1= lgb_daejeon[0]\n",
-    "lgb_daejeon_2= lgb_daejeon[1]\n",
-    "lgb_daejeon_3= lgb_daejeon[2]\n",
-    "\n",
-    "lgb_gwangju_1= lgb_gwangju[0]\n",
-    "lgb_gwangju_2= lgb_gwangju[1]\n",
-    "lgb_gwangju_3= lgb_gwangju[2]\n",
-    "\n",
-    "\n",
-    "xgb_seoul_1= xgb_seoul[0]\n",
-    "xgb_seoul_2= xgb_seoul[1]\n",
-    "xgb_seoul_3= xgb_seoul[2]\n",
-    "\n",
-    "xgb_busan_1= xgb_busan[0]\n",
-    "xgb_busan_2= xgb_busan[1]\n",
-    "xgb_busan_3= xgb_busan[2]\n",
-    "\n",
-    "xgb_incheon_1= xgb_incheon[0]\n",
-    "xgb_incheon_2= xgb_incheon[1]\n",
-    "xgb_incheon_3= xgb_incheon[2]\n",
-    "\n",
-    "xgb_daegu_1= xgb_daegu[0]\n",
-    "xgb_daegu_2= xgb_daegu[1]\n",
-    "xgb_daegu_3= xgb_daegu[2]\n",
-    "\n",
-    "xgb_daejeon_1= xgb_daejeon[0]\n",
-    "xgb_daejeon_2= xgb_daejeon[1]\n",
-    "xgb_daejeon_3= xgb_daejeon[2]\n",
-    "\n",
-    "xgb_gwangju_1= xgb_gwangju[0]\n",
-    "xgb_gwangju_2= xgb_gwangju[1]\n",
-    "xgb_gwangju_3= xgb_gwangju[2]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **Soft Voting**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "voting = []\n",
-    "mcc = []\n",
-    "accuracy = []\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm', 'vap_pressure',\n",
-       "       'dewpoint_C', 'loc_pressure', 'sea_pressure', 'solarRad', 'snow_cm',\n",
-       "       'cloudcover', 'lm_cloudcover', 'low_cloudbase', 'groundtemp', 'O3',\n",
-       "       'NO2', 'PM10', 'PM25', 'month', 'hour', 'ground_temp - temp_C',\n",
-       "       'hour_sin', 'hour_cos', 'month_sin', 'month_cos', 'multi_class'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_seoul_test.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CSI score of soft(test) : 0.3248062015503624\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "probas = []\n",
-    "\n",
-    "# 1 Fold\n",
-    "test_preds  = get_proba('seoul', 'deepgbm', 'ctgan20000', 1)\n",
-    "probas.append(test_preds)\n",
-    "test_preds  = get_proba('seoul', 'resnet_like', 'smote', 1)\n",
-    "probas.append(test_preds)\n",
-    "# probas.append(xgb_seoul_1.predict_proba(df_seoul_test.iloc[:,:-1]))\n",
-    "\n",
-    "# 2 Fold\n",
-    "test_preds  = get_proba('seoul', 'deepgbm', 'ctgan20000', 2)\n",
-    "probas.append(test_preds)\n",
-    "test_preds  = get_proba('seoul', 'resnet_like', 'smote', 2)\n",
-    "probas.append(test_preds)\n",
-    "# probas.append(xgb_seoul_2.predict_proba(df_seoul_test.iloc[:,:-1]))\n",
-    "\n",
-    "# 3 Fold\n",
-    "test_preds  = get_proba('seoul', 'deepgbm', 'ctgan20000', 3)\n",
-    "probas.append(test_preds)\n",
-    "test_preds  = get_proba('seoul', 'resnet_like', 'smote', 3)\n",
-    "probas.append(test_preds)\n",
-    "# probas.append(xgb_seoul_3.predict_proba(df_seoul_test.iloc[:,:-1]))\n",
-    "\n",
-    "voting.append(calculate_csi(df_seoul_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "mcc.append(multiclass_mcc(df_seoul_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "accuracy.append(accuracy_score(df_seoul_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "\n",
-    "\n",
-    "print(\"CSI score of soft(test) :\", calculate_csi(df_seoul_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[   2,   11,    0],\n",
-       "       [   6,  417,   58],\n",
-       "       [   7,  789, 7470]])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion_matrix(df_seoul_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CSI score of soft(test) : 0.46608315098458075\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "probas = []\n",
-    "\n",
-    "# 1 Fold\n",
-    "test_preds  = get_proba('busan', 'deepgbm', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "test_preds  = get_proba('busan', 'resnet_like', 'ctgan10000', 1)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "test_preds  = get_proba('busan', 'deepgbm', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "test_preds  = get_proba('busan', 'resnet_like', 'ctgan10000', 2)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "\n",
-    "# 3 Fold\n",
-    "test_preds  = get_proba('busan', 'deepgbm', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "test_preds  = get_proba('busan', 'resnet_like', 'ctgan10000', 3)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "\n",
-    "voting.append(calculate_csi(df_busan_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "mcc.append(multiclass_mcc(df_busan_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "accuracy.append(accuracy_score(df_busan_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "\n",
-    "print(\"CSI score of soft(test) :\", calculate_csi(df_busan_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  11,   13,    0],\n",
-       "       [  11,  202,   68],\n",
-       "       [   2,  150, 8303]])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion_matrix(df_busan_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CSI score of hard(test) : 0.572269457161506\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "\n",
-    "# 1 Fold\n",
-    "probas = []\n",
-    "test_preds = get_proba('incheon', 'deepgbm', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('incheon', 'resnet_like', 'smote', 1)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('incheon', 'ft_transformer', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "probas.append(lgb_incheon_1.predict_proba(df_incheon_test.iloc[:,:-1]))\n",
-    "probas.append(xgb_incheon_1.predict_proba(df_incheon_test.iloc[:,:-1]))\n",
-    "\n",
-    "# 2 Fold\n",
-    "test_preds = get_proba('incheon', 'deepgbm', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('incheon', 'resnet_like', 'smote', 2)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('incheon', 'ft_transformer', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "probas.append(lgb_incheon_2.predict_proba(df_incheon_test.iloc[:,:-1]))\n",
-    "probas.append(xgb_incheon_2.predict_proba(df_incheon_test.iloc[:,:-1]))\n",
-    "\n",
-    "# 3 Fold\n",
-    "test_preds = get_proba('incheon', 'deepgbm', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('incheon', 'resnet_like', 'smote', 3)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('incheon', 'ft_transformer', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "probas.append(lgb_incheon_3.predict_proba(df_incheon_test.iloc[:,:-1]))\n",
-    "probas.append(xgb_incheon_3.predict_proba(df_incheon_test.iloc[:,:-1]))\n",
-    "\n",
-    "\n",
-    "\n",
-    "voting.append(calculate_csi(df_incheon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "mcc.append(multiclass_mcc(df_incheon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "accuracy.append(accuracy_score(df_incheon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "\n",
-    "\n",
-    "print(\"CSI score of hard(test) :\", calculate_csi(df_incheon_test.iloc[:,-1],mode(np.argmax(probas, axis=2), axis=0).mode[0]))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  87,   74,   21],\n",
-       "       [  22,  788,  395],\n",
-       "       [   2,  140, 7231]])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion_matrix(df_incheon_test.iloc[:,-1],mode(np.argmax(probas, axis=2), axis=0).mode[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CSI score of soft(test) : 0.2852112676055334\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "probas= []\n",
-    "\n",
-    "# 1 Fold\n",
-    "test_preds = get_proba('daegu', 'deepgbm', 'smote', 1)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('daegu', 'ft_transformer', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "# 2 Fold\n",
-    "test_preds = get_proba('daegu', 'deepgbm', 'smote', 2)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('daegu', 'ft_transformer', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "# 3 Fold\n",
-    "test_preds = get_proba('daegu', 'deepgbm', 'smote', 3)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('daegu', 'ft_transformer', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "voting.append(calculate_csi(df_daegu_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "mcc.append(multiclass_mcc(df_daegu_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "accuracy.append(accuracy_score(df_daegu_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "\n",
-    "print(\"CSI score of soft(test) :\", calculate_csi(df_daegu_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[   1,    0,    0],\n",
-       "       [   1,   80,   47],\n",
-       "       [   2,  153, 8476]])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion_matrix(df_daegu_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CSI score of soft(test) : 0.31884057971011603\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "probas = []\n",
-    "\n",
-    "# 1 Fold\n",
-    "test_preds = get_proba('daejeon', 'deepgbm', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('daejeon', 'ft_transformer', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "test_preds = get_proba('daejeon', 'deepgbm', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('daejeon', 'ft_transformer', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "\n",
-    "# 3 Fold\n",
-    "test_preds = get_proba('daejeon', 'deepgbm', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('daejeon', 'ft_transformer', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "voting.append(calculate_csi(df_daejeon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "mcc.append(multiclass_mcc(df_daejeon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "accuracy.append(accuracy_score(df_daejeon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "\n",
-    "\n",
-    "print(\"CSI score of soft(test) :\", calculate_csi(df_daejeon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  15,   23,   15],\n",
-       "       [  10,  337,  271],\n",
-       "       [   0,  433, 7656]])"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion_matrix(df_daejeon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CSI score of soft(test) : 0.4759725400457121\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "probas = []\n",
-    "\n",
-    "# 1 Fold\n",
-    "test_preds = get_proba('gwangju', 'deepgbm', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('gwangju', 'ft_transformer', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "test_preds = get_proba('gwangju', 'deepgbm', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('gwangju', 'ft_transformer', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "\n",
-    "# 3 Fold\n",
-    "test_preds = get_proba('gwangju', 'deepgbm', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('gwangju', 'ft_transformer', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "voting.append(calculate_csi(df_gwangju_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "mcc.append(multiclass_mcc(df_gwangju_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "accuracy.append(accuracy_score(df_gwangju_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "\n",
-    "\n",
-    "print(\"CSI score of soft(test) :\", calculate_csi(df_gwangju_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  10,   12,    8],\n",
-       "       [   2,  406,  235],\n",
-       "       [   0,  201, 7886]])"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion_matrix(df_gwangju_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "plt.figure(figsize=(10,5))\n",
-    "sns.barplot(x=['Seoul', 'Busan', 'Incheon', 'Daegu', 'Daejeon', 'Gwangju'], y=voting)\n",
-    "plt.title('CSI Score of Voting Model')\n",
-    "plt.ylabel('CSI')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.4078715882283252"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(voting)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.3248062015503624,\n",
-       " 0.46608315098458075,\n",
-       " 0.5763157894736463,\n",
-       " 0.2852112676055334,\n",
-       " 0.31884057971011603,\n",
-       " 0.4759725400457121]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "voting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.5106142349456536,\n",
-       " 0.640202275543952,\n",
-       " 0.709448778435959,\n",
-       " 0.45579515959653394,\n",
-       " 0.453960121993875,\n",
-       " 0.6218724605270242]"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mcc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.9005707762557078,\n",
-       " 0.9721461187214612,\n",
-       " 0.9264840182648402,\n",
-       " 0.9768264840182649,\n",
-       " 0.9141552511415525,\n",
-       " 0.9477168949771689]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accuracy"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Analysis_code/find_reason/ busan_trend.ipynb b/Analysis_code/find_reason/ busan_trend.ipynb
deleted file mode 100644
index 1bbc656fd226cec2d9a974801cbe74ab0e372ced..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/ busan_trend.ipynb	
+++ /dev/null
@@ -1,157 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "26f60e36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 분석에 필요한 라이브러리 임포트\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy.spatial import distance\n",
-    "\n",
-    "\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "28456d07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "busan = pd.read_feather(\"../../data/data_for_modeling/df_busan.feather\")\n",
-    "feature = ['hm','PM10','PM25','multi_class','year','month','hour']\n",
-    "busan = busan[feature]\n",
-    "busan = busan.loc[busan['year'].isin([2018,2019,2020,2021,2022,2023]),:]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "84183ace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['hm', 'PM10', 'PM25', 'multi_class', 'year', 'month', 'hour'], dtype='object')"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "busan.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "5b942b62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 월별로 시간의 흐름에 따라 PM10, PM25 변수의 변화 추이 시각화\n",
-    "\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "# 월별 평균 PM10, PM25 계산 함수\n",
-    "def get_monthly_trend(df, variable):\n",
-    "    # 'year', 'month' 컬럼을 기준으로 월별 평균, Q1, Q3 계산\n",
-    "    monthly = df.groupby(['year', 'month'])[variable].agg(['mean', \n",
-    "                                                           lambda x: x.quantile(0.25), \n",
-    "                                                           lambda x: x.quantile(0.75)]).reset_index()\n",
-    "    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']\n",
-    "    # 날짜 컬럼 생성 (월의 첫날로)\n",
-    "    monthly['date'] = pd.to_datetime(monthly['year'].astype(str) + '-' + monthly['month'].astype(str).str.zfill(2) + '-01')\n",
-    "    monthly = monthly.sort_values('date')\n",
-    "    return monthly\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "variables_to_analyze = ['hm', 'PM25']\n",
-    "\n",
-    "for i, var in enumerate(variables_to_analyze):\n",
-    "    if var in busan.columns:\n",
-    "        trend = get_monthly_trend(busan, var)\n",
-    "        ax = axes[i]\n",
-    "        # 평균값\n",
-    "        ax.plot(trend['date'], trend['mean'], 'o-', color='blue', label='평균값')\n",
-    "        # Q1, Q3\n",
-    "        ax.plot(trend['date'], trend['q1'], 's--', color='orange', alpha=0.7, label='Q1')\n",
-    "        ax.plot(trend['date'], trend['q3'], 's--', color='purple', alpha=0.7, label='Q3')\n",
-    "        # 선형 트렌드선\n",
-    "        if len(trend) > 1:\n",
-    "            z = np.polyfit(range(len(trend)), trend['mean'], 1)\n",
-    "            p = np.poly1d(z)\n",
-    "            ax.plot(trend['date'], p(range(len(trend))), \"r--\", linewidth=1, alpha=0.6)\n",
-    "            slope = z[0]\n",
-    "            ax.text(trend['date'].iloc[0], trend['mean'].max(), \n",
-    "                    f'월별 변화율: {slope:.4f}/month', fontsize=10, color='darkred')\n",
-    "        ax.set_title(f'{var} 변화 추이', fontsize=14)\n",
-    "        ax.set_xlabel('날짜', fontsize=12)\n",
-    "        ax.set_ylabel(var, fontsize=12)\n",
-    "        ax.grid(True, linestyle='--', alpha=0.7)\n",
-    "        ax.legend()\n",
-    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
-    "        plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n",
-    "    else:\n",
-    "        axes[i].text(0.5, 0.5, f'{var} 컬럼 없음', ha='center', va='center', fontsize=12)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e149d5dc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analysis_code/find_reason/ daegu_trend.ipynb b/Analysis_code/find_reason/ daegu_trend.ipynb
deleted file mode 100644
index 391514049ae2121ae22f516cdf848bbc1a0ef872..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/ daegu_trend.ipynb	
+++ /dev/null
@@ -1,157 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "26f60e36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 분석에 필요한 라이브러리 임포트\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy.spatial import distance\n",
-    "\n",
-    "\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "28456d07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "daegu = pd.read_feather(\"../../data/data_for_modeling/df_daegu.feather\")\n",
-    "feature = ['hm','PM10','PM25','multi_class','year','month','hour']\n",
-    "daegu = daegu[feature]\n",
-    "daegu = daegu.loc[daegu['year'].isin([2018,2019,2020,2021,2022,2023]),:]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "84183ace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['hm', 'PM10', 'PM25', 'multi_class', 'year', 'month', 'hour'], dtype='object')"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "daegu.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "5b942b62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 월별로 시간의 흐름에 따라 PM10, PM25 변수의 변화 추이 시각화\n",
-    "\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "# 월별 평균 PM10, PM25 계산 함수\n",
-    "def get_monthly_trend(df, variable):\n",
-    "    # 'year', 'month' 컬럼을 기준으로 월별 평균, Q1, Q3 계산\n",
-    "    monthly = df.groupby(['year', 'month'])[variable].agg(['mean', \n",
-    "                                                           lambda x: x.quantile(0.25), \n",
-    "                                                           lambda x: x.quantile(0.75)]).reset_index()\n",
-    "    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']\n",
-    "    # 날짜 컬럼 생성 (월의 첫날로)\n",
-    "    monthly['date'] = pd.to_datetime(monthly['year'].astype(str) + '-' + monthly['month'].astype(str).str.zfill(2) + '-01')\n",
-    "    monthly = monthly.sort_values('date')\n",
-    "    return monthly\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "variables_to_analyze = ['hm', 'PM25']\n",
-    "\n",
-    "for i, var in enumerate(variables_to_analyze):\n",
-    "    if var in daegu.columns:\n",
-    "        trend = get_monthly_trend(daegu, var)\n",
-    "        ax = axes[i]\n",
-    "        # 평균값\n",
-    "        ax.plot(trend['date'], trend['mean'], 'o-', color='blue', label='평균값')\n",
-    "        # Q1, Q3\n",
-    "        ax.plot(trend['date'], trend['q1'], 's--', color='orange', alpha=0.7, label='Q1')\n",
-    "        ax.plot(trend['date'], trend['q3'], 's--', color='purple', alpha=0.7, label='Q3')\n",
-    "        # 선형 트렌드선\n",
-    "        if len(trend) > 1:\n",
-    "            z = np.polyfit(range(len(trend)), trend['mean'], 1)\n",
-    "            p = np.poly1d(z)\n",
-    "            ax.plot(trend['date'], p(range(len(trend))), \"r--\", linewidth=1, alpha=0.6)\n",
-    "            slope = z[0]\n",
-    "            ax.text(trend['date'].iloc[0], trend['mean'].max(), \n",
-    "                    f'월별 변화율: {slope:.4f}/month', fontsize=10, color='darkred')\n",
-    "        ax.set_title(f'{var} 월별 변화 추이', fontsize=14)\n",
-    "        ax.set_xlabel('날짜', fontsize=12)\n",
-    "        ax.set_ylabel(var, fontsize=12)\n",
-    "        ax.grid(True, linestyle='--', alpha=0.7)\n",
-    "        ax.legend()\n",
-    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
-    "        plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n",
-    "    else:\n",
-    "        axes[i].text(0.5, 0.5, f'{var} 컬럼 없음', ha='center', va='center', fontsize=12)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e149d5dc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analysis_code/find_reason/ gwangju_trend.ipynb b/Analysis_code/find_reason/ gwangju_trend.ipynb
deleted file mode 100644
index 37a66f6d17d36e0c5ad4fee1fe1a61967639e587..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/ gwangju_trend.ipynb	
+++ /dev/null
@@ -1,136 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "26f60e36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 분석에 필요한 라이브러리 임포트\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy.spatial import distance\n",
-    "\n",
-    "\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "28456d07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gwangju = pd.read_feather(\"../../data/data_for_modeling/df_gwangju.feather\")\n",
-    "feature = ['hm','PM10','PM25','multi_class','year','month','hour']\n",
-    "gwangju = gwangju[feature]\n",
-    "gwangju = gwangju.loc[gwangju['year'].isin([2018,2019,2020,2021,2022,2023]),:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "5b942b62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 월별로 시간의 흐름에 따라 PM10, PM25 변수의 변화 추이 시각화\n",
-    "\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "# 월별 평균 PM10, PM25 계산 함수\n",
-    "def get_monthly_trend(df, variable):\n",
-    "    # 'year', 'month' 컬럼을 기준으로 월별 평균, Q1, Q3 계산\n",
-    "    monthly = df.groupby(['year', 'month'])[variable].agg(['mean', \n",
-    "                                                           lambda x: x.quantile(0.25), \n",
-    "                                                           lambda x: x.quantile(0.75)]).reset_index()\n",
-    "    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']\n",
-    "    # 날짜 컬럼 생성 (월의 첫날로)\n",
-    "    monthly['date'] = pd.to_datetime(monthly['year'].astype(str) + '-' + monthly['month'].astype(str).str.zfill(2) + '-01')\n",
-    "    monthly = monthly.sort_values('date')\n",
-    "    return monthly\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "variables_to_analyze = ['hm', 'PM25']\n",
-    "\n",
-    "for i, var in enumerate(variables_to_analyze):\n",
-    "    if var in gwangju.columns:\n",
-    "        trend = get_monthly_trend(gwangju, var)\n",
-    "        ax = axes[i]\n",
-    "        # 평균값\n",
-    "        ax.plot(trend['date'], trend['mean'], 'o-', color='blue', label='평균값')\n",
-    "        # Q1, Q3\n",
-    "        ax.plot(trend['date'], trend['q1'], 's--', color='orange', alpha=0.7, label='Q1')\n",
-    "        ax.plot(trend['date'], trend['q3'], 's--', color='purple', alpha=0.7, label='Q3')\n",
-    "        # 선형 트렌드선\n",
-    "        if len(trend) > 1:\n",
-    "            z = np.polyfit(range(len(trend)), trend['mean'], 1)\n",
-    "            p = np.poly1d(z)\n",
-    "            ax.plot(trend['date'], p(range(len(trend))), \"r--\", linewidth=1, alpha=0.6)\n",
-    "            slope = z[0]\n",
-    "            ax.text(trend['date'].iloc[0], trend['mean'].max(), \n",
-    "                    f'월별 변화율: {slope:.4f}/month', fontsize=10, color='darkred')\n",
-    "        ax.set_title(f'{var} 월별 변화 추이', fontsize=14)\n",
-    "        ax.set_xlabel('날짜', fontsize=12)\n",
-    "        ax.set_ylabel(var, fontsize=12)\n",
-    "        ax.grid(True, linestyle='--', alpha=0.7)\n",
-    "        ax.legend()\n",
-    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
-    "        plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n",
-    "    else:\n",
-    "        axes[i].text(0.5, 0.5, f'{var} 컬럼 없음', ha='center', va='center', fontsize=12)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e9b71ce7",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analysis_code/find_reason/ incheon_trend.ipynb b/Analysis_code/find_reason/ incheon_trend.ipynb
deleted file mode 100644
index b767250447669b29634ff0cb544a63f87e3ac090..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/ incheon_trend.ipynb	
+++ /dev/null
@@ -1,157 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "26f60e36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 분석에 필요한 라이브러리 임포트\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy.spatial import distance\n",
-    "\n",
-    "\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "28456d07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "incheon = pd.read_feather(\"../../data/data_for_modeling/df_incheon.feather\")\n",
-    "feature = ['hm','PM10','PM25','multi_class','year','month','hour']\n",
-    "incheon = incheon[feature]\n",
-    "incheon = incheon.loc[incheon['year'].isin([2018,2019,2020,2021,2022,2023]),:]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "84183ace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['hm', 'PM10', 'PM25', 'multi_class', 'year', 'month', 'hour'], dtype='object')"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "incheon.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "5b942b62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 월별로 시간의 흐름에 따라 PM10, PM25 변수의 변화 추이 시각화\n",
-    "\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "# 월별 평균 PM10, PM25 계산 함수\n",
-    "def get_monthly_trend(df, variable):\n",
-    "    # 'year', 'month' 컬럼을 기준으로 월별 평균, Q1, Q3 계산\n",
-    "    monthly = df.groupby(['year', 'month'])[variable].agg(['mean', \n",
-    "                                                           lambda x: x.quantile(0.25), \n",
-    "                                                           lambda x: x.quantile(0.75)]).reset_index()\n",
-    "    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']\n",
-    "    # 날짜 컬럼 생성 (월의 첫날로)\n",
-    "    monthly['date'] = pd.to_datetime(monthly['year'].astype(str) + '-' + monthly['month'].astype(str).str.zfill(2) + '-01')\n",
-    "    monthly = monthly.sort_values('date')\n",
-    "    return monthly\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "variables_to_analyze = ['hm', 'PM25']\n",
-    "\n",
-    "for i, var in enumerate(variables_to_analyze):\n",
-    "    if var in incheon.columns:\n",
-    "        trend = get_monthly_trend(incheon, var)\n",
-    "        ax = axes[i]\n",
-    "        # 평균값\n",
-    "        ax.plot(trend['date'], trend['mean'], 'o-', color='blue', label='평균값')\n",
-    "        # Q1, Q3\n",
-    "        ax.plot(trend['date'], trend['q1'], 's--', color='orange', alpha=0.7, label='Q1')\n",
-    "        ax.plot(trend['date'], trend['q3'], 's--', color='purple', alpha=0.7, label='Q3')\n",
-    "        # 선형 트렌드선\n",
-    "        if len(trend) > 1:\n",
-    "            z = np.polyfit(range(len(trend)), trend['mean'], 1)\n",
-    "            p = np.poly1d(z)\n",
-    "            ax.plot(trend['date'], p(range(len(trend))), \"r--\", linewidth=1, alpha=0.6)\n",
-    "            slope = z[0]\n",
-    "            ax.text(trend['date'].iloc[0], trend['mean'].max(), \n",
-    "                    f'월별 변화율: {slope:.4f}/month', fontsize=10, color='darkred')\n",
-    "        ax.set_title(f'{var} 월별 변화 추이', fontsize=14)\n",
-    "        ax.set_xlabel('날짜', fontsize=12)\n",
-    "        ax.set_ylabel(var, fontsize=12)\n",
-    "        ax.grid(True, linestyle='--', alpha=0.7)\n",
-    "        ax.legend()\n",
-    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
-    "        plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n",
-    "    else:\n",
-    "        axes[i].text(0.5, 0.5, f'{var} 컬럼 없음', ha='center', va='center', fontsize=12)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e149d5dc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analysis_code/find_reason/ seoul_trend.ipynb b/Analysis_code/find_reason/ seoul_trend.ipynb
deleted file mode 100644
index 173def5e11f934b134edfe6cb7c7246a81fa8f0c..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/ seoul_trend.ipynb	
+++ /dev/null
@@ -1,157 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "26f60e36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 분석에 필요한 라이브러리 임포트\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy.spatial import distance\n",
-    "\n",
-    "\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "28456d07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seoul = pd.read_feather(\"../../data/data_for_modeling/df_seoul.feather\")\n",
-    "feature = ['hm','PM10','PM25','multi_class','year','month','hour']\n",
-    "seoul = seoul[feature]\n",
-    "seoul = seoul.loc[seoul['year'].isin([2018,2019,2020,2021,2022,2023]),:]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "84183ace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['hm', 'PM10', 'PM25', 'multi_class', 'year', 'month', 'hour'], dtype='object')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "seoul.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "5b942b62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 월별로 시간의 흐름에 따라 PM10, PM25 변수의 변화 추이 시각화\n",
-    "\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "# 월별 평균 PM10, PM25 계산 함수\n",
-    "def get_monthly_trend(df, variable):\n",
-    "    # 'year', 'month' 컬럼을 기준으로 월별 평균, Q1, Q3 계산\n",
-    "    monthly = df.groupby(['year', 'month'])[variable].agg(['mean', \n",
-    "                                                           lambda x: x.quantile(0.25), \n",
-    "                                                           lambda x: x.quantile(0.75)]).reset_index()\n",
-    "    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']\n",
-    "    # 날짜 컬럼 생성 (월의 첫날로)\n",
-    "    monthly['date'] = pd.to_datetime(monthly['year'].astype(str) + '-' + monthly['month'].astype(str).str.zfill(2) + '-01')\n",
-    "    monthly = monthly.sort_values('date')\n",
-    "    return monthly\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "variables_to_analyze = ['hm', 'PM25']\n",
-    "\n",
-    "for i, var in enumerate(variables_to_analyze):\n",
-    "    if var in seoul.columns:\n",
-    "        trend = get_monthly_trend(seoul, var)\n",
-    "        ax = axes[i]\n",
-    "        # 평균값\n",
-    "        ax.plot(trend['date'], trend['mean'], 'o-', color='blue', label='평균값')\n",
-    "        # Q1, Q3\n",
-    "        ax.plot(trend['date'], trend['q1'], 's--', color='orange', alpha=0.7, label='Q1')\n",
-    "        ax.plot(trend['date'], trend['q3'], 's--', color='purple', alpha=0.7, label='Q3')\n",
-    "        # 선형 트렌드선\n",
-    "        if len(trend) > 1:\n",
-    "            z = np.polyfit(range(len(trend)), trend['mean'], 1)\n",
-    "            p = np.poly1d(z)\n",
-    "            ax.plot(trend['date'], p(range(len(trend))), \"r--\", linewidth=1, alpha=0.6)\n",
-    "            slope = z[0]\n",
-    "            ax.text(trend['date'].iloc[0], trend['mean'].max(), \n",
-    "                    f'월별 변화율: {slope:.4f}/month', fontsize=10, color='darkred')\n",
-    "        ax.set_title(f'{var} 월별 변화 추이', fontsize=14)\n",
-    "        ax.set_xlabel('날짜', fontsize=12)\n",
-    "        ax.set_ylabel(var, fontsize=12)\n",
-    "        ax.grid(True, linestyle='--', alpha=0.7)\n",
-    "        ax.legend()\n",
-    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
-    "        plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n",
-    "    else:\n",
-    "        axes[i].text(0.5, 0.5, f'{var} 컬럼 없음', ha='center', va='center', fontsize=12)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e149d5dc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analysis_code/find_reason/daejeon_trend.ipynb b/Analysis_code/find_reason/daejeon_trend.ipynb
deleted file mode 100644
index be316c4db6040bc5184791a7f81a441a64b4986c..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/daejeon_trend.ipynb
+++ /dev/null
@@ -1,157 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "26f60e36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 분석에 필요한 라이브러리 임포트\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy.spatial import distance\n",
-    "\n",
-    "\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "28456d07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "daejeon = pd.read_feather(\"../../data/data_for_modeling/df_daejeon.feather\")\n",
-    "feature = ['hm','PM10','PM25','multi_class','year','month','hour']\n",
-    "daejeon = daejeon[feature]\n",
-    "daejeon = daejeon.loc[daejeon['year'].isin([2018,2019,2020,2021,2022,2023]),:]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "84183ace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['hm', 'PM10', 'PM25', 'multi_class', 'year', 'month', 'hour'], dtype='object')"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "daejeon.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "5b942b62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 월별로 시간의 흐름에 따라 PM10, PM25 변수의 변화 추이 시각화\n",
-    "\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "# 월별 평균 PM10, PM25 계산 함수\n",
-    "def get_monthly_trend(df, variable):\n",
-    "    # 'year', 'month' 컬럼을 기준으로 월별 평균, Q1, Q3 계산\n",
-    "    monthly = df.groupby(['year', 'month'])[variable].agg(['mean', \n",
-    "                                                           lambda x: x.quantile(0.25), \n",
-    "                                                           lambda x: x.quantile(0.75)]).reset_index()\n",
-    "    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']\n",
-    "    # 날짜 컬럼 생성 (월의 첫날로)\n",
-    "    monthly['date'] = pd.to_datetime(monthly['year'].astype(str) + '-' + monthly['month'].astype(str).str.zfill(2) + '-01')\n",
-    "    monthly = monthly.sort_values('date')\n",
-    "    return monthly\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "variables_to_analyze = ['hm', 'PM25']\n",
-    "\n",
-    "for i, var in enumerate(variables_to_analyze):\n",
-    "    if var in daejeon.columns:\n",
-    "        trend = get_monthly_trend(daejeon, var)\n",
-    "        ax = axes[i]\n",
-    "        # 평균값\n",
-    "        ax.plot(trend['date'], trend['mean'], 'o-', color='blue', label='평균값')\n",
-    "        # Q1, Q3\n",
-    "        ax.plot(trend['date'], trend['q1'], 's--', color='orange', alpha=0.7, label='Q1')\n",
-    "        ax.plot(trend['date'], trend['q3'], 's--', color='purple', alpha=0.7, label='Q3')\n",
-    "        # 선형 트렌드선\n",
-    "        if len(trend) > 1:\n",
-    "            z = np.polyfit(range(len(trend)), trend['mean'], 1)\n",
-    "            p = np.poly1d(z)\n",
-    "            ax.plot(trend['date'], p(range(len(trend))), \"r--\", linewidth=1, alpha=0.6)\n",
-    "            slope = z[0]\n",
-    "            ax.text(trend['date'].iloc[0], trend['mean'].max(), \n",
-    "                    f'월별 변화율: {slope:.4f}/month', fontsize=10, color='darkred')\n",
-    "        ax.set_title(f'{var} 월별 변화  추이', fontsize=14)\n",
-    "        ax.set_xlabel('날짜', fontsize=12)\n",
-    "        ax.set_ylabel(var, fontsize=12)\n",
-    "        ax.grid(True, linestyle='--', alpha=0.7)\n",
-    "        ax.legend()\n",
-    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
-    "        plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n",
-    "    else:\n",
-    "        axes[i].text(0.5, 0.5, f'{var} 컬럼 없음', ha='center', va='center', fontsize=12)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e149d5dc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analysis_code/find_reason/make_trend_plot.ipynb b/Analysis_code/find_reason/make_trend_plot.ipynb
deleted file mode 100644
index 3a4866f251838a774d005359bb6bf62588022f8f..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/make_trend_plot.ipynb
+++ /dev/null
@@ -1,176 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import numpy as np\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False\n",
-    "\n",
-    "seoul = pd.read_feather(\"../../data/data_for_modeling/df_seoul.feather\")\n",
-    "busan = pd.read_feather(\"../../data/data_for_modeling/df_busan.feather\")\n",
-    "incheon = pd.read_feather(\"../../data/data_for_modeling/df_incheon.feather\")\n",
-    "daejeon = pd.read_feather(\"../../data/data_for_modeling/df_daejeon.feather\")\n",
-    "daegu = pd.read_feather(\"../../data/data_for_modeling/df_daegu.feather\")\n",
-    "gwangju = pd.read_feather(\"../../data/data_for_modeling/df_gwangju.feather\")\n",
-    "\n",
-    "feature = ['hm','PM10','PM25','multi_class','year','month','hour']\n",
-    "seoul = seoul[feature]\n",
-    "busan = busan[feature]\n",
-    "incheon = incheon[feature]\n",
-    "daejeon = daejeon[feature]\n",
-    "daegu = daegu[feature]\n",
-    "gwangju = gwangju[feature]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x1500 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualization of PM25 variable trends over time by month\n",
-    "\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "# Function to calculate monthly PM25 averages\n",
-    "def get_monthly_trend(df, variable):\n",
-    "    # Calculate monthly mean, Q1, Q3 based on 'year' and 'month' columns\n",
-    "    monthly = df.groupby(['year', 'month'])[variable].agg(['mean', \n",
-    "                                                           lambda x: x.quantile(0.25), \n",
-    "                                                           lambda x: x.quantile(0.75)]).reset_index()\n",
-    "    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']\n",
-    "    # Create date column (first day of month)\n",
-    "    monthly['date'] = pd.to_datetime(monthly['year'].astype(str) + '-' + monthly['month'].astype(str).str.zfill(2) + '-01')\n",
-    "    monthly = monthly.sort_values('date')\n",
-    "    return monthly\n",
-    "\n",
-    "# List of city data\n",
-    "cities = [seoul, busan, incheon, daejeon, daegu, gwangju]\n",
-    "city_names = ['Seoul', 'Busan', 'Incheon', 'Daejeon', 'Daegu', 'Gwangju']\n",
-    "\n",
-    "# Create 3x2 subplots\n",
-    "fig, axes = plt.subplots(3, 2, figsize=(13, 15))\n",
-    "axes = axes.flatten()\n",
-    "\n",
-    "for idx, (city, name) in enumerate(zip(cities, city_names)):\n",
-    "    trend = get_monthly_trend(city, 'PM25')\n",
-    "    ax = axes[idx]\n",
-    "    \n",
-    "    # Mean values\n",
-    "    ax.plot(trend['date'], trend['mean'], 'o-', color='blue', label='Mean')\n",
-    "    # Q1, Q3\n",
-    "    ax.plot(trend['date'], trend['q1'], 's--', color='orange', alpha=0.7, label='Lower 25% (Q1)')\n",
-    "    ax.plot(trend['date'], trend['q3'], 's--', color='purple', alpha=0.7, label='Upper 25% (Q3)')\n",
-    "    \n",
-    "    # Linear trend line\n",
-    "    if len(trend) > 1:\n",
-    "        z = np.polyfit(range(len(trend)), trend['mean'], 1)\n",
-    "        p = np.poly1d(z)\n",
-    "        ax.plot(trend['date'], p(range(len(trend))), \"r--\", linewidth=1, alpha=0.6)\n",
-    "        slope = z[0]\n",
-    "        # ax.text(trend['date'].iloc[0], trend['mean'].max(), \n",
-    "        #         f'Monthly change rate: {slope:.4f}/month', fontsize=10, color='darkred')\n",
-    "    \n",
-    "    ax.set_title(f'PM2.5 Trend in {name}', fontsize=14)\n",
-    "    ax.set_xlabel('Date', fontsize=12)\n",
-    "    ax.set_ylabel('PM2.5', fontsize=12)\n",
-    "    ax.grid(True, linestyle='--', alpha=0.7)\n",
-    "    ax.legend()\n",
-    "    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
-    "    plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Analysis_code/find_reason/trend_analysis.ipynb b/Analysis_code/find_reason/trend_analysis.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ff27d78316ad798de1e191e08dcc2f4d950fe5e6
--- /dev/null
+++ b/Analysis_code/find_reason/trend_analysis.ipynb
@@ -0,0 +1,89 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e21b44f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ox4oVK/j3v//NY489RlJSUsj26667jvvvv59XXnmF77//ntdff53evXszd+5cbrnlliadC+DUU0+lZ8+evPbaa0Hldrudd955p972v/nmm3g8Hu67776QbT179uTqq6/mpZdeClkReu2116JSBf9cOf3007FareTn5zep/U3tx/lJS0sLrKz1s3v3btasWRO2zmhwuVxhE5++9tprjBkzhtNOOy2o/MwzzyQ3N5e33norqDw7OzsoLCb4Yq6PHTs2qM+xefNmDh48yJ///OeQ2bfz588nPT29uZIEAoFAUA+x7D/Uh91uR6fTRdyu1+ux2+1NqlP0H0T/QSBoaYQzQyBoJNnZ2SFlmZmZAFRUVIRs69atW0hZQkICQ4YMCVvucDia3CaTycSll14aFOIJfEsx+/Tpw8knnxzx2LVr1zJlyhSMRmPY7eeddx7bt28PiUPZs2fPkH3T0tIAKC4ublL76/tOKysr6z32sssu44MPPggkE3vnnXc47bTTwtYZDUajMcRhUVVVxd69e5k4cWLI/iaTidzcXPbu3RtUHu77At93Vvv72rNnDyqViqFDh4bsm52dTW5ubjQyBAKBQNAK3HzzzWg0GrRaLZ06dWL+/PlcddVVIXGWa9Ocd3ZdCgsLueyyy7j00ksDsZ7rcskll3D55ZczduxYRo4cyYUXXsjixYt56KGHePbZZ9myZUujz+fnmmuu4eWXXw4K8/jhhx8iyzIzZsyIeNzatWsZNWoUXbp0Cbv9vPPOo6qqij/++COovK36HOH6cbW57LLL+PrrrwP7vfPOO/Tp04cxY8Y0qQ31UTvOtZ/169eH7XMA9O3bt1l9DoDhw4eH7GswGML2VQUCgUDQfGLdf2gIg8FQby4Ih8MRcfygPkT/wbef6D8IBC2DcGYIBM3A7+2vOxOg9ra6+F+iLcW1117LqlWrgl7gr732WsTBDT+VlZWBl3A4srKykGU55AWt1WpD9vV75eUGEnc3hvq+09rMmTOH4uJiVq5cicPh4IMPPmixGQ5A2FkF/tUiN954IxqNJuSza9cuDh8+HHRMuO8LfN9Z7e/LarWSkJAQMfFZVlZWtFIEAoFA0ML8/e9/Z8OGDWzZsoV9+/ZRUlLCwoULIz7z/UT7zq6NxWLhvPPOo1u3bhFjUNfHTTfdhCRJfPfdd00+du7cuRQVFQUlrXzllVeYPXs2BoMh4nGN6XOAL2Z4beKlz3HeeedhMBgCqy/ffvvtFu1zAGFjpVdVVfH444+H7XN88803zepzAKSkpITdX/Q5BAKBoHWIZf+hMaSlpYVNRO6npKSE1NTUJtcr+g+i/yAQtCQibolA0M6pnRTs8ccfZ9WqVeTn54ddblib1NRUSkpKIm4vLi5GkqSoOittQdeuXZk4cSJLly6lrKwMl8vF9OnTW6z+cE6FhIQEAB5//HGmTJkS9rhoX+CJiYlUV1fj8XjCnlskABcIBIL4ITs7m8GDBzf5uGjf2X7cbjcXXXQR5eXl/Prrr/UOAETCaDSSmZnZ5NCQ4JuJ6E/keeaZZ7J//36+++47/vWvf9V7XGpqar3vMf+sv3gNT2AwGJgxYwZLly5lyJAh/PHHHy0+GBGp33HNNddw5ZVXhj0m0mBCQyQmJgK+wZ9w/bzCwkJ69eoVVd0CgUAgiEys+g+NpU+fPnz22WfIshw2CXi07z/RfxD9B4GgJRErMwQCBTB//nwWL16M0+nk1Vdf5dxzz6Vz5871HjNu3Di++eabiIMZn376KSNHjsRkMkXdrnAdoJbksssuY9myZbzxxhtMmzYt4GxoLVJSUsjOzqa6uprBgweH/UTrzDjhhBOQZZn169eHbNu6dSsFBQXNbb5AIBAI4oBo3tl+rr32WlavXs3nn38e9fumsrKS0tJS8vLyojp+/vz5LF++nCNHjvDaa68xdOhQRo4cWe8x48aNY+3atRQVFYXd/umnn5KVlUXfvn2jahO0TZ/j22+/5bnnnmPs2LFRf39NYeDAgZSWlkbsc+Tk5ERV7wknnADAunXrQraVlpaydu3aZrVbIBAIBC1Pc/oPjWXChAmUl5eHfQ9s27aNoqKiiLk9GkL0H0T/QSBoKYQzQyBQAJdccgkul4vFixfz3nvvMW/evAaPueWWWygvL+fxxx8P2fbjjz/y6aef8re//a1Z7TKZTNTU1DSrjvqYOXMm1dXVLF++nMsvv7zVzlP3nE8//TSHDh1q0XonTJhAdnY2zzzzTFC51+vlrrvuatFzCQQCgSB2RPPOBrjnnnt46623+PDDDxkwYEDU57/zzjtJSkrizDPPjOr4U089lR49evDqq6/y+uuvN6r9V199NYmJidx9990h23bv3s3LL7/MX/7yl4ghOhuDf/JFa/U7Jk2aROfOnVm8eHGb9jneeustNm/e3KL15uXlMXr0aJ577rlA7jE/9913X5OTuwoEAoGg9Ym2/9AUxo8fT58+fXjwwQdDtj300EN07dqV008/Paq6Rf9B9B8EgpZChJkSCBSAPynYLbfcQnp6OlOnTm3wmB49evDAAw/w97//nQMHDnDppZdiMpn4+uuveeSRRzj//POZOXNms9rVv39/SktLeeyxxzj55JNxOByceuqpzaqzNklJSZx33nmsXLkyYtinluaee+7h008/Zfjw4dx9992cdNJJ2O12tm3bxm+//RZV/HIAjUbDv//9b2bMmIFKpeLqq69GpVLx9NNPs2PHDk488cQWViIQCASCWBDNO3vJkiXcf//9/OMf/yAjIyMkebckSQwYMCDox/zMmTOZNm0avXv3RpIkdu3axaJFi1i7di3//e9/SUpKiqr9kiRxzTXXsHDhQmRZblS4hMTERJ577jkuv/xyKioquPbaa0lPT2f16tXcf//9DB06lAULFkTVHj/9+/cH4N5772XWrFmUlpZyzjnnNKvO2qhUKmbPns3TTz/NRRdd1GL11seCBQt45513OPnkk7njjjuYPHkyXq+XXbt28dFHH/HBBx9EXfdzzz3HpEmTOP/887n55ptJTExk8eLFLFu2jLPOOqsFVQgEAoGgJYim/9BU1Go1ixYt4swzz2T27Nlcf/31qFQqXnnlFZYsWcKHH36IXq+Pqm7RfxD9B4GgpRArMwSCBtBqtUiSFDYpUrhtOp2u3v0jlWs0jfMt6nQ6dDpdSPm1116LzWbj2muvDZmZoNPpUKlUIfEU//a3v/HZZ5+Rn5/PtGnTmDRpEu+//z6PPPIIH3zwQdCSy4Z0hdt29tlnc9VVV7Fw4ULOOeccvv7666jqitR+ALvdziWXXNLo7682/u+xbjsifcfgS7D1yy+/cNFFF/HEE08wduxYzjnnHF544YWQ+Kf11RNu24UXXshnn33Gzp07Oeuss7jgggswGAysXLmS5OTkiHUJBAKBoO2I9C4PR0u9s7///nsAHn74YU444YSQz+DBg0NWDKakpLBw4UJOO+00JkyYwN13302fPn34/fffmTZtWrPaP3fuXLxeL5deeinJyckhx0Dou3X27Nn89NNPuFwuZs+ezcknn8x//vMfbr75ZlasWBEU1rKpfS/whT74xz/+wSuvvMKUKVP473//G1VdkdoPvj7H1KlTo4rN7a8vXJ8j0vWk1+tZsWIFN910E6+//joTJkxgypQp/Otf/woMvtSupyl9jjFjxvD9999jt9u58MILOfPMMykqKuKnn34iNzdX9DkEAoGghYlF/yFSO+p7xk+ePJnvv/+ekpISzj33XM466yz27NnDV199JfoP9dQl+g+i/yBoOyS5dmp6gUAgaEcUFxeTm5vLzz//zIgRI2LdHIFAIBAIBArF6XSSm5vLf/7zH6ZPnx7r5ggEAoFAIGgHiP6DQNDyCGeGQCBod2zcuBGr1cqDDz6Iw+EIrPgQCAQCgUAgaEl27NhBZWUlL774Ij/99BNbt25tcNarQCAQCASCjo3oPwgErYcIMyUQCNodCxcuZNKkSTgcDl577bVYN0cgEAgEAoFCWbRoEaeccgpbtmzh/fffFwMRAoFAIBAIGkT0HwSC1kOszBAIBAKBQCAQCAQCgUAgEAgEAoFAENeIlRkCgUAgEAgEAoFAIBAIBAKBQCAQCOIaTawb0BZ4vV6KiopITExEkqRYN0cgEAgEgjZHlmWqq6vp0qULKpWYy9AYRP9BIBAIBB0d0X+IDtGHEAgEAkFHpjX7Dx3CmVFUVERubm6smyEQCAQCQcw5ePAgOTk5sW5Gu0D0HwQCgUAg8CH6D01D9CEEAoFAIGid/kOHcGYkJiYCvi8wKSmpRep0u92sX7+e4cOHo9G0769RSVr8KE2T0vSA8jQJPfGN0vRA0zVVVVWRm5sbeCcKGkb0HxpG6IlvlKYHlKdJaXpAeZo6uh7Rf4iOlu5DdPTrsD2gNE1CT3yjND2gPE1K0wNN09Sa/YcOkQC8qqqK5ORkKisrW2wwQpZlKisrSU5ObvfLRpWkxY/SNClNDyhPk9AT3yhNDzRdU2u8C5WO6D80jNAT3yhNDyhPk9L0gPI0dXQ9ov8QHS39vXX067A9oDRNQk98ozQ9oDxNStMDTdPUmv0H4cwQCAQCgaADIN6FTUd8ZwKBQCDo6Ih3YXSI700gEAgEHZnWfA+KDF5R4na7WbNmDW63O9ZNaTZK0uJHaZqUpgeUp0noiW+UpgeUqakjoDS7CT3xjdL0gPI0KU0PKE+T0COIB5RmN6XpAeVpEnriG6XpAeVpUpoeiB9NwpnRDDweT6yb0GIoSYsfpWlSmh5QniahJ75Rmh5QpqaOgNLsJvTEN0rTA8rTpDQ9oDxNQo8gHlCa3ZSmB5SnSeiJb5SmB5SnSWl6ID40KSMDiUAgEAgEAoFAIBAIOgQejweXy9Um5/LPPrTb7YpI4FlXj1arRa1Wx7hVAoFAIBC0PqL/0DzqatLpdKhUbb9OQhnfpkAgEAgEAoFAIBAIFI0syxw+fJiKioo2PafBYODAgQOKSOAZTk9KSgqdO3dWhD6BQCAQCOoi+g8tQ11NKpWKnj17otPp2rQdIgF4lMiyjM1mw2g0tvuLUkla/ChNk9L0gPI0CT3xjdL0QNM1iUSUTUf0HxpG6IlvlKYHlKdJaXqgdTUdOnSIiooKsrKyMJlMbfKdybKM1+tFpVIpwka19QBYrVaKi4tJSUkhOzs7ZH/Rf4iOlv7elPasUJoeUJ4moSe+UZoeEP2H9kBtTbIsU1RUhFarpVu3biEaW7P/IFZmNIO29jy1JkrS4kdpmpSmB5SnSeiJb5SmB5SpqSOgNLsJPfGN0vSA8jQpTQ+0jiaPxxMYiEhPT2/x+iNRe+6fEgYj6uoxGo0AFBcXk5WVJUJOxTFKe1YoTQ8oT5PQE98oTQ+I/kO8U1dTZmYmRUVFuN1utFptm7VDJACPEo/Hw9q1a+Mi8UlzUZIWP0rTpDQ9oDxNQk98ozQ9oExNHQGl2U3oiW+UpgeUp0lpeqD1NPljXJtMphattzFYLJY2P2drUleP/zttqzjigqajtGeF0vSA8jQJPfGN0vSA6D+0F2pr8juf2vo6FM4MgUAgEAgEAoFAIBC0C5QyuzGeEN+pQCAQCJSOeNe1PLH6ToUzQyAQCAQCgUAgEAgEAoFAIBAIBAJBXCNyZggEAoFAIBAIBAKBoEPg8cAPP8ChQ5CdDePHg0gTIRAIBAKBoCFEHyI+kOTa2TsUSmtkUJdlGY/Hg1qtbvdLlZSkxY/SNClNDyhPk9AT3yhNDzRdU2u8C5WO6D80jNAT3yhNDyhPk9L0QOtpstvt5Ofn07NnTwwGQ1R1LFsGN98MBQXHy3Jy4JlnYPr08Me0RALPefPm8eqrr7J+/XqGDh0adp8xY8bw+++/43a7ozpHYwmnp77vVvQfoqOlvzelPSuUpgeUp0noiW+Upgfiu/8ATe9DKK3/AKGaYtV/EGGmmoHT6Yx1E1oMJWnxozRNStMDytMk9MQ3StMDytTUEVCa3YSe+EZpekB5mpSmB+JT07JlMHNm8CAEQGGhr3zZssjHer3eZp3b7XYzcuRIXnnllbDbt2zZgtfrbbMEms3VI4gN8XhfNQel6QHlaRJ64hul6YH41RRtH0Jp/QeIjz6EcGZEicfjYdOmTW2esb01UJIWP0rTpDQ9oDxNQk98ozQ9oExNHQGl2U3oiW+UpgeUp0lpeqDtNMkyWCyN+1RVwU03+Y4JVw/4ZltWVYU//uhRW9Df0cQ2mD17Nh999BEOhyNk2+uvv86VV17Z9EqjxGaztdm5BC2D0p4VStMDytMk9MQ3StMDbauprfoQdfsP0fQh4qn/APHRhxDODIEgTrGUWCjbU0bZnjLK95RjLbBSvqc8UGYpscS6iQKBQCAQCAh+Z4f7iHe2QNDyWK2QkNC4T3Kyb/ZkJGTZN9syOTn02MREic6dE0hMlAJlVmvT25uUlMSpp57KRx99FFTu8Xj44IMPuOSSS4LKv/32W4YPH86AAQMYMWIE33zzTWDb77//zoQJExg8eDCDBw/m4osvprKyMlBfbm4uzzzzDAMGDGDw4MFMmTKFgwcPNr3Rgg6BeIcJBIKORlv0IcL1H6LpQ4j+QygiAbhAEIdYSiwsm7MMa6nvKSfLMlarlZ2mnYFYe6Z0E9OXTMecaY5lUwUCgUAg6NDUfWeHQ7yzBQIBwNVXX82DDz7IxRdfHCj78ssvGTNmDCkpKYGygoICbrzxRj777DPy8vL4448/OOOMM1i3bh3p6enodDrefPNNunfvjizLzJ8/n8cee4wHHngAtVrNoUOH+O2339i4cSM6nY5HHnmEm266iQ8//DAGqgXxjHiHCQQCQfwj+g/BiJUZzUCtoJT1StLipz1rclQ5sJZa0eg1GFIMGFIMaBO1gX9r9BqspVYcVaHLzNoT7dlG4RB64hul6QFlauoIKM1uHV1PuHd27U+s39lKsw8oT5PS9EDbaDKZoKamcZ/PP29cnZ9/HnpsdbXMkSMWqqvlQJnJFF2bJ0yYwIEDBzhw4ECg7PXXX+eqq64K2u8///kPN954I3l5eQD069ePs846i08//RSAE044ge7duwO+BJwXXHAB69atCxzv8Xi4//770el0AMydO5dVq1YFtislWWxHozXuq1i+w8SzL/4ReuIbpemBttPUFn2IcP2HaPsQ8dJ/8B8Xa8TKjCjRaDSMHj061s1oEZSkxY9SNGmMGnRmHfYqO4kJiegT9IFtboc7hi1rPkqxkR+hJ75Rmh5QpqaOgNLsJvTUOvbYO9vj8mCvtGNKMyGpfJ39WL2zlWYfUJ4mpemBttMkSWBu5ETxM86AnBxfmIhwsaolybf9jDMgdBxFIiGh5Wakz507l9dff5177rmH8vJy1q1bx7vvvhuUUHPbtm28++67vPTSS4GympoaBg8eDEB5eTmPP/4433//PWVlZTidTnJzc4POU/vvjIwMysrKjmmVMDf2ixPEDa19X/nfYc4aJ16PF0OyIbCtNd5h4tkX/wg98Y3S9EDbamqbPoSy+g8+rfHRhxArM6JElmUqKiqQo8n+FmcoSYsfJWnyeryU7iileFtx0EOqvaMkG4HQE+8oTQ8oU1NHQGl2E3rqHO+VObz+MBV7K7CVxT45ntLsA8rTpDQ9EJ+a1Gp45hnfv+tOKPT//fTT4RwZPj1ut7vF9Fx55ZW89dZbyLLM0qVLmTVrFipV8M9ym83Gww8/zIYNGwKf3bt38+c//xmA888/n4qKCt588022b9/Os88+G3KeSDMnW1qPoG1oq/uqZFsJpX+U4nG2bgLeeHxONBelaRJ64hul6YH41RRtH0Jp/YfW0BQtwpkRJR6Phx07duDxtO5Lvi1QkhY/StLkcXqQkfG4Pcje+HqoNwcl2QiEnnhHaXpAmZo6Akqzm9ATTOXBysC/42EFpdLsA8rTpDQ9EL+apk+H99+Hrl2Dy3NyfOXTp0c+1m63t1g7OnfuzIABA/juu+9YvHgxc+fODdmnT58+rFmzJuzxR48eZfPmzTz33HOBMBJbt25tUhtaUo+gbWiL+6r2b023s3XfYfH6nGgOStMk9MQ3StMD8a0p2j6E0voPEB99CBFmSiCIczwu34NcpVchqZsem85SYqk3xqk+SS+SuUVJ7e/W4/ZgLbBSnlqOWuNzyYvvViAQCDoOjioHliMWAExZJkzpUQbVFwgErcb06TBtGvzwAxw6BNnZMH58+BUZrcm8efO46667UKlU9OvXL2T73LlzOf3005k2bRqnnHIKAPv27aNHjx4kJiYiSRK7d++mb9++/PHHH7z55pukp6e3rQiB4vB6jkcB0Bq1MWyJQCAQxB/x0IcQ/QcfwpkhEMQ5XqevU6nSNH0hlaXEwrI5y7CWWiPuY0o3MX3JdDHo3kTqfreyLGO1Wtlp2hlYlie+W4FAIOgYeN3egCMjoXMCyd2SY9wigUAQCbUaJk1q23PqdLpAMk2As88+m+uuu44HHnggUCZJEqZjWUFHjhzJe++9x+23305NTQ06nY5Bgwbx1ltvodfreeutt7jooouQZZmMjAyeeOIJ7r///kBdJpMpKExE7boFPqqqqrjrrrtYuXJlIAb4fffdx+mnnw7A9u3bue6666isrESSJO6++26m17d8RwHIHt/KDEktoVKLIB4CgUBQl7buQ4j+Q3iEMyNKJEnCaDTGRRb35qIkLX6Uosltc+OocSC7ZWSVjPWoFZ1Jh9vWuGW/jioH1lIrGr0GjTH0dnfb3FhLrTiqHG0+4N7ebVT3u5VlGbfWjcFsQJKkmH63LUF7t09dlKYHlKmpI6A0uwk9PqxHrXicHrRmLYZUA06LM7Ctse/s1kBp9gHlaVKaHlCmproxqZvKiy++GPS3Wq2msLAwpMxisQT+Pv300wMD63U555xzOOecc4LKzjjjjMC/a9cTru7m6lECF110ERMnTmT9+vWoVCp+//13zjvvPH7++Wc6derEtGnTeOmll5g4cSKHDx9m4sSJ9O7dmyFDhsSkva19X7ltbmRkvG4vapU68B5rrXeYEp8TStMk9MQ3StMDytSktP4DxEcfQjgzokStVjN06NBYN6NFUJIWP+1dkz5Jjynd5BsMr3TgdXvBDRV7KkjKTQJ8s/71SfpG1acxatCZdThrnGiMmqCZNrGK6d3ebeTH/90C6BOC7REP8dKjRSn28aM0PaBMTR0BpdlN6PG9s9N6p1G6sxRDigFbqQ23w41KrQpMJGjKO7slUZp9QHmalKYHlKcpXmclRovS9ETLd999x9KlSwODMiNHjmTEiBH8/vvvaDQahg8fzsSJEwFfnPLbb7+dV199laeffjom7W2t+6r27063zY3X7cXr9lJzuAaNofXeYUp7ToDyNAk98Y3S9IDyNCnxfRsvmoQzI0q8Xi9Hjx4lIyMjLrxSzUFJWvy0d03mTDPTl0zHUeVg7QtrKVpThMvlQm/Uc/a/z0alVjU5H4O9wk7pzlJ0iToyB2S2YusbR3u3UV1kWcbpdKLT6RQxk0Bp9lGaHlCmpo6A0uwm9Pje2Rd9cFEgh9KBHw+wcfFGOg3pxIl/PhGIXQ4lpdkHlKdJaXpAeZpkWcbtdqPRaBTRx1Oanmg56aSTePLJJ7nvvvsAWLVqFatXr2bRokU8+uijAUeGn4kTJ/LMM89ErM/hcOBwHM9TWFVVBYDb7cbt9k1wUqlUqFQqvF4vXu/x/BT+co/HgyzLYcs9Hg+lpaWkp6ej1Wp9K8HdwROn1McCt9dNnhupXKPRYMowcf4b5+OsclJTXMPKe3xht3pP7U3f8/sCoEvSYUgzAERse1M1uVwujh49Snp6OiqVCrVa3WKaZFkOKpckCbVaHdLGSOVC0/GZ18XFxaSlpQX+bs+aAMrKykhNTQ169rVXTV6vl/LycjIzM5FlOUir0BRc7na7g+qre13429/S5f73rf+Z3ZR6mkJrtD1SeThN/jK32x1kp7q2akmEMyNKvF4ve/fuDXqwt1eUpMWPEjSZM82YM82c/JeTKT9QzvJblmPSm9An6Ensktjk+mqO1ADgrHY2sGfboAQb+XHbfcuyLTYLWp0Wifb/w1BJ9gHl6QFlauoIKM1uHUWPpcQScFb48Tg8VB6sJK13mm91Rq80AKqLqtGZdWhN2kBZrFCafUB5mpSmB5SpyeFwoNEo56ez0vREw+LFi5k6dSq//PIL/fr1Y8mSJbz11lvk5ORQVFTElClTgvbPzc1l7969Eet7+OGHA46R2qxfvx6z2efMzszMpFevXuTn51NSUhLYJycnh5ycHHbu3EllZWWgPC8vj6ysLLZs2YLVaqWiooKUlBQGDBhASkoK69evDxrAGzJkCDqdjrVr1wa1YdSoUTidTjZt2hQoU6vVjB49msrKSnbs3+Er1ELGlAyqf67GITvYU77HV14OyRXJDBgwgKKiIgoKCgL1NEfT4cOHSUlJQZIk+vfv37KaduwIlBuNRoYOHcrRo0eDbJicLDTVpyktLY1NmzYFkgYD7VpT9+7d2b9/P3q9Psjx2F41+SdTpqens3XrVmw2m9BUjyaDwYDNZsNoNOLxeLDb7YF9VSoVJpMJt9sddG2o1WqMRiMulwun8/g4mkajwWAw4HA4ggbs/fkt7HZ7oI3+/+t0Omw2W5BDx2AwoNFosFqtQU4Eo9GISqUKCe9kNpvxer1B34s/51OsNNntdpxOJ1u2bAmxk9+p3xp07B6MQNAOMGeZ0afpMXY1ggUqD1ZG5czwur0N7ySIiqqiKqxHraCDBEMCOqOu4YMEAoFA0C6wlFhYNmcZ1lJrSLmzyokx3UharzSmL5mOOdMcCMvhsrli0VyBQCAQNILu3buzYMECbr31Vr766itmz57N6NGjAaioqMBgMATtbzAYsNvtyLIcdkXLP/7xD2677bbA31VVVeTm5jJ8+HCSknxhgv3OvZ49e9K9e/fAvv7yvn37hp0dP3jwYNxuN+vWrWPEiBGBZLDDhw8PaoN/JvKoUaNCyo1GY0g5+AYga5fvPLSTjT9vRIcuqNyvuUuXLnTu3DmkjU3VNHDgQOx2OyNGjECtVgfa3hqa/G3PyMggLS0tpFxoCq/J6/WSmJgY0FO77e1Rk9frZf/+/QwcODDQ3vasyePxsG7dOsD3jKi7ikFoOq7Jbrdz4MABjEZjYJvfyVwbjUYT1tGv1WrRarUh5Xq9Hr0+NASf//0hyzJWqzVQp//8dYkUtilcG1UqVdjyWGkyGAzodDp69+6NwWAIspNwZggEAgyZBrwWL1UHq2BM049P75vO4fWHAZCRFbF6IF5wWXwDVm6LG9tRG7pc4cwQCAQCpeCocmAttaLRawI5MByVDtxWNyqtCn2i3pfjqsqBOdOM1uj7YRDLxN8CgUAgqJ/LLruMXbt28c0339CtWzfuu+8+hgwZwpo1a9Dr9UEzXAFsNht6vT5iaK5IA0DhBpL84VXqUnuAtW6534niD+HhrzscTSmXJClQbq+w47b43l0uiyvs/pHaHo0mv57a52lpTc1pe0fX5PV6w+ppatsjlbe1Jv9s83B6mtr2SOVtrcn/LIh0TQpNx5/BkiQF6o70HG+t8mjP2xRiqcl/jdR97rXmClDhzIgSSZJITk5WRJxRJWnxoxRNbrub7R9uR5+sJ61nGqX7Sqk8WNnwgXXrsfliBPpXZzirnEhqKaYDLUqxkdPqxFnjBBmQwVZmw5hmbPeDWEqxjx+l6QFlauoIKM1uHUmPxqhBZ9bhcXqoOVyDSqMioUsCxlQj9orjg17+lRlue+zfA0qzDyhPk9L0gDI1RRpUaa8oTU9T2b17N59//jn79+8nOTkZ8IWdmjt3Ls8//zw5OTkcOHAg6JiDBw+Sk5MTi+YCbXNf7fx0J1uXbgVaPzSxEp8TStMk9MQ3StMDytSkxPdtPGhSRhDTGKBWqxkwYEBcGLG5KEmLH6VospRY2LJkC5ve2MSAkwYgSZJvZUYj0SfpMaWbcDvcOCodyF4Zr8eLtczqm3njcGNKN6FPCp1F1Nq0dxv5v1tnlROvy4vX7UXySjiqHNjKbTH9bluC9m6fuihNDyhTU0dAaXbriHqqCqvwur1ozVqSuiaFbPev3ogHZ4bS7APK06Q0PaA8TZIkYTQaFTO4ojQ90VBVVUWXLl0Cjgw/J5xwAuXl5YwbN46VK1cGbVu5ciXjxo1ry2YG0Rb3lct6PDyis6Z1nRlKe06A8jQJPfGN0vSA8jQp8X0bL5rEyowo8Xq9FBUV0aVLl3af2E5JWvwoRZOtzJfYx5BqwGF2MGDWANJ7pzf6eHOmmelLprN/5X4O/nyQzAWZdJ/YPejBo0/SY84Mja3X2rR3G/m/2+3LtrN16VayhmRx5I8jYIeT/3Yy6X3TY/bdtgTt3T51UZoeUKamjoDS7NYR9fgHexK7JIbtyNcOMxUptnpboTT7gPI0KU0PxLEmewm46pkUpE0CQ2ZIsSzLuFwutFptzH+8twRK0xMNQ4cOJTExkaeeeoqbb74ZlUrFnj17eOmll3jllVcYNmwY99xzDytXrmTixIkcPnyYxx9/nLfeeitmbW6L+6p2rqfWdmbE7XOiGShNk9AT3yhND8S5pij6EEp838aLJuHMiBKv10tBQQGdO3eOv5usiShJix+laKrtzCi1lzJqzqgmx50zZ5qxV9op21lGRr+MJjlDWhMl2MicacZlcaEz6+gyqgsV7gq8+7w4Kh2k9UpruII4Rgn2qY3S9IAyNXUElGa3jqjH4/AAx8NJ1UWXoGPMLWMibm9LlGYfUJ4mpemBONVkL4HVc8BRGnkffTqMWxLWoeF0OsMmymwsTqeTJ554gqVLlwK+JKdTp07lnnvuISEhIWT/V199leuvv54//viDHj16RH3e+trTHD3tHbVazWeffcadd97JsGHDUKvVmEwmHn30UU4++WQAli9fzg033EBNTQ1er5f77ruPMWOiSFzYQrTFfeV31mcNyWLwxYNb5Rx+4vI50UyUpknoiW+UpgfiWFMz+hAt8b4VfYhQYv8rSyAQRMTvzDCmGptVT8W+CgBSeqY0s0WCupTtLgMgtVcqCUcSqNpXRfHW4hi3SiAQCAStgdfrRVJJIIFaHyEBqE5N3ml5bdwygUBQL64q3yCESg/qMP1qj8233VUV1pnRHLxeLxdeeCE5OTn8+OOPJCQk4HA4eOSRRzj11FNZtWoVRuPxNt19992sXbuW1NTUQMJaQcuTkZHBCy+8EHH70KFD+emnn9qwRbHH78zIOz2PTkM6xbg1AoFAECeIPkTcEUeuLoFAUJfAyow0AwD2CjuH1h2ifG95k+qpyK8AfAPvqx5cxf5V+1u0nR2ZEy49gQEzBpDWN42EPJ9XvHRHaSDZukAgEAiUgdvmxm1zk9YnjYwBGbhtbpwWJ26bcn8oCATtAo+9nk+dUDlqPWiMoR+1HmS5cfU2kbfffpuysjIWLVoUmEGp1+u59957ycvL46mnngrs6/V6yc7O5tNPP8VgMDT5XAJBc/C/z7SmjrtqRyAQdDBEH6JdIlZmRIlKpSIzMzO+lj5FiZK0+FGKJr8zw5RmQpepY9cnu9ixbAe9p/Zm9A2jG1dHuQ1HpQMkkL0yhb8UktQ1ie4Turdm0xtEKTbqdnI3up3cDa/XS+4JuWTPz6bT4E5I6vYdE1Ep9vGjND2gTE0dAaXZrSPo0SfpMaWbsJZacTvCOy5M6Sb0SfrA34c3HsZZ7aTT0E7oE/Vhj2kLlGYfUJ4mpemBGGj6YVbkbemj4IR7j/9dsRWkMO2S3aCqM4D7y9WB+NhGrxf8eiZ90qTmLV68mFtuuSVsbOlbb72VK664gjvuuAPwfXc33HBDk+qPhqaGrRXEnra4r/wrM0q2lmAvt9P1xK4Y05oXISAS4tkX/wg98Y3S9ECMNLVyHyKo/wCiD9FSbYh1A9orKpWKXr16xboZLYKStPhRiia/M8OcaaZbr27k788HoPJgZaPr8IeYSuySGEhG7ahytGxDo0ApNvKjUqno3bs39I51S1oGJdpHSXpAmZo6AkqzW0fQY840M33J9HrfnfokfeAdC/Db//sNy2ELUx6fgr5fbJ0ZSrIPKE+T0vSA8jRJgLoZAyvr169n9Ojwk5BGjhzJnj17qKmpCRv3ujWQJEnxMzaVSFvcVz0n98R61Mru/+3GbXeT0DmhVZ0ZSnpOgPI0CT3xjdL0gPI0Nbf/AKIPEQnhzIgSr9dLfn4+PXv2bPeeUCVp8aMUTSfdchLWEiuJOYns2bOHpK5JAFQdrGp0HbXzZfhnjcaDM0MJNipcU4haqya9bzpqg7rd66mNEuxTG6XpAWVq6ggozW4dRY8504w508zGNzdSsq2Efuf1I3dcbsR6/Mm/Yx2CSmn2AeVpUpoeiIGm8e/Vs7HO+VMGgcYcupvbAs46k4VOegUAWZZxOBzo9fqwMyMborKykk6dwucf0Gq1pKamUlVV1WYDEc3VI4gNbXFfDZw5EICK/RWUbCnBWeNs4IjoEc+++EfoiW+UpgdipKkV+xAt8b4VfYjwKOOKjwFer5eSkhK83vYfF19JWvwoRVNidiKdhnRCl6SjpKSEhC6+B5Sj0oGjunEOCZfVhVqnJqVH/Dkz2ruNNry+gRV3r6B4a3FAj8vuYu83e1nz/BrkunET2xFKsE9tlKYHlKmpI6A0u3U0PWW7yijZUhIIxREJrdG31Nxlq3+/1kZp9gHlaVKaHoiBJrWhno+uzs6qyJ+6P8pr1eOWNcf/biKJiYkcOXIk7DaXy0VFRQUZGRlNrrc5KDkpqFJpy/tKl+C7b1rbmSGeffGN0BPfKE0PxEhTK/chgvoPog/RYghnhkDQjtAatZgyTABUFTRudcaQS4cw671Z9L+gf1w5M9o7brs7sEImrVdaoFxSSaxdtJbd/9vdpBU0AoFAIIh/ag7XAJDQuf7ZT4GVGfbYd/YFAkEtPDbfDMq6H4+t1U45ePBg1qxZE3bbunXryMvLQ6erO2AiELQtslem+lA19kp7wJnR2MlzAoFA0CEQfYi4QTgzBII4xVZmY/M7m8lfkR9UnpiTCDQt1JSkktDoNcKZ0YKU55eDDMY0Y1AsWZVGRUZ/n2e8eEtxrJonEAgEghZG9spYS6xAI5wZxvgIMyUQCI6hTQJ9Ongd4KoI/Xgdvu3apBY/9ezZs3nmmWfCrth96qmnmDNnToufUyBoKvYKO5/O/5QPL//w+MqM6tZbmSEQCATtBtGHiDtEzowoUalU5OTkKCI2nZK0+FGCpqqCKrYs2UJSbhLdJ3YP6EnOTebIhiNNSgLux+/M8Dg8yF4ZSRXDGHft3EZlu8sASO2VCgTryRqcxZGNRyjeWkyfs/vEsplR097tUxel6QFlauoIKM1uHUmPtdSK1+1FpVE1mBA1XlZmKM0+oDxNStMDcarJkAnjloCrnslA2iTffmFozqzH+fPn884777BgwQIee+wxzGYzTqeTRx99lE2bNvHKK69EXXe0dMRZnO2d1r6v/GERtSYt+kTfb8bWDDMVl8+JZqI0TUJPfKM0PRDHmprRh2ju+1b0IcIjnBlR4r/JlICStPhRgiZbmW+pmjHNGKSn+8TupPZKJaNfw3HxDvx4gC1Lt9DtlG4Mvngw+mQ9M/87E41BE/OEf+3dRn5nRlpvX4ip2noyB/peYiVbS5BlOebfdTS0d/vURWl6QJmaOgJKs1tH0uMPMWXuZG5wMoDfmRHrnBlKsw8oT5PS9EAcazJkRnRW1IckSc364a7RaPjiiy+4//77OfHEE1Gr1RQVFTFkyBBWr16N2RwmmSi+wQKtVhv1eSPRXD2C2NDa95U/F5TWpG2TnBlx+5xoBkrTJPTEN0rTA3GuKYo+REu8b0UfIjxx5u5qP3g8HrZv347H44l1U5qNkrT4UYKm2s6M2noy+mWQd1oeSTkNL2Er211G5b7KQF2SJKE1auNicL2926iuM6O2nvR+6ag0KmylNixHLLFsZtS0d/vURWl6QJmaOgJKs1tH0tPYfBkAPSb14MSbTiR3bG6Lt7EpKM0+oDxNStMDytMkyzI2my1siIfGYjabeeSRR9i6dSubNm3i559/Jj8/n23btkU8ZufOnXTv3j3qc0aiJfQI2p7Wvq/8zgyNUUPn4Z05+e8nM+jiQa1yLlDecwKUp0noiW+UpgeUp6ml3reiDxGKcGZEiSzLVFZWxtyALYGStPhRgqbazoxo9VTsqwAgtWdqSzev2bRnGwUl/z7mzKitR6PXkNrb950Xb22feTPas33CoTQ9oExNHQGl2a0j6ZG9MqYMEwnZDTszMgdk0mtKL1LzYvv+VZp9QHmalKYHlKmppQdW+vTpw9KlS7n88svZunVri9bdGJQyUNSRaO37yp/jSWvSkpidSLeTu5HWK61VzgXKfE4oTZPQE98oTQ8oU1NrvG9FH0KEmRII4ha/M8OQagjZVrK9hIr8CrqM7oI5M/yyMoCK/AoAUnqkBMq2vreV0p2l9L+gP1mDslq0zR0FtU7NmU+dScW+iohx07MGZ1G6o5Tqouo2bp1AIBAIWoPeZ/am95m9FfUDSyAQxI4TTzyRPXv2xLoZAgEQHGZKIBAIBPFNR+9DCGeGQBCn+J0ZpnRTyLYNr2/g6LajjDWPxTwxvDPDUeUI1JHcPTlQfnT7UYrWFNH1xK7CmRElkkoirVdavbOV+k/rz4DpAwIJ9AQCgUCgDBoTqtFWbqN8bzlak5bMAU2P0S8QCAQCQVtS25nhcXoo/K0Ql9VFrzN6xbhlAoFAIBAEI8JMRYlKpSIvLw+Vqv1/hUrS4kcJmmqvzKirJznX55zwhzoKhz/ElLmzGa3x+AwbfZJvcN1R6WiNZjcaJdioNnX1GFIM7dqRoXT7KAElauoIKM1uQk94ijcXs3LhSja9tamFWhYdSrMPKE+T0vSAMjXp9e23TxcOpenpCLT2fZXcLZneZ/cme3g2boebn/71E7899xtet7dVzqfE54TSNAk98Y3S9IAyNSnxfRsPmsTKjChRqVRkZSljVruStPhRgqZJCydhPWoltWdqiJ6kXF/y78qDlRGP9zszaoeYglrOjKrYOzPaq402vb0JY5qR7uO7o0vQAe1bTziEnvhHiZo6ApHsZimx1Ptc1ifp6w0rGCuUdh1G0uOyufj8hs8xdzYz+f7JqDT1/8jSGH1dbH8M8lihNPuA8jQpTQ8oT5MkSWi1ygm9ozQ9HYXWvq86DelEpyGdAF+OKCRABmeNE0NKaNjj5qK05wQoT5PQE98oTQ8oT5MS37fxokk57q42xuPxsHHjxrhIfNJclKTFjxI0JXZJpNOQTr6lvnX0JOX4nBn1rcxQ69Qk5SaFhEKKF2dGe7WR2+Fm69KtrH1+LR7n8baH01P0exHf3vEt619dH4umNov2ap9IKE0PKFNTc9ixYwd6vZ777rsvUHbo0CHOOecchg4dygknnMCiRYti2EIf4exmKbGwbM4y3pv1XsTPsjnLsJRYYtjy8CjtOoykx3LEgvWolcr9lQ06MoDAiki3PbbODKXZB5SnSWl6QHmaZFnGarUqJl+O0vR0FNryvpJUUiB3hrPG2SrnUNpzApSnSeiJb5SmB5SnSYnv23jRJFZmRIksy9hstpgbsCVQkhY/StNUV48/zFTNoRq8Hi8qdejASu+zetP7rN4h5fHizGivNqrIrwDZF/6rdvLvcHrcdjfFm4txVDsYzvAYtDZ62qt9IqE0PaBMTc3h5ptvZvLkybhcrkDZjBkzWLBgAZdeeinV1dVMmTKFbt26cfbZZ8esneHs5qhyYC21otFrAjP6a+O2ubGWWnFUOeJudYbSrsNIemqO1ACQ0DmhUfVoDPGxMkNp9gHlaVKaHlCmJq+3dULtxAql6ekItPZ9Za+0+2bcmrSoNCp0iTpcFheO6tb5zajE54TSNAk98Y3S9IAyNSnxfRsPmoQzQ9As2mtYjHinqqCK/av2k9wtmW6ndAvZbso0odar8Tg81ByuIalrUqPrjhdnRnulbHcZAGm9Iyf/9uNPsF65vxJnjTMQkkogELQsH3zwAZ06dSIvLw+32zd4vGnTJjweD5deeikAiYmJ/POf/+T555+PqTOjPjRGDTpz+OeE2xHbQfGOTs3hJjozjjmlXDZXA3sKBIK2QvxuEQgi89tzv1H4ayGjF4ym91m90SXosGBptZUZAoFA0J4QfYj4Ii6cGS6Xi4ceeohly5bhcrno1KkTixYtol+/foAvTMS8efMoKCjA6/WyYMECrrvuuhi3WuAPi2EttUbcx5RuYvqS6eKmbiJlu8vY8s4WsoZkhXVmSJJEUk4S5XvKqTpYFeLM8Lq9SGoJSZJCjtUl+gbKXFYxwBINfmdGaq/UBvc1pBhI7JpIdWE1JdtL6Dq6a2s3TyDocFitVu655x6+/vprXnzxxUD5N998w8SJE4P2HT9+PDNnzkSW5bDPR4fDgcNxvJNaVeUL5ed2uwNOEpVKhUqlwuv1Bs1K8Zd7PJ6g2UR1y/3/9+/jdrvxuI+XeT1ejm4/ii5RR1I337Pdv68sy4F2+NFoNIF6/UiShFqtDmljpPLmaKqtS61WI0lSSBvVajVAyJLxSOWx1lT73H5NVYVVeGUvxkwjbre7QU1owCt7cdlcuFwutFptTDQBIedt73ZSmib/PuHaKDQFl7vd7qDnYV0kSYpYXlNcw7JLl2ErtYVs92NMNzL97ekkZCUE1RPp3w2dtzZOp5MnnniC//73vwFNZ511Fvfccw8JCQmBOp599lleeeUVJEnC4XAwevRoHn74Ybp2De5D1qe1ofLa//eX+z/+77i2neraS6BM/M53f3gp/yQs4cwQCAQdnViPffr7EEuXLgV8fYipU6cG+hB+nn32WV5++eWgPsQjjzwS0odQAnHhzLjrrrvYv38/v/76KwaDgVWrVjFjxgzWr1+PVquNyzARarWa/v37H//R2o6JVks8h8Vo7/axlfl+aPnDGIXTM/xPw1FpVKT0TAk5/uDPB/nt2d/oNr4bY24aE7Qto18Gs96bhVof2++mvdqobE/4lRmR9GQOyvQ5M7a2L2dGe7VPJJSmB5SpKRoeeughLr30Urp06RJUXlRURPfu3YPKjEYjBoOB4uJiOnXqFFLXww8/HJRzw8/69esxm33vsczMTHr16kV+fj4lJSWBfXJycsjJyWHnzp1UVlYGyvPy8sjKymLLli3YbL5nu9vtpqamhtTUVNavX0/1/mqsVisulQu9RY/T6sRaacWb5EVCwmPzoJW12O12dq3dFahbrVYzevRoKisr2bFjR5DOoUOHcvToUfbu3RsoT05OZsCAARQVFVFQUBAob64mt9vN+vXr6d+/PykpKaxfvz5oUHLIkCHodDrWrl0b9L2OGjUKp9PJpk2b4kbTjh07AnqAgKbdG3ZTWVFJUVURzrXOhjVt20Rlha/+dWvXMWbsmJhoyszMRKPRBPTU1tRe7aQ0TT179qR///7s2LEj8IwQmiJrMhh8iYg9Hg92uz1QrlKpMJlMuN3uIKe0Wq3GaDRiKbVgKbGg1qnRGDVIkhRw0MiyjNvmxlJiwVJqISErAbvdHtRGf7JLm80W5NAxGAxoNJqQ+NFGoxGVSoXFYsHr9TJr1iy6du3KDz/8gMlkoqKigieffJJJkybx5ZdfkpGRgcfj4bTTTuPSSy/FYDDg9Xp57LHHOPfcc/nxxx9DNLlcLpzO4wPNGo0Gg8GAw+EIckDodDp0Ol2QJr+DS6VSYbPZsNlsOJ1OtmzZEmIniyX+8jV1RFq73+ef6Ob/Xd/azgwl9mOVpknoiW+UpgfiV1Nzxj79/ZZo8Xq9XHjhheTk5PDjjz+SkJCAw+HgkUce4dRTT2XVqlUYjb5xw/POO4/58+djMBhwu93cd999nHvuuUF95paguZpaAkmOcTAyWZZJTk5m3759pKUdHxy87rrrOPfcc+nWrRvXXHMNv/76a2DbV199xfPPP89HH33UqHNUVVWRnJxMZWUlSUmND8cjqJ+yPWW8N+s9DCmGsGExnBYn9go7s96bFZKEWlA/615exx8f/0H/C/sz/E9Nz7Ww8c2NbPvvNnqd1YsTF5zYCi3smLgdbt6b9R7IMO31aZjSTQ0ek/9dPr889Qvp/dI54/Ez2qCVAkF4lPgu3LNnT6CDZjAYWLhwIW63mwceeIB58+YxZswYrrnmmqBjunXrxsqVK+nZs2dIfeFWZuTm5lJaWhr4zpo7499P7RnK5XvK+eCSDzCkGFBr1RzZeAQZmawTstAYNL6Y1ZUOZv53Jsk9koPaHA+rGMJpqk17mEnekKZPrv2EqsIqJv1zEp2GdGpQk9vlZtdnu9AYNfSY1AOdQRd3mpRoJ6FJ2ZrsdjsHDhwgLy8PvV4f2M9tdwfaX/enraSS0Og1lO4u5f2L3kefqEdr1h7bCBzb3WVxYa+0M+v9WaT3Tvc5OOzB7fTX78+JU7c8Em+++Sb/+c9/+Omnn0JWBV5yySUMGTKEO++8M2wdsiyTmprKtm3bgpz2zVmZEa7cbreTn59Pt27dMJvNQXaqqqoiPT1dUf2HtqC99bs+ve5TqgurOe2R08galEXJthJs5TbSeqU1OsSiQCAQxCP+d1zPnj1DBuLrvutrI6kk1Dp1YOxTn1SrD1ELfx/iovcvCox9Rqq3bh+iId58802ef/55Vq9eHdKHuPjiixk6dCh33HFH2GO9Xi+pqals3749ZOJfS1Hfd9ua78GYr8w4cuQIOp0uyJEBMGjQINasWcPOnTvjNkzEhg0bGDFiBFqttl3/0HA4HGzYsIFhw4ahVqsb/UMjEBbD/1+ETrzH7QnUFe+aGipvKztZjlrwyl50KbrAfuvWrWPo0KGBttWnqXxvOV7ZS2JuYmB7rDXVtVNTNYVre1trKssvQ/bKGFON6JJ1Qe30er1s3LiRIUOGBM0kyByUCUDprlLsFjsavSauNNUur20nj8fDxo0bGTFiRMgP4Hi3UzhN/mf28OHD0el0ihg0aqomJYaJuPnmm3nggQfCzg7R6/VBs3b92Gy2wOyVcMfUHiDzo9Fo0GiCu0x+e9Ql0kyiwCD3sVn/w4cPD9Sr1vhsJUm+QTetWYvL4sJj86Az6gJ9HUmSQtpRX3mkNja1vD5NtfX42xmuLU0tj5UmWZaD7OPHnGXG4/CQkpMSVB5Jk1anZeCFA2Ouqe71Vpv2aielaXK73fz+++9h9TS17ZHKlaJJo9EEPQ/9vH/R+2GPA8gelc2keycF9i/ZVoKkCv396HV7UWlVQfV/Mu8THFUOZGqF0kNi9iezQ44P95vUzxtvvMEtt9wS9ru79dZbueKKK7jzzjvD1mGxWJAkiYyMjJDtkc7ZULksy1itVkwmU6A88A6q9R3X/t4Fsae+Z19L4F+Z4Q8zlTkws8XPUZvW1hMLlKZJ6IlvlKYHYqPpvVnvRdzm70P4Kdlafx+iNsuvXo69yh7UfwDC9iHqY/Hixdxyyy1h3+3+PkQkZ4bVakWSJNLT05t0zvqo3Yeor+/T2sT8ik9OTqampoaKigpSUlIC5bt378bhcGCxWOIyTIQsy1RUVARmqrTHJeB+Tdu2baOsrIx169YhSVKjl4BbC6xYrVYMKQa8Hm/AaQS+TrFJa8Lj8bB582ZM5aZ2oSle7JS/LZ+aihoOFB8g82gmaWlpVFRUBPQA9MnrQ/n6cjb9tInsqdmB8iFDhlCeX05lRSWFNYVUrq0M0VSwvABHqYPuF3bnlLNOicm1F05Te7DT2CfHkqJNYdeuXUGaunfvjsfjYdu2bUHO1H79+pHcI5kKZwW//fAbuhRd3GkKZydZlgOhC2qHx4H2Yae6mvzP7NLSUrKzszukJqWFifjiiy+wWq3MmDEj7PacnBwOHDgQVGaz2aipqSErK6stmhiRuk4wP27bsQkXGhVetxdbhQ21Xh0oj1ci6WmvhNMz+f7JMWhJy6A0+4DyNClNDyhQUzNiGaxfv57Ro0eH3TZy5Ej27NlDTU1NUNxrgK1bt/K3v/2Ne++9N6yjvTnEODiDIEpa874KODOMobOOWwvFPSdQniahJ75Rmh5QoKZmvm5FHyI8MXdmGI1GZs+ezS233MLzzz+P0Wjkyy+/ZNmyZUydOpWKiopAIvDaGAwGrNbwyVf+8Y9/cNtttwX+9oeJGD58eFCYCPDFdK3tLPGX9+3bN2zCwcGDBwdmxK5bty5Q3/DhwaGA/DOURo0aFVJuNBpDysE3sFW73D/Am5GREbRyxV/epUsXOnfuHNLGpmoaOHAgdrudESNGBFYxNEZTeWo5O007A0u0jRqjL9zUMeec2+pLkHnCCScEkiXHu6ba5bG0007dTtQpaoaeNJSMjAy8Xi+JiYkBPQCSLPH1f77G4/Yw6IZBmDJ8DiOP3YOt1EZySjLjzhkXCAFWW1Pxq8U4ihzkdc5rM03hVmbU1dRe7KRSqUjyJIUku9y/fz8DBw4MmqGoVquZ+uzUiDP740UTBNvJ/4yD48+9um2PdzvV1uTX45+V0BE11XY4K4H8/HwKCgoYNmxYoOzw4cOAz9HxxBNP8Ne//jXomFWrVjF69Oiws2NjiT5JjyndhLXUiuWoBbfVjdftxV5+fDWXKd2EPqllO6KC1qVsTxmOSgdpfdLQJwrbCQStxaz3ZkXcVncGZeagzHpDRNTm/FfOB3w/2i0WSyD8UlOprKwMOwEPfLk4UlNTqaqqCgxE/PWvf+XNN9/kyJEjzJs3j5tvvrnJ5xQImoLslfE4fL9V/CszrKVWjm4/isaoocvI1glPIhAIBLGmNfsQze0/gOhDRCLmzgyARYsW8fDDDzN+/HicTiennHIKN954I+Xl5VRXV8dlmAg4viTXX3c4YrUE3FZqw1HlCNnmR5+kDySm8YciUavVjQqd4C8PhMVAwlHpoHxPOQDp/dIxJB8P+aHWqBv9vbdk6ItoNDWmvLWX6qtUKpyVTlSSisROiYGB/3B6ErITqC6oxnLIQlJnn2OtbH8ZEhLmLDOm5NCcDhqNBkOyAcshCx6rp000hbNTJE3+NoYjnuwULqSCv7z2eS0llkbfi7WJB03+51uk+6y92cmf7LN2eWPt0540RWqjUpYf+7n++uu5/vrrg8oW1sqZIcsyLpeLt99+m0svvZTq6mruvfdebr/99hi1ODLmTDPTl0ynqrCKr277KlBuyjBx2sOnAZGfFYL45Zenf6FyXyWnPnAqnYd2bvgAgUAQFU2JPy2ppLDvVkklhQw0+OuVZRmNR4PGoIlqMCIxMZEjR46Ql5cXss3lclFRUUFGRkag7LHHHuOxxx6jtLSUhQsXctVVV7F48eImn1cgaCxej5feZ/fGZXUFnBmlO0v56V8/kd4/XTgzBAKBYmnNPkRz+w8g+hCRiIuRDb1ez8KFC1m4cGGgbMGCBYwdO5aDBw/GZZgItVodEhs/XrCUWFg2ZxnW0vArV8A3w3P6kumYM83N1uK2ubGWWvG6fbHdbWU2VBpVTMNixLN9GsOZT5+Jvdwe5HAKpyc5N5nqgmqqDlaRPTwbgIr8CgBSeqZErN8/u7e+QdzWpr3ZyOP08P1935PWK42hVwxFpQl+iYXTU/de9Lq9SOrgF13tezGeaG/2aYjG2Ccc8WofUJ6NWgKtVhsU7/yjjz5i/vz5PPLII3g8HubNm8esWZFn37QFkexmzjRjKbagM+vQJegYcvkQUnqkBJLIxStKuw7D6dny7hb2fLWHvuf1ZcCFAxpdl//HkegPtSxK06Q0PRDfmiLdjw3dp5Em0TWGwYMHs2bNmrADEevWrSMvLw+dTheyLT09nWeeeYaUlBSeffZZkpOTo25DXZqjRxAbWvO+UmvVjL4+OIyJLsF3TTprnC1+Pojv50S0KE2T0BPfKE0PxL+maPoQzX3fij5EeOLCmVGX6upqli9fzv3338/mzZvjNkxEuAsmHnBUObCWWtHoNWiMoSb2Ox8cVY7AAF00WmqHxbCV2wLODEe1A7XO9/CJZViMeLVPQ0iSRFLXJJK6JgWVh9OTlJsEP0PlweO5G0yZJrqO6UrWCZGdfQFnRnXsnBnQvmxUnl9O8aZiKvdXMuyqYWH3qaun9r1YWVCJ2+omrU9aYMZTuHsxnmhP9mkM9dmnsc/KeENpNmoud955Z9Df3bt358svv4xRayITyW5VB32hwNL7pdPn7D5t2aRmobTrsK6e6qJqrCVWZE/T4sP6nRkum6vF2hYNSrMPKE+T0vRA/Gmq/bvF7Qg/6FDf75bm/O6cPXs2zzzzDBdddFHIzMynnnqKOXPmRDzW4XDgdDpbPIZ4rH9HC6KjLe8rf3hEZ3XrODMg/p4TLYHSNAk98Y3S9EB8ampOH6K571vRh4jQhlg3AIITvBQWFjJ9+nRuvfVW0tLSmDBhQiBMBBAIE/HnP/85Vs0FfG1eu3ZtXCen0Rg1aM1aZI+MSq3yzfY060IG7aLV4g+LMe21aXQ6oRPZI7PJHpnN0MuHMuu9Wcx6b1bMZjS3B/s0hUh6knJ8Do+qguOx8HPG5DDhrgn0n9Y/Yn3xsDKjvdmobHcZAGm908IuEaxPj8aoQWfWBZL6RroX44n2Zp+GaIx96n7i2T6gPBt1FOqzm/9Z7n+2tweUdh2G01NzuAaAhM4JkQ4Liz+Jqtseu5UZSrMPKE+T0vRAfGry/27x/0YJ96nvd4vFYon63PPnz0etVrNgwYJAPU6nkwceeIBNmzYFJu45nU4KCgoCx1VUVHDllVcyc+bMoJxbLUFz9AhiQ2veVx6XB0e1IzA5EY6vzHBZXK2S7DUenxPNRWmahJ74Rml6IH41NacP0dz3rehDhCcuRmoeeughli9fjsvlwmg0smDBAi677DIgfsNEtAdkj0zZrjLs5XYkjUSnwZ0CKyZaCnOmmfK95YEk0wBqnTruw2LEM6U7SylcU0h633S6ju5a777Jub6lYlUHmpbYNx6cGe2N2s6MaNAn6rGX232zm7JbsmWClsTj8vhiW+ri4vUo6GAEnBm5SVhKLBzZeASNUUO3k7vFuGUdG8sRX4e9qc6MQJipGDozBALBccyZ5phMstJoNHzxxRfcf//9nHjiiajVaoqKihgyZAirV6/GbPa1qaSkhGnTpmGxWDAYDKhUKubMmaPY5J2C+KF4czHf3/s9KT1TmPrsVOC4M8Pr9uJxeJoUV14gEAiUhuhDxBdx8Ua6++67ufvuuyNuj9cwEXGPdPwHtOyWKdtbRkb/jAYOajrFW4oB0CfrcVQ6Yh66qL1TvLWYre9upfuk7g06M/yzdx1VDhxVvvBeLqsLQ6qh3gRDfmeGyxLb0BftiYAzo090zgxdou8HgaPGgYyMRHQJoAQti9flxW1zozPrqDlcQ+XBSkyZJlJ7pMa6aYIOiD9kYFJOEiXbSvj1mV/JGJAhnBkxxO1wYyuzAVE4M46t7nJZxbtWIOjomM1mHnnkER555BEAdu3axRlnnMG2bdsYN24cAF27duX333+PZTMFHRT/e8ofChdArVcHVpU7qh3CmSEQCAQxQvQhQhFvpHaIpcQScUa9x+EJOBMklURqr1Q8Dg/le8pxVjmxHLYEBlVbipKtJQDkjM1hzxd7WjWuZkfAP2hiTG04qY7GoOH0f51OQucEdIk6Cn4u4MeHf6TT0E5MfmByxON6TOxB9/HdUevjM7FSPFD7PvM4PRzdfhTZKyOpJMr2lKFP0jfJM681aZHUErJbxm11B/1YELQOQTZ0e7AWWClPLUetUSPLMrs/301VQRUaowZDmsFnE9l3D6Z0S4lt4wUdDrfdjbXYl4w+OTc5MCOycn8lsizX66AWtB6WYt+qDK1ZG7BJYxErMwQCQST69OnD0qVLmT17NsuXL2fQoEGxbpKgA+PP7VT794kkSegSdNgr7DhrnHGbP04gEAg6GqIPIZwZUaNWqxk1ahTfXHstW157jSvWrydr6NCw+741ZgxHfv+d293N/zFrKbGwbM4yrKXWkG2Osp04Cj/C0OuWQIgUnUkHJvB281Kxr4LK9XeQNPSvwPFM9n4tanX9A9suq5W1Tz7J7g8/xOvx4HE48DidnP7ie9jKc1n/xPW4pInoEsN/D21JYzQV/PgjH5x1Fim9eyN7PLisVoZeey2j//rXmA4aVe47yNHfHuW3LTY2PqVl2IIFDL322oh6Mgdmsvvjj1m9cCG2MhuOGolOg+8J2qd440a+v+02rCUleOx2Bs2dy0l33BHY7nG5+PWhh9i1bBkelwtzp05MWbSItH79Avvs/d//+O2RR3BWVeFxOuk2eTITHn0UrTHY6WIpLmbxCSdwXWEhKk3kR0xjr7uW4mmTiZuqq1E14nx17zO33U11YTWSWuLTaz8FfAmeasdFbEiP/weBf/VSvDsz2to+LU24Z6Usy+ySdiF7ZCwlFtx2N16PF5VWheyR0SXpUOvUeJwe7JX2Fg/L19K0dxt1VCLZTaVRcer9p1J9qBp9kh6tSYtKo8JldWEtsWLOis9BBKVdh3X1+PNlmDs1/fvvOrorxjRj1OEJWwKl2QeUp0lpekCZmvxhHFqSE088kT179rR4vY2hNfQIWpfWvK/8KzPq5osbee1IkGgVR4YSnxNK0yT0xDdK0wPK1NRa79uO3oeIiwTg7RWn04ns8dBp5Eg2v/JK2H1KtmxB9nqRWyiBjaPKgbXUikavAfs6tAYL+mQ9LpsLe3kNyB4sRyx43b7QKU6LE6fFicasQWvSIsseqg6WhdVSH/aKCt6dMAFkmYtXruTKDRv40/btXLVtG90nDmHgzIGodRLj/jKGC16/oEW0NpeGNHndbrKGDePKDRuYu3kzV6xbx/5vvmHbW281+hyFP/3EwVWrGr2/vbycd8aPp3T79oj7bH/tDkxdxnHW4i+Zs3o1W19/nb2ffx5Rz9GtW/n+L39h+mefMWD+iyT1v4g/Fv8Dl823wsNy+DDLzj6bcffdx9xNm7hy82aOrF0bdM3+eNddlG7fzqW//sqftm1j3H338fGMGXhcvo5t4erVrLj5Zqa+8QZXrF/P5evW4bbb+f7220Pa88fSpfQ677x6HRl+GrJRtISzi9tmg0Ymr6t9nxlSfDP2NQYN+mQ9hhQDGr0Ga6k1ZIVUJD3+e1GtU+N1e7GV2nBanLht8T1bt7Xs0xbUtaE+RY8+SY8kSdQcqcFtc+N1eTGkGEjskojH6cFlcaFN0OJ1e6kuqo57+0D7tlFHJpzdVBoVnYd1ps/UPoG/E3MSAajYV9GWzWsySrsOa+tRqVVkDMggvW96k+vJGpxF/2n9yRqU1ZLNazJKsw8oT5PS9IDyNHm93oZ3akcoTU9HobXuK3+ft+5kq26ndKPbyd2avDKxsSjtOQHK0yT0xDdK0wPK06TE9208aBLOjCjxeDxs2rQJr9fLgNmz2f3RR7gdoaGftr7+OoOuvLLFz68xanAc+QGv/QiV+yqxl9lRqVSotGqSuiZhTDPidrixV9ixV9hxVDjQJemQAEOyAVO6KZA3wa/FU4/D5bubbqLvzJmMvftudAnHY0Zr9Prjg9aSLxdDPMxmboymuuiTkxn9l7+w7Y03Gn3Mvq+/Zv9XXzV6f5fVStn27TgqK8NuL9m0Ca/TjSnnFIxpRnSJiZz8z3+y8cUXI+r5/Zn/0HncLPJXllKxrwJ9Wn8yh41g37E8M1vffJO+s2aRc8opgM9mk556lp/ue5xVD6zC4/Kw8T//4fTnn0djMACQO2ECOaecEqjj4IoV5J1zDsnduwfqGHrttRT++GNIe7YvWcKAOXMa/C6isVFjaapdIqExatCZdSRmJ9LlxC5kDsxEZ9aFzFqC8Hr0SXpM6abAvSjLMlqzb7a1vcKO2+EOuhfjida0T1vit6HGoKH0YCmVBZUgg8asIbFrIsndkvE4PIFnpUrtiw1sK7Phsrri1j6gHBt1NJpit9Sevtwt8ezMUNp1WFdP9ohspjw6hRMXnBjjlkWH0uwDytOkND3Q+ppi8SPadmySkFKoqyceBiYE9dOa91UgZ4ax7VaOi2df/CP0xDdK0wOi/9BeqK1JbuSE4ZZGhJlqAXRJSeSeeiq7P/qI/hdfHCj3ejzs/OADLluzhu/+/OegYw6vXcv3t99OTVERssdDz6lTmfDoo+iOLdd5d+JEBlx6KRuefx6v241aq+XUp5/GnHMCHvshyta8itdWTMWmV0BKQJV1E+asRKz5LpyFr6KxVKJSS2QNG8XYfz6ORu8bpP7vxIc5a9G5OCoO8+nF53Hxd98F2rTippvoctJJDLriiqC2WktKyP/8c8548cUQ7Ts+2oExzUj2yGwAdi5bxorbbkP2elGp1Ux68km6n3Ya4Fvd8eWf/kTp9u2otFr0yclMWbSIjGPx3SJpzp04MXC+za++yq8PP4xKo0GfnMzI225j88svM+vYwLWttJSvr7+e4vXrcTid2C+5hIkPP4ykapzfzpydTXVhYeDv/C+/5Ke778ZtsyHLMjmnnMJp/+//odJoWHLyyVQcW9a159NPOWfJEjIGDqRy3z6+vu46KvPzkVQqhsyfz6hbbwUgsWtXFhw9GvH8+7/5Bl1qfwCMab7wTV3Hj2f5zJlk//3vYY85+P136LpchWP5Tpw1Pi92zzMns//rr+lzwQVU7tlD+uDBQcck5XbFeqSIgp/3U74nF7VOhzEtOAxG+qBBHF6zhl7nnkuXsWP5/IorGHnbbSTl5uJxOvnlwQeDbANQsXcv1QcPkjtpEgCfX345nUaOZMe77+KsqSEhO5uz33yTHUuXsvHFF7Hb7XiuvJKT7zkeFsty5Agrbr2Vop9/RqVWkzZgAJOffpqUXr0A+Onee5FlmaLVq6kpKgJg2PXXM+LYPRbJLgA/3XMPuz78EEmtxpSZyVmvvUZyjx4R7VEbCQlJ1bTwY+ZMM9OXTI+Y4wZocu4NQXRIkoTs8iVeT8hOwJBiwFHl4Mwnz8SYHhwqbeU/V1J1sIoTLjuBfuf1E/YRtAn7Vu5D9sp0Hto58PxP6ZECQHl+eQxbJogWp8VJVUEVKrUqpqGmBAKloNPpUKlUFBUVkZmZiU6na5PQsLIs43A4UKvVishfVFsP+GbBlpSUoFKp0OlaZwa+IL4JlzMDoGJ/BVUHq0jsmhiYYCEQCATtDdF/aDnq9iFKSkqQJAmttm3DqAtnRgtxwtVX8+uDDwY5M/Z9+SXZY8ZgSEkJ2tdy5AjLzj2XqYsX0/PMM/F6PHx30018dc01nLtkiW8nSWLD888z88svMXfqRNHPP/PxjBnM/OZ31IZs0kbfT8X6R3AxHsnYj6wTsnBXllNtOYih+xRmfvMYKT2S+fC88yj4bhkjbrwR8IWtSO6eQvKk4Xy74BrKd+8jsUcOXpeL3R9/zIRHHgnRdnjNGjqNHBmYte/H4/Kw8Y2NeF1ezll0DgD7v1lN3yseY9hVY6nev4VPZs3imn37UGu1eN1uTvy//yN7zBgAtr39Nl9fey2z/bP7I2i+Jj8fjV5P0S+/8OOdd3LxypWk9e1L5f79vHfaaSTk5ATa9L+5c+lzwQWcvWQJv/3yC0cef5xNL7/M0PnzG2XHko0bA4PmAIbUVKZ/9hmmzEy8bjfLzjuPLa+/zpB585jz00/8tHAhstvNKQ88APhu7OUzZnDKQw/R88wzcVZX894ZZ5AxaBA9zjijwfNXHShA0vo6iv7BLK3RiNpgwFUefjDLXlqMsVd6wJFhTDeS2ieP/C8+8/2dmUnl3r3Bx5SX4XVW43XWgGTAWVODvaIi6Fqt2L0bz7HVRt0mT2boddfx5ogRDJk/nz3Ll5PcsycTHn00qN7tS5bQ76KLAs4jSa1m3XPPMfuHH0jo0oXt77zDsvPOI2PwYC5bt47f16yh4IEHKPjhB3LGjwfgw/PPp9d553HO228jSRLblyzhvTPO4Kpt29Do9SBJrHvqKWZ9+y3Zo0djPXqUN4cPJ2f8eLKGDQtrFz+Oqirmbt6MSqPh14cf5vu//IVp779fr028Hi+SSqr35ePxwMqVEj/9lI7FIjFpEvjDPJozzWIwPB6QwNjFiNlgxpBkwGk5fr+k9QoeZOx/QX82vLqBsl1lwnaCNmPrf7dSdaCKiQsnhjgz4nllhtKRvXKTHdl+SraVsOqfq0jrm8aZT5zZwi0TCDoeKpWKnj17cujQIYqOTWhpC2RZxul0ttngR2sTTo/JZKJbt26oGjkBTKAs0vum47a5SemZElS+56s97Fy+k4GzBgpnhkAgaLeI/kPLUVeTJEnk5OS0eZ4T4cxoBrWNlTthAl9fey1VBw6Q1K0bAFtef50Trr465Lj1//43/S++mJ5n+n7Y+lcwvNi9OxX5+aT07AnAiJtuwtypEwBdxo5Fn5RE5b5aCV5kGUmjQq1Xo9aqcQNqUxe0aScG6h1w6aXsWb484Mzws+297ci6IXx5w2Oc9txCjny2ksyho7GUuLGUlAXNFreXl6NPDe28lO0uw+vyok/Wk9jFF9c7oddUCn8to/fUGrqOG4fGZKJi927SBwzAlJGBKSMjcHzvCy7gq2uuCaoznObynTvJPOEEdrz7LkPmzyetb18Akrt3Z/Rf/8r2d94BoHzXLiyHD3PC1VfjdrvR6vWMvP12fr7nnkY5MwpXr+ane+7hnLffDpRln3g8rIRKo6HXuedyZN26iHXs/+YbkvPyArbVJSYy4qab2P7OO41yZjirKxl65Uh6TD0NjeH47akxGCBM7EBLiQVHZQWSRhsYnE3pmYK1zIG9tApLiYWBl13GuxMn0nfmTLqOG4etrIxvFixApTUAMh6niv6zZ7Pillt8oaaMRvZ9+SW7li2j59SpgXP1v/hi8j//nF8feghDaiqnPPhgSPLvHe+8w9TFi4PKBl91FQldugA+m39+2WXM/PJLVGo1Gr2evHPPpXD1anLGj+fAd9/hsdsZe9ddgeMHzJnDjqVL2fHOOwyeOzdQT/bo0QCYMjLIO+ccCn74gaxhw+r9fsfec08gLNrAK67g96efrnd/gOqiaqwlVpJyk8IObC9bBjffDAUFasAX6z4nB555BqZPD63P4/JQtqsMZ42Trid2bfD8sURJibcA1Do1usSGZxz2mNgDnVlH7sm5bdCq5qE0G3UU6trN6/FSU+RLNJ2cmxwoT++Xzqn3n0py92TiGaVdh349sizzwewP0CZomfLoFEzppibV43+Pxzr/jtLsA8rTpDQ90HqadDod3bp1w+12t1loDbfbzfbt2+nduzeaRuSEi3fq6lGr1Wg0GsUMtCiZ1rqvek3pRa8pvULK/bkyHNWRV5k3B/Hsi3+EnvhGaXpA9B/inbqatFptTK5DZXybMUCj0TB69Gj+V2v2yqC5c9ny+uuMu+ce7OXlFK9bR49330WuE5ft6ObNITkFNHo9nUaO5OjmzQFnRmJu8ECaMSMDR8Xx2fnSsbAF+ozjiSUlbfBgqykrC2tJSVCZrdTKr8+uobqsL9bvPubd8/vjKf4QbdpJvDfrPd9x6SamL5mOOdOMPjkZR5hVAcVbigHIHJQZ6Pwa0tKwVREIq+M/f/qAAcheLxtfeIFdH35I9cGDqLRaX1LmWoTTbC/zJSyvPnCArrNnB23vNHJkwJlRum0bFbt3s7jWgPZWjwd9cuRBoOING1g8bBhum42awkKmLFpEl7FjA9urCwtZ8+ijFP38M87qalw1NXQ7FjYrHKXbtlGwalVQGzxOJ1lDh0Y8pjYagwGNwZc0tDZum42RJ50U9AC0lFhYNmcZsqym6Nf9uB0aJLVE5YFK9n62E2dJBcvmLGP6kulc+PHH/Hz//VQdOIAuMZGT7ryTPZ9+jUprxlHlYMqiRfz68MO8O348HqeTrqecwrAbbwzY/ci6dbx3+ukM//OfmfbRRxz45hu+XbCAw7/9Flj9ULxhA163m86jRgW13dy5c+DfWqMRU6dOgZBWo0ePZsvWrdiPhd4q2byZrsdye9Sm6ymnULJpU+Dv+q6T+qgdSivcvVEXGRlbqQ2v24tKEzpT7csv4dIbQ/OKFxbCzJnw/vuhDo0jm46wcuFKzJ3Nce3M8D/j2jsep4fKskoSOieQUmeVXCSMaUZ6nRH6gy7eUIqNOhrh7FZzuAav24tap8aUeXzAXGfW0XlY57pVxBVKuw5r67FX2HFZXbhsLgzJhgaODFNXHDgzlGYfUJ4mpemB1tfkD2fQliENlGYjpenpCMTiWeF3ZvgjALQk4tkX/wg98Y3S9IDoP7QX4kGTcGZEiSzLVNZJ4jz4yit5d8IExt59NzuWLqXvrFlIKlWIM0OK5LWS5aBtYWfHHBs1ddvcyF4vbrsb1bGEXS67C9kbPKoqSVLI+Z01Ttx2N6bOfXDk23EcPYBk3Ufy0BtQqTS4bW6spVYcVQ7MmWY6jRjB4TVrcDscvjA/xyjZ6hsIrj3wrjVqsVWBs9oZcv6f7r2XglWrmPTEE2QNH47bbufZWsnEI2n2J5RxWSyo68RxVddqj9tmo8vYscz4/POAfZKTk+udZZQ1bFggzNXGF14g/4svGHjZZQB4XC7enTCBwXPnMu3DD0ns2pX1//43h9esiVif22ajz4wZTHn++bDb7RUVfHjeeZzx0kuk9+8fsj0xJ4eqAweCylw2G66aGpw6HSZZDuhxVDmwllpR6VJRG614PSmYOplI7JyIrbAatSEtYMfsMWOY/umngTotR46gNpiQ1DocVT67nrxwIScvXBjY55sFCwKOnbVPPsnwm24KbB8wZw7ZY8bw6oABjLnzTrRGI9uXLKF/HWdTOPw29NuodsKg5twbTU08JElSqBeiDvZS30CWpJFQaVSB1S9umxtZhvvvD1+FLIMkwS23wLRpx0NOAWQOyAQJLIctWEutTZ7p21Y09h6Kd6qLqnFUOrBX2Enslohao0ZCivlM6ZZAKTbqaISzW1VBFQCJOYntzpZKuw5r66k57FstY8o0hXVoN4Q/karbHrvnjdLsA8rTpDQ9oDxNQo8gHmhNu7msLtQ6dci7rjWdGUq8DpWmSeiJb5SmB5SnSWl6IH40iaCYUeLxeNixYwfeWo4Cc+fOpA0YwIHvvmPr4sWBkDh16TRiBAU//BBU5nY4KN6wocEZ/FqzFlO6CbfDjdft61jYK+zYK+y+ToZXxpRuQp+kr7cegKTcJNRJo5CrluLV9UdnNqAz69AYg31cCV26kDt5MuuefTZQJntljm73zabPGlTLmWH2/XAPtxR114cfMunJJ+k8ahQqtZrSrVsbbGNt0gYM4PDvvweVFf38c+AGSunTh+ING/C4XAH7NGX52AlXX82RtWs5vHYt4MufoTWZGHv33SR29c2gP1qnzao6g++pffrU6+xwWSyUbd8edqULgD6zHzuX/Y/DGw4HygpWraLTqFH8sXNnWD3alD6opf2+zqfXN5PXU7MLXXrfiO3Y8uqrZA6bBBA2ObWzupo9y5fT8+yzfX9XVZF+LIm2n+S8PDRGI86qKmRZZse774asOKqPcPdQpxEjKPTnUKlF4U8/0Wn48EbXXdcuTUWfpMeUbsJaasXr9qLRaXBUOQL3mtvhxqE2sf9w5PtMluHgQahzq6M1aUnN84Vt8zsE45Fo7qF4Qp+kx5BswFbmW1mjMWioPFKJvfy4DRt6Vu7+cjdf3v4lpbtK27Dljae926ijEs5uVQd9zoyknKSQ/cv2lLHxjY3s+WpPyLZ4QGnXYW09fmdGQueEBo4KT2BlRgydGUqzDyhPk9L0gPI0CT2CeKA17fa/m/7H0guXcnTH0aByfaKvn+yfqNiSKPE6VJomoSe+UZoeUJ4mpemB+NEknBktzJB58/jxrruQVCrS+vULv8/8+ex87z3yv/gCAK/bzYpbbqH76aeTWCuZdTgMKUamL5nOrPdmkdAli+RcL2c8cQaz3pvFpIWTSO+fEQgP1RCSJJE6cDKS+yAeaSjWo9aI+05ZtIjtb73Fr//6Fy6rlYp9FcdmcHhJyjn+I98/CzHcAHlCdjYlGzce217F6oUL0Zobn1x35K23suXVVwPhhsp27mTNo49iSE8HfAPh5k6d+OEf/wisBnFUVmKP4Dioi0qj4ZSHHuL7224DjoUhKi7GUuwLp1X0yy/srbW6AcCQnk7V/v2Bv3tOnYqlqCjI8WM5cgTXsXBaiV27suDo0aBQVrXx0B3b0RrWP/8K4HMqrL73XoYuWBCx3caup+E88jV4KkEGR9kfOMt2Ysg8nu/D/33IXi/b3nqLrYsX0+vCeb5z1Djx1noQVRcW8vH06Yy89dZAWKYT5s3j14ceoiI/HwCvx8MvDzxAl3HjMHfqRMEPP2DKygrkM4mWruPGoU1IYPU//xlYabH1zTcp276dvrNmNbqeunZpKuZMM9Nen0bnoZ3JHpnNuS+ey6z3ZgV90q6ZjpWGr99Dh0LL/KuZircWR91GQf2YM80MvGgg2SOy6T+9Pxd9dBHDHhzGjHdnBGzY0LPyyKYjlO0sY9+KfW3XcEGHxL8yo3a+DD8V+RVse28b+1bua+NWCQLOjE5ROjOOTQ7xur143d4G9hYIBAKBIDb4VyxrTcGhV1pzZYZAIBAIBNEiwkw1E7VOFxT6qOfZZ/P1dddx8rE8AgBIEhrT8VAypowMZnzxBd/fdhvf/vnPyF4vvc8/n0lPPhlUrypMSCW1Toc504w504yp2+kUr/5/rLr9Z0579ilSemagTzIFDc75jwmqt1Z8OF1iKipDNrIml5pDNREH9kwZGcz+8Ud+e+wx3jn5ZBxVDizFVWjNeiyHx5PUrRtqnQ5dki8htL/DU/v8pz//PF/Om8e6Z59FpVYz9t57Kd+5E4/LhVqrrVcz+BJ+n//ee/zvyitx22zokpLod/HFgfBDkiQx48sv+f7WW3ljyBCcXi+709I465VXMIRJYF73uwDoO306vz/5JLs//pje06Yx/uGHee9YjozkvDwmPvZYkEOjz4UXsvnll3lz5EgGXXklI266iUt++IHvbrqJDc8/j8ZkQpeQwHnvvx+SLDsc9nI7aaNuo3jNf3n9hHfxejwMmTePvjNnsvbYipEd775LdUEBvS78EwDapJ4k9b+Yiq2v4LCo8BQbSRt9O2gMgB17eRlvzjwNSaXC7fCFnLrou+/Qp2Yy/GoJtV7NLw88wO7ly/G6XGiMRoYvWBAItwXQ+/zzkT0ePr/0Utw2G16Ph67jxnHusXwl299+O+yqDLVeHxQKDAhxYPlt7+fC5ctZcdttvNyrF5JKReaQIcz8+mvUx2yl1unwqoL9sGq9PigMVTi7aEwmX+wnP3Xuy7pU5FcgqSRSeqbQ+6zeIUvocg9GPDSI7OzQssyBmfzx8R9xvTKjveO2uznwwwF0Zh0jrh5BWu80TBUmUnulNjr5Vs/JPTmw6gD7V+1n+J+GRxVmRiBoDJUHfWErk3JDV2ak9EgBfM8kuVaoQUHr0+yVGfrjzxqXzRWY4SoQCAQCQTzhOha2um6EBl2icGYIBAKBIP4QzowokSQJo9HI6BdeCMrcrlKrua6wMGhflVrNLRZLUFnW0KFc9O23Eeuf9dVXIWW193fb3agMfeg8+RlmvDsDndnX0bj4u++CjskZPz6orqt37qRsTxnwCwDWgysxdRuPu+ZY0tF6UgjoEhM55Z//5JR//hMAW5kNp8VJ0rGZpLO++or8Ffkc+PmXwMqM2m1O7dOHS1auDKqz9/nnN1qzx+XC3KULV6xfD0DVwYN8evHFTFu2LLCPOSuLc95+G4/Hw5YtWxg8eHCQfWrTddw4Ll6xIqR8dq0wRyf86U+c8Kc/BW3vf/HFgX8nZGdzxbp1QdtT8vKC8lM0BVuZDY0pk3H3vk33Cd0D5R6PB6PRiCRJ9L/kEoBjdvRhzhmHOWdcUF3+/A6G1LTAdxaJsXffzdi77653nz4XXkifCy8Muy1zyJCwKyfOeOGFkLKrjoXq8t9DAy69NMhGxvR0zl68OGI7TrrjjrDtr004u9S9B8Pdl7XJX+FbhdJjUo+wg4fjx0NOji/Zd7i8GZLk2z5+fOi2zEGZAFTur8RR7YjLAS6/fdrrwOmer/bgrHaSkJ1A7rhcvLK3yXo6D+uMPlmPo9LB4Q2H6TKqSyu2uOm0dxt1VMLZ7ZT/O4XKA5Wk9UkL2T+5WzJIvhAP9nI7xrSGHeNtidKuw9p6ao40z5mh0qg44dITUOvVqLXNC38YLUqzDyhPk9L0gPI0CT2CeKC17OZxeQKrB+uuzDClmxh94+hW+a2ixOtQaZqEnvhGaXpAeZqUpgfiR5NwZkSJWq1maAP5LVoTS7FvEFZr1gYcGU3Baz9M2a8vojFnkDbqVpLUOiSadjEa04whgyo5J+VwweILArM4WhK31cpns2fjqKry3UCZmUxZtAhz584h+8baPtFiL7cDhHyv8a5neD1hsCIR75qGXj6UpJwkek7uGXa7Wg3PPAMzZ4Zu8z/Xn346OPm3H0OygcScRKoLqinZVkLOmPrDy8WCeLdPfXjdXnZ8tAOAAdMHIKkk1DRdj0qtovuE7uz8ZCf5K/LjzpnRnm3UkQlnN3OWGXNW+JWRap2axC6JVBdWU7G/Iu6cGUq7DmvrSc1Lxev2ktg1Mer6Bl8yuKWaFhVKsw8oT5PS9IDyNAk9gnigtezmDzEFx0NG+9EYNPQ+s3eLnxOUeR0qTZPQE98oTQ8oT5PS9ED8aBIxM6LE6/VSXFwclLy4LfHPFjR3anzOidqoDJ1JGvJPkgbfhssGlnILjhoHToszqEPTVLRGLcY0Y6vMQNQnJzNn9Wqu2rKFuZs3c/F335E5ZEjYfWNtn2ixlfpyaxhSDUHl9elx29w4Lc6QT0N2tJRY+OWZX/j12V9bTkATiHcbpfRIYfhVw0nqGhr2xc/06fD++1AnkhYZGb7y6dMj1z9y/kjOeOIMuoyMrwFyP/Fun/pwO9x0Gd0FcydzwBkVrZ4ep/YAoPCXQlw2V0s3tVm0Zxt1ZKKxW0rPFAAq9lW0TqOagdKuw9p6Rl4zkjMeO4O0XqErZtoLSrMPKE+T0vSA8jQJPYJ4oLXsFggxZdAgqdpupq0Sr0OlaRJ64hul6QHlaVKaHogfTcKZESVer5e9e/fGzICWI76VGdEkpdQn6TGlm3A73Ngr7Ngr7FSXVGOvsFNVWEXlwUqM6Ub0SeGXk+avyOf7hd+zf1X0CZZbm1jbJxpcNhduu88BUXfmbTg94exY++N2uDGlmyLa0ev2kv9NPgd+ONB6ouqhPdooHNOnQ69evn8nJ/t+DMyeXb8jAyB7eDbpfdPjNg9De7aPzqxj9PWjOXfRuah1PsdqtHrSeqeR2DURj9PDwdWNTJTSRrRnG3Vk6trt0PpDbF6ymeKtxRGPqZ03I95Q2nXY0nqqD1Vz9I+jOKodLVJfU1GafUB5mpSmB5SnSegRxAOtZTe/M6NuiCk/JdtKOPDjAewV9hY9rxKvQ6VpEnriG6XpAeVpUpoeiB9NIsxUOyWwMiNCWIr6MGeamb5keiCvhcftYfPmzfTL68f3d36P2+Hm5P87OWIy8MPrD3Po90NhY3t7nB42LN6As9rJmJvGxO1AbTxiK/OtytAYNCFLfMNR147h0CfpI9rRH/vUbXfjcXliFs873qg+VM3mJZvJOy2PzsNCQ5iF49Ah3/8vueQQL7zQjU8+8YWYUlBoxHZJSzx/JEkib0oeR7cfjcp5LBA0ROGvhez6bBcep4esQVlh9/E7M6qLqtuwZR0bj9ODSlKhUjfvOfLzEz9T+kcp4+8aH5chBQUCgUDQsdEYNXSb0C2iM2PtorVU5Fcw6b5JZI/IbtvGCQQCgUAQBuHMaKeoNCr0yfqow0yZM82BQW63242p3ESnIZ0YOGsgOz7cwd6v9pJ3Wl7YpC7+2aPhBl1UGhU7P9kJMgy7ahjG1PiK7R3PJHRK4NwXzsVZ42z0MbXt2FS0Zi2SSkL2yjirnXEXhz1W7Fuxj/3f78dR5WiUM8Nmg/Jy37/PPvsor7+eS36+xLZtMGhQ/ccW/FpA0Zoiep7Wk8wBmS3QesGmtzfR9cSupPdJb7E6B84Y2GJ1CQR1qTxYCUBSbuSQdp2Hdua8l86L+p0vaDp7v9nLxlc2kndGHicuODHqejRGX1e7OSE8BQKBQNCyeDwexo0bh8MRPCls7969LF26lKlTp7J9+3auu+46KisrkSSJu+++m+kNLb1uhyRmJ3LyX0+OuN2fC7Mpv1EFAoFAIGhNhDMjSiRJIjk5OWYZ3IddOYxhVw5DluVm11VbS/8L+rPz050c3X6Ukq0lZA0OdlhYSixYi61IKomM/hmhdakkdAk6nNVO3wB5jJwZsbZPNKg0KhK7hE8y2hp6JElCl6jDUenAUeVoc2dGPNpIlmX2fb8PgJ6nhk/8XRf/qgyjUaZ3byOnnQaffw4ff9ywM+PgTwfZt2IfhhRD3Dkz4tE+dbGUWIJWJpXtLmP9K+vZuHgjUx6dQmLXxICzrzl66p6nLvWtgGpN2oONBKHUtVt1gW+1RVJOZGeGxqAhoXPbrAxq6vWutOvQr8ey2YLsldEYmtdV9h/vDyPZ1ijNPqA8TUrTA8rTJPQoD7Vaza+/BucNtNvt9OrVi7Fjx2K325k2bRovvfQSEydO5PDhw0ycOJHevXszJELOxtYmVnbTJbSOM0OJ16HSNAk98Y3S9IDyNClND8SPJuHMiBK1Ws2AAQNi3YwWuYBqazGmGcmbksfuz3ezZekWJg+eHLRvydYSAFJ7p0b8ga9L9Dkz6hsMaW3ixT4tRWvp0SfpA86MtiYebVS6s5SaQzVoDBpyTmpcOJCiIt//u3SRGDhwANOm+ZwZy5fDHXfUf2zmoEz2rdhXb6z8WBGP9qmNpcTCsjnLsJZaA2U1h2twWVzoEnV8/KePMaWbmL5kOuZMc9R6ap/H6/LisrlC8tDUPk9bEu82EoSntt2cFmcgxGB9zoy2Itx9VZe617vSrkO/nlXLVgHR5SarjT9spMvmanbbokFp9gHlaVKaHlCeJqGnY7B06VJOO+00UlJSWL58OcOHD2fixIkAdO7cmdtvv51XX32Vp59+Oibtay27ed1ekIgYVtHvzGjp3E9KvA6VpknoiW+UpgeUp0lpeiB+NAlnRpR4vV6Kioro0qULKlX7zgtRV8vAGQPZ88Uejmw4QunOUtL7Hg/XUl+IKT/6RD011MQs2SW0T/sc+PEAFfsryB6RHTJLv7X06JP1cJCYODPi0Ub53+UDkDM2p9GzcQsLff/v0kWmoKCQs8/uAqj49Vc4fBg61xOpyn8flf5RGnd5S+LRPrVxVDmwllrR6DVojBpf7heHB5VWRXL3ZPCCtdSKo8qBOdMctR7/eVRalS8ckNcX3q126Jja52lL4t1GgvDUtlt1oW9VhjHNiM6sq/e4ot+L2PvNXjL6Z9B/Wv9WaVvd+6ou4a53pV2Hfj01h3y5yZq7IiawMiNGYaaUZh9Qnial6QHlaRJ6OgYvvPAC//rXvwD45ptvAo4MPxMnTuSZZ56JeLzD4QgKW1VVVQX4Qjq73b53gEqlQqVS4fV6g5Kn+ss9Hk9Q5IXa5R6Ph8OHD9O5c2e0Wi2SJAXq9aNW+35LeDyeRpVrNBr+WP4H615dR95peYz+82gkSUKtVgfaqDFp8Mpe7FW+BOCR2t5UTS6Xi0OHDtG5c2dUKhVqtbrFNMmyHFReV1ND5UITgWdDQUFBQI+/je1VE8Dhw4fp1KlT0KTg9qrJ6/Vy5MgRunbtiizLQVqFpvjQ5PV6OXz4MF26dEGj0YRck0rXVFdXSyKcGVHi9XpDHuxtRcW+Cn546AdS81I55e+nNLu+ulrMWWZ6nNqD/G/z2frfrUy4a0JgX//KjMxBkUPi+GctxzKuZiztEy0FvxSwf+V+tCZtkDPD44Hvv5f56ScbJ58sM2kSqFtozNtvq1g4nuLNRl63lwM/HACgx6k9Gn3c8ZUZMgUFBYwa1ZkTT1Tx22/wySdwzTWRj03smog+2bc6pmx3WVyFmoo3+0RCY9SgM+uwHLGg0qgwpBowpZlwWpy4Hcdfns3VozPrMKWbsJfbcdlcmDJMgW21z9OWtBcbCYKpbTd/vozEnPAhBmtjKbZw8MeDuG3uVnNm+PHfV267G5fdhTHleBjCute70q5Dr9fLwYMHqT7sczS1mDMjRmGmlGYfUJ4mpekB5WkSepTP5s2bKS8vZ/z48QAUFRUxZcqUoH1yc3PZu3dvxDoefvhh7rvvvpDy9evXYzb7JgBkZmbSq1cv8vPzKSkpCeyTk5NDTk4OO3fupLKyMlCel5dHVlYWW7ZswWq1UlFRQWFhIQMGDCAlJYX169cHDV4NGTIEnU7H2rVrg9owatQonE4nmzZtCpSp1WpGjx5N5dFKKisqOXjoINJaCaPRyNChQzl69Ch79+7l8JHDVFZUUri3kFGMoqioiIKCgkA9zdF0+PBhCgsLfeGu+/dvOU2VlezYsSNQXleTn+TkZAYMGCA0RdCUlpbGjh07AnqAdq2pe/fuFBQUUFJSEuR4bK+aZFnG6XSSnZ3N1q1bsdlsQlOcaZJlmYqKCrRaLdnZ2WzZsqVDafI79VsD4cxoh9QcqaHmUE1gyWdrMHDWQKoKqsibkhco87q9GNOMWEusZA6MPOjqTxIWyzBT7RF/qBFT+vFB0mXL4OaboaBADfQBICcHnnkGWiL/3Jg/j2HsbWPR6MWj4NC6QzirnRhSDXQa0qnRx/mdGdnZx8vOPx9++80Xaqo+Z4YkSWQOyqRgdQHFW4rjypnRnnBanIGwOInZDQ8KR4spw+fMsJXaSMpJinmcSEH7p6rA18FLzk1ucN+UHimAb0JDW+B2ujmy5Qh4IWNgBvoEfcMHKQS3xY3b7kYl+SZ4NAetKbZhpgQCgUDQMIsWLWLevHmBvysqKjAYDEH7GAwG7HY7siyH7QP+4x//4Lbbbgv8XVVVRW5uLsOHDycpyRdK0u886tmzJ927dw/s6y/v27dv2NnxgwcPxu12s27dOkaMGIFO5/u9PXz48KA2+Gftjho1KqTcaDSGlANo0ZKckkzv/r0ZMmpIQFtGRgZpaWnsKd2DZbWFZKOvr9KlSxc611p6Hq2mgQMHYrfbGTFiBGq1OtD2ltCUnJwcVF5XU91yoSm8Jq/XS2JiYkBP7ba3R01er5f9+/czcODAQHvbsyaPx8O6desA3zOi7ux4oSn2mvx60tPTO6Qm4cwQBFFz2Bf6wNyp9UKaJHVN4ozHzwgqU2lUTH5gMl6PN2JMTfCFmQJwVsduZUZ7xO/MMKT6Os7LlsHMmVA3x3thoa/8/feb79BoTYdYe0P2yiTlJpE9Mrve67sutVdm+Dn/fLjrLvjmG7BYwFzPrZo1KIuC1QW+VU+zom19x0X2ypTtLgPZFzatNa9pQ7IBSSPhcXpwVjtDcmcIBE1lyKVDyDs9D5Wm4WdOSvcUAGylNhzVjsC7trVQa9RwbIWxo9LRoZwZzjJf/8WYbkSta95SyKzBWQy6ZFBQyE6BQCAQxA8Wi4X33nuPbdu2Bcr0ej12uz1oP5vNhl6vjziZRa/Xo9eHvis1Gg0aTfCwiz9sR11qD7DWLfc7UfwhPPx1h6Mp5X7nvT5RH7Td38bsYdmcdNNJJHZJrLft0Wjy66l93pbQJElS2PKmtr2ja/J6vWH1NLXtkcrbWpM/5E04PU1te6TyttbkfxZEuiaFpthrkiQpKExbc9seqTweNUVqf0sgnBlRolKpyMzMjMnSXEuxBaDZswX9RNJiKbHUu7pCn6QPGyN+0EWDGDB9QEwH+mJpn2jxOzOMaUY8Ht+KjLqODPCVSRLccgtMm9ZyIafampjeQ2GubVOmiZNuOwmv24ulxNLo/Ae1E4D79QweDD17Qn4+fP01XHBB5OP9IdscVY6IM71iQazvoYaeP7ZS3/0iqSSSuyVjL7eT3CPy7PaW0COpJIypvtVp1jJrzJ0ZsbaRIDpq202lUpHUtXGJv7UmLaYsE9ZiK5X7K8kaHDl3VUsgqSRSeqZQkV/huxe7ht9PadehSqUio1MGhomGBvOYNIaswVmtbqv6UJp9QHmalKYHlKdJ6FE27777LpMnTyYjIyNQlpOTw4EDB4L2O3jwIDk5OW3dvACtZTeX1bdy0L+SsC5JXZMa3VdpCkq8DpWmSeiJb5SmB5SnSWl6IH40CWdGlKhUKnr16hWTc1uO+JwZCZ2aF8fZTzgtlhILy+Ys8w0oVjrwer0YU41IquMDraZ0E9OXTA8Z9I31AB/E1j7R4La7A8lBjWlGfvgBaoWvC0GW4eBB+OEHmDQp+vOW7S5j52c7MWWYGHLpkOgrioJY2ch/bfvDEoUj0rUdDn8C8NzcYD3nn+8LB7Z8ef3OjNSeqVzwxgUYU42Rd4oBMX3GNWAjR5UDnVkXWM5oTDdiTDciEdkR1FJ6jOk+Z4a9zI7cPYy3sQ1pb885gY/m2C21ZyrWYivl+eWtNkDutrsD91btHFi1E8HVRmnXoUqlYujEoTCx4X3bA0qzDyhPk9L0gPI0CT3KZtGiRTzyyCNBZePGjeOzzz5jwYIFgbKVK1cybty4tm5egNayW8CZYQzvzGgtlHgdKk2T0BPfKE0PKE+T0vRA/GhSjnuojfF6vezZsyfij/vWpOZIy4aZCqfFUeUIDCQ6qhy4LC6qCqqoOVLjW4Kq12AttcZtXoxY2ica/KsyNAYNWqOWQ4cad1xj94uEvcJO/jf5FK0pal5FURArG/mvbY1egyHFgCHFFzpIn6THkGJo0rUty8dXZnTuHKzn/PN95Z9+6kviHgn/bP94I5b3UDgbGVIM6BJ02Mpt2EptlO8rx+Py4La5cVlcuCwunBZn4ON3DraUHrfNjdPi9Dl0JfC4PFhLrSHnaUva23NO4MNvt8rCSn5+8md2fLyj4YOO0dp5M47+cZTqwmrKdpXhqHbgcXuQ1BJelxdLsSXs9a6067Cl9bgdbioPVgaSvbc1SrMPKE+T0vSA8jQJPcpl3bp1lJWVMXny5KDymTNn8uuvv7Jy5UoADh8+zOOPPx7k3GhrWstu/pxOkVZmeN1eDm84zIEfD4TdHi1KvA6VpknoiW+UpgeUp0lpeiB+NAlnRpR4vV5KSkra3ICyLGMt9jkZWmplRn1ajGlGDGkGVGpV4KNP0qMxRl7UYym28PtLv7Nh8YYWaV80xMo+0RIIMZXuG9SunUy6Phq7XyT8s25j4ZSKtY00Rg06sw6VVkXV/ipKd5aiMWjqvbbrUl3ty4kB0KlTsJ7x4yElBUpK4JdfGlefHC6uWIyItX3guI10Zh0eh4fyPeW4rW4kjYQhxYAxzYjb4cZeYQ/5uB1uTOmmwDUerR59kh5TuilwHkelA1O6icQuiXgcnpDztCXxYCNB0/HbrXxPOftW7GP/yv2NPjalRwoqjQqPsx4PaZRYj1rZ8OqGQI4IR5UDR4UDSSXhdXuxFlvDXu9Kuw69Xi9Fe4twu1rGUVn6Rymf3/A5Pz7yY4vU11SUZh9Qnial6QHlaRJ6lMvLL7/M9ddfHxLm1Ww2s3z5cu644w6GDh3KlClTuO+++xgzZkyMWtp6dsscmEn2qGxMGaaw290ONyvuXsFP//qpRfsfSrwOlaZJ6IlvlKYHlKdJaXogfjSJMFPtDI/TQ2JOIpYjlhbLmdEQiV0SKa0qBWhUwlFnjZOdy3diSDUw7Mphrdw6ZZDRP4NzXzwXt903eDJ+POTk+EIYhRvfliTf9vHjm3feQAiRDpys3Z97QWvSNinxNxxflZGcHJrkW6uFs8+GJUt8oaZOPjlyPdZSK788/QuWIxbOfeHcuMmbEQ/IyFTsqwg4cbUmLQnZCbjtbs566qyAAzAckfL6NAVzppnpS6ZHlT9IIKiPqoIqAJJyGx+HOuekHGa9N6tRCcObgsfp4YeHfsDj8NDvgn6MvXUsar3PqVHwcwGb395Mt/HdGHTxoA5xvf/x7B8U/L8Cznj8DNL7NC9xt8bg62rHchWXQCAQCMLz/PPPR9w2dOhQfvrppzZsTWwYftXwerdrTVqQANn3O9+YFn8ryts70eYqFQgEgo6KcGa0MzR6DWc+cWabnlOfpEeXoMNZ40Sf3LAzo/YAeTwlNI5nVBoVidmJgb/Val++hZkzQ/f1f51PP9385N+6RF9yU7fdjcfpCczI7Uj4O471DYpH4njy7/Dbp03zOTM+/hj+9a/I9eiT9BzddhSP00N1UXWrJNlrr7isLp8jQ4KE7ASSuibhsrpw290Y042k9Upr9TaYM81hf0DIsozH4QkMVgoETSHgzMhp3P3emB+6UP9Ku3DPeVmW2bh4I4c3HMaQYuC0B08LmiyRnJvM4EsGt7gDJZ6o/d26HC5sh2xoEjU4qh2U7Slr1iCCf7Wff7JCU9oSDjGgIRAIBIK2RJIk31hAtVM4M1qBls7nKBAIBB0BMQITJSqVipycnJhncG8JGtIiIZHWJw1ntRNDmqHB+nQJvgFyr9sbs4E+Jdhn+nR4/324/HKw1urbdO3qc3RMn978c2hNWiS1hOyRcVQ5Ii4vbg3ixUYel2+5tEbf9OvUn/y7a9fwes4807dC448/fJ9+/cLXo9aqSeubRsmWEoq3FMeFMyNu7HNsObvWpCU5Jznqelpaz5FNR1i7aC0J2QlMvDs2mYLjxUaCpuG32+bCzYDPWdAQjfmh61856agOPxDudXmpLqomsWtikGPCUeXAWuKrN2NAhm/2ZS0acnK39+uw7nfrcXmo3l+NRWXhk3mfAM0bRPAnVG3MyozWGNBo7/YJh9I0KU0PKE+T0COIB1rDbrIsg+zL4VcffmdGpD5GNCjxOoxGU+1cgeHCHbtt7kA+x7Z2ZijNRkJP/KM0TUrTA/GjSTgzosRvQCXQGC1qrbrRszDUejUqrQqvy4ujyhFTZ0Z7Yc9Xe7AUW8gdl0tqXmqgfPp0n+Ni1arj+y5eDHVy1EWNJPkSX9vL7TFzZsQa2e2L4xXNrOPaKzPC6UlOhkmT4Ouv4ZNPIjszLCUWTJkmnBYn+1fuJ6138GqDWMzEjRf76BP1ZA7KbHY9La3HkGKg6mAVNYdqcFQ7GhWCr6WJFxsJmoZKpaJr166sLlwNNC7MVO0fui6bC2upFUOyIbCCwm1zYznqS+BjSDKE/TFsL7fjsrmQ1L6cMwCyR6aqsAqVRoUxw4jH6an3x7Lb4Q5x/Lb367DuIIKjxoFGq0FtUGNIMTR7EMHfB/K6vXjd3nrfNa0xoNHe7RMOpWlSmh5QniahRxAPtIbdPA4P7816D41Bw4VvXhjxd7suUQeHfGGmWgolXofN0eTPFRgOtyM2oSqVZiOhJ/5Rmial6YH40aQc91Ab4/F42L59Ox5PyyfhrI91L6/jk/mfsOfrPS1WZ31a3DY3Tosz5FPfDENJkhqcIdraxMo+0bJv5T62Lt1K5cHK0G37fP/PznYBjU8m3VhilQQ81jZy29w4LA5cNpdvFZHT0+C1XZfazoxIeqZN8/3/44/D1+GfibvupXUc+v0Qv7/wO+/Nei/os2zOMiwllmhkRk2s7QM+G9UOzdKY508kWlpPcrdkknsk43V7Kfi5oEXqbCrxYCNB0/F4PGz4aQNuuxuVRkVCp4RGH6sx/n/2zju+jfr+/8/TtCTvlTix4+y9ByEJGexNIBAoe5RSWlpWafstHZSWH7TQUkpbSssooRCg0FAoUPYIEEZC9t7TiffUvvH743yypi3Jsi2Lez4eecg53Z0+L71PNz7vZcJoMSL7ZESPiCzJyJKMJElIHimwjsVhAYHA+9o6iqygSAqyJGN2mLHmWhkwcQC55bnkDM6J+bkNuxt47cbXeOdH70TVkwnHofa9CQjIgowl24LFYYnqVEhov0ETQ/GWmtLGYjAZMGWpfyc7lkyxTzCZpinT9EDmadL16KQDPWE3v0t9vhS9YqBXVjS0ygupdGZk4nGYaZp0PelNpumBzNOUaXogfTTpmRlJoigKzc3NampmL9JyRI0EVuTUfW40LdZcK/YiO656V8xIAHuRPTARHo4l14K7wd1njaX7yj7JojWhthWEZr/4/XC4fZ701FNrePrpwaS6D91J/+8kTFmmpMosdYe+slHwse13+5H9MqDenGsp1p0d28EEOzNi6Tn3XPje92DVKqithZKwJAMtEteaZ1VLvShgdpgxmtUHir5KLe7L31B3zz/R6Ak9QxcOZcP+Dez/aD8jThuRsv3GS387z+moKIpC3YG6QB+YRLPCtLJFvlYf9dvrAQIO2dwhHVkerYdb8TR5Av8XvSKyX6b5UDPOaieDjlOb/ZjtZvKG5OFzxr5e24vttB5pBYjIRMq041DyqU6fVPWQMpgMGEwGZFHG7/YHJoS6QvSJVG+oxmgxMnDqwKQ/P9PsA5mnKdP0QOZp0vXopAM9YTe/W3VmmO3mTvtc9oQzIxOPw+5q8rv8tFSpGbMFQwu63qCHyTQb6XrSn0zTlGl6IH006c6MfoazWo3OTiSSMxkcJQ6WLF+SdBNK7Yant6P9+yueRnXCKbwJ9cGDIMuQlaWwaFEDTz89mFWr1GWpKlGXldd1H5RMIvjYlrwSB1cdxO/0M+rsUYGb+HjLOmk9M2I1AAcYMgSmToX16+H3v4cpU6CsDObPD23gbrFbsOZa8Tv9KJKCJb9jwquvUov7imAbVa2pwt3gpmRCSUgvkXRoglu5oJINyzZQs6kGd4Nbb4ioEzc5I3JY8K8FSM7EI1osORZshTb8Hn9gmeSXUAi9oTRajZjsHbd5iqBAGzFLGHVGVn4WuUNyaTnYQs2mGirmViQ87v6C1qcnVc4MgLFLxiIYhITKbmrBIJpzpat65jo6Ojo6OomiZWZogRKxGHHqCAZOGUjx2OLeGFbG4ax1xpwXadjVgLfVS1Z+Foqs4GnwdJolo6Ojo6OjOzP6FYqi4KxRnRmOAT0/iecocSQ9WTj3B3MRjEKf1JHvb4geMXAjGZ6ZsW+f+jp0KIwa5cThUGhqEti2DSZM6OWBZhDBx3bJ+OT7MWiZGYMHd77e6NGqM+O3v+1YVl6u9kNZNKVjma3IhsGsV/+DDhtteHoDx9Yeo2h0EYUjCrvesBdxlDooHldM3bY6Dnx8gLGLx/b1kHT6EUazEWtx4tdIQRAi+ur4nD7aqttCluVX5of831nnxNfiI39YPo7ixK/tA6cMpOVgC9UbqzPamWHJsWB2m7uc2EmEKVdO6XqlTvB7/Fjs8WV06Ojo6OjoxItWurWrIIfuZAh+3dFKCrvqXRHvST6J1qpWvC1esnKzAoGNkk8NUhHQAxl0dHR0oqHPmiWJwWBg+PDhvdrBXYskRyCljZp7Qou92I6twJZUU+VU0Bf2SRZ3g1piypQVGS0b7MwYPXo4s2er/09lqamqr6r4/KHP2f3m7tTtNA76k42ioSiRDcCj6VmxAv71r8jtjxyBiy6Ct97qWJYzMIfi0cXYi3qvEXss0sU+3mY1iimRklLR6Ck9lQsqATiw8kBK9xsP6WIjncTor3YrnVQKQPXG6pDl/VVPLBwlDopHFQeapPcVsih3/O2TO1mzczLNPpB5mjJND2SeJl2PTjrQE3YLLjPV22TicRhNk1ZS2GQ1kZWfFfiHolbd0K73PpcPySchizKyX8bb4k26V2CqyDQb6XrSn0zTlGl6IH006ZkZSWIwGCgtLe3Vz2w7pkZd2ovsgXr6qaAvtPQ06a4pONW0fmc9PqcPs8NM495GoKOEjubMGD5coLS0lHnz4P33VWfGDTekZiwth1vY994+JL/EyDNGpmancZAONnLVuWirblOzAEoTi1Sur4e8rFpyC1oYmA0GJ5TaAGdrYB3JkMstt0TP/FAUEAS45x74/oDuqOgZ0sE+0OHM6G45tK70SBJ8/DEcPRq9DFgshswfQv3OeioXVnZrfMmQLjbSSQyDwcDWR7ZiK7Qx5eopCR3bsR5og5fHWkdrEC56xKj9Mbp6WC6dWAoCtBxqwd3oDmQSZspxGKxfQMAv+iOWJ4un2YOvzUdWfpbanD2OsficPmRRDmTsJTuhkSn2CSbTNGWaHsg8TboenXSgJ+wWKDPVhTPD2+KlcW8jBpNBvR9IAZl4HHamyWQzYXFYkGWZ5v3NuOpcCAYBa54VRVHLSXpbvCiKgiIpuOvdgZKXifYKTBWZZiNdT/qTaZoyTQ+kjybdmZEkkiSxefNmJk6ciDGeGa8UoJWYspemNmq7J7TUba/jwMoD5JbnMuqsUSnZZyL0hX3iJTzV1Nfmw1ntxGQz8eLSFwH1hmXJ8iXs26dOsFdWymzYsInjj58IGFOamaHdGPV2f5N0sNHBTw+y7vF1DJk/hHk/mpfQttUHa3n2pssYmF+P5Qu1DJzb48GWlRXovdHsLMLTvByI7dA4ehTaHGArCF6udNqErzdIB/soioKnWe0n090o6c70rFgBt9wChw93LNPKgC1Z0vl+s/KymHP7nG6NLVnSwUY6ieNqcrH9g+3k5uQy44YZcW1jzbViL7LjqnfF7KGjlY7ytnqjriOLMmabGUVSQhqDB9PZw7I1x0rB8AIa9zRSs6kmkJXU34/D8O9WFmU8Pg82my1wHu7uJMKqB1ZRvaGaOXfMYejCoXGNxWgy4hjgQDAKIfZKdCz93T7RyDRNmaYHMk+TrkcnHegJu2XlZ1E2oyyifGU4tdtq+fiejykcXcjpvz89JZ+dicdhV5okv0Td9jo1OEGAnME5WHOtuOvcnPHHM8irzOPDX35I65FWjr/t+EA55L7qFZhpNtL1pD+ZpinT9ED6aNKdGUmiKAput7tXO7gbzUYKRxVSMLyg65UToCe0tBxuYed/d1I2o6xPnBl9YZ94CU41NdlMgZsTBQWT1YToFnHVu/C2eAPOjKFDVT2zZysIAuzZA9XVMCCBiP5Yked95cxIBxtpDVaTmaSqq2qhOLse2WAFsw1FUfB7Wsgy56oTYJIbfPXk2lqoa+28L4coqpG4kleiflc9giBQMkHdpq9Si9PBPqJHRParqdfdjUaKpWfFCrXcV7hMrQzYSy917dDoK9LBRjqJ03KoBVmSsRXb4m4I7ShxsGT5kk7P0/GcyyWf1Glj664elisXVlIwoiCkb1d/Pw6Dv1tZlHntxtcQW0TOfexcbHlq9kl3JxE0O3d1Po/XzomMpb/bJxqZpinT9EDmadL16KQDPWG3smlllE0r63I9S7aaVehri8zsTJZMPA670uSsdSK6RQwmA4WjCrHmWPE5fRjMBvIq8ygcUUj+0Hy8TV7MdnOf9wvMNBvpetKfTNOUaXogfTTpzox+RPnx5ZQfX97Xw4gLS456w+Nt7d0J8v6ElmoaDS2qtqNnhoIsQ14eTJoEGzeqpabinWTtLPJ84SR1AszXkrqb0/6CNmGUzER5dQ3kABhsYHKAvw1FsKh/t0fzmk1dH/9erNiK7IheF4qkqH1xAHejO2VRwf0VLRrZaDHGPembCJKk/i6iXYe1MmC33gqLF3ddcqpxXyP7P9zP8JOHkzckL+Vj1enfBJcWPLL6CJJbwuww07CnAYhvgtpR4ohrErsnI/fGXTCux/bdl2jfrbvBjdlhxigZKZ1UitmcmhriWj8s0dO1czrYzpJP4rM/fEbLoRZOf/D0Th1ROjo6Ojo6PYXmzPC3+ft4JP2bnLIczDYzBpMBa070Zztbodp3tC97Zejo6OikO7ozQ6dH0C7OWuS7TuK4XVBTo/49bJiajQEwb15izoyuIs+ff0It36PV6Ozr8ka9SXecGTXVMBKwWgHJhdCyFZuoAEWBdbKzoWwg7KuNPmEuCFBU7uDa15YgOtXv//UbX0eRFU594NRAaaW+Si3uawL2ye8ZR87HH4c6+MJRFDh0SF1v0aLO97X5+c0cXnUYwSAw9eqpqRymThc8/PDDPP744wiCgNfrZdasWfzmN79h8ODBAJhMJiZOnBiyzb333stZZ53VK+MLLy3oqnPhbHCyq3YXRz47AnSUFvw6/s7TCe2cY7KbUnot1JyxWqPVeNjx3x0ossKhTw4BasZrqjNzdXR0dHS+3sT77Bd4tm/zfe2eF1OJIAiBnmOxOO6m4zj+1uP171hHR0enE3RnRpIYjUbGjh3bqzXCeurGoSe0BDIzerl0kUZf2CdZWqtaUWQFe7E9JPpcm2TNy4PiYiNms6pn3jz461+Jq29GPJHnP/6Flf83RY0AlbxSj0TARyMdbNTdzAyK250ZfrXpt9kIAhLaqVUQ4Oc/hzMvVv8OtoP2U37oIcgd6ADUScycQTl4Gj3YCmx9OnGVDvbJr8zntAdPQxblbu8rmp6jR+PbNp71hi4cyuFVhzmw8gBTrprSKw8g6WCjdODcc8/lhhtuICsrC1EUufvuuznnnHNYt24doNb1XLNmDSZT39zyhJcWdDe6MVlNZBVkkZWfFVJasD84M2RJpnFPI0aLkfyh+Rl1HHpbvAiCQPHg4pTqMdvUDI9Eoiy3vbQNd4Mbo9WI5JVoOtCU1DUhk+yjkWmaMk0PZJ4mXY9OOtATdvv8oc85vOowU66ZwuizR8dcT8vMUGQF0S122TA8HjLxOOxMU6yAhvB7g3TKwsw0G+l60p9M05RpeiB9NBn69NP7MYIgkJ+f32sec0VReOmSl/jvDf+N2bgzWXpCizY57Hf6UeTer6XW2/bpDs5aJ61VrRETtpozY9iwUD3z2vtUr10Lbnfn+44n8nz/YSONLeqpoDedT+lgI60MWrKZGdDuzBAMCIBBEBBEZ8h6p5+u9l1oDxIPUF4evR+DNU8dS6p/54mSDvYxZZkoGlVEybjOe47EQzQ9ZV2XCI57vbzKPGRJpmlfE3ve3kPDnoaQf85aZ9c7SZB0sFE6MGzYMLKy1Cwmk8nE3Xffzd69e6mqqurjkYWilRZUJAWTyYStwIbFYQmUIOovbH5+M2//4G22vbwNyKzj0NPsQUAgtzQ3tZkZCZSZAvWeT7sel04sBaD5YHNSn51J9tHINE2ZpgcyT5OuRycd6Am7+V1+RI+Iwdj5tJDRYgxMsqeqb0YmHofRNFlzrVhzrTTsbKBxXyOeJk/IP9Erpm1J4Uyzka4n/ck0TZmmB9JHU/96gk4jRFFk3bp1TJs2rVeiPT2NHkS3iNPrDGQ9pIqe0BLcC8LX5uv1i3Nv2ydZFEVB8qk9EozWUM/mIbWyBMOGheqprDQxaBBUVcHq1bBgQez9xxd5LpB35XlcfLWlVyNB0sFGWp+QZI7PY9XABLBYAFlEAUTRj8nfgmAO7ZmwZInad+E734HHHoMTT4R33onehyErP4tmmvE0960zIx3sk0qi6Zk/n8BvKRqCoDqd5s/vfN/OWievXPMKtdtq8bX6+O+3/ou92B6yTk+UEco0G6UKl8uFIAgUFRV1vXIYXq8Xr7fDqdvS0gKo37UoqhPRBoMBg8GALMvIcocTWlsuSVJIQzRtHUVRkGUZRVHw+/2Bc76iKOq1QJQQRTEQ5SJJUsjYYi03mUzq9kHLBUHAaDRGjDHW8kQ0FY4pRFEUajbWIIoikiSxfv16pk6disViQRCEwHfV1djTRZO23NviRZIlappr8Hq9GI1GjEZjtzUJZvVm3+/2h+wn1thFj4gsqsdK8fhijqw5QuP+RmRZTliTLMusXbuWKVOmBMaWCk3Qd3bKNE2yLLNhwwYmT54cEuWma0ofTZIksWHDBqZPn44gCCFa+6Mm7bw9bdo0LBZLhP3CNYVr0+kbeuK+z+9SswXiCaywZFtwN7jxtflwlHb/fjYT72OjaXKUOJh46US2GLZQNKaIuXfMjdguuKRwW3Ub655Us4vn/6SLh5AeJtNspOtJfzJNU6bpgfTRlBnfZh8RfhPak7RVtwFgK7Z1GTmRDKnWYjAZMNvN+F1+vK3ePok06E37JIPoFpF8ErJfBgFEn/p/LdU0ODMDOvQIgto348UX4ZNPOndmxBt5Xj7KhjG1PrK46GsbTbx0Ip4mD/YSe9crh6H1M7FZ3GDMAlM2iq9BTXcRnSCFps0YjXDeeaozo64udkNprU9GX2dmQN/b58jqI7QcbqF0YilFoxKfmA4nXI/mrOgsgP+hh7pu/q2VEbIV2gK/a2ueNRCt0JNlhPraRunGli1b+NGPfsRdd92F1Zr4dee+++7j7rvvjli+bt06HA7VdiUlJYwYMYJ9+/ZRW1sbWKe8vJzy8nJ27txJc3NHFH2RQT1225xtGPwGrJVWDF4DMupEU2trK36Xn02bNmFvtDN58mQsFgtr1qwJGcPMmTPx+Xxs3LgxsMxoNDJr1iyam5vZvn17YLnNZmPKlCnU1dWxd+/ewPK8vDzGjRtHVVUVh4PS9hLRJPtkRFnEVedizQdrULIVmpqaWLt2LePGjSM/P59169aFHJvprglg+PDheFu8tLW2kUUWa9euRRAExo4d221NLtHF6HNHYx1kDVk/liarvz27VfZz1HeU5qZm3GvdDNk3JGFNhYWFAfto56RUaOpLO2WapsrKSiRJYuvWrSHOVF1T+mhSFAWfTw2A2bx5M+6g1Oj+qElR1PN2fX09ZWVlXWpyOlOfXaqTHKm+79OeO+MpGzXpikmggK2o854PiZCJ97HhmhRZ4cjnR7A4LEz8xkQKRxR2vgMFDq86jNFiTIv+JJlmI11P+pNpmjJND6SHJt2Z0U9wVqs3kdkDsvt4JPFz2oOnYbabycrL6uuhpBXWXCv2Ijuuelcg+tJgNuBt6njYsxfZ2X9UncwYPjxyH5ozo6u+GfPnq5O1sUpNxRt5nqmMPGNkUtuJIuw9lEtdWxGTTPUgAeZcfGYTZsEA/iZ1RWsRmHMD22k9iHfsAL8fzFGeGwpHFuJr9UVE9n8dOfjxQfZ/sJ+p105NiTMjnPvugy+/BJMJCgs7HFQaf/hDZBmwzrAV2XA3uJH9MoqkBEqGAYhePaqxJ/nhD3/IP//5T6qrq7n++uu55ZZbQt4/44wzqK6uxm63c+mll3LzzTdjMEQGBvzkJz/h9ttvD/y/paWFiooKpk2bRm6u+lvWths2bBiVlZWBdbXlo0ePDolwbdrXBEC2Ixuzw4yiKDQ3NwciWXJycvBIHiZNmkTBiIJA1O7MmTNDxmY0GrHZbBHLQZ3YCl6uPfgWFxdTWFgYsXzQoEEMHDgwYuzxanJOdlK3pY5CXyGV0ytZu3Yt06dPx2JRveLTpk2LGHu6azIYDHgrvUw4ZwLNtmamT58eyMzotqaZsaOuo2lq3N3IdraTV5rHnDPnUPN8DYIsUDGoImFNsiyTk5MT0BM89v5qp0zTJMsyBw4cYPz48RFZDLqm9NAkSRJr164FYOLEiRFZDP1Nk6ZHy2DsSpOWpaiTnjhrnZ2WCg6O+g9Hy8zQejt1xohTRyQ3wK85R9cexVXnwpJjoWJORZfr2wpVZ5Hkk/C7/CEVLzqjO8eBjo6OTn9Dd2b0E7TMDMeA/nMByh2c2/VKX0McJQ6WLF+Ct8XL4c8Os+7JdRSNLWLuDzpSTq25Vn53imprLTMjGK1vxqpVIMsQZU4OUCPKH3gALr008r3gBtQHV+6jekM1FXMrGHzc4MiVdUKoqYGa5hKuenQ5R/6vBQwgSSK7Nm1i0qRJmIztp1ZzLmR19HsYMgQcDnA6YfduGDcuct9jF49l7OKxvaQkvdFKbQU7BVLF//6nNmcH+Otf4dpr1R4zR4/CI4+oWU9btya2T0EQsBfZ1RR8p0935PYiDzzwAA888AD19fX88pe/5Nprr2XZsmUAHD16NDDZs3//fq666ipcLhd33nlnxH6sVmvUjA6TyRSRRquV7QgnvBmato4gCBgENSNDQID2c7AgCGrJEJMx5DNipe1GWy4IQtTlscaY6PJwTQOnDKRuSx21W2oZfvrwQMkTbYItkbGniyaAirkVlB1Xxpo1azAak7dHrOXxavI71ckla56V7KJsHMUOLDkW/K1+LDZLQppkWQ7YJ/yz+6udMk2TVsInmp5Exx5rua6p+5q081ushpf9TZMgCIF1utKUKaUxMhFnrZMVl63AVe+KuU5npU4TyczQSY7db+0GYNhJw+Iq6Wy0GDE7zPidftwN7ricGd09DnR0dHT6G3oD8CQxGo0RdWB7kp7MzOhtLb1BumtylDgoHFGI0WLE4rBQPLqYwhGFgX/2Ygf79qnrDhsWqWfKFLDboakJtm3r/LP86pxIhMMjuAF13fY69r23j/pd9akV2gl9bSNvq5eazTW0VrUmvK1WlsjkKMGYNwI81Ri9xxgzaQ5Ge5ladipnRIgjA1QbjB+v/r1lS3cV9Cx9bR/oKLWVijJ1wXr27IHLLlMrgt1wA1x/ver4W7RIdfz9v/+nbvP001Cf4E8id0guA6cOJHdQzztz08FG6UZRURF//OMfefnllwPlQYKjVocOHcq9997LSy+91OtjE90iPqcPv8uPzWjD7/Ljc/oCEwn9iQGTBgBQvbEag8GQUcdhT/yuZFHG3eDGWRNfqRZvsxpZqZ37Fv9jMWf96aykapRn4nki0zRlmh7IPE26Hp10IJrdtFKnJquJrPysiH8mqylQ6jQaifTMaKtu49j6YzQfau5y3WT19HfCNbkb3FR9qT44jjg9/swWLTvD3eDuYk2V7h4H8erp7+h60p9M05RpeiB9NOnOjG6glVPoDbQH4J7KzOgJLYc+O8RXf/+Kqq86KUjfg/SmfZIlYNewCYqGBmhtn2MfOlR9DdZjNsPs2erfXZWa+tOf1Ndf/rJjIv3uu2Hfvo4SOtqESaI3ON2lL21Uv7Oe937yHp/e38UXGIUjR9TXwVoSy/5nYdvvsFa9AKsuh12PxtxWKzW1eXPnnyGLcucr9AJ9/RvSGrRrfUSSRZLgww9hxQorb74J55+vOgJnz4aHH45cf/58mDYNPB74+98T+yxBC7fvJfraRumI1+vF5/PFrOUpSVKvRplqpQVFr4inyYO3yYu3xYu3yYunyYPoFbEX2fukt1SyFI0pwmgx4m320nKoJWOOQ0+zB1mUU66nYU8D/7n6P7z7k3fjWn/wcYM59XenMu1atcRLd+tlZ4p9gsk0TZmmBzJPk65HJx2wWCy8df31/M5goGbDhsByk82ExWEJ/Gta92tqP7yuUyeFoiiUTCiheFwxluyuj4ed/93JBz//gH3v7QssO7JqFS+ceGKn2z0xejTNBw7E1NMZfpeLz+65h3/OmMGyqVN5ctw4Hhsxgub9+wF48bTTOPTRR12OvTcJ1rT33b0oskLxuGLyKvI4/Mkn/DE7m2VTp/LUpEk8NmIEX95/f0iZN4CsAvXZJ15nhkb4caD9i8dZFU7b0aOsOOcc/nX88SybPJn1j8Z+vgXY/sILPD19OsumTOHJceP4z/nnUx8Wddl29Cj/Pvtslk2ZwlOTJkXdp6exkfdvvZVlU6cGbN6wY0fg/d+bTIH3tH9733gjYj/OmhoeGTAAWQwNGurrc99DdjtyCvsN9LWeniDTNGWaHkgPTWnhzGhpaeHmm29mypQpTJ06lXnz5vHuux0PfNu2bWPhwoVMnTqVadOmsWLFij4crYokSaxZs6bXGp/kDcmjcFQhOYNyUr7vntJSs6mGnf/dSe3W2q5XTjG9bZ9kmXnjTM75+zkRvRu0rIyBA8Fmi65HKzXVmTPjiy9g9WqwWODb34YxY9TlxcWhTY37wpnR1zbStCYzgahlZgwa1L7A34qiKOyotqg3oy3bQY4ebT1hgvoaKzOjaX8TL178Iv/91n8THlcq6Wv7KIoSKDPVnXJNK1aoDsGTThK48kojZ50lsHkz5OaqmUnRekQLAtx6q/r3n//ckd2UbvS1jdIBn88X0vy0qamJq6++mosuuojCwkJcLldIE9T9+/dzxx13cN111/XaGLXSgktfXMrSF5ey5LkljP7paJY8F7Ssn6X9G81GZn1vFqf89hTsA+wZcxy+efObPH/B86x8ZWVK9Wi1yOPNwrFkWygeU0zB8IKQ5eGTHfGQieeJTNOUaXog8zTpenTSgYDd/H4GzJjBpieeiFinraaNui2b1fRjpfPAKEEQWPTLRZx6/6lYc7p+HrLkWHAd/iTEMSH5fMhd3CjHWqer49DT1MTzCxaAonDJRx9x9fr1XLdtG9du3UpOeXncn9+bhGsaNHMQw08bzuhzRgMgiyKlU6dy9fr1XLNpE1etXcuBd99l6zPPhOxHy8zwNHoiPuPIp59yaOXKuMck+9poXHsvkudoQlpevfBCxlxyCWP//ncuXrmSLU89FdVpoDF43jy+8dFHXL1hA9du3cqwM8/kXyefjN/tDtnnuMsu4+oNG7hs1aqIfXpbWnh+4UKKxo/nyq++Ur+nzZspGDUqsI4iSVy5Zg1Xr18f+Df8rLMixrPjhRcYce65GIICmHr73BfNVqLbrf4+U0AmnsszTVOm6YH00ZQWBTAvvvhiFi5cyLp16zAYDHz11Vece+65fPbZZwwYMIDFixfz2GOPsXDhQo4dO8bChQsZOXIkkydP7uuh9xozbpjR10NIGC3Co7ej/fsTBpOBnLJIB1VwialYxOPM+POf1ddvfANKS6GsTP3/0bB7mb7KzOhLUubMUBQQ1TQal2U4yNkgOaFtL+SOjthWy8yI5cwwO8yIblFtIq0o3Y7I7a9o3wEkX2ZqxQq46KLo94stLWrz7/ZnoQguuQR+9CPV1i+9FL3vTNQxSzJN+5uQRZmiMUUICP2yjFB/oba2lsWLF+N0OsnKysJgMHDZZZcFGoA3NjZyzjnn4Pf7MZlM2Gw2brnlFq644opeHaejxBFwVoiiiL3RTsGIgn5bh9xZ6yRvSB4AzQeacR120VjQiNGkesl7s8lkqhpeKooS2I/Jnlq7aBGRoie5c0HL4RZW/W4VsiRz1p8iH9h1dHR0dL5ejLv0Ur566CEm3/iTkOXN+5uh9RNsA47H37w3pZ9pybbgPPgBrQdHdr1yCnj/5psZfdFFzP6//wtZbooWiZSmFAwvYPb3Z8d835qXx6w77mD1Aw8w4corA8tthTYMJgOiN/K+Yf8776CIIhULFsTcb8PuBgSjQG55Lkg+JFcVSPFnedRu3IgsSYy97DLWrFmDJSeHeb/6FesfeSSq4wAIOJhAdZRN+fa3WfunP1Gzbh2D584N7HP85ZcDRN3nmt//niEnnsiUG24I7MuQZCmbbcuXM1+rHdxHxGMrHR2d5EiLp+j333+fF154IdCEbMaMGUyfPp2vvvoKk8nEtGnTWLhwIaDWvv7BD37Ak08+yUMPPdSHo9bpCkuO6szwtfr6eCT9j3icGXPmqBHke/ZAdTUMGBD6fnU1vPCC+vf3vqe+aqXjjx0LXVd3ZiRGiDND9gayMCTBgZI7DhrXQPOWqM4MLTNj507weiMzA7QsBFmU8bv8cTV9y0S0rAyj1YgpK/FLlSTBLbfEDnzRsi8WLw7NUtKwWuG734W77oI//EF1CMbyK2llhFz1LhSPgrdJPbY8DR4Eg7pRfysj1F8YPHgwX331Vafvr1u3rhdHlPmEN5lUFAWXy8VO+86A87W3mkymsuGl6BYD5f1MjhQ7M9rPYbJfRhZlDKbOE6P3f7gfT7OHQTMHkTs4F0uOhcY9jSCA6BUxWdPi9l1HR0dHp4+w5OZSceKJHHg3NFLeYBGQvZswF5yPe//ykPeOrVnDhz/4AW1VVSiSxLAzz2TB/fdjcajXx+cXLmTc5Zez/pFHkEURo9nMiQ89RMXChdRv28ZHt16Iv3kve1/+Hcu3vchlq1YB4Gtt5b+XXELtxo0AlB1/PKf+9a+YskIzqw+89x6f/7//xyXvvx9Ytud3v8N+9tlMvvbakHVdtbXse+MNTouj3uvOFSv44PbbUWQZg9HIogcfpPLkkwE1u+Ot666jfts2DGYz1rw8Tn30UYrbH8g606yx6ckn+eK++zCYTFjz8phx++1sevxxlr79NgDu+nre+c53qFm3DsFgwDF7NjMef7zLcWs4yspo1WoYA/veeovNj/wMyeNhzf9TOPbRCZz85z9jMJlYPm8eTXv2qN/da69x9vLlFI8fT+vhg7h2P4xLbEAwGBCFGWBfQG5FLkZbIcUn/DnQizAeDrz7bsh3ADB4/nxeveiiuIPtFFnG19JCdnspg3j2uf3551n88stxjzMWTXv30nroEBWLFgHwxpVXMmDGDLY99xwtdXUcHDaMs595hu0vvMDGv/8dwWBgzCWXMOdnPwvsw1ldzQe33UbVZ59hMBopHDeOkx56iPwRau+TT++6C0VRqFq1irb2yYGp3/kO07//fYCYtgL49Be/YNfLLyMYjdhLSjjjH/8gT6strqOjExdp8TR0/PHH8+CDD3L33XcDsHLlSlatWsWjjz7K/fffH3BkaCxcuJA//vGPMffn9XrxejsmZVtaWgA1GlJsr5lnMBgwGAzIsowsd6RgasslSQpJ5w9frr1q64hhtfi0ZijhqTexlptMpsB+NQRBwGg0IvpEFKHjoqEtjzX27mjSxigIQrc1WXOtKCi4m92BfcUae3/R1JmdEtXkbnaz9u9rcQxwMOHSCRiNxsDY9+wRAAOVlTLapuGfm5trZOJEgU2bYOVKiQsuUELG/uijMn6/keOOU5g2TUJRjO3ODIGqKhlRm7QxmbDkWJAVWa3fLoq9YqdomnrTTu5GN7IiY85Wy38kounIEXX8AwZIiO5GDIoCghFFsCDnjENoWI3SuAml7NwITQMGqLZraRHYsUNh/PgwTWYjJrsJn9NHW10budbcuDWl0k6x/u4tO2UVZHHyAyfjd/k7PSZjaVq50sDhw7EnDBUFDh2CDz+UWLhQiarp+uvh3nuNrF4t8MknInPmRNdkLbBy3tPn4WvxYTQZ+d/3/4ff7Wfh3QvJHpANAtgL7NiKbCH7766dtFdtna7sFG4vHZ1kCG4yKfklfC0+DFYDWflZ6vHmFgNNJnvamRE8lmj1oBMZSyArw2rCYEltFVatzBSozgiLqXMn9e43d1O7pRZ7kZ3cwblk5WVhzbXibVF7lBSOLEzp+HR0dHR0+h+TvvlNPvnpL4FLAsvMwh685iEohDoS3HU1vLL4HM5ctoxhp5+OLEm8cdW3eHzc6Yy8+Bec9rvTQBBY/8gjDPrFW9R5B5DT8BmvXXoh39q3j6Jx4zj7+Xd56bRTKFtwBUtfvjOw79qNGznuxz/m3BdeQJYkXj73XDY+/jjTtWi6dioWLeJ/V11F65Ej5AwejOTzUf/RR4yI4rA4tno1A2bMiHCIRKN2/Xou+fBDbIWFHFm1iv8uXcq39u/HaDYjiyLH/fjHlLU3m9z67LO88+1vc+knn6gbt2u+6K23cAwYQNVnn/HKhapmk9VK1eef88lPf8olH31EYXvvjxdPPpnsoCyE/11zDaPOP5/z/vUvvG43z55xBuv+/ChYj2fEaSMoGlXU+fg3bAhMkANkFRRw4RtvYC8pQRZFVpx7LpufeorJ11/PZZ9+yqe//CWKKHLCPfcA6vP0+zddg6X0ZLKHzADFS+0n/w/MAzGYBsX62E5pq6oit7IyZJnZZsOYlYWrpgZHeBRlGE179/LZr3/NqAsuCEzSd7VPS24uzfv24Xc6eemMM2irqiKnooK5d91F2XHHJTT+bcuXM+biixHa5xwEo5G1f/oTSz/4gO1VVWTv3s2Kc8+leOJErt64ERSFFeeey+GPP6Z8/nwAXj7vPEacey5nP/ssgiCwbflyXjztNK7dulXNDhIE1v7hDyx97z3KZs3CVVfHP6dNo3z+fEqnTo1qKw1vSwvXbNqEwWTii/vu48M77mDxSy8lpFFH5+tOWjgzli1bxplnnsnnn3/OmDFjWL58Oc888wzl5eVUVVVx6qmnhqxfUVHB3r2x0ybvu+++gGMkmHXr1uFojzwoKSlhxIgR7Nu3L6Sednl5OeXl5ezcuZPm5ubA8uHDh1NaWsrmzZtxB9X9a2tro6CggHXr1oVMyE2ePBmLxcKaNWtCxjBz5kx8Ph8b2yMXQJ1wmjVrFs3NzWzfvj2w3GazMWXKFDa+upEv/vQFBdMKGPqNoeTl5TFu3DiqqqpCaoUnq0n7TC16dezYseTn53db0+DswYh+kcN7Dge20TTV1dWF2LC/aOrMTolq2r5mOxte2YA514xntCdE04YNZUA+sI+6uhxKSkrIysoKiTAeO3Ys8+bls2kTvPxyNRUVBwOaBMHCn/8sA0bOOms3a9bUM3PmTIqKRCCLPXtcrFmzOaDJq3hpbmqmpbWF1atXY7fbe9xOsTT1lp32bttLc1MzdS11AAlpqqpSb2Cdzp1sWreVoc1NZOWWMXPWLHatrWVgcxNSyyfsdq5m7LhxEZoqKyewaVMOmzbJuFyRmkwOE7VHaln76Vqyh2en/NiL105Dhw7FaDSycePGkPNeb9ipsaWRvU2qpkNrDiWsaevWUUDnDw8An366F4ejPqamSy6ZydNPm7j77mbuuWdXXJqsxVaa9zazp2oP2ZZsVdPoKdTU1PSInRobGxkwYEDE9Slck9Pp7PL70Ol5jEYjM2fO5N1vf5vN//gHV61bR+mUKVHXfWb2bKq/+oof9LAj6siqVerD+gcfxFznidGjueiddwC1NKLJZsLd4Mbb7CWnIgdLtgUBNegivCyC3+VizYMPsvvll5ElCcnrRfL5uPi998gbOpQXTzuN43/604iIvXjRGl5GI1qJhmgEevTkZjFz5syAwzIahz/5hH+fcQb5I0eiSBJ+l4sp3/42s374w6jRigaTAYPJgCzKiG6xy4w7bSzB2Vy5Q3Kp3VxL88HmhJwZ7poaDv/qV+w4cgRFlpl6001MvfHGmOtXff45n/3617QeOgSKQtGECZz8pz9hLykJrFOzYQMf3n47rtpaJI+HCddcw/F33hmxr01PPsm73/kO1+3YERJx2Hb0KB/feSfVX32FIAhY8/M58Q9/YMD06RH7qF63jv9dfTXXhJ1ru7JRT9J65AjPz5/Ptzp5FkmEvtbTE2SaJl2PTjqg2e2d9gnaigULaDt6GDmrAdFdCoDY/DmyeQbeVtVBr5U63fbME4y95BKGnX46oJbumXrzL9j576m4atV70Lo6+G/Vzbx9oTZRPYefmHJ56ZGdfOO2SYES0uHlUwvHjWPcZZcF9jvu8svZ8+qrEc4Mg9HImIsvZueLLzLj1ls58NZbVJ54IvaC0P5QoDaBtkZZHo3pt96KrVC9Lg6eOxeT3U7T7t0UjRuHvbgYe3FxYN2R55/P29/6Vuj2N98cmJwfNGcO1txcGnfupGTSJLY//zyTb7iBwtFqtn1eZSWzfvhDtj33HACNu3bhPHaMSd/8JgCWrCxOuece3v3OHdiGldC4t5HTf396zLEfWbWKT3/xC85+9tnAsuCJe4PJxIhzzqF67dqY+zjw7rvkVFTiq56A6BaRJJCt8xCca2k7NhNrjrXTkrfvfu97HGl37thLSlj6zjt4m5owjRkTca4wZWXhd8XOhl3zhz/w1YMP0nr4MMPPPptTHnkk8J62z3C0fcqiiMFk4rNf/YrTn3iCnMGDOfTRR6w45xwu/eSTgA0AXjrjDFzV1ZjsdsZdeinTb7454LgA2P7cc5y5bFnI50y89lryKiqYOWgQ8uTJvHX11Vz01luBMlYjzzuPI6tWUT5/Pgfffx/J4wnJ1Bh32WVsf+EFtj/3HBOvuUbd5vzzKZs1S/3uiosZfvbZHP74Y0qnTo35HQHM+cUvAr08xl91FV8lWXEmE8/lmaYp0/RA+mhKC2dGZWUlN910E7fddhtvv/02l156KbPaTwpNTU1khXnks7Ky8Hg8MVPcfvKTn3D77bcH/t/S0kJFRQXTpk0jN1eNctYiw4cNG0ZlkIdYWz569OiokeQTJ04MZGR4PJ6Ac2TatGkhY9AMO3PmzIjlNpstYjmoE1vByzVtRo+R3JxcKodVMnPmzMDyQYMGMVCrG9QNTRMmTMDtdpOVlRWIFE6FpuZ9zZjMJnJsOYH3tLEXFxdTWNjxMN5fNEFsOyWqqdBaSF5+HkWji5g5c2aIpsZG9e8TTxxKcbG6n5EjRwb0aGOcNw8efRT27Clj5szSwPJ//Qvq6kyUlirccccwrNZhGI1GhgxR9ba2OkI0DBw+kCv+fQXWHCtGi7HX7BRNE/SOnVpfbkXJVxg2bljCmrQyUwsXjmJiuQvDpnxwlODx+Rg19TQMnmUgeZk5vhRjbl6EpuOOM7BpE2zdauCSSyI1OYoc5OXnMbJ8JOUzOyJ/UnXsxWMnRVECGW7aeS94jOGagpf3xe8pXJPLFV9k9bx5w5k5c1hMTbffbuTpp+HDDwspLZ3JkCFda8opzsHb4GVUxSgGzxzcY3bSrkN2ux3o2k5alqJO3+Pz+VAkKdDA8+SHH45Yp3bzZhRZRumB5mpbn32W8vnzyW0/oBNq4Bl022XNteJp8qgOjSj9n0At8fDiKacwcvFiLvnoIyzZ2QCIXm/gATIdGngGlx70+XzYbLaY62oNPLXITm9zM68uXYqjrCyk5nUwJpsJX6sv0DfjyKefIktS1DrKvhZfYCwa9mKB2lV3UzX3Xoad1EkNyjBevegiJlx/PVOuvRZ/WxsvnnoquUOGxKx5bTCbOemhhwLNNj/68Y955zvfCUQMOo8dY8VZZ3HOCy9QfsIJiF4vr196KZueeCIwmQPwyc9/zrE1a7AWFCCHOeMUWWbitddy5j/+AcCe11/nP+efzzd37oyIwt36zDOMufjiyO+oCxulkj2vv05uRQUl7b36ZL8fyZfaEqq9qae3yDRNuh6ddMAXdu4Zd+mVbFq+BtFyGu6GBsS2g8iOi/BpzgyviL3ITvOe7Uy89qqQbWXRgDlvGGLrYVasUPv57aMiZJ1msZif3d6ApRJOPV69JvndoddrW9C9LYC9tBRXUDBOyHivuIL3vvc9Ztx6K9ufe45Rl1wSdT1rXh7exsYuvo3OP79o3DgUWWbD3/7GrpdfpvXQIQxms9qAOYicilDNtuJiPA0NALQePMjgsMZ5WrkigPqtW2navZtlQZPXkt+Pu17BNgxGnh7ZX6Rm/XqWTZ2K6HbTduQIpz76KIOC0r9bjxzh83vuY/dr7yG6XVhsMkPay2ZFo37rVqrXfIbfsx5ZklEkBUH0oxgG4qp1oUjqs0GskrenaE02gzBarYgeNbAi+Fwhut2YOzlvzLztNmbedhu+tjbW/vGPrDjrLC5+/311PiZon8Fo+1QUBdHtZtHvfkfO4MEAVCxcyLhLL2XrM89wwq9+BcB3jh7F0f7c1Lx/P/+76ir8LlcgqKJm/XpkUWRg2DOato2mxz5gQMixY7LZcFZXA1C7aRODTzghYqyDTzghUE4NOj92OiP4czv7vcRDJp7LM01TpumB9NCU2jz6JLniiit4+umneffdd9mzZw9ms5nJkydz+PBhrFYrnrCTntvtxmq1xqzVZ7Vayc3NDfkHakkT7Z82KWQwGKIuNxqNnS4XBIEtW7YEynsEr6u9LwhC3MuBiOXaRJS7zo1BMJBblhuyPNbYE9UEsGXLlsDna99rdzVZc6wICPid/ghN4WPsL5o6s1Oimtz1ql1zBuaELBcEI/v3aw6MjtJTwXq0MWpNwNetE/D7O5b/+c/q9t/+toDD0bF80CB1eXW1gCB0aDIYDeQOyMVqt/aanWJp6i07jT5nNFOumELRyKKENPn9Burr1e99yBATptyRGCb9HHnolWqUvsGEYdjlGMbegsleGlXTpEnqvrduja4pKz8Lg2DA3+ZPSFMq7SQIAps3b0aSpAj79YadajfXsuvVXTTubExK08KFBsrLY/e5EASoqIBFi4ydapoyReCkk0CSBP72t/g0WfOsGAQDokvsUTtp1yHNgRGPnXT6HkmS2LhxI7IsM+7SS9n9n/8geiP7FW156ikmXH11j4xh42OP0bhzZ7f3Y3aY1XKSre4QR1owWgPPOT//ecCRAWoDT0MaHZOaM8Oca2bjxo0RZfE6Q2vgufXpp2OuM/yU4Yw+d3SgHNb+d97hQHu97WAUWQlE1AZPOmSXWBBbj9C091jENrGo3bgRWRTxjB+PJEmBZpsbO6lBPnDGjIAjA2D2T34SMs4t//wno5cupbz9Id9ktXLyX/7C+r/+NUiDjKOsjCWvvRa1REjO4MEhTpwRZ59NVmEh9Vu3hqynyDI7XnghEPGrof2GErFRd9j54osc/fLLHtt/b+vpDTJNk65HJx0Ivn/QmHbTtzCxkYv+dRGDp9RiLz+espmDGXbKcACWvriUJcuXYLJHTmKLbhEUBZPVzC23aEuj3Tgr3HorWPJsZJdlM+qsUTGv+aDe2ytBYwxm4IwZeJuaqNu6laNffEHzgAFRj8MB06dzbPXqqPdHXRH8+Z/edRfbn3+e+ffeyzWbN3PZZ59FXT8cTZ/f6cRoCc2mNAY1PBTdbgbNmcPV69dz9fr1XL5mDRV3PkT+zJ9iyjJRuSC0rBJA6dSpXL1+Pd/csYNFv/89+958M/Ce5Pfz/IIF2EtLyBnzPYrn/IYZt/+wU72i283opRfxrb1buWbTBubc9y9KF97PgPm3M+36aZz3xGnYjU9x4t0T4i7/mVNeTsvBgyHnCr/bjb+tDXtpaZfbW7KzOf6nP8VVU0Pdpk0h+wwmeJ+2oiIMJhP5I0MdQPkjR+IOmux3BAWA5Q0dygn33svOoBJN25YvZ2yYA0ojWE+4XYMRYkWcK0rIe50dO/EiCELsJo9dkInn8kzTlGl6IH009bkzY/fu3bzxxhu8++67LFq0iOHDh7Ns2TJOP/10HnnkEcrLyzkYdtI7dOgQ5UF1CjOdtuo2ALIHZnexZnphL7Zz1iNncd7j5/X1UNISZ41a8sVeYg9ZfvQo+HxqU+IwZ38EQ4dCWRn4/bB6tbps/Xr45BMwmSC8kkRpqTqJK0lqKvHXmcr5lUz8xkRyB+cmtN3Ro+qr1QoFBYAlD4pnQ+GsjpUqlkDZqWCOHqk8caL6unlz9M8oHlNM2cwybIWZ5cFPhCOrj7D+H+s5/MXhrleOgtEIsVorafedDz0Uvfl3OLfeqr7+/e8QT6UmrYm7tznxBzCdrxdaA8/d//lPyHJZktj5738z9hvfiNjm2Jo1PL9wIY+PGsVjw4fz7k034Qs6MJ9fuJANf/87y6ZO5R8TJ/L0tGkc+ugjAOq3bWPZ1KlUr1nDOzfeyPK5cwPbaQ08nxw3jifHjeN/114bNYKuatVHuHb9ASDQyFpo/g+uwx9HrKs18Jyh/Yg6YeeKFTw1eTL/mDiRZVOmcOC99wLveZqaeGXJEp4cN46nJk/mufnzqduyJfB+09r7cB54j6Pv/Yiqd35A9Yf/h7d+W8j+Nz35JI+PGsWT48bx7PHHs/1f/+LF004LvG+yePEcepwdj1/HV5dcwic//WnMyZhoRGvg+cxxx/HUpEn8Y+JE6r78G9Oum4K9yM7yefPY8OijbHz8cZZNnUpd+yR+8/79vHja6VS//wOqP7yDzU92OAgGTh9D2el/R1Liv/898O67lIdlfgxuL58Q78O2p6EBY5BDonnPnoiJhuyyMloPHUJqz64RDAamffe7gcybuD6nsTHkcwAOffghOeXl5A9XJ+Wemz+fTU88wbMzZ7L28st57eKL8ba08PGdd/Lk+PE8NWkSm558MmQfjbt3s+Kcc3hs+HAeGz6c1y+/HGdNTeD9N66+mtW//z3PzpnDPyZO5B8TJ7LjxRcDY3p6+nT2vPoqn/3qVzw9fTqe9khhWRR55zvf4cmxY3ly/Hj+c/75uOOIxtTR0dFJJY6BAymeOJ62A+upWf0/coafiMVhwZqjTrgXjijEUeJgwPTpHP449DrtaXbibzlAk3Eshzu53VZQ+8x9ttpE9oAcBs0YFFcD6FiMu+wy3rzmGoaffTZCjKCG7EGDqDjpJNZGyVxNhF0vv8yiBx9k4MyZGIxG6oPuHeKhcNw4jn31Vciyqs8+C+jPHzWK6rXrqNteTcOeBhr3NFL1VhV+p5/iCcW0HGnBWRv74WHSN79J9Zo1HGsv0Vu7YQNmu515d9+FtVB1GtSs3xiyTfi1tWDUKI6tXo2jxEHhiEIMBgMWhwWLw4I5y0x2qYWWfbswCPE/lwyaO5fD7feOGodXrmTgrFkh5Zy6wtfSEriX6mqfRouF4kmTImzUuGtXxH1HMIokBYJjFEVh+/PPRwRBJMqA6dMDpbeCOfLppwwIy+LvjETug3R0dBKjz50ZLS0tDBo0iLy8vJDlkyZNorGxkblz5/JR2Envo48+Ym7QA3im46xWL4CO0p5tpJlqDCYDeRV5ZOV33bjr64jmzAi367596mtFheqQ6AxBIJCd8emn6quWKbpkCQwK6/llMoFW9vpYWHDn9le28/lDn9OwW38Y7wxtrmrQoNhR/10xYYL6umcPhGU6AzD+ovEsumsR5cd/fZy24WiOAM0xkAxLlsAVV0QuLy+Hl15S34+Hs8+GESOgqQl+9jN47jn48EPVKRgNW5ENe7EdwZj8g57O14dJ3/wmm8MmYPe/9RZls2eTlZ8fstxZXc2Kc85h9p13cv2uXXxzl9rHJaT+c1Azy2s3b+aURx7htUsvRfR6KRo3jqvXr2fAzJmc+uijXLZqVWCz2o0bGbl4Mddt28Y1mzfjqq5m4+OPR4x34OwTkL3HkLyNqjNDERG8W8kqnRGxbjINPK/dvJlT/vpX/nfVVYHJca2B53XbtnHNxo1MufFG3vn2t4M0Q+u+t5Ed16Hk3Y59+BU0rP0TiqxuX7NuNZ/89Kcsef11rtu2jXNeeIFP7rwzpFTQmvt/xMzvX8YNh/Yw7dlnadyxI6r+mONvb+ApSer5YeWXBQy483WuXL+Jq9evp/nAATY/9RQAl336KVNuvJHJ11/P1evXUzx+PIqi8OqFFzLh2hsZcOLvGXTab9j57xfZ354VkVuRS87gHApGFMTtiGirqiInLPgnuNlmPGx49FEmXNVRmsRWUkJzWK8Id309rtpaPFraYoLsfeMN7KWlFI8fH7J82/LlIRMSgtHIxsce46L33mP6s89SOmMG/zr5ZAxmM9dt3crlX3zB+r/8hab2GynR4+HFU05hzMUX8629e/nW3r0UT57MKxdc0LFPQWDtQw9x1j//ybWbN3PBq6/y7ne+g/PYMbIKCrhq7VpGnHcec37xC65au5as9hrurupqcisruXbbNq7buhX7gAF8ce+9SenX0dHR6Q6Tr7+eT372M2RJwZw9iJnfnclZfwotJTj5hhvY+eKLgSwAWRTZ9Og9WIsn4jINjutzjh6FrKIiWg4c6NZ4x11+OcdWr2bs5Zd3ut6pjz7Ktmee4Yvf/jakT4Pf7Y4oXxiL7LIyajdsANSmy6t++UvMjvjnVGbcdhubn3wyUFqoYedOVt9/P1lFamZ/dsUYfE4Lzy68gn9d9AIvXfwSh/+9m6Nf7WXri1t5cemLrLhsRUyHhsFk4oR77+XD9hLp9tJSXDU1OGtqsBXa8DXuYv/bb4ZsE26DYWeeibOqKuD48bX5kLzNKJIPX5uPnMGDuamuLqSUVVeUL1iA7PezfflydZ+tray66y6mff/7MbdpCro38LvdvH/LLeQOHUpJe184bZ9b2/uDRNvntJtu4r2bb8bX2gqoPUV2/+c/gUxlv8sVUpKpef9+PrzjDiZedx0Ahz/+GHtpaUh/jWQYPHcu5uxsVv3qV4F7ri3//CcN27YxeunSuPeTit+Ljo5OdPrcmTFlyhRycnL4wx/+EEiZ3LNnD4899hhXXHEFF110EV988UXAoXHs2DF+97vfcdNNN/XlsIGOeuQ9iSzKuOrUi7djQM85M/q6eUtPkO6aunJmtAciBoilJ9iZUV8PWv+wWPcaZWXqq5ZhoFG1pop97+2j+VBz5EY9RF/ZSPJL1Gyuoflg4lq1fhmDtfv+5m1QsxJcR0L1tO2Dw6+CJzIFZsAAKCxUM0qDel+nHX35Gwo0wM2LTItPBO37/f73JX796z28+67Evn3xOzIADAZYtEj9+6GH4LLL4MQT1cyoFSsi159y5RQW/2Mx4y4Y162xx0O6n+d0ohNst4oFC2g5eDAk9X7zU08x8dprI7Zb95e/RDTwXPTggxx8//3ABC7EbmbZGdEaeB75ODLbwmA0YsqfgfvI54geEdm9DdlYiegx4XP6QppMpqKBJ6hNFctmzw6sO/L880OaYSqSgrn4RGTZgSzKyEIFgjELX6Pqfd77+stRG3hqhDfwNFutzPjBD9jeXhO7K7QGnp65dzJ0qHp+uOYXx3HaBSUMHQr/edXEsDPOouqzLwM9M8I58O675A0fTun0+QDYivKZfvPNgbrcWXlZnPPoOcz/yfyoEbHvfu97LJs6lWVTp/LiqacCarNNY1ZWxHmiqwaeGrWbNrH1mWc47sc/Diwbf8UVbFu+nCPtjjB3QwPv3nQTluzshDJZNPwuF+/fcgsLfvObkOWSz8fuV15hTFg99Wnf+x7WvDyMRiMjzzuPlgMHOL69QafZbmfIKadwrL0k1LblyymZMiXEGTP7xz/G73Ry8MMPA8smXHstBe1Rn/nDhzNg5kyOaemuMTDZ7Rz34x8HbDHhqqsiop4TIRPP5ZmmSdejkw4YjUaMFktIiZxhZ51F68GD5I1Sz/3ZA7NBEDDZO7L/7cXFXPjmm6x+4AEeHzWKJ8aMAcFEweTrKSg1AyBhQSS09I6IFal9WVkZjFxyFZ/e9SueOe54Dn/yCUaLBUOUMkzB4zNaLBjM5sD/bUVFFI0fT9ns2Z0eh/biYi795BP8TifPzZunZpxOmMDy44+nrf2BrKvPP+WRR9iybBnLpk7lXyeeyJQbb8QxcGAgWKKr7fMqKznvxRf539VX8+TYsbxxxRWMueQSitqd775WH9ah30OQWnHv/BWunfdi9i7DbHPjGODAZDXhqncFSlmGfxcAo5csQZFldr/yCrlDhjD/vvt48eST2b/i+7TufpXJ3/6/kP5poy64gLotW/jnjBmsffhhzHY73/j4Y/a//TZPjh3L/hW3IlY/iSy68bUl199JEAQW/+c/bH3mGdZdcQXPz5vHmIsvZkzQRP72559n9e9+B6jX7DeuvJInxoxh2dSpLJ8zB0tODhe+8UbgOhnY59NP89SkSTwze3bEPid985tUnnIKy6ZO5YkxY1j54x9z3r//HWji7mls5KXTTlOzeKdO5bVLL2XGLbcwtb0cxbZnn42alWG0WgPlwbRjLtypZTCbQ47bC159laY9e3h8xAgeHzmS3S+/zEXvvIOx3X7xHPvhtgL1/iEkIjLst5oomXguzzRNmaYH0kNTnxcrNhqNvP766/z0pz9l6tSpGI1G7HY7999/P/PaZ2lfffVVvvvd79LW1oYsy9x9993MDnqo7QtMJlOgSXlP4qpzgQJGi7HHMhx6UsuuN3bRcriFkWeMJG9IXtcbpIjesk93cNepIfnhtSu1+ahhQf09O9OjOTM++gi+9z3weGDKlI7l4QwcCBs2RGZmaLW5tZutnqYvbeSqc/HeT97DlGVi6YvxR1dAhzMjkPVy9C049h6m4Vcza9ZFHSvu+qvq6DA5YGBo0zZBUEtNrVyplpqKla0qi3KgjEtv09e/oVRkZuzfr5ZfMxjgzjuNDBw4Iqn9rFgBYYHzgJqlc9FFiWV5pJK+tpFOcmh2+19Qmv6Ea65h81NPMfcXv8DT2EjN2rUMff75iMnhuk2bIh7STFYrA2bMoG7TJvLbLxzJNCSMt4GnNddKzrCFNG1+ClPhiQje9SjmaXgaPRjMqiatyWRPN/C05lqxF9lx7VKQfNnIovp9eZu9IDjwu1rIHTocd00VI84KPQ9HbeA5ZWqgXPgWScKaF/u+JbyBZ/43H+WKn84JlD3O5QiLuJ/Kw5/x2YWt7LPXYyscx/6P9kdtCFq/dSuHV67k9W+cguyTce1SqP1EprQ9orErYjXwVPz+iPNEVw08QY1g/e8ll3Da3/6GXUvpBArHjOGCV17hs1//mpaDB7Hk5HD8T3/K4Y8+ittxFcxb11/PyMWLqQxrbrr3jTconTYt4JTTcAwcGPgNNe/fT8HIkYGJBVAbeGrNXetiNfCcN4+6jRsZ0u6lzo3ye+mqZFRWQUGIU8leWhpS0zsRMvFcnmmadD066UDAbmG2MxiN3HjkCC9d8hJ+lx97sR2D0citYbVRS6dM4eKgEo47/ruDAysPULkgm/Ln4PEjb0eU7v8776l95sph/nx45wcm8qb9hoV3LWTQTPVh6JL33w/Zpnz+fJYG9Vr6ZlgwxeannmL8lVfGdRxacnI44Ve/CjR/DmdplN5TwRoLRo3iG2FVPkae11ECu6vtJb8fx6BBXLVuHQAthw7x2iWXsDgomslgziVv8newOCw4a5y0VrWSPSgbq8OKDx+ityOIYfDcuVzywQcRn3lpUEmjSdddx6TrruPTBz7l4MqDFE+dxgm//F7g/eyyMq4KCugA1RG/5LXXAv+v2VzDez95L2lnBqiOnIujfD8awaVQjRYLl2llIrrY50VvvdXpOnN+9jPmtAcphJMzeHDAFtEomTw5aubEaX/7W+Bv7Zi7Nqyc1fiwTCFbURFnLVsW87O0huMhY//5z0P+H81W4b/LaL/VeMnEc3mmaco0PZA+mvrcmQFQXFzM34JOMOFMmTKFT+M4OfYmiqLQ3NxMXl5et2pGxvM5FfPUh6ye+pye1LL/w/3UbaujZEJJrzozess+3eH8ZefjrHXG5czoTM++ferkeFsbPP+8uuzgQXj55egTrLEyM3rbmdGXNtI0WnJjN/6KRYQzw98CgGLKprmpqUNP3gTVmdG0OcKZAWqpqZUrIVrp1roddXzwsw+wFdk459FzEh5jKujr35DmzOhOZobWC27BAhgwQKGpKXE9kgS33BK9L5uiqL+9W2+FxYvj67+RSvraRjrJodktmIlXX83zCxYw5+c/Z/sLLzB66VIEgyHCmdHbDQmjRdo7Shxc8toPWD7nGU66dxJvXdPAic/+iJKKARiMqjPDmmsN1OjWGniarIn9lsMbeB5euZJFv/89pdOmIXo8PJydjaPEwZLlS3j5nL9ROnc8RzeovcWGnjiUA68UM+O2hQw97WT+d8UzXTbwtBSMwjLkNk648wRyxuV0+bsqnTo1MPmw7q9/4y93vImiqHXtDPj5LgtYwzUs42VahcF8U/4BU1kTMzNDdLsZdeGFnPrII1Hf9zQ18fK553Lq3/9O/rBRmLK6voXPKS+n5cABmoKuTfE08JQlide+8Q3GXXYZI849N+L9stmzQyZNnNXVWHJyunSQhPPFfffhqa/nrH/+M+K98BJTwQR+Q4oSERUZTKzfixL2e4laMzLRZpydNLztikw8l2eaJl2PTjrQmd38Lj9+l5ptcPDjg1RvrGb0OaMZcsKQmPsbc+4Yxpw7BoA/+tQAnXDC+8xZstVzrrc18efFhh07eGXJEnIqKli8YkW/OA5Fl4vXL70Ub0sLgiBgKynh1EcfDWlCHYy91I65wIzJ2P1pNq13orshSk3iLtDs1B1nBvS/c8W0Lqq39Dc9XZFpeiDzNGWaHkgfTX1eZqq/IkkS27dv7/EO7jllOZzwfydwwv9FRpalAkmC99+XefjhWt5/X45ZAz5ZtAlyX2v3LqSJ0lv26Q4Gk4GcspyIyHut3GSwMyOWnhUr4BvfiHzmbmpSb0ijlcBJF2dGX9pI05hM1H+EM0NsA0Ay2EP15LU3xmiO3mhOawIezZlhcVgQPSKepsjmu71FX9pHUZRAmanuZGa093Bl6dLk9Xz8MZ03RVTUpojB1UVajrTw9g/f5r2fvhd7wxTQH85zOpFodpODJj4dAwdSOG4cB99/ny3LljHxmmuibhutgafo9VKzfn3cEfzQ/YaEjhIHE6++ks9+cSujlpxHs6OV/OH5FI4oDDQbhd5p4OkocWCymbDYLIGGlwaTAXOWiZzBuWpDzDgaeLqr96DIIkabMeHfVdPob1LoWkM5agPPQWzAh513+TktDEZRwOg5gNdLoAxXrAaesfA7ndRu2sKb31/Bp/fHF+AzaO5cDn30UYieeBp4vvf972MrKooZGRnO5iefZGRQH4p42P7882xbvpxz//WviO/C19rKwXffZVSMlLd4z30Dpk+PWiqtatUqShNo4BnTiZgiMvFcnmmadD066UBndtPKUpsdZtyNbmq31NJ0oCnufS9ZogYBtVfzCRDeZ86Sk/wkeeGYMVy7ZQsXvfkmZru9XxyH1rw8Llu1ims3b+aaTZu45P33KZk8Oeb6iqLgbHMGsjy7g63QhsFkCGSdxkPj3kbe//n77H13L2f+6UzO+OMZ3RpDf7BRIuh60p9M05RpeiB9NOnOjK8xK1aoNd9POcXIXXeN4pRTjDFrwCeLdsPTWxPkmUC0zIxodBUxDmrEePg5RgskiSgzldO7zoy+RNOoOXASIbgBOBDIzMCUE7pi3jg1nMl9FLyR5Sq0JuCbN0d+hpaN4Hf6kfyZc+GLF9EjIvvVG/dkbARw4AB8+aVqgu6UgAp3+sWzniAI1G+vp2Fn52VKdHSC0Rp4CgYDhWPGRF8nSgPPD269lcpTTolo9NwZmdjA0+/2B/72NIY6grtq4Dlg+nQMllxatr+AVbtvaW7GE2eJrGM1Jv7HvZyL2sCzjVKyqcGB2mR7CJ9TxqfIEoHMjFgNPD/44a/Z/p/tNOxuwFldjb+9ZFLO4MF845PtGG3D456g0ppt1rSXdIingefq3/2Ouo0bOf2JJ2Kuo2UgKLLM1meeYcuyZSF9NbriyKef8tGPfsQF//1v1FJeu15+mYqTTsKamxv3PqMx+qKLqNuyhU3/+Ic6XkXh83vvxZqXx+C5c+Pej01v4Kmjo5Pm5AzO4bwnzuOke04KBBRoDo54WbJEzcDQuPBCIvrMaRH/fqcfnVAUWYEEk/o6Y8y5Y7h4xcXMuGFG3Nu0HG6hen01DbsbyB+aj70o+V4MOjo6/R9nrZOGPQ0x/zlrkytxlg6kRZkpndj4XX5MNlPK03dWrFAj98MnwlNdAz4wQZ5EKmomc+TLIxz89CBl08oYumhoYLnP1xEF3pUzI5GIca15MaRPZkZf0h1nRkQD8PbMDMw5QNB3Z3KAYxi07YXmrVAaml2lOTP271dLhGVnd7xnybYgGAUUScHb7MVe/PW6ETVajJz+h9PxtnjjKqUSjeASUwMHQpxzphFov5dE1tOcUaJHRPJJGC193yBLJ/2I1sDznRtvZN4993SsFKOB54e338573/8+iiwz8rzzWPTggyH77aoh4dTvfpe3rruOjY8/zsIHHuhWA0+/r4zD76xlSNYQBk0dRDhaA88vH3iA5+bNQ1EUZL8fg8nEBf/9L7lDhsTVwPOt669n7cMPYzAamXPXXTTu3Ink92Nsb9goBp1+PU2emA08RbcbS24uYy65JBBxr8gKhbN+TPOWf/LyGXPxC7C7sJAznniCrCh9IMK/i7Iy2MwSFvAgE3iFLSzmf9zHt1FLDDYwnK+4nUWGVwNOl1EXXMCmxx/nnzNmMOHqq5l+88184+OP+fe5V7Lhr3/FXppPbnkx5770UqB8k1au01XjQvSIXZ4fBUHg3H//mxWXX84/X3wRRZaZfP31EQ08Ww8fZtYddwDw2a9/ja2oiGeOOy5kX2c+9RSlU6firq/nxVNOQTAYEL1eymbP5uL334/oeRLruwL48v77ET0e/nP++SHLp33ve0y+/nq2Pfssk7/97Yh9mYIaeAIB24d8XtAyo8XCxe+9xwe33srn7b+r8gULWPzyyyHji1aCLHjZhGuu4dUlS9j/1lvM+uEPGTR3bsg4AvtJsJSajo6OTqowGA04Sh04Sh00H1RLWbpqO3dmvPPjd3BWO5n7w7mUTlBLDwZXwbRYIkuodqfMVKbTerSV1qpWDLkG8vK7X147mb6J2sSkveTr9eyoo+OsdXY6j6WVwP064ax1suKyFbjqY18L7EV2lixf0i+/G92ZkSSCIGCz2Xq8RtiHv/yQht0NzL9zfqDJVnfpzRrwfVVmqrfskyy122rZ//5+zDZziDPj4EHVBjYbBPe8jKYnmYhx6MjM6GtnRl/aKNAzI6ebPTMUBfytAAjmPGw2Z6ie/AntzozNEc6M4mLVxtXVsHUrBM8bCYJAVl4W7gY3nmZPnzgz+tI+BqOBwpHRJ8biJbjEFCSvZ/58NcX+yJHo50xBUN+fP79jmdlu7nBGtfScMyrdz3M60dHsNutvf8MYdJHVGngGE08Dz3C6amYJMGTRIr6l1TRsJ9kGnke/Okrdp3U0Tm+M6syA3mngueZva/C0HmLskrGMXTwWwdDR66GrBp6+Nh9Gax6FM77HRS9dxNZtW5k4cWKIfYIJb+CpnSf+euSTwHliNdexmusA9Twxr3AbA2eXB8pMxWrgOfKSe6laXcVxNx/HiFNHhLyvNlW34m320nyomaJRRVHHF0ze0KHM+tvfYuoJbuAJcHNYP5dwbEVFnTbfDCf8uAG44JVXOt2mfOFChp99dsRyrWmoJEnYbDZyyssjjp3jf/rTkP/nDR3K+f/5T8zPOu3vf49YdkZYVkrx+PFct317yLJv7dkT8v/cIUP45o4dMT+nMzLxXJ5pmnQ9OulAvHbT7ju7ysxwN7hx17sRDB37awhKKq6tjdwmVb0YILOOQ9Et4mvzIftlBEnA7/QjCELgmt9bOGvUe0ZHqYPtr2zHVetizOIxSU9UZpKNQNfTH0hGUzpP2vf1nJer3oXJasJki5z6F90irnoX3hZvQt9Luhx3ujMjSYxGI1MSqE+dLM5qJ7Jf7lYT3HCSjehPhkCZqV6O3ugt+ySLFinjKI3e/Hvo0NB+lNH0JBMxHvz/8DJTJeNLWPzU4kA2TU/TlzYKNJdOMDOjtVX9B+3fo+QCRS23YbTmMWVKSegGeRPg8H/VRuBRmDBBdWZs2RLqzACw5ltVZ0Yf9c1I999QZxw8CF98of6GLrxQXZasHqMR/vhHNWNNEEIdGuFNETuWC1hzrXgaPT3qzOjPNvo609/tFt7Ac+Mz28jLzUN09e4Dezgzvz2Tmd+eGfW9rhp4atcES7YFs8WcsH2CzxPhaOeJ6280IWwgZgNwDa1fUKzrU15lHjUba2g+GJ8zoz8eb8ffeWen7/dHTZ2RaXog8zTpenTSgc7stvut3TirnVTMrQhE5btqXSiKEnPCSZtoN9s7sueCqyvW1ERuoz0npiJQMROOQ2uuFXuRHVe9C1+rD1mUyRKyAvcVoE6iJlMNQBZlPr3/U9wNbk6656S4stUDcwwlqjOj9XArg2cPTnoCNxNsFIyuJ/1JRlNPTdqngnSwkclmwuKwIPklJK8UcEoDiN7En9/SQRPozoykkWWZuro6iouLMXTSRDGcrtKfgkuSSD6J5kNqhJzf6adhT0NK0qOSjehPhlTe8CRCsvbpLYKjJoKJ1S8jmp5kIsahIzPD6VQn5g2e0GMyfPK8p1Ly+tJGFfMqcJQ6GDB5QNcrB6H9JnJy1H9IZpj4MxDbkAUTdTU1oXryp8CU/we5o6Pub8IEeP/96E3AtcbXfeXM6Ev71O+sp3pTNYUjChk4dWDC22slpubP7zjeu6NHa4p4yy2hjuDyctWREa0kX7Azo6dI9/OcTnT6u920Bp4aJpsJr9eb1iUntAaesQguPZisfZYsgcsug2efDV2unSdOGJ3H3uJhXWaddVUGMW9IhzMjHvr78RaNTNOUaXog8zTpenTSgc7sdvDjg1RvqCa3Ipch84YA6lyCr80XM1DN71LLHppt0Z0Z0TIzisYUMfXaqeRWdK+nEWTGcegocbBk+RK8LV5W/nolTQeaGHvVWEaeMDKgKdlnacEocGzdMUSPiLvRTU5ZTpfbBJeZSkUWTSbYKBhdT/rTHU3apH00kpm0TwXpZKNj64+BAiUTSmJ+T/GQLpp0Z0aSyLLM3r17KSwsjNuAXaU/yX6Z1qpWcgbnYDAZkPwSLQdbEAwCr1ynpuSnIj0q2Yj+ZBgwZQBnPXJWYGK2t0jGPr2JFjURXs+yM2dGuJ5kIsZB7c2Qna32adi/1cn6n/VNSl5f2mjQjEEMmpF42baI5t9GCxTPBtRGvBF6zNlQMDnm/iZOVF+jNQEvHluMwWQgK793fzsafWmf6o3VbFi2gWEnD0vKmRFeYgq6r2fJErX03h//CD/4AVRUqL/XWKX4tGy6nnZmpPN5Tic6mWY3k8OEy+XC25Yezoyt/95K495Gxl84noLhkf0uomHJtjD0pKFk5WV1yz7tPco55RR4910YMgT27tXOE6WUTiztch++FnXSoTNnBpCQMyOTjjfIPE2ZpgcyT5OuRycd6MxugUnsYjtGixFboQ3BIOBrje7MkEUZ2a9mlwdnZoSXmdJKUGvkV+aTX5nf43r6E44Sh/qcrIDZYcaZ7SR/WD4mU/em2gRBIKsgi7ajbbgb4nNmuGo6qj+kypmRCTbS0PWkP6nQ1HSgCUVSyB+ej0DflkJKKxu1zxd6W7zddmakgybdmdGLdJX+5Gn04Hf7EYwCWflZeFu9GEwGjDYjWflZKUuPSjaiPxksDku3fiiZiCzKAedBvJkZsUgmYhxUR9WuXVC1N/SYbD3WiuyXySlTHWp9mZKXjkQ0/+4mWhPwaJkZky6blJoP6YcEyqwkUV7v4EH4/PPQElOpwmiE889XnRm1tdDZtdteZP/aNW7X+XqSyvrZyaLICq9/93Us2RYkn0TTvibKZ5fH7czIH5rPnNvmACCKyUVubdyoOqYtFvj5z1VnhseTWO8xWZQDkbKxnBmFIwspm1EWaNaqo6Ojo/P1RlEU3HVuoKNfxuJ/LA7phRGO3+0P/B08LxGcmSGK0NQEBfFdSr/WKLKCu1G1gTnX3MXa8WMrtNF2tA1PY9eZ+qJHDDQNd5Q4Oipk9OH9mY5ObyPLMs5q1bmbPSgbc1bqfo+ZgsGYGY4v3ZnRB8RKf9JuKkxZ6vt+px+DyYDVYQ2sn4r0KC2iv7OJvmgR/Tqpwd3gBgUM5sio+0SdGdARMf7xx2oZpLIy1RHVmf0GDlSdGVotVO2Y9Lf5kbwShnJDSo+5dKN2ay1mh5ncwbmBm754CGn+DeA6Aq27wTYI7DGM5qmFw6+o/TXG3BzylubMOHwYmpshLy9BIRmKVmc2mYyu4BJTqcguC6e8XHVieDzq72dAjEplc26fk/oP19FJQ7Rrhb/N38WaPYfP6aP1iNrQqHxuOU37mgKTCr3FM8+or+ecA8OHq383NHREtSqKguSTkHxSzJIfgUwugZB6tsEUjSpi0S8XpXj0Ojo6Ojr9FV+rD8knAR3OjM4cGdBRYspoNYZMbAU7M0AN3gl2ZsiSTOPeRnxtPgZOHdjnDWDTBU+zBxQ1m8KcnbrJ06wC9VmosyoKGqYsE0ueXYLoFTFZTWkRbKKj09toGWcARrM+oRlMVn6WWsI8Q07bujMjSQRBIC8vL+kLuOSXaKtuQ5E6UiO8rV5kUQ5ZRzAKGK2p/xGed5460RetL8aZZ8aO6E8URVHY/NxmfG0+plw1Ja7GVamgu/bpSVz1LhDUm83w8cVyZnSlx2hMrFm7NslbWwfB+RYGkwHJKyGJUvw7S5K+spEiK7z7f++CAuc/fT62Alvc20Y4MxrXwa6/Qck8hLE/jKFHUZ0ZBiOM/DYYOyax8vPVLI8jR9TsjLlzIz9TFuWEHC6poi9/Q93JzIhWYgpSp8diUW126BDs3x/bmdEbpPN5Tic2mWY3a44Vk9mEz9l3D8taXy6TzRTIIownilHD7/JjMBswmo1J2UeWYfly9e/LL4ei9r7coqiWdMzJgZZDLbxx0xtYc60seTb6TZYlx8Jpvz8Nn9OXsuMj0443yDxNmaYHMk+TrkcnHYhlN1edOtGdlZ+V0ORd0ZgiDObQZ4zgMlOgBu6MDmr9J/kk3r79bQCWvri0W8/2mXQcKrJCxQkViB6R/ML8lGmyF6nOqUTuaUxW1SZmh+pU6Y4zI5NsBLqe/kD351llfH5QBCMNTQaKOm9V1+Okg41EtxqcLEuymgXu9OOz+wLLEyUdNIHuzEgao9HIuHHjkt7eWeukraotZJnoFVHkDudG9sBsvM3eHnEAPPWU6sgoKoKnn1ajwquq4I471IbE1dWJTdJJUvTMAEEQ2LZiG5JXYvS5o+Oq9ZgKumufnqRkXAkX//viiKbobW0djda0qE6NVOsJODNqwpwZ7Te0wR7tnqKvbORz+gL1AmNFx8YiomeGv/03bM6JrcdaAlnF4KmDlh0RPTQmTIjuzKjeWM3KX68ke1A2Z/7xzITGmQr68jeUbGbGoUOxS0ylUk9lpfpZBw7A7Nkp2WVSpPN5Tic2mWa34tHFXLLskpiZBL2B1nzckmMJRDG6G+LPzFj9yGoOfHSA6d+azpjzxiRsn48+Us/j+flw1lmQlaX+83igvl51Zmj3cqIn9oOD0WykaHRRXJ/pbfGiyEqXfZUy7XiDzNOUaXog8zTpenTSgVh2C276rHFk9RG2/XsbhSMLmX799Ihtsgdkc9rvTotYrmVmlJaqjozwJuCmLBOCQUCRFXxtvm7NU2TScWgvsnPCj09I+X6TuafRCGRmtCbvzMgkG4Gupz/QHU3VR0QO7pOw+2VEDOz8zEeWBUYMFcnro4rpfWkja64Ve5EdV70Lv9uPp9mDLMpqO4P2eT97kT1madtYpMtxlxnFsvoAWZY5fPgwspzcpK82WWzJsZA9KJvsQdnYim0Ixg7vlsFowDHQEdEkuru43XD33erfP/sZnHGGzPz5h7n1Vpnjj1cfvv/wh/j3t2IFDB0KJ54Il12mvg4dqi6Hjgnj7lxIE6W79ulpjGa1MVsw+/errwUFkeWGUq1nYHtP5ZrwG1Rr15MtqaKvbKSV8TDbzQlnPET0zBBb1FdTTmw9ggB57fWkmiM7fcdqAm62mxE9It6mvmmq25e/Ic2ZkWhmhlZi6oQTIktMpVLP0KHqq/abjUbNlhre/uHbfPbgZ93+vFik+3lOJzqZZjeDxUCroRVrfuKZVKlCu7+wZFsC2XaJlJnSrguWbEtS9nn2WfX1ootUJwZAYXskmBblqtUkl3wSstQ9269ftp4Vl69g24ptXa6baccbZJ6mTNMDmadJ16OTDsSym6tWzcwI7tUmekRqt9TSsDss1aITvF5wtVczGjNGfQ13ZgiCgCUnNeWLMvE4TLUmW6ENg9kQEvAai20vb+P9n7/PgZUHABi6cChn/ulMZnx7RtKfn2k20vWkP8losuZaaZPs7N0pYvB7MCJjxUcObQg+D3t3irRJiU/ap4K+tJGjxMGS5UtY+uJSTvp/JzFg0gDKZpRx6gOnsvTFpSx9cSlLli9JuDduuhx3ujMjSbprQIPZgMluwl5sJ688j7zyPBwljohmLPZCe8o7xD/yiFqjv6ICbryxQ4uiyPz0p+o6f/lLZJppNFasUB/eg5tPgxqheNFF6vuWXPWGR4uc7A3S5QeWCJ31y0i1nkBmRrgzo32yJdmUs0Toa2eGdlwmQkSZKb9aox1zJ84MTy1YCkF0Qu0n0Lon5N+MCaoRwpuAa9G2nmYPitL1DWyq6Sv7KIoSKDMVb2aGJMGHH8Jf/6r+P1o/oE7tE2aTkH+e2oh9xePMkEWZ+u31CT1EJkp/PM/pZJ7d0kGPdn9hzbUGAgUSKckQKG2Xa01Yj8fT4Ui9/PKO5VqpqYAzIyh6NVbAQN2OOrb/ZzvVm6o7/czsAdkANB9s7nJ86WCfVJNpmjJND2SeJl2PTjoQy24jzxjJeU+cx7TrpgWWaY4NLWsjHrSsDEGAkSPVv7X+isGkqhdDJh2Hkk9CUZSUaxq6cCgX//ti5t4RpRZxGPU766leXx0I5sjKzyJ/aH5SPQg1MslGoOvpDySjKavQwaO1S/gXS/mMOVRRRhVl7KeSf7GUF1nK3+qWkFXY++kZfW0jR4mDwhGFZOVnYXFYGDRrEBOWTqBwRCGFIwoTdmRA32vS0MtM9QGiWyQrLytwYdHqTEsetU+B6BGj1p5OxQRzczPcd5/69y9/qUYQikG7PftsmDIFNmyAhx9W14mFJMEtt6jNLcPRGl7eeis8eUXvZ2akM+ueXIen2cPYxWMpGN7RUS2Z5t/JElxmCkfQsaUQSD3zOZOvo5fOaM6MRD3zitK5MyMqnlpYdRm4j4LzEDSuhdrP1B9HO2cVFFGcs5wtW0pCNtWyEhRJTeNOtCRWf+bUB07F2+yNyF6KxooV6nko2KH629+qztoue/9o9vHWx17HWgRzl0NWh30qK9XXzpwZ2vk90NBXRyeDOfb+MdZtWMekb0zq1kNzsgRnZgQcwU3xOzN8Ler2yURsvf66em9VXg4LFnQs1zIz6ttPLwaTAcEooEgKokcMNE4P5ti6Y2x6dhMjzhjBgEmxa33mVuQC8TkzdHR0dHQyG4PJgKM0dEJKm6By17tRZCWiIfi+9/ex8Z8bGTx7MDNvnAl0ODPy8zvKTYcHvkHqnBmZxNon1rLnrT1MuHQCVKZuv101cg9Gy9BJZnJSR6c/8/HHsPuoA3BwFDeDqMKBCyMyjag35A1V6nqJ9JnNJLRnpUyaU9KdGb1IcM0y0Rs5SSyLMmabGUVSYj6EJ1PTLJjf/159sB47Fq66KvJ9QVBLTy1dCn/8I9x+O+TmRt/Xxx9HZmQEoyhqXfmqegtmejczI5058uURWo+0Mvzk0MYYvenM0MpMHam3Yh/ScUwqkoIsysiSjLvRjSAI3T7m0o1knRmNjWr6NQSVMBLbnRmmGM4Mf4s6UW7KAWMWKCIYrGBqTwOX3OSY68m1tbD3WAn19R3RvEazEbPDjN/px9vszagLT2cIgkDhiPg6dWmZYeEO1WPH1OUvvdSFQ0Ozj8EKxiiOE8mtvu9vCXFmaJkZBw7E3rV2fPlafSiK0ucNsnR0epKaj2twGp2MPHVknzgzDCYD9lI79mI7ueW5nP/0+QmNI3BdSLC0HXSUmLrsMghOpA0vMyUIAmabGV+bL2ZmRrzXp7whai1KV61aA9dsMyc8bh0dHR2dzCWrIAsEAvMK4QFCnmYPrjoXfpc/sExzZhQUqD0zINSZ4ax14m3xIvklfE4fdTvqQsphW3OtX9uJdE+TB0VSeqTXabwEyo2128TX5mPn6zuRfBJTrpzSZ+PS0elpjh7t+PsIg2mkgMW8gh0XAjJKe0Gi4PW+bmjOZ4PZgLc1M+aWdGdGkhgMBkpKShIqAaXVLOssUlfySRgtxpjvd+cmoaYGHnxQ/fuee8DUbv1wLUuWwLhxsG2bWrblxz+Ovr94TwZOv5V8ejdCORn79AaKokTcaGjs3au+RnNmpFqPNhl/qN7BucuWILu9gfG569zYimyBSJCeujHtKxsFJouSbP5dVARWbdOgzIxO9RhtYClQS00ZTGDq+D6NspfBg2FvjVpqKjiyNys/C7/Tj6fJQ255DK9iD5GuvyGNeDPDFi8Go7ELPUabahPZC7Iv1DklR563gstMaZ8VjjYZqTVI7IkbhnS3kU50Ms1uBoOB7MJsaCZqVmlvMOqsUYw6a1Tg/1rfjHgQPSKST82MteZaE7JPY6OamQGhJaYgsswUqKWmfG2xsx6Dy111hjXHSlZBFp5GD80HmykeUxxz3Uw73iDzNGWaHsg8TbqezMTtdvOb3/yG1157DVmW8Xg8/OUvf+Gkk04C4OjRo1x//fWBcho33XQTN954Y5+NN5bdvvr7V5iyTIxZPCbgyDcYDWoQZZ0LZ60zwpmhOTG0EsPQcb0qKICS9hgercyUs9bJistW4Kp34ax24mvz0bS/KZANCWrQZSL11zPpONQadNuL7FhKLCnV9OkDn+KsdjLvx/NifreSXwqMQcvSkXwSm57ZBAJMvmJyUoFVmWQj0PX0B5LRFN4r040NGQMGZGy4ceGIul5vkC420gLLD31yCFedi9MeOC3pfaWLJt2ZkSQGg4ERI0YkvJ2jxIGjxMHbP3wbX5uPE358AvlD81M/wCjcey84nTBzZmi0crgWgwF+8hM1c+P3v4fvfx/sUXqQx3syKBxoQa7t3TJTydqnp/G2eNVJEyEyBbSzzIxU6ykqUp1ZoghtsoPyEUFjGZmyj+mUvrJR6cRSJl0xifzK/IS2i2j+DTDq2+BrBHt513pyRhCrTdGoUfDxukhnhjXPSuuR1oTKpaSKvrJP88Fmjnx5hLzKPAbPGhxzvXgzw7R00rj0tGwHyQt548GUHXO1igrVgeFyQV1dxwNfMAaTAbPdjN/lx9vSM9EP6Xqe0+mcTLObwWCgeFAx9S31/bLkhObgNpgMmLJMCIIQt31eegl8Ppg0CSZPDn0vvMwUQPmccnxtPsz26JkUiWQO5g3Ji9uZkUnHG2SepkzTA5mnSdeTeYiiyJlnnsmJJ57IqlWrsFqtKIqCJEmBdS688EJuuukmLr/8clpbWzn11FMZMmQIZ511Vp+MOZrdFFlh1+u7UGSFUWePCnnPXqI6M1y1LhgTui/NqR58PdIyMwoLO+5ttcwMb4sXV70Lk9VEdlk2olfEkm0JZAaKbhFXvQtvizchZ0amHIfBzoySEVEeDLpB/Y56nNVOXHWumN+tq04NljRajIF7CK0cGIrqvIpW3rIrMslGoOvpDySjaf58tdzrkSNgUTz4drpHbwABAABJREFUseDCRjZOHDhxCw7Ky9X1ept0sVHwXGxwRl4ypIumzHHh9TKyLLNnz56km560Hmml9XBrQnUQk0FrjPvww2pTb1B7ZgQ75qNpufRSdVK9thYeeyz6vk84IXYJKlA/o6ICzr5tNGc9chaTr5wce+UU01379BTOGrUJm63AhsHU8fNTlK4bgKdSj8HQUQv12LGU7DJh+spGxWOKmXjJRMqPL09ou4h+GQCFM2DgKWDOjUNP7NPt6NHq6+bNoctLxpcwaNagPinz1Vf2qdtRx4ZlG9j1xq5O14s3M0xbr0s9YqvqyADwdN5812rtcOZ21jdDK1nTU1lp6Xqe0+mcTLObLMs4fU4URUkbZ8b2V7bz6f2fUrstSrHvMASDwNCThjJk/hAEQUjIPlqJqfCsDIgsMwUw44YZzLl9DjmDopcmTNSZAV33zci04w0yT1Om6YHM06TryTz++c9/kpeXx1133YW1PeVaEARM7aULNm7ciCRJXN5+gs/JyeFXv/oVf//73/tszNHs5m5o74lhFCKyEh0DHNiL7ShyZBqzNpkVXKYwWpmp8AbgJpuJnLIcCoYW4Ch2YHFYsDgsIRke3dHTH1EUBU9je2ZlvjXlmrSsGs1hEo3gyg9aBobRYgxU/Ej2/ixTbKSh60l/ktFkNKol8gFO5y0u4QWyUO+ps1Hn3x56SF2vt0kXGwVnz4uu7vXFTRdNemZGksiyTG1tLZWVlQmn1yiK0nEDESM6LxVEa4xrtUJLS+h60bSYTGp2xg03wAMPwI03BpXWQZ18/7//i9yXhuYseeghyC6xAfGXfEgF3bFPT6I5M+yloaku9fXQ1qb+rZWwwVOr1uoHZEmkpWoTcpGIwdj+szXnhtTxT5SyMtV7HT4pXLejjh2v7sBR4mDqNVOT3n9XpKuNYhHVmRFEd/SMag+k2rIldPnUq6cmNsgU0lf28TarNx5d1buPNzNMW69LPXJQhIKvGej84jx0qHpMHDgAs2ZFX8debEf2y8j+nrnQ97ffkI5KptlNlmW8iheFvnNmfHzfx7jr3cy4YQZFo4uo2VTDkS+OMGDyAErGdX6dtBfbmXPbnMD/u7KPJKkZX5s2wUcfqcsuvTRyv9HKTHVFIo3Iy6arJ7eBUwZ2ul6mHW+QeZoyTQ9kniZdT+bx/PPPc8stt8R8/91332XhwoUhy+bPn89FF10Usxea1+vF6+0IYGlpf1AWRRFRVCePDAYDBoMBWZZDJoK05ZIkoQTVUA1eLooiNTU1lJeXY7FYEASBlmMtyIqMPd+OJEsYBXW2TpIkZt08KzBObZ9a5onX6UVWZMx2cyAjpa7OABjIz5cpKVGPi7o6Bb9fQhI7xqUoCgodYxTo+AxJlCK0xtLk9/sDeoxGI0ajEUEQAttrGI0dmuJZbjKZIrJsBEHAaDRGfO+xlidiJ1+bD8knqc6wbBM1+1OryZKnZlS46l0h+wkeu6fVgznbjK3EhiRJgeUmuwm/14+7yY29xJ7wsSfLcoiNtDH2RztBx7kvWE9/1iRJEjU1NVRWVqq/yyCtXzdNF1wAL7wg898rPIg+aKCAUmqpKHLy279KnHeegrZZb2rS9FRUVGCxWCKOyd6y05AFQzCYDez/cD8+l/q80RuawnWlEt2Z0QfIfhlFUo3dU86MWI1xvd44G+Oilpm6+251wvvOO9XyVGVlMHcufO97HRkb114L77wT6jQpL1cdGV19xtcNLWpCq2WpoWVllJVBVhaqI2PVZWrzYcCoKIxyuTC22js8RdYimLs8aYeG1gQ83Jnha/VxcOVB8irzetSZ0Vc07GnAYDKQU5bTaX+acCKcGf42aFij9sIoiLOpWttudbvc0WDscGgFZ2bE6sHwdSFQM76LRrzB6aTR+mYIAomlk1oKoWgWNKxVG7X7msEQOx176FBYtarzzIyT7z05zg/X0enfGG1GJKQ+c2Y07mnEWe0MRJ9mFajO0M6iGJMhWpCIxQJr1sCQIaHrRiszpSiK6twUwGgOvf4oihLIzIinefmgmYMYNDOGd11HR0dHp1M2bNiAzWbjwgsvZNeuXRQXF/OjH/2IM844A4CqqioqKytDtrHZbGRlZVFTU8MALcU9iPvuu4+77747Yvm6detwONRnv5KSEkaMGMG+ffuoDequXV5eTnl5OTt37qS5uSPjbvjw4ZSWlrJ582ZcLhdNTU2sXbuWcePGkZ+fz4bPNtDc1IyYL7JmzRomT56MxWJhzZo1IWOYOXMmPp+PjRs3AnBwz0Fam1sx2Uw0Nzezfft2duyoBMoQxVpKSlR9oijwwQfrMLW04vV6sWHD7XbjanapDa8dJiwWC2bM+Hw+Nm3ahL3RHpemrVu3BvQIgsDYsWPJz89n3bp1IRNy8WoCdRJt1qxZAU3BtpsyZQp1dXXs1RpVAnl5eYwbN46qqioOB13cE7GTu9qNz+cjuzCbHbt3pFxTnbMOgIYjDSHrB2s6ajxK+S3lKJLCzp07A5qaXE14mjys+3IdI0wjEj72CgsLaW1tDegB+q2dgMBveuvWrSGOx/6qSVEUfD713nvz5s243R33vV9HTYNKv2DksBa278hmG+MYfcZQfvdQDgcbV6PJ7W1NiqLQ1NREfX09ZWVlfWano8ajyJNlmv/TjNmlOrF7Q1NLrOj3FKA7M/qAQI0ygaRSMruis8a4GsGNcWNhtcLpp8OTT3Y0Dgew2cDtVicLH3sMvvlN9TPvvht+/WuYMgW++qpj3+5GN7v/txtFUZh8ee+VmkpHvK1etV9GDGdGoMSUv0V1ZBisYLSpHlCDAcWcq95ISG71fX9L0s4MLWI9vMxUboVaO6z1SCuyJGMwZlbE1qoHVtF6pJWT7zuZ0omlcW+nNQAPODNch2Hb7yGrFI5/ovONpfaTu98Jkgt8TWBWAstHjFB/T/X1ajp3+PORLMohZckymXgzM7R00osuinwvODMsrnRSKWjC05wH3hpwV0FW5IOqRnATcB2drztGe/fKGHQXrQ6sJUd1QGqlNtyNXTszRI+IYBC6dG7HChLx+aIHiUQrM/XZg59x4MMDTLt+GmMXj434jJN/czLeFm9Eo9ZwnLXOTsvXWXOtcdcs19HR0fk6Ul9fzz333MNf/vIXxo4dy8aNGznnnHN4+umnWbRoEU1NTYwZMyZiu6ysLFwuV9R9/uQnP+H2228P/L+lpYWKigqmTZtGbnttZi0TZtiwYSHOEm356NGjI6LjASZOnIgoiqxdu5bp06djsajXu8EFg6nLr2PIpCHMnDkzELU7c+bMkLEZjUZsNltgufKFQp2pjqz8LPLy8pg5cyZmszaGEqxWyM1VaGkRGDx4GqWVjey07lT3JRvxHPJgtBgpGlyEgKD2ZLBYmDRpEgUjCuLSNH78eDweD9OnTw9kMQBMmzYtYuzxaApG06ShTcQXFxdTqF2gg5YPGjSIgQM7Mh0TsVNrVSumk0yYLKYe0eTY72Dzps0IHiFhTeXDy6n11jJqyCgqh1XGrUlbLssyOTk5AT3BY+9vdgI1Ev3AgQOMHz8+IjOjP2qSJIm1a9cC6jkiPDr+66ZpwsgJfGI9gIyBKgZhnwBlo6FUKuozTZqeovaU7b60k+gVOZh/EAEBySv1iibdmZGGGAwGysvLk0rN1eqVmW3mqCmq3SWZxrjRtKxYAf/4R+T2mtPttttURwaoE4Ynn6w6M9zu0AlEv8vP5uc2Y7KZes2Z0R379CRTrpzCxG9MRBZDy87E7JdhtIHJgYCCxW5CMFuhPZUXuXt1+GNlZjhKHRgtRiSfhLPaGbO2d3fpKxsFapJ3EfkfTkQDcLFVfTV3PJxE6DHnqhk03vp2e8kgi+BrAC0921qELSeX4cNhzx611JTmzKhaU8Unv/mEguEFnHr/qYmL7QZ9ZZ94MzNAnTh88kk1OyyYaJlhndrHVQWyBxDAYAShfR3Zq75vjmwOpF3fDxxIQFyKSdfznE7nZJrdDAYDk5dMpujqIuxF9q43SDGyKAeCRKw56nlDy8zQalh3xpYXt7D1X1sZc/4Ypn9zelT7JBMkEq3MlMmq3nZrjVeDEQSBolGRD1vhOGudrLhsBa56F4qsIPkkjGYjgrHjftJeZGfJ8iU4ShwZd7xBZv6GMkkPZJ4mXU/mYTAY+NGPfsTYsapjefLkydx22208+eSTLFq0CKvViscTeQ1xu93YbNEdzlarNdB/IxiTyRToxRH8+dG+f2OMKBytbEdFRQVmc8ccgrfRi0EwkDMgJ+QzTCYTzhonq36/ClmUOf33pweWA8y5ZU7I/k0mE01N6t9FReq4SkoEWlqgsdFEWZkx8JmmLBMCArJfRlCEQA9QQRAwmowRWmNpMpvNAT3B30X49sksD+5/Ekys7z3R5cGaCoYUMP//1FRwWZZTrkkLTvA0ehLWZM21YhAMSB4psE6ix140Pd3VpNGbdgLVPuXl5VH1JDr2WMt7U5PBYKCioiLmuomOPdby/qJJckpIkoCHLECgqUkNcuxLTZoe7bNi/c562k51W+swO8wY2uc5/C4/tixbj2uKNf5UoDszkkS7CUyGnu6XkWhj3Gha4nlwf/FFuP/+jgd3LdI//PO12s+iW+y1CPPu2KenMZqNEeUlOmv+DWotUltW12UnEiFWZoYgCOSU59C0t4nmQ8097szoTWRJDkQOa5Ne8RJZZqrdmWHKBmLoySpRS4G19z7h6Ftw8CW1nNHIG9Rl7b1PJk5UnRmbN8NJJ7Xv2mZC8kp4mrqekEs1ffUb0jIz4m16rjkVBg2C3/1OPa7nz4/MyIhpn+P+Dl9+W7XjxF+AOex4j9GbJp7MjEOrDrFtxTZKJ5b2SMm2dD7P6cQm0+xmMBgYOWlkn31+cEM7s0O9r4qnWaaG5uC2ONQo12j2STRIBEIzM7TygaasdmeGJ/n6sd4WL656FyaridajrfhafeRW5JKVr94jiG4RV70Lb4s3xJmRSWSapkzTA5mnSdeTeZSWljJaq/PazsiRI3n77bcBtazGwYMHQ953u920tbVRWhp/ZncqiWY3V1174+fiyGACo9VI3dY6EOLL8tYagGvXr9JS9dmkpgbGtz83im4RBbWOvSIquBvdmLJMUZ30yejp7/SEJluhDaPFGHAaReODX3yAoijMvHEmuYM7grCmXTeNKVdOwV6SXLBJptlI15P+dEeTp9mDKIIXKwIy7r3V7H7TyYjTR/RIEHk8pIONJL/Eez95D4BBswZhzbV2ej7pinTQBPD1DcfoJpIksW3btojGK/EgCAJ5lXnklPfMJHGijXGjaenqwR06Htw1tEj/1lZwOjuWWxyWQDJBb5Wg6I59+gLNmTF8eJQ3ZS+K8yDupoMhzda6SyznE0BuuXoT1HKo59LC+sJGvjZfICHCkh27H0I4ktTh9Ak4M8IyM2LqySqBnBHqv6JZYHKopaa0Ze0T5RMmqKsHNwHXJqe0Cf7epK9+Q4EyU/nxOe+0cpHHHac24V20KHppqZh6Wrar5dzs5VA4tcMuYfYJJ9iZEcvp63f5qd9RT9P+pri0JEp/O8/pqHRlN0mCDz+E555TX9PdvH19HGolpswOc6AsolZmKp7MjEC2XrsDNZqeRINEoGMySBTV+yLoKC3qd/sjtm050sK2l7dx5MsjcX2WyWZSIy5NBlDUey2LwxJRvrSv7dMTZJqmTNMDmadJ15N5zJo1i02bNoUs27VrFyNHqs75uXPn8tFHH4W8v3LlSmbNmtVnGS3R7Dbvh/M474nzqFxYGbG+NdeKwaxeI+Jx7mvOjAK1ShQl7bfAtbXqvuxFdkSviLdJvW7Kooy73o2nyYPoFbEX2eMORoqlpz8iesSQEjmp1jRw6kCWvrSURb9cFPV9RVGo2VRD9frqiIDJ3MG55A/NDwRsJEqm2EhD15P+dEeTt9mLKNKemQGFGz9i9V9Wp7yHXiKkg40Cc7ACLPj5Ao6/9fi451qikQ6aQM/MSBpFUQLNTxKlcGQhZ/35rB4YlYrWGDeWMyK8MW40Lck8uOfkgN0OLpe6vP1eEMEgYHFY8LX58LZ6u/XDiZfu2KenED0iH979IY5SB7Nvnh3Si6LTzAzJDZ5jCJIRRSlPmVc5VpkpgLyKPABaDvecM6MvbKRNWpkd5oQyhGpr1QlFg0GNUgI6MjPMamZGXHrs7R5s1xFQ5I5yRnQ4M1atUicxy8pg9nT1t+J3+dVSIgk0LO8uffUbWvCLBXiaPAGHWldoz6GTJnW+Xkw91R+orwNODF0ueaDuM8ibGNWhoTX7bWtTI6+1kjLBaA90ndW27w7peJ7T6ZrO7BatwXR5udofJrhsWjqhKAq1B2vxrfVhMpuYcPGEXv18b2t7ZkVOx4O6VmbK2+JFUZROr5vhzoxo9kk0SATU/mJaj7GGBsjN7Twzo35nPeufXM+AqQMYfNzgiPejYbapmSjRnCMamXieyDRNmaYHMk+Trifz+O53v8utt97Kcccdx8CBA9m2bRsPP/wwb775JgALFizA7/fz7LPPcvnll9Pa2spdd93FD37wgz4bczS7GUyGiF6MGoIgYC+203a0DWeNM7Ceoii8ev2rmLJMnHzvyYE+dVpZRM2ZoT3z1NaqpY6WLF8SuGau/utqjq09xoRvTGD4yWo0XqL9mjLlOPziT19w6JNDzLhxBsNOHZZyTV09+3saPWoJa4Eue24lSqbYSEPXk/50R5Oj1IFrwFCO7clHwUCbYgNcOGucfVIKF9LDRpozw5JtSclcYjpoAj0zIyMxGuH886O/F29j3GQe3AUhdrS/NsnQU5N6/QFnjZPazbVUfVkV4siQ5Y66+9GdGep3ZpA9CC3bUjae4DJT4eeh3IpcBKOA6E2+FEY6okXwJhI1BB3NvwcMgEDZPy0zwxTfpDugNpQ2mEH2g6c65C0t82PzZrjsMjjxRBg93syxGvVY0XpJZDoFwwoom1YWmKTrCs2ZMTmZdjzuY9C8TT15lS4MfW/r/bDtQTj2XtRNs7I6HIKx+mZofT++LrbT6R5ag+nwQIQjR9TlK1b0zbjiQXJJbH5uMzte3dHrn61ICvZSe8gEir3IzgX/vIClLy7t8qY9ntJ2WpBIrF0JAlRUdASJaGjZGfX16qt2XovmzAh3qsSDloXRnbJVOjo6Ol9HTjnlFG699VYWLFjA2LFjueKKK3j00UcDPTQEQeA///kPTz/9NJMmTWL27NlcfPHFLF26tI9HnhhaeSGtHBWA5JNw1bhoOdgSCJRSlMgyU1pmRk2N+uoocVA4opDCEYWUTijF4lAnxrRliTgyMglPowdFVpLOfuguzlq1JIa9yB4RrNe0v4nNz29m73t7+2JoOjq9yoDJAzg2bA7bGQdAs6iek5w1zs42y3i0OTBLjgVFURA9IpK//2fz6JkZGYjHA6+8ov6dlwfNzR3vRWuMGw3twf3IkeglVMKzOzTKytTamtH6ZrQdbQv8kL6OaCfR4JqVkqROUPl8atR/hBNJcqsZAIoIigT+ZrV5tCLTXbQm0z6fevOq3bgClM8u5+KXLu6V/ia9STKTRRCl+TcEZWYkUC5OMEDeeNWWUodjb8UKuOOOyNWPVAmsPGJl/gw1hfvr+pAQC1lWnT/QdWZGVKo/VF/zp4C1MPS9khOgfjXUfAiVl0SdxRw6VHVC7d8P06dH7l47znwtX9/znk58dNanSuu3EN5gOp0w2tRB+Z3+LjMhUk3pxFIWP7E4ZJlgEOLOAo3numA0qtkxF10U+V5nQSKFhep9lBbtGnA+RKktro1Di5CNBy3TQ/JJKLLSrfq3Ojo6Ol83rr/+eq6//vqY71dWVvLWW2/14ogSw1XvYsOyDeQMymHiNyZGXUfrpRHszAhcg4J6Obnd6jMhRC8zFU5Omfr803a0rZsq+g5nrbPTQMt4M020EjZaVmhP8OVfvqRxbyPH3XQcBcMLQt5z1bb3TYnSF6NpfxObnt3EgCkDAhk0OjqZTEtQYZEGbzZQi7P66+3M0LLYrTlWVv56JVWrq5h96+x+f07QnRlJYjAYGD58eFI1M3e8uoNd/9vFsJOGMWFp6ssx/PnPaj+LigrYtg1Wr1adC501xg3XEvzgLgihEyydPbh3mZnR2juZGd2xT0+hOTO0FN/wciKyDCNGtJcTOSsXrEXgrVcdGLKIyaCALKrR5JY89X1zAlkBYWRlqTerjY2qvYKdGb3VpL23bZQzOIfJV04ORMzHgyTBB+2ViCwW9f9GIzD4PCicDjljgAT0TLknYv+dTWJ6sbJlixtXvYeiUXEPu9v0hX2cNU4OrDxA9sBshpwwpMv19+5Vy9plZXWUtYtFhB5FUR0VAAMWRW5QPAeMf1FLgrXugtzREasMHQqffx67Cbg2OSp6xB4pE5aO5zmdrolmt2QaTKcLBoOBkeNHcpjDyKKM5JUCkyPpjqIoEc6MWL+rJUvgxhvhr38N3UdnQSJa+TktMyN7QDaDZw+mcGRhxLrJONsNZgOCUUCRFESvGDWjLRPPE5mmKdP0QOZp0vXopAPhdmutamX/B/vJHpTdpTNDi94HtXwtqNmCWvCB5nQ3GiFbraAbKDOlZWYEUzKhhKnXTo2YWO+Ont7EWetkxWUrcNW7Yq5jL7KzZPmSLh0aWn8uW6GtxzQ17WuiYWcDbdVtEd+5Ztto5ca0HpHJzsFk2rlC15P+dEeTt9VLW7MZrQBRg9eBLIee/3qbdLBRcJkp7RlNuw4kQzpoAt2ZkTQGg4HSQPH8xHDVuWg93NojWQqNjXDvverfv/oVOBxdT3zE0rJkCbz0UvT63bEe3INLFwUz41szUL6pBG6oepru2KenCL7R0MqJhE9ga+VEXnqphCVnLQd/C2z+NYLzIELuKGjZpdbwH3uL6siI0Zw4XsrK1GPm2LGOng29RV/YKK8iL9APJB7CHU6rVqkT2Gr9+rGQNzawbrJ6uprErGYATo+djTssVByf8O6Tpi/s07ivkQ3LNlA4qjAuZ4ZWYmrChK6j1SP0tO1VHRVGCxTPjdzAZIOi46Fmper0iOLMqGzvtRjLmWG2mwMTjd4Wb8rPf+l4ntPpmmh2S6ZPVbpgMBgoqyjDYDKgSAq+Nl9aODN2vbGL6k3VjDhtBGXTotfOlP0yw04ahqfZE+LMiPW72rhRfb3hBvXeKlaQiIYWJKBNEpWML6FkfPTrdqLODC2yVjAKSF4pUOYiPOsjE88TmaYp0/RA5mnS9eikA+F207ItOptszx6Qjb3YjsnacV0OODPsHQ7w4BJTWuBiZ5kZiT5TRaMvj0NvixdXvQuT1RTImgxGdIu46l14W7ydfr+iVwx8n1n5WT2mSeuFoTlOgolW/UFDCygNNABOkEw7V+h60p/uaPrgFx8wZn0j21nIUQbhxIFfpE8zM9LBRsFlprRrQXedGX2tCfSeGUkjSRIbNmxIqoN7tBuIVPHb36o3IxMnwpVXxrdNZ1qWLFEn6j74AJYvV1/37YtdpipWZkbOoBxyy3N7bYKjO/bpKbQbjawie6eR+KCWE5HMJZAzAmQfisnObnE2iskOniNqI+luOjKg8ybgO/67g7duf4s9b+/p9udEIx1tFEyi9esT1iOr54GuJifXMp2VLKTF0n17J0Jf2CcwmRdn5ow2sRhPiakIPdnDYfqDMOq7quMiGgNPUl9rVqpZUWEMHaq+xuqZIQgC9hI79hJ7j9S0T/ffkE50otktmT5V6YIkSWzcuBGzQ72nSfaBOVk2PbeJt37wFvs+2BeyvG57HYc+OUTTvqaY2xotRo6/9XgW3bUokJEY63e1ezd8+qlaEvKuu+DSS1WHRmeO1HBnRmfE07tDe99eZEf0iniaPJiyTFjzrUg+CU+TB9ErYi+yB/aTieeJTNOUaXog8zTpenTSgXC7BcoLdRIsM+K0ESz+x2KmXTctsMzvVp9BgifxNWdGQVDQf2fOjFSQDsehyWbC4rBE/Ivm4IiGVmLKaDFitpt7TJNWwipaJonBaMCaayV7QHbEe1pmht+Z3MRlOtgoleh60p/uaPI2eRFF8KHeAztx4Pf3bc+MdLBR8bhiJl85mYq5FYE56O44M9JBE+iZGUmjKAputzupDu4+p/qgrz34p4pDh9SIcYDf/Cb+utpdaTEa4y9rEcuZ0dt0xz49hXbDubvKEX85kRNc4G9FUaCe4Qy3FCH4GqBpExTN7PaYYmXSgHpz1rCrgYZRDYxgRLc/K5y+sFHL4RZkUcZR6ujUmRhP/frlf1zJ4vkOjIWTwGiJX4+nFtb/H4hOmPccZWXx1Tjv7UnMZO3Tnfqz2mRevDXjtcyMeJwZEXoEAXJHqf9ikT9VLenma4bGdVA0K+RtzZkRKzMD4LzHzut6cEmSjuc5na6JZrdk+1SlA5oes8OMr8XX686M5oPNNOxsiPhcrWeGNtEQL7F+V8uWqa+nnQaDBsW3r/AyUxqyKEeUc4w3M8NR4mDJ8iVxn2cz8TyRaZoyTQ9kniZdj046EG63QOPnKBH5nREtsFJzugc7M7TA29patRxyeEWRlsMtNB9spmh0UVLZx+l0HDrrnLQdbaNwVCHmrPjnaLRMiazCLARB6DFN9iJ7yOcFM/366Uy/fnrUzwx2ZiTTWyudbJQKdD3pT7KatNKxogiedmdGI/kMXjKbWScl0OM0xaSDjYrHFFM8phiA5gNqQ+XuODPSQRPozow+QSsBkOrMjF/+Um3+vWABnHVWSncdN7GcGU37mzi06hD2YjsjTkv9xHh/QPKrnssmf3xNnI8eBYw2mLMM2XkUZWcrSt4MqH4HGtak1JkRzfmkpQ63HGqJfLOfsu7JdVStruK47x/X6XHYVekngyBy9awHaFwJxectV0sVxYslH7y16oylr5H58wu7nsQcrDBvjkK6J9N1t/6sp0m9QY+3zIrmzJg8OfGxxoXBCKUL4fCr0Lo7KWeGjk48JNtgOp2wZFtw4ux1Z0YgdTo79DyslWRwN8Z2Zkg+9brcVT8bWYann1b/vvrq+McWnpnhrHXy2g2vIRgELv73xSHrzr1jLu5GN/nD8rvcr6PEEVdTUh0dHR2dzEUrM5WoI8FgMpBTnoNjQMd1JLjMlEaxOveFJEFTU+h7AGv+tobq9dUZ0Ui2aW8ToE70aZN+8WDKMjFk/pCE+l0lg5aZ0VmAhtb/JBiLo+PeyOf0Yc3p2XHq6PQVWo9KUQQvWeTlQXOzFcPI4ZSM7+vRpQ9a1ll3nBnpgu7M6AN6oszUli3w1FPq37/9bcfkR28Tq2xR86FmNj+3mZKJJV9bZ8YZfzgDyS+xcmUCkfiCANZCMOYCa1CKZoFrP9grUjKmzspM5VaozcUzyZkRb+RrV5lF2VltAHi8ApgSnFAymMFWBq4qcB3CWFDY6SRmuXKI2wZ/xsf3FHPSr09K7LN6me7Wn9Xso0VUd4bLBbt2qX/Hk5kRws6/gCJBxRK1ZFtnlF8A5YshK7Iu5JD2th4tLepDXn5+guPQ0QliyRJ1oly7lmt01qcqnZj1vVmYTKaoDSh7Eq2pZfh5vbP60hr7P9zPl3/6kvK55cz/Sey0l48+goMHIS8PFi+Of2zhzgyT1YQsygAREZIFwwsoIPFGqrIo07ivEVeti4q5qbk30NHR0dFJf+LpmQHw4d0f0nywmRN/dSK5g3MZNGMQg2aEphhGKzNltdI+IahmZ4Q7M7IHZlNNNW3H2rqtpc8RAIXANTpeCoYXMO9H83pmTEHEE6ARDYPJgCnLhOgR8bXpzgydzMXb7EVWwCcbETExZIga+BhPqddMp3GfeoLPKcsJzEGH99jrj6R3mG8aYzQaGTt2LMYkwiQDZaa66cyQJPjwQ3juOfjWt9TIwSVL4PgEmwR3R0s4WqR/XR34goIztQtnTzQ9j0YqNaUSo9nIgkUGystjO5wEASoqQsuJBPSUHA8zHoTBZ6dkPJ2VmcotV50ZniZPj0Ta9oWN4nVmdFXSKSerFQCzzQGCehpNSI/mjHIdAtTf7UsvdTiXNMrL4b4HTAwskQJZC71Fd+yTbP1ZT3N7ZkYcPTO2blUzWUpKYMCArscU0KN4ofp9OPoOiHE8EGQVR3VkANjtHSn4sbIzdv1vF2/f8TbbX9ne9WclSLqe53Q6pzO7VVWpr9PaS1sPHdp5n6p0QNNTOKyQvCF5vd78O1ZmRjxlprRrgtnWcT8WzT6ag+mSS8AWo8VONMLLTAV/N6nqo+N3+3n79rf55L5Pou4zE88TmaYp0/RA5mnS9eikA+F2c9er17euykw5q524alyBksfRiFZmCjr6ZtTURG6TPVDt0dB2NDlnRjodhwXDVOGCsXsRoT2lyVZow2gxYjSH7rf5UDOv3fgan/z2k5jbnnjPiZz5pzOTKgWWTjZKBbqe9CdZTZ5mT0iJqcpKdXndjnp2v7k7MKHf26SDjb7805e8efObVG+qJqcsh/I55RSPjT8DLZx00AR6ZkbSCIJAfpJhuLZCG6JL7JZnfMUKtaZ/eCmceHtbBNMdLeEUFYHJBKII1dXqpDyAJUedZOgtZ0YqNaWa4HIighBaWiiinMix98B5AKF4Dvn541I+ls4yM8w2M/ZiO646F82HmikZl9oG1H1ho3idGdFu2IPJtbVis0FJWUf9xYT02CuALwLODFAnKxcu7EjpfvNNOOUUaDmQxZsfdfST6C1SYR9XvYvWqta4688mUmYq0RJTAT3H3gPJB/bBkDMyvo01RGdEJs7Qoerxsn8/TJ0auYmnyUP9jvq4SsckSjqf53RiE8tuLpeaAQBw771w5pnqNV6S0ru8VF8fh9p9RTKZGdEcqOF62trg3/9W/06kxBREZmYYzAYEg4AiK4geMRDU4mn2sO/9fdiL7FQuqEzoM6w5Viw5FnytPlqrWikYHjoT1df26QkyTVOm6YHM06Tr0UkHwu12wdMX4G5wB653sXCUOmg51BLosRGNaGWmQHVm7N4dvQl4TvtzULKZGelwHGrRyYqiIIsy/jY/Pqcv7qhlv9uPyWoKZFr2lKb8ofksfWlpRCmptmNttB5pxWiNfaOYSNmscNLBRqlE15P+JKvJ2+wNlJiyWjuCHZu+3MnqtfuZcvWUgNOyN0kHGwWy2HOsFI8tpnRi9EDNeEkHTaBnZiSNKIqsXr0aUUw8su6kX5/E4n8sjnjgjJcVK9SJ8Gg1/W+5RX0/EbqjJRyDIfoEuea48bZ4e6VRTCo1pYJDnx3ivTvfY9vL24DOI/FfeikoCrfuMzj0MlLL7lA9oguaNnd7XF01bA+Umjqc+lJTvW0jWZTxO9USb9qkV3B204cfqv//4x/hG9/o2C48g0YQIMfWyoQJYLB0ODMS0qOVNnKF/ogLC8HSHlw8dqw6galFF3ube+e3o5EK+zTuaUR0i4E6tF0x9wdzWfSrRXHddCfS/Bs69MhH31MXDDgx/np8sgibfgWrrgBvaCdfLeojVmaGdqx11qw3WdLtPNdXPPzww0yePJkpU6YwduxYrrzySo4cORJ4f9u2bSxcuJCpU6cybdo0ViR6kUwxsez20Ufg9arly04/HXJz1cCAHTv6aKBxoump3lLN5uc3c2jVoa43ShGyKAeyEcIDRLT60qJXDPTGCCeagzvcPi+9BE4njBoFc+YkNr5wZ4YgCB21at0dtWpbq1pZ/+R6NvxzQ2If0E7O4JzAfsLJxPNEpmnKND2QeZp0PTrpQLjdDCYDjlIHBlPnUzpaRL6WmbFp+SZev+l1dr+5O7BOtDJT0JGB3GlmRpLOjL48Dq25VuxFdkSviLvRja/Npz4ruvy4G9yIXhF7kb3LAKtPfvMJL1zwAgdWHgB6TpMgCFF7Ymg27ak+Wpl2rtD1pD/JasrKz6Jw+lCqKCM3t+Nc5kL9bThrYjtze5J0sFGsLPZkSQdNoGdmdAtJiv5w3LOfqTosOpvTvPVWtaZzIpGcqdRSVqY6WkKcGe03ArIoI3mlXilD0Rf2iUXzgWZqNtUEbvpAdVgMGwbTp0NODrz6qlpaKsRuHvXOUbGWdOjx1MGX1wMCzHsOjF33F4iF5sxobga3O7J8Rl5lXo/WQe1NGwVKZQnqiTxadlN2thqFC/Dd78KJJ8Jtt4WuU14OD9zbSlkJYOpwZkACerQyU87QST9BUB8aDh9WHxoqKzt+O4qs4Gv19XiDuWC6Yx+FjpNUvA2m8obkkTckL651N25UXzt1Znhqwd/uiJNEspybQfxc/b99qPp+VhwZR75G1fHka4YD/4Ky0wJvTR8NH+TkcuBA9P30pDMD0us811ece+653HDDDWRlZSGKInfffTfnnHMO69atw+PxsHjxYh577DEWLlzIsWPHWLhwISNHjmRyj3WO75podnvrLfX19NPVc8GkSfDpp6rjLuG+ML2MJEnUbqtl07ObqFxU2Wu9G/xuP/ZSO36nH7MjNPvLbDdzwTMXYM21Rp0AgI6Mt/DzarB9li1TX6++OvF+ZFqZqYYG9b5NENRSU36nP6QkVKBfUF5y1/OcQTnUb6+P6syAzDxPZJqmTNMDmadJ16OTDiRjt4Azo73HhrPGScvBlkDpa+i6zFS0zAztudbb7MXv9oeUbIyXvjoOHSUOlixfgrfFS9P+Jj7+fx8Hen4t/OVCcgfnYs21dukkcDe4UWQl5B6kNzVp2Tad9Sur+qqKhl0NDJgyIKlKC5l2rtD1pD/JaCoaXcSA8+ew6QH4/+y9eZwcV3X2/62u6n32faTRvq+2bHmTF3nBNthJDLIMwUD8Y0lCSFgCIQlJgJiEJQnw2iQvIS+EEAIOYBDGbMaAV2xjW5ZkS9Zq7dJo9ulleu+q+v1x5/a+T89Ma9SPPv3pUXV1VZ2+t27de55znrMshczwGbNLZsDstpFpmImAXqmWY5omRsxAtVWe+l8L/a5OZpxnePrp3BkZEqYJp0+L/SqRnKoGctVhUO0qFs2CETeI+CIzrqk925CDZ+ZEY2REvC9alKO9TBPCg+JvRzcw+be9HexdEDoH4y9DxxUVX1dzsyjuFomI9lqyJP3zTe/axCXvvqTi49cSZHqdrcHGDx9S2L49mxSURMbb3w7/9m/C8fSmN4n76dw50bevvRbUfj8cBazpZEbJcC2AplXi3dDBknyQdHeLe3xwsrktmgVbg43oRJSwNzyjZMZUoIeTD7iuDVNLZcyFojJT4WF49u5EJoVqmqyYGEJhQhCAO/9E3EtbHihMaMjjTBwXf3v3wtGvJj5+90q45E/b+faxB4Ds40jn5EzLhF1IWJIycGmaxr333suXvvQl+vv72blzJ5s2bWLr1q0A9PT08JGPfISvf/3r3HfffbN0xbnxyCPi/fWvF++pZMb5ABntMx01lvLB3mjnjv/MXZFbUZSi5EAxEuHECZG1pyjwjneUf30yM0PXwecTz1w5/0mVsChVAjEfmuZPZlGerX4WZR111FFHHbWH/p39nHzqJD0X97DkxiUF95U1NaTjWwYZpdbvzCczJTMzcpEZVpcVe5OdiC/CxMDErEi4TAXuTjfuTjehsRA2t5jDvOHf3kDT/Kai2S4SUsqymNRXNbD7v3YztHeIi/7gInouFvIO0sdQqG7K6WdOc+yXx1AsStVlo+uoo5bgn4zpaWxMkhnjUTfYZpfMmE2kktY2t43gaJCH3/0wiqLw5h1vzhvwdT7gwvIo1wBCYyEe+7vHsDfbed1nX1f29/PJAVW633Qgl3SRoijYm+yExkJE/JGC0QNzEfkmGtJhnbOAcXxCyEmBIC8kmaEo0L4ZzvwYxnYWJzNSo9MzoADrljWxa39nTjLjfB7cMmFz29j4jo0YBtxWJLvpySfBMESWjKrmIJraNoO1CewVTgg1J1zy+Zwf5UrntjfbBZkxHqZ5QWmZC7OJeEjIuhhxA82lYcQNovHC+rNhb5ijjx7F1eFiyQ2FF2WDg+L3URRYuzbPTjGfIDIsdlCdmKaJYp4FVQNnj9geGRX7FSIz5HFsbRD1gGkkjgmg2kJ0NIwyNuQjF5kx3ZkZdWQjGAyiKArt7e386le/ShAZElu3buX+++/P+d1IJEIkkmwrn0+MnfF4PCnrYLFgsVgwDAPDMBL7yu26rqdJwmVul+9yn3g8zvHjcOiQhqqa3HijGJ/WrjUAlVdeMYjHjUSRtcxIGE3TEseVUBQFVVWzrjHf9qnYJD9THSomJtGJaFbacb5rn22bQuMhDNNAdYnvZ7bTN76hACo33gjz5sVJNasUmzQNXC6VYFBhbAyamkw61nXQMK8Bxaag6zqqqiauw+q2Eo/Hy7bJ3SPmVL4zvrTf3mIRzpjM31JVVRRFOW/aKbPvzTWb5D65rrFuU23YlO/v89Um+Z7aToVsmm0piTqyMXJohBOPn0C1q0XJDJldIDMzEmSGM5vMKKcAOMCmd29CtavTJnM0E5A1+3o399KyqKXk7xlxIxGsNBNkhv+sn7EjY/jP+RNkRkJmqoBvZTaCTeqoY6YRnYjiHVMBlaamJDE7EnJDAwQGA8IfMIf8W6VASkxpTg2LZsHqtGLqJiZTz86YbdTJjAqhqiobN24su4J7dCKK77QPm6cyvTJJFFRrP6jclmLnziRUtn5yK6pNxd09/ZOdats0VeRLAS1IZkxKTGFrQbW50u1puzRJZkjtilzIiE7PhX9/Rzu3f+YBzp3L79CVC5xqDv4z3UbONifr3ryOJ54onN0EJWQ3uReIVwqqZY/sC7JvAHRf1E1TX1NCa30mUIk9Un82OBokNBbCiBtgJhcJQF792YlzE7zyzVdwdRUnM2Sk+vLl4MofiDRpiBM0NwomWsM8iHnAOQ/0CBhlEAxaAzg6IDIOegjsoq6HzQUQydunUsmMak+gam2cqwW8+uqr/OVf/iWf/OQnsdvt9Pf3c/PNN6fts2DBAo4dO5bz+5/97Ge59957s7bv3r0bt1uM352dnSxbtozjx48znBKq2NfXR19fH4cPH8br9Sa2L126lK6uLvbt20coFAKEo29iYoLW1lZ2797Ngw+2A0vZsMGPzaah6zas1kPAOnbtirFz5242b95MNBrlFamxhugDl112GV6vl4MHDya2O51OLrroIkZGRtJsbW5uZs2aNfT393MmpdNO1SbDMDh25hjxWJzoRJTdu3enOeQ2btyIzWZj586dab/rdNukHFfY9+g+rMuttG5szbIp2hnF1EwOnz6MpctCV1cXBw8exDAMdu3azde+djGgcs89VGxTQ8MmgkE7o6PQ2uqFy8CBg6NjR+kP9XPRRRcx2j+K1+Olf0xkE5XbTk2qyMzoP9Sfdj1Lly6ls7MTm83G7t27E9tXr15NS0tLzbRTuX1vrtm0ZMkSNm7cyMGDBxNjRN2m2rOpr68PVVV55ZVX5oRNhmEwPj5Od3d32vMpl02BwIUZ0VprSJ33SWKiFBLB1enC1eFKyE3Jmk2pmRmVyEwBRYmUQqiVeWwiu6K1PEJCrm8UVUnM96fTJkmYhMaS92o+9YdUTIXMqJU2qhbq9tQ+KrXpmX95huMPDbCYK2lsXJIYy4YCLugCPaoT8UUqlnStFLPdRgl1kkmJqVR/UiwUq4jMmG2bJOpkxhRgs5VPSCQiIdzla0qCkLjp64OzZ3NHliuK+Pzaa8s7biW25EOuAuBAxQXPK0U1bZoKTNPMGzUhpbgyC4EDGRJTGfY0bwDVJupnBE+Be1Huk2dEp2dBD9HROEqT05eXzHjyH55kZP8IN376xqq34Wy00XRmN5VtjxET2Te2ZLZFrsyMy/7ksvIvpgoo1540/dnjHoZeHcLV4SLqixIcC7L+99fn1Z8Ne8WCoJQJRlGJqZxQUFx9oEgSqoJMCdskmREZTdQ9cUzyMj6fqD3TnJE4Y2+yozk0bE02YsFYIo29WqiVcW628dGPfpT/+Z//YXBwkPe85z188IMfBMDj8eBwpPcph8NBOBzOSS597GMf48Mf/nDi/z6fjwULFrBp0yaamoTDWEaGL1myhEWLkmOv3L5y5cqckeTr169PZGQYhoHVKuYBmzZt4tOfFvts2+bG6RR/33nnKv7kT2BgwM6KFZtRVRWn08nmzZuz7G9ubk7bLu3q6OigLUUzQm6fN28ePSkPnqnYJCOAvR1ehr87TGwixqZNm9KuT052M699qjadefYMB//jILHLYqx/6/osm3b9chcT+ydYs3YNGzZvyLJpxWdWZNm0bt06dF3nuedUzp7VaGgw2bZNwW6vzKbubpWhIeEoymeTzbTR3NLMqo2rWLN5TdntZMQMLO+24Op20Xtpb+L7qe1ksVgS2+W1z1Q7pW6vZt+bKzYpioJpmqxbty7ntddtmn2bUrPp5Fh+Ptskn0OappVkk8xSrGP2Ied9ksyQBEUhNPY2csd/JSUZM2WmTBM8HvFZPpmpfJkZU0UtzGND45OBJnGD/d/fj2JRWLNtTcnfc7Q40uaT02VTJplhmibONid6VC9Iak01M6MW2qiaqNtT+6jEprAnTDwOUexpBcDHPCrONieh0RCBocCMkxkwu23kbHOy8R0bE6SFoihoTo14KE4sGKv496iFfleaGGAdWdB1nZ07d5Zd+ERqlqVGQpQDVYU86hiJ4Pz77iu/+HcltuRDvsyMmUS1bZoKwp4wRswARUSlp6JwZkaSzMiyR7VBy6Qnd3Rnji9nYDI6PeulOtEmu2JqjZNURCeiIm3vtDf3DhViptsoMBTAc8JDZ3NpTuyC2U3je2DspTT5rrLtGXwCnt4Oh9JvaLloSM3MmA1U2j7uTjdty9pY+rqlXPnBK1nxhhUc+dkRzj5/loaehryT7YRmfHNxzXhJZpRTFNk0TbxeT9pivWzYmkGxgBFNZHVoKkz6Ajh5MvsrFs3CXQ/exR3/eUfViYxaGudmG//yL//CwMAAIyMjOBwO3vnOdwJgt9sJh8Np+4ZCIez23EWh7XY7TU1NaS8Q8h/yJZ1CFosl53ZVVQtuVxSF3bt3J+Q9DEPjscfEPrfdJqQ9FEWhs1Ojr09c18GDWmJ76rGlIypzu3REZV5jvu1TsUnaY2+0o6AQnYhm7Z/v2qdqU2gkxPjRcQKDgZzX7mxzoigKUV+0ZJtAZGF885vi/3fdpeB2U/K1Z27v6BD9bGws3SZVVRM2Rf1RLIoFV6uronayOW2sfuNqFl61EKvVmra/ruvs3r077dyy71dq02z3vblmk2maiej9zGus21QbNimKwq5duxLScOe7TXLclnOiUmyqY/aROu+TgXKlkBmZiAeFbJj0R/j9orYTlJ+ZEfFHOPPbM5x65lTZ11Er81iZYWGaJi//98sc+tGh0r6Xo17GdNrkaHWknVdRFG794q3c+cCdOFryOySnQmbUShtVC3V7ah8V+1l9UeJxCGeQGePjcPmfXc6Nn7mRpr6mabjiwpjtNnJ3uln35nWsfuPqxDY59ktiu1zMtk0S9ZnJDENqxldKZgBs2wbf/74oUpySEUxfnyAytm2b4kVOEZlkRmA4QMQXYeTgCKOHR2lb2kbn+mQWQL5I7VqFtCcfMu2J+qPCoaIqWcXECpIZfW+Erq1g5hkk2jYLImNsJyy8s0wrkpgMDs5LPjUvaGZk/wi+0+d3VNahhw9x6EeHWPWmNfT1XTy17Kaj/wkTJ2Djp6BtU4EdC8DeLuovBE+nbZZ9ITMCyjRNTN0suSBdrcDZ5qRxfiP+s36GXx1m/uXzc+4nFxKlRAdIZYaSyQxTh5hf/N5TgkVIVCkKKEnGWAYmnDhRbrZIHdVGe3s7999/Py0tLXzpS1+ir6+PU6fSF9inT5+mT7IENYDnnhOOhM5OyAjsZcMGIYv3yitw9dWzc32lQi6WjbiBHtXR7NM/xcxMnc6EXPinSjJIGHED0zDT0qt1HZ58UuGJJzr4zneEM++ee6Z2jTLKdXRS7XHX13Zx5KdHWPf761j/lvUAXPzOi1lx+4qytLrrqKOOOuq4MJGa9V+o8HM+ONrEs1H6I6TElN0OzoxEfklmjIyIeoKWjGWI96SXpz/9NA29DSy8emHZ11ILkORA59pOTj5xktBYiHg4juYoPI+xN9lZeO1CGnoaZuIyc8pMlYJ6zYw65jpM00xkZkRwpBUADwSgY+M8aiCRoGZgdVkJjYaIBSojM2oFdTJjhiEzM6YaobttG6xcCS+/DH/xF3D77cL5WgtyeZLMGBwE/2CAh96+Q2joj4YIe8LYm+1pUSSudhfbHth2XhAageEAO+4W9uRDpj3NC5t543+/EUPPdqQWJDMUBeyTXpBchffarwAUUT+jFJhxiI4JD74jeULr5CiQj8xoWiAY7GpnZsw0pNPL2WLn/vth+3bxE6cSGiVnN8X84l2bwuR1UqaI8CDoUZFtQ+7MjOOPH+fFf3uRnk09XPd311V+zhnCxMAEnhMe2pa34epw0bWhC/9ZP4OvDOYlM2QBvWKZGboOr74q/i6ZOIj7UfyHccUtQFvR3QvCOS9rk5wc5crMqGPmEYlEiEaj6LrOli1b+OlPf8qf/umfJj5/8skn2bJlyyxeYToeeUS833prtpNgwwb4+c+T2Ui1DM2p8bp/eh22BhuqdWYmI7Konb0x97gh9a+lFEQqhvYN8fjHH6d1eSuv/z+vZ8cO+OAH4cwZFVgOiOdAvmjUUiHJDOksAkGkxMPJ53rzgmaaF2Ro1JWJwFCA0cOjOFocdK3vmtKx6qijjjrqqF1EJ6LoURHsVmpmxsvffJkTT55g/VvW8/r/8/q0z2Tx70yJKUiSGbou9mtvT/9cOvIDQwEM3cCinl9BVwC9l/TibHPSsboDW4ON6ESUiYEJWha3FPxex+oOOlZ3zMxFMgUyYzLgQ86Z6qhjriEeiou5dUpmRktL8vPx8Tz+tgsAgaEA0UAUV4crsV5KZGaEzm8y4/x72pznyNSorBSmCbIO3LveJQoV1wKRAWKgUBThfx84GSE4GkSzC814i2ZBtas4Whw4Whxodo3gaLBgpkMtIeJL2iNtSH0VsifX5K5gzYxicHTA/NvAWWRkNmIQPCmKFk+cgNDZtI+tRWSmZDre+Z6ZkZAxarInspsypaT6+sT2otlN8UkywzqFVEVrM1gbxM2c0ia5MjM0u4Ye1RN1JWodZ54/w9OffpqX/t9LAHRvEEYN7s2vnSXbp1hmxtGjEA6Lwt9Ll5ZwMXoIwqNgxjEUDeIB8dLLWwigh5LfTX3pIWyTftQTJ3J/dd939vGLj/yCU78pPwW/jsKIRqNpxU89Hg/33HMP27dvp62tje3bt/P888/z5JNPAjAwMMDnP//5NHJjtpFKZmRCZh+dD2SGoih0ru2keWEziqV6he4LoVhmhlz4y6jLtO9OjjmaQ2PHDkFwp3QlQDhv3vxm2LGj8muUjh9JZsjCezJTt1o49ZtTPPNPz3Dk50eqetw66qijjjpqC+GxMCgiAKjU4IFYKEZwKMjEwETWZ5LMyJSYAhGwI+vB5SL3ne1OLFYLpm4m6nicb1j35nVc/ZdX07qklYZ5gpzx9/tn5Vp0HZ54Av73f8V7qoqLs9WJalexuqyYpsn+H+znJ+/9CQcfOljwmM0Lmrnpszdx/aeun85Lr6OOWYP0kUQNFR2NpibhG5Vj18CxIK/94jWOP3Z8Fq9ydnDwRwd55AOPcGDHgcS2znWdzL9yPvam4vLetYx6ZkaFUFWVzZs3l13B3aJZcHW4CuoaloKRESFLoSiwZMmUDlWxLflgtUJHh5jwDE86ZDWn0M+1aBYUi5KWmRKPVHdBD9W3KROaU8ubXVOqPbqenBRmMcWmCQe/KKSIFr4ZVXVWZo8RgfDApLzOpHPJiANm4v9aCTJTABPnJqoacTPdbZQJ6biSTq9t22DtWlizBhwOEf1cUnaTHhUvEGTEJMq2R1FEdob3gJCaahA3sszMGBkRfURVUzRSPTNHZkylfcaPiVWRLBgvo4Q9xz1EJ6KJdOdUSNuKZWZIial167Kj2NNgbRL3T2QUYmNgxHHY7RBPyTCytxcnpFKPY0SEZJURF7UzLOLmMazt+EJNecmMicEJxg6PVX1hNNP3UC1ieHiYO+64g0AggMPhwGKxcPfddycKgLvdbh5++GHe9773MTExgWEY3HvvvVxxxRWzds2p7TYwAHv2iO233JK9byqZYZrJ7LFawmz2w2KZGXKuFfaEMQ0zjWRJPhMcfPCDuWUHJT70IbjjjsqCRjJlpqRshczMMOIGhx4+hL3JzuLrF1csJdg4rxHIdsDka59yJTNrCXNt7Jtr9sDcs6luTx21gNR2e8uOt5QVDCgzOHIRDpJsz0VmgFibeL1i3bp6dfpniqLg7nbjP+Nn4twEDd2lZ63XYj9snNdY8pw9GohidVrT5hZTsSmZIZrc1tcn6qVu2ybWg3c9eFeipo3/rB//WX9apmcuaA6t4ozNWmyjqaBuT+2jEpukwkMEsR5oFFNiWlvF2DVwyMvYD16kaWETS26covO0TMx2G+VaK216Z4Uy6ZOYbZsk6mTGFBCNRnFmCksWwarfXcWq31015XO/9pp47+sTjtipohJbCqG3d5LMSIngkAt0I2ZgYqIwvV6ZatuUC/5zfhRVwd3pzllMFuCF//sCvjM+Nrx1A90bk6zF6KjQHlWUZApvAjGfKBCtKLD4bUAee/QIDD4mHOKr/zzd02Wa8NrXhOPdYgNHD0Q9gAlRH1g00EOJzIyhoaTzPBWuTheqXUWP6EwMTNA0v3qFk2aijSRSMzMkPB7x3tMjsptKgszKsKigpqd2l22Pq0+0XSBZN0P2BcMQfaSrK5mtEPHMbAZTpe0jyYyWJS1Aet2MoVeH6Lsiu17BpX98KcHhYELWLB9khHpRiSlHJ2x5QJAQuz4IeozIqr/F3rwoeZ9Ym8R+pRxHFns/+V0Y+BXMez0sELVqDjzWxIi/My+ZIfvcdGSgzeQ9VIuYP38+L730UsF9LrroIp555pkZuqLSINvt0UfF/y+9NElkpmL1ajEme71icbtgwcxeZ6mQ9pz6zSl8Z3z0XdU3I/UfitbMaHGAAopFIeKPpGV+ySiu00P2rIyMVJgmnD4NTz9dxnMiBZkyU1Znemp3xBdhz3/tAYUpLbAa54uV20T/BKZpps1JMseJSiQzaw1zbeyba/bA3LOpbk8dtQDZbhbNklZ4uhjkWD74yiA/e//PcHW4uP6T1wOFZaZArE2OHMmu5yfR2NsoyIwcWR/FMNv9UI/pxAIx7E12FIuSNzAgFx7/+OOMvTbG1k9uZd6lSRnaSmySGaKZgRVnz4rtQjkg3dcQGA4AldVNKQez3UbVRt2e2ke5NlndVpbctIQfHRRz7KZJd0Jrq1BOCFnE+BccCmbNkWcCs9lGslZOvrVSxcetgX5Xl5mqELqu88orr8xaBfejR8X7smVTP9Z02CLle4ZSyAwZjRgLxBg7PIYem77fbibaJxaK4Tvtw3vCy8ihkYR2aSZGD40yvG84y15ZE6G9HbRMWjE8+aGtDSzW/PYoFjj6dRh8HAIZaXMnvg2efaDahUNWD4oMDSMO0VGIecCIoLna8Yeb0HWRDZAJRVHo3thNz6YejNhUCygnMdP3UNQ3yUqnkBkyUjbf5D0nUutlpDwIK7JH1s1IKQKuaUlZEtlHZLZCPByflkymXKi0fYy4gf+M+I1alyRDvLo3dmPRLASGAjm/1zS/iZ6Le3C1F56QSzKjpOLfkqhQbJiOHvaccaG7FkPjMvEqRmSkHkd+p2kFaG6w2BPb5i0Vx8lXMyNBRlWZzJjt51AdlSG13aTE1Otfn3tfuz0ZBVmrUlOp9hx99Ch7v72XsdfGin+xCtAcGlaXNWe2FwgSY9u3t/GWH74lS8JO3o8TsdJSrPNlLxZDlsxURmZGgmhvtE9JnquhuwEUIWcqI9Qg9zgxFcnMWsBcG/vmmj0w92yq21NHLWAq7SYzM0KjIbwnvHhPJTOVC8lMQTLQKl8NKVk3Y2KwPDKjFvrh+NFxfviOH/LjP/4xIIgZEAGLxRAeD4OZHvFciU26Tt4MUbntQx9Kl5wCEkXg3V3Fgw6OPnqUfd/ZV3aWfy20UTVRt6f2UYlNzQuaufJDV3LQLWrJpmZmAPgNcY/Ew/GEc3+mMNttlAj8ylgrmaaZs6ZvKZhtmyTqmRnnKWRmRjXIjOmAJDOGh0DWCbM6rTQvbMZ72kvYEyY0FsrrgDgfoDk1mhc34z3hJeqLMrRvKDGZS4V03mZONArWy5BkhqNISqjFCq0XwcjzMLoTGiaLCAw9LSLIVTtc9u/QerHYvvdeCJ6B1R+C5nWTh2hCcXSCTzhrchVH2vqJrYWvo8ZhxI1EvZpUMkM6lzKL2RWEjNDXGqd+YU2roft6aEn3zHd3C6JFRkBZXVYsVgtGzCDijaB11e7Q7T3lxYgbWN3WtEihDW/bwCXvuQTVNrV0RCkzVRKZAeAT+pBm0xoIViEKwzopvhlLLgIXLRLvo6NC/q8xo2tMZ2ZGHecvdJ1EZkauehkSGzaIovd798Jtt83MtVUK+UyPBWamoNwt/5JDmysD+SSo5P3YPr80MiOzxlKpyCszFcogM4pI7BWDalNxdboIDgXx9/tLkjOthmRmHXXUUUcdM4v939vPxLkJlr9+OV3rSpMPyiwUnlq/sxSZKcifmZEgM86Vn5kx2wiNixp6MuCh99JeXv+l1ydIjXwwTTPx3XIyZHLh6aeza3alnyuZIdoxuI+zz59l1R2rkmRGCRmU+767j+BQkJ6Le6Ysd15HHbUK/yQHKTMz5Bzc41fpa3UQHg8TGArkXRvMReSSmXr1wVfZ98A+lt26jM3v3TxblzZl1K5HbI7i+X99Hu8pLxe946I0yaFyITMzli+v0oVVGdJBPzScJDNATHbsTXYCQwHc3e6EwyM0GmKM/JGctajdrKDQ0NWAo8nB2NExYoEYnhMeNJeWmMzFQ/EEmRH1Rxk7Kmy0N9kZHBT25CIPkmRGkcrg4WFwzhOFiAd+CW2XQuAEHPhn0GPQdwcsvDO5v3sRRMfB2iiiyifR2ysmqPmKgM8kpkPH2zRMNr5jIxFfJI1Aqygzw9UnyCCLteiuRdG8Rrwy0NUF+/cnFw2KouBodhAcCRL2hEuKwJktjB9P1stITeEsVNg7Holz6OFDOJodLL15ad7Uz0AAjh0Tf5dMZnhfFe9Na6AadQlzkBlNTaIPjY2J7Iz169O/Ip2U50sB9zpmBrt2iTGoqQmuvDL/fhs2wHe+U7uZGamQ4+tMRz1VAvmcueQqB319QsohV1SkoghJz2uvrew8mTJTznYnXRu6aFncknYd1SjC1zivkeBQEN9ZH51rS8w8q6OOOuqo47zCwO4BRg+OMv/y+SV/x9nmFOUSJ59zUvIQSpOZgvyZGfM2z8PZ5qR5UXPJ11MrkJkKsj6hvdFekqMz4otg6uLHnCo5UGrm57lz4BoNMnZkjOH9w0IVQskmqnLB1mAjOBQ8L+ZnddRRLqKBKKpVxecTQZOpMlMgxrhVXW5BZgwGaFtWjvPn/EZCZirFB2bRLGnBvucraoLM0HWdLVu2EImkOzCPHTvGd7/7Xd7whjdw4MAB3vve9+L1elEUhY9//ONs27Ztlq5YoJKCJ57jHsaOjE054q2aMlNQmS2FkJqZsYZkBKKEq9NFLBAjHoqjR3W+d+f3sDXY0Jy5u2Ql2s3TWZAm057mRc1MDEwQGAwQ8UR48K4Hcfe4MWIGvtM+FFVhx9t3JPZ3tbsYvGYb4C5CZiQ/zLInPAzP3g2hQQichPGXYPQFUaA4NCCc7WdVWHx3Uk7H1iLeo560Q/X0wMsvF59MxcPxRFRpNZCrIOl06HirNpV1b16Xtb2izAx7G/TclPs8Vepzsk9ImSmA7ou7ifqjU85sKAdTKf4t62XkQqZWZWgsxCvffAXVrrLslvyD2quvCmdjT0+OOjP5sOQeaL0Es3Ed6nCekLJyIAuGp5AZILIzxsaELmcWmTGNmRmzXXirjsqgqio/+5m4B173OhK1i3IhtQh4rUL2w1okM048cYIzvz3D/Cvms+SGZE2KzrWdaA6N5vkN3H+/0KRWlHRCQw5T991XWfFvSJeZMgzoWNXBTZ9JPkOqSWY0zW9icM9gziLg+WAYBvFwHKvLOu21zKqJuTb2zTV7YO7ZVLenjlqAqqqJIt6lOLElLJqFtuVtjB0Ri5/UzIxiMlMyMyMfmdE4rzFRa6JczHY/DI9PkhllEhLye/Yme6IuqES5NpWa+dnbCw5DXOfoIRGR52xzZp0/F6YyP5vtNqo26vbUPsq1aedXdnLyiZN0ey5hhFVZMlPj4+DudTN6aDSv5PV0YrbayDTNRGZGas2MRP2+KZAZtdDvaoLMUFWV559/Pm1bOBxm2bJlXHXVVYTDYe644w6++tWvsnXrVgYGBti6dSvLly9nY9EqsNMDTdO47LLLyv6e7DCpE4hKIGWmqpGZUakthSAfyv1jdlztLoKjwbwEjh7VCXvDRANR3N1uGrob0nSj46F4Qru5VAf2dNgEYsLiancRGAngPeVFtavYm+0oioLVaaWhu4HgaBCLzYLVYUVXdSyaBc2pJSZJ0p7hMxHykxmTTtdJmamc9sR8orix5haZFnoIFBVs7eKlhydrY/iSZMaCbdB7K7jSI3lke+UjMwLDAR798KPEw3G2f297VYom5bIpVcc7F7FVSV8oBJmZURaZkQcV9zlDh/AAaC6wiSdurnTuKz9YIHR7GlCpPat+bxUdqzpo6M2WXDv1m1O8+r1X6b6om0vefUlie6kyK2VLTAG4F4B7ARpw2WWLyvhiHsjMjGg6mbF4Mezenbtuhr3JjubQ0OzVfeRO1zhXx/RCttuHPiT+n69ehoTs7wcOQCxWmPioJnRdSBqcOyeeEddem9uhn9oPZ5LM8J318dwXnqOht4GrP3p13v28p7ycfuY0jlZHGpmx8W3J+eO2laK45vvfD/39ye/29QkiYyqxM3IhZRjg80FLS/rn1SQzlr5uKT0X99C6NOmRKjZORH1RRg+PYm2w0rW2NLmS2cZcG/vmmj0w92yq21NHLUDTNDZfupmj4yKqsdy10K1fvJVDPz7Erv+3K22dVUxmSgYQ5ZOZqhS10A8TUlGtSamok0+dZHDvIIu3LqZrfe7nYmhsUp6qLZ0EqcSma6+F+fNFhmgupGaIHv+lILCCI0Fal7UmMkqKodL5WS20UTVRt6f2UYlNYU8Y3YBgXPTzXJkZ7ovFeDnTZMZstpFpmGx4+wai/mhaxpn0RcdClZEZtdLvaoLMyIXvfve73HTTTbS0tPDwww+zadMmtm4Vuv09PT185CMf4etf/zr33XffrFyfaZp4vV6am5vLcuxWg8zw+5OREdXIzKjUlkKQzvFTI262/WRbwYhk31kfO+7eQTwUJzwWJh6O0768PS0DoNxMlumwCcSkcdsD2xh6ZYinPv0UVpeVW++7Ne0cvjM+fvHnv0BzahiGgUWzYHPb0nSp45E4w5PFtnPWzIhMfjiZmVHQHtUJ9g4InQM9As7Jia2iiiLfqWhYQi7Ia8gnM+VsdRLxi3Ta0GiorEigfChkk+bUsDqtBMeCOJocadkIlWQ1hb2iRouzzZkmdyQn72XJTE0cE1JdrgVpNU0q7nOH7hcF3JfeAwu3A7kzM2YaldrT0N0gCtHmgee4J2ubLFZbSIoKkpHplXDYVRsTZGaGHgY9Cqq4rxcvFptPnMj+SmNvI3c9eFfl58yD6Rrn6phemKbJyZM+fvvbJkApWC8DRNZPY6N49h8+DOuyk8yqjh07RDHKVA3nvj64//5sx35qP5xJMiM0FmLsyFjRibhc6MsoynzYtk3MqS6+GFwug5/8ROG665SKMzIS53eAywXBoHjmTCeZ0ba8jbbl6Q+0YuOEPH9sIkZgJIC7o3ZlDCXm2tg31+yBuWdT3Z46agGmaTJwYgBDN7BYLBXVapDqAtWUmQIY2DOA56SHviv7Cq4DUlEL/TCRmZFCCvTv7OfE4ydwd7nzkxk5SBCozCZVFTXRvvrV7M8yM0Tldbq73Nz6xSITyBRIR2a587NaaKNqom5P7aMSmyK+CHocwoh+npmZMTYmAn56L+mlaX7TdFx2XsxmG1lUC+vuyl44JsiMCjMzaqXfFc9JmyX8x3/8B3/4h38IwK9+9asEkSGxdetWfvnLX+b8biQSwefzpb0A4vF44mUYonK7YRg5t+u6XnB7NBrlwIEDxOPxrGPH43FM08Q0zazt0UAUExOL3ZK2HcjaX1aHz7zGw4fF9o4OE5dr6jZJW6LRaOLay7Ep1/bOTmHTuXMmthY7TYuaaFrURPPiZtqWtdGypCWxzd3txtXpomVxCxbNQiwYY/zkOIZpJK7FNE30uD6rNsl2cnW4iIajaC6Nrou6aFnSkmaTu8ed+L7NbUO1qagOFcM00mwaGTYn21HPtuni+4hf9jXiruUYhoGu62n2pNpkmCaGtRkTMBVNvE9uN02TuF7cpp4ecaz+fiNn3zMwcHcLu7ynvXn7WDl9r5BNpmniO+tj/Ng4Y0fHMOW/SRtkXyhkU+r2E0+f4Ofv/zkv/NsLadc4MiKur709/7VnbT/zY3jl7zHOPZa2PRaLcfDgwTR7Sul7ur0bwzTR/ScS2zs7xXcGB400mwzDIBpOHj/fGJFvezntJO8hXdez2q/S+6ltdRuGaTB+fJzAeCDRToGxAIZpYG20FrRJkhlr12aPBTlt6v8F+pmfEg8MJeyJxWI5+2TJNmFH73sT5tJ3YprJc/b1ies+caL0sXyq7VSJTXXMPnRd59vfHsQwFNasgYULC++vKEnpMpmdNJ3YsUNILmUWozx7VmzfsSN9u67rHDx4EF3XZ5TMyFXQLheks0dGUcLkPZqDGJeEfm9viGuv1adMZEikSk1F/BF2vG0H37vze5iGyarfW8X1917P4hsWV+dkGUhtn0zEQ3ECowGMuIERNwiNhogGollSmrWGQjadj5hr9sDcs6luTx21AF3X2f/ifkzTxNnmTFMyKBUWTZAgqdnQpcpMFcrM2Pu/e9n9td0J+aNSUAv9MFEzI0VmSkpmZUo2psLV4WLhdQuzaqBWYlMwCD/5ifg7M+Chr09kjspAklxzmlJQ6fysFtqomqjbU/uoxKaIN0I8DhEcuFzJTHJJ0I6PCynW7g3dFZHAU0EttpHVPTUyo1ZsqsnMjL179zI+Ps61k9UW+/v7ufnmm9P2WbBgAcdkJdgMfPazn+Xee+/N2r57927cbhFx1tnZybJlyzh+/DjDKWEGfX199PX1cfjwYbzepIzI0qVL6erqYt++fYRCIUzTxOPx4PP5aG9vZ/fu3WmNuXHjRmw2Gzt37kxsM+IG8Ugc0zDZe2Av2inx86uqymWXXYbX6+XgwYOJ/Z1OJxdddBEjIyNptv72t31AHwsXRtm5c3die6U27d+/H4/Hw65du1AUhdWrV9PS0lKSTQCbN28mGo3ySoqXJRazApcSCik89dRu3G49r03BM0EikQgtPS3ggNEDo8TH4qjjKna7HStWotEoe/fuxTXumjWbUttpz6/34PV4cWpO9u3bl2ZT8EyQYDCIYTdoam5Ca9bQnTpej7hOi25BQeHcgBg8vN7D7NzpLWhTW1sbfr8/YQ/A2gV2moCJCT86DqxmO0ZEwe3QUSwW/H4fqhHkyN69RKzjwqaJQU7t+i4mCl7XVQmbmpuDgJvXXptg5879OdvJr/iZCEzgO+1D79Q5k+LpqqTv5bKpxy5SRPw+P4H+AGbcRLNqGLqBYlHw+/zEgrFEXyjWTvJ+OrfnHD6/D3uTPc2mM2fWAw20tYlxphSb1hhnaQbODHrpH0z2m0WLhITR/v370+r/FOt7B09NMN/rIRx8iZP+nWzevJnWVh2wc+xYkJ0796GqKq2jrfz2//4WdbHKkreJDJt8Y0RzczNr1qwp2aZc7WSaJtGomPDKca+YTQvcCxg/NM6gPoh7UTK6V7bTvtf2EbFHCA+GeeaHz3DLu27B6/Wyf9d+vB4vqlfNup8kmpqaeeWVNZNt/Co7dwaL29S/g+DQYU40/gETttV4PB5GR0fp7e0t2abcY8TalL4ntsfjrcAqTpyg5LF8qu0kn0Ol2hQIzLxGaB258fzzLUBxiSmJDRvguedEdtJb3zp916XrIiMjVyFs0xTEyoc+BHfckVtyqmt9F6/7p9eVLHswFUT8YpxN1YDNBRk1KaMoASYGJvjJH/0Ee7Odbd9KpprI27CrKwpMPVNCoq0NTp8W0oaaXUtkQ8QjcRp6GmjoKS2KtRScef4MvtM+lt68NG+2m5TMnBiaIOZPLmSiE9GEY8fV7qpKtkgdddRRRx3VQ9Qj5uauzvKz5E/95hSHf3KYzvWdXHzPxYntpcpMjY4KyURLjnDYxt5GRvaPMDEwUfZ1zSbmXTYPd7c7LVq7FDKj56Ieei7KJa9QPr78ZSHpuWiRkBR9xzvgBz+AN78ZHnggfb4lHbFhTzirBmEh1GJNszrqqAZM0yTijRCbzMxoSt7KaTJTFyKiE1ECwwEczY40Ekdm5sWDtR28VAw1SWZ85Stf4T3veU/i/x6PB4cjfUHmcDgIh3MP4h/72Mf48Ic/nPi/z+djwYIFbNq0iabJ3m2ZfAovWbIk4YRM3b5y5cpEVGvq9vXr14toal1n165dieNt2rQp7RpkQZTNmzcntkX8EU4oJ1BUhcuvuRyLmj4TaG5uTttf2tXR0UFbSt7n44+L761ebUvbvyybwsNYdC/4/axdaMf0T7Bu5SJUVUW1jEA4VpJNcrvT6cza3tQk9KF7ejaxalV+m8ZbxzlsPwxAQ2sDfocfQzdw29zYXDZiwRg2m40NGzbQuqw1v00p29euXUs4HOaSSy4RNk1e+1RtAtFODZEG4i1xLr35UvrW96XZNN46zmHXYRxOIY/Uvrgdk+Q1xgIxkT3kFYPINdesYNOmwjYZhkFjY2PCHgA1eEL8Zg2Nom4GLWm/cWNjE0rMYMOGDdCwTNhkCbBSeQzs7Rib3584x9KlYkI8MdHI5s2bc7aT/aCdA2cO4D3tZcXvrKAnRR+rkvspl03eE8KRblfshJUwFqeFrqVdicgjTddQFIWFzQtpbm1O7L+sNam3ZmuyJdpJtt/uPbsJNYawN9nTbAqHxXnb22HevHkl2aTu/SFEYf7i1czrSPYPwzA4efIka9euTSuIVKzvrd50M5ZdP6ZZjdN56aWoqkpfn/gsGHQnbDj99GksWOhs7ExsyzdGyO2l2pSrneQYB8lxr5hNBx48wN4H9rJo6yI237k5bX95Pyk3KLz2yGu0RsW93NzcTF9HH4GWACvWr2D9ZAh6pk0DAwqjo2CxmGzbthans4hNcR+81k9Dg5s1l78J3eJk165dtE+GSJdqU6ljhKxjcPJk7rF853/sZPTQKBvv2Ujnus7Edqi8nWQblWqTzFKsY/ag6/DEEwpPPCH6f0acRl7MVBHwp5/OzshIhWkKp/zTT8P112d/bm+y07m2c9quLxWlZmakykzJOaMkE1R7OiMj9ao7O6u72JdD2dgYWKwWUABTZEakSn2kotSaJZnY81978J/107aiLa+jRUpmHv3lUXZ/LRkU07K4hWv/VgQS2ZvsValNVUcdddRRR/UQD8ZRFKUsMiMwHCDii+Dv9+M54UGxKIwdFQyGrkPMawfceWWmOjpI7Ds+nrvWoCTlzzcyY/1b1mdtk3X/Js5Nvy0+H3z2s+Lvv/97cDph61ZBZsRi2c99R4sDzakRD8V56A8e4tq/u5aOVR1Fz7PoukV0rOmoP9frmHOIBWMiaHwyM0NKTEE6mWGaJsd+eYyJwQnW3rl2yjWMzwcM7BngmX96hs51nbzuc69LbLc32ena2HXeBy3VHJkRCAR48MEH2b9/f2Kb3W4nHE7XOg6FQtjt9pxstN1ux27PbhhN09C0dJMtFkvCQZSKfNXZ5XZFUXC5XInvZh439ZwSUT2Kq92FqZvY7NmRhIqi5DxO5jUePy7eV6wobf8sm8LD8MIfiOLRgM00WR0OY3vekfw97e1oWx5IFo7OY1Oh7b294gE9PKxlaXynXqOqqYnzKopCQ08DiqKg2tK3q5qadY587aRpGi6XC03T0vYp9doLbdejOv4zfiyKhc7VnYnjS5ukPanXrpDsp4qiYJowNia2zZ+vkXoa1fsKDDwKLRfBPBG2a5pmtj2Tx7coSlJQMwXK5HZN1UicwNYqtsd9WFQ18b1588T7uXMKqqolDpfaTq2LxXd9Z3x5+1g591Mum+RvFvaGUVBwtSXvsYg/gu+Uj4g/wk/+6CdZGqUSrnYX2x7YhrvTnWi/WCCGoijYm+xp1ygLgLe15b/2rO1xEaWj2ltIbThd13E6nVl9TiJvH2tcCBYVjLAgGK0diRomg4PJ9nC2OlFQiPqjJY9jU2knOcYpilLwPkuF57gHBUXUvMlhr6Zp9F7cy7FfHGNk/0jiPLGJGBbFgqvNlXU/SchHwsqVCo2NJYx744fE8d2L0ZwtKLqOy5U8fqk25dweHYfgiBgn7WLlJ+sXDQ9DMKjgdqcfx3vKy9iRMSLjkSm3X+ZzqFSb8tlWx8wgWYdCBURb/eEfwpe+VLzA9EyRGefOlb+foig4nc4Z10wtNzNDj+iCPHBZE2RGZuaCJHLmzSs92rEUpMpMKYqC1WklFowRD8c58rMjWKwWFmxZkKitVU7Nkkw0zmvEf9aP/6yfnot68raPu9NNcDiIzW2ja2MXQ68MYRombcvKKSI1O5itPjddyGePdEDmQy0TThdKG52vmGv2XChQFIWFNy5k7fvWYsZzpFDmQGA4wI67dxAcDRKPxPGf8TOwZ4Bzu8SDPB6HO3HxA7bR2pp7PLHZhPyRxyOkpgqRGf5z+bMZctlTi/1QZmaEx8PEQrGcQQcRfwSry5oVnFquTV/8opgbrF4Nb3+72CbXFEePpu8rnwk3ffYmHvnQI/jO+pgYmMCiiWso9Exwd7lxd5X/vKjVNqoUdXtqH+XaJGtvGoqGjpo3M0NRFF7+5stEvBEWXr2Q1qV5UtGqjNlsI5mJJTOzJJxtTm769E0VH7dW+l3NeTa+853vcOONN9IhQwAQ0hqnTp1K2+/06dP09fXN9OUloKoqF110UVnfcXW4eOM33jjlc7/2mnivuPh3zCeIDIsdVCcK4Ert33pIfB7z5SQzSkVvLxw6VLpzRGo0S81KI2YQjVWm3VxJ+5SK8WPjmIaJo9WBsz2/5l6+646H4uhx0A3BJXRm/sT+IzD0G9E+k2RGQXv0PJqZubZbm8W7EQc9OJnRkSwAHgzCxARpjLZE0wLxZPCdrk50dz6bTMMkNBZCQUFza0QDYhA2dVNEtBpCJ1S1qjTOa0zTi42H4gRHg0R8kbTJXCKCN4V9jsVEQV3IPSnPi9hklI41/UequM9ZNHD2QvAshM6AoyOhTRsOJ9tDattGPPkdG9VEJfaMHxc5nIUmB10bhHGe4x4i/gj2Rjsb7t7AkpuWFCwYKJXEpFO3KHyT7EezlKaq4pjw2tdg6ClY/h7ouwMQi7zmZvB6RXbG2rXpX5FO07C3cBHicjCd41wd1YWsQ5Ep39TfL7an6iHnguz3J0+KIIHUiXouVBrV39tbfJ/M/VL7oaEbHPnZEaITUdZuX4tqrVLRiRwoNTNDc2hoTg09qhP2htPIjMyIJEkebN7cU7V6GZDMzJAEuubQiAVjxEIxdv/nbvSoTvfGbmxuW96+ImuWFOsrmfIYeZ+1psngnkEAltywhKFXhkT2imFWpMM+k5hrY18ue1IdkPmQGrxRa7gQ2uh8xlyz50JBWruVGFAb8UUIjgbR7Bq2BhuBASE5qmgK9gY7E+NxXARpdUawWvOPJZ2dgswYHoY1a7I/l2SGPH7Z9swC9JhOdCKKo9mR9tyzuW3Ym+xEfBEmzk3kXNf84sO/IDAY4OZ/uTktM6Icm0ZGBJkB8A//kIyRSyUzpMRn6jPBNEw8xz0APPLBRxLXPh3PhNluo2qjbk/to1ybLJqFJTctwbMbeCZ9jSTn36GQ8Ku4u9xEvBECQ4EZIzNms41KDfwqF7XS72quAPhXvvKVROFviS1btvDkk0+mbXvyySfZsmXLTF5aGgzDYGhoKFGQdSYhWfqKyQwJ1QmaG1N1Eo2GMTWXcG6r1SmKIx3kxcgMqd0cj8QJe8JZr3gkXrZ283S2z8TABIqq0LaiLXdmUAn2KA0uIthpb0/K0yQQFs4FHMmCYjntsTaBvR2MCMQ82S8jIj63pozoqg20yfaNehKbGxrEC/K3V9P8Jro2drHg6gUY8an/rrlssjfZsWgWEW2kgBE1Er9bNBDF2e7EYrNgUS1EvBE8Jz0oqoLNbcPmtqE5c/OzuRxXUh9WUbKLreWFaSYyM9DSyYwp9TnXJDEbOA2A2y1eAIOyO6Q4w02jtGisqaBce6KBaGIB07KkJe9+jmYHPZt6WHzD4gTh19DTQM9FPQV142VEeslkhveAeG8WrEJVxwTbJCkY9aZtXrxYvJ88mf2VBBlVIMq2XMzmc6iO0lGsDgWIOhSFaqi1tcH8+eLvffsKn2/HDtEXb7gB7r5bvC9enF24OxeuvVZkAOQLtFEUWLBA7CeR2g8Vi8Kur+5i3wP7EmTDdEFRFawua0kT9Du+fgdv+eFbaOwV47aM4sqcV0iZKbfbU9X7KlVmCkg8q8KeMHpUT1xLNfpK4/x0MqPQOHHln1/Jmu1rWLBlASgimKCahOt0Ya6NfbnsSXVAOlocWS/NriWCN2oRF0Ibnc+Ya/ZcKJhKu2lODXuzWGdZNAuKItZPhiqeR80thb9frAi4lGYKjgbRY6UVhZ3tfjh6aJSH/uAhfvq+n2Z91jBvUmpqMFtqyjRNwuNhMLPnEeXY9E//JALrNm1KD1JYvHiSwAgkf+/UZ4LVZcWiWVAdKs42Z0nPhOhElMM/OczBHx3M+Xk+zHYbVRt1e2of5drk7nJz5YeuRL36SiA9KLepKbmmGR8nkZ0UGJq5OpKz2UalBH6ZuRYdRVAr/a6mMjN27drF2NgYN954Y9r27du384lPfIInn3ySrVu3MjAwwOc//3m+9a1vzdKVigY8duwYbW1tOWVApguRiNCrBli+vEoHDZ4B/2lgUdKpWgXIyM1iZIbUbk59+IY9YcZeG6NtRRuOZkfZqfTT2T5LbljCwqsX5i2glcueTDy/207wLjdLunN8mIfMyLLH0QlbHhAZNPlgbcrOrrG2QDwEMS8wP7G5txeOHBHttXJl9qE0hzaldLRM5LLJ3elmy0e3JOourL5jddp3vCe9PPKhR1AdKhP9E8SDcUYPjdK9sbtgmpt0zuQiM1paSotYFhcdEVktkJWZMaU+13UdNCyFpqS9XV1CUm5oSNzr0hmOKVj2fIVdq4Vy7fGc8AAiA61YpPQNn7qh7OuRZMbGjSXsrEdgYjKFrSlJZlRtTNAmCcJYNpnx8stw4kT2V2TfqzaZMRvPoTrKw1TrUEhs2CCc7Xv3Qr5YjqlG9auqkDK68878+9x3X/qYmdoPNU3D5rYRnYgKArqtOsERuXD5n17O5X96eUmT8Mz06mKZGbp+EsNYV7X7KlVmCpJkRmBQLKYsVguaQ+PJJ6feVzIzM/KNE4qi0L2xm+6NYq7hbHUS8UeI+CJ5ZRxrBXNt7Ctkj+bUEvJjmYhHardw44XURucj5po9FwoMw+CXn/gli5Yt4tJ3X1r2MzZV9lizi+dQfHIYaS6S8SmVBIaHc39ub7KjOTTi4TiBoUBaQe18mO1+GPaI9aFUhUjF1R+9GluDLaeufjwUR48IwibzeVnMJpk5u3+/mG8BfPrT6UXV7XYROHLqlAhi7U7xF2hODd9ZX0JaKvX5UOiZEA1Eeek/XkK1qVnr60KY7TaqNur21D4qtUkqbqRmZlgswtczPj67ZMZstVE+mSmAn77vp0ycm+CWL95C65LyslRqpd/VFJnxta99jT/5kz/J1vV1u3n44Yd53/vex8TEBIZhcO+993LFFVfM0pVWhrMvnuXV771K94ZuLvqDytJyjh8Xi9mGhhzyRBXBhPCA+Et1kN8dXD5KJTNAOLFTyYpH/+JRRg+NcuWfX8m8S+ZV8aqqA9WmFpxAZtqTibHfivfugmRGV/ELcXSWLwVma4HQubTMDBCZNEeOwMBAeYerNta/ZT1r71yLETfQHDlqI2gW7A12XOtdDL86jB7RCY2HcLXlL4S36ndXERwNpmmFSrmPsiSmsMDqD0F8QsiAVQtd12Vt6u4W97vMzLCoFmyNNqL+KGFPeNrJjHIh050LZWXkgmmaHPjBAWyNNpbcsATVls0sxePJmhklZWYEToJpgL2ttPuoXMjMjBxkBuQmM2R7yYjwOi4cVFKHIhc2bIBHHslfN6NYVL+iiKj+O+4oTOC+6U2wcKFYRGfirW8tXrPB2mAVZEYewr/aqESvNReZEQolyYaurukpAJ6o07SsDc2hJaJX7U2iBlw1+op0IE0MTGDopUdM3f7vt6M5tVnXv62jjjrqqCM39JiOd5+Xk2dOsvmPNld0jM61nUSD0USQVGzS/93UXOR7RcgMRVG4+q+vrulaPpkIjQtJZkdr9pqqUH0J+T3NqeVcq+ZDrnpYNpuQec7EsmVJMiMzgKUSCW7pzNSjOnpUz7neqqOO8xGxUAyLasHnE306U4q3tTVJZnR2zzyZMZtIkBk5stgN3cCIG8SCsZm+rKqhpsiML3/5y3k/u+iii3jmmWdm8Gqqj+BwkNGDo1OKVJQSU8uX55eBKAsyql9RwVbdoo/lkBmZ6FzbyeihUYYPDLPkxiVVva5agCQMpBRXAqYBkclZoiPzwypB1s3IIDMKtVdqEcp4OE4sGEvrx9WeuMoU6EJQrSrNC5vBkjuiJhWr35gdgSKdVm3ldHvVBj3Vy04phFzp3L2X9BKPxLOKzdUCxo8Vr5eRCtM08Zzw4Gh28PJ/vwzA4usX59z3yBGRleZ2JwmDgmhaCVf/ryAGp8MxJ++hjKyoRYvE+0xlZtRxfqCSOhS5UKwIeLUyQHbtEgtom01kckxMiJo1n/scPPmkqDeUJY+YAluDjQCBGSMzSsHpZ09z4skT9FzUw4rbVtCypIV5l8+jeVHSeyMlplwuk4aG0iQySkWmzNTlf3Y5QKIAqxwfqtFXnO1OVJuKHtUJDAVwdmbPOU3D5OVvvkznuk56L+nFolpyRp/WUUcdddRROwiNCie6alPLkl9Oha3BlhalG598VLcUITOKyUwBzLu09gIQCyE8nj8zoxBCY6IdyvHp5MucjUbhrruyM2eXLYPHH88uAg5irTV6eFSsg0uE1WUFBTCFg3M6M2frqGMm8cq3XuHww4cJx9cBG7Nqv6YWAV88SWbkko+bi5A1M3KpZsh5f53MuAChKArNzc1lRbDJjjKVBeOUi39nIiLCBA1ra9Wj8aZEZqzr5OAPDzL8ap7wjyKopH1KwdC+IXb95y7mXz6fDW8tVbw/GzLaPiszIzIKhi6KQtuTXvaq2rPo90XRYteCtM2yvTIzM1ILjkX8EYJDQTSXltAeh8oKjuWyafTwaN5aJLkwlYlYZZkZ+THlNgoPQfA0NK8D1ZFYNMi+ArDlL2auTlC59mx+72aWv2F5yYurX/3Vrxg5MMLF77wYANWuJlLeU6Hr8J3viL8XLswddZ4TmltId02iqveQNb/MFOSpmTGZfm+xVo+Imq5xro7qQtahOHs2d/9VFPF5ah2KXEglM2SmRSqqlQHy9a+L9zvvhN/9XfH3tm1i+9mz8MMfwpvfnHr96f1QOkmmk8wwTZNf/dWv0JwaW/5iS1FpO3+/nzPPnkGza6y4bQWrfncVq353Vdo+kgjq64OWlureV5kyUxKJDJHJCNlq9BVFUbj2767F2erE3enGVMyscWLs6BgHfnCA1x55jW3fLpJqU4OYa2NfMXv0mM748XFUTZ2xgpVTxYXWRucb5po9cxmpAWXDB4YhLsgMGUQ01YAymZnRPMXMjEow2/1QykzZm5088YSYH/X2imdsbCLM3gf2EvFGuOavr0n/niRBcmR05LKpUOasRGbmbGoR8EzYG+30XtqbJhtWDIqiYGsQGf7lyIDOdhtVG3V7ah/l2iRVD4Ixsf7IzMxIDShybxBjZXAoRzrUNGE222jJjUtoX9Gek/iUPulKMr1qpd/VyYwKoaoqa9asKes70YBY2E+FzKha8W8APQjhYRR0HO5WCI8IySnVLhyBU0Q+53gp6FjdAYDvtI+IP1LUUZGJStqnFIwcHGH8tfGChYpLQV4yIzo2SWR0gpJ0elbVnsbcnSdfwfbUgmMWq4XwWBhFURJRLPFQPFFwrJzJdKZNvjM+Hv3Io7i73dz+77ejWvOnv+YadE3DzKkVGgvFmBiYwNHsSJu4JeQ+ysnMCA9B4JSoZ+JOJ4Om3EZ7/krcg5v+GZrXJPpGoQio6US59qg2lfYVpTNDrUtbGTkwwqnfCC2bRE2QFGSmYx84IAiD++8vLnWTdX3VvIcS2U2ly0x1bejirgfvqs75JzFd41wd1YWsQ7F9u3BGpy5m5Rwwsw5FLqxZI/YZHxfO7r6MElfViOoPheCBB8Tf73pXcrvdDu99L3zqU8KWVDIjsx/OBJmhR3RGDoyI8xd4VkjIsV9KQ+RCksxQqn5fZcpMSWTWc0rtK5kop6/0bkpv5Ex7Bl8Wk5CuDV2JTL+zL57l6KNH6VjVwdrta4tYNLuYa2NfMXv0mE7EIxbrDb0NWJ21n0VzobXR+Ya5Zs9cRWpAGZAIKgueCPLgXQ8CpQeU5XNY6WGxvZjMlAyyKkRmBIYCnPntGVSbyvLXFy/uOdv9MDQe4tw5uP/9Dp4fSW7v64P/888qsZ+JKNJYMJbmvymUmZHLpkoyZwuRGUBZRIZEgszwlz4/m+02qjbq9tQ+yrVJzqX9cTGXLpSZ0djbyI2fubGgjFy1MZtttOzm/E5jOZesJDOjVvpd7WmVnCcwDIMzZ86UVcG9GpkZqTJTFcPaBPZ2iI6DEcEEopEAZmREbIsMC8kpa/HCXYUgHSbj4xAOl/ddR7ODxvliJBo5OFJk72xU0j6lYPSI8ESU47TNhbxkRtMquPYHcMnn0zZPlz2pKJZJozk1nG1OLJoFUzcTRSllIdNykWnT8cePA9C8sDmvc8reZMfV7iIeiRP2hBMvzwkPg68MEh4L42p3pRf6PjLGIx94hMc+/ljasWSEbFmZGaM7Ye+9cOJ/itpTNmSmTPA0kD+d2zRNjPj09QOJ6e5zXRuEgWNHRENk1gCR6diZk39ZyHjHjjwHDpyC3X8FJ7+btrmq9tjbYeGdsOgtaZ5pKTM1OCicwqmYjsiFmRgX6qgOtm0TEgLz56dv7+srXpRbwm6HlSvF37mkpq65BpwFAu0URRSULBTV/9BD4PGILKgbb0z/7L3vFfJSzz4LO3cmt2f2w5kgM2TatEWzoNqLkxkyelJGU+rRbBkpKTM1b55Z9ftKkhnj42AYsO+7+9jx9h34Tvu4/lPXp0khyr6iZTxay+krqcg1TgzsEVEmPRcl5SxDoyHO/vYsw/urGHY7TZhrY18he+KhONGJKEZcaBuHPWGigWhFkXQziQupjc5HzDV75ipSA8ocLQ5Uu4qpmtgabThaHGh2LRFQlg/51k7ypUfiBHHR0l04cFBmZhQKsvKd8bHrq7s49ONDJdk32/1w34thdr4Ep0fS1yBnz8Kb32ZlyCd+E/85f9rnjfMbWbR1EV3rsuvy5bKpkszZfGRGPBQnGohmvUp5JlQyP5vtNqo26vbUPsq1SWZm+KPiPs5VMwPEHFy1qXRv6Kahe2qByeWgVttoKjJTtWJTPTOjQsgG7OnpKbmCe83ITDk6YcsDcODzMPoiRvfreNWzkg3r1qDt+7iopbDsPeUXls5AS4twvkQiIjujJK37FHSu7cR/1s/w/mHmXza/+BdSUEn7lALpeG1bMbX6InlrZoDIyMggkqpqT3gERl8AixV6b05sltdSKJNG1VQsVgtGzCAeimNzZxcTKhWpNimKwoknTgD56yaAKKy+7YFtWZP2gzsOcuTnR2hZ3MJNn7spLTopV6FXqDAzIz45mdUasz6achu5FsDY7gSZIYmuVJmpQw8fYs839rD4hsVc8f4ryj9HGSjHnnO7z3H62dPM2zyPviv6Cu4LItLM6rYmstVAFKEaOyruL81t54MfdFdWyNizD7z7wWITZEMF9hSF5oKl/1/W5tZWaGgQNQa+/GW49FLhOC4WRV0ppmucq2N6sG2b6LNPPKHzzDPHuPrqpVx/vVpW/9iwQWQo7d0Lb3hD+mdf+1o2iZaJYlH9UmLq//v/ILNL9faKjIxvfxu+9CX45jfF9sx+uOr3VrHkhiVTzmAsBBlVaGu0JYhCXRdRjakyEdJWZ6tgecLjYYy4wffu/B6qTeWOb9yRyPyUxOn8+WbV7yv5nDEM8PnAiBlEvBFUm5qVRQGiCLvVCvFJ34SqwqFDhcmqVASGApz6zSkUVWH57cvT7NGjeoKw6Lk4OQlxtk9mr4wV6UQ1gLk29uWyRzogg6NBYoFYIogh7A1jGuLhmBm8UUu4ENrofMZcs2euQwaQyWh8m9uWWIPlykpPRb61k8T7/gR+/Es7n+8rHKlcisxUQ6947gcGApimWTSQZzb7oa7Dg8/NJ04jftLXdXKt8dTuRu68LoK/30/bsuSCcf5l8/P6JnLZVEnmrPT1DA2B35/+TMjX5sWeCVMhM+bKWFG3p/ZRrk2SzPBGRN8vRGbMBmarjQzdwHvSi63RhqvDlTUeV4PMmO1+VyczZhCxgOgolTqAdR2Oi+D1qWVmgCAqNnwSRp/HdC4lEjgnsgKW/n/w2ldh8Few8C6wVO6FUxTxUD5xQjgXKiEzjv3yWM1ECYa9YYLDQVBIm9BUgryZGTOB8AAc+XdwzU8jM2QmwIkT8MQT+Z2wmlMjGosSC8amRGakYuTAiKjF4dSYf0Vh4srd6c5Kpb7kjy7hzG/PEBwO4j/rp6Er6UTLR2ZUlJkRmyQzrNlkxpQhMzMC+TMzVJuKETMSGq+1goE9Axx95CiKRSlKZqSmzPtO+xIR0qOHRzn1tJCcCltcjJ3ZBuReWBUsZOw7IN6bZ14m5Yc/FOQtwF/8hXjv60vKYj3zz88wMTDBlo9uSas5U8eFA1WFrVtN3O5RNm9eUjbRtWEDfO972ZkZe/fCn/+5+Puee+DXv87OavrgBwtH9Z84Ib4HgszIhQ9+UJAZ3/kO/PM/5ybkmxeUXpCyUsjMDFujeAZlStJB+r0nMzMivkhi/NRjetozLJXMqDbsdnC7IRAQRLrmENPvfAuIoSFBTCmKWJR5vXDwIGzaVNr5giNB9vzXHlxdLpbfnj5hHD4wjBEzcLY7ExmwkJTLkNkrdcwuUh2QZ54/w+6v7QZg3VvWsfR1oh7UVLXy66ijjvMLksgsJSMxFbnWThIDUQiSdPjlg1yXjIwIYj6X/8rd6QZFZD+Gx8M1XWT66afhqbH89S9NE/r9jYyOjeDv9+fdrxRk1svKRK56WM3NYo06OgrHjsFFFxUmpaD4M2HjOzay7s3raFowNfWNOuqoFZimmZCZGg+KuX4hmSkQfovBvYN0b+hOC+qZa4h4IzzywUdQLApveegtWZ83LWiia0MXrk7XLFxddTA36LvzBBbNgubQKs7MOH0aYjGw2bKlKiqC5oLuG4RTW6L3VrA1Q2gQhp+a8iny1WEoBb2X9HLVX1zFlo/MXNHjQpBZGY3zG6eUXWMYyaiWLDLjyL/D/n+CiWMVH78obC3iPepJbNqxA37nd8Tffj/ccIMgn3JJ+UjnjyTnqgEpMbVgy4KcRaCLwdHsYNnrRQjLq997Ne2zmcrMmBLCw2LWHA+A91XwH6Wv5ShLu47i1I8SOH2KsaNjRCdEKrH3pJexo2OJV2A4UN3rKROe4x6AkgqTpqXMtzmwaBYsmiUtZT40GsRO/sm6RM5xxbtfvDdPs45jZBR8RyDmA5KyWLGM2yJVFmvstTHGjoydF5HPddQmUouASwSD8Ja3CDnHN7xBZFecOAGPPy7qX8jaFz//uQiKyIf//m8xDN14IyxZknufyy6Dq64S/fw//qMqJlUEmZlhb7SXJElnb7KjqCIiyXPSk/iuYlHS9ocqza9yILUAoZRnPP7r4xz95dGEHrrEsckpwIIFIsMLYNeu0s8lSYrgcDBLUishMXVxT1qUVmpdEUOvrVT4CxXuTjdty9pwtjgTkdi2Bhtty9poW9ZWJzIuIASGA2nzvszXbM8D65gZtCxtoWlNUyKTrhqQDr5i66EOUc4Sw8jvnLdoloRjbGJwokpXOD0oxTfhp5FImCwyI+wNl/yc/M530uuMZSarFKqHlSk1JZ8J+V7FngntK9rpWt+VJe1bRx3nK2KBGKYuSN7xYO7MjNT5N0D/zn72f28/53ZV4KA8jyAzsGwNtpxZcitvX8lNn7mJ5bdONUp+9lDPzKgQFouFzs7OstJqrv2bAkLVJUA+yJYsqa5sSZotFg367oBj34RTD0LX9dlP3TJQrA5DITjbnCzeurii81bSPsVQrXoZo6NJh1JnppLX6E5RZHr+76Vtrqo9snhxPABGnB0PaWzfTpakj3QEffvf0rdLMiNVIqgSSJtM3UxE5C+5MY8HrQSsedMaXvvZawzvG2bo1aGEjun0ZGZkR7RU3EbhYXj2btHuEydAAZ4+whLdwoMfgEjAxg/eeCUhfQXxKPjP+hl8ZTDtAVxq8b9yUKo9pmkyfkyshEohMyQ0p4a7050oatrU14TVIUhCTStNCzwrbTs8In5HxQKNq9I+qvqYcODzQtJq7UfR26/jgx/MvocgXRbrq3fZ4dxEwaiqcjAd41wd04+ptJskMw4cEISC1Sr61oED4n74xjeS0ZIya+n220UtjEOHxKL6bW/LPq5hwH/9l/g7tfB3LnzgA/Dcc/Dv/w4f+xhoWro9gaEAZ54/g9VlZelNS8u2sRTICbrVbSvp3rvjDgVHi4OIN4LvjCAgbU3pmYWSDFmwQKGtrfr3VXu7CEoZG4N2R3L6/cKXXuDGT9+Iqz0ZGSXJjKVLRTbGY4/B7t2ln8veZMfqshILxggOB9Pax3PCA0D3RenRFI5mB4pFwTRMIt5ITUfUzrWxr5g9qRk81Xp+TDcutDaaTmQWgc6FcueBc619LhQoKNjt9rztVkhuMR8kmVEsM8NqFfuMj4ugPEluZKKxt5HgUJCJcxN0riksWT2b/bC7Q8dJlDB2zDzxvX4asTtg4lw6MfOTP/4JsWCM2//9dprmJ9eEug5PPWXhlVeWEAxaOH4c/vAPxXzkHe+A3/1d+PCHs7NI77svd+bssmXwwgv5i4DPBObaWFG3p/ZRjk2mabLkpiXEQjE8z4jBrlhmhrtbPCdninCdrTbKzGKvJmql39XJjAphsVhYNqXCFeWjKsW/AaJeUcS482pYsC3blnm3wakfCLmb0eeh48qKTzUVMmMqmI72Ua0qri5X1epldHSIiWECRlwUXwdwpDsZqmqP1iDkwwwdPezhgx/sKOgI+sd/hPd3kywspiQdJVMpQiltOvviWWKBGM52J13rswuplQpXh4slr1vC0UeO8ur3XqXrXnEsmXqYLzOjPDJDOMHQsrXgK26jmE9E+asuUB1gxkF1otqceIJgTEBwXMHaAVa3k8BgAEURjjkQ7SKL/1WbzCjFnvB4WGhVKtCyqKWsc9gabTTOa8TWZEsQGSDqTvT0gGcwt5MyVzo2kJSYalgKWrojrupjgiQFo16efjo7KjwVUhZrYNyOjaS251QxG8+hOqaOqbTb4sXgcolsjPvuE7JFX/2quCe+9a2kDEQqmprgIx+Bv/1b+NSn4Pd/P9u58fjjcPKkkDUoVmD6zjth3jzo7xeSV29/e7o9/n4/u/7fLpoXNU8bmWHoBlaXldND9pLuvaefhtu/fDuaU+P0M0LKL/WZEIsln80LF1ro7q7+fSUjw0ZHYe3i9OzOzOdTKplxySXi73IyMxRFoXF+I2NHxgicC7DsyqQ9Wz+xFX+/P/EMSXzHIp4robEQwdFgzZMZc2nsK2ZPqj66zEqqdVxobTSdSM1olVldqahkHjjX2udCgaIouN2527iY3GI+yOCuYmQGiCC88XEhhbgmTwJ0Q08Dgy8PMjFQ3FE4m/1wdfswb3U8zkC4mZ9xW9bniiJsaW9LrzGhR/WEOkFqhkPy97cA6ev4P/5jUUvPYhFtUSrhlK8IeDHkI7W8p70MvjyIq8NF35XFaxzC3Bsr6vbUPsqxyd5o58oPXYlpgv8TYluxmhnuLjGGBoZmJqNxttooNTOj2qiVfjd3KLwZhmEYHD16dEYruFel+DfA8G/AfwSGnwFFybZFc8OSd8Cq90Pb5imdaqpkRnAkyP4f7M+SDiqG6WiftdvXcsd/3sHK31k5pePkrZcRGRWeF4sVbOkzyqraoygJR+yLz3qLOoJOnLMTUV3EI3HCnrColdFoQ1EVwp4w8Ui8oiKU0qaeS3q4+V9u5tI/vjRN8qMSrL1zLYpFYWD3QGISncjMaKyGzNTkxDxHzYwpt5HqhIbF0LQSbC0omps4bsIxByagOVXsLXYsmgXFoiSKAeZa2FYDpdozflzMDJr6mlBt5aWMqZpKU18TjqYMp5oCH/947u8USsdO1svIXmFVfUywTZIZMV/J41tAT+r2VwOz8RyqY+qYSrs99FCyKPRf/iXce6/4+01vEvJQ+fBnfybGusOH4X//N/tzWfj7rW8tXmTaaoX3vU/8fd998OtfG9x33yCPPWag65UVmCwXK29fyfbvbsdy1eUl7X/unCh0pyhK4v5LdUIMDIjnndUK7e3Tc1+lyUw50sftQmSGrJPx8suFZcIyIQuxes940+xRFIWm+U05a145252oNrWqMpLTgbk29hWzZ+PbNnL5B0Rfj4crCyCZaVxobTQTkPO+zFcl88BasKeO0hEPxYkGokQmInhHvEQmImkBZb/4RXG5xVyIxURQBJS2HiqpCHiPePaUQmbMZj+M+sKsWwcR7Hmlnz51fwvbv3snt3/59sRnoXEhFWuxWrC6RWBCPrlLiZtvTmbNqqrInH3rW8V7ocyZSsiMHTtE4MsNN8Ddd6dLR48cHOGl/3iJo4+WfsC5NlbU7al9VGJTOJxcH5VKZgSH8mc7VhOz1UYy+CVfZsbAngF2vG0Hv/rrX5V97Frpd3Uyo0IYhsHw8HDJDWiaJr/661/xxN8/kUj5KRfyQTZlMmPoSfHedR2Qx5b5t0PvLUJ2agqYMpkxGuTlb7zMwYcOYuYK0c6DctunHOTSnCsHecmM8OQHjq4saa+q22NtAcAz5Cm6axA3bX+4jbsevCvvqxKJI2mTaZp0rO5gwVULKjAkHQ09DWz+k83c/uXbE5PpxdcvZvWbVtOypCWxXygkXlBmZsbit8Py96TXmZlEVdrI3gW2DlDEfWef9G/Jrq9YlAThM90OjVLtkfUyUn/fauDWW+H73wdHhqxrX5/YnjPKTNGEBFhTdvHvqt9D2uRMKebNlrvKg7Ze0aDVJDOma5yrY/pQabvJxXI0B0fwwx/md1aAmNjLovT/8A/JCT+Iyf0PfiD+LiYxJfFHfwSaBi+9BK97nYU///NubrrJwuLF8Msnp5/MkJg3r7Tnceo9mkt6UDog5s0DmJ77Sj5rxsbA3mzH1ZWUlcpcaKSSGStXJrNxDh8u/XxS+sJ3xleyPTd99ibu+v5dNV8Qca6NfaXYs/j6xbz5B29m6ye2zuCVVY4LsY3OJ8w1e+Yq7E12XO3JgLKwJ0xwLJj4Ox6J42xz8Yl/tOfNsgcht5iLDJfOPRCZmcUgsz+HhvLvs2jrIm75wi1c8oeXFD3ebPbD0HiI3l646w+cWfP4+fPFWuPOuyxZxH94XGT8O1odKIqCrpNX7hLEkv7P/7y8YASJcsmMYjXEnn2x/PnZXBsr6vbUPsqxKR6OE4/E8fmS2zKT11LJDNNMkhkRX2RGAkRmq42kzzkzoFfColmI+CIV+SVqpd/VZaZmCHpUZ/hVEcZg0SrjkKoiMxUeAu8B8WTtKrGGh2kASkW1M+TkQMo3lIu2ZW2oNpWoP4r/rJ+mvuxaBTMBPapjsVqmTGRAKWTGDDgRJqPKe9o9Je2+YJWbtmXJJ4MRN/Ce9hIPx4vqoc40lr8+/QbJJXMiU6pVNZu9L4iua6ZwZeXDbgc/KWQGk/JSCthc1U8ZrASy6GTrktLrZZSKbdvEeLdvH/zVX8HrX19E/3fZu2DpOyfHrGlGIjPDy7XXCpLl7NnCslgbLrPzyqvnj+Z5HbWDYotlkLUh8t8ff/Zn8PnPJ7Mz3vEOsf0734FIBNavh80lJmM+/XQ6ISJx9iz8wXtsfPZS6O3VMeJGxXOeUlDqvXfttaLg39FfHiUwGGD+FfNpW54MQ5UL/77SVBcqQqrMVNuyNm78hxv5yR//BKvLimpNb7RUMkNV4eKL4dlnhdRUPmmPTDTOExmEE+cmaET8/ehHH8XV4WLTuzblDEDQ7PVlQa0is4/UUUcdcx/uTjfbHtiWmDfqcZ29e/eyYcMGVE2MCTv32nntTfkDylLlFmUtLQm5HmpuLq0eZymZGe5Od1Vlb6cLkpS47DoHv/wrWLcu+dmvfy0CCXIhNCai4ZytIo21VKnZXL9/MUgy49SpZJ20fCg0T5TS0Z//ko2/2kzFgbV11FFrOPijg+z91l6aNy8HLqOxMZkFJSHn35GICGZ1uW1Y3VZigRiB4QDNC0pgcs9DFJOZklmdqXXZzjfUVy0zhEQnUbLlBUqBaVZJZmroKfHevB7sRULSw8PQ/wj0/wSW/H9C/iYVsghyzJf1VYkFnU1AZ8WZGRbNQtvKNob3DTO8f3jWyIy9/7uXo48cZd3vr2P1HaundCxJ7BTMzJhuLH4HLHwLGxwLSnYEpeL0s6d59l+epW1FG7d+8daSTxsYDqRNyA/ef5DgiiCrfmcVznYn9iZ7VSbA8jxRfzRnat25M3bATVvblOrbVx9mDOJBcVFaUyIzg5S2aV3ampalZMQN4uE4odEQY4zlPXS1f1uJZbcsY+HVCzFNk7GjYyWfJ1+tlcztp0RteO65J4cTLzxccPzB2gSOaSLbZM2MmA9VFZrE27eLpku9l1JlsZzNdjSnNmU5tTouPFRjsdzYKLIz/uZvRHbGW98qsiukxNS73lXaeCgXzPmuI4aVfa+KujfRiWhWbYZq4Pl/fZ7gcJCNb9/I/fe3s3179j6ZknSB4QBnnj3D/Cvmc93fXZe279mz4n1+dtJd1ZAqMwVJUjOzEHk4nLyepZNc/KZNgszYvTt3Afdc6L20l9ff/3qcXU727NtDYCjA6MFRxixjXP5npclz1VEb2PPfov3WvGlNGglXx4WHiD+Cv9+PrdFG07zZWRPVMbNIJQfi8TiucRety1rRNOFPGH2htOPkWovLzIxSJXdlZkYhMuN8QdgjyAxnq5OR0fTPjh1LkhknnjzB8ceO03dFHytuW5GQmZJ1pUr1cVTiC+ntFdKfoZCoa1YooLWUeeKpQRujY+BsPX+dl3XUkQpZhzKmiLl0riDVxkaxDtB1Mea5XKIIuOeYh8Dg3CUzOtd2svpNq+lcl9sXYnUJdrROZlyAsFgs9PX1lVzBXXYSqdlcLoaGhKalxSJ0DyuGJDO6kgv5nLaEh+HZu8F3SBQMH3oSnPPSj2VtFE7WuD/v6VbSTkfjAwwNdaLrpUV9ZKJzbWeCzFh2S2lMTrntUwxjR8aITkSrErUoMzN6MhMw9LAozO3IZDmqbw9NKwBQKc0Jm9lucjHtOeEpOfI2MBxgx907CI4KfUI9puM56eGcco5DDx3CollwtbsqkqzKPM8P3voDRg6OEPVHcfe40exa2jWGLS5cbKOtrYzzxIPgfVVIdE3+fqmoShvFJkQ9G2sjNDVhm/RxpbWLRUFBNI6Jie+Mj+BwkB1v24G1If/4Uu5vm8uezDas5DwyZT44Gkwrapp5DHuTHY+HRNrowoUZO8kxKjK5AjENUDJ+e3s7bHkAHJ3Vv4esycwMEFkk3/9+7uKL990nPjfNJVUtiFx1m+qYEVjicdZ8//tYzp2DLVtyMNvZqNZi+c/+DL7wBThyBD7xCRGNuXOnGOPf/vbSzlF0wYyCP2xldCw2bWTG8P5h/Gf8rL1rbeLee8970iUzUu89SEZRSkdEKlIzM6brvkqVmYLcclcgHBamCQ0N0NEhtlVSBNzeaMfeaMcwDPr6+hjeK7xPbSvbctbLABg+MMyBHxygoaeBS95TXCJktjDXxr5i9pzbdQ7PMQ+DLw/SvrKdaz52Tc1nalxobTRTiIfiRLwRIt4IjmZH3nu5GGrFnjrKQ652K1XqNNd+8plZSvFvSGZmFJKZAjj6y6N4TnhY9burErK/uTCb/VDOBRytjsTaXOLIEZENDqJI8MCuARzNDkFmjCW/B1P7/YtBUURQw6uvCoWOQmRGKfPEKDYiYRFoYppmST6puTZW1O2pfZRjU9grSMm4KubSjdllTVEUaGkRmdEDRwM4wxHWbFuDZtewNdoYO5oMBq1W8GcqZquN5l06j3mXzsv7uSQz4qF4yeOBRK30uzqZUSFkA5aKVDKjEsisjAULkjr6ZSNwGiaOC4d559WJzTltifmEk9DeJZy4RgwUO2iTlUH1EIRHxN/WJlG8OBN6CJs+Sovbx4i/k6Ghyh7knWvFzGl4f+lhIOW2TyGYpsnYa2KQa19ZToGF3MgrM7X8PUImx8h28FbTnkzkc8LOny+Ijly1CRp6G7C6rMSCMbynvLQuLT4LjvgiBEeDaHYNzakRGAqgWcVDxNXhIh6KExwNEvFFpvQQifgiYqKpiMye0Ij4u2tjFwoK8VCc8GAQOxHa28s4T/As7P0UODrgyv/K+njKbaSHwNTBjEM8BPEAbgc4rJOcYUgHNV3j1DTNRJaGf8CPzW2jdUlrVjHISn7bXPZktmEmSjlPZsp8LsiJxMsvi/93dGTrXybGKItdjD8Tx0CPgHsBaA3i94yMiv1SyIyqwTkPFm5Py6Tatk3I/Fx1Fbz4Inz0o/DZzybJwGrI1KViOseFOqYPlliM5osvhiefhEceESvVK6+E667LmxpRrcVyY6NYoH/726JvSthsgqTIWYsmA6UvmGPTVjdDFrWTOrDbtsGBA/B3fyc+X7YMDh1KJ+Kl4yEwGMiatOciM6qNVJkpPabz3BefA2D9769P2y9VYkpeoiQzdu9OykWUCmnPM//7DAA9F+WXsowFY5x9/iwtS1tKP8EsYK6NfcXskWuIiDdC/4v9RP3RRFRwreJCa6OZQDwUJzIRwYgLKU3/OT+NvY15M10LoRbsqQV861vf4gMf+AALUyJm7HY7zz77LKqqcu7cOd7znvdw5swZDMPgT//0T3nve987a9ebq93KkVvMhCTXyyUzimVmvPbz1xg7Mkb3hu6SyIzZQGpmxtCB9M+k3wWSko3+fhG82bqklUVbFyVklqfy+5eCZcuSZEYhlDJPjGLD7hBZ/XpEL0ktZK6NFXV7ah/l2CQzMyKKmOPnkw9vbYXQaIBnPrKDnXrlQZmVoFbbyOpM+qXjoXhZfupasWnuUHgzDF3XOXDgAHqJ1ZxigamRGdUp/m0Kzf+Oq0Tk9yQK2mJtEpkCigaxcdDc4pVKXqjO5PbUl+pEUZLRiJVKTXWs7gBF6D7niqjMhXLbpxAmzk0QC8RQbSrNC6eehpaXzAARWa5mR1lV0x5A1E45+1MYeAwQjqATJ+Dxx5NO44ceyu/YUhSF1mVi5pvKZpcCzalhdVuJ+CMYikFDTwM2ty2nc3wqaJzfiEWziJdqwe62J84jtd7LKv4dn0wR0HI/JStuI2uTyCAwIpPEYVw44mMeGm0e3A1BLA6TeJREwT/5ingjOFudtCxpwaJaMGIG48fHifgiWF1WbG5bxb9tIXs0p4bNbSMejuM77SMWjJV1Hnenm7ZlbXlfcgJx8qTYf9GiAgdTnaC5xO+HIX7PzDGqiD0VwdEBS++BeW9Ivxw16Xh0OCrLRisVVbepjhmB7nJx4NJL0T/3OVFJu6FBVNKW6XF792YVpJCL5XxObEURwQ7FFss7dsADD2RvD4VEhl6hIuISpSyYf8uVrPnA62heVP3UbdM0kzqwKTKCIyPJfQYGsjVzpfM34o3w4PYHGT+WTONIlZmarvuqrQ1cBIgOjOE56SEwHCAaiKKoCmNHxxg7OkZgOJBGZkisXSu0sj0e8awuFad+c4rn/+15nvvRcwzsERqXhYp7J7JXRkuba80W5trYV8weuYaQOB/qLl1obTSdSC0CHfVFMeIGRtwgPJ4sAi0zWkvFXGufShGPx7ntttvYs2dP4vX888+jTk7e7rzzTu6++25efvllnn32Wb7xjW/ws5/9bNauN1e7SanTXCiUZQ+Vy0wVy8yQBIb/XH71BpjdfrjgqgUs2roId7c7sTZ3TCaSHjmS3K+xN53MWHjNQrb8xRYWX78YmNrvXwpKLQJeyjyxt0+jo1PsUGqwyVwbK+r21D7KsUlmZoQRN2+uzAwQZIadCOExEZTpaHFkvTS7lgjKrCZmq418Z30EhgOJAIhMWKyWhHJJuVJTtdLv6pkZFcI0Tbxeb5p2fSFMNTOjKsW/3Qth7V9lhQ0UtcU5D6KjEB0Hz8ugNaZLIfkOgmWyK1ls0LSKVJ6sazKKo1Iyw+a20byoGf9ZP77TvsRiuxDKbZ9CGD0iZGxalrRUpZBpQTIjD6ppDwDB03DkK9CwFHpuBMQk6/rrYeNGeO45EZVy6aX5D9G2vI2hvUOMvTbGspvLY9ligRjxcBwTMxEtW20UKpAtx91SJ+8AxCYn5NbcEUYVt5GjU0ghxXziHtv9l4LUuuwr7HtK4V3/CcsXWvnf7+SPbAqNhvjxH/6YiD9CLBDDd9pHNBClbXlbQpKqXJRiT9QfFcSJu7JxrRhkvYyCZAYIEsg0BOmaK0uMabiHCkBeryRjJPSYztOffpqIL8JNn7mpovpJqZhJm+qoHhLtpmlikL30UjAmJ5onT8K//Ztgla+8UqxOe3tLrstSaLFcjSLiUFoUor2vi9e/Y3rIvHgojqmLE8vMDEh3rgQCYs4xLyW7OlXuSo/qaQXxUjMzpuu+alAC3MkOGs8E+f6bwXPcg2mYPHTPQwnJIFe7i+PLtwHuNDLDZoMNG4TM1K5dsGRJaefs39nP0V8dRVmkYHpNrA4r7avys/jO9iThM93F26eCuTb2FbLHNM1E5L3m0IiH49OW8VRNXEhtNN1IzWh94V9fYPAVsZCwNdq45Qu3oChK2dIYc619pgOvvPIKuq7ztslCRY2NjXzqU5/iy1/+MrfddtusXFO+dpNZ9r//+6JQtESm3GImKpWZKpaZIcmMiYGJgvvNZj/ccPeGxN9y/nD55fDUUxlkxmRmRtQfJToRzVlMd9s2+J//yZbrLPb7l4JSyYzUeWImEvPE+xWuXHQdmkMrmfyca2NF3Z7aRzk2ycyMoC76c77MDOnzicdFUKZFsxAcDoICTfOTX8ongT0VzFYbPfHJJwgMBrj58zfTsaoj63NFUehY05Gm9FEqaqXf1cmMGYIRN9Ac2pRlpqaWmTGJcqVOVAfY2iEyImRcMp2FRkzI44D4POYTdQUm0TkZxVEpmQGw9eNbcbQ6ZkUjeOxI9SSmDCM5YUqrmREagAOfF4TTqg9M+TxFkdD792R9tHq1IDMOHix8CFk3Y/zoeOEdc0Bm2FgbrCjq9BVD7ljdwcihEVwdrrTtFWVmJMiMaSi46OgULz0qsgoAXL009Lo5NgRhDdoK3PtjjKGoCs2Lm4kH43iOewiPhTFixrTeM9KhMl1kRkmZGSAyWUBkaMwkwsOCgHItSErwkZ/MsJg6Yy8ewxINE93Tg7ZxhahCdvCgeAUCMDEBwaCodi4Fe+uY+5BpBIsXwyc/KQbh556DX/8arrgC3vWukuqyFEI1iogDVSFWpoKIXyxcVJuKakueJJfmdSqZoVpVLJolEaEkF/KGkczMmM6MaZcWwUWQiK5hb9ZE/SOLgmbTsDfbEzJ9p5UImWQGiCLgu3YJqak77yztnNIJ43nZQ3NLM13ruwo+E+xNdhRVwdRNwp5w1rOzjpmHETMSfdbd48Z7wnteZGbUUV2kFoFO1MkwRDaVq71+n04HfvWrX7F169a0bddeey3bt2/Pqy8eiUSIRJL3p2+y8Fs8Hic+ufiwWCxYLBYMw8AwkhGzcruu62kOotTt8jNd11FVFUVREse95RYwDBUSQUwme/fquN1i3SOzTVIjaUdGLICFlhaTeDy5XVEUVFXNusa2NgVQGR01iUT0xHM+0yZnlxPDNBKZGYVskvYAWTZJ5Lr2Qts1TUs7biGbFEVhcFAc56qrDJ56ysLx4yaRiIndbsFis2BvsRMaD+E57cHd6cbZ4sRU0h2AK1cmf8sPfegIV121mK1bLdhsok0qtWnxYvGbHz1KUZt+7/fgu99VuPtuC/F4sn/On2/yxS8avPGNChbLvMT+mX0yVztB9nlnq51ybS/3fpL75LrG89GmfH9fCDaZpknIE8I0TSbiYk7f0GAQT8lEkDa1tEweLy7uWyNu4O/3Y9EsNMwT5Ks8T+q9UQ2b5Htq35uJdor4I5imia3BlredbvjHGxLb4/H4tNiUaVc1USczZgiLrlvEousWVcxelZ2ZER4WpIKEdx/Y2pJFvK1NwnlaKhqWgLNnUqxZTZIXAI3LBcERPCUcvkZ6mlJXFcgMd1d1C/GUA5mZIZ33UzrWaDIroDP15w+dE8XW9RmSdpBkU8ybJcC9apV4P3So8CESRcCPezB0A4taegRnxCMm+lrj9A5B9iY7PRf3ZEWX6pNjalmZGbLQvZY/Q2LKUG2g2hOkYHe36PdDQ6XppCsouDvd2BptqFZ1WokiQzeIhcS9nhodXU2UTGYYk1Gq6vRcRxpME6JRQTw88+dgDMPWLwB9wvkcCHDZ/gDvJUjbXgWY1Fb+xCdQBgfZ9NoJ9HAcy30vwMc/CuvWCUNffFFE47tcQnYoX55sHXMf8+YJb/UddySLJACMjLCNp7hj53U8faCDc+eE5NO115ZGHFSriDgUL3h/3boRDv14lNYlrXSt78p7nEog62WkSkxBMlDA4YBwGA4fBumHCgwHiPgixEIx9Kh4CHvPeFEUhdFRsMbsxBV3RXW9SkWLjCFAQ7HbEs+leDieIB3ikTinJzPSMsmMSy6B//zP0oqAS3tN0yQWiKGHdNwr3TT0NDB2dCxvFLeiKDhbnQRHgoTGQucdmSHtzofpKOw43Uik/ivCoe094U0QejOJav22c7GNZhLh8XDa/8ePjtfJjGlCf38/izImoE6nE4fDwdDQEN05Uuw/+9nPcu+992Zt3717N+5JHd/Ozk6WLVvG8ePHGU5Jcejr66Ovr4/Dhw/j9XoT25cuXUpXVxf79u0jGAzi8XjYtWsXa9asoaWlhd27d6PrOq++2oCur6ejwyQaBZ9P4ac/fZWlS8XacvPmzUSjUV555ZXEsY8eXQ504HKF2bnz5TQ7L7roIkZGRjgmtQ8Bl6sZWINhKDz22B5aW+M5bfKP+PF6vKhHxeQkn0379+9P2KMoCqtXr06zSWLjxo3YbDZ27tyZ9rvmsklVVS677DK8Xi8HUyLzUm167eBrQiO+wUpLWwtDQ2sAaGx8Dbt9GZGIym9+c5qbblrI8ePHmVAmmPBM8OJjLzL00BAOp4O1f7uWkJlct7/00lqgiWXLJrj88qM0No6ye/fUbQoGHcDFHDsGHo+XQ4dy2yTbqbVVJR7fnHIeH1/+8n5UFY4fL7/vtbW14ff7E20EzFg7pfa95uZm1qxZQ39/P2dSJp7l3k/ynt6/f38a8Xi+2mSaJtGomBfv27ePUCjZJ+e6TbFwDHWZSmwixnhQjDWh0BA7d57IsikW8wE2QuE4fr+ftq42DFP4MjyjHhRVwYgaWLEyPj7Oa6PJwjlTtck0TTweD6Ojo/T29s5IO5m6yfDZYSyqBVuDrertVI5NktSfDtTJjAphsVhYunRp2RXcKy0AW1bNjPAwPHu3KH4LgAmBk2Do4JoniAd7u5C2mSyMW9wWBdSUyXI8kPxb1sywOIBsMqNzijJTlaDS9smF3k29qFa1oDRDqZCRo+3tQv86gbAU68ytPVVNewCwTXpVDB3iE2k1VEolMxp6G7jonotoWdIiKlSXCBMTe7Md0zRp6myqWAapVOSKQpXBR+XVzJhMlc6TmVG1NrI2TpIZfjo7hWctHhep4KWSL1bH1DMlitkTnYiCCapDnbbsj7LJDEt+abEsewxDFAoIBERO/vz5YvtvfiOE6YNB8VkwKDy3vb3wox/Bo48mU3tG9sIaFbZ4IdICv/wlNDTQpbvQcHN2vFmks2rA7/wOGAZn/LvwDERpeOdWelauFMe59VbxKhNVHxfqmBGU3G6aBpddlvx/fz88/TTqL37B9evWwdVXC11AtbT7vVpFxCVkwfuFC036+xXuu8/gz/7MgqrC7v86zcEdB1l1x6qqkxnxSByr25qXzMiUiQgMB9hx9w6Co0HioXgiEuv7b/4+IG7xO3HxdOc2rFY3hjE995XVNpmAY0AsRSVIdaSPn6dOi/dcZAYUJzNS7dUjOt4zXlBgwBhgcM8gL/2/lwoWOHS2J8mMWkWueyjV7nyYjsKO1UChMSEhU+u0JrKJJKE3U6jkt72Q2mimYJomhi6iILsv7mZwzyDjx8aZf/n8so9VC/bUAhRF4amnnuKaa65hdHSU5cuX8zd/8zdcddVVeDweVslFUQocDgfBYO4+/LGPfYwPf/jDif/7fD4WLFjApk2baJrUP5G/+ZIlS9LIErl95cqVOaPj169fj67rjI6O0t7ejnVyIblp0yYAnn9erKcuv1w8D3fuBJttPZs3i2OpqorT6WTz5s1Zx+7tdaRtl/6Kjo4O2lIWH4qi0Noq1iTz51/M2rXpx1myZAkdrg48poez1rNEBiOMHhmlQ+2go1XInNiabDR2NCZs6unpob29HYvFkog4ljZJyO2p15jPJonm5ua8NkXsEZ7656doXdLK5fddnlifX331UlassLBvH4RCfQmbhjYNcSZ6hr7mPrx24XBdd8k6UpewDz0kr9HFxRdfXDWbNm4Ei8UkGFQIhfLbJNvp179WSL0wt7uRK64Q37FYLAy+MkjseIxla5bRurQ1sR3y972NGzfS1taW+P9MtVNm3wOYN28ePSnyFuXeT/J6Wltb0/xy56tNhmEwPj6OxWJh/fr1WdHxc92my6+6HIAXPiy2L1/exebNHWn7O51OVq508DgeLBaNxkYHFtWC1WFFiSi4rC5sTTZigRgRb4TW1laWpOi5TtUmwzAYHR2lc9IpOhPtFPFFONZyDAUFW4ONjsbqtlM5NtXJjBqExWKhq6u6C/R88HqThS0zF7c5EfMJIsNiF0RDfAJBRjjA0QN6WHwe8yXIjLy25MsUSN0u/zYNMOPifPFAYrs89MBACddeAK98+xXO/PYMm/94c1HnSDXbZ/3vr6/KcaBAvYwSyIyq9jeLVRBQ8YDIzshBZhw+XDgbQFEU1m5fW/ap9ZCOs82ZKMYqF+pSE7payHe8eCie8EWXVzNDFgDPHTFftTZa8gfiXnJ0Y7dBS4vwrQ8NFb/eQjaXi0L2xENxQt4QRtzAbrMTDUQrPk8hlERmRIPg94E/ClYdrAE4G4R+H4TC0P8wGA1Y1q2ja8sWOHYM/vVfhQdTorkZ/vmfxd+PPipIDpkl4XYn6xmsXSv2ldvPNEJ8H0S9IiT9i18EoNGA//iC4Eg+1Q8LFyJWl0Dsp0Em/IOEbC0ZjGb5mMnnUB3VQ8XttnGj6Kc7dwpv/f/7f0IP6q1vFSl/FkvB9K1Sal309RUvIp4KVYWFCxX6+2HRIksiQ0RqSk+Htn/Xui62f2d7hmxAUsP7mmvEz3P4sPh/xBchOCoK/imqgkWzoDm1RA0NXzCOiyALu4S803TeV6oKGBCNQduKNkLjoYS2OAieNBgUbZE57gmHhphHyKycXEi1195kT2iW25vsWFRLQs4q4ovkdBg7Wh2oNrXsYoAziVxtlGq35sxe3hSzezZRqM81zmvkLT98C7FQjP0P7gdIPHNnCpX8thdSG80UFEVh27e2oUd1jvzsCIN7Bhk7OlbRsWrBnlrA9u3bedOb3kRTUxOmafKzn/2M3/u93+PZZ5/FbrcTDoezvhMKhXA6c9dns9vt2O3ZWcKapqFp6X1eynZkQjqBcm1XVZXejMFfHlcS3ZddpnDkiJgqHDumknHatOtIFgBXsq4v3zV2dYnvjY9rWccOjYZ4+A8eJjgSZPz4OJjwvW3fw2JNHiOVsLRarVn2ZF5jpdsVJb9NMX8Mi2LB1e5CVdVEMERvr8bKlbBvHxw9aknsf8X7r+CqP7+KsdfG2P/d/ThbnWjW9GMfOCDe16/PbqOp2KRpYh1x4gQcO6Ywb17hdpJB3H19InN2eDj9dzj66FFOPnmSTe/eROfKdJWOfH2vJ00be+o2paJQO+W6P8rdnsumQmPf+WhTriyxYtdY7vZat8k/KaDR0mJBy1Hvrb1drI90XUk48q0uK3pEJx6O42h2JLZbLJaq2JpqU+qYkO8+q2Y7BUNBLIoFq8uaUE/JdY0v/t8XOfv8WS5+58UsvSnpaK6mTfmuvxqokxkVQtd19u3bx/r16/M2Xir2/2A/Q3uHWHbrMhZctaCsc8msjO7uMpVHZMZEZEgUxnV0TUrkKGAk0+py2mJtEtkbkdG0fdPg6BAR+XH/5D5xQaCYerIWg72d1i4RiTLVzAx/vx/vCS9Drw4VJTPKbZ+ZgiR0suYECTIjt13TYo+tWZAZUQ+4kkLhy5YJh8vEhAgEnl9+wFdO2JvsuNpdIjo2InQOw+EwDkfy4eFqd5VckKzU8+RCEBcR7OVlZvS8DhpXQHNucqtqbdR9Q9p/u7oEmTE4KOqZ5EKmzdFAlHgwjtVlTdSzKPe3zWVP6nnCo2GMuIGJ0FaXqLgNYzGRCaFp0NBAeMDD8sF9XESA5a8E4NWgqIL75jeL/f/5P+DVEQgHRSaLqcNbAjBPhYPjsD8gSAfLEDSDHo2y7+WXWd/Xh/qGN6STFe4Up8mnPpX/GlesEC8JbTGcO5Qu6YdwOC5YIHiTkycnyYxJyN+mGprntTrO1VEYU2o3qxWuukq8BgeTtTaefRYeeURsv+KKDB1DgemqddHebgIKg4MGIK5nOskMidSIurGxJOe4ZYt4Ty3gCaLgHyGwaGJyL3XnYwZAnO7J5/J03leaBsREZkZrlxNna7pDTCoezJ8v5LJS4XKJZ8D+/cJpdfvtRc7l1LC5hZxVLB5DUZSEzYUKHF790auxWC0VZxLPBAq1kbQ7F6ajsGM1UKzPWTQL9kY7G+7ewMa3b0yrFTOTkL9taDyUuCaJzN+2WBtZXVYCwwEcTQ40h5b3OLWCWnreqjaVBVsW0LK4JRFZXS5qyZ7ZhDtl/qcoCrfffjt33HEHP//5z+nr6+PUqVNp+4dCISYmJmaNCCrUbi++KN4vuyz5TJekfj4kyYzSr6GzU2TvSwIgFQnC0qHRsaZDSN5qSiILP5OwnK1+KGs3OlochMMgA4e7u5PTfFmvFEjIQia+15rxgCZJZqxapfPyy9W1adkyQWYcPSoCNgrht78V77/3e/DlL2e3U7nzs7k2VtTtqX2UapMeFXUaNLuWIDPy+UrlGKfryeBLRVUw4gZhTxhbg63qQZmJ65yFNkrUNW0oHDgZD8eJeCNlr9dqpd/VyYwKYZomoVCo5BoYY6+Nce6lc/ReWr4g89SKf5uiQC2Imhm59shli6NTyFBlOOnSIOV2iuzTvkc4VaZKZnSu6eTUU6cY3j9cdN9y2ycfvKe9OFocVasJkD8zQ4p952ahq2VPGqwtQL8gM1Jgs4kMoCNHRE3iQmRGLBhjYM8AYU+YFbetyL8jQuv5jd98I/0v9NO2sg0ssHfvXjZs2ICqJZ3lU43Gc3e62fbAtoLO4s9tsRMMuMvLzGi9WLzyYFraCNFXDh/OvWiQyLR5/4P7OfroUZbevJR1b14HlP/b5rJHnifsDbPzKzsZPTjKtX97bVpNG7vTgtuRLBjFSy8JkkJKNgUCYobd0gIPPZSoM0FsMgr4DW+AN76Rc3sGeQf/Q8zqouGQCxrcyTQvgGtfB1ddDjYTXE5wOqGvFxx2uHoypSilNpAZjxPauROzuVlUSawGpFxbzJv10aJFSTIjNdLd0SwWQNUgM6arz9Uxvahau6U+SBYuhJUrhdTZj38sJgy33y5qsqRgqkXEc6GjQ5AZIyNJexKL5RmKIJfjY1tb0uTXXkvWqJIwjezfPDJ5iTLIYDrvq8lHXWK4y8SkRHDeLNxLLhFkxu7dxckMCYvNIgiUYKykucxsOcrLQbE20uM6FrW2CZlUlNrnUp3+swU9pjN2RGQD9FzSk5i/ZaKYTYGhAN6TXoINQbrW1n6GQK09b91d7inVE6w1e2oJuq6jaRpbtmzhox/9aNpnTz31FJdddtmsyXPlazefT6zZQJAZ0jlfKpnRWgYnJmMlhgssx0sllWerH8raM45WR2L+YLOJ5GtJZmQGRAAJ+UWpLpA4XjgZfLpmjcmZM9W1adky+PWvk+fIB9PMJjN8PnF9MkCiXDJjro0VdXtqH6XadOzXx9j55Z0suGYBPp9g+Zpyq4HT2goR7IQUF/GICP40YkYamQHVCayt1J5qQsqRFpv3W12C7Cg3G7tW+t3sz4ovECQ0b13ly4qUXfw77cR+MOJCVshaTloHwglYSpHwIvvIDKRz50orYpwPnWvFeUYPjmIaJopl+hepv/0/v2XsyBjX/t219F3RV/wLRZCfzJhM2chDZkwLlr1LRLO7sjOFVq0Sk7hDh+Cmm/IfIjQe4jef/Q2qTWXZrcuKFgEPjYbY9bVduLpc3PaV23CNu2hd1lr19DN3pzuv4940od8j/i4rM2OmEBqE4Gmwd0DD4oT/XvadfEi1uX1VO6efOY1qU2lbVg5jk4JYLBm63d8vVi2BAO5gEHcgwK3vXom5+hZ47TWU73xTpPJMTAhvXG8v/P3fi+/+z/+IcGOZBeFyJcOPFy4UYeDys4aGxM3xmrKCP+HfWbvSwp99Jsf1VYuQmAoShG42mbF4sXg/cSJ9u73JLp4Dc2fOWUctYNEiuOceITn18stiNSvlMU6fFvfm6tWgKIlaF08/TdlFxHOhY1KadmQk+UyezsyMgz86yLmXzrH0dUtZdJ3QYpLOiK4ukRVls4mh6PRpSF3XONudGIaRlhEhh6Pe3CoKVYWqgUacyAREA+mfxUPxxLXkIzM2bYJvfau0IuAS7avaGTs3hqvzwikS7D3lJeqL0ry4GWdLbjmY8wUDLw9w7JfH6FjTwcrbV8725WCxWrBYLRgxg9BIukxaqTAxCQyJGyA2ESMWjlWl1tdcx+lnT3Pk50eYt3keq+/Ik6pbR1k4e/Ys3d3diXXID37wAx555BE+85nP0NPTQywW49vf/jZve9vb8Pv9fPKTn+QjH/nILF91Nl56SUzZFy4Uz0FZkq0YmTE2qVJWDpkh1yWFgqxqHTKj3NHiSKyvurqEj0L6W1LJDNMweeofn6L/xX7xvYzMjMOHRXZoS4tYxqQGi1QDMqC1GJlx/LiQJrfZYOvW5FxoaCiZJS7rjUX8Uw+qqqOO2UTEK/qwzW1LELj5MjNaWyGIm6c6tnHvg+J7EwMTPP7xx7G6rdz6f25FUZSqBNbWAlydLla/aXXOLLJUVEpm1ArqZMYMQXaQfFEKhVBW8e+sE3vEu60FprnQMiBmUnoYtOTiUTruo9HyihinIjAcwNANDN0gMBzgxFMnaF7QnPi8WgNPYDiQiJjWYzpD+4Yw4iLKfOzo2JTPIydMi3uHhc4/CLLJiIERFvVG/EfTIsqnDU3ZRe0kVq2Cn/ykeBHwxnmNaE6NeCiO77SPlsUtBffv3ykmgV3rumYtYjIQSEa/lkVmjO0Ssm0Ny8AyjUPnwKNw8nsw/3dgxR9XtGiQjrrwWEjMri0W4cw8fTqZITExIbIZbrhB3Ldf+EJa9oQlEsH6pjeJAz76qMigABHa43ZDYyPKmjWCgFi8WLw3NIjPWlqSF/O5z4HdnpvFvOSSZFXbDJw8bcEkXaIpJ4y4IOXU6kZRlARr4cwMSNb9kFj3lnVVrcNTRx1psNlEWGZq4fDnnhMhfa2tQoZqyxbUzk6uv746pxSZGcnaXpAkM2IT1Z8cjx8dZ2D3AD0XJ9mHVGeEqgpnxP79wsGwOWXuZFEtNPakr3TCk+v5IvK8U4a9yY7F7cLqCRL3xwl7svcJKUICsVBmBpRHZmgODXurveQpoPe0l5f/+2U0h8aWv9hS+olqBIZuiOhZA1Rr7WeZFIP3lJeTT57ENEx6L+ll3//uQ7WrXP6nl8/K9SgoNM1vwnPCQ2A4gLun/DlxLBhLk3MIjYawzq+TGcXgOelhcM8gDd2CQBo+MMzZF87SvqKdBVvKkzCuQ+CRRx7hX/7lXxJ1LlatWsVjjz2W0AJ/6KGH+KM/+iM+97nPoes673nPe7jrrrtm85JzQkpMTZZnS2QXDA6K2pvNzdnfCYWSZH65MlNQODMDhHxJYFiQlqlr9lqAlItytjo5mhIMAcnf7sQJsV602UCxKIwfHU98PzMzY78oZ8TatZUHbRZCqWSGzMrYtEks17q6BLGSRmbI+Vng/HRe1lGHRNgrSEl7sz0hM1UoMwNgwO+mbZmYt7QuaeVN//MmGnsbZyRIeibRsqiFTe/aVHS/OplxgUJVVVavXl2yRph8YFSSmTElmSkpAWVtybtLubbkRTwEz70d9Chc+/2Ec9HhEAPI+LiIAi2XzAgMB9hx9w6Co0H8/X7ioTg/fs+P01LAUouJVWpT6nlApMH6z/hRVIWf/slPc56nXAwMQEfjMNvn3Q2/GU3/0DTht+8Uf9vbhczXJKFRtTYqEbIIeDEyQ1EUWpe1MrxvmLHXxkomM+ZtnjfjNkmMTv7sNptICCgJhg6vfFL8veVbSXmhFFTNHllgfPLeFQ42k8FBRRATp04lZZsk+XDTTeJG+9nPYN8+Oo4NcfH+g7iOG7BVF1kMR47AV74ijm2xCOMXLxZkhqKIE8kfxe1GcblYtnChsOfOO8XL5QJVJeKLJO+/3l5429vy25Mp/F4iSir+DeB9FV7+O2heDZv+Je9u09LfGpbCwu3gzmZc8pEZ1STxZuseqmNqmPF2u+su2LxZ1NV47DExTrzrXaK2RhXQ1SUy8kZHZyYzQ0YTyuhCSM/MAOGM2L9fDHubi8ydIpMJLFJmarrax93pRtm+je/dH+GeW+D9n8je5/f/wE6w352XzLj4YvF+8qSIqC1lPqUoCg0NDSWPPaZucvb5s1VPs68mCrVReDwMk0qHo4dHaV/ZXlEg0UyikD2p6wc9onPi8RPYm+2zRmaAyHDynvYSD8WJ+qM5nQCFbAqNCkei5tRwtDiyHIO1iFp43mZK3AztHeLA9w+waOuissmMWrCnFvDud7+bd7/73Xk/X7RoEb/4xS9m8IoKI1+7pdbLAOHQ6+kR684jR8QUIBNSYkpVy6vJKZ+zxcgMQzeYODeBoik0LWhK1M0oxZ7pRqrM1OCkn0UGNPT2irisQEBkOvS1iUBH1akK6UxFBAmMHRVpLfYmO/v3C7/A2rXTY1O5ZMaVV4r3VDJDotz52VwbKy4Ue1IDdHOhljMQSm0jmZnhaHYkMjOKkRnj40mlGMUigjOmG7XS53Q9Oytf+qbLrRdSKzbVyYwKoSgKLanRx0UgO8iMykzpIXAvEkWeFVW8y+0pKNeWvFBTnJZRDziTYY69vUkyI0PCuygSxcTsGs4OJ4Fzwg5HizhfZjExqMym1PNoTo3gaBCLZsHWaMPR4sh5nnIxOAhNTh8N1lFRLF3NsYDTQ6LwesyXIDOq1kapCJ0T2QbWRui6Lu0jWWi6GJkB0La8LUFmLH1dHg8M4qHqPekFBXo29UyPTSVAplS3t5cRPaOn6IFouSUVyrJndFRU9ZY1JAIBcYMvWgSnvLDjOChBaPLxxmcDTNDLa0OTur2f/WzKtWiCYLjiCkEa2O3Q1YXS0MXQLgs0ulm0dq3Yd80a+PSnxQzd4cg2/h3vSLcHSFiTssoxdIOH3/0wtiYbN//zzbjaczNCuR6Y5TzvSiYzIpPh4JbCpMm09Df3Qlh6T86P8pEZ1cRs3UN1TA0z3m6KInSLli6FN79ZFFxYs0Z89vDDYjy66iqhS1GBDnhnpxhLUmWmXO0urvv4dWmEQ7WQSwc2k8xIk9h4vfg710TdBPRIHAskCoBPZ/u0LXAzjpthHdpykCyHJqUp8pEZLS3is2PHRDMWkoHMtDcWjeXcnglnu5iXRHwR9Jhek9kNhdooMBRIZNQacSOxmJ+uwo7VQCF7UmVq5f0U9UcxTXPGM1zjoTjBkSB6TEdRFfSIju+sD3dH9py44H00KbPY0NOA1WXFNEyigeh520YzhUwyQxb/Hj82nvc7+VAL9tRRPvK1WyaZAeI5ODAwmaGYg8yQ66GWlvKyCWRmRrGMcavLChYw4ybxcDynlNxs9cOF1y5k4twEDd0NWfMHKTX18suwf2eAF74hAh0DwwGiviiOVgdPf/rpxLFc7S4OO7cB7snMjOrbJMmMkRFRAyOfw/b558W7jFXJJVUsyf1SZabm2lhxIdiTGaCbC1MN0J1OlNpGqZkZxWSmZPBPLCbcL+4ZNHs2+lxwNIhpmDiaHag2lR07ctdL/PS7rVgpPzOjVu6jOplRIeLxOLt372bTpk0l6f3LIpjlkhmhULLTlZyZYW0Skf2RUTAmH1Rxf/o+9vaE3nu5tuSFoogMEH1IyFtlkBn790+tCLjm1HDb3ER9UWwNtrRIu9RiYjA1m2TRssBQAItmwdnqTJwr8zzlYnAQnIBVQxAZWp6R1EifYFStjVIxcQyOfAWa12SRGTIz4+RJ0QedBYLm2paLp8PYa2MFT3fuJdH4Has7sDfap8emEiAzM8rKEIpN3j+aGyyTjp1wWMwqJ8mIuM/HodOnWfXWt6IZBvzXf6UXvQ4EBJnQ2Ajf/a6YJUtYrbB9u/CA2xpBs0CzBusvZgI3jz3WiX0Q4Wj8xCcEgeFyTeY+p6xAJj1bmi9C/893AGD0zMMCgsAoI0siX/uMHxsnHo6jqEreaMp8D8z77y+9wHDZZEYRWbaZ7m+yZsbJk0mlLxCTixf+9QWMuMGN/3jjlM4xW/dQtTExMcE3vvENXn31VRoaGrjhhhu47bbbZvuypg2z2m42W3pGRlOT8IL89rcibOnyy0W2VhkC2i0tcUCbLAAuxiPVpjL/8vnVvfZJlJqZASIi1d5kx9XuIjgazHqG6zpYDAjiYuFyQY5MZ/vI585YjselrPEB+ckMEFJThciMTHtN0yQUDOF0ORPO70IFDm0NNiyaRRRFHA9PqcjwdCFXG9mb7GgOIXuJAppdIx6OE/aEE4Xfp6OwYzVQqM+lkhmSwDMNk1gwNmMZJ6l9KjAYIB6OY2+xY8QNgsNBbC4b7i532m+br41kAISt0YYe1dGjetq5zsc2minIaPIEmbFMjNO+Mz7ikTiavfTrqgV7KsGzzz7Lj370I/bs2UN/fz/hcBin00l3dzcbNmzgjjvuYOvWrbN9mdOGXO02PCzmmooCl16a3HflSnjqqfx1M2RmRrmKCaXITEliUrWqxAIxQiMhzFYzi7CcrX648W0bE3/nqme5YoVYph3bH6FhMtDR0eIgHoxj0SxZAZXH/BHAzZo102NTY6P43YeHRZDrphzqMeGwmBdAemYGpBNPzYuaufbvrk3YUAzn61iRDxeCPZkBulnfqUKA7nSi1DaSmRn2JkdRmSm3W8SAxuNiDi7JjLHXxjj4o4M4mh1c8p7c0tdTxWz0uZf/+2VOPH6Ci991MQfMNWzfniyFKnH2LPz1vU7+5rYWVpRZ/6xW7qPz/w6eRei6XnwnRLV3GYVULplx/Lh4b24uQ9/f0SkkiqTEVC5k1GQo1ZaisLVCeAii6VFCsgj4wMAUD99ko+ei0qp0TtWmBAHlro6Or2GIycSidjGYJhDqF5ksji5R9DkPqtZGElJ6LOrJ+qizU0TqeDzCGbRxY9YuCUgyw3Pcg6EbeYuAp0pMSVTdphKQmpmBYYgNqaRDMAjXXCO8z7/4hfAajR6Ho4ch7gD1ORHFvGsX/Pd/J45rMU3cbrcowKtpYlYp87zdbvGyTval7dvhjW9MFsS2pvSxlWvh9xaJ/nDlnZjdsOsfYZmciM4v7iC0NdpQVAVTNwl7wrg6Kiv8mqt9hvaJC+lc25kzKnTHDvI+MLdvh+9/vzRCo2wyw1Z8gJyW/hYaFDUzGpaAJdmOfX2iC0Ui4r6XEjYW1SKIPUU4pKaq0Tkb91ClcLvdDA0NiftkEh6Ph82bNxMIBLjmmms4d+4cX//617niiit4+OGH58RCIxdqpt2uv15UiTx+XBAaTz8tQjtbW4VnvaWlqP5EqfrZ1UK5mRnuTjfbHtiWM93+yGH4u9vA2Wync3GyX05X+8h5XC4y4+RJMW66XEk7cmHTJjGO5qubkWmvHtfZu3cvGzZsQNUEGV9IXkBRFBxtDoJDQUJjoZokMyC7jdydblbctgLNobHg6gUoisKp35xi9RtXs+J2wW7VsqxCvj6XIDPcVlSbimpX0SM6EV9kxsiM1D71y4/+krAnzDUfu4ZXvvUK/rN+LnvfZSy8dmHWb5urjTLvRdM0GXl1hP7d/Wx46wacbc7zro1mClKeSxb0dLY6cbQ6CI+H8Zzw0LEq/xoiF2bbnnIQDod561vfyqOPPsptt93G9ddfT09PDw6Hg3A4zMDAALt27eK2227jmmuu4fvf/z6N5WgnnUfIbDeZlbFqVbojr1gRcElmlFP8GwoXAM8VPGDEDREhPjndzSQsZ7sfZs4fIBkQcfIkrEMEOiqqQnAoiKmbaWNvLBznxKS/RibDT4dNy5YVJjN27xZR511dyYAqSdCktpW90U7fFX1lnXu226jauFDskQG6Jiambqb5aKYaoDvdKKWNwh5B8BtWe8LvkI/MUBQx1g0Pi7FvwaQyYywU4+QTJ3F3u6eNzICZ73My8Etz2vjgH2b7ZUBsG1B6+ee9vRx/uPxz1MJ9NDe9BDUGRVG468G7MHSjbMdVavHvsrLJrU2w7x+h9SJY/HZQZ0gv2NYi3vOQGVPJzABy6m1OB2KhGPGgGOSltuRUMToqokABtFR+RA+Jwt9mBZXRpwLZVjmKFyuKmBQ//7yQmipEZpRSBFyP6Qy+LEJfUsmMqkEWtE7Ngli8WMzoDh+GZ55J7LP0yQDvZDHjbe8SaSd/+7fpx7JYROhrQ4M4ZjwOTXZY4IbWBTBv8vrXr4e//usEUWFYrQzs2kWfPMYHP5j/egt5qqyTi6/4BJCciKamCBeDoijc/u+3Y2+yVyRtVwjDrwqPZdf6bBt0XZid74GpKPChD8EddxSWnIrHk1kdxcmMyVSbAkTgtOKl94t6QZd/BVxJoslqFV3lzBmxGJJkRmI8McVEw9FcWU2R8xGhUChr4vMP//APdHR0sHv37oTj4ezZs9x88818/vOf56//+q9n41IvLGTKUMmb83/+RxAa69fDli2wYUMGEy/QMXnrTUwohMPJBLAzvz1DYDjAgqsWVEyoZsI0zYTOcymZGbKAp7vTndNB6j0K48D8GaqdKyNgZYZgKo4dE+9Llxae75VSBDzV3ng8jmvcReuy1pLJQWebM0FmzASqoe0cC8YY2D2AzW1j49s2cvbFswzsHkC1q7Qtm+H5VRWRmpkBwhEVjAQFqdc7c9fh7nTjaHFgxA1sbhvzL59P65JWnG3Okmte+M74CI2H6FrflQiGMA2Tpz71FOHxMCtvW1m2Q/5CgWmYCcdN6u/durSVcy+dY/zo+Jz+7f7yL/+SkZERTpw4QWdn/kzcoaEh7rzzTv7qr/6KL3/5yzN4hbOHXBJTUJzMkKR6uWSG/PnlujZ1Pp9JWJ594Sy7vrqLliUtXPs31wKzTyrHw3GigSiOZgcWzZI3MwPEHEKqY0vJxVgwlibzF4lAXBfLxgULkmv9amPZMhFzkq9uRqrElJxDFCKe6rhw4DvtY+LcBJ1rO6vm16oF9F3VR2gsRNwqnokWS2E1kVQyQ0L6rQKDAWLBWNX9JrMFGfh14Jg9TSkjE6YplnpPPy3i28431MmMGUQqG1qqnnzFxb89rwgZobgflr6z8osuF7bJGdE0kRmpMDGnjdywOq20rWgjOhGtml60nCy1tkIap2VMMuOWGb4drZNFrONBUbQ9g/BKJTMKQVEUrv3ba3F3uWnIk6Jm0Szc8oVbGNgzQMuSltwHMk1BLqRmSTgcwrMTDMKPfyy2TUwkCYt77xU3zpe/LFJIUvH2t4tZXDAonlxuN3R3c7LZzQH6WNeOCIH90IeSmRNut6g7IWeBd94p3gceg4N7oO3ipHe9qSmd/o9XKcJhUv6NeBCMOF1dol9IrqbUouWNvdWPSDNNk+H9gszoXJe9mHz6aarywOzvF2Ok1ZocO/IiMhkO7pilRby1RZAZMS+QnjWzaFGSzJDKPhbNgq3BRnQiSsR3YZEZuTJ5HnvsMe699960CMr58+fzqU99ir//+78vicz42c9+xhe+8AWGh4cxDINrrrmGL37xi7gmbxZN01i/fn3adz7zmc/MaSmripHq7P7AB4SX5Lnn4CtfESv1j3wkSehOorkZVNVE1xVGR5PJY3v/dy+eYx4a5zVWjcyQC41YIJYzMyNfAU8pnZgJOV71lRekWDEKyUzJTNxCElOQjMg8fFg8FxrKywwvCdJZOhNkRrW0nTWnxk2fu4mzL5ylfVU7o0cEYySlec5XZJIZtiYbwZFgyXrn1URwOAgmqHYVe5O97OfXgR0HOPbLY6x+02o2vUt0ZMWisOi6RRz60SGOP3582uTpzndEA1GsbjH2pcrDtC4TZIYsRjxX8eCDD/LII48UJDIAurq6+Nd//VduueWWC4bMeOEF8V6IzJABRamoVGZKBjCYpniWZTZJKpludVnZ98A+wmNhmhc210QNpoGXB3j6H5+mbWUbt37h1oKZGSdOAJPbrW4r9mY7qlVNm8+GJx8xa9aUGXhaJooVAc8s/g25a2YAnHz6JBFvhMXXL55Tzu06ciMeFj6KiC8yp9r78j+9HEj6qpqaCt+DqUXAJeyNdpztTkKjITwnPXSuKfyMOV8gA7/GJkpr72r6aGcSdTKjQqiqysaNGyuq4F6OnnzFxb/Hdor3ts1Fn6xTsSULCemi6SMzvKe9BIYCNPQ20DQvdy5ZNWxytjpxtpYWbVYK5ESiI1MNx5x0giv5b8eqtpGE5hYEihEXkmRqujO4nCLgadJf8Xh6hkQohLJhA80Lm2k+thu+swsCAdRAgEs8HtSGBhH5++tfw4MPph94/Xp4//vF3wcOCK+N2y28ZQ0NQiZKVcWNE48LT7+UbrJNDt4XXyxek3jmFfgtcG0b4t6QhXALQdacyVP8G6rYRpoblr07kaHR1CT4lUhEcDJFMxWqhFz2+E77iPqjeSNdS72/i+0nJaYWLCihHnGJmRnTcg+BIJ9C5yCaneG0eLFICjpxQvxfRh+biGKnQ/uG0vTCy41WmzabpglmjpSdgYEBVuXwNF9xxRUczbdiy0BDQwPf/OY3mT9/PvF4nHvuuYdPfOITfP7znwdEGuzOnTtrRrLqvGm3hgZRP+OGGwTD+MILSbbg+98XmkmXX47mctHeLgiF4eEkmSEXTLFAeUXlCsHmtrH9O9uzJA3l8zW1gOeKFbBnj+C5yyEzprN9pMzU+Hh6LR1Iz8wohO5uwSf19wtN76uvLrx/Jfa42l2oNjWrnsF0oBJt51w2KYpC+4p22leIH1nO30LjM5NdMhUUaqObPnMTsWAMzTFZd2KSxKvmfVUqJgZFxqi7251FTkf8kTSCMdOmWDDGyafEw73vynT2cPENizn0o0P0v9Bfs5GRsz1u2xvt3PnAnRjx9LFPzsX8/f58X82J2banXExMTBQlMiS6uroIBvOTo+czMtvNNJOZGZdfnr7v0qXiGeP3i2dkT4ZKc6UyU5omCJCxMfHcL9QsDT0NQvrWohAYCtA0P33dPhv9UBLckhTMlZkh/S79/WBOLi8URcmZ/STJDCkxNV02TYXMyMzM2PXVXYTHwyVF6p9vY0UxXIj2yHpKhm7M1GVNCeW2UbHi3xKSuB1Pd1PSsrhFkBknpofMmI0+JzMzehYWvr/tRLiFR/F+Q8f8/TtyBh7mQq3cR7Wxsj9PYbOVxnR5TnjY8997aJrfxIm2S8rSk0+VmSoZpgmjKWRGCSjVlqJoWAIdV4B7SdrmapAZsmhYPBpHj+hEJ6JEA9GsYmISldikx/QEk5nv/JUiQWbIeZA+ucDWw4LQ0GOgBJLbM1C1NpJQFJGdERkV5FNqZLtpsnppjGaCjL4SgMMB0YiNjSJdaN++ZJZEMChmzHfcIQpiZ8o2Afzf/ytmv4cPix/C7QanE3XRomSqwYYN4imTSkjIkFOXC/7+7/PbUsz7k4K0mhmFEB5O1p2xNsH828HRDf6jyW0ZRaer0kaKBRa8MflfxGT09Gnx05VKZpx5/gz9O/vpuaiHhdcsrOhSMu2R9TLaV7Vj0bJZhqJZFCXuV3K9DNOE/5+98w6T46rS/q+qc/fkrNGMRtJIsiQr2iPLQY44J2w5gG3SgjFpwSbuwsISl7R8YGAxBsyyS/CyGMsGvGBjDNhytoItycojjaQJ0uTUubvq++P27TTVcbonyHqfZ56Zqa7urlM31L3nPec9NWeLuhlZyEwVfAxBLMPJQK5NXv+RI4nRx2NdY4R8IR6/5/EE3d1soo+TURSbigRFUSYskpqbmwkGjZ1yakYmS+CCCy6I/m02m/nkJz/JO97xjvwvdAowm9oNEN7zG24Qf+u60Jd46imxaFm5knNLz+V3vSvo74+1mdwgp3qmTgbxzjyvl2jhv+TIyldfTS2xAWLtBRNLERWrfaTTSNdFTar4iNhsyQwQUlPd3UJqKhOZAbnbs/bdaznjvWdkvakpBKS2c8gfikoZSRhpO2eyyVnrpLSplJL6IqSuFAGp7FHNaoK+/IZPbxC1M6Yhwtl9wg2QcE8D7gDPfu1Z+vf2c8N/35DQbvE2dTzdQdgfpqy5jJplic/ryoWVlDWXMXpslGMvHGPhm7Jf000lZsK8nbz2aljbwHUPXJdXbZuZYE+2WL9+PT/96U/53Oc+l/HcBx54gDPjK2GfZIhvt6NHRRCB2QyrVyeeZ7OJoJpDh8RzMJnMyFdmCgSBMTiYuVZWvOxtqufJVPfDqFxbpYNwWGxdIXH9UF8fiZkbB38A0oU2xmdmSBTDpnRkxvHjsSLwbXGun1RkhrXEim/Il/X6bDbNFdngjWaPySrWC1MRoFIoZLIpHAyjh3XMdnPG4t8Scq5Lzo6uWFBBz9YehjuG87vYLDCVfU7X9Wj27oY32WhqEvsdIxnwMCZq7eOUWSDsD0cDZ7LBTBhHp8iMPCEjPdva2jJGe3oGPPRs6cEz4OXuR3LTk89LZsrbJYpwq2aoTFPsIA9bMqL2XPGTBLmAyofMSC4mFvaH0UIawfFgdEFiVEwsV5ssTgv+ET9jXWOUNJQYDubk78kFksxwVpSBrVqQCJpfkBe6BmE36JFFha06JjeUpz2A6Fh+f0y6yWoVK7TxcXj2WdjRB0Pd8MpPwdIgOiDA177Gha8e4RuAZQ/o3wLlfXfBmWeK2fDll2OyTCUlhJ0l7PvtboYP9nH2O96JWloSfX18XOO1b79I09nNtNx1V8ymUEjYNG+emIjq6xPDYooEqVWelszw9cHzt8ei/o1gq4ZzH4wSGgUdR0morxdkRi6apwP7B2h/vB3VrOZFZhjZU7mwksXXLDasiwJCLi/dA1NRxOvnn5/+u7MmMxQFTvtIhpMEitY+VklmjE54KZ7MiI8+trgsaCENi90SjQwzij7OhGL2uWJA13WuueaahGvt7Oxk7969E2Sgtm/fznxZwTBHDA4OYrfnJ9/l9/vx+2PyLaORcJ9QKEQoIiWnqiqqqqJpGpoWi3KSx8PhcEIWSvLxcDjMtm3baGtrw2KxRD9XQka5JNcXSXXcbDZHP1dCURRMJtOEa0x1PGeb3vteVLeb8PPPoz/3HLeNfJ8n+C59fQ4YHiHkcmG2m9F0De+IN/reYth0/LgCmLBYdFyucFTxb/FiE6Cwf79OKBT7nHibjh1TAJU5c8JomoKqqgQCAbZt28YZZ5yByWTCZBKSEoVoJ4tFj5RjUujtDVFeHrOpvV0BFFpawhEd8tTttGaNzmOPKWzdqhEKaWn7nqZpvPLKK1F75DWms0lDg/DE48Xoe/JvXdfRNI3+vf2E/CGqFlXhrHKi67r4jlCYUChkaNO2n2xDC2gsv3k5znoRIFHRWsGV378Sk8k04RqLbVOu40nTNLZt28batWsTotyM2km1qagmdVpsGj8xjq7rOGoc0Wsy2U34hn2EfCHan2pn8dWLJ9ikqioH/ngATddovbzVsO+1XNjCjl/u4NBTh5h3YWzNMlPaSc7b69atQ1GUhPYr5ByRq00mmwl7tT36nmxtkvaceeaZWK3WCX0y2aZk26Ya3/rWt7j44ovZtWsX73rXuzjjjDOoj9sznDhxgm3btvGzn/2Mxx9/nKeeemoar7Z4SF73yayMVati9arisWRJjMyIi/sA8peZAkFm7NsHjzwi/k8llw2klaObjnWszNazV9oZGBBZkpCYYSKzOzu2R2pipAhoDHlD+CIxiPHFv4thk/QFHTsmrskW546Q9TJOPz3RoSuHSF9fYjZoLsEms22vkQlvJHtkvx05OoIW0vCN+NIGAs8UZNNGXS938dzXn6NuVR1j694EZE9mTMjMaKkAKBqZMdV9LugJQuRx7ii38t3viqB5RUn0zygKhHUTp68Qf8dnAWfCTBlHs38EzwJIvdvjA5as9eTPPx/+/vdYpN6CBanfNwEyK6N8BZhmhh67jMQeGxN+dVcOAUTJxcQ6X+pk+wPbqV5azbkfF8RJIYqJHd18lJqlNZhsJi7+8sUJ6fISk/me48fFb1dVrXCCB0cFifHy+8QLZ3w7Vvw5Oepf01DdbsGIhEIxL+nzzwt6Ob7OxHXXidf/9Cf4/e9jqzQQwv3vfrcII/nzn8HcAq6lYKkRwueSVbvqKkrOD/CD37oYDzq55h9d1C+NPAEuvFD8xEHVdV5/y28JeUMsf+tVCc7u7sf2c3TzMbxDPloumCKNpDSQZEbaxXtwVBAZqg1MBvE4Ya94PTg6ITujIHAfFYSkaz7Ya1JqnqaDdJIXUi+8ZmkNNUtTZ0CYTKR9YALce2/64t+QA5kxE5BlZoaE2WHG4rQQ8oUw2UwZo49PJvzyl780zMI4Q1Y0joOmaXzlK1/J63vuv//+vDMzvva1r/HFL35xwvHt27fjijy4amtraW1t5fDhw/TFhSU2NTXR1NTE/v37GRmJ9YeFCxdSV1fHrl278HqFY394eJjR0VGqq6vZvn17gvNq1apVWK1WtmzZknANbW1tBAIBduzYET1mMplYt24dIyMj7N27N3rc4XCwevVq+vv7OSQXEkB5eTnLli2ju7ubzrgFSd42NTTgufJK/vvP5Xj7nXR1jMAXvsAJj4fQWD3e/nL277KxyLuoIDbVhmrZ9uA2QpUhGi4VURLHjtUDC6iuDrN1a+zzKyoWAnXs2OFly5bY58fbdODAPMCFx7Of/v5q6urq2L17N8PDw2zbtg1FUVi6dCkVFRUFa6eSEgfj4zaee24Pfr/G6tWr6evr58CBKsCMx7OT/fttadtp7tzjwByef97Lli0707ZTVVUVY2NjUXuAgts0mb5n90SeVT4f455xfOPimTVwcADLSgserwefx8fOnTtxDjkn2KT5NXY+tBOn1UnrZa3s69437TblOp5aIg+L3bt3J5Cpp512Gnt/tpeuE13MuXoOJptpWm1a/fbVWJZbGBgciH53U1MTrVe08vS3n+bFX7zIcO0wiqIk2DR4cJCO7R0oJoWqM8XiK7nvtZ7Viv4Lnf2b92N9yoq13Dqj2knXdQIB4fiTc7nEVIynbb/Zxp4n9lC5tpKas2ombZN8Dg0MDDBnzpyMNrndbqYTa9asYefOnXz5y1/mXe96F/2RcHqLxRJdV5SXl3PttdeyZcsWlsiCESc5UhX/lliyBB5/3DhDMV+ZqU2bYOtW8ff3vid+Usllz0TIQEh7hT0aJFZdnVgyDASZsW+7Db/JScjvMVyj6zoM+Z34sUXJjGKhvj5WC6yjI1E+00hiCmIETSiUmA1azMzZU5h+JAcCy5oZmltLGQg82+AfEWslq8uatcxUSjJjfgWKqqBrBpGYsxRLNy4l6AlisprYuFEk0r/vfbFMNBDz9r33Kmi/EfW4gt4gjrR5aDMPp8iMKYDUtfUEs0vF+d3v4O1vT6ypsWFDDosEWS+jOjuJqYJD14Wz1+SIejBLS4VSkMcjsjNyrQESX0wsMBbA6rKiKIqhbn8+GDk2wq7/2YXZZuacj5/DnDVZ6uXkgBMnwESI5nIPDIWgqglMIRithiMnYOwQ+CJZFIsWiQrJXV3wzW+iejy09PWh1taCwyFWjgBPPy1mZCnL5HLFPMhLl4pj8bJNFRXitZoa+Pa3U1/s2rVYAfcCOHwI9gxCfZruqygKla2V9O3qY7B9MJHM2NINQGNbY4p3Ty2ylpkC0YfNLgiNx/5XIp54rYjFN9v/Ewa3iqyDOZdFI2tyycyQeuFy0TJVkA/MD34wkXypqoIf/zi7OSxrMiPsAxQwTeNiTGZQGZAZMrGgoyOR2KlYUEHlgjzy+mc5br/99qzPvUFKGuWIJ554gldffZVf/OIXCcevvPJKTpw4gdPp5LbbbuMjH/mIoYzVpz/9aT72sY9F/x8dHaW5uZm1a9dSFgn5ke9bsGBB1GEXf3zJkiUTouMBVqxYkRARKz9vrazoHIGM2m1ra5tw3OFwTDgOwokTf1w6rWtqaqiKY27l8cbGRhriNCcmY1MoFMLWKHYGY54SuPNOGp59Ftumv9PQ04d590oc5uvBZJq0TQf/eJDRfaM0ndcUfa2vT9g0Z07i54fD4ho7OxM/P96moSHx90UXLaamRvy9fPlyfD5fQmYGFK6dGhpEcEN9/XJkQpLJVIPbLb7/mmtW4HSmb6crrxQPhcOHnaxc2YbDkbqdNE2jtLR0QmZGOptObz2drT/cStAb5OIvX1zUvjd8eJiXeAm73U44EMZrEQ5VBYXBA4OUzy9HdaqsXLmSytbKCTYdfvIwpc5SKporqFlWQ2U4cW6dDeNJ0zSOHDnC8uXLEzIzwr4wHX/rQNM12j7fhslqondXL6/+8FUqWipou2FqbVJUhWVnLpsQ8R+qCPHqz14l5A2xsHwh1UuqE2zavnk75RXltFzQQm2T8K4Z9b265XWUDpdyWtNpVJ9WPSU2QXbtJOdtiM3l8deeyiYoTN/TBjTUPpWmiiZWtK1IsInjcOjPh6hYUMGCsxZkZZO0pzqyGM5kk8xSnE40NTXxox/9iB/96Ed0dXXR3d2N3+/HZrPR0NBAc3PzdF/ilCMbMgOMyYx8ZKY2bSInuWwQOv3PfeM5Bg8MctX3r5r2AsQyyMtR6eCYQb0MicWLwYOLnnM38qHPGe/5jh6Bj7/JhmZ3kWcycdZQFCFDuXOnkJqKJzNkZsb69YnvsVrF1n94WOzJkskMKUVzCicX4gOBw/4wf/zHP0Zfu/bH16IoSkECgacTvhExjm3ltpxlppLJjPJ55dzy0C1ROa7ZDqvLytp/SFyPbNwohFne+U7x/7XXwqOPigDT3/0hQmZ4pr4W22RxisyYAsiOUV6TXUG7e++deCzdIiEBug6ORhHZnWW9jIIiHIDnbxO/N/xaOIIRD+A5c8TDNx8yIx4y4lwyspOFrum89N2X0EIajesaabkwgwdV1yEYFMyMySSYmrEx2LFDzBIySwLgbW8Tv7/2NTb+rZtzCHDR34EehKTTsmVgfjscehJOvByVbUJGdlVUwDXXoNlsnDhyhOp161DjZ+pPfzr1dS5YkGNKz0QsXSqyg/btE9xKOlS1Vgky4+BgVO845A/Ru1N44GcKmZFVZkYy3Ich5IWyJbEi98WEdJBHCo/nlZlRKcZJoYqfDncME3AHqF5cnfFhv3Gj6MpXXhk79u53Zx+xlTWZ0fl7OPwLmHs1LP5Adh9eaJQtg3m3QOnESW1eRCljfBxG47gOhZhusK7puHvdWEpmXsHT2YZjx45x11138fDDD2OLy7/v6emJOrA6Ojp4xzvegcfj4TOf+cyEz7DZbAnvlTCbzRPSaKVsRzJSFUNLLlYsnVGp0nNzOa4oiuHxVNeY6/F0Num6TkWFiPrqHzTBihWYVqxguO5cOv7fwzRbQbFaQdcxP/KIqI+0ZElC9etsbQqMBVAUBUe5I/qanNPr6hLPlxv9Y8cUAgFztDxT9LMCpuh75883Ry9HyquYTKaEzytUO0kifWTEFM1S6+gQXy5KU8Xel6o9WlrUSAFWhb17zUh5eKN20jTN0J50125z2Oh5ReiCKpqCYk7dVyfb9+TfckxYHBbMdjNBd5CQT8jvma1mTObY9cfbdPjJw6iKSuuVrROu5ekvPc3ggUHO/+z5hoVbi2VTpuPJ7RSVbEpqo4BPRM2aLWasDhHE4x/0c+TvR/Ct8bH8ZuNQ4Km2yVpiZd6GeXT8rYOOv3RQv7w+QZZv+PAwqqKy5Oolaee9C//1Qiwui6G2/kxoJ3ldqebDYs7lviEfqqLiqnElvK6qKsHRIF0vdOEf8bPqtlVZ26QoSvScTDbNNDmWuXPnMje52NEbDJoGMuknFZmxWKi+pc3MyHY/FA7D3XfnJpcNosbVcMcwnn4PAwcGmLO28AGDuSBeZqo3st+Ir5chEb13nS6qWo2dvs/tBg+wZmnmrPNCoLU1RmZIhMNC/RkmZmaAsG14WATEyboepzIzTn7IQGDvkDdBBaCkvsRQfWS2QfoB7eV2Ro+JY/kWAFdU5aQhMtIh3o9kt8fmLLNTPN/fkGTG/v37+fWvf83Ro0cTInUk7HY7991332S/ZsbBFIkwzKaCu+wYp62wpNWTT4d0i4QEKAos+WBOjr1cbMn8YVZQzEAAAsNRMgNiZIaUW8oXMiXOP+pH13XDTU8qm9x97qhcFQDhMB1/2kvPth7MdjOrVoPy7LMxUsLthquuEiuBP/5RaH+53URFuS+6CG67TcyKP/95YiZEfKjL2Wfzx//UeA0ny29xcdqVLlFQFYQs1PXXGxvrcsHll2PSdZaHw8KeQhbk9HTC0KtgrYTaiVVETztNmL1vX+aPqloknhCDB2NVlXp39hIOhHHWOCmfV55wfkH7XZbQtBwzMwB8xwWRoSjGklMRFNQeKTcWFGRGPpkZk5WZSrZn3+/3cejJQyy7eRlr3rkm4/uTiZfXXsvue3VdFDSELMgMfyRX0pwhFIMi9rfypeLHAA6HmDp6e2OFhpMx2jnK+PFxVKuKtcTKyJGJGR4SyVE00zGGphI9PT3MybKqvNvt5oYbbuArX/nKhGjX+Ejc+fPn89WvfpWPfOQjhmTGVOBkazeTycTKlaKd4tOXG85biKPprlhh2tFRQfr/5S/i+bh+vdh5Z9nGENt4W0tjGzM5LyZHVlZXi68ZGhJrj5UrE1+XYzI+aVHaU8z2kc+e+AKEuRT/BvE4OuMMcSu3bYN0tW7zscfismCymkSmxJB3Sopoh7whzA4zlQsr0dGFFvmwD1upjbB/Ys2BtrY2Rg6PMHxoGNWssuCSicEb/lE/vmEf3sHCkPrFQqo2kvsHizPm4Jd9PzA2tU6okC/Ei999kZKGEla/fTWKmrgWXXTlIjr+1sGRZ45wxp1nYHaYozZd8e0r6N/bn1amEpj2iO10mO55WzpgHVUT16GVrWK/MXxoOOW+KBnTbc8p5If4dtu7V8TSORyklDiSmRnt7URqMcVey1VmavNmspbLTg6Aq15SzXjPOAP7E8mM6eiH8y+cz/iJcVy1ruheJVVmBsCBA6k/a/du8Tu++HcxbTIqAv7668I1UVqaeB0S9fWCzIrfQ+ZCZpxsc8UbzZ54B/XZHzsbk2Xm252NH2/w0CABd4CAJ8DY0UEqgXKzDUidbZKqAHixMdV9LuAOEPQEsZXaEmpgxPtg4wkdi1MEVOZCZsyUcTQpMuOxxx7j9ttv59prr6W1tdUwasMowvFkQSAQwOHIrCsmO4atxBLVk09Gsr68EdItEgw/MAdka0tWsFZAyAOBIXDGomakP+mxx4SGY7piYelgK7NRtaQKe7mdsD+M2WYSmRJms4j07OmBnh5CQ0OYQiHwemHBAtyNi3h8409obv8bFi2AWQughgIE3Co99qtw1jrpuvt/KLmwHkt1uciQcLlElS0QYdYXXJAo6SRDOZqb4Yc/TIg0TcDFF/N7t0jIcJ0DxBd0z7KtCtpGEqP74cCPoHJNSjIDIE6+NyUkmTF8aBgtrKGaVLq3CompOW1zDDdXRbEpDUZHYyVEsopECo2BJ0L3O5tFDY00KJg9ZklmiLT+fDIzpMxUyCu0MrMt6BSPeHt6Xxer4Nrl2dUI6RZNz7JlsGcPbN8eI2XToa9PDFlFEcMqLSSZYUvvIJGY6v4GgpBJR2ZYS62o/SrB8SDjx8f53T/8Dke18TU6q51sfHBjAqExHTZNBg8//DA//vGP6evrY82aNXziE59gucEufHR0lKampgkFUo0QDod561vfylVXXcXb3/72rM6f7ijT2dZumVBZGQJMxEm0U1JfkugELy+HL30JDh8WAs/PPCPCCb/6VTHgvV7hlUkDuYmJjyqT82JyZKUs4Pnyy8IZkYrMmDt34rxUzPaRzx6ZFQK5kxkAa9cKMmP79szn5mqPoijYq+y4j7vxDhaXzEjWdo6HxWEh7A8bajsHAgHanxAenebzmg0jDWWGYiFrRxULRm0UcEcyM5yx+Soa0DPF8iDjJ8Y59uwxrCVWw4CGmmU1lDWXMXpslCPPHKH1itaoTYqiULss+/pi4UCYse6xBMnSmYDpnLd9gxFpHAMyo6ypDJPVRMgXYrxnnNLGDOGpEZxsz6E3CmS7SYmpM86YWO9BorlZFIr2+0WgkEzY1/XcZaZ6evI/r/q0ao48fYSBfQMTXpvqfrjqbauif0sHf7rMjGPHRKlJowLrksxIXsYWyyYjMkNKTK1bZ+xXkbbFkxnzzp9HZWvlG3aueCPZI+XunbVOFlw8OcWOqUSyTe4+N5tu34RnQKifjHWPEfKGGO0cRR3ayq1A+V+cuPs2ppTPSiUzBdCzrYedD+6krLmMs+82SHGaJKayzx155ghb7tvC3LPncsG/XBA9Hj83xxM6ZXPLCPvDOWenzIRxNKnd/N13383PfvYzbrrppkJdz6xBOBxmx44dWVVwl5szi9MS1ZN/61uF/12iqQluuslYYioZKRcTug7jh6BkYU5kRi62ZAVrJXi6RWZGBJs2iQJkIBIYfv5zg2Jhui5+VFXINB05EpNsGh8XK7XLL0c1q1zRsk+Mws89Jl4PheAznxGew6efRn/qKUb6+qidNw+ltBTsdvwlzYyPhPG46tDsDsImG0GsDA9p2BQbzlonL3qvoPGzt1G12MA5umIFUYHrZChK2nuuaSmiRwdegSP/CxUrYeE7U76/4G0kYa0Qvw30/iFGZmSTmVE6txSz3UzIJx4s5fPK09bLKJpNaSAnbqfTeFGaAC0I3i5QAFst2BvSnl5Qe6IyU6JWRz6ZGWaHOSGytnROdgtWiXh7gmNBxrvHQcmdzLjiCtF/+vrE3NU4sSskQGZlzJkjtF7TQpIZ9sxkRtH6m66D74QYQ6VLJswD8+cLTePOLihBkEvxMFlNVC6spH9/P2jgHfaimlXKmspQTLHPCnmF3Ip/1B9dpE3HGJoMHn74YW677Tbe/e53c/3117N161bWrVvH2972Nu69996EBZGu6wn63elw991343A4+PKXvzzhNY/Hg9vtpjZSBbGjo4NPfOITvOc97ymMUXlgtrVbJoTDYUZHDwHLEjIzDCFFnxcuhFtvFRODogjP/uc+J56x550nfhvsytNlZhg5I5YsEWSGkcSGjDBtappoTzHbR5IZk8nMAOHAApGZkQ752uOockTJjGJCajt7B72gCEmSZJgdZvb9bh+Lr15MaWMp4XCY7a9sp/PvohEXXWmsXSozFAslt1gspGqj+MwMCUnaTHVmhvuEKADtrHMavq4oCitvX0nIH2LehnmiJsML21h3zjpszuwD2wYODPDXf/krVpeV6//z+qyyDKYC0zlv67qeNjNDNamUt5QzeGCQoUNDWTkoT7bn0BsF8e32yiui3c46K/X5JpOQdn79dfEclGTG+HhM0ThbmalskyiNzqteIlISB/YPJGQPTXc/TJeZUVMjNPhHRwV5cPrpE88xIjOKaZMRmZGq+LeEUUBceXM55c3lxm9IwnS3UaHxRrPHaB0x02Fkk3/UL2RHbWbMDjPjJ8ZRzSq2ChvBURtBQqj+xH1yMtKRGQAD+waiQSTFtqeYkOvD5CCfVGTG2ffkTt7MlHE0qW/u7e1lw4YNhbqWkxbrP7yedR9YF3XMbNwoHvRHj4ogxfPPFz+bN2dHZqRcTLiPwNZ7wF4H638CSoosgWJD1hQIDEFHB08+6uYbH3XThhsXbp7mQsYpZVnnk7x80zaW3exmWYtHEBdXXil0tI4ejd0MRRFZEHPmwOWXi2MNDeJHZki4XDHthuuuQ7vySg7v3k31WWehygHWPojPXMKR5g0J2oFKM9TrOkFPEJ8/nDq7YhIYHIwtGhMcLr5eGN0Htmw1jwqMDGTG0oh6TkeHiOpJl2gli4AP7Btg/Pg4zhon1hIrXouXhtXpiYCpgoyEzSgxFXKDtwf0sJAwsteIYxLhIjtGLJEo2ElkZiiKwtU/uBpbmQ2zY3IPmb7XRbh1xfyKhLGTDpLMaG0V/Wj3bnj11cxkRtb1MiDnzIyiQPPDS+8Vf2/4XzAnOnqkHV19NtamiD4GkXURGA2gmlUC4wGGDg1Rtbgq4X4bvW824Rvf+Abf/va3+cd//MfosS984QvceeedrF+/nscee4x5stAIZOXEGhoa4gc/+AGnnXZaQvFVRVF4/PHHCYVCXHvttQSDQcxmMw6Hg7vvvpu3yXpGp1AQlJeLzVI8mRHyhTj2wjFC3hCLr1488U1mc2xB43TCLbfA88/DffcJvYQLLxQyjHEwyszIJrLSSCYiFZlRbBRCZgpiZMb27fDLXwo78s14NYJ0mk6FRJOr1kX/3n5euvclWi5qYf2HE6uYbvnRFvY8vIfDfz/MBZ+5AEzgPuam+Zxmhg4NYbKbcPe5J2xgZYaib3jmZ2YYwcgJIYm8kDeEFtJQzVOz1h8/IYIrShpSZ+lUn1aNf9TPaOco4VCYjl93cPQ7R1nx1hU0ndOUVcFRi1MUovT0ezj4+MGoE1SiUEVLJ8jOJmEmFUf1j/rRw2IPKQm6ZFS2VjJ4YJDB9kHmbZhneM5sxurVqwnGRwFmgNlsZseOHUW8oulHpuLfEkuWxMiMK64Qx6Qzz2rNmBAZxfnnk1YuW1Fiz6FkVC6oRDWr+Ef8ePo8MfnJKUbIFyIwHsBeYUc1q2nXDzK7c+tWsYZIJjN0XWSeQ2qZr0JDkhmHDolASVXNnszIJSDuFE4eyHVE0B2k65UuHJWOqJrGbITZYcbqsuKqcRHyh3CUOwhiJgSYTOn3yfE1M5LVImQm6FjXGOFA7lkKMwnRwK8k6c5UZMZsxqQ8XBdddBG//vWvufvuuwt1PSctkjcbo8JHya23xqLfJ7NIAGAwUgXM1VJYImNgQIx6jydWR2LBAvFE7eiA3/8+lkHhdoPWDjcAwWG0b3+bEz/z824ghBk3LrZxBuOU4sHBcRr43t+c/MfPXJhKnbGQkYUL4d/+TZAUdvvErIdbbwUw1oZ1uUSmRg6kRLEjv6QTuqoKLPHEeKQmAubi61EbwhKJyggMG2oA1dfHolIOHjSOSonHuZ88F1uZLarHeOV3riTgDuQlcVQMZF38230kci9UQfhESIUE2KpjGRSFhvzcpJoZ/f0TNW/TIZ3TIRdIiam6FQar/RSQZEZjo5BD2b1bON2uvjr9+7ImM8L+2PiZTjLDZBe1gsIBQQqmIDM6+lx8+cGNKZ0nI0dGePyex7G4LIx1j6EFp85JNVXYs2cPG5OqwM+bN48///nPfOUrX+Gss85i06ZNnHvuuVl/ZmVlZcYMju3Z6PCcwqQQLQDeH3uUhPwhXvy22GW3XtFqGHEfhcMBF18sfjo7BakhJR59PrFbP+sstLDQCcwlMwOMMzPiZaamEoWSmdqxQ9znQACkutqEjNdJYCrJDIDBA4OEA2HDeW/BxQt4+otPE3QH6fhbB646Fx6PB6fTiaIo/PaF3xrK8M0mmSkjSCdEPKltdVlFxqgupKYkYVNsyMyMVJJjyRIQuqYz0D6AqqoMtg9icVgM2yj5M373rt/Rt6ePwFiAP9z1hwnnZvqMrGxJulYjFOJ7CoXAeABbmQ1FVVKuCyoXipDToUMpQk5nOcrKyvB4PHzkIx/J6vyTIeI6HQIBESAE2ZEZkPgcjJeYynYLbDIRlctOlseWn3HvvcZ7FJPVRMWCCgYPDDKwf2DayIzurd089/XnqFlWw2XfvCxtZgYkkhnJOHZMuD3M5hjJUGzMmyfur98v9lmlpTFCZf164/cYkRmB8QDdW7rRNd2w3tQpnDyYe9ZcbvzFjez41Q6e+dIzLLx84YSAkdmI+FqsspRtJv+IzMwIh0V2WnzBcHulHVuZDf+on5GjI7Oa8JEypPF7JUgkM8bGhEqQZfYk7BhiUk/6++67j+uuu469e/dy6623Mm/ePCxJd8RqtSYU3zyZkG/BE02DkUggfGLRyfwXCQAMbhW/q9omvub3CzkHSTiMj4twjPXrQddpePRRlM2bhWa1POdznxNPwN/9LibICCI8/5prxJNbVcXnVFTEakn4q4EXITDEixd9mg//hx03LoIkDqjn2MBzbIABeEtpUh0Qq1Xkd6bAK/e9wuG/HmbNu9aw5Nolhueka59wKMxY1xgmqyln+Z18IAvuTBgKERmhaMHnNChKgR1JZuiauJak61AUQba98oqQCspEZjirJ8oPpIvkn+qiQVkX/266DtRfgMkGzhQRbpYysCdKLhXMHmcztL4n+vnV1bE5ob8/9aK70JD29O7KrV4GJJIZa9bAr34V23ilgyQz5mUKLPRHPIEmO5iMZS+SUbT+ZqmAcK8gvRyJqXOSzDhyREQfp3OMqGYVi8NC3Yo6Qp4QZlvmR/R0F97KBeXl5QwMDNBokJ7z2c9+lqVLl3Lttddy//33c4UMHzxJMZvaLRtUVQmSIRgU5Hd5eeLcH3QHJ9Q8SImmpmjAAiA8/f/7v/DQQ1y1Zg36e86GxbFNRjEyM4rZPskyU8FgTF4vWzJj0yZxi5J5vK4usYb87W8TCY187HFUOTDZTGghLef35oOBA2JOr1488QGtmBTsFXZCvhBhX5hwMIyl1IK91I6iKIYyfDC7MjOM2mjhmxbSdHZiB1VUBWuJlcBYgMB4YMrIDJmZ4ao3foZJCQiTzURwPMho96i4VpeVkjklhL1hwzYy+gxntZOQN0Q4EI468cFYbjEfJMtVJCPV90zXvF02t4yNv9oYJXONULmwMucAiNn0HHr00Uc577zzcLvdfPCDH5zuy5lWmEwmdu0SW/vKysyOdCMyQ2ZmZCsxJSHlsu++O7EYeFOT8FGkI9LrVtQZEnJT2Q/ls0AS3enWDxBbQxw8OPE1KTG1ZMlEh2CxbLJYxL7i0CEhNRUMinXAggWpbTCSKvYOeXnh/72AtcSaFZkxm+aKbPBGskc1q9gr7FQvqab98Xa8AzNbdlMilU06EwPYJJmRKY7Z4RAuxkBArMHjyQxFUSifX07vjl6GO4YLTmZMZZ+TmRnxWeweTyyYXmJoSMwbBx8/yJ5H9tB8brNhTbRUmAnjaFJkRkVFBWeccQY//vGP+dGPfmR4jsPhwO12G742m2E2m1mXKRQigpf/42WC3iCr3raK0jmljI/HNqDlSXKFaRcJ39HZeMEAHHEnkhLnnAOmEPz1aTg6BnUvQuBlcc6VV8Ill4j80vg2UhTx5Fu/HrPFwvyWFjG644tbuyIL+Ouvh6uuir0WH+0ybx68//2JRvRXw3EdShZyJDCH4SzuUbZFxWLXD2F/GN+I8QY1U/uEA2HcJ9yoFnVKyIyUkR+SzMiQmZFLf8sJqllIGgXHRXaGAakiyYxMRcDjU/bDwTB6WE/IyEhO2S+aTWkgI2HnN/TBmEG2hQwptpRB45U5fXZB7bFVQ/MNcZ8tuL2+PrEYzZbM6Hqli66Xu6hfWU/LBdnoNsUg7QmMBxg5ItjX2tOzIzN0fSKZAdkVqs06M0M1w5zLxN9ZhJUVtb9ZyoVkXGCiXNv8+eJ3R0f2H6eq6oTUUCNMxxiaDM477zz+/Oc/szK5EnMEN998M01NTdxwww28613vmtqLm0LMtnbLBLPZzPnnt+FyiWVJf79Y26hmNVpHSUYW54Xly+Eb3xDZGS+8gPKD/xAaS+97H7qm03sCQElLZpw4IRbxZXHJdDIzI5nMKHb7JMtMHTsmosTsdoOABwOEw2KNaJSQJB9h99wjFDtNpvztWXbjMpZtXDYlNQt0XWfooPCupdpAmu1myprLRB2PPi+uOhfWBmv0+gzl+2qclDaVpnTAzxSkaiPVrGIvnygrdM0Pr8HitESzYKcCmTIzJMwOM2PdY5jMJkxmE6WNpdhcNgIEspZKtFfb8Q56CQfCaCEtgbAppNyilKvQNVGjKT57LPl7ZsK8nS67rXpxNbc8dEvWhMZMsCcXVFdX88c//pFrr72W22+/nYr4aMA3EGS7yS19W1vmJXA6MiPb4t/x2LhRPF/+8z/hrruEe6C9PXOE79p3r51wbKr7oczSs1fY0fX0NTMgfUBEquLfxbaptTVGZsi9ViqJKTCWKpZ7jIA7YKxyEYfZNldkwhvVHhlwmi4jcaYgnU3uXjcePJS3lKNG2ItsMzMURcx5J06IOTDZ11C5oDJKZhQSU93njGSmpK/Vbhdx6SMjMTIj5A8x3j2Opy/7vjFTxtGkyIy77rqL8fFx9uzZQ2tr64xgZ6YKuq4zMjJCeXl5xo1e10td+IZ9LL9ZPO2Gj/uoZZRq6zj2gxFSYt48oR196BAbR57mhk+6Ofy6G0+/B1tzPa3/74OY0OCD/5L44aoqCmVqe8EfEhHCcxbEiAfpSTvtNPjnf46RFA5HlL7UdZ2R225LbUuaDAlD1JwlfphcsbB0kE4R/4ixZEum9tGCIrppqjaCkyUzculvOcNSLsiM4DDQPOHlbIqAx6fse/o90XaxllqjqcTJKftFtSkFBgehprSPj59zOzw7kPhi2CdqMNjrwdEA5z44IfMiHYptT12dIDNOnIAUvuAJGDw4SPvjokpcrmSGtKe0pJTLv3U5Q4eGso4AHRgQ0UIQK20DYuGd7FBMRtZkhr0OTstObgCKPYakLNhEMkPaMTgouOeSAirKTccYmgw+/OEP89Of/jTtOWeffTbPPfccV2fSI5vFmG3tlgnSnpqactxuhf7+WKSotcQaJTMmhbIyUTPrsstEpEekCNXoy3v5TPg3vMA51FnPAiomvK2+XsybBw7AmWfGXpMBI8kyU8Vun2SZKSkxtWBBduqYmzcnBrskQ9cFQbJ5s8h4zdceGQ0/FRjtHCXkC2GymShrTv2AcFQ70MM6nj4PYyfGsFfZsZcZ1xAAQYxc+8Nri3HJBUWubWREcBQbMpozEzGkoOCscTLWOYamazhrssucTP4MR7WD8Z5xPAOeomaf6Oj0vt6LHtapW1mXkjCY6fO2oio5jdmZbo8RFi5cyG7pQX6DQrbbyy+XA0pGiSmIkRlHjgjVRrs9RqbnmpkhYTLBP/yDINbdbrG+l3UWc8FU90PvkJjHHJUOxsfF/YDMmRm5kBnFtqm1FZ58Utzz114Tx1JJTIGxzFTUyamLzNl0AVSzca5IhzeaPUeeOULf7r7os3s2ZGaksinkDeHuc6OaRLaJXBtkm5kBiWRGMmTdjKHDhZVrnOo+JwuAx8tMSZUY6XMdGYk9B2RdNiltmg1myjialCD3n/70J+69916WLFnyhiIyAMKhEPtee41wb6/YNe7dK54qILx4Dz8MP/85/PCHLHz1YVbs+y1WRAcx/ewBvszn+LT6Dfj+90Vow+uvi/f6/dDfj2pWaT23gZVvXcGSm1YKptFkgo9+FP7lX+CrXxWaVPfdJ8iGwS1wTj28/11idXHrrXDttTHNApcrloPociWM9nA4zN69ewnLCtUFhKwDkqqPKwo0N6epA5ICsgBeqsyMdDaFvCH8o360kIaOTsAdIOAOEPIWr7huSjJDav5nkJkqZhux5B9h7TehxDhPWS5O05EZ8Sn7KCKaUDWrWFwW7BV2zDZzNGVfoqg2pcDAAJQ5Ril3DIBqE+SfpULIFPkHRcHv0LiQMDKqk5EGBbdn9AAMbBEkC/kVcItKbOShFy7t0dGpXlLNoisXZf1eGSlUWxtTjJPRz3LhnQo5FQDPAUXtb9ZIip0BmVFWFpMTlLalQ8gbis5J8T9G89N0jKHJYMOGDfzsZz/LeF5raysvv/wyjz76aPEvahow29otE6Q9Mu6hry/2mqVELJAnTWYgorH++q9/46VHu6OBGn0eF900cov1d9g+/89C52LnzoT3GUWlhkKxhX1yZkax20c6j4aHBSeTa72MbDNZ5Xmzob8NHhQ7qsrWyrTR5woKFS0V2CvtqK7sMthmA1K10cHHD/LKD1+JSj1OJ278xY28+WdvziqbuaSuBGuZFUuVBcWc3yZXrvOD49lvrvOBHtajklae/tRRidM5jl7/zes89S9PcfS5o1mdn6mOFMyOeeEUJkK228svi/+zITNqa0W2pK7HXBWTycyQMJtFZggkqlFnQsgXIuQTa9qp7ofRzIxKe3RvHi9GkQxJZnR2CsGLeKQq/l1sm2SwSHt77L5nk5kxOhojb0wWEyab8N1lWp+dbHPFG82e468d58D/HYj2/cBYgHBgZttuZJN/zM/4iXH0sI6t3IbJaorukQkGMBPKqqZofBHwZFQsqMBZ5yx4rayp7nMtF7Sw8PKFCbWJ5J5gzpyJcrcWR4TM8Ga/3pop42hSmRkOh4NAYPIb1FmJ555j/r33otbWxoiBRYvgk58UpMOrr4LTieZw4VNdBEuro7qsXWuv5dtcRv0cF+/4Rol4gsrczGXLxE8qGIU96Hr6ehnTAV2HsAeTycl3v6vkXwckBWRUWi46yLYyG85qp3CqjwgyQwtqCZ/hrHbmL4WRBilrZqhWofk/XQXAASpWpH05PjPDoEZ4AswOM44KB2GfmNhK6kuiuumFlAbIF3LSNpsAkwPMLkCDkcOALiLsXS05ExlFwetfEQRL23ehZKGh5mkmSE1YGYk0VYiXmJJYu1ZsBl59NTV5OTYWW1xkJDMCI5F6GYUfrzlD1p5J0W9aWoTjsqMjdd2Z+Pkp1Vgp1vw0E1FeXs5111033ZdxCjmgpkYHRGaGRFTKoABkhnfQy4lXTyREhneb5/EA7+WlZg/vfds2IUXV1SXS1wYHYXiYxYsWsHmzkhBZefy4qF9mNqeOyCwW5CZC10VkVK5kRrEyXpMRDoZ59uvP4h30cunXL82qhk++GDwgHs5G9TKSoagKlYsqGRkeEYWwT2L0bOuh84VOKloqqFsR66gdT3fQs7WHuevnMu+8TAWmCgNFVbLOslDNKtWnVYs2yhMWl4WK+RVYXBZ0dJQiNbZqUilvKWfkyAiefk9GGa3pwMCBAXp39GZs657tPbz6X69SNreM8z513hRd3SlMNbxeNZoVkA2ZoSiC1H/lFUHqn356YcgMEBkBmzeLR+8735n5/Jd/8DLtT7Sz7oPrcgqSKhSiNTMq7BzNUC8DxPO6okKs4dvbY5nxuh7LzEjntikGJJnx178KWU+rNSbna4SKCuFmCgZFsElzRITBWmLF6/cWZH12CjMXMtreWevEZDUJ4n7AMyUy64WCruvs+OUOVJOKjo7FaYmOZV0Ha6SclKsm8z5ZznlGZEZVaxVv/umbC3np04LTb53obIgnM2TtjMlkZswUTGpn8r73vY/Pfvaz/PrXv55Q+Pukx9Kl9L75zVS3taGWlSXS+qoKX/4yAMFRP4ee2gTAubUVAJxwzGc/UFFLsiJC/lj+TzC4DcqXZz632NDC8NytEA7Aub9k48ZywzogVVXw4x+nLxaWCjJiK5XMlBFctS42PrgR/6ifnf+zk46/drD46sUsvTFGECXXdSgUUmZmrP5ywb+r0Fi0SCyEh4fFIiiT48dkjTFT8YWHZgKkrEd82Rc8nRDygGqB0iWgzZCJ3FwqyIxI9o6R5mkmRDOY8ix+qgU0tty3hfqV9cy/cH7WEgbygRlPZqxZA3/4Q/q6GTJzobIysSiXIfZ9FwZegaV3Q8OlWV1X0VC5RvSfcmNicP58kZGSLjMjfn5KhWLNT6dwCoWArAVRLDLDPxaTL5SQ5G5ZgxM2bBA/Mmri+efhD3/gH4400Mm5HNt1NiCIR7kWaWzMLi29kLBYxPw2NiaeSbmSGTLjtavLuG6GoojXc814TYZqVjnx2gnC/jDeQW9RN76VrZXMXT+XupWFZ5ae+coz9O/tZ8M/b0ggBGYDAm4xbiyuxD3W4IFBOv7Wgb3SPmVkxlRDVdWEqMJiwlHtYOToCEF3kIBn5jn24qPJ00E1qQwfGj7lnDzJsX+/C01TaGycKJOYCvFkBkxeZkpCZgRkm5lhK7OBDv37+qeHzIiMJUelgxMRUYx0dQgVRWRnvPKKkJqSZMbx42JfrKqx7M+pgiQzZBbs2rVCAz8VFEXsIbu6xJopSmaUWvEOnCIzTnYE3cKvYXVZo/KN3oHirukKjT0P72Fw3yBz18/lvH86j9LG2LWPjcFnIuV4vvrLzPtkSWbIOfCNgngyQ/rB3vBkRigU4rnnnmP+/PmcffbZlCdXswbsdjv33XffZL5mRkKprUU74wyU5cvTphXITmGymaKp8yORQCWD25XnxSgiuj5DhH3qtys4HI7C6Z2pJlDtgswIDIG1PFosbPNm+Pa3hVPz0kvzIzIAbOXpa2aksslV68JV60I1qVhdVqpPq6aqdZIruSyQqcBYJhS8jeLhPgpDr4kaBDUTRTcdDhFZ3tEh1NQykRmOGgeeAQ+2clta53dRbUoBSWaY4me+kEhMelsAAQAASURBVChqibNZZMrkSWYU3B4pPRYhM/LJzIiXmcpU4C0ZiqIQOhGi88+dnNh+gvkXzc/6vakyM0BkZqRCThJT/ojH1JpdWFlR+1vVGeInBaQ9mWSm5PyULaZjDE0Gq1evJhjMfnzZ7Xa2bdtWxCuaHsy2dssEaU9tpMRQvMzUso3LaL28lcoFkwz/JKYBG0+S9xpFVsr7evXV0NqK8/vPcf1ff8+cvz8Cf38rXHRRlMxIlpiKt6eY7VNVJTZgg4O5kxkmk1AZzTbjNV97FEXBURXZ+BaZzFj4poUsfFPmGyDl9nRdRwtoBN1B8axKIxMacAfwj/inPEMxF6RqI2mX3GhKyMhDOSaKjSPPHKHzxU6azm7KWH8rnzZK9RnZHs8XIW8omvVhdVnxDfsY7RzFWTUxA2U6522pce6oSl87pLJVzLOeXg/+MX/agKLZ9hw6tX4QUBSFAwcqgOyyMiSS5RYLmZkBsGOHkGFyZkjeql4ioh4G9osN2VT3w/mXzMfT58FZ6zRePxggnsyQkFkZra2iBkk8im1T8lqhqUlIVqZTuZBkhmER8AxkxmybKzLhjWaP9EVaXBZWvW0VuqZT1pSmeOUMQLxNfXv62PGLHQCsv3s9LecnrkE8nTCECBSqNFjTJyNdZoaErutoQS0hQHcymMo+p4U0vENebKU2zPaYwyuezJDKUMlkRsiT/RprpoyjSZEZiqLwgQ98IP0XmIuXlj6dMJlMrF69OuN5cgKRUjsgmHyI6ajnDF9fehkcS1lOhYuztSUnWCvFNQaH475HFKO02QSZ8fjjIuUxn6Qee4Wd6tOqsVfYDZ20mWyKRmZk2BgUCpMlM4rSRhIjr8PBHwsiw4DMACE11dEhpKYuuCD9x5nMJupOzxz9WFSbUiBBZkpCj8zo6uS0twtuT5TMEGN9MpkZWlAj6AkmzENGcPe5EzIDLF0Wgp4gznonQ4eGss4MMCIzZAr0669DICDSopORH5mRWZYEpqe/SWRLZuSK6bQpH+zcuZPFixfzjne8gyYjL3IS7Mk7xJMEs63dMkHaI+eo+MyM2mXZr0UyIV1mhqEzQlVh2TIsH1jGJ3/o4WLvFm5c1IoCaM8+z210UF1xNugLEvQTp6J9qqvFfJAPmQEiEMQo47WmBu6/PzFQZDL2xJMZ0wkjGT4LloSAllQyfNEMxTxqR00VUrVRNDMjicyQYyBdJl8h0ft6L0c3H6WkoSQlmTGZNkr1GVpIrF3QY0FMhZBbjP+eocNDaCENq8uKFtLw9HmwOC24alwJ3zNd87au69Hs2kx7FqvLiqvehfuEm+HDw9SvSr3pmG3PoVPrBwGTyUR3t0jHmAlkRlOTyA7p6oKtWzNnBEoyY/TYKEFPEIvTMqX9cPXbY9+V7d5c1s04eDB2LFW9DCj+2Przn8XyRotI6zz8sMgA/+53UweJGtVdXHnbSoLeYEZ5x9k2V2RCMe1J3kcnoxgZ9pnsiZIZDgtN67Pw9k8Tku9dc0kzIx0jDLYPous6c86cw8JLJy6UpWRSWVl6KXSJTGTG4b8dZttPttHY1sg5HzsnVzMMMZVjaLRrlD/945+wldvY+MvYhBBPZrgjcbzyHlhcFlz1rpzWVjNlXpgU0/DlLxdOIsfr9fL1r3+dxx57DE3T8Pl8/OAHP+CSSy4BoKenhzvvvJPOzk40TeNDH/oQ73//+wv2/blC0zT6+/upqalBTaNRIAupmJ2xWy3JjLwyM3x98PztokAxCEdsYEgUMDZHwiFs1XDug1kTGtnakhOsFeA+Iq4tCevXi4dqby88/bTI0MgVtlIbl3/r8pSvZ7Lpgs9dgHfQi7M6O/3fyUDTUiyYAsOw6ytCb3/l5zJ8RhHaSCKq959a23jpUnjiifRFwHNFUW1KgYEBqLImZWboERZamRz7XnB7zBEyI5R/ZobJasLishB0B/EN+dKSGe4+N5tu34RnQFS403Wd0a5Rwv4wI0dHOPTnQzirnWx8cGPGhZgkM+I12+fPF3PeyIjYBBg9/7ImM8L+aMbKtM5z0Q8Pg79XyJWVtk54WdrT0VHgr52GMTQZPPXUU/zkJz/ha1/7Ghs2bODd7343N9xwA1YjZuskxmxrt0yQ9lRV1QBqAplRSGSdmZGERYvAi5M/jl/AgB1qgP7jIVaxgwuPPQ3/Wie0Ms49Fyorp6R9pLRHe3tsM7FgQW6fEZ/x+qUvwd/+BrfcMtGZMRl7pPO0mGSGu9eNoio4qlNHeCXL8GmaxtDQEJWVlVGbUjkJohmKecotTgVStZGUh5iQmREZA5LgKzbcvWLnG1+vJhmTaaNUnzGwf4Dn//15HJUOLv3mpVl9RjaI/54/f/zP+Ef9bPj0Brb8cAu+ER/nfuJcGtsaE75nuubtwFgALSS8lrIvp0NlayXuE24G2wfTkhmz7Tl0av0gommfflrjL3/RARNnpE4InoBkMqNQMlMg9vWbNom6GZnIDEelA2etE0+fh8GDg9SuqJ22fphLZgYYZ2YY1cso5tjatElkZSZLTHZ1ieO//a0xoWFEZqSbH+Ix2+aKTCiWPcn7aCNku4/OBZnsia4jXHlED08RjHwQ4VAYk9mEoihoIY2AO4Cn3zPh3kkyI6M8dQTpCoCDCAoIjAUYOpwmdSNHTOUYknslmXklIcmMhobY/C9/O6udXP/A9Tl9z0yZFyadNnH48GEeeeQR2tvb8fmElEk8spGZCoVCXHXVVVx88cU8//zz2Gw20YnjqqPfdNNNfOhDH+KOO+5gbGyMyy67jHnz5nH11VdP1oS8oGkahw4doqqqKm0D1p1ex1seeUtCQVcpM5VXZkZwVBAZqk0UMA4OC5kcXQNHI4S94vXgaE5OvmxsyQlS+iUwPOElVYXrroOf/hR+97v8yIxMyGSTrdQ2ZfUcBgdj6VwJC6bgCIzuE5k0GVCUNpKItlVqMiO+CHg65CINUFSbDBAOCyKxqi6iFRiOOGfscwShoYfFWArn57QpuD2yX0yiZgbAVd+7CmupNSHV0Aj+UT+eAQ9mmxmzw4yu6Qx2DGIym4RutQ6eAQ/+UX/WZEZ8ZoaiiOyMp58WUlOTIjMkmWuyCyI3CxS1v/l74aW7RDHy83874eViZWZM9RiaLC6++GIuvvhiRkZG+PWvf813vvMdPvCBD3Dbbbfxnve8h7VSi+wkx2xrt0yQ9lRHimbEy0y5e930vt6LrdRGY1tjik9IjfhIrcFDgwTcAQKeAIPtYgU+cMwGuNJGVjocQiP62DHhyKmpgc3KBfya8/nPq/ZxTuuLgq1vbBRkRl8fh/fsoWrDhqKTGVu2iN/19bGya7lAZryOjQky47HH4D/+IzE6bTL9LUpmDBSPzHj9oddpf7ydFbetYOXtK1OeFy/DFwqFODhwkAULFmTMAJeZGTNZZipVG8m1U3IgwlTLTLlPCDIjU3HsfNso1WeUNpay5YdbCAfCOGuc2MsLF23vqnVhcVjQwzpWl5V5G+ZR0lBC6ZxSw0Ln0zVvSyLRVmZDNaf+XjlXWl1WAu4AXS91JTgrk0mg2fYceqOvHzZtkpl4sba68074/vezk2yWDvneXrEXKlRmBohYAElmZIPq06rx9HkY2D9A9fLqKeuHQW+QoDuIvcIuakLlmJlhRGYYZWYUa2yFw6IPGNXK0nXx3L/nHhHkkCw5lU9AnMRsmysyoVj2JO+jkxHyhrLeR+eCTPZEMzOcFnwjPgb2D2CymGhY01Cwa5gsJvggdJ3R0VFcZa6oTKVvyGd478YisY1lWSpnZcrMqJhfIT63cwwtpKV97maLqRxDUjYumcw4flz8njMn9vdk6obMlHlhUmTGww8/zO23385FF13EmjVrqDOgtrNJ8/zFL35BeXk5n//856PHFEWJLn537NhBOBzmjjvuAKC0tJQvfelL3HfffdNGZuQC1axiNRdQZgoEkWF2ge84KGaw1Yj/AbSpidRKi6iD3HimePObBZnx+9/D976XXVpYKuRaC2CqIRdLVVVJ0jrBcfHbkn5zWHTIzIwUbQWZyQwjeYFkFEIaYDKQD61RbxkmRzUEBxLHSiQDAhDZTVmQTEWFOdIvIjJT8QtRuWjNBrkW0DQ7zFhdVvxjfjEP2804qh0E3cGUbZsMIzIDRN2Mp58WRcDf+c6J78uezIiEfttqJjd5FApyDIX94seU2M/nzxe/jx8Hn2+ivu4bDeXl5bzvfe/jfe97H3v27OG///u/ufbaa6mrq+Mf/uEfuOOOO6KO8VOYPaitFTvs+MyMvj19vPjtF6lbVZczmZEcqeXudRMYCzB8ZJidv9wJQP1hJ042UpdhnluyRJAZBw6IBIyuLgAF15lL4dalcNtt0Yp4ykMP0fL3v6Ps2wfnnSe8FQVeqMvuLcmMXCSmjPCmNwnS5uhR2LkTVq2a3OdJOKojZEYRiYDBA2I3Vd5SqEJyiZBFk2eyzJQRtJBGOCAiYZIdI1MpM6XrepTMSJeZUQxYHBZKG0sZ6xpj8OAgjWfmToimw8gxEcTjrHFicVioX5mnFmwREfQGsZXb0kpMxc+VQU+Q8Z5xTuw8Qfuf26PnFCMieDrwRlw/pIrG7+lJH40fj9JS4cTq6RHPwUKSGbJuRrZFwKuXVHPs2WPRuhlThe5Xunn+35+nbmUdb/rqm3LOzOjuFvIsLld6MqNY2Lw5UVYyGbou1jmbN4sgh3gYBcSNdY/Rv68fZ7Uz6yyNU8gMuY82Qrb76ELiup9cR8AdwFXrouvlLp792rNUL62eUWSGRNQHMe7Hc8yDa5ErqqKS6t7lmpmRqQC4s9aJxSlktkc7R6PkxmyBkSRvKBQLNJszJ+ZvORmKoE+KzPiXf/kXvvWtb/HhD394Uhfx61//mrvvvjvl63/5y1+48MILE46df/753HzzzYaObL/fj98fW+CPRnp5KBQiFBIDQVVVVFVF0zQ0KToYdzwcDidkmSQfl7/lOfJzJUwRSjw+u0QeF5kZCiUlYUIh8X6z2TwhG0VRFEwmU+I1hkOYdB0F0NGj0fS6pRyFyHFdJxwOiZ6bo03yGhVFycmm5OOKqRQToAeGCMd9jrTpkks0nE6Fo0cVtm4NsXatga0Z2umF//cCnS920vbBNloubMnaJk+/hz0P76GkoYTlG5dnbVNO7RR3vKdHA1Tq63VCoXDMpuCoWHmoLrRQKG07ARO+txDtBGC2lqMDesiL5neDyTbBptZWADOHDukEAgpmc6KtjmoHGx/ciHfYi67Frl1RlahN1lIrtkoboYitRbXJoJ36+hTARECpQTv752ghMS8oioJJjdiqR2wyl6Faq1Eh6zlCnmN0jXnZVHo6yoJ/QHe1QChEXZ2Yrn0+GBoKRbQh0/e9XMYTEJ3TNF3DP+ZHRxfMvh57LRwKR+ddI5s0TaYyKtTVhQiFYjatXKkBJrZvF2MhuZ2OHDEBCi0tE+97vE265wSKroOlCjQtq7lc/o5vp/j2m1Tf0y2oigm0EGpwBF2tTTi/okLB6TTh8cDhw6HoxiifdpqMTcm2zQQsW7aMr3/963z1q1/l8ccf57/+67/47Gc/yxVXXMFdd93FZZddNt2XeApZQvqP4smMbAtMGiE5UkvWyAKikVqK14MNf0YyY/FieOqpmMSGdAjMnRs5wRYjIPW3vpWhUIja7m4R9lpWBnfdFfNoFAAyM0Pqbk+WzHA6RZbrH/4ggkQKRmZUOTDZTEULGAkHwgx3DANk1O3OF1KaZyZnZhhBMSnc+MsbRb2rpAg7mVkcGA8UPaDHN+wTpIrCtDjCqxZVFY3MGO0U68DSpolekHAgXLDin5NB7bJaNv5y4wTlg3jEz5XWEiv+UT8WpwVbhQ0FpWgRwdONN8L6YTLR+MlYskSsz/fujQVWFkJm6swzxXd3dYlna6aSJnUr6mi5qGXKnanyGSAJ7mwzMyorxfpmYEDUzZg7N+YYXLq0WFc7EVImJp/zjGSmerb3sPX+rTSd23SKzDiJYa+wRzNUowEqRcy2nSx0XWfo0BB6SMc76I1ecyoUOjNDURTK55fTv7uf4Y7hWUdmyP1WvALNiRPieWEyQW1tbN6PJzP+/oW/M358nA2f3kBFS8UUXvHkMCky4/Dhw9x8882TvojXXnsNh8PBTTfdxIEDB6ipqeFTn/oUV155JQDd3d20JIXqOhwO7HY7vb291Cc9hb72ta/xxS9+ccL3bN++HVckj7+2tpbW1lYOHz5MX5wmQlNTE01NTezfv58RqQcFLFy4kLq6Onbt2oXX6xWRSm43Y2NjVFVVsX379gTn1apVq7BarTz50ycZ3jVM2dIyqs+spq2tjYEB4VQdGjrMli39mEwm1q1bx8jICHv37k2wcfXq1fT393MoUiHSFuxkqd+Pwwp+7xhqwAsouMdDWG0eXFYIBALs3rkTv2UoK5v27NmD2+1m27ZtKIrC0qVLqaioSGnTFhlKGEFbWxuBQIAdO3ZEj5UGvZxeczZudQ674s6XNrnd/bS1mXnmmSp+9KMePvaxcZYtW0Z3dzedcWEH6doJYGRwhNe3vk6fqy9rm4b3DXPgfw5gq7Wx4KoFWduU3E7hMLz2Whmjoy7OPruF007r58iRQ9Hzy8vLWbZsGXv3DgHVOByjbNmyJ2rT8WMHcYwM4/aO0rllS9p2qq6uxu/3R+0BCtJOJpOJdW1tBMM67rFh2rc8TchUNaHv6To4nevweEy0t0NpqXE7HR89Tt/AxPG0Z88eRo6MwJEpsslgPO3fXw0sprxcY8sucSGq5qXBdIh5rSvo9s2Ns2mI2lpPTnPE/PnzKS8vZ8+ePfh8sQjQvG3aNww0AkFMpu2sW7eOkhKd8XGFp57aSXOz33COgFjf6+7u5vUnX2d41zAlC0pYetXSlDY5ceL3+wmOBjEFTXiGPSgoWEusjI6NEhwPEvQE2blzJ2fUn5HSpqEhK+GwGUXROXZsCz09MZus1oPAKrZtC7Nt23bOOivWToGAQk+PCO9qaSGtTQOH+igPLME/WIb58OGs2knO2YODgzQ0NETn8km3U6TvtY4EseijVARGGPFZJ8zlLS2r2bMHnnzyQLTfxNuU7bw3GZvcsuLXDISqqlx99dVcffXVvPjii7zlLW/hkUcemZEEzGShKArl5eUzOqMwF0h7KiuFPUNDIpbCbJ4cmSGRNsotKPpHpshKqRd+4IBY0IvMDGOni1JZCVdcIciLnh544QUhMgui8ibAWWdNKr1WbiQkVzlZMgPg+utjZMZnPxs7Ppn+1nJBCy0XtBStrw53DKOHdZHdWZt9DbNcbHLWOClrLqO0McuwvWmAkT2KomAvtxtKKzmqHNzw8xuwlliLPo/IrAxntTMnuYVCzXNVi6o48vQRBg8WPoRw9JggM8qbY1lB7l43L//gZcZ7xrn2R9dGr3+65+1svlfOlUakT3JU63TbU0iczOsHo2h8BQ0dGYSUOho/GUuWiOzoLVti5EghMjNcLli5UkjIvvRSZjKjenE15378XEAE4UxVP5R1k6RjN9vMDBC1twYGxBpCRoLPn28sD1mssRVfhzDX84zIjGzXZyfTXAFTZ8/I0RFUi4qzxonJUjxiPKf1UCTLwTvonbHKJt4BL2FfGNWiUjG/AoX01xhfADwbZCIzQEhNSTKjEJjKMWRUM0MSnPX1ItHciMwYPz7OWNdY1vKlM2VemBSZUVdXR3d3N3OynV1TYGBggK985Sv84Ac/YOnSpezYsYNrr72Wn//851x00UUMDw9zmtS5iYPdbsfjmVhk59Of/jQf+9jHov+Pjo7S3NzM2rVrKYv0dBmJvGDBggSiRB5fsmSJYXT8ihUrJkS+AhO0OuXxeks9g4cHqVtRx9q2tZhMpiiDuGbNAtra5kffU15eTltbW/R/2Tlqamqokr1uvBLT84Jps1kUsFhAtVJeUSnUVkIerFYrK1euhJLWrGxauXJlTjbFX6M87nA4ko63gdmMS9dpS8pikDa94x06zzwDW7Y0sWSJ2NE3NjbS0BCL1EjXTifKT1DiKqGptolVbauytuno+FF6K3qpW1KHw+HIwSaB8vJyjh1bx8c+ptLZGRvATU21fPvb1dx4YyxyFCAQELPm4sWltLW1Ra+xocYFIxWU1S6hYWlb2nZSVZXzzz+/CO0EKAoWZy3lKqxZPh9Klxj2vaVLVbZtE1JT11+ffTtNi01MHE+9vcKmujo1dnz8EKZX/x/sf5rGs/9r0jY1NDQYRscXyqa6Ohgfh7q6lbS1pZgjiB1vbGxkyDFE/75+KhsrWRCpMGtk0/DhYWw2G/YyOxaXhfKKcvSQWOgoJoWAGsCn+Vi5ciXl5eUpbZI+/Pp6OPvstgSbbrppOe95j874uJnKSvFe2U4HD4r3ORw6NTUKup7apoaGW4FbE9qj2HN5pnZSty+E8cMQGqW8ctGEubylRURh22xLaGtLnCNymfcmY5PMUpyJ8Pl8PPTQQzzwwAPs2LGDW2+9lbvuumu6L6soMJlMLDOqGjlLIe0Jh0WUqK6LTX99fWwxLQsQFhK6BuEIGZCtTMT+/SJzJBAQ12q0fE1on+Zm8SPR3w/PPSd0P5YvF9VOV63KHBKbhGQVlEKQGddcI36/8orYuEjbJtPfir1JkQ7qqsVVOX1XLjZVzK/gmvuuyev6pgq5tpGiKlkVgy4EfCM+FJOCqyG3iP5CzXNVi8Q6YKi9cMU4JWRmRllTzAtiK7cxsHeAoCdI787eaMTyyTpvnww4mdcPRlH2d/FjGummg/kcoYUO5nP82DwgfYFfSepLOSinM0n+eBI4+2xBZrz4Itx0U/bvm8p+KKUGHZUOAoGYMzNTZgaINcRLLwkyYyCijpXqsotl0/nnC6Koq8s4U0dRxOtGRdjjpYplfR3fsI+AO8Bo52i0DhlMrK9zMs0VMDX2aJrG+Ilx0GMEQrGQzh5Pv4fdD+/GWe1k+c3LBZGngB7W8Y/4o8TeTIGu64x1C0dpZVNlViRQvgXAh4dFQJGRimzlAuG7KxSZMZVjyEhmSj5H5L4gntCR98DiFM8PWWMlE2bKvDApMuMtb3kLX/rSl3j44YdzKu6WDFVV+dSnPsXSSK7eqlWr+OhHP8p//ud/ctFFF2Gz2RIinSW8Xm/UGR0Pm82GLU4yQMJsNk+4TulUTYYpxcZUHtc0je7ubhobGxPqeyQj7A+jKiq2Elv0HBnMXV1tIv5tqT4n4RpN5qhGvKJFOltEFijhc0xmSPqsVDYpikJPTw+NjY0J9yKVTbkcT2fT9deLwfPqqwpdXSbmzUvdHkbHbeXC7uBYcMJ3pLMpMBpAVQRTLu9bLjY98ojCW95imrCQ6OoSx5O1S3t7I+RFg4o5LqpNDbtFW9rKE9rKqJ00TTO0J9drT3VcWfpRFNWCWrJA9DF5jXH3felSomRGLu00bTYl9b1YSnX8cT+ggNk1aZs0TaOzs5PGxkZDe3O2SQXG20VR8qozAKivVzh0CAYHzQnDO921u2pcqIpKYCwQPSfV+YqioCgKqqKi6zr+sB+7zR49rigKJrMp7biRD8zGxolj3+Ews2KF6Ec7d5pZvDjWTjJSuqVFQVFAUXJrj0ztFD9nxx9PRt59z1ohxnNgxHDek3UzOjtNyVPzlNk0med0sbBt2zZ+8pOf8Otf/5rTTz+d97znPbzlLW/B6Szu4n86Ed9uJ0sxRWlPVZXKwIDw+SeTGbqmo6j5OcfDoTD9e/qxldkonycigQJyCaRmji6Nz8w4dkz8XVdn7MhJ2z633w433CAmseeeg/vvh89/XhQIkukoWSBZ2qMQZMacOSJh5OWXRSHw9743C3umGVIzvWpxblonM9mmfGBkz8ixEQ783wFK5pSw9M1TqGWShKb1Tbxl01uy3uBKFKqNKhZUAODp8+Af8ydIJ0wWZU1leAe9CTISZpuZeRfMo/3xdtqfbI+SGdPV57Y9sI2hw0Msv3k5c9ZOLngwHifDGHojrB+MCPeXWM9p7GM+HZzJVsyEWDz2bmA9tLcLb/eCBUIPKa5t5XNw+3bxuxASUxLr14vHYbZ1M3RNZ7RzlIA3gN/ln5J+GC8zJZOeTabsslNkQMTBg1ASKWmYql5GscaWyQTf/a6okyIDRySkG+jee41jK2TAx/gJNw/fvgnvgIeQL8RY1xgnXjtB785YykZyfZ2TYa6Ix1TYE3QHIdI+Q4eHos7xYiCdPe5eNwceO0BJYwnLb16OalZxVDrwDnrxDHhmHJnhH/ET8oVQzAqmclNW2SP5ykxpmnhvuUG5tqrFVdSvqad2eW0OV58aUzmG6k6vQ9f0BOnWVGRG/D3IlcyYKfNCTp6ND3zgAwm1KPx+P//3f//H4sWLWb9+veHiwW63c99996X93Lq6OpbIJ2wEixYt4s+RdP6mpiaOHj2a8LrX62V8fNyw6PhUQDouGxoa0jagjEaMl0goSAHwsBdC46BHUmhD7tjxHJGtLTlD1yHsAZPTsEhvba0oxPnss0Ia4UMfyu3jZeq9UQHEdDZ5B8U9yieqLR/t0rSanCZ7rNBzGhStjSQqMwtsZyoCniuKblMSZCRNQkRsKJLZZZ68hnDB7QmNw7ZPiE51waOgqIYF3DJBasNmqxce8oo5Rdd13KNu1DI1qk+fDVIV/5ZYs0b4AV99NZH0y7r4N4CvVxRoN2W/CCt6f7NGVkPBEcOXpV3SzkJgqsdQoTAyMsKvfvUrHnjgAY4dO8bb3/52nnvuOZZPZSXFacRsbbdUiLenpiZGZkDi2ifgDuTtiAyMBaJzUCzrUbxWVZW5PveCBeKZ7PGIzAVILYWRsX2cTtiwQfz09govga7Dv/0b1NTAhRdmLBpeDDID4LrrBJnxhz8kkhmT6W/P/ftzjHWNcd4/nUfpnMJKNUUzMxblTmacrGNI2jPWPcaB/ztA1ZIqQzJjzyN7GDo0xNI3L835/uUKRVUm1O3IhEK1kdVl5dJvXkp5c3nO15AJa9+91vB46+WttD/eTufznQTeH8Dqsk5bn+vf18/A3gGWXLMk88k5YLaOoTfa+sEoGv9V1vIqou+aCXHmnG7ufFtkc7Nvn3gAaJqoBbVggXhWrVsXJTPks7MQElMSZ58tfm/ZAsGgEI5Ih0NPHeLl771MzYoaSt9cOiX9MF5m6nhkH1VXl3n9ADEy48CBWImtdGRGscbWxo2i4PvddyfKjzU1CSIjVSH42ohP1hT2M3bCg90lapG5T7hRTErUqW1UX2e2zhWpUGx7Qt4Q/nE/WkikDnsHvFhLrJhtxQkoS2dPwC0Gu3RUg6ib4R30iroZrUW5pPygw1jPGFpIw1XjwjPiwaSbMvogcpWZstvFj88nZJYMyYzWKi758iV5GGGMqRxD8y+az/yL5icck2SGFIFwOMSP1xu7B/mQGTNhXshpVDU1NU3Qn/zc5z6X9j12e2Zn07p169i5c2dUAgXgwIEDLFq0CIBzzz2XT37ykwnveeaZZ1i3bt2Mn1Rlh7C4YpOIzMwwGjwZYSkDWzX4B0A1gyNSvTI4HDvHVi3Om07oOjx7K4R9cM5/g814o/XmNwsy43e/y4PMiDx45eIkW8g0U+nkzQVG2qXxMNIuTUlmLHi7+ElT1G8mQZIZcaUAZhWkLmAimTEufheAzCg4JMml64KstJQmpAlnC0nayX6fCrYyG85qJ54BD75hH94hL5pJw6JZos5DZ7UTW1l6Z2Q2ZAbEIsMkciIztt4DwTFY9wNwzcviDVOA6vVgr4eyiZKIELOro2PqLmmmYfPmzfz4xz/m0Ucf5ZxzzuGf//mfufHGG7Fk2vWewqxBTY3wpcioR9WsYrabCflCBMbzJzNk0EL8/CMzM5Ilm4xgsQifzsGD8Le/iWOZdL2zgmSYdR0uuUQIkn//++KiNmyAyy4z9OrEX7PVmnq+zBXXXw+f+xw8+aQgbgoRoDzUPsRY1xiefk/ByYwVb11B/75+ak6rKejnJmPzVzfTt7uPcz9x7pQXnc0X0f2D03h+7N7STe+OXhrbGotOZkw3apcVJjoyW1QtqqK8pZyRIyMcefoIi69ePKXfH49oAFbV1MiKzVS8UdcPmaLxw5j51H/MwyS3/VdfDZdeKhbVhw6Jh16kltrCwF4+pzxEu76QdlqZ42wBvcEw4DBXLFki/BojI7BrF6w15gmjqF4iHoKDBwYp0TIH9RUC8TJTvfvFsWxjYuPJDOl6mi7+bONG4T/ZvDkmKXn++enVLm22iN9pJJJE6jBjspqidZAsztheL7m+zilkh/h9tHfQGyUzQMg9OWucWe2jCwmjdYR8lnj6J0r1TxdsZTaxV/CGUFQF1aziH/fj03wZfRAyMyNbmSkQRG5Pj5BZinM/n7Q4flz8js/0q6oSJPngoLgHuZIZMwU5kRn/8i//UpSL+OAHP8g999zDWWedRUNDA3v27OF73/sejz/+OAAXXHABwWCQX/3qV9xxxx2MjY3x+c9/no9//ONFuZ5CIjqJOEQH8flAJrfklZlhr4VzH4RgGu1zS5k4bzqhKGB2CjIjMJSWzPjkJ+HvfxcLoFwIHlu5mNB8I7mRGTJCPZ/MDCPt0kznyQmkIdX+eSYUXxo/DMO7wDEHqifWaoDCZ2ZMNWRmRkJEbDQzYwampKtmMDsg5BXj3VKaX2ZGhPTzj/jTyry4al1sfHAj/lE/7X9u5/XfvA5z4ZrPXYPJLFbIyTqqRshEZsgNzquvJh7PmswI+wWRAYK4nSmoOx8wEKqNoBiZGbMNF154IYsXL+azn/1sNHjhkUceSXm+3W7n+uuvn6rLO4UCQEb/ycwMgPX3rMdkMeWt8S8jBbWQhsliikaa+cfFhrsmSz/44sWJZMbcuXldjjFUVXgTNmwQg/yZZ4R4+JVXitd37RIVRCMBPvFrnfr6wsU0rFwp5pojR+Cpp0SmxmThqHIw1jWWkRDPB/M2zGPehuIT0kFPEP+IP+sMxZmATGSG3NQbZScXEpu/thnVrLLmnWtw1c3AwI88EfKHUM0qqslYcnPhZQvZ/sB2Dv3l0LSRGbqu4xuMOGCzIDNSRa9mm1k7k/FGXj/kHI1vtYoH3uLFcMUVscOVLjy181nce4ALeIb5J4B7l8JHPyoyOV59VTxAqqpy3puqqpCa+vOfxaMvHZnh7nMTDoTRQhr+cT9Drw0xVD2U014jHyx40wK8A16cNU5ObBbHsqmXATEyQ+7pIXXNjKmAyZS54Hsy6uqgP0JmAFEiA4iur04hf8Tvo//yT3/BO+hl/iXz6fhrB6VzS7noCxcVrW+ngtE6Ysm1S5i3YR41S4sbRJILXLUu3vr7t9K7sxd3n5v6NfXs3LmTlStXZpwXcs3MgEQyIx0C4wHCgfCsCibw9HuwOC2YHeYoEZQsMwWJZAbEkRnek5jMKBYuvfRS7rnnHi644AKh8e5ycf/990draCiKwqOPPspdd93F17/+dcLhMHfeeSe33HLLtF2zqqrU1tZmzAxJzsyQElOKEtNczBn22oKSFdnakjMsFeAfFGRGCixeLBYDe/bAn/4Eb31r9h/vrHZSfVo1rvqJE1s6myYT5ZRtrfv489LKTGWJorWRxNBr0P5TqL8wJZkh05MHB4WjKlsHUioU3aYkGGdmROTZspD6yoSi2GMuFWRGJIMkn8wMW7lNFPvSdPxj/qg8mxFctS5ctS4OBA9gdVmpO6OO6sXVOdmUicxYFVE06+xM7EdZkxn+CCtlsgsJuywx1f0tGbJmRldXTtL6aTHdNuWK1tZWAoEA999/f1bnOxyOk8YZEY/Z1m6ZEG+PHM/xZMa88/JzVssot/HecYLjYi2lhbRoNmbICx6cVDZkF+W2ZIlYZ8j5M1VmxqTaR1HEYJ8/P1ZVb3wcfvAD4X044wyeHF3Pu/99GSA+/9gxcfp3v5taIiKXr7/uOviP/4Df/178Pdn+Fo3iG5g5UXy52iQzcXPN5J0qGNkjZWpTkRlScikwFijademaTvfL3WghjbX/kCHUOgmFnOd8Iz72PLwHz4CH8z553qQ/D2DPpj3s/s1ulm5cyuq3r57w+vyL5vPaf73G4IFBhjuGKZtXNuXzdmA8EI3uTZdNHh8RnCqqOjmqdbY9h97o6wcZjf/00xo7dvSxalUtF16opo3Gn4DmZnaf+Xb+9Cdw4OGetUe54soIk37iBPzoR+LvkpLYc+yaa7LTYSJGZrz0EnzgA8bnuPvcbLp9E54BD2PdYwS9QQYODXD0x0cTIrDjazYUCmveuSb6t1wHZJuZUV4ugjVk1mljY+oAzJk6turrof8AhCK+SkVRqJhfgdluNiR1JWaqPfmimPa4al2oZpWwP4y1xMoZ7zmD7pe68Q/7cdW5ClrzSSKdPUZy9zM1O9VV62LBJYKo1jSNFnML1Qsy+yByLQAOscDWdGTG7od389p/vcbCyxay/iPrs/9wA0zlGPrjh/5I0BPkmvuvoWyuYHhSkRkQ84/ZK+0465yY7dk5KGbKvDAjyAyAO++8kzvvvDPl6y0tLTzxxBNTeEXpoaoqra2ZheaSGdF4ialJt/3e74KlFObdIn7niWxtyRnWiBhnGjIDhCzCnj1CaioXMsNV5+Lyb11u+Fo6my79+qX4hnw4a3OPxjfSLk1GY6M4D4QvQy6YJpAZr39NRJkvei8404eIFq2NJKTef2A45SlOJ8ybB0ePiuyMQpAZRbUpCcaZGRGZqRyc4qlQFHssZaI+RCQTS97zXbtENlOmtGIA1aRiK7PhH/HjG/KlJTMkRo6JItaL1y3O+SGVicwoKxMBygcPiiCwSy8VxyWZMS+T39Mf8ZLaanKKHCt6f9OC4trCfiiZP+HlhgYRLBcIiPkjKzmtDJjqMTRZHDhwYLovYUZgtrVbJsTbI+coueGfDGSU26G/HGLbT7ZR1lzGhf96YfT1b3wdHn7AxvvmZefwWJwUXJ2OzChI+8i5s6RE1NN46SX2/OxFDj/0EndRx+f5InqE0OjqEjIiv/3t5AmN668XZMZjj0k+ZXL2SDJDBoIUCl2vdGG2maleUp315kkiV5uylVucLhjZI6Pj4p0Q8YhmZowVLzNDZkSpZjXnAKBCznOqWWXvI0LjtO39bQVxCI0eG0ULaSnvr73czrKbl+Gqc1HSUDIt87Ycc9ZSa9qo6fiI4FRIjmqdbc+hU+sHsd6/5BKVSy7JPzououCNFycHzUsJLwETCC/Xv/+70EI9ckT83rkzlt73ne8Ib+GCBYLkmDdvgoSirJvx4oupv98/6scz4MFsM2OvtKMFNaw2a3SONqrZkC/cfe6UY6J3HzixUW8QEJkKixbF1jYNDaKOptEebKaOrbo6eB0IxvGd2WTbzVR78kWx7RnrGkM1q5Q0llA6p5TSuaWMdY3Rv6efuWcVMiVYIJ09mTI8ZwJ0Xcc35EtYY+TSRrkWAIdYvaBUZIa7z40e1gm4Axx/9TiD7YMJr4cDYUzW1M/k6XreaiEt2ubx6yQjMiP5Hpx+y+mcfsvpWX/XTJkXZgyZMdugaRqHDx9mwYIFaZ19195/LUFPMDqJFKT4Nwj5puN/EX+3vGVSH5WtLTkjSzLjzW+Gb3wD/vhH4eizFqC+XzqbbKW2vDdCUrv0ppvSn9fbKyaMoaFYOueE6I/hHRAcB/09Gb+3aG0kIdsqRfFiidNOi5EZ500yOK7oNiXBMDOj4VIoWxKrPTMJFMUemTESHGPTJvjIR8S/e/fCxRcLZ1w20bxXfOcKbKW2rBxGuq4zemwUXdcZ1oZp1ppzskc+MNNpwK9ZI8iM7dsFmREOx1LnM2dmxJEZOaDo/W3sAGz/J3A0wPqfTHhZVaG5GdrbxR6xEGTGVI+hQuAnP/kJ3/ve9zh27Bjz58/nnnvu4V3vetd0X9aUYja2WzrE21NbK+zp7485EoY7hhnvGadsXlk0Sgiyk5Jw1bpE8USXlZYLWqhqjbHRJ4LgIXfNa4lUMlNFaZ+qKsKXX8Xl770SlaPMoQcdFSt+Psz32aqfySucxT33uHjzmzOT1Olw4YXC53T8uCjG2tY2OXuKRWZse2Ab493jXPTFi5hzRpZprxHk2kYyqn2mykwZ2ZMpM0OuY4spM+U+IbJXnbXOlBKVqVDIcWR1WSmZU8J4zzhD7UMFiSwd7RRBImXNqT0grZe34h/1M9o1KopdHuukqbkpak+xJUNyqfEnM2uzxWx8Dp1aP0yu3TZtgl/8Ivb/Qw/BCy/E7SPKykT6tEyhltB1seE4dEgs3EMhsaj9whdEpF5HB1gsnNU2B1DZt0/sf9MVGDc7zDhNTnyDPvxef8FrNsRngIDIMtM1HcWkoCgKlg64CSe1zo1A5nGzaRO89lrs/23bUmdUztSxJddKoRxv70y1J18U2566FXXc/Jubo/N37em1jHWN0burtyhkRjp7jMiMoCdI765eQr4QLRcUYDM6SXS+2Mnz33ye0958GmvetQbIrY3ylZmCmG8oHnLuGDs+xujRUXq29XB8+3GILIG0oMZY9xilc0sTpNrikZxdNlVjSMrwQkwVSNdT18wA43uQDWbKvHCKzMgTmqbR19dHS0tL2gZUzWpCWu+kin/HQzr0zM5JFy/O1pacEXWQD6c9bf16sRY6cULITMtI7WyhR1IklLgo7aLZhFi0XH+9kHCIx5w5EAyKyPQ3vUnockuJKZcLnn8+LpJeFnUGsGSWOCqmPeIaMmdmgHAGPfkkPPIILFyYXWZAKhTdpiQYZmaUzDeMos8HRbHHIp7ML2we4+a3TMwGyjaaN5cNrm/YJ5woCrhVN5qmZW1PKBTr8+nIjLVrxTXLuhnHj4uxYzJlUQh3EmTGlIyhNIRgS4sgMwpVN2Oqx9Bk8dBDD/FP//RPfPrTn2bVqlV0dHTw8Y9/HJvNxm233TbdlzdlmG3tlgnx9tTUCHuGu2OOBHevm8BYAEeVI8Epl62UhL3SjqveRf2qxGjUXGUipFSiRFeXcWRlsdpn82bo7FKAFo4iNpAu3Hhwciu/4WZ+y2vHVrP15+dx1j9kHx2VDKtVlOp46CH4wx/gjDMmZ08xyIyAO8B4t8iMzKd4da5tJGtHzVSZKSN7MkVUWkuLLzM1fkK0kZGcayYUehxVtlYy3jPO4MHBSZMZuqYz1iXCOcuajD0gE5yhuo7H48HpdBZdDkci5A9hK7fhrCl8XbfZ9hw6tX4QyLfdNm0S+4W89hGKAlLaW0YfdXTEimT99rdw4AA1VivfqG5my8B8XvvjeVx0R3rHrdVlRUcn5AmhaRqmybD4SYjPADE7zPiGfIx2jWIttVK5sBJ/OIQTD9WlfjKRGbneu5k6tuLJDFlHRw8LCWItpOGscRrW15mp9uSLqbDHZDFFs17qVtRx5OkjCQXBC4l09qx62yqWXLckYR3hHfTyzJefweKyTDuZoes6u369Cy2koZjy8+PlIzOVLjNDzh3WEismqwld0zE7zZhtwm3uG/IR9AZRTEp0bRkPo+yyqRpDgXGxHrS4LFHpuIEB4WOBRJWYQpAZM2FeOEVmTDEKlpnhi+Q52qa50Hc6WCvE7wyZGaoqslgfeEBITeVCZvz9i3+nd0cvGz69gca2TF5QGOseY+/v9lLeXM6Sa5dkPD8VZBHsz39eZCvMmSMc+0eOiKjIPXtg3TpwR/gKtzspkv46d2xVVIB6DZNGvCNW1w3lezZtggcfFH8/9pj4yTYzYCZAkhkJmRkzHfUXES5ZwifuXmkoayab6p57mHQ0r8ToMbEqcNW7UC25PZx6e4WsickU2+MYYc0a8Xv7dvFbOvebmrKoJZEnmVF0RIgnQl4hOaVOdEDJuhlv1CLg3/nOd/jhD3/IW94SyyZsbGzkU5/61BvKGXEyQ8pMjfb58ViFI8FaYiXkDWGym6IL/1ykJFbdsYpVd6yKBi5I5EpmbNmS+P873gGf+czUPcNk1lo8hqjih3yQUkZZz0ucy/NYnnoc/uF0McH39WVvYByuu06QGb//vVinTAaOKofQ1E4RgZYPBg+K3ZOr3pUQ8FMszHSZKSO0vb+NlbevjNbGSMZUyEzJzIyS+ulfp1YtquLYs8eifWcycPeKAsSqRU1pm3RoqBaVkDdEyB/CUmLBXmZHUZSCyuGkQtP6JprWN02Y+96IOLV+yB/hsCgeXpB9hMkkInPi04v/8R9F8aeODqpe6WD1wGvseX65IDM2b4atW4U81YIFoMeiOVWrSllzGb5g8eZls8OM1WXFP+pHNatYXBbxvwYQyrgnLOi9m2bU14MfGz7FScgv6uuEA2FGj42iqEr0GZ9cX+cUJod5G+bRcn5LQddQ2cJaYp2whnBUi/VQ0B0k5AvlLPNZSHS93MXwoWHMdjNLb1ia12cUQ2YKwOKwYC2zEhwX98lWZsNkNgkJUF0QgXo4ElBtVkR2GYXLLssHMrglvs3l3qOqCmxxwzqZzOjf18/WH23FWevk/E+fPxWXWxCcIjOKCP+Yn60/3oq1xErb+0RRZUlmTD4zQ5IZM8yhFw9XC9SeA2WZJ6frr4+RGd/7XvZS+LqmEw6E8Y1ktxAaOTbCwT8epGpxVd5kxoEDgswwm+GjH01sy4ULRUbG+vViXZcMGcXxh4fGuaYaMNkMnZ5TDumI1XVRn8Ga2EEnFdEzAxAIiBqskJSZ0f8S6GGoWBG7BzMJ1evYvBOe35H6FF0XfW3zZrjoIuNzerb1cPS5o9QsraH1svT6hoHxABaXJWW0YjrIehkNDelrAq2N1BHdtw88nhyKf4NoKz0M5fkteooGcwkoKuiaGEO2iTskad8blczYu3cvFyV10iuuuIIbb7wRr9eLw5GbJvspzDxIEnNwEGgQjgRLwII6pKKa1QR9+lwX+0rSwiAXMmPTJrj11onHp/IZNieNktIYZfyFy/gLl/Kmt0UyIPbtEzrlCxfCOefAmWeKNM8scPXVYg7esWPy803t6bXc8tAtk/uQJAweELunqsW5Z2XkA0e1g7LmMkrn5l9fbqph5ISIR8PqBm74+Q1FKSgqMZnMjEJDZvAUgsyQElOlc0szymeZzCZGToiMS3uFXUQ8KmKBM1UOi+S5742IU+uH/LF5c0zK1QjZ7CPSwm4XqfuLF+PbB599Ca46pPMBEDWjLBbx4X/8I85RPwuG7fRUnIM55Keh1E2P35yzjF2u0IIiKl7WngkEQAVqMpAZRb93U4i6OvDgYuuCjXzmQUGCa2GNP/3jn9BCGm/66ptw1jqLLp93MmO0a5TnvvkcdSvqOPO9ZwKkrXc0HbA4LJgd5ighHy//OpXQdZ1d/7MLgMXXLs5rLRMKgTeyZM6FzMimADiIexUcDzLWOYbVZcVULtpS13RGjo1EAz5ArGdl4Mx0QQa3xK8djSSmYCKZoYU0Bg8MRrOCZwtOkRl5QlVVmpqa0qbV+Ef8HPn7ESwuS5TMkDJTBcvMsE8+MyMbW/JC5WrxkwUuvVQUmD52TEjPSGdnJshixv6RxMi0VDbloj+bCo89Jn5feKExKbVgQeq6HzKK4xtfGePq74CSZeH2orVR9AvMooh8cExkZ8SRGcWKSim6TXGQE7WiJI299p+CtwfWfgPKl0/qO4plj1E0b67nDR8Z5tCfDxH2hzOSGc3nNtN0ThNBX5Degd6c7MlU/FuioUEsqnt7RX3BnMiMugvET44oen9TFEGIBYbFGEpDZnR0FOYrp3IMFQIjIyMTHA5WqxWHw0F/fz/Nzc3TdGVTi9nWbpkQb4/MzBiKkBlANNVZD+UeXezudeOsmajXr+sxSbv6eoM3xiHXZ1ix2uf880X2WVeX8bUoCjQ1KZx3WURSprUV3vteIWr+P/8D//u/wltyS2ZioboaNmwQ0p1//KPKjTfmb08xHKkDB0SqZPXi/FIlc22j8uZyrrnvmry+ayqQT58zWU04rMXdOIcDYRRVySszo9DjSNbLcZ9w4x/zT4rEGTkmNmPZBG2YbCaspVYR8eghGnk52zHbnkOn1g8C+bRbIfYR2WL9evH7pZcV8Xxdu1Zs6nUdBgfxb36V/h2iQnj5eBcLj/6NJWGN4EANbmcNg6Y6OsmstJArgl7hoDPbzOi6IDPsZM7Wz+fezdSxJQM/uoZcVLXGyIqaZTUMHxoGSKhLJjFT7ckXxbRnYP+AyDSwGbtYtZBW8AyNdPbsfng3QU+Q1stbE57jjmoHY51jeAe800ZmdG/pZqh9CLPdzLIblyW8lm0byawMKJzMVDxcdS5CXiGDl7APUYhK2GlBDS2o4R/xpyQzpmoMSZkpKUMKxsW/YSKhI6XIsiUzZsq8cIrMyBOyAdPBSO+2YDJT/sLJTGVjS7HhcMDll8OjjwrJhyuuiEk3pXOQ28rFZiZZBzmVTbL4o9SAzgeSzLj2WuPXN2+OsaBG0HUYHXIzMAA1LdltEKekjZZ/ClQ72BM9Q8WKSpmsTeGw+M6ensx9RZIZlZVJ54SFFvJk685Akdoo5GZhbSdL5sD+ntPSnpou6lc+XLMtfqooClaHNWd7siUzFEXsbZ54QpCXOZEZeWJKxpC1IkJmjBq+XOjMjJkwd+cKI8eoxTIDstOmELOx3dIh3h5JZvj8QnIOhDMQxCJb1/WsneO6rvOXf/oLQW+QN331TVQujFUTHR0VzghIL2kHuT/DitU+JpNY39x8s5gD4wkNeUvuvTfuGWWxQFub+BkdhZdfFoslEEzO3/4GZ58tJhaDe3rddYLM+MMfVD784ZnV32R0fT71MuDkHkMSO361A13TWXLNkkmtWSeDDf+0AS2sQR4qR4VuI2uJFVeDi6A7iLvXPSkyo7SxlOYNzRPq8KSCs8ZJYCyA5tOmNEvi+f/3PN5BL6vfsZqa0wqbiT8bx9Cp9UN+7ZZuf5DPeemwerWQMhkchIMHRcIGIJ5R1dWEV65lzHYQszfECftcRhquxj58HJevn6rgCSr1MTorG8Hvh298Qzzf5s8XvxsaspduSIKsBWFxWggGY1NaJjIjn3s3U8eWJDNkVqtE5YJKhg8NM9wxTNPZE697uu1x97nxj6aWU8w1k6SY9gzsjwRqLEnsWL27enn5P17GWePkkq9cUtDvTGfPwccP4j7uprGtMYHMcFY7Gesci9aEmiwytVE4EMZkjTlgdF1nyw+3EHAHmHf+PFEfitgzPds2kvUy7HaxZM4W6QqAx8PqslK7fOImQ1EVKhZU4Kpx4R30MnhwMKH4djKK2efi733YH6ZhbQMlc0oYbBfGdR+wAa4Jc1nyPciXzJhunCIz8kQ4HGb//v0sWbIkZcGqKJnhio2uwhUAjxQAKEBmRja25A1Z6NrsFBIsaTA3Uifsv/9b/EDmmgzRzIykCTSVTbKApVHBnmwwMiKcA5CazMgmisNm9uPx27Oul1HUNpKoXGN4uFgRPZOxadMmEWkb76BK11cMi3/HF2E3TZ7MKEobje7lLPMX+NQNC3nv/d9NE80ryJxUkJlIueiF52NPtmQGiLoZOZMZWhh8x4W8nik3R8aUjCEpVRYwLgIua2YcPSocvZMNZpgSmwoIXddZt27dhCiO0dFRLrnkkglOCbPZzI4daTTWZilmW7tlQrw9JSUmkZkYEOnfIJ7TqkUVkpDDvqzTsMePj+Pp96Ca1QnyQHIzXlIisjrTIddnWDHbZ+NGIWll9Py69940UldlZYkFxfr7Yds2QWg0NAgZqrPPToiUuf56+OQn4a9/1fnKV3o455x6LrrINIH0zyYw4JUfvsLAvgHOfN+Z1C6b3LrTN+LD0ys20PmSGSfzGJL2HHjsAIHxAPMvmp+SzNj2wDZ8Iz7OvOvMoslNycyqXFGMNrri21dgLbFOmlCQtSiyhdzk+8Z8ORGyk0X/3n7cx93oWuFrZsy2MXRq/SCQT7tllxWYfh+RLaxWOOMMkVD40ktxZEYEtjIbzmonngEPIT+M+e0Md1dgNtdQsaACdF3UbLDpIlJh927xnAMhs/itb4nF8/79YlNXXZ2R4AgHw4QDYQDMTjPuiP/WpIIltZIfkN+9m6ljS5IZIyOCK5L6+RXzKwAYOmwcpj6d9rj73Gy6fVNap7uz2snGBzdmTWgU055UZIat3MZY1xiefk/BszPS2RPyiIV4vMQrxOpmeAeyC3JMh0xtpAU1xrrHKJ1bGrU7HAxHa3R6B7wcevJQQhtm20b5FP+G7DMzsoH08wY9wZTP6mL1uXT3fttPtgEw3u/EyUbmzEkcH8kyU3KdE/aH0cJaxrXfTJnnTpEZeULXdUZGRtIWZYuSGY4iZGas+qKIADblL5ckkY0teX4wPHebcBif/VOwpxa23rQJ7rtv4vFMetaSlEjOzEhlk3Tm5hvl9sQTwkGzdCksWmR8TjZRHC+3r+dQ40PMW53dPS9aG2WBYkX05GtTPvU75ESdEIGjBUGLeNsKkJlRlDYyl6IAl144CvdnGc1rgGjx0+H0ZEbQE+Txex6nrLmMcz55Ts725EJmSCm57dtjaaIZyQzfCXj5/WLe2/CbnCK0pmQM1V0oagS5jOUO5s4Ve7BAQARWTzYKbjrnhXzw4IMP4vdnX6zWbp/8820mYra1WybE26Mowv/g6YqRGYqi4KxxMt4zjm8kezLjxA6hI1V9WvWElP1c6mXk+gwrdvts3CgkrbLNLDTE6afD178Oe/fCiy+KlNH+fnjb2yAYBEVh1y4zZjOEQgqf+5yYlJNJ/2wDA0Y7RxlqH8J9wj1pMsPqsnL5ty9ntHM0IXM5F+TTRs9+41l6d/Ry9sfOpvHMwsuYTAbJ9ui6Ht1DJDsh4nH4qcMExgOcfuvpRa2dkQ+KMY6my0az3YyOqNGnhbQp0UDXdR3fYGTPUgQd7tn2HDq1fhDIp91yzgqcJNavF2TGiy+KR1I8XLUuNj64MRqEGPQH+c27fkOpq5TLvnEZjmpHLNK+9d3iTR6PiAIaGBCLaF2HH/5QHC8piRUkv+iiCdGiIW+IwHhAjFubSdQJGAEzIcxZPH7yuXczdWxVVhJZE0Bfn3jWQ4zMGO4YNnzfdNrjH/XjGfBEJX2SIes++Ef9WZMZxbInHAxH5bqSyYyyprKoXOFg+2BBM+1S2RO/jkheazmrRRRQITIzMrWRb8hH0BtEMSkJwcSOSgdBTxCTxTShDbNto3yKf0N2ZIbM5kpG2CeI0ZAvRMAdQEfH7DBjspnwj/vRAtqE9xSrz2UzPkJjHmz4M5MZcf7qkDeUtmYbzJx57hSZUUQYZWYUrAC4ooJtagon5g1FEU7ikBsCQynJjMnUZIjKTGVZAHyyMlOZJKYgxyiOmVTUb/QAjO4BZzNUxYqWTGVETybk21cMMzNCkYrgilIQUrAoiNRUaWkcN4zmrasTJGCmwrUyMyMwFkgbETJybITxnnHC/nBeUSO5ZmaAKFArA+0ykhn+fvHbljkSa1ow5/K0L1ssgtA4dkzUzShESv9swlvf+tbpvoRTmALU1MDRCJkhNwPWEivlLeVYSiwE3IGUm4R4SDLDSAomFzJjJj3DJEymAhQLVVVYvlz83HZbTHfrhRc48O+P8tBjZzGHczlGM0R0/uNJf8g+MEBufGV266Qu26xSvbg673oZ+SLkDeEf9RfEhmIj7A9HI/zSET7WUiuB8UBaeYd80ft6L6/+16vUrahjzTvXFPzzpwsyQ8xZ68wqw0LOVYqioId1vENerC5rVnPYZBD0BKPR5NMlMzaTcGr9MDnknRWYB84+W/x+6SXj1121rqjTMhQKUdJSgsUr5jmjmg04nSKKUEJR4EtfEgtp+bN5M1wQqaf36KO4DnXSyjH6RksYoRxrqcjo8g378A2BBdAdoth1JkzlvSsmFEWsmbq7RUBVlMxYUAHAeM84IV8Is33muQfNDrOYd/0hVJOasEcN+Ys7F2eL4cPDaCENa6kVV32i41hRFGqX19L1Uhd9r/cVXDbQCFpQQwsJx3ryOmLehnlUzK+IElmFgGwjELKyWlh8dzgk1jN6SEcLCalGOe7s5XaxJ8izDWVmRq5khvQHjYxMVEpIzB6beF1aSMPisKCH9WiQqHxGB8bEOtxZnd3cUijIex8OhVEUBcWkROt7BYPChlQ1M3w+UUTd4VAxWU2EA2GCnmBGMmOmYObNVicRpHZa/ARSsALgswXWSvD1CjIjBSZTk8FZ46T6tGrKW7Jjh2SUUz4yU+Ew/PGP4u/rrkt93lRHwBQMg1uh41fCIRtHZswke/LtK4aZGVJiyuyamY5xiMkWhX1svCHIm99sYfNm+MQnYOtW+OhHs1tEW0usqGYVLaSJTXyNsS7LaKdYFZQ25ZivGUEuZMaiRSJj3O2OHZs3L8ObAhFWqgC1gqYLLS2ijx45IpRhTuEUTjbU1MABbOh2JyF/4mbAPxxzuqZb7Ou6Tu8OwVhMlsyYSc+wosHhiNbTCC9eyjdf2MAZvMBF/I1Omvg917OD1VHS/+67xduyDQyQG7V8iYBCa17ng3zkFqcLUc1iJVZzxgi2MhvjPePRDXQhMXpslIG9AzMq40MLaTz3zecYPDjIVd+/Km3WSioM7B/gqU8/RVlzWdqi8EYODT2s4xvyoQWFk6aYDgs51iwuS4LW+CmcQr4oSFZgFpBFwF99VTrJ0p/vaHQQbg8z3DHM3LPmZvclpaWwcqX4gcSHWUkJtpCHC9cOE/acAFXBf9PthFefiTI0yMO/9PLv353DZec7sn7uTNW9KzYkmRFfN8NebufCL1xI+bzytM+b6UY4GObEzhOYzCZqV9RiMs+sa42XmDIiymtPF2RG7+u9LNu4bMLrhUa0hoPChKj9yoWVCXXoCo3ho8MEx8U6JuQPoQU1RjpHcPe6Uc0qc84oTDTfZGWmdF34ZivjbkVy9pgRkmuAJGMq1rRGGDo0hH/YT8VCUc8DIBRZTiaTGaWlYv4Kh4WfbO5ccNY6hSxfMDzFV54/TpEZeUJVVRYuXJi2gnt8sSmJgshMeTqF09m1AFpuncQHCWRjS96wVojfaciMydRkqFxQyeXfmhgNncqmq75/Fd4hb0IRpGzx4osiwr+iAs49N/25GaM4zvwN7NgNjVdBzfqM313UNpKIttXwhJdS2dPYCN/7Xu5RKeEwPPOMyv79y/B4VC68MLsFYb59xTAzo4DFv6FIbWRyxrxvwTFMtiouukgE4W7dCs89l93HKIqCrdyGd8CLd8ibmsyI6FeWN5fnZU8uZIbJBKtWiVR0gPp6UcArLXx94rct94jeKRlD4UAke0QHp/GGbP58ePbZwhQBnxKbTqHgONnaLdme2lrw4ML81o3cctvEzUA4IDK/7BX2lIv90c5RfMM+TFYT1adNHO+5kBmQW2TlbG+fzfvqeGBgIwo3cDqvcy7PoyKcrws4RJU+yGudqwmROuJfBgY89Qc3bSv9hPwinX7gwEC0qCBkt2FL1vT1DHgwWUyi9oEqNvy5al7n00apZElnApLtiZeGSJc9YC0Vznz/WOEzM8ZPiOzV5AjTbFGMcaSaVQbbB/H0eRhqH8q6gHc8ZNCGqy69XckODf+YnzHfGFXVVVGbiumwkGRGsbIyZvs890bFZNutIFmBGdDSItb0J04IKdl0e2ZVVZm/Zj6H2g+lrNmQFeLnyUsvhUsvxRIOY+nuhsOHsS9dCnVV8OgzrH7mT3wBK039zfCb+WIzEp/5kQLZ3ruZPLZSFQFPJ704U+wJjAdAE2vIydQtKpY9iqpQMqfEcM0KUHe6uPn9u/sLWnsplT3xcvfFrPOk6zpBdzCqlgJCmhGptqSItjPbzVjsFhRz5mvJto3ylZmyWkXCl8cjHPmVSbxOfPZYtgj5Q4x2jhpml03VGJKBFvGZS5HEDBoaEs9VFGF3f7+Q25o7F669P430TBJmyrxwiszIE6qqUpdhF7385uUsvnqxzO4HClQA3H0Mep+Fsr6CkRmZbMkb1sjskIbMKEZNhlQ2WUuseadNSYmpq64SmpOZkDaKY9cBkQmRBZEBRW4jCUlmBI2LF8fbc9NNYvL/5S9zXxTHNLpVQAyETIXeJfLtK5LMSMjMsDfA8k8ChZmEi9JGigLmUgiOip+ItJzMpH722ewLSV/+rcuxlljTpg+PHBNtX9ZclrM9gYDQYIXsyAxIJDMqKwXJlZbUimZm5J6eOyVjaOBl2P0NKF8Ga79peIqU0ioUmVF0m06h4DjZ2i3ZnprI8Bz0uahqTdwM7Pr1Lvb9fh/nfOyctDJDJ14TElM1y2sM9elPiJepz8GXmW1k5WxvH0nm66jsYiW7WBl9bRU7uIo/4cXBFtp4kbNpp5WEhWoETty89rlNHLZ4CIwHcJ9w07e7j0NPHoqdkwUJEa/pq5pVRo6MEFSClDWVoahKXprX+bSRrDsg5UYng0JnmiTbk0rnesL3RLImiiEz5T4h0ibzCf6B4o2jqkVVeHo9DLYP5kVmxK9zMiHZoTGHqdOGjJIZ1cUjM2bzPPdGxWxoN0UR2Rm//70IBMxEZrSsbuHwpsMpazbkDZMJrXEux0+YqDCV4NB1lKuu4nf/czovbe/gIzUd8NprwrO5dKlg8H/7W1iwIPaTq5eUmd1Gcs2UTGakw0yxRwYJO2ocGYsTp0Mqeyb7XF989WIWX704ZQ2ByoWVmO1mAuMBRo6MFEziKZU96dYRWlijZ2sPngEPrZe3pr2f6e6Lf9TPlh9vYfz4OKpFxVYi1iRVC2MOfXe/eH/F/Fi2QL42JSPfzAwQfgePpzBFwH0jPh59x6Pous4tv7llgq9lqsaQlBWTWUvhsPATgbEPrapKkBmDgxNfy4SZMi+cIjNyQPxgDofDHNh/gMVLFkcruCdPcqpZnZB+XJDMDH/EY2gvjNRKOBxm165drFixovDV6LMgMwqhZy0fHJJ5LoZNf/iD+J1OYioZKaM4ZL0Gc3azb1HbSMISYdiCwylPkfaccw783//Brl25kRn5FO+Ox/nnC0e5zABIRqq+YigzZS2Huguyv/gMKFobWSJkhuwziOLZLpd4AL/+eizLOh1SZWPEIz4zI1d7jh+PXK4l6T6nwKZN8JvfxP7fu1dkLaQltXyyZkbuZMaUjCGrHEOjKU+RZEZHx+S/bkpsOoWC42Rrt2R7JJnR3z/x3MB4gMBYgIOPH6SxLTXrWbeyjhW3r6C00fgZmWtmhkQ2kZWzvX3Skf6/4wZe4BzW8xJn8yLns5lfcQebuQAFDT2O3LfhR/F5MJeYUa0q3gEvihor4JgrCWF2mKNZOWanOUG+KFe95HzaKCozNcnMjORMEyPkmmmSbI9RzT0jyD1GYLzwMlOTzcwo1jiqWlRF5/OdDB7MY/dNLDOjrCk3J+VUzwu6pmOvsEfr1RQas32eKwb27t3L6tWr+cxnPsPnP/95AHp6erjzzjvp7OxE0zQ+9KEP8f73v3/arnG2tNvZZwsy4w9/EM+kVMED4XCYzvFOdF1nrGsso3xLrhjvGefpLz6NyWbiloduAZuNXf7F/IXF3HQ18H5im1JNA5tNpL3/6U/i2NKlQtMXoL1dbDJt6aXlZnIbyTWTDAiRcPe5OfSXQ+hhnVVvW5Xw2kyxJ+iNZRoA6Ojo4dyLDxvZU8jneqosCNWsMu+CedG/C4VU7VMxv4Jr7r8mGq2ffI2b/20zuqYz96y5KZ8z6e5LYDyAp18c19GNYmLyRrZ9Lt/MDBBkRldXYcgMe7kde4Ud76CXocND1C5L9NFOxRjS0WOZGRbRv/yRpaHTYUz4JBcBzwUzZV44RWZkieTBrOs6Ho+HHc4d0Ukr0yQXDscG3aQyMySZUSDdeF3X8Xq9xalGHyUzhlOeMlk96yc/9SRD7UNc8m+XULNUeFGMbBo6PMTBxw9SuaCSRVcuysmMw4eF09hkgiuuyOmtxpCOaUt2EW9FbSNfn3C+BoZFHQlPF4y1x163lE0gzlavFmTGa69l/zWTKfQuoSgiqsSIzEjXVwxlpgqMorVR0w2g+cEei0A0m0Wk05NPwjPPZEdmZEI4EGb8uOiXZU1lOdsTLzGVKZs1b1LLnz+ZUdQxJBElBI2zm6CwmRlTYtMpFBwnW7sl21MbeVzITK14tF7Ryr7f7aP7lW68g96UEioVLRVUtFSk/M58yYxsMNvbJ1OASJ9Sz/a517Ndvw5H10F6ELnnN/Nb5tPBi5zNVtqYMwdKSgQJoZpVTDYTZps5oU5BriRE0C2cEfnUOohHPm0UlZmaZM2M+EyTZB1qyJ3kAYMxtLyWa390bbQIeCpEZaZmYGZGscZR1SKxkMubzIgL2sgFQV+Q3T/ZTZfSxRXfvsIwY6yQWPimhSx808KizUOzfZ4rBu6++24uueQSgsFg9NhNN93Ehz70Ie644w7Gxsa47LLLmDdvHldfffW0XONsabdAxIn297+LHzDOwtd1nZAlxDmfPIeqhVVRJ1yhILM9KuZXRP01EzI75aalpQU++EHx4BweFpt/GdY8PAzf/KZIhW9sFOfOny82Y0lyDTO5jVLJTAXGAux6cBfWEisr71iZ4JCfCfaEvCH8o34Rea6Db9THaOcoQXeQkjm5PaOM7Jnscz0cCKNa1IxyTus/nJ0iRy5I1T4mi4myucZefkVVcFQ58PR78A54U5IZRvdFC2uMdY6JwBAdUIRso63EFqvTEYewT9RfCPlChq/LjJtsbEpGvgXAIeYTKgSZAVC1uIqul7oY2D8wgcwo9hgKeUPomk44IO51OBAWdVJHxb2tTbFXSiYzdvxqBz3belh24zLmbUhfyHQmzAtwiszIGsmDWdd1gmoQe5kdRTFOk9/98G7cvW5aL2+lqrUqOuBgkmSGr7BkRlHhbIbac6EsvRZlLnrWydDCGuFAGN9I+g3qcMcwB/94kPo19TmTGVJi6rzzCuQQD0ZYLXN+m8SCwdcHz98O/gHQNRg/LI5vvgmUyILSVg3nPphAaKxeLX7v2JH9V02m0LvEt78t9FfNZhH9Hx9ZUlEBDzxg3FcMMzPGO8DbA84mcDVnb8hUo/FKw8Pnny/IjM2b4UMfyvwxx189zpFnjlC1uIrFVy2e8Lp/zE/Voip8Qz7slXbC4dyKP2VbL2NSpFb9xVC6SLTZTESUzBgHLQTqxEdsPJkh7T2FUziZkC4zo7y5nJrlNfTv7ufQXw5x+q2n5/UdxSQzZjuyCRD57ncBFG6+OfIs0GEfp1HPCe7gQd7K/9J2YSv7dgbQmIPJZkqrqZ0tspVPKgac1U7K5pVROicPPQIDmB3mlKRMriRPMkxWU8qspHgsuWYJi65YlLd8aiqEfCH8I4IgyTczo1iQetTj3eME3IGciLGQL4SnTwSlZSMzFQ+T1cTo3lE0u8ZY91hasrWQKKbW+SnE8PDDD1NfX8/ChQsJhcT43bFjB+FwmDvuuAOA0tJSvvSlL3HfffdNG5kxG7BpE3zxixOPpwpYUhSF5vOaMWej4ZwjZB2OeEmfjOsHKSYfL6RfVgaf+5wgODo6xM9rr8WkAP77v4Vc1fz5wnmhTYyGnwlIRWaUNQvZx8B4QDi3s8jmnwrYymw4q514+oXcJTqEAiHCQ2F8Qz70sE7YH56ghJIv5HNdC2mEg+FoFgikf67veWQPezftZdlNy/Je1041HNWCzPAMeKgmvaSCvC/+UT9D7UNoQQ3VouKodoiM3XI7QU/Q8B5pIQ2Lw4Ie1lNmxjqrnXm14WRlpqDwZMbggfwCLfJBdHwMiPGhhTRQYgEufg94cFLVYHxvk8kM9wk3g/sHcfe5p+LyC4JTZEaOiE5yuoYpaMLisqBGnL7JA7jzxU4G9g4wZ+0cqlqrohJTTqd43uWNAstMFRUVK8RPFpB61nffDT/4AWzYIKI5MmUu2ctFtJ3ceKVCVH+2Mnf9WUlm5CIxlRY5ykwVDcFRQWSoNjA5QO0CNEGyqFYIe8XrwVFDMmPnzizqHEQwmULvIHRXP/1p8fd//Afceadw5D/wAPzqV7B8eWrSyzAzo/cZOPoQNF0Hi+7K7uJmEGTdjGeeyc4pPnJshENPHiLoCRqSGc5qJ1d8+4q8i5JlS2ZMitRqviHn65pSWEpj3sPQWCwzLQ7zIoEObjf85CewZIlx6v0pnMJsRToyA2DRlYvo393PwScOsvyW5RPmmxM7ThAYD1C3si5Biigep8iM9Mg2QCT+nB2sZgerWT53hPvf8worRv/K/p3C4Vs23o2mWnE7aibFwGYrn1QMlDaWcs0PrinoZ2phjbGuMRzVjklnm+SDQpMYEv5RP846J2FfeFrsSgdbmQ1nrVMUAT80RP3K7OtmjHYJz4et3JZybkkFRVGw19lhVGR3TBWZcQrFh8fj4V//9V958skn+fGPfxw9/pe//IULL7ww4dzzzz+fm2++OeVa2e/34/fH9qOjEW9bKBSKkiSqqqKqKpqmocU5veXxcDicEO0af1y+Fg6HMZlMIqAylOh/kJIfyUFJqY6bzeboZ0ooioLJZJpwjamOy2sMBjXuvluJkOiJ90fsVXTuvhuuu07HYlET7JHXWEibBg8Noukapc2l0Xt24oQIJ6+uDhEKZbYperyhARoaUM87T7RHIIAeDoOuo2ga6u7dKH//O0o4TMvICOGvfQ3mz8fU24titRIqL094fk5HO9XUqIBKb69OKBT7HNWkUtpUysiREfoP9tNY0Ri9B6LtEr93qvqerdLG9T+/nsGDgzz9r09jspi48vsiyO/4a8fZ+sOtqGaV0WOjOKodWY0neU78d4bj7oWmafTt7SPkCVG9tBpbmU1IWuk64VA4anO8TX17+/C7/VHPajqbQsEQw4eGcTW4sJfZs+t7aWxK9Xfvzl76dvVRs7SG+rWJz0mTyYSjyoGma4z3jhvaJO+L/DwdHcWsoIU0zA4zFQtEtpN3yMul/35pLNNaIWqTzC4NBUNYbJaoTfFZp4qq4KhwYK+yR68jlU3yGmXfGx0V/dnlCqPr6oR2NbJJorJSNFZ/f5hQKCZXH98e4TA8+6zC8eMKc+eqnHeehqIYt1P5/HI0XaN/fz+apiW0k/wd3/dS2ZTNtUdLHETGR2A0wNChIV749xdwVDq4+KsXA/Bf/wUPf93JtS0uwzmiqsoUuQcaoZCGalVFwL4nmLHv5WJTsl2FxCkyI08Ex4OE+8MoFak3dTKdXkagFaT4NxQ8M8NkMrF06dIZoetoMglC4wc/EI6QbC7JVi42JPFsr5FNUlpA6iZni7GxWIrstdfm9FZjhAMQjix0s5SZKnobmRxgdonCxYoJzE6ihbG1iSTRokXgcIDXCwcPwmmnZf6KyRR6HxqCt74VQiG49Va46y6xHrzoIli8GB58UEicdnSIoJhkGGZmhCOss6kwUYdFa6PAiMggMbsSMkjOOkvUp+jpgUOHoLU1/cdkW/xUbs5ytUeSUJnaebKkVr6YknlOUQURGBwT7WZAZvzpTyJLXdPgfe8Tx4xS77PBTJq7TyF7nGztlmyPJDOMZKYA5p03j60/2oqn18Px7ceZc0bipLH30b10v9LNmnevYdmNyya8PxSKEdTFIDNOlvaRASLPPKPT3u6htdXJBRcoCesqec6mTeLZqqqw9WA5dvulDLafgfvph7ADjb07KBvvwmuroL9yMQOViwnkuIWQWbRAQrRjPpgpbTTWM8b4cZEhkCwrkAuS7ene2k3f7j7qVtQxZ+3UFZ2WcNW5ePNP35xR5ioditlGtctrGT8+nvP1WUusLL9leV7a3iaTiXkr59HzfE+07kYx8dfP/RU9rLPuQ+tSSoVMBjNlDM0EfPWrX+WOO+6gMSkap7u7mxaZThuBw+HAbrfT29tLff1EIu1rX/saXzRIS9i+fTsul9hv1NbW0trayuHDh+mLe1A2NTXR1NTE/v37GZFOA2DhwoXU1dWxa9cuvF4voVCI7du3s3TpUioqKti+fXuCs2vVqlVYrVa2bNmScA1tbW0EAgF2xKXVm0wm1q1bx8jICHv37k2wc/Xq1fT393Po0KHo8fLycpYtW0Z3dzedcUy5tOk3v+mhs3PuBPsldF2hsxMee2yYG2+sZO/evYRCIV76+0sMbR+isaGRde9aVzCb2re0ExgO0DneSWBXgEWLVuN2iwmgq2s7w8PhjDalbKf29lg7LVvGwmuuoa60lANPPEGwvZ2jXV3o/f2sevZZnAcO0D82hq++Hv+cOYwvXcqySy+d8nYKh+cCzXR3hxO+t6mpiYr5FXTt7mLbX7fRjYhQW7hwIbW1tZjNZrZv3x49f6r7nobG0o8tpbGyEb/LL2yaB651Lka2jPDid15k6ceXMuKLjZtU42nBggUsXbqUvXv34vWKPbGn0xO1Y2RkBO+ION5/qJ85K+eALkjPnTt34hxyJtj02muvceDFA4TGQvSGejmd09Pa9MSnnmDs4Bgtb2lh4aUL8+t7STbV19djMpnYsWNH1KaeJ3sYe26MpdctpZvuCe1kq7QxMjzC7i27GakfMWwnT6cHr8eLo9JBKBjC7XdjbbRicpjwaT4cJgfhUJiDXQdxKuK+lJeXs2zJMjo7OxNtcgib2tvb6RtItKmmtoY9e/ZktAkS+96xY4uASgYHO/B65+TU9yor1wGwZ88Jtmw5OqGd/vM/h/jOd+bT2xsLfJgzR+MjH2nnoouGJrRTz3gPI8MjjAyPcOTgERYsWZDQTqFQiKGhIerr66NzuZFN+Y6nQDjAso3LCOgB2oeEXPyOznl4cDFnDoZzRGWl2GPt39/Hli2H6TrRhcfjIeQNZdX3srVpdLR4a6ZTZEYe0MIaAwcG0EM6Yz1jlDUaLzKT0+kLUvxbC4mIXyhYZoaiKFRM6qIyQNdFJoLJCWrmRfOyiO/i4EGhuZkpi0XqIMdrBhvZJJ24qTS6U+HJJ8V1LFqUndM+I8JeMNlBC4h7kgWK3kYSluyYNpNJ1Gl4+WWRZZvNfcmk4w2ippr8rHBYROd3d8MPfyhkeRYuhB//ODEwdO5cuOQSeOopQWp85jOJn+n1ih9IyswIRcgMc2HIjKK1Uc8TcPgX0HApLL07etjhEITGc8+J+5SJzIgWP02hF54cZZarPdlmZuRNagXHRS0KW7UYPzliysaQtUKQGQZFwPOuFZICU2bTKRQUJ1u7Jdsja2YMDAjSTk2SwDZZTSy4ZAH7/7Cfg08cTCAztLBG7y6RdlG/yjjiWmZ8KEoSQV0gnEztYzLBxRcrXHxx6uecyQQ33QR2O/h8Yj5Kfp7sW3AFtq5DlB57nTkDL9BUuoUdc67ER/bZpf4RoXdtspkI+WJRWkZ6yZkwE9pI13W8/WJxERgPRCPx8kGyPcdfPc6+R/ehh/W0ZIZv2Mfrv3kdLayx7gPr8vrutNel5p+FU8w2OvcT5+b1vpL6Ela/Y3Ve71UUhfrF9Rx//jgjx1LXxSoEdF2nf3c/4UB4Um2QDjNhDM0EtLe38/DDDyc4aiWGh4c5zWCDY7fb8XiMCwV/+tOf5mMf+1j0/9HRUZqbm1m7di1lEWF3OU8sWLAggSyRx5csWTIhMwNgxYoVEyJfAdauXZtwDfJ4W1vbhOMOh2PCcRCOrbYVLRASa1dFUWCsnRqbRtXSSGCOuQzFISIIGhsbaWhomHCNkN0i3+MpT7Bp8OAgT37/SY6WHaXtnW0FsclhduDQHTgqHJx3zXnYSm0cFT5L7HadCy5Yi6LEgrhS2ZRTO6kqi6+7LrGdli+Ho0epbW+Hjg6UI0fQzjsPk8MBr77Kul270BctEpvcOXMyt1PccXntNTU1VMVtclPZ1NUlrn1gwMSZZ7ZF99OqqjK6YJQSVwnVpurod6iqqANxxhlnTOh77j4388vmJ1yft9uLFy+tla1Yy6xR6fVC2SSj5qVNa1et5U8f+hMjR0Y4/J+HaftgW/T9ildhsH2QuZVzWbw4pkggI8xLS0ujNg1VDnHAdEBcT0U5rjNc9O7shQCE/WFQwOl0snLlSipbKxNsWj5/OYdMh1CrVc699tyMNp1+wens6d9DVaiKJUuWGLZTPn1PUZSEOeLVHa+y37Ifi9PCyrWJxTVNJhMltSWUV5TTUNYQvdbkdhqqHGK/cz8AZouZ8opyqIjYhELQE8RkNiXcl0KOp2Sb5DWCmPcURfy9cuV8HA7xOdnOEU88IX7bbA20tdUlXPszz9Twmc/UTtivHz9u4jOfWcL//q/GjTfqE2zqX9KPu9eN3WNPaRMUby6fmOkkvq+hwXg8yX/N5lra2qpxdbjYuX0nQU+woM+nU2TGDINqUimfV07fvj7GOsewldkMU02T0+kLkpmhmuH8hyEwVDCJIhnhsXbt2qLoVfL824Rj76z7wZk6WkNi7lyhfTc2JgiN5cvTny9lpuIzM4xsyldmSkpMXXttgfTtreVw/kOCmMryA4veRnlg9eoYmXHrrZnPlzreN92U+hy/H9raRP21+++fKEf0vvcZj5877hBkxi9+IaSo4m+rjOA1mZIKRBWYzChaG1kiFy1JzDicf74gM555Bt71rvQfk6n46RMfe4KwP8w5HzuHqkVVOduTLZmRidRSFPG6lKGNYuBl2PsdqFwNq7+S8XqSUfQx5OsT81z5SihpFQTuWHv05bBaxt13T1wYQRa1QlJgJs4Lp5AZJ1u7JdsjCQZNE0EcRnWmFl25CNWs0npFotd88OAgIW8Ia4mVygUTM5sgViuppqY48mwne/sYQVWFH2X3brHuiiczJNkwqtZxULex16GzqGqEYb0c0LD+9lcwv04UQm2eWH8qXtO3rKkMLaxN0E3OVS853zZ67t+f4/j246y/ez1N6ydXeykwHohmmqBDYDQQfc7mimR7kjO7ZXBHT48g+qU0YTgYZv8f9qOaVdre32a4F5kuTHYcpbJ5uhAKhega60LTtaJnZgQ9wWjfyjUAK1ucbPNcvrj77rv5yle+gt0+cezabDZ8volrZq/Xi8Nh3C42mw2bbeJcZjabJ9xn6VRNRqpsGZPJlNBucrynar9cjiv+fswvv0NIC8dfI9E8/VgNRVNtymufOzc7Qleep+s627dvZ9Xpq1BVlcBYAN+QL2W/z8Wm0aOjqIqKs8aJs0IED8YkKhUsluzaI9d2kjZFx1ZZGaxYgWnFCnkCqlz4h0KYTpyArVvFosnphMsvh6uuwqzrEAyKYxEoimJoa7bXLpOJQiGF8XFzQlkQWSR99OhowncYzRXuPjebbt+EZ8CY1APxXN/44MaEgtmGfW8SNvmH/AweGKR3Vy89W3s48H8Hon6hVNcRCoXYunVrgj0mc6wtVUXF6rDirHLiHfQyfnyckvoSQaaYTROudeTQCKqiUrmgEpvTltGm+pX17HtkH/17+6N9aDJ9z8geECSMgoLFaTG8FmeNE1VR8Q37Jrwef18URUFHxzvgxVZqw2RN7Pep7ksxbEq+xvGIantlpSnq+8l2jpD+pF27VJ59Vo2tq8Lw0Y+qKfbrCooCH/+4iY0bY8Fa0qZlNy5D1/RoNqW0KX4Mqaqacu4oyFwe1/fkfmnOHOP7HiuCrmI2q9hKhU876AlmbKdcbCrmGuONu3qZJBzVDizlFnSPzlD70IRNt67p0Q1gQTMzQEiZ2Aobjphrsd+cYCkVTr7AUFZkhqLA0qXwyiuwZ09mMiMqM5VUADzZpnxkpjQN/u//xN8FkZiKh0Fx4HQoahtFv8QHwWFQLWBN38dk3YzXXsv+4zduhNtvFxkU8Whuho99DH70I9i7Fz77WeP3//M/iwyZ5Oj1jRsFAbJ3rygQfsYZsdekxFRVVRJ3VGAyA4rURpYIaRk0JjO+/nWx4c8ESeKFfCFCvhBme6z/6ZrOyJERtKCWoGWeiz3ZkhnZFKe9914Dp4U/Eo5tq8n6mpJRtDHk64Pnb0/cBHb8KuGUEXc1vpEHAeOMurS1QtJgSuaFUyg4TrZ2i7fHahV799FRITVlRGaUzytn7bvXTjjeu1N4GmpX1KaMSJbOCAOFj4LhZG6fVFi0SJAZ7REONp6ECPlDhINhtJCGexyO1rZAQMP5/9n78vA2ynP7MzPaZVvy7thObCfOvhGSsCSQEAhQlkIT1paWtre9dP21pb30tpRCd2ihLd17u97bsrQFAm1poeyEHRKy73HsJN5XWbZ2zczvj1efNJJmpBkttkN9nkePEnkkzadv5lve877nVNhhqi0Dtm0DnnuOJvOzzwbWrqVSDwDOaic2P7A5qXo2FdYya1LAo1BtSntPSIwHy/JFaDQESYzpCctU/cub+ZwqTYDk9kQCCTJjyxZ175Mf/Qi44lJa/0pRCdFgNG/5LoY3f/omPMc9WPq+pWmVIUZIhlzvo0xtVq7/oqEoeIEHb9IXQB08OAhnjRO2cltOxI+5in7fsc6xnP3F9IAlX5mdZpisxduqv9PGOaN48skn4ff7cZVGllVjYyNOsFT+GAKBAMbHx1EziYZNRem3VA/FtC9V91BMRS4JS6IoQrAIKG0oxVjnGDwdnoKQeKUNpTjr5rPIFDcGFtwr5voByNJHVA5C/169mh6hEJmLHzuWKE0/fBj48Y+p3LW5mR6zZ9MjR9hsifVZfz/SyAwA8Pf7IUWlpHE1tT0hbwj+IT9MVhNM9vQxKhqIwj/kR8gbMjy3q+HAowdgcVrQeHZjkt9RyBtCxB9BSV0JQqMhOKocST5PWueh1T+edg8ctQ5YHBZY3Vb4+n3w9fkyzq1Dh2jvVzlfX2yuelE1wAHj3eMIjARy8nJNhVp7UhVi0s5jcTXW3LIGJTOyS56HPCGMHh8FJ3CoXlwdn/tyXe/ogZ5xLlcD8C1bgK99jf795pvAhg2JNUZFRe7envPfrS1VUuz5NjQWAi/wMNlN8f7JJgGeagDOrhV27WTDVFhDTJMZBsFuWlmWYSm3IBKKIOKLYKRjBI7KBHPONiJAEciMUw0WN+DvAsIe3W9ZuDBBZmRDSW0JqhZWZTXjY5tXI5PGW2/RZF9WppIp/k6EOA74TlAAvQhkBkD+FwDwyU+KqK09hrVrZ+O88wQIAvDhD1MwXKN6G4B69rrLBVxxBfCXv5AZuJLMYJUZaXIkRSAzigJWgaVSmbF2La2Hjx5NBBY0P8ZugmARIIZFBEYCKJ2RmPnH+8YhRSTwZh4ltfp8XFKhl8wA9JvTJiEc68g8yIyiQc8mMDyEMrsXg2OZ5QEL7RUyjWlMBqqraZMxOGhMnrF3Vy8AbYkpYNr8u1hobaXno0fpOZWEkCUZT3z6CYgRERu+uQEldSWwlllhq3YC4rXAvn3Aq68Cf/sbERoA6UPW18NZ7SxIQCNfsGSWbN5RmcBIHgAw2UzxoA9v4uMVJ0YrTVLBKjNe327Gh76uLU340EMCeDMPKSIhPBYuGJkxdGQInmOepCAgoJ9kyAd65Rhf+NoL6N3Ri/O/fT5qlmQfDCRRwrNffhZSVMIVv70Czhrj16O1wgqTzQRnjRPhsXBefZwJuXr8TcMY2tvb0dnZidNOOy3+Wm8vzUFPPvkkvv/97+OWW25Jes/WrVuxevXqnCXlpjyYh6IUBmQpWdZVxUMx7e25JiyBgumMzEj10soFNpcNLee3JL02ZdcPVitlci5YkHht1izgP/6D5tGODmDnTqCpCbjlFsq2fOABOqalhTZfOkvXamsTZIZyfWavtOOyX1yGkhkl4AV917fJboLZaUZkPAKz05xE8EZDhQl0y7KMPffvgRgSUb24OonMYCipL0H57HLV89ZzHtYyK3gTj8BwAEFPEK4mFzieiyco+Pp9qJpfpTrmDx2JkRnz9JEZFqcFriYXRjtGMbBvALPOmaXrfUbBAtJKckcJPesytt4ZaR8h42+zCaHR5HEg3/VOPhiLhUaSlDeyINMa46qr9HOFU22/vv1X23H8heNJfoNGyQyL0wKz05xWfTOVMU1m6ERqhposy4j6o7C5bRjrHkPIQ2wwu5nZAMKbeQhmuiAKIjPV+xww/BZQtRaoOSefJk0cmAlueET3W1g1hh4yo3pRNS783oVZj7vit1cgMBJASZ3+YO3f/07PF1+c3btDN4a3A51/B9xLgFlXF+hD84QY29jLAOQoVQFEfYnXVbBsGT13dtIgqJZ9mwpJolgHANx4owxZHsKqVS3x9deOHZmJjExs+A03EJnx4IPA976XWNOpmn8DgBj7oqlOZjCZKRUPBpeLSKWdO+k3yST3xXEcbOU2+Pp8CHqCSWSG9yR9dmlDaU76zMFg4nfWQ2YACeNZ3TISwZgJ1VQkMxgEO2CyA2KYdm18YnFnNmXfBAL6PUWmMY2pjKoqyvBn/hZa6N/bj8P/OIy5l8xF9aJqDO6nN9Qtr9N+z1QNRpziYNJSjMwA0je7NUtrMHRoCLIko2KOYtIXBFoULFuWMDuLRoEf/ADgOEinnY43t/OwrliEZR9YHl8XTzSyyS3qQbEqTZSI+COQZeDen5kzShPefDOHe8+zIjQSoMzTHAL0avD1UbKHcr1caM8nNYgikSV65Bh5Mw9ZkjHcNqyLzBjvHY97tjiq9fnVpYITOGx+YDPMlsKQRlqIy+IWSWJqGoRPfOIT+MQnPpH02te+9jVEo1F861vfgizLiEQiuP/++3HDDTdgbGwMd9xxB77whS9M0hlPIEZ20nPF6QBnLFyklbBUX0+FBlrjRHlLOU6+fBKeDk9Op6wHE1WZURCUlgJnnkkPgDbRTFdnbIwIjldeodfNZiI6br4ZMJkAn4/kqVSqx2pqgCNHEr8FA8dxKGs0EBWOYbxnHN6TXpQ2lmr6yOYD/4AfYkgEb+I1YzgcuCQiQ4yIhtYZjioH6k6rg6XEgrmXzMWCzUQqDewbwFs/ewstG1tw2odOU53XZ5w+A4JZQNUC/fvTmsU1RGbsLyKZ4ctcmaEHbL3z6j2vovutbsy/cj7mXT4v6Zh81zv5gFVm6CUzsq0xACqO0gOt/bq304uhw0NoOKMBlpJCBQ+zg5FMLBYdDicSevWSGQ1nNODqP02R2KROTJMZOpGWoSbLCAaDsNlsOPrEURx89CCa1jfFb2ZHlQOb7tuUZHJYkMoM7wGg/2XAka5LnCsEQcCyZcs0tc7yRg5kBjMB10NmqEGtTZYSi+5BhZXR//GP9P/LLsvtPFTh7yRCw0AQvWh9ZC4jybLQEGXcyBKRGVKUMuE5gf5uTp8lysooGaS9naozNmzI/nX79hGp53QCp58uIBJJbpNellvtuHe9iwblnh7g+eeBjRvpdTaQp5Etc/6TDKVthVnRFq+PWGXGeGI3r8C6dURmbN2a3btk43c3wuK0JElMAYibWbpmJphWI+1h/WGzGRvfBMGApFIov8qMoo9zDMEBwHccsFYAJa3xl0tKgBl1QPuAQa+QDJiwNk2joHin9Ztae6pit2k2MuP4S8dx8uWT9DmxyjGry4qymdo7k2KTGf8O/aMGVpnBZKbU4G5xY+jQEEaOjaDp3Cb1g1jmh8kE3Hor8PrrCD35Aqr+uRvR59zg33sfYM4vSJtrH7HK3HwqM2RZBsdzyWROnkhtT8QfwdAw0DWgHYRgyR1D4xaUIIDQmD7CPBvC4+F4EITtaYyQDKxLcumjl17SL+9Q1VqBrte7MHx0WNdnM5+L0obSnOShWHtM5uJvnSeCzHinjXOFgtmcyC7nOA6PPfYYbrrpJtx1110QRREf/ehHcc0110za+U1Iv8mKTPaoX3UPmA3KhKX3v59Iz+9+N53IULaHyRyNtOuPF2hBlmUc+ecRuJvcqFpYFQ90T0QyRNH6iOcTUVuXizSZw2EaFNvbSdeTadN/5zskXdXSQiRHczMwdy5gtcbbzn6LfNojQ4avn8jv4EiwKGTG6Anao5Y2luqqGPEN+jDaMYry2eUQrOnnrNae7m3dGO8Zh6PSgZUfWxkPCJfPLkfzhmbVahCGxdcsxuJrFhtqU82SGhz5xxH079PZCRmg1T/xxGqrGS+8oJ442PN2D8Z7x9F4dqOmcomjygFfrw8WpwUt57cUdO2jBT33UChElz+gX2Yq2xqDobyc4ra57Ne3fmsrxrrGsP5r61G/kjI8J2LcZnL7LGknVmQIs1kloTcGJjPn9VL+kRFri6myhpgmMwxAmaHGnOIFQcAZnzoD9Svr0Xh2wkyQ4zgyIFJUYRSkMiOenZxZqsQoLAUrO1CB2U3POZAZBw9SwoGeal45NuKwRWiubVIro7/1VhooC1JGH4llVZiNyfkUpY9s1WTmpsz633UrXWcLbwHK5tEiVkMfdflyY2TGyy/T89ln04DJcclt0puVrnacxQJcdx3wi18A992XIDM0KzOq1+j7MgMoSh8xmSlJpEoZU3JG4bnnUqaTHt8MpRSeEqwyIzWAqLc9SompovmPFsAzo6jjHIMQW/CKyZm/HAd89avAJdcaL73PhAlp0zQKjndav6W2pzo2ZQwMZH5f/ep67H9oP9qfbceci+fg3NvOhX/Qj5FjtF5Qy/iaiGCEav8EB1Qr5OKQwgCfoV8zzKXFhp7rTUlmaK27ymfTzsfT7tH3xbW1wJVXotu+GAcP/g2NNUFwdjsNgL//PZXhrlhBEhsGkcs9xGR7Ug3IjWD46DCe+sJTaDijAed+5VxwHIf+vf3Y//B+lMwowaqPrcrpc5XtifgiCAWBCLJnVAZFK0oAhMfCOX0vQGauLFlr9Pgowr4wLKUWeLvoet+2x4rOTu0EHK2qWaN9pDeh5eQhH+pqTQj7wujZ3oPhtmRCQ23cYOscZdKGUUzYuM3Rteqoyq2CRC/eafNQIfCVr3wl6f9NTU3417/+NUlno46i95uk0EuXch9XWMLSBz5A/n6PP05V9Klg7WFkxnj3OCRR0i11pAZfnw/bf7kdvInHNQ8nyKeJqsyYsHvLYqGySlZaCdCAfN11tEHv6CA/K58PuP12oKEBZ8qvw4dRiPubgWBT3N8KADwdHhzYcgAmuwmrP7E6a3si4xGIIdLNj/gjefebGhiZoXfsjgajkCUZI+3pnrYMyvbIsoy9D+4FAMy9bG6SZBLHcRmJjFzgG/DB7DRj9oWzUTmvUtf8lQ1q/bPuq+vw+CNhrLm8DMe7E68rpSF3/G4HRo+PorS+VJPM8PX5EBgOgDfxqJo/ceoI2e4hJjEF6Ccz9K4xPvhB+o1y2a9XzK3AWNcYho8Mx8kMoPhjQmplBmtrXZ12bEbpmePxJBLR9GIqrCGmyYwcIYoitm3bhlWrVsFkMmHmGqqUUG4IUuHvBBywwu3OoxQrFNvFF3BDnNqWgiOHyoyWFpqfAwGSimxpyXz8Pz/9T4x1j+GSH1+CssaytDYNHBhAx/MdqJxfidkXaIvhaZXR9/QUrowe0RiZYdI58qLIfWSrTr6eXIuB6FsAZKB0jubbACIzHntMv2/GK6/Q89q16m3KxThOiRtuIDLjkUeAn/+cKmw1KzMKjKL1kWABmt9HJAaXvkBkv8WePeRHUq6+bssIlrGoXCgaaY8Rv4ycIIYTQUSrPk3StI8o9jjHIMSCD2IAgAQg0WcXX5yDV0gGTFibpjj++c9/4vvf/z4GBgYgSRLOOecc/OAHP4DDQX1x4MABfPzjH8fo6Cg4jsNXv/pVbC6UwHsOeKf1m1p79FRm+AZ8eO7Lz6F/bz/EkIi/bP5LPKOIwVHpwOYHNidt7IpNZqj2T3AAePV9iQqxVEgRINAN2BsAXqNPrZWUPDDBhIbe623WLEoyCIVoDp6pUgBcPrscpY2lugwjlfB0jGKsZAaki2IC3X4/7Zx+/3vShly5khYGs2frYsRzvYfYZj0fmaljTx8DZPLLYMkzYlhEz/YeOKocWHnTSsOZ/6ntueCuC2B5NgzvB7JnubpqLEAvMspeZYJvwIct79sC/xBJb4Z9Yfh6fRBsAh665iEAgDfigAOb4Ufm/YsyUJBLH+lJaHHAh+Ffb8ELEV9cjmZg/0CSRKbauMHWOZmqvjKBtae5pBm7frcLVpcV591xXk6flQ0LNy3Ewk0L40laxcA7bR76d8GE9JuSwMhE0OvEFVcQmfHEEwkVQgZlexzVDlz8w4vhmuXKOyDOxoayWWVJnzURyRCTfm9xXEL2EaAN9dBQfCPcwnXgMryK054PAZ8FRTs3bwaWL0d0zIfjz7bBWumMkxmZ2pM0l8pU2WdzFdbrJ05mzNJHZpQ1lCE8FkZ4LAzPCU8aKZzanp63ezB8ZBiCVcCCTQs0PhUYPDSIiC+S5Ofi6fDAVm7T3ebU+VYNavNXJmj1z9OvleKGz2SWhiyvtGP0+Cj8g9rnw6pHKuZWTJiXgp57iElMOZ36EwH1Js1eeSXFV3LZr1fOrcTxF47HvVSA4o8JsizHyQy2j1KSGVowmajYy+ul5N8yexgv3/UyooEoLrz7woxr2Ukf52KYXr0UEL4BHx6+7mEMHRoCb+EhizIEixBnyBqOAFfBgRJ+M5BlQ6AKWS5aZUZR4ZhJWfCl87IfG4PJRKZUe/aQ1FQ2MkOMiJAiEoKeoKre40jbCI4+cRTB0aAmmZFLGX1OYEbOptyMlosO5yxg6C3AfyLroUZNwFllxjkadi/5GMcBwJo1VEnb0UF+J9ddp2EAHvUDI7tIwsm9RN/JTyaa36v5p9paYN484PBhIosuv1z7Y/p296HjhQ6Uzy5P0rx0N7sRDUV1LxRTUXQyAxLQfAMQHp669w0DbyF9YTlK1RlC8iKald4/8QRt8GSZ7otZxZFM/bdASUkJ/vCHP6ChoQHRaBQf/OAHcfvtt+Oee+5BMBjElVdeiV//+tdYv349ent7sX79erS2tmIZ2+hNo+DQQ2aEvCH4h/xUwt7nQzQYhdVtBQca7KOBKPxDfvICUGzqWGblhHpmRLxEZPBW8sZJRXiECExeSFSjKiEG6P0R76RVZ2SDyUTz59GjVJ2hRmZUza/C5b/IMMlogFXasMoOOJ3A5z9PF8jrr5OZ1sGDwLe/TX8fHydtvgLDXmGHq8kFZ21uSUXRUBTHXzwOAJhzUSLZo3pRNXgTD/+gH+M94yit15+soobSGaW4+L3AjC9nT+7YfOcqmMyrc9ZmZvehyWqCyW6CGCFtcmupFTa3DdFAFNyYH1aEspIZ+Xo+nXsuJWSMaOQ+cRzQXBeCVfTD7DDD7DBDDIsQrELc5FRr3FCT08wFgkXA8JHhCTE8zUUOaxrTyAtigB5ylKoJOSGrh2I2nHEGzdf9/VS9dcEF6sdxHIeK1sJknjEyIzUz/5TyzCgUOC4p5XrgguvxuUevxX809eLcD3ZQ1mgstb38xB6cvv8P8NsqEf5fHyyL55NMlQbMTjPAkZ+QxWkBx3EI+8KIBgpj/g0kxu5MRHTq95XOKMXQ2BAi4xEEOO1rN6kq49K5mqTEiZdP4JXvvgJnnROX//LyOEH22g9eg6fdg/V3rEf9quyb4NT5Vq0davOXUeiNaf3587RHzUSuDOyj2GP14qm1dmVkht6qDMBY0qwg0H79nHNomfq5zwH33JM9Blgxl8aw4SPDJEs6AfN4xB+BFJUAIH4NM5mpbOuyiooEmTGnWUDfThoko8EozPbi+oMVAoWtA/s3R8gbwsixEUT8EYS9pDkrSzJsbhtsbhsisgkO+OHUaQSbBtGfkC6Zyia4qXAtABZ/GZh1laG3GfHNYDeuVmYa00fOpD9rRKs3L8RlpvLb7BYNzI/Fp5/M2LcPiEQyH9vZSeslQUh4mamBGcc1NCS/3tiYvTKG4xIlzPfdR89MZiqpMiPYC+z7DnDg7swnfYpg3Tp63ro183Fj3WM49vQx9O7sTXr9jE+fgUt/cunUJTMEG9B8PTDvk0XUsSoAxABt+vgYmREaVt0ECgKRTitX0v+z9ds0MmPdunVoiA0YJpMJt9xyC5566ikAwFNPPYUVK1Zg/fr1AIC6ujp84QtfwO9+97tJO99/B7B9czaZKQBw1jjBmygBxN/nh8VpIW8flY0ekMisnJRghGAnvyveTJ5LEQ89ouN0z0fGAVPsGOVDjQCZgmBSU0oT8Hwhy3KCzEiVfKiqosHw298G/uu/aHwfHARuuYUyF15/nUpFCgRnjROX/vRSrP/q+pzef/LVk4j4I3DWOlGzNMGmmWwmVC2kiz51fs0VLLlDDcrkjpIqO2wuW96ZzCa7CRanBWa7GWanGdZSa/w+LCnJLFXAcUR+GfF8UsPAgHZ3s+++7Tb6t8lugtVlBW/iARkZxw1ZljHWSYlEuRjcKsGIqpA3lHM1zDSmMeXAPBSlUIzIcFNyDpvjpJCmh2I2sDUvAPztb4U8aW0w3w0mXcUwEZUZUx01NYAMHofG6ikL8L3vpapIAMLK0zC0aB1CllKEX34L+M1vwD35JL1xeBj461+BXbtglQNwVDrA8RwsJTRvyJKM0FgIQU8Q0VAUjkpH3qSvLMvwnoipB6jsUa1lVjgqHYiGogh6gvFH2BeGzW2DLMmIBqJJWfKpmH3hbJTNLMPCzQs1j6lfVQ9LqQW+Xh86X6NgUTQYTZBms8s136sGk90Ek80EMSQiPBbOuu41ihefjaKiczcW4ACA9Kg9i2mdGIj5iA1pEz7L3r8Ma764Bs3rmwtyboUCk5nSa/4NJK+rUtczakmzgpBIwBVFfcnM5bPLwfEcgiPBuP9VscGqMkw2U7x6hlVm6CEzALq9eTMPTqAfgnmuTHVMV2YUGDa3DWJEhBylgYNtDgCAJAWjKMs1hs2qMsxlCV32dzAYmbF/f/ZjrS76PbR0kONmehp6gEB+5tOGMNUrMypWAad/X5fJfHMzMeJjY8ChQ8CSDEUOTGJq+XJ6TzRD0obSOE7NsCoTbriB4iJPPkkxEdXKDEYoGTBhn1QEB8gzwlajKrN07rnAb36TnWhjeuH5mJ+qofiVGVMcbBMYGqINH2RAilIlCVsdqWwCN2wAtm0jw/r3v3/iT/udiuHhYdhiGsDPPPNMnMhgWL9+PX6kESUMhUIIKaJp3ljqTzQaRTQ2aPE8D57nIUkSJEmKH8teF0UxSR4k9XX2zI6JpgyGzExNFEVdr5tMpvjnMnAcB0EQ0s5R6/V82qRslyAIlF1ZIQIQMDAgIxoVVc9djMb+zdMcHhwNUpWlLIHneMig30iMiohGo+A4DjwvoL9fBsChoiKKaLQ4bVL+noIggAMSfSbL4HwdQNiT6AQpRL5GoUFAngtwSPpsyDL42GeIiv6eiH4C+17Fb8/6KfXamz1bAMDhyBEJ0aiUdLyy/2SZAgT2MnvWa2+8dxxhfxiCSUBZY5l2m9xuSNEoYLcD118P/q23wP/+95Duvx/ymWdCvv76nNpUyPup7ak2SLKEpg1NEEUxqa3VS6rRu7sX3Tu6Meddcwz1EztGFEWExkI4/NfDsJRZsGnTIjz8MIcPf1iG15vYeTc2yvjhD4ErrhCT1lK5jhHs2pZkCc4aJ0pqS+L/p/OUcdtXJLz/MwAFRpRRAPr/D35AbWLno2xT6u+u1U833wz4/RxaWmSEw0BXV+J7Ghpk/OAHEs5bJuKRX9NvZ3aaIcsybOU2SLIUr+pSjhsAIEsyVnx0BUaPj8JWbctpLGf/NtlMsFfb4ev3YbhjGNWLqgt+7T3xmSdgdphx9i1nw15uT7qfRBF4+WUOvb0cGhp4rF0rgeOMjxHsWdlPymsytU2pbZvGOwxqHoqyTKS9xU3/z8P36YorgN/9jsiMe+/VJkZHT4zi0N8PwWQz4fSPnJ7TdwGJygwlmRGNJvaE/1aVGSnIaABeV4foOevR+WonXO9egQUXNEIOBinDYXCQNpr//CecAK5b4ES4aS7Cm66j94aCgDVR2ZCL94MaLv/V5Rg9MYrSGekBNGe1E5sf2KxJLO/78z50vNCB3f+3Gw2rG9K8IzmOQ+vFrZhz0ZyMGfQmmwnz3j0Pex/Yi/0P78fMtTPJ60IG7JX2jMmyWpAiEoaPDgMcJVoopRLzRdexEBZjHyTwOAhtkmZMdMCJzJUZ9go7ms7Vrs6ZLORSmQEkkmb1SkgZSa4GAJPVhLJZZRjtGMXwkWFNv9JCgjfzmPOuZGn4XMgMjuNgdpgRHgsTmZGbsveEYprMyBGCIGDVqlVpDu4cz8E9y42RjhFAQlKmVDRKP7jRmy6OiIckDAosUaDVloJClilzUXBQG3TAUGVGTB8uOEpkRmqbmKYjC+aqIR/zaUOQYhsCA2TGhPQRg8VFDx3geZLkfOUVkprKRGakSkxlaxMzjjOKhQsp4337duAvf9GozBBjk7ZQuAmmqH3U9ltg4BWg9Sag8d1pf2aVGdu2kRS5Q6NZ8ftEoXEaDUYhWIW0RZyR9hSdzAj2k4avtYqqNHJAUfsndRM48BJw7A+AayGw4PP0msomcMMG4O67yZsvF0zouHAK4Ze//CVuvPFGAEB3dzcuvPDCpL/PnDkTx44dU33vnXfeia9//etpr+/YsQNOJ23KqqurMWfOHLS3t2NAUXrQ2NiIxsZGHD58GKOjo/HXZ8+ejZqaGuzduxeBQIJIHB8fR3l5OXbs2JEU1Fq2bBksFgu2bduWdA6rVq1COBzG7t27468JgoDVq1djdHQUBw8ejL9ut9uxfPlyDA4OJrXV5XJh4cKF6O7uRqdiFV+INu3YsQMLFiyA2+3G8PBhAAvR3R3Ctm07Vdvk7/RTEFWUwVfyEDgBXDkH76gXbrcbYlSE3+/Hnj174BhxwG63Y86c5QgEaKzq7Hwbw8NSwdvEfscdO3YAALVJAPx+P6I8D4kLwxkeglngAVsVAoEQeEmABT6EJTssMiBHxhDw9kHiLBCFUghyEGU2YGxsDPv3J36Diein6upq2Gy2eHvibXK70669pqbTAVjw1lsj2LbtSPx15bU39NYQTj56EuVLy3Hdj67Leu2NtY1hPDyO0vJS8CYenZ2d2dtktaLx+uvRaLej86GH4O/pgWfbNnDBIOZ3dMB10UW621So+2l2zWx0bu+EP+DHaMUotm3bltRPA8IARj2jGH9hHA3XNqB1bqvufmppacGqVauwb98+DB8bxoHfHICpxISG8xuwebMbf/lLH/78ZxI+Xr58FC+9ZIbdTm3yd/kx9OYQzC4zLv/i5YbHiJGREfj9foT5MEwRE0xmE0pLShEMBREMBCEGRET8EZy1qBMPP1yF665LTUbhsHgxsGjRYWzbpt4m5bin1U89PafhT3+ygedl3HHHHrS2+vHjHzfhL3+ZgbPOEnHPPW9BEIA9e/wI+AOwl9thq7Ahao9izEdJQrzAwy7YIUbF+LgBxO6nCxeis7MTO3Ylrhmj91NzczMEQUDQEsSoZxTbn9uOKn9VQa+9od4hdOzoAAA4LnWgxF0Sv59+97sR/PCHzejvTyS0zZgh4TOfacN5543k1KaRkRHU1tamzU+pbfL5fJjG5KPoa1m2ThXD5BMlhoC1DwLm/BLwNm4kn+mODmDvXmDpUno9tT2RQARtT7bBXmHPmcyIBqMY76GkNXeLO/764CCFI3g+JcGtwJjqa/OMZAaIAOp8tROedg9QsgCC04lVbje15+67gZERBHYcQNdDr6C2tAwVcyqAYBCBG2+GN2iB88zFKDlzCclTVc6hHzxHcBwHe7k9YzKqs9qpSZqs/eJaBIYDqDutLr4HVusfPVJA8y6bhwMPH8BI2wj6dvfFK04r5+V2MQk2AZzAQRZlREO5S/qotafaTVn1EWT+zJpmO3z7MldmTAb03EO5VGYwGEmaNZJczVA5txKjHaMYOjKExrMaiz4mOKudOONTZyS9ppfMYF6rTN7TZDclyIwMmCrj3DSZkQfC4TDs9vTB1WQ3wd3sxuiJ0Xh5nYwEmZHLTQcAKD8NOHcL6f0XGFptKRhe/xBJrqz6CVDSrOstixbR84EDCX0/LTCZKWVlhrJNcZmpDJNhvubTurHqR0RoqJg5Z0LR+yhHLF+eIDOYxJMaWGWG0i+jWG264QYiM+67T8szI7YpK3B1TNH6iEmSsaqeFDQ10bXZ2UmqHOefr/4xcfNTTzCu47jj9zvQ/kw7lt24DAuuTDY+09ueopMZJx8Buv4JNF0HtORewlDUe0i5CRRsAGcGyuYBpXM033LOOaRT39FBj+Zm4187VceFycK//vUv7Ny5E3/84x8BAB6PJ16lwWCz2RAMJu4BJb785S/j85//fPz/Xq8XM2fOxIoVK1AWm7xZZnhLSwuaFFrC7PV58+apZscvWbIkngEdDAbj5MiKFSuSzoEtDFetWpX2ut1uT3sdoGCd8nXWrqqqKlQomFz2en19PeoUrnD5tEmSJASDQdhstrgJ3Nlnz439ftakxa7yHEfKR3CEOwJO4OCudKdlAAkmAQ6HA0uXLkX5nHJwHIfjZFcAu13GueeeDo4rfJsWL16MQCAAm80Wz17H+BAcDgdkcxkg2MGFZ9M8UtICmxNAaBCcxwOLaxY4XgAnjsPJDQOWCsglbjo2OorS0lKsWrV0wvuptbU13h4gcY2lXnsDA/T68HBFUl8pr71udMPzLw8coUSgOOO1two479rz4prWhtrE82j4z/9MtKmtDcJjjwGvvopFdXUQ1q4lQfbSUs02pV57b/zwDfS83YMZn5qBWWtn6b6f9v1pHywWC2aeORNrLlyT1Nb6+nrUvKcGQ48MIeKPwCW5tNuE9H7iOA6hUAiLFy/GADeAbnc3SupK4HK5Yu9LaKJYrWVxO5FVq1ahS+pCz74elLSW5DRGlJeXw+FwIOKLgItycDbSuGSz2mC1WhExRxCUgmic2Yhl59GcFY0Cd98toqYG+NjHeOzdy+HVV+fjQx9Kri5ibVLrD2U/+XzA9dfT65/5jIwbbqANwEc+wsWSUniceSad+0j5CA47DgMAzDYz3FY3fYhMniaQaOxg40ZqP+V6P8myHK/aaz29FYc6D6HWXosVq1bovvaUr2v1k1k0w+V2wWw348y1Z8bPfevWKtx6a3Xa/qS3V8Ctt87Dn/8sYdMmWXeb2DzkiGW/sPkp9dxZm1iV4jQmH0Vf9w2/TdKIXCxIFegBzHPz+kinkwiNxx+n6oyliWkwqT3uJjd95XAAwdFgTmbSoydGAZkSuJTvZ34ZVVV5el/qQE59FBxIroxJRR6VMUqwqhSPh2T9rCliH6yahVW3AIr2cBxQUYFjQ27s7pqJmooaXAAAPI+jM87F6Mt7MHtPB0p6jgKSBPz4x0RmPPUUOb+3tJCO9AQZBvMmHhvv2ojASCDeHlmWEQwEceCBA6haUIXGsxphr7BnrSKxllnRuKYRR/5xBNt/tR1mpxlhXxjWMitVacBYNQoHDma7GeFx8hjJx58g9Xo7bXEED9mA8aD6Z7KY1poLHHj6H9pkxtEnjyLkDWHWObPy9gIzimz3EJuSco2r6k2aZWRGdzcwOgq4dOT7tl7SisazGlE5P7Gxmej9ei6VGQBgdtA1o8f3ZirEIKbJjBwhiiJ2796t6eDurHLCUeWIlzyLYkKxLmcyA6AAeJ7ZEanI1paCwFRKZEbEo/st8+bR/Ofx0AJEsf9IA5OZYmWGqW0KDhPJkakMUGk+nQo95tOGwBv7nSekj5QYegsY3g5UrAQqV2c8VI8JuNeb+PvatfRczDZdfz3Jb7/2WuK1w4dp8SwIUJAZhavMKGofMXmi6LjqnzmOSLYHH6QsAy0yg2WlSBEJEX8EFqcF3k4vxLCYZh5qpD2MzJgxA4VbjCs/Z/Qw9ZkUBsbajH1ODBN6DzkagJYMzF4MpaXA6tV0nT7/PPDhDxv7mgkfF6Y4Tp48iZtuugmPPPIIrLHdmdVqRTCYLD8YCARgtVpVM7GsVmv8vUqYTKa035jJdqRCu9qMXo9Go9i3bx9WrVoFnuc1+87I6xzHqb6udY5GX8/UJlmW4+1hv+mMGXQuY2McRNEU37cqz1Ew0Wdy4MCrkPscOCITTEL8fQm9aw5ms77+MNomAPH2KM+X4+h8qEK2VvE6nS0UxEpcClSO0Gux1yejn5TXW+p3p/6feWYcO8ZBEExpSSQmkwlVc6vAczzGu8fJfNki6GqT2WzOv03z5wP33IPojh3ofughND36KPhDh4DPfIYCJ9EoTGb1zTs7R1mUERmPIDgS1OwPtdcXXbUIJbUlcNY4VccCi9WChlUNCI2FwMmc/jaB+oiN5XJYBs+RATe7ngYHE5/R3c3F+8VkMsFR7gDP8YiMR+LHGx0jIr4IAkMB8CYedrcd1jL6bnYPksQbj/Z2IBjkYLMBN98sQBDonrzlFuCWW3hcfjkfX6sr26R+PqZ4VuRjj5Gn2qxZwDe/ycNkYsQiHdvWxkGSTLBYaNxg7WTnJ0syRjpGEBgKwN3iThs3Bg4MQJZkuJvdcenf1N8gWz9Fo1Hs3bsXq1atgnuWGzzHw9fjS2pbIcby4EgQPMfDUemI/10UgZtv5jXMXOl6+MIXBGzenEiCztYm5biQ2la1c5xeY0wNTMi678D3af3N9kjBHqAsPzIDIKkpRmZ85Sv0Wmp7TDYTSmaUYLxnnGThlhknM9zNbmz83kaEx8JJr0+UX0ZOfRQcoEqYkLa3A6yVVAWeJ6HhdidI6YEBCmon/b3ZHZfKZJJ0yvbIsoxjz1IlacsFLfQmiwWOyzdg7zEnArOqUH/XBfThrP379tFGXJLotVmzSF+3oYEYFYtFNWv18OOH4R/yo2ldU7rnlk4ERgLY8r4t8A9RNbAYFhHwBRAZoTnTNcuFktoSbH5gc0Yiwjfgw+G/HUbvzl70bE9ojo/3jGPH76jiz1HpyPo5SpjsJoTHw4gEIrAjt6Cw2vUmBsNYvBh4ersZHJecpKuMablnlmLNLWvgqFKPhxx54gg8xzworS+dUDJDzz2Uq8yUUbjdFN/o6QEOHszs+8pQOTc5O6vY43bYR2Od2WGOr4/yJTOyVWZMlRjE9MqkiOAUmrJRxfVg+3dMorWUA77jQHhE91usVvKjOnqUSrsykRkldSWoWlilOtDKkhyXn8okMwVQ2dkttwDf+17y61o6eu9YeHYDXf8g8qwAZMbrr9P6paVlYnwVZswg4kJ5TtddR/34ox8Bm09nZMYp4plhil3XGUiCdeuIzMhkJi1YBJidZkR8FMixOC3wnowZq83Mzfzb50ssKBoqC7QYT13U+zup1D3QBRz+qf7POQWwYUPuZMY0EvD5fHjPe96Db33rW0nZro2NjThx4kTSsSdPnkRj6u5tGgWFy0XEsShSdVymcV8r+0ft9Uk37xQ1SvGlYOI56iP/DDlKlbRRn/b7phhaWmijOzZGcQi139leQcHukDcEz3FP2qat6DCZgBUr0CeKmLlwIXhGVh44APz618CqVcBZZwFz5qgGR9g6UMtjTfNrbSbM3jg74zFrblmjS64iE9gGkm0ogWQ5kJ6eZCNKVgGeGrjTC2+nF75+HziOg63CBk7g4htjIPk+3LePnhcsSHz/5z4H/OlPVA37mc+QvGc2bNmSrlcNUGyrRJGvVV9P/x8fB9raEhmSqeclQ0bUH4UUkTDSNpK2F9h932707+7HmZ87E7MvyNyHeuCa5ULJjBLNAFA+YDKgyuSrl15K/62UYGauL72UmzzrNKYRhxRJ7DVcS4ChN6kyowBgJuBvvpmQdlGDu9mN8Z5xjLSPoHaZcXMLwSKgemH63oBVZkxJv4yIl/Y8vJWqYlIhBujvEW/e+x6eB6qrqQ/6+9PJjJK6Elz78LVxI+FUDB0awnj3OASrgJlrEv6aNUtowTB8ZBjRiAST8oe++WYgEqGBqr2dHmywf/BBYM8eKk9nj9mzAacTHS92YOjgECrmVORMZoS8IfiH/OBNPMZ6xiCFJUQRBW/i4ahywOK0wD/kR8gbykhChLwhhMZCsLqtiPqjkCU54XchcIgGoro+B1DMXxwgRSWERkMIu8O6suH1IBqIYsYM4N2bzdjzBqmOMNTXU8EMxbRMaFqn7ocR9oVJagxA9aKJ22uLIvDiixxeeaUSPh+H885TTyTOR2bKKBYupPvlwAF9ZMZEY8/9e3D474ex6NpFWP6B5RDFxHiXKX4KpJMZFqcFZqcZkihpv2kKIXcRu2loIhqIIuwLJz0C3jBMiEIQMsslZcThnwP7vwuMtxf0fCcEzDxMaZipA3p9MxpWN+DC712I5R9Ynv5HDrjqwatw2S8ui2emZwKThX3Pe4AHHqAgY3t7gYiM0DCw+2vAQXXz2SkDxyx69p3MeuiSJXRN9/UlBs5UqElMFRNbtqiTK11dVHnz9punGJnBZKYi6jJTQEL+7LXXgHCGmEbcN8MTRNgXjm+cyxpzWw0w5t/pBEqsisW42Z3+4K2JxXgmRFI+BxxVM1kqjH3OZCI8StVN3sMZD9uwgZ6fe05d3m4a2SGKIq6//npccskl+MAHPpD0tzVr1uDFF19Meu3FF1/EmjVrJvIU/+2g1KQeHFQ/xlpmhaPSgWgoiqAnmPaIhqJwVDriwVogMcdMOJlhLiMCVQxQYkZogCpN2UOOUgBCEun/op/kJEU/EBkhg3BrZaLKborCZgNmxmITR4+qH8NxHNyz3QAQ3+hqITwext8++je8dOdLxdkYOZ2JyFRdHQ2o+/aRrvdtt1F0NwVxucURY2SGHuRLZADqZIbC+iBpkwoA1lJr/H1S1NhvHBoLYfuvt0MwCxCsAsw2c8b7kJEZSuUokwn4zW8o2PDQQ8Bf/5r5O7dsoXWYWnD+zjvp7wwcR8QJQBmRgPq4EfKEqEKbA8SQiPBYGJbSRAXGWCetnXJd56SielE13v2rd+OMT5+R/WCDYEasyuSrHp2xZL3HTWMamgjFIlq8GSibT/8uEJkxYwapAgJUoaEFNZmjQmDSkyH0QLDT3lQWqcLT5KSHGsGRBzL5ZnAcp0lkAIhXZcxcMzNJGqmkrgT2SjukqITBgyoLP7OZSIoLLgA++tGEXs/atcTCyjIFXX76U+CttyDLMqL7j6CufxfcoV5A4emTC8wOM3gTDxky5IgMwSygvLkcJrux3G53sxszVs1AxbwKuGa5YC2zwuK06Pqc1PlLikiQohKCXu11by5gCQlLTjfj9deT//bgg/piWoMHBgEZKJlRkpPBeS7YsoW4rI0bBdxxx1xs3CiguTl5XcCQr8yUERg1AQeA/n392H3/bvTv1TCnKSBY0ja7boaGaK3IcdnJ21QyY91X1+HqP109JU3f1TBdmZEHUkty2QDlH/KTbqsCQR9gBhC15DFADW+jjOXG9+T2/gwounmLJcamG6jMAGjw+PvfjQ0eDKxNHMfB4rSolpar4Zln6PnGG4FNm4x/b0aERyjAaa3IfmwKJtRgxxkjM/wnMh8HiiXMnUvVo7t2ARddlH4MM/9mElMMxWiTKFLGnxqY98rn7zoXz/6tAUJpS0G/u2h9ZMpOZixcSMHDoSHg7bcpMVUNF3znApidZpispvhi015pTwqcMBg1/47HcdhiXA1SKOtnJk7ADphiCyjORKQoZzL+OezjJvIe6n4C6LgfqDufvDM0sGYNVVd3dVHwcK7BSv7JNt6aCvjsZz8Lu92Ob37zm2l/u/rqq3H77bfjxRdfxPr169Hb24t77rkH99133yScaQLvtH5Ta091NW2UlYFYJZzVTmx+YHNcHlINqfrDbONd7MzKtPbYqqkSbOBl4NBPAFsNsPzbycdIYYCPrTMkEdj2CZp0VtxNY1eB9K5zgZHrbc4c4MQJyoTX4vzKW8rRtzNhgKmFkfYR+PooeYAXCps/ldamykpydLziCogHj+Dw/72G7n9FIYjAuQsHIfR0AkuXxgl95qWWDSFvCC987QU0n9eMee+ep4uwCHqCkCXZUACAtUcpFwDQJcSueyYN0tWVqHYyOxNzd9gX1q0xL0syXvv+awiPhjH7wtlY+99r0+QmGdh9qEZmAMBpp1FV8113AZ/8JCVX7NjB4bXXqpMyK9n6LBNx/7nPUTey7l2wANi2LUFmZBo3Rk+M4uU7acF5/MXjWHzNYkooG6a+zpfMmIhxW60yI5tMhNHjGN5p89C/C4rab+FYRbS1ErDHBplAd8E+/oorqDLjb38D/vM/6bXU9uRDZsiyjJ3/uxNljWVoWtcEkzUR6prIyoyc+0iOAt4DtN+zlgMl+ct7qSGbCXgqWHvEsIgTL1FsIC4xFQPHcahZWoPjLxxH/95+1C3PkhLOMHduYvMjy7SRtdsRGA7AMtKPhoGdKPnTMPCXWGR27Vrg4otJ8oHJVukAx3OoaK2IB5cdVQ4IFgFiRNR3njHwAg/BJMBZZTwhMnX+CgwF8MyXngEncLj0p5eCN/GGfDcYUq83lhRhcVowOpp87NGjyf6vAwcG4Gn3oHpRdfzeAygYDwDViydm3coSHVLXBywR9eGHk0kYVplRbJkpIDcy4/jW4zj6z6MQQyIqFlQUddxmlcZs/ccSG6qqiEPMBEZmMANwI0k5U2ENMU1m5AiTyYTVq5PldzItsF98EfjWR4D5rcYHKAC0MWaSK9bCDipqbSk4ciQzlCbgesDM63JtU1cXcOgQZZUWpVSbeR4YNJ6ekD5SwhGrOQ0Nk0RGlgqG5cu1yYxIBHjjDfq3sjKjWG3SU47/4o75eOno/IL2cVH7KG4Arl2JwPP0+/71r/QbaJEZyg3y6Ela4ZTNTN/g621P0c2/pViZCccniIwcMOH3UElskT/ekfEwh4P6autWSkgyQmZMeJumIEZGRvCzn/0M8+fPTzJf5TgOTz75JGpra/G3v/0Nn/zkJzE+Pg5JkvD1r38dZ05inbBmv02Q+WOhodWeqip61qrMAGjdZGRNNBGZlZr9Y6sGQoM0H9acC5TOyfxBzllAcJB8zrIdW0QYHSdaW2ks0qrMAIDy2bSmG2nPQmbEyA52fDaIIuI+CjNm0IZbba+UqU1bHuXw2c/OQ2dnjET+NnB91U58Z8VDaFleBnf1fFhDUQRH1M/JN+BLWscfe+YYenf2IjASQPXi6qyBhl1/2IX9D+3HwqsX4rQPnqar3cr2KIMQAPnGRWP5UYsX0zqrq4v8lgAKrDD5yJA3lEZmpLaH4eCjB3F863GYHWac/63zdfXR3r2J80jF7bcDjzwCHDlCcug+nwCArnsm8VlRYVwuKbUyA9AeNyrmVECKSHj1nlex7ZfbYHaYIVgEInncNoz30ho8l2CR2jUnyzJkUQZvyo+oU/ZRYDgA3sxDikhxU9nTF1jR2OhEV5c6EcTMXJUBqmyYXj+cmih6v4WUZEYsGF2gygyAyIzbbqPEQb8fcDjS2+NucQMgpQtZlg0F1wLDARzcchAcz6F5fXPS3yaqMiPnPhIDgL8HQKzCTiisR6oSjNDRUlTo29OHXf+3C84aJ9Z+cW28PSdePoGILwJHlUNVAqxmSYLMyAkcF188eo/0YqByAYJLV2Le11aTsVJHB22cAMq6uPtuKidtbiadzJaWjB1stplRObcS/iF/wSr1jEI5f8mzZWz87ka4ZrrgrHXmVN2pdr3N3jgbNUtqYHFasLsj+fhDh1L+/7dDOPnySaz46IokMmNgH2Ui1SwufilTpkQHloiamugwGZUZ+/frf09FK7EEQ0eGij5us/UD8xDW65cBAOWxpR+rzNCLqbKGmCYzcoQsyxgdHYXL5UoaeLQW2ME3gREAjlzjEOERQJbIeJJJNhUIWm0pKOIyU8YrM4Dsg4csy/jbR/6G4EgQV/7+Slhd1nib+nb34cTLJ1CzpCZtYZOKZ5+l55UrEzd3QcEy683GaOQJ6SMlTE5ayIaGAN8JwLUw4+HLl5O8gJq0065dJN1VXp6sd1ysNk1WOX5R+8heBzRdT32SAevWEZmxdStlSWaDt1PbL0Nve4pOZogxMoPPr+R2wu8hZzM9+0+Q3AyvPd1u2JAgM266Sf9XTHibpiDKy8vjJLYWli9fjleY1t0UgGq/TaD5Y6GhdR3qITOMYiKCERnvq5Gd9Fx+WvYPslYRmREaBDC/wGepH0bHCWYCnonMqJhbgfrV9ahaWJXxs5gMlZ5AuZqPQtznKkUSQatNWpl9fx7aiJefXoBHlryMpQdewLJDR9CHdQDelXScb8AXNwpl8J70QgyLGD0xioeueSirwSdLDujbpRElUoGyPfPfPR+NZzbGZZLYNV9WRlUzjMxQwlpmRcQXSfPNUGsPQ3g8DF+/DxWtFUmSTFoQxQShoEZm2O3ABz5ApAaTa2VgmZXnn5/1awAkr8/UyIxMqD2tFp7jHvh6fXj0A4/CXmGHf8APk92Eh655CIBxk1Yg/Zrb//B+HHjkAOZePhfLblim+3NSodVH23+1Hdt/tT1+vt//xmZc/5H081WauRpJkpxeP5yaKHq/sTWIpRKwzwCqzwZsMxIRxTyxZAnFnTs6iNB497vT21NSV4Kr/nSVbkUFJVg1R2lDaZpU0kRVdmr2kVbCiiwB7X8A/F0k72UqpQQIofCePAzZKjM4nsPQoSEEhgNJ7YmGorBX2NF8frPq9cd8M8a7xyGJUl4VmaMnKOHO1VxOUdkZM5Iz9crLaWJpbyd5yeefp2O+9jW6Xp94AmhoACclR7utLisEhwDePPlq+xzHoWF1Q16foXa92Vy2eGLD8NvJxx9OUT92VNJ1FhhKVKuKYRHDRyi6PRGVGbn4Qk2UATiQiF+1twPBIEmyZgPzkxs5OgJJkuD1eos2boc8RGakVmboITNYZYZ/0IfhthC6t3fjxNYTqFpQhdZLWuPHpSaBTJU1xDSZkSNEUcTBgwd1O7izEi9XesxQH0IxnQZLJWUoFxBG25IT7I1A9VrD2YlsE9PbS9lpbrf6cRzHQQyJpDvoCcJUYoq3aejQENqebIMUlbKSGUxi6oILDJ2mfuRYmTEhfZQK5yxa1PpPZiUzlsX2cWpkBpOYWrOGqgcYitUmPQP30pm70VoFIDqnYL4ZRe0jSznQckPWw1hW3vPPA/ffDzQ0pGe29u/tx7Fnj8Hd5Ia72Y3GsxtVjb30ticjmSEG6ZrnhER1llEIFqoUynPcm/B7yFZDElnRAJXoM+k2FWzYAHz969RvRvaLkzIuTCNvqPbbBJo/Fhpa1yEjM7RkpnLBRJAZmvdVeITIfY4D3DqCl3NuonHLkd9GNV8YHSfmxJZpbW3ax5Q1lGH97euzfharzGCZtlowKi+g1qZsmX1dXCOufvh67H/9UvR99jfgmpoo6/fll6mxZ52FkKka/iE/TFYTTHYTIoEIZEmGYBFQ1lAGMSxmNfhk0hrDR4cRHg9rSjcpoWyPzW1L8ndj909NDc3pQPqm//xvnQ/BKsT9MxiY8SlrjxLse6SopMuwtK0NCIWItGhRUegUReBXv1J/L+sTljCUDcp1nJLM0DM/hsfCMNvNsJRaUFJfgvBYmKQ7XFbY3DZDJq1KpF5zvIlHeDwM78n8vLsy9RGA+Pleti6Ehx924tpr6bdmcLmA3/7WuK/f9Prh1ETR+00pM2VyAotvLejHcxxVZ/z4xyQ1deml6e1h8tC5gBHoanPORHluqfaRVsKKLALBPiDio3WecybgWoRi29pmIzNYhr5/wI+ANxBvz+wLZqP5vGZIEXV/ptL6Ulz6s0tRNrMs7wAnUw9QS7ijP7hoA8XMB30+ChYBVPbz/POA1wu7N4Qzu3oQ9NXi+JwLEOVN8I2Noqy84h1B5GYbE5h8kMVCnpqplRn2StpzJCVxdHrBCRzsZXaU1BWvQojBaCKqKCbWQSdO0P+LqXhUV0cxSI+HyKBlOrYAZTPLIFgERINReI57cLjvcFHGbVmW86rMqKgAHPBhw9AWPHSNH8HRIAKDAVhKLNjxux3x41KTQKbKGmLyKcl/E7CxVSsYnxWMzLBNZdeqDCibCyz+EjDrGmNvK0ts3rJJTbEbOLWcnukiZ9MuluXERmvjRkOnqR85khmTAmYCHsyeXbg85rt+8CBtdpWYaPPvc8+lbE6t9QnHAV/a9FOsMn+FAlPvIHR0UPt8PuD976f1Xapx1njfONqfaUfPjh60bGjBubeei1nnaAfasyEjmeHZDYwfA8aO5Pz54G2k22vTqb06VcBxieoMX0fGQ886i7I8+vpy8weaxjsIzG+GE4jIK5L540SgOsa5nGqVGZoYibH1JXP0VVeWzY1lV+rzMIgjOACMtWk/ggVkh1SgpzJDD6SoFA/0lrdok9nZSAiA5AWUAVw16M3s23a4DGsf+jzW3HMVBTIEgSL1P/wh7Hd/A82enXAKAVicFkTGI+BNPBzV5Henx+DTXmGn6gwZ6NutvzpDC+yar65OrIdTKzOcNU7YXDZwvPrCx2Q3kUGpzQTBIsR95PQQLQzML2PhwuTEFIZsv3/8XJ2Z12czZybLJbW20vd5vfoDHhzPoXJeJUprSwEZ4E08bC6bbpNWPWAVOCzgli9YH6U+lOf7rncl7oMrr6TnJUuMExnTmIYmqtcBcz8OVJ1dtK+44gp6/vvfyfKgkGCVGUq5HIaJqsxQhTJhxexOfoCjxAemvhENANGxRAJLNEZ0FBDZyAyL0wJHNWXsj3Ykj3G8wMNkUx9HOY6Da1ZhMrXHeyhm4pqlMxPY6UxMkk4n8L3vAXfdhdB7P4Q+ZwuksIRgQEbEF8FpHU9hyZ4/oenIU6jp2YWyYB9F+nUgGogi7AunPaKBaPY3q8Db5cXeP+3Fob8fyn6wTnS82IEDjx6At9MbJzNOP52ejx5NXk85qmKVGcOJa6x8djmu/tPVuOCuCyaE8NHr9+RwJEzC2Zrk9tvTYx2FBscZ983gBR7lc2jtO3zUoIaTAYTHw5AlWiznWplhRQh2+AGTCdYyK3gTD97Cx5NeTFZTPAlkqmGazJgg5F2ZwTavBfbLOBWg1zeDkRnMBIeBDc7KTDc1HDpEgVmrVdv0Mm8wMsOgzNSkYNbVwNoHgJYPZD105kwi6qLRZEkwWdY2/y4WBIFkKYD0DTP7/zln+8ABBavKmBAEeoDRA7TIVcGWLcB112lntrJJnt0HqfdJrlAlM8QALb7laOKRy2KcfU7qo8CL+qKBkRnj7RkPs1oT98fzzxf3lKZxCkAMEBHoLdzGZjJQDJmpicqsVIURialcwbI3X75G+/Hq+4pKaMyeTc9DQ4lEHDXIsozAcABjPWOqfx89MQopKsHsNMeDImowIi+QCTlLTK5ZA3zjG8B//zfE+YswY/wwHOERRIIRyP19MEtBw/4KdacR+d67q9fQ+wCg7ek2HHj0QPx3VRJ4WmSGXng7vRjYN4DwuL6gjRIscLBkifrf9f7+zPRXa32WKpdktSaqhfRKTSlRWl8KV7MrvkcoFJjeOpNTKQQkSULv7l4MHBiIByeUOBLLCykvp8x2gBKGCi2bOo1/Y5TNBRouS1TkyzL5J2aSwDSIdesoHtLfT2bgahjYP4DnbnsOr9/7uqHP1iIzZHmS1w8MLGGFPcxlQNlCoHQeER1iGIh4AO9BYHQfVXZHPIAUomoZc2EMArJ5ZgAKI/bjHsiyjP69/arjUrGw4ZsbcMVvr8CMlToj3angOKC8HKazV6N/zlrsKT8XwdEQgp4gjpla0c/VgR8bxczBt7HS+wJs3tiPsWsX8MILlCEYTRAU1jIrHJUORENRBD3BtEc0FIWjkhIfjGCsewx77t+DtqcylMMaxNEnj2Ln73Zi5NhI3Ath+XKaT8NhahoDk5nyDyZLHfImHqUzJiZexRJRs+GGG4CrrkpfM6bGOoqBXEzAK+aShlMxyQzIwJx3zUHT+qa4fxdbE9TpyAO12wEry2sRKKmCN/HgBV41qWKqYeqe2RQHx3Gw2+262cq8KzPEADH2RZCXMNqWnCHLlGkgODLqx6di4ULg6aez+2bEg7SjwaQ2BUcoaJutMoNJTJ1zDt3YRYEYCyAbrMyYsD5SwoA3C8fRJPnii7QGYD68x46RRJjFkjCrTLyneG3avJlkKdT0t+/9oYxZ1T7yVyugHmnR+2j3HURorPhurAQ5ASPGWfZyurjHusbgH/TDXql+znrbk0RmmMsSXitSiLwiGMLDlHmkZzGu/JyIh4y/OVNy9MPgon5S7iGdZAZAVTTPPktkxqc+pe/jJ6VN08gbWfstFIv+R/2ggWpq551otafQMlOimCBGihmM0Oyf0Zj7cflyfR8UHAQGXgLAATPfo+89RZAbMzpOlJZSoKOvjwoWVq5UP67tX21462dvof6Meqz/arrkVDQURcXcCljLrBm/OxcSQq1NejP7VI/jOGD2bISvvAav/1GExWmD95gHi/y7UcP3IzgyD0OYj35e34VXu7wWh/9+GL079ZEZyvYc+ccRjLSNwDXThdIZpUkyU2yzn0pmdG/vRve2blQvrEbTuibV75CiEvwDfsiSDCmHdGhGZqj5ZQD6f/8rr6TAher67F71KoMFCyiQf/Cgft8NBrYZzxep15yzxgnBIkAMi/D1+VBan3/QR4pIEIMipLCkOuwzeZAFC8hk/cwzgTfeoACO3nUDw/T64dTEhPfbib8A7fcBMy4E5n+mIB9pNgOXXAL86U/A44/zeO9709sjyzL6dvXBUaN/nyZGxLgXYGo1oNebSLwvNpmRtY+CvVSxaXbT/1linbMJWPkjej72e2DgVaDxPUQuAbTnKVAcKFtlBkBSXd1vdcN73AtZkPHCL15AyYwSXP6Ly+NBUzVEg1G8+dM3MXR4CJf+9NI07xK94DgOzpr8kw6d1U5sfmBzPKtcFEUcOXwEc+fNhSAIgCTBFhiBY2EzveHQISIzRJEu1lmzgIsvhnP5cmy+fxNCY9rJAKm+AnrAKk/GOsdy8hlRu94i/ggAwOw0xyszqqqAuXOBvXtJKoklCTCZqcAQ+aNMxpzAElGvuir9bxxHsYyZMymxRQ1aJuGFRC5kBvPN8J7wonpNdVF+W2uZFWd86oyk13pjS0+96zKXG0A/EIkCNrsAW7kNZoc543umyhpimszIEYIgYPlynRtaFIDMaHk/0Py+5OBggWC0LTnjjY8CwX5g5Q+B0tbsx8egd/BgTHjQE0xqk16ZKSYxVTS/DIBKd+d8lMy+DGDC+igPKMkMBiYxtWpVullSsdu0eTNNaC+9REGQGTNi/hEIAy/F6ivNhZP7KnofmUuJzIikZ8EayWw9cxl1hBgS8dcP/xW1y2tx/rfSowN62iPLKWSGrZoMiiNeGqve+kTi4CW30QJdz2KcfU54FNj+KUCMAMu/nSyzZ3BRPyn3UMXpwIKbdY13TPL1+eep7F5NxiMVp8K4MI10ZO03RyPd6wARGlNcllCrPYWWmRoaShC2jCgpBjT7Z+VPKFOybFH639QQHgHafgdYK/STGfGTsGtXDkrGyrxzGSdaW4nMOHpUm8xgMjueYx7Vv1cvrMbFP7gYshrLrkAuJIRam849l645LfKM4yhgfu65wOv3vo6uN7qw8uMr07zUZE4Axwkon12Oo/zZkN3DqB1rw9yOpzBTtmBPafYy05olNeB4DuPd4/AN+LIGN5TtUQYhAHWZqc7OZP+I4SPDOPL4EYhhUZPM8A34IEsyTA6T4cxRIDuZwTIru7rUEyuUv78gaKzPNAIQCxaQJE0ulRmFQuo1x3EcShtL4TnmwejJ0YKRGQDAm3lwSA8QMDJj/nx6vuYaIjMeesg4mTG9fjg1UdR+k2Vg4GVKFiqdH0uijKXwBwpb/nPFFURm/PnPPJYvX46RkeQxIO7Z0O9H2BfWRUiOdY1BFmWYneZ4gBagmPTf/kb/ttspwa6YyNhHUR95Ucoy4FqcPM/zZtonlc6hpLWRXZQEZtBvVA+UZIaWFxHrg9Hjo6i0VYLjOFS0VmQkMgBAsAro29WHoCeIocNDcVPwyYSz2pk0D1fPS90/KhaV114LbNpEG+f2dsrMjG3KnEd2wfn3v1MJ6+zZpG80cyaVPCggivrnN2eNE4JVgBgSMd47jrIGY9U3atdbfB3hMMcrM8rLae7Yu5fmkksuoddZfEyKSAiPh+Hr8+HV77+KhjMasOLDKwydSz5g81oqWKKDy5VZBl7NJLyQYPHIbMnVStSvqselP78UZQ1lmjKgxYARmSkgphwUIzMsTkuchMmEqbKGmNrpflMYkiShv79fd3ZT3jJTAE1oQuFnYKNtyRlMWik8YuhteskMVpkR8obibRJFEcHhYNLf1SCKCXmXopIZAFWlGOzHCeujVBz/M1UEjB/Leigbz5RkRiaJqYlokyDQhPbe99KzIIAWkkBMn7RwsgNFb48pdv9E08kMI5mtVpcVyj2yVtaLnvaMjZE/B6CYMG3VtPC21yaXUvNmel0vAWGrpiAgZ6Gxo3I1vZ89DGYnTco9ZK8D6s7PaP7NsHo1SbwODwN79uj7+EkbF6aRF7L3GwdYYouFU0BSTas9hZaZYkHdykpKlisWNPvHZAcqV+mfv22xHyA8UpREFL3IZZzQYwIeDzYN+hEa0yZYsmVt6fG5SvVRUGuTKKbFE5I+A0hIGElR2rSzyl01mGwmuBY0YrBuGfbN3YR9czdhsGw2/KZYoOGZZ4CtW8loNAUWpwVL3rsEZ33+LF1BOGV74kEIezKZoZSZ8vko05iBkRNa8lGyLMPXR5N1SV2JaqA8E6LRRCBdi8zQI/GplJBSXZ9pQGkCPllQu+aY1BTLBs8XYpQSbbSChalkxtVX0/PWrYlMTL2YXj+cmihqv0W8wP7vATv+G0CMkbTHFvcFJjMiNMyhowN43/vSPf6SPBuO6/OlcTe7sem+TdjwzQ3xeYfp6994Ix0TCBRfX1+zj2SRvBplGbBWZZY6ZiRSKEPphBp0em6xZJNIJBGjUsI3QPOFYBMgyzL2PLIHofEQKudVYrhtOP53NXAch+ol9AX9ew2efwzHtx7Hy3e9jONbj+f0/kzQdQ+ZzURWXHABaSMuXUqvNzbShm10FHjsMeDuu4E//IH+5vcDL76IJ355HHOaotiwQf3aTgXHcQkPphPGPZjU2hP105rT7EhUZlRUJOYOpQm4YBZwzpfPwcbvboTJZkL/vn6MdY7FPc8mCnfeSc+bNwPPPivhF78YxbPPSmhvp9cyVREpUSzZRSZ7f/hwkvqYJnwDPoz3jUMMixhqG8KRN49g8MgghtuGs95DRsA8W1jikCwbJzPcsS1nNKL/e6fKGmK6MiNHSJKEY8eOoaKiAryOFNq8KzOKCKNtMYzgAC2QZJkCyaP7AUtF4u9ZMqwZmdHRQfOEQ6PitKyhDFULq1BSWxJvU6mtFGKYNgeZKjO2b6d5yeXSzkKcTBS9j7Tg2UOZITXrgJLZGQ9VkhksyyOT+fektYmRGSaHdvQkBxS9PYwMVKnM0DtZVdp98HSEAC4R9ODNPIbbKG1DWR6rpz2sKsPlokB8EqIpk3SgW99Jqr3HVmNImk4Nk3a96YTZTMG6J58kYlVPssNUb9M01JGx3+LkhUBeM+ERksObwqSGVnuUZIZW5p8RTJT5d8HuK7Obxi0pSjJ7thxOPDQAiH7A3kim8Dkgl/boMQG3OC1w1jrh6/PB0+5B7bKEq6osy5CiEgRz9nNmQXAWmE2FLAPf/35ysFutTd/7HlUslJUBJSWJ+QlIlzBiyS2scpehf28/Iv4ITNb0+SaMUgyWno5oKLaLPXGCBN//8hcasM8+m3a7sfNZcr2GuYQKlO2J+NQrM2pqaP3rdtOeoqsrkSBlKSXCRMuc0T/gR8QfAW/mYbKaEPbR/K/XsPToUZJocTpJcUMLGSU+783dqDoXMkOrbbmatKpdc9ULqxEeD8cNVPNBNBBFJBCBFJUgy7JqH7H2s4BUUxPF1d56C3j0UeATn0j9VGPtmcbUR1H7LRzzxbC4Emtue8wQLzQEiCFAyD8JbMsW4EMfSn+d6d4//DCNFe5mN/wDfng6PKhepC+ByeayxU1wt2yhz9PyEmTfU2io9pEskzqFHKbktJLmLA2JrReCBsgA5rmVyd/EWgmseQB2ezVKSykprb8/OT7lG/Bhy/u2wD9ERH3frj6Mdo/CbDHjxW+8CI7j4Kh0YPMDmzWrDmuX1uLkyydzJjP69/bj5CsnUVpfqlltmCvyuoeamugBUAZFd3eilL6rC8e+8yecfErCLTChE404jHnYgqvomrtKxsOPcKrXnGumCyNHR4hAODu/9siyHE+KsDgtSZUZ8+bRv5VkBgDMXDMz/m/WZ9WLk+85I9UmRtHWBjz4IP37ttuApUslOBwHsGrVKggx2a28pEQLgKYmquwKBKhgZ+5c7WNT7yFZluH3++FwOOJEa7Z7SC8O/fUQ9j64F62XtGL1J1fD66VzBAzKTME4mTEV1hDTZMYEIa/KDDEI7P4qmX8v+HzeQb0JhXJiDfZTINbXDlgUWpaxiVWL0KiupkzMoSEafJkfQyqa1jXFJ7xojDI12U246k9XIegJqm5OGZjE1IYNxdHZi+PADygzY/YHcwtsTDScs4jM8GXPjFi8mH674WFaKNrtiVK8ohmq54I4mXEKmX8DCjIjPVNCj7xD6wwfen+6BQ8N++E96Y2TfGPdY3j7128DMDaxiiLwz3/Sv10u+n/SvcPM7hlyIjNiqQX2Iq1MJgK+48DIbsA5M6tx8IYNRGY89xzpfk7j3wjMJ2bsGCDHVpNSFIiMJsaqApo/TgQYmREO04a5LM9TnygyIw2yRGuw0nnArGupQkMPOI76LNBHPihG53wxkPDbsVROqNyYHjIDAMpnl8PX58NI+0gSmeEf8OPvN/0d7hY3Lv7BxVmrMzZvBn7603SpHKaVvG8fSepo4fBh4Fvfon///OfA9ddT8Py//ovmxvb25PnJVh7zWFNUZoz1jGHn73ciMES7QHNIvfwnbvD5H/9BAs+vv06Pn/wE+PrXyXHR5yPmwSCDJ0ZESFHKcmN6xUrPDIDa4/EQWcAyBa2lscqMFC1vZljau7sXUlSCpdSSRnjoMSzdG7OLUXA1mmASny+8IOKVV45h7drZOO88Ia+1NQvenzwJjI8TWaUF1mb/kD9BPKUgF5NWNcy7fB7mXT4vr89Qnm9oNAQpKkGKSAh6Etemo9IBS6k1yTOD4ZpriMx46CFjZMY0ppEGFgS3KCRGTCW0Bon6yOvBmV9g2YjHH/NsGGk3puhg9HuKuu9n6H2aZEM5gfY00ZQEldSEFauCzNCbDWLQc6u2ltZmfX2JIDdApLh/yA+T1QST3YSgJwhe4OGodsBebkc0EKXxyhvS3C8yaanBA4OQolJWaapUjJ6kwBmrWJiSEAQqG41BnD0XF+z7MTicRBOOoxkdKENszy5LuAtfxp6PzMCVXAuEOS1UrhFbGDPfjFwqM1IhRaSkdYSyMoMZQqeSGQyyLGNgHy06ahYn1qxbtqgnKfzoR4UhBL/7XZJYvuQSivOpVT4YkbIsBnie1iI7d5JaTCYyQ3kPSZKEwFAAkijBVm8Dx3G67iG9CI7SWoGtaVhVRlmZdgJ4KlhlRjgQRVilYCTXJJCJwCkUFT+1kVdlRmgQGD0ImE6eWkQGkDyxmspiRuaWhOmVDjNLjqPqjJdfpsFDi8xQfy8Hs9Octcx/QvwyAGDwNSKnWt5f5C8qEByxSdqv4bikgM1Gg/z+/VSdwarOFiworr65YdhqgLmfINmjUwlM2kKlMkOZ2cqCPwxs/fv1r4Tg/Q1NrFULq+KLFUeVA4JFMDSxpi5qTpygNVnSooa3AlVn0QZo1tWAI7vUUhreCWRG/8vA8T+ReaIOMgMgyYg0cmga72wwn5hdt5IUQN0FVBlXOheY/SE6poDmjxMBh4Mefj9VZ5yyZMbYUSIkx9qAlg8Ye6+1OkFmGEVQoRsjTqx3ih6ZKYDIjM7XOuFp9yS9PtI+AlmUIUv6zSRZkHzpUuDLX6aMsu5u4IYbgG9+E7joIvXECFkGPv5xIBSiY973Ppr3rr2WyIzeXlqPKMdTe3nM8DJWmRENRvHSt1+CLMpYdO0inPmZMzWDL0kGny4XcPHF9MW9vRQpkGXamZtMGGtejG6uEfUbF6F0RnZPBZZNCaTLTDFZkIYGIheUJuBxmakUMsNZ7cQFd12Ap295GryJx8bvbkwL4usxLM3ml5EKQQDWr5fhdA5h1aqWvOeyysqEH8rhw8Dpp2sfm2r2qoZcTFqLBeX57vvzPhx75hha39WKhVctjB9jLbNiNOLE+Dj9tuz+BGjd98Uvkmddf/8kjI/TeOeAkRlWxaaN42gNPnYUCORPZhjx+GuJSRl6Ojy6PvuVu1+Bs8aJhZsX4rXtVt3fUwx9/SR4DwFdf6P4h8VFvldq3lfKhBVrJf32UgSIeJKTQLOBeW5JIaqmUSbBKL63poYSFrTke0x2EwSrgIg/Ak7gUNZQFo+naBHFDGUzy2AtsyLkDWHoyBCqFxpbu3pPEAngbnIbet9k4qWXgI4uM4DZaEeymoUZEbyA9WjxtKP7Ty9hpjuWDfjDHwIOByqDXSgd78H48fznpfg6gqM+ZGRGeTkF+wFaWykTA0aOjWDw4CBkSUZ4LAzBIqCilVRUil3h1NUF/O//0r9vvVX7OD2xDqWUZTGwcGGCzLjiiuzHm+wmiCERobEQJEGC2WkGz9HaMts9pBehUbqnra5kMsNIhYqrxoo+OCCG/Ah6ipsEUmicYpHxqQOO4+ByuXRv0vIiM2L6hsUKYhhtS04Q7IBZpBJWjk/OitdhZqkkM7JBlmVDbQoEEt4OmYyF8oYUJSIDMByUmJA+UgMLQPtO6Dp8+fIEmcH0nNUkpoBJbJO1Ami4tOAfW/T2sMoMFc8MILu8w3nLgYd+QxMrL/DgTTw4noOt3BbXz1ZOrFrt0b2oKWkGlnwlvzYHYzOyLX8yY9Kut5IWeh7vyHroihUUGxsdBXbsAFatynz8pLVpGnlBs99MDsrCMzmB1ptym/OZrKMWikCIZLoOq6qI7BwcJPnhXCGKpOgDkMZzEtlX4DantWdkJz2XL6P1ixFYqxPnaARiAIiMk9QYAIQ9gODMSW4sl3GCVWZ0d1ORQZqMYAzuFjcA2ggrwf5f3qI/APP00/R8zTXko8DwxBPAffcRqbFrF5Fiyjb94Q8kzWe3A7/4RWJT29BA5+3zkX/n/PlU+h/yhhAeJ41hz3EPho4OYfuvtmNg/wAcVQ6c/83zM8qSqoLjkneO11yD0NMvYvie3wNDPoysPR2Rr3wGKEkQGsqAOmsPy34z2U3geA6imPCcYUFq5puhJDOUMlNsDcxQv7Iel//P5Rg5NoIZK3KbS42SGco2FWp+WrCAyIyDBzOTGUC62WshkKk94fEwBKugS1ZNDex8yxrL4JrpQtWCKlTMqUg65o3n6LmlJdnAuKWF5HG3byepqY99LP/2TGPqoqj9FiczUsxf42RGDhXWKTDi8bfiHDesZda4bFQmBEeDOLH1BMABS65bYuh7Co20PhraBnAmYN4ngOYbtasslGsV3gQ0bqL1IJdjuG4kZmLpXkzrhxQoTcC1EBgOADLA8Vy8WlAPmG9G56ud6N/Tb4jMCI4GiYzmgNKG7EkARlGseyjTtRSGFU+AYg/nXCnjvRfHZCxiqfNVB1/ChfUnYQkdAu7YThmC73qXroh0anssJRZceM+FsfUElyQzVV6unhjQ9WYX9tyfMGysXFAJ3sRPSIXTPffQun7dukTMSKuPiiVlqRd6fXyVMDvN4MBBDlOCDwpMtrDKDDZOsuuQVeHoQXmjEz/HZrx/fQifu1P9mNQkkKmyhpgmM3KEIAhYuHBh9gNBNyjzB8xJZioU2wQrMyUKCCNtyQssE14yIMgWg57BI+KP4B+f/AdCoyFc/ZersXDhQnS+0Ynubd2oO60Os9aqZ4a/+ipl9NXXJ0rZiwImu8NxhiWOJqyPUsGMi4P9RMQImReUy5eT5uGuXYmNthaZMWltKhKK3p6y+UDT9YnguAqYvIOavMawIsM2EqB70GQzaRqBqrVnwsu2C1iZMXn3UDM9+4+TXE2GYKjJRIu5v/+dgnPZyIx32j307wLNfvPspWvEUZ87kaFTL7mQhEam65CRGQMGY/lKpFaCPfaYohLs0sK3Oa09HhYQOM34ybN1m97KDCY3Fhoi4poZh+chN5bLOFFRQZvekREiApj3ZSoq51Zi/pXz4xl8DIzMYGRHNogiyesBwIUXJv/tpz+lhJOODuDTnyavTdamgQHgC1+g4+64I5kw43mSztixgyQVGisSGsZiWIT3pBe9O3tx8uWTJC3FAdWLqiGJeZoZchx8dbOx5Ymd8HrXw+VpQ+2/OnDsyD8BjkfD2EGMWqogzZiJzQ9eBWe1M94eMSziou9fFJeBHBpKzLeswlWNzGAyU1JUghgSYbIlb+8q51aicm5KgNIAciEzCj0/LVhAma+TZQKu1Z6nbnkKQweHcP63z0+SWssFy29cjuU3qhtmpZp/K3HNNURmPPSQfjJjev1waqKo/RbWIDMqz6C5rDSDropOGNG9L2ssw6b7NukKlrHqjZK6EphspknV10/ro5YbaO9WfholrejFnA/ncRbKjZr6vkMPmeGodCAaiMJR5TActKxbXofx3nHD2dxMaslZ68woEZ4rinUP6b7m6jkqN6xM3GfCN74GobeXFjrHj9Mzm/wfeYQCYC0tiUddXZwUS20Pb+JRNZ8WDGNjtL4CaF0H0BwyMEBzCiMz7JXJCRzMo8ZIJVUuFU4DA8D//A/9+yuK/MdMfcRiHcXy78gEJu3JZNT1QLAKEKwCEALC3nC8MrhQYJUZzAuuN1bUbWRsKy8H/HBiIOpExZzsxwNTZw0xTWbkCEmS0N3djfr6+qymJ8wvA8hRZoFl9FmLU5lhpC15gbcCFndOmt96Bg+T3YTgSBCyJCMwEoAn7EH/3n60PdkGk9WkSWYwiamNGwvqB50ORmYIDsPZnRPWR6kwl1JJbHgU8J0EyjIvZJlp8ZtvJsw3165VP3bS2uTvpsCSva6gviVFb09pKz2yQBCA667Tltdg4AQOJrv2FKDWHkOLmnUiXeccBwy+HpPOOd8YMTHrGqoKKtE5s2bApF1v9joyTBRDRM44GjIevmEDkRnPPQfcckvmj560Nk0jL2j2WzxorghmyTJVL2YhkgEY1ksuFDJdh0waZ1BnLD8V2SrBnviLFxc7C9vmpPbIEWA0tvAoVw8yZkT9u4DqtYBNZ5CTyY2FhoFtn0w03OwCTr8n9m9jlSa5jhNz5gDbtpHUlBaZYa+w4/SPpqfJM9mp8tn6KjO2baMKZpcrncR1uagyY9064I9/pGTFujoJ+/eP4K9/rcDQEIdly4DPfz79c+fPT5AZ5yxOaBhbSiwIeAIQgyJC3hB4Ew9HjQPRYLQgGsZML1moKEX32Gz0CHNQ7bZDkKNoGjoCs/dtjA+XQvyHE9h8IaSSkngfVc5LBDgYCVhZSWQ3kJCJUM7FglXAZb+4DJZSC22aY8hFqzwV4TBlcALGyIxCz0+5mIAXElrtYYE6b6c3bzIjE9T8Mhiuvhr40peAF16ga6Zax/AwvX44NaHZb4WoUNSqzKg9jx4FgF7d+9MX+DByLLNUHIC4nFzna50I+8KwlFkw3DaMxfVA6wwr2nqdmfX1Vw8AYwWo7FT8/pIkoa+/D7U1tYk+Kp1rjMjIF2Lst+N49bURgNrYcNXXp/0xvMDDNcuFYCiYVvWXDa2XtGLupcYJMO9J+h1dM3PJAM6OYo19eXk68Dxl1dbXp+tptraSJtSRI7TBlmXgPe8hg4mBAUidneixWjFjwYK09rCqDIuFqlcBSvJ4+WWaU1i1asQfQdhHMpXOWiesLiuG24Zx8pAVQPb1UK4VTvfeSwopq1YlJ7Jk6yNBmAB5OBWw2P3Bg/qtbDhwsLlt8PZ4EfQEC05mMH+tVM8MI2QGI7rY9aIHU2UNMU1m5AhJktDZ2Ym6ujrdZEZJSWIzYgisMqNIMlNG2pIXTE4y0MwBbPA4coQqXcwqlY4cx8HqsiI4EkTAE0DncCfEYaKjmdmjGp55hp6L7pfBvA7MxksmJ6yP1OCYBUjHKDM0CxiZ0dFBz+XllEGrhklrU+/TwImHgcYrgNb/LNjHTmofpaC+PqFV396ebOwGkK5i2cwy2N3aE6paewyVbbf/H2nEzroW8OwGPPvIBNsImVG9lh4FwKT1D8eTxrD3MJn5ZiEzzj+fnl94gQJ3M2dqZ5xMpWtuGvqh2W9xOaPT6LnveeDI/wAVK4FFWZgtJZheMiQAXOzBvjy7rKNRZLoOWTZ5LmSGnkqwb30LuOg7ABdvs9oJGmtzUnvGDlB1hK0KsNcbb4StxjhpbqumClZBEfiQo/Q5E7h+aG0lkiGbCXgqwr4wfH3kIKhXZopJTJ1/vvo6ee1a4LbbgG98A3j/+wFZ5gEkAm7vfa/6upBlsSsD4Ca7CRanBfUr6jF6chTjPeNwVDvgqHEg5Cns/WFz2+Dv85MZpwyYSp3Yu/S9sA8cR3nPflie+Sew8yVId91FfVRbm9RHqX4ZgHplBsdxKGtMThSKBqN4/GOPo351PVb8xwpDEiFKHDlCZpylpUlep1lR6PlpKpAZau0payxD95vdccPaYiFTZcacOSRTuWMHVa79p46l7fT64dSEar8Vqipz1tVA5ZlA2aLCnrQCenTv7/m6D3/9AFXRMaQG0lk1WmiMxmxfvw/hsTBG2kfQ8VwHAODj1Q7c0bMZfs6p+j0//+EAhDcK8Lul/P6cLMPl84ATgjRvc0JuVbFiGAj2AZATSgl6IcVkrTMkwrD12fbttOfQ2mvIkBEMBGG1WjWr+dWQq/xMeDwM3sTD1ZRMZohiYbLxizX2Ka9tLWTydOh6qwsnXj6BuuV1aDlfocKwfHkiwBIIUOUGq+rYswd48EGYBgbIkXr2bIzXz0Wn3IjS+lKMWGjBUFGRuO7ZHNK2O71alRM4uJvdeOH2FwAAQd4BBzbDn4XQyKXCyeOhyluAqjKUl8tUnZ9aW6n/xsZoDcaSS7LB6rJC6pQQ9AQhQ2VTkyMkUUJ4nEgoVpkxkWTGVOijqXN1vIOR5JcRHKAMZa2Hmq5yXGbq1DH+LDRmziTt42g0syElM79hLGXAQ/rSWvrHHg9N4sAEkBmsMmMCTTwLgqW3A2sfBCqzaN6AJLuSYnMjVA25ZUsRz88oohRkMSr1NemQJcDfCYxmF2pk8hpAIqMy+e88SmpKIFiMrQINlW1HxwFJJK1X5nnhz19v95QEk5rytWc99MgR6r9gELjxRqrUaG6eYvfQNAqP0DBVv3Ec4I6lwZtKabzydRj/PDEADL8N+I4X9DSNIh8yQ08lWHcPJawVDYxgci8vculmCpj5t7MpEfzwZ/gxigDmm5HNBDwSiKB/Xz/691HknVVlOKodsJRYMrwzAUZmpEpMKbFkCT2rkVu33qo+RrJNOwsEp6JsZhkqWivgbnIbCtLoBQcuvi6NG1JzPEadDThYdS78X/o6uZebTODCYYQ/9QV03Xw3+v76GiDLqqb3amSGGtqfa0dgOIC+3X0ZqzCzQSkxNZnSyIzMOHw4IZsxFcCyh1k2ca6QZRn/+OQ/8PR/Px0P0CrBSBwtOdxrrqHnhx7K6zSmcSpCWZVpdqc/eGuiQjET3EvJU9CRQtzLMlXojx5ISB/mAaZ735CS21NXR69vXJeoouM4DuO94wh5Q7C5bbC5bTBZTfAN+uAb9MFkNVEQjyN5HUeFI35MieDHr38WigfqGBob6Xsuv6hAv1vK7y+byiCIY4AUBqIB/Z+Tiv4Xgbc+CbT9ztj7xAD1lxyl7wz20FpS4bm1ZQvw9a/Tv998U32vEQ1EEfaFEfFFIAZERHyUvc+8nfQiGorCP+jPfmAMi69djGsevgZLrluSdL7NzXSe73vf1N0bbd4M/OUvybEQgGJY2YyyPe0edDzXgd6dvdoH2e00GbIMhw0bIH3nO+jbtAnyGWcAwSDGdxzBjt/uQMdDb8L1/a/iI/gNLrU8Q5kpoVB8Duk4nLjP7JV28tLkOPKpid1DVtGPprpQxrnfZgOWLaN/iyIRYw8+SM+Z5uqf/5z8VRcv1memPRVgsSTWxnp8M9g9xPEcZFlGJBCBf9Bv+B7SghSV0PquVjSuaYx7p00UmTFVMCXIjPvuuw8VFRU47bTT4o8zzzwTYuwO6OnpwWWXXYbly5dj6dKl+OUvfznJZ2wMjMxomRFj7l++Rvvx6vvUCQ1eOLXJDDFAE2nURxNs2JM2sWYCxyU2MpkGD8ZKsk1jcJhIDa2SrhdeIBmeBQvSF1UFB2vrqUZmCDZdO9gtW2gzJaXITTM5kCmz4DhVyQwxBLz5CWDHFxPlwxnAyIzUIA6bWFMfyolVFIEXX+Tw1FOVePFFLr4YyZahy3GJSoLE71yS2BgZMQ/0d1EwNpijPs1UAiMzxjOTGVu2ANdeewrcQ9MoPEIDVLVU0prIvo/7rXQa95rynyQCNJhBDHkCwPZbuXhm6K0Ei8SHLjlhmF0ocCaSmWDVMkYhicDJR4Gjv6IsS70IxDaz9jpgyVeBNX8EylT0ZYqIOTF1v2zj/slXT+LZLz0bN4802UyYtW4WGs/Sl7I2Pg689hr9W4vMEEV1GSklPve59I1zNjKDAwd7hR0cX7woPSN0Ij6Ve9juiC9uOVHEUEUrRv/+EqTv3AXcfjvsLzwBQFYlM/r7SQKK4dizx7Dtl9sweHAQsizj0N+o0fPePS8vg8Zc/DKKgaYmCpqEQonq36mAsplUEePtzI/MCI+H4T3pxeD+QZjtyVU0LBkX0CYzWDbwc8+R18o0/g3BKhRTHxoyQ4bwxkdp/xHMoElkAJs30338zDMiGhpor3733cnBXpPdBHOJGbIoQwpTBnJ4PLZvGQtDlmQ6xmGGFCZJPXuFHRanJU7gXnwxVXgC5OH4/PNUsZ4UVGa/m2AhAsLkyO13i30OF+oHB4mqK0vn5P77s6pOvb8589ySQkDEQ8STFKX9VMRDr1sr8fhTZbj66vTAJdtrPLPVSl4ZoSiCniCCniAi45H4v6OhKByVDl1eGMdfOo5Hrn8Eb/70TUNN5wU+7v3E5EZTk1um6t6oqYn2cQ5HYt1SXZ3dnJrNJcwzRBc4Digvh3/+fMibNgE334zR06nE3+Swoq9mKSowjEvDj9ENdscd8TnEfXQbSsJDMFuJwOBNPHgTD8EixO8hjgO++tXMpxAMAmefTVUp2QgnRnb8/vfAd79Lr335y+nkz1SGHh9fa1nyPRTyhsCZOPACj5AnZOgeygST1YTVn1yNc798LnghWUXj34XMmBIyU9FoFJdeeinuu+8+1b9fddVV+NSnPoUbbrgBY2NjuPDCCzFr1ixceumlE3ymCfA8j+rqal1lNUxmqq4qRz3r5d+moESRYKQthqE0s5RClH0aHiEfBmaMqdPMctEiqqI4cADYtEn9GDYohEfDqG6pRqeHZj4tmakJk5gCgJp1QNWanAzQi9pHBUAuxtCT1qYikRlFb49gI1JTEkmyTMg8AaZWZrCJ1T/kRzSkHvBzVDrwzFYrvnAe0NkpACCt08ZG4IwzkhckWuXh8RJaVolkLqH7HTBGZvS/CHQ8CMy4EJj/Gf3v08Ck3kPVa8jvxNmkecgpdQ9NIy+o9lvZfODMXwFRRfaatZLun8g4kRMls9M/TAuSIsopRQA+N4kZPch0HeZTmaF3IW5mK1nRT3J2gjXZd8QgktrT8n6g+QZAzjEVnOOBjvuJgK6/PD3jVQvuxUDrRymJpaQl+/EZkOs4wbLPspEZTEpq5NgIZFlGRWsF1t6iXx7wxRdJPrS5OUGgpCJXE0o2Dw4OAqMe3adUUJiddO9F/NprP57nUTlrFjzLq7B3byUWnWbCjNl+cNvbAXCoqZaB198ATjsNVVU2WCxEZHR3J6Q8u97sQuernSibWYbQWAhjXWMwO82Yc2F+nlO5khmFnp8Egfpz926qUtC6VooFrfYwea/AUAARfyRnOS9m4ml2mtN8To4epWvc7U6u0lFi7lxSItm1i6SmPvKRzN83vX44NVG0fouMASM7yN+pLIUx4zhKthhv1+X9pheCAGzYwOH880P44x9t2LYNuOGG5GMYsSeGRYwep4CKFJUghsV4sFuWZThrnfAP+iHY0ivO2Rj27ndn0dn3HqG9i7NJv89VKqI+IDwMXhBo3c+ZAOQoX8jIjFC/PoF+5rkV8QIH7wVGYw13zCSVBQAiX4ZPLKjOuNf4rzuc2PvmZkR9dN6SJKHzZCcaZzbGrztrmVWXt1RZQxmkqISBfQOQRCkecNWLXPZG2VDsse8f/6DnSy+l6pef/ISIu7a2zPOWa1aiys+IP0lqe9hag6uuwu7GS3E3gHevlHDVV7sBrxezZwM2PowrQ3/G8u4jMHnMCJVWoywooM26GBFfJMl4/eKLyZPpzjuTv3fmTODTnyapqMOHqR9SwQinhx+m/3/2syl+XwJVO2Rr01TCokU0x2YiM5zVTmx+YHM8uVqSJJzoOIFZzbMM30NGkQ+ZEQgQOWXTYdM4VfpoSpAZmbB7926IoogbYrNbaWkpvvGNb+DnP//5pJMZc3SupFllRtz8W7ADJjtVKFhcSCqQ0dJ2NmgYbQRG2mIYyokVAAbfANp+Q5kKi75Er+k012JMaCYTcFaZER4PY0HTArw5TpkAqTJTTHfx0Ufp/xNmIsSb6GH0bcXso2wQw8CB71EwbeWPVYPouQQaJq1NYixYqNQjLwCK3h6OI9mZsAeIegFUZTw8NSM1dWJVwzNbrbj+I+lmeZ2dif69/XYqJ/3c55L7vLGRiIx45kmEyao5AQubJQ2QGfHM5Bx06lUwqfeQtTLdUDEFp9Q9NI28kLHflAaRHEfVGZ69wHiHfjJDFpOrHkUfwLtzPNvsyNSefMgMPWaK9TPIjwxAIlFADFHAxYg/jwJp7eG4WEAiB3AcJW74u4DQoH4yw9mUkfw0glzHCUZmnDhBgXO1DSdA2YScwCHii8A/4IezxtjmTCkxpbV3N+TXpEBJCV1DnZ3AsWOGTqtgsDgsqF5UDZND+xpifbRj6w6A4yG2zAU+fDr+8Tpd+K3Wk8D//i/JUZ12GjZUn4Wnuhahq4uPkxlMQz48Fkbn6zSZzLloTjzglyv27qXnXMiMQs9PCxYkyIzLLivoR2eFVnssTgts5TYER4LwdnlROTfzXK8FJo/L9jFKKP0yMsW3rrmGyIyHHtJHZkyvH0496O83KRbt1Rnh9XUA++8mouIMFQUMJZlRQPA8j4sucuGPfyS5o1QIZgGuJhfCY4kEDTEsIjQeigd7eZ6HtdQKa6m6p8MeKhrE0qVZToYlYYX68yIzOAAmW0X+SgzWarrhxTDFUVhiWCbYqukxYyPFlAI9AGSKuwB46QV9e423Dzpx3nmJubxqbuY9pxbczW6YnWZEfBGMtI2gcl7m8XHw4CDe+sVbqF1Wi9M/cnrOiQyZUOyxT0lmlJRQ1cLWrbTWyfS1JXUl4E08xLAIX78PJbX6rp/U9jAyw+wwYyS29i6v5OMGDxYAjbMtuPXod/Dtsl9jVvk4XPII6sv70V3hhq3ChtbjzwCBAIZFF4T9C2ALnQbAhXe9iySQlZ4lN95ISRUhlfACI5xuuomy/lPX8qIIXHcdfY6ycmUqz0964pEAxV2UZEWu91AmRINRSFEJZqcZHMchEEjEnY2QGaWlVB0jSSQRr+e9U6WPph7dlYJnnnkG69evT3rt3HPPxXPPPQdZbXcLIBQKwev1Jj0AqgBhDymm4yFJkurroihmfD0cDuPIkSNxKSzlsdFoFLIsQ5ZlRKNRDMdMqJ1OCTJAj0Af5LEjkD17IMWqLmRQdkFUpM9gn516jlqv59qmSCSCI0eOIBwOx889W5v0vA7WHlM5ovYmRO1NECvPAkxOyGEPorZZ9LqpXFeb5s2jY/bvlzXbVNpQiqpFVbC4LNi3fR8dJyQy46LRKB56SERzs4wNGyirDQA++1kZDz0k6m+Tjv4oZD9JkpTUR0XpJ61zlzhInn2QfF0Qx0+onntXl77Koc5OcdLbJEfGIMkyopytoP0UjUbR1taGSCSSdo4Fa5OphM49OJL12ktUZiQ+y15pR8WcCrhb3ChrKos/3C1uuJor8IXbHZrjKiCjokLGrbdGsXmzHC8P/+MfRTzzjIgjR6LYtClx7lLES+cKG2RbHWQAUtiLaMCjr5/8XfR+S3VB+omN2ex7U/tvwu4njdc7O/Vlfvf05NemaUw+JElCW1tb/BqFGKaKKzWwrHwjvhlMXkCOEgHCmQzJOhpFWnsUYGRGLjJTzExRDSygd9ttsX+LAcoslWPtjozm3OZ4e4IqO69cwCRCQzn8CAC1q+33wIF7cnp7pv7JhNpa0nmWpMyyPoJZiMsjDB8dhrfLm2EeSYcevwxDfk0pYMT+sZjKnx6pxUIhGogiEogAXPL3pn4X66OwjwJ2FicxR/0DdKFb586itMh3vxvo7MQnxJ/g8/gBuroA34APw23DiARIz7xjawdOvnISYX8YNUtq4Bvw5Xz+oVCiMscomZHrdZcJk2kCnqk9zec1o/XS1jR5KCNgiSZqshPZ/DIYmG/G008Dv/51Zs3yYvTPNIqPrP0WHQeG3gSGtiW8l/SAmWBrJd6w5IBgYckMSZJQV0d7y7ffpiq9VJTUlqCitSL+cDW54KhygDMliAt7hV3VHzMcSlSoM98ldSjmrJLWHFoSg+iHDCAkCvkb/fKmRDKYUcnQxiuBFTEdn4gnvsbMJTEg17HCN+DDSPsISutLEfaFceyZYxhuG44/1OYmT4cHnmOeuGxfrokMmVDMsa+vD9i2jf59ySX0vHEjPTM1EC3wAo/SRpKZNSI1ldqeOJnhNGNkhI4pL09+z/z5QBRm9EvV6C1fiPaZ5+HQ0mtR2lAODhzGnDMg8ibU+dpgfeD3WPGnL2Ix9mLzZuC9a47jvLqDEMK0vj54UJ3IYJBlkj7MtCxMlQmdyvOTHpmpVCjbEwlE4OvPfV2mRNvTbXjkvY/gle+9AoCuPwCwWmNezTrB84lrRK/U1FTpoylfmdHd3Y2mpuTsNLvdDpvNhv7+ftTWpjPnd955J77OXI0U2LFjB5xOYsiqq6sxZ84ctLe3Y0Cxy25sbERjYyMOHz6M0dHEQDJ79mzU1NRg7969CAQCkGUZHo8HFRUVqKysxI4dO+KBKQBYtmwZLBYLtm3bhgMHGgE0IhKhO1kSRYTHByGIESASQVhsR0nlHESjUYT8fhzZswch8wjsdjuWz4wiuP+36A7UYLD0XQAAl8uFhQsXoru7G50KujqfNvX29mJwcBAcx2HBggVwu90Z26TEqlWrEA6HsXv37vhrgiBg9erVGB0dxUHFrsNuM2M5b0I4MIq9bzyNiKlCd5ui0TEAp+HAAQmdnT2YNUulTatmY96l87Bjxw709PSg6b+aIPpEeL1euN1u/OAHHfjv/05nEbu7gWuv5XHnnYdx3nkjxtpkt2P58uUYHBzEMUXqX2qbyn0vwBbpBF//LsxcdrmhfqqoqEB7e3u8jwAUt59S2jTTa4Yj7IH30KtoOmNuWj8BMwFkLzv2eg9h2zbvpLYp6BtBcNSDjgPHEDKHC3Y/NTU1YWBgAF6vFyHFrF7INoUkKwKjHnTv3YYxWyTjtTdvHs22PT0cXnxxB5xOMWObjh5tRGdnppJWDsPDwG9/exjve1893G43ysrehtNJbdqxI7lNrf2dEOQAju1vw/Kz5gNCGcaGjuP4W08haJmVtZ9Cox0IeD3oODKIUMe2vPuJjdklJSWYMWNGfCwvRj+ptmmWjPH2Z3DCW45x2+J4P7E2Eem+KMPvT5gxAzm3yecrzOJpGvlBkiQMDAygqamJynN7nwLa/wg0XAG0pGgsxM3jO7J/sFLW0dEQ00sOJVd86pR1NIK09ijAPDNyqcwAKFPrrruA//7v5NdZJdjFl5YBr8baHBlNmJNGvLSBBwy3mbWnpf87AKLA0juMSXylgklqhnT+CLIMDL5KmaHOFpIIOxnT+Gv9WMJTRScy9U8mcBxlEe7eTQFtRpCroXx2OUY7RnF863GcfOUk7BV2XPm/V2aVSejqouw2jgPOP1/7OD1VOo2NMb+mFMyfDzz7LHCsy4pFOqQW89UwBvTLOrLvYn3EfDWY3jub0mpqQDvNiy8GLroIT20/iecfH8eMQz7886e/R/ORp9AvN2DYX4Oe7RTVMZeY8fjHH4ej0oHND2zOSc7g0CEKMLhcQL3BIslcr7tMmGwyQ6s9K/5jRd6fr6cyg7VfC3v3AmYzBYRvuolea2wkUjhVr70Y/TON4iNrv0UUQVAxqP+DGZlh0SAzbDEyw2+gwloHJEmCw9ENt3smPB4Oe/YAzToKEPTiWDsQjdIY1pjJxomtkzg+P4+RmFRoWDTBrEMZKitsNdQ3wT6gbK6x95pdCU3gyChgrcgpMSCXscI34MOW922Bf8iPoCeIwFAAgwcG8fZv3o4fozY3sSC+a6Yr7Tz0nm82FHPse+IJel65kgztAUrUuP128jMSxcxyWK5ZLox2jGL0xCgaVuuTc0ttD0uKMDvM8cC0Gpnx6j9IUkgNfVWLEbbPRdARQMPNF+GuxzxoRwvOOgukDfoKBc9RWwt7XzMW4GwcxEJd55sKteqaqTw/sXl4YIBImkodxZisPXK7jO0/244ZK2dg3W3r8j4XJk/J1pKM1KurMz72VFRQe4yQGVOhj6YEmcFxHLZu3YpzzjkHQ0NDaG1txa233oqzzz4bHo8H81VSUWw2G/x+v8qnAV/+8pfxeYVToNfrxcyZM7FixQqUxbSe2I/e0tKSRJaw1+fNm5eUWcZeX7JkCWRZhiiKePvtt+Oft2JF8kJWiI1Uq1atwv33s++qBDgOvCDAZncAIQo6mblhQJwBk8kEk8OBpUuXAiVzaBPY8zjs0RNoqZuJ5oWr4r8XANTX16OOjZR5tGnRokUIBoM4/fTTIQhC/NwztSn1dbvdnvY6QME65escxwE7ZsEitWH5XBdQqb9NDQ0SzGYZwaAAUaw33CZRBH7yE0ZkpN7hHDhOxs9+Ng833ywabxOAqqoqVDDROZU2cfueADfcAVSZ423S20+SJKG0tDTeHva7A0XsJ0WbuKOrwfUMwxUbsFP7aeVKHl/6EtDVJUOW00dPjpPR0AB85CPzIAiT2ybr/A/DEvJgad35gLmsYPeTJEk4fvw4Fi1aFD/fQrfJWlIFi8uNsjn1kGesynjtCQJQUyOjv59DSckKrFyZuU0vvZT2daooK5sPl4vP3KaVK8EdPA9cZAzLF55Dry/7Kkp5B5ZYa8n7IwbVfor6YOVDsLjcWLr6QsDkyLuf2JhdGVt1sLE89dyLdu2deACl3hewsPYiyHPTx72PfKQOd94px4J16vcQBes4ALm1iVUpTmOKYWQnbYDVfHBKW4GKleShkA2pso5q0CnrWCiwyoyREQoomHJYdbLN1rnnAp/4RHJ5O6Boc/sfgf6tdLDJAZx+L63mc2izOToMRPporLIZ2CWrwSiZER0D9t1F537Ow+SXZKuhzEzfccCdMb20oGhtJTKjrS3zceWzy9HxHFUEAEBJfYkuvWeWqbhyZeYNIavSufpqHX5NKWBbiIMnnLgti9RioTSMU2UdR08S0WNxWrDgPQs0v4tlVMYrM2LJuEk+CRwH+/xZOPA40NMxjJoRP0L2cswd24+W8A6M2Gagr3IBxqtpH+Ef8iPkDeXULqVfRt5BuQKgGGQGk53t6UkdWyYWwVEa6Kyu9HlAKTOlBWaSm0r2KTXLsxnQTuMUhxgAwsNUnQhQVZ/eCsXw5FRmAJQVvHq1jKef5vDmm0BzrEpPq1pO+Xq2Yw7H7p2lSzOMYWKAFihyFODtCW9Fo5WdYoCkdTkOchj0ORyXX1WsrQYYPUDSV3oRGSMiy1oFWMpjfqXDgLUir8QAIwh5Q/AP+WGymuCsdSI0GoIkSrC6rOA4DtFAVHVuipMZMf+IiTrfQuGf/6RnpQziqlVEpo2MkPfrGWdov9810wVwiSB1LmDXvtmRqMxQhAgAJJJTQqEs9xDH4XC/G68GZ6O0lPwisPj9VG7S0QEcP47qng5UgsaPZdiFK/A3tKMl/uhFHWQdYkBGqmsmE04nMGsWSbAeOACcc47+97pmuSBFJfTu7IUYFiFY8ltssHWDzW2DKCYqnR2O7MRZKk5VE/ApQWZcffXV2LRpE8rKyiDLMv75z3/iiiuuwKuvvgqr1YqgCm0YCARgt6sz51arFVZr+mLQZDLBlLKT5nlelU0SNHpf+TrHcfHNWurnKr9zbIz+7SrjEyF0SyUx/8E+0nkeOwqupAXgOJgEU2LHHxwABw6CozYtCqB17rm0iYKfQlI7MrVJ7+scx6W/XtIMbvwYTIGTgCnZJDJTm2w2HvPm0ebq4EEeLS3abeJ5Pq1NpLuovRuTZQ6dncBrr5nizLDuNmU5d57nSbec4+Kal0b6SZIk1T7SOkejr2dtU0kznXvgpOa5U6CB0wg0cPjRjwCrwlBq0tpUf7HqZ+R7PzEJH7X2GD13rdc5Rr5IgaTxQOsc583j0N8PHDtmwplnZm6T3qyWxkYhviHQPHezGVj6Ffou9qJrgeqEo9pPY73gwIGzusDbkjOq8+knjuPix2iNHUW79pzNNJYHT6qO5VYrnyVYxymCdbm1SasN05hESCJ5YgDqptUls4FlX9P/eUwvGaCLqOtxYLwNmPNRMhOfYLDFsSzTAlnLvDYTWKbbBz4AvPe9KgewNvMmCiQoX9ejMQ0AwYEECSRG4fK/BnA+oKyVvH7yIYHY+4I6ZaaYX5C1EhBiRhXOWURm+E9OKJnB5HAzmYD7BnzgTYlsQACwlFgw3EY7okwEgR6JKYbNmykgm2ogmebXlAKlf1SqhnExofyu8FgYPW/1oGRGCSrmVGi+h1VmMBNpRmZUp1x6DbGEzb4+wG92oa35fCDgh+XgHszkTmKWawzHq50QvWOwjWXRd8iAXM2/iwWlofvgYIIszRVbtqhfT2qVDFrwDfgQ8oYQDUbhH/THDcEZ9BJkvImHvdIOR2Wyn5ssZyczimGSO41TCMqqzPAoEJOvhjyuv0IxXpmhMT4x/7pgH61b+MJeSKtX03zwxhvAB67KXtnmrKJ7KjQWylj9tv84xYNUJaZSfzcpCkhjwPhR8rsQbPoqO5Wfw5sArhR82A8u4kkwKLlWxVatBRyzAJeBeb/vOeDob4CacxRkBkW180kMyAUmuwlmhxmj1lHIIplaM7Jerd+8J2kdxqQrleerhUKebz6IRIB//Yv+rbT1NZmo8vTRRymBIxOZMf+K+Vi4eWFeQe7TPnwa5l8xH64mV8bKjBCsGI04EA1lriDdeYDuoTPPZL8zT6Wa9fXAmjVouhY4/iTAdQFeuQzH0YQ5aMM5eBkcZOzGMvwcn4IJESzBXrSjBaNwp32XkeqaycbChbmRGe4WN+yVdgSGAujb04f6lfn5gjLSa/teGy79YmItc+AA+ZgYWctMkxl5gEk/ARSYueyyy3DllVfiiSeeQGNjI06cOJF0fCAQwPj4OGpy2RUXCDzPo7GxUVdZDTNiKWVzmBggIoN30WQ5doRKAUUVFpZpLFuLl0lppC0FQVw2o93wWxcupM3VgQMJLUIl/IN+PPWFpxAJRtB8TTO2v7UdjWc2ouGMhqLoLhoCMxbLwRBswvsoFc5Z9Ow/qXmI0UDDpLepwJiQ9lSeSUSoS19kYf584OWXExvhTJhS2S/MYLBA5t/AFLje2Lg33pGILqTg3/0e+ndBUr+NH6HsPZMzbtCYF/q3Al3/AOo2AjMuBLr+CgT6gNrzgfJl+X++CjJdhyYTLZCHhyn4aHTZNjSUMAVVm/OTEEpZgfs79ZEZwQHg1ffFgzmCLKPZ3wNOCgD+dmB4OwUi1jyQG6ER98zQWZnBdM5tiQo0OGaRBrrvhPp7MiCfcYKZgGuRGUxKwjfgQ3gsDP+QH5Apu/LAIyQorCVzJMuJygw9ZAZAY+CVVwIvvihh//4RLFpUjvXr+YyBDBYAPno09+qgfFE+hyIJ4z3jCPvC8WAOA+ujeZ+ch7A3DHezG+FwYv+Qet/EyQyFJL5ss6PLMhu91rmoq6c3VI51oKn3Zdh+4gXevZGiEaX6ZcryITOKMT8pMyIPHcqPzDBayaDWHnb9j/eNY/Q4ZRS7Z7uTqpL0ynwtvmYxFl+T/kP39gJeL2Wvs/sxFbmY5E6vH05NqPYbq8ocOwrs+kpylHrF3YDFnZ2Qz+aZYa0kHwb7DEAWoUyqyQesPSzA++ab6ZVtqqcTk1XJdsyfP0z3nar5t7KadWQHMPAqVcoCwKyrgRkX60tkSKmKlSUJY/19cNTUgmP9lGtCRPXZAM429p5ATArMVkdtkCWq8o1Ba69RUwP8/OeF32twHIfSGaXgeC5jkD48HkZgmKpYWGUGO98HHkhPZrFYgAcfNF5xVqyx79VXaayuriZyTomNG4nMePpp4NZbtT+DJTIYQWp73E1uuJvcAKBZmTF/PuCHEw8EN+Pr94Vg0VDXtJZZ8cn/onvobI3LUEk4Heda0CGT158VQTThBCRwqKwESoe68HH8EgDggRsdaMYxzMbTuAiNM7mk+MJUn58WLSLiSq9vBmuPIAioX12Ptifb0P1Wd95kRtATRE8P8KPHrUhdAhitymTXCLtmsmGq9NGUIDPUIIoiTCYT1qxZg1tuuSXpb1u3bsXq1asn9cdjHagHTFbfXqpg7pUa1rZaWnhIoXTmnmXyFVEWwkhbCgL3UloQ5ZBdyDaljz8OnH56ejm42WGOT4TRE1F0PN8BR5UDDWc0FEV30RAKQGZMGhwz6TnQQ2a1gkX1MBZo0FOyPyltEoNEHppKqdqkgJiQ9lSdSQ+dSJiAZz82U/aL4WwdtolSBuyDg0DvM7QJSvUFSEXZAmDh5wE+f+1yhkm/h+z1pH0vBilYaVcfaKb8PTSNvJHUbyO76Ll8OWk1ayE8SlWctizRu+G3gdH9gCvmv1Iyh8iM8WNFJzO0UFWVIDOM4umnyYB6yZIsetcAyXHZqmmMDw4AgS598lwRbyyj0goIdnAATIFuyrC01lK/hIbouFzWYmXzgZX36n8vq8ywKTzh4gkFuZMZuYBVZmjJTDEpCbPdDFuFDeHxMKSoBEe1A2a7WVNKAgD27KHKAocDWLNG/zkJAnD++TzOP1+HUDEo+G2zkVxZR4d2QLiYsJZa4ahywD/oh6fdg5olyeyEWh91x+JRgpCeVamszEDssjI7zKhZVgPexMfHkn7XXIxVm9BUU06RlEceAa64QgczSGBkRmbjXHUUa35asIDIjIMHgbVrsx+vhlwqGdTaE7/+nWYIVgGyKMNsN8Nko+11putfL1gySksLmXqqIZdkren1w6kJzX6zVdPcb3LSnBPx0p5NsOpLlIjLTGmsMTgOaP1o7ieuAdYecyyGe+AABYTLdFbRZTtmzx56ViUzgERlZ+kcIjCO/4UkK8WgsQQTWzUlpvAm8CX1mOEy6G9RSDBfE3s9UH6a6iHKvcbNNwM7d9KzWtAz21ihJtWXitL6ZBI9GogmyeICJMcIAI4qWj8owfazpaXko/b//h8QDmf3EVJDsca+f/yDnt/1LiKflWAJG6+8Avh8RMwXCpnao1WZUVdHv+XYmBPDcGJRhkv9tdfoWYvMANQJshBsCMych3vvpf9ffXUzviR/F80xAaomHMdy7MbT3MW494cyhHu+R3qjzc3gm5rQOGtW+g85RcBMwPfv13e8so8azmhA25Nt6HqrCys/tlKXJKsWAp4g9u0Dgkj32jJalWnUAHyqrCGmxBXS1dUVl2gBgEceeQRPPvkkNm3ahHXr1iESieD+++8HAIyNjeGOO+7A//t//2+yThcAkS0HDhxIMorVAsusspfHmPtzHgJOu5s2t2seAM59mF475yHgjN8CnOJqY5l8RazMMNKWgqC0lRZEVWcZetuWLcAvfkH/fv55YMMGKqHasiVxjMluAm/mIcsyjm0/BlmWYS8nOTKWea41ZnAcMHNmkTLPZTkvMmPC+ygVlnIKopW0ZtZjBw2W551HGRTnnac9eE5Km/xdwM5bgT13FPyjJ72PVKCU19ADthhxu5Nfb2w0qLfs2QW8tBnY+aXEa9FxoON+oOvv2SUvbFVA7Qag2kCEKwsmvX94IRGQHO/IeOiUvoemkTeS+o1lAapJTDGceAR49f1Ax32ZP1iWE5/HNrDMuHo8i+lBHsh2HTIvhEcfBV54gTa+evHkk/T8rnfpOLjlBmDxl6kqpXyZ8blWsAMmJ2QAUVGEzFsoQJGPGShA/h2lc/RLTGhVZgA5VWbkM06wwP+xY5n7zWQ3wWSmAC5v5uGocMDitMSNrNXAJKbWrdMO0mrBSJt4PhEI0TsXFgOsOoPJbymh1h6lxFTqfp7tH3v7Eq/xPA+zzQzBpJgwOB4j9gaErv8g8L3vAddeS1FxgHbhDz5InasyJweDCRIrl8qMYs1PhfDNMFLJwJCpPWa7GZYSC3gTD44nGZVs179e6PHLyCVZa3r9cGoiY7+NxS6WsvmJymZ/l74PnvtJYO4nCloRrQesPVVVIpqa6N7btq0wn+31EvEJGCBkS2LjYw7qETj2v8Cbn4DY+Y/C3VuyRFWmw9v1ywUyX5Msfcn2GjfeSP9//nn14zJdc1u2UCxmwwbgfe9LxGaY3JLq54VFjBwbwVjXGAb3D2K4bZgeR4dhKbHA6rLGX/MNkH/Jrljez6pVwCc/SZw8APzqVxmbaLg9+UDNL4OhtRVoaiIpqq1bM3/O3j/txdNffBp9u/syHxhDansOP34YbU+3IRqMalZmcJy+ddHgIHDkCP37rCxhu82bKWHk+eepkub554H2dnqdxRdKGt3YiRV4FJtxL27Gn2beQvGFK0VamwwPA489BunuuzFy440QB2JJ3YcP04cr4sWTCUZm6K3MUPZR7bJaCBYB/n5/3CMmV3QeDSEQBIJQX0SrrWW0YFRmaqqsIaZEZcaTTz6Ju+++O+5zMX/+fDz33HOYEVt1PfbYY7jppptw1113QRRFfPSjH8U111wzmacMWZYxOjqaxiqrgZEZbjcSGQC7bwMi48AZvwAcsV3JeDuw71sUOF7+HQBSXOOwmJUZRtoyWdBbDs5xHGwuG8YHqOTb5XbBVk5sZUEzz41CDJLGKACY9Zf4M0x6H3EccObvCuoAOSltYqZupgKmRMQwIe0Rw6RXK4u6KkuUlRkaykZp2LyZ7rf77wc2bBjCV77ixnnnCcbui6iPtGeZbi+QqESI+sjgNhft2Dww6fcQQFJTY220Sao2WDauginRpmkYRrzfokHAG1sJa2TPAUhsSH0dmT840BWrMDADZbGV9gSQGZmuwy1bgB076N/33ksPvZr0kpQgM3QmkhOa32fgYBVEvJAkCYKpFEDh5jzdiMvsKcgMZ6w6UgqRWbzJkf4+DeQzTjQ2kpRDOEzB36Ym7WPjch8ywPH6zb/1SkwpYbRN8+eTkfmhQ+qBholA+ZxydL3RhZFj6TX8sizD4/Hg4GMHYSu1ofm8ZvT306Sb6pcBJILSkYiBvb3TSZEmhvFxSsd94QXSFzn7bIpWxHa0Bw/SPVhRAdTWqn5iRhRrfmJkht4gghpyqWTI1h6T3YSIL4JIIBJPojKCf33+X+BNPNZ+cS0cVYn7Ww+ZkYtM6PT64dRExn7zxaSAS+fRmttaoVkFnIbK1dmPifpJbpgzFUYWE8ntOfNM4Phxkpo6//z8P3tvzI6soSE9Mz0JkghERin+wiRh/Z20j+ENhMpiBIjsbMZoV4HuLSkKvPkJ+vfaB7LHEKQo+WsBgKOequI9u6jytEZd3P+CC+j5pZdorrekiC9oXXOZYjOf/hTwxRagzp3+fdEgTVrRQBSPfegx2Nw2WF2JYOzgoUEc33qcmhCT6du5k/btp51Gx9x0E/DYY8D//R9w552Aho2uKoox9h0/TpWMggBcdFH63zmO1jq/+Q2tfTKtaT0dHgweIJKndln2yVfZHlmSsf1/tgMA6lY2YHSUrl+163/+fDIkz0RmvP46PS9YkOUeioERZGrIrDxgomQLAJAkSCdOoOupp1DKsisfeYTIDEGgyaypidYz9RNLvjIwMuPECVpKlWTJnVL2kclqQu3yWnS/1Y3ut7rjkmBGIcsyuOYmnEQQQWS+AfSseYySGVNlDTElyIyPfOQj+MhHPqL596amJvwrE8U7xcFkplxM/k+WE0FVQbEhZYbgY0eBwz8Fqs8h86moHwj0k2xCPiaUUwlRH5E35rJEtrIGjJaDW91WjA+Mx49RbioYM/yBDwB+f+JzshlI5g3W37wZ4NUlmqY8CkhkTBqKSGYUFcyc1nsYOHA3YK8Fln0r8XeNcWHOHMroHB+niUzvnL+d1kG4/PIBrF/vMk7wRVSqkAQrVVwEB6kE2pWBzOh9JuENoiFpdkqCZXwF9WXbTOMdDjkKzLqOSIpMAQdGXPpOZDbeZFUZrkWJ+6YkFnDwd5Ivl1A46bZsMKpJn4qdO0lGx+nUYbAnhgEpSBKCec5VssmJiKkSJmtV4aiM3ufIVLRuY4Jg0oJaZYZgA87+XzJnncC5WBAoWe7QIfKcyERmmBwmgAPKGrIT1aEQ8OKL9O9cyAyjYIHgfLL580X5bIoEqJEZACCFJOz8/U7wHI+m9U3xygw1nxmLhV6P9McIjYA6o6H1OgByIV21irIdXnuNmEOrlSJbY2M4sMMMwIbFi6fW8q8QlRnFkJ1l0lJiyHiWohSVMHyEIgi8ObkMh7UzE5kx0aa+05iiWPYN8kuwuIuzz+l7ATjyC5K7XXJbwT/+jDOAv/wl4ZOVL7JKTDEEOoG3Pg3YaoAzf0O/XdRH6ya9ksThUfLt4jjA0QxgX+4nroRgIZIlPEJ7h2xkRrCXBgDBBpjdwNCbwMF7gdK5mmTGkiVEmg8MUPB63brsp5UtNgNQAkRlbfocxAkcXLNcGGkfQTQURWA4AEmU4JrlIpnEGJQyfbt20fW8PFbEfNFFtB45fjwR15lMsKqMNWu0g/4bNxKZwapSteCa5cLJV07GzdCNIBKIxP/tCyekurTIDCAzmaFHYsoIMpEdcfA80NiI8WXLEhPYf/0XbR46Ouhx5Ah5gAHAs89SpemcOfRobjZe7msQlZWJe+bQIWDlSmPvb72kFXUr6tB4Vu4yTRzHYeGNq/Hy77Mfq2ctM20APg1VyHKCzIhLt4jBxEivXGxwApWDjh0BRrYD9gaajADglRhbmY8J5VTC8T8DJx8FGt8NtN6U8VCjxnY2V7JunL0ima3cvJmq7d94gybi97xHW5O+YLBVAeseA0T/1NoV5gK9Kf5TEWKMwRJOITJDaU4rhSnzihMAr2L1oTEuWCwUiGpro3iFHjLD600sbBYuHM98sBZERhqlpCrYY1lCgW7ApSF0KoaAgz+if6994J1FZtSeD9Ssp43mNKZhcgLN12c/zlZHawExSPcOy9JPRdx/47TEa5ZyMsEOjxJpUpYhIlZA5KJJnwpWlXHBBemZgmnw7Ab2fJ027Ct/EDuJICUPZPIiUYOpFGFTLeyFvE8HXiIDb2dzdjKj9ePUz46UTY6WMWuR0dqaIDNYBqcaLA4LGlY36PrMV18FAgHSbc7Fj8EojEouFgMVc2inGA1GIUtyWvUKC4Izk1SmrqBGZgCUbXyo3wrJ5kA05Ec0pE5cOCodcbPcNPA8sQMLFpBGCMM//oGmn76CD+F0zGpYC8hzp8y6j5EZ7e0khWVLl4rOClbJoLW3UKtkyAZmbCuGjZMZ8aomjvwho7BzAAEAAElEQVRVlGDXbDZteC1T36Ina01j6oDjAIe+MTgJgR6KPTgaM89PLOkioLO0ySCYCfgbbxTm81hlRtY5hklxWcrpNyxpBjz7aM2kl8xgslS2OsCUpzRlKmw1MTKjP8nIWxVKvwyOowQIAAhrRyh5niph/vxnigvrITOyxWaCsGI04oBn0I/SUvW5qXJ+Jca6xhDxRRANRDHSNoLKBZWwOBILvmgoCllOyEwxMkMQgP/8T+C224D/+Z/JJzOYX8all2ofc8EF1CV79gC9vbT+UQMzP89FgijiJzKDN/PwjtOcVFKCuCeNEmxdlMlXs9BkRs4wm4mkaG5O/1tZGW0snnqKFpY8D2zaRIyXz0eP6uqCr2EWLaKknAMHjJMZetfK2ZBLVaYWjBqATxVMkxk5gud5zJ49O6sJ+fg4lWkDisqMeJa+kJylH/ECkGgyjY4BYQ9tzJmHhhjIz4Qyz7YUFKyMczy7JqXRcnCrywoOHBwOBzhwsLnTdzrHjtHzhz6UKFksOngB4I1LTAGT1EepGO8ADnyPrsdVP8n74yalTXHfksKTGUVrj9Kc1lQS20RwlHEDZB0X5s0jMuPQIR3ZEADefpsmxKYmGStXzsqtPawyw6xCZozspkCdFlhWssmZkySbFqbEPVTA9gBTpE3TMAzD/cZxgLOJCExfhzqZIYmAJ5aGqPTf4DjyOhp5m+67IpAZau0xmoSghieeoGddElNso26JLbTe+iQRv0opT53gOND6oZD7HmaqGhzIfmzVmQX84vzHCeaboWUCngtYZuLGjbntL422aSqQGfZKOzbfv1mVWOB5Ho01jehEJ8xOMziOy1iZAdAGdccOJ+w3bMY1m0Ka32sts+ozn1ZmMl58MZ7+YRlm4zVsOvk6cFsVcMMNtHvXiWLNT3V1tJ8aHSWCLRcyTBAowK8mOwvQ+PTtbycTrdnaY7LEKjNyIDOCo0EA1FdKkisUoiRUIHNlBkNmGY9kTK8fTk0Y6jcxTP4J9kbtik4AGN4Rq7g4C1jyFe3jGJnBsv8LMEkq23P66XStdndTcK4hz3if/soMBQEAUIzCsw8I9Or/MhbPKJld+HvLVkvrPyYflQn2WjIyN8fWQlZGZoyQ9K9GcscFFyTIjK9/Pflvau3JFpvxw4lHsBkXfCqEd79b/ZjAUACPf/xxlDaUYrxnHBFfBKPHR1E1vyppHOzuJsl2szl5Cvrwh4E77iBT7X379Hs7Fbp/AgHguefo35lkLKuqgBUraJ/97LM0paqBkRnek16SEspynynbE/ERmWF2mDXNvxmyrYui0USV1ESTGYb6aPVqesgyXSxtbcCsmOrL228D990HOBxUytPcnEjgyBMLFybIjGwoxnwbDUUhRSTce68Z11zD5V2VmYsB+FRYQ0yTGTmC53nUaO0wFGB+GRaLInsonh3uUF8IOJuI4RdDQKifAhEMkvaGJVfobUtBEZfN6Mi6IDJaDu5udtOGYIyDpdSSVLIIUOY5y3ibUxjJz6JjUvooFebSWFUAb1xHVAWT0qZo7N4rEplR1PYIdtJJ52K/u2BN/DvDuDB/PgUFM2VeKPHWW/S8ejWXe3sYaZRaAcM2CpnIjLhefGF1MKfEPVRgvBPb9O8AnudRU+4Ahl4D3Mv0kVwlLQkyAyopNtFxwL2U/p6qZT3v00QsCjmkMOuA2nWYiya9Eh5PIiNMl/l3iJEZsdU4HwvM+jv1kxliAJDC4KQwrII9YUYgBvS9PxOsMaI5NJj7Z4wfo6pWwQ4s+Jzut+U7TrB10tGj2scYlTliZEauElNG28Q27X19FASPJxdNIDiO06yQ4HkeZfYycBwXN41WGoCrgQX6erxOVMwp8JqmvBx/GLwUx3AJLvzYUUB6NVFe/vbbpG21YkXGkqlizU8cR3GIN94gCaZcK3vYtiM1ACAIVFn2yCMUbGL79EztiQai4E08rC4rTFYTwr5w/HU9CI3SGk6pGw/QPSdJlHiq17dEl4wHptcPpyo0++3Y/5EMUcMVVPksy8CrN1CF4pm/yixlGR6i52zVf9ZqIkXEMCVR2apyb0gMyvY4nXQ/79pFQdRNm3L/XFk2QGawygxW1dJ0HdB8g7EEpPFYlmRJS+HvLVvss0I6yAxnEzD7g4n/m92xQU6ipDeNilNWdfnGG+keAGrt0ROb8cOJptOcqNCItQyD1m0mqwnVi6rh6/OBM3FpVYsHY8HihQuTp5z6ejICf/RRMgL/0Y+yn5NWe5QQRX2EMMMLLxCh0diYfT7auJGm0Kef1iYzSutLwfEcIv4IAkOBJA+lbO1hlRlmh1nT/Jth7lx6HhqiR2XK7b9vHxU1lJUZymMoCHK6hziOFkZKFnTlSvoBmDzVK6+Qq/mCBcDYGPDHPxLB0dJCZIdDvx8d883Yvz/7sWrtCY2FcPLVk4j4I1i4aaHu72XofK0Tr33/NdSeVouHHz4fH/94Ir4JGK/KNCozNVXWENPpGDlCFEXs2rUrq4O70vw7Hq+P6/ZruMVwQkLnOjSc3+ZXB/S2paBwzKQFUWScFkQZwEqotPgOjgNmzkyUUC3ctBDv+eN7MP+r83Hh99N3yiy7sLoaKC1sorQ2hrcD++8Gup/I6e2T0kepsFRQMF2WyGg2T0xKm4romTEx7eESlVqSvk2y0gRcDxiZcfrpUu7tYb+zWmUGkLlEPU5mGBCr1oEpcQ8BQO+zJIfTvzXvj5oybZqGIYiiiKNvPQx5353Arlv1vSlbNaPFRRmVZ/wqPfPOVlU0IgNQvw7z1aR/5hnaUC5YoF5VnoZ4ZUZsN8aCEv4M5SEM5jIK4kghINAF2XsI4ZEDkMMjQMRDr1sr6bhcwSozsq3nxo8BA6+qj5GSSH8b3mboq/MdJ1hlhhqZYS2zwlHpQDQURdATTHtEQ9E0maOhoYQv08aNOZ2S4TaVlSUkHSazOkMLoihi3859kGUZFidFa7JVZrD9elf+y7E0+P0k4wRwmH3xXOCDH0zoVO7dC/zud8AttwB/+AMtLlT0DYo5P+XrmyGKwO2307+//GXg+eeBBx6g51deoYDZX/8KfPe7yvekt0d5/Yd9YVhKLODNfMbrXw2sMiO1klzpl1Fola/p9cOpCc1+G3oD6H+JlB0AumDssUHPn2WQYPtwaxZygheoSgCgio8CILU9hZKa6u2loBxT0ssItqe1xwZVS7nxSmpFZUbB7y1GZuipzEgFLySqNDJITc2eTWutaJQC+UqotYfFZrSQGpvJBo7jUFJXAmdV+v78QGwcZBJTSnzsY/T8hz8QoaAHmfpnyxb6HTZsIOXFDRvo/1u2aH8ek5i67LLs4zRL4Hj6aXVZIADgTTxK6mn/PHoyu9SUsj1KMiNbZYbTmehDtXURSyg688wEqT9RKNg95HBQyc5llwGf+hTpzH8wRvb5/eR4/9RTFPW/+WbgO99JdExPTyKpSQWMzNBTmaHWnrGuMbz107ew78/7IEUlw01TVnRu3gz88IeJ83r+eVrDGZGXZGSGx0NrpGyYKmuI6cqMHCHLMgKBQFYH9zTzb0ARUM3A/plKqHoh0JP5uAJAb1sKCt5MZa++41SFkiG7IxdjO1mWEZbCcFSn/3ZsQ97amvan4mG8g4KXvAmo16OZkYxJ6aNUcByRUN5DVKHhzOAEqgOT0qbKMyjoV5Z/eWEqJqw9vJlmGSmsK0BpVF6DkRmrVuXRHsdMwL0EsKVEKt1LgTN+mdgMqaFIZMaUuIcAMnEe2ka/QY0OYdoMmDJtmoYhyLIMYWwPZAvAuVV2Z2pwLQIaLqfnTJgEXXu16zBfHVdDElOAgsyI7drssR2aHjLDVk2eQxEvcPR/IA++hS7xdMw86yaYhNgy2VyWn7xnnMzIIjPV9zxw8jGg8Uqg9aPJf2PyYuFROled5Eq+44RSZiq1kNZZ7cTmBzYnNP9VoJQ5EkXgJz+hz2lu1p9tnopc2rRgAQW4Dh1KBMwmGp4OD97+7dsQzALW374+/rosy/CP+iFDhtlB4tZ6PDOAzHJuueLAAeqjqiqV77/xRroxX3+dHq+8QgY4CxdSxUZMnLuY81O+ZMaf/0xZp243cTJxT8MYfvazhB77qlWkJ//CC8ArrzgwOEiVD4Jg/PrXQtATIzNSPP/0+mXkgun1w6kJ1X5jZtUAUDov8bq9gfafgS4Aq7Q/lJHsenyZbDPIlyHQQ2v6PJHanjPPBH796/xNwFlVxty5gD2bhUW8MiOPivCWG4Hxo0Dp3MLfW3Eyoy/7saMH/j977x3nxlnt/79n1LW92muve08cl8SO0wupQELAKZRw4YYbuAS4hHIJPzrhcuHSAgmXy5fea4IhEEhI79WJk9hx7/a6bK9atZn5/XE0ajuSRlppV7vez+ull3ZHI+k5OlOe53zO+RzZ312fuFm760W6PJxdDP+ii+AnPxEJpOS5l5U9Zmzm6qtHfk6+8ja5sC2W+W4lC37JJTKX2L8f7rxTbk+5kMk/GzZIrCndbW1tsv2uu0YGhw3DXr8ME+ecI0otR47IfTZTxUPN7Bq0oGarui/ZnjiZUZG7MgMkRnD4sOQknHVW6msmmXHGGTmHUHSU7P6kKIkGItOmydzFMCR7ZN8+KUVRFJms/vd/y2uzZyckqlavjktymmTG7t3CiWTr7WdlT8PiBjzVHkL9ITq2dTDtlPwmxOnzhiMx0YtTT7VXnZmOZNKrt3dkpU46ymUOMUVmlBjJlRlx+Fpi5Ys5FqKeZnlMVlTOjZEZ+6FhbdZdMzW2mzlTbqbr18NQx1B8QaFFNYYODdFT14PDKXdSc0FhVmaMqcRUvFfDWJWCFBnBDgmeOP0yae5+ITXYPNpAz1ihboU8JjLMRsBa0FYwy6zM2Ls3Jc5giY6OhD7zqaca7NpV4Bjnvg2waGzs9OcmZ0tEZpQFgh1CRkWHoGcTDKSJ0E+U82gK+cO8hgJoUaqDL4OqS/BgYE9u31fOg0X/bv2aFpZgvpmFaYW9v4S+LSI5VTHb/litYOM4zZaEYCLTQtcwEs2/7ZMZsVWbGYwxpaXsVhF6m+QR7gFnBQPulVIh6yzSNNn8vUKd2aU1TY1uq+ufwyt62MPHhRStHYPO2cgaTlUlie3YsZHVNBVNFbZ6MmzYkDqH279f1obmHK7UWLJEAtLjWZmhulSOv3wch9sxogm4FpTsNpPMyCUzZWZUlqIy47XX5DmjBnlTE1x5JVxxhbBc82NNg7//fUmPPeOMkjakGw2ZEY2KzjpYExkAN94oPM1PfiLHZlUVHD3qAESXo7U1cdwmH//hoTCBjgDeOu8IYiIbFFXB3+gfISdiHqt2+mVM4QTGwG65r/imJfpGQVKFos3KDLcNMmOMmoBv3CjxxEKD4bYlpiIDiflOsrztwT9Bz8sw9x1QY0MCpvF0eUDWbO6CUDEX5r0zUTmSCVoYXv6kHAtn/SohKeWpl6rPUObKDEglM+zAvOynIzk2Ywe5ZCqzVWaoqhDPn/mMNAK3Q2ZYQdNkfmI1VzWnbB/5iPQkSj4mt2+XuYzbnZDqygavV5J4HnhAKpAzkRln33I2qiP/cojmU5q54NYLcPqcPPMn2ZapMgPk3vLQQ9krM8a9+XepoShCbCRn1ygKfOxjQnDs3y+TokcfhRWxGNI99zAzGOJc/1xeC8xl9656Tjo5v2QyRVVoWdPC/of3c+SFI3mTGenylOZcsNBeQy6XzHUGBqSqLReZUS6YIjNKDMvKDP/MWLDvBEfFXOAxyRqxAbOx3eOPS7XY8LCw4StWCJGx4R0bCHQF0MIafYf60DSNbTO34aqQhaG/wc/6365n925ZdIxpZUaczMggLVbOCHbA0++QyW64V54HdsC+XyX28TRIZutUILb08DTIceSyJ5U1Y4ZUWQYCQmhkWxSb0h9LloyPpjiQ1IhvkpEZ5nkUOAqBQ0JmdL2Qus/UeTQ5kXwNBRxamJrAfpSQQyTHFHV0vu/bAq9+AWpPhlX/Y71P/zbJ2BvYlZ3MSBurJWyONVMSAmTXcd28WTKM/H77EgUjKjOSZabsNiqNDMbJhKBrlJ1H02EGibSQzAcySVgETTIjAzHlnz3mZIbbLYTGvn2SgWZXQiwZhWQ8Fhvmva/QbP5ioGpGFU6vk2gwSn9bPzWzEjfa6qXVrDprFd5qCYKPp8yUSWbk7EehKKmT6XPOkQjI73+P+rvfMa26WrSoCy3ByYBkMiPfPsS/+IUcx42N8OEPZ97vf/9XpBr27hUN+WRkOm6f/faztD3XxpoPrGHR6xfZHtOSK5ew5MqRk7MpMmMKttAfO1CSqzIgSdo1x0XCbs8MgKaz5XNzVYkWiJNOEvmbgQE5v+02dU7Hli3ynPMaZugw681S8Zhc7d6/XciMhrX2yIxSwtMgfTxyIXhcLohOX0JaCmD2ddJLpXJe1re/7nXy/PLL0lagMYfq2E9+Is/XXAPve5/EaIaHpd/y+ednfy8kZPoCXQGiIWtCw13rZ9cmCdZakRkA73mPENRPPy1+L6SP0hNPZK9yNAw4dEj2S856/8c/5PmCC+S4tYOLLxYy44EHMt+DCiEyAHx1Pnx1UopkVmZkIzPMhMd0MqOzk3hC43hUZow7VFXYumTGbng4UeY1MIDyyit8tOJ+2gPg/FQlfO1GKdfo74+VbuY+IGauncn+h/fT9nwbq9+zOq8hxuUpY4kT5vFbKJkBUsUzMJA4diYCpsiMAuFwOFi6dCmOHCkDlpUZuZCp2WQxmlBawK4tRUfDWmmCXm1/lu5wiH7h8uUih7N7t5AZof4Qga4ATo8Tl9/F4NFBFKeCw+3AW+slOhwl0BUg1B9izx65uIxpZUYkpmGa3kPAJsbNRyAZK6EuaajqaYBIjKFz1cqzNiyvR/rzCsSNi039u0Q/1Dej6BryJbfHPP9VrzwMJMM/x3VBVWWy8vLLUkaabVGcaP49SnuyRRfaH4ful6DpHGiwKHtf9p9CaIxSxiwd43oOQeI8clVJdQaGXP/UWF1qAefRuNs0BXtIvoY6fGB0oTo9cj9w19v3vRYU8t/pTyUkel6R53RZt2RUzofeLbEmlVnSx9LHOmIMI8ea7Tg0kxDMZorf+56o0mRbxJkSUxdeKFlsttB0juhJm7+fb4ZcgyKDsaaXNtjZQamUUnzTWLz0tOKeVw43rLkjQUZbwTASlRneDGRGxWwhQQMH7X91Ea4TCxcKmbFnTx4EUwyFZjxmQyE25Su5WAooikLtvFo6t3XSvbs7TmY4HA5WrFtBTU0NiqIQCIjaAeQmM3p7ZV+7gRQ7yFmZkQmnniqPgQGU555j+pNP4jCb0z33nBgzd+6o5fAWxIqmhoaEWMim3Z6MUAi+9CX5+1OfSm1ymw6XS5JArJDpuDUrKwKdGd6YBwwjtWdGsTE1f5iYsPTbQKwp3ggyI3aRMJOErBAdlgfYIzNqlxeVSE+3x+EQabfHHhOpqULJDNuVGe4aWPBvI7dXzIXOZ0U9Ihe6N4ERlXiGq3r8zi3Tz96W1GusTTJm2jSJr2zZIkTutdfKdit7gkH4zW/k7xtvFLmnq68WIuOvf7VHZtiR6Xt1u4fAIxXMmJGZXJk+XRqBb9ggjcDvuCP791rZc9RmoVH6fsn9Muzikkvgk5+URP9cigl2kOl4syszBSPnRc8+K89Ll2YnQ0qFsrw/JevVvf3t8Pa388+D/Tz9u/3UVu1nsVlGe++98PDDMt+ZNw/mzcOxbJmlPdNXT0dxKAy0DTBwZICqGfYVXDJVZtidD1mhrg4OHLDXBLxcfDTVALxAKIpCbW0tSo4JuSWZEewQeaVIWqpPchPKSO/IRzGaUFrAri1FR8UcmPkGqMqfVch08XX6nHhqPKhOFadTiA13hRunL8HbjUvPDLMhW4EyU+Pmo2Q4fBJ4c1ULkeGskIdVwMsGxsWmbV+HjTdnbqI7CpTMniJcFzJlXqQj0S9jFPYYBjx5HTz9TuuS5r7XpAl231br99csg+kXFb1Je1mcQyDni6sKFCegjOo8KhubpmAPDh84K1D0MKrqRPE05Of7A3+ETZ+Atr+lbu95WZ7rVmV+b2Usu2hwb15jHfGwGGuu49DhkKy1t79d+gY7nXDPPTLXt0Le/TJAghEnfypRmeHwSAZpy6Vg2GxOZ5IZVYtKc15VzpPrdKbPjfQLYaUoCY3sdPhjJNaQfTKjGNeJebGEzrvvlgV4Pv3+8sl4tItCbDLnjbt25Tf+YqNugRyjPXsTqW/p9pj9MjweKfu3QnV1gsAodnVGwWSGiaoqlIsvxvfFL6L4/eLke++F//kfSaH9xz+kE3yBcLkSCUn5VNr8+Mdw8KBUF910U/Z9n3hCZNUyweq4jZMZHaMnM9rbpbpfUUT3v9iYmj9MTIzwm2FAf4zMSE8ONCsUg51yb7H8QAcs/yws/mDRk7zswOo4NKWmCu2boWmJa1hOMiMTzCoGO+oRB/8Im78Ur7Yuybk1fBy6X5TnjPvEyIxR9P4wpZKSpaas7PnznyVYPmuWVBoAvOUtidfsyuhXNFVQv6A+42PrAbnJ5VItNBuB//Sn8POfZ5+nWNljt+I0eb/+/sT1306/DBMrV4pS4+Bg5kb3hmHw8Oce5s//8meGu7MnLSbbc/zV4+x5YA+9B3pzNgCHxLxo9+7U32u8JaYmyv1pzinVbGYFt+97E49uaZTf8OKLpVzo5JPh+HG46y6UTZvEnoMHhQV86iloa8Ptc9J0spAgbS/kN5GLV2bUynV7tDJTkCC+7JAZ5eKjKTKjQESjUV544QWiOXQRLWWmDt0FL3wIDt+durPZhPKcOzM/SiBBYteWckK2DDtVVTEwiEQiGKTeTYPBxKJ6bCszYsRVgZUZZeMj1Q01yxOBsVFgXGyKxlIdSyD3VTJ70q8LZ/8R1nwPTvkCnPV7W9cF83zZuTPz1xhGamVGwfboIVk0hfus+2PEy96zZIqVAGVzDoFUZABoowt4lJVNU7ANI9xPJBpBz5fcrpwrz8mZgpH+BEFRl6WZeGXshje41/4qEyDUDkPZyd98jsPFi+H975e/P/5x0PXU1/v7ZY4PcPnl9odpiZM+CUv+Q/Si7WBAMh00/7zxOa9MiSlPQ6x6ywIVs6XKQ7GfCTXa68SGDdIwGeAvf5GKmblzZbsdFJrxmA2F2DR3rkhmhUIS0B4v1M0fSWZEo1Ee/NWD7Lx3J/2H+1P6ZWRaJypK8ftmaJpwDmbvrNE0nk7xkaLA5z8vpQzz5smXfOYziYyvAhpI5ts3IxCAL39Z/v7sZ3M3BS7kuPU3yb19qGPI3ptjeOgzD/HALQ/QfzjRq8hc38ydm0eFWh6Ymj9MTIzwmzYs9ziHe+TazFUFM14P898lckpWcLihcR3MyOOGO7Bbqqyj+R3nVrA6Dk0yI1OgNxf27pX1vs+Xua9DHIE2mUelX4Mq5sZeP5D5twN5n5kgF/v9S3Ju7fs5vPpF6Ho28z5xmd40MiPcC8celESyHDClppLJDCt7TImp97wnUZl22WVyrdq3D155JedX2YL5OZkkpkyYyj5DQ3DDDdnnKVb2nHtu7ox2vx9OO03+1jT49relPUprayLhww5UNUEaPfCA9T6KojDUPkSwN0jfob6sn5dsz94H9/L8Hc9z9MWjtiozZs+WpIlwWDLyTYw3mTER7k8bNsC3viV/P/NM0jH3WAOsWwdve5uUgN5+O9FzzxV7urslo+ZXv4IvfYnI+z7AvCNPERmO0LGlnZ7nd9C9u4vuPd107+nOOpeYdeYsZp09C3+jH01LNAAfKzKjXHw0RWaMApqN1C7LyoxoLIBllXnsbZJKhUyPEmmp27GlJAgchqMPJPQ+bSKXXIC31oviUPDWpa4ADh+S5+rq3FqQRUURemaMm49KiDG1yTCSyIwcTagLRMnsSb8ubPsG7P4xuKttXRfMyoxsZEZbm2QhOhyJDJiC7DGJO9UhUjXpiJMZFpGCvm1w5D7bfXTyRdmcQ05fLBiZZYFkE2Vj0xRsw6g5iaBzRv73g4qkTEFz4d3zauy1OYlmj1bwt8aazwcSQfOcA43K4tjhJ9exms9x+IUvSILHyy/LfD4ZDz0ki8NFi/JIODDJ0wICoimY81ZY/EGM+rWlOa96XoHdP4Tjj1i/nktiCoSUOudOWPlfeX11ofaYvS760tbSZs8AO4RGIRmPdpCvTQ5HoiK3WFJTmiYZoL/7nf2KlfoFslrs2dODkXTMtj/dzgvfe4Hjrx7P2S/DRDH7ZmzYIAvx5AzT006zT1pZIcVHqip60jfcAN/8Jnzwg7I4Mgyp2LjrLslitAmTzNi2Ldv3J/zz8Y/LHGfOHJFFyYVCjttCZaa6d3bTua0TxZFgrsaiX8bU/AHuuOMOVqxYwcqVK1m6dCn/8i//QlvSCbVt2zbOP/98Vq1axerVq9kwmhOiSEjxm9MPp30Hzv69VCSmY/EHYPa1xV33vPZV2PoNUZkoAtKPw3Xr5PnVV0WmPl+YElMnnWRDunDr1+Gp66F7Y+p233T5PbVw9mbnoQ5ZW6pO8M+Kby76ueWJ3QyCHZn3yURmBI/D9tth/29yfs3558ulevfuVNI/2Z69e2WupihyOTdRUZFIQvnzn3N+lS3YITM2bIDrrht5/802T0n3j8MBt96afSyBgJAe3/ue3Cu/+EXZfvhwfgkekKhmueuuzPMHU4ay72B2MgMS9kQCEQBcfpetygyreVE0mqiKGs/m3+V8fzLnxukFppbHnNMJXq/Yc8opcuB85zsEbriJx16q5tUnBwh0Btj3++c4+ob3cvis69h+/vt54ZLP8MgVtzHUnqbkE8Pq96zmnP/vHCqaKmhvl+NHVUV2rVDkQ2ZAefhoiswoMSwrM+IB1eLKqExIHLkXdtwB7XnoC5BKZljFL+oX1lO1uArVmXqImzfmBQtGLdmbH07/IZz9m0R27KTA6AOxYwo9lMiumcjnnqIkmsParG6woxW+MTaPX75csk8KRjJxZ3WSJVdmpJ+8HU/Azu9B+6OjGMAEgHc61J8GvlEIW05h4kL1EHXUAnnehHwzZMGsBWVxCvYkpkDeZ2Ya2pWaCnXJQj7UQTGni42NkpQN8pysS29KTOVVldH+hMjabfnSyNe0sPTSsIOKOZKdmq1B+mgwsBsO/016Blmh9hSRypp9TebPUBRpGD8GyNXrAiTRPtdaJlfGo6KIVEW+vTgKQTH7ZpjB/wsvhHe8w37FSs3sGry1XuoW1MUDDwBaUH5Il98Vl5kaKzLDXJiny4HlQ1rlBY8nof8SDgt7+fTTUr3x7W/DSy+NLNtKQ67KjHT//L//J9uvuEIqdHLBPG6zVcakH7cVTTK3HO4cTiGqskELa0SDktloNvKE0vbLmEICV155Jc8//zyvvPIKW7ZsYe7cuVxxxRUABINBrrrqKr70pS/x8ssvc++99/KpT32KV199dZxHbYFM1Xy50L8Djj8myYV2kS0pqQhobZWAnKbBpk35v99+828jMwGgqInefdmkic3X/LNknlUqmNKTwSyEbyZb3LFeKOGenEkfNTVSnQ+p1RnJ+NnP5Pnii4UcTkay1NRooWlCaEFmmalizVMgUSWR3sNi1iyJPzc1CbnyoQ+N/l5pJrNv3Zp5/lAzxz6ZYSKZzLBTmQEj50WvvSYVLtXVQghOIRVFOea8XoJNs9nDAroal+Kr96E01LNrweW0t6xErfDSEtxH6/4nCQ2E5T2/+Y1o9JoOSoJ5PE6fLtxJociXzCgHTJEZJYZ1ZUZps8MnFMzgSg4Zi3QsWiQLiZ4e6Oy02EHBUsPNLKEb034ZIFnqrurSTnTGCsNHJYMln4lvOcA87xTVumJgIiFPqSZTb/nYMSnFtUKyxNSokIus9U6Tk1cLysQ6GebCKFsj40mB8tYAnUKZQnUk9UzYL8+9sbS1bBJTJirnixxFJu3sdIRM4f5Y5VekP0FWjhL/8R+yCG5rg9tuk22GAffdJ3/n1S/DvI64alO3922DJ6+BVz832uEWB2YFXchq0oL4puksITrLAMXqdeFwwO23W79mTtO+8x37zb9Hg3yliTJhNMF/1any5l++mYv++yLcFYmoejKZkW9lRjY/5UIxg0EFweORH+1rXxPNkkgE/vSnxMHR02P5NtOXr7wyMrM1k38A/u//7AWcko/b9OVEpuPWV+8DBfSoHm/OmQum7rXqUlP6+5mBpdFIfU0hN+bNm4c3puPldDq59dZb2bt3L0eOHOH+++9n9erVnB/rZjx9+nQ+/vGP89Of/nQ8h5wKPceJqUdlvZYpieH4w7Dtm5krBq3gi83RSyQXqyijk5qy3fw73BPrU6XK2iQdFXNFqiubJGxcYioPnaFCECczsiRnzHkHzFovlbjJMKt29Wiih2cWWPXNMKFpCTLDqsLtiivkmrh5c6JHaaHYvVsqc3y+zHGbYs1T7rkHfv97GftTT0kD9N/+Vp737ZOK4o0bMxPh+dwrN2yw7tmUPn8wKzP6D2VYuFsgPCSBb1eFvcoMGElmmBJT69ZJpv8UUlHsPnBOn1P6+1b5GWqcS8fstexb/Ho2LX07L7YIsY6uSxnIQw/BHXeg3/xRord8CiMWBG3f2YuD6Kiaf0OCzMgw7SpLTILI6vjA4XCwYsWKnB3cLckMLYvM1DjAri0lQbzB1j45+22WS/h8ovN34IAsSk+OxXajw0J1Gxj4HD4igQgKSnz7gaTKjIIR7JCgTia4qosuBzauPjKhxWp99bA8IgMSuNYKqAFmHGxKDrKXoCxnTO3JMyuqtlaCIu3tIjW1Zs3IfZKbf8Mo7MklqaY6ZdEwfEwWQsl69qY9vuKTGWVxDkHm86WA86hsbJqCPcR8rADVPgeKORfIx/eV8yQoMbgfGtbBghuhZ5P0MsqFRe8H9UP2vifcHbvPqdL0O3g0If+WlvVXyHHo9YqyzNvfLs833AD33y8LAJcrzyz9cGzFlt4bw9scy7w8Jov4bMkEPS/L9ad2BQ7fjNKcV56YtmUoi0yEHbQ/DgfvhNqVsDC3Xk6h14li9rpYv14WzOkVEa2tEhBevz6voRVsUz6VGZomi9GjR0VK6NxzJdCRK/ivKBLQuOqqzARNerKNw+GgtqKWwGAghcxoyjGVLEbPjHwW5hdcYP9z8/aRyyXRk3XrpFxLUWTS8rnPyaT9zDNF9ypWOrprl7ytq0syW0F+j9tug499LHsCci7/mFi/XmRAbr459TeaOVOIjvTjVnWqLLt6Ge4Kd4pkVDYEe2NNPGu8KcdFqWWmpuYP1ggEAiiKQkNDAw8++GCcyDBx/vnnc3smdhYIhUKEQgkiqz+WQRSNRuPa4qqqoqoquq6jJ1Ugmds1TUup7EnffvLJJ2MYBoauoTzzTnTPNPSTPgNuiVqaPtU0DTqfQt32DahajHLatxLbY1CGO3AAhrseLUn7XFEUHA7HiDEqioLD14KBgT7UhjFKm4C4PdFoFIfDgaIorF2r8de/OnjuOZ1oVE+1KQlW2zdvdgAKy5cbRKNJtqbbNHAA1TDAOw1VdY4c+/z3oi7+ELphoCf9Nsk2MbAHxTAwfHNQdD2rTena8vnYhLMBJ2AE2zP7qenCxHZNS7IVVGelrNuHO1Fd1Vn9dMEF8JWvOHjoIQNNM3A4HHF77r1Xo63NQUODwVVXjbSprs7BhRfCgw8q/OlPGh//uJHVVqfTiWEYqcdkzKZNm3RAZflyA8PQ0LSRx+ThwwqQ+xp2+LBGNGrEP3/FihWAnJcDA3DTTXLMfOxjsHp1qk2q6sAwYNcunXA483cl3yvPP9/apkhE5+abldj9SRnxfkUxuPlmuPJKg5rZNRiGQe+BXiKRCIqiWJ5PhmGwfPly+fxABN3QiaLGJdrq68l67C1cKL/hjh06hqHEyAyFdevk3IPsfrK8Rlhsz+cakWxT+rWjKOfTKGw6fNjAzjF39CjxsZvXbfMer2kaWjTxWjQcpWdPD9FglGkrp4Eiv0FUdcuzrsMHPiAHSUcH3fe/yM7v3s9A4Bku+c7rqbrrJ9zOXmoGWzF+Nxdt1iwpq6muzstP1dVyLHR3k9NPVjZl8lMp+2pMkRmjgNtGnXJWmSlH+VRm2LGlJPDPlgVLZEAyJOw26kQm+AcOyIT/1KUe/A1+Al0BoqEYoWEYKQsDf4OfvW2SkV9wZUawA55+h8hvZIKnIbUhc7AT9v5MAhkLbsj8vhwYNx+5qsWmUJdINenhRHZHpFf28TTIfnliTG0aA3m3MbOngCbaS5ZkJjMMIyEzlVyZUZA9dvrD+GZIqXTyeWToifLpEpAZMI7nEIw8j0Ld8lt56hO/VQHn0bjaNAV7SPc9yAJaT1rI2PV9cjWjokjjzsZ19sZhpzLQHGvfNrnOu6rkWm8Ykr2ohYTkSJu/FHIcvvWtoijz/PNyfTKrpiMRkda3ChZawiQz3GnzB3c9OLySeTl8FCpmjXyviaMPCEkw719g9rWlOa/iZEaXdfJG2z8kg7L+VBl3JhiaEEt59FwpxJ5i9rro60tkaf7ud2J+MkFQCAqxyS6ZsWHDyAB2a6sck/X1xQv+a2ENh1t+AD0oi8ZCKjNGQ2aUokG7iYLPI1Prsq5O0n+ffVYkFn7/e1i3jg2V7+Ld7x75trY20U7PhnzJmfXrhfh49FGDq6+Gvj6FH/0osxTeqnevyv2hSTArODw1iYrhUEiygaG0MlNT84dUvPbaa9xyyy184QtfwOPxcOTIES655JKUfWbNmsXevZmlGr/61a9yq4X4/qZNm6iokPVHU1MTCxYsYN++fXR0JMjt1tZWWltb2blzJ31JjYrmz59Pc3MzW7ZsYXh4OL6+XTbHT01kkN6udnZGdsUlCFesWIHb7Wbjxo14It3M7etF699C7SqNcDicIpM1t3s70/0wGPHwmrkQAHw+HytXrqSzszPF3pqaGpY1tRAMhujd/zIHBjeOyqbXXnstTiABLF26lNraWurqdgNLePLJMBs3vpxiUzLWrFmTYlMopLBrl5R1zJ7dz8aNiaY66TbVBJ5hen8vUWMeTcCRI0c4nHRxj9u0d29Gm+r3P49b6+XQ/gDTfZ1Zbdq0aVNK4NSuTQBOJcoaIBrsYdPzT6Kr3tx+WrYsbtPc3iieaC+D+zcza8X8rH5yu/txu9dy7JjK0093c8459ezevZtQKMRtty0CGrj22jAej4cXXhhp05ve5OHBBx38+tcBzj//tYw2ORwO1q5dS19fH9uTyiVNm555ZhioYMaMdjZu3DfCJoD+/mogtxZSf/8ONm4UYnHevHk0NDTw2muvMTw8zLe+NZfDh6czd67GF7/oyOinp57aByzK+V1Hj5LRpnvu6ePw4cylEoahcPgw/PGPR3jbtdMJDAfo7e3l2ceexVXpyng+zZkzh4qKCjraOgj1h9j46i5gOopiUF2t8OKLmY89Xa8ElrNlSwRNc/D00yqgUF+/g40b+3L6KdexZyLfa4Rpk3ndM1GM82k0NglBnfuYa2khxSbDMJg/fz7Tpk1jy5YtdO3qIhAIEFEjVDdWExmKEA6F6W7vxuFxoA1rOHUnuqaPsKlhxlL2T+vDCPSyceNG/qyey8ucyo3T9xHatIm+P/yB41dfTWDRIuoPHGCx00lvdTV7NY1oTQ0oiqWfenrqgCV0d2PLT+k2ZfJTfyZZkCJgiswoEJqmsXHjRtasWYMzizhZ3g3AxwF2bSkJHG7wzZQS2KH9eZMZ998vi9KKpgrW/3Y9oX5ZFGhRjc2bN3PKKafgcMpC0VPt4UtnyW9ecGVGpF8CEapHslXToQ3L65H+BJkR7pIgibe5YDJjXH3kbRJyxqxGGT4Cr35BgllrYtlJBVSjjLlNnkaY/66SSUyNqT0FkBmLF8vi3SqIs2ePlBQmy1gXbI+zCmqXi6RNJiz9qBw/jqSFdKgzlj3tSgT9iohxPYdg5Hm0/zdw/FGY8XopC4e8z6Nxt2kK9pDme02Lxu9PTkfMb3Z9X3+qBCyqRxnhylQJ6W2C038EL7xf+k2c9EmoirH/wQ7Y8mUhtI/8PV4VUOhxqCjwpjcJmZEm/xovt7/rLhuERsgkM9IWiIoicgsDu2G4LTuZMbhHnqsWlu68ctfLmPSoJAIkj1cLw67vy99n/To7mRGXGjuYeZ8kFGqP2TOgrc06y11R5HU7VTSPPCIVDYsXw9veZnsIGVGoTWZg+MgRGBiAqqqR+5gSRek2m8ek3YaY2YL//W39PPqFR9GjOm/++ZvRNI32I+1U+avGvGdGKRu0j/o8crkkw2LtWmHEXngBLRjh5pvAawS4kr/xLGdwkNmAYnmcZkI+5IzDAeefr3Huud3cc08z992XZ1+fLDBlpry1iXN+zx45X6qq8v/d7WJq/pDAJz7xCX71q19x/PhxbrzxRm6++WYAent74xJUJrxeL8FgcETCnIlPfepTfOxjH4v/39/fz6xZs1i9ejXV1ZKwYGbvz5s3jzlJTQfM7YsXLx5RxQCwfPlyotEoL730EqtXr8bd9RgAta1rWbPi9Pj+ZibymjVrQAujPv0TABR9CJ+vWrabn/2cAyJQWT+HNa0JqSTTtsbGRuqTRPcVRYHgYbxeD9McBk2nnQaxjPFCbFq6dGncHofDER/7W9+6gA99CNravMyZswafL8mmJDgcDnw+X3z7pk2g6wr19bBkSTW6ntg/3SZl72aUtlqYKQufGTNmMD2pe64tm2bchjK0j5Pq16K6q7LatHr16hFjt2NTHM9V4WSAU09ujSe2xG3yDVG/yCfzA1dVfLtpk7plKfQMU9Poz2nTokUG55wDDz8MmzbVceaZGsFgkNbWU3nySVm33XSTO6NN69fDhz8MW7ZUMWPGGmbMyGITEihO3m6OfdcuGetFFzWyZk3DCJvk++GrXzVoa8t8/Xc4DC65ZDHzYoe3rktwePXq1Tz/vIM//Un88KMfqfj9mf109tlZ1rVJaGnJbFMgUJPpbSnQ+qvpO9RH87xmFKdCq6eVyrpKlGGF7j3dzKybyaKYhrSmabz00ks0NjZS4a7AW+tl1nyRnq2tFamobMfe/Pnw3vdCR4eHQ4cMdu+Wsb7znYtSJKoy2WR5jaDA8ynmRNOmpqYmli9fPiLjH4pwPhVo07/92/TYMSfkUzoUxYjNjRVgcbz646WXXqIu9oMuX76cbm83O/078VZ7cblduCpd6LqOV/Hir/UTcUUI9YVQHeqIse+8eyeqQ2X2stmsWbOG//WpPIrK5ZdeiOcWg4aBARpcLnC5UAYG4KGHqOvu5lTDgMpKjMsvl3lVOMyM6mqmxz5/aEjs6e7O7ScrmzL5aYrMmMCwrMyY9RapRDA1DE90VM5NkBn1p9p+W3qGXUVTRbz5XjQaxd/jp25BXXySHo3C/v2y76h7Zjh8Qkbp4VhDzqRuUXqaTq6dTPVyh7cpEWjztyaIOO80cE0Qu7xNMPva8R5FcWCSGcHjoper5k5tNc+XnTtHvmYS/qtWjWx8ljcaT5dHNlhd++KN66aXRAasLJB8HtWvga4XRHawajS6d1OYEEj2fTRKyNUDlQvy69QW7AA9AjUnS6R1++1QtVjIBkW1R4js+j50PA1LboYGC705gP5tck+rmQ8tlybOx6oFcMrnhNA48EchLhvWgBbFEzkMg3WQBzmjaYmmvOmwK9cDJMlMNYx8zT9TyIxsPZ6iAQjEosGVJTwXVacQGKFuqdhMJjNCsVR8hzd3hY4/1pU40g/hPnDbWxznC7NnwDXXyNdZBQrs9rq4/355vvTSog4xb9TViXRTR4fcC09La09ip3/E00/b+65sQWh/g5+h9iEwJJitelT0sA5+0brOtzLj6FGZ4xYSk84V1M+HtCopamrg4ot54lGpjJnLcdawkdfxMEeYwdOcxXOsYwB71Y2FkARnn93DPfc0c889UlVmNVWJBqMMHhtEcShxzfNc8Df68Tcmqt2SJaYm63SonPCNb3yDb3zjG3R1dfHFL36RG264gV/84hd4PB6CwdQeU8PDw3g8HksiA8Dj8eDxjEyacjqdI0ijZKmlZGSS/nI4HHESxeFwoAyK1ppauxTV4uR3Op1yUfA1y/xh+Ai4axLj0DWISqBC8TZaklojxhjsgOgQSjSAEg2gDm5NWd+qrmpUi3t/NptMe5K/v7HRydKlIiW9aZOTN7whySYLmNu3xQoxTjkFVFVBtahIjdsUOionWOUsa1sBdn4PtWcT6tKPQ82ykTbVLJCHDZtyjT3n9nnvRnG4cfqnjbjYq0f/gXrkPpjzVpj3zpG2ehti84aezLaS8NPFFwuZ8fDDKh/4gI6iKPzud06iUYXTT4cVK5SMY585E844Qwrq/v53Z0p/CKv9FUWx3P7qq/Idp57qSDE3eexOZ+Z5ivm/pilcdpmTRx6Re9kTT2g89VQDvb1OPvYxkZC64Qa4+OLMNgFccIHDdoJHJptmzszdhMLPED0/+Qt3/ijRq+XI86nJi/4GP+t/uz4e9zKvR+d97jwigQgHNdleX5/dJqfTSXMzNDZKD9pf/9qsJoKmptT3ZLIp07GU7/b0a4RpU6Zrx6jPJwqzyeNRc8yNlaS5cWLspkwYiE0Op1wnFEVBQcFT5SHcHyY8GKZyWmXcfqsxhvpDKCj46/w4nc74PG7mzNj+1UnzoEsugUsuQenvRzlwQIKhM2fKAF95BfUHP0BtaoK5c5lnzGM2C+nunmPLT+k2WcHq3ldMTJEZJUQwKA9Iq8yY89bxGE75omIe8GRCj9sm8tE+Bjh4UBZ7Hg/MmJF7/5zQg9C7RQIUtSuADDeoSKzZlssiBXAiwuGR4Em4TwIwE4XMmEzwNMDcd0jgHx072o2LF8uzFZlRtObfo0G8+ff07PtNFlTOleehA+M6jMmEn/70p9x0003s2LGDuXPnxrc7nU6WL0/tKfGVr3yFN5ir44mAdIlDLSQBekWVDD1FGSlxaIVoAMK9UomQicxovgBQrfsLNa4TgmPzl6R6w9+KQ3GyKBDAMeBP7G9jLEXR6jeMRAPw9MoMAF+sqUA2MsNsjOptlHtbCbVd8TQJmRHqIEWuYPiYPPtackcvHZ5E36GhA+BeUbLhZuoZUFUFP/+5/V4X5UJmgMwdOzpk7phOZuQ6Jk1UVko1UaEVK06vk6oZVQy0DdCzp4eGkxtY+N6FLJqzKK+eGdOmJfp4HD+eIDesYNUD5Oc/l4zM5LGnB4Ng7Bq024G5aN/PPD7J11jGNs7iad7MX1jKdv6X/0BBR8FAt5gbjYacWbu2D5fLYM8ehZ07rSWgdt+3m00/2cTsc2dz9i1n5/zM+RfNZ/5FqRm/pe6XMQVrNDQ0cPvtt1NbW8sdd9xBa2srBw+mVsAdOnSI1tF2WS0W+mMT+qrF2ffzzUyQGckB+XCPnPCqA1y1ub8veR4S6QfFAc/8a+o9y848xCZOP13IjOefB7vTtS1b5DltymeNaRfIOipbpWuoE4aPS2/PNDJjzDHjssyvxRPCMgQ4Wt8k9vqzVKgmwWwC/uijcu8wDPjZzyTO8W//lvv9b3mLkBl//rN1s+tc6OxMVByuyDHFyTRPaW2Fz34Wvv51qXZbu1buY8eOOUief1VXwze/mXtM2RI87N4r7VS8zp0ewqMFcHqdOH0jQ7XR4SiBrgCh/lCczJD3Ksw4Tfz/6t9kW67m3yaWLJHf/Be/kP/tVqCeqMh0zIHMK/PpA2f29VWdKnpUZ7h7mND0ENpw5k7ypjylWdFpjiHrram6WlheU4IDJLv7xhtF13LfPpq3buIqlvD97g/n08p4XDHVo76EMKsyFMW6lH0KMTSfCyv/CxbYuDsmwZzk790rGtu5YOo1L1ggJXejRqhHdP61cELr3wqToTIjHd5p8mwGYCYCgh0StAr35d633KEoMPftMO1CkWWygWQyI30Cld78u+SIDsGOO+CVzyUG03QOrPrqiUP2mlIxsSy3KYwOn/vc57jzzjupq6sb0WjMlNN4+eWX448JRWTASInDSF8s079WgviqJxFgyAZT/s0M4FvB6ZNFc/M51q9PvyxRDWboGK5aNLUSw1UrARGbYymKVr8RFam25nOsyQx/bGY/nEWHZyA2OagcbcmmDZx0C5z9G2hMWykGTTLDJplrXj8C9qSmRoP16yWR65FHZOEGEgy3u1jbu1cCCU5nfg2kS4VsiTB2j8kbRWEt40LPTvC/boEcr917ulEdKtVLqpl1ziwUVbUtM+VwJCoMsklNbdgAc+fChRdKs+wLL4SGBrHDMODf/x3uvHMkGdLaalPqbQyRXFFhoLKVk/kx7+UWvs4fkPnDSWzla3ySa7mTGSR+mNGSMxUVOuedJ3OWv//deh9/k1RYBDoD1jvYwBSZMX4IhUKEw2E0TeOss87iscceS3n9scce46yzzhqn0SVBC4miAeQmM/yxAHcg7SIRjiVHmBKIuZA8D/HPFpLEXSf3/Tzu/XZxeqzI+7nn7L9n82Z5To7XZUTzebDwfdllceN9yvaPfO3Yg3D47kQy1ngiF5lROR/qVllXsFrgtNOkGK6vD156CTZvrmT7dgW/355U5FveIs+PPCIyxvnilVfkecECezG05HnKb38rz/v2wfveB489JveNjg44ZhG26O8X0sYOzCB2ofdKkxCBkaec+f9nPyt/O31O3BVu3BVuXD5X/G8rgiMd5m+epJSUFea9Zk9McXWKzMiN9GPuF78Qv23cKEReLniqpd9vNBQl2BtEi2jomi5kVYf0APY3+PFUj6z0M+UpPTUeDCMx/8uW0GKJ6mph+a67Dj75SdTv3s7P+Vei0ZHyv+WKqcqMAiDZTQ7a2tYyPKxy3nnWk2KTzKiuTgqea0EItktgO4/+EKWEw+FgzZo1GcuDSg5fS0ENf2fOlB6BgYAslpMn/VY2mRfogvtlpMPXItUZwQ6ZyHgyrDojMTJjFJUZ4+6jdNSulL4GBTT9NjHmNh35Bxy8C1qvimu9FxNl56M0mCTe4KAEbMzqJE2TiSqkVmYUbM9rX4W+12DhvwtRaQXVA8ceEjIw3B1rfhzrtVEilJ1/XJWSCR7sFO37AjK+ys6mcYKu67S0tHDPPfewoGgX+NJhVH5z+CQAoUdAccp12JT9S5c4tIIpo2T2iCgEqiNGaKvgb0UBquoqUmU3bIylKFr9qkuCEZlQOR+mX5To+2GFwRiZEdunpOeVN8M8wUwMsFuZVjEbup631TejGPY4HEJEnHoqfO97Qorv2gWLcvfC5IEH5PnMM4uX2DMam5Yuleekno9x2D0mr7pKMizTs/LcblnU2gn+182v4+DjB+nZ25Niz8CANICG3JUZIHPhw4czkxmZeoCYa5Q3vQm+/31ZhL/lLSOrNwo9bEp1HmXKbA1QQYAKCQK1TOPst62j6ofPcdHggxxkNvdzKcdb1/Kd7xRGzpj2vPGNCg89JGRGUmuEOEy5qEBH4WSGeWyax2opMDV/gHA4THt7e7zSore3l/e9731cc8011NfXc8011/D5z3+exx57jPPPP59jx47xzW9+k1//+tfjNua43wZ3yhzaU587QO2LRbjSSX3fTDjl8zKfyGsQvsx9P+3MQ5I/KstxuG6dPD//fOY2X+nIi8ywg8pYowUrMqPtHzCwS+ZhSXGMkp1bkQEY2AmoUJ/UL0ALyVoCEsTVKGHe8+++G376Uwc7d0rD42uukbhWLixaJNUxW7bAPffAv/xLft9vkhkrV+Y/5nRMzzGtsi1pGsP69bJvoffKTFn9M2cK0XHBSrjzx7ItGozSuaMTQzOYfup0FEaeBObxFhmIcGDjAXz1Prq75TiwW5mRLr9uEonjhYlyf0o/5h57DH76U/jMZ+Chh5L3G2lPer9fgKe+8RTdO7tZ8S8rmHPeHDzVnpTqGxPxyowaL319CeIhbzIjDb4qJyF3NYSlb0ZlljzscvHRFJmRJzZsMC8+Cqa0S2urXHzSJ8eWzb8H9sDL/5/c9Nb9cAxGbA/hcBifz6KhdRlDVSXb/OWXJYspPYMp3SazMmPU/TKSUTFXJhdaUKozXBb6uEWqzCgrH81/V1E+ZkxtMrPfM03Ai4AxtSfSL9cT1Q21J+fc3e2GefOE1NuxI0FmbNsmN8HKytznkC2Ee6X6RclS/qQ6YzIpRyWbyGam0GhRVucQgH9OjMw4UHD5etnZNA5QVZUPfOADRfmsUChEKJSYWJpNy6LRaLziw9QR1XUdXddTxqGqKpqmjWh0mbzdMAyCwSAVFUIApFeSmBNDTUsqMdaiCcEUNdGQ1HBWoQIGgGGgaVGIRuN6zeljVHxzcADG8DG04d64TKCqqqh6COPVz6M3nosx/VJQndY2aVEcqCjuOgzEHl3XRTdVUVEg1hwuGpdssrLpzDOhtdWRtYnezJlw5pka0SiZbcqwXVVVVP8M9MUflu1p/jNtUvt3SaSkYj5qbIzDw8N4vd6ENrldP2XZ7nQ6403zRox9+CgYBoa7CSOL/+LHnn8O+GZiOCoxotGsx56iKCn2mGMsxCa/X+O881Qefljl7rs1/vM/HZltio39n/9UAZWLL9aBzOdNPufTaGxauFDm7zt2GDE97dRjsqXFydGjBlgEDZKPSYcDrrhC4emnHWzfrvPhDyuEwwq1tRq6ruS0qXpONbqh07Wri8H2QQ49f4iqaVUMVbcATiorDeTSnt2mGTPk9z1wQMMw1BRbpQeIw/L8MvHSSwbhsBYPxJx7bqr/zK/O10+KohAOh3G73SPGPtrz6bbbFN76VgeKYqTYpijit8/e3sDaN7+FU//rTbz8qy2sfu45rl0+wMn/oaO0HyX63GFYsQLV47Ftk2EYhEIh3vjGCj72MXj8cYPubo3q6lSbPHUedENnqHMILaqhOtSsNj35lScJ9YVYfeNqmpc1o+tGrDJDYcGCKJpWwHXPhk3mfcjv9+NwOEb4L91P6f6aDOjo6OCqq65iaGgIr9eLqqq84x3viDcAr6io4K9//Ssf+MAHGBwcRNd1br31VtaZUfZxQjgcxqe6pCLRWZU7yl97Csy9HqrTKjhcldBQoL6sHoHogFRHZkrms4lM89gVK6SPX3e3VFOtXp09aNzTkyB1T861LAp2SmKIb2Z2ueTkyoxkRkXXIBCTibWo7CjJ3Lxvq/Qsq1qYSmaYlZ3OCjkerBAdkn5pWhBar7T1dY2N8vzjHyuY98N775VYmB1C+C1vETLjz3/On8x4+WV5zofMyASTdMgEW5KmachEnNiFSYg8+ii88Y2SwPD3v8sx352Ua+RwO9DCGhighTScHuvQbTgcpv9gP8/f8TzVs6vpaZRFvp3KjA0b4FvfSt12xRXWsc2xxERc337+8/DrX0u/mYceSsi1gbU9yf1+ARa9fhGdcztpWd1C/YLMzms5rYWKaRVUtlRyOHbNq6uTJO/RQFHkmDl2TK67s2dn378cfDRFZuSBTNlNbW2yPb28zLL5txbL1ClhQDVfaJrGq6++ypo1a0raoCUrel6FnpelBLLOvv7zkiUJMiMZVjYVrTJDjyQICpBA7NAB0eSusPj9orGeGaMgM8rCR0XGmNsUJzNGeaXPgDG3p/NZ2PFdqD8Vam+19ZYlS+Q82LlTZCYg0fz7tNNSFwgF2xMn73Jc43wzhMwIHIGa5bDvV0JwTLsQHO7s7y0AZXkOVS0SzWK1MHvL0qYJjq9+9avceuvI82nTpk1UVMgx3dTUxIIFC9i3bx8dph4M0NraSmtrKzt37qTPnAAA8+fPp7m5mS1btjA8PIxhGPT29nL66afT0NDApk2bUoJdK1aswO12s9E8OQFP5DCrDANd0xgM6Phi2oqBwQi1tRJoCgUC7Nq8mZCrB5/Px8qVK+ns7GTv3oSkVE1NDcu8zQR7D7Lr+XsIeBYmbPLtJnDsRQbbdrOvrQkUxdImT+Qwy7UoLmJkT3QILRIEdx1VVZW4gEAgwM7YWDLZBHDbbWt561tNOiY5KCnPH/zgTjZtks+wsknVA9RUV7Lk5LUcOXKEw0lpbnb9pOpvxau2MT3SSDOwefNmjh07Rm1tLYqisHTpUmpra235CWDNmjWEw2FeffXV+DaHw8HatWvpP76drld/ga646Kx6Y9ymYM9+Qn29HN7bxVDbRvHTsmWZbRqaRYfxr9ABdGzMeuzV19fzzDPPUFVVFQ/8j8amFSum8/DDc/nDH4b4z/+spq+vj+1JZQ7Jftq5cy8PPLAGUFm8+AAwr2A/FcumcNgLrGLnThgaGmbLloSfDMNJdfUajh5VsHNM1tTUcMEFy1i48AgPPOBkw4bpfO5z/fzqV905berq66Kvt4++3j52PrmT5779HFVzqxg+bzWwnOrqEH19wZw2uVxzgBZeeukYmjYt5dh76aVqDh8+iWw4fFjhJz/Zyamn9sdtynrs2fTTnDlzOHDgAB6PJ4UgLsb5NHeug7vuWst//IfBkSMJHzU3R/i//3NzzjmdbNwYu+6thOrzLmTZsmUcPnyY/g0baHj4YXSPB/X005n25jezzzDo6OzMapNhGITDYc4++2xmz9Y4eNDDD36wlwsv7E6xKRqJ0j/Qj6EZ9BzpoXZGbVabdj23i0hfBM9ZHpqXNbN3bz89PTUoikF//4ts2eLNfC0fhZ/M+9CqVatoaWmJ358y+WloomhO5IGZM2fy4osvZt1n5cqVPPXUU2M0otxImfed9El7b6qcl6gwKBb0sFR4GoY8TPnhPJFtHnvPPYnrrlkFlSmBFBL9MmbPTou9WKHzKdj9YyGEsv2OvhmSgBUdFmUNnymzfERkph3eERWVJZubm79xsD11eyBJYioTsWXK+6pOmHlFTgJswwbJME9HZ6d1zMsK69fDf/0X3HefqGjkE2g1KzNWrbL/nkwoiqRpCeBwSLD7jDMko3/jxpH9QRRVweV3ERmKEBmKWJIZ5vE2PSLHocvvortbXstVmZFvbHOsMFHXt3PmiGznd78r1Rmve52canbtWXrVUrgq9/esuD5xoDwX60k32qoME8lkRjaUi48mztExzpDsJutmPSZRn16iZlmZUeKA6oRF51NSrmloeZMZYK8JeFEqM5xVEqwNHJJJhbMCWfCqku0Q6oCqBanyS4s/LP1AlEl2uhmG/BYTpbF5/NybJL1LTF1UUyfVBhYvhn/8I/V8KXrzb7u/s38GdL8o4w93w8E7pZpj+kXZ3zeZMO96eUyh5Lj88ss5fvw4fr+ft7/97Xz4wx+WSoI0fOpTn+JjSdoh/f39zJo1i9WrV1Mdq6033zdv3jzmzJkT39fcvnjx4hGZ5ADLly+PZ7G/9NJL8c9bvTopw45E1u6a5CY2g3XwjILqcFBVNw0iPlBdVDskI8bpdOL0+znllFOgckE8wNvY2Eh9UmqWoiiwfQHe4HFOnuvDmLkmMcZN38Pv9+Odez0NrWsz2zRYh+NpuZ9VV/qgZwcRI4KjZjaq6oRoGH/SWDLaBKxbp3LnnTJ/Sm/c+O1vw1VXJbIPrGxSDt2FeuA22HEJMxb/B9OT9ATifpozizlNqsjbeZtz+umkk04iGAxy6qmn4nA44mO35afYdp/PN2I7QLVPpcq1BTwNzF2zJm6Td9VncAcOs6xmObiq49tnzJhhbVMex56u61RVVcXtSR57ITbV1UmW7MsvV9HXJ0HV5P2T/bRzZz2Dg05qaw3Wr59dFjatXAlOp8HwsEJXV6qfbrlFZccO8PkMampS9bVbW+G22wze/OaRx+SMGTP4yld0/vIXg2eeqWNgoCa3TYsMIhdH8NZ5cetCaE9vnc5A47LY93moqfHktGn1aoU//hE0rQWHQ0k59nbvtte9sbp6CWvWGCNsGo2fdF3nwIEDnHTSSSkyBMU6n9auhTe9SeGxx7QkqQ8HbjfousV1z7TpAx+Aq69Gee451Oefh69/nXlveANz3vjG+ILOyibzug3w5je7uOMO2LFjIZ/4hD7CpuPzjzPUMYQ+qMelGKxsOu2009jn2odeq7P6LHnv0aNyX5g9G84557Ts1/JR+Mm0p6FBKmPN+1MmP5lVilOYROh6AaIBqDkp/4bdzgppJj10UHo3OTyg2OvhZweFBFnzkpjK1WPChOqU/iCDe6U6wyQzhvbJc+W8seuSa/oo0i8xB0esSjduSxadRLPBux7NuXYvJOZlhZUrpVfT/v3wz38m+mjkQjgsqgHmZ4wWRZE0LSHWrRMy47nn4D3vGfm6q0LIjPBQGF995iz4yLAkObkr3PTEiJlsZEax/DyFVHz60/CTn4g///Y3kfIsJWw1/84D5jQjF5lRLphk0dXS4YknRnarT4ZViZo1mWFWZkySgGqxUJFFkzIL7JIZui59NWCUlRnBI1C5SJL1Vnw5IY/T+yqEOqWJsacxdVKoOkAtvLdEWSLYDi/cBKhwzh/HbiI3GmixILtjkhCJ5gQ8eFwmp2ruy3lyE3ATRW/+bVdWzRubNQ4fSTTP806zZccUppAPjh49Gg/27N+/n3e9610EAgE+/elPj9jX4/Hg8YxstuZ0OkdknpiyHenIpB+avF1RlHgwKlNGS8p2R+JvRVHAnZp2qMgLOB1O6bScbYw1J6NEBnB46xP7DuyBgV0oqgvHjEtSPmOETQ5n/JqvqG4MxQFEUPQQSuz8VSzGksnWq6+GN7/ZSn9YwWqammKT1i/Wu+sy+kPd/3PUw3+DWW+BBYmVYjY/mRIuyeO15acc2xVvk/gv0oOqKvFG6mr1fNTqkVIVGW0ytxuGJIHE9rGySdd1S3sKtWnJEtHy375d4Z//hOuuUyz3V1WRowK4+GIFt9thz6Y0FNsmp1PmgTt2wM6dCnPmyPa//EUINIBf/1rJoIktkk5WY1+yROWaa+CPf4TbblP55S9z23ThF6VEcvvfpLLFXeWmq0temzZNiU+tstk0a5b8feSIOmJ/u4vb1lZH+qk6aj9FkyTmrMZfjPPJ6VS46KKRx0fOsc+cKZHQt7wFdu1CratDdTpFF+KVV+Css+DUU3GkSWSZ1+wrr1S54w649141JnuWOsaK5gqGO4cZ7hqWa2EGm4ywARqoikplQyWaBn/9q3xYc7OCojjjQaR8/WHHT0oScZPpepg4b6bmZuUCRQ/J3Llylv31V7BdVAQq5iR6Nx36M/RuhmX/Cd7z8x+Idzpow9I7cmCPfHYRUGiQ1azMWG6n/Z7ZDN1nI525eklsbZL0Ww/GggrFrnjJBmeFPKJD4s+KmAZMw+mSJJutOsbhFjmtyKBUhGchMwqJeVnB7MP07W+L1JRdMmPbNohEJH6WS+bGDjL1WUoeZ2ur7DceMFXr0hvdR4dj0qgOFT2qM9w7jK/eF9+ejkhAyAyX32WrAXix/DyFVEyfLtevr35VqjOuuCK/9xuGwcCRATCgunVk/FCP6kSGI7gr3SiKUnjz7wyYaGRGFlHzKSSjkBI1a5mp8gyojnfzlqwNtrLAbI5nRWYk23T0KAwPy6RnTqFzLcOAvb+U7JPZ10Lj6VKFUbVAgiQL3yu69/lmt9jEuPsoGe46Ka/VgpIhUiDG1KYx6Jkxpva46yQrxzCE0LABk/wzyYxwOFHKa1WZkbc9elQa0UHu39mfVFlikhnZsoqKgLI6h5Kha/LbFYCytamMkJy1OnfuXL7yla9w1113jeOIRuE3bViuZekPbTj3e020XgWrviqSbiaO/lOeG88cQZTkHIviQEGTfjn5jiUGU3/47W+XZ9s/T6hLnj1ZVmy+WETXDF4k4+BdsOenEuRJGU+Jzit3nQRFDEMCCqPBnp/Ck9fBkXtz7lpse66MSW7fc0/2/e6Plb9fcklRvx4YnU3piTB798K//qv8/dGPSpy7kGPyE5+Q59/9Dg7m7s0eRyQQQVEUXF4X7TEFkWabMvTmAtaqAbgZxMkU71QUmDWrdEGcsr8/KYpkeZid1pua5Nz82c/Emb/6FRxJVL+a9px3nvQZa28HK5WiBZcuYOW7V1I7tzbr15tNPJ1eJ3ff42DuXPjmN+W1F16QrOYNG0ZpYxaUvX+mYIkqbT/qxg/Apk/Yf9PuH8LmL0k1homwef/Ms29d8jzE0yQNwfUQDO4uaC6bfhzmE2RNRn6VGbELpt9GBHDxB+DUb8m638RgrDKjwprMKNm5ZSU1VTELZrxeZIezwR2bJ4WzRymLKctkVs9s2CCX00cfFbIqG5L7ZRQjV9LhEGkyGPl55v/f+c74VR+YZMbmzTA4CJ5qD/4GP9FQlGBvED2io0d1wn1hgr1BoqEo/gY/nupE4pXD4SAylCAz7MhMlav8lomJfH/6xCck/rtlC/z+97LNrj1b79zK39//d17742uWr3fv6WbDOzbwt/f+DUhcK4tNZvTYWKKUg4+myAybKKRELXtlRvn0zHA6naxdu3Z8s27MbI5QtzQPtgkz07yjI/WkS7fJ7JcxZ440EysIXc/BwC4JIM++NvN+hi6BfhM7vwe7vj+qwEVZ+CgZqisRPErX7rSJMbfJJDOyNXobBcbcHkVJBP9tSk2Z58vevUJkvPqqPDc0SHPwZBRkT3IvGTs9M0B60MRLpKdn3n+UKLtzyMRrX4Unr5HqrjxRtjaVOTRNG199z0L85qqWgIMegkjvyIcektddBVQBakFof1T+brk877EoGLhUUCM9ox9LvjDvq+4sZIYZrBi2iI4cf1iyU5PuYyU9rxQlMdZQTM++f6eMoW9bnp/lFN8FskfNS2GPmWn2j39kDkz098Ozz8rfxSYzRmvTokXy/Ne/CuFy9dWSgHTmmfC1rxU+rjVrRCM5GpXgiB1oEY2eXT3U1tbiqfbEyYwmm3kxZvWFVdZpchAnHaUO4kzI+9OKFfDxj8OXvyxi5lu3ikg84OzsZO2iRTidTtxuuPRSecvf/z7yY+ZfNJ+TrjmJmlnZieFgXxCAo70errlmZADXlNQpBaExIf0zBZxOJye1OlEVxV4g3kRcmjYWxDcMURQA+2SG5TykD9y1gCJr30gfqF7bw7I6DgsJshpGHmSGFpIG4JBbZioTzORLi8qMkp5bZlVNIWvv+NwjO5lRTFmmY8ekcHRoCN71LunXmIukNZPsiiExZWL9epEmSw/4traOX18IEzNnyjh0XcjxiqYK1v92PdfeeS3X3nkt1/35OlrPaGX66um8/ruv59o7r2X9b9fHG0ebx5se0gH7lRnlLL810e9PdXVwyy3y9+c/D48+6mT37rU8+aQzJ5nXsFiux+2b21OkH02YSRAmmWUmshRLZspMwn/yyezkY7n4aGIeIeOAQkrULCszyrBnhmEY9PX1UVNTEy+hHnM4vBKYHT4qGZJue30zKivlJtDWJhl2Z5wh29NtGnW/DEOHfb+Uv1vfFJu4WaD7Jcl+aTob5v2LHCzHHpRMlVnXFPjlZeKjdHinyYQoeByqF+X99jG3qfXNsUl3nhlINjEuPvLNkOwgm2TGzJnSgC0QgH37Es2/16wZma1SkD2GDrXLRfZEycGVe6fDuXfKub/1awl7SoSyPIdAfic9Kte9XFlVaShbm8oIgUCAoaEhmmKRwf379/Of//mf/Nu//du4jakgv3mb4KzfZq+Ec1XnVxkYDQAKdDwpzS19LVBrI6UxbSzGkfuI7vsDzmlnoix8X2FjKRRmZUY2MsOUkRg+lirJpwUhEIseViUmByU/r7xNEogIdQDLZN6w/zfQcolUd9qFKTExlJ3MKIU9Z50li7WuLiEszj575D6PPCKLoEWLRpLlo8VobEpubPrAA/IAmU/+4Q+jSHiJ4ZZb4OGH4Yc/hM9+NnswYbhnmLtvuBtd04lGorh8LsyezflWZgQCsu5ISaBCgjTvfjf8/Oep21tbhcgoVRBnQt+fmppE6PrKK+OLPuNPfyK8cSPuFStQzjyTKy9dzYYNHv7+d/jiFwv7mmBvEMOAx571jrlu+YT2z4mGYEfifmsYhI88jjs6hOKsFnknO/dbv1mhGFsvRIcSiXd210XZ5iHBdnjtK9ByqSS8DezJ/DlJ47U6DgsJsra1yfXP4UhU3mWEWQ3uqsqv76MeAcUh8/bTfxCT7Rp5cyvpuRUnM2IV+VoYOp6Q9VP10uylDO5Ymn6OyoxiyTJt2ADXXZd/c+lSkBkg33XVVfD44wZ79gRYsMDPeecpZdEPYt06IbOfew7OP18IDZOsAFj0xkWoTpW6+XVUtaQes+bxFh6S89luZUY5y29NhvvThz8syTF79qQm9LS2SpJJprlX49JGFIdCoDPAUPsQldNSk3DNJIh0MqMYlRkbNkhhKkjl9T33ZB5vufhoqjLDJrKVqJlIz26yrMyoWyUSD9V5LFhLDE3T2L59O1ouqrDUiEtN7cvrbVZ9M9JtMiszCu6X0f4YDB2SrP5ZWVZ+ekSkLA7/VSZ7eihRcjuKRtll46NkxEtd7UkcpWPMbZr1Fpj/r/YlVPLEuPgozybgpqICyPmSrfl3QfZ46kW+ZvXX7Q0m3rzO7JlRuvSPsjyHAPyxqrQ0mRs7KFubxhFutxtXUjSyp6eHSy+9lOXLl7Nq1Sre/va3c/PNN/P+979/3MZYsN+8TQlpQ6tHPuTB9tvhybdKRYa/FRrPEJmCfMiV2PdqdacyGNQxIkOFjaVQJEs1ZZOZ8jTEJPl0CCZ1dB7cJ5/hqU8s8hmD88rTKM9mVqw5Jm+elWkmmRE4aL0SjaEU9jid8PrXy9+ZpKZMiSkzg72YKNQms6msVfn84GDinjgaXHqpJPgPDcH3v599X2+tF5ffhWEYDA4OonrVvGWmfL5EwMJKmsUw4Pnn5e+PfhR++1shmvbtK2026qS4PylKvB+N9q53seuMM9CjUfjZz7j2hU+whO1s3JjaKB5AC2v07Ouh/bUc2dMGDGh+jg9mTnDLJKkzWkwK/5wICHbA0++AJ6+NPxyHfgfdL8L222Tb0++Q/bIhvl6IXSRMiSlXlfRTsItM85CmM+GsX8Oct8Ez16eMd8QjabxWx2EueTwQ8vmssxL/m1UZS5aARduzVJhJDPlUtrz8aXjimgRJ4/BKLw2L366k51bzebD0ozD9Yvl/+Ahs/w5s+VLu+Zs5T8qhFFEMWaZcfU9ASNr0n8gwUmWmig2HA849V2P58s2ce65WFkQGZO6bEX/9w+tY+4G1I4gMSBxv8y+bz1m3nMXMM1ptVWaUs/zWZLg/3X+/VCinI1fFpdPrpH6ROK59y8g5hFmZ4a2VGEqxZKbM+XH6mDONt1x8NEVm5IFMJWouF9x558hFgUlmpFRmNJ0FC2+E+tWlHOrERMVceR46lNfb7DQBN8mMgiszQl2SzTnr6uzyOQ2xPhpaEA5tkEZbIO9Vc82uJhhMMiNUmMzUFIqAprNg6UdgxhtsvyW5b0bRm38XAsMYs54ZZQlTYi/PfkFTsMbOnTuZk9QYaebMmWzatIktW7bw8ssv88wzz/DOd75zHEc4zjAbdephyczsfA5UtwQgalfkDohYwexJMXwka1C96IgOSQIBpJARI5Asx5HcN2MgVrJZWejEoEB4YkSPKfUQv/7lQWYEO0QqQwvA8HHo2RRr4h57FOLHPGFKTWUiM8yKh1L0yygE2YIrkMiAH+26TFES8gJ33CH92qww1DFEz94evHVeIkMRvNO8VDRVMHSomzq6qXMP2f7ObH0znn1W1JJ8PvjCFwroSzMFgc/H4MqVGB/9KHzlK1Ssv5zGVdJ9fctX/yYr/ZjuTd+hPu778H089T9PZf3IWWfNovpdV/EU5+T8+vHSLZ/COCPSH1uDesBVi+HwoRuKVD94m2V7qCt3/0LzHhhsl/tmvN9UEavV3TVJ43WD4gJXberDxnjtJJAODkqAbWhIrtl/+Ytsb262cQ2vWgAL3wctr7dvm+KQhIjxnqtXL4Hpr5M+GZAk02ujsn3aRbDyv0SpIAdGK8tUaN+TtjZpPOxwwMkn5xzmpEEuMsMO6ubXMefcOTgbaonG8mizVWZAectvTWSY800rZCPzTDQvl2yW9s0jY2zB3lhlRo2HUCiuhDkqmalCycdywJTMVJ4wS9QefVTj/vsPcPvt8wiFFMssAFNmKr3kewoZ0HIZTHtdooTSJuyQGabMVMGVGbOvgebzc1dXKArMvV6arLXdI5U4IATIBC2TywiTzBgurDJjTKGFJRvJWZn38VXWqFqYIo9iB2ZlxqZN8Fqst5RVZcaYoP1JOPIPyTRqOvfEJDMq58pz4JAslHLJc01hCoXCzPAMdUl/m+Hj0LcF2v6a2MfTIDIS+VRWeKfRXnUFVcvORzV0WfSPCQxovVLkstQc2kC+mRLkDxwCYqvGwdjEoKrQiUGBmLVeHs5Y6Xi8MsPm9S/Zj0MHJTD19PXShNVEIX7ME5dfLgGHLVtg/37RwTaxbx/s2iWvX3hhpk8YW+QTXLnggtF913XXwac/LU3Af/5zWLZMgtEtLZJ1HOweYsM7NhDoChDoChDsDaJ4Ff6595+ctldhJbD/W36GLlifIjWRCa2t4gcrMuPHP06MKSW5agqFo6EB3vAGLn4ennoZXnxe42L1CfjnP2HOHCqXn4pDCxHslZ4oDlfma2I565ZPoYzg8MlaUhuWe6yrOnYPUUQFIBdcteD0iaTk8LEkicYSSO8aupD0ekikj5xpa2cb4zWDrDffnHrdnjUL3vpW+O534W9/k+z9QCBB9j36qNyLssm44GuRuUM+qJwLPS8LmbH/99IvpOVSqJyf3+cUG/mQGRWzEiSIDSTHvJ56ai9nnz2fCy5w2CLCC20ubUpMLV0KXvutVyY8TjtN5kttbfKwyrI3K/7qF9ajOjKvFc2qDJdLpKVzwfTzE0+kzlOmEh4Kx2jnm9NOmca2u7ZxfPPIGFuoP1aZUePlSOz093iyV+GUerzjiSkyowA4HOLIxsYBNM3gW99SuPVWeOMbU+PVljJTw0clU8Fdm9BtHmcoioLP5xtfTbokPVAGB0e+nkUP1IrMSLbJMPIgM5LHkQmOHHfX+jUi29G/Q0o/o0NCgiTrh+apJ14WPkpHxRwpK67KJU5qjTG1KXAQXvyoBHjO/HlJvqIsfWQBk8z461+l2diMGfJIR0H2HLkP9v8ams6BRVlkfMzzrH8HdD4LtSul8bAptVQCvf2y9Y93mpSpa2G5P+RR9l62Nk0hK8bNb8kZnh6f/G1ooPrA4ZEgiZkxmc/9SXUQbHw9SsNyUMdw9eOqkuxKO2g6B/yzpPrERIbKjJL7JzkpQgsnKjTsVmakZOpWSy8oMwMWRvixVPbU1cE558Bjj0l1xoc+lHjNrMo480yoLkEf+EJsKjS4UghcLvjYxyST7T/+IzWbrbUVvv6pEIGuAE6PE3+Dn8hgBEM18NZ4GYwqOIiiDwUI9YdskRmZKjP6++H3v5e/b7xx9Hblg8l2f7Ky541vhFtvhf9+7c189KErcO/YDM8+i+veu/EoqwngIbhtPxXLZmdsxjJeuuWTzT8nCpTokPjMJMNtv1GJkfq7pQl4/Wlwyudzr2kLGqQqMQ4tCAO7oOakjE3Bsx2H2YKsb3kLXHZZQnkhGbl6MhSEuHrEfpFpGjok630LMqOk55ZhSCVmsF0SQONkRmnYzkTMK8Dy5fYD3IWStKWUmDJRjte+igpYvlzInOees+5P8Jd//QvhgTCv/+7rqZ1bG3/NtKft2TZUVaWdZsBDfb39PFrTz+WCcvRRPhjtfLNxWSOKqhBol74ZFc2JeWByZYZJQOSS5SvFeMvFR+URTZ+AcDgcrFy5kpYW0cTduBHuvRfekKT2YtkA/JVPQ7ATTr2toKbJpYBpy7ghOcswE7JkGZpkxu7dsmB0OFJt6upK+GJ+tgQKq3FEByVI4PDkHEccoU7oeQn6toumKchksX+7LXusMO4+skL1Yjj50wW/fUxtigbk2WkjRaFAjJuPel8TsqZhXXbd+BjM88XkDOfNS5w3ySjInkg/hPtEwiYTks8zbViaEfZuho7HEvuUIKu4LM8hkEWff44s+IYO5EVmlK1NU8iKcfebmeGpxKaAg7tFIhHsZXimf9x422MHTWfKw4QeTSQupFW3jak9Zs8ppz//4JTDJ9dK1RXL0k0Keif5sZT2XHFFdjKjVBJThdg01hnwTbHbV3pZflsbfOiDcMs8mD7XiavSxUDbAOhgOJyEceACnM6o7e8yyYz0zLo//EGylpcssW7SXkpMiOtCHrCy57TTYNo0OH4cnnjGyUUXrYbVq1ECAdSPPARHBuB73wOfLnqeZ54pC5HY4v+Z255h4MgAX/v4Kt75sZFVw6XULZ9s/jlRoHjqcSkquAoos5p1NRhRqFok6wUba4aC4Z8lAf/ooFQQVi223C3XcZgpyLpunfTNsMp/NIyEbOBVV1mcO+1PSvJAxTz7CRhmX8/+HYk1jrltxJhLfG5t/Zqsa2uXJ8lU2qjM0MLSAzTcA7OvtR39LMSeXCQtWJO0ZmXGqlV5fV1eKNdr37p1mckMRVGonVtL++Z2unZ1pZAZpj1333A3gc4ANW+9DPDklJgqZ5Srj+xitPNNl8/FqvesoqqlCk9NqvzPtJXTcFW4qG6t5vlNsm20/TIKGW+5+GhKz6JA6LpOe3s7jY06N90k2269NfWCbVmZEY1p4GbruzDGMG3RdX18BpCcZWgYsSxDh219zdmzpbwqFIIDscTuZJvMrI0ZM3KU26XpkuKsksBs8JgEfezqkkb6RfbBWRHrleGUQEUeeqHpGHcflQBjalM0NtvNN1iUB8bNR7t/CDv/T4LhNrB9e+r/Tz0lJdnpjZ0KssfO75x8nrkbYhVqeiKzuIDzww7K+hyqPw2az5GAZB4oa5umkBFl4zezceUopfd0XaejbQf68ceh8/kiDMwmwn0Q7i2sT4fqhDN/CWf8dERAp+T+0SOw6wew5csx2Suk+Xch2U3e6RIkytIzpJT2mH0zHnkEBgbkb02DBx+Uv0vR/BsKsylXU1lFEQmTYmTAaxp88pPWr5mH6+HD8rfTK6SirusMDwnz4XTmdzhkqsz4yU/k+cYbx17ptGyuc0WClT2qCq+Pye7//e9JO/v9+Jv8oCh0Xvo20Vp77TX4+tfhc58TUXige083XTu6eN2FOnfdJX1NklFK3fLJ5p8TBYazkpCzGSOX7LEVms+BaRcUt09GRqhStaAocp/WApZ7FXocPvEEHDuW+fVMPRmIDAgZ8OJHhdixg3iPqmGppIwMAgqEeix7VJX03FKUxJwteDw/mSkM2HEH7PtVRn9YoRB77PQ9WbVqJNFkkhmljJGW67XP7Jvx7LPWr9cvlLlq9+7ulO2mPaFBSWIZCEoV4Ghkh8Yb5eojuyjGfHPpVUuZefpMnJ7U2oOTrj6Jcz55Do1LGovW/LuQ8ZaLj6bIjAKh6zp79+5F13U+8QmZgD7/vMilgixkzG7w8coMQxetSig7MsO0ZVzh8MnEIjIoWZPOCnk4fNnf5oBFsSIXU2oq2SZTYsp2828zazUac6CrSiYOOcYxAv7ZUHOyZLxWL7FtjxXKxkfpMIzYJDWY91vH1CZz0uYoXWXGuPnIH5vAmhPaLNiwAd797pHbzZLsZEKjIHvyIY0cPpHbU5zy0AIFnx92ULbnEMC86+GkT0qWVR4oa5umkBFl47eqxVIJVDF7VB+j6zrtOx+GrV+HQ3cVaXA2cPAP8PS/wL5f2tt/+Ch0vZCYhymKZfVXyf2jOOHY/dJ83TcDTvt2dlk+2zBij1SU0p4lS2RuFQ4nCIyNGyWZp7ZWktFLgUJsyhZcKXYGfE79YSASgd4+UFCoW1iHq9GFFquWcudoAZMOs/FjMpmxebNkeDqd8K535fd5xUDZXOeKhEz2vPGN8pxCZgD+RplvDuoVkh7+la/Axz8OK1bEu7I2bH6M+p7deP0O1q9PNLz96EeFINy3r3QNWCebf04UGAYEAoGCOPwUHHsIjj8qwf1SweFNEO3DRy13KfQ4LFjGJRC7SHobE6oL2WBWkz99PfS8IooL3S9C9/Pw1HXw5LXyehKhUfJzyxMjM4YOJslU2iAzHJ5E/CncY/vrCrUnU3PphhiXds89cs81MTQkvbag9GRGOV77TDJj40biDbyT0bBYfrjuXSPJjD179hAZjgDQF5AJxESuzChXH9mFHTKvGPNNc843mubfUNj8uFx8NEVmFAHTpsH7Y+tQszpjIGluECcztOHExhLK3UxomIHMPDIGICGdk551Dgk9zfyafxuJxta+VqCAlDanX8qAFadknU9GvPp5CSZ1PjfeI8mOMqyIKhp89sgMTZNGelYLIHPbRz4yUhIjL+T9OyedV6XQ7Z3CFKaQGQ6/aGgXYSoYdsYW14HDhVVKFAJzEe+2mX72ymdh85dgaF/pxmQHigKeRvk7OiAyVzXLRveZRhQGdtoitYsJRUlUZ9xzjzybElOve50E0ssJmYIrxc6AtxtoC8fUwLx1XjwNHkJhuSdmaK+QEVaVGWZVxlVXQfPoiq+mkAWXXCLH+c6d8JvfwO9+J02IvfWyzgt0xtYziiJNy667DhQFIxzB09XGgkOPUvmtW+E3v0Hfsw8wePe7RVpnqgHrFOLQhmWOHR1C1RN/p8QWckGPQPdL0t9u789g27dEGrmU43XVyv0p2C737HzGmwUFy7gMxy6SPpupzCm9xhoTiguuupJWk2eFb5o8h7pgxZdg8QfBZVN5wCSXQt3Z9ysS1q+H/fuFnP3tb+X5+HH42tfk9Y99DP70J1l7/uIXMn2sq4PGxjEZXllh2TLpMRYISCFfOszKjN59vWiR1MW6Fkz83zs08SszJgMyzTcVRY51O/PNY68c45VfvcJwt1w3dU0n2BfE0GWdZc75RluZkW28pawQLQbKbJkxcXHLLdI749lnZSFnNtn1+UQCCUgE+lSXPKYwEmZAM5vmvgWsmoCbyLsyw/x+QxM9e3dtXmM5oWCWKpu63+UK89yzO9mbSLBJZuTMFE0qyS64CVghcl5ViyDSO2qpmwkPw5DFnseU3prCFCYOws5m0BTJ8oz0g7sAPe+8v9QkM2ymn/lnyjkWaIMDf5R52PwbEtVtYwlPk/QLKlYgyexXFO6DirGNgF5xhWRs/f3voOtw//2yvVQSU6NFtqayxYLdQJs7LTHYJDfc7vy+z1x8dnSI5KphwK9+JdvGuvH3iYaaGli6FLZsgXe+M7F9+bQZfOxfXMw51zoqFxrW2D7/CjyhPuZfNo3QE8/x7p7n2MI3mDfPA8EgeKeSPE54uKplXhjqAj2EYhg49ABKRE+kzHoa7MmU6hF49Qup24otOZU2XgDU2AUt2id/2x1vFuTqyaAo1j0Z4mulPPrTAZJsWdUIRkTmOZ7aROJWAb3GRgVPrKI03AP1q/N8b70knYTHhswA674nn/iESIP/3//B298ulZwdseKWnh6RP7799vINoJYCqgpr18JDD0lVZXp1SsW0CtxVbsIDYXr399KwKHHu6kHJjledKj39MpmZyJUZkwXmfPPRRzWefHIvP/vZQg4cUOK9fHPhlV+8Qveubmpm1TD3grkMHR/inn+/B1eFi2t+f01KA/BijreU8+NiYypiUiAURaGmpibewX36dKnO+M53pDrje9+T/VKaf5dpdni6LeMKk+QZJZmRbFNBlRmmbJLDQ0FVGUVGWfkoGcm6nXmiYJuCHdkzYFzVI6VDzHOvhDJT4+Yjm2RGviXZBdlTCGnkrrMfjBwFyvYcMvH8e6Ua7LTvQJW9i1XZ2zQFS4y73zJlRhaYMakoCtW1jdDbDKF26QMxJmRGTCbBbjDG3wrdm2BwD/S8JBEQC3mnMfGPWZmx83tyP2u+IP/fLNlfqkcqQcNdMLgPfIloeqntOfdcySY8flyyLp95RraXkswYrU2ZmsoWCzkDbUj1RaUnSngIDMOAKIQHw7hQ8Kj2m3+DyHaYveOOHJFASHe3aByXqgl7Loz7da7IyGTPhg1CZKTjtfYm/u1bTdx1Jqy3UJAM9UkA1GhsQn3LW9gx7yr+59vHqKz3UO0OSpbc/PnSNHzVqqTMuNLaM4Uyg7cJzvptfN2j6xrH9u1j3rx5OMzm1VbrHis4/TLfNu+dqkv6Q5ZwvDLoaGqSTtJ4Cz0OTVmUa64R4iL5OptVNtCUmbLVY8ICZp+NLOvJkp9bXrMyoz3/95qVrHnITJXCHkWBO+4QifaNGxNEhglT/rgUGeHlfO1bty5BZrzvfamvKYpC/cJ6jm06Rvfu7jiZoSgKfpcfBQWX30VPzLUTuTKjnH2UL8z55owZUWprDT7yEYXvfx8++MHcvcyalzfTvaub9i3tzL1gLsE+iU+6q4QgLmZlRvp4c6FcfDRFZhQIh8PBsmWpsgC33AL/7//B00/DnXfKNlWVcuNzzwVHmZIZVraMG8zsDT0K6NiVv0gnM5JtKqgywyQz1PLIiiorHyXDnFAF859QFWSTqV0a6sq8j6dBJtLJE/u6VTJpz7MnQT4YNx+ZE/JgJ2jhRFPfNORbkl2QPf5W6Q3kqs3vfWOAsj2HTHiahcwY2m+bzCh7m6ZgiXHzm1XGZDoKyJiM2/PqrBiZcbik11og1q8pz8oMXyx1qeNJeb+71lKiakz8Y2ZWaiHY/WPprYVNMiOTH50VUuWmhSSQFJPtLLU9bjdcdpnMe9//ftF6njEDZo+uFUtWlPu1L1egLWR4mLvMjxYKoIUkOObAQXQohA9wKeBv8OOpthfAVhRZzO7dK4vbH/9Ytt9ww/hl1JW7j/KFlT2mfKcVDEP88pGPSKZjuh+CvbLO8NbKOmPfAZWjzOC0ecji8a1vFWbwpz8VIuO006T5SZECB5PNP5Ma3qb4msYBLFy1uPDP8s9MSgSoL9rxlIKk8ebCaI5DUxbl5ptTK89bW4XIsAyC5yszlY6aU4TQUDJfWEt+bpmJhH3b4fgjMiavTV0mc76UR2VGKe3JlGiX6/o5GpTztc/sm/FcBvXuBZcuYPrq6TQvT6gZOBwO5sycwy5lF64KF90x107kyoxy9lEhMO2ZMQM+/WnYulWqH847L/v7mpc3s/3P2zm+WZKGzSQIb40XXZfkFSgumWEX5eKjKTKjQOi6zpEjR5gxYwaqKgH3lhZhUe+4Q/q8gRxkF14oN9YffKeON6y8Kj8JljGAlS3jAjPL0NABXRpLqx5b2aImmXH0qDRer6wUm6qrZ3D8uNhkuzJDG5ZAgBEFlES2eb5Zq0XMfi0bH6UjTmbkX5lRkE3J2qVWjaK14YR2afJEumGtPEqIcfORqzrWsH4IgkehYo7lbvmWZBdkz7L/tD/uImeH50LZnkMmKudC72YYOmD7LWVv0xQsMW5+s8qYTIfdDM8kxO3xzUTlRSEzSg0tIOQt2O+ZYcpKhGP13ZULLAM5Y+IfT1LgQVES5IYdZPNjqAu2fEl+m7a7YdFNY2LP9OnybCaPHDlSWpmIiXDtyx5oq+Cyc9cT6peFqa7rtB9v58tfnsZ9/1T4wr/D+g95qGiyn/xkkhlPPCGZnYoiZMZ4YSL4KB9Y2ZNdvtOgxuhFOxTgsYen87pLUqNxuqbjb/bjb5IM732xVj7z5yMM4dlny6OzU9KXu7rEqboO//gHrFmTOPGKZM8Uyh8F+82salfdiXUtKgzE5AsKuPfnjWAnHP4LzL42Xok42uMwL1kUw0g0Is9XZioZSvbwWUnPrWCHJCzMfivs/Sls/i9Y+N5YQgS5/WhWsubRM6NU9jzxRGqfp3QURf7YAuV87TPJjK1bJZ5VnZZbNPuckVkiuq4zYAxwxsfPwOl2ckdMSa5oZEYhihijRDn7qBAk2/OOd6j8+MfSniAXmdF0chMoMHhkkOHu4XgShKfGQ3u7JA+p6qimAgWjXHw0RWYUCF3XOXz4MNOnT09x4CmnWO/f1gZXXDuTu+66sez0/zLZMmYYkWVoCJEQ7kn00MiRLVpbKw0O29ulCd+qVWKTyzUdUGlokH1sj8Phi2W8K5LpaMJO1moJsl/H3UeZEC917RASSrE/tlHZ5PBJAN+Ixr4z6f1jrV1qfu14+UhRYOlHhCT1Zr6b5VuSXTJ7SpQdngtlew6Z8McmqHmSGWVt0xQsMa5+yyNj0i5Me1paY8GBwKGifr4lzIW4syJjNVoKgh0S4I8HcQBXlWUgp6T+MReFejAxFk9D4ry3uyjM5MeqBXDKF+HlT8OBO8FZg153Gh37NjO98hRUhzO/77GBDRvgf/935PZSykRMlGufGWj7/e+ln4KqwiuvmEGGijhZEY1G2d21m8OBZfSg0HIyVOTpHrOq8qtfleeLLxZCabwwUXxkF1b2ZJfvVLiEB3ES5fCON8IlqXOaltUtXPWTq+L/m2TGvHlpH9PYCG94Q+L/jg5hq/72N2E+zjpLiA2fRYJPnvZMofxRkN+Sq9rDvYnq9oEd0L1R/raqai82tn0D+rbG+lW9GyjOcWhfNtCAkz4pCRee0vXpK9m5la5OMLhf+ntuOR6TxSa3HxvPhIq5WdeL6SiVPfnKHxcL5XztmzYN5syRfiIbN8LrXpf7Pbqu097fzppz1uB0OosrM1WoIsYoUc4+KgTJ9tx0k5AZf/qTyLNOm5b5fe4KN7Xzaund20v7lva4zJS3xhsnAqdNE9nSsUa5+GiKzCgiNE36ZVihlOVyEx7pWYbRQdGiTA6M21h4L1kiZMaOHSIvC7B3r0RpbVVlFCtrtUTZr2UJTwOoDpEFC3XbL3MtBvQQ9G4RDdjqHGVugTbJpJmszZUbz7C1W0El2cXGiXR+5IOKufI8tH88RzGFKRQMo3Y1nPxpqTIqNRxeaL0q936Quhgb3Ber/kTkJvb/Rv4ei0BO8jgMAyJ98rfTB09eW7xxVMyB4BEYPgav/TcO1c2iQADHgD+1YWwR7DVldqyq/abmvQKHA66/Hr74Ralcefxx+T2sYOqGN+Xplg0bJFkfYGBAnl96SbaXWwLVZEIu+c4Afqrpp94bALInaGQkM9IxbRp84xvCij39NPzmN/DYY/DZz8rr5ok3hSmYSK5qd9fJvQfAWS2ysJmq2ouNWeuFzDjyd5h9zdjLbitq4ZX6Y1xNbolkP6puOc8Vp1R6Kg57fvQ2J2Sqxhn5yh+fKFi3TsiMZ5+1JjMGjg7QtbOLhsUNVLWM7HlTVJmpQhUxppARp54qPn7uOfjJT0R2KhumnTKN3r29HN98HIdLJtKeGg8Hitz8e6JiEkb1xg/Zy42hwjPAUE+YJx+v4vwLbWQSnkgoQrbokiXiA7NvBhBv/m27X0axslZLkP1allBUmH6J3OTyqMooCrSQZKREBkRyJFtz781fkH4Eq78ONeOv7zeeyKskOx8E2+Glj4nky5o7su97opwf+aAiVpkR6pZj2lXkpoxTmEKp4W2CyjFadXqbYOGN9vZNXoz5W2E4JjLraZYM0bFajKUvCvWwkOvFDihF+sWuygXgrMAwDDRVxXBVS6O+Itqba95bKpmIiYjLL5cKlvvuy01mNOcRa9qwQSpg0gml7u7SVcZMQZBLvjOAn2nefpa0Do18MQ1798pzTjIDwOmUHhqnnQa9vYno1dGj8N3vwhlnSMVG4xgmGE2h/OHwSRW3KQXpn5FYO41FVXvD6UK2Dx2AtntgzltL/52jxThVk2eFwyfrXiWp0tLEOKkTFIJ85Y9PFKxbB3/8Y+a+GS/9+CWOPH+EU997KkveJDrrw0eHOfz0Yerm1tHTIxJuRW0AbipiWGECHXPlgptuEv/+4AfwyU9mj8E0n9LMjrt30Luvl4pp4gNvrbckzb8nIqbIjAKhqipNTU0pZTW5yuCuOf0u1q/dgPfoVYDNRfgYwMqWiYjkJuCmTXv35tkvAyR7cu/PJbt09rXFHmZBKGsfLf5gQW8btU2uamleF+qGUGdCpscK0YA8l7Bfzbj6KNwDnc8DOsx4fc7d7ZRk521PZEC06LM0xRtPlPU5BFJh5G0WUmjogK0GymVv0xQsMdn8NmHscfjkvhENgBGRBuAmkhZjJbcneVGoOIW4NP8v2qJQkc90VqBg4HKHUVwVsr2I3zNeMhET5phLwmWXJciM9OR5VVWpq2uiq0s22iUzyrkyZiL6KBus7Mkl3xkw/Jx8MgR7AiM+74Xvv0DPnh6Wv305LafOsF+ZkY7a2oSOrtMJJ50kMlSqCldckZc9Uyh/jNpvDg9UzpfqxmxJYKWAosCc62DrN+Dw3dB6FarqHrvjsOdliAxC9VL7SgIFVJOPyblVqJynYcCxB2TtPOstCXmqLCiVPfnKHxcL5X7tS24CblVo17CogSPPH6Frl0g/qapKdE+Up3/yNAvfuJje3jXAxG4AXu4+yhfp9lx3HXz0o3DwINx7b9ZbNdNXTuf13309NXNq2PX3XWBA7Zxa2p6X18eLzCgXH02OI2QcoKoqCxYsSHFgrjI4v0cms1V15dUA3MqWcUXfNtj6ddj367zetnSpPJtkxoIFC9izR+4AtiszQAKJ7Y/Lo0xQdj4qAopikzs2GQ11ARareZCZgKlN7izdxH1cfRRsh53/Cwd+X7SPzNue6KA8l5AwGg0mxDk0/ZJYY0R7M9AJYdMURmCy+S3Fnr6tcPBOuY+XEqFuIXFNySi7UN1QcxLUrsy8y1j5x+zHVeKqRiXUgT+8ByV4vOifPV4yERPxHLrgAunrvH8/7NqV+pqqqtTULIj9bT+jMp/KmLHGRPRRNmSyx5TvTA8otLbC+z5aQUsLBDpGkhm9+3rp2tGFFtLo6IBAIBbrnTOKQTY1SXOWb3wDLrqoIHumUN4Yvd8UkSUar7l649nga5EEqKP/HNvj8PDdsPVriT4hduFtkn5UmR5pFY5jYpMn9p35+lFRYPePRGIz1GnrLaW0J9v1s1RVheV+7Tv1VOGljx+XYHc66hfJBKF7l1TkqapKXVUdiqIQJdE8YaKTGeXso3yRbo/PBzfcIK99//vZ3+v0OqmdW4uiKCy+YjFn33I201dNj8/9xktmqlx8NDmOkHGAruvs2bMHXU8spM1yuUxSpZWeIXw+WHLyGGdC5ICVLeOKcC+0PyEZFHnArMzYuROiUbFpzx4JcOdVmWFKUPhm5PX9pUTZ+SgZhiEZ+cP5BUpGZVOkD6IDif4X2jAEj1trl+qhRMCrhPqs4+oj81gNdYMWLMpH5m1PnDAaYw1cmyjrc8jE3LfB/HeB316axYSwaQojMNn8lmLP8Udg7y/zDxbkiz0/hqffBW1/K/pHj5l/KuYIcekpreSeYehEo1GMwCGIZskuLQC55r2KArNmFV8mYiKeQ5WVid/hvvtSX9N1nRdflEzbhgb7majjVRljBxPRR9mQzZ7164WkOu00+f8Tn5AeGBe8QdZ7gc6RZEawN9bIs9Ybr8qYORM8uROlc8PtztkQfLL550TBhPeb6oDpF8uaYe8v0Hu3cuC1h9H7dsHAHnkEO4rzXcGOxGcO7JEki+iQrAmL+T1pGBMf+WdC5TyoXpT/ez0N8hzusbV7RnvSf9/0h83f17x+PvII/Pa38rxvX+nkEUvqnyL8Jj4frIzl21hJTdUvFDJjoG2A8FAYXdc5euAohmEQ1EXGvqIC3Hpx/JOwrR0GdycScUqICX+dS4OVPe9/vzzfe2+iZ5YVhjqG6N7TPeLRtaubOrqZXpVbxrIUKBcfTclMFQhd1+no6GDOnDlxRipXuVyFZ4iTTwaHu7yCfVa2jCs8sWz7cFdeb5s3D1wuGB6Ggwd12to6OXhwPpBnZUaZkhll5aNktD8O274JtafAqq/YfltBNpnapd0vgxEF33QpkQ0HYfioyPSka5eaQXbVIVrlJcK4+shVJY/IgDR9LUID3rztKfPKjLI+hwrEZLTpRMBk81uKPf5YilAgS7p4MRCOacS7iykKLBgz/7gb5FFiGJ5mwkonPiMgC9iKfHVsMmO8ZCIm6jl0+eWiAHTfffDhDye267rO3r3SuTuffhnl3EB1ovooE3LZ43DAOefAiy+Crsv//sbMZEaoX6TePDUe9r0q2/KWmBoFJpt/ThRMeL8FO2D3D6H3VZFBfOrtNAwHUQ74EzcNT4NIO42mp1OwA55+R6xyH7k5De2VIv6X/z9JhivG91hgbHykFp4I4a6DQFtiHpUDlvak/75WyOP3tSN/XCyUzD9F/E3WrZN7yXPPiSRRMrw1XvzNfgLtAXr29NBwUgP9XZKoEtSkMmNBa3H9A0giqUmAhTqgYr699xWACX+dS4OVPYsWwcUXw4MPSu+M//mfke8b6hhiwzs2MHhskOHuYbSIRvXMalBgwVZoBfp+6mfouvVUNI1tfLlcfDTxj44yQ7ZyubddO0TLdMZeo3KiwcwYCHXnJSHhdCYqMHbuVDh61INhKFRU5Lc4LEcyo6zhjf24JZCwGPldTbDqG0KcNKyFs/8A634C9adB4xnyf/qN2SQzHP7M6aOTAebxah6/Y40yJzMmBAxDrns9L1uLoE9hCuWOOJlRoJ6zXYRKR2ZMPiiEnC0is6hHYGh/Ua8v4yETMVFx2WXy/OijEEwrouzpkSBEPvPV8aqMmYI1li2T520xlb2aOTWsfPdKVrxzRcp+WkQjMhQBJDCVV/PvKUyhUGjDsiZKf1hVtZcKkX4JolctgsoFGO56NLUSw1ULrlpJOgt1Ze9RYfd7Ql3yea7a2BrQKf97Gov3PeOB0frRnDeF7JEZlkj/fdMfE/n3LRRF/E2S+2ZYoWGRxMrMvhlaUAMgEJF5ROu0IvtHGxYVDiMqFRrBDhg6OLbXjkmID3xAnn/8Y7j/fvjd72R+qIk7CfWHCHQFcPqcRINRDM2g72AfnhoPAxEvEZwow4F4csSJiKnKjBJg/XpptvfEE1La3dIiCwnHSwEYomxlWMoG7lrRkDZ0kZzy2A9WLFkC27dL34xo1AtIVUZeMew4mTEOqWwTESaZEeoEXZMKiFKif5ucQw1roW6FBGU89aKBrlpc0uLyR5M8yO5rgf4d40hmxH5n1yT/nUsJQ4Pn3iPn0Rk/LXq22BSmUHL4Z8nz8NHS3Q8MI5FRmMf8IOOia6wXY2M1DvPzDAPVCGF4Z6AM7o4Rz0bRCQ3Lee8YN50udyxfDjNmwJEj8ltdcknitULIjPGqjJmCNU46SZ63bpVnX52Pk645acR+oT4JPCgOBVeFq/Dm31OYgh2YVe2hLpHetUJ6VXup4fDJWs4w0NWI/G1etDKNcTTfo0eEzHD6E3GYYn7PWKBYfjT78tmszMgK8/e1wkT7fYuFIvwmJpnx4osQiYjySDLqF9Zz6KlD8b4ZWlDDgYPBkOxYYx4Cox2LecwNHwctAApSERTskHiDOlvWqmN57ZhEuPJK6ZHW1ZVIdgFJUrn9drggJjfmrnSjutR4e1jV6yGogwtwuaNjPu5ywhSZUSBUVaW1tTVjWY1luVyZasrnsmXMoagSoAh2SoA8TzIDYNculebm6UCe/TL0GOMMZVWZUXY+Soa7HlSXTBTDXQlywwrBjngWgKrrzGmIog7tk46XIDfDbAFcw4COWCfL5liqoaJIZUbG8dXCrPUlP+/G3UdFrszI2x5Xtei3eqcV5fuLjXH3jx2oTvDNlGyXoQM5yYwJYdMURmCy+S3FHk+TSP9pIQges93/JS9ow/L5kFiUZ0OeAYCS+WesAkpp36MYBj5HBEXTZT41fER+v8B+qM5HgzM7xlImYqKeQ4oiUlM//alITZlkhqqqaJrMdZvy5LDNypibb05tBt7aKkTGeFXGTFQfZYIde8zKjAMHYGhIdMutEOyL9cuo8aIoSpzMmF861Y4RmGz+OVFQkN+8TVK1ni0LO9f6q0RQogNU0Imi1Jb2i/RYKZwjey+ZYqBk51ax/BjvmWGPzMhpjxERdQbPNIkHlDkmwrVv0SKorYXeXnj11UQ/JhOzzp5F1cwqGhY3oKoqPqcPHZ2BYIzMqE37QG1YJKJ8LQgjYRPmMdf+OOz8nqxRl38ONn9e4mWzroY51xX92jERfJQPMtnz179Ct8Vp2NYmSSq/+d/ENqfPSTQgxEUwlqvkdCRCaGONcvFR2ZEZ27dvZ+XKlXz605/mC1/4AgBHjx7lxhtv5PDhw+i6zgc/+EHeb3ZNGSeYDswL014nFxI7i+8xREG2lBruhgSZwWLbb0s0AVdQ1Vogz34ZoQ4hU1RXWfmpLH1kQlGEwAi0Sb+GTGRGmpakCrQA7E7aJ5d+Y+AgDB2SoG/D6SNfNwzJbk+u0PC1wIIbCjAsP4y7j0pEZthG61XyKFOMu3/somJujMzYDw1rsu46YWyaQgomm99G2OObCYN7pW9GKcgMcwHu9IPDm3v/PAMAJfPPWAWU0r5HAdzJrx+9HxxumHbR6L5nHDGRz6HLLkuQGd/6lmxTVZVgUEisvGRRYyjHypiJ7CMr2LGnsVHIqI4OqRA/9VTob+tn4MgAtXNqqWgWdkOP6Pib/fgbRHZ4PCozJpt/ThQU7DdvUxlW++ooA7txG1Gp5ixlEmE01rfGzpxhlCjpuVUMP8YrM+w1AM9pz9ABkawK90LN8tGNbQwwEa59qgqnny7SQ889N5LMqGqpoqqlKv7/2R86m1BfiN8/JdtqatI+sH+bJOwaEfDPyW8w3iYhQ5wVsi6tWQwL3wvbvwNdz8DCf8vfwByYCD7KB1b2aJokoVjBMCS89uUvw3/EckQrmivo29+Hw+OIy5R6StcKNifKxUdlR2bcfPPNvO51ryMSicS3XX311Xzwgx/k+uuvZ2BggEsuuYTZs2fzhje8YdzGqWkaO3fuZPHixTjsrhbmv7u0gyoQBdlSangaRJ7CrGaxCZPM2LHDIBQaAirzq8zwtcA5d8kNuYz6K5Slj5Jhkhmh9sz7JGtJOnwYGAwFhqnw+1BQ5EZp6jdmmqi1PynP9aeNrLQ4+gAc/APMvHJcgurj7qO6VdKA3Vec4OG421NkTBh7KmKTzKEDOXedMDZNIQWTzW8j7KmYFSMzDgHriv+F8X4ZeSQc5BEAKKl/xiqglPQ9I+ypukn2SaqUtMQ4ZerawUQ+hy6+WAIVW7fCoUPS00LTNPbsCQBVBZEZMAaVMXaOF4jvo+ka+/btY968eThMubkyPqZywe4xt2yZkBlbtwqZseknmzjywhHWfnAtCy+X7KrGpY1c9ROZp0ajUskBY0tmTORz6ETG5PKbilExC61vN47AYRRXFSVr51oxSyr1x6Ayo+x9VLcSVn45u5JCEnLaY87JogFAp9xb8o6Zf/SQxEaMKFTZT8w1sW5dgswweytYQdM0BmsHWXz6YnofFHtq08kMPSZFpIXzHgcAA7HM06pF8tx8ARz4g5CQR+6FWW/J/v4855sF+yjPeYqdsRQDVvY88URqNW06DEOSUwYrwFcnZIbqUHFXujkSC7fZJjNKMN8vl+tcWZEZf/rTn5g2bRrz588nGpWT7tVXX0XTNK6//noAqqqq+NKXvsT//d//jSuZYRgGfX19GJOgSWtZ2rL0o6C6pUoiD5hkxsGDCqGYbmBelRkgWf3exjzfVFqUpY+SYUoLDdtoAh7TbzQMg4gewXBUoNjRSU2WmGqy6Gaph+X7jz+SSmaE++RzXdUlzcgZdx+5a8B9StE+btztKTImjD15kBkTxqYppGCy+W2EPbPfCrOuKV2WpbtOrvEl6oM06f0DiUrJYAdE+qQhZHoCR65KyXHERPZRfb0EKZ55Bv75T7jxRrGnPbY4LZTMKCnSKmst4aoSPefoAACqYTA9EEA94k8cW2V8TOWC3WPupJPg8ccTTcD9TVJ9MdRhnZx1+LBkaLrd0k9lrDCRz6ETGZPNb4a7gbByHB8BGNgDlSXSWlNcicbXJUbZ+8hdl1cySFZ7DC31cw0t79jNWGNM/DO4R6qHQ52xhlaR3O9JwxlnyHOmJuA9e3toe76NiukV9HnFHlOyqDqdzDB70Zp97fKGLknGJpmhOkReavvtcOhPMOMNIjFrBTvzh7S5QUE+KmCeYmcsxYCVPUeP2ntvLCSOghKv5jQrM7x2wloF/P52UC7XubIhMwKBAJ///Od54IEH+OEPfxjf/uCDD3L++een7HvuuedyzTXXYBhGIgiahFAoRCiUCIr29wsTFY1G4ySJqqqoqoqu6+i6Ht/X3K5pWopz0rebz+Y+5ueaMBkqzWxHr0ch0ofDIwHV+PYYnE5n/HNNKIqCw+EYMcZM24thkzl2RVFy25Rj++hscoKmo6rkZVN9vUp9vUp3Nxw/LhfVOXOiGEY52JQ69nz8BIz43vLwU2yMtStRVQ961RL0pPGk2KRFccTOGUULogzuwRn1YRg16GIgCiKJkdGmpZ9E6XwKo+ZUiEZTbao/E5UfQv9u1KFDGP5WNE1DOfgnlEMbYOabUBe/r2R+Mvex+t3Lxk952JTp70w2qa98EiU6AMtuQfPPLTubzOdkP+WyKdvYS2aTtxUHBkrgEHo0jJ6U4TRam9Jtm8IUSoKKQhdLeXz+whtL+x2THWalZLBDtMQVF/iTysXtVEpOoWBcdpmQGffdJ2QGQG+vLMny7ZkxJkirrB0BbVikYSGWOOKTe5SqYriqZa12ghxT6U3A/Y0SfAh0Biz3NyWm5swZP+3rKUxhzKHFRN8Ng4hah08JSYPhwb3FvT6Y32N3+xTyQ6hbKg8cXklg0cJA+MT+fUMd0kMEVZrO62H5X80vofL0mJr2jh3w4x9Lcm6yfOTxzcfZ/JvNNK1oIjo3ynH3cXp6RJ0hLjOlDQvBpMcqMvSw/J+vf1Z8SXybLOPdfAEcfxSazwclS2a+nflDMeYGBcxTSjYWG2hpsbef0wnR4dT1e2hAmn97VBvr+rH6/ccJZUNmfOUrX+H6669nRlpaypEjR5gzJ1Xbzefz4fV6aW9vZ9q0kc1mv/rVr3LrrbeO2L5p0yYqYt3YmpqaWLBgAfv27aOjoyO+T2trK62trezcuZO+vr749vnz59Pc3MyWLVsYHh7GMAx6e3vp7++noaGBTZs2pQSvVqxYgdvtZuPGjQB4Im3M7fo2NU3zCK7+Aa+++mp8X4fDwdq1a+nr62P79u0pdq5cuZLOzk727t0b315TU8OyZcs4cuQIh5Pqkwq1aevWrfT29vLSSy+hKApLly6ltrY2p00m1qxZQzgcLhublixp5pln5H+XS+fYsY00NNizqbn/L6j6MK1nfIiwe2bZ2FRfX8/AwEDcR0CZ+amFBQvPYd+ePXTsTnxvsk3BztdYFAigqSp+xzDO6BDuSA+DvR501Y+qD1Ph1nBCZpu2HgcWQvtrljbNHJ5GVXgbte2P0ld3Jdu3b2da31Zqh3vpd3QzZzEl85N5ndq6dWsKmTqWfqoIbsUX2YfSuI4Fq68YlU2GYRAOy+THvO5ls2lB+2vUV6gYBmV5jTCv2V1dXbS0tNiyqVR+ymqTYbA8qFHpVTi48zmO9Sea6Y3WpqGh/GT7pjCFKUxy+Fukt0mkD7QG8CRVpWarlJzCqHD55fDFL8KDDyay7np65FpflpUZJmKVtaAnZCsA9Ij8ryDBG2cFGAa6GpG/7VTfThKYTcDjlRkWZMamn22i47UOlr55Kfv2zQbGtvn3FKYwbnBVSxZwqAv0EIph4DAC4KmVnn+RfnndlIMpxvcEOwBNqjmTG1QX43smKo49LA2cZ1wu8lv5wvx9+3fItd/pgEhv6j4n2u/rqpaer72vyG/irgUUCAfkt/ZOy+s3efxxIS40Dd77XtnW2gq33y59shoWSyP3468cp++xPvrv76e7+zoAKmqSjv/oQOz+rMrfhi5/5+sfhzv1f9UpcmW23+8bKRFuophzg2zfY0otjdVYsuDcc8WfbW0iPpIORYHGFg/Nc/wMdweIhhJzrugg+ACXCv4GP55qG3pTZWBzKaAY410bAuzZs4crrriCTZs24fV6+eIXv0g0GuXLX/4yN954I+vWreO95lkcw+zZs3nssceYZyEwalWZMWvWLLq6uqiulpN2tFUMuq7T1dVFc3MzDocjdzZv72bUzZ9DqWiFtd8f96zr5O2RSITOzk4aGhpQVbU8MpSHj6Ec+DWKoqCe/Mm8bLrhBpVf/lL+b2012L1bw+22Z5P6/I0Q6kRZ/XWoWVY2fgJob2+nvr4+/n9Z+CkfmwZ243j6bRiuGpTBXaBH0XUd1enFqDkZtBBKtA/lnDuJ+lIJTNs2dTyJuv2bqL5pGKf/CE3XUbZ/E6XjSYwFN+KY/eaS+Qmgu7uburq6lIqxsfSTsuM7KO2Pwtx3os5726hs0nWdnp4empqaUqrQLG0yDNSnr0UxNDjjp2jO1PLlcT/2kq7ZTU1NOJ3O8qzMCHZAtB/1+MOoLj96zQp0hz9hk7sW1T+tYJtM8r2vry9+L5xCdvT391NTU1PU30zXdTo7O2lsbIxfzycyLO05/FdpYj/vXYUtlLMh2C4BU3dtSeQMTgj/DOyBJ68VealInxAaigq1K0TiMzokgYlz7oSqfBqPjQ0muo80TUiL7m548klYvVqnokLs6OmB2trxHd8IJB8vTh/0vpqqvW1E5X8FUH3QuA4DSYhwu93SF63Mj6lcsHvMHTkCM2dKECoQgN6d7Tz0qYeobKnkyh9eCcCjX3yUoy8e5fQPn87PH1/Al78M738/fP/7Y2VN/udQKe6FJwKK/btN9GsfkKLfbq416urqUDseh/2/gaYzYcWXR9+70vyezbfKPW7R+6XnookS9fCZED567n3S72D116DmpKy7ZrQn2CHSzt0bofFMqDkFhvZJsLxqYdn2SCqpf449CFu+Ag4XrPwaBI/B1q9LEPnU22TeauM32bABrrlmZJDbPCXuugve9IYod153J4Yu99raGbV8/qU3s3mzSFheel7s+O/bBttvkzc6K8TfC99n3z9mN+pCkTJ/qJDG89ow+KYDquXcoCAfpXyPX9YK0aGEdF10SHp/GGFwVslcF8QnpgRdieYpmewx/Qypvk7282XnDhHqTyUb3vQmqf780Y/g8qs8VDRlIClg5O8f6RU7fTNgFHOzfHxUyvlDWVRm3HzzzXz5y1/GayH85fF4CJrCYEkYHh7G57Nu4uTxePBYdERxOp04nakmmwGvdGRqZJK8vSWpPij9c0duD8uR6awERbHcX8mwPdMY892eySaXy5Viy8ixF769YJucDuh8Slhgw7Bt04YNcPfdif8PH1ZYuNAZZ7Gzjl0LQ6Rb/OSfWXZ+mj59euaxj3L7qG0yDAj3oQbbUSvnpZYhErPJ4QRFQdEGJUNAdeFwqEJiRPpSSt9GjGVwLxy8E2fzBdA4sqFsfP/ms2BPBQTbUfq34qxdHpPQUMBdlZ9NObZb+ak5S0rlmPipchZ0KBA6lnXsdm2yqnyzHKMWTOimOivH9tjLsT3TNTvTeTZu51O4C55/V4qmpUpaG72YpqUjafKZj02ZbJjC2EJV1azXiokGS3uO/EMaHzafX3wyY/u3oXcLnPQJaD6vuJ/NCeKfZPhmyOIyOgTRfnCXV88wK0x0HzkccOml8Pvfi9TU7NlypXe5kuQhyhWGJtmnw0cTK25DESIDJb5NQcHj9iAC1RMfdo+5lhaorob+fti1C+bEemYMdw3H5ZGDfbKu9dZ4MYs1x7L5N0z8c+hExaTwm7cpHkRVgQbzmle9EGqXQ8PpoycyzO9x+CRQ56yAlkuLPx+xwITwkbtOruFm8+4syGiPt0n6JsyRagDa7oFdP4DaU2DGZUUecPFQMv8YBhy5T461WW+G+lWga7D35zK/MjRb5IGmwc03W2frm7zCRz4CV13lpGZODX37+/B4PLj8Lnp6ZL/6ehLnmaJIn7nhw9C3XSqgKufbP8c2f1FIkYXvg5plI1/Xo3DsITj+kJCQ6RUc8cFr0kskuXdDht56o/KRHpWKoUh/zMakskctCPqwVJMqsTVxuBvqayll4/pM9qxfL4TFzTenNgNvaIAf/EBeh4oRZMWuTugBFqyBinz5Qj0s6zNn1agqp8rlOjfudPF9991HIBDg6quvtny9tbWVgwcPpmwbHh5mcHBwXH9ATdN45ZVXRmTlZkQ0Ju/h9GffbxyQty1jAbeUzqGFpZbKBkx2M0n5B5DyrWuukdezInhU7hLOirIriyxLH6XjuffASx8TrchsCA+AoWE4KhjSazD8rTLZzKbf2P44tD8J7Y9m/2yHG5rOlr+Px/Y1j59MpXVFQln4yJwUDB8Z9UflZU8k9hsrakmbrI8GZeGfbEjWtHTVjnyonoSmZQxlb9MULDHZ/GZpj9l/IXCo+F8Yjq3WStTM84TwTzpcsWbqUWtd/3LDZPDRZbFYz333wbFjYkdTk1GUGF5JobikiWjD6VC/Vh61K8A/W7bXnAyAEekj1PEyxlAJrgHjALvHnKKk9s3wN/hBAS2sxbMrQ33y7KnxxHtmjDWZMRnOoRMRk81vKfYoiiSsKYpk/ffvlsfAnpGPYI61pon+bbK2988cEyIDJoiPPLH5Uzg3mWHbnoZYk4e+LdmbDY8zSuafnpcliO5ww6z1sk11QP1q+bt7Y8a3JuOJJ1ID2+kwDOg6NMT9v+/GW+slNBii63gX0VAUvbObOrrx6UmywpXzYenNsOK/RWYt3Gs/TmAYcg4N7LbutyA7wb6fQ9dG2PfLkefq0AGIDMDAztTjIou0UUYfBTusrwfp3xPplz4e3rREYFcFuOokXlIxWwgNQ4doaXu8ZDvm1q+H/fvhkUcS88LLLjOJjJEIhaC9Xf5ubSX7bzKwB4JdiZ4pkIgjaKOb75fLdW7c0zT37dvH4cOHWbVqVXzbsWOSVXzffffxrW99i0984hMp73n88cdZu3btuJbuGYYR751hC3Eyo7QB1UKQty1jAYcbXFVyUQp1yd9ZYJ/FTjROGoFA7MLum1GcjJAioix9lAxFES3IoUPS5Mpn0dXI1NcEcHrB0DHCYTCcCZ1NK/1Gw4D2J+TvpnNyj2X6JYAC0y+S/82LtaO0RGJZ+KiIZEZe9mjm9a2y7M4dE2XhHztw+IT0jg6CFsqqYT9hbJpCCiab3yzt8c8CnisNmWEuhkpEZpwQ/kmHIzY3jQ5l3qeMMBl8ZC5aX3wRXpM2YOXZ/LtAGIaOog1BSIeK1txvKHPkc8wtWwbPPit9M9RrVU5976l4a7w4PSJDaZIZ3lrvuJEZk+EcOhEx2fxmaU+wA556mwSGVS+4LRIMY5XKOTPde7fIc4xkHQtMCB+5Y3LAZnJIFmS0p2+7JEL4ZsbiAM2Sud+3Tdbts95c/HEXASXxj2HAgd/J3y2vT/y+APVrRObM5pz16NHsr/sZ4mo28MpnA/gdIYY6htA0jeGDQa4MSgz1uU/6mfvH9akZ/Q43VC+Rc6J3sxB8uTDcJoF+hzs2r7dAuFeOhcG90i/EPzupatOQuFCwXeI7rqpYVYiaNS6T8brw9DusiTLDkO8IHQdnNbhroHLhyARLhx8cgKcpJrnUB+E+iWOUMEab65hzOOCCC6TZ9z//CX/7m5AWFkJD8ePD44GGiiy/CUg1TLhXHs4asdHhB7pHnbxULte5cSczbrrpJm666aaUbck9MwzDIBKJ8Jvf/Ibrr7+egYEBvvCFL/Dxj398nEZcIMwFYokDqpMKngYhM8JdwNysu9phsQ8dkv0uuCDDTmYA2CoQP4XcMMmM4eNQZ/V6k0w8Y4ywpkXZtXkzp5xyCk6HU0pd2x9NnQAADOySG5TDKxOCXKhZlloGaZ57ZubpZIZ57Ib75CY1VpVgZmXGifAbjwWMqCwGIJY9Ne5FlFOYQn6IV2ZkuTEXguiwlIlDIrNwCoUjXhGpiH6w4pR7ZrZKySkUBS0tsHIlvPIK/O53svBvbjaI6TWVJ6JD0kDUWUHKfSn5eDH/VpwYKCL1OXxUqgtPECRXZgAsuXJJ/LXIcAQtLJmMhttLLH9vqgH4FKZgItIvWdbRIVCCCbkoE9pwolI5F5nRF2OKx5DMmBAwA+ujqaDY/f8k8/ukT0JzLNmw+YIYmfFY2ZIZJcPcd8DBOxNVGSamvS6RYGkDFsrvKfAQwk8At9+Jv9rBcM8wBgaq18UwXpxECfcGCPWHhMwI98aC+G6oWZ4gM2ZcnnswA7vkuXKBVJlYwcz0V92ALpUOpsIKOgQOAjHlk8p5gCKBOTM+Y3e+maxgkF4lMrQv1tw89j0VcySQn5ygYzVPcVaC4gEcZTH3PessmDFDem898ABcccXIfcx458yZoESz/CaRXpGT0kJCLkV6wBGbhxlRIXLKwObRoiwjJC6XC5fLBYj2+F/+8hd++ctfcsopp7Bu3Tquu+46rr322nEeZZ4ws8PLsDKjbGFmJNu40eZisW3tN5xUmTGF/OGN9VcIHs+8j+qW5kJVC6ByASFXq9wgK+bCnh+JnNT+X6e+p+NJeW44PXERzgfTL4GWy0aSJJMRTr9kI0BRqjNsQ1FlgpIpa2MK+UFxye9Zs4yyDmxNYQqZUCoyw5REcPrKVtJuQsCslNRDsuDRYwEjpz/2f8i6UnIKRYVZnfHgg3KdL9vKDPN4ifRC/04JEEZ6Ew89BN5GmbfHjikl0oehukW/evjICXVMLYvl02zbNvK1YK+QsQ6Pg8PHJKewuhrqToAp6hSmYBuualm3KQ65fji8EkNxVmSRu0mDFoLB3fJ37fLSjXUiwiQzbFRmWCI6JJn4kNpAvOlsWRMO7JZA6okCRYG6VbDyv0cm2uSpWHDuuSIflOltCtJfq77Zib/BT0VzBYpDQXU5ieAGhzP1vZu/BE9cDV0vSD8TECkwOxn1JplRtTD7fooiCZWKU+bpTl/sfK0C/1w5f13VQkgkzx1CnfKcz9zA4UtcC8yHbwYobokTWX1PhnmKfJ5L5sBlMPdVVTA7L/zxj9b7tMVOq5nJhTXJv4nDLdL5gTbAkN+karH4INIrklN6VBQgwj3jbvNoMe6VGVb4zGc+k/L/nDlz+Oc//zlOo7GGw+Fg6dKlGRuujkDVImFlqxaXdmAFIG9bxgomq2uDzMjFYtvaT4s1ivaXH5lRtj5KRi4yI9QFz75HsmNW3IrD4UzYpCgw/1/hta/Cvt+K72tOijXT+rtMmirmSgaIqzp3Jk6wQ7QrO5+D1iuFeQ91Jxqd2fmMPDHuPgp2yM3b4YPoEZm0JAfC87Q5L3tqlsGaO/If8xhi3P2TLzy5fTXhbJoCMPn8ZmmPSWaEuuX6XaxEjhL3y4ATxD9plZKWKMF9sliYLD66/HL4+tdB1+VeHQopaFoWOdTxgnm87PoBHH8Ypl0Ac69P3cdcCJvHlGEQ6TqAZ+9XZC638suyFirTYyoX8jnmzMqMHTtEBjfUE6Bnbw+eag9Or5OKaRU43I6U5t9jrdA5Wc6hEw2TzW9Z7fHPgqG9sj4f2i/Jb/lguE2Sg7x1tubUxcKE8FEePTMs7enbKtd1X0tq8N5dA3WroftFSVCc+/YiD3z0GDf/aCE5Jiuzl+E5HHD77dLvVVFSOQdFAYwE2aEoClUtVTj9TkKaJHw6XWkfGI7F0Vy1UrGgxtQwgsdyq5EMxMjAqkW57fPUx4LjYUl6qF4q2901YvNpt8v3J3/29m9J3GLN91LmBvZ8pBPPy3fXQ+1J8v3p35OM9HlKpn3GMUZ03XXw3e/C3XdbS01ZkhkmooPSM0SPygHimS6JxGv/LyH1bhjw4n/I8bjiViGCCrC5XK5zZUlmTAQoikJtba39NzSfK48yRN62jBU8jVLSZqP8yWSx29qsiWZFkdfPzeaCkz4B+kfsMdVjjLL1UTJykRntj0vpoaGB6kKBVJuazpKMjm3fgmffLZNYPSLMsqLCa1+JXZhz6KSamoo9L0sD+X0/H8k229VazQPj6qNkHUk9DCiw7Rup++Rp84Q45vLAZLMHJqdNJwImm98s7XFWyMIm1A3Dx6QarxgwF94lJDNOCP+A3AvS7wdaOCbd0Thy/1LCJOMzIW2hNVl81NGRGqz4058U5s6VQEam5o/jBm+TZPs5K2D6xZnP6ZifFKCqeiEMPimNQQd2yjxvgiKfY27OHPD5YHgY9u2DyOYDvPzTl5l93mzO/sTZvOnHbwLgf/9X9h/rfhkwec6hEw2TzW9Z7VEconvfv03WN76Z+VVkVs6Hs38v2d9jyBZOCB9VLoxVETTk3NXSnt7N8mxm+iej+TwhM3pfBcqPzCi6f7Z8WRIuZ70lc+LO0CF46SNCrp31m8ySTTGsXw933SX9YJNl1GfMgG98Gvp/nNjmrnDjrnBzPBZ+cSVHd3UtkQTkaZCs/ZbLZJxKOuuRBl2DwT3ytx0yA1XIkaEDsdhdEtmguoRgSJ43VMyBQ3eKPHnPiym9tXL6KNQpFVtVCxMS/orT+nuskDz3DbYL+VK1EHzTM79nFMjnmEuWmrr/frjyytTXzeOh1aoVWeCwEBkOj9hjkKh8Sf5NapdLnxOMgtdn5XKdK0uZqYmAaDTKCy+8QDQaHe+hjBpla8us9XDuBljwnpy7miw2jJyvmP9/5zs2st1Ul1zoywxl66NkeJvlOdhu/frxR+R52oVABptaZYGHYUhgw1mZkDdw14kmoKmTmgmmpqK7XrIPIkNS5uiqlYedzygA4+qjZB1JT7NkIJn2FmjzhDjm8sCEsUcbjumSpz0sSN0JY9MUUjDZ/JbRnpVfhXPvLB6RAeBtgdaroPHM4n1mGk4Y/6Sj81l46jrJlBtLmGT8k9dmfjz9Dtkvhsngow0b4G1vG5k/09YmGZkbNozPuDIi3AtDB+VvqwBWGuI+ar5YNhx7UBbZExT5HHOqCktjSalbtxJvwhroSG24OV7Nv2FynEMnIiab33La46wEV0w+N9SZ/xeoDvBNK3yABWBC+MhVCXUrbDWBtrTHbKxuJd/VeKYQJSv/u0iDLS5G5Z9gh6hEmI+2e+HYw7Dvl9C7NWWekgL/TFmLR4eE2LeB9eth/3545BGoj+Xv/OIXCXlKE7qh09vbSzgskwlXMkcR7pFJhqKCu1a2LXo/zPuX3EkrWkAkvivnCpFoB95pUL041qMmR5hZdcLs6+Tvg3+SZJoYsvpIC8UIk6DMS0aLXT+ArV+DrudH/1kZkO/84Zpr5O877xz5etbKjOolULtC1E0cWSriG88UUsvTnHvwGVAu17kpMmMU0DTN/s6RfjnpyjDrH/K0ZazgcMvF1yZMFjv95G5tle1ll+WWJ8rSR8nwtQgZMfvakcf54H4Y3Cc3rqaz45tH2KR6hBRRHNLAGkSDsnppfjqpAJ5pwtKjQ//2/LVWC8C4+8hKR3IUNtu2Z99v4Pl/h7a/F/Q9Y4Vx9082pGvYW2l9WmhalrVNU8iIyeY3S3v8M4rf16J6ESy8UeQDS4gTwj/p8E5PZOKN5Vw1mYxPJuFzkPET2UeaJhmXVj+zue0jH5H9yga9r8pz5TxwVdl6i6ZpULdGmsMufH8JBzc2yOeYS24C7m+UzNFApzWZMV7NvyfyOXQiY7L5Lac9Zg9NG5JI5YJJ7aNoINGLpMaC2Hb6hCjJI4Yz1ijIP1aJF8/dIFUofdvghX8fkXgRh6KK/BZA90bbX+lwwAUXwEWx/uHPZ4i3G4ZBOCJ/O5MrM0yJKU99/v5wVUlz9zXfza+yyVUbawZuA9NeJ3GfcA8cTW0pYOkjw4DAAVH5cFXnlsmyA7PqxJTUKhHyOebM1tCm1FQykhuAj4Qi665cVTez1sOSD0HNUttjskI5XOemZKbGCps+KaU/q75iK6NpCoVh/Xq46ip49FGNp57ay9lnz+eCCxy5KzK6NsLBP0D9Gpjz1jEZ66SDswIWvtf6tfZH5blhbe5FsMMj+n3B45KF422moCbIqnOk2OSJAEMTKQgtIkGHsUCoHQJHbEnCTSEDJriG/RSmMIUJDv8sSSKJDkv5vo2MzaLCJOOtoIest09QPPFEqnREOgwDDh2S/S64YMyGlR1xWZEV+b1PdUhA5ARDchNw/41CZgx3DbPl91toe6GNRW9YxN69wmKMR2XGFKZQ9jDXFKpLgpaumoyVyiPQv1OqDBvW2VJ4OCHR/oTEpqZdkF9QuH9brF/G9NzZ/bomQfSxbgpUCiQnXjh8cizqkYS0kaElEi+s1moNa0Ryu2ujVEbkgTPPlCz9Z56Bf48VM0SHJSPeMAy0YY3oUBgXCm41KVPe7DvrTpMTiw5J35OqRYmKjdEi03mZ7XxVnZIEu/N7cOguqRbIppAS6pSxqx6p1I4mJQgUGoOoNskMexUzY4FsUlNmZUaKzFQhv/0kQflSppMNWuxkK1YTzBMBekTKvjbdIiVlNuFwwPnnG1x6aRfnn2/Ya6Q4tE+04wJthY93CtYwDDj+qPzdfIG993gaZWJQs4yCiAwTvtiV3ukv/DMmGhQFho9CqCPWP2MMEB2SZ2fl2HzfZIW3SSR5Mj2miIwpTCQEO2Hn/0kfpGIh0CZ9OAy9eJ85BYHqgIpYVLXEGWpZETgo1ZRMXh8fPVrc/cYEPa/Ic93K8R3HBEFyZYav3oeiKhi6wbFXjtG9s5vwQHhcZaamMIWyxYhK5X4JiEcHslYqp6DvNUmyGj4yVqOeeGj7G+z/rSgn5IPqk+CUL8C8d2ffb9+v4Nl/FV9MJpiJF+EeUYDwtYgUdi4VhLpTZY0+uFfmsXngzJiy6jPPgLvKg7/BTzQUJdgbJNgbJDIYIToUxEcQJ1H8DX481Z4EmZHeG2XzrbD5S9mrRIKd9hJCC1QWiGP6xUKKhbrh2AOZv6dvq6jcoIOnDrTB/L4nEyoXynOgLZUcGUdkkprSdSE4IFaZkfzbD7cJITN82N5vooXlWMwjxlqOmKrMKBAOh4MVK1bY7+AeHYy9sfzIjLxtGSsoTuh6QU6yUJdIVthE3jYFYlcGn/3vGEuUrY/SERmUQLqrMpHl0btZ/OeskMqMGHLaZDYUHw18LcLwjwGJWD4+UuU6Ex2UBYAnR9ZMBuRlj3l9K2Myo3z8UzxMRptOBEw2v2W0R1HgyL3yvOTDkr02Wmz5L1lwrPxvkTEoAU4Y/1ihaiH07xAJiWnnl35w6TCi0jAepP+W17oZ40T3UYvNJFi7+40Jln8Gel4VLWYbGOGjcA8cfUAkEMz+aBMI+R5zyZUZKAq+eh+BzgA9u6UZa1jx0B8rxJw7t/jjzYWJfg6dqJhsfrO0J1elsmGAuyZ7go/Z06Hm5OIN1iYmjI/cdfKcQ75rhD1On1QZ5EKoS3oatD9u3VtjnFAU/0QHIdIn81u7VS3uGqmE6N8p0lQtl9j+utWrwe2Gzk44PlDB+t+uJ9QvQWjDMAgGg3z2s17uvlvh//tXWP9xj/Rq6m4VsqB6ceoH1iwXaazezfJ6OvQIPP9eqYA4/f9lr94YrbKA6oRZ18Len8WTMEf4aPgo7P+1SHXNuBRa35L/92SCu0ZUQILtkshTgrVFIcfcddfBHXckpKY8HujogEhEDruWFsCV9Nsf+IP0Jpt+carKTKbfZOOH5Hdd9dWCzs9yuc5NkRmjgNttUw9Ojyaa2pRphrhtW8YSiiKB2ECbaP7lQWZAnjaZmRt5fsdYoix9lI4Dv4XDfxMtvgU3yLbqxbDsY0J0pAWzxsSm9NLKUn5VufjIXZOYaBVIZkAe9kRiZIarfMkMKCP/FBGT0aYTAZPNb5b2uOtl0RsdlglzxezRf5G56E7PMisyTgj/WKEy1qx9vCoztGDi71BXRjIDJraPzj1XJALa2qwTHxVFXj/33LEfW0ZUzJFHHkjxUd82ydR118KMN0gAY4Ihn2NuwQJpxDo0JJJh/iY/gc4A0aBIgHQOSj+h6dPBP05Lw4l8Dp3ImGx+s7TH2zQyANf+OBz6s1w/qrMEgg0D+rfK3+NAZsAE8ZE71lXaRpVAQfY0nw/HHoKOJ2Hh+8rqmj9q/wTbYx/UKAF/u6g/rSAyw+OBU0+FZ5+V6ox3vrNCyApiMlOaRnvEQQ8KDYugwjx16lfLIx21p8DBOxOkXzoG90v80uETebdcsDpf80HLJdJbK6mCIMVHDh9ULYaKECz5qFQSFxNVi8Sng6UhMyD/Y+7MM6X6oq0tITVlSkxNm5bU6N387fWQJO/WnypqDrngnyVrs6H9BZON5XCdm5KZKhCaprFx40Z7jU9MiSkAR/mRGXnZMtYwAxahzrzelrdNJpnhLac0uATK2kfJMKspQu2JbQ4vTLtwRMPWrDZpwyJdlP7IR/uvGJ+RB8rCR6bNilsyXENdQmoUYHNe9pRx5ZmJsvBPkTEZbToRMNn8ltEeRZHJMkDgUBG+KCjECCQyCkuAE8Y/VqiKlduPdRNwE87KRJPM6BBoQ5a7TXQfORxw++3yd7qUuPn/d76DPZnUMsUIHzWcLkRGuFeqricY8j3mXC5YFJPi3rYNll29jDP/88z468d6hcwYL4mpiX4OnaiYbH7Ly57gcSHajz+cfb/AQUmycnigcn5xBpoHyt5HwQ4Y2CNZ8NEhkaYZ2JN4pDWwTrGnfyfs/aVI/uRC7QpJrosMQM/Lox9vpkfftuyvZ7OnUPimS0WGtzm/9zWdKz1c5r0z769MlppKhmlPd4yTqrMzPa5ZJtJtwfYEMZOMgV3yXLV4bPqdqC5RYon5TOvbyeZn/oLWFzs2Q12w+EOw/PPFJzIgMfc17S4yCjnmVBWuvlr+NqWmTDLDsvl3INaIzVx35ULlXHke3G97TMkol+tc+VCkkxmmnrzDW5oTcDLDzKo3Nf9KgWhAFldQ1pUZEwJmFuXw8cLeb2r/hboyN/zMpYdYjM+YaEi32TBEU14PyuTf4S2tzeY1rswrM6YwhSmMMfyzZPE7dAhG2/IlLPIsOLy5tYmnUBj8s6QZaOV8kRnI1oix2Egm3V1V4u+hwyWvwhkvrF8Pd90FN9+c2gy8tVWIjPXrx21oqTAM2P0DOSaaz5PzrxCoTpE/OHgXHP0nNJ2Z+z0THCedJD0ztm6Fyz7aimEYPHvbsxi6waGO8SUzpiD4xz/+wbe+9S06OjrQdZ1zzjmH2267DX+sXGbbtm28//3vp6+vD0VR+NznPsf6sjk5TzA0XyjVXb1bZJ3pyyBH3Bvr0VC9tKyqAcoCwQ54+h2xRtUDEsju3wZH/pHYx9Mg0jVWmfZdz0lGf7grt+Sg6pDgfds9UlVjR5oq23itoEckIdU3M7Ovs9lTKBz+wkrqKmbJowCceSZ8+9sjyQwTPbEpcn190sZgO7hqR87lHN6Y5NWOmNTURamvx8mMhQWNNW8k+1kbxmHoLAqBo9+dUPUw/ViKWEPjWUJOVS0p/mePAulSU+ZcMaX5N0iyl0lK2T2+zB55Q/uLMdRxw9QVfiwQb45bflUZZQ9TIqeUZIZZleGumWrQPlqYGQrBGJmx5ydSxjr9InuB9NHqLhbrMyYarGze9f+kjLX1Kph5RelsNnSZAEQHy7pnxhSmMIVxgD824x4+nH0/OzDnAe66sckUOxGhOmHZx8f2O60SEFSXSByEOmTu7G2aXAkIMaxfD1ddBY8+qvHUU3s5++z5XHCBo7wqMoLHoO3vcmw0nze6z2q5VMiMnpdiPVHyzGqdYEjpmwGEB8MYulQ87T8q0iRTZMb4orKykl/+8pfMnDmTaDTKu9/9bj7/+c/zzW9+k2AwyFVXXcWPfvQjzj//fI4dO8b555/PwoULWbGiNFIkU8gCbyPUrpQs/+MPw9y3W+9nNpyuKZ8+DWWDSL/ca1UPuB0i3ak4JOANklQQ6pL9rNaMvZvlufaU3N8V7JCAaXRIGjvPuDxVksnOujR5vFZJLOEeGbOaZEMyctlTCDKpHZRI+cHEGWfI86uvinxhRVrIyiQz4pUZhgEvfEAqHtb9cGRvj9pTYmTGlpFkxmBMarRqUVFtyAjTz/9/e+cd5kZ19f/vzKiutq/Xdd1tbGNcsSnGYEIMIZCE4BiCTQoEAoQSkhDCG96XJASIaQmBhIRfCA7FVNNCCYSWmI5Z3Huv67JdK63qzP39cTVqK+1KWpXR1fk8jx7tjkaj+50zd+6de+49R/XzvoFkgYZ67rS0VAHW2uzbMZqyoYac0Bwfairpyozu0AfmytT7yvrKDPcefq0U6XMVOTPygVLGbxLkzEgffUaeP8crM2wDhX+oygv6OQw4eQN08FVAU4HaWanfXPsbdzFbxyg24jUPOZM3UJbq1GInZookA7MeyN3xCYIoXvTlzu4shJnSV2ZYa3vfjygudGf84feAg68A9acAg88Cdj/GH7arp3GbC9qmKwowbx6Dw9GKWbNGG8uRAfCk3wAPN5Hpqgwd+xCgZhrQvpYnAx99cf/LZ2CODU1c3rQJ8Lv92LtiLwCgbEAZdu/lkZ7JmVFYTjst4qAzmUy48cYb8b3vfQ8A8NZbb2HGjBmYN28eAGDw4MG44YYbsHTpUvzxj38sRHGJwWdEnBkjL0o8AGcfzCdSVBcmX0ZRoNj5uZNMAFjsZM5kUQ1UX2TGfl+OouiZ9u69fHLC+9+MnfSWzooJxZ54wqkeelS2JZ+QmkxPupgrucOkaxvXkWhcL5UoCKoXaP6In5exP0j554cPjwxsNzYCodsSAD4W3WNlhtrNbQZE8qNEU30cn1zQGZc3Q/XysgH5c2bo2AYAgQ5AC8CmHgJMMsBUbv9QcvBSQpaBhQt5WNLnnouEHu2xMkMP5ZvOqh/bEL5iR/XxSSupJrI3GOTMyBBFUTBr1qzUMriXDQUm/iTnZcqUtLTkG+sA7m1nWlpfS0tTzVTgpEcKEx86RQxto2hMDr78L+AC9j/PHRkV4xLeXItGU4oYTs/A04FBZ2S8xNpwevqJaHoAMTWVAqLZLakebzPP3RN089V6zh2xAw+pzMrzNkdWnHVu5MdijMfQTfUYaVIy9kkGY3zFqq8ZqJme07KFsdUD/hZua9kMVI4Dpt2WdPec2ij6mktEPq+5ApSlBx0hZ0Z1ejPRk2qqOxFo/hjY/wJQO7tn+F2DrpzN5JqbNAkogxsH1/tw4NNOfPKHT2CrtuHU/zsVv53fhhoADbVWAPlfFS7afS5btLW1wWbjTrt33nkn7MjQmTdvHu7XE94kwOfzweeLDJ46nbz+BoNBBIM8+bssy5BlGZqmQdMiz7f6dlVVwaKeSeO3z5gxA4wxMMYgSVL4uDq6TeNjmSfbbjKZwkmEdSRJgqIoPcqYbHummgCE9QSDQSiK0rum6tmQZSvQfQha2zootVN7ahq+CKbR3+Waoo5jWE35tJOmQQbAwMAkGw+rI5shgUGCBAYGMAZVDQLBIGRZhqIoXI9zPTQ1AFjrIFkHQgKSa/K2QfG2gMk2SLahAAsAllowfWWG6oXkbYEUcIJZByTXpAah6Nc60yB5DoCpXiB02pmlGhIDJADM08THHyQZrGwYJMXGt0fp0Y8/axYPeRVd/j7tZKqBNPYqSDv+CtgHQ5ryW0CSoGpRdjJVwmSr791Ofg/kLX8EGAMbci6UskEpX3snnaTghRckfPSRhlNO4dsZY5gwYSYCAd6/rqgIcqnuw1AASOZyBJkS1h/WVDEJ2tirwKomhz9TFAXo2gmmaYClBppSCQSDub9HhHQwJkGyDQa698NskgHJBFY+BkwLQgrZUcnVfc+9B3JbI+Ty4dDqTs7qPUK/b0uhZ6D4e0dv1x53Zkh45RUGviBQwtChDIxFNEmeFkgA5LLh6dmpbARY13aondsBc33ONMXryibkzOgHfr8fdrsYcZsNq6V2FnDqSxktfUpbk8GXVxnWRjr6Q7dsBYJHgH0v8O2VE/nAU4IHVMNrShND6ckk1nn0wAljCPi8UKy2SN0w6CBDqhjKPllCRE2ZsnTpUvzoRz/C1q1bMWrUqPB2I8a8Fs1uPfSEZ+W18MHxQAfw0YWxX+prVl58rGTVw1dSdu/nIQtSOUa29BQ5aelx7wYar+eTE055On99o3D4itQGzXNio77icwP5u+YKWJYwjEWcGTXT0v56Qk1b7wc61vKZlh9dwEOcRJNrTf0g3WuuocaNb+FFlDm78fqPGdwHOgAAzy18DifskTAbwN7fl8E9dwEc9fl3aIh2n8sGDz30UHhlRlNTE84888yYz4cPH45du3Yl/f6SJUtw66239ti+evVqOEJxYerr6zF27Fjs3r0bzc2R5MQNDQ1oaGjAtm3b0NnZGd4+ZswYDBw4EBs2bIDH44GqqlAUBRMnTkR1dTVWr14dM3g1depUWCwWNDY2xpRh1qxZ8Pv9WLduXXiboiiYPXs2Ojs7sWXLlvB2u92OadOmoaWlJUZvVVUVJk2ahKamJhyISvaTqaaNGzfC5XKFBxz71LR6AwZ7hqPK8zk6Gx9Dw5fvLn5NebTTkIpujATg8XjhUwOhrT7YbDLsNhvc3R7A343t69fDZ27HmDFjUF9fjw0bNqCy7XUMcHeg0zYW1U5nr5rWr1+P8d3dUGUZmlyFqqpqME2Ds4s/Y8qaHyatGw6gV00Ht0aOY5bbUaYehqZp4d/0+c1wMAYJQMDTycNmAVC93UDFMbCbAK/Phy0hPQAwevRoVFRUYPv27fB4IqGhUrFTQ9u/4fAH0CIdi9GO0Qns5MTs2UP6tNMIdwXsgX3oWv8KRpz4w5SvvWOPnYgXXqjGO++4ccYZG8PbJWkEgKEwmTRs3twISQLKfFsxKRiA2VGXXNPBOuDgYQCHw9deQLXhYHAmWFBBa2NjfupTFbeT3+uEJllRFmQwyQxy1Sg43X4g6IKi8ety9JRBOalPNe73Mdj9OqrGnIkWNj7r9whVVTFu3DgMGjQofC/X6e3aO+EEC+rrVTQ3W/Hhh9xZcPSoBpfLh40bdU0NMJXdjFmjj0vPTkPORps8CXt2dCCwpzFnmnSnfi6QWLQLRVCcTieqqqrQ2dmJysrsxNwNBoNobGzErFmzYDL14RNSfQAYH+Q14IB5WlqKBNE0GV5PTEIxJ18K6O/kUyUco/jDatwDquE1pYmh9QRcfSfMihs4YYzB3d0NR1lZ2OuecJChYz2w7UEegmLSz3IkoP8Y2j4Zkq6mXLSFRuGWW25BY2MjVq9ejQ8//BDjxvGkdV6vF1OnTu0R83r58uUpxbwueP+hCEiop2sn8OEFyeMcqx6+9H/u8uRh8LJxjGzpKWLS1qMFgQ8v5Ik1E8VZzgXeFuDTS3kf+ZSnI+Ei/O3Aobf43yO/Hd49ZzZK9Zqb/VDvicnTdPznrA71F/d+HnNbsQCnPBNJxJkChtWUIZlcc20723Drscvh8pswZboC/4GjgAZUjBmATxsVmBHErOlBXPj8Bagdm9/wedR/6Mm///1vXHfddVi/fj2sVivmz5+Pm266KcahoWlaeJarlOCZPtHKjOHDh6O1tTV83vqzMiMYDGLVqlWYOXMmLBaLcWb8Z6jJ5/OF9SiKktoqhs5NkA6+DDb4LCj1J8Zq8h4FLLUwWWzFpSmF7Vmxk3s35I+/DWauAlMiDlSJBSGxIBhTAX8H1DnPAOVjw/o///xzzDa/CLlrM9j4ayAPO7t3TZ3boHx8EZi5GjA5wnUlfL6CbkiBDkinPg9WPia5Juf20HGqILl3QVJ9YNb6cB+BAZA61kOqmQYmW0ITXvbxA1VN5isz/O1hPQCvw6tWrcKMGTNiVqb1aSdPG+SVlwJMgzbrr1DKGzKzU/cRSPuehnTwVaBmBuQJ1/DtehQSUyXkskEJr73PPpMxd66M+nqGgwdVSBL//Wef3YLvf38aBg7k2wFAOvwO5B0PQqqdieCxtyTWZJR7hHs32AcLw9cLUz1wOdtRUTMUkgSwAL9e1DnPQKk6Jjf1qXMTlPU3Q7LWQTvxH71r8jYDQSdkKUpTVBQZ2VINuWxQ+B6hqipWrVqF448/HhaLJa2VGQBw7rkMb7whx3zW0MDwhz9oOP/8yHHyeS9PR5PT6URdXV1O+g/F/5RWDOx/CdjzJE98dMw1hS4NEc/n1/KB3ok/57H6iPSJTtBVNoKHFAm6+QO9pS43CbiIvuk+CGy4jce/POkfvTtT45KsMcagyjKYuZJ3QpPZ0N/OfydRPE6CyAOapmHIkCF47bXXMHZs7AAYxbwuMMniHAOpxzHOxjGI1JFNfBJC13Y++JwPZ0ZnaFVG+bhYW7v3AruX8W0N3wQUa8KvZ53errmgC1j1Mx6POhnZXF1QyOtfX5VROSktR0afGKFO5ymEl9UGdPhN8AQtMMsyIAOuox4EUAubFZCk3IVfIFJn//79uOKKK/DCCy/AauX3GavVCq/XG7Ofx+OB1WpN6MjQv6N/PxqTydTDaRQdliiaZKG/FEUJh5bSB4r0Yycine2SJCXcnqyM6W7vTZOuJ/r3ey173VT+it8OAJt+y1eETr0dUtXE4tGU4vZ+2yn0twQpcg0H3TwPhKRAKh8NSBJMigkI/Y6maZDAIPuPQJYkoG56+HkyadkVEyCFfiP8Oy5I3sNA+ZhQvg6pb036cQKdfHKwbIJUPhJASIe3hU+cBCDpyY9VN39e9TQB9qH8+FF6glEhlRL9blJN7Z8BYEDlMZArhve6f1JN/lbIK78HdDcB3QeAjjXA0f9AliSErRTVf4i33/HHA2Yz0NwsYf9+E8aM4dtdLt4+19RE/W6wI3y8pJrgB478h5dl/JWZacpSfZKirhdNsUOTeV8gfK3qdszVfa/6GJ4H1NcGOdABOUF+PlmWIftbgZXfi1k1KwOIURSyoRLVh5AkKaw72b0jURlffBF4442e7c3BgxK+/W0Fzz8PRAcbyOe9PFVNuZwYRs6MfKC6+btCCcAzYvv/A1w7gfFX8QYwmwRckSRHfc1cJ/pGf0ANunhSMfuQyAMrDTrlH2s9j3uu+vlMFcfIvr+j25AxaHKA/613RBPZMODi71R/iAIhyzKuvvrqhJ+lG/M6H/Guo+ON6seOxnAzpvrQFK0rPBMnKs4x/O2QvEcAUxmYPfQAyBgkxmMzI67s0ZqgH4MxSGo3JNkCJpt4iqvQMTRNhQJkVVP0+TTUrMoMNPHTHfu7fWnSykZBcm4D69wKVntS7jW1rwMDg1Y5GSy6nlVPg2YdCHiPgB1eATbojIw1pWQnNYhQOlQ+aOg9xNs4x3BIip3HE9dUwN8GZqqCZLKF44yHT3tULHDNUpeSnfR94suigI/TaHGL6MODUHGxwLNtJ+Zu4jNbKydD0rS0rr2UNKndfCWvuTpvmvRZlfKn34Xkb4UkSTHlDn/HNgDqiU+AWQeEf1/fL9VZlYwx2GwAnEBXF0NNKNC7z8UTmdrtLBTXn8+4z/esSsZYjJ1605TLmNeFxu1245vf/CZuv/32cCx9gIfV2LdvX8y++/fvR0OP7KuEIfB38MlVksQTgBPJUSPhYMBU3q4xDx8TsdT03F+Soc16BLL/MGAbnMEPMsC1gz+PmsoBU0V63/U08Rxs5oGRpN8AoHkj78HQeJu5hq/Q8TUnTtKdKUdX8PeBp2Z+DH3ioLmSP2+zAJ8ooCdG72Pyp80GzJwJfPYZ8MknCDsznE7eTtZGj7/7Q4Ptll5WkYIBO/4f74uPuICfr86NPPF3X4nMc4F+XTIGWfMAQTOvz9HXa65QbHxCrnsvn8xjPTHxfnGTP3uQxQm8qgpcf33izxjjp2bJLU345sj7IFeNB8Zdkf6PdB8EXLuBuln8HBQZ5MzoByknTdNvrslmIRkAQyeAc+0AOrcAnsNpOTNS0uRp4u/WWsNXYEPbKBoWDN3cWeIOURRFoylFDKdHsQBVU4C2L4C2Vak5M6JINvMshrCz1rj3Nx3D2ScLiKgpm6Qb8zof8a4ZY+js7AwvuzVCHOX+aOru7kZnZydWrVqFSZMmobq6Gps2bsKYUJxjiQVRwTqBYBe6vCZosh2y5oGidcOmqfB7PAk1dXV1QQ4dQ2YtsAWbYDab4S+bhG5vIHyMw7t3Y9z0Y7KmadOmTWE9kiQZKt51Jppqa2vhcrnCeoC+Y0NvP6RhsLMDbs+HONA6OfeaOtahu7sbu5oUuFsbYzQ1sePg6NwKz5rHsa+uMmNNqdipTD2EqeADt27nUZT5dwMAfKwWFVV2+AMBoLsFitaNoFwBmCthc1TDq5XB6+Pxx2XND5vshxVI2U4jR46EoijYtGlT2JlqDRzAFFWFyQw4nZ0xA82VFZWQGEN3dyS2eTJN/bJT2xTIljHAIaBO253WtdeXJnf7QVj9ewHI8FhGo7yqPj+aDhyANXAA4zv2QzGXwe6ohsfjQSDgD+9vMwNWXyt2b1+LFm8VAIST+QJIOd718PLhsNm43do7gqitlhBwBhC0VwMATEoA3d3dWL9+PSqcFXm9R+jtUGtrK4YMGdKnJrfbDRFRVRUXXXQRvvrVr+K73/1uzGdz5szB66+/jmuuiURVWLFiBebMmZPvYsYgWr8vYz2eQ8Dh94AhX+GRFTpDOQQcIws+wcqwNjJX8lnjvtbYCWrWaj4m4mvjE+HiBrIVReEjp47hyAwJsA/jg6aeJr4KM9XyWgbwCAP+DkBWeA42HX3MQVNjtytWPhHBewSoOjaxnnTwNgOdm/g5qO+HMyNcAHvIDi3coR89RtjH5M+TT444My6+mG9zuXiezJroYZeq47ijqnJC8oOZHHxsrWsn0LkBMFcD634D2AYCJz2SibLMiLsuJcZg0rohBbTIhEprXe4dLBXjI86MAUmcGTpprjDN5J7wwQdAVPPeA8YAk38v2vZswYBRgeQ79sbam3m9n3lv79dKAoxwn6OcGflg4xKg+WO+smDYufn//WJn013A0Q+5t7Hh69k99pH/Apt/D1QfB0xfkt1jlxJ6HGRzNWCyA649oYYpFLYr6OadDAPGQRaeA/8EdvwdqJkOTLst+X7RNlT0pN/68kLG45AmsuHOfwD7XwSGfxMYe1muVBBZoOBtYR4YNWoU3nnnnXDOjHRjXuc63nU6MVKNPuO/V00dW6PiJZdBcu0IPYhawCqPBTQ/pKj7SUJNzh3AhxeAyTZI3XsApkEqGwpmH8ZnwYdiLmunPAul6pjcaxLRTr3Eu5ZX3wCYyqGd9ASU0BLxnGhiQci7/gbWvh7qjPvCE0vCZfe0QPrsMoCp0GbeD7lidO7s5NoJ0yeL+HXrawW8PCkmamfzGfxBF9Cxia8oUCyAZOIrFyzVYOXj+b5RscA1x+jM7eTaCeWTRZDM1dBkC8A0PnlAtkEylfF+VVwscMNfe9GaFDukru1AoJMPClRN5jMa86HJtRPKxxcBlmpIpvLYlTUAJNUNKdAJdc4zYI7IJKp061Pnnk48fu7zWLPNBtlqxpfP0BDsDmLzTgv2H5AwYbQPg6q8+NYz30LN2Brj2CmBplzGvC4k1157LY4ePYpnn322R3/A7Xbj2GOPxeOPPx7OuXXqqadi2bJlOPHEPga6QpRCv6tgrL6JDzCP+T4wYiGw42/AgVf5WMv4qwpdOuOSLMTe/heBpjcASxVwwsP9C72dMD8SA5xb+UCvpY47nFIZF+grJKDmB2RL7DZPE3BkBXd0lY/qf8jAzs3A1vv5JM3+jBdFP2trPh55xFIbbu9SGS957jng29/mKzS++IJvu/de4MYbge98B3jiiTTLtOPvfKxg6Nl81c2uR4GBc4Fjb8pQZIbkKfRjrxz8F7D9r0Dt8cDU3yTeJ2bMKxSNxHuUrwaTLVkd83r6aWDx4t73WXjCcvzx+scxbMbpwKQb0v+Rdb/mE16PuYZfAzkgl+0grczIEH1GS1VVVd+zl4OhuLrZXOqWRdLSUgj05XG+lpS/krImfWVGPmJC9wPD2ygGOaUVNMWlqW8Mq6f2eAB/5zMuVG9qK5C8h8C8zdCUCsjMB8lSm3w2RDAUZspU2FlQfWFY+/QDETVlm3RjXuc63jUQazf92IkolnjXia7D2HjJMn9Q69zIQ/C4dwJlwyMxk5OUnR+LQereywdyzVWAfVgofi7C31VkJauaZFlOWK+K1U6MMXR1dSW8TyQte+VYQDEDqhtysA0wD8qhJhMw4ceQkPihRLEPAOpPBJo/gdz8LlB1RWaaUtmu8L8laIA/1OesPCYS2xsSX8nLKkJhMjTAewRSoBMS8wGyLSYWeKp2Yoyho6MjVo8SKZfcvZfPnAOAsmGR54m4WOC9aU3bTpIEOY3909YkyUDFGKBjA3dieA7w/n4uNenbw3G3Q3bt2g5JCwIV40KDYny7IivhckTf51KNdy1JEqyhLpfPJ0HVFFgrFLhDj4V2O79HKqZI7PZ83SPi26G+NOUy5nWhaG9vx4MPPogJEyZgxowZ4e2SJOHNN9/EoEGD8Morr+Dqq6+Gy+WCpmm49dZbU3Zk5ALR+n390jP4y9yZceQ9YPi3gI7QyoyqydkvaBoY3ka2+sSDwhN/yle7dG0HtvwemHYHIMlgQR/8n1wJS91kSBOuS+05MtkKEHM54HHzEFBlDX3PtGcseXl7o2Js0nBQGdmnahIw+6+RaCvZwFLNJxpK6eWjOukk/r52LdDdzcMVNjX5ANhiV2akSvUU7szoWA84uvi2VFfOZJMoOxesDlWEJqV07039O66dPJ+L6uarYRKQqZ4hKQxPDq/bD6sV/LkqExyjuDPDvSetrxnlPtezl0OkhKqq2LJlS4+ZQgnRb3wGDcOSlpZCYA05M/TYfymQsqawM2NohoXLD4a3UQaIpsmweuzDeOdAC/JBg75gKuA5AmgB+H3d/P7lPQxAS7x/EYTRAwxsn34goqZsY8SY16LZrVc9qoffI1QvvxeFcmjAva/nvvEwFsr54wEgA/bBfHJI0B06Zm5i6JaUfZIhm4CxlwOTb+azNAvNkK/w9yP/AVR/7m3kbebXrCTz/F/R15wk89mmlhoeX9lcxa9V79GMf67POuRr4+E0WJDXnxxe/2E23Qms+R8+IzUDUrsv+Hm9ZkGegDSNSUtZI9DBV40FXYBzc9IQH5lec4oCOMxBmOFHZ7MffrcfAZcfZvhhkQuXh0K0+1wm1NTUgDGGLVu2YM2aNeHX6tWrMWgQd+BOmzYNH330EdauXYv169djcV/TZHOMaHbrl56KYwDm56GoD77GJ20F3TxHaddOfh8vAEVrI9kEHHsjd1a0fsFn7HfthHroLXib14Md/QBwH0jt3NrqeRLructjX/P+BTR8E6iZAQw/v3cnBWPAmpuA3U9GJgZniha512ZsH0nKcvgyOW1HBgAMHw4MHcrzKTQ2cj179vAQj+GcGUzjScbVPvKVepv5yhm1G3Bu447BIF8BWpJ1qHw0MOt+vjIp5e+EJvAGPZFxxTgy1XPqqUBDQyTSVjySBEwaeQB1dcg8T1D5KP6epjPDKPc5cmbkA1VfmWHswT7DojszfKk7M1KmuzicGUWD/oAa/8pH4iYiMZIUWp0BoH1V3/t7DvGHeckEvzwAgByaNXk48f7mCt4ZNVdnq8QEkTXmzJmDFStWxGwzQsxr4dFn5Wk+PlgY6OAJGq01/KHS1wJISu+z8lo+5YOdYPx7QVfkWIEOfux8xNAtVYadC9SfnNt8YozxB2aWxFmuUzODx+quOym3/Qn9uvW38utUtvIwSNHXnKWaX7s6tkHcwZGrsgSc3LGiBflLL08ur39NBdpX85nO8eE7+kOi+wLT+PORFnJomKvyW6f1vo0k8YEf5xYetiQLWCutKKsrg8MWhB1euFq88LR5Ifm9sMMLM4IoqyuDtbLnakCCIHrB2wys/CGfpNX2BfDppUBrI9Cxjm//8ALg48UFG4wtWuxDgJEX8Znp2/8CfHgBlC+ug8O3FVLbF8BHF6Z+bm31fIVE9KtyHDDhWn7PP/rfyIrDRDR/xJ3pB18FkGFUfPd+YP1vgS33ZfZ9gCdIVrPTJmQDSeJ5MwCeNwMAnE6+ci68MsPXAqy8EvjoIsTEUIzG28zt+OklvN60fQG0fMbf1/1vadYh2cSdE3IaKxFNFZFwUp6mrK7eURTg/vv53/EODf4/w1fn7edrSTNemTGav7v2JL9WDIx4a0aNSO3x/AKz1va9L9ETPe9CLpwZ1jrA38aX7ROZk2w5aTQ06FQ46k/h73UnJN/HXMlnmrr28NUZljIofhdgLgN83aElwTN62vCYaxIeLhU0TYPfn58Ooh732ev1ChMuIV6T2Ww2RDIuI7Fw4UL86le/wooVK8Ixr++9914sW7as0EUTG31WXqL4twde4jPAjr0x+aw8LQi0fswHsUcsAAafmXi/fMTQJXKH5yDwxU9423Pyo8mdApIMzPpz5PNgjma02+qBGX8AvriePylOWxKZUKPjawU+vyriVJFMfIawpGR38oZeh/Y+y2OZV03iA+2M8USN+qB/Lq5/1w4+y9Dk4DMVs0Wy+4LmBzb8jl8Poxblr06rPl4WSQIqJwGuXaFEs+08gWw/cdQ7sOCpBVjzCx+eexT4wXzg7MXAzfMBuw244yXAVmWFo54muxFEWgSc/F5sqeN1FozPTJZkPrlK9fDPA07qI6RLzXR+79fzXfhaoUGBYq3LzrmtncVDQNXO5O1YIhgD9j7D/274RuYTglkQaP2c3+PdF6WfwJwxYMNtfKxoyq28Hc4Gej8h2A14D/H+g2NUyv2Hk08GXngh4szo6uLPfhFnRmjMzFKbfFq/XodkK69HvpDTQrby71Ed6h01arWQbOP9MX8b4N7FV6JniQULgOefB66/PjYZeEMD8Jf7WtFQ5wVkJfOQ+WUN/PtBN3eCFZmtxRjRKQCSJMFut6cWI2zcD3NfoH6QlpZCYKnjlUySeaOSQjlT1nTc/2apkLnF8DbqbeBKJ+6h2/Ca0sTQemqm81dv2OqB0d8PxScdCG3yr7F/x04cM3Y0lA238DAM467MWiPn9/uxe/fumASVuYQxBrvdjn379hnTRhmQSFN1dTUGDx4sjMZ0sVgsMJsjy7YdDofhYl4b+l6RAUn1JI3L/DM+G1s29Z7wb/yPgNZVwJjvpdTuZ4uSsU9faEGgfQ1fej78W7mxQfs6/u4Y3vfqBv2hGoCkqahSWiC5d/H+IRBxtPc3gWTlMcBxN/MZnQMSTADIweSNXuuQ9zAfyBlyNgDGy8XU1BNLppJUE4jd59Db/MG2Yjzg2p2R0yTt+8L02/ks69rZfLVOOuVNtE9K5dX4LMxgNwAJKBvJByPMFdy2UZOokl5zffyOo96BCSc70P4osOkQ0BwE2gEMHQvUFSAsuY5o97lSQTS79VuPtY47HzU/d4pbopIGJLs/5xhhbKTYAZOd5zqTTfxep4TyNfXn3EoScOwvet+n5RPAvZfnhxr2jcx/q3w0MOAkvtJ337PApJ+nZx/3br4yQ7EAjpGZl0Mnvv+g+vjztSTzVZ9ASv2H2JUZElwuvoIyHGZKb7viJ2MkQrED5VV8Fa7nEK9DuvOoFOuQez+fdAWZryKKx1zJxybb1wCymU+6lk38nPnb+SqeoDuUV43TXz0LFgDnnQd88AFw6BDPpXHqqYDS3QlsrOcOqHRWk0Qjm/ike9ce3t9Psa9nlPscOTMyRFEUTJs2rdDFyAqG12IbCJz6UloP0obXlCZFoSfNBF1FoSkNil6P6geOvMsb47E/gFIzCZNmh2agjP4esPMR4PDbwPAFkQf5DGGM4dChQ1AUBcOHD0+YpJJID8YYuru7cfQoj9s+JJWsYQKybdu2Htv0mNdGoejvFXGkrUeS+Utf4t59IBTKKEEbb60Dhp2T15lCJW+fMAzYeAd3agw8jffFsk1HyJlRPbX3/fRrxdcKqD4oLIhJJgcQnWLBXMEjUQS7kh/HWscnXvR2PSlWYPD85J/3Nnmjex9fqVE5MTv9IdXPE9wCfEJC13b+oN21E6ib3feBo89bMhKdN08TX5nh3stDgaRy3lLVlAzHSD5Qlkl54+mrvNEDSgDv0wQ6QgW38AEccwWw+ud8cEI2QwEwCYi95lI8L8cey983bQJ27+Z/j87igpdMEO0+VyqIZrf+65F4PfQeBbRA1srVH4SyUfsaSADMJlPEkZFt4ieqRq/KGPaN/uepGLWIOzOOvg+MXASlbFjq9jkSClNbO4s7VvpLfP9BCwJfXMffp93Ow1am4CSfORMwm4GjR4H9+xV4vbxs4ZUZ/jScGQDP3WGr531xufBhDwtah1iQT+gwlfHoE4kmZEy4DtjyR77P9Du5QwjgDoHNdwEN5wG2AeGvZEOPogCnnx63sWIscNLSmJwwGTFyUeh4x6RRHmPc58iZkSGapqGlpQUDBgzofSCOMT5bQLbkdWZhOqSspVBkcN6SaoqeoZZolYdBQ1YY3kYZIJomw+vRVKBrG19GOvC0np93ruexS20DgEFnxOoZejaw7zk+Y6N1JY+jDvA69PnVvDGfcmvKHc5gMIju7m4MHToUZWU56hzHwRhDMBiEyWQq+CyCbBGvyW7nnamjR49i4MCBFHLKoBj+XpEmGesJOAHndp4DwDaYL5H2HuWODuuAgi1xJ/uEkM08wbVrF9C1I/vODMZSd2bo4RA0FfAeBoMJfmUALBYLJEj8WvGGRpnNlZEHy2iyeT0lmryx/0Vg5z+A+jk8hEYaJLWRczMfoLPW8lAAFWN5EnTXjtQOHB1GItk56XHeQqs/ZBMfWAHL6LxldN3p5ZXM/G/7IMSkd8yWnW31wImP8jw+yfAeBT74Fn+GqxgPptjgDwRgMZsj11yK52VSaF7I3r3Ahg3870I7M0S7z5UKotktK3rsQ0Lhoo1xPoSykW0QWPdBqKZqKGD83pctGOOT6PY+C0z5dSSBcetnfEWgyc5DTPWX8jHAgBN5Poi9z0Kb8JPUx/Ca3+d/D5zX/3LoxPcfaqbxJPaaP+UVlzYbMGMGsHIl8NFHGlpbAUBOsDJjQJIjJEC2AdYc5khLg4LWobIRfFJDsJtP7IgPRc8YcOhNPvlz1MVA9XGRzyrGArUzeqQWyLmeTFdl6NSnn0fSKPc5cmZkiKZp2LVrF2pra3s3oOcQT8BjrgBOeSp/BUyDlLUUEQk1xc9Q87XxmV3mqtilfWnOPssHJWOjIsbwely7gNW/4J3DAXN6Nny1xwOz/xwa+DBBCwYjekw2YOxlfOBgwEmR76jdfFY1kFaSUFVVAfCQQPnE5/MJky9DJ16T7hwKBALkzDAohr9XpEm/9CgWICjz0C6KBfC38AcFay2/3xRgiTvZJ4qKcbztcO3M6GGnV9x7gUAXnwlYMT6171hrgUAboPrh9zhhto+MOKf1iSqKPXl87b6up+1/5bGOB385/RjdtbO4M6PlE97fTHPgP6GNbIOAUYt5ey1JPO+VuRKomJBe2Xo7J/HnLdjFHYqylfeJg+6M6mG/rjvvYR4HXzbxOOK9lTfhj/dR3oALWPMLfk2PvSxxknu1m8/QBAO694GVH4NuH2C2OSLXXIrnpa4OGDiQz6J94w2+zQjODJHuc6WCaHbLih7JWP16oWxkHwZmqkCXK4Cq1CJ9p44kAYff5SsNtz7AQxkDwI6HebszaB5vB8wVvR8nFQafCRx+D2h6HVrNbBzcfgS1limQldC1kyh8Ydd2wLWXtw+WmrTb9ZSpOIY7M7q2AYNOT/lrJ5+sOzMAp5MbJiYBOMDDIRUhBa1DssIdYJ1b+DUQ78xo/ohP+DA5gIav9/y+7sjwNocT3GsMOLh1fc9rzmDjjelglPucse7+IqJntM9CIrmSZt/z3FM/7OuJZ5WnQvwMNb3BMjkoWRghPhXjeMMZcPIkotEzCXQcI5PHBB385Z7bwvc3C3+liSgrJIwEnVOiqDCVA1C4M8NzmG+zDeSxZvX7C1E4ykOzBLtSXAmQDh1r+XvV5DRmlUmAtR7oPghLsBmSO8noSncor4SlNvXcFZ7DwMF/8b/rTkzfmeEYAdRM5XlAmt7guV76i30wD5ER/n9I5kkeAT7z03uY56wBQgmwu0IR3mSg3AFA4gM3srmXA+UY2yDu7PIe5WXV86moIeeBXrZAZ2hl16D0QmMcejPSF0r2PaUMsA/l9ybVB6lrK2QMAVCdkaRjj+XOjF27+P9jxmR0GIIgiPxiqgCkjuwf19sMHP2ARwZo+wI4+DofL9MCPI/E7ieAQ//u/yRTbzOw7v/4SsdgN5RPLsZ41Q6lqyzSf0gUvtDXAvg7+WcfX5y7Ca+VockJzp5hcnvj5JOB++8H3npLAmPxzow0w0wRsVSMjzgzoh1MjAF7n+Z/95aY3tvMV3Y6twCKHYqlFuO7u2OvuWxcT59fy6/PiT/r33EYA9oaec6MYedlNKZTKIrcXVwE6JnucxVnsFTwHuU3Fff+/h9Ln83FND6bw1zF/0+0XJ0gREGS+NJHAGhbFdnOGG9000H18+8FXfx/Uz/jmRIEUbrYB0cGnE0OPihMGIOKUIbirh38np9N9OTf1VPS+561HoAEifl52ETv0Z75K3ytfHvX1kg71ReH3+bvtTNC4Y0yYOi5/P3Qv3k7aTS6D3Cnjfcof/lb+fkJurhTAODtecX4nisi8ompPOK08bXElhdR12Gwm+txbgmtokgBLQAceIX/3XB+71ONJRlwjA4NdKmwBfZzh1AG6KGmdAq9MoMghED18IkP8S/VU+iSFT9R51bWPNk/twEnHyczV/MJDfrf1nreBillkUmm/f0dXyvvazpGgjlGQ5XLwczVod+28vCFvhb+t7majw2pfl4u+xC+PRtlSYS+MtW1M63cByeFAiXs2sXbsLIyBqvum68/BRhyZmr9aapDPdFt0rU9dnvLJ4B7X9+J6QNOwHuE9zeCLjDJ1POa6+/1FOjikz46NmRnHGbLfcCuxwHPgf4fK4/QyowMkSQJVVVVfc+C1WcWpjvDK4+krKWQ6J5lPaFRH6SkSQ3Fyk20vNxgFIWN0kQ0TUWhp/Z4nsysfRWA0KzRlo+BzfcAQ78GjLs8vGtSPQdeBfY9C0y8AZBCYYwKdH9TVeCDD4BDh4AhQ4BTT+UJspIhYtglETWJTlHcK9Kg/3ok/uDgbw+FfCzsPBuyTxSOUaHkyE7+oJ/NGYkjFvKl/HUnpPc92QJUjIfqboXZFkocz7TYQWb7YD6jMtDJ80tUJViJGI0WSvgIAEPOTq880dSdyPNOeVuA5g8Sr2hMQEIbuXbxgfrqqbH5qNz7+SxW++DYsI+pIJv5+bNU83fVF0l8HRfjub/0ux6VNfAyMjWyTfXFJvk1OXg/RPXx85VK8sqjK/i9xlqX2kpvSeEzZzs2QVG7ILl2AFWT+v5eHBMnxv4/osA+W9Huc6WCaHbLWI+5ktdhX2vycG/WutRX5mWRordR3LmVGINF8kEKaLGzyrN1bssa+EB+sBuAxleC6GQz1Ki5BjA5IIFBtsiQzKGViEDi8IXVU/jKPNsgXrZchT21DQYcwwHbED6xQA993gcjRvBn30OH+P+10U14KrlGqA4lJ8bBpPJ+MMBX3QI8UkxfeUJNDu6YC3RC8h6CYh4de83193rSw3zbBvAQ4v1Bknh/v2M9z1dT3vfS0YLbKAQ5MzJEURRMip9mkwh9ZYaBnRkpaykkegIjX2rOjD41aT7+gCRJReHMKAobpYlomopCT01oZUbXTr6E11wF7H2ON9RxK5OS6vEe5oNE+57jjTlQkJUZL74IXH89cCBqAkFDA19yu2BBz/2jE2RnyuWXX46lS5di9erVmDZtWsJ9TjzxRHzxxRcIBlOfXZMp2dBE5J+iuFekQVb0SEp6iQpzCNkn+suWUBLw3aEk4Fl0ZlRNymhQGAAkSzXs0Q/8QTefBadjG8wfIjs38okrrl08F0YyWj+PONPSda5EIyvA0HP47LaDrwGDzkgpyHhCGx36Nw971fB1YNwVke1tXwA7H+GOjHSdGWXD+Usn6EZ4pYMlu86M/tcjKZSAPIp4O5srgaqJQOdm3qfxHOp94IUxnqgd4IM9KYc3kyFVjoOpY1NoFms3kEYi3BdfBG67LXbblCnJ+yv5QLT7XKkgmt0y1mOr5yFaepvZXKCY9EVvo7hzKwHo8aSRzXMrW3l77T3K7+U109PKw5guEiRU2E2AcysAFpo9H5p8HHRxp3/lJD4+ZB+as3JECiQBs/+S0ddOOgl46SX+v9ksQVV7n9QXA9Wh5NiH8bFbSzXvG9pCzyfH/R8PUznojNSOUzYUcPkhqR6UoxmZhqlMiO7MsDdk53jlo7gzw70npd0LbqMQFGYqQzRNw4EDB6BpWu87hmPKGzfMVMpaCom+MkNPaNQHfWqKsYvxZ04UhY3SRDRNRaFHC/ABi6CbD7Qc+CdvuFiQ59CICjeVVE/9qdwZ2PIZ0PSv0FJUP3eQdO1MP2RVBrz4IrBwYawjAwAOHuTbX3yx53cYY/D7/WD9CJUSDAZx/PHH45FHHkn4+YYNG6BpWjjBea7JhiYi/xTFvSIN+q3HYEvcyT5xjP8RMPvP/Rvkzxaha4UFXfC628CCrp7Xin49qV4+EME03nf0HEx+3EP/5u+D56cxwJ2EwWfxwRB/W2TVQx8ktFH7Gv5eHec410N/udLIY5JKHctyPezXdZdOeRm404MF+UN4b5Oe2r4IJe60A0O+klZZmBaEz1QPVjYKgJTyedH7Ky1xjy+99VfygWj3uVJBNLv1S4+tHqgYm/xVoPyXQtgo6txqjtE40GmF5hidu3NrH8ontUhKlrOM94QFnAh2bAYLOHmonqArtOrPFwm7WAS8+CLw3nuR/3fvBkaNAl5+wQt0H4xEIOkNqkOJkSTg5MeBEx6KODIAntOl4bw0ktLLQMVYMEmC6m0F8x7OXhm7Q6H3HcN73y9VHKHYl649Ke1ecBuFIGdGhqTtzDDwygyjXIy9YtGdGamtzEjZmWFgu0RTFDZKE9E0GV6Ptxn4eDFw9L/8gX7N/wArr+R/d24CPr2Ufx5yRiTU420GvriOz5xp+wLY+ieexHX/C8CHF/BX1DFShTHA7U7t5XQCP/5x4vDt+rbrr+f7xX+3vd3fY1u6foBFixbh5Zdfhs/Xc3noo48+iu9///vpHbCf+P0GjMtO9Irh7xVpkrEefYm7Fgp1E//SfAVZ4k72iaNqEuAYGVlmnw0O/gto+TS1h22g57Xi70DQ08Jn4+vXim0AX90TfT1pPr4CUQvyWZ/m6p7Xk/doKPQigCFn9V+bpQqYvgQ48RGeSDsFetjI2wx0N/GcDdVxIbL05f/eUHLS3og+b76WxHUs0XnLQj3M6LpL5Z6QqLySzFeXagH+gG+uTFzeg6/y9yFf6bv/n+CaC/g9gOaNlMXS+3lRVd4f6a2/8pOf8P3yjWj3uVJBNLuJpgcQT1Ne9MgWoHoyb+8kc+5+BwBT7OhGHVj52Eh+KL1dcYwCysfl9PeTF4wBvraUdtWd5J1xXYCDB4E7frEBh165Clh9Uw4KmR8KXoe8zbwvoU/UbFsFOHdkNnFTKQPsw/lEx+79gOrOThl1Z0ZZtpwZo/i7e3dKuxfcRiEozFSuKWvgy8DLxxa6JMWNvjJDn23Xn9BQqoc3GIqNz57THRulnOiIEB89AZq1nsflhMZjQcoWoGwkwAKRZFTJZmLox7ANCtUXBpQfE4nVqHr6PkYCuruB8ixFqmKMr9ioqor/RALQ80dcLsCRhk+zsrISX/rSl/Dyyy/j29/+dni7qqp44YUX8Pnnn+O6664Lb3/33Xfx85//HF6vF3a7HXfffTfmz58PAPjiiy/w05/+FG1tvPM6efJk/O1vf0NVVRVUVcWoUaPw85//HA899BAURcGQIUOwdOlSDB+epY4LQRQSAy9xJ3KIFgR2/YP35Wb9iS9t74u4a0VVg9i+fj2mTJkCkxJ6lNEHleOvJ8aA/c8DA07kuTPiryctAAw4mc/M1JNO95fKCf37fsfa0HGO6TnobioDyobxmZeuHTwXVjL08+bvBFb/nIdXPfYXsaEzkp23aPJVD1O9JwA999F8wMY7AUhcY6LyTvwZX5WaitOqr2uuaycPWSUnH3j74IOeK0ijYQzYv5/vd/rpfReJIAhCWOQ8hf2WTFCVCh5CSJKAoCUyscJcVZiJrqqXTzD0tQFzn+m1DH05yQdUtGDjRmDQsXU0az0T9Mmf0ROouw/wk2sbyFdnWOt4/yDFfhGzDYTqOgqzogCSia8k7S9Zd2aM4PXB38knCqWYu6XQkDMj1ww8LbUEc0TvmMr4ki7FDgRcmTkzohMdyUrEQRIdBqBAiY4IIm+YK3knybmFN6i2gXwmadCdejIqcyV3aPhagGB77BLMXCVIMxCXXXYZ7rjjjhhnxr///W+ceOKJqK6uDm87cOAArr32Wrz++usYM2YMtm7dirPOOgurVq1CXV0dLBYLnnjiCYwcORKMMVxxxRW45557cPvtt0NRFBw6dAgrV67E2rVrYbFYcOedd+LHP/4xXtIDpBJEsWOrJ2dFMXDwX0DXVmD09/ufKLprO39wN1fyFR+pEn2tBIPwmdv5RCGTqed+8Uz+n+THLRsGTP4lD0eVbTSV55kq6yVfRyKShZjSKR/LnRldO3t3ZgD8fKhe7shQLHyCVaIBeKPUw1TvCYn2mfkHfl0pSeKtW6qA0RdnVpboa05RgK3389Blm+4Ept6eMDyZnpi1L1LdjyAIQjiSTSbN9iRT/XiMQdY8QNDMB2/jwxfmoyzxKLZInpCu7TxvSBL6cpLXOlrh8QA79tfhmKnZLWZJoE/clMy8/xYOOyaHVmumMPlTJ+qa88t1sJZVQtKC/b+eGOMTU7UAnzifDRQrn+jSfZCH7LRMz85xcww5MzJElmXU19dDlovf51k0WuY8mXIcxYSaingWaNHYKA1E01RUevRkZ5KUdCZqn3rsQ0JhK7r48XqZndgXZWV8hUQqvP8+cM45fe/3r38Bp0X5kRlj8Pl8sFqtkKLuI2UZpDM67bTTcOWVV2Lfvn0YMWIEAB5i6rLLLovZ769//SuuvfZajBnDw4JMmDABZ599Nl577TV8//vfx5QpU8L7SpKEb37zm3jwwQfD21RVxW233QaLhXdwL7nkEtxzzz0xv2GKH8gjDE9R3StSgPQYm37p8TbzPtPeZ/jMtLIRsQ/ZqfaZ9OMAwKG3ePtTNYkn5k7nOCEy1uRtBtrX8QfURMmzs9kHbFsDbLydO0mmLek52B31WzF6GAPaQyszkg1oVIwDjr7PV1emQnilx7H9aqvToSD1SJ9YoV9vQRdgKudOpegwaRnYOUaPJAHH3gSs+inQugrYdBcw8qIe3xldDwyoqERLV++/NSSVBUHRdSgRmr/3pLlxmkW7z5UKotlNND2AeJpypid6kmmyiXDZmGQa9zsSY7DJfkgBLTKuZBvA8y8Fu3Jblt6oOAbwHAac23p1ZvTl/K6r4CsKmjvrcEwWi5dPDFGHTA6+gkIK9d3sQ/jKnVQmfya65hQ/pEBn5JpLFg4zFSQJmHZbZt/tjWOu5X2mFBwkhrARyJmRMbIsY+zYFEJHacG8JDPqDylrKTRpnMOkmmz1vLMvWyKhcYqAorFRGoimqaj0yGagZhp3RMjWxLv0pUex81UdwW4+4xOZD5BIUuqhns46C2ho4HFBEy2xlST++Vln8cmTUZ+gvDx7S5gvueQSPProo/jVr36F9vZ2rFq1Cs8880xM7MhNmzbhmWeewcMPPxze5nK5cNxxPAZ6e3s77r33Xvz3v/9FW1sb/H5/jxBS0f8PGDAgHJKKa5Vgs+VpWTaRNYrqXpECpMfYZKwneqm99yhvL1bdELsyI5Wl9vFL9j1NQNADuPcBh99J/Tj91eRtBt4/H+jcwP+3D+MDwHrI0QzK0etvrf45j7PMVKDz6/wBMZqo34rR497Pl/gr1uThqvTQtbozqC861vP36im975dFClaPvM3AR4t5CC5/B38oD3RyW1tqub0zsHMPPWXDgDE/AD7+LtD6GbDr0R5JQU9kwAs31GHhH55Cs7Pnb+n9lVNPTUFTfNiLaLQAr1f2YckT2MdpFu0+VyqIZjfR9ADiacqZnnxNMo37HQlAjydfI4RbrDyGT1Lo2tbrbn05v2sd/DmxvLYuWyXLO4asQ/bBqe/b2zWnqcDBfwJHV4RC3RtoEnV8jrZeMIqNxHAZFwBN07Bz586+k56s/gXwwQKgbXV+CpYBKWspInrVtPcZ4KNvA3ufy3/BMqTkbFSEFJ0eydRrctKU9DhG8mSk/clhkyaKAtx/P/873r+p///HP8Y7MvjKDK/XC5Zuxu8kfP/738eyZcvAGMOzzz6LCy64oMfsBI/HgyVLlmDNmjXh144dO8I5Nb7xjW+go6MDTzzxBDZv3owHHnigx+9IvThxs62JyA9Fd6/oA9JjbDLWoy+1l618EFg28ZusuZq/ZGtkqX2qxzFXhmbJm3iownSO019NASegdgOmCj7JyN/OX55DgOLIqBy9/pa/jQ8gyyaej0M/bwk0x+hxDAdOeoSHvkq2iqJyAjDzXmDWn/suC2MRZ0ZNkrBVOaBg9Sjg5KtGVT9PDO5vB4JePuBvytzOCfVUjOfHlGTuOJGtMXaWFCumHNOKSrszrf5KYk2tPY4ffkkmHrZCVhJ/nkCzaPe5UkE0u4mmBxBPU0712OqBirHJX9lyHkT9juYYjZ1HAc0xOvZ38lWWZFSE1lE4tyaerRfi1FO5EzzZ4+GAilbY7cBxx/czJGgBMUwd0vOL2Yekn5g+2TVXOT7kxJD4qk49X0s65CIsapoYxUbkzMgQTdPQ3NzctwFVN1+doSSe/WwEUtZSaI6+D6y+Edj9ZJ+79qqpa3so1tygHBQyNxSNjdJANE2lqUfKqyNDZ8EC4PnngWFxYcgbGvj2BQsSfy8YzELCrRCDBw/GpEmT8N577+Gxxx7DJZdc0mOf8ePH4/PPP0/4/ZaWFqxfvx5/+tOfwmGoNm7cmHY5sqmJyA+lea8oHkhPHIqdD8pLpsiAsMnBt6d7HMb4wK9Sxld4ZHIc9FNT+Rieh42p3KlhqeavDMrRJ7Yh3CGhevnIQ5Jz10OPbWDvuTD0VRupPFu4d/NVNSY7UD6uH2LSo6D1SJJCuS3s/JqVpNCs2sEZ2zmpHmttKJGszHNoKNYYO9dUAw/+Of3+SkIUeygxrMZDaIVf3bxuybaQc0Xp9XrrVQ9haESzm2h6APE0kZ48UT424hhPtgoPfU/qqy1vxeTJgGIv3pUZhrGRbRBQNbnfSbZj9EgSMPGnvO/QtRPYcDt/T/TyNic+4OZ7gE8vBY5+2K9yxeBtBjq3AtsfAtb9iv/dS1mMYiMKM5Vrgm7+rmQQmJ2IJeACOrfweHWZogX5gx3AZ1QRRKmRjeRmhUqQFsWCBcB55/FEaIcO8WW3p56awgzHLHL55Zfj//7v/yDLMiZM6BkO5JJLLsH8+fNx3nnnYe7cuQCAPXv2YNSoUaioqIAkSdixYweOOeYYbN26FU888QTq6oq380kQhKCYyvgDmBYAAh18tncmMDU0sFzR9765QlL4oL5zc2hiy8Dc/ZZs4qtafK08rFblBPCAA3mkfR1/r5ocmzdCdGQTHxzqCs1ytaURIiJdykYA3fu5I0P19UhK/5WvAHv2ZKm/4mvpGV5M84EHfA+hukIT6ejZkyAIoihQLIBjFL+/d22N5IBKgD6p7/rrY5OBNzQwjJv3NQyZ2mLIHLDFR2hSQLaxVAFjLgM+/BYPU7n32cT94mQhMd37AW9L9iaURoeydO3mKz/2vxibhytbYVizDDkzco3uzMhFRSg1rKFBPl9b7/v1Rvc+vvTc5Eia+JgghCQbidbylawtRRQFOP30vPwUAMBisYSTcQPAOeecg6uuugq33357eJskSSgLZRU//vjjsXz5ctxwww1wuVywWCyYPHkyli1bBqvVimXLluHCCy8EYwwDBgzA73//e9x2WyShV1lZWUyYqehjEwRB5A+ZD/p7jvAH7arJmR3GUgNUTEge1z9fmMq5Q0N1c2dDLrEP4zMtA04+c9/eS2JF51bg4ItA/cnA4Pm9H9e1Bzj0Btcy+rvJ96uZCoy8kIeFLDXMldyhoXZHniFygaQAVceiN0dVVvorQQ/QvYf/ba6M1KOgmztRdGQrgBJyXBEEQYjAoNN5bqsUxqj0SX3//a+Kjz7ahVNOGYPTT1egKItzX85SINcTN8uG8f6b1sH7iNaBsattVU8kPGS0A0FTeV8S6PeKkTDx4WCDLu7I0CcuJSuLASBnRobIsoyGhobeM7irfj4zBuCz2gxKSlqMgDXkofa19LlrUk3OUFKlinGGTsoeT9HYKA1E02R4PWkmWkuoJ1/J2nJEtCMiE/72t7/F/K8oCg4ePNhjm9vtDv8/f/58zJ+feFDq3HPPxbnnnhuz7ayzzgr/HX2cRMcG+q+JyD+Gv1ekCekxNlnTUzYcCLh5uCLZHOnfpksWnN1Z0WSpAZA8b1TWUGxA+Si+TD/JOQvr6fgv0LqSz9Dsy5kRcAIH/wXYB/XuzCgfw195xjD1yFILoP8Oq7715OGZwnuIz5i0VEfiqwN8hmb0s5Gp75VPhrEPkRai2U00PYB4mkhPHhl+flq7KwrwpS9JmDDBjqFDJRhRUiYU1EY5mLiZVI+lmq8cVT2Atyk0USiqL5Ho971HQisvLdlfWazYuS7Vy/sa0ZPx48pilHpEzowM0Q3YK2o3f5ckQy/1TUmLEdBnVQU6eCXuZWZfUk1d2/l7kYWYKhobpYFomopCj57gLAWS6knjGEZCkiThBv5F1FQKFMW9Ig1Ij7HJnh4ZqJqItNPteQ8D3QeBcnvWVikXnY0sdUCVLan+sJ7VoZBQ1dP7PmbFWP7uOcJzYhQydFcCis5GfZCyHj2ZZy7yiTlG8sTyWZiNKZp9SgXR7CaaHkA8TaTH2MTo8XfyWfXWuoLktMwWBbVRDiZuJtcjhcJU7gXsg5HSpIju/fzd3ksW+P6gT8DvYwWKUeqRIP67/KOqKjZv3gxVVZPvFJ0vw8CrAFLSYgTMVdyBwRjgb+9116SaitSZUTQ2SgPRNJEeY8MYg8fjAWOs752LBBE1lQKi1S3SY2z6rUf18P5s0M3D3Oh/q57Y0DaJCHqAbX/hA7zd+6KO4449Tj41xejpXznS+i0g9rei7tuqqmLLxtVgzq18Q830vo9tcoQefgG4dibep30t0Po5TxCdZwpej7Js51716L/l2gW0rwLce3JzTWl+wFrP61O0Ji3kQNG8KWsuuH2IjBDNbqLpAcTTRHryjOoFOtb3OdYV3j1az5F3gZVXAdv+nONC5paC28hWzyeMJHulOamzVz2yiYcWsyTPkRKD7swoy5EjwVwNVB7D87f0QsFtFIJWZmQIYwydnZ29DyJJJmDAiXw5voFJSYsRkCS+XNx7lC/96uVGklTT0HMA55ZQEsbioWhslAaiaSI9xqfQDW4uEFGT6IhWt0iPsclYT29L7ZnGl7ozLfnsNcaArX/kiQVNDsBay1fWJiLNJfsZacpnzqdez53Kz525OvxbjDEEmleDMRVS2RAeOioVKsYBnsNA147EDpB9zwPta4DxVwLDvtYfRWlTsHqUIzsn1BP/W0zlK8f9bZGVMv29po6+D7gP8tU9/iSaWJCHh9DUlOuYaPe5UkE0u4mmBxBPE+nJMxtuA9rXARN+DAw5s8/dY/T4WvlGSw5zROUBw9soTfrWEzXpnQV43zoZ3aGM79nKlxGPbIlN/J0Eo9iInBm5xD4IOO7/Cl0KsbCFHvA0f2bfH3o2fxEEQRAEQRCJ6W2pPdOArffzPGQ7/h8w8z6eSyOa/S8AzR8DZgcw9/neB+jzkWspnzmfevutzk3cyQMF6NwA2L4EACjz7wDMSG1Vhk75WODoh4lXZmgBwLmJ/109NU0BRUwh7az5gC9+wh0a024DbIP791uu3byeqX5g4k+AyonJ99X8vQ9AGDifGUEQBBGi4hjuzOjalpIzIwbdmWEtbmdGyRJ08ygyshlwjEi8T1kDUHVsJNRoiUPODKK4mHaHoUN2EQRBEARBCEFvOZKm/w744nqgaxew/tfA2Csi/bPODcD2v/LVGeOuAAbOyV+ZeyOfOZ+S/VbFWCDYBex8FNh0JyApgGUQKr2rAMnHQw107Uxt8LliHH/v2tHzM+c2PghuqcrdDD6jUkg7183mA1H+TqD+lNSO4W3u6XwJuoCNdwC+dqB2JjD0q4BE0aEJgiCERg+FroedTAe/7sxIMWQRUXiiw0BqAT4xQe0G3Pt5/y2eEQv5K9dlSWW7ASBnRobIsowxY8b0nsFdX3Zj8MH3lLQYhRTPZUJNHRsAUzl/qJOVHBUwNxSVjVJENE2kx/hYrdZCFyHriKhJdESrW6TH2ORMj7mSOzDePw9o/QzY9wJ/8NICfBk803ionZ2PAIO/nNXB5aK30aAvA2v/j8+ifH8BFPsw1PqaIPkCwKbfcQeHtY7P+u/tvJWHZuYxjTsulKiZ+R16MvGpBXkOKXobxZGynupp3JnRsQYYdk7fB/Y2Ax8vjsyoBQAwwHOI55zRQxX3EV43XUSzT6kgmt1E0wOIp4n05JmKY/i7ey/Pn9FHIu8YPb4WvrHIV2YY3kZpklBPspCYlmoePtTXkr3Qp32RQXhOo9iInBkZIssyBg4c2PtOB14Gdj8ODJ4PHHNNXsqVCSlpKTISatr2IH/AnvJroG5WYQqWISVjoyKG9BgbSZJgNhs7f1G6iKipFBCtbpEeY5NTPbaBgKmCOzACnTw5saWczzJnDCgbymcKBpxZH4gtahsFXdzRE+wCmAZJdUGpmQJAAyDzWXC+FM6buQI45alIfoZoop0ZBaDobRRHynpqpgG7n+AODab1vZoi4OS2lq087wXAHRlagIeNcgzneTCoDhEQz26i6QHE00R68oy1jucY87UBrl08pFAvhPUwFkkaXuQ5MwxvozRJqKe3kJj7ngMOvd1zZYbqBSDHTlzJBhmE5zSKjcRwdxUAVVWxdu3a3pOvBrt53FSDLwtOSYtR6NwMrPo5sPHOXnfroSnYDXgO8r/15XtFRFHZKEVE00R68oi3mYfhSPbyNvf4CmMM3d3dBU9UlU1E1FQKGLpuZQDpMTY512Op4g/ekgJ4D/EHnuopQPWxgFKWk58UwkaSAjjGArIZLOBCt/MomKmcJ0tX7H1/XyeRI0P1Ac4t/O+aadkpb5oIYaMoUtZTPo7bMOjm/ZFUUez8e5qfJxCXTEDleJ4oPgeIZp9SQTS7iaYHEE8T6ckzkhRZneHc1ufuYT3eVkBT+fct1bktY44xvI3SJKkeWz0PPRr/mnQjz5+mBYHN9/B3ADj8DvDhQmDrn7JfyGRl0V9xkymMYiNamZEhjDF4PJ7eB5FUN39XHPkpVIakpKXQ6PFk3fuAtlWAbUDsQ0Kct7CHJtdO7rG2DUwcf87gFIWN0kQ0TaQnTyQMyRBHkvAcmqb1++f9fj9+//vf49lnnwXAG/OvfvWr+NWvfoXy8vIe+y9duhQ/+tGPsHXrVowaNarfvx9PNjQR+cWwdStDSI+xyYseewMgHeaJCREKaSSZAfhz8nPC2MhUBpSNALyHEdDssLEsRYRybuUPv7YBPAl1ARDGRiFS1iMrwNjL+GCSI4NcJYFOfhHYBvHZtUF3RuXtC9HsUyqIZjfR9ADiaSI9BaDyGKDlU54EvA/CeqAAoxbxCbxycQ/xFoWN0iBtPbIJOPYmnpeubQ2w/SGeN6ttNRBw8RUa+jhoKvnVcoBRbGSIK/2BBx7A3//+d0iSBJ/Ph9mzZ+POO+/EsGHDAACbN2/GVVddhc7OTkiSR2ZpWAAAS79JREFUhFtuuQULFiwocKlTINjN3025mZlWMkQPXmoB7tCQpNiEh33FFu7azt/1ZIkEQRQniUIyRJNqeI4M0DQN559/PhoaGvDhhx+ivLwcPp8Pd955J770pS/h/fffh90eKdMtt9yCxsZG1NTUIBgMZrUsBEEQhkFSIjMJifSwDQKz1EFzdmX2fc8hYNtfeNs3816+rXoKcMJDPOaywfP2CcmQMzP/rmMUdwpKxZXbjyAIgsgSdSfwla19hJiKwVwBjFqcuzIR+cU+CBj9XeCLnwA7/w7sWgp4mngure79wN6n+X6p5FcTGEPEP/r617+OlStXYu3atdiwYQNGjRqFr33tawAAr9eL8847D7/97W+xZs0avPHGG/jlL3+JdevWFbjUKaDPpjEZe2WG4YkevLQO4N5KSeHJvM3VfLs+eJmMsDOj+EJMEUTJoHp7ecXN8FWsgMne86VY+SqsVI6bJk8++STa2trw0EMPhVdhWK1W/PrXv8aYMWNw3333hffVNA1DhgzBa6+9Bput9+RtBEEQRAnTn4FrUznQvoavxtCfOyQJKBtWsBBTRD+RTAivcCIIgiBKC8dIYNi5QPnoQpeEKCSVE7mTSrHzMU8m8XFQy4DUx0AFxxArM0aPjlRUk8mEW2+9FQ888ACamprQ2NiIGTNmYN68eQCAwYMH44YbbsDSpUvxxz/+sUAlBhRFwcSJE6EovTyAqPrKDGM7M1LSYgT0eLKKPZQYzxyJx6z5YneN16THHCzSmYNFY6M0EE0T6ckCH1yQ/LO6WcCUX0f+79iYOB8RC/J7QzSfXgYEnChjcTE8Tn81reI99thj+MlPfgIpwUzXn/70p/je976Hm2++GQBPjHX11VendfxMIEdJ8UH3CmNDeoyPaJokSUJ5eXnCtqVPzBU8hKr3KE8WWj0l+wXMANFslLaezs1A60pgwMk8ZEhfaAG+uiadfCn9QDT7lAqi2U00PYB4mkiPsQnr8bcAfpVP/FWshS5WvxDWRpnqUeyAYgOg8ckO1prIJJi4MdB8YRQbGcKZEU93dzckSUJdXR3eeeedsCNDZ968ebj//vuTft/n88HnixjW6eTeqmAwGA71IcsyZFmGpmkxMcf17aqqxsQAS7Q9Oj56fAgRRVGAgAuMMWiwAqHPdYPHJ0sxmUxgjMVslyQJiqL0KGOy7Zlq0jQN5eXl4d9WFAWSJCXWlKDsOdcEHpeNMQYwBkk2A1oAUrAbTLGDMQaJMahqELKm9dQUcELxHoUEIGgfGbZFQTVlYKeKioqY3zWcnUgTqqure2wvZk2VlZWQJCnrmoLBYPh4iWItSgB6bGWMb09lf5Z4/+iBIhY6ZsxxJKnX2I+rV6/GrFmzEu4zc+ZM7Ny5Ey6XCw5HYgd2wrIn+c1Ut0d3IvT7JGMsfI6j7UShroyBJEmorq4udDGyBukxNnnRo3rS295PhLFR6PxIAMwAEPTHbE+ZinHcmdG1g7drTf8C6ucAA0/LZmnTQhgbhUhbz6F/A4ff5X+n4szwNAGBDsA6ELAPiWynOkREIZrdRNMDiKeJ9BQI71GgfS3PiTDgxKS7hfVs+SNvc0Z/Fxh5Yd6KmQuKxkYpkhU9nZuiDlh4J49RbGQ4Z8bGjRvxi1/8Ar/+9a9htVrR1NSEM8+MjT06fPhw7Nq1K+kxlixZgltvvbXH9tWrV4cHmerr6zF27Fjs3r0bzc3N4X0aGhrQ0NCAbdu2obOzM7x9zJgxGDhwIDZs2BBOdtLZ2YnZs2ejrq4Oq1evjhnAmzp1Kqzl43D4aAeObj0In5kPPs2aNQt+vz8mTJaiKJg9ezY6OzuxZcuW8Ha73Y5p06ahpaUlRm9VVRUmTZqEpqYmHDhwILw9U03r1q3DkSNHUFVVBUmSMHHiRFRXVyfUZLFY0NjYGHNec65pIE+66/U6ockBmIMW2JkLivcwXAErNH8XFK0b29evx7AJNT00yQji2NE3oELpwur124yhKU071dbW4j//+U/MzD3D2anENY0cORIHDhyAyWSKcaYWqyZ9QPzkk0/Gxo0b4fFEHqizoUlfUaCqKrzeUMinGf+ALMsoKytDMBCIOY+KyQw7gEAwAEXVwMonAqayiONGd7gEuyEFO6EGA7CAhypUj7s//Ft2ux0WiwWe7m5obndMeUwmE7q7u2OcBXa7HbIsw+12o7OzE+Xl5XC73XA4HNA0Lea8VFdXw+l0wmazRTQh4sQIBoOxmhQFdrsdgUAAfn8kjJbJZILNZoPP54txQFgsFlgsFq4pdN5jNHk88Hg88Pv92LBhQw87ud25SSRKpEcwGMTq1asxY8YMmEyG64alDekxNjnVY67k8Xp9rclnh1nr+H5ZpOhtFHfeGGPo7u5GWVlZxOmeznkrHws0f8ydGf52oPkjvpK5gM6MordRHGnrqZnGB5ba1wD4fvL9zJWApRZw7QGYCkDjTo1oqA4RIUSzm2h6APE0kZ4C0doIbP8rUDuzV2eGrud4UzPPH2Cty1sRc0XR2ChFsqKnbChffWupzW7hMsQoNjLM1XHjjTfiiSeewJEjR3D55Zfj+uuvBwB0dHT0CKOhDxTps07j+eUvf4mf/exn4f+dTieGDx+OGTNmoLKSdwZlmYcnGT16NEaOHBneV99+zDHH9Jh1DW8zjhtlB2M2qKqKjRsPoFJuBro6MGN8JWCKZJNXFAWYcC0GjVMxKKps+uDVrFmzepS7qqoqZruubcCAAaitre2xfejQoRg8eHCPsqelCcCxxx4Lr9eLmTNnQlGU8EzfGTNmxJRP3x5f9pxrcu+GxWKB2VzJH85YOaRON2CuQrm9DMwqQQpomDJlCuTKAck1SRJmDE48kzzvmpCenfSVJrqe6LIbxk4lrknTNOzduxdTp06NmS1frJpUVcWqVasAAMcdd1zC1QCZavJ6vdi3b1/4s8hKBocuACbZCpO1HPGYTWZAkQGTGTBZIhoUPeRUAGAmyCYeaspmswE2W3iwyGQtA2QZ9vLES3DLysoSbnc4HKioqIDL5cLAgQP5b8pyuOyBQACdnZ0YMGBAnKaIPUwmU8IG32w2w2w299hutVphtfYsp94mhjWFjmm32yFJEiwWC8aNGxcug24nfZUiUXjiVy8VO6TH2ORMj62eJx7sLV6vuTIniQmL2kZx501Vg9i2fj2mTJkCkxJqI9I5bxVj+btrJ+A5yP+unprlQqdPUdsoAWnpqQ7lK3HtBAJdPBxYImz1wMSfAJv/AFiqgWl3AnLcrEuqQ0QUotlNND2AeJpIT57xNvNQUUE30PYF4NwRGyY5rk1QVRXQWvk/AjgzgCKwUZr0W4+lDqgu7xlKu4AYwUaGcWbcc889uOeee9Da2orf/OY3uPTSS/HYY4/BarXGzHAFAI/HA6vVmjS2bLIBoEQDSXp4lXh6xP/yNgMfL4bi4zcKhTEc090N0ydlfOANSJhNPpmnKtF2SZISbk9WxnS3J4tppoeMURQl5vfTKXuy7dnSJEkSt7ckAZIZqJkOQIIU+gySxB8AQ98tBk3p2EnTtIR60i17su2kqf+aglGh5BL9bjFq0u+xye4dmWoymUzhYye7j/e5PS70QnhvfXuS42f6uwB36jQ2NmLs2LE9Plu9ejXGjBkDi8WS4Ju9H7u/26M16a/ocxx93gmCILKKrT4nA63CE33egkH4zO18hUW692lvMwCFD3ro+eEAnhi8a2fOBsKJPrDWAo4RgHsf0LEOqD8l+b6tjXyyVsM3garizO1HEARBZIHQmCO8LYB7Nw8d+cH5sYPYCcYc4W/j7xYxnBlEAuTizoWSCww3slFXV4f7778f1dXVeOCBB9DQ0BCewauzf/9+NDQ05LdgASdfDi5bgVCeBlWWwcw8pjxUTySbvK2e33gySeRHJCfTmMx7nuEPFLWzACX5QCNBEEVAgcKaAMCiRYtw//3348ILL+zhULjvvvuwePHirP8mQRAEQSREH/TwtQLd+wE1FK5QtgArf8j/TjToQeSHmmncmdG+Nrkzw9sCtPNVsBhyZuJ9CIIgiNJAH3NUbDzqi+rmjgxzNf88fswRgKT5gGA3H3sUZGUGEUWe89IVE4ZzZgA8gbff74eqqpgzZw5ef/11XHPNNeHPV6xYgTlz5hSmcIodMDkgASivskOKnrEcPbDmbwc+u4zPjDr5cUM7NhRF6REax1D0NnipevjNu/KYmMHLsKZgB7DnSUCSgbnP5bfcWcTwNsoA0TSRnjzRj7Amdru9Xz99xRVX4Omnn8Y111yDe+65Bw6HA36/H3fffTfWrVuHRx55pF/Hz4T+aiLyj2HrVoaQHmMjmh5APE0Z64meaFU1hYeY8rUAlho+8JFg0CNfkI0AVE8HDrwaypuRhCPv8Qlw1cfFJv7OMaLZp1QQzW6i6QHE00R6CoRiB6w1gMcHsCBfvaejxeZfnDphGKT1UsgBkjhccjFRNDZKkYz1FHACZ18YxUYFd2b4/X4cPXo0vNKio6MDV1xxBRYuXIja2losXLgQv/rVr7BixQrMmzcPhw8fxr333otly5YVtuBqN2TXbqB8DL/ZxBN0A1oQ0AKGdmTo9BYapeAkG7wMdAJr/oef5+Nu7fGgZrFY+GwogK/MUIp7aZahbZQhomkiPXkiw7AmiUJrpYPJZMKbb76J2267DSeccAIURUFTUxOmTp2Kjz/+OCZPRjQ8509uYlz2VxNRGAxbtzKE9Bgb0fQA4mnql57QRCtoPkAyAbaBkYGPZA/AeaDkbVR9HJ9MpXYnz5vR+hl/Hzy//wVME9HsUyqIZjfR9ADiaSI9BUJvxwNdve5mZqExMoFWZRSNjVIkIz0FzEuXCkawUcFHQZqbm3Heeedh4sSJmD59Os444wyccMIJ+Mc//gGAJ1195ZVXcPPNN2PatGk488wzceutt+LEE08sbMHd+xDwdACdGwDv4Z6fq9383ZR4cMtIqKqKxsZGQyRxSYqtnic4jH7VzgSGfY2f4yPvxOyua9I6t/INFeMLUOjsURQ2ShPRNJEe4+N2u/t9DIfDgTvvvBMbN27EunXr8Mknn2D37t3YtGlT0u9s27YtJuF6NsmGJiK/iFa3SI+xEU0PIJ6m7OjRACh8AlWyZNN5hGwEPkN29l+Bk59IbpPpdwLH3tR7To0cIJp9SgXR7CaaHkA8TaSngJgqeJuuBQEWSLiLqqpYu6UJ2ohvA0O+kucC5oaislEK9EtPojHQ6FeBHBlGsVHBV2YMGzYMX3zxRa/7TJs2DR999FGeSpQazDEaqtcHM/PxeKiKnXvGQgnC4dwSWZ3RtTPyRUrEl11GXAAcfgdo/Rxw7eIrZaKQurbzP4rcmUEQhDEZP348nn32WSxatAivvPIKJk+eXOgiEQRBECWHDFQdC+7UKPhcNUKnbGjvn8tmYODc/JSFIAiCKB5kC1A+jjvDpeTDtgHTALARZwOmgg/tEkReoSs+U2QLvOYRsNn9POlesCuUJ+MH/MYTdAGeI4BzE9C5PvI9SsSXXexDgPpTgaPvA3ufAyb/T+QzxgBXyJFEzgyCIHLECSecgJ07d/a9I0EQBEHkFHJkGBLG+LseelgLApJSFKGICYIgiAJhqSl0CQjCsFCPN11UD19xEXRDZh4wxQE4xvDke0wD3HsBNQAoZYBsCq3YqOYv2RpJxEdkj5EX8veWjwH3/vBms9rKnUqyCXCMKkzZCIIgCIIgCIIoTbb9Bfj0Ev6MqHP4HWDlFcChtwtWLIIgCMKgRI05hl/dB/grDmvgEN+u+gtQUIIoHOTMSBU9m7zmAwIdkIKdqLRpkIKdgOYBbIMA+1DuvLAN4MuGJRN3apgc/JUoUbgBUBQFs2bNKng2+oxxjAQGnMhnPe1/HgDXNG1sOSRJAhyjuUOjiCl6GyVANE2kx/gkS9BdzIioSXREq1ukx9iIpgcQT1O/9SQa9Ai6+fYCQTaKwnsU8LUB7Wsi2w6/DXgO80lXBUA0+5QKotlNND2AeJpIT56JG3MMv7yHeUh13xE+QdpcCSA05mVfAWXVdUD7qkKWPGsY3kZpIpoewDiainuEN5/EZ5NnDF6fFzarLbJE2NcKfH4lAIk7M8wVhnVgxOP3+2G3F0dZEzLi29wjXT0tvMlfORv2E/4ff6ATgKK3UQJE00R6jI2maZBlsXz4ImoqBUSrW6TH2IimBxBPU0Z69EEPXysf+EiEtS486JFvyEYhaqYDbV9wZ8bwb/IVGs5tgKwAg87IcilTRzT7lAqi2U00PYB4mkhPHokfc4xm92PA0Q8B20BAsYU3a55mKABgqctbMXONoW2UAaLpAYyhiUZA0iEqm7xaNgprd7qglo2KZJO31gEIOTYsdUDlJJ7TweCoqop169YVPBt9v6gcD8z+KzCYPxSoqop169dDtQzknxU5QtgoDtE0kR7j4/EUbpZqrhBRk+iIVrdIj7ERTQ8gnqaM9eiDHnOXJ38VKE8f2SiKmun8vXMjz5Whh5aqOwGwVGWtjOkgmn1KBdHsJpoeQDxNpKcARI05xrwm3wxUHsMn6m77E8AY1IAfztZ9YIyFxiKLn6KwURqIpgcwjiZamUGIg68l4sVWg7AGDgCuGkAJXebmSkq8ThAEQRAEQYiBrZ76tkbG2xxK9i3zFTRNbwIHXuGDUZWT+OdkP4IgCKIvFBsw6Uag8Trg0DuAbTBQMRmK1g2oFt7G+NtpzIsoGciZQYiBtxn4eDHgbQGCXVBULyb7fVDaKiOznqx1BZuhRhBE9nA3u+FzJgmpAcBaaYWjnnJJEJxly5bhxz/+MUaMGBHeZrVa8fHHHxc81idBEAQhKPqzia8V8B4BAi7g00sBzc9DTK3/Lc+zSM8mBWfp0qX40Y9+hK1bt2LUqFHh7Zs3b8ZVV12Fzs5OSJKEW265BQsWLChcQQmCKG3MlYDnIODaA7SvgmIZAIfvMKR2C/DRt/k+NOZFlAjkzOgHSQdBkiXcK2Aivr4o+gGdgJM/LDCVe6QBSCzIZ0OZq/m597Xy/Yr0xl70NkqAaJpIT+5xN7vx4uIX0d3anXSfsroyLHhqQQ+HhqTnN+oHfr8fv//97/Hss88C4Mssv/rVr+JXv/oVysvLw/s98MAD+Pvf/w5JkuDz+TB79mzceeedGDZsWL/LEE02NIlOMBjEOeecg2XLlhW6KGGMWLf6A+kxNqLpAcTTJJoeQDxNaevRn01kK2CtB1QvAA2QTYA1FPO8gM8motknU2655RY0NjaipqYGwWAwvN3r9eK8887Dww8/jHnz5uHw4cOYN28exo0bh6lTpxasvKLZTTQ9gHiaSI+BCDgBSQFMFYDmBVgQTFIAk0OYMS+gyG2UANH0AMbQRDkzMsRkMmH27NkwmaL8QXoiPs0HBDp6vjRfQRPxJSOhlmLFOgAwV0GSTDCbbZCs1fzmXiSJ2JMhlI1CiKaJ9OQHn9OH7tZumKwm2KptPV4mqwndrd09Vm5IkgSHw9GvwX9N03D++edjz549+PDDD7FmzRo0NjbC4XDgS1/6Ukz+iq9//etYuXIl1q5diw0bNmDUqFH42te+lvFvJyIbmoj8Y9S6lSmkx9iIpgcQT5NoegDxNPVLj2LnCVvNlYClhocGKWso6LOJaPbJFE3TMGTIELz22muw2Wwxn7311luYMWMG5s2bBwAYPHgwbrjhBixdurQQRQUgnt1E0wOIp4n0GJTyMUDVZEi2gXzMy1wuxJgXIJCNQoimBzCOJnHOaJ5hjKGzsxNVVVWRgSQ9EZ+etyERBoxhl1BLMWMfCta1HYwxSCYHBFAkno0gnibS03+C3mDSzyRZgmKJzABQrApM9p5NGNMYAt5Aj+MyxqCqKhRFCesx2dJrAp988km0tbXhtddeCx/DarXi17/+NTZt2oT77rsPN998MwBg9OjR4e+ZTCbceuuteOCBB9DU1IShQ4em9bvJSKSJ6B8+nw8+X8QR5nTy9jwYDIZnbMqyDFmWoWkaNE0L76tvV1WVJ+JLsp0xBqfTiZqaGsiyHDMTFIjMdIlPqpZsu8lkCl8LOpIkQVGUHmVMtr0/mjRNg9PpRGVlJUwmEyRJKmpNwWAQnZ2dqKysDB+7mDVJkoT29nZUVFSE7xOkyViaJElCV1dXzOo+0mQsTYwxuFwuVFdXQ9O0GK1JNUV9F5IFqJwcLg8AsIALEmNQ1SAQDOZVk94OVVdXQ1GUHvaL1xSvTRRkWcbVV1+d8LN33nkn7MjQmTdvHu6///6kx8t1H4LaW9JE7W2Jt03gbUp4NQYkaNbBUCwOAPzerrcrCmPFoSkb7a2BNaXb3oqmKZf9B3JmZIiqqtiyZQtmzZoV65EqwkR8SbUUK5YawORA0NsFk1IhhDNDOBtBPE2kp/8sv2B50s+GzBqC0399evj/5o3NkOSetVsLapDNsYsOX7nsFXidXqhBFYpJgRS6Kyx6dVFa5Xvsscfwk5/8JKHj4Kc//Sm+973vhZ0Z8XR3d0OSJNTV1aX1m33h9XrhcFB+kGyxZMkS3HrrrT22r169Onye6+vrMXbsWOzevRvNzc3hfRoaGtDQ0IBt27ahs7MzvH3MmDEYOHAgNmzYAI/HA8YYOjo6cMIJJ6Curg6rV6+O6XhOnToVFosFjY2NMWWYNWsW/H4/1q1bF96mKApmz56Nzs5ObNmyJbzdbrdj2rRpaGlpwa5du8Lbq6qqMGnSJDQ1NeHAgQPh7f3R1N3djY6ODlRXV2PSpEmorq4uak3r16/H4cOHUV1dDUmSMHHixKLWVFtbi8bGxpiBCNJkLE0jR47E3r17YbVaYwZCSZNxNDHG4Pf7ccopp2Djxo0xKzGTaZo2thw2xtDV5YQmRyZZVFVVg2kaXF1OKFo3tq9fj6DNmVdNejs0ffp0DBkyJNw+JdPkdrtRajQ1NeHMM8+M2TZ8+PAYO8ST6z4EtbekidrbEm+bqgCvzwe/l7crjAGMOVDjqEWXswsIusLtyugpg4pDUxbaWyNrSre9FU2T7tTPBRKLdqEIitPpRFVVVdhLng2CwSAaGxuFGLgUQkvXTuDDC3isQJMDmqais7MdVdV1kCUJCLp5qK+5y4GKsYUubdoIYaM4RNNEelLH6/Vi9+7dGD16dMyy/qe//nTS7+jOjLadbVh+wXI49zt7dWb84KMfoHZsLQDgxYtfzIozo66uDp9//jnGjBnT47NAIACbzYbOzs4es302btyIX/ziF5g/fz5++tOfpvWbvcEYg9vtjgk1lezcArlpC4uBxx57DLfccgtGjBiB1tZWjBs3DjfffDNOPvnkHvsmmlU5fPhwtLa2hs9Zf1dmqKqKVatWYdasWTCbzUU1EyfR9mAwiFWrVmHmzJmwWCxFN7sofrvP5wvrURSlKGdMRW/XNA2ff/55WI9eRtJkHE2apmHVqlWYMWNGTAxi0mQcTfp9e/bs2ZAkKbVZld17gI8uBDNVhWbQRsoDhFZmBDqgznkGKB+bV026nuOPPx4Wi6XPmaJOpxN1dXVC9x9GjRqFd955B+PGjQMAzJ8/HzfddFOMQ0PTtPB5TzSxJdd9CGpvSRO1tyXeNrl3g32wECw05qVHUuDOM4AF3OF2Rak6pjg0ZaO9NbCmdNtb0TTlsv9Q/CNuBJEISebJkQiCKBouWH5B0s/iHRf1k+thdph77BdwB+Dt9MZs+8Yj30g48J8unZ2dGDRoUMLPzGYzampq4HQ6w86MG2+8EU888QSOHDmCyy+/HNdff31Gv0v0j4ULF+L8889HZWUlGGP417/+hW984xv4+OOPMX78+Jh9rVYrrFZrj2OYTKYeTj29YxdP9MNRsu2SJIWvw2TOwnS2S5KUcHuyMqa7vTdNLLSMXe+4plv2ZNsLqUnXE/37xapJH3SL15Nu2ZNtJ03916Q/wCbSk27Zk20nTf3XFB1iJaUyhvaXJCn8d4/jSRJMigkIfTefmiRJCu/TlyYRJumki9Vqhdcb25/0eDywWq1J+5G57kNQe0uaqL2ltin8DCFJ0BBpXiRIse1KEd8j0m5vM9hu1Pa2P9uNqCmX/QdKAJ4hkiTBbrdnPChmJETSAtUDBN2QVDdM8ENS3XxVhurp+7sGRigbhRBNE+npPyabKekrOl8GwJ0bekMb/ZJkqUeZ9WNYyiwxx0yXiooKHDlyJOFngUAAHR0dGDBgQHjbPffcg8OHD6OlpQU2mw2XXnpp2r/ZF4k6GkQsDocjPBNEkiSce+65OO+88/DGG28UpDx0rzA2pMf4iKZJND2AeJr6pSf0bNLjVcBnE9HskwsaGhqwb9++mG379+9HQ0NDgUoknt1E0wOIp4n0GBRBx7wAgWwUQjQ9gHE00ShIhiiKgmnTpiX1QhUTQmgxVwLWOkDzAYEOSIFOVFiDkAKdPLyU5uOfm4tzabQQNopDNE2kJ78EPUH43f4er6AncZIpSZJQVlbWr0b3uOOOw+eff57ws1WrVmHMmDGwWCw9Pqurq8P999+Pl156KSZua3/JhqZSRVXVgs00NXrdShfSY2xE0wOIp0k0PYB4mjLSE/ds0uNVwGcT0eyTC+bMmYMVK1bEbFuxYgXmzJlToBKJZzfR9ADiaSI9BkPwMS9AABvFIZoewDiaSm/NaJbQNA0tLS0YMGBA0c+MFUKLrR6Y8xQQ4AlmNE1De3s7ampqIprMlUWXnF1HCBvFIZom0pMfrJVWlNWVobu1G0FfYsdFWV0ZrJWxy/wZYwgGgzCZTBkP/i9atAj3338/Lrzwwh7HuO+++7B48eKk3/X5fPD7/T3iW/aHbGgqBQ4ePIhBgwaFnRcvvPAC3nzzTfzud78rSHmMWrcyhfQYG9H0AOJpEk0PIJ6mjPTEPZskpEDPJqLZJxcsXLgQv/rVr7BixQrMmzcPhw8fxr333otly5YVrEyi2U00PYB4mkiPwRB8zAsQwEZxiKYHMI4mcmZkiKZp2LVrF2pra4v+ohRGi60+fOPWgkFs39yKWQ2jIQsQ51UYG0UhmibSkx8c9Q4seGoBfE5f0n2slVY46h09tvt8vn7Nxr/iiivw9NNP45prrsE999wDh8MBv9+Pu+++G+vWrcMjjzwCAPD7/Th69Gg4FEFHRweuuOIKLFy4ELW1tRn/fiL6q6kUePPNN3HPPfeE41hPmDAB7733HoYMGVKQ8hi1bmUK6TE2oukBxNMkmh5APE0Z64l6NjESotknG1gsFpjNkVxsDocDr7zyCq6++mq4XC5omoZbb70VJ554YsHKKJrdRNMDiKeJ9BgQgce8AEFsFIVoegDjaBLjiicIgiBKBke9I6GzIteYTCa8+eabuO2223DCCSdAURQ0NTVh6tSp+Pjjj+Fw8DI1NzfjvPPOg9vths1mgyzLWLx4MSUALxCXXXYZLrvsskIXgyAIgiAIg7Jt27Ye26ZNm4aPPvqoAKUhCIIgCKI3yJlBEARBECnicDhw55134s477wQAbN++HWeddRY2bdoUjqM8bNgwfPHFF4UsJkEQBEEQBEEQBEEQhHCIsc6lAEiShKqqKiHilIukRUc0TaLpAcTTRHqMTy6SVI0fPx7PPvssvvvd72Ljxo1ZP35fFDrxFpE+otUt0mNsRNMDiKdJND2AeJpID2EERLObaHoA8TSRHmMjmh5APE2i6QGMo0lijLGCliAPOJ1OVFVVobOzE5WVlYUuDkEQREnj9Xqxe/dujB49GjabrdDFEYrezi21helD54wgCIIodagtzAw6bwRBEEQpk8t2kFZmZIimaThw4AA0TSt0UfqNSFp0RNMkmh5APE2kx9gwxuD3+yGS/15ETaWAaHWL9Bgb0fQA4mkSTQ8gnibSQxgB0ewmmh5APE2kx9iIpgcQT5NoegDjaCJnRoYYxYDZQCQtOqJpEk0PIJ4m0pM++R6E9/v9ef29fBCvSZTrT2ToXmFsSI/xEU2TaHoA8TSRHsIIiGY30fQA4mkiPcZGND2AeJpE0wMYRxMlACcIgiDyitlshiRJaG5uRn19fV7iLTLG4PP5oChKweM7ZotoTQB3bDQ3N0OWZVgslgKXjiAIgiAIgiAIgiAIIruQM4MgCILIK4qioKGhAQcOHMCePXvy8pt6SCaLxSKUMyNeU1lZGUaMGAFZpoWXBEEQBEEQBEEQBEGIBTkzMkSWZdTX1wsxYCSSFh3RNImmBxBPE+lJj/LycowfPx6BQCAnx49HXw7Z0NAgjI3iNSmKApPJJIyzRlToXmFsSI/xEU2TaHoA8TSRHsIIiGY30fQA4mkiPcZGND2AeJpE0wMYR5PESiBzaC4zqBMEQRBEMUBtYfrQOSMIgiBKHWoLM4POG0EQBFHK5LIdFMc9lGc0TcPOnTsLnvQkG4ikRUc0TaLpAcTTRHqMjWh6ADE1lQKi2Y30GBvR9ADiaRJNDyCeJtJDGAHR7CaaHkA8TaTH2IimBxBPk2h6AONoImdGhmiahubm5oIbMBuIpEVHNE2i6QHE00R6jI1oegAxNZUCotmN9Bgb0fQA4mkSTQ8gnibSQxgB0ewmmh5APE2kx9iIpgcQT5NoegDjaCJnBkEQBEEQBEEQBEEQBEEQBEEQhqYkEoDraUGcTmfWjhkMBuF2u+F0OmEyFfdpFEmLjmiaRNMDiKeJ9Bgb0fQA6WvS28ASSJWVNaj/0Dekx9iIpgcQT5NoegDxNJW6Huo/ZEa2+xClfh0WA6JpIj3GRjQ9gHiaRNMDpKcpl/0HMc5mH3R1dQEAhg8fXuCSEARBEERh6erqQlVVVaGLURRQ/4EgCIIgONR/SA/qQxAEQRBEbvoPEiuBKRaapqGpqQkVFRWQJCkrx3Q6nRg+fDj279+f9azs+UYkLTqiaRJNDyCeJtJjbETTA6SviTGGrq4uDB06FLJMUSZTgfoPfUN6jI1oegDxNImmBxBPU6nrof5DZmS7D1Hq12ExIJom0mNsRNMDiKdJND1Aeppy2X8oiZUZsiyjoaEhJ8eurKwU5qIUSYuOaJpE0wOIp4n0GBvR9ADpaaIZlelB/YfUIT3GRjQ9gHiaRNMDiKeplPVQ/yF9ctWHKOXrsFgQTRPpMTai6QHE0ySaHiB1TbnqP9DUCoIgCIIgCIIgCIIgCIIgCIIgDA05MwiCIAiCIAiCIAiCIAiCIAiCMDTkzMgQq9WKX//617BarYUuSr8RSYuOaJpE0wOIp4n0GBvR9ABiaioFRLMb6TE2oukBxNMkmh5APE2khzACotlNND2AeJpIj7ERTQ8gnibR9ADG0VQSCcAJgiAIgiAIgiAIgiAIgiAIgiheaGUGQRAEQRAEQRAEQRAEQRAEQRCGhpwZBEEQBEEQBEEQBEEQBEEQBEEYGnJmEARBEARBEARBEARBEARBEARhaMiZQRAEQRAEQRAEQRAEQRAEQRCEoSFnBkEQBEEQBEEQBEEQBEEQBEEQhoacGUS/8Hq9hS4C0QtHjhwpdBGIPqA6ZHzIRgSRfaheGR/qQxgfqkfGhuxDELmB6paxof6D8aE6ZHzIRr1Dzow8wxgrdBGyxm233Ybrr7++0MUgkrBkyRJce+21hS5G1qE6ZHzIRsZHJBuVEqLYTdR6JRLUhzA+ItYjso/xEclGpYRIdhO1bokC9R+Mj6h1iGxkbLJtH3Jm5IlXX30VACBJkhCV7J577sE///lP/Pvf/8YLL7xQ6OJkhWeffRadnZ2FLkZWuPvuu/HPf/4TjY2N+Pvf/17o4mQFqkPGh2xkfESzUakgkt1ErFci9R8A6kMUA6LVI7KP8RHNRqWCaHYTrW5R/8H4UB0yPmQjY5Mr+5AzIw80NjbivPPOw+LFiwEUfyW766678Nprr+Hjjz/Gn/70J+zatQsAoGlagUuWObt27cKiRYtw7bXXoq2trdDF6Rd33XUXXn/9dXz66adYvnw51q9fD4/HU9T2oTpkfMhGxkc0G5UKItlNxHolUv8BoD5EMSBaPSL7GB/RbFQqiGY30eoW9R+MD9Uh40M2Mja5tA85M/KAyWTCRRddhMbGRpx99tkAireS/eY3v8Hbb7+Nl156CRaLBWazGX/729+wc+dOyHLxXk6qqmL+/PnYs2cPLrnkErS3txe6SBlx6623hu2js3LlSuzYsaOo7UN1yPiQjYyPSDYqJUSxm6j1SpT+A0B9iGJAxHpE9jE+ItmolBDJbiLWLeo/GB+qQ8aHbGRscmmf4jwjRcbmzZsxdepUbNu2DQcOHMDXvvY1AMVXyQ4ePIi1a9fimWeeQW1tLVRVxdlnn40LLrgAy5Ytg6qqResxPHDgAE4//XR88MEHCAaDuOyyy4quQ9HR0YHNmzfH2GfWrFk499xz8bvf/Q4ej6fQRcwYqkPGh2xkfESxUakhgt1Erlci9B8A6kMUA6LWI7KP8RHFRqWGKHYTtW5R/8H4UB0yPmQjY5NT+zAiZ2iaFv77s88+C/89efJkds455yTcz+gEAgHGGGPBYDBc7uXLl7MLL7ywkMVKm0Tn/JNPPgn//dWvfpWdf/75rK2tLZ/F6jfBYDD8rmtcvXo1+853vsNcLhdjrLiuN6pDxodsZHxEtFEpIJrdRKlXovYfGKM+RDEgSj1ijOxTDIhoo1JARLuJULeo/1A81xvVIeNDNjI2+bCPxFgRuauKBKfTCYfDAQBQFCXhPscddxxGjhyJ119/HQDP7C5JUt7KmA7xelRV7aFrzpw5+MY3voH/+Z//KUQR0+bw4cOorq5GMBhEeXl5wn3OOecc2Gw2PPLII6ipqclzCVMnXoumaT2WoZ1xxhmYPn06/vCHPxSolOlBdcj4kI2Mj2g2KhVEspuI9Uqk/gNAfQij1yFAvHpE9jE+otmoVBDNbqLVLeo/GB+qQ8aHbGRs8mkfcmZkmd/97nfYsGEDXC4Xpk+fjgULFmD69OlhAwWDQZhMJgDFUcn60qNXtldffRUrVqzAvffea0gd0dxxxx349NNPYTKZYLFYcN1112Hu3Lnhz6NtZPQORV9adPusWrUKf/7zn7FkyRIMGjSogCXuG6pDxq9DZCOyEZEbRLKbiPVKpP4DQH0Io9chQLx6RPYxtn0A8WxUKohmN9HqFvUfqP+Qb0SrQwDZyOg2yrt9Ml7TQfTg3nvvZaeddho7cuQI++c//8n+9Kc/serqavbaa6/F7KcvH2KMsalTp7J58+bluaSpkaoexhjbuHEjGz58OHvrrbcKUNLU+dOf/sROOeUU1t7eztatW8ceffRRNmzYMPboo4+yjo6O8H7RNvra177GzjrrLNbV1VWIIiclVS2MMbZ//3520kknseXLlxeotKlBdcj4dYhsRDYicoNIdhOxXonUf2CM+hA6Rq1DjIlXj8g+xrYPY+LZqFQQzW6i1S3qP1D/Id+IVocYIxsZ3UaFsA85M7LI4sWLWWNjI2OMMVVVGWOMLVu2jA0fPpw9++yzjLFITLBoI55++uls//79eS5t36SiR1XV8GcPPvggu+qqq5jb7S5MgVPgyiuvZG+88QZjLGKDV199lc2dO5c9+OCDMR0GPfYjY4wtWrSIHThwIL+F7YNUtejX3JNPPsm+9KUvsSNHjhSmwClAdcj4dYhsRDYicoNIdhOxXonUf2CM+hBGr0OMiVePyD7Gtg9j4tmoVBDNbqLVLeo/UP8h34hWhxgjGxndRoWwjynbS0tKEU3TEAgEcPjwYXR1dQHgy2QYY7j44othtVrx4x//GJWVlTj77LOhaRpMJlN4mc1//vOfAiuIJV09qqoCAIYNG4YVK1b0iJVoBFho2ZLP50N3d3fM9q997WuwWq245ZZbUFlZie985zvQNA2KooRt9NRTTxWw9LFkokWWZUyePBnjx4+H3W4vYOkTQ3XI+HWIbEQ2InKDSHYTsV6J1H8AqA9h9DoEiFePyD7Gtg8gno1KBdHsJlrdov4D9R/yjWh1CCAbGd1GBbVPRi4QIiF/+MMf2MKFC1lzczNjjHukdO/To48+ympra9m6desKWcS0yETP0aNH817OdPj73//OZs6cGfb+Rc9+ePnll1ldXR375JNPClW8tMhES1tbW17LmC5Uh4xfh8hGZCMiN4hkNxHrlUj9B8aoD1EMiFaPyD7Gtg9j4tmoVBDNbqLVLeo/UP8h34hWhxgjGzFmbBsVwj7GcusUKSyUQ/3888/H0KFD8dJLL8HlckGWZWiaBsYYvv/97+O6667D22+/HfMdI5KJnmAwCACor68vWLlT4Qc/+AHmz5+Pv/zlL2hpaYGiKFBVFYwxnHfeefjlL3+Jf/zjHwgEAoa2EZCeFt0+Rk0iRnXI+HWIbEQ2InKDSHYTsV7piNR/AKgPYWQbiVaPyD7Gtg8gno1KBdHsJmLdAqj/AFD/IV+IWIfIRsa2USHtQ86MLKBnXR81ahSmT5+Ozz77DK+99hpcLld4qSDAb+JbtmyJ+Y4RyUSPnpXeyLDQ0sizzjoLXV1dePjhh8MNsN/vBwCMGzcOPp8PZrPZ0DZKV4vR7UN1yPh1iGxENiJyg0h2E7FeAWL1HwDqQ0R/x4iIVo/IPsa2DyCejUoF0ewmYt2i/oOx7UN1yPh1iGxkbBsV0j7GPStFhH5TB4BLL70Ufr8fb731FlpaWnDRRRdhwIABAIDKykpYrdZwfDCjIpoeIFbTGWecAb/fj1dffRV33nknfvrTn2LYsGEAgK6uLgQCAXi9XlitVkPeCEXSoiPaNSeaHkA8TaLpAcTUVAqIZDeRtOiI1uaKpgcQ77ojPaQn34ioqRQQzW4i6xGhvRVNDyD2NSeCHkA8TaQne3qMe1aKBFVVoSgKAOD++++Hy+XC//7v/8JisaCxsREXXnghLrvsMjQ1NeGJJ57AU089ZeiLUTQ9AGIqzD333IOmpibcd999MJvNePPNN3Haaafh6quvRkdHB1588UU8++yzsNlsBS51YkTSoiPaNSeaHiC2kRJBk4h6AAilqVQQ6X4hkhYd0dpc0fQA4l13oukRsb0VTQ9A/YdiRLR7hWh6RGtvRdMDiHfNiaYHELPNFUWPEfoPEjNyQDGDsXTpUuzbtw+yLOPLX/4yTjnllPBnf/nLX7Bs2TIsW7YMY8aMAQAcPHgQL774IjZs2ACTyYRrr70WkyZNKlTxeyCaHgB44okn4PP5YDKZcNppp4XLDnBNTz75JB5//HGMHTs2vH3ZsmXYs2cP/H4/Lr74YkyYMKEQRe+BSFp0li9fDoA3tl//+tfhcDjCnxXjNSeaHgBwOp2orKxM+FkxahJNDwC8//77sNvtYIzhhBNOABDpHBWrplJApDZXJC06orW5oukBxGtzRdMjWnsrmh6A+g/Fimhtrmh6RGtvRdMDiNfeiqYHEK/NFU2PIfsPPVKCEwlZsmQJmzNnDnvqqafYd77zHXbllVeyrq4uxhhjn376KTv33HPZ3r17GWOM+f3+QhY1JUTTwxhjd9xxB5s9ezZbunQpW7RoEfvFL37BlixZwhhjbPfu3eyiiy4KawoEAoUsap+IpEXnjjvuYCeffDL74x//yE466ST285//nP35z39mjDG2ceNG9pWvfKWorjnR9DDG2AMPPMDOPvtstmHDhh6fffbZZ0V3XxBND2OM3X777ezEE09kF110ERs+fDj705/+FP6sWO/dpYBIba5IWnREa3NF08OYeG2uaHpEa29F08MY9R+KFdHaXNH0iNbeiqaHMfHaW9H0MCZemyuaHqP2H8iZkQLPPfccmzdvHnM6nYwxxvbv38+GDh3KVq5cyRhjTNM01tHRwRhjLBgMhr+naVr+C5sCoulhjLE333yTzZ07N6zJ5XKx9957jy1YsID99re/ZYxFyh+tyYiIpEXnk08+YSeffDJzuVyMMcZaWlrYk08+yS6++GJ24403MsYiWorhmhNNj86SJUvYkCFD2HXXXcc2bdoU81kx3hdE0/OHP/yBnXbaaczn8zHGeGdoyJAh7L333mOMFaemUkCkNlckLTqitbmi6WFMvDZXND2MidfeiqaH+g/FiWhtrmh6RGtvRdPDmHjtrWh6dERrc0XSY+T+g5zddR5i0tzcjG9961uoqKgAAAwdOhRDhw6F2WyG0+kEAFRVVfX4nlGTHYmmBwBaWlowbdo0VFRUwO/3w+Fw4JRTTkF1dTWWLl2K3/72t+Hy67EEjYpIWnRcLhcGDRoEh8MBVVVRV1eHb33rW7juuuuwY8cO3HjjjWEtshy5LRn1mhNND8CXqba3t+N3v/sdXC4X/vCHP2Dz5s3hzyVJQlVVFRhjMdedUTWJpufIkSM4cOAAHnroIVgsFni9Xpxwwgm4/PLLsXfvXgDFp6lUEKnNFUmLjmhtrmh6APHaXNH0iNbeiqaH+g/Fi2htrmh6RGtvRdMDiNfeiqYHEK/NFUmP0fsP5MzoBRZKJ3Lo0CEcOHAAHR0dAIB//vOf2LZtGx588EFMmTIFF110EX75y18CMPaNXTQ90eiVS//b7/fDYrHg5JNPxvXXXw+n04kVK1YUuJSpIZIW/ZqbOHEiLBYLdu/eDUVRwBiD1WrFrFmzcMMNN+DQoUN4+OGHARjzRq4jmp5oFEXBjBkzMH/+fDzwwAMJG1+A9BSKmpoaLFq0KByHUk+qN3DgQPz973+HpmnhfYtFk+iI1OaKpCUekdpcQCw9orW5ounREa29FU0P9R+KD9HaXNH06IjU3gJi6RGtvRVNTzSitbki6TF6/4GcGQnQbxa6Qb75zW/i9ddfxwUXXIAZM2Zg4cKFYIzh9NNPx+9+9zssWrQI27dvx6uvvlrIYidFND1ARJPOlClT8MYbb4Rv3haLBc3NzXjmmWcwduxYWK1WvP/++4Uoap+IpCUe/ZorKyuDyWTCK6+8Et6uaRoURcHMmTMxd+5crF+/vpBFTQnR9MRz0UUXYejQoSgvL8fDDz8Mt9udsPEtFkTSY7FYMHXqVFitVgCR+8ZXvvIVVFdXx8y+IQqLSG2uSFp0RGtzRdMTjWhtrmh6ohGpvQXE0kP9h+JBtDZXVD06xd7eiqYnGtHaW9H0xCNSmwuIo8fw/YfsR64qfg4ePMgYY0xV1XDcr927d7M1a9awV155hV111VXs8ccfD+/v8/nYd7/7XfaXv/ylIOXtC9H0MJZY07vvvstqa2vZd7/7XXb99dezU045hf3+979njDG2efNmNm/ePNbS0mK4eHQiadH5z3/+w15++WW2Zs0a1tnZyRjjMR6HDRvG/vGPf4T308t/4MABNmLECPbf//63EMXtE9H0MBarSU+8p5dfT+jW1dXFFi1axC6//PIe8R6Nhmh6GIvVpMevja/zLpeLzZ07lzU1NTFVVRljjK1evTocu5LIPyK1uSJp0RGtzRVND2Pitbki6xGhvRVND2PUfyhWRGtzS0FPMbe3oulhTOz2VgQ9jInX5oqsx+j9B3JmxPHoo4+yqqoq9sEHHzDGeBIT3UCMMdbd3c3mzJnDXnjhhfDnjDF26623xtxQjIJoehhLrEm/Uaxfv549/PDD7KmnnopJSrNmzRq2ePHigpU5GSJp0bn99tvZSSedxM4++2y2cOFC9rOf/Yy1trYyxhh77733mNlsZg8//HCP711zzTVszZo1+S5un4imh7Gemm644YYejY9+n9Ab3yuvvJJt3LixEMXtE9H0MJaapkAgwFpbW9nMmTPZ3r17GWOMPf7442zKlCnsyJEjhSh2ySNSmyuSFh3R2lzR9DAmXpsrup5ib29F08MY9R+KFdHa3FLQU8ztrWh6GBO/vS12PYyJ1+aWoh4j9R/ImRHHn//8Z3bKKaewUaNGsbfffpsxxmIa3vb2djZjxoyYWQRPP/00mz59OtuxY0fey9sXoulhLLGmYDAY7gQl4oknnmCXXnop83g8hppJIJIWxhi7//772bx585jL5WKMMfbf//6XXXrppezzzz8P7/Pee++xoUOHsrvuuot9+umnjDHGnnnmGTZ16tTwDdEoiKaHseSaGhsbGWOxnnf9OnS5XOzrX/86+8UvfsH8fn/+C90LoulhLHVN+r38nHPOYW63my1fvpydeOKJbP369YUpOCFUmyuSFh3R2lzR9IjW5paKnmJtb0XTwxj1H4oZ0drcUtBTzO2taHpKpb0tVj2MidfmlqoeI/UfyJkRQjfOJZdcwh5//HH23HPPseHDh4dv7pqmhQ338ssvs4qKCnbZZZexH//4x2zGjBmG866JpoexvjVFd5Ciefzxx9ns2bPZhg0b8lbWvhBJi05zczP7+c9/zrZt2xaz/eKLL2Y//OEPGWMRXY2NjewHP/gBO+2009g555zDpk2bZjhNoulhLDVN8ega3W53eDmyURBND2OZaVq0aBFbuHAhO/744w153ZUCIrW5ImnREa3NFU0PY+K1uaWoJx4jt7ei6WGM+g/FimhtbqnpKbb2VjQ9jJVme1tMehgTr80lPcboP5AzI461a9eyVatWMcYYe+ihh3rc3HWv2sqVK9nDDz/MXn75ZbZnz56ClbcvRNPDWO+a4mcJbNy4kS1YsMCQN3XGxNLCGGPr1q1jLS0tjLFI+RsbG9miRYsYY7FLjD0eD2tvb2c7duxgzc3NhSlwH4imh7G+NSWaaZOsY2sERNPDWOqaVFVlgUCAnXXWWWzIkCE9OiBE/hGpzRVJi45oba5oekRrc0tNT7G1t6LpYYz6D8WMaG1uKekpxvZWND2l1t4Wmx7GxGtzS1WPkfoP5MyIw+PxxPz/t7/9LWnjWwyIpoexvjVF3zg6OzvDsQWNiEhakrFx40Y2ZswYdvTo0XAszmJGND2M9a7JyI1uMkTTw1jvmlauXGm4mWylikhtrkhadERrc0XTkwjR2txS0lOM7a1oehij/kOxIFqbW2p6iq29FU1PIkqpvS1WRGtzS0mPEfoPJpQwS5cuxdGjR2G323H88cdj7ty5sNlsCAaDMJn4qfnhD38IAPjBD36ApUuXYv78+dA0rZDFTopoeoD+aZIkCZWVlYUsfgwiadFJpAlAjKaxY8di+PDhsNls4W1vv/02ZsyYgQEDBhSs7IkQTQ8gnibR9ACZa3rrrbcwdepUzJ49u2BlL2VEanNF0qIjWpsrmh5AvPs56SE9+Yb6D8WJaG0u6TF2eyuaHkC8+7loegDxNJEe4/Uf5EIXoFDcddddeOSRRzBmzBhs3LgRjzzyCC655BIAgMlkQjAYDO/7wx/+ELfccguuuOIKvPHGG5Bl45020fQA/dckSVKBSt4TkbTopKrJarUCAA4fPgwAWLZsGW666Sa4XK6ClDsZoukBxNMkmh6gf5r+53/+B16vtyDlLnVEanNF0qIjWpsrmh5AvPs56SE9+Yb6D8WJaG0u6TF2eyuaHkC8+7loegDxNJEeg/YfCroupEBs3bqVzZ07lx05coQxxpjX62UHDhxgp59+Ops7d254v/ilNH/+85/ZzJkzmdvtThgDrVCIpocxsTSJpEUnVU1er5epqsqmT5/ONm/ezJYvX85mzZpV8CVp8YimhzHxNImmhzExNZUCIt3TRdKiI5om0fQwJt69j/SQnnwjoqZSQLT7OenhkJ78Idq9TzQ9jImnifQYV09JOjM2b97MvvKVryT8bM6cOWzevHnh/+Njm3V2duayaBkhmh7GxNIkkhaddDQxxtjll1/Ovve977FTTjnFkMnDRNPDmHiaRNPDmJiaSgGR7ukiadERTZNoehgT795HekhPvhFRUykg2v2c9EQgPflBtHufaHoYE08T6TGuHuOt7csDEyZMgKqqePbZZ8Pb9KU0H330EWRZDscNjF/+aMSYgaLpAcTSJJIWnXQ0AYCmaXj55ZfxyCOPYPLkyXkvb1+IpgcQT5NoegAxNZUCIt3TRdKiI5om0fQA4t37SA/pyTciaioFRLufk54IpCc/iHbvE00PIJ4m0mNcPSXjzFi5ciU2btyINWvWQJIkXHTRRfjiiy+wfft2ALGxwR566CG43W5s2rSpkEXuFdH0AGJpEkmLTiaa1q9fDwD48Y9/jA0bNmDChAkFK388oukBxNMkmh5ATE2lgEj3dJG06IimSTQ9gHj3PtJDevKNiJpKAdHu56SH9OQb0e59oukBxNNEeoytJ0yhl4bkg9tuu43NmTOHnXPOOWzOnDnsscceY52dnWzx4sVsyZIlrKmpKbyvpmnM6XSy0047jT3zzDMFLHVyRNPDmFiaRNKik6mmp59+uoClTo5oehgTT5NoehgTU1MpINI9XSQtOqJpEk0PY+Ld+0gP6ck3ImoqBUS7n5Me0pNvRLv3iaaHMfE0kR5j64lGeGfGvffey0477TTm9/tZe3s7e/fdd9ns2bPZzp072Zo1a9iZZ57Jbr/99h6JTG666Sa2fPnyApU6OaLpYUwsTSJp0RFNk2h6GBNPk2h6GBNTUykgkt1E0qIjmibR9DAmnibSwyE9+UNETaWAaHYjPRzSkz9E0ySaHsbE00R6OEbVE4/Qzox9+/axq6++mu3atYsxxlggEGBdXV3shz/8IXvhhRcYY4w1NjayRYsWseuuu449+OCDzOv1skcffZRNmjSJ7dixo5DF74FoehgTS5NIWnRE0ySaHsbE0ySaHsbE1FQKiGQ3kbToiKZJND2MiaeJ9JCefCOiplJANLuRHtKTb0TTJJoexsTTRHqMrScRQjszPB4P++9//8v8fj/TNC28/e6772Zf+cpXmKqqjDHGdu7cyR599FE2Z84cdt5557E5c+YYLlM7Y+LpYUwsTSJp0RFNk2h6GBNPk2h6GBNTUykgkt1E0qIjmibR9DAmnibSQ3ryjYiaSgHR7EZ6SE++EU2TaHoYE08T6TG2nkQI7cxgjLFgMBj+WzfiRx99xM455xzGGAsbUae7u5s5nc78FTBNRNPDmFiaRNKiI5om0fQwJp4m0fQwJqamUkAku4mkRUc0TaLpYUw8TaSH9OQbETWVAqLZjfSQnnwjmibR9DAmnibSY2w98ciFTkCeaxRFCf8tSRIAYNKkSfD7/XC73eHP1q5dCwCw2+2oqKjIbyHTQDQ9gFiaRNKiI5om0fQA4mkSTQ8gpqZSQCS7iaRFRzRNoukBxNNEekhPvhFRUykgmt1ID+nJN6JpEk0PIJ4m0mNsPfEI78yIR1VVeDweNDU1obOzE7Is44knnsDixYvR0tJS6OKljWh6ALE0iaRFRzRNoukBxNMkmh5ATE2lgEh2E0mLjmiaRNMDiKeJ9Bgb0fQAYmoqBUSzG+kxNqLpAcTTJJoeQDxNpMfgFHppSL4JBoPM4/Gw008/nTHG2PLly9lJJ51UNHHB4hFND2NiaRJJi45omkTTw5h4mkTTw5iYmkoBkewmkhYd0TSJpocx8TSRHmMjmh7GxNRUCohmN9JjbETTw5h4mkTTw5h4mkiPsZEYY6zQDpVCsGjRIlRUVGDVqlV47LHHMHny5EIXqV+IpgcQS5NIWnRE0ySaHkA8TaLpAcTUVAqIZDeRtOiIpkk0PYB4mkiPsRFNDyCmplJANLuRHmMjmh5APE2i6QHE00R6jImp0AXIN4wx+P1+bNq0CYcPH8aHH36I8ePHF7pYGSOaHkAsTSJp0RFNk2h6APE0iaYHEFNTKSCS3UTSoiOaJtH0AOJpIj3GRjQ9gJiaSgHR7EZ6jI1oegDxNImmBxBPE+kxNiW7MuPjjz9GVVVV0Xqh4hFNDyCWJpG06IimSTQ9gHiaRNMDiKmpFBDJbiJp0RFNk2h6APE0kR5jI5oeQExNpYBodiM9xkY0PYB4mkTTA4inifQYk5J1ZhAEQRAEQRAEQRAEQRAEQRAEURzIhS4AQRAEQRAEQRAEQRAEQRAEQRBEb5AzgyAIgiAIgiAIgiAIgiAIgiAIQ0PODIIgCIIgCIIgCIIgCIIgCIIgDA05MwiCIAiCIAiCIAiCIAiCIAiCMDTkzCAIgiAIgiAIgiAIgiAIgiAIwtCQM4MgCIIgCIIgCIIgCIIgCIIgCENDzgyCIAiCIAiCIAiCIAiCIAiCIAwNOTMIgkiJJ598EpIkhV/V1dWYNWsWfvOb36C9vT2jY3744Yd47bXXslxSgiAIgiCMAvUfCIIgCIJIF+o/EASRDHJmEASREoFAAOPHj0d7ezva2tqwevVq3HTTTXj33Xdx7LHHYu3atWkf85133sHzzz+fg9ISBEEQBGEEqP9AEARBEES6UP+BIIhkkDODIIiUkWUZ1dXVqKmpwejRo3HBBRfg/fffxze+8Q2cf/758Hg8hS4iQRAEQRAGg/oPBEEQBEGkC/UfCIJIBDkzCILoF5Ik4YEHHoDP58NTTz0FgM+iuOmmmzB27FjY7XY0NDTgyiuvhNPpBAB8/PHHkCQJt956Kx577DFIkoSzzjorfMwjR45g8eLFqKysRHV1NS6++GI0NzcXRB9BEARBENmH+g8EQRAEQaQL9R8IgiBnBkEQ/cZqtWLBggX497//DQDYuXMnOjo68Nhjj2Hnzp14/vnnsWLFCtx8880AgJNPPhnt7e246aabsGjRIrS3t+Of//wnAMDr9eKMM86Aqqr48MMP8dFHH8Hr9eL8888vmD6CIAiCILIP9R8IgiAIgkgX6j8QRGlDzgyCILLC5MmTsX37dgDAxIkT8f/+3//D3LlzMXToUJx00kn43//9X7z66qsAEE7gZbPZYLFYUF1dDbvdDgD429/+BlmW8cwzz2Dq1KmYPHkynnzySWzduhUrVqwomD6CIAiCILIP9R8IgiAIgkgX6j8QROlCzgyCILKCw+GA2+1O+vmYMWNw4MCBPo/z+uuvY/HixZAkKbzNZrPh5JNPxqeffpqVshIEQRAEYQyo/0AQBEEQRLpQ/4EgShdToQtAEIQYtLe3o6amJvz/22+/jaVLl2LDhg1oaWmB2+2Gpml9HmfPnj24/fbbcdddd8Vs7+7uxujRo7NeboIgCIIgCgf1HwiCIAiCSBfqPxBE6ULODIIgskJjYyOmT58OAHj44Ydx7bXX4sorr8Qdd9yBESNGYPv27bjwwgtTOtYtt9yCiy66qMf26M4KQRAEQRDFD/UfCIIgCIJIF+o/EETpQs4MgiD6zdGjR/Hiiy+Gk2gtWbIEd999N66//vrwPlu3bu3xveilnDrDhg2D0+nEqFGjclZegiAIgiAKD/UfCIIgCIJIF+o/EERpQzkzCILoF06nE9/+9rcxb948fPnLXwYAHD58GJMnT47Z76WXXurxXZvNhkAgELPtS1/6Eh5//HF4PJ7cFZogCIIgiIJC/QeCIAiCINKF+g8EQZAzgyCIlNE0DR0dHWhqasLKlStx1113YfLkyZBlGU8//XR4v7lz5+Luu+/Gli1bsH37dtx0003Ys2dPj+ONGDECK1aswJYtW7B27Vp4PB5cffXV0DQNX/7yl/H555/j6NGjWLVqFZYsWZJHpQRBEARBZAvqPxAEQRAEkS7UfyAIIhHkzCAIIiVMJhO2b9+OmpoajB8/HosWLcKaNWvw17/+Fe+++y4qKyvD+y5btgxVVVU49dRTceKJJ6K1tRXPPPMMFEWJmQlx3nnn4fjjj8fxxx+PBQsWoLW1FXV1dfjwww/R0NCAs88+G8OHD8f555+Po0ePFkI2QRAEQRD9gPoPBEEQBEGkC/UfCIJIhsQYY4UuBEEQBEEQBEEQBEEQBEEQBEEQRDJoZQZBEARBEARBEARBEARBEARBEIaGnBkEQRAEQRAEQRAEQRAEQRAEQRgacmYQBEEQBEEQBEEQBEEQBEEQBGFoyJlBEARBEARBEARBEARBEARBEIShIWcGQRAEQRAEQRAEQRAEQRAEQRCGhpwZBEEQBEEQBEEQBEEQBEEQBEEYGnJmEARBEARBEARBEARBEARBEARhaMiZQRAEQRAEQRAEQRAEQRAEQRCEoSFnBkEQBEEQBEEQBEEQBEEQBEEQhoacGQRBEARBEARBEARBEARBEARBGJr/D2QC+jvOrCa5AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from utils import analyze_trend\n",
+    "\n",
+    "# 서울 지역의 hm, PM25 변수 분석\n",
+    "result = analyze_trend('busan', ['hm', 'PM25','PM10'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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79izGjBnjtr5arUZ6ejrOnj3rstxTXoA9s/p5nTlzBhKJBP3793dbNyUlBenp6S3ZDUIIIYQQ4kcPPfQQpFIpZDIZkpKSMH/+fNx5551u8zLU15pz+OZSKpWNzv1gMpm8/h5ozN13340PP/zQZajXb7/9FqIoYtq0aV5ft3fvXgwZMgSpqaken7/xxhtRU1ODkydPuiwP1G8QT7/r6rvtttuwceNG53pffvklunXrhqFDhzarDI2pPy+Gw4EDBzz+BgGA7t27t+o3CAAMHDjQbV2lUunxtyshhLBCDRmEEBJAjruCGt4xVP+5hhwn3Kzcc8892LZtm8vJ/ieffOKc5M6b6upq5wm7J4mJiRBF0e1kXiaTua3ruHtHbGKSbl80lml9s2bNQklJCbZu3QqTyYTVq1czuxMKgMe7jxy9RB544AFIpVK3R25uLoqLi11e4ykvwJ5Z/bz0ej0iIiK8ToqYmJjY0l0hhBBCCCF+8ve//x0HDx7EkSNHcO7cOZSWluKZZ57xeg7o0NJz+OaKjY31OOm4Q2lpKWJiYpq93blz56KwsNBlguuPPvoIM2fOhFKp9Po6X36DAPY5RuoLld8gN954I5RKpbMX9vLly5n+BgHgcW6VmpoavPrqqx5/g2zatKlVv0EAIDo62uP69BuEEOJPNO4EIYS0M/UnDHz11Vexbds25OXleeySXF9MTAxKS0u9Pl9SUgKO41r0wyYQOnTogDFjxmDlypWoqKiAxWLB1KlTmW3fU4NCREQEAODVV1/FhAkTPL6upSf7kZGRqK2thc1m8/jeNNk3IYQQQkjoSUlJQd++fZv9upaewzdXt27d8P3330MURY8Tfp88ebJFF+ITEhKck35fc801OH/+PDZv3oyXXnqp0dfFxMQ0el7r6C0QqkMaKZVKTJs2DStXrkRWVlaL82uMt98hd999N+bMmePxNd4aIpoSGRkJwN5w5Ol3X0FBAbp06dKibRNCSFOoRwYhhLRD8+fPx7Jly2A2m/Hxxx/jhhtuQHJycqOvGTFiBDZt2uR1XovvvvsOgwcPhlqtbnG5PP1YYum2227DmjVr8Nlnn2Hy5MnOhgZ/iY6ORkpKCmpra9G3b1+Pj5Y2ZPTr1w+iKOLAgQNuzx09ehT5+fmtLT4hhBBCCAkhLTmHb67Ro0ejsrISe/fudXvu2LFjKCws9DqXR1Pmz5+PdevW4dKlS/jkk0/Qv39/DB48uNHXjBgxAnv37kVhYaHH57/77jskJiaie/fuLSoTEJjfID///DPeeecdDB8+HJmZmX59PwDo3bs3ysvLvf4GSUtLa9F2+/XrBwDYv3+/23Pl5eUe6w0hhLBCDRmEENIO/elPf4LFYsGyZcuwatUqzJs3r8nXLFy4EJWVlXj11Vfdnvv111/x3Xff4fHHH29VudRqNerq6lq1jcZMnz4dtbW1WLduHW6//Xa/vU/D93zzzTdRVFTEdLujR49GSkoK3nrrLZflgiDgySefZPpehBBCCCEk+FpyDt9cV155Jbp164bnn3/e7bkXXngBHTp0wPjx41u07XHjxqFTp074+OOP8emnn/pU/rvuuguRkZF46qmn3J47ffo0PvzwQzz22GNeh+n1heNGLH/9Dhk7diySk5OxbNmygP4G+eKLL5CTk8N0u5mZmcjOzsY777zjnHvQYfHixc2eCJ4QQpqDhpYihJB2yDFh4MKFCxEXF4frrruuydd06tQJzz33HP7+97/jwoULmD17NtRqNTZu3IglS5bgpptuwvTp01tVrp49e6K8vByvvPIKRo4cCZPJhHHjxrVqm/VFRUXhxhtvxNatW70O9cTa008/je+++w4DBw7EU089hWHDhsFoNOLYsWP4/fff8cEHH7Rou1KpFP/+978xbdo0SCQS3HXXXZBIJHjzzTdx4sQJXHHFFYz3hBBCCCGEBFNLzuGbi+d5LF26FNdccw1mzpyJ++67DxKJBB999BFWrFiBb7/9FgqFokXb5jgOd999N5555hmIoujTEEuRkZF45513cPvtt6Oqqgr33HMP4uLisHPnTjz77LPo378/7r///haVx6Fnz54AgEWLFmHGjBkoLy/HpEmTWrXN+iQSCWbOnIk333wTt9xyC7PtNub+++/Hl19+iZEjR+KJJ57AVVddBUEQkJubi7Vr12L16tUt3vY777yDsWPH4qabbsJDDz2EyMhILFu2DGvWrMG1117LcC8IIcQV9cgghBDGZDIZOI7zOGGap+fkcnmj63tbLpX61hYtl8shl8vdlt9zzz0wGAy455573O5gksvlkEgkbuOtPv744/j++++Rl5eHyZMnY+zYsfjmm2+wZMkSrF692qVbdlP75em566+/HnfeeSeeeeYZTJo0CRs3bmzRtryVHwCMRiP+9Kc/+ZxffY4cG5bDW8aAffK9Xbt24ZZbbsFrr72G4cOHY9KkSfjPf/7jNj5yY9vx9NyUKVPw/fff49SpU7j22mtx8803Q6lUYuvWrdBqtV63RQghhBBCAs/bub0nLM/hPZWjsfPEq666Cr/88gtKS0txww034Nprr8WZM2fw008/YfLkya0q/9y5cyEIAmbPng2tVuv2Gkf56ps5cyZ27NgBi8WCmTNnYuTIkXjvvffw0EMPYcuWLS5D2zb3txhgHy7pH//4Bz766CNMmDABX3/9dYu25a38gP03yHXXXdeiuTwc2/P0G8RbfVIoFNiyZQsefPBBfPrppxg9ejQmTJiAl156ydlwU387zfkNMnToUPzyyy8wGo2YMmUKrrnmGhQWFmLHjh1IT0+n3yCEEL/hRFEUg10IQgghJBBKSkqQnp6O3377DYMGDQp2cQghhBBCCCFhzmw2Iz09He+99x6mTp0a7OIQQkibRQ0ZhBBCwt6hQ4eg1+vx/PPPw2QyOXt6EEIIIYQQQog/nDhxAtXV1Xj//fexY8cOHD16tMneMoQQQryjoaUIIYSEvWeeeQZjx46FyWTCJ598EuziEEIIIYQQQsLc0qVLMWrUKBw5cgTffPMNNWIQQkgrhVSPjI8//hj33XcfTp48iU6dOjmXHz9+HPfeey+qq6vBcRyeeuopl+54FosFjz32GDZt2gTAPp7ja6+9RuPyEUIIIYQQQgghhBBCCCFtXMj0yHjqqaewatUqxMTEwGq1OpcbjUZMnjwZ//rXv3Dw4EH8+OOP+Mc//oHDhw8713n66adhMpmQk5ODnJwciKKIJ598Mhi7QQghhBBCCCGEEEIIIYQQhkKiR4YgCFi6dCnuuecedOnSBZs2bULXrl0BAOvWrcPy5cuxcuVK5/rvv/8+jh07hjfffBOCICAjIwNHjhxBdHQ0AKCmpga9evXChQsXfOq6JwgCCgsLERkZCY7j/LKPhBBCCCGEtFWiKKK2thapqamQSELmXqiQQr8pCCGEEEII8YzF7wkp4zK1iEQiwYIFCzw+t2nTJowZM8Zl2ZgxY/DWW28BAA4ePIjU1FRnIwYAREVFISMjA/v370d2drbbNk0mE0wmk/PfBQUF6N27N4M9IYQQQgghJHxdvHgRaWlpwS5GSCosLER6enqwi0EIIYQQQkjIas3viZBoyGhMYWEhJkyY4LIsPT0dZ8+edT7v6QeDYx1PDRkvvvgiFi9e7Lb8v//9LzQaDQAgLi4OmZmZOHv2LMrLy53rpKamokOHDjh58iRqamqcyzt16oSEhATk5OTAaDQ6l3fr1g3R0dHYt28fBEFwLu/Tpw/kcjkOHDgAwN4qVV1djdGjR8Nms+Ho0aPOdSUSCQYPHoyqqirk5uY6lyuVSvTr1w+lpaU4d+6cc3lUVBR69OiBgoICFBYWOpcHep8cBg4cCLPZHJB9cuTYo0cPZGRkhMU+OQTjOHEcB1EU0aVLF5w5cyYs9ikYx0kURZjNZgwbNgzHjx8Pi30CgnecevXqhV27dkEulzvveG3r+xSs4xQREYEtW7ZAq9U6s2zr+xTM41RQUIDq6mpotVrEx8eHxT4F4zjt37/fmSPHcWGxT+FwbqTT6TB58mRERkaCeObI5uLFi4iKigr4+1utVhw4cAADBw6EVBryP/NCGmXJDmXJDmXJDmXJDmXJFuXJBuXIFqs8a2pqkJ6e3qrfEyExtFR9nTp1chlaavz48fjb3/7m0pghCAJ4nocgCFi+fDl+/PFHLF++3GU7d9xxB8aNG4c777zT7T0a9shwBFleXu780SGRSCCRSCAIgssPUsdym82G+tF5W87zPDiOc5n3w7EcAGw2GwD7hc6amhrExMSA4zjncgepVApRFF2WcxznzKF+Gb0tD/Q+NbXcH/vkyDE6Oho8z4fFPjVVdn/ukyiK0Ol0iIqK8qnsbWGfgMAfJ1EUUVdXh+joaGc9bev71Nhyf++TRCJBVVUVIiIinBff2/o+Bes4AUBFRQWioqKcWbb1fQrmcbLZbKipqUFUVBR4ng+LfQrGcbJarc4cHWVp6/sUDudGNTU1iIuLQ3V1dVAu0rcFNTU10Gq1QcvI0WhVv3GatAxlyQ5lyQ5lyQ5lyQ5lyRblyQblyBarPFmcK4d8s5RCoXC5OwwADAYDFAoFOI7z+LxjHZVK5XWbCoXCbblUKnVrWXL8uGvI29wb3pZ7a7GqvzwuLq7R9TmO87jcWxmbu9wf+9TUcn/sU/0cw2Wf6gv0PjmGbWNR9lDZJyDwxykmJsbj8paU3dvycKt7gPd9cuTZUFvep2Adp/rfmfW15X0K5nFqmGc47JOvy1ntk0wm81gv2/I+Bfs4sTg3orvZQh/HcS7D7ZKWoyzZoSzZoSzZoSzZoSzZojzZoBzZCqU8Q36mvrS0NFy4cMFlWf2xtDw933CdtsBqtWLPnj1ud/KR5qEc2aI82aAc2aI82aEs2aI82aAc2aI82w861uxQluxQluxQluxQluxQlmxRnmxQjmyFUp4h35AxYsQIbN261WXZ1q1bMWLECADAgAEDkJubi6qqKufzNTU1OHHiBAYNGhTIorZaw+EFSMtQjmxRnmxQjmxRnuxQlmxRnmxQjmxRnu0HHWt2KEt2KEt2KEt2KEt2KEu2KE82KEe2QiXPkO8jPn36dDz99NPYunUrxowZg+LiYrz66qv44osvAAAqlQpz5szB3//+d7z77rvgOA5PPPEEZs+eDbVazbQsNpsNFouF6TYdHK1aRqOx3XTdl8vlHodCIIQQQgghhBBCCCGEEH/y57XecNGca9b+vtYbclfM5XI5ZDKZ898ajQbr1q3DggULUFdXB0EQsHjxYgwdOtS5zksvvYSFCxeiT58+AIBRo0bh7bffZlYmURRRXFzs0uuDNVEUoVQqceHChXYzEY1EIkHnzp0hl8uDXRRCCCGEEEIIIYQQQkg7EIhrveGiOdes/X2tlxNFUfTLltuQpmZNLyoqQlVVFRITE6FWq/3S0CCKIgRBgEQiaRcNGYIgoLCwEDKZDBkZGcz2WRRF50Tv7SFHf6M82aAc2aI82aEs2aI82aAc2WKVZ1PnyyT4GdFnhx3Kkh3Kkh3Kkh3Kkh3Kki3Kkw1fcgzEtd5w4es166au9bI4Vw65HhmhxmazOSt2XFyc396nfntSe/nwJCQkoLCwEFar1aUXTmtRDw+2KE82KEe2KE92KEu2KE82KEe2KM/2g441O5QlO5QlO5QlO5QlO5QlW5QnG43lGKhrveGiOdes/XWt14EmKGiCY5w01vNteKLT6fz+HqHE8aXCcsIYm82GvXv3hswkNG0d5ckG5cgW5ckOZckW5ckG5cgW5dl+0LFmh7Jkh7Jkh7Jkh7Jkh7Jki/Jko6kcA3mtN1z4es3aH9d666OGDB+1l14SgUSZEkIIIYQQQgghhBBCAo2uS7Ln70ypIYMQQgghhBBCCCGEEEIIISGL5sgIIJsN2L4dKCoCUlKAK68EeD7YpSKEEEIIIYQQQgghhBDSHHStN7CoR0aArFkDdOoEjBsHzJpl/2+nTvblDhqNhul7zps3DxKJBIcOHfK6ztChQyGVhk97Fs/zGDJkCHj61mCC8mSDcmSL8mSHsmSL8mSDcmSL8mw/6FizQ1myQ1myQ1myQ1myQ1myRXmyEagcfbnWy1qwrvWyvmbdUtSQEQBr1gDTpwP5+a7LCwrsyx0VXBAEpu9rtVoxePBgfPTRRx6fP3LkCARBCLtJhMxmc7CLEFYoTzYoR7YoT3YoS7YoTzYoR7Yoz/aDjjU7lCU7lCU7lCU7lCU7lCVblCcb/s7R12u9rAXrWi/ra9YtRQ0ZLSCKgE7n26OmBnjwQftrPG0HAB56yL5eWZmhye152k5jZs6cibVr18JkMrk99+mnn2LOnDktSCB02Ww2HD58OOwaZ4KF8mSDcmSL8mSHsmSL8mSDcmSL8mw/6FizQ1myQ1myQ1myQ1myQ1myRXmy0ZIc/XWt15fttYVrvQaDgfk2WyJ8xhQKIL0eiIhgsy1RtLfeRUdzAJreaF0d0JzePFFRURg3bhzWrl2LW2+91bncZrNh9erV2LNnD/7yl784l//888947LHHYDQaoVKp8PLLL2P8+PEAgH379uHhhx9GRUUFAKBPnz54//33odVqYbPZ0KlTJzz22GNYunQpeJ5HSkoKPv74Y6Snp/teYEIIIX6lK9XBVGM/4bFZbdDn61EZUwleau92q4hSQJPg/oem/us88fY6QvyppfUy0K8jhBDiH/S9TAghhAV/XOvVan1bP9Sv9T766KN47733IJPJgn6tlxoy2oG77roLzz//vEvl3rBhA4YOHYro6Gjnsvz8fDzwwAP4/vvvkZmZiZMnT2LixInYv38/4uLiIJfL8fnnn6Njx44QRRHz58/HK6+8gueeew48z6OoqAi///47Dh06BLlcjiVLluDBBx/Et99+G4S9JoQQ0pCuVIc1s9ZAX64HAIiiCL1ej1PqU+A4DgCgjlNj6oqpLj/6G77OE0+vI8SfWlovA/06Qggh/kHfy4QQQtqrQF7r3bNnD3777TfExMTgpZdeCuq1XhpaqgXUantrmS+PH37wbZs//CDi0iUdamvFRrenVje/vKNHj8aFCxdw4cIF57JPP/0Ud955p8t67733Hh544AFkZmYCAHr06IFrr70W3333HQCgX79+6NixIwCA4zjcfPPN2L9/v/P1NpsNzz77LORyOQBg7ty52LZtW/ML3Eo0KRJblCcblCNblGfLmGpM0JfrIVVIoYxWQhmthCxS5vx/qUIKfbne7c5GT6+r//D2uvaI6iYbvuTY0noZ6NeFAqqX7Qcda3YoS3b8lWVb/l5uKaqX7FCW7FCWbFGebDQ3R/9c6/Vte6F+rfdf//oXFAoFgOBd63WgHhktwHG+d/mZOBFIS7NP9uJpzDOOsz8/cSIHnvffXSJz587Fp59+iqeffhqVlZXYv38/vvrqK5fJWo4dO4avvvoKH3zwgXNZXV0d+vbtCwCorKzEq6++il9++QUVFRUwm81uXYnq/zs+Pt7ZNSlQpFIpsrOzA/qe4YzyZINyZIvybD2pSgq5Rg59uR4KUQFzjdm53KE8txzFB4oBAHXFdTBWGiHTyGAz28cZlUfJoYhQONe3mqwB3IPQRHWTjebm6KjPxiojLHqLc7nVZIVFZ0Hu97mISI5AYr9EJPRKAAAINgGmWpOzPtdnNVkh2i6ftJlqTTj942mPnwNVrApSpdTltaGG6mX7QceaHcqSnUBk6fg7YDFYYNFboIpTgYO9p2kofi+3FNVLdihLdihLtihPNlqSo3+u9QL+bJcK1LXejIwMyGQyAMG51lsfNWT4Gc8Db71ln7Ge41wr+B+jeODNNwGJRITVagPP887hPViaM2cORo8ejaeeegorV67EjBkzIJFIXCq3wWDAiy++iFtuucXjNm666SZkZWXh888/R2ZmJr7//nu88sorLuv4o+zNIYoiqqurodVqg16WcEB5skE5skV5smGqM6HyTCUEUYCEs3fQ1Ha8PIhn+alyHP78MADArDPDUGGAqcYEidS+bmRapEtDBqG6yUpLc7ToLagpqAH+ONcSrAIEq4ATa09ArpEj6/YsZ0OGaBVRV1znrM/1CVYB8ki589+mGhMOf37Y4+fAUGlAYp/EVuyt/1G9bD/oWLNDWbITyCxLckoAALychyIy/M5RqF6yQ1myQ1myRXmy4e8cfb3W6+/ONYG61gsAVqs1JHoL0dBSATB1KvDNN0CHDq7L09Lsy6dOtf/baDT6rQzJycno1asXNm/ejGXLlmHu3Llu63Tr1g179uzx+PqysjLk5OTgnXfecXZHOnr0qN/K21I2mw0nTpyAzeZ+hyVpPsqTDcqRLcqTDWOlESJEiLwIVbwK6gQ1JLLLpwXadC0yJ2Yic2ImMkZlQB4lhyrOvl5kh0hEpkYGsfShieomGy3NMSI1AtqOWqgT1FAnqKGKU0EeJUfGqAxkTsxETGaMc12O55z1ueFDFacCr7h8ki5Tyzx+DsABFp0FFqPFU3FCBtXL9oOONTuUJTuBylIULl9FCqdeGPVRvWSHsmSHsmSL8mQjEDn6eq3XnwJ5rdef16ybg3pkBMjUqcDkycD27UBREZCSAlx5pf9b5+qbN28ennzySUgkEvTo0cPt+blz52L8+PGYPHkyRo0aBQA4d+4cOnXqhMjISHAch9OnT6N79+44efIkPv/8c8TFxQVuBwghhDBjrLafiCjiFYjuGA0JJ4FZZ4bVYP/xn5SVhKSsJABAxZkKnPruFJTRSsg1cpftCIIAY6URoBuGSJCIf9wCxYFDRGKEc7lZZ4axyoj+c/ojtkusy2skUgmi0qLc6nP91zmoYlQY+pehbp8DmUYGmVrmMrQUIYSQ4OAkHJTRSvv3t9D0+oQQQkhr0bXewKNfXgHE88DYsYF7P7lc7px4GwCuv/563HvvvXjuueecyziOg/qPWWUGDx6MVatW4dFHH0VdXR3kcjn69OmDL774AgqFAl988QVuueUWiKKI+Ph4vPbaa3j22Wed21Kr1S5dtupvmxBCSOgQLAKseis4cJBFyFq8HVEUUX6qHMYKI+SRcucFZUICxVxnRl1JHRJ6JECmbnldbon6jSaEEEKCz9Gz1GalO5kJIYQEBl3rDSxqyAghEgnbkb7ef/99l3/zPI+CggK3ZTqdzvnv8ePHY/z48R63N2nSJEyaNMll2cSJE53/X387nrYdCBzHQaVS0ViCjFCebFCObFGerWfRWyCPkkOwChBtIiw6CziOc/bG8MbT81KlFKIgwlhpxLGvj2HUP0a122NDdZMNX3M0VBigL9UDAGqLaxGR5Nqw0JL67M/XBQvVy/aDjjU7lCU7gcjSarBCFEQINvvcSOY6s0sv03BB9ZIdypIdypItypONcM4xGNd6RVF0XrMOxrXe+qghI0QEu0UrXPA8j/79+we7GGGD8mSDcmSL8mw5RZQC6jg19OX6y5NgWgFTtcm5jjpODUWUwuvrGo47zct4qGJVMNWYcO6Xc1BoFchekB2WJ41NobrJhi85iqKInBU59km3OfvErvWHg3Jobn1u7utsZhtM1SZwUg6qGJXH1wUb1cv2g441O5QlO/7M0vG9rCvToexEmXOeDEudxfk3IRS/l1uK6iU7lCU7lCVblCcblCNboXTNmhoyQoQoirBarZBKpe3y4g8rgiCgrKwM8fHxzHu4tEeUJxuUI1uUZ8tpEjSYumIqTDX2hgtBEFBZWYmYmBhnloooBTQJmkZf50nxgWIc+uwQzqw/A5vJhqEPDYWEb1/Hh+omG77keHLdSVSerkSHYR0w6u+jEJHseZinltZnX19XfLAYe/69B8poJca/NB7KaKXb64KN6mX7QceaHcqSHX9m6fhevnT4ErY/vx0AIFPLkDwwGQPmDgDg+fu8raJ6yQ5lyQ5lyRblyQblyFYoXbOmhowQYjKZIJXSIWkNQRBw9uxZxMbG0pcVA5QnG5QjW5Rn6whWAZyEQ3SnaNhsNpwuP43OnTs3+fdHk6Bp9EJAbJdYaBI1+O2133Buyznwch5XPHAF6+KHNKqbbDSVY/WFahxadggAkL0gGxkjM5r9Hk3VZ19fp83Q4tiqY7DoLLAarNB0C72LZVQv2w861uxQluz4O0tNgsY+ZKZGjpTBKRj7zFjm7xEqqF6yQ1myQ1myRXmyQTmyFyrXrOloEkIIIe1I7ve5WP/geuz/cD/zbXcc3RGjnhgFZYwS3SZ1Y759QgSrgJ2v7YRgEZAyJAVdr+0a1PLwMh7pI9IBAOe3nQ9qWQghpL0qO1EGAIjvFR/kkhBCCCHEn4LflEIIIYSQgCncWwgASOyT6Jftpw1NQ/KAZEgV9lMMXakOxmqj1y6o4TTkA/E/s84MmVoGeaQcQx8cGvSuzQDQcUxHnN14Fhd3XMSQe4fY5+0ghBASMM6GjJ7UkEEIIYSEM2rICCE8zwe7CG0ex3HQarUhcWEjHFCebFCObFGeLVdbVIvaglpwPIek/kl+y7J+I8ZXk79C5dlKRCRHgJO4v486To2pK6aGRWMG1U02GstRqVXi6uevRm1RLVQxqiCUzl1SvyQoY5QwVhpRdKAIHbI7BLtILqheth90rNmhLNnxd5aGCgP0JXqAsw9zufnJzTBWGzF+yXjINXK/vGewUL1kh7Jkh7Jki/Jkg3JkL1SuWdMtYyGC4zioVCr6kLUSz/Po1atXyHzA2jrKkw3KkS3Ks+UcvTESeidArpH7PUtDhQHlJ8thM9mgL9NDHimHMlrpfEgVUujL9Y1OutyWUN1kw1OOoig6/5+TcIjqEBWMonnESThkXGmfp+PcL+eCWxgPqF62H3Ss2aEs2fF3lo7eGNqOWsgj5Cg/WY7qc9UwVYfHuUV9VC/ZoSzZoSzZojzZoBzZCqVr1tSQEQjGUqD2jPeHsRSiKMJsNrv8UCfNJwgC8vPzIQhCsIsSFihPNihHtijPlivaVwQASM1OBeD/LCVSCSKSI8AreAgWAdXnq8HLecg1csg1ckhV4dUxlOomG55y3P32bux7fx9sZlsQS+Zdx9EdoU5UQ5uuDXZR3FC9bD/oWLNDWbLj7yyV0UpkjM5A2rA0AIBCqwAAGKuNfnm/YKJ6yQ5lyQ5lyRblyUZAcvThWm+4CKVr1uF1BSEUGUuBnbMAU7n3dRRxwPDlMNvUkMlkTN/ebDbjtddew8qVKwEANpsN1113HZ5++mlERES4rf/xxx/jvvvuw8mTJ9GpUyemZQkEx5dVcnIyJBJqp2stypMNypEtyrNlrCYrLh2+BABIHeLakOHPLHkFj5guMai5UAOrwYqyE2VI7JsYEndzsEZ1k42GOV7ceRF5m/IAzt5gEIpjoMd1j8NNH94UkvWa6mX7QceaHcqSHX9nmdA7AQm9E5z/VmgV0F3ShU1vz/qoXrJDWbJDWbJFebLh9xx9vdY7YgWgTPC+TgsF41qv2Wxmfs26JehT4W+WGnvFligAWbT7Q6KwP2+pYf7WgiBgypQpOHfuHH799VccPHgQe/fuhUajwbhx42AwGFzWf+qpp7Bq1SrExMTAarUyLw8hhJDguXT4EgSLAHWiGlFpgR2WR6qQIr5XPDgpB6vBCovOEtD3J22XocKA3//vdwBAr2m9QrIRA7B3tw7FRgxCCGlvlFolAITl0FKEEEJCBF3rDRpqyGgNm7GRh9l1XV4BSFXuD14BNOya422bzbR8+XJUVFRg6dKlzhY5hUKBRYsWITMzE2+88YZzXUEQkJKSgu+++w5KpbLZ70UIISS0JQ9Ixrhnx2HgnwcG5YKrVCGFItI+3EM43iVJ2BNFEbvf3g1zrRnRmdHImp0V7CI1SbAKKNhTAHOduemVCSGEtIqxyojqi9UuQ10oosJ3aClCCCEBQNd6QxoNLdUa22d4fy5uCNBv0eV/Vx0FOA/tRqIVkNi75kilfxyOXXd5brUb+79mFW/ZsmVYuHChxwtWDz/8MO644w488cQTAACJRIIFCxY0a/uhSCKRICEhgbrgMUJ5shGoHHWljXejV0QpoEnQ+LUMgUD1smV4GY/kAckuywKdpSJKAVONKSTG1vQHqpstV//7SxAEKPVKHPr0EM5vOw+JVIIBdw6ARBr6uW5+ajNKj5Tiir9cgS4TuwS7OACoXrYndKzZoSxbx9N3elVelTNPVuek57aew4EPDyB9VDpG/W2Ufdt/zJERjj0yqF6yQ1myQ1myRXmy0aocGV/rdWrj13qd16yDLDRKQcBxHPPWsQMHDiA7O9vjc4MHD8aZM2dQV1fncfy0tkoikaBLl9C4cBAOKE82ApGjrlSHNbPWQF+u97qOOk6NqSumtvnGDKqX7AQqS6vB3oVVppYhrkccOI6DWWd2Lg8XVDdbxtP3l81iQ83FGkAEVPEqbPrrpjbx/ZU6OBWlR0pxbuu5kGrIoHrZPtCxZoeybDlv56S7sdv5/6zOSctOlAEAYjJjnMtUMSrII+Xg+PAb7o/qJTuUJTuUJVuUJxvhnGMwrvX645p1S1FDRmtcuaqRJxu0yEX3AaQeTtSsOsBs7w5rMhqhUCjADfuISfGqq6uRlJTk8TmZTIaYmBjU1NSEVUOGIAjIy8tD586dqQWbAcqTjUDkaKoxQV+uh1QhhVTl/tVuNVihL9fDVGMK+QuBTaF62Xx5m/NQebYSncZ1QmyXWOdyf2epiFJAHaeGvlwPq8lzo4U6Tu0cBqKto7rZMg2/v0RRRG15LXg5D5laBk2ips18f3Uc3RGHlh1CSU4JDBUGqGJVwS4S1ct2hI41O5Rly3n6TjcajVAqleA4juk5afkJ+ySr9edP6nlzT/S8uWerthuqqF6yQ1myQ1myRXmy0aocGV7rddGGr/WKogiTyWS/Zh3keQGpIaM1+Oa0RkngeUoSCfBHJbBarVAoFM3crneRkZG4dOkSMjMz3Z6zWCyoqqpCfHxoTprZUoIgoLS0FB07dqQvfQYoTzYCmaNUJYVMI4NVb4VULQWHy39kvF1IbmuoXjbf2Z/PouRwCTRJGreGDH9mqUnQYOqKqR6HPLOZbOAVfNgMeQZQ3WwtqUoKuUYOQRTAW3gkJSdBIpHAZrbBZrIFu3g+0SRqENczDuUnynF++3n0nBz8i2lUL9sPOtbsUJatV/87XW/RQ6aRQfLH8Bsszkn1ZXroy/TgJBziusW1enttAdVLdihLdihLtihPNlqVI+NrvS3brnfButbrvGYdZPSpCBSbwd4i1/BhMzT92hbq27cv9uzZ4/G5/fv3IzMzE3K53G/vTwgJjrrCOpQcKbEPy0LaPYvegrJj9qEXUoekBvz9NQn2xhPHw6K34Nclv+Lw8sOI7RIbNo0YhD1ezoOX8cEuRrN1GtMJAHB+2/ngFoQQQkKAudYMfYEegkVgul3HsFLRnaMhVdL9mYQQQoKArvUGHDVk+JssClDEAYIJsFS5PwST/XlZFPO3njlzJt566y2Pk6q+8cYbmDVrFvP3JIQEn2Cz/1CsK6qDzdI27mIm/lN8sBiCVUBEagQiUyKDXRyoYlTQFetQdrwMNjPVT3KZKIgoPVEKU1XbnqA1Y1QGwAEVpypQW1Qb7OIQQkhQVZyugKXagorTFUy362jIqD+sFGAf2mrzk5ux/uH1Hn8HE0IIIa1G13qDhm5d8DdlAjBiheeZ6R1kUYAiHnKLhelbz58/H19++SXuv/9+vPLKK9BoNDCbzXj55Zdx+PBhfPQRm/HZQolEIkFaWhp1wWOE8mQj0DlGpUfBVG2CRW9BbWEtojtGB+R9A4XqZfMU7i0E4Lk3RjCyjOwQCWWMEsZKI8pPlSOxb2LA3tvfqG62Tt2lOphrzKgyVEHbXesyNF5booxWIql/Ei4dvISi/UWInBTcBkSql+0HHWt2KEt2RKsICS+Bpc4CUWDXsOCtIYOX87h06BIAwGq0QqaSMXvPYKN6yQ5lyQ5lyRblyYbfc/T1Wq8ygflbB+tab6j08qCGjEBQJjRZeTmwrxRSqRTr16/Hs88+iyuuuAI8z6OwsBBZWVnYuXMnNBrPw3nI5XLIZG3zhM/xZUXYoDxbTleqc5kXQA01qvKqnP/257wAHDhEZUSh/EQ5dKW6kLgLnyWql74TRRFF+4oANN6QEUgcxyGxXyIubLuAS4cvhWVDRjho+B3WEOvvMKvRCn2pHhJegujO0VBpgj9Jdmv0v6M/+Lt4aDtqg12UsKqXpHF0rNmhLNmRSCXAH1NiGKuM4BVshgzsN6sfSo6WuJ1HSJVS8HIeNrMNxipjWDZkkNajLNmhLNmiPNkISI4+XOv1h2Bc6+U4jhoyiCtRFGE0GqFUKpnOAK/RaLBkyRIsWbIEAJCbm4uJEyfi2LFjGDFihMfXnDp1itn7B5rNZsOpU6fQvXt38HzbG1c71FCeLaMr1WHNrDXQl+sB2D/fJpMJCoXC+flWx6kxdcVUphcCDZUGWE1WaJI04HgOvJKHpc6CynOVUMeqmb1PsFG99F1VXhUMFQbwCh6JfdwbDIKVZVK/JHtDRs4l9EO/gL2vv4VL3Wz4HeYJy+8wm8kGXYkOok2EIkYBiVSCytJKqFVqcBwHq6H1k8IGWihNPBsu9ZI0jY41O5Rl61kNVogQwSt5mCpM4CU8agtrEZEcwWT7KYNSkDIoxeNzCq0C+lI9TDWmsLqhh+olO5QlO5QlW5QnG+GeY6Cv9frrmnVLUF+lEGKz+X+s8G7dumHlypW4/fbbcfToUb+/X6CJoojq6moaD5URyrNlTDUm6Mv1kCqkUEYroYxWQqKSOP9fqpBCX65v9G7n5rIYLLDUWVCbXwtdsQ6mKhPkajkEqwDdJR3MOjPUcWooohTM3jNYqF76zlBhgCpOhaT+SeDl7idwwcoyKSsJAFB+ojys5skIl7rp6Tus/oP1d1juj7ngOA6chINMI4OxyghjpdH+3yojrCZrm/7+CnZ9CJd6SZpGx5odyrLlFFEKqOPUsJqsMFWZoIhSQB4nhzJGCYVWEZDvdIXWvm1Tdduec6khqpfsUJbsUJZsUZ5stLccA3GtNxDXrH1BPTLaoSuuuAJnzpwJdjEICXtSlRRyjRxmgxlWoxXSJKnzbgCrie0dxmfWn0HywGTE94rH0AeHOpfvfHUnAHv3+/ie8X4bzoqEptQhqZj8yWRY9GznYGqtiJQIqOJUMJQbUHaizNmwQUKL4zvME1bfYZcOX8L5X84jeWAyrnjgCiT0SYDNakNOTg769esHXmr/zvTncHz+Yqgw4MDHB1CZV4nr/+/6oN+9RAghgaBJ0GDqiqnOxm5/fKef23oOMrUMiX0SIVO7D5Oh1CoBAMZqY4vfgxBCCGlL2su1XmrIIIQQP6s+Vw1jhRHVYjViu8Qy337VuSqc33YeUoUUwx4ahpjMGOdz17x+TViNDUyaj+M4rxejg4XjOHS+ujPMdeY2e5c9YaPogH0Olx6Te6DHTT0AAFarFepKNWK6xEAqbbunqvIIOQp2F8BqtKLsRBkSegV+DF1CCAkGTYIGmgQNBKsAm2hz+U5v7d2xoiji4McHYagw4OolV3scOtNxbhFuPTIIIYSQ9q7t/joMQwoFXcxpLYlEgszMTEgkNGoaC5Rn64kQwUk48DwPQ7kBxnijfdJDhg5/cRgQgfRR6S6NGADCshGD6qVvzHVmyNQycBLvd4EHM8v+t/cP+Hv6WzjWTVEUYaw2gpfx4OU86orroIxRMtv+gDkDkNA7wWWy1nDJkZfzSBuehnNbzuH8tvNBa8gIlzxJ0+hYs0NZsnF201nseW8P4ofEQ3KFBGc2nsHJdScxYO4ApA5ObdE29aV6GCoM4HjO63xEyhilvTEjzDrCUb1kh7Jkh7Jki/Jkg3JkL1SuWVNDRojgOK7Fs8eTyyQSCRIT3e/KCQW6Ul2j44mH4rAZoZxnW8GBQ3yPeFSdr4Lukg5VeVWIzoxmtv2yk2Uo2F0AcEDW7Cyv65lqTDi+5ji6XtuV2SSLwUL10jf73t+Hwr2FGHLvEHQc3dHjOpQlW+GYp81iQ8WpCoADZGoZLDoLTDUm5/jjLHTI7uDy73DKseOYjji35RwubL+AQfMGQcIH/sdUOOVJGkfHmh3Kkg1DpQEQAG2MFhKJBFV5Vag+V40zP51pcUNG2YkyAEBMZozH+b8AYOCdAzHwzoEtLneoonrJDmXJDmXJFuXJBuXIVihds6amqRAhiiL0en27mYjGX2w2Gw4dOhQyk9A46Ep1WDNrDVbNWOX1sWbWGuhKdcEuqotQzbOtEUURnJYDr+RhM9tQk1/DbNuHPz8MAOh8dWdEpUV5XW/327txfPVxHP3aPxM/BRLVy6aJooiifUUw15obvXs+2FkKVgGlx0pRW1QblPdnLdh5+oNgEQAAvIxHZIdIQGIfqqOuuA42U8v206wzY/fbu2GoMHh8PpxyTO6fDEWUAqZqE0pySoJShnDKkzSOjjU7lCUbhgoDRFFESU0JbDYbukzsAgAo/L2wxfNXOBoy4nvGMytnW0H1kh3Kkh3Kki3Kkw3Kka1QumZNDRkhRBCEYBehzRNFEQaDISQ+XPWZakzQl+shVUihjFa6PaQKKfTl+kZ7bARDqObZVlgMFtQW1sJUa4JFb0FESgQEmwBjhRGmWjbHuvf03ojvHY9+M/s1vt6M3gCAvJ/z2vxFY6qXTavIrYCpxgSZWtbocDbBznLPu3uw6W+bcHbj2aC8P2vBzpM1q8EKU7UJglWAyIngZTyi0qMgiiKseit2v7MbFkPzJ5Lf/8F+nN14Ftue3+Yxq3DKUSKVIH1UOgDg3C/nglKGcMqTNI6ONTuUJRvGSiNEiBBVIkRRRHSnaMR2i4VgFZC3Oa9F23Q2ZPRqfw0ZVC/ZoSzZoSzZojzZoBzZC5Vr1jS0VAD4MqSQOl4dwBKRYJGqpF4n3bWarAEuDfEXRZQC6jg1agprUJtfC07CQZYog0wtgyJSAUOFAVKFlMkkx8kDkpE8ILnJ9eJ7xCNlcAqK9hXh6MqjGLZwWKvfm4Suwr2FAIDkgcnM52RhKaFPAs5uPItLhy8FuyikHsd3mKOBXbAKEK0ijFX2u2cjkiNgKDeg8kwltjy1BWOfGQt5hG8TyhfsKUDez3kABwyaNwgcF2YDmHvQcXRHnP7hNPJ/y4dtgc3rUCiEEBJuHD3vZFGXh6PoMrELKnIrcOanM+h5c89m/R2wmqyoPFsJoPEeGbVFtfj9/36HRCrBuMXjWlh6QgghxLu2OHx8OKCGDD9zDCmkL9d7XUcdp8aU5VMAP7RlmM1mvPbaa1i5ciUAe/eq6667Dk8//TQiIi6Pk//222/jww8/BMdxMJlMyM7OxpIlS9ChQwdvmyatUFtUC6vBiuhO0Y1OxEvaJk2CBlNXTMXh5Ydxat0pJA1IgnyEHP369YNEIkHl2UqkDEpp1R81URSbfQGw3+x+KNpXhLzNeeg9ozeiOngfioq0bY6GjNQhLRt/OlCS+iUBsPcgsRgsYTk5fVvk+A4z1Zhwct1JnPrfKWSMznCZoF1fpsfut3aj/GQ5dr+zG1f+48omt2uqNeH3d34HAPS8uWfQJr8OtITeCUgelIzEPokQbAJ4UEMGIaR9MFbaG8BlkZf/vncc3RH7P9yP2vxalJ0oa9bfgorTFRBtIlSxqkZvBOQ4DiWHS8Ar6PuWEEIIe75e6526YqpfGjPa87Xe0L1NM0w0Z0ghpdL7OOYtIQgCpkyZgnPnzuHXX3/FwYMHsXfvXmg0GowbNw4Gw+WxqW+88Ub8/vvvOHToEI4cOYJOnTrhhhtuYFqeQOB5Hj179gTPh+5Jq6HKgJqLNdCX6VGdXx3s4jSqLeQZqjQJGtRcrIFcI0fXa7ti0PhBiO8ej7huceh6TddWN2Js+tsm5HyZA6vR9548cd3ikHpFKiACR1e23bkyqF42zlhlREVuBQAgZVBKo+sGO0tNogbqRDVEQUTpsdKglIGlYOfJkiZBg9guseDlPOQaOeJ7xCO2S6zzkTY0DVe/cDViu8Vi0F2DfNrmvv/sg7HSiMi0SGTdluV1vXDKEbBfUBu3eBz63NInKI114ZYn8Y6ONTuUZeuJor0nH8dx6DWolzNLmVqGjFEZAIAzP51p1jYTeifg+nevx7BHhjV6Q49Ca+/1bDPZmnWuHOqoXrJDWbJDWbJFebLh7xyDOXx8sK71sr5m3VLUkNEKVqPV68Nmdp1QhlfwkKqkbg9ewTvvrJZKpeA4zus2m2v58uWoqKjA0qVLnS1yCoUCixYtQmZmJt544w3nup07d3ZWSqlUisWLF+Ps2bMoLCxsRUKBx3EcoqOjQ3aoCsEmoCqvyvlvU40JohC6Y/aFep6hzFh9+WJyhyEdPOaoK9Fh81ObUZlX2axtn992HmXHy3DyvydhszRv8qp+s+xzaZz75RzTSccDiepl44r2FwEAYrrEQBWranTdUMgyKcveKyNYEyGzFAp5sua4m1YV416XojtFY+JrE6FJvNww6+076eLOizi/9TzAAcMfHt7o8ErhmGMwUZ7tBx1rdijL1hMsAjoM7YCEXglI7pTskmWXa7ogbUQaOo3p1KxtchwHbboWyf0bH1ZVqpRCIrNf6gi1OQhbg+olO5QlO5QlW5QnG63JkeW1Xl+221zBuNZb/5p1sNHQUq2wasYqr8+lDEnB2EVjnf8uPVrqcQghwSpAIpNAFEXodDqo1Wqsu2udxxOumf+b2azyLVu2DAsXLvRY0R5++GHccccdeOKJJzy+Vq/Xg+M4xMXFNes9g81qteLAgQMYOHAgpNLQq961BbUQLAKkKikiUyOhilGF9NBSoZ5nKCvaXwSIQHRmNGRRMuzZs8ctx0OfH8Klg5ew89WduPaNa30aN12wCshZngMA6DW1FxSRzZtnI7ZLLNJHpUMRqYBM3TaH8aF62bjYbrHofUtvn3r9hEKWSVlJyNuUFxbzZIRCnqx1u74bEvokIKG356E/6p9j5O/Kx+53diN7QTYiki93aRZFEQc+OQCzzoxeU3shrnvj5xbhmKOuVAd9qR7Fh4qhiFK4je3uzzF0wzFP4hkda3Yoy9bj5TxG/X0UrFYr9h3Y55JlQq8Evw4vyHEclFol9GV6GKuNLg3ubRnVS3YoS3YoS7YoTzZakyPLa731teVrvaIoQq/XQ61WB70xgz4VIaRha11rHThwANnZ2R6fGzx4MM6cOYO6ujqX8dMA4OjRo3j88cexaNEiKBStn4w40Gy25t2hHihmnRmGcgM4nkNESgSkSiksBgsAwGoI3S7PoZpnqGs4R4GnHAfNG4TiA8WouVCDQ58dwqB5TQ/PcnbTWdQV1UGhVaDHTT1aVLaRj48M+h+f1qJ66Z02Xesyl0FTgp2lc56M0xUw68yQa3ybNDpUBTtP1lIGpTQ5RBkAiIKI/R/tx9mfzuL0j6cRmRrp0jgrCiKM1UacWHsCfW7t0+RF+3DK0TGGbvnpchgrjJCqpYhMiXRZx59j6ALhlSdpHB1rdihLdlhkqSvR4eCyg0jql4Su13Ztcn15lBz6Mj1M1eHTIwOgeskSZckOZckW5clGuOYYrGu9rK9ZtxQ1ZLTCjFUzvD7XsEUuoU8CZBr3u58tOguM1UaXZTd9dBOT8lVXVyMpKcnjczKZDDExMaipqXFW7r/+9a/4/PPPcenSJcybNw8PPfQQk3IQ+52WvIyHKIhQaBWwmWywmf74UhXtY9qr4lRQRLW9hiPiThREFO8vBtD4ZMtKrRJDHxqKbf/ahpP/PYnU7NRGu8rbzDYc+eoIAKDPLX0gVbbsK7ytN2KQ8KKOV2PwvYMR2zWWJvtuwzgJh+z7s3Fy7UlABPSlenuPtHrHVKaSwVBhgKnG5LcL9qHIMYauOlYNc40ZgkWAPEIOidR+l5bVYHWOoduecgmGmpoaPPnkk9i6dSs4joNGo8HixYsxfvx4AMDx48dx7733orq6GhzH4amnnsLUqVODXGpC2ibBKoDjGz/nrC2sxZmNZ9B5XGdoM7SNrltytAQXtl2A7pLOp4YMpdY+lEbD39qEEEJIY+hab2ijOTJaQaqUen00HCKGk3CQSCRuD07CuV1U9LbN5oqMjMSlS56H6rBYLKiqqkJ8/OWhDV555RUUFxejrKwMSqUSd955Z7Pfk3imSdBgzpY5mPbVNMxePxszVs1wPoY+NBTRnaMRlRYV7GISRjgJh4mvT8SQ+4Ygvkd8o+t2yO6ALtd2AQDsfnM3zHVmr+vm/pALQ7kB6ni1Tz/gmlJ5thLbntuGqnNVrd4WCQ35u/ORvzu/zU1s2X1Sd8T3iA/pofbaI6vJioLfC1BxusKn9RWRCkSmRkIWaT+ZrzpbhbriOsg0Msg1ckhV7fv+GYVWAYVWAQkvgdVkhVwjp1wC7JZbbkFKSgoOHDiAgwcP4u2338Ydd9yB8+fPw2g0YvLkyfjXv/6FgwcP4scff8Q//vEPHD58ONjFJqRNOrH2BFZOWYn9H+z3us7BZQdx/JvjOL3hdJPbKztRBgBuQ/N5o4xRQqFVhPR8hIQQQkIPXesNbdSQESBWgxVmndntUX9IIZWq8UlZm6tv377Ys2ePx+f279+PzMxMyOXuQ3jExcXhrbfewrfffovq6mqmZfI3nueRlZUFnm96roFA0yRo0O26bkjomYDYLrHOR9btWUgZlALRJmLXm7tCprsWENp5hrrIlEh0u74bOAnXZI6D7hqEiJQI6Mv02Lt0r8d1BKuAE9+eAAD0ndXXp/k0mnJ01VEU7C5Azpc5rd5WIFG99C5neQ62P7cd+bvyfVqfsmQr3PKsK6rDtme34ZdFv/j8Go7nENs5FvJI+/mFodyA6vPNO5cItxzrU8XZz/UM5YaAvWc459lcmzdvxoIFCyCR2H8CDR48GIMGDcK+ffvw008/YeDAgRgzZgwAIDk5GY8++ig+/vjjYBa5WehYs0NZtp6hwgDRJkKuknvNsstE+80857acg83S+BAgzW3IGLZwGKZ+MRVdJnRpZslDF9VLdihLdihLtihPNgKVoy/XelkL1rVe1tesW4puAfMzRZQC6jg19OV6WE2eK7I6Tg1FlML5o4qVmTNn4q233sItt9zi1hL4xhtvYNasWV5fazKZYDab2+SYcp4+sMFiqDBg73/2YtBdg7xOMifhJRj+yHD8+OCPKDlcgpP/PYmeN/cMcEm9C6U827LGcpQqpRj+6HBsenwTqs5XeZwnQCKVYPxL45H7Qy46X9WZSZn6zeqHizsuIn9nPirPViImM4bJdgOB6qU7fbkeVXlVAAef5jRwCIUsRVHEhe0XcOnwJfSf07/Zk9iHklDIkxVDpf1iuzJW2azXcTyHuB5xqDxbCUudpUWTrIZTjvWpYlWouVADc50ZNqsNvDQwP1LDNc/mGjZsGF5//XUsXrwYALBt2zbs3LkTS5cuxcsvv+xsxHAYM2YM3nrrLa/bM5lMMJkuj79fU1MDwD7BpNVqP+933JknCAIEQXCu61hus9lcbmLxtpzneXAc59xu/eUAnOvzPA+r1eqc2LLhubxUKoUoii7LOc5+00XDMnpbHsh98mW5P/bJkaUgCOB5Piz2qamys94nXbkOgihAFiVzLmu4T4lZiVDHq6Er1eH8jvPIGJXhcZ+sRisqz1YCor0hI5zrXmNlF0XR5bMdDvvU2HJ/7hNg/9vo+LyHwz4F6zhJJBLI5XJYrVaX605teZ+CfZwcf8s5jgubfQr0cap/TuSol/X3yWq1QhRFl+/XhjiO87pcHimHKk4FQ7nB67VeVZzKeXNXc7ff2A3Of/rTn/DWW29hxowZHq/1zpw50+t7Oq71Ovbf17KIouiyji/rO86H6x+nhseqJaghw880CRpMXTHV48z0Dooohf0ETqeDRsNubOT58+fjyy+/xP33349XXnkFGo0GZrMZL7/8Mg4fPoyPPvoIAGA2m1FSUoK0tDQAQFVVFebPn4/p06cjNjaWWXkCwWazYe/evRgyZIjzJC9YRFHE7//3Owr3FMJUY8L4F8d7XTcyNRKD5g3Cnn/vwaFlh5A8MBnRHaMDV1gvQinPtsJQYcCed/egwxUdnHeZ+ZJjfI94jHlmDBL7JoKXeb6wFZEcgYF/HsisrNp0LTKuzMCFbReQsyIHo58czWzb/kT10jPHBPNx3eN8nm8nVLLkOA45K3JQW1CL1OxUpA1NC1pZWiNU8mTFUGFvyFDFNP/uG4lEgriucRAhgkPzhgwLtxzrk8qlkKqlsOqtMFWboI5T+/09wznP5lq2bBmuu+467Nq1Cz169MCKFSvwxRdfIC0tDYWFhZgwYYLL+unp6Th79qzX7b344ovORpH6Dhw44DynT0hIQJcuXZCXl4fS0lLnOmlpaUhLS8OpU6dc7orLzMxEYmIijhw5AoPhcs+dnj17Ijo6GgcOHHC5eJCVlQW5XI69e/dCFEVUVVUhOjoa2dnZMJvNLkNj8TyP7OxsVFdX48SJE87lKpUK/fv3R1lZmcv+arVa9OrVC4WFhcjPv9zTL5D7VN+QIUMCtk+OLHv37o2OHTuGxT4F+jjlHcuDrkqH8yXncXHLRYwfPx51dXVu+9R5fGfsX7YfO5btQImyxOM+1Z6uRVVlFSKTI6GOV+PMmTNhW/ca2ydRFGE2mzFy5EgcPXo0LPYJCM5xUigUMJlM6NixI86fPx8W+xSs49StWzfk5uY6L0yHwz4F8zjl5+fj2LFjiI6OBsdxYbFPwThOPM9j8+bNzhw97ZNSqYTBYIBKpYLNZoPReHlOC4lEArVaDavV6nLTCs/zUKlUkEfLce3718Jcax8anJfyzu8Vm9VeFnmkHPJoe0OG0Wh0KaNCoYBMJoPBYHD53CiVSkilUuj1epeGApVKBYlEAp1Oh9mzZ2P58uW455578MYbb0ClUqG6uhpvvvmmc+hUADAYDLh48SI6dOgAwH7DzcKFCzF16lQoFArodDqXfbJYLDCbLw91LpVKoVQqYTKZYLVaYbPZwPM85HI55HK5130yGo0wm804cuSI23Fy3PTTGpwYSuPYBElNTQ20Wi2qq6sRFeU6T4HRaEReXh46d+4MpbJ5dyQ2hyiKzoYMlhPx6nQ6PPvss/jf//4HnudRWFiIrKwsrFmzBtHR0QCAgoIC3HTTTdDpdFAqlZBIJJg1axYeeughyGT+m3jVH9lardaQ+bF+9uez2P3mbkikElzz5jVNNkyIoohtz25D4Z5CaDtpcc3r13i9oB0ooZRnW3Fm4xn8/vbviOsRh4mvTgTQvBx1pTq3hk9Trcl5h7oiSsF0MtjiQ8XY8MgGQARG/3M0tJ1cJ1pk/X4shFu99HTM6/N2DBq+bs+/96D4YDF6TO6B7jd09+nYhVKWe97dg9M/nkb3m7pj8N2Dg1qWlgqlPFk49s0xHFp2CJ2u6oThDw9vcv2KMxVYNWMVlNFKt15lAGDWmWGsMmLGqhmI7eL9Rolwy7FhLtUXq1FXVIeIlAho07U+59JSrPJs7Hy5rRAEAf/+97/x8MMPw2azOXsvJyQkYPz48fjb3/7m0pjhuCtSEASP5+eeemSkp6ejvLzcmVEg76S02WzYv38/Bg0a5OyF01bvpPRU9kDeHerIcvDgwZDL5WGxT02VnfU+rbt7HeqK6zDm2TG4aLyI7OxsZ++W+mU3lhvx37v+C1EUMek/kxCRFOG2T8dWHUPO8hxkjMrAlX+/0qd9qsitwOHPD0Mdq8bQh4eGxXFy1Mvs7Gy3u2Hb6j41ttyf+2Sz2XDgwAEMGjTIZWSMtrxPwTpOoihi3759GDhwoMswPm15n4J5nMxmM/bt24dBgwaB5/mw2KdgHCfH+a8jx4b7ZDQaceHCBXTu3BkqlYpJjwl/L6/Pca33u+++c7nWu3r1amfjTX5+PiZPnux2rffBBx90u9brSw8LvV4PtVoNjuMaXd9gMCAvLw8ZGRlQKpUux6mmpgZxcXGt+j3RZn4dWiwWvPDCC1izZg0sFguSkpKwdOlS9OjRAwBQVFSEefPmIT8/H4Ig4P7778e9994b5FIHn0ajwZIlS7BkyRIAQG5uLiZOnIhjx45hxIgRAIAOHTpg3759wSxm2NGX6bH/ffvEdv1m9/OpdwXHcRj64FD88MAPqC2oRfmpciT2SfRzSQlrjrviU4ekNvu1ulId1sxaA12ZDsYqIyQSCWQaGaovVEOukUOdoHb28mLRuKAr1WHjYxtRfrIc5loz1ty2BhEpES7rqOPUzN6PuHMcc3253us6no5Bw9eJoojqc9UQBRF1l+pwaNmhNnfskrKScPrH07h02PPEZSTwHENLqWKb1yPD25iw/hwrti1w7L8iSgFFpAISmcTvY+gSV7fddhtyc3OxadMmZGRkYPHixcjKysKePXugUChc7sQD7HeyKRQKrzcZKRQKKBTuPeCkUqlbo5HjR3hD9S/6+LLcW2OUY7njooGjzJ7W5zjO43JvZWzuctb75Mtyf+yTY0iPxsre1vapPn/ukyiKMFWZIOEk0MRrwBXY66OnfdIkapA8IBmXDl7ChV8uIGt2llvZLbUW8DyPhF4JPu8TBw6lOaXQJGnC6jg1HCKlNWX3trwt1z1vyxvbJxb7Gmr7FMjj5LiIzPO8x/K0xX1qrIzNXd6SfXL8La//ura+T4E+To6L7Z7qpeM8zbGOY31PQm25Q0REBF566SW89NJLAC5f6z1+/LjzWm9aWlqzrvX6UhZfM3PUkfrZezo/bok205Dx5JNP4vz589i9ezeUSiW2bduGadOm4cCBA5DJZJg2bRruv/9+zJ49G7W1tZgwYQIyMjJw/fXXB7voIaVbt25YuXIlZs6ciXXr1qFPnz7BLlLYEUURu9/eDYvegrgeceg5xff5LpTRSox8fCQUWkVIDC1FmkewCig+UAwASBns+xwFDqYaE/TleggWAeYaMyCx3w0q4SXgpBykKin05XqYakxMLk473i8iKQJVxirYzDbwCh4ylb113mqwMn0/4s5xDKQKKaQq9z/J3o5Bw9eZ6kz2ieUVPDRJGtgMtjZ37BL72Rtuq89Vw1Rj8nl4LOI/jqGllNG+9Zpszrxg7QnlEhpOnz6NH374AefPn4dWa+99uGzZMsydOxfvvvsu0tLScOHCBZfXXLx40Tn0KiHEd1aDFTaT/W5dVYwKKGh8/S4Tu6AitwIS3vOckYPnD0b/O/pDFHwfTEKptf/tMlV77/VKCCGEhIP2dK23TTRkiKKI9957D+fOnXMOQTR69GiMGjUKGzZsQEZGBmw2G2bPng0AiIyMxL/+9S+8++67baohg+X8GI254oorcObMmYC8V6DxPI8hQ4Z4bakNhDMbzqD4QDF4OY9hDw/zekLuTVJWkp9K1nyhkGdbUnqsFFaDFQqtArFdLw8P0twc1Ylq2Cw2mKpMsOqtkEgliOkcA07COX8UsqTQKhCVFgVexkMdqwYnudyy7u2iWzCFY72UqqSQa+SwWWwQbSKkSvufZ9EmwlBhQPHBYujLLvfaqMmvgUVncb7OWG2ERCqxXwzVKGCG2adjF0pZKrVKRKVHoeZiDUqOlCB9RHqwi9RsoZQnC8ZK+93pvvbI8HVesKYa18Itx8ZycUyc589h/MItz5aqqalBamqqsxHDoV+/fjh9+jRGjBiB77//Hvfff7/zua1btzrvamsL6FizQ1m2jmAVkD4qHRadBQqNosks04enI21oGni593Uc50a+UmjtjcNWo9V+s04j224rqF6yQ1myQ1myRXmy0R5z9Pe13kBds25Km2jIuHTpEuRyudvE03369MGePXtw6tQpjBkzxuW5K6+8EtOnT3f+QKzP03i2AFxmUK8/ZptjPDBfZ2f3dXl9ju1LJJKQGoOtNfvkq/q5t3ZsPVEUYTQaoVarnWOwBnK8QJ7ncXbTWQiigKxZWVAnqZ0T4rRkn0qPl6JwTyH63dYvKGMgiqIIi8UCpVIZ8mMg+rpPgP/Gdby4+yJEUUTqkFRnfXTkaDKZoNFoGt0nm/WP50QgplMMSo6UwGa1QRGlgCxCBovO4txewwxauk+O757ItEhwuDzWoYjL33uO14bKcZJIJDAajS7DfbTVuuc85rAf19JjpVAnqBGRbB8f2mq2ora4Fr//3++Qaew9ZThwsOgtqC2uBXhAGaNEREqE82Jz/b9ZNqvN3qvHyz4B9qFTlEql21AFwfg8JfZNRNWFKhQdLELKFSkhc5x83SfHeKuOsUBDue75sk89p/RE8pBkRHWKgtVq9enzpIhRQBGjaHSfHOdm3vbJarU6c3SUhdU+Bes7on4uAKAr0mH/h/th1plx9ZKrnfvtj31idW7UcN/amv79+yMyMhJvvPEGHnroIUgkEpw5cwYffPABPvroIwwYMABPP/00tm7dijFjxqC4uBivvvoqvvjii2AXvVnMZjNUquYNB0c8oyxbThGlwKi/jQJweYLqxrKUSJt345cvZGoZJFIJBKsAY7WxzfRQbQrVS3YoS3YoS7YoTzYoR7Yc1xWCrU00ZGi1WtTV1aGqqso5QTVg7yJuMpmg0+nQsWNHl9eoVCoolUqUlJQgKcn1DvcXX3wRixcvdnufAwcOOFuYEhIS0KVLF+Tn58NsNkOn08FmszU5O3tLZpx3sNlsiIyMhCiKMBgMzuUcxzkvgNYfu1cikUCtVsNqtbo0zPC8bzPOO/hznwB7q50gCG775PiR7ZjJ3rGN/v37o6ysDGfPnnUu12q16NWrFwoLC5Gfn+9c7jhOeXl5KC0thSiKqKqqQu/evdGxY0ecOnUK1dXVzvUzMzORmJiII0eOuJSnZ8+eiI6OxoEDB1wyyMrKglwux969e132aciQITCbzTh8+LBL7tnZ2Rj8t8HY+flO1KTUYO/evS3epxMHTmDrI1sh2kQUm4rRb1I/pKWlBXSfHMeoW7duyM3NZXacHNLS0gK+T47jVF1djRMnTjDdp2PrjwEm+/wY9ffJ8QNu5MiROHr0qNd9qj1fC71eD4vEgpikGMR0iUHZuTJwMRyqq6phM9ggE2UwGo3I3Xv5eLR0n0oulTjfj7fwkMvl0Gg00NXpYDaZIZgFWPQWlFwqQXy3+JA5Tn369MG+ffsgl8udF9/bat3T5+thNBqhggomownGOiOMOiOsCitkChl4jodEJoE1wgou0r6vKrUKEYhA0eEimCwmVFfZy69UKaFSqlBbVwtTjQkWvQU5OTnoG9PX6z5FRERgx44d0Gq1ziyD+XmyJdhQXVWN3IO5EPYKIXOcfN2nixcvOs9VEhMTQ7ru+bJP6ADUiXU4WXASKAjs58mRI8dxAf8uD8Rx6tu1Lwr2FKCmpgbqzWrIomR+2ydW50YNz+/aGp7n8f333+Of//wnBgwYAJ7noVar8fLLL2PkyJEAgHXr1mHBggWoq6uDIAhYvHgxhg4dGuSS+85ms+Hw4cOtntidUJYsNSdLx00d2nStc7i9fe/vQ9nJMvS5pQ/Shvo+1Jujt5uhwgBTddsZarMxVC/ZoSzZoSzZojzZ8DXH+tc6SeMMBoNPvTJY3fTuDSf6+x0YufPOOyGKIt59912oVCps2LABd999N6677joIgoChQ4fi7rvvdnlNRkYGtm7dis6dO7ss99QjIz09HeXl5c5Z0x13p1mtVpw+fRo8zyMxMREymYxZr4n6HI0XKpXK44QpodbzorU9MkRRRFlZGfR6PTp37uy8Y7G1dx3abDbs378fgwcPhlwu98sdr7pSHaw6+3qCzfVLj5fykEfKoYy9PJ54a/bp0GeHkLM8B1KFFGMWjYEqVmUvS73oldFKRCZF+uUuXpvNhgMHDmDw4MFuE/yE4h2vweyRYTVase1f21CRW4Gpn08Fr+KdZXfUy+zsbLfPTv19qjxTidV/Wg1ltBLyCDkA1z8CFp0FpmoTpn89HdpOrkNjtGSfynLL8M0t30AZrYRMI3Pe6V9xugIytQwRyREwVhsx/evpiO8WHzLHSRRF7NmzB4MGDWryuyPU657jmKtiVOB4DpcOXwIn4ZAyKAUcZz8ehkoDpn01DTFdYpxlrMqrwtfTv3YeOwAuPWrMOjOMVUZM+2oa4rrFed0nm83mlmUwP09mvRmmOpOzd0moHCdf98lisWD//v0YNGiQ83whVOuer/sUjM+T2Wx25sjzfFjsk6fjtOGRDSjPLccVD16Bzld19ts+sTo3qqmpQVxcHKqrq53ny8RVTU0NtFpt0DKyWq3Yu3cvXfxggLJsHZvZBolM4vwO8TXLHa/swIVtFzDgzwPQa0ovAMAPf/kB1eeqMeqJUUgf3rxhJx2vHfPMGKQOTm3x/oQKqpfsUJbsUJZsUZ5sNJWjIAjIzc0Fz/NISEhwuUmSuBNFEXq9Hmq1utGcRFFEaWkp9Ho9unXr5ja0F4tz5TbzqVi6dClefPFFXHnllTCbzRg1ahQeeOABVFZWora21qWngoOjYaAhhUIBhcJ9QkVPM6hLpVJkZmaiqKgIhYWF7HaoAccd2+3pw8NxHNLS0jweC8eP6pYsd/T2AOD2oXHwttzbHwrHcl2pDuvuWAd9ud7leZvFBkudBYpoBTTx9rGwG97105J96npNV/zy9C8wVZvw5Y1fIjI10m09dZza4/v5uk++LOc4zuPy1hyn+lgfJ1+Ws94naYQUE1+eCKvR6nEM34bD9ngqIy+1XzTiOA4cOJfX1f9/b2VvyT453k/CXX7eZrbBZrJBqpL69fPky3JP+2S1Wp0X8Bo+19bqnuOYA/bxpDlw4GW8S1k5jgMvdd9XT8eu/nLH65o6ft6yDMbnSa6WQ66Wuy0P9nGqr7F9clz4dQwr1dj6ob5PZp0ZJUdKoEnQICYzpsn1Gyt7c/epfo711wm3v0+pQ1JReboSlw5cQreJ3ZzL/bFPLL7L6cc0IcRXOStycOLbE+h9S2/0vrW3z69LykrChW0XcGbDGfS8uSesBiuqz9t7kMX3jG92OVSxKhgrjRCsdMctIYSQyyQSCTp37uz3a73hojnXrB3Xer39tmitNvOLRKFQ4JlnnsEzzzzjXHb//fdj+PDhuHjxIi5cuOCyvsFgQF1dHRITE1v93nK5HBkZGfYx7BvcUceK1WrF8ePH0bVr13bzQ1Emk/mlYvvrwwIAphoT9OV6SBVSSFV/TMYriqg8WwlLnQXg7B9aUw2b7ssWvQWKKAXMdWYIFgGCVYA6Xu183mqwQl+uZ/Z+nvgzz3DkbSLC5uRoNXgeh9zb8tZquF11vBp1RXWovlCNqNTQvOs23Oql1WCfiFKwCuAVPMw6s3N5U69rznJPQjVLxzwKbU2o5tlcVeeqsP257YhIicCN798Y8PcPlxwbkzokFUe/OoriA8UQrIJfxoh3aA95Ejs61uxQli1nqDBAFETnebGvWXYc3REHPjyA2oJalJ0og81k74muTlRDFdP8cc7HPjO2TZ5LNIbqJTuUJTuUJVuUJxtN5RiIa73hojnXrP11rdehzV4xr62txbp16/Dss88iJycHf/3rX12e37ZtG7Kzs5lNRMJxHGQyGWQyGZPteZKdne23bbcXUqk0IDlKVVLINfa7hmuLa2Ez2sAreESmRsKitzB9L17OIyotCrpLOuhL9dAkaiBTXa6HVpP/Jt8MVJ5tnWAVYDFYoIh0710E+J6jIkoBdZwa+nK91+OqjlM7xwxuLW/vx8t4SKQSWI1WmOpMzmGuQkU41cv6x8BUY4JgtTdYGqsu9zL0dMxZ1ZVQzLK2qBZ7l+6FqcaEa9+4NtjFaZZQzLOljJX2OqiMUTaxJnvhlGNj4rrFQRGlgKnGhLITZUjs2/qbbzxpL3kSOtYsUZatY6i0z7ejilU1K0uZSob0UenI25SHMxvOICI5AkDLemMACLtGDKqX7FCW7FCWbFGebPiaYyCu9YaLUKmXbaYhw2azOVt0CgoKMHfuXDz88MOIjY3F6NGjYbFYsHz5csyePRu1tbVYtGgRHn300SCX2neiKKK6utplwlXSfIHO0WK0oCa/BgCgzdCCl/PMGzIAQBWvsl9UrjZBX6qHNkPb9IsYoHrpm+JDxdi6eCvSR6Rj1N9HuT3va46aBPuwZKYak9d1FFEKZr1vGns/fZkeW5/ZCsEm4ML2C+g93fdhAfwtnOpl/WNw4r8nkPtdLjqO6Yis27Kc63g65qzqSihmqYhUoPhAMSDa7+h0zJfRFoRini1V/yJUoIVTjo3hJBxSBqfg3JZzKNxX6LeGjPaSJ6FjzRJl2TqGij/+hsSompWlrlSH+J7xOPnfkzi9/jS06VqYdWYoo5WoOFPB9Dy4LaJ6yQ5lyQ5lyRblyQblyFYo5em/PuyMvfDCC8jOzsaAAQMwffp0zJkzB4888ggAewva2rVr8dlnn6Ffv34YOnQobrnlFsyYMSPIpfadzWbDiRMnqDtTKwU6x+oL1YAAKLQKqBPUTb+ghThwiMmMQXSnaERlBG6oH6qXvinaVwSI8NpzoTk5ahI0iO0S6/XB+sebt/dLG5qG4Y8Oh1QhRc7yHFSerWT6vq0RbvXScQwUEQrINXLEdY/z6ZizqCuhmKU8Qo7oztEAgJIjJcEtTDOFYp4tVf8iVKCFU45N6TC0A5IGJCG6Y7Tf3qM95dne0bFmh7JsHUevPlWsyucsdaU6rJm1Bj//42dcyrmE/N/ycfTroyjaV4Rdb+7CqhmrsGbWGuhKdT6Xo/hgMX5+4mfse39fq/YnVFC9ZIeyZIeyZIvyZINyZCuU8mwzPTKeeuopPPXUU16f79ixIzZs2BDAEpH2zlRrgqnKBHBAdMdo5+TM/sLLeGgS2+8dSKFKFEUU7CkAYB/vPJx0vroz8nfnQ7SJbequ+LZq8PzB6D+nPyAGuyTBl5SVhKqzVbh0+BI6ju4Y7OK0S8EcWqo9yRiZgYyRGcEuBiGEMGOz2GCutc/11Zy/IfXnIoxIjkBdYR0AgFfYfwPZjLZmzw1oMVhQklMCmyX4F14IIYQQ0nptpiGjvdg4fz6Ofvop7jhwAIn9+3tc54uhQ3Fp3z48avV9bgRdqa7ZQ5AU7NyJX//5T9y6ZYvX133UvTumb9wIbcfmX2iy6PXY+/rrOP3ttxBsNthMJtjMZtzy88/QduqEVRMnYtg//4n0MWMa3U79fbNZbdDn61EZUwleynvdNxYcQ0qpE9SQKqUwVZxA2e6XIJEn4Nvr34JgNaH/Pfcg+69/DWrXq7qiImyYNw91+fkQBQED7r8fA+69t8nXnf3xR3x3663ovmQJMGSI2/MFO3di13PPQVdUBMFqRXTXrrj5228BADaLBbtfeAG5a9bAZrFAk5SECUuXIrZHD+frP+zaFRK5HLz8ci+GgQ88gKx581zex2ax4D/p6Zhz+DA0if4ZdqMlHHWfk8ZAV6yDRCpB8oDkYBeLKY7jMOKxEeDlfNC7D7YXUgX9WQbsDRkn157EpcOXgl2UdiuYQ0sRQghpuxwN4RKZBPIIebPv3pSqpFBEK6COVUMeJXfeLGbmzM2eG1CptTekmKq9/w4mhBBCSNtBV0xCBMdxUKlUqLDZkDR4MHI++ghXv/2223qlR45AFASIzTghdHTT1Zfrva6jjlOj71QLukwaj6gM+52BNrMZgqXx+R58WccTY1UVVo0fj66TJ+PWrVshj7BP5GY1mSD5Yy4UX7bdcN9EUYTRaMQp5SnnhVd1nBpTV0xl2phhNVjt2xMBVbQKZp0ZFr0JvDod6q6PYcqqGdDE81g3YwY0KSnoc/vtPm23YMcOCDYb0kePdns/wD6xt67Y3p1a21ELc2019KfeQdXpLMR2Ge5xm+umTcOA++9H79mzYa6txaoJExCVkYHM66/3Wo5D//kPjnzyCSI7doScd7+Inbd+PX557DHcsGIFErLsY/nb6h2rX598EjXnz2P27t2QKpW4uG0b/jttGu44cAD8H5MoCVYrpq1fj5iuXRvN5Nz69Yjv2zeojRhlR4+i6swZdL3pJucyR/28dLAQAJDQNwFSpeevVMfnuy02BjS8sG6qMTGbbLyl2nKeoSZUs0zonQBwQF1RHfRleqjj/Td0H0uhmmdLOIaWUkYHvkdGOOXoK0OlAdXnq/3SIN4e82yv6FizQ1m2TvqodAD2HFuSJS/lwUfxrS6HQms/Z23shr62hOolO5QlO5QlW5QnG5QjW6GUZ5uZIyPc8TyP/v37g+M49Jo5E6fXroXV5H7CdfTTT9Fnzpxmbbt+N11ltNLtIVVIoS/X4+inH6Py1ClWu9SozQ8+iO7Tp2P4U085GzEAQKpQQCL1vX2t4b6pYlSISYmBKkblsm+sTl4VUQqo49SwmqywmqxQaBUw68wwVhlhrjMDggh1nBqKKAUUWi2yH3sMxz77zOftn9u4Eed/+snj+xmrjPb9LdNDX66HscoIq0EPwVwMUTB63F7p4cMQbDb0nj0bACCPjMTIf/0Lh99/v/GCcBxu2bwZqrg4dOnSBTx/+YeEKAjYeN99uP7zz52NGACcDRSiKOLQe+9h/LvvQqq0XwBLHz0aaaNG4VwLhn87vmIFes2a1ezXsVS8Zw9Ofv21x+cK99obMhobVsrx+a6fY1tj0Vvw2+u/Yf1D6+11PYjCIc+GRFHEtue2Ydebu2DWBS7fUM1SrpEjtmssAOBSTtvplRGqebZE1m1ZGDhvIKI7RQf8vcMpR19UX6zG2jvWYvvz2/0y/El7y7M9o2PNDmXZcppEDUb9bRRG/W0UgOBm6bj5xqKzQLAKAX9/1qheskNZskNZskV5skE5shVKeVKPjBAhCALKysogiiLkUVFIHzcOp9euRc9bb728js2GU6tX47Y9e7D5L39xeX3x3r345dFHUVdYCNFmQ+frrsPol1+GXGPvhaDPfQ2qDiNRd2wLRNEGjuOh7XMHFHG9YNUVQnfiXRi4Smy8916oExMxa+dOAIC5thb/u/VWlB4+DABIGTYME957z3mB2uH8zz9j1/PP49bNm53LNj3wAFKuuAJ97rjDZV19aSnyfvgBE5u6mA7g1Jo12PLIIxAFARKex9jXX0fHq68GYO/V8fOCO6A7tg+cVApeoYG2758hKhIhl8tR9tuzkCcMg/7CJqy57k3INUqMe/NNl6Gqcj7+GLtffBESqRQKrRaDH3kEOR9+iBl/NCYYysux8b77UHLgADiJBN2mTsXkT/8Oq9H9QkPRrl+x742dLr0/NCkpqC0ocK6Tt2EDdjz1FKwGA0RRRNqoUbj6//4PEqkUK0aORNWZMwCAM999h0krViC+d2+MXzIYvzz8F9Tmn4eMkyA6ORuqDhNx9QtXQ52ghiLqL157m5zftMltaK4OV16JddOnQxRFr62p/efPd/5/ZWUl0gUBEom93bNo926oExKQNHCgx9fqL10CL5dDFRvrsjyuTx8U79mDLjfc4PF1npjr6nBuwwZMWLoUAPDrU0+B43kU/Por9JcuQR4ZiWs++gilhw5h1wsvAADSRo/G1W+/De6P8prr6rD1r39F3vr14GUyaFJTMfa115A8eDAAIOeTT3Bp717oiopQfvw4AKDblCm48o/trZsxA0W7d8NSV4dlAwZg7KuvouP48QCAA+/8G4c/WQWAw+6LiYjt+BESBwxw2w/H5zs+Pt6ZY1vDSTiUnSyDvkyPPe/twci/jgxaWcIhz4asBisKdtu/K4bc5z6Um7+EcpapQ1Ih08ggj5A3vXKICOU8myttaFrQ3juccvRFVFoUVLEqGCoMKD1ayrxXRnvLsz2jY80OZclOMLOUR8jBSTiIgghjtRHquLbRw9MbqpfsUJbsUJZsUZ5sUI5shVKedDRDhCAIOHv2LETRPstrv7vuwpGPP3ZZ59yGDUgZOhTK6GiX5bpLl7Dmhhsw9IknMC83F3fl5gIAfrr7bpf1DAU/Izb7b5Cm/g2KtJmo2P8ORJsFUk0qND2fRHzfAZiwdKmzEQOw39HfdfJk/Pn4ccw9cgT6S5dw+MMP3cqfPnYsKk+edF60t5nNOP3tt+h6881u6xbv2YOkwYPdGkM8KT14ELf+8gvuPHIE4997Dz/ecYdzCCPBakW/+Q9C0/sZxF7xHDQZV6Ni//soPVEKq9kKgIOh4GeoujyIqT/uwPh338V3M2c6e7oU7tqFX//5T0z9/nv8+fhx3LByJX594gnYzJfvhv5x7lx0vuYazMvNxdwjR1B+/Dj+d9uTOL3+NDSJGsR2iXU+IjtEQaaUujQqlB46hOguXZz/VsbEYOr332NuTg7mHDyI6vPnceTTTwEAs3bsQP9770XWvHmYc/Ag4nv3hiiK2DT/dgz75+OYf/Y05hzcB1vNfgj6E+AkHGK7xDY6ZFZdYSEi09NdlslUKvBKJfQlJU3mL4oiiouKIAiX72AqOXQIcX36YP///R8+HzIEywYMwOaFC2GsqgIAyLVamOvqnP92qDp9GvpLzbuz+vTatUgfOxYKrRYAwPE8Drz9Nq5+5x3MzcnBmFdfxfezZuHYF19g9u7dmHv4MGwmE44tX+7cxvo//xkQRdx16hTuOnUKw596Cmtvugn60lL7NjkOhz/4AH3vvBN/Pn4ct+/bh3MbNuD0f/8LALhp1SqM/Ne/0Pn66zHn4EFnIwYAVJ87h+uWr8fIJWuQ/fij2NBgfg8Hx+e7fo5tjVQpxYhHR4CTcLiw7QLObzsftLKEQ54NOeYjkKqkAZsnw2YDtmwRsXRpNbZsEdHMIaz9rt+sfrjq2avQIbtDsIvis3Csm8HQ3nLkOA4pg1MAXO7lx1J7y7M9o2PNDmXZclaTFaIgOv8dzCw5jnP2ygiHeTKoXrJDWbJDWbJFebJBObIVSnlSQ0aISh89GjUXLqDmwgXnsiOffoq+d97ptu6Bf/8bPW+9FZ2vuQYAnD0XLmzejKq8POd6qrQJsBgUMFYaUVcWB45XwaorarQcsb16OYf1kfA8es2ejYLt293Wk/A8etxyC06tWgUAOPvDD+gwahQUUVFu6xorK6GIifEhBWDQwoXOO/s7jBgBqVqNqtOnAQDq+HgkDrh857IicTBsdRdgrbOiKq/Kuc8Smb0MqcOHQxEV5Rw+68RXXyFr/nzEdu8OANB27Ijsv/7Vub3K3FzoiovR7667ANiHTkq7+jaU52zGxR0XwUkaHxuuYOdO7Hj6aQx74gnnspQrroA6IcGemVSKLjfcgEv793vdxvlNm6DNzHQeW3lkJNKu+hMMBTtRdb6q8fAAmKqqPDYYSZVKWPTe50xpjLG8HHk//giLTodZO3fitt9/B8fzWDdtGgB7Q0nPmTOxZeFCWPR6iKKIvPXrkbtmDcT6X3och/V33oll/fvj88GDsWPRIlgMBpf3Or5iBXo2GFaq29SpiOvVC4C9TtQVFmLUc89BplI5ny/8ozGu6swZ5G/bhnFvvukc+qrj1Vej+/TpOPDvfzu3mTpsGLrceKO9/Go1us+YgYvbtjWZxbB//gM9buyL7AXZ6PWnP6HsyBG3fQgncd3j0OdPfQAAe97dA31Zy+oQcWessg8PF6j5CNasATp1AsaP57FoUTeMH8+jUyf7chKadKU6VJyp8PrQleqYvZex2oj83fmoPFvJbJukcY7hCf3RkEEIIYG07z/7sHLKSpxYe6JFr7carDDrzG4Px5yBzaWMtQ+n3NyJwgkhhBASemhoqRDWZ+5cHPn0U4x4+mkYKytRsn8/On31levFYABlOTlucwhIFQokDR6MspwcxPb5Y3xSRazL2KAiVBAsOnCNDHHWcHggdWKi8072hnrddht+fuABDF64EMdXrHAbUspBodXCVOnbxRFv7x/XqxdEQcDx5R9Df/pj6G1VECEBYO+tYaoxQWq22fe5/vbi42GsqAAA1F64gA4zZ7psP2nwYBz/8ksAQPmxY6g6fRrLHEMFiUD1xUoASvSe3htyjftwJyUHD2LZgAGwGgyoKyjAhKVLkTr88iTctQUF2PPyyyj87TeYa2thqatDxh9DZXlSfuwY8rdtu1wGAPqyWohiEqovVDceHgBeoYDV6D5/htVgcF74by5OIoG2c2cM/dvfnMtGL1mCpampqDp7FtGZmZiwdCl2v/givrryStjMZnQYNQoDHnjA5bjP3rUL6sREcBwHXUkJNi1YgC0PPeQcckxfVobi33/H5AZXVjXJrkNuSFUqxPXu7fJv6x+NCWVHjnjs/dNh1Cic+OM4A3DrtaKKj0fFyZNNZqGsVz85iQSquDgYSkshy8ho8rVtVZ8ZfVC4txAVpyrw2xu/4arnrgqJCZ/aOmPlHw0ZMf5vyFizBpg+HRBF1+UFBfbl33wDTJ3q92L4zFBpgGARoEn03vss3OlKdVgzaw305d4bD9VxapehDVuj/FQ5tj+3HTFdY3DtG9e2enukaUn9k8DxHGoLalFbVIvIlMhgF4kQQlrEUGmAKIiQqWXNep1jbkB9ud5ro4NjLsLmuPbNa+lclRBCCAkT1JARIjiOg1arRUW9k6y+c+bgq9GjMfypp3Bi5Up0nzEDnETi1pDBeZtsRRQbPMfBZr08dohgEWA1WSBrxnUzjuPc3t8hefBgmKqqUHbsGAp/24URz/4fKs5UuK2niO2Mot/3wGoyQapo3olo/fffsWgRzm3YDEXqzVCndEP1uTII5x6HTCMDzIDNZIXNLIBrsH+O4bssOh14uWtjBF+vPFaDAanDh2PaDz8AAE59dwr7/rMPqlgVuk3q5rF8iQMGYOavvwIADv3nP8hbvx69b7sNAGCzWPDV6NHoO3cuJn/7LSI7dMCBf/8bxXv2eN1fq8GAbtOmYcK77zqX5e/Ox/bntqP6QjWMVVX49sYbMfGDDxDXs6fb6yPT0lx69QCAxWCApa4O6sREr+/rwAHQaDQuJ//qxETE/NGLxYGXyRCVkQFDaSmiMzMhVSgw8plnMPKZZ5zrbLr/fpdGHU1S0uX/T0zEVW++iWVZWc6GjJNff43MG27waQiyhsfRWX4vnw2x4WfD04+bhld5GxBsIvJ+PgupOgmRqZHO7Xj6fDg+3+HwI0oilWDEoyPwv3v+h/zf8rHvP/uQOSHTbT1FlILJBVVPwilPB0ePDFVMyxoYfWWzAQ895Ll6i6L9o7BwITB5MhDsebx0pToc++YYjq06hvQR6Rhw5wCX5/1Zx1rKX3XTVGOCvlwPqUIKqcr91M1qsEJfroepxsQkE0OFvTHY3/XRm3D8jDdFrpEjoXcCSnJKULi3ED1u7MFs2+0xz/aKjjU7lGXLOf6GOG7O8DVLTYIGU1dMhanG+xBQLfnbH07HkOolO5QlO5QlW5QnG5QjW6GUJzVkhAie59GrVy+cq1cpNMnJiO3VCxc2b8bRZctw3R9zKTSUNGgQ8rdvR48ZM5zLrCYTSg4eRGL//rD8cS5oM9vAR0qgildBd0kHTgT0ZXpo5PY7Xji+9SON9Zo1Cz/cdgds6IHVM7/1vqK8K3a98CpGLf5ni98r99tvMfL5t7FlcS5sRgGKiCpYAMR1jkP1xWpYqwCr2eq1oSa2Vy8U79uHrpMnO5cV/vab84MZ3a0bSg4ehM1igWjjcHTlUQBAn1v7+DSGfb+77sK+N95A8d69SB4yBKWHDkGmVmP4U0851yk7etTlNRKeh816+Q6kmG7dcGr1apd1tBlaSKQS8DIe5ro6VBw/7rWHS+qIEdhab7gsAMjftg3J2dnOybAbxXHI6NgRfL0rmsnZ2S7DMgH2RpqaCxegrTcfSH3m2lqcWbcOI5991utbCTYbOOnlXE+sWIHhTz/ddBkbkdC/P0r274fVaHRpECncsQOJXiYr98RTg4jNZMXRlUdRUyzH2EVjG3294/MdLiQyCWoLalF1rgq1hbXY9/4+t3VY3h3eULjlCVyeI8PfQ0tt3w7k53t/XhSBixft640d69eiNMrRA6H6YjXqiupw6dAl5P6Q67KOP+tYS/m7bkpVUo+9AQEwHTIjkD2EPAnHz7gvUoekoiSnBEX7ipg2ZLTXPNsjOtbsUJYtZ6qy//h0NIY3J0tNgiak/q6HGqqX7FCW7FCWbFGebFCObIVSnjRHRogQBAH5+fnO3gIOWfPm4dcnnwQnkSC2h+cftVnz5+PUqlXIW7/evi2rFVsWLkTH8eMRmZYGRZQCEikPm9kGwSJAqpAiIjkCEpkEvJyH1WSFOk4NdUI8as63bgLfXrNno+TAPnDqwZAqpFBGK90eUoUU0qRbcfLrL7H7pZdc5mqwGAwQrL5djIlISUHNuZNQx6lh0dfCcOG/gEQBXYXOvs8yHhw4r12QBz/8MI58/DFKDx8GAFScOoU9L78MZVwcAHsDkSYpCdv/8Q+cXHcCxiojlDFAh+xYt215IpFKMeqFF/DLI48A+GNYrJIS6P6YZLtw1y6c/e47l9co4+JcjkHn666DrrAQ+99+27mM43S4+fMbMPHViYhKS8P9ZWUuPR3qSxs9GoLF4pz82lxbi52LFmHgX/7i0z4AQGlpqcuEPglZWZBpNNj/zjsAAFEQsO3vf0fn666DOj4egL1RwqG2oAD/nToVgx9+2DlUmGCzobbe1VTH0FL9/vxnAED1+fOoOnOm0WG3fBGVno6Mq67C5gcfdE4Sf27jRpxavRr977nH5+2oGhwXALCa7PvoGNe8MY7PdyhMjMSC40652G6xiEyN9PgZd9wd7g/hlicAmOvMAPx/4bio8WmRnAqDPEy/oweCIloBicx+qiJTywJWx1oqUHXTVGNC+ely2Mz+maHd0bCmig1Oj4xw/Iz7ImNUBoY9MgzDHh7GdLvtNc/2iI41O5Rly4iC6PY3JNhZXthxAZv+sQk5X+YE5f1ZCnaW4YSyZIeyZIvyZINyZCuU8qQeGSHCUSkkMpnLMDmdr78eG++9FyOfe+7yyhwHqVrt/Kc6Ph7T1q/HL488gp//8heIgoCuN92Esa+/DsB+Z0tS/1Rk3TMeyVeMcL7ux9u/wqCHJyJp0BVQRClQfjQdG/78Zxz+8EOMeeUV8HI5JB6GXqpfPl4uh0R2efxTVVwcorv2gE3T2XnnqCiKbt2PrKYI3PD598hd/RG+HDkSoihCsFggkUox5X//Q1RGRpPvP/7dd7Fh3jxYyyuhgATDXv4Hdr/wJLo+2R1ZAwdi411fY8D949Fx/LjLdwTVe722Y0fctGoVfpwzB1aDAfKoKPS49Vbn3fccx2Hahg3YsvBhHJp/LSBKEZkWC31JFtQJcW7HsGEWANB96lTse/11nP7vf9F18mRc+eKLWPXHxXltZibGvPKKS2NGtylTkPPhh/h88GD0mTMHgx58EH/avh2bH3wQB999F1K1GvKICNz4zTdQRDR9txLHcZi8di02zp+P35csgWCzIWvePJfeOye++gq1+fnIfuwxt9dLZDKUV1VBEARI6vXguPHrr7Hxnnuw7803wXEcMq66CuPfe8/5/O4XXsDpdesgWCyQqlQYeP/9ziG2AECwWLBuxgyYa2ogkckg4Xn0mTsXAxYssJdpxQp0nzEDkgY9IaQKBYQGy2Qa1xz4Bp+hiR9+iO3/+Ac+7tEDEqkUkRkZmPbjj1D90WDFy+UehxirvyxtzBjsefVVfJqVhcxJkzDsycUQbRwgkSJ1cKrL6xrWAeDy5zs5Odklx7ZOrpE77w4XbAIk9Xp1+XNCxXDMM/u+bAy6axBEofEhzVorJcW39RYtAiwW4E9/AhqOAGiz2XtsFBXZt3fllf4bhkqulkMZrYS51gzBKrgMdRSKk3YGqm6WnSgDAFSjGrFdfWtcb45gDy0Vjp9xX2gSNeic2Jn5dttrnu0RHWt2KMuWMVYbAREAByi09hOIYGdpqjah9Egp5BGeezS2JcHOMpxQluxQlmxRnmxQjmyFUp6c2LALQDtUU1MDrVaL6upqREVFBaUMVqsVe/fuxZAhQyCV+q99qexkGeQRckQkRUAitVc+c50ZploTk4kl9731Fmryy3Hq5w5QRith0VtQda4K0R2jnRO1mnVmGKuMmLFqBmK7tP4CTN7mPJQcKUH3G7sjMj3SJUdREHH82+M4u/Esrnn9GpdJ52wWC+oKC6Ht2BEAUHPxIr679VZMXrPGbUJpU60JZ346g15TeoGTBH9MuEAJVL1s6Njy5UjOzkZsg7k4QsWFXy9gx0s7EJkWiRveu6HJ9YOVo79UnKnAqhmroIxWQq6RQ1+mR/WFasR2i4UiUsH8M95QuOUZSDYb0KmTfWJvX/76JyYC994L3HcfkJxsnyj8oYdch6dKSwPeeovtBOH165ih0oC6wjooo5WI625vgPR3HWspf9XNhp+5gt8LAAAyjQyJfRKZ5/HTYz+h/GQ5rvznlUgbltbq7TUXfcbZYpVnKJwvh7pgZ0SfHXYoy5apPFuJ9Q+thzJaiSmfTwEQ/Cwv7LiAHUt2IL5XPCa8PCHg789SsLMMJ5QlO5QlW5QnG5QjW6H0e4KapdoRURSx+YnN+P7e7513WxbuK8S6eeuw+63dbsNaNUfFyZP4pE8f5P34I/rMmQ8AsJlsqD5fDYhATX4NBBv7LkiiKOLEf0/g7MazuHToktvzVpMVuT/korbAfRx/q16P72fOxCd9++LTfv3w45w5mLB0qVsjBgAoIhXoPa13yDRiFOwpwI8P/Yhdb+0KdlH8ovfs2SHbiAEAhXvt4+74MqxUe2CqNUGwCqjMqwyJrobEO563Nzp4wnH2x6efAi++aG+gKCkB/vUvICMDGDMGmDbNfY6NggJg+nR7I4c/qGJVAGefEN3xt6s9q/+3Oq6be+9AFpxztgRpjoz2zKK34NjqY9jx8o5WnZcRQkgwSGQSZFyZgdTs0DlHVmrtf8tCbThKQgghhDQfNUuFCIlEgoSEBL920bHoLc7xtB0TykZ3jIbNbEPp0VIUHyhGyiAfxx1pILZHD9z5x8TVFWcqAAA1BTXOYVIEq4C64jpEdWB7d1pFbgWqzlZBIpOg89Wd3XKUqWQY/shw/PyPn5H3cx46XNEB6SPSAQAKrRazdu5sdPs1BTWITI10GxorFFSdrfL7MDSBqJdtjSiKKNpnn2jA14aMcM9Rm6GFqdoEm9GGmos1UMerm35RK4RbnqIoYtuz26CIUmDQ3YO8TubMytSpwBdfALNnuy5PSwPefPNyz4pHHwXWrrU3fOzYAWzb5nl7omhvAFm4EJg8mf0wU3K1HBEpEagrrIO+XB+0eRt84e+6aTVYYTPZIFgFcBIOVosVNosNVgPbYbYG3TUIulIdIlNb31OzJcLtM94cnIRDzvIcCBYBfWf2hTZd2+pttuc82xs61uxQli2jTddi5OMjXZYFO0vHEFem6rbfkBHsLMMJZckOZckW5ckG5chWKOUZ/BIQAPZK0aVLF79WCmOlEYB9KApebr/SpI5Xo9ukbgCAQ58dYnr3X1SHKCiiFdBm2H+E1xXXwWZhOzFp7o+5AOwTZCoiFR5zTOyTiF7TegEAfv+/332+o1dXqsOPD/yIn5/4GRa9hWm5W8uRaW1+rV96ujgEol62BbpSHSrOVKDiTAXyf8tH3aU6CFYBvIJHxZkK6Ep1jb4+3HOU8BJEZ0YDAHSXdM6Jq/32fs3Is/6x8/Ro6tgFgkVnQeGeQuT9nAde5qfJJhroZv/aR0wMsGIFsGULkJfnOjyUTAbMmAH8+iuwdGnj2xNF4OJF+9wZ/hDVIQrRnaP9Mh8ES/76rCuiFFDHqWE1WaEv10Ow2r/3TVUmGKuMsJqsUMepoYhSNLEl36SPSEfPyT2hiGSzveYK9+/MxkiVUiT2SwRwufdfa7XnPNsbOtbsUJbsBDtLx99Gc53Z+fezrQp2luGEsmSHsmSL8mSDcmQrlPKkHhkhQhAE5OXloXPnzn6rGM6hIqJdh4roPb03zqw/g8ozlcj/Ld/ZY6G1REFEVIcoiBAhkUtg1VtRU1ADRQSbCyNmnRkXtl0AAHS9risA7zlmzc5C0f4iVJ2twu63d2PMojFN9rI48tUR+12vHAepKrQ+KppEDXgFD5vJhrqiOkSl+Wcc5kDUy1CnK9Vhzaw10JfrnctEQYTNbMOaWfaxdNRxakxdMRWaBM8TsIdrjvXvApfwEii0ChjKDag6V+XXO7l9zdPTsWuoqWMXCI7v5vqNzP6Wk2P/76BBIq644myTWfo6fGVREYPC1VO/jsnUMmejMuseCKz467OuSdBg6oqpMNWYcPbnszj6lb0HZFzPOAycOxCqOBUUUYqg1mOWwvU701epQ1JRvL8YhXsL0WtKr1Zvr73n2Z7QsWaHsmwZi8ECqULqMhxvsLNURCoADoBoH14qlHt2NiXYWYYTypIdypItypMNypGtUMqTjmaIEAQBpaWlfh1f3tEjo+GY10qtEj1u7gEAOPT5oVYNVyTYBOhL9c47R41VRpiqTFBEKKCKU4GX8czuHM3bnAeb2QZtRy3ie8bb399LjhKpBCMeHQGJTIKifUU4/ePpRrddU1CDvE15AICs27NCbmgpjuOcvTKqL1T77X0CUS9DnanGBH25HlKFFMpoJZTRSqhiVYhIjoAyWgmpQgp9ub7RcXfDLcf6d4cbq4zOh0wtA2CfH8dqtDK7O7whX/P0dOzqP3w5doFgrPL83exPR47Y/9unj+hTlik+jjro63pN8VbHjFVGGCoNqCmsgVwj91sdayl/ftY1CRrEdonFoD8PwpTPp0ARpUDtxVpIpBLEdoll1oihL9Mjf1c+qs5VMdleS4Tbd2ZzOYYtLDtWxqRHaHvPsz2hY80OZdkyu97Yha9u/gpnNp5xLgt2lpyEc577WQyh1cu+uYKdZTihLNmhLNmiPNmgHNkKpTxD6zZz4leOu35VMe53ofS8uSdyv89FbX4t8rbkIfPqzBa9x/HVx3H4i8PoObUnukzs4nW91t45KoqiszGi67VdfWpo0GZoMWDuABz85GCTwzHlrMiBKIhIzU5FQq+EFpfTn7QZWvscIeermPWiId5JVVLINDJwcK9rVlNo3h3uL/XvDm+o7GQZfnvtN3S/obvf58rwlVQl9TrvRCgcO2cjc3TgGjIcPTL69vWt4frKK+1zaBQU2IeRaojj7M9feSWb8jVWxw4vP4zzv5xHbLdYj3/Pwp1EKkFUhyikDklFwe4C1BbVIgWMWpAAXMq5hF2v70JiViKufv5qZtslvotMiUREqn1emOJDxUgfTn/jCSFtg6HCAIjw+3xfzXXzsptD7sY0QgghhDQfNWS0I43d9SvXyNF7em8c/PQgagtrW7T9yrxKHPnyCCAC8T3jEdvF81jmVqO11RcPRZuIjCszcH7reXQa18nn13W/sTtSBqc0Oul4ZV6lc8iqrNuyWlVOf9J29H+PDOLKWGFEbWEt1IlqRCRGBLs4QaVJ0HhsjIztEovUQamISG7f+TRHY43M/lK/R4YveN4+6ff06fZGC0+NGW++yXaib2917Ir7r0D58XLUFdbhyFdHQvp72p8iUuyfsbqiOqbbdTSsteWhN8JB6pBUnFp3CoV7C6khgxDSZjjPaULsbwg1YhBCCCHhgYaWChESiQRpaWl+HWssZWAK+s7qi9TBqR6f7zapGya9Own9b+/f7G0LVgG73tgFwSqgw9AO6DS2k8f1CvYU4H93/w/7P9jf7PeoTyKVoN/Mfpj03iSXO36aypHjOJdGDE+Tmx/+/DAAIOPKDMRkxrSqnP4U3SkakR0ioY7z313vgaiXbYlZZ4ZFb2n2+PztLcf6jRiePmOt1ZI860rqUHeprlVD5/mLs5E5QD0yysqA4mL7//ft63uWU6cC33wDdOjgulyjsS+vP1G4P6nj1Mi+PxsAcPTroyg9XhqYN/aBvz/rglXArjd34cjKI84eT3XFbBsygtGw1lB7+870JHVIKngFm5ZByrP9oGPNDmXZfKIowljh3hhOWbJDWbJDWbJDWbJFebJBObIVSnlSj4wQ4agU/pSUlYSkrCSvz0sV0hZPGn1k5RFU5VVBHinHFQ9c4fWuF3WcGsYqI85vO4/e03sjulN0i97PoeH7NCfH/N352P3ObgyYM8DZYGHRW1B2vAwWgwVdrvU+NFYoSBmYghuW3uDX9whEvWxLHGOVO+aC8FV7zbG2qBa73tyFXlN7IW0ou/1vbp6iTURtfi0EqwBw9gu0vCwwk2r7wqKz16tAzZHh6I2RmQlERUkQFeV7llOnApMnA9u3Az/9BLz4IqBQADfe6KfCepExKgMFvxfg3JZz+O3133D9O9dDqgz+KY2/P+u6Uh3yfs4DL+cx6olRAOyfM5YMFfaGjEDO2dJQe/3OrC8pKwnTVkwDL2/9dxXl2X7QsWaHsmw+c53Zfq4F15szQiHLMz+dQd6WPGSMzED3G7oHtSytEQpZhgvKkh3Kki3Kkw3Kka1QyjP4TSkEAGCz2XD8+HHYbLZgFwUAUH2xGvm7831at+J0BY59fQwAkL0gu9G7imMyY5A+Kh0QgcNfHG5R2S7+dhH5u/I9znPha466Uh2+vf1b5P4vF6tnrsbX077GqhmrsHbOWpSeKEVtYS02ProRulJdi8oYLkKtXgaTCNF5wbm5DRntNcczP51B2bEy7Pm/PTDVsptQu7l56kp1zh/W1eeqWzx8nr9kL8jGLatvQY8bewTk/RwNGX37tqxu8jww9v/ZO+/4Nsr7j79PkiV5b8dJHGfvTQbZgz0LBJIwyh6lpWW0lBZaSoEWWn4tBQqUFspuGIGww8ogBBJC9iB7x3svTUu63x9PTl6SrXGyZPver5dfkk83nvvec+v5js88eOghyMmBqipYsSIybW2PST+ZREJ2ApYSC5uf39z5DfBBpM91pe8m9U4iuU8yAJYSi6qZT7FQWqqnXjObo9PrVHFigGbPnoR2rNVDs2XwKPcPY5KxxfUrFmxprbBSvqu8y5fkjQVbdhc0W6qHZkt10eypDpod1SWW7Kk5MmIEWZapra2NSBkWhfLd5dQV1HUodF2+p5zlty1nwxMbcFqc7c7rcXlY/4/1yB6ZfrP6kT8rv8N2jLtqHEhQuKGQin0VQe2DLMtse3kba/+8lqOrj/r8PRA7OuocxCXEiYdsWUTam9PMmNPMxKfHY041Y620+hSajTVkWcbdGJmLSWf0y65CY30jLrsLj9uD7JFxWpw4Lc6Aykz1VDuOvWIsKf1SsNfY2fTcJtXWG4w9ZbdMQ2kDHpcHQ4IBj8uDo94R8LHrLPRGfadlFChC32PHhtc3DQZYuFB8f/NNFRsYIMZEI9N/OR0kOPHtCW9JpGgS6XNdcWQk90kmMTsRSScRlxCHs779e3UwKBkZ0Swt1VOvmf4It29r9uw5aMdaPTRbBo9yrWqd0RcLtjSlmgCw19qj1gY1iAVbdhc0W6qHZkt10eypDpod1SWW7Kk5MnoIHreHFb9dwSc//QRnQ/sDHlnDs0jJS8HZ4GTv+3vbnVfSS4xcMJLkvslM+emUgNqSkpfCwNMHAk16FIFStrOMhqIGDPGGgJwm7SHpJNIGpqEz6HDUOmgoaSAuPg5johFDfPRLlATCD2//wDuL3+GHt3+IdlO6LaYUEwmZCTjqHHhcHiRJwlHnwF5jx15jx+VwkZCZgCnFFO2mxhx6o55pd01D0kkc//o4x7893qnbN6WYcDe6kV0ykl4izhyHx+XBWe/s0ceueUZGuFxxhfh87z2wRcGPkDMmh6m/mMp5T58X1YH3zkIR9k7qnYTOoOOyty/jktcuUbUPK5otsSbU2hNx2V188rNPeP/a91XNatPQ0NCIBMZEI/lz8uk9qXe0m9IGc6pwrnSFIDUNDQ0NDQ0N/3SN0VqNsHHUOkAWg/em5PYHPCSdxLirx/HNI9+w7/19DLtgmPfhr828ksSg0wcxcP5AJJ1vXQxfjL1iLMe+Okbp9lJKtpeQOz43oOUOfHoAgAHzB6gSvWxMMpKUm0RDSQO2ShvOBme7OiKxht6kx2Vzdfk06VgmMTuRBUsWsHPJTvZ9sI+86XlMvGFii3lMKSYSsxOj1MLYJnNoJiMvG8nut3ez6dlN5IzJ8Xs9iQRpA9JIyk1i2l3TSMpNYsVvViDpJc575jx0el3Uj50sy6x5cA3mNDOTfjKJuPjgypYFv70mR8bYseGvb/p06NcPTpyA5cvh0kvDX2ewDD4ztvWM1KR5RgYIbSs1kWWZKbdNwVZl84qJa0QPg9kAEiBDydYS+s/pH+0maWhoaPglY0gGM389M9rN8Ini8HfUao4MDQ0NDQ2NroyWkREj6HQ6Bg0aFDEFeG+qb5o5IIdD3rQ8MoZm4LK72L10d5vf3U53i8yOYJwYAIk5iQw5dwgA5T+UB7SMrdpGwXqh2zHknCE+5wnFjil5KRgSxGBQfGa8X6HyWCStfxoAtcci48iIdL/sKiRmJ5LSN4W0gWnknZpHxuCMFn8dDYT3dDuOvWIsqQNScdQ52PjsxrDTEQO15643d6HT68iblsewC4bRZ3IfzGlm4sxxmFPMAR27SOOsd1K8uVgIOHeCAPmJE1BXB3FxMHRo+H1Tp4PLLxffo1FeqjVFm4o4+tXRqG0/0ue6Iuyd3Ds5IuuXJIn+s/sz4qIRURVP7+nXzOb0mdwHEH07VDR79hy0Y60emi3VIxZsqZSW6uqOjFiwZXdBs6V6aLZUF82e6qDZUV1iyZ7Rb4EGIDpFTk5OxDqFIr5mSgus/IQkiawMgAPLD2CtsLb4ffur21l+23JKtpWE3KbRi0Zz9hNnM/bKwMKCD395GNktkzkik/SB6T7nCcWOkk4ia3gWaQPTSOmbEvBysUBqfiogonTdTvV1MiLdL7sSoy4bxUX/vYhhFw4LetmebkedQcf0u6Yj6SUaihtotDaGt74A7Tns/GH0mdKHcVePQ5IkJEkiKTcJaBoQjjaKk9mYbERniHz/UPQxhg8Ho1Gdvqk4Mj7+WDhJokXJthLWPLiG75/+PmrHN5LnuuyRsZaLe7GSkVG8tZhVv1/Flhe2qL69aNLTr5nNURwZxZuLQ3YCa/bsOWjHWj00WwaP0+L0qcUYC7b0lpaqdyB7ol/fO1RiwZbdBc2W6qHZUl00e6qDZkd1iSV7Rr8FGoBQgN++fXvEFOCVwbJgaojnTsglbVAa9ho7G/65gapDVVQdquLApwfY+cZOak/UYqmwhNwmc5qIiA4E2SNz8PODgP9sDAjdjvo4vRBO7ULZGCDE9OIS40CGukL1RxAj3S+7IqH0Ec2OkD4onfkPz2fWvbNoKGnwXk9a/1nKO76mBGrP9EHpzP3DXLJHZnunKQPAitZAtFGczK2FMSNF67JSavTNiRNh2DCw2+HDD1VoZIj0Gt+LnLE5OOocrL5/NRX7K0LuY6ESyXNd0klc9tZlnPfsecRninu5y+6idHsp5bsDy2zsiIaSBgq+K4h6ucJQ7Oh2w1dfwRtviM/ucLm1lFvQG/XIHpn64nqOrDwSUl/W7kE9B+1Yq4dmy+BZ++e1vHXJW5xYd6LF9FiwpRIwYk4z47S0rxcZy8SCLbsLmi3VQ7Olumj2VAfNjuoSS/bUNDJiBFmWsdlsEVOAD2WwzFphpWhjERV7K6g5WsP+j/Yje2TqCurwNHowJhtZ99d15I7PDbs8i6XMgrXS2mLAsUX7a+yYU824rC76z/ZfIzpYO7psrqCmxxqSJJGan0rFngpqj9f6zVQJlUj3y66Cx+VB0kshO7o0OwqScpNYduUyrJVWv/MkZCawYMmCdq8pHdlTlmW/x6rPlD4kZCd4s5mijSKsbE7rXEeGIvStRt+UJCH6/eCDYhD5xz9WoaEhtUNkEm7971YKNxRyeOVhn877QPpYqET6XNfH6Unt19R3lRJTDSXqOOaKNhWx+d+byZuRx+x7Z6uyzlAI1o7LlsEdd0BBQdO0vDx48klYsCBCjYwwlnKL93rZUNpAY0MjH970YQsR9kD7snYP6jlox1o9NFsGj63KBrJwGjQnFmyp0+tYtGxRlwtaa00s2LK7oNlSPTRbqotmT3XQ7KgusWRPLSOjh6AMlgWTkeGoc+Cyu8gYlkHagDTMaWYabY0gQ1xCHMl9k7FWWnHUhVdrtHhLMR//5GO++8d3eFxt05EB4jPiOfvxsznvmfPQG8OvI29KMZGQmYDL4cJeY2/z53K4SMhM8ArDxTKp/cWgVrQjaLsz+z/ez7KrlrHzjZ3RbkqXxlHnwFppFSL1DhfIYgBf+TOYDKpcU7559Bs2/muj97rXnCFnD2HyTyaTMyYnrG2oRSjZcuGglJZSQ+i7OUp5qS++gMpKddcdDHqjHmOKEUkn4ax3ojfpI9LHYgWlVJqz3tlCtypUOrs/qsGyZXDZZS2dGACFhWL6smXRaVe4KNdLg8lAYk4iOoMOj9vTbfuyhoZG10cJnGvucI0luroTQ0NDQ0NDQ0PLyOgx9J3aF1OKiexRvjMe2sOYYMSYaMReZ8dR40Bn0JExLAOdXudzoDBYskdlE5cYR0NxA4dXHG63dJRaD8aJ2YksWLKg3QEAU4op6kLAgZA9Khtbpa3L6Xt0JaoPV+Osd2ovQCrhsrmwVdiQ9BKJOYktnJMuR3jZUOV7yilYX4Ckkxj+o+GdlukQKp2ZkeFywZ494ruSkaEWI0bAhAmwbRu8+y7ccou66w8GU7IJPOBscNJQ1ED2mOwWtTzD7WPRYt9H+6g+XM3A+QPpNa4XAAazAXOaGXuNnfriejKHZoa1jc4udRYubrfIxPAVGCTLIlvozjvhootAH34MRFQwxBswpZrwNAonhjGxKdK5q/ZlDQ2N7ofL4fJqoHUlZ7iGhoaGhoZG10LLyIgR9Ho9I0aMQB+hN+3cCbmMuXyMd/AjWGRk6k4IDYbEnETMKeoNchjMBkYvGg3Arjd3tRGtrtxfGXAt02DsmJidSMbgDL9/XcGJATBw/kDm/mEuA08bqPq6I90vuwrVh6sBSB8cWukuzY4tMaebMSYZkd0yVYerghZd9GdPWZbZ/up2AAaeMdCvc89pcVJ1sCom0iKVl/7OGDg+cACcTkhKgv4nK/Sp2TeVrIw33wx7VWGTnJeM3qjHZXdhrwrf4R4okTzXizcXc2TFkTZC5km9RVaGGuWlbFUnMzKiHE0bqB3Xrm2bidEcWYYTJ8R8XRl9nJ70gekhDw5q96Ceg3as1UOzZXAo9w+9SY8hvmWsZKzYcs+yPaz47QqOfX0squ0Ih1ixZXdAs6V6aLZUF82e6qDZUV1iyZ6aIyNGkCSJtLS0mI34djvcuOwuDGYDKfnqR/4POWcICVkJ2CptHFh+wDvd4/aw9pG1vH/N+1Tsq+hwPbFux66GZk9wO91eJ16oGiSaHVsiSRJpg9JE+Z86J5UHKvF4fJeV87u8D3uWbi+lfFc5OoOOsVf4rp3kcXtYdtUyPr/rc2yVtrD2Qw2m3jaVRe8uYtgFwyK+LUUfY/RoUBIU1OybixeLz6++gqKisFcXFjq9jrRBaWQMySAhK6HTthvJc11xYCi6GApKeSk1BOxjpbRUoHYsLg5sfYHO113R7kE9B+1Yq4dmy+BoXlaqtc1ixZb1xfWU/1BOXUFdVNsRDrFiy+6AZkv10GypLpo91UGzo7rEkj01R0aM4HK52LhxIy5XZMoElO4spfZEbdCRzwoGk4Fe43qRPbpliQ610Bv1jLlS1Dr54e0fvFHKRZuKsFXaMMQbSB/U8SBypO0Yy9hr7ULDREV6sj0Vao7VIHtkTCkm4jNDG9zT7NiWOHMcmcMzkfQSjloHlfsqkd2BXZ982bN5NsbQ84f6HbzW6XUk9hLZVvVF9T7n6Wz0Rj0GU+QrPSr6GM3LSqnZNwcMgOnTRRT80qVhry5szCnmTs8siNS57nF7sJRaAEju08qR0TsJU6o6ek6xUloqUDv27h3Y+gKdr7ui3YN6Dsqx/vSGG/ibTkfZ9u1+53391FP5uyHy957Cdet4a/78duf577Bh1B4LLUq+0Wpl/Z/+xGuTJvHKhAm8OHIkzw8eTO3RowAsPessTqxZE/R6o3XeFHzzDU8mJfHKhAm8PHYszw8ezPePPRYTWaStaSgu5t3zz+eV8eN5/8LZWI6t8Hn/UGx54ptvePf883l53DheHjuWjy6/HGt5ud/1L7/2Wv5uMCA3C3Y5vHw5b59+Oi+PG8dLY8bw5a230mi1tlm25tAhXhwxgm//+EfvNHOqaJu9VtzrSrdu5eVx40Ld/YhQX1jI84MG+f1du56rh2ZL9dBsqS6aPdVBs6O6xJI9NUdGDOF2uzueKQRcdher7lvF8p8tb1O2KRj0cXp0+sh1mYGnDcSUbqKhpIHNz2+m6lAVO/+3E6fFSa9xvag9Xoul3NLheiJlx1jm6z99zXs/fo+C79qpsREiPdGezak+JMpKpQ0Kz/vc0+3oC1OyiazhWUgGIcxcfbQ64GVb27NwQyFVB6owmA2MWjiq3WWViPbWJXq6O0pGRmuhbzX75hVXiM833lBtlaog03kDQJE41y1lFmS3jN6ob+NQHXP5GBa8vsBbojFUZI/s1WyJdkYGBGbH2bMhL09oYfhCkqBfPzFfT0e7B/Uc3G43HpeLXpMmsfO///U5T/muXcgeD3IE+sXu//2PuuPHm9rjdOJpbD/QJpB5fGGvqeHNOXNAllm8Zg3XbtvGDXv2cP3u3STn5YW1bojOeeNxuciZMIFrt23jup07uWbLFo6tWMHu118PeB2F337Lia+/Dnh+e3U1b8yeTaUipBUgH156KSOvvJJrt2/nvDeW46r9DgMHfM7rdrvRxcVx2hNPcN2OHVy3cycp/fvz5U9/6ncfbBUVyG53C0dGXFIS5776Ktft2MG127bhrK/n2z/8ocWyRd99x3s/+hGpgwcjNxtwUZz+ikbi7tdfZ/iiRUHts9oc+uQTynfs8P7vaWzE7Wy/pLJ2PVcPzZbqodlSXTR7qoNmR3WJFXtqjowegFIqwmA2YDAHH3nlsrlwWpxt/lw2dT1xtiobFXsqKNlawoYnNvDWxW+x/ZXtFG8uZvtr21m6cCnLrlwWkDOjp6EMbNUeq41yS7of1UfE4HrG4Iwot6T70PyaggSp+alIOskbKRcKu9/dDcDwi4Z3uB41NQXCweP2sPqB1az/x3pc9shHNiiODLWFvpuzcKEoW7VhAxw5ErntdITSxxz1Dir2VVC8uRhHvUP1+1ZnoZSNSuqd5LNkhxrIHpmZ98zklFtOUS3DI9Lo9fDkk77FvkFMf+ihriv0DZ33DKbR/Rh5xRUcfP99XA5Hm99+ePllRl97bUS2u+P556nevz8i627NqttvZ9hllzH9/vsxJiV5pxtMJnSdkG3SGZhSU5ly993sfvXVgJc5+uWXHPvii4Dnb7RaqdqzB0dt4O8R5Tt24HG7GXXVVQD0nTqIc57/B7X7/G+316RJpA8d6v3/1Hvv9dlOj9vN6rvuYv7jj7f5rd+cOST37QuAzmBgyq9/zdFW67CWlnLJxx+TO2VKi+nK86Gj1oHs8bDvrbcYeeWVAe5xZNi/dCnF338f1TZoaGhoaGh0JTRHRg8g1FIRphQTCZkJuBwu7DX2Nn8uh4uEzARMKeoMeDjqHMhumYyhGaQNTBOROwYd5nQzidmJGEwGrJVWbxSNRhNp/dMAqD2uOTLUJjU/lV7je5E1MivaTeny+LumuB1uEnMT0Rl03mtKsCUU5t4/l5GXjWTEJSM6nNebkRHl0lKOOgclW0o4uvooemNkR1qtVjh4UHxvnZGhJrm5oFQQiYbod+s+5qhzYKuy0WhppL6oXvX7Vmeh9FXFCRcJdAYd+bPyGX7h8IhmX6rNxRdDTk7b6Yrz4tVXIcRg7KjS2c9gGt0PY0oK/ebP5+D777eY7nG72f/uu4y4/PI2y5Rs2sSbc+fywtChPD9oECtuuw2npSmA6M25c9n+n//wyoQJvDRmDK9OnOgt21S5Zw+vTJhA6aZNfHnrrSyZMcO7nLO+no8WL+bFkSN5ceRIPr3+elx2e5vtH1u5krdOO63FtBU//zk/+BjEt5aXc2T5cibdeWeHtti/bJm3FNEr48dzbOVK72/2mho+WLCAF0eO5OVx43hj9mwqfvjB+/vS007zu88KO198kReGDuXFkSP537Rp7H37bZaedZb3d1tlJR8uWsQLQ4fy3+HD+free1tkGXREYu/e1BcWev8/8vnnvD51Ki+PHestr+Q5mXmwZOZMtj/3HDteeIFXJkygYrcI9Kg9epR3zjmH/w4fzosjR7LpH//wri+5b19uq6igz7RpAbfp2IoV9Js7t8W0vrNnc3zVqoCf4exVVejNbd9Pt/3rX/SdNYuM4cMDWoeh1TqGXHQRaQMHtplXuV7aa+2c+OorkvPySDtZxumN2bPZ+d//8urEibw8diwfXHopjro61t53Hy+OGsXLY8ey88UXW6yv+uBBll1wAc8PGsTzgwbxyVVXYSkr8/6+/Npr2fj3v/O/6dN5acwYXhozhn0na2/aq6t59ZRTOPThh6x/6CFePeUU7NUieMrjcvHlT3/KiyNG8OKoUbx/8cXYqqo6tIWGhoaGhkZPoHuEqnQD9Ho948aNi4gCvJKRYU4LzpGRmJ3IgiUL2nUcmFJMJGYnhtW+1hiTjMQlxOGsc6Iz6EjJS8GYaATA5Wg/AjGSdoxlUvNTAaHnoCY91Z7NGXb+MIadH54Qs2ZHQaDXFLfTzZe//pLpv5reRtgYfNvTlGJiwrUTAmqHNyNDBXHkcFCczKYUE5IusqJZe/aI6PTs7JaDvpHom5dfDitXCkfGvfeqttqA8NXH9i7by4FPD5A7MZcpP5sSkfuWQqTOdaXkU2t9DIW1j66l+nA18x6YR0peiqrbjgbB2PHLL6GsDFJS4K23oLpaaGJkZMDMmbB6Ndx9t8jc6Eqo+Qym3YN6DsqxXnNSz27sjTey4c9/ZsTixd55jn7+Ob1PPRVzWlqLZS2lpSy74ALOfeUVBp59Nh63m1W3384XN9/MBUuWiJkkiW3PPstln39OYq9eFK1fzweXXsrNR46QOXIk127bxpvz5jH997+n/xlneNddvmMHU3/zGy586y08bjfvXXghO154gVN+/vMWbeg3bx6fXnMN9YWFJPfti9vp5OB77zH7kUfa7GvJxo30mjSpzSC2L8q3bWPxV18Rn5FB4bp1fLRwITcfPYo+Lg6Py8XU3/yG3qeeCojSWF/+5CdcvnYt48aN46hO53efDSYTRd99xze/+x2L16wh46TWx9LTTyfpZGkrgE+vu46hF1/Mj95+G3djIx8tWsSOF15g/C23dNh2gPLt20kbPNj7vzk9nQWffEJCdjYel4tlF17IrpdfZtxNN3Hlt9/y7R//iOxyMetPfwKEjtiHl17KrEceYeDZZ+Osr2fpWWeRNXo0A5o5XIKhoaiIlP79vf876h3EJZjQm81Yy8pI7NXL+5u/a9D2555j9DXXtJhmrahg6z//yY8DzFLY9txzjDq5Drcb1q6F4mJxHzC08hV5S0vVOtiz5L0W2RiSXs+O559n8VdfYUpNZcOjj/L26acz6LzzuGH3bhqtVt6cPZt+8+eTNnAgLrudpWecwcyHHvLuw4a//pUPLrmEK7/9VqxTktjyxBMsWr2a9CFDqDl8mP9NnUre7Nkk5uZyzZYtfHrddfSdNYtxN910sm21WEtLSenfn+v37EGSJL74yU/Y8MgjzPvb37TruYpotlQPzZbqotlTHTQ7qkss2bPrhNz1AIxGY0TWG454Z2J2IhmDM/z+RWowyO10E5cQh6SXgnbARMqOsYziyLCWWVUvUdMT7RkJNDsKArmmbHx2I5X7Kln525XUnvCdZaTYU3HUBoMyGNxQ3BBV8UxlcLozhJV9CX0rqN03FyyAuDjYsQNOBoJ2Kq372MhLR2JMNFJzpIbkPskRu28pROJcH/fjcSx6d5FfHYyGkgYsJZawsoxqT9RyYt0Jv+dcZxOoHZ97Tnxedx2cc47QaZk3D8aNg9deE7899RS89FJEmhlR1HwG0+5BPYfmx7rfnDnUHT/eQrNi18svM+b669sst/WZZxixeDEDzz4bAJ1ez7zHH+f4qlXUNKsVeMrtt3sHqftMn44pJaXDUlIZI0d6B411ej0jr7qKwrVr28yn0+sZvmgR+09GrR9evpy+s2ZhSmnroLVXV2NKT293u94233kn8RmiRGjfGTMwJCRQczJNMSEry+vEABhy8cWUbtkCNNmyvX3e++abjLvlFjKGiaCX1P79mfLrX3vXV33gAJaSEsbeeCMA+rg4pt5zD3sDFJMqXLeOb//wB6bdd593Wu+pU0nIzgZEeaXBF1zgbbMvjq1YQeqgQd5ja0xO5pTbb2dPgG1Y8fOf88qECbwyYQJLzzwTAEdNTQsn0sr7VvLWJW8h6eN8im+3vgaV79zJ7tdfZ+pvftNi+trf/pbJv/wlptTUDtt15PPPKd+2jXE338yyZTBggMgKvfJK8fnEE7B3b9P85lQzkl5Cp/Nw8IMPGN7MwQcw8ec/9253yMUXU3fsGNN+/3sA4hISyD/jDEpOOlj2LFlC9vjxLRwxp/7mNzRaLBz/6ivvtNHXX0/6kCEApA0aRK/JkynZuLHd/TIkJDD1N7/xlo4cfc01FDQ7X7TruXpotlQPzZbqotlTHTQ7qkus2FNzZMQIbrebTZs2RUQ8RRnoiwXxzoCRRfZFan5qUPW/I2nHWMaUYvJGGak5CNVT7algr7XjbGhfcC8Qerodg2XG3TNI7Z+KrcrGyntXUn24pQi4Yk+nzcnnd37Oyt+txFrZ9qXZH0m9khhy3hDG/ngssjt6joxQs+VCoT2hb7X7ZkYGnBwriUp5qdakDUwjuW8ynkYPhd8XdrxAGETyXNcb9d7sxNYk5Yoso3AE7AvWF/DNo9+wZ1lwYq+RIFA7FhTARx+J7z/5SdvfL74YHnxQfL/1VvjuO3Xb2VXQ7kE9B+VYe5qVLRp93XXsevllQAz+l23ZwoCTg9HNqdi5k76zZrWYZjCZ6DVpEhWKNxxI7tevxTzxWVnYOyh7ozgRFBJycrCWl/ucd+SPf8zet94CxGDxqKuv9jmfKTUVR3W1z9+C2b7s8bDtX/9i6Vln8eLIkSyZPh2Xzea1pSzL7e5z/fHjZLWKEug1aZL3e+Xu3dQcPOh1BLwyYQJf3HJLuwLkZdu28cqECfx3+HDeOessZj70EH2mT/f+Xl9YyKo77uD1qVN5ceRIvv/LX3D5cB40b0PB11+3aMP6hx/G7aO8ly/OePpprt22jWu3bWPhl18CoDeZWpQHs1fbQQaP00FcfMt3ztbXIEddHR8tXsxZ//631yEDUPz995Ru2cK4m2/usE11J07wxS23cP6SJXz4iYnLLhP3hObU18O778KyZeJ/c7qZxe8tZvQCAzkTJ7bIGgFIzM31fjfEx5M+ZAj6uLgW01w28ezm63wB6DtzJhXNxLtTfPSdjspEmdPTW7z/JuTkYDvZX7XruXpotlQPzZbqotlTHTQ7qkss2VNzZPQAlIyM+Iyu48gwmA3kjsuNeORsdyK1v4gg0nQy1GPve3t594p32f7q9mg3pUcRnx7P6Y+eTvqQdBy1Dlbet5KKfRVt5juw/AC2KhsNJQ1B1YnXGXRM+ekURlw0Ap0herdBJSOjM5zM7WVkRIIrrhCfb7zhX4i5s5AkifzZ+QAcX3u8g7m7JkoJtnAE7G1VJ4MeutCzwn//K0qJzJkDo0b5nuf3vxdZQk6n+Cwq6tw2amhEmzHXXsue119HlmX2vvUWwxYuRNK1vfdJ/koFyHKL33wFGAWb3ShJkl+NiNxJk3DU1FCxezfFGzYw8JxzfM7X65RTKNm40aeYeTDb//aBB9j75pvMfuQRrtu1iyvXr/c5f2uUfW60WNC3ilDUm5qeSVw2G32mT/c6Aq7dto3rdu7kim++8du+nAkTuHbbNm7ct495f/87Rz77zPubu7GRN+fMIT4ri4vee48b9uxh6m9/2+7+umw2hl56aYs23LB7NxeczMiw19TwxuzZVDZPX+iA5Lw8b6aPx+URAtpuJ41WCwm+hItO4nG7+fjyyxl55ZUMvvDCFr+tuv125v/jHz77Z3OcFgsfXHwxs/70J7InTuaOO3w/ayiT7rxT3CskSUKSJPYsWRKQyLeunchTf+eL3Op8wVdAXrAPRu2cLxoaGhoaGj0NzZHRA8iflc/Yq8aSM9b/Q6VG1yd/Zj7DfjSsW9RHjxWqDomIqcQczaHW2ZiSTZz2p9PIGplFo6WRL3/9JQc+PUDVoSqqD1VTf6ieHS/vwGlxMmDeAK9ToCvhLfsXxYyMSPGjH0F8vBAYb6faRaeRP0s4Moo3F+O0hJ9l1RxLuYWqQ1XevmktsFJ9qNo7zVJu6Xgl7a2/zMKKe1ew6d+b/M6jhu5LV8vedLng+efF91tv9T+fTgevvCKceMXFcMklEGAQsoZGtyAxN5eMkSM5vmoVP7zyCmOuu87nfL1OOaVF+RoAl8NB2bZt5IwfH/D2dCrUTh555ZV8dt11DL7wQnQG35KOSX360O+009jy1FNhbevAe+8x7/HHyZ08GZ1eT2Uzoe9AyBg5kpLNm1tMK1q/3uv8SBs6lLJt23C3k4HRHmNvvJHSTZso2STuAeXbtxOXkMD0++8nuW9fgBbi5ND2GKQPHdpuOaNGi4WqPXsCznAB6DNjBgUnRc+VZzBnzV5yp0xp1xGx8he/ID4zk+knSzY1x1ZRwao77miROQLw2uTJbD4pdKQ4Qgaeey6jr76atWvbZmI0RwZOnBDaGSCE54+vWMHQBQsC3ldf9DrlFJ/l0YrWrSNn4sSA1+PXgaihoaGhoaHhE03suwfQ+5Te9D6ld7SbETAum2+NB3/TNQRDzxsa7SZ0K2RZpuZwDQDpgwOrwayhLsZEI/Mfms+Ke1ew8/WdFH5fSHKfZGRZprakFo/Vg96oZ/3j69nx6g4WLFkQcBaXy+6ivqgenUHn1ZjpbBptYlAj0hoZVVVNUeijfUssqE5SElx4Ibz9tsjKaFZlIyqk9U+j96TepA1Iw+NSL6rRUm5h2ZXLvKXNZFnGarWyP2G/dxArITMhqL7ZmrqCOsp3lbcr+qxGRkZXy9785BMoLISsLJFp0R5JSfDBBzBlCnz/vXB8vPSS70BZDY3uyLibbuKb3/8eSacjY/hw3/PccguvjB3LoPPOY+A55+BxuVh95530P+MMkpsJV3eEOTOTumPHwmrvyKuu4ts//IHT//nPduc787nnWHr66cgeD6f84hfEJSQA0GizoY+L8+sEaU5S796Ub98uMkHq6lj3xz8Slxj49XrSXXfxxsyZDL/sMrLHjaNq/342PvYY2ScH4XudcgqJvXqx9t57mfvYY0g6HY7aWmSPB3MAGh86g4FZjzzCV7/8JZd//bUoi1VWhqWsjMScHIq++47DH39M/mmneZcxZ2ZS3CyzZOC557Lq9tvZ8tRTnHL77YAQdzempBAXH09y377cVtE287U98ubMwdPYyO7//Y9eU8/B47JRf2AZ05/9k99lNj/+OBU7drBo1Sqfv990UrekOX+TJK7etMl7LFfdcQeG+HhmPvwwIBzUgaDMt/LuJzFlj6b6sJXcCaEHfw277DK+uf9+dr70EmOvvx5Zltnw6KOYUlPpO2NGwOuJV+F80dDQ0AgGS7ml3fcKU4pJq4yiEdNojowYQa/XM3ny5JhQgI8WphQTCZkJWCutuBy+nRYJmQntlpDR7KguPdmetkobjjoHkk4irX9aWOvqyXYMF4PZwKRbJrFn2R6SeiVhTDLicXmQCiT0Bj1p+WnojXqslVYcdY6AH7oOfHqAbS9uI39OPjN/PTPCe+GbU39xKpNvndxU+yBCKNkYAwZAcnLL3yLZNy+/XDgy3noLHntMRMZHk3l/nKf6Oh11DqyVVgwmA4Z4AzKyyLCRQELCZXMF3Tdbowh4KyL1vlA0MiylFmSPjKQLfoReKS3VGeLzHRFIv1REvm+4AUwBVJYbNEj0x7PPFhkaEyaIciM9Ae0e1HNQjvVKk6lFuaOB553Hl7feysw/NRtkliQMJwf+QYheX/rZZ3z1y1+y8he/QPZ4GPKjHzHv8ceb1m80tim3o2+1rQk/+xmf33ADO154gbn/938BLaM3GtE10yKIz8wkc9SoFiLcvkjIyuKKb77h+//7P96YORNZlvE0NqIzGLjko49Iyc/vcPtnPPssn990E1ueegqdXs/0Bx4QQt4eD5MnT+aEydTu8qn9+/OjpUv59NprcdlsGFNSGL54sTfSXpIkLv38c7666y5eGjUKQ3w8erOZs194wacjo7UtAIYtWMDmxx/n4AcfMOSii5j96KMsPf10sf1Bg5j7f//H4Y8/9s4/9JJL2PnCC7w2aRKjr72WU26/ncvXrmXV7bez7dlnMSQkYExK4sJ33mmjZxEokiRx0fvv8+Utt/DtAw9jKW0g65TzGL5woXeevW++SX1BAZN/9SsmT57Ms2efTXxmJq9PndpiXee+/DI5Jx0/bexhMnm9zvbqarY98wwZw4fz6smsh4YGuBOJF/iMBlpqXrgxIp8sQNH7ZEzfiVUfYkidSX1xPbkTmmlimEwtSoLp4uLalgxrNk1vNLJo5UpW33kn3508r/LmzOGi995rmt9o9Fl2rPm00dddx4cLFnD088+Z8utf02fGjBbt8K7n5DTteq4emi3VQ7OlukTSnq2DsHwRbhBWrKD1S3WJJXtKcrBFTbshdXV1pKamUltbS0pKdMryyLKMzWYjPj4+KHHrQNZbtrMMc7qZlLwUVdcdCcL1DkfKjl0Fp8VJ7fFaMgZnoDeGf4HpyfYs3FjI1w99TWr/VM57+ryw1tWT7agGVYeqWLpwKeY0M8ZEI7XHa6kvrseYaCR7TDaNlkbsNXYWLl1IxuCMjlcIFHxXwNo/ryVjaAZnP352hPcgujz7LNx2G1xwQZMwskIk+6bdDr16QV0dfP01zJ6t6upjguZ9My4hDnutHUOiAUOcAQkJp8UZdN9szeb/bGb/R/sZcckIJt7gu1yF7JH54PoPiM+KZ94f52FKDlwzBkQ/WHrZUtxONxc+f6HXMRItOuqXhw/DkCGizPjBgzB4cODrfvJJ4cDQ6+Gzz+CMM9Rrd6yi1nkeC8/LsU60bdRdnjc2P/kkLpuNUzvQfogkgdrS3dhIQ1ERqf37A0KE+uPFi7lo2bIW4tHdmYOfHWTjMxvpM7UPc++f2+b3SPZLt1sEahQW+paekCTIy4MjR8R1f+mFP6XRPZ1x15zCmMs7SThMRbrLOR4LaLZUD82W6hJJeyrvLkoQVmtcNhcuhyusd5dYQeuX6hJL7xOaRkaM4Ha72bFjh+oK8I46B6t+t4rlty1Hdse+zyoxO5GMwRl+/zryCkfKjl2F5T9bzop7VlB9JPAat+3Rk+1ZfUjYMH1Q+GWlerIdI4IELpeL5L7JSIR2E1UGasPRFOgqtCf0Hcm+aTYLPQKAN99UffUh4XF5KNpURNkPZaqvu+5EHZX7K6k4XBG08G171Bd3nJEh6SQufuVizv772UE7MQAarY24naIPxEJpqY765X/+IwaszjorOCcGwO23w7XXisGvRYtg/3746itRAu2rr8T07oZ2D+o5dPVjXbVvHy+NHs2RTz/1lkCKFoHa0mW18skVV/DSmDG8PHYsn157LWc+91yPcWIAJPZKpP/c/vQa18vn75Hsl3q9cFD7QhlneeIJMR/AsEW3Iunj2g2ci2W6+jkeS2i2VA/NlurSGfY0xBswJhrb/PlybnRVtH6pLrFkz+7TSzV8otS8NqWY0Bk0v1V3JyU/BVuVjdrjtWQNz4p2c7o01YfVc2R0Nm63EDUsLhap9LNnN73AdXVkZHRxOsy5ZkypwQ/YKiiODGeDE0e9I6TB33DwuDyseWgN5jQzU38+VZUMKn90ttB3c664QpTxWbpUDDQEUK48oux5bw87Xt1B70m9yRmdo9p63U43DaXCKdZYH5qgqz8CKS0VLvo4PTN/MxNHnSOifVENHA548UXxvT2Rb39IkihLtWeP0MsYPVoIhyvk5Ym+GqYOrIaGRghkDB/O9UGKbUcbU2oqV65bF+1mRJXeE3vTe2L09BgXLBDOijvuaDk9L09Mb349V0oU22vtndY+DQ0NjVhFlmU8Lg/6uNh+/tfQaI7myIgizcsouV1urAVWqtOr0RvERUQNkR1bdezUvNaIPGn90yjdVkrtsdpoNyWidIZAVf7sfEypJrJHZ4e1ns5m2TLxIldQ0DStOw3MSUgk9krEVeMixGQMQGhvxGfEY6uy0VDS0OmODHuNnZKtJUg6iWl3TYvYdmS5/YyMSHPaaUKMubwcVq4U+gTRpN+Mfux4dQcl20pUdWBZyixerROPw4PH6UFnCj94wOP2YCm1ABF2ZBj15M/Kj9j61eS990R/6tNHlEsLBbMZbr5ZODJcrSS5CgvhssvgnXe6xzVTQ0NDoyfQeDKGQK8XAT1jxsC2bW0Decyp4p3YUds1MzI0NDQ01KLR2kjZ7jJ0el1UndEaGsGiOTKiRGuRHVmWsVlt7E/Y7603pobIjpKRYU7rOY6MWBCfiRap+akA1ByrUW2dsWbPzhKo6j+7P/1n9w95+dZ0hh2XLRMDcK2r2nTHgTk16lwm9UkSjoziBjKHZqrQqsCx1zRdmyNZs7OwEGprRSbEiBG+54lk34yLg4UL4V//EuWlou3ISOmbQtrANGqO1FCwvoDBZwVZl6g9JGFLV6MLR70Dgyn8RyxHrQNzqhlng5P4zPZLPhV8V8DWl7aSMSQjagL2auKvXyoi3zfdJPpXKLjd8OCDvn+TZZG1ceedcNFF3SebLdbu5RqRQzvW6qHZMjDstXaMSUZ0ev8O/Ejb8pNPxOeCBSIL1Gr1ff1Wsnm7ckaG1i/VQ7Olemi2VJfOsKfepAcPeDwePG5Pu9fwrorWL9UlVuzZ/XpqF8FR58BaacVgMmBOMxOfHk9G3wzi0+Mxp5kxmAxYK61h1+9UMjJioeZ1Z2AwGJgyZQqGaNcviRKKI6PueJ0q64tFe7Y+d1r/qXXuqEln2NHtFpkYvkrzK9PuvLNr13932Vw4LU5cVhcJcQm4rCf/t7k6XtgHSnkppXRPZ6Jcm01pkc0EUcpKDRsGRmPb3zujb15+ufhctkwIgEeb/Nki8+DY2mOqrTMhM4HMYZmY0k0kpCbgcXvC6psK8RnxXPzKxVz6xqUdOrwkvURDUQN1BcFf/6uPVHNi3QnqCtW5d4SLv365Zw+sWQM6ncioCJW1a1tmrbVGluHECTFfdyAW7+UakUE71uqh2TJwPrvjM9665C2qDlX5/D3Stqyra7pe33ST+Cwu9v1MbE41ozPokHRdU/hV65fqodlSPTRbqktn2VN2y0gGcS102cN7Z4lFtH6pLrFkT82REWUUkZ24xDgko0RcYpyqIjtKRkZ8es9wZMiyTE1NjapCq12JlH4pANiqbDgtzrDXF8v2jKRAVc2xGioPVHrFb8OlM+zYnQfmTCkmEjITcDlc2Gvs2GvsWCot3u8uh4uEzARv3eNAyZ+Vz4QbJtBncp8Itdw/SkZGpK/NSlkpf/oYndE3Z82Cvn3FQMNnn0VsMwGjlFAq3V4adkRm877pbHBiMBqIS4lDdslh9c3WBKJb0VzAPtjjefSro3zz6Dcc/PRgSO1TG3/98t//Fp8XXihK5oVKcbG688U6sXwv11AX7Virh2bLwJBlcb9Dbirb5GueSNryyy9FmcChQ4UuHIDNJp47WpM2MI1FyxZx7pPnRqQtkUbrl+qh2VI9NFuqS2fY02VzUbqzFLfdjcflwV5jVyUIK5bQ+qW6xJI9o+9K0QBEp2hoaCA1LVXVMiM9TSPD7Xazd+9eJk+eHBOews7GmGgkISsBa4WV2uO1ZI8MT9+hp9pzz7I9HF11lLFXjWXM5eELC3SGHbvzwFxidiILlixooSm0c+dOxo4dG5amUJ9JfegzqfOdGNB5Zf+UjAx/+hid0Td1Oli8GB5/XGi12GzRFaFP7p1M+uB0qg9VU7C+gCHnDAl5XUdXH+X0v57uHbxRq2+GQlKvJJDEi4mjzuF3QMkX3v4YI88Kvvql1SqE4yE0ke/m9A6wDHCg88U6PfVe3hPRjrV6aLYMDEedA9ktg+T/mSbStlTKSp1/PsTHQ2qqKKtZXCy+NyeS5Tw7A61fqodmS/XQbKkukbSnEoRlqbDQaGn0TrdV2bzf1QjCigW0fqkusWRP7WjGALJHpq6gDkuNhdTU1LAEbFszYN4AUvNTyR7VtQSLNUJnxAJRCL8zBs5iAY/Hg6PGgTHF6B04DJfqw9UApA9KV2V9nUF3H5hLzE709mmXy0VCdQLpg9OjfhMNFa9GRoQHjqMp9N2cXr3E51dfiT+Irgh9/ux8qg9VU7G3ImRHRvmecna8tgOdQcdFL1+EOdXs7ZvGZCOSLJHSNyWsdm54agMNpQ2MvWIsOWNy2p1Xb9STkCkc2Q0lDUE5MpSXl1guQ/n221BTAwMGwFlnhbeu2bNF/yss9F16BEQWkRLZq6GhoaHhG8URbkoxoTN0frEHjweWLxffzz9ffPbpIxwZRUX+9cE0NDQ0eiJKgGDF3gq+euAr7/Q+U/sw6eZJQOcFYWlohIpWWioW0IG13Iqr3qV6bbq+U/oyZvGYThey1Ygewy8czvALh5OQlRDtpnQKlfsqqTpY5X2RChe3003dCZGL3pUcGbNnQ26u/98lCfr10wbmWlNztIYT607gcnRuGm2jVUTARDIjw+2G3bvFd3+lpTqDZcvgt79tO10RoV+2rPPbNOiMQZz7z3M59Y5TQ1pelmW2v7odgIGnD2zhNCj7uoyPb/mYHa/vCLud5bvLKdtRhuwJLIU3MVe8dDQUNwS1Ha8jI4bLUCoi3z/5icjyCQe9XjjRQFwbfdFVnb4aGhoanYly/4hWRt+WLVBaCklJMGeOmKZcv/1lIW/810a+vOdLqg761vTQ0NDQ6M4kZiei0+talOd2O9xkDM4gY3CG5sTQiHk0R0YMICFhMBtESYhuKLLTmUiSRHx8fJdPG44VYtmeTqsTl8PlHQi2Vdo6WCIwao/XIrtljMlG4jPVGdTrLDum+An+Vjb7xBPRKeWjNmrac9XvVvHNo99QX9i5gt/T7pzG4vcWM/TcoRHbxsGD4HBAQgIMHNjsB3s51B+C+kNIlsOk6iuQLIe907CXq9aGWBWhN6eaSRuQFnIfKtlWQvmucnRxuhbl5yRJImNoBhKScECEUUPU4/ZgKbUAkNQ7KaBlknsnA9BQEpwjw6vZEiMZGa3P8a1bYcMGiIuD669XZxsLFsA774jMi+b06gUGA2zaBPfco862ok0s38s11EU71uqh2TIwlDLG7TnCI2lLpazUmWeC0Si+d+TIqD5UTcWeCizlFtXbE2m0fqkemi3VQ7OlunSGPeuLxLtv5vBM8mbk0W96v4htK1po/VJdYsmeXaYmR11dHb///e9Zs2YNkiSRmJjIgw8+yBlnnAHAnj17uPXWW6mtrUWSJO6//34WRKNeRZAoYjqSXkKPHnutHYPJoIrIjsfloeyHMuLT40nplxITHS7S6PV6xo8fH+1mRBWP20PN0RrqC+vpP6d/WOuKVXvKskzN4Rpkj0xy32Q8Lg+2ahv2WjueRk9Y6/aWlRqcrto50xl2fPxx2L8fzGZIT2/58pabC08/HZ0SPpFATXsm9U7CUeegvri+0zNwIl2CQdHHGD26WQS7vRzWXQmOSgD0wEiAimYLmjJhxhIwh1+SMBgR+nnzwt5cSHhcnqCORfNsjKHnDW2R/abX65lx4QyKlxTjqHNQc7SG9IGh9StruVW0LU4XcIZd2sA0MoZmBFXX1t3oxlnvBGJHI6P1Oa6IfC9Y0FSmTA0WLICLLhL9r7i4SbvlnXfg8svFdXX4cLjlFvW2GQ1i9V6uoT7asVYPzZaBEUhpwkjasrk+hkJHjgzlHumodUSkTZFE65fqodlSPTRbqktn2LOuQFSg6DO5jyqaoLGI1i/VJZbs2WUyMhYtWkTv3r3ZunUr27Zt46mnnuKaa67h2LFj2O12LrroIh566CG2bdvGp59+yr333suOHeGXdYgUisiOy+HCXmPH4/LganThqHZgr7HjcrjCFtmxVdlY/fvVfHbnZyq2PLbxeDyUlZXh8YQ3mN2V8TR6+PzOz1n3f+u84sghrysG7WlKMSFJEm6HG9kjI0kSkl7C0+ihvqg+7HOn6pBIMw918NEXkbbj7t1w//3i+9NPi4Hh1ath3Dgx7frru48TA9S1Z3KfkxHsQZbi6Qr4FPpurBNODJ0J4tKQ41JxkIAclwpxaWK6o1LMpwKxLELvcXtY9/d1LLtqWQuBu44oWF9A9cFqDGYDoxaOarlOj4eKqgqyRmcBULq9NOT2KZFSSblJATtVh184nLMfP5uh5wWe6aOU5dMZdBiTjME3NAI0P8fr6+F//xPTwxX59oVeL5xoV1whPvV6IU7/0EPi99tug5Ur1d9uZxKL93KNyKAda/XQbBkYqf1S6T+vP9mj/Qc/RMqWpaWwcaP4ft55TdP79BGfRUW+lzOlnnRkhPmeFA20fqkemi3VQ7OlunSGPesKxbtect/kiG0j2mj9Ul1iyZ5dJiNj1apVvPXWW+hOhpVOmjSJU045hc2bN2MwGJg4cSJz584FIDc3l1/96le8+OKLPPHEE1FstX8UkR3lAapwcyFf/fUr+o7uy7wH5gHhi+woqb7mdHOPyMYAcXIdPnyYjIwMb1/paRjMBhJyErCWWak9XtuhQGx7xKI9zalmModmYk4zM/ry0Qw6fRCHPj/E7nd2kzk8kxl3zwjr3Kk5UgOIjAy1iKQdXS649lpRQui88+CGG0QpqXnz4N57xeDc66/Dww+HX1c+VlDTnkrJnvrizist5Xa6WfPwGuLT45n686nojZGp96UIffvUx9DHgyERWZaxNtQQZ05suk941Huxj2URep1eR0NJA43WRo5/e5zhFw7vcBnZI3u1L4ZfNLyNoLbSN7PHZFOyuYSS7SWMuDg0pVGlTyrOtkhhTDYy87czcdlcMfOs0Pwc/9//dDQ0iMyIk495ncLvfw9798KSJULLZf36risaG4v3co3IoB1r9dBsGRh50/LIm5bX7jyRsuWnn4rPU05p+RzRUUaGUpbWXquOvl5novVL9dBsqR6aLdWlM+zZf3Z/knsnkz4oHVmWsVXakHRSzJSZVQOtX6pLLNmzyzgypk2bxuOPP86DDz4IwNdff826det47rnneOyxx7xODIW5c+fypKLk2AqHw4HD0TRQU1cnvJEulwuXS5R00ul06HQ6PB5PC4+TMt3tdreofe1vul6vR5Ik73qbT0/ISsCULiJCPJIHXbwOZ72TtIFp3m0qyxkMBmRZxt2skLgkSej1+jZtVKZbK614ZA+mVBMul6tT9glo0cb2poeyT/7arkxX2qrM0x32qaO2+5qe0i8FS5mF2uO1ZIzICHmflO+yLLewTTT2SWnj/k/247A4SMlPYezVY9HH6Rl56UgOfHqAuoI64lLiMKWLPh/KcRr747FU7K8gfVg6LpdLlX3y913Zp3D63iOPSGzapCctDf7zn5b7dMEFEqmpeo4fh1Wr3MybJ3facYrk+QQEfJ51tE8JOQl4ZA91hXUtrpOR3CdbtY3ircXoDDom/2IyuInI+bRrlx6QGDXKA5yc7nahl2VkWUaS3Uh1+zA7XdCoxxOXDLKMJAyMO8B9be98mjnTQ16eRGEhyHLbQXJJkunbF2bOlIHO73v5M/Op2FvB0TVHGXzu4A73SZIlBp4xkIOfHWTIhUPaXCOU9mSPzkaWZcp3leNyulrkvwa6T7UnavHIHq+zLZi+J3tk3B53C8eEv32Ki48jb3oeHo/Hu41YuEaI+46bf/1L9OOf/EQOqu+pcT799786jhyRWb9e4oILZL791k12dtd7jlDr2aj1vmloaGhEE19lpaB7l5bS0NDQUIPhP2oK4Nrywhb2fbCPEZeMYOINE6PYKg2NwOgyjoxXXnmFc889l++++47hw4ezZMkSXn/9dfLy8igqKuLMM89sMX+/fv04fPiwz3U9+uijXodIc7Zu3Upioojizs7OZvDgwRw5coTy8ibh07y8PPLy8ti/fz+1tbXe6YMGDSInJ4ddu3ZhszWVqBgxYgRpaWls3bq1xUvpuHHjMBqNbNq0CRA1ut0eN5JeorqomgMFB7zz6vV6pkyZQm1tLXv37vVOj4+PZ/z48VRUVLTY19TUVEaOHEnhoUJqa2rBCps2ber0fVKYPHkyTqezRamvUPepqKiIgmYF11vvkyzL1NTUUFxcTP/+/bvFPoVynKrcVbgaXdQcqwlrnxRPa11dHQcONPXJaOwTwOD8wexeupu62jrSzkpj6/at3n3KGJrB4e8Ps3bpWjJOyQj5OOlydTRYG9hzbA8cU2efZFnG6RQ16NXsewcOJPDww6Ju0D//CYmJtWza1HKfFi8ez3/+A48/XkVS0qFOOU6RPp9Gjx6N0+lky5Yt3oHaUPepwlFBbU0t1l1WNm3a1Cn7VH6inNqaWuLS4ti8eXNEzqfS0joOHJgKQJ8+VUAWu3btwlN3gKFWK26djoQkPQZjKjrrEVxVO3HrEnHpkkmINyJ73Kpc96qrK7jttmruvXcYIAPNnRkysgy33baf48cNUel7o6aPYtN/NnHou0PEr4jHmGbscJ+yZmVR27uWHXt2tDlOJ06coKamhkPyIRr1jUh2iR2rd+BMdQa9T4UFhVjcFnSp4joc6D7VLq2l4kAFfa7vQ3zv+ICOU2dfyzs6Tlu2bKGmpobXXtvPjh1jMZtlFi60sWlT599zH3vsAIsW5XPokJlzzrHy0Ud28vK61nOEWs9GFkvXE8bV0NCILLZqG6ZkU8R1v1rT2AhffCG+B+vIULIpu2JGhoaGhobaeCsUFHVehQINjXCQ5OahVzGMx+PhmWee4a677sLtdnPFFVfw5JNPkp2dzRlnnMFvfvObFs4Mj8fjjVJrXSrBV0ZGv379qKysJCUlBej8yGSPx8MPm39gzOQxGAwGVSL0tr+2nV1v7WLw2YOZ/NPJXSbaOpyoQ4/Hw4EDBxg2bBhxcXHdYp86aruv6Ue/Osr3T35Pzpgc5j08L+R98ng8HDp0iKFDW9Zbj1ZGxp6397DrjV0k9k7knH+eg06v87a9cn8l+gQ9yb2byrDEynHyeDwcPHiQ4cNF5IMafc9mczNtmp6dOyUuusjDe+/pgLb7tGGDnpkzITFRpqDATVJSbERbBzK9vYyMffv2MWTIEK+zLdR9stZYee/H7wFw2duXEWeOi/g+nfjuBF//6WsyhmRw5t/OjEjf27JFZupUA5mZMqWlMnr9yX2qP4h+3eXIcWlIcUngtuOoOYqJekAG2QWSHiY/g7vPxaJOWYjHqXnb33tP4pe/1FFQ0LS+vn1l/vEPD5dcIke17634zQrKdpcx4YYJ3uikUK8RLpeLAwcOMHToUI6vPo4p2UTOuBz05qbyYcHukzI90H1a8esVVB2sYsY9M8ib3lTuw98+1RyqoaGsgZT+Kd7rZ7SvEY2NjRw4cIC//nUEr72m59prZV56KXrX8l27ZGbP1lNfL51si4TH03WeI9R6NqqrqyMzM5Pa2lrv87JGS+rq6khNTY2ajdxuN/v372fYsGHePqYRGpotO0aWZZYuXIrb6ebC5y8kqVeSz/kiYcvVq+G00yA7G0pKWpZPra8H5fSrr4ekVs0q2lzE2j+tJWdsDvMfmq9KezoLrV+qh2ZL9dBsqS6Rtqe1worL4SKpVxI6g46SbSWsvn81yXnJXPCvC1TfXrTQ+qW6qGVPNZ6Vu0xGxo9//GMOHDjAihUryM/P58EHH2TcuHFs3LgRk8mE3d4yosJms2EymXzWezaZTJhMbYWADQYDBkNLkygvd63xd+D8TW+9Xl/TJ06f6HO6giRJPqf7a6OjzoFO0pGYldhiuc7cp46mB7tPgUwfPXp0h23vavvUnED2KWNgBhIStcdqvQMRgbTd1/SRI0f6nC+Utofb9/RGPQazgQnXTMBoailKmzU8y287Az1ORZuLaLQ0kj06m4TMhIDa3tE+ud2wbh0UF4+irAxmzwZfpg+27z36qIGdOyEzE/79b93J8ea2+zR9OgwdCgcOSLz/voHrrgt/nwKdHsnzadSoUW2mQfD7FJ8az6SbJpGYI66TyjyR3CdHrbg2J2QmBHRtDuU47dkjvo8ZI6HXS97p6A0gSU3XBL0Zc+YI8DjBXgLWInA74MC/MNT+AOP+2LRiezk01vl8cJAAQ1wKmFuKfSptXLhQCM6vXQuLFkF5Obz0ksSZZ+p9zu9rn3yhxnHKn51PxZ4KCtcVMnpB0/2jed9zOVys+dMahp43lLxpeX6v8Uaj0XsPGnL2EJ9tiPQ+JfdJpvpgNbYKW5vffJ1Ph744xKHPDzH2qrGMuXxMi9+idY0wGo307j2apUvF/7feKiFJ0bvnjh8Pb78ton5feUVi5Ei4+24933wjIn579xbXdr0+dp8j1Hg28rcPGrGDXq9v97lNI3A0W3aMy+bC7RBOV0V3wheRsKVSVurcc9tqwCUnC+dFQ4O4RreKx6L3Kb1ZtGxRzOhCBYPWL9VDs6V6aLZUl0jbc/8n+9nzzh6Gnj+UybdO9gp+W0oseNweb7BoV0frl+oSS/bsEj304MGDLF++nBUrVjBv3jwGDRrEK6+8wtlnn82zzz5LXl4ex48fb7HMiRMnyMtrX3gslvB4PBQUFLSIqAsXe7Vw7sSndx/Bno6IhB27Iil5KSCBs94ZVv3XWLPnqMtGceELF9JvZr925ws10WzfB/tY93/rKNpYFNLyrVm2DAYMgPnz4corxeeAAWJ6OGzcCI8+Kr7/61/Qq5f/eSVJiIEDvPJKeNuNFdTsl5IkMeLiEfSb0S9iotutUa7N7b30h8uuXeLTp9A3gNsG9nJkeyn2hnJkjxOMmZA0EIzpQgw8c0rT/NYS+PYK+Gah/791Vwpnhx/0eiFCP2+e+H/rVjX2NHzyZ+aDBJX7KrGU+S6ds//j/ZRuK2XLC1uQ3f6vL7FwzUzKFaGnDcUNAc1vqxKlhMzpkeuPweB2w6pVHm6+2YLdDuPHw6mnRrtVcM458NRT4vtvfyuuu2pf2yNFLPRLjc5BO9bqodmyY2zV4v4RlxCHweTb0alc059+upJVqzy0SkILmeXLxWfrslIK7ZWXkpoHdHQxtH6pHpot1UOzpbpE2p71haKElOLASMhKQG/U43F5/L4LdUW0fqkusWTPLuHIqKuro0+fPqSmpraYPnbsWKqrq5kxYwZr1qxp8duaNWuYMWNGZzYzLDweDwc2HmDtI2tZ97d1qqxz0JmDGHf1OLJG+I9U727E0skVTQwmA1N+NoW5f5xLXEJcyOuJRXuaU81+Xz7qCuv4+k9fs+r3q4JeryzLVB+uBiB9UHpYbQQxoHXZZdCsbDkAhYVieqgDXna7cEy43bB4MSxc2PEyV18tHBpffQVHj4a23VgiFvtlMCgv/pF0MiuOjDFjWv0QlwKmTPA4wHoM6vYhWwvAWQONNaK0VMpQmPIM5DbTnipZAdVbwGUR64hLa/mnM4GjEhrrOmzb5Mnis5VkQNSIz4hn8NmDGXf1OPSmts4sp8XJnndFisvYK8e2Wwe8dd+sPFDJzjd2Urm/Mqg2lWwr4YMbP+D7p78PajnAWx6qvjiwOred0R8DRXH+nn66jmXLhGbZsWPw3nvRbZfCbbeJ6F+AylaHNNxreyTp6tdMjcDRjrV6aLbsmI4c4c2v6b/4RSann65Txel75Ajs2SMCJM46y/c8HelkdFW0fqkemi3VQ7OlukTannUF4n0tpa8o6yNJEkl9TupkFHYfnQytX6pLLNmzSzgyxo8fT3JyMv/4xz+8Rjt06BDPP/88P/7xj7nsssvYsGGD15lRUlLC3/72N2677bZoNjskCr4roGhTUcgR5c3JOzWP0YtGkzYgLfyGaXQ5hpwzhD6T+nRalHkk2fvBXkp3lHY4n8FsoPD7Qsp2lGGtsAa1DVuVDUetA0knhX3OuN1wxx3g6zRWpt15JyFFpd1/v3h569ULnnkmsGXy80UdYYBXXw1+m90dW5WNgu8KKNlW0inbC6QMQ7js3Ck+22RkmLNhxhKYtRR6nY6cfgrHMu/APeNNMW3WUvF70gDQNyvfVrkePG5wVED9AWisBb0ZDIniTx/4IHisOTIApt42ldGLRnsFQJuz9/29OOudpPRLYeD8gUGt98DyA+xasovj3x7veOZm1BfVYy2zYq8JXog02IwMb4ZQlDMy/Dl/a2tjx0HgdjedW60J99quoaGh0dXwZv9ntH0GiFRADzSVlZo5E9LSfM+jODKK/CRZr/vbOr6858tuFX2soaGh0RGyR/a+I6TkNekTKE6NusKOg9I0NKJNl3Bk6PV6PvnkE/bu3cuECROYOHEi11xzDY899hgzZ84kMTGRDz/8kPvuu4/x48dz5pln8uCDD3JqLNQiCAJTltD0aLQ0hjR4oaHRHWkoaWDbS9tY9btV3owJfyRkJpA9StToP/5NcAOHyrqT85LDdv6sXdv2xa05sgwnToj5guHbb+Hvfxffn39e6GMEilJe6tVXfTtYejLFW4tZ++e17H53d6dsb9qd01j83mIGnzU4IuuvqWnqf83K4jdhzobkwSA3giERq2k4JA0W05IHt9G5AGDY7WK6zgQeF1gLoG5vSO075RTxeeRI26j2WMNea2ff+/sAGPfjcUi64EpR5I7PBaB0e8eO2ObUF51M+e6THNRyAEm9hSPDWm7F42o/YkaWZe/zRjQzMiLp/FWTSF3bNTQ0NLoi/jIyIn1NVxwZ/spKAfTpIz79ZWSU7ymnYk+Fdx80NDQ0egINpQ14XB50cToSsps0QfOm5TFiwQgyhwUxwKChESW6jGpfVlYW//73v/3+Pn78eL799ttObJG66HQ6evXpRUVuBZYSC3Un6sIaVHA73ZTvLic+I57U/NSOF+gm6HQ6srOzfYpe9jScDU5KtpXgtDg7FJ31RyzYc+eSnchumd6TegdU8il/dj7lP5RzbO0xRlw8IuDtKI6MjMEZIbdVIdA09sLCwNdpscB114kXwGuvhQsvDK5NCxbAz34Ghw4Jh8isWcEtH0uo3S+DjWBXg/bKE4WLUlaqXz9I9Xf5d1nBZUWSIK3XkI5tqTOIklIJ/URZqobDosyUxyGcG0GQlqYI0MPmzf7LQnQ2LruLwu8L0cXp6Ddd6PDsfmc3LruL9CHp5E3vWHerdd/sNU4I2FQfrsZR78CUHJitFEeG4pQIhviMeDKHZ5KQnYDL7sKYZPQ7r6POITQ/pMhmCHVEMA4CRWMlGgR6bY+1UiaxcC/X6By0Y60emi07RnECtH5njeQ13WKB1avF9/YcGR2VljKnmkXmY23XCh7U+qV6aLZUD82W6hJJezYPlmpernvAvAEMmDdA9e1FE61fqkss2TP6LdAARKcYPHiw1+lQe6I2rPU1lDaw+v7VrPjNCjWa12VQ7BgLJ1e0sZRb+Pav37LtpW0hlyqLtj1rj9dy9KujgIiGDoR+M/qBBFX7q4JKF1ccGWkD04JtZhuUl6eO+PWvRXkoi49mut1C0+KNN8Tnb34DBw9C377wxBPBtykxsUlP4+WXg18+llC7XyqaApYyS4cR7F2BDoW+QZSIAiRDEoOGjgrClpIQBdefjOBxhVaSIRbLSx396ijr/m8du94UBrRV2zjwyQEAxl89PiBh0NZ9Mz4jnpR+KSBD2c6ygNsSTkaGJEmc9bezmPWbWe06MaCpLIgpxRRR51pHdBUHQaDX9kDn6yyifS/X6Dy0Y60emi07JmNIBv3n9W+jxxjJa/qqVeBwQP/+MGqU//k6cmSYUkVggaPOEXwjoojWL9VDs6V6aLZUl0jaU9HHUIS+uzNav1SXWLJn9FugAQjhlEOHDjXVpjsRXm26WKl53dkodowFAZpok9I3BSREqbLq0KKNom3PHa/vABn6zexHxpDAMiXi0+PJGZMDBFdeqvqQehkZs2dDXp4Q2PaHJImXq5//XGhY/OEPUHqy+owijjh/Plx5pfhU9DD++1//9YA7Qikv9fbbYA1OQiSmULtfmtPNQuRZJuK1kl0OF6t+v4p1f18XMaeJUsO/jdB3c046MmRTVmi2NKaDKQt07Q+U+yPWHBmWcgtJuUk02hop21nGiXUnsFZaGX/NeHpP7k1yXmAP+776Zu4EUV6qZHtgGiwetwdLqeiHipMtUiRkJTDr3lmccvMpEd1OR3QVB0FH13ZJEplQs2d3brs6Itr3co3OQzvW6qHZsmP6z+nPjF/NIH9WfovpkbymNy8r1d5zdiAZGQCO2q7lyND6pXpotlQPzZbqEkl7Zo/KZswVY9pct0EEcZXtKsPt7B5ib1q/VJdYsqfmyIgRPB4P5eXlJOWJMhLhZmTYqn3XLO3uKHaMhZMr2uiNem80b+3x0PpTNO1ZeaCSgvUFIMHYq9oLLW9L/mxxYz629ljAy5z+yOnM/t1sMoaG78jQ6+HJJ33/Jkni7/XX4emnYdAgqKqChx8W0WVnneVbHFHBV/ZGoMyeDQMHQn09vPde6OuJNmr3S0mSvCV86ovrVVmnP+zVdkq3l1KwrgBJH5zeQqAEk5HhMWYGZ0u3TWRhxKWCuRcgif/dwdWYVhwZGzcGtVhEsJRbWHblMj644QPKdpVRvLmYZVct451F77DmoTXs+2Af7131Hpbyjk8+X32z13hRXipQnQxrhdVn7dpgkWWZRltju/MYk4z0m9GPAXMHhLwdNZg9G3Jy/P8eKw6C5td2XwNosiwy5vThySypjvZs1HPQjrV6aLYMnUACekK5pstyYPoY0KSR4U/sW8nI6GqlpbR+qR6aLdVDs6W6RNKemUMzGXvlWPrP7t/mt+W3LWflvSu7jeC31i/VJZbsqTkyYoyUvBT0Rn3YJR6UCPxoindqRJ/U/qJUWc2xmug2JAS2v7odgIGnDSS1X3A6L/1m9CNrZBYD5w8MuKxWYk4iedPyiIuPC7qtvliwQJSFav0Sl5cH77wjMi1uuw327xf/n3qqSJX/8kv/YtySFJ44ok4H11wjvr/ySmjr6K4oke9KSZ9IoQgrm9JMAZUqChZZbnJktJuRkToaht2GnBugQEVcCpgyhSZGY03bP49D/B6XEtDqJk4U/bmgAEoCS1SIGI46B9ZKKwaTgaTcJHQGHS6HC3OaGXOaGYPJgLXSGnL5iZwxOSCJY+9scHY4v8vuInN4JhlDM0LuI0e/Osrbl77N+r+vD2n5zqY9B61iglhxECxYIK7Zffv6/j0ry/d0DQ0Nje6G4nhvTUdOX4Bf/Sr4a/rOneK5IT5eZCu3h5KRUVMDNh+xFl01I0NDQ0MjUijlpuoLI/s+rKERLl1G7LunkDE0g4VLFyLpwhvg6qkZGRotSc1PpWBdAbXHwsvw6WxkWWbQGYOwVdoYe2Vw2RggXk7OfOzMCLQsOHr1EgPL6ekyd9xxkFmzBjFvnr7Fi5teD5deKgbHnn4abr/d//rUELy95hp48EFYsUK8DOZ1rF/cI1AyMiIt+K1cmyPlZC4uFhk+ej2MaE/rPqGv+HO54EgA9Z3M2TBjCTQ2i9DxuMFeDMY0MCQJJ4Y5O6B2JieL9u3ZIwS/O4qs7AwM8QZMaSYaihuQ3TKWUgtpg9IAURIsVIyJRs7957mk5KWg03ccpJDWP42z/haeArox2Yin0dNhhlHZrjLstXYyhmSQ1Ct4YXG1uP12KCuD7GyIi2sZQZuXJ5wYCxZErXltWLAALrpIXIuLi8WA2SuvCP2ha6+FHTtEH9fQ0NDorridbj64/gMALn3j0jaaTIrT9+abxXOJgskkAnf+/W+48UZICuLWo2RjnHaacGa0R2oqmM1gt4uAiYEDW/5uSjWhi9NiOjU0NHoObqeb0p2lpOSl+HzuT+mbQuXeyogH9mlohIt2944RdDodeXl56PX6sJ0Y0HMzMhQ7xoIATSzgFY8PsbRUtOwpSRID5g7gvGfOIzEnMeLb2//xfna9uUv1NMoVK8TnuefCjTfGM3++5Df6TJICj+QNR/B20CCYM0c4RV57LfT1RJNI9MsBcwcw8zczGXbBMNXW6YtI6xcp2RhDh4oX+I4IypbmbEge3PR39DXY8zdwlIv/A3RiKMSaTgaA3qD3Dsa4HC4kgrsf+7NnWv+0gJwYaqGUFbSUWNrNStv/yX6+/cu3FG4o7KymtWHpUuEE0Ong3Xfh+HFYudLDP/9ZycqVHo4ciS0nhoJeLxzKV1whPp98UpQHPHoUfvnLKDeuFdqzUc9BO9bqodmyfZQMU12cjrhE39nMCxaI7GOAadPs3mt6nz4ikOGWW/xnIfsi0LJSIJ6r29PJGDh/IIveXcT0X04PvAExgNYv1UOzpXpotlSXSNmz9kQta/64hi9++YXP35WMDEUQvKuj9Ut1iSV7Rr8FGoD6naKnZmTE0skVCyiOjLoTdQGXWGpOtO0ZbukdR72DQ18e6lDA+eDnB9n5v52q37QVR8aZZ0oB2bGzBG8V0e9XXgnuBTJWiES/TB+UTv6sfO8AcKRQXvzNaZG5Ngck9A1QsQFqdqGTG0O3ZdLJ8Mb6A8EvC0yZIj5jyZEBkDYojaTcJDKGBK+XE0jf7OhaHMq1ujWJ2YlIOgm30+11nvnCG/SQEZ2ghxMnxEAWwL33inrpej2cdpqOn/88k9NO08VEOalASEkR11RJghdegI8/jnaLmoj2vVyj89COtXpotmwfW1XTu2Z7z+sHTj4iXHKJmdNO09G7N7z1lrjWv/EGPPdcYNurrIT1J6slBprF2Z5OhqSTIlLiM9Jo/VI9NFuqh2ZLdYmUPZWSUYrDojUpfUWJ4O6SkaH1S3WJJXtGvwUaALjdbvbs2YPb7ebwysMs/8Vydry+I+T1DTt/GOOuHhfSQExXprkdezqWcgsuu4tx14zj1DtOpepQVYu/QIRrI21PS7mlRZsqD1by6e2fsuXFLVTsqwioje2x/u/r+f6p7zmy+ojfedyNbupOCAdG+qD0sLbXnOrqJiHj+fMDs2NH4ohqCd4uXAgJCbBvH3z/fXjrigZd+TyPdGmpgIS+ZRn2/B9suxe3rSx0WyafzF6p2x/8srTMyIglh1qcOY7U/FT0ccGPoLfXNzf9exMf3PhBh3Vnl/98OR/d8hHVh6uD3r6CztAkFN5eeanmA1GdjccjnKo1NcKp9cADTb911XN87ly46y7x/aaboKIiuu1R6Kr21Age7VirR3e0Zevn7lDeDRSU+0dHzzP79onPhIQTXlvOmgWPPSam33ln0/Nye3zxhbhvjBkD+fmBtbG9jIyuSnfsl9FCs6V6aLZUl0jZUwnaTMnzrWmoBPTVFYQWBBtraP1SXWLJnppGRowgyzK1tbXIsozb4ab2aC2J2aGX1MmblkfetJ5X/L65HXsylnILy65chrXS6neehMwEFixZ0G4/i6Q9fbXR2eDEUmpBek0iNT+VxJzEDtvYHv1m9qN4czHHvznOmMW+Q9Rrj9ciu2WMyUYSshJC2o4vvvpKvHCNGAF9+8ps2tSxHRVxxMsuE06L5rOrKXibnCzS/V9/XdR0P/XU8NbX2USqXxZtLqKuoI4B8wZ4RSDVxu0QN/6oZmS4LOAW4pZyXCa1tYWh2TLlpCPDcgTcTtAb25+/FePHi75cUiKiJf2JJ3cl2uubtcdrsZZZKdle4vcFwuP20FDUgMflwZgcnD1bk9Q7CUuphYaSBnJG5/icJ5plKP/+d1i9GhIT4X//E9oYCl35Xv7nP8Nnn8Hu3XDrraJ0VrSDfruyPTWCQzvW6tHdbKnWu4GCNzCjnYw+WYb9J2MdsrIqkeWmlOK77oJvvoH33hPPvVu2QGam/+0FU1ZKoT1Hhsfl4Zu/fIOjzsH8h+ZjMHeNYZHu1i+jiWZL9dBsqS6RsqdSRttfRobiyGi0NOKsd2JKMam6/c5G65fqEkv21DIyYpCUfmKAo/ZE1xJo1ogdHHUOrJVWDCYD5jRzmz+DyYC10oqjzhEzbTSlmnA2ONEZdCT1TSIuIS7sNvab3g+dQUft0Vq/55MS9Zw2ME3VFHOlrNQZZwS3nCKO2HpQNy9PTFerVrxSXurNN4UQogZseWELW1/YSs3RmohtY/ovp7P4vcUMOnOQ6ut2u8XgKXSQkeE4GSYelxK086EFpmwwpgrRb4v/rCd/JCTA6NHieyyUl3LZXDgtzjZ/LlvoQt/NyR2fC0DpjlK/81grrHhcHnRxurAdq0m57QvYu+wuXHaxb51dWmrLFvjd78T3J58Umi7dBbMZXn0VDAah+bFkSbRbpKGhoaH+u0EgGX2FhWCxgF4v07dvy/VKErz0EgweLLSRrrlGBAD5wu0WDmJQz5Eh6SVKtpZQsafCW/ZTQ0NDozujZIX7C6jSG/WMuXIMU26bgqTveqX3NHoOmiMjBlF0DSylFtzO4NN2XHYXJdtKQhZ41ug+GOINSHoJR62DRksjxkQjxkQjhvjYiToyxBswJhpx2VzIbhlDvIG0/DRV2mhMMtJrQi8Ajn9z3Oc81YeEIyNjsLpl2L78UnwG68gA4aw4elREKy9ZIj7VFrydP1+UqaqpgQ8/VG+9XZnk3iIKxd/Ar1roDLqQyhZ1xJEjYLNBfLwQdfeLo1x8mgJUl/eHJEHyyRFoFcpLRQtTiomEzARcDhf2GnubP5fDRUJmQthRSb3Gi2tR2Y4yZI/vSBalJm1SblLYjtXsUdnkzcjzPlO0RhmEMpgNnRqJarXCVVdBYyNccgnccEOnbbrTmDQJ/vAH8f2226CgILrt0dDQ0FBQnrtb/wX73B2IxpJSVmrQIIiLa3vfS00VQTpmMyxfDn/5i+/1bNggNDLS02F6ENrciiPDp0aGJHnv6/ZazZGhoaHRvZFlucmR0de3IwNg7BVjGXLOEIyJ4WWGa2hEktgZzezh6HQ6Bg0ahE6nw5RiwphsxFnvpK6gLui6/bUnall9/2riM+O5+OWLI9PgGMTthq+/1rF//0isVh1z54Zfgqc74LK6qCuoIy4xjsSc4Eo0Ne+XkaTR3tgkPtU7GZ1eve31n92f4k3FHF97nDGXj2kzOKhkZKipj3HsmBA31Oth3rzQ7KgsGyn0erj6anjkESFQu2hR5LalNpHql0m9RQR7VxU4U8pKjRrVwbXPUSk+TVnh2zJ5GFRuClnwe/JkePHF6DoyErNFCbv2olBNKaaASm20Z8+MIRnEJcThbHBSfbjap4aV4kRTQ3R+4PyBDJw/0O/v3mjajM7Vx7j7bti7V4iwPv+877JLnXXviST33isEv7//Hq6/Hj7/HKK1O93BnhqBoR1r9ejOtnQ5XFQfqSa5d3LIpTSzR2XjcXna1WNUHBnDhuHXlhMmwDPPwI03wv33w7RpcNppLedRykqdfbbIdgsURezbn0aGKdWEtSK6GerB0p37ZWej2VI9NFuqSyTsaauy4bK7kHSSN2u7u6P1S3WJJXtqjowYQafTkZPTVMM6JS+Fij0VITkylPTYaIh3Rotly+COO6CgQAeI6NO8PFGyQs0o9q5IXIIoPN5obaTR1khcfFwHSzTRul9GAluVjYaSBpBBb9IH7WzpiL6n9kVn0FF3oo7a47Wk9U/z/tY8MkFNR8bKleJz6lQRbQaRt2MoXHONcGR8/rl4yevdu+NlYoFI9UslI6M9ceRwaLQ1svbPazGnmZl21zRVHXbQJPTdrj4GNGVkmLPCt2XGZJB0kDYupMVbC35HS0sgMTsxLF0qhfbsqdPryBmbQ+GGQkq2l/gc/PFmZPSO/AtGan4qs+6dFfHtNOfjj+Ff/xLfX37Zfz30zrj3RBqDQZSYmjBBlBr8179EdkY06A721AiMWD3WlnKLKs7iziRWbakG9lo7zjonlXWV9JnSJ6QMwEFnDGLQGe2XyVQcGSNHSu3a8oYbhF7GSy/BFVfA1q1NTggITR8DOhb7NqWKjAxHbddyZHTXftnZaLZUD82W6hIJe8bFxzHtrmnYa+3oDOId1O2GtWubxgFmzwa5UTi6PY0eeo3rpWobOhutX6pLLNkz+q4UDUAowG/fvt2rAB+OTkY0xTujwbJlQiSudemGwkIxfdmy6LQrVjCYDSLiVg6+P7Xul5FASVs0p5nJGp6FpFN3JNOYaCT3lFyQoHJ/ZYvfJEni4lcu5tx/nutX9CoUlLJSZ54pPjvDjqEwfLhI0Xe7hdhuVyFS9lQGjyNVWspebad0eymFGwpVd2JAgELf0KSRYcoK35YpQ6H/YkgdGdLi48YJkeeKClEju6vTkT2V8lKl233rZCiODDUyMkA4a62VQnejNaYUE/1m9KPfjH6qbKsjSkubykj98pdN10dfxOo1M1iGD4fHHhPff/3rJtHbzqa72FOjY2LxWCsi00sXLvX7t+zKZVjKLdFuagti0ZZqkZCV4B3IiqQ+xN694nPoUE+Htnz6afFMUFYGixeL8oMg3u+2bxeBDuecE9z2FUdGRQU4nW1/V7JRulJpqe7cLzsbzZbqodlSXSJhz7iEOAaeNpCRl4h3tmXLYMAAUW76yivF54AB8NY/y1hxzwo2/2ezatuOFlq/VJdYsqeWkREjyLKMzWbzKsCnD0wnpV+KN5o+GGzVJ8tFpHX/jAy3W2RiyD7KjSvRvXfeCRddFDtlpnx5viPdttS8VOzVdhw1Dux19oAHUVv3y/YIJNrOlGxi7/t7sVXbGHzWYEBkYfQa1wuDKXKXo4k3TOTUX5zq85zQGXSkDUhTbVseT1NGhqKPEYwdO5trr4X160V5qV/9KnoR8cEQKXsqg8cNxQ3Isqyq+Ds0uzZHKFtOychoV+gboM95kDISUoZFvW+aTKK9W7aIrIz+/aPSDNXoyJ6543NJ7pvs95qT2j8Ve62d1H6+dS2C5cObPsRaZuXsf5zdbvmPSND8XpebKwb0y8vFQNUjj7S/bLT7pZrcdht88IG4L1xzDaxZI665nfkM0J3sqdE+sXism4tM+9JhcNlcXpHpWMrKiEVbqoVOpyMhJ4GGogYaShqCDn6TZRlrhZX49HivQ8QXTaWlPB3aMiFB6GVMmiSyM+69Fy64AF5/Xfw+dSpkBSntlZkpgiUaG6GkBPLzW/7eFTMyunO/7Gw0W6qHZkt1ibQ9lUDg1qsvLIRb70nhwUmgi6tH9siqB5l2Jlq/VJdYsqfmyIhRhl0wjGEXDAtpWaXudXvia92FtWvbF9GUZThxQswXSb2BQGkqgdU0LZIlsFw2l/e7Kc2ErdxG1aEq1QbJFJRoO2ul1efvsiyDLGrEux1ukCBnTI63jYZ4A05Xy1Cp5m0Pl/YErdRmxw4xWJeYCKee2mmbDZnFi0Wf3LVLDCZPmhTtFkWPxOxEJJ2E2+nGVmUjITNB1fV7y/5FwMnscDRFe3eYkZEyXPwBuFQ4z5w1ULsb4pIhrSMvSlsmT25yZFx6afjNiWVS81O54LkL/P4+4doJqm4vITMBa5mVhpKGNo6Mkm0lOBucZI3IIiFL3b7u614HYkBpyRLhwOop6HSiXMrYsUKwNicH6uqaftfKYGr0FBSRaV+4HOo982m0j8vmQpZlTEkm6tx12KvtWCosSAQ+WGWvtvPhDR8i6SQWv7fY50CXzdaUaTlsWGBZl0OHiuvlZZfB3/8u/hT27BH3lmCulZIkHMbHjwvncWtHhjnVjN6oR/ZEf1BGQ0NDI5IUbixEH6cnbXAmd9wR5zcQ2EIiO3fryM31YK2wql72W0NDDbTSUt2QnqSR4a/maajzBYPbDV99BW+8IT47yrDqzBJYphQTCZkJuBwu7DV27DV24kxxyB6ZxvpGrOVWEjITMKWoM5rUPNrOnGb2/plSTV4dipojNdiqbCT1TmLG3TPIHJ7Zpo3N/1wOl6ptVHA7mw7Upuc2sf4f66k5WqPa+lesEJ/z5oHR9/t6TJGWBhdfLL7/+c+B9+fuiM6gY9Z9szj7H2eHLH7ZHkrZP7WvzW43vPaa+ExKgl6dXc60dDX88CgUfhTS4s11MjTURRHz86X7smfZHr7967eUbC9RdZv+7nUgomKVCN2eRL9+IhsDWjoxQCuDqdHz8Hg81J6ojYmIvp5E83eDsl1llO0qA8Dj8lB3oi6o5+7m2f/+onUPHBCDYmlpkJ0deDv9JcPW14d2rWxPJ2PkgpEsfGchp9x0SnAr1dDQ0OhibHl+C6vvX82KpVXtBwIjUW5LorIK6grr/M+ooRFFtIyMGEGv1zNixAj0reoLyLKM7JGDqqfekzQyAhUnVlvEONjMis4ugZWYnciCJQvalHo6vPIwDSUNDP/RcFL6pnSYwu+vX/qjebRdo62R6oPVIvLLLSPpJcb9eBzjrxnvTUP31cbmqCn+WF9cz/dPf4+t0sb5/zofSZI48e0J7DV2hp43VJVtQJMjQykrBcHbsbMZMkR8vvee+IPYjhKOpD3zTs1TfZ0KkcjIaH0tamiAgQPbOXZuO1SsB1MOpI1Wx5bJJ7MH60ITAIgVwW81CNSeHpeH2uO1pA9K905zO91IeklV/ZT2dF+UgSg1nxXau9dB4Pe6WL9mBovb3XRtbU1nlMHsbvbU8E8sHmuX3UWjrdF77/M0enA2OCOS+agmsWjLcGj+bvDZnZ/RaGlkwvUT2PbSNiS9xBl/OYPU/NSAnruV7H9Tmn+nh+K0Hj4cDIbAbKncQ3wR6rWyPUdGVyyZ0t36ZTTRbKkemi3VRW17uhvdNJSId4Ead8eVKupIxmGvo76wnt4TVR5I60S0fqkusWRPzZERI0iSRFpaWotp6/+xnoJ1BUz/1XTypgU+uDbi4hHUTakjbWBah/N2dWbPFgOuhYW+B04kSfw+e7Z622yvpuBll4n6rsoAotstBOY++aTzS2AlZie2eRnJGBxcjXRf/TJQ9EY9nkYPOoOow6sz6Og/t3+LWrq+2hgp4tPjqdxbidvppvpwNfHp8WJgWUI1jQy7Hb7+Wnxv7sgIx46RZtky3/XqffXnWCGW7dkeag8cB3MtampEEex5HIxpMOM1dWyZPFhcbB2V4KgCU3DXmdGjRamhmho4dKjJsdYVCcSe9lo7H974IR6Xh0vfuJS4eKGFdXjFYbY8v4WBZwxk6m1TVWlPcu+Tui8lbR0Z9qqTQQ8qlqFUq9xjVz3H/RHtMpjdzZ4a/on0sQ5EDy0xOxHZI1O6o5Qjq45w6ItDNBQ3eMtTWEotNNoacdY7Y9qR0R3Pm8TsROLT45GQMCYaGXHRCMp2liHpJRKyEgJ+JvcGzbVz/2juyAjUlpG4ViqOjKKiwOaPdbpjv4wWmi3VQ7Oluqhtz4aSBpDBYDaQO6TjgLp6UjCZC7t8RobWL9UlluyplZaKEVwuFxs3bsTVvF65LKKYao/XBrWufjP6MXrhaO8ARndGrxeRx+3xxBPqRTh2lFkhy3DllWJgLidH1APPzYUbbwxs/ZEogdUeHpen3d999ssA0el1ZAzLoNf4XkJ7IMph1gazgT5T+gBwfO1xqg9XA5CSl6Ka0Pj69aImcG6u6AMK4dgxknTUn0FEvsVamalI2rOhpIF9H+7j0JeHVF+3og+jRkZGyMfOXi4+TaLOgyq21Jsh8aRKd/2BoBc3GmH8ePG9q5eXCsSe5lQz5nQzslv2lvYAqC+qx+PyYDCrF2PiLyPD4/J4ByPVLHWmVrnHWL1mhko0y2BC97NnONhsNh544AEmTZrExIkTGTlyJKtWrfL+XlxczPnnn8/48eMZO3Yszz33XBRbGzyRPNaKHtrShUv9/r294G02PLWBD67/gNX3r+bo6qO4nW50Bp23tKcx2YjsknHUx7bAcnc9b5TsUEkvYUw2Mv+h+Zzx6Bmk9U8LeB1ePcZ2AjMUR8aIEYHbMhLXyj59/C/jqHPw9Z++ZuXvVga+wijTXftlNNBsqR6aLdVFbXvWFQiHRHJeMnPmSOTl+c+AlyQwZSeTmQH1hW1L03YltH6pLrFkT82REUO4W404peSJtC/lwqPhmwULROSxLz2CG25QN5q8o0ghEKK7u3cLwWclDTo1QG1ttUtg+aOuoI7VD6zm+2e+73De1v2yPRx1Du/LDYApyaRqmZRwyZ8lVP6OrT3mdWQ0L+0SLs3LSrV+OAjGjp1FMJFvsUak7Fl7vJYtz29h/8ehlUlqjxl3z2Dxe4sZePrAsNcV8rFzVIhPU6Z3kiq2TD5Znq0+/PJSXZ1A7Jk7PheA0u2l3mn1ReJlIbmPekEIikaGtdLaQh+o+UCWmjpEapZ7jMVrZqhEqwxmc7qTPUPF5XJx7rnnotPpWLduHVu3bmX37t3MmTPHO8+ll17KlVdeyfbt21m3bh0vv/wyy5cvj2KrgydSx9qfHpry53K4KN1Ryt7392KrsmFMMjLkvCHMuncWqfmpSEg4LU4kvYTH5cFZ78ReZ8dli/4LsT+643njLXOZakaSpJCc594M0wAzMiAwW0biWtleaSmdQUfhhkLKdpR1KcH57tgvo4VmS/XQbKkuatpTcUik9E1pNxBYGb+469EcJv90EmOuGKNaG6KF1i/VJVbsGTsjjBptSOknHBm1JwLPyHBanBRvLQ46i6Orc/bZTZHHd911lNtvF/98/rlwLKhFoBFA990HO3ZAaakQN62spEPPd79+6pbAag+nxUnJlhKOrDziHdAPoXEs1QABAABJREFUF0+jEG+sOljlfcGJNVIHpOJudFNzpIYflv6A0+IkLiGOqkNVVB2qwlJuCWv9X34pPpuXlYploh0lHIsoEeyWEktEhEh1eh36uPBTxEI+doojwxyE8mYgqKiT0RPoNV4osjcX2lYEudXMpjSlmMifk8+Ii0fgbmx68Gwh1KpitpxS7jFW7nWxgmaX2OC1114jNTWVBx54AJNJOPAkScJgEAO5O3bswO12c9VVVwGQnJzMQw89xH/+85+otTkWUfTQZI+Mx+XBmGjEmGgUZaIkyJ2Qy6z7ZnHxqxcz5adTyJ2Q6xWZttfYcdY7QScywywllqBEpjXCR3FktNa3sNfa2ffRvoCefbwZGX4cGbIMe/eK74ojIxAica1sz5FhiDd4S946amM7Q0hDQ0MjVJQSUUqgtBIIbGjlx87LE9MX35jMsPOHkT1S5fdFDQ2V0DQyYpjUfiKMv76gHlmWAxpsqDlSw1d/+IqkPklc+O8LI93EmOG774QjIy9PZuHCEsaOzeOdd0TE8iuvwC23qLOdQCOAzjwTxo5tOe3JJ0XdeknyXQ5GzRJYHZE1PIv82fkcX3ucrS9uZf7D88MazHI73TSUNuBp9GBMNqKL0+G0OL2/x0K0naXcwofXf0j57nKcDU1tqy+uZ8sLWwBIyExgwZIFIel2VFc3DcJ2FUdGLEQJxxpJvZJAgkZrI446B+ZU9cruqEnIx86bkZGlanuaMjIOhKTYPWWK+Ny8GTwe0HXzMIte44Qjo/ZoLfZaO6ZkE5YS4UhVMyNDkiRm/npmm+kdDUKFihLldemlvtoiPjvzXhcrKHbx9QzQk+3S2bz55pvc4U9JGFixYgVz585tMW327Nlcdtllfp/DHQ4HjmYRM3V1YrDA5XJ5U+91Oh06nQ6Px4PH01TSU5nudrtbDB77m67X65EkqU1KvyK66Ha7vcu43e4W05tjMBi88yhIkoRer2/TxubT3S6xbuWv6mAVBrMBY7IRJJHhlTYgjSm3TSF1QCqyLONyuTClm7j49YtpbGgU25Rh5/92cnztcQafNZjRi0ajT9BjSjd5981f29XeJ1/HQ5mu2FKZR83jFK19AmioaECWZeLT45v20eXh41s/xlnvJCUvhZxxOe3uU864HPQmvfc4t257WZme+noJnU5mwAB3Cxu1t0+y7OHxxyUWL9advFZKzeYTyz/+uIxeH/j51KuXDtBRXCzjcjVtV9knY7IRa5UVS6UFU4YpZo6Tv33y993Xceqo7bGyT+1Nj+Q+Kd89Hk+L9XflfYrWcWreJ7vLPkX7ODVfJtx9qj1Ri0f2kNRHBOy5XC4uvBDi4vS4XOI6e+ONMs8840avB2WXu/pxUp5Xmrezq++Tr7Z31j41f8YMZ5/UKE2lOTJiBL1ez7hx41oowCflJqEz6HDZXVjLrV6RvPZQomzUEpPtKijlU+bMgfHjxxEfr+eee0SN+EcfheuvF3oV4aJECvkr6dKeuLji+b7jjrbL/+xnnS+oPP7a8RSsL6B0eylFm4roO6Vvm3l89Utf7PtwH8h46//7imqKdrSdUhLBnGHGZW+6eCbmJKLT63DZXFgrrTjqHCE5MlavFoNTI0dC31amDNSOnY3SnwsLfTvX2uvP0SSS9tQb9SRkJmCtsNJQ3KCaI8NpcbL2kbXEp8cz/VfTw46CD/nYtXJkqGbLxP4w8m5IGRbS4iNGQEICNDTA/v3i/65IoPY0p5pJHZBK7dFaSneUkjU8C4/Lg86gIyEr8uK3mcMymXXfLFWyg1ozdy6YzWC3t5yelycG6wO518XqNTMc/D0DZGbCv/8d2WeA7mjPUNi+fTvx8fFceumlHDhwgKysLO655x7OOeccAIqKiujfv3+LZeLj4zGbzZSVldGrV68263z00Ud58MEH20zfunUriYniWSI7O5vBgwdz5MgRysvLvfPk5eWRl5fH/v37qa1tyqQeNGgQOTk57Nq1C5utKcN1xIgRpKWlsXXr1hYvpePGjcNoNLLpZDSFx+Nh69atTJ48GafTyY4dO7zz6vV6pkyZQm1tLXuVkPmT+zl+/HgqKio4fPiwd3pqaiojR46kqKiI/Tv3Y7VaadQ1EmePw9PowWa3UVNTgyRJuG1upJMDIj73aXAO27dvx2azYc+1Y220Ul1QTcbgDDZu3Ij7mP99UlB7nwqanYy+jpPH46G0tJR+/fqpfpyitU+1RbUkDU8ia0RWi32S+8s4tzjZ98E+ynRl7e9TNuiz9SQNSMLtdrfZJ4tlCqCnd28HO3duA5oGSTrap3794JFH0nniiUGUlja9vGVnO7nrrqNMnGgAAj+fkpKGAFmUlsJ3323yRiAr+1TnrMNSY2Hrhq2k1qYGdJyc1U5cFhc6vY5x48ZRV1fH4UNNxyMpM4mp86eGdZza26e8vDz0ej07duzoUn2vvX2K1vk0btw4qqurOXLkSLfZp2gcp+HDhzNu3DhvZmN32KdoHqfS0lLvvVyNfTKcaiA1PxVTXzEWs3XrViorJWy2yd5lJElm69amfbIV2+iX3I+kfkkcrT7aJY9TXFxcCzuqfZy6Y9/raJ8Ue4azT0rQTzhIciRqZ3Qx6urqSE1Npba2lpSUlKi0oXn0VPMBrk9+9gl1J+qY+8e59JnUp8P17PtoH1v+s4V+s/ox6zezItnkmOK008RA8rPPytx8s7CjzSYxcCCUlcFLL8F116mzrSVL4GTVgRYoh+2dd9ofkHC7heOluBi+/hqeew4GDBC1ZH3pfESSrS9tZe+yvaT0S+Hcf57bRs/CX79sztE1R1n/t/W4nC6m/GwKOWNyfM5nSjGF5CBQi6pDVSxduBRTiomGkgbM6Wbi0+O9++y0OLHX2Fm4dCEZgzOCXv9PfyqO5e23t607GYgdo8WyZSJKGNoOiEtSx/05GkTanit/t5KyHWVM++U0Bs4PX88ChC7NJz/9hLjEOC578zJV1rlsWfuR7z6P3YabwFYKE/8KqaNiqm/OmgXffguvvQY//nFUmxIywdhzywtb2PfBPgafPZj8Wfmsvn81yXnJXPCvC1Rvk73ajrvRLTKOIswf/gAPPywE3P/xDygpEZlBs2cHnnEQS/1SbZRngL/8RZS/vOYakTkaSdSyZyw8L4dDXFwcc+bM4ZlnnmHEiBHs2LGDCy64gFdffZV58+Zx0003ceqpp3LzzTe3WC4/P581a9YwcGDb+4GvjIx+/fpRWVnptVFnRugpGQQ6nc5bMkutCL3KA5W8e/m7ImClzkF9QT3mNDPpQ4XWWKOlEXuNnUXvLPJG6vvbJ2uFlS/u+oJeY3sx695ZMRnFq9jSYDCg1+u7fCRlR32vvrieT3/2KRIS5zx9Dsl9m7IDg92nF17Q89OfSpxzjoePPmqypfHki04g++TxSKxbp6ew0ENursysWTJ6ffDnkyzriI/X4XbDsWMur/i3sk8rfreC0u2lnHrHqQyYP6DD41RXUscHV3+AtdLqbb/YTtM2EzITuPSNS4nPjFf9OCkRxsoAXXfoe+1Nj+Q+Nd+H5tvsyvsUreOkrKN19mJX3qdoHie3243L5UKn0yFJUkT2afNmmDatKa79rLNkPvmkqY3f/eM7jn99nPHXjGf4JS1rBHaV4yTLMo2NjV47KtNB63uhZmQoz5h6vT7kfaqrqyMzMzOs9wktIyNGUKJZJk+e7H3xAMidmEtS76SAhdi85SJ6UEaG0ylKSwHMnNlkx4QEA3ffDffcA488AldfrU7Zhv0nS8Dr9U26HBB4tKleD/Pmie8XXQTvvw9Hj8KLL8Ktt4bfvmAYvWg0h788TN2JOg59cYih5w5t8bu/fqlQe6KWjU9vBGD81eMZcVHsh1FLOonMoZkdzxgkzYW+W9ORHaNJe5lCt9wSe04MiLw9k3snU7ajjIbiBtXW2VyTQC1OP104P53OltPbvRYNvQ3spZDQD4itvjl5snBkbNrUdR0Zwdgzf3Y+5jQzfSb3we10kz8nX/VSTwCHvzzM9//8nt6TezPvgXmqr785tbXw1FPi+/33w/z5oa0nlvql2ijPAJIkHBkffSS0tNTIGvVHd7ZnMOh0Ou655x5GnEz5GjduHHfddRcvvvgi8+bNw2QyYW+dSgTYbDbi432fmyaTyau30RyDwdDG1srLXWv8Zcr4m+7vGBoMBlwulzcbQ3lp9zV/c22QQNqo0+nQG8RLqCRJOOqE88aUZkIn6bzrbD1Q4G+fUnJTuHTJpe22MdjpoexTe9Ob2zKQfQqn7f6mq71PrWne9vR+6eSdmkfhhkIOfnKQKT+b4rONHpcHW5UNc7rZ7/FT3pVGjNBhMLS0pa9zw18bxfuS71qTwZxPvXpBURGUlxvIz2/5W0J6AjpJR2NDY4t2+TtObqsbW5WNOHMchvi287hsLmxVNm+Gt9rHSQxAbm73et7V+l4g0yOxTy6Xq917Y1fcp47aGKl96siWXXGf2mtjsNOD3SdZlltcMzuaP5R9Ut77lTGtI0datjGtXxonOEF9UX2XPU5ut9unHf3N3xX2Kdjpau5T84xfZZ5Q9kmNd5FuXoW66zPp5knMvX8uOaN9R7m3xl4tXsDM6bFZ1z0SbNkCNpsozzByZMvffvpTMf3AAXjrrfC3deIEPPaY+P6//4kskCVLxOeRI8EP+iYkwO9+J74//LDYj87EmGhkzBVjADi6+mjQ4sbFW4px2V3kjMth7JVjO16gm3L0KBw8KB4EWpXX7hIsWCD2QenPt90mpn/5ZVONzJ6EIvitiC+rgVL2T81r86uvCifGqFGwalWA16KMidDnHIhTT4fBi7MaTiyDI6+FtHhPE/zOGp7FqMtGkTYgjcxhmcz89UxOufEU1beT2EtkwjV3zBVuLOT4N8e9wQ9q8fTTwpkxahRccomqq+52zJoFOTlCX2n16mi3pmeQk5PDsGEty98NGTLEm/6fl5fH8ePHW/xus9loaGggJyew5/CegNPqxF5tx+PyoDfocVqcOC3OoPXQulu2VVfB4/L4/W34RSLq9sjKIzjqfYtf1xfV8+GNH/L+te/7Xc++feIzVspEtif4bU4zozfq27WLLxTR+9Z/vpwbGhoaGtGi8kAlBz87SPXh6hbTjx0Tn4pO4bFjQqdQQcnKqy9U731YQ0MtNEdGN6MnamR8/bX4nDWrrb5sUhLcdZf4/uc/t7w4h8JvfyucDbNnw6JFIlLoiivEZ6jZHjffDPn5IlLouefCa18oDD13KFN/MZXTHzk96JfKEReNYPbvZzPj7hlIup77QqpkY0ybBl2w2gbQFCV8xRXw178KB+Dhw7B0abRb1vkMmDuAs/5+FpN/MrnjmQPE62RWKSNDluHZZ8X3224Tke/hXovCxmWFQy9BwfvgCd4Dpjgytm7tmQ60SJHcW7yIWEotyB7hrN715i6+/eu3VB6oVG07DQ2ilBQIB72PYB6NZuj1Tc6ed96Jblt6ClOmTGHnzp0tph04cIAhQ4YAMGPGDNasWdPi96+//popU6b4jE7raZhSTCRkJuCoceBp9IAEjTZRTspeY8flcIWkh2avbZsFoxE5Pr39U5YuWkr5nvI2v+WMySFtYBpup5tDnx/yubziAG8vMENxZAwf7neWTkVxZBQVtf1twvUTWPjOQkYvHN25jdLQ0NDoBE6sO8HGZzZy8PODLaYrjowZM8QzqdPZ0tmb0lcMamiODI1YRHsq7wLIsux1UHSEt3xJD8rIaC707Yuf/xzS0mD3blFTPlTWrxcRz5IkBmvUCiQzmURNcRDC5A3qVbMJCJ1Bx+CzBqMzhHY5yDs1r0c5znzRXlmprkhioig1BaJP9jQlpYSsBDKHZWJMUk+0Rm0n8+rVsHevcNYGXIbJWgilq6HhcMfzhkJ8HzAkgtsJ1uMdz9+KYcPE/litYt+6O5ZyC6U7Stn5xk7WP7GeygOVVB2q8v5Zyi2qbCchKwGdQYfH5cFaIWp626vUdayBEKyurIQhQ4SjX6NjFH2i99/XnHedwc9+9jPuu+8+SkpKANizZw9PPfUUt51MQ5wzZw6NjY3873//A6C+vp4HHniAX/ziF1FrcyyRmJ3IgiULGHf1OHpP6s2Un09h4dKFLf4WLFkQsB6ardrGB9d/wIc3fBh0NLxG6Nhr7LhsLoyJbZ9xJEli+EXD0cXpaLQ1+lxeedf0VwrR4RCZoRA7jgxFF8NXRoZOrwspO0hGPBy7XW6cVqc3UEBDQ0MjlqgvEo6IlLyW0ZaKI2PIEOgnKg5z9GjT78l9RCCUo87hN0NPQyNaaLmPMYJer2fy5Mltaol5XB7eu+Y9nPVOLnn9Esyp7Q86jF44mvrietL6p0WwtbGDxwPffCO+CyHRtnZMTRUCzA89JMo3LVgQfKSoxwN33im+X3cdTJqkSvO9XHONEP48eBD++U+491511x8oHpeH0p2l9J4oQpd82bP6cDVbX9zKtDunkZCVEJ2GhoG/0gfBlkRQ8Hhg5Urx3Z8jw9/5Hcv8/OeijNrOnfDJJ3CBuvrDYdEV7am2RoaSjXH11UFkAVVtgYP/geyZMPq3gMq2lCRIHgrV26BuPyQNCmpxnU5cW9esEeWlxowJv0mdTaD2tJRbWHblMuoK6rwvGGv+uIbU/FSvUzkhMyGoQUF/SDqJxF6J1BfW01DSQEJ2QpNjTSVNDpsN/u//xPf77oNwS592xXM8FObOFdlv5eUiKCNUTZGO6Cn27IgzzjiDO++8kzlz5qDT6UhMTOS5557zamZIksT777/PLbfcwl/+8hfcbjc33XQTCxcujHLLAyfSxzoxO5FxPx5H1vAsskZmkTE4I+R1mdPMuBvduJ1uKg9Ukj0yW8WWhk93PG88Lg/OeiGs5e95pP+c/vSZ3MfvO2dHeowHD4pn4+RkyM0V06Jty/ZKS4WKtcyKRbYIe3gge3S2T+eQ2kTblt0JzZbqodlSXdS0Z11BHdDkmFBQnBb9+8OAAeL/o0dh5kwx3WA2EJ8Rj63KRn1RPabhwWVbxgJav1SXWLKnlpERQzhbK7YiouXjEoQCZN2Jug7XkT8rn9ELR3fJAeZQ2LULampEBPnEiWKaLzvecYeI9N2xQwhrBsuSJfD992Idf/5zeG32RVwc/PGP4vtjj4l96mxcDhfLf7Gcr/7wVYtyI83t6bQ4+eYv31C6vZTtr27v/EaGgVISweVwecsgNP8LtSTC9u1QUSH6xqmn+p/PV7+MZdLTm8TnYzErI9L2PLzyMJuf30xdYcfX3UBwO90gqZMtV1goIrhB6AAFjONkGQlTVovJqtoy5WT9+fr9IS3eHXQyArGno86BtdKKKdWEzqATf3od8ZnxmNPMGEwGrJVWr6BuuCTlNum+OBuc3uhntRxr//0vlJaKlyG1hNq72jUzFOLi4KKLxPdIl5fqCfYMhJtuuon9+/ezd+9eNm/ezDnnnNPi9/79+/P555+zc+dOdu/ezS9/+csotTR0In2s0/qnMeqyUQHr9/lDkiSyRwvnRfkPbcscxQLd7bxRynhJOgljsu9Bd32cvt3AuY70GJuXlWqe6BBNW7bnyKgvrmfNw2tY++jagNfndrppKGnAViGcGMq0zqK79ctootlSPTRbqosa9pQ9slcjz19GRv/+MHCg+K5k0yl4dTKKum55Ka1fqkus2FNzZMQIbrebHTt24Ha3fQhK6ScuOrUnaju7WTGPoo8xY4aIAPVnx4wMEWEOIisjmEFZi0VoY4CINFUehtXm8suFQGpNTVOd8c7EYDKQOTQTgK0vbkWW5Rb2lGWZDU9toKFYRPSecrP6orSRRCmJ0LoMQqglERSUslLz5olBKV+0d37HMnfdBUYjrFvXVMItFugMex76/BD7P9zfRhgtVGb+eiaL31vMwPkDw17X88+D2y2y0MaODWJBR4X4bObIUN2WyUPFZ/2BkBbv6o6MYO0ZlxCHLk7ndWaYkkwREQtVBOwbihu80bTGZCP6uPAjahwOoasD4l7p7zoYDF31mhkKSnmpZcvC1/HyR0+yZ0+nqx3r7FHCkVH2Q1mUW9KWrmbLQGiu1xVIOaXqw9Vt3j+9GRl+Mvp86WNE25btaWTIHpmi74so2VoS0Lo8bg+WMguyWyYuIY64pDg8Lg+OOkdIovfBEm1bdic0W6qHZkt1UcuelnILnkYPujhdizGO2tqmwFklIwNalpYCGL14NHP/OJfep0RoACzCaP1SXWLJnpojowuQ2i8V6Dgjw1HvoHhLcY9yeHSkj9GcX/4SEhJg82b47LPAt/HYYyICesCAJuHwSKDXi/JXIBwZFRWR25Y/xl8zHr1RT/mucgo3FLb4bf9H+ylYV4DOoGPWb2dhSu566YWJ2YlkDM7w+xdKCZcvvxSf3UUfozl9+ohSaiCyMnoSSvqtEsWiBjq9LmQtGoXGRvjPf8T3n/0syIV9ODJUJ/lkRoblGLiDF3BVHBnbtol97QkkZIsMSr0pcmm6vU/pzfCLh9NrXK8Oo2mD5dVXoaCg5fVCI3BOP12UwCwpgXXfyk2RFhUVIo3022/FQ8vSpULsS0MjihxYfoBjXx/DaVEnIi9njMjqqNhToWkMdAJKWUFTWsfP8Lvf2c1nd3zGziU7W0z3amT4KS0Va0Lf0L5GhpJ94rK5AsqqOLHuBCflMTAmG/E0eoQjo9YRVoa3hoaGhto0Lysl6Zqc10o2RmamqCrhz5GROz6XPpP8lxrU0IgWmkZGFyDQjIzK/ZWs+eMa0gamce5T53ZG06KKLDc5MmbP7nj+7GxRKufxx0VWxjnndCzYffy4cGSAqP9tjvA1/JJLRImsrVvFdpVtdxYJWQkMOG0Ae97dw3dPfsfs+2djLbByuPowG/65AdktM+GGCWQOy+zchsUodntTHzzzzOi2JVLccw+88IIYR9u6tamEW3dHiWCPtVTaDz4QL+I5OULvJygUR4Y5gnXITRnir7FOiIsnDw5q8cGDxaBubS388ANMmBCZZsYSaflpGIwGzBmRu8H0ndKXvlP6AnBktcgbV0N4vrGxycl5zz2Rv0d2OWRZ3Cjq66GhQXw6HDB1qvj93XcxFhXx/MB6Dm6rR/5FPfzvpzB6NGzYAB9+KOZLSGhZcF5DIwrIHpkdr+/AWe/kjMfOUEXTIn1gOoZ4A42WRmqO1ZA+MF2Flmr4w6uPFMD1v8/kPmx/ZTsnvj2BpcxCYo4I9sk7NY/EnERS81N9LheLjgwlI6O0VGS+NddKjEuMQ9JLyG4ZR52j3fLMdYV1HPjoALkTcxl71VjyZ+Vz6ItD7F66mz5T+zDpZiGiaEoxha1vpaGhoREu9YXiPVYpEaXQvKwU+C8tpaERq2iOjBjCn2hKoBkZysOpWlGWsc6hQ2JALy6uaUwA/NsR4O674ZlnYP16WLVKREK2x29/K8Yg5syBSy9VqeHtoNPBn/4E558PTz8tMkAiVcrKF5ZyC3uW7aFkWwmyW6ZwQyFuvZuNlRtFCnVSHNtf2c6Qs4doD+iIkkt2u4j0Gjmy/XljQRQpFAYPhkWL4M03hSD9W29Fu0WCSNszuffJjIyS8DMyHPUOvnn0G8zpZmbcPSOgcg7+UES+b75ZlP0KGNkDjpPaN8aWjkjVbTn+UeEs0QVfY0iSRFbGypWwcWPXdGQEa09JJ7UR4Iskvcb1YtZ9s4iLD78G1BtviJeenBzRJ9UkYDvay4XjzB9xKeo572RZ1JusrxdCQmazeBjZs0c4KhRnxfDhcN55Ip3z4YdbrsNggClTRGe3WsFgYNC8fF7flsz2omRm5fRCAlGvcNYsESqnwjnaVe9BGsETqWNddagKZ72TuIQ41QJaJJ1E1sgsSraUULarLOYcGd3tvDGnmek9uXdAxy9tQBq9xveidHsp+z/ez8QbRCTLiItH+F1GlpscGSNazRZNW/bqJS65LpdIdstpJu8iSRKmFBP2ajv2WrtfR4bskdnw5AbcTjd50/IYf814JEmiYWwDB5cfBBkyBmd0yv50t34ZTTRbqodmS3VRw54D5g8gfVB6m6zv1o4MJSPj+HFRvljZtLvRTcH6AuqL6hm9eHRY77DRQuuX6hIr9tQcGTGCwWBgypQpPn9TMjJslTYarY1e8e/WKOUi1Iiy7AookfBTp0L8yV1uz44gnAI33yycBA8/3L4jY906MUgjSfDEEx1nb6jFuefC9OnC2fLoo/DUU52zXRACtPZqO0m9krCUWWi0NJI2KI1aWy14hEiUrdKGo86hOTJoWVaqvf7RUb+MdX77W+HIeOcdOHAAhg6Nbns6w57NNQXCxV5tp2xnGcZkY1gPgHv2wOrVwuH5k58EubCzRjgzJJ3ImDhJRGyZ0CesxRVHxqZN6g+OR5pYPtftNXbqi+tJH5ROv+n9wl6f2w1//rP4/qtfiaQBtQjYjvZyWHdlk5POF6ZMmLGkfWeGwyHe3urrm/4aG0WaJIgb8fHjwlGhlH76xS9gzBg4eBDWrBEOh+Rk8ZdyUlAxO1t04ua/JSY23TCuvhqAUTZY9QI0lMMvj8DUbMR8KhHL/VJDXSJ5rBUNgZxxOej06lUn7j+nPyl9U7w6bbFCdzxv+kzuQ5/Jgd+jh180nNLtpRz64hBjrxyLwdz+0EFFBVRXi0tc82fFaNvSYBCX47IyoZOR00qn3pQqHBmOWoffdTSUNtBQ3IDBbGDqL6Z6n+cUx4e13Bqx9jcn2rbsTmi2VA/Nluqilj1NySZvCcfmKI4MxYHRp48IDm5sFNfIfidfEyRJYv3j65HdMoPOGNRuxlosovVLdYkle2qOjBhBlmVqa2tJTU1tM9BlTDSSPyef+PR4PC7/SpCK+FpPychQhL6bl5Vqz44K99wD//63GHdYu9Z3WSqPB+68U3y/4YbOLacjSSIr4/TTRTvvvhvy8ztv+wCJuYk02hrRxenQx+vJHZ/rrQGrZP5oNAl9d6SPEUi/jGXGjxfBxcuXi3Jnzz8f3fZ0hj2VjAxblQ2X3dXhy3t7KPWkzWnhXZv/9S/x+aMfNT1gBowhEcY+AK564cw4SSz2za4s+B2sPf2JgkZCLPSzOz/DVmnjzL+dSdbw8HVS3nkH9u+HjAz46U9VaGAzArZjY51wYuhMoI8HtwdsbrC6xafFCn3KxHw/HBdpPs0zJ045Ba66Cior4W9/E+vU6YTjITUVLr5Y3JRHjoQhQ1o6JJSi62efLf58YTI1deh2iI+HCy5ochg3zzJVg1g8zzUiQySPdfFWITDQe6K6qcKDTh8EHWRIRwPtvBGOj6Q+STQUNXB4xWEGnzUYe40dc5oZvbFtVObeveIzP78pyAxiw5a9ewtHRnFx22xPRc/CXuv/HSe5dzLnPXse1YerWwRzJfVOYugFQ0nqlRSJZrchFmzZXdBsqR6aLdUl0vZUtDCUjAy9Xly3Dx0SmdbKe6bOoCMpN4n6wnrqi+q7nCND65fqEkv21MS+YwS3283evXv9KsDP/PVMTrnplHaFwzoSX+tu+BL67siOIC7M118vvreu+KDw+utivCM5WTgVOpvTToP588HpjM72JUmk+mcOy8TmtIEEBpPm92xOVZUQjoeOS5QF0i9jnXvvFZ+vvCKqpUSTzrCnMcmIMVnUbgq3vJQaZf8aGoTtIQSRbwC9CTInQ6/5LSZHxJayDAf+BZvvbL/kjx+Ucd+dO0Xptq5EoPY0pZhIyEzA5XBhr7G3+YuEWKiSZbT7nd0cW3us3QGbjvB4mu5Nd94p7pVq4na72btnD26LpSnEF6CuDr74At59V5wQ/3kNPigTTgxDIrxeCP89Dm8Uwvsl8EUVlJ0UJXY4xF96OowaJS7cSpRCTg48+KAQ0Xr2WSGK9fvfN2VOnHmm8ObOmSOWGTJE3RQUmspXvvNOU9KHWnSHe5BGYETqWDfaGqnYI3SWcif2DK2W7njeuBuD2xdJkhj+IyF2se/DfVTur+TDGz9k+c+X+5zfnz5GLNiyI8FvvVHfodi3KdlE7viW/d+cambyTya3W3JLTWLBlt0FzZbqodlSXdSwp8vuYutLWzn05SHkVg+WrUtLgX/Bb6X8bV1h8O900Ubrl+oSS/bURia7ET1JI6OoSHiMJQlmzAh++d/+Fv77X1EaaMMGOPXUpt8aGpoGbX/3u+hpaz78sCiP/eKLIotkyJDO3b7eoMcj+88A6umsWiUGm0aPbno56s7MmiX+vvlGjPX9/e/RblHkOf2R04nPiPc6NEJFKfsXTkbGkiViDHfo0I4dZ1FHkqB6uxD7rj8AGZOCWrx/f8jKEuPXO3cKOYHuRmJ2IguWLMBR57+Mhdpiocm9kynfVU7hd4UUflfIaY+chnlsaH3yww9h1y5RQekXvwhhBceOQU1NUxmnhgaYO1c4FL78Et0XXzDg8GF0GRkiQ2LWLFGGyWIRqWHJySI7wgAkN3uUnX/yYpxggHgDxDmAWjFtxgz/DwwGQ9SFtM89V0QwHzkC27Z1biaohkZHlO0sQ3bLJOYmejMW1cTtdFO5v5K4hDjSB8WWTkZ34rM7PsNabmXeQ/MCFmvvNa4XsixjrbBy9KujOC3COVx1qApoea+KRaFvBUVz0JcjY/qvpvstl3Z45WF0eh395/aPegSqhoaGRqDUF9Wzd9leTCkmBp85uMVvrUtLgX/B7+S+ybCxSThcQyMW0BwZXQhHvQN7jd0r/t2anqSRoWRjTJggqj8Ey8CBYkzk5ZeFw+Djj5t+++tfhaNk4EC44w41WhsaM2eKgY1PP4WHHoJXX41eWzTaEmhZqe7EvfcKIfp//xvuuw8yY6ucteqkDUhTZT3hZsvJMjzzjPj+05+Kcd2gqd4OzipIGQHx6pYF8UnyMOHIqAvekaEIfn/2mSgv1R0dGSCcGZ2pNZSU27LsRTj9UWQzyvzyJ1bSHA1wsL5JAHvAAOGF+vDDlo4KgL/8RXy+8IKo8QFCCyI5WYzc5+RAnz7IM2ZQNWAAWZMmoUtLE54tECNRTzzR1Jj6Q/DNh03/D0pRWik+XI3Q2DUGnhITxT1/2TKRlaE5MjRiieojIitK7bJSCrve2sXut3cz8PSBTLtzWkS2oSGC3lx2F8bEwAI0LOUWPrzhQ+qL6tEZdZT8owRbhY2KfRUUbSoCICEzgQVLFpCYndhlHRn+nBiWMgubn9vsLTGaNy3P53xOixNLqQVzmpn4jO7/Hq6hoRH71BWIDIrkvi2DD6zWpkfwQDIyUvqKZ+uumJGh0X3RHBkxgiRJxMfH+430KNtVxsp7V5LUO4kL/3Ohz3nGXjkWS5mlzcWqO6I4MlrrW3Rkx+bcd59wDnzyCfznP2IcRZJERQkQ5bLNUU5uefhh4ch4/XWRRTJqVOduX0JCp9ch0TUGgjqTYBwZwfTLWObcc4Vexvbt8PTT8MAD0WlHV7OnN1suxIyM9ethxw4RrX3ddSE2ouhTKP8WhtwMeT/yTo6YLZOHQulqqN8f0uLNHRldiVjum0ppKYU22Zsej8h4sNmalFDXrRP6Ec0cEl9lL2LLln5caVrGPaVfwB+arWPmTPEmJMui/l5SknBCKJoTCrffLvQjkpLaeuZGj4YRI2jctQtpzBhRuDcQlDJmcUnQcARkd+c47VTkssuaHBl/+lNTZatwieV+qaEukTrWYxaPYfCZg/G4I5Opmz1KZAeU/VAWkfWHQnc7bzwuD856kU0R6POIo86BtdKKKcWEId6Ay+5CZ9BhTDJiTjPjsrmwVlpx1DnadWTEgi0VR0ZRUWDzy7LMhn9uwGV3kTUyi76n9vU778ZnNnJ87XEm3jgx4iWmYsGW3QXNluqh2VJd1LBnfZHIoEjJS2kx/fhx8ZmcDGlpTdP9lpY6ObbYFTMytH6pLrFkT82RESPo9XrGjx/v93elNl1DSQNup9unwFr/Of3bTOuu+BL6ho7t2JyhQ8WYy9q18JOftPxt1Ci45BIVGhomkybBggViYOOBB2Dp0s7ZbnOh2Xh9PI3WxjbTezJHjojSZgaDqIbSEcH0y1hGkkRWxuWXw1NPwa9+JcYhO5tI29NSbsFR56ChpIFja46hN+oZcUnTi2mwJX88jR6QQi/79+yz4vOKK0TAe0g4RG1zTC1FniNmy5Rh4rN+vxjUDvKBp6sKfsfiuW45UEDj0UL0B4pJL9hOnNuOPTGb+uJ6dKXFJL6/BKPsFFkTsixSAx5/XCz85ZcidOtkKSc5JZV/PCkcDyOvnUr8TwY2lXlKShLLAmRnw913+29UdvslTUKyo60QGuvB3Auc1SB7wO0AY1pw64ki558PRqMQUf/hBxgzRp31xmK/1IgMkTzWkYw0zx6ZDRJYSixYK60kZEZfULS7nTdKUIWkk4IumWmIN2BMNCJJEjqDDlOyyZvV4XKId4PGRjh8WMw/otVYfizYsj2NjKpDVexcspP4jHim3jYVgMMrDlO6rRS9Uc+pd5za7sCNIoBrrbCq3u7WxIItuwuaLdVDs6W6qGFPb0ZGn5ZBzs31MZpf1vyVllIyMiylFjwuDzpD15FZ1vqlusSSPTVHRozg8XioqKggKysLnY+6IeZ0M3GJcTRaGqkvqlet5ElXpKpK1OaGto6MjuzYnGXLRL1/X+zZA++9J5wI0ebBB0Vb3nkHnn9ejBX17i32PdBA1UBRBGitlVZcDheyLON2udEb9N4HeLUFaLsiSjbGtGmBidwG0y9jncsuE3otBw+K/njXXZ3fhkja01JuYdmVy8Q5YHdRX1iPzqBj55Kd3nmal1EIhJn3zGTG3TOQPcEr+JaVNTkwQxL5VvA6MloOIEfMlokDQacHZy04ysGcE9TiiiPjhx/EOLrKusoRI6LnuscjnA11deLP6RT1FUF0kvJy8btSyunnP8eS8v/snXeYW9WZ/z9XXaPpzTPjcS/Y4G4DwWAb0yFUQ0JCNtkkmyVtQ9jwS7LZZLMppG2yqRsS0rNLKIGYEiCQhGKMqcbGNuCG+4zH9vQijfr9/fHOHWlmpFG7kq5m9HkePZqRrq6OXh2de+/5nvf7NrD1+v+i7vjroKrU9wcIKDZ6rDN54N0PYAt6mEMfZ33hAhzNdTKgRQ9qX/7yiKucZ56GP2+TZIqPfHUaNEzT9zMOf9QU4xgcAH8PoIK1Qop/DxyCQC+EBiGU/cklPSgvh0svhT//WeqZ6yVkTKRjUJHxKdTv2lpipXJWJT0He2h/s90Qi7MKNZbxiM4OTWc1ZSgQYrBTrDIV09jXHzwIwaDo2VNHJS8YIZbjWUsFvUGOv3Kc0iZZnePp8LD9V9sBWPwPi4cn8uJRUicnKe52t34NjoMRYjlRKMZSP4qx1Bc94qlZQY3OyIhVHyP6/5YWGcstQzPFjioHa7+8lrKmMhRz/lfip0KxX+qLkeJZFDIMQjgc5uDBg1RXV8fsFIqiUD6tnM49nfQe6x0jZHh7vHQd6MJV74pbQ2OisGWLLBqdPx+mTBn5XKI4aoRCUv9CHWde8dZb4Zpr9BcLUmXRIqlzunkz3Hxz5PHmZvjRj/QVW0YXoA0FQ+zatYvFixdjtkgg9C5AW4ikWh8j2X6ZMd72iL1KLKzl4EiuuGM8zGYpPn/zzVLw+xOfkEnNXJLNeGo2Cha7BVupDfdJNyjS7xWTMsZGIVkUkxLzwj8Rv/mNzFefdZZkaKVFOCT1MQAcIzMyshZLsw1cM6WGQd++lIWMpiapvXzihFiZnXOOfk3LJinHMxAQayWzWa4cjh6NCBV9feLPsWaNLI/6zndGHrRKSiJCRleXHNjq6mD2bFG8Kyvx9fg4yCxaZs5Cdbk4ua+PkD+M2WmmttJBcNDCbt9iFp11Po451WPbpyiEQnL8aWuTJgD88z9ntzZ20nG0loO9BgYOQzgg/S40NJHkqIXBNsnM2PsTWPE92d7g3HCDCBkPPKCffV/OjkFF8oKWRQhy3vbGrjd0PW978Qcv4u32sui9i5IuEJ0O9Yvq6TnYw6k3TxlGyJhIvxtNyLBXpnfSZraasZZYCXgC2MvG7mPPHrmfP39sEqYRYhktZGiJotpvx9vtxe/209/aT+fbnbzyk1dwt7upOa2GBdcktorKZUaGEWI5USjGUj+KsdSXTOOpquqwFdRo23nNOmrGqMNsQ4Nc0/t8ckmiCRuKojD1zPjWekam2C/1xUjxLAoZBUTFtAo693TSd2zsRGX7W+08/63nqVlQwyXfvSQPrcsdWn2MtWsz20dLS/znVRWOHZPtzj8//ffRg3iZI62tMuHxwAP6ixnaBW8wGKSku4SqOVVYLMXhAmRR9FNPyd8XX5zftozA2w4v3AS+zvjb2Gtg9d0Zixkf+AB85SvSB++6C/7pnzLanSGxOC1YXVbMdjNqSMVkMWF1WoGIjUK2CYXg5z+XvzPKxvB3yaBmsoC1Uo+mJUfZfAi6QU09XlrB70cfFXupQhEyCAaxdnWJ95zXK9kR4XDkgKUVudYyJ/x+sWCaNw9eeQWefFIEivJyuYVC8rr6eviHf4g8XlYm9xqj/RE1errwW0owlTuwuWyUT1PoPdIb0xYkFhs3iug/+ni5eHG6AdIZR52Mabu/C51bYeoV0BzlC+k5Cnv/RwSN178AS28HW7r+bLnhqqtkFdwbb8DevcYsmlvEOERnEYJMXng8HvaV7BuRSZtKFmE0aljl+CvH8Q/4WXxTdn/49WfUs++RfbS/1Z7V95msaEKGsyp9i7Da02sJ+UNYHdYxzxm50DdExHe/X7R/Rzjy21FDKj2HewC49+p7ZQJQEWtQT6cn4W9HEzIGOwaz+RGKFClSJCm83V6xBFegtGGkD3S0tVQ0JpM8tm+frJ8anbFRpIiRKM5MFhDl02TSovdY75jn9Dg5LRTiFfpOhVhpxZlsly3GyxzRVhMZJXNkMhAKwa9/LbVvnU5YsSLfLYoi0CcihskutiqjCQ3K84G+jIUMux0+8xmZf/3Od6QA9UTsfwoKFruFgCdA0BscFjJSwdfn4/lvP4+jysHq/7c6JTuHxx+Xk83qanj3u1N+66hGDNlK2ar1qx6cDPM+Bkr6qzWihYy8EApJ8Wut0PX06SIy7NwpnleaGNHfL+kyV14JR4/S/ItfYKqrixSxrq2NCBmlpeBwyL1WW0JLLXznO+HqqyO53NG4XJKalyGOKgdmmzkpf9uNG0Usj3X8+djH5GMZwX4RWxW4j4DFBVOvgrI5kefK5kDpXNj5JRg8Dp4WwwsZVVWS7ffEE2Iv9e//nu8WFTEy0VmEFqcFVVUJmAI4ysU+KN0sQo2ut7vwD/ixuqxUz4uRtaUjWsHv3sO9+Pp9MVf9F0kfR6WDpjObqJlfk/Y+TCYTJkfs44fRhQy7Xc6nurrk+q7JGfntmJ1mTK0mUEWUsJXa8Pf78bv9Sf12XPXy/GD3YMF5yBcpUmTi4ahycM1vr8F9yo3ZOvIiPZ61lPbYvn1jC373HOmh5aUWSmpKmH3R7Gw0uUiRlCgKGQZBURQqKirGneTSLKO0wj3RDHbJCpB0i8kWCm53ZFIrVkZGMnGESHpxIpLdLlvkO3Mk2XhOBkavTB4clEXUydh75TSOZqdM6MUi7NPtbW6+Gb7xDdi/H77+dblwzVbtltHkMp62UhsBT4CBkwNpCcWDXYOc2nUKe4U95fZqRb4//GERztJGEzJiCFhZjWUGIgZkoeB3KCQ3m01EiN27I0JEf7+IPO97n2z75S/DyZMjX3/bbeKZ0dYmRWI0MaKpSbz+AGXqVAY++lFqFy2CykoRIKJTb9/znvjty4FHm8VmwWJLfOqXb/vFlPpl75sQ9ICtQrKARuOaBsu+LUJGpVFSScbn+utFyHjgAX2EjOKxfOKjFWNWVRU79uHCzJBZFmHbdlnRM2XpFEzm7E7OOiodnHXLWVTOrBzOGMsnE+1307SqiaZVTWm9NjgYuw9FPz6ekGGUWDY1RQkZQ3Nx2m/H4rAQDoQxWU2UVpTiL/UPLxRMhL3cjslqIhwIM9g1OCxsZAOjxHIiUIylfhRjqS+ZxlNRFEpqS4azxaKJZy0F8Qt+9xzqYdddu6hbVFdQQkaxX+qLkeJZFDIMgtlsZuHCheNuUzmrknnvnEflrMoxzw12i5Ax0TMyXn5Zig81N8cefJOJI8hka3OzWOPEmqhRFHk+k6wPPch35kiy8ZzoxFuZnKy910SMY1mZrBq+/34pSK+Rjdoto8llPEsbS3G3u/H3+fH2eVOeyNHG5lRF5gMHZCITZPV7RlQugSVfAWXsIT8nsVRVUENibZUCWk2Q3btFdygtjbFRd3ekuLV2f+aZYrv03HNy0NCECo8HLrxQ0lva28XmyWqVHZeXQ03UKtVLLpEDgZY1UVYmS+VBqjFfemnMNptdLuZcfnlKn9OI5FtET6lf9uyU++oz42ccORvlpuFpkQ/hyk6x8ky59lr53W/fLgV0Z2d4zTgRj0FFYqMoCqWlpRAGdLjOPLH9BACNy3OzsmfOxXMSb5Qjir8bmaAvqSnB0+mJK4iV1JRgL7ePK2QYJZaNjWLb19YGjBpXw4Gw3AfDKe9XURROv+F0zHYzZnt2V/MYJZYTgWIs9aMYS33JVjz9fjh+XP6ONZemZWmMzsjQ6mwMHB/QvU3ZpNgv9cVI8SwKGQYhHA5z/Phxmpqa4hZOKakpYdXHVsV8ztstK0YmekbGc8/J/dq1secrkokjyArSH/1IJqEVZeQEtbbfH/4w/3Y5+c4cSTaeExk97L3yFseQF8zZGRM2bhQBZzTZqt0STS7jabFbcNW78PWml82ireZzVKb2PWi1MS67DOZkOq9jq4Dq2JXCsx7LA7+Btr/C3I9Aw0Xjb+v3R0SHkhIaGupZ3NjBvLZNtH6zn9Mah4QKq1V8zQC++U0piq1hs8lZeHm5bFdTI/+Xlclt2tDE9fTpchCw22MfTNK0cZooY2a+RfSU4jjzH6B2NZiSXME9eBJ2fFHqt8z/JJRMj72dtTxjG750qa2Fdevg6afFXuqzn81sfxOlXxZJTNAfpOPtDuxOO1WzMrNRC3gCdOyRjL6G5Q16NK+gmGi/m1AgNMZiJBGuOhcb7t4wXEw+FvZyOz6zi46h5M/5MRLjjBJL7XpJm8iLxlHlwNvtRQ2Nk4o4DtmuIaNhlFhOBIqx1I9iLPUl03i+ef+bBL1BZl0wi/KpkZp6LUPreBwOKb83mrhCRpMIGYNdgwQ8Aawlqdst54Niv9QXI8WzKGQYhHA4TEtLCw0NDWl1islSIyNRfYxU4rhhg0y2ji5k2twsIoYRvL/znTmSab+cCOixMjmncQz0SnHnwTbpIFXLY67Ez4R8127JRTyj7RKc1U6cNc5hv/FU0ETmVMbmwUH4zW/k74yKfCdB1mKpqpI+BzJhvG0TVJaMrCtx5ZUyY/vQQ/DUUyJkaFxwAdx4I2ctHmRG2+uc2lXGac2lMuBVR/m0f/SjIlhomRO2qMnsc86JXyXcbM5K5zTqmJmMLUg0RhDRk46jooysi5EISwmYHHDycTjxlGRqxBJ87TVSSDxPYsYNN4iQ8cAD+ggZRuyXRfQn6A1Kkc+BoNTESXHiOpqTu06ihlRKG0spnRIrJU5/VFXl0NOHaH+znZU3r8TiyN+l6kT73Tzx6SfwtHs4/2vnUz2/js2bRYxOZAvqqnMlrBGx80W5b26OnT1plFhqx6xYInzF9AqcNc6UF57kGqPEciJQjKV+FGOpL5nG8+DfDjLQNkDD0oYRQkZ0oe9Y67jiWUvZXDYclQ68PV76j/dTPTe7NbP0otgv9cVI8SwKGQVG0Buk92gvZruZyhmVw49PhhoZfj+8OHSirNfE/YYNMtma7Ml8rimUzJGJTL5XJqeM9wSEhyYnVVWKe9v0PdnIt+1MNknFRiEZhq2lkrgwDoUkZvfdJx7O06bBFVck3/a4nPg7KGbJyrCWJ94+Hh4P9PaOtHFqbJRiMS0tMuMa/VxFBdw69AHu/Qs4T0mxD0108A55T592WuSx8nK5HxIrZq2dxpf++nXeWwZrYok6c+em/3kmAen253yL6FnFWgan/Qu0PAQq4O2A0lkjawuFBsHXKeNnnoSM666DT34SXnkFjh6VJKIiRRJhK7Nhq7ER7gvTc6iHqjnpZ2VYHBYaljdQMaNCxxYmZtddu/B0eJixbgYNSydfJki28PZ4CXqDPLXJymcvGbuIKxNbUKMX+tZoGioREuuc3WK3YLGnPzUSGAww0DaAYlZGXKMXKVKkSC4JB8O4T7oBKG8eed03Xn0MiGRktLbK3Fv0+rCyqWUZCRnada4R59yKFB5FIaPA2PPQHnb9YRezLpzFO259x/Djyz64DE+Hh9KG3KyYygfbtslK5Zoa0NOazWw29mRrvMyRigr49a+NkTkykcn3yuSUCHrkpljFTsjfBd52MNllYk4nCk7cSYHxbBRCgRBHnjtCxbSKhKsTNYatpRKIzKOLyYNoBg8/rMNv/ODvwd8DK38YETKCQREburuxt7ZGKms/+qi8sSZGDAxIZfemJsmc2LQpsl9FkVoR8+ZJVoTTKXnKmihRUQHlUgSbmxpg/Q/APrboHAsXxh3UtWY99xzcc0/xxDdVkrUFGd2fo0X00RhKRH/zW5JNMeM9I2tgJMJcItv7e2Rs9ByFsnlgjZqwDadnJ6cXDQ3icLZ5s4wPt96a1+YUMThadpWqqljLrQTcAYKDQXoO91BSE2PcTYKGpQ05FxIURaHujDqObDpC+5vtRSFDJ8LBMP5+P21t8O8fdzJ6dMvUFnTPHrk3upARKyMj1YzFeBx+9jBb79jK1LOnsvZLa9NtYpEiRYpkRH9bP2pYxeKwjLn+jM7IiEV9vVzODQ7KosRoe+OypjLa32ynr7Uv9ovHIdZ1bi7qahaZuBSFDINgMpmoq6tLmKJTPk0moXqP9o54fOb5M7PVNMOg2Uqddx7EC1OycSw0ojNH7rhDCixfdFH2B/6JGs9U0GNlck7iaC2HcADUMFgdoJgkM8PfBdYhgdNek9mK/CHyLe5kO57xbBT2PLyH/X/eT2ljKXMunoPJkvj9w4EwKONnZMQrJt/fn8LEQjAoA6PJJGeJx4/LDnp74LU3YHoprK6F/fvhpz+VM1TArKrMVBRM110n+9m1S5bMlJVJVsT06VJHAmD9ejjrrIhQUVISmdWeMkVsnkajqiKq0Qu+Y2BPbZZDm2xobYWbbpK/jXzia8QxMxlbkFhoIvqHPyzalkYu7BeTimOgHzpelD42832pv4ligtKZYsPn74H+/VC5JPlaGznghhvkuP+nP2UmZBixXxbRh9FZV6qqgl+OOf2t/fh6fLjqXUlnERoBTcg49eapvLZjIv1uvL1eVBXeeFPBx9gxLlNb0EQZGUaJZXSNDL0zcEtqRTB0t7t1aWs8jBLLiUAxlvpRjKW+ZBLP/uP9gGRQKKP8ozQhQ8u8GI2iyHO7d4u91AghY6jgd19LakJGvOvcXNTVLPZLfTFSPItChkEwmUzMSaKia8V0WS3Y19KHqqpjBiejoWcKWXSh73gkG8dCRMscURQRMrZsiVx4ZIuJHM9k0WNlck7iGPJC6RyZmFtyu4gXr90qnWTZd8BerVvx2kTiDohDUJr1khOSr34599K5vHX/Wwy0DXDwqYPMvTSxrdF5/3YealhFDccOVLx6I2aCVKo9lDHAHZ8Y4Jr6fswWBd4xlIn3i19Ad3fEymlwEL70JfGj2rwZnn12KEvCBN2DUF8q339tWPyqhmyclNJSqsrKIurwF74Q/8Oko0wpiqx079wqE8XlyQsZGzfKJPpocnHimy4TbczcsAH+/nf42c/g6qvhX/81NxkxScWx6zX54ZTOBEeMioXJvZP0z963RNhQgxBjki9fbNgg48OWLZHzqHSYaP2ySITxsq72PLyH/Y/ux1HpSEp4j6avtQ+r04qzOve19+rPkN9z555OwsFwym3Xi4n0u/H2eOnsgm6vA4h94ZCJLWgyQoYRYhmdkVFSm17GYjy07QY79MuAjoVRYjkRKMZSP4qx1JdM4qkJDZrwEE2ijAyICBmjC37PWj+LqWdNTckBJt91NYv9Ul+MFM/8SylFACmccuDAAcLh8LjblTWWoZik4Oxgp5woeTo9HH/tOL3Hesd9ba7ZuFEGwvXrZSXt+vXy/8aNqe8rHIbnn5e/x1v5nmwcC5mzzpL5ybY2OHgwu+81GeKZDBs2SN2C0TQ3JzeZmpM4tj4CZjtMuQDqzoHKxVC1THzfg31SDFcnr3dN3IH4QlpfH3z+8/GFjkzIV7+0OCycceMZALxxzxuE/KGkXqcoYAoFoKNDlrdoRa137GDP7Q9wUctv+RQ/5t/5BhfwFADz2M83+CL/xrd418mfcPI7v4O//CWyU5tNvGeWLYPLL4d//EeorJTnrr0Wfvxj+MlP4Is3wz/Mg3csHCr+XgWXXAKrV8OiRYSnT+dAT092Y1k2X+779iX9kkQnviAnvqHkvoKcMRHHzKNH5f6d75TJrVzYSSUVx85X5b76zAzfTYGKhVC+UCynDERzs2iXqgoPPpj+fiZivywSwVXnonpONdVzqqmcVUk33VTOquQdt7yD+sX1lNSW4OtNzSptx//u4KF/fIh9jyU/butF+bRybGU2Qv4QXQe6cv7+GhPpd+Pt9uLzgpfE2QWp2oIGg/D22/J3PCHDKLHUhIzBQTlPjf7txLqlks2oZWT4+nxJnx+mg1FiOREoxlI/irHUl0zi2d8qGRmj62NA4hoZEMnWGC1kOKudVEyrwGxN/kIglbqa2aDYL/XFSPEsChkGIRwO097enrBTmCwmShtFBdXU1pM7T7LpK5t47c7XEr5PKCQLde+5R+6zNQmkpZCNHri0lbSpihlvvAE9PeBywfLl8bdLNo6FjNMJZw7N22Rr0NeYDPFMFs2r32qFu+6CZ56ROelkVoRnPY6Bfjj5tPzdfE3k8cqlct+9Q/e31Gxnpk4d+fi0aZFV9N//vpRX0HucyWe/nHvZXEpqnHg73Oz/y37JhtixQ5TWJ56QdKmHH4684D//Ez71KbjlFvjiF+Hb347MEhw8iLpjJ3W048fGEWbQjohNR5nOD7mV2/kSn+c7PHfjT+GrX43s94MfFPHi+uulTsXq1WL5BDJI2O0iXPg65DF7bczPk5NYVpwholrZvKRfku8T33SZiGNmMqu39CZhHMNBycgAqDlLh3c07unw9dfL/QMPpL+Pidgvi8Qm+rs2WUys/eJaLv/x5VTOrEx+H6EwJ3ecBEiroGemKIpC3elyLDz1Rv7spSbS78bb48XuAC+JM2xSzfw6fBgCAXA4xI0yFkaJZUmJlO8C/eu4WV1WLA4xu/B0ePTdeRRGieVEoBhL/SjGUl8yiefAiQEAyqeOFDJCIbl2gvHP6WfNkvtDh1J+6zHku65msV/qi5HiWRDWUqFQiNWrV+PzjVxNdPDgQe677z4uv/xydu/ezcc+9jF6e3tRFIX/+I//YIPRPCd0onxaOf2t/fQe66VhWQPebikmmyj9O1dFdrKRQqZNVq1eDZaC6LXZZe1aeOEFicsHP5jv1kwODhyQ+zlz4H1p2LFnlXAA6s8HzzGZNNaoPUfuazJdsRyb6Noto+3jzjsPPvIR+NWvxP3of/9XRCDD4vXK8rz+/sj9qlVy1fvMM7B9O/T3Yx4Y4PzO4+w42cBbf3QyZ7of68/vkH24XGLZ1NQku+z18tYeB/bK+Zz+4bNRystFbJgyRba/7jo6qq7juzFWWntwsYdIAeyG5jQ/VwIhIydULZFbCuT7xLeIoKr5ETIS0rcbgm6xSyufn/5+QqMsQNQQBAcMVSfj+uvhs5+VxSd33ikrnosF74ski6t+5IryZGxpu97uIuAOYHVZqZlXk83mxaXujDpaX24dnpApkhmOKgeLLmnigb01KN3p13yLhWYrNX9+/BqGRqKxUeo+HT8OCxbot19FUXDWOulv6cfT4aGsaaytS5EiRYpkmwtuvwBPhwera+SFd1ubZNBZLMOXqjGJl5EB8PaTb9Oxp4PTrjqNqtlVCduS77qaRSYuBTElbDabefnll0c85vV6mTNnDueccw5er5drrrmGX/7yl6xbt44TJ06wbt065s6dy5IlqU2eFAIV0ytofamVvmOSkTHYLRfi6RSTzYbXeCoraZP1YNXqY6R6cj1RWbNGFnZrcSmSfaKFDMNhr4bTPjW2aEr5PLllEa12y2g+9CGZ07/pJrj3XnC74Y9/lBV7WUdVxb6pv18smMrLobMTXntNVBVNqHA6RW0B+NznIFos16qdTZ8uf5eXy1lfaSlll7sI/PoQvj4fe98Mseh73xMRY9QV/GDnIHs9M3HYHZxx9tkxm6pHMflx0YQMnWzFckXxxNcYdHfLTwUMJmR0viL3NWdKbYtUsZaDvQZ8nRAe+t2rIXAfAVQomQ7OBtkuz2zfLiJwIAAf+5g8ZuSC90WMiaqq7PvzPk7sOMHaL60dV8w4sf0EAA3LGlBM+anFN/ui2cy6YBaOilycNEx8mlY20bSyie4lsDmDmm+xSFQfw2g0NsKePdlZCFFSW0J/S3/WC34XKVKkSDwUkzJmEQNEhIlp08Yf58fLyGh5sYW219qoW1iXlJCR9evcIpOWghAyYnHfffdx4YUXUllZySOPPMLy5ctZt24dAA0NDdx222385je/4Yc//GF+G5okJpOJ5ubmpCrATz1rKvZyO3ULZWJKy8hwVMU+2c91kR29V9KqaiQjY7xC35BaHAuZ1avle3v7bThxQqzys8FkiWcyZCJk5CyO2az8ngbvepfM719/Pfz5z+Kx//DDInCkRLRAs38/pr4+Zre0YOroAI8HLrtMhIaHHoKXXpKZ12BQtr/iChncOjrgsceGi1xTVib1IjQ+8hGxYyork1tpaeQ9zz9/hFpjAuZaZ2N++hBTV8+MWDqNYlhkjjM2gz7F5Mdl2gaZ7LXHLoac09+4v1dWwDsTD1jJFJRvajLeie9EGzO1bIz6etH9ckXCONrrwTUj/WwzRx2svhsCfSMf3/N96N0NU6+AWf+YdwFQr0UoE61fFolPvO/a0+5hx//uIOQLsf/x/cx/Z/xMprZtcoLesDxLJ5dJYC9LXMsh20zE341mC/rud4+0/aythZ//PD1xNBkhw0ixjC74rTezL5pNw7KGrFqyGSmWhU4xlvpRjKW+ZCOeyWZYaxkZbW1iWBC9CLGsqYy219qGLe4TEX2dqygjz2d1uc5NQLFfRnC3u/H1xa+XZi+3J6wLZaR4FqyQceedd/Kd73wHgL///e/DIobGunXr+JFWjXYUPp9vhE1VX5/8EIPBIMGhyS+TyYTJZCIcDo/wANMeD4VCqFG/xHiPm81mFEUZ3m/04yC2WRoNDQ0oioKqqiMeB7BYLMOPV86ppHJO5fBqKk+Xh7AaxlZuIxgMoigKZrN5uO2bNim0tMQfHbQMiWefDbFuXeafqb4+DCQejRobGfNZR7cdZAK5rc2C1QqrVoUJBsd+H9HbN0TN6mfjexrv8ejvabzPNN7jyfS90lKVxYvN7NypsGlTmBtvzN5nam5uRlXVEfvJxmcy+vd04IAKKMyYESIYVFP+TE1NTfp/JlVFOXo3as07sFSeFvszESDctQPV143acNG4nzUb39Mll8Cjjypce62Jp59WuPhilUc2+ig3DfDqM266jvTTVO5h2WUNMK0Jjh5FeeIJGBjA7PGg9vcTLi9H/eIX5T1/8hNMPh91gOpyES4tJXzOOSguF+Zp0wgDamkp6pDNk6mxERMQnjeP8H//d/y2n3560p8JoOkdTUw9ZypmsznmmG02mxnsGiSshrFX2IdfF6vvXXONwgMPmPnQh1T6+iJi1NSpKj/6kcK11yYe9+K23VKNqbI27mcymUw0NDSM2Fc2fk9K2xMob/8cat+BafGXkup73/++wo03moaOi9HvKL9FOXaFaG42xhihPa7FU/tchTzuHTwYBkzMmKESDIZyNpZHxzEcDo/9TA2XQ8PlWGL8/pI+PlmqwFI18jNNezemge9C13aYdwsm8nce4feH+PSnzUN9f6RILdquyqc/LeOHoiT+PelxbjT6sxUxHtpF5mhc9S6WfWgZr/38NV7/7es0Lm+MaX3jd/vp3NsJQOPyyZ3yFi+WhUjIH8JskzHnuuskyysUgiVLYOdOKbWVbobXnj1yn4yQYQQ0S5VsCBkz183Uf6ejMFIsC5HRk3kllNBzqGf4/2Qm84qMpdgv9SWVeEb36ZM7T9LyUgtTFk+h+Rx5vdanNSFDEyriUVMjCxHdbjh6VGwDNbQC4v3H+5P+LJqA/s//DF1dkcebmuDHP85udnGxXwrudjcbb9qIpzN+/aaSmhI23L1h3PHPSPEsSCFj165ddHd3s2ZoKebx48e5+OKLR2wzbdo0Dh48GPP13/rWt/hqdNHUIbZv347LJV9cXV0dc+bM4dChQ7S3tw9v09zcTHNzM/v27aO3t3f48dmzZ1NfX88bb7zB4GDEc3nBggVUVlayffv2ERerS5YswWazsXXrVkAm9N1uN2vXriUYDLJz587hbc1mM2eeeSa9vb3s0c4WAafTydKlS+lp66G3p5eDbQdp39pORUUFCxcu5Pjx47S0tLBlSw2Q2F5m+/YTuFzHMv5MDsdW6uuXc+qUjdEX34LKlCl+1qyxx/1MHR0dw9/fo4/WAXM46yzo7pbPpDH6e9LiOH/+fKZPn67796SxatUq/H5/St9T9GcCxnxP8T6Txui+N3/+THbubOBvf/Ny440lWflMJpOJsrIypkyZwr59+7L+mYz8Pe3dGwBshMP72bq1J6XPpKoqVquV5cuX6/qZnP6DTO/6Jarye6rf+Wd6BwJjP9MsB4Ht/0H/oMrb9eWgmPT/nnbswN/VhRIMEqysZMFpp1G5fTuHtm1DGRhgtsfDU5cGeM/fv8RLL9XwrYW/Z6nvVbxeuahuA+6ovoz5nx3kktPeonrfPtTSUmYtWYIbONzfz8BQHEqvu46FK1awfe9eAqGQCLqtrVQMDLBw5UqOT5kS+UxuN3W9vcyprc1633v11VdHWHWsWrWKvlN99Pb0Yu4zs3Xr1nH73oYNS3n44UH+939LWLu2kxtvPMnatQqLFi2kpSV7v6eysjI2bdpESUnJcPuz8Xty+nuZ3tuD4nuNysUk1femTYM775zL175WO8KusKYmAJhpazOzenWIH//4TZqbfUl9T9key48dO4bb7cblclFfX1/w496uXX1AJWVlXWzduj9nY/mrr746HEdFUcb/TD09+h1zT5mZ0x/CEj5CYO9j1J9xXd6+p7vvPk5Ly+nEQ1UVWlokY3Xu3NycG7ndRbsUoxMKhdi3bx/z588fFss05l0xj5YXWzi54yQv/fAlLvr2RWOso07tOoUaVimbWhbTmiKXnHrjFG/c+wauehdn3xLbnjGbjBfLQuOJW5/AfcrN+q+vxz61Dq8k8/O978Ell0hCq8cjZcFSJZmMDCPFMpsZGbnASLEsNEZP5qmqis/nw263D58DJzOZV2QsxX6pL8nGc3SfHuwaxNvtxV5hp6RWBnStTx85In06UUaGooi91BtviL1UtJChLYDoa00uI0NjwwY5X402y3nsMVi6NKXdpEyxXwq+Ph+eTg8WuwWLc6wEEBwM4un04OvzjTv2GSmeiqqOXOdYCHzyk59k9uzZ3HbbbQBcdNFFfP7znx8hZmgr+MLh8Bgf2FgZGdOmTaOzs5PyclEZc72CPBQKsW3bNlatWoXFYkm4krLvWB89h3uoP72eJz79BL4BH5f/5HLKp5XHzMi46KLEHe3vf9cnIyMUCvHggwrvfreJeELG734X5h//MbmVlB/5iInf/97Ev/0bfOMb46861OK4cuVKbDZbQax4TXdV/P33K9x0k5lly1S2b1ey8plCoRDbt29n5cqVI35Hky0jw2QyU1Gh0t+vsHNnkIULk/hM7hOogd7h93/zzTdZsmTJcNYVlnJw1GX0mZS3vo3S+RJqwyWYF94S+zMpoG55L2rQQ3jZd6FsXuLvKRQirKpw8iS0t2PyeDC53YT6+lDnzZPshb17Md9zD4rbTdjtliXCjY2Ev/xl+Uyf+xxhRUEtL5dlHWVl7DntOs69qprK3iOU0Uc/5QxQygCl+LGBonDffWGuu04d93tSVZVXX32VFStWDMcjX33P3+fnzXvfBBMs/8jyEdtv++U29jyyh4UbFrLkA0sS9r1rrlF55BGFn/wkxMc+pmb+mUJ+lOMPozjqMTWsJxRWx3ymUCg0Jpa6/548J8HXjmn7rSioKMu/R9hSTlgdarulHFPJlLifSVVNPPtsaLig/HnnqZw4YeLii03s3w+NjSpPPhli4cL8j+WBQIBt27axYsUKrFZrQY97ZrOZT39a5cc/VrjttjDf/nY4Z2O53+8fjqPZbB75mfp2g2s2mO1ZOeYqh+9COfYAStUylGW35+17+sMfwrz//YnP3e6+G268MTfnRn19fdTU1NDb2zt8vlxkJH19fVRUVOQtRsFgkK1btw5fT4zG3e7m8U8+TnAwyNIPLuX060eKZb5+Hye2nyAcCjNr/axcNTsmJ3ed5Ol/fxpntZNrfndNwiLlepMoloXEn276E/5+P5f/z+Wc8lVy2mniitnbC7Nni3f6vffCjTemtt/eXqisjPwdr8sbKZb33CP129atg2ef1XffoUCIvmN9+Af8TFkyRd+dD2GkWBYaXQe6uP9d9w9P5qmqSn9fP2XlZXKsGwwS9AV51/3vonpO9uzBJiLFfqkvycZzdJ/uOdKDr8dHWVMZJXUlI/r0ez9RzV//Cr/5jdSyHI+rroJHHxXLwY9+NPK4u93NIx9+BJPFxLseeBcmc/IWQxdeCE8/Hfn/scfEATqbFPuloPUTR6UDm8s25nm/24+3x5tw7NMrnnqcKxfct+l2u7n//vt56623hh+z2+14taUlQwwODo5Q16Ox2+3Y7WO9Vy0Wy5gvRLu4G008BSre4/G+6OjHFUUZvsXaPvrxHb/bwYltJzjzk2ey8uaVDHYPUtZQNuJ1WtvPPz+5Ijvnn28mVvPT+UzvepeovVoKW6RNEA4r/OpXZm64AVyu2J81Ou7PPy+PrV0b//uIflwmnk3jtj2T7ynR4/G+v2TanszjWts1y/4dOxR6e6GiovA/U7KP5/oztbdDf7+CosC8eRaiXxbzM3nbMb/8fikkC5hVlfkeD+YXIqvesdeIR7ulLr3PFOiA7lfkBzz9unE/k+I6HeXYi5jeegbKg/KaRYswKQqmu+6SmhJRN9OXv4ypvh7++lepOQHgcGAuLZV8U4tF7pcvF/smraZEZSUm7f2/+11Gf9MLQuKzf6R3ZszPpAC33WZmw4aIV2aszxRtoZfsmJ2tvtd1vIu3//I2illh4bULKW2IFADx9noxKSZKakrGjPWxvqfjx6VvTJ9uTtzHkvlMgXY4cjeY7dBwAeY4J5vxYqnL78nfiemVD8hvwX0Mwn544SZMFlekf2i/BUdd3M904YUjv48ZM+C55+Dii+GNNxQuvNDCX/8Ky5al3vaUP9M434c28avZdo23vdHHPYCjR6VPzpplwmIxJdxer88UHcfobSz4YNeXwGSBs38NVOp/fGq+Alo3Qs8O8BzHXNKky2dK9Xtqbk5upVNjY+7OjSbzReBEwVXnYuXNK3n5Ry+z665dNK1sonJm5fDz9jI7M9YmWK6ZI2pPq8VkMTHYNYj7pHvE8bVI8oSDYfz9fgAclQ5O7JDHGxvldPCmm+Cb39RE0dT2rWVjNDTEFzGMRjYzMgZODPDEp5/AWmLlhvtiFD8rYggsTgs2l42wGsYUMGF1WTEpcnwM+ooWikUKD61Pq2EVk8WEoyoyYa316WRrZEDEfkorEK5RUluC2WYm5A/hPuWmrDF2ncjRqCrsGDr2NDdDS8vYecIiucHb5xWxq7ks5rVDoVBwVyT33nsvF1xwAbW1tcOPNTc3c/To0RHbHTt2zDD+XdmgYloFJ7adoK+ljxUfWTHutuZsF5ONwZEjclMUePBBSVdubJS5zosuEnHiuuukAHAMTWmY48elRoaiSIHrIhEaG6Xw9IED8MILcPnl+W7RxEUr9D116siCV3EJ9MnErckOZqesGDaZUK2SMUVoUJ4P9KVeTFZVYXAQdt0NwTDUrYSjA3D0yYgYMTAAZ50FZ58Nr78O338JeveAvQNqdsgV56JF8sPq6xOz5OnTI0WuNW+B666Da6+Vx6zWke1oaJAq3imwebMUpx/vox07JttF1dY2NPWL6mlY3sCJ7SfYdc8uzvnXc4afCwfDKCYFZ1VyVZKPH5f7ptjzpqnjHbL+sdfmrxB89G/BVgX+TlDMYK2U5zP4LTQ0yGrKSy+F116D9evhiSek2xfRB+0CJpGfbs7oeg3UMDimgK0yO+/hqIfqldD9OvTvgzhCRrZJVPBeW4RitIL3RYzPrAtnceyFY7Rta6Nzf+cIIcNImG1mqudV07G7g1NvnioKGWni7ZXFfopJwV5uH57A18rmaELGX/4i/uXVKSxE14SMBQt0bHCWyWaNDM2SI+AJEPAEsJZYE7yiSD4ZODFA36E+HKc5KKlJw1etSBEDoYZVgt6hmoyOkdO8qkrSNTJArKVArKWiURSF0qZSeg/34j6ZvJDR2gqdnTLfePnl8MtfSv2NIrlFDav0HO4h5A2hmJThmieFSMEJGT//+c/59re/PeKx1atX89hjj/HJT35y+LFNmzaxuoBmvk0mE7Nnz05aFSufJp2u91hvgi0FrcjOe94DgUDUfsolvUzvIjuPPCL3550H11wz8rnHHxc/1r/9Tdrzxz+OnSPV2LxZ7pcuhYqKxO+bahwLnTVrZJJ98+bsCBmTLZ7x0ISMOXNSfKHZCRYXCiqOUiuKNapuTNg3clu3W5YnDAxExAiHQ5Q/VYXbb488HvDBqWfhH+fA8mvgz6/IMofSUrmVlUV+VDNnwkc+Bkd+CC4nXHA7lFdF3veWW+K3X/ML0IlkLxrvvx9WrIi9ui8UgueeM7Fv30I8HhPr1ukrwqbDkvcv4cT2Exx+5jCnX386FdNlsFrzhTWoYRU1nNjBMRSKiDy6CRm+Drm3xxcIcvYbNzvBXgWBXggHwRLlvzn6t5ACNTXw1FOSmvzCC/JzeewxyeALhWRs1Cyp1qzJfl+ZaGNmKqu39CRuHDtfkfuas7LbgDn/LH3UlsSJR5aIXoSiKCPFjFQXoUy0flkkPsl814qicNanzqJrfxfOGiddB6T65smdJ+lv7adhWQOljaWGKHpbd0YdHbs7aH+zndkXzs7pe0+U3423R4QMR6UDRVGGzzU0IeOMM+Q6a8cOOf+KthFJRDL1McBYsdQyMrRT6lId9TGLw4LVZSXgDuDp8AyfD+qJkWJZ6Hi7vJhNZroPdGMvH2dlZZGEFPulvqQTT0+XB8KyCMBsG3ly2NkJXm9kEUwi4mVkAKz78jrs5XYs9uSnkrVsjAULIjU3ciFkFPvlSNSQitliBhuUNqZ+8DNSPAtKyNi2bRtdXV1ccMEFIx6/4YYb+PKXv8ymTZtYt24dJ06c4Hvf+x533XVXnlqaOiaTifr6+qS3r5gmJ0Yntp3g+NbjlDaUJlTUNmyQ+c2uLlnB+uSTMjGrt4gB8PDDcj9axADJrHj4YXjnO6W43Ic+BP/7v2I7NRpNyFi7Nrn3TTWOhc6aNfC730XipDeTLZ7xSEnICAahpw/a/RBwg8+PMrccu80O29vhiBvcHrk9ejtc815R9vbvh5/9TPZhMsmPdcYMmZlVFKlLYbfLFZfnNeg8CFNmQdVy+MDy+CvuKyth3TXw4p/A3wOmk8N2VrlGu3hMxB13wK9/LeLcjTfClVfKx964ET79aWhpMQEyBjY3y2RfNsaxZKmZV0Pz6mZaXmhh5x92suYLkSXSikkZU0w1FidPQjgsX/0UvWyVh4WMmrib5PQ3rokXQX0LBldUyPHsmmvEe/Wyy+C222RsjC4Snou+MpHGzL4+6O6Wv/MhZIyJYzgkGRmQfSEjT1kYo9EWoci4F3m8uVlEjGT78kTql0XGJ9nvOhwM8/QXnx4uEAqyOjngDuCsduKochii6G39GfXsfmA3p948lfP3nii/G03IsFfKRO1oIQPgfe+TiaY//CF7QoZRYllWJqXb3G5Z6DBvnr77L6krodfdi7vdnTUhwyixLHTqTq+jbVsbakil+1A3ZVOTW11eZCzFfqkvqcZTRWWgbQAA1xTXGHv91la5b2oC29gSCWPQhIzRGRlAWucEr78u90uXigkE5E7IKPbLCCaridqFtYT8oZTqmwy/3kDxzL+UkgK/+tWv+PjHPz7mh+lyuXjkkUf493//d5YuXcrFF1/MV7/6Vc4uIH+JUCjEjh07xhR9jIeWkQGw6aubeP13ryd8zeCgiBggkzkmE2zbFltpzYSeHti0Sf6++urY21x4oaz6sVjkpPkTn4htnaBN0CdrnZBqHAsdLS6vvCIqu95MtnjGJBik7a1uSnCLkNHeLkvAH3oI/u//ZNb9D3+QbcNh+Jd/gf/8L7j3BDzYAn85hjrgp7/3FGrXIQj6ocIGsxxw0drIsoQFC+DrX4cf/ED2+V//BVFZZlx/vczon38+LF8Mc6fDrOtEwEhkG6QoUCnFpunbo3OAkkezSonXXEWRLIz588HnkxC/971QXy8C6PXXj5zMAzkxu+EGETnyyZJ/WAIKtLzQQuf+zpRfr9lKNTTomDWQREZGTn/j5hJwNoCrGUicpZIKpaVSlO6KK+RYd/vt+ekrE2nM1LIxqqtl4ieXxIxj324IDIC1DMoSzJrpyeDJ3L1XDDZskPO0Z54RD/tnnpELy1QEuYnUL4uMT7Lfta/Ph6fTg8VuwWw34+v1EQ6FMVlMuBpcWOwWPJ0efH3pZ8zpQe3CWlBg4PgAg92DOX3vifK7ic7IgEh2bPTikve+V87BNm9Ozbd8z9ApZSIhw2ixzGadjJJasSjydHgSbJkeRotloeOY5gAFfD0+vF1ZuJieJBT7pb6kHM8QWEuscgyvHys0HB8SMpK1itWspU6dEov4TNEyMpYty62QUeyXIwkOBgl4AlI7y+0fvgUHk6sNZKR4FlRGxh133BH3uaVLl7Jly5YctkZfVFVlcHAQNdZsfgzsZXbsFXZ8vXKB4ahKbNyvTZQ5nTJRuHat+Itv3Aif+Uy6LR/LX/4ii9IXLhx/lctVV8Fdd4k36513yuqY730vMsnZ3Q27dsnfyQoZqcax0Jk7V1ZvnzwJr76qv1f2hI2nqspMULSN08CAzII6nfCnP4nK198PPh+XPQsH2cCcOZfKD+nBB2XGXbNxcg7VQDCZ4MMfBrUTdu+E8mooK0e1KFja98EywDQAJdPFTue8tVA2U17rcCRZgANovASmXJB4u2hmvAdm3gTO/K00TsYq5be/ldIcO3eK7dx990lGzIsvxt6nqsprb71VVuTny2aqYloFM9fP5PDTh3nzj29y5sfPZMt3t1BSW8Lq2xLbHGrj89SpOjZqWMiojbtJbn/jivT9LOF0yur1mhoRM0aTi74ykcbMfNlKQZw4arZS1avAlIMfejgEu74M3TvhzDvANS377xkHszmzukETqV8WGZ9Uv2uzQ2xVQr4QCgpmh5mSmhICnoAhit7aXDbqzqjDYrdItkiSNaf0YKL8bhyVDqaePZXquVL8IlZGRnMzrFsn14X33guf/3zi/YZCkkwMiYUMo8WyqQnefrswhQyjxbIQGewZJBwIo1gUwsEwzjon7uNueo/1Ut5UuJ7x+aTYL/Ul1XiG/CFKG0px1bmG62QAwxPUWkZGsuf0lZUy1dHXJ9cDCxdGnhvsGmTX3bvw9ftGuBCMh5aRES1ktLbKnKElizPSxX4pnNx5kpAvJHVU4pzbldSUJLTYM1I8C0rIKCK42934+nxYS630H+8HIOgLDnvcxvO01Vanaiuir79eTlgfeEBfIWM8W6nR3HijpPb+0z/B978vc8Jf+YqcHP/85zLx1NwMtfHn4SY1iiLixQMPyCqqSVf0U1XB748Uue7vF2VnypTIEtbo5+rqxHMGJONBG4RLSkSUOP98mQ1taorYO5WW8oM/lbKTJr4xB1iyBP7nf+K36ayzoP8AdNmlToUFCAXwWqfiMJ0UW53+A+DIsFObUhy+8zgJF02yVilLl8rt9tvhF7+Aj30s/j6NUiR8ziVzCHqDzL18Lm3b22h9uRVnlZOua8cfm2Fkyq9u+IaKfWfa1wqIl1+OLWJoGKWvFAKGK/Td+arcZ9tWSsNkjtihtf0F5t6cm/ctUiSHKIpCxYwKuvZFjlOjM9/zzUXfuijfTShomlY20bQycnIRS8gAWVj27LOSZJyMkHH0qGTP2mwGOk4kSTYzMrTzPE97doSMIuljL7fjrHFycsdJevw9uOpdBJQAJSUlmCwmgt4g/gE/trIkvHeKFDEA9nI7JTUleDo9405Qv9EuE9TJChmKIlkZO3bI2s9oIcNkNXHgSfHdDnqDYwqLj2ZgQIRjkGv72lqZIgkEZAyeZowpigmLr9/H7gd2Uza1jIU3LIxbb8wIddFSoShkFBjudjcbb9qIp9NDwBMY9sLrPdrLrrskfSGep602aait+N2wAT71KVnp3Nqqz0pgv18yMiC+rdRoPvxhGeA+/Wn46ldl8uSppyLtbWmRE+R8++AblbVrZWL4uefg3/89363JEFUVZSsQgKoqsWrasmVk1kR/vyhfpaUyw71t28h9XHutFFfw+6GjQ8SImTNle634gKLAl78saUClpWOXZp9zzvCfbjdsGnIKmjOHxDZO0YQGIdCL4j6MM2RHrZqH4jkMgT4YPA69u6EshQri7iPgPga15+RmRXKW2LBBhM5kCjFrdlPJ8PDDcO65kTrn0WS78LO73c2Ttz6Jp9PDW/e/RcAdYODEAGa7mfvfdT8Qf2yGSEaGrkLG6Z8H70kom6/jTtMkNKQuqCEIDVmVWEoij+tEspMS2Zi8mGjkMyMjJou+JFkZ1ctz956Nl0H7i3DiaZj1j2AuFgMtMvFwVjpx1btwn3LjrMldxkMyaIu34lFoF95GIJa1FEi27L/8i2TD79oFixePvx+tPsbcufnLhk0X7bNr51560rCsAcWsUDMvfn2yIvnBVedi7RfXsulrmzBbzaz/1nr2HtzL4sWL8XZ5efVnr7Lyn1dSWq9jBfgiRbKIq87Fin9eQdnUMkobYvdbe7mdP35YjpOpnNPPnClCRrQNvXZMVlEJuAMce+nYcO1e7b1GH5N37ZIpnsZGsYsGWcB46JAI4kUhI7u8/tvX8fX5qJlfw4oPr8BkKajqEnEpChkGwWw2s2DBAswJzgSjPW0dlQ4CngABdwBHhQN7hZ3gYHDY03b0IKKt+G1ulvumJvGdf+EFccr5l3/J/HNs2iQpaFOmQColSm65Reanv/Ql+P3vxz6veZs/8MD4YkaycZxIaFkYL7wgk7V6fnTd4tnfL15hfX1y6+8X37HZs+HNN+WL1cQKVZUj5xe+ILPY994rWRKlpRErJ82Xb906WLEi8vhQBgUg/mmf/Wz8NiU5Y3zwoNxXVcktKazlUmDZ1wm+LggHsVodKKF+sFVB2A/hIOz/GTinQM2Zye336P1wchM0X5Xe6uCeN6D1UXDNhJnvSf31OpKKVUqyRcJ/+EO45x740IfgIx+JFGePFAmPbKt34efosdnitKCGVUwWEzaXDUelY9yxGbJkLVXSLLdxyPqYGf1bCPsg0A/eU2BxRmzO7DWynQ4k21eS3S5VJtIxKJ8ZGTHjWDIVSq7LbUOqlssYPXgS2p+Hhgtz+/46MZH6ZZHxSfe7rphZQdnUMsxW4/SR6MVbIAXKFbMyImMkmwXJJ8rvJuQPYbKaUBSFYFBKvcHYjIyqKnFYfeghycr49rfH368mZCxYkLgNRotlNjMyaubXUDM/eyKG0WJZaBzedBiby8a8d86jaXkTrtkuKioqUBSF5nc0Gy4jrVAo9kt9STae7lNudv1hF6qqctUvroorZmiLk1I5p9e21a4Hoo/Jfa19hLwhHr35UWylkQymWMdkrT7G0qWRfU+fHhEyzj03+TalymTvl6fePMXBv8lk1pmfPDNjEcNI8SwKGQZBURQqKyuT3t7itGBzyaBhspiwl9uH/4+XVhZtLaVx/fUyAf7AA/oIGZqt1FVXiTNPKnz+8/Cd78h89miS9TZPNY4TgcWLIx6GO3bIvL5exIxnMBjJjlBVkdFDIXjssbGZE5/5jDTuD3+A7dsj+ygpAbtdhIzycjHX1YSIsrKIYqAoYuMU76QymaunDDkgmZPDk+JJ4aiD1XdL5sXeH6P07MI88yaYsl6eDwfg0P9Czy5o/bN4vic6cfZ1ykQawJQ0J9P8XdC+RbJB8ixkpIJWJLy1dWRdDQ1FEf3K6ZR6Md/+ttwuvFBOmn7wg7GvS1YcTRWL04LJYmKgbQCTxYTVZU04NmvtAZ0zMpIg62Nm9G8BYOAQvPlNsFXC8u/KY9Zy2U4Hkukrzc3Zs+GbSMegfGZkGCaOiiJZGQd/D8f/UrBChmHiWSTrpPtdKyiGEjFg5AKB/rZ+/P1+KmdVDns4J1ogkCkT5XfzxL8+gfuEm/O/dj6h6npUVa7RYtn2vu99ImTcfTd885vjX8tpQkai+hhgvFhmU8jINkaLZSHRe7SXtq1toMBp15w2JpbRIsbAiQFK6kowmSfG6uVsU+yX+pJsPPc+shc1rDJl6ZS4IoaqpndOrxX8PnRI7kcsqC53MBgcxGwz46iUGp/xjsnR9TE0clXwezL3y3AwzKs/FVveOZfOoW5h5tfaRopncWQ2CMFgkFdffZVgMPnCeqqqEvLJyvRk1LXR1lIgQgaI5cqpU0m/dZz2wCOPyN/J2kpF8/zzsUWM6P1r3ubxSCeOhY7ZHFGyx4tNTFQVvN7IjN/Bg6Js/fWvsHEjod/8hl333ivx3L5dlrV/8pOiOn3961KtHeRK56WX5Eg3OCgVok4/PTI5f/XV4nv17W/DT38qM8vacvxp0+A974F3vlN8spYvH7lcIM8rY9ISMkAmZsvmQKCXsLmEXSfKCDpnyGMVC2DJ12Dm++D0LyT3GVsfk+KzlYtSs6OKpnJoKcTAIfD3prePPKAVCYexodL+/93vZIzbuBEuu0wef+opqb0Ta0Jbe+zWWyMJPnqhmCONVMPJFcPS3VrKfQyO/BE6Xhl3s5yMmdpvoWwO1J0jdQfCAXA2yGM6iRgwfl/R+OEPs2eDMZGOQfnMyBgRx6Ab3voOnHgq9o852zRcLDWJ+vbCwMHcv78OFHK/fOGFF/j85z/PpZdeyuLFi5k3bx5Llizh4osv5jOf+QybNm3KdxMNRarfdXAwiN/tH3PTCoQaAYvTgq3UhsliIhwMY3PZsLlsWJzZXY9XyL+baLzdXkL+EDaXbbg+Rn197OPglVfK+qJjx8TZdTxSETKMFkvtXCtbQkb3wW5aXm4ZUXRXL4wWy0Ji94O7AZi2ehpljWVxY3ngbwd4/JOPs3vj7nw0syAp9kt9SSae/gH/cK2KhRsWxt2up0cWvEJEQEiG0RkZGhanBXu5HZPFhKqqCY/J8TIyIPtCxmTul7sf3E3fsT7sFXaW/uPSxC9IAiPFs5iRYSBCqc6mKWArs6GGVMz2xLMyo62lQFTZVatg61ZZgXNzBrUsX39dTnydTrgojbp8enmbpxzHCcCaNVKbZPNm+PTH/WLjNDAgR62BAXA44MwzpfbET38ayZgYGJAMi+98R8SHv/1Nak44HJIZ4XJF6ko0NorYoNk4lZZCxZAnoqLI0q145HqZuY6kLWSA2Oj4pMDGoGnKyOdMlrFZEX17oTzGFWHIB21PyN/N16TRkCFsFVA6EwYOSzZI/Xnp7yvHJFsk/Lrr5Hb4sJRB+b//i7/PbBV+NlvMVM6spPdYL6765FaJauOzbtZS/Xvh0P9B9QqoHb84ck7HTIsL7NViueZphXL963fE6yuKAr/9bfZrLU2EY5DHE7EfyVeNjOE4dm2HU8+LAJuPjAhbhdQlOrVZxJS5sYvkGZ1C65der5f3vve9/PWvf+WKK67g/PPPp6GhAYfDgdfr5cSJE2zbto0rrriC8847jwceeICysrJ8N9sQJPNdJ1sgVMt+yDe2Mhuedg/+AX9O37fQfjejCQfD+PslZo4qB21vyePx7BUdDlnk9tvfSjL1eNmLqQgZYKxYZrNGBsAzX34GX6+Py350GVWzk/WlTR4jxbJQGOwa5Mizsix9wXWRjP5YsTSZTYT8Id64+w2aVjVRNUv/73AiUuyX+pIonm8/8TZBb5CKGRU0LG+Iu52WjVFfL6YYyRJPyACGC3yHvOO3MRSCnTvl73xkZEgbJl+/DPlD7H1YDtLL/2k59jL9zuWMEs+ikFHAKCjULqwd/jsRsaylQE5Yt26ViZ9MhAzNVuqSS0TMSJV8e5sbElWVGSVNeJg9W7IfXnlFjir9/dDfz00H+nmYi9m8+R2o27aj/PY3kX0oilxlnHkmWCwiTtTVjawpoX1hH/iAFNK2yNCgBoO4t26V5xoaxhrqTgK0GhlpCRkDQ7mYjnrCimP8bY/8USae53wYpo3ygT/5tIgizgaoGX9SOiGVS4eEjB0FJWRApEj4s8+G2LLlIOeeO5vzzzfHXFU4c6bUfB9PyNDIxoo8V72LkvqSpMZmrxe6uuRv3TQ/b4fc22N4R+SbkuYhIaMlK0IGjCwo39oqotbBg/D221l5uwmHdtFTXi4ad17pHMoqynTsy4RpG6D2HSJoFMkJn/vc5+jo6ODw4cPU1cXP2jp16hTXX389n//857njjjty2MLCxlXnYsPdGwqmkLZ2Ee4f8BMKhjBbjGWDZVS071cxKdjL7cMZGeOdzt90kwgZ998PP/4x2Gxjt+nvjyzASFbIMBLatWRPjySSp3PdOh4ldSX4en14OjxZETKKpE7XgS5MFhPV86upPW38c+OZ62dy7IVjtL7cyovff5HLfnBZzgvkakWV46H3+Jzr9yuSGeFgmH1/3geIMDdebZd0rWI1IaOjQ6aiohnOvlBARY17vXvggExlOZ1SHlUjl0LGZMRsM3PZDy/jwN8OMPP8mfluTlYoChkFTjKTZCCL7rWT11hCxhe+AM88I5Np1dXptUWzlbomzQXj+fY2zwnhsNwsFgn2gQMja0pUVkqlPb9frJjcbtleQ8uc2LtXXjskRDStncLA/1Zz6hQcsC1k7m23RUQKlyticqso8M//HL99ep/JTwC0jIzZ6SzCdYuQobpmgSfBtiGv3O/7GbiPQNOV8n2pqtTTCLqlloavMzMrnqql0PIwdO9Ifx95xGyGdetUXK5OVq2aNa5FUL7F0WTHZ01IsdtTKCifCN/Qcnq7frZNulHSDN07RcjIItEF5Z1OOdb96Efwr/+a/nFuPEIh2LRJYcuWGtxuhfPPz56FVbaJvujJq7ufGoauITE9n0JG2Vy5FckZ999/P0888cS4IgZAfX09P/nJT7jkkkuKQkaKuOpcBTMxZbFbsJZYCXgCeLu8SWc7TnYGuwcBsFfYURQlKSFj/Xo5L2prgyeeiG0XvE/mz6ir0/G8JYdUVso5l88n18eaF7xelNSW0P12N56ORCf/RXLF1DOncvVvrh53sl5DURTO+pezeHz34/Qe7mXXPbtY+n59rFmSIbqocjxiFVUulPcrkjmHNx1msGsQZ7WTmetmjr/tYblPVcioqJDxvbtb9tEUNU1kcVqYsnQKFvv408lafYzFi0deExWFjOxTUlvC4vcuznczskZRyDAIZrOZJUuWJF0BPp53bbzHT5yIzJ/X1498bt48WLJE0r4eeQQ++MFUWi4cPSolFBRF/FXTQfM2v+GGyPythjaRksjbPNU4ZoyqRiyahrIj6O8XtcVigT//WUQHzeLJ7Yb3vldm1/bvh9/8Rj6QJjpoy/6tVrjgAhEhtOe0DAqA979/RDOsQPWvgM2waXs5c/+pXJePl/N4GoxgMHLwTysjo3QONF+FyTWbJRUJ4jj7AxD2w+tfgM6XYc8PwF4DaggGT8hz/i449oAUT05XzKg4AxQTDLaB9xQ46hO/xmAk2y/zIY6mOjbDSFsp3SaNfUMZGY7xV53l5TfuHFLTB7MrZERz7bXizbpjh5To+frX9d3/xo2alZUZkCVHzc1yTMu2lVU2yGd9DIjql+63JRvN4oLy+P6/OUUbTPJcvykVCvFYPjAwkFDE0Kivr8fjKU4YQmF+18nirHES8ATwdHlyImRMhFh6e2SRjKNKsoK1hRPjLeAwm6V03Q9+IPZSsYSMVG2ljBZLRZEM2EOHJCZ6CxnaZK+73a3vjjFeLAsJe5l9hMXKeLF0VDpY9YlVbPn2Ft66/y2mnjU1YSaHXkQXVY5VdyBeUeV8vl+xX+pLoniGA2FsZTbmXz0/YbZQuhkZINcB3d0yVjadHnlcQUkoYkDs+hgQETK0+h3l+kxdjWGy9Uu/20/3gW6mLJmSeOM0MFI8i0KGgbDFyt0dRbqetpqtVFNTZHF+NNdfL0LGAw+kJ2T8+c9yv3q1rM5Jl2R98McjmTjGJRCIiBGVlSJFHzsGr702UrBoaBAbJr8fPvvZkfuwWKRgdWWl/F1dLUcOTYjQZsRXrJBR3W6PXcH4iitSavqaNWKjsnmzuEPpRUbxLHCOHRMxw25Ps3ZB5SK5qSq2ZPwEp6wHaxn4uyE4ACYHlEwFW40UR1aDkpER6EtfyLCUQMVCCAeH9lN4QgYk1y/1EEeTJRO/cd0LfUNURkbii66c/8arlsG8j0Jp7la4m0zwn/8pxxC9szI2bpQ+Nlosa22Vxx94oPDEjEwuevTCZrNB25CtVPVKMOX/pJmWR6D1z7Dws1mzRcsWhXYsP/vss/n1r3/Nf/zHfyTc9le/+hUrV67MQasKg0L7rhOhLQSwOCyEg2G83V68vV7CgXCCV2ZOocdyWMioFCEjmYwMgPe9T4SMRx6Ry57R5WdSFTLAeLFsbIwIGXpTUitG9J727AisRoulkQmHwnS93RVXhBgvltPPnU7LuhaObDrCSz94ict+dFlSk7d6YXFasLlity/etUY+36/YL/VlvHjOvWxu0pZB2jl9OouTZs2SxcqHD8O5Q0LG6MV5akgFBUK+sXMdWkZGdH0MkDW61dViUHL0KCxalHrbkmUy9cudd+1k/6P7Of1dp7P0A9nJIjNKPItChkEIhUJs3bqVVatWYbHE/1rS9bSNVx9D44YbZJLnb39LTxXV6mOkaysVTbS3eVubnGiuWZPcZOOIOJrNMgtttcr9m29GRArNzul97xPz11//WkZaf1QRwfe8R/Kr29ulJoWWFVFXF5lxtNngE5+IZE2Ul48UJi6/PH5jrVa56YS2qnzzZt12mXS/nKhotlKzZsUWAJMlpTjaKkXM8J6EQA/4rOAaWioWdEM4cUp0QpZ+U7IyCpRU4qmHOJoMmfiNZ0fISK5GRl5+465pcssx11wTycr4/vfh9tsz32coJH0rVsaPqsqh4NZb5b0NsHglafItZGj98izlZTFoqz07Pw0ZTf/bkiF3/C8FJWQU4rH8e9/7HuvXr+eNN97ggx/8ICtWrGDKlMgKs5MnT7Jt2zZ++9vf8sQTT/DUU0/lsbXGoRC/63jEWiBgK7NhsVvw9ftQFCWrBcknQiydVU6mnj2V6rmi3CcrZKxYISLF3r3w4IOydisaTchYsGDsa2NhxFhms+D3sJCRBWspI8bSyBx74Rgv/NcLTD17Kmu/tHbEc8nEctXHVtH+Vjsz1s7AZC7ca6dsU+yX+pJMPLWC24nINCMDRMiIdUz2dAxl6tS7sJXaxhyT42VkgGRlZFvImEz9snN/J/sf2w+QtYwMI8VzYn+bE5R0PG2jrUticfrpcjK6Zw88+qgUekuW3l549ln5Ww8hA0Z6m4/A4xEhQrNq6u+HuXNlBvDNN1H+8hem7t2L6f77Zdv582XpbTgMmneyyyWiQ2mpCBc2m3hraVkT2nNaasmKFXKLhaLEHpnzwOrVMtl+8KCclOs6KTpJ0YSMtGyl/L0weBxcMxHzrxSw1UjmxMAhMGfBPqGARYx00MTRv/9dtEVVhRdfTDPLZhzS9RtPND6nTNANQfHFNmSx7zwRnZXx4x/LoaGmJrN9bt48UiAbjapKZtfmzXGOaQYl39ZSAErYBzYnmCxQFecYnGuaLoeTz0D7czD3I2J5VSQrLFu2jF27dvH1r3+dD37wg3R0iDhrtVoJBAIAVFRUcOWVV7J161bmzy8cYalIchRaQXIj0riikcYVER+pZKylQC5vbrpJjpl/+EN8IaMQC31raDHISkZGnQgZ2bCWKpI8qqqye+NuAKrmpFfMxVZq48qfX4nZlrvVKAF3AF+fD4sjfoZEkclJ77Fe3CfdNK5sHLfAdzTp1siAiO3eoUOxj8lvPfAWB548QNOZTay8eeWIY3JHR+Qad8mSsfuePl3WERfrZGROOBTmlf95BVSYcf4MGpYlWK0wASgKGZOERBkZIPZS3/iGrF5ORch44glxZDrtNNENkiYclpoRAwPyf2OjVF37+9/H1p34/Ocl0+EXv4DduyP7UBTJqmhqEhun0lK806ahLl0qtlDa6j2bDb77XREoYi2tP/PMFBpuTMrLRVPZvl0mzW68Md8tKnwyEjK6t8Hu70PF6bD4G6m/3lYLlaViL5UtgkMrxSwl2XsPg2A2w6WXSk2gffvgrbf0FzLSRfeMDJMDzvyp1FQxZ7H/ZIL7KAwcEHupHGZnXHutpDe//rrYZmSalZHsBEg2JkqySb4zMgBUk53wsv/ChM84gkH5AiidCQOH4cTT0HxVvls0oWlububOO+/kzjvvpLW1lePHj+Pz+bDb7TQ0NDBtWu4zu4pkRiiUWsZ1IRUkLwSSzciAiJDx97/L67TXhMORYt+FLGRo51zZOD6XN5ez7EPLcE0p9t18cuqNU3S/3Y3ZZmb+O9MXuzURw93uZrBrEDWkYraPHbhiCavudndSYmw4GKZtWxuHnj7EoacP4Wn3YLKYKKkpYeDkAJ52DxUzK7CXZicDDUBFxdvrxV5uT3qSvEhuefOPb3Lk2SMsuG4Byz+8POH2bjd0dsrfmWZkwNhj8sLrFnLs+WP0HOyhYlrFCMFPy8aYM2esPSEUC36nS6wx5eDfD3Jq1ymsJVYWXJNkqmSBUxQyDEAoBJs2KWzZUoPbrXD++fpbUCQjZNxwgwgZTzwhg54ryXMvzVbq2qtCoJpEXDh8WCyZojMnzj5bZhFffhnuu08yJjQ/jtNOg898RkSGZ54ZWeC6oUHOmkFmod75zshzLlfExum001DnzKFz61ZmrVolwkY02aoiZCDWrBEh47nnikKGHmQkZAwckvvS2ek3IJsixtu/hNZHYc5HJtVk3OLFcgH+xhtw8cX5bo2gCRm6CSsmM7imy82oHLkPTj0Hcz6UUyFDUWRi5rrrIrUyMsnKSLSqNdXtjIDPF+mTOc3I8LZL3R6AUBB7oAUGqsA8dCy3lqdfG0gvFAVqVkPPm3DkXilAPvpi3wjtnIBMnTqVqUZRn4ukxcaNsW0ef/Sj1Gwe+1r7OPT0Icqby5m1XucqzROMkD+EyWpCURQGBuT6DpITMubOlUs37bLt05+Wx1ta5BLOYtG/SHYuyWZGhr3MzsINC/XfcZGU2PPgHgBmXTQrYws6d7ub+667j869nZgd5pgCa0lNCRvu3jCi2PvGmzbi6YxvMWZxWli4YSEntp0YnpwMB8OYbeZh6yC/20/AE8Db5c2qkBFwB+g93Cufpb6EqpnpZbEUyQ7udjdHn5NZ/xlrk1MltIVJWunXVBktZIymel41JXUleNo9tG1ro/kdkcnGePUxNIpCRurEGlPCwTB9x/pQwyoldSU89vHHRoxDE5WMhIx9+/Zx7733cvToUcLhsQXXHA4Hd2h2PkViEjmpNwPzgPRO6hOhXTSMdw24dCnMni3WRH/5C9xwXWhkZsTAADidYmI3OAi//z2hnn6WbeznTPp5734P+H4EDoeoG2+9JTUgNNHhjDPkjZqaZAZRe7y0VEZXkO2/9734jUwwq2I2m1m1ahXmQjIj15G1a8UyRa86GZM9nvoIGbOMGUdbFahh6NlRcEJGJvFctAj+9CcRMoyClnabDzu4vPXNkqETXc84vkxZ4pprIlkZ3/++CPjpcu65clgcHIz9vKLIMV2rYVQIHDsm904n1ObKmczbDi/cBD5ZNmZWwyxTVXgxql/aa2D13fkVCbztcOCXknHXpUL39rFZT0Zo5ygMeQwqkhWM+l1v3CgLpkbXE2ptlccfeCD5654T20/w1h/foua0mqwKGUaNZSo88a9P4D7h5vyvnU+vrR6IOOwmw/veJ0LG3XdHhAzNVmrOnORL/RkxltmskZFNjBhLI9J7rJfjrx4HhbgrlFOJpa/Px2DnIOFgGNWjopiUEeJIcDCIp3OoXsDQBKKvz4en04PFbsHiHDvtFhgM0LG7g/2P7cfmsuGodDDj/BlUzanir5/5Kxa7Bb/bj9VhxR104z7lxlHjIDQ4tqiyHnjaPYSDMqc32DE4/DlGF3eORbFf6kuseO59ZC9qWKV+Sf1w3aNEZGIrBZGpt+5usZMfLYYoisK0c6ex96G9HN1ydISQMV59DMiNkDHR+mWsMaXnSA+KScFWZhNRadQ4pCdGimfaQsajjz7KTTfdxJVXXsmcOXNiFvuw27OnGE8E9DypT0RrK1gIMLOsDw73y2imKHJ22toK/f0oAwP8pLmfLxy8nAceWMoNdc/LmWs0CxfKTKDVCn4/eztrecU3C3NFGbfcVhZJJfmnf5JtbLaxqxWnTZNblvD7/Tidzqzt38icd57cv/GGHHCqdFhIMVnjqaoZCBmqCu4hIWOoUHdKcQzFmRWN93g6VA6dVfS8AeGQrOIvINLtl1oxMaMIGaqaBWupzldFSKtaCuWJfR/y8hvPo5ChKPCVr0iC349/LMmA6WZl/Md/jC9igBSVN8D5XtJE18fImbNAoE9EDJMdzE4I9KJ6DqPYqqFkmox9vk7ZLp8CQaAPAj0iBAf6IOwHR9TSZqO0MwaT9Vg+GTHadx0KyST46OsdkMcUBW69VUTmZMbKaedO47VfvEbn3k7cp9y46rO36tBosUwVb7eXkD+EzWXjxJBInUqG4LvfLZmLr7wC+/dLYn269TGMFstsZmSAZA71tfRRMb2CssYYvioZYLRYGhEtG6P5Hc2UNcWPfyqxtDgtuKa48HZ7cZ90S7YTcqJksplgyO2l53AP3Qe76T/ej6/fB0NjXzgcxt/vp2p21bB1k73cTtNZTZzxrjNoWNaAyWzC3e4eUVRZVVXUsErAE8B9wo3FYRlTVDkT7OV2nDVOeg73oIakseFgGG+3l6GPl9T7FfulvkTH0+/2c+AJmZhIJdsrU6vY0lJZ1NTRIdcHMYt2nzudvQ/tpfXlVkL+0LC9lFEyMiZiv7Q4I/VzyhrKCAfC1MyrQVVVQr7sCJ0aRoln2kLGpz/9aX77299y/fXX69meSYOuJ/Vut5iX9vVFCmFXVsqsttuN+q1v85kjfVjxcvpdQBkys+J0yghz7NhwdsSC8xvxPFfCY4+B978X4fiXfxlZAFsTpywWuOUWfn4L/An48PVgjl5xmuxSH50JhULs3LmTVatWxRTXJjpTpkidkn37YMsWuPLKzPY3mePZ3i5JSIqSRuq8v1uKfSsKuGYkH0druazm9XVCOI6fqr1GtsuUsjniOx90S62C8sIplJpJv9SEjDffFMe6WCVzckl/f8TqQTcho/0FOPF3+TuBkJG333gehQyAq6/OPCvjzjvhO9+Rvz/1KXjwwbGWKT/8ob7Zlbkgr/UxzE4Zl7wnCAZVrE5npD5GvDExHzgapK2OKWPrdxipnUNM5mP5ZMOI3/XmzSPHxtGoqlyKbN4M55+feH/OKif1i+s5tfMURzYf4fTrT9etrdEYMZapEA7KpCmAo8rBiVfl8WRspTSmTIGLLoInn5S1bf/5n+kJGUaMpSZkdHSA3y9r7/Rk5107Ofb8MZZ/ZLmunuVGjKXRCAfDtO9uB8af9E0nlqUNpQQHgwS9QXoO9gw/Xjm7cvjv1lda2fl/O/G7/XhOefB2eTFZIhccJXUlOMolm9NZ7WTlP6+kek5khX2sosqv/fI1jr9ynDmXzOH0d50esx5HurjqXKz94lo2fW0TFoeFoFcyMC7570uGxYtE71fsl/oyOp4HnjxA0BukfFo5jSuSV6O1c/pMrGJnzpRx8tCh2EJGzWk1lNSW4OnwcOL1E0w9ayo+X6SsbaKMjJYWmRvNxqKvydAvndVOHFUOFEXB7/Zn9b2MFM+03/3UqVOcpy3/LpIysU/qVVy4KWWAMrWf0mMDbH5uGeevV+Cpp0QGjbZ6uv56OOsseO01+MMfZBeKIsLDokUiZDgc9M1exiPhMvoo58OfK4Oa8ogg8dGPjmjBLBV8v4WBY/DX12q4+ur4y1RVNVIf4+qrdQlLER1Ys0aEjM2bMxcyJjNaNkZzc+TnkjRaNoZzKphtEEyckgvICt7Vd0d84mOhl/+6YoLKxdDxkthLFZCQkQlz58rFqtstJ3f59nfWbKUqKpKvS5QQX4fc23PlC5QGziY5XgX6pb/rIc6lQKZZGY8/Dp/4hPz91a/Cl78sxcP/7d9CfO97ZhYtUnn9daWgMjE0ojMy8oMqQjCgWivJVVJISpidxv59TQCWLl1KIBBIenuLxcLOnTuz2KIi6ZLsivdUVsbPWDODUztPcXTz0awJGYWONgmqWeCkUug7mve9T4SMP/xBjnWakLGgwOuJ1tSIeUAgACdP6m8WUFJbAoCnI359hCLZwWQx8c6fvpMTO05Qu0DfY7ViUqieW01fq3jSD7+nyUQIWQld2lBKw4oGBrsGad/djsVukVXqCthKbVgdiT3ZRhdVXnD1Ajre7KBzf+eIjA69OPXmKWwuG7Mvnk3rK634en04KhxUzS7Wycg34WCYvY/IwLtww8KUvns9FifNmgVbt8avk6EoCgs2LEANqcOWV2+9JdMfVVXxx9aGBlkbHQzK8X+8Wr5Fxkfv8aAQSFvIOP/887n33nv5tGaYWSQlRp+sl9LPd/ksCiNTNE4d+QFQImdY3d0iUtTVRYpgAyxfLt435eUyExa9xNhs5uDy6/kbsqrGumz8dimKrB790Y/E2mo8gWLnTkkFczqNUzS3iAgZv/61fnUyJit61cdIGUdd7ixJqpaKkNG9A6a/KzfvmWcsFnHI27EDdu3Kv5Chu60UgE9WoRl6otVsB3sdeE9JVkZF7ieirr5aDp/bt8N//zd885vJvW7bNrHbCIfhgx8UeymQlUQf/KDK974Hb7+dzdVFMr63tcmK0jVr9H2fvGZkAPh7QA2CYgarvnYc2UGFoGdsZkaRjCgvL8fj8XDLLbcktX2+V4YViU99fXLbpWJ5NG31NLb+bCvdB8S+ZTzrmMmKt8cLgL3CjqIow9eeqcQZRPB3OsVaauvW9K2ljIbJJJfSx47JuVhRyJhYKCaFxuUpdvYksZZYqZk3cvWL3+0nMCji+4y1M5ixdgZdB7o4tuUYjkrHsA1MujSubMRsN+M55aHr7a4x758J4WCYY1vEe27G2hl0vd2Fr9fHYPcgVRSFjHzj6fBgK7OBCjPWpXZynmmNDEhc8BvgtKtGHhCi62PEm2M3m0W8OHxY5hSLQkZqhENi/2ZxWLCV6pxSWACkfdZ/xx13cNVVV7Fnzx7e/e53M336dKyjKn7ZbDYaUl32MUkYfRLpxsUfeB8DlDJAKf2U0U8Zj80Y8h+76ab4Oysrk1sctMyPZAeHG24QIeORR8ZPtdWyMS6+GEpKktt3LjBC8Zl8ohWV3bpVvNsztbCbrPHMSMioOVu83p2RH7oh46jVyejbDSG/ZI8UCJnEc/FiOcF64438Z5NpGRlTp+q0Q1VNOSMjb32zpDmvQoaWlXHNNfCTn0hWRqLi1kePwjvfKRk9F10Ev/jFyBP0+fOhoiJAb6+V7dvh7LP1bfPGjWJLOdrC6kc/0s/CKu8ZGYPyowiYq7AYMx9jJAOHwN8JpXPBZNwx1JDHoHF46KGHOPfcc3G73XxCS38qkhRG+q67u+Fb3xp/G0WRcUw7f00Ge7mdKUuncGL7CY5sPsKiGxdl1tA4GCmWqTLYLQWcHJViYZNuRkZZmRwn770XfvWriJ95qkKGEWPZ2ChCRjbqZGir6T3t+gsZRoylUehr6aO0oXSEldN4FEosLXYLM9bOIOgLYrbq2+aOPR34+/3YK+zUL67HWe2k51APg12p1WYslFgWClo8SxtKufzHl+Pp8KT83etlLQViLZUsiepjaEyfHhEyVq9OvW3JMFH7ZcAToPtgN2a7mYaluZtzN0o80xYyKisrWbFiBb/4xS+48847Y27jdDpxa+bfRUawZo2ctLe2yryTionniZzBD5/Ur838vbQJj2QnylavlpPcEyfg6afhsstib2cYWylv+7AVjwU4c0E1DB6JPK+XFU+BMGuWrO4+flxquSfjNxwPi8XCmWeeqVvbComDB+U+LSHDNU1uQxg2jiXN0HixTMARzndrkibTeBqp4LfuGRnBAQgNeeomIWTktW9OvwGarx7qf/nhqqsiWRnf//74WRk9PXDFFXJsXLRIshZHrd/AarWwdi38+c9Sp0hPIWPjRlloMLq2VmurPP7AA/qIGXnNyAj0QtCDophxVc/JYbXxdFHlpqow8PaQeG28Nhv2GDQONTU1PP7441x55ZXcdNNNVFZW5rtJBYGRvut9+2SM3bdPLDp9PvlJx6oP+MMfpp5ZNmPtDLr2d+nS1lgYKZbpoGVkZCpkgNhL3XuvZHyDiBtVKSzUNmoss1nwu6QuOxkZRo2lEQiHwjz7lWcJB8Ks+891CW2R0ollcDC2XXC8x9N9TSzOvkXn1TFD1J1Rx+U/uZyBkwOYzCYcVQ5QIOBOzd6x2C/1Y3Q8FUVJuSaKzxcZ2zK1loLxMzJAspJaXmwh4AmwY4co3fHqY2hku+D3RO2XwcEgAW+AcDCM2Wkero2R6piSKkaKZ9plTm+++Wba29vZvXs3gUCAcDg85lYUMeJjNssKShh7na79n85JfSy0Fb/JZmSYTHDddfL3Aw/E3qalRew1FCXPdRi87fDCTfD8u+D5d6E+/y6CmzagDv3P8++S573teWxkblGUyKq2TO2lVFWlp6cHNdZV5wQno4yMURg2jooCp90CU68AsyPfrUmaTOM5oYUMLRvDWp5Uhk1e+2blYqheCbaK3L/3EFpWBkhWRkdH7O38filL9eabMvHx+ONS12Q0qqqyYoWsYHvhBf3aGQpJJkasr0l77NZbZbtMCAYj5wx5ETK8J0ENolorCfjcqEE3BN0QSm1VYNYJDUq7gh4p+G2tgHAABo6MX+MoTxj2GJSA2bNn89ZbbxVFjCQJheCZZ1R+9Ss3zzyjZjweZMLf/y5C7r59Ytnz8svwpz+NXVRVWpq+CDtj3Qyu+7/rspaNUai/Gw1nlZOp75hK3emymEub0EpHyPB45Hip9an+flmlu3Fjcq83aiy1c6+sCBlD1lKDXYOEg/otFjJqLI1Ay4stuE+6CQfDlE1NbDeXSizt5XZKakoI+oJ4e7xjbkFfkJKakuHi2Om+Jh8oikLlzEqaz5bJolUfXcWND97IgmuTL4RT7Jf6osXz6Jajw8XXU0UTB0pKUqsDOJpoa6nxvt6eQz28/KOX2XX3LnZslzEvmYyM6LbqzUTrlyPGlG6vHFtC5GxMMVI8087I+Mtf/sJrr73GHD1m+SYpGzbIyXssm4gf/lA/m4hUraVAVnf+7Gfw0EPw85+Lr3w0jzwi9+ecI7U38kagD3ydYuNjdqKqKu7BPsrKyqXoTWhQng/0TaqsjDVr4L77MhcyQqEQe/bsYdWqVZPOfzptIcN7Cnp2Qdk8cMnReTLHMRtkGk9NyNizR4o8jl5Vn0t0t5ZK0Vaq2DdlxfCKFSLO//d/j7VBUVW4+WbJUCwthccei++lHQqFaGg4CJzBli3yWj2SCjZvHnmeMBpVFXuMzZszy8JraZGJKpstvcmutLGWg71GPkigF0xWfAMnsZSURAJor8l5Ufi47fR1QtgX9XgphL1SpNzfC+1boMw458fF3/nEJ2I7pwCyalNv27lkUFW44w5pSygk1wkPPijXCkuXikXR5s0iavzP/8g5Vrrt09teZTSF/rtpXNFI44qIxamWkZFqjYyNG+E978ksG9CosdRioS0q0RNHpQOTxUQ4GGawaxBXvT51lIway1zjbncPF7QHmWDb/pvt+N1+Zl4wE1+fD0vd+PFJJZauOhcb7t4w4j1HYy+3j1g1n85rEqGqKn3H+hjsHsyanYzFkXq/KvbLCKP75mjifefRrwsFQ7zy1Cu039+OvczORd++iJK6kpT6SrStVCbXIpqQ0dcndpHV1bG3qzu9Dkelg+42L47ek1itjZyewDk420LGROuX0WPKtl9to/XlVhZuWMjcyyPuBqmOKalgpHim/e5OpxO/369nWyYlGzbISf1XvhLi9tvNLFyosmuXomvhzlStpQDWrhXltrMTnnsOLrhg5POGsZXSMDul0KaqEjYF5G9txA7HP5BMVLSMjBdflBW2E2Dczilud+SCb/bsFF/cvQP2/lgKaS+9Xfe2ZQVPq7R7yvlgMVDBmywxfbpMSA8MSPHKRCdZ2UT3jIzKpXDmT2V1uNFRw9D+vNTImP4uMOVHUdKyMq6+WrIybrttZK2Mr30Nfv97yZD84x/Fimo8FiwYwGpVOXFC4fBhfQrKJ7taNNNVpdpFz/Tpkp2ZMxx1sPru4WyGUCjI/l27WLx4MRbz0AHMCDaRo9o5AlWFlgeh7W9w9H4ZS6ffkPs2Fpl05Mp2LppQSASJtjaZDF6zBsJhuOUWWQAF8IEPSB0he9TCQLNZxNYFC0TI2LkTurriT4wkg6qqdB/spmp2lSxiKjKGUAhOnZK/UxGpE2UDKopkA15zjT4uArkmm9ZSiqKw4uYV2Fy2SVmINZu4291svGkjns6IbVdwMEj/8X5QYLBzkLcff5sNd2/QdULPVedKeX/pvGY8Wl9pZfPtmyltLOXKO6/MeMx79Y5X8bv9nPGuM6icWalPIycxsfrmaEpqSsb0zdGvU1WVntYeVJ+KvdTOxn/YGPN146GXVazTKYsRTp6UrIx4x2vFpNC8upnDv32b6RylbmFj3Fq7GlrbsiVkTES0MSUcCGNz2WhY3kD1nAxOogqUtC9TP/rRj/KlL32JQKAAJksMjtkM110nZ4gnTug/eZCqtRTIxPe118rfo+2l+vrgmWfk72uuybh5+uLvwhFoATW7/nBGZ9EiqKyUiVqt2FKR5NHqY1RVpeYBDIB7qBJWqQ6zl7li11dh/8+gxwBeSzlAUSJZGbt25bctmpChW0aG2SaZQAZaDR4fBfbdAYfvgcEszCKkwJVXSlaG2y0TcffcA88+C7/9bcR66o474PLLE+/L4VCHxY4tW/RpX7IraFNdaTuavBX69nZIFlHZHLmVzsFnbYbSOZHH8i1iaDjqIm2KvpXPhYX/D+Z8GEwWKE1VBS9SJHVyZTsXzcaNMkasXw833ST306fLGPrzn8sx9jvfgd/9bqSIEU1DAyxcKG3ctCn9tqhhlcc/+ThP3vokPYd70t/RBCToCw7bP7S3i9CkKFCXwlCaSjZgIZJNIQNg3uXzmLF2BtaSPKb+TkB8fT48nR4sdguOSgeOSgdBbxCTxYSr3oW1xIqn0zPuqvhCZcqSKZisJgbaBjIe80L+EIefOczR544SGIzM6blPudny3S1s+a5OJ7GTiFh9M/pmsVti9s3Rr7O6rIT9YUwWE+XTy+O+bjz0rHkXbS81HtPPnU5fLzTTwrIliS31tIwMra1FkmfgxAAAZY2JbfQmImmv0w4Gg2zZsoWZM2fyjne8g4oYZtEOh4M77rgjowZOFubPV1AUle5uhY6O1E4yx0M7wYTUhAyQVVy//rWkhP/P/0QElieeEDuW+fNlRZWRUAYOYAkHUTzHJvUkgskE554rFiibN8OqVentR1EUnE7npFvhllF9jIEhIcMVETIMH8eqpTKR3LMDas/Kd2sSokc8Fy2Cl16SOhk33qhj41IgHM5CRkaK5LVvKgqUTIW+fZKVMWTFlg8UBS65ROyl7rlHbtH827+JvVTi/Ug8V69WeeUVhRdegH/4h8zbt2aNHMNbW2NPWCqKPK9l46VLXgp9q2HY+R+gmOD0z4NruvHHzHgoCsx8D0xZN1T42xgUYjyXLl2a0mIph8PBtm3bstgiY5Ir2zmNeNkfx4/LzeEQa9NkMrbXr4fdu2VxlFabL1UUk0L5tHL6jvVx5LkjVM1KdfXJOPsuwN9NNE9+5kkG2gZY/7X1nAjWA1Bfn1qWtl7ZgEaNZTZrZGQLo8YyH1icFmwuG4HBAAFPAJPVRMWMCtSQStCXeFFjIcbS6rTSuLKR1pdaObblWEZj3vGtxwl6g5TUllC7IJKKrKoqR587islqQlXVpOJTiLHMJlrfBFBRYeiYqaoqQW8QNayihocejAqZ2WkeFuIUs4Kzyomr1oXf7U+qT0ejiQ56nNPPmiW1rg4dGn+7ujPq6B60Y8PHovpTwPgpgJpVb0+PLJYu19lBdqL2y8BgAF+viFqlDaU5e18jxTNtIUNRFD7+8Y+Pv/Oin03SlJaamTFDBpw9e/QTMvr6ZIUppL7i94ILpJjpiRNStPS88+RxrT6GYWylolAcU7B6Txqy2GauWbNGhIznnoN//df09mE2m1m6dKm+DSsA0hYyVDUiZERlZBg+jpVL4fgTYi9VAOgRz8WL5T6fBb87OsT6TVF0rEfQ8jCEfFC/FpyJd5r3vlnSHBEy8sjGjbKCOB4rVya3Hy2e550nta70ysgwm8Xr/oYYTkXaueQPf5i5tUdeMjJObZbv31o6XNsl7/0yU6JFDE8rtD0Jsz8oYk0eKMR47tq1i3nz5vGBD3yA5iRW4jgcjhy0yngkOwH72mvxhYxYNlGxxpLxsj80Kivhne9Mrk3r10um29NPJ7d9PGasmUHLCy0c3XyUpR9YqtsFdiH+bqLxdnvFeqLUxok98liq5xp6ZQMaNZZau0+ezI4Vr6fTQ9fbXdhKbdSfUa/LPo0ay3yihlRQpC6J1WHF707O/rxQYzn93Om0vtTK0eePsvh9i9Me845sltUr09dMH7EPZ5UTgHAgTMAdSMoarVBjmQsC7gDtb7YDEA6GCQfDPPrRR4eFjkU3LWLqWTJRF/KGOP6mrHKzWqwZrbaPrpGRKclmZJjMJvZ5mqnkAI2BoyQSMsrKxP2iu1sWXZxxRuZtjWai9kuTxcT5Xz0fd7s7pxl/Ropn2ofrr3/963q2Y9ITDoeZPTvA4cN29uzJfFWlhmYrVVkJrhTtGW02ESv+7//EXuq88yQT47HH5HnD2UoBqnMq4cETmMJ+lNBgvpuTV9aulfvnn0+/4Gw4HKajo4Pa2lpMOTVMzy9pCxm+dgi6xVakJFIN2NBx9LaDpVTa3fsWdG0fWVDXCL70o9Ajnpq1VD6FDC0bo75ex4LjrY9Jdk3F6UkJGXnvmyVDE5R5FDISTc4pCnzmM7JiOJFQoMXzHe+oBUzs2qXfCqMNG+RY/J73yLFYo7pafOj18MDPeUZGOARHhtJfmq8brtGT936pFyE/7PwyeE+B+wjMuEmOD7HI4lhbiPF86qmn+OUvf8m3vvUtzjvvPD784Q9z7bXXYktkuDzJSHai+f/9P7jrLrGCes97IqsgI0XCI9vGKxKeKPsDZPFTstkf2jZvvin1G+rTnOdtWtWE2W7GfdJN19td1MyrSW9HoyjE341GOBjG3y+TuY4qx3Ddt1SFDL2yAY0ay7o6yWIPh6UP6p0d2/pyK1t/tpWpZ0/VTcgwaizzia3URsWMiuGJ4WQp1Fg2ndmEyWKiv7Wf3qO9VM6oTHkfQW+Q46/Khcj0NSMzos02M1aXlYA7wGD3YFJCRqHG0siYS8zYK+L4MyZBNqylEmVk9PXB9s7prOcA9ZXJCYrTp4uQcfSo/kLGRO2XZquZxhW5z/w2UjwzWndw6NAhHnzwQQ4cOIDX6x324dQoWkslTzgcpra2C2hk71799qtdcKRqK6Vxww0iZGzcCD/4gVyc9PRIIdRzztGtmZkzJFqoqkogbMFu8sHgcbCOtTybLKxcKcWZOjoky2fhwtT3EQ6HOXjwINXV1XkfrHJJ2kKGlo1RMm3EZJVh4+hthxduAl8neI7JpNuWG0XY0LDXSHFbA4kZesRTEzIOHACPB0ryUONcE5p1u3BWVfB3yt/22vG3HSLvfdOZfyFDT2sWLZ6rVlUza5aJQ4fEwuySS/Rp6wUXyKpREMvArVvh/e/Xr5BvzjMyTm2SjAVrGUy9avjhvPdLvTDbYM4/wRu3Sz2Yt38JjimxVxZkcawtxHiuX7+e9evX09vby7333ssPfvADPv7xj/Pe976Xf/qnf2K5VohmkpNoohnE7ikQkJppr78On/ucLE5auBB+9avxi4SvWQOvviq3hx5Krk3JZonU1kp25K5dUpPo3e9O7nWjsTgsTD1rKkc3H+Xo5qO6ChmF9rvRGPZRV8BeZh/+TlKtoxSdDagoI/tKKtmARo2l2SxFbNva5Ka3kFFSKyeXno74hX9TxaixzCWqquLr82G2m4fFi9L61O1VCjWWNpeNhhUNHH/lOMe2HEtLyGh9tZWQL4SrwUX13LGFgh1VDgLuAN5uLxXTEs+nFGoss4WKWEcpJgVriZWGFaIiBzwBfD0+Lv3BpVTNFlsws81MX4u4iZjtZhpWNKCqKn0DfaioKKS+GjUYjFzb6GUtBYkzMnbuhJPU83LTddz9teQyZadPhx07slPwu9gv9cVI8Uz73f/0pz+xYMECnnzySUpLS5k+fTozZswYcyuSPDNmyGT8nj367TNTIeOSS6C0VCZxXn01Yit15ZWZW1jogrVcLv7DPhhsQfEMSc/hoKyADPvkeavOhnsFgM0GZ58tfxdqEb58kbGQUSiFvgN9ImKY7GCrEfFFBayVcjPZ5fkJaNVWXy8r8VRVPLrzge71MQJ9IkaBjHuFwHBGxrHx/UqyiF4e4KM591y5f+GF1F43HlqG3bx58MlPymOvvabPvkOhSE2tnJy+hUNw5F75e9oGsDhz8KZ5oG41zL0Z1IAsugh0yzmJNs5O8LE2UyoqKvjoRz/Kiy++yPPPP09paSlXXnkly5cv58c//jGdnZ35bmJe0SaaYaw+pihy+8MfJFPi5z+Hdevkseefh1/+Mn6RcFUVYaG+XqyivvIVEUGSIZXJ8gsukPtnnkn+NbHQVhQf3Xx0zMK6yYi3xwuI1Y5iUtLOyIBINuBoi+LmZnlcLyE9X2Sz4Hc2hIzJTtAb5PXfvI6n3UPvkV7CocQFhSci08+VMa9tW3od9+hmmTWesWZGTGsqZ7Wckw12TW6Hi3QJuAOceP0Evcd6URQFs8WM2WLGZDahmBVspTbsZXbsZXYs9sjiR21bk8WUloCh0doq5/U2mz72xdHWUuMdYnfsABUTC5cnb/epFfzOhpAxUWl9tZVDzxzCfcqd76bkjbQzMr74xS/yve99j0996lN6tmdSM3OmnHRmQ8hItT6GhsMhFzD33Qff/z489ZQ8fuWV+rQvYxx1soIx0AdvfQe1bz8tztXMtryGEvbDGV+AqmWGWk2eS9askVVumzcnV6i2iKxg0FIxUxYymi6HsrmFJ5yZneCwgL9bxD9LlA9d2Je/dmWZRYtk8mTXruRrIOiJJmSkOz6Pwdch97ZKMOXOLzMjnI1SNyDkBX9XXgQYvTzAR7N6tVi56FUnA2Q8B8kMOfNM+XvbNrlYyXRxQVubrNq2WHJUfP7UM2KDZquAqUY5qcgSlYulr3s7ITgoGUhl80GJ+tIm8FirFwsXLuTb3/423/zmN3niiSf43e9+x5e+9CUuvfRSbr75Zi6++OJ8NzEvaBPNsSyifvjDyETzRz8qt5YW+MY3RNgYj1BI7k87Dc46S7LAvvENaG/PzGYomvXrRYjJVMhoWtmExWnB0+GhY08HdQsn53m/xmC3TD46KmUyKRMhA6QPXXNNcrVUCo2mJjmOpiNkJKovU1InQoav10fIH8JsmwAByyO9x3p5/lvP07FHzndtZTYC3sCYCd/gYGpFkQuRqWdPZe2X19KwLL0fdd3pdfS39Y+xldLQ6mRoY0mR1HCfchP0Bgl4AiNqtiTqm9rzqqoSGgwRsAZQFCXlPq3NZUyfLvZ5maKJDW63uH3Eq+erLXhYtkzuvb1e7GV2FFN8UaYoZKTO3kf2cvL1k5x969nMvnB2vpuTF9IWMg4dOsQNsapOFkkLRVFYtEgmng4dAp8P7Olb4g2jWZekm5EBER/d++6LPPbpT8sFiyFW4TjqxEYl0INidUHleihfCFYX1JwlkySTFO1i8m9/g3vuSf3CQ1EUKioqdCucWAgcPSpiht2exmSerQJqVo15uCDiaC2XiTVr+kXFcoVe8dSEjHzVydDdWkoTMuzJT+DkvW+aLLDkq9JmW1VemqCXB7hsG4mnlpHx0kv6CA0wUshYsEBqX7ndklWk2aWli3bR09ysf8HTmHRtk/tp14N55MqtvPfLbGB2QulsyT4K9MPAARlzc8BEi6fJZOKKK67giiuu4KWXXuLGG2/kwQcfJBic+JNX8dAmmp99NsT27SdYvryB8883xxx3mpuljloiIQMka+MjHxn52kxthqJZu1Zeu3eviPvpHg/NNjMrP7oSV72L2tOSs1ZMRCH/bqIzMiAySZ/JylyzObnaJ7Ewciy1RQra4pJkSaa+jK3UhtluJuQL4en0ZFS4V8PIscwmh545xKs/fVXskOpc1J1eR9AXxNcTexFASU0J9vLxJ1MKOZY2l42pZ6a/EmrBtQtYcO2CuM87q52giBVSMhRyLPXEXm7HWeWk51APaljFZDENj8casfqmvdxOSU0Jnk4PQV8QVVUJ+8J4w97hmCbTpzX0rnnncMjx+fhxycqIJ2Ts2CH3S5fC5m9tpuXFFi785oXUL4pfIyibQsZE7ZcDbQMAlDakbqmXCUaKZ9qXqvX19Rw/fpzGVJcoFomJ2WxmzZp5lJdLkZy339an2E2m1lIbN8J///fYx48fj/jnGkLM8J6E4CCKycrcJefHL6Y5yTh1Su5PnpQCjxC/iGMszGYzC9MprlHAaLZSs2frs4IBCiWOJlnJXwDoFc/Fi+U+X0KG7tZSmpDhSH4SxxB9s2pZXt9eLw9w2VcknmecwfAxfdeuyOqkdOnthe3b5e9166Q9K1fCc8+J9aNeQkbOXEEXfhYaLoSKsSc7huiX2cBSAuXzJYs0h+PtRIun1+vl/vvv51e/+hU7d+7k3e9+NzcX004xm+HCC81ceGHiya1kL9/mzh35f7LZH8lSVQXLl8uK+Geegfe9L7XXR6P3qsRC/t04q500n9NM1RxZIKBlZOTrst3IsUzHWmrjRjlnGK++zIYNMulTUltCf2s/nnZ9hAwjxzJd3O3uSF2XUYQCIfY8uIeWF2TAmbJsCqv/32pC/lDc14BMDLvqXHGfh4kTS1VVdZ9YXPy+xSz9x6WYzMldDE+UWGaKq87Fqk+s4tWfvoqr3sX629eP+W5i9U1XnYsNd2/IuE9raLUs9DynnzkzImRoWeHRBINyvQNyzdPZagUVjm45mjchYyL2y3AwjKdd7Ar1OKakgpHimfY03Y033sjXvva1Sb36SU/C4TCtrS0sWCBnRHrZS2ViLRUKyYVKPP9cgFtvjaSe55Wh2gRqyTRajp8gHJ6cfpnRbNwoRWBHo51kb9yYeB/hcJiWlpZJFc+062O4j8Ghu6BrrGH9ZIxjNtErntrEb74zMnS3lkqy0DcU+6aGXh7g0fE0m+Ed75DH9aiT8fzzEA5LfQytndqFxKuvZr7/nBf6VhSoXjkmGwMmeL+0lIKzCcwlOXvLiRLPbdu28fGPf5zGxkbuvPNOPvjBD9La2sqdd97Jynz4AxqQZL9rLRMt3tyXokhGdqxMtA0bZLx45hm4+265P3Qo/YVN69fLfab2UnpTyL+bxuWNrPn3NSy6UU50MrWWyhQjxzJVISPV62PNXkqvOhlGjmU6uNvdbLxpI/e/6/6Yt403bWTbL7cR9AdZdNMi1n91PY4KB646F9VzquPekpnwnQix3HnXTh796KP0tSZXY8s/4OfIc0cIesefw7PYLUmLGDAxYqkXba+1YXPZWLhhITVza5Lum9F9unJWJR67h8pZlSn1aQ1tcZKe5/Rawe9Dh2I/v38/eL2SLT5nDkw7V2xdWl5oGbd2lSZktLToP684Efulu92NGlYx28w4qpKvRaIHRopn0svWP/7xj+PzRRRCn8/HY489xrx58zj77LMpKRl7QeZwOLjjjjv0aekER+sU8+Y18corCnv36rPfTKylNm8eudpqNKoqhUE3b04/1Vg33DKihktm0NLSQkNDA6awDzpehKAbmq/S77287eMX5LSW570mR6KTbEWRk+xrrhl/lbHWLxsaGjDplZ5gcNIWMnp2wZH7ZGKueuSESkHF0dsmE+KlcxNvmyf0iqeW9dbaCt3dsjI0l+iekTHjRpiyPubEcDwM0Te9p+Dks1IrY3r+LCv18AAfHc/Vq+Gvf5U6GZ/4RGbti7aV0tBTyMhZRkbffihpGlmLZxSG6JcTiEKOZ29vL3/4wx/41a9+xbFjx3j/+9/Pli1bOP300/PdNEOS7HedaSZaJjZDo7ngAsn+1kPI6GvtY/9j+7GWWFnyD0sy2lch/26icbuhv1/+zreQYcRYaudgyQoZqV4fL7h2AXMunkPtQn0sz4wcy3Tw9fnwdHqw2C1YnJGpKRUVBakNYC+zc9anzuK0K0/T9b0nQiw793cy0DbAsS3HOOPdie08jr14jFd+/ArV86u59L8v1a0dEyGWejBwcoBTu06BArMumJX2fjKNZzbO6aMLfsdCq4+xZIm4WjQsa8BaYmWwa3Dc2lWNjXJOEQyK6K7bAj8mZr8cOCG2Uq4GV84tnowUz6SFjObm5jHZF//xH/8x7mscjtwqRBMBPTMyBgehs1P+TkfISPaELp3iaLozlJGBaxZoGsPA27DnBzJZ0nSZPsVvve3wwk3g64y/jb1GCpDnUcwoKBHKYBw8KPepZ2QM9cHS9E9a8kYoqpCbr1P82wePg3Vi15cpL5dVIEePwptvwnnn5e69A4GI9ZtuQobZAa7YRfsMja8TDv2fWGLlUcgAfSfngOE6GXpkZIwnZOzYkXltrZxkZIQD8NY3peD1kq9D+bwsvpkBGR5rVQj0QmAASqZKsfsiI9i8eTO/+MUveOihhzjnnHP4t3/7N6677jqsVh3O5YoA+ttEpYsmGB88KJMvmUy8uE+52ffnfdjL7Sx676KUVhRPJILeIGa7GUVRhrMxSkqgzPhl0HJOqjUyUr0+blqp10nexMbitGBz2VBVld6jvahhlapZssIo6AvGnQSd7Ew/bzontp3g6JajSQkZRzeLf0/z2eNPDnl7vbz2i9cIuAOc/5Xz9WjqpODQUzIfMGXpFEpqc5d5O5psChnxMjKi62MAmK1mpp49lcPPHObo80fj/obNZjnvOHJErsn1FDImIvmqj2E0khYyvvjFL2azHUWGOG1ooYEeQoaWjeF0QmVl6q9P1kfVEGVS3IcBUF0zAb88VnGGiAq+TikqWnt25u8T6JP9mexSuHM0ocGhieC+vAoZBSVCGYy0MzIGClDIsJZHfiPhoYw7kxXCQVklb3bI89by/LYziyxaJCdNu3blVsjQfntWK9Tqs0ivcCkZupjydsiEbgoZJUbn7LNlVdLhw5kVsu3tFQ95kPoYGrNmQU2NLFrYuTO2Z22y5CQjo+1v8j3bq8GVq2IcBmD0WKuqct6ihqWm1yQYa1Nl3bp1zJs3jy996UvMGvIzePDBB+Nu73A4uPrqq3PVvAmDHplomVJWBqtWwcsvS1bGBz+Y/r6mLJmCvdyOr8/HyZ0naVxuhIuU3PPkbU8ycHyA9V9fz4le8SZvaIhvJTaZ0a5jT54U+8ZEi0wL6vq4wAiHwnTs7SAwIAWmXfXJW+lMVprPbuZV06v0HOyhv61/XM98b6+XkztOAjB9zfgLn0wWE0efE9Ej6AtisRfrjybDjHUzCPlDumVgpUM4nF1rqUQZGdE1AaedO43Dzxym5YUWVnxkRdwMgunTI0LGOefo1eKJiZaRkev6GEajOCIZBJPJRF1dHaWl8uPeuzdiAZQu0bZS6exH889tbY1tUaQo8nws/9ycs/KH4D6CqWQmdXVtkuqkmKB+DRx7CE5t0kfI0DA749tihOMXacoVep1ka/0y36ljuUJV0xQy1PCwmEbp2GKTho2jo06yh6Kt0sI+2PYZCPnhjC9C1ZK8W6WNRs94LloEjz+e+zoZ2sq/xkadisqrKrz9c7DVQPM1YE5uab4h+qa1DGwV4O8FTyuUpaoiGofR8SwrkxTr11+XrIwb0kw40epjzJ07cqWSosgE4JNPir1UukKGqmbnomcEIT8cvU/+nn4jmG1xNzVEv9STWGPt27+Ezldg6hXQfF1WbSkLMZ5z5szB7/fz85//PKntnU5nUcggve9a70y0dFi/Xh8hw2Q20by6mQNPHODo5qMZCRmF+LvR8PX4CAfD2EptnBiyK86XrRQYO5ZTpsixNBiEjg6oj1+TFoCVK8FmA78/9vOjr48DngAnd50k6A0yc93MjNtr5FhmiqfdQ2AggMlionJ2JTaXDb87TqB1YCLE0l5up35JPSdfP8mxLcc4/Yb4tovHXjgmmS5zqhJOglpLrJhtZkL+EINdgwm3nwix1IPyqeUs++CyjPeTSTxPnpTxyWzWN7sh2loq1jzl6IwMkHpNFqcFT4eHzn2d1J4WW+DJVsHvidgvT7v6NMn4qcl9xo+R4pn/FhQBpFPMmTOHefNMmEzQ1xcpzJYuWpp4OrZSEPHPhbEDVTL+uTnF4oKK0zFZS5gzZ07kx1W3Vu47X86OdUNwAIhfvChfZFLEMRqtXxphsMoF7e0wMCDxSWkyb7ANQj6ZmHOOXXJt6Dg66mTiWLtVnA5TLpTflPuQ4UQM0DeeixfLfb6EDN1OMAM90Po4HL4LlOQHZcP0TS0rwzOOJ14BECueq1fL/ZYt6e83lq2Uhh51Mk6dkgJ92gRMVmh7EnxdYiHWePG4mxqmX+rJ6LG26dKhcfaY/J/FsbYQ47l//34OHTqU9O2tt97Kd5MNQSF+1zCy4Pc4NUGTYsZayfY69sIxwsH0C1IWaizDoTC+fllU5ahyDGeA5jNDwMixtFqhbmj4TZSpHgrB+98fETGSuT72dHrYfPtmtv5sqy7tNXIsM8U3IP22tLEUZ2UM5wOdmSixnH6uzAIf3TL+LLBmK5UoGwNAURQc1ZIh7e1OPIcyUWJpFDKJp7YwaepUsOi4bH3aNBnjvF4RS6I5eVLmLhUlcm0NYLaZWfSeRZx1y1mUN8fPOtaywbMhZEy0fllSW0LTyiYqZ1bm/L2NFM/8t6AIIIVTDhw4gNUaZvbQgu5M7aU0ISOTiTLNP3f0Ppqb5fFc+ecmixbHcHjowqVsrhQVDfmh42Wd38wLfbulyLMakscGj4O/e2TNgTwwngilkYwINSaeExwtG6O5OUWvec1WqmSGZAKNouDiWD8kALZvlmwTg6FnPBctkvs33sh88iQVtIw53epjeDvk3lYlVjVJYpi+6ZwYQkaseOpRJyPbQoaWJj51qqw01Z2QH47eL39PvzFhzSrD9MtsUrVCDtADB0XgySKFGs9f/vKXLF68mMrKSpYtW8bvfve7fDfJ8BTqd33uuTKhfOxYpFZZutSfUY+jykHAHaBte/oeqoUaS1+vT9ZYKWAvsw8vjMtnRobRY5lMnQxVhVtugYcflmuE229P7vpY88kPuAMEBgMZt9XosUwXFRV/vyhEttJsnIiMZaLEsvmcZlCg++1uBk4OxNxmsGuQU29Icb4Za5Kz9nRWOYdfm4iJEst0CQwGeOF7L3D8teOoOlxQZhJP7Zxeb6tYmy2y2Gm0vZSWjTFvHrhGmZYs3LCQORfPweaK/7vWMjI0EUYvJnu/1BsjxbMoZBiEcDhMe3s74XCYBQvksUyFjGhrqUzYsEEGq2eegbvvlvtDhwwkYrQ8LBYN/W+PiCMgkwRaVsapTfq+r2fIc8vskBXQYR/4OmRC4vV/g8P3QtAd2d7bDv0H4t+87bo2L54IBfCRjyT3/Y2J5wQn7foY7qGjbpz6GAUXx+oVslLY1wU9OU5VSAI947lggVg7dXVlngWXCtrFckZCRvSY0r1dxhvFmtKYYpi+qWVkDBa+kDE6nlpGxrZtMJiGxh2vPoaGJmTs3i0ZZemga32MWMe6g78RkcrsgMqlCXdhmH6ZTWwVUDZU7Lzrtay+VSHG8/777+fzn/88H/jAB7jvvvv4+Mc/zm233cY999yT76YZmkL8rkEmPs4ecoB95pnM9qWYFKafN7RCeXP6yzsLNZbeHlk97ahwoJgUwwgZRo6lJmSMl5Hx3e/CHXfIpeVdd8EXvyjXx9//vjzf1BT7+tjqtGJ1iXjv6fBk3FajxzJd/H1+goNBwiH5XH63H79bHssWEyWWjgoH08+bzpzL4l/AnnrjFKhQc1pN0rVHnNVDQkZ3ckLGRIhluhx9/ihHNh1h2y+36bK/TOKZTavYaHupaGLVx0iFbFlLTbR+6ev38ca9b3DkOZ0VnyQxUjyLNTIMyGmnwaOPSp2MTMjUWioaI/jnxuXUc9C3D8oXgnPm2OenrBNf7nBAVpfHWDGfMqFB8A+toCwZUgpMNnBNF5uIoAcO/wFaHoLmq6H2HHjlZin0GQ97jXho62gvMbqI48svS6bGn/8MHg+U5N5az9CkLWTMfC9MOV+fvmUETFZouEj83K0Tu5CUwyGrR/bulayMXFkvZGwt5W2HF26KjCn+XhFS+/ZC99BJdBbGlKwxbC3Vmt92ZIEZM2SC4/hxyZpYuza110fXx4h1PG9slH7U2iqCR6r7h8gFScYXPaP7pYavU+zP7HXw0gcKp19mm+pVcv7StTWh3dZk4wc/+AE/+9nPuPHGG4cfa2pq4nOf+xzvfe9789iyItli/XoZ7555RhbcZML0NdNpebGF0oZSfRpXQGiTjvZKSS3WJufzKWQYnURCxt13w+c/L39///uReldmM9x4I3zmM2KtEgrFznYvqS2h192Lp91DxbQK/T9AAWMvt1NSU0LPkR7CwTBmuxlf38h6kyU1JdjLU0mVn3yc+7lzx31+xtoZ1C6oxdubvNX2sJCRREbGZOfQU+LOMPui2XELWucKXRcnjWLmTJlXOnRo5OOx6mNEM9g1yNHnj2K2mZl72dwxz2dLyJho9LX0sesPuyipKxm20ZysFIUMA6JXRoYe1lKGZ0SR5dir4SlphnN+L5YreuFpFWHEVinp21rmhckOrmaY+89iy+M+BofvgUN/kNWo1kopFD6a0ODQRE+f7pM70SLU9ddLSvThw/A//wOf+5yub1XwpC1kKKaIoDVRmJvhLEIBsWiRCBm7dsHFOZpLzNhaKtAnY4bJLmNKcFAspaxlMs5kcUzJChVnwJk/BWceTbyzhKJIVsYDD4i9VKpCw6ahZMLxFhOceab0qXSEEtDxomd0v9SwVkodIcKF1S+zTfUqOHw3BHpjV06cxOzZs4fzR3X6Sy+9lOuuu47BwUGczuz7pxfJLevXw9e/Dk8/nfnPoXZBLVf/5uq8Tyjlg+GMjErxt9cyMvJZI8PojCdkRBeg/8xn4NZbx762tFQyIg8ejFzHR1NSW0LvkV5dMjImGq46Fxvu3oCn3UPXwS4IQd2ikecH9nI7rrrksgiKxMdV70o6GwOkxg4KhHyhLLaq8Olv66f9zXZQYOb6mfluTlaFjFlD022pZmR07Olg2y+34ZriYs6lc8Ycm6dNk/vubujvh7KJvY4ybQbaJPW+tHHyLdIYTVHIMAgmk4nm5mZMJpPhrKUMzeBx8d4228DZiEllOI4j0EvEsJaLNUagFxRkoibQM3Ibe62sZp/xHmjfAkfuhYHDkYkdS5wTiLAv9uM6YrPBV78K//iP8O1vw0c/ChXjLAyK7peTgbSFjARMtjhmG73juWgR/OlPuS34rYu1FETGFEUBZUjI0MaYJMYUw/RNixMsiYsPGp148Tz3XBEy0in4PV59DI0zz4SHHkq/TobuFz2xjnUWl4j+hdQvs03ZXHjHb6UAehYpxHj29vaOEStsNhtOp5OOjg6maVe9RUZQiN+1xjnnSO2BEydkcUGsCeFk8XR4xqzqjiaZidFCjaWz2knz6maqZsu1jxGspYweS+1cbHSNjF274NprIRCAd79b7KVGoygwf75kRO7bF0fIqJMUeHe7e+yTKWL0WKaDq86Fq85F3em5XeAw0WKpqiqdezsJB8PUL6qPPB5WUUypi7oLrl3A6defntRrJ1osU0HLxmhc0UhJjT52F5nEU7cs6xjEspYaHIw4ycTKyHC3u3FUOggFQnQf7ObwM4epmBGZgLKX2ymvc1FZCT09Uivr9NP1ae9E65f9bf0Aecs2NVI8i0KGQdA6BYi1FEhqVbr2P4FAZFXJhBYytCLLrlmgmDApDMcxJv5eWbUcT0xIhKMOas4GkwPqVsPsD43dxloeWWlafx7UnQsdW2DrLZFt+vbIdk69Kv0mz/veB9/5Drz1Fnzve7L6LR7R/XIykJaQ0bdP6rRULoGmS2NuUrBxVFXJeHIfFYs2g6B3PBcvlvt8CBm6ZcyFpUAiptQKJBZs3zQo8eKp1cl44YXUVhr39cFrQ+UTYtXH0Mi04HfWLnr8PWC2x85EHIdJ0y8VJesiBhRuPGOtprdaxy8UP9kp1O8axOrxnHNEvH3mmfSFDHe7m403bcTT6UFVVYLeIBa7ZcRkXElNCRvu3jCumFGosWxc3kjjckkxCIXE8giMIWQYlVgZGS0tcMUVchxeswZ+/3upqRYLTciIZwutFfzWIyPD6LEsJCZaLA88eYBXf/oqtQtrufi/Iinmf/+3v2N1WVnxkRWUTy1Pen9mawyftDhMtFgmi6qqw0LGrAvjuIOkQbrxVNXcZGREW0u9+aYca2prxy7Qiz4eD5wcIDAQ4OEPPzxsWwaR4/H06S56emQOVG8hY6IwcEIyMsoa85OyYqR45l9KKQJAKBRi9+7dhEIhamuhuloGov3709vfiRPyeosF6usTb1+waELGkK1UdBzHcOC38OIHoO1v6b9fcFAyMmwVcNotUDZn7G20XYaigCMqnzvYL7YanhbJ7MgxZjPcfrv8/YMfwKlT8bcdN54TjIGByMVeakLGbqnT0rU17iYFG0f3IRHg9v0EQsl7qmYbveO5aJHcv/mm1CLINm63FHAGHTIyNMrmQ+VisKbmvWyovtnxMuz5EZx6Pt8tSZt48Vy+HJxOKSqfSv2rRPUxNFatkvuDB6FznFJMscjqRY/nCPS+Ice9FDBUv8wVIX/2dl2A8VRVlTPPPJPTTz99xK2vr48LLrhgzONLlizJd5MNQSF+19FccIHcZ1Lw29fnw9PpwWK3MNg5iOeUB5PZhKPSgaPSgcVuwdM5fsYGFH4sQY4HoZBciuTzetDosdRi8/bbIqR1dYmI0dICCxdKxqPDEf/12iLEfftiP9/8jmZWf3Y1C67NIM1oCKPHMh0693ey/Tfbads+TrX1LDDRYjn1rKmgQMfujmHRbODEAB27O2h7rQ2bK7XFTqkw0WKZLCd3nMTT4cHqstJ8tn4TvOnGs6tLrjMhYtekJ9qCpyNHItfMWn2MZcvGLtSKPh6XNZRhspgI+UPYK+1jjsfZqJMx0fqlJmTkKyPDSPEsZmQYBFVV6e3tRVVVFEVWIb3wgthLxSuaMx7R/usGyPzJHu6ojAxGxnEMjilSU+PUJph2bXrvZ3HC0m/CYCs40rwisJTJa72nwHsy5YlHPbj2Wpn42roVvvUtETRiMW48JxgHD8p9dTVUVqbwwlFiWiwKNo6uWVKzYLANOl+B+jTM97OA3vGcM0fsLDweWZk+e7Yuu42Llo3hcunoAaqYU171Dgbrm/374cTfJWuu/rx8tyYt4sXTapWsieeek2N7siuNNVup8bIxAKqqROx4+20Z1y+NnRwWk64uEXIhUmxPF8I+qYuhKClnQRqqX2YbVYU3vg49O+DMn6V/bjHuWxRePO+++258vuQtNx3jzTJOIgrxu45m/Xq5f/ZZmSTJ5BrG4rTgrHYS8oXwe/yUNUUOuEFfMOHrCzWWQW8Qs92MoijDtlK1tXIcyhdGjuXGjfDJT8rfHR3SB+128Pkki+Uvf5Frg/GYP1/u4y1UqJxRSeWMSl3aa+RYpkvba23seXAPnk7PcDZRLphosXRWO6ldWEvHWx0ce/EYp111Gkefl1nhKUumDNfNSZagL8jLP3qZwe5BLvj6BZgs8QfkiRbLZAkHw5RPL6d+UT1mW/IZLIlIN55ahnVDw/jia7o0N8uiWL9fMtimTo3UxxhvztLitOCodtB/vB81pKIoCrYSEda043E2hIyJ1i/zXSPDSPEsChkGRRMyUlm5GY1W6NsgmT/Zw9cl9+NMIg9Tdy68fSf0vw2e41CS5lJoRZEC4pngaBAhI9Cbk9oYo1EU+OY34ZJL4I474F//VecJrAIk7foYAyPFtAmFokD9GjjyRzi5yTBCht5YLLLi7vXXxV4q20KGJjRPnVqs7TsCbVz1tOS3HVni3HNFyNiyBT784eRek0x9DI0zzxQh49VXUxMytGyMKVMka0Q3An1ybymlmAA8DooitUNCfuh6DZouz3eLDMF73vOefDehSB446yyx1G1vlyxJzfoxXZw1TgbaBvD1+NL2iS80nrztSQaOD7D+6+s5cUKE0XzaShmZjRvhhhtET45G01Bvuy25TMVEGRlFxqdjTwcAdQtzWyNjIjL93Ol0vNXB0eePctpVp3Fks5zkTV+T+oW+2Wbm2AvHUEMq3h7vsEVakQhNq5poXNlIOJCDlP4k0M7ps1EfA+Saedo0EUwOH5Zr2eiMjPEwmUzYK+x4u724T7qxzRqZIZQNIWMiERgM4OuVg1O+MjKMRPHK0qBoJ0TpFvyeNELGqh/B6v+FsnmJt7VVQNVy+fvUc6m9j6rCsQchkJo9xhhCgzJhoYZk9XQ4AO5j8niOuegimRzz+8evkzFZSEvICAfBM3S0TUZMK0Tqz5f77m2Z938Do9lL7dqV/ffSrdA3yNjRt1duvk4ZX4LuvIwpGTPBhYzoOhnJkGx9DI1062RkpT5GaBB8HaAGwWQv7H6ZC2qGvrzONIucFCkyQbDZRPSFzOylNKwlVhSzghpWk8rCmAj4enyEg2Fspbbhmg9FIWMsoRB8+tNjRYxofvxj2S4R84YuQ0+ejFiHjqb1lVb2PbaPoHdy9MNkUVV1WMioXZj9mlETGXe7m9LGUvxuP8e3HmffY/s4tesUAW+A0obSlIvNK4qCs0pWuAx2Fc/f4qEoiq7ZGOkSCsFTT8nfTmdyY1c6RBf8VtWIkJGMi0xpQykoYLKYUBk5+BaFjPGx2C2882fvZN1X1mXVJq5QKAoZBsFkMjF79uzhCvCa7USmQoZuhWSNjK1KrEgYG8cxaCvKTz07/pnraNo3w4HfwGufhnAaRwVrOdhrJPsi0CM3s10mwr0npf6AvUa2yxFaVgbAb38bexVRwnhOIDRrqZSEjMFW+Q4tTrEui0NBx9E1DUpnyudsT3IGNstkI56akJGLgt+6CBnamBLygq9dxpFAb2R8CfuSGlMM1TedQwcsfw8EBvLalHQZL57nnCP3e/YkV8dCq48xZ05yPrfpChm61seIPtb5umTcIFzY/TIXVA8VOenZkZVaGZMunpOYifBda/ZSeggZCgoWh1wjpDqBXIixDIfC+PplxaajyjFsLdWYO7eemBgxlps3R66X43HsmGyXiPLySIzjZWW8/KOXee3nrw17nKeLEWOZCX3H+gi4A5jtZipnVub0vSdSLLWiyo9+9FFOvXmKtq1tPPj+B2l7rY1Tb5zi4Q89zMabNqYsZjiqxJ9osHt8IWMixTJZWl9tzZowmWo8N24UgeGOO+T/Z56R/zdu1L9tmpBx6JCIGX19sgghGdtce5mdKUumUDGtAoWRGZLZEDImUr9UTArlzeU0rdSrwGbqGCmeRWspg2AymaiPqsKmDQR796bnEatZl0z4jIxRjI7jGGrfAWYbeFqlvkZpEh4y4SAcukv+brwMTGko7o46WH13xGoDpF7Hji/IZM/sD0HTZWMLhWeZc86Bq66CP/8ZvvxluPfekc8njGeeCYXkAqOtTS4g1qwR38Z0SCsjY2BI/XDNGtcjyOhxTEj9Ohg4DO3PQVMKnjVZIhvx1OwrciFkRFtLpY02pnRtg7e+IzUIVvxgZD+0liccUwzVNy1OmeT2dYpIaD0t3y1KmfHiWVMjx/Y9e+DFF+HKK8ffVyq2UiAFxU0mGQ9bW5PvX7pmZGj9svct2PVVOd6u+NHwYgOg8PplLnDNiPT93jegeoWuu5908ZzETITvWiv4vWlT5nUyACwOCwF3IC0ho9Bi6ev1gQooMmGkCRn5zsgwYizbkqwrnex28+fLtnv3RhYWROOsdeLr8+Fud2c0YW/EWGZC++52AGpOq8Fkzu3k2ESKZXRR5dL6UvqPSxa9YlEobSgdUVTZVZd83TJn9VBGRmdiIWOixDIZ+lr7eO5rz2F1Wbn2d9cOC+Z6kUo841nktbbK4w88ABs26Ne2WUMmFIcPR+pjnHFG8nWYLPZIrKKzMjQho6VF5njSndOJZrL1y2xjpHjmX0opAkgF+B07dgxXgJ81SzzoPJ7IpFcqTAprqQO/gV1fg+6dww+NjuMYLCWRlY8nNyX3PieekmLHtgpovjr99jrqoGxO5FY+D+beDPM/LvvNsYihcfvtcn/ffZGDkUbCeOYRbeXB+vVw001yn8nKg7SEDH+PCFsJbKWMHMek0DKZ3EckAyDPZCOeWkbGnj1it5ZNdLOWctSBv1tEjLrVUD535BiTxJhiuL5Z4PZSieKpWaZs2ZJ4X5uGDlHJChkul1xIQGpZGbpmZID0u0CP9Muas6HitMLvl9lGUSLnJl1bdd/9pIvnJGYifNcrV0JZGXR3Rywr0iE4GMTv9oMiBVl9fT78bj/BweQEjUKMpbdHztEcFQ4Uk2IYaykjxjLZLJVkt9NsoePVt9Qmjz3tnuR2GAcjxjITOnYP2UotyL2tVCHEMhSShS333CP3iZpqcVoom1pG/eJ6quZU4ah2UNZYhsWZ3kR7shkZhRBLPTn0lNTIrF1Yq7uIAcnHczyLPO2xW2/V12Yq2loq2foY2vE4+ubp8tCxp0OO08hYazZDICA2fXowkfrloWcO8cZ9b9BzuCdvbTBSPItChkFQVZXBwcHhCvBWK8ydK8+lYy81Kaylul8XP+koz+3RcYzJ1Ktg/idh+g2J3yPkhyP3yN/T3w1mR2ZtHk3jJTD9XSKS5IklS+C975W/v/Slkc8lFc88oK08GJ0Srq08SFXMCAYjk3kpCRnTroPz7oeZ7xt3M6PGMWkc9bD8v+Adv9P/N5AG2YjntGkycRIMwv79uu02JpqQocv43DN0BlmZhDFpDAzXNzUhw5eE95IBSRTPZOtkpFofQyMdeyktI0M3IQOg8VJY8pXkjrMxMFy/zAU1Q0JGp/5CxqSM5yRlInzXFotk2AI8/XTqr7eX2ympKSHoC0Ym9isdmKwmvD1egr4gJTUl2Mvt4+6nEGOpTTbaK+WzGcVayoixXLNGFvzFS6hWFDk31PpiIubPl/t41lJaoWRPR2ZChhFjmQn9bZI5kI9C30aPZbqL9sxWM/YyO6VTSqlbUJdRpktJjfTbRDUyjB5LPVHDKoeeFiFj9kVJuHuk8x5JxjORRZ6qJm+RlyzR1lLaIth49TFGH4+jb/2t/QTdQfx9ImRYLJFrY73spSZSvzz09CF23bWLrre78tYGI8WzaC1lYDQLij174OKLk39dOByZKJuwGRnhIHiOyd+pFlmuXCS3ZGj7i0yoOWrFVmqC8tWvwh//CI89JiuFtVXDRiTRygNFkZUH11yTfEri0aMyge1wpHGxZ7LKbaJTsTDfLcgqiiJZGS++KPZS2sr2bKBl2WWckRHyQd+Q0l2VnpBhOGa+T6z2zONPMBUq2tj6yiuS+WOLU6ttyxYZ65Ktj6Fx5pnwm9/A1hTmwjURV9di32YHVK/UcYeTgMqlYilVvUJqcaVjY1mkyARh/Xp4/HHx+f7/7J13mBtnufZ/o1FdbV/vuq1L3B13x06x43RIQgIBpyfAgUOoOUAIAQ6EJCT0EiC0A3zAAQIhIYmTHEhII92pjrvj3sva21daraRVme+PR7OSdiWtetnVfV26tDsajWaeeWfmfd/7ee77i19M7bv2Rjur71uN1+GNu46l2pKSvEqpIJK4AYpGWioleNqipXgHIwl5wmSgqnDPPZIApSjR4wqd3PjpT5MfSwxXkZEtImOk4YLvXYCr1TXQZssQ5FsuKB6sdVapavMFc/9jJYLjG4/j7nBjrjIz8dTCZg1nWyIvGejSUocOSfUExK/ISPQ8DvgCvPydl3Edd7H1/q2s+toqpkxROHRItn366dnb55EA3V+pclxlgfekOFAmMooYw3WI4qG9XSZIFKXwGTg5Q9+RkMmyHSw5yuDwu+Hg3+XvKdeJ1ncuoGnQ9gq0PAUnfzmvht86Zs6Ej30Mfvtb+NrXpHQ1geVDQZFK5kGykiy6rNS0aZlrMY94aBoEfbm7HgoIncjYsgWuvjo3v6FpWZSW6nlH7oPWMWArnPFXVmGqKvQe5BSzZolXRkeHZDGdemrs9VL1x9ChV2SsWxcmdhOhpwe6u+XvrFZklJE6jDZYeGeh92JUYceOHSxatIivfe1r3HHHHQC0tLRwww03cOTIEYLBIDfeeCOf+tSnCrynqaH+2WdRXnoJamvFhbi6Wtj5KVPA7QaXS0oQLcVLGOuG3y+9JIkmxhRHrPZG+4gkKoZDRUMFzSuaqZtWB1A00lJJw9MGr16XuCrT0iBeTFkgM1avlgnhz38+emzR3CwkRioTxXpFxu7dsb1dKhqFyEjVbHmkQ1EUKseWJ+YikYukvXQx7fxpTH/XdBRDkU4MFAD7nhWPzClnT0E1FTbpJNsSeclgwgRRj/H5ZM4FROEjHhI9j8+981yevvlpjr5+lAMvHGDyZGFJ9CSrMgRBf5C+ViHBK8eX75dQQtJSbrebO+64g1NOOYUlS5Ywd+5cnouoN25paeGSSy5h0aJFLFiwgF//+tcF3NvUoaoqc+bMQY14GumG36lKS+kdsaam+NmeJQ+XlPNROTVqpiZWHGMi0A9H/wmbvyETgbGg+UV33j4Jxp6Xjb2Oj8NroGsTtDyd299JgNtukzHtSy/B06HdSDqeGSJZ/U9NgzfeSG6bqWQeRBIZSaNrE7x9Mxy4b9hV8xXHnOP4s/DGf0p7LSByFU/dJyOXht9dXeANJaVkTGQE++X+VLsobeZxxLTNIsFw8VSUsLxUIp8MnchIRVYKxLTebIbOTti3b/j19YFCQwNUZqtffPzfsO9P0JvEDsRBuV1mF+V4xsbnP/95zjvvPHx6SiFw+eWXc91117Fp0yZeffVV/vjHP/LEE08UcC9Tg6qqTFi0CEN9vRAWO3eKPpOu07BpE9x6K3zuc/K69Vb485/lM02T0tyXX4bNm+UG0d0dezYtx1i8WHgYpxPWr898e137utj//H76OpLPhi/F62bc4nGs+uoq5l89n74+kSmEwie2JR1Ln0NIDIMFTLVDXwaLfJ6oYiNFrF4tEovPPw/33Sfv+/ennu0+nL9ltioySrFdFiuKNZaFkAuKB4PRkBSJUayxzDb6e/s58rqcnGnn50ZWCpKP56pViYnqVCXyktu3sDE3SI5EXV1626o7qY7518oA/O3fvM3kMXJ/zJa01Ehpl642F1pQQzWr2OptBduPYopnSVRk+P1+Lr74Ys4991xeffVVLBYLmqZFmYxcfvnl3HjjjVx//fU4nU7e9a53MXnyZN7znvcUcM+Th6Io1NbWRi1Ll8jQO08jVlYKoDdEZNijZaVixTEmFBUO3g/9PaIxH0sCw1QlXhpBf24lHhQFJl4CO+6BY/+CSatByT/H2NwMN94IP/6xVGW8+90pxDMDrFkTOxPqnntkEKFpsG2bSF89+GDy10Mqg7a0jL6du+VlGzvsqvmIY36ggKcd2l6CKVcXrGwnV/FcsEDec0lk6PfnhoYsJMOOOU1e8cjYJFCUbXPv78G5B2Z9FipKq9IkmXiuWAH/+If4ZHzhC0M/T9cfA4TEWLxYpKveemv4e1rWjb5BiIzuLVItWZneIK8o22W+4O2ErvXQeGbWPIlGdTzj4OGHH2bs2LFMmzYNv1/uoZs3byYQCHD99eJ7VVVVxV133cWvfvWrkhpPVL73vfFXmDdPUnkdDmEJHA6p2ABh2V98UZZFkhc/+AHU1MDDD0tJYU1N+DV7tnS4fD7JQrFYstI3UFW5/z32mEwsx6teSxZv/c9bdOzoYOVXVjL5zMnDf4HSv250s1SrNXyKC4WUY6napOo+FoLxJcPShaqmXgE5GCaTPHN37hSfjMGykLVTalnxpRXYx2ZWKVTq7TISr3zvFbSgxoLrF1A7pTbvv1+ssdy/P7n1YiXt+d2xxwTxlmcLxRrLbKN1aytBX5CaqTXUTU9z9j4JJBtPpzOxzw+kJpGXLKZMCc+fTJwoj/90f2Pu5XM58sYROnd1Ur/7DeAcDh3KzhzDSGmXrhNSyWcfa0cpoGxKMcWzJIiMe++9l5qamoGyb5AgGkN1xiNh4OH3+9mwYQNLliwZOC5dWuroUblJVSWpuKFPCI8KImOQP0asOMaEQZVJgqOPw4kXE2t5G/JwmTSeBXv/AJ5WMfock+GILU3893+LvNT69UIwXHZZkvFME8Ppf15xhcj8RJIXJpM8KD2e2NtUFGn7qWQepEVkxCHTYiHpdlnsGHO6+IG4DoPrQOr+NFlCruKpV2Ts2yeJrPYcqFJkTVYqEhnco4qybXZvFSKj71DJERnJxFP3yVi7Nrb8k+6PMW1adMZTsli+PExkXHNN4nV1o++s+WMEvODYLn9n4NtSlO0yX9j4FXAfB2N11voCozqeMdDX18ftt9/OM888w29/+9uB5c8++yxnD2IPV61axRVXXIGmaTEHj16vF683PLHqCKXA+/3+AYLEYDBgMBgIBoMEg2GdcX15IBCIMk6Mt1xVVRRFGdhu5HKAQCBAIBBg48aNLF68GHOoLDsy8QubDePcuUMSwpRAANVqJfi97xH0+6G3F7q7URwO1KoqgsEgmv5QPHwYw7ZtKE4nwauvJtjYCK+9huHee1HMZpSaGoJ2O9qMGWiXXw6ahuHFFzHU1BCw29GqqmRQY7ejGo1xj+mcc+CxxxSeey7IF78YHHKskTAajUOPSVFQVZVgMIh9rJ227W10H+5mYmDiwPJY50NfrsdyyZIlmM3mrJ6nZJYPd0yR+64v97q8GMwGFEUJjQeNjBsHmhYkEMht20u0XI/lKaecgqqq8Y8p4EfVNLneQtechhYeJ+jLIS/XUzLLI8/TzJkGdu40sH17kPPPj77mDVYDzSubk2p7ifY9EAiwadMmli5dKvEpkraX6jH5vD6OvHmEQH+AedfNIxgM5v16CgQCbN68mUWLFmGI0AJL95gStT2/X+OVVxRaWmDiRANnnaWgadHHtGmTym9/qxfJDT9ZOX48A8ekVkimdl9HH36vP6YZr32MHVOlKSqWwx2T3+/ntbtfw93hZsVXVlBRVxHzWDVNY+PGjSxcuDAqW7sY214m94hxy8bx3t+9F3enO6fH1N/fz4YNG1i8eDGqqsY8Jr8frr5apaVFoaFBw2yGlpZwu2luhh//OMD73qehH0I2rqdHHlF4443wOX71VZg6VeMnPwlyxRWpnycUWP7Z5Tx989PYg17M9HPokCUr93K/38/69esH4pjt85Svttd9pJugFqRyXOWQfcznMfl8voE+pslkSvuYBp+rdFASo5n777+fz3/+83E/T3XgUWyDDv3d75eHjt7YqqqgqUmltVVh1y5YujS5RnjokAEwMGGCht8fXj/fxzTc8kwuLEUxoRgsUDEFA+HOrB5H/bsJj6lhJYYj/4S2V1Fm3ohitMgxeTtR9v0ebfKVqNXT83NMmgGl8VyUo4+hHP0nyphTC3KexoyBL3xB45vfNHDrrRp2e5A336ylpwdWrfIPMO2JjglUXnwxyLFjGuPHw5lnaphMQ9ue6H+qaNrQjpp+GA8+KO9ms8aFF2pceaXC+96n8PTTAa6+2hBaN/L78sW77w6GrqXkztOePSqgMH360IFRvGNVe/fJQKpiigz6E5wPvV3qfxfb9TR43+O2PcUKdaegtL+G1vIcyvSPFuQeAXK/TuZYU7nv1dWF77lbtwY45ZTsH9OxY/LYnTAhiN8fTLjvCY8p4CKomAmiRi9P8R4RK5aFbnuadQKKYzea8yBK/akl9XzSr/VAIBC37S1dasBoNNDSAnv3+gdIBH39554LAgbOOkvaSKrHdMopcj97883oPkCsY9q/X/oLkydHr5v29dS1FSXgA0sDQVMThgwmJiLbZSkOOmIdUzLLjfWnoB19nGD7G2i1S7NyTCn1jRIcUzYGHsWA73znO1x//fVMGMQoHzt2jCmDypNsNhtWq5XW1lbGjh1agfnd736XO+8c6m2yYcMG7KGJ/8bGRqZPn87+/ftpa2sbWKe5uZnm5mZ27dpFT0/PwPJp06bR1NTE1q1bcbvdA8vnzJlDbW0tGzZsiGo3CxcuxGw2s27dOjRNo7u7m/Xr17N8+XL6+/vZvHnzwLqqqrJ8+XJ6enrYEZElYrPZWLRoEe3t7eyL0KWrqalhrsHAsSNHOFJbO2DE09jYyPRp09i/ezdt69ZhdLuxnHEG4yoqaLRaOb5jB44jR+hetw6Dx8P8v/2NCqOR7o6OgXZ06LOfZeYpp1D7739z9LXX8NlsBCorCVRUcNJ73sNZZ03BTIC3XuzntVe3YTLDsmXL0jqmNk8bPd09vPP6O5gWm5g7dy7Hjh3jSEQ58ODzpMeypaWFKVOmZPU8RSLdYxpynubO5bEbH6NjfwczPjGDtw9PBmYzbhx5aXuJjknTtIHfSXRMR3duYWZfH0FFw6rtxVw9EU+wEo/HjaIFUIL9WNV+LBT+mGKdp+rqycAEXnutg//6r8a452m4tpfomDRNo7+/H6Co2l6qx7ThuQ10tnaiVqjsbNnJ9Irpeb+eLBaZLO3o6OBghCh/Ns4ThNvePfcc5vvfH09rqyXiO/DZzx7glFM6efbZMTz6aBM7doQ1PlU1SCCgEJvQ0Bg7tp9VqyxRxzT1i1MxBozMmTuHjo4ODh86PPCNqqoqTl56Ml3eLrat25b0Me3evZvtL2zH7/RjfMHI/FXzY56nmTNnDpBskf2RYmx7yd4jJo+ZjFWxsnnzZoIRRPDsObMxm8y8+syrmOvCOu7ZPKaWlhY6OztZv349iqLEPKaf/nQKTz89nooK+M1v9jNuXCubNlXT3m5iwYIxvO99dWzdupV167J3Pb3wQh1f/eosBuPIEbjqKgMPPQRnnpneeWq8uhF/fwP9j1k4dIis3MtVVY2KY7bPU67a3uBjOvLaEXq6e5jWMI1AIFCw66m1tXWgjzlp0qS0j0mff88EihaLri0yjBs3jr/97W/84he/YPfu3YwZM4Yvf/nLXHTRRQDccsstTJkyhc9+9rNR32tqamLLli1DBh7f+MY3Yg46nn322SGDjr1798ZshNu3b495wjZt2hTzhL311lsJbxZ6Z/m8884byA4A+PSnT2bjxmr+8he45JLumI2wtbU1qhF+//tzePTRWr7ylR7e//7tA8vzfUw6El1Y3d3JHZN+YR05ciR8YWmaHNOMGQPHpMfx5JNPZsqUKYmPqa+Pae3fwRTowrjw61RNu4i33nqLMV0PUtv3Km7TVGrP/W3ejsnk7+Ck9u9hMZuwn/MX9ra4C3KeXnttG+997xLc7mies6nJyxe+cIBzzumKe0xvvtnMD3/YHCUT1dTk5Y47uvjMZ8ZFtb21a2u45Za5DIf/+I+jfPCDx6isDEQd07//Xc1PfjI1qlNoMGjceeduLrigM+qYEp0nTYPzz1+O262yYwfU1Q3f9hTNx/yuO7FX2Ng/7nZOdIfjG+s86YOOlStXsm3btuK8nkjuHhE48RITu+/FZ6iF035P09ixeb9HzJs3j7Vr12I2mwc6JZkcU+T19KUvLeWVV8zceech3v3uY1k/pqeeWs5tt8Gll7Zy66370j9Pwafp3fMYRyzvortiZcJjSnSPqKys5Nlnn6WmpmYgloVue8H9f2NM75P02JZhnHdLST2fDh8+THd3N7W1tTQ1NcVte5df3sybb8I3vrGbCy/siDqmBQtcbN1q5/bb93Dxxe0pH5PNtozFi43YbAGeeeYtVDX+Md122xyefbaWb33Lyfnnhwe26V5Px175Lrb2x+mxLeN4zTVpn6e33nprII6KohT8vpfXtjdNwbfh63T1GdnXeCsoSsbHlFLfKMExuVwuLrjgAnp6eqgutFZNmti7dy+XXnopGzZswGq18o1vfAO/38+3vvUtbrjhBk477TQ+/vGPR31n8uTJvPjii5x00tAqxFjJUZMmTaKjo2MgRvmuyFi/fj1Lly6NXZFBgQhARcHg8xHo6kJzOKCnBxYvRjWZUJ5+msCuXeB0ojgc4HCgXHstwRVn8t76tVzmuJdL329g/MwqlJoamDWLwAc+INn5zz+PVlWFsa4OraqKQEWFGP4oStS+H3zpIK/+6FXGzB7DBT+4IGkCcP369ZxyyiklU5Hx8PUP4+nxcOFPL+Rv/6rjc59T+cAH4KGHCkvU6rFcvnw5qpqgIsOxG/Xl94G3GxRQDGa02oWSmOjcLi7a5lqUs9YQtJ9UHORzxHn63e8UPv1plQsv1HjySWXIeWp/px3HIQdj5o2helL4HppqRYYey1KuyNj+6HbW/249E5ZNYNXXVxXkegoEAmzYsIGlS5fmrCLj0UcNXHGFXlU0mJTQsFrB45HlZrPG6tVwww1BOjrgmmuGJu0pimz/gQeCXHmlmpd7+ZOff5LuA92cddtZTFw+MW5Fxttvv82SJUsGzkGi81HsCSquNhePffgx3B3u8Loacl8KjZds9TYuu/eyASPrbB5Tf38/b7/9NkuXLkVVh1Zk/OEPCp/8pPzeQw/B+9+f+/teIAAzZqiheZ6hBJuiaDQ3K+zdG0RR0jtPDgc0NMgcVHd3ALs984qMdevWDcQx0bEWS9uLdUyB/gCuEy7MFWbsTfaCXU8+n2+gj5lJRYbD4aChoSGj8URJEBkmk4mzzjqLX/7yl8yZM4fNmzdz6aWX8uc//5lzzjkn5YFHsQ069Pf169ezbNkyjEbjwPJPf9rA735n4Lbb4M47k2uEF16o8txzCn/8Y5Drry/irMMc3CxSHXQoB/6McngNSuMZKPNvxe88guHtG0ELEFzwTdSGxXk9JmXbtzB0rUNpfj/Baf9ZkPP00ENBrrrKwOAHVGTHafVqhuz7I48oXH21YUiFhf69u+5SsNuDvPUWbNighOSihi+bvffeANdco8U8pkAAXnlF4cgRAzffDJ2dCr//fYAPfzi8fqxjjTxPJ05Ac7MRRdFwuxVMpiTannMP6qZbUEzVBE+/l+Aw52OkDDoCgQCa34vhjf+AgBsWfx9D3by83yM0TeOtt96K6pRkq0Nx880qP/+5wk03BfnhDyMqcLJ0TJ/7nJH/+R/42teC3HlnBhUZ6z6D1neEwNz/hobTEx5TontEIBAYEsuCt70TL2HY8SOomgVLf1RSz6dYHbxYx3rLLQZ+8hP41KeC/PznwYHlLpeBujqNQEBh714/kyenfkygUlsLLpfChg3+Acm0WMd0xhkq69YprFkT5L3vzfweEVx3Ezj3oM2+Ca3pnLTPU39//0AcVVUt/H0vn21PCaCtvQ4t4CW49Gdgn5z3vlG8Y8rGwKPQuPTSS/noRz/K5ZdfDkhyk05k3HjjjcyZM2dIYlRjYyNbtmxhXCJHzRAcDgc1NTUFi5E+aNfHEyUJTZOXwcBH3tfJln/s56aPOvjQZQ59lgPe8x7RgPzKV8SfIxI/+pFIV/397yIiX1VFr9vAW/fupG/cNC559OOiT+rziYZkxARmJEotlsFAkAc+8ABo8IF7P8B3fmzlm9+ET38afvWrwu5bUrEM+uCdH8I73xfJTFMNVE4H1Qp+Fzh2hP0xVj0EY8/N3wEkiZdeEm+XadPCsrWReOV7r3B47WGWfmIps987O63fKLV2GQ+vfP8VDr9ymIUfXsi8K+cVZB9yHctAQKQ7Exl3A8ycCZ/8JHz4w9DYGF4ey0eysRF+/evUzegzwQvfeIGWt1s49XOnMv1dsXWYR0q71NG5t5MHr3wQo8WI0SZ9zY4dHRitRqonVRP0BfF7/Vz54JXUT6/P+u8niufLL8P558sj7M474fbbs/7zMfHCC3BuErfd55/PzHOovibAZMdWfvQLKxfcmN59UsdIa5eFRrbimY2+ckmcTYPBwJe//GXmhNyvFy5cyBe+8AX+8Ic/cM4552CxWPDEEMx3u93YbENd3S0WC5YYLqtGo3HICdEHd4MRyTQnszzeidaXq6rKokWLMIa0YvXlc0MJ6zt2RPuCJNpH3Ux20iSRrxhu/VwdUzLLkz2mqOWKMkRUXF9fj6PJZEq476qqgqcNqqZBoA9aX4Tu92E8eD/4nVAzF0Nl88Dv5PyY9OWT3gvBPqiZV5DzFAjIRG4s6ATFZz+rMmGCjDEDAQOBgAGvFz7zmaFeF5Hfu+02gNRNzJubVQbvqr7vRqM8yAGOHxePj5/8ROWjH41uIonOk15JPGmSEjJfTuI8eQ4DClSehEFVYx7V4GyURYsWDUwKxULBrqcUlquqCmoFNK2A489B+0tQNy/v9whN01i0aFFURUZGxxQB3fD7nXdi3z8zPSbdIyPW/Tnpffd2QN9RFMWAsX4xgy+QVO4RetuMFcuCtb2qqXIBe44NXMil8nwym81D4hlr/RUr4Cc/gddei24H4o+hMG0aTJsW/dup7Pspp8iEyoYNRhYvjn9MukfGtGmp9RdiLvf1YnDtk3PWsDSqXaZ6nmLFMd76ebnvxUDu2p4RpXYBSufbGBwboSZsmJ7uMaXUN0qwj6U+CHzyySfp6+sbIDEGo7m5mUOHDkUtc7vd9Pb20tTUlI9dzBiqqg7RKC85RPTzT72onj/9o54/HoQPXTZoPbsdfv5zMSmPNC+vDMmz1NZCVxd0dFDR2c2EExs4pFrwOr1YNr8tQvSKIqRHdbWYE151lWT9P/ccalUViywW1BMn5PNQpUexwtvjHcgYtlRbBoyAk+Dfco5h22XfMdj+A+gKVaiZasE+CbSAkBgA9qnQuxcCHnjne6AYoWlVPnY/acwKKa4cOCDNcvB0Q8WYCgD62vrS/o2RcI1rmkb7O+0ANM5tHGbt3CAQgJdfVjl4cAlut8pZZ0G2Q/ryy8OTGAC/+U3sCeLVq+Gyy2Q73/42PPssXHJJfkkMAGudFQBPVxyDSkZGu4wFo82I2W7G0+1BC2oE+gNYqi34+nz4vbmT24wXzwMH5Pz7fHDllfr8Sn4Qy1w+k/XiYfGYI4xzvMM79xlY/r5x1EyqSXtbI7VdZgK598l5Gj9efGWTDU8xxbMkRiRNTU3MmhWtxTZjxgyefvppYGQMPICBEvBIhLibKLPjRNC0UWD2vePH4NwN0z4i5sODECuOQ+Bpg1evC00IHpbO8MtXgOe4DAI8J8B5Hay4D6x57GDVLU1sPJ5jJNPhOnECzjwzve2vXAkXXiiTbIsXw2mnCfEWiwBRUjTt/uQn4Vvfgq1b4ckn4eKLk/teWkbfALaxSRl960iqXZYKxl0gGXJjzyvYLuQqnjqRsXVrTjY/QDRnZPbdtUneK2eAqTLxukmg6NqmbYLcAPwu8PWAubbQe5QSkonnihXyvmWLzLvpySgvvCDvmWQygcjYv/SSGH5/9KOx13G5oF3mERhkC5Ae3C1gqgZjFVgyz04runaZTzQsg863oWMdTMrOjMWojmcI+/fv58iRIyyOYPeOHz8OCMlx991386UvfSnqOy+99BLLly+PSRQVK0bSudYn9159VYoorNZBKyiKLLRaYfCY793vHvjTAGw/9ijujj6cx5xYTj5ZMnB6euQm7HBATWiyxO2Gxx4DrxeLpoWJlR//WMiTBx6QGYDq6jAJMn8+TJwo3/V4ZHmeiT9Pt0wyWmusKAaFUNNm/Pi87kZcxG2X/T2w/gvg75PnffVs0Pzgi6GhbRsvy4MavPMD6N0PJ32oaAimsWOlOTgcMr44+eTozysahchwtbky+p1Sv8b72vtwd7pRDAr1M7OfzT4cwpUOCiBsU3Mz3HNPdkmCZCd09Ws1FlRV+oSKIkTGY49Bfz/kswnY6iUx2N3pTrheqbfLRHC1yzVrG2OLm5SYbQyOZ28vvO990ndfuhT++Mf83vqSfZZk+sypmDOZln376ett4fWfvM67fvguDGr6fbBSb5dep5eN/7uRqglVzL18bkbtL1aVV6r3vmKJZ0kQGcuXL2fLli1RElG7d+9mxowZAKxYsaLkBx6BQCBmmY5OZOzeLezZcOSXwyGTEyD96RGJ3r3QdxQMpiEfxYvjEPgcQmIYLFAzH1Cg76AQGpYamYzxdsh6+SQyCtwRT7bD1dQk4z1VlXGawwGDuMSYuPFGuPba8P/33ANXXCGHHUlm6GH46U+TZ4hra+HjH5cs5x/+MHkiQ5cbT4nIGP8ueSWpzJd0uywV1C6QV4GQy3jqg85jx6CzE+qzPL7SKzIyuj93hzIW6xZlvD9F2TZVM1jHQrAf+rtKishINp4TJojcwIED8MYb8K53yfJsEhkgREY86NVo1dVy/8wY1TPhjHvB153xpoqyXeYT9cuA34Bju2Qfq4Nnb1PDqI9nCJ/+9Kf59Kc/HbUsUlpK0zR8Ph9//etfuf7663E6ndxxxx188YtfLNAep46Rdq7nzJGKguPH4fXXM7s3Lv3EUpEFaa4Guxnq6mKvGKr0CLhcbHz5ZRZPm4axrw8qZBKaujro7pZOwoEDUglSVSUP9nXr4C9/kfUqKuQGO3cuXHONVHo8+WSY/NDf6+vjylulAp3IsNTKxKw+OVqwigxP2wAZEQj42bJlCwsWLMCohtqlqVrGWOYamPAekY6a80VAi01i6DBWwrEn4PAaOPSgkB4T35v4O/pv5RiKIlUZ69bBzp0xiAy9IqM9/YqMkXCN9zv7aZjTIBWVlvwew5o1MvYcPIQ7elSWP/RQ9siMhobk1ktm4vfMM8P3wmefFXW9fMFWFyIyuuITGSOhXcaDx+HB0yn3V/sYe15+c3A8g0H44AclAWrsWHj00fAjKV9YtUomvbOVhBoPk6co/JFTOcv/BK1bW3nzF28y69KhBuMg1Ye6R0ksjIR26TzqZN8z+6horODkK04e/gtxkI17XzHFsyTO5mc+8xluuukmTj31VMaNG8f27dv52c9+xpNPPgnAWWedVfIDj3iYMkXKUj0emSiO4TMYBZ1dq62VfviIQ6Af+kIHmUI2fFyoNjDaIeACX6+QI/ZpoAXDOqyFgM8JLU9D7UKZHMoTkmXQH3ggejCZrGbi4O2vXi03zVjM8E9/mnpH8qab4Gc/E23Gt9+Wyo/hkHZFBhSceCoj+6iulvvuwYOwbVvmnbFI+P3hiYW0KzI0DbpDFRlZIDKKFst+KYTGCMbKlTIH9uqrQmQ4nTL5AaKxnQl0ImPTptjyFhAmMqZOzey3oqAoYI4zOVhG8rCNg3lfhZp5GZMYZSSGyWQayG5TFIVHH32UT3ziE3zve98jEAhwww03cOWVVxZ4L0cvFEX6m/ffn7nu9uSVk1P7gsWCv7ZWDA8iB+wRlR4D0GcGFiyA//qvsMyV0xnOiOjrg+eek7TayJmEn/xEZqT+8heZUaipEZKjpgYWLYJJk+S7fX3SSYmTDalPMlpr5Z5RUGmpyMp3QNU0Zvb1oTor5KQGvGAZI14X1kaYej0oBnnB8KTD9I9C5VTY/2epzo/4rZiwNOStyn72bHmW79o19DOdyHC3J85sH+mom1bHu3/47ihPpnwgEJAxZ2wpZGmaN90kck6ZKqe0tcFddyVeJ5WJX1WVycZf/ELsf/JKZNTbUAwKQX9w+JVHGLSARve+bgDsTXZMFUMTafOB22+XahyLRUiMSZPyvw+qmt0k1HiYPBncVNDaNJ/KDf/g6JtHWf/b9aiWoRuuaKhg9X2rE5IZpQ5nixOAynHpqzDk896XL5QEkXHBBRdw0003cdZZZ2EwGLDb7fz6178e8MwYyQMPVRUDqK1bRV4qWSJjxMpKuQ7K1Waqzu5kiWoD+2TJAFZtYT3WQmHfn6DlKWg6C07+0vDrZwnpMu2ZMPSR+p/paPVFYvJkSXr761/F6/Fvfxv+OykTGVoQGOrTMuqgadDzDrSvhZM+MqImnefPl0nerVuzS2S0tkpCpqoOVcBIGu5j4GkXI8zqudnbuWLDCGpP8bBihdyr1q6V/8UfQ57zk1OcbxuMk06STMCODti8OUxsREInMrIiKxUMhCahRvl9MZtoXFHoPRgVuPXWW6P+nzJlCk899VSB9qaMWDj3XCEyHnlEKjQy6SfmDPq9r7Y2folbZaV0ToNBITN0okP3c5w4UR4CPT2wf7983tgoM1br1skDA0RGq7pa0v2vvVa+8+ST1BzvZ85JHioavQRPtNF6vAEwFEZaKrLyXbWhaRoBgwHNVIXS3ylShN428HYKuWBIY0pi7LkwZqVIBOu/pahgGNR/CLjzWmWvq2Hv3Dn0M32yra+jj2AgmJFcykhANiR6UtF7f/bZxBLKmgaHD8v2MiFNt22D975XLuOKCuEgszHxe9VVQmQ8+mj8JJVcYOJpE7n6katRDKOvj+c85iTQH0C1qlRPTs+UOFUEAvDiiwpr1zbgcolM4Le/LZ/9v/8Hpw9VVs8bsp2EGgv6GOhAXyMzTAaC/iDuHjcNMxqi2qDf7aevow+vwzuiiYze471AZkTGcPLx2br35RMlQWQA3HDDDdxwww1xPx/JA485c8JExnByObr++sglMg7Ie+W0LE+YGMBaBG54OiZcLERG+6vQ3503aZV0mfZMGXpd/zMbuOUWGes9+CB897vDZxunTGR0rIOdP4ExK2D2ZzPZ1dLHjh/JpHrN/BE16TZ/Pjz+uJTvZhO6rNS4cRlMwBjt4g/kc4CapxFMGTnBypXy/vrrMmjJlqwUyH132TJ46imRl4pFZOhG31mpyGh7Cfb9EcZfBFOvHXb1Msooo4xk4fPJ+5YtcN118nc6evb9vf20rG/B1+djxkUzsr+jycJgECKietCkWKLS5sWLhdTQ/TwcjuhKjxdeoMHppEHTYBu4v/woauCn+LAx7uk/Q1tLtKTV0qVCkPT2iiZxdbUQJNkmo/XKd00jqHhQPC0yrlEUIR20/gy3H0FaBPvBfRQqJg0d0+Wxyn72bHmPRWRYa60oqoIW0PB0eQYqNEYTgv4gAV8Aky2WRHRqiW3J6L0fPw7//Cf84x/wr38lt4/JGHTHw5NPwtVXyyU6bZr89vbt2Zn4XblSKrqPHYNnnoFLL01/P1PBaCXctKCG1+klGAhSM64Gvzts7h35dzYRbtMqEK3K8aUvwYc+lJOfTQnZTEKNBZ3IONaiYG+003uiF4JCfJrt0UR1Lg3XiwW9LSEiY3z6REa+jNrziZIhMkY6VFVl2bJlMR3gE3WIBmPEV2T0hgwNKmOXpiSKY0mharqY3Tl2CqEx5eq8/XS6THs+GPpksHgxXHCBZN385CfSmY2H3l4xL4dhiIwInV/aXwP3Cckmc4ZYkGG0d0dMu4yEtx2q5kDvU6JTbB0b/Xm29YgjzoGqaSybU4fadyA86M7i782fL+/ZNvzOitG3uRYmX56N3QGKuG26j8OuX8jExJIfFHpvkkYq8Zw/X+aTnE5pa9kkMkDIC53IiIWsVmR0bZLM2mCGk1IhFG27zDeO/hPa1sKMj0sCR5oox3P0YKSd6zVr4LMxckbS0bN3d7l59YevYrQZmX7h9GGzwYsqlrGIDx1VVWIOFwwKKeFwcORtB54/W2loAONJk0DVwoZ2Dod0RCZNEh3W++6T7ZhM8hvz5sH114se5hNPhH9bl7waMyZlTw8l6KaWI+JSrChgnShVGGoWJ/L9Lsmkch0S4/AM7pmZQK/IiCUtpRgUzvzqmViqLViq00tGKap2mQba3mnjua8/x/hTxnPOHecMLE/VhHY4vfdrrpFktTffTH0fb71VJLrf//7UuL1f/EKOIRiUid01a+RymTs3OxO/BgNceaXE5O9/zx+RkQxKvV0OxoD3giKkRdAXHPAh0lHRUJH2dRwL8dq0jlNPzdpPZYxsJqEOhk5ktLQADQo1U2qw1ljT8tMZCe1Sr8ioGl+V9jaSrd4aroKzmOJZJjKKCP39/dj08uII6IbfO3YMvw394T9ijb5d++U9DpEB8eNYcph4iRAZx/4Fk64AQ/5uGDrT/tJLGgcP9jNlipmzzlKG7XDlmqFPFl/6khAZv/sd3HFHfMNm3ei7oUHGZzExSOcX93EZLLkOQIv49CSjvTti2iWEY9J3TDxrutbD8WfD2saQXT3iwecAUILB6IF0Fn9vQcjHfOvWsG5kNqBXZGREZOQARdk2VZtMjiuKeCOVkNRUsvFUVSkPf+YZIRyy5Y+hYzjD76xVZOTIt6Uo22W+0bURurdCx1sZT8qV4zl6MFLOdbY1nSvHVQ5MSnl7vANeEolQSrH0eQMYKytRqqo48I4MBMeNI3Glx9Kl0lmPVenhdovmocMhM7M67rlHqjf+9Cfp2OhER3V1uNLD2QudPqgOQGWIXAh4wWiFqhmgAb7u7AbANkGSWvoOSbKNZYz8n2fMDCVRt7eLH/zgMUjzaZlnG5ZSuxyM9h3toBFVkZGqCe1w9waIlhdevlykni65RO4X8aSQQe4rhw7J751+Onzve0P7ZYMrR844A26+GX71K/n8Ix+BX/86evIwWxO/V10ll+Cjj4qHqjVPNlpv/OwNHEcdnH7T6XEnVEu5XQ6GvdHO6vtW43XEr+Yazmg6FSRq0yDt8uab4QMfKDJZxRxgwgQ5Rr9fXrY6c1okho5Sb5cDFRlpSktt3ChtKxFS8esplniOzjqxIkQgEGDz5s0EAoEhn6VCZIx4aSn7SUJixBnQJ4pj7C+4ZVJ68CtQBCZsjWeCuUYmbzveyPvPqyqsWhVgzpwNrFoVSPqhqXfUrr1W3gvxsH3Xu8Qfsa9POpLxkJSsVKTOr6lWlhmMoQFSrSzXtXfjIOV2WezQY2KqBmOlyAOgSDySjElavxc6B5qxhl6vimasycnvzZ4t7barK7slljqRkTbR7D4OJ16QzPcsoWjbpqkaTJXSo3cfK/TeJI1U47kipMj2gx/IIGbcuOw9v3UiY/t2qT4bjKxVZOTAt6Vo22W+UR86iZ3rMtpMOZ6jByPpXKei6ZwMVJNKRaNUADiODt9fKLVYPvOlZ3jgAw9wYssJjh+XZcP6Y1RVSQnBsmVw3nmSgn7WWeHPvv99mZ398Y/hG9+AL3whPDs7ebJ0aPQT8frr4TLnjVvhry3wmz3wk83wh2MEnnehVc0GbPByK6x3wFsb4Z135ERHkiXpwjoWzCHmwO/MfHtpoLIy3M+LVZWRKUqtXQ5G2/Y2AMbMHQMMT0pomsjpXHqpTLItXCgTncnIP918s8yNvPkm3Hab8Gx6pf7gJCUlZH/4pz9JRUZFhTTpc84Rae+NG2W9NWskAeTcc0Xq7txzJRnuV7+S73//+/CHP+TOv+L006Wf6HRKEky+0L6jnfZ32nG1xvYRLfV2GYlDrxxi29+3Yau3UT+9Pu4rm74M2X7elTKMxvA9tD+i0FtDIxhI7TlR6u3S7/UPVAKlIy316KMiSXfkiNw39ftcJFLx6ymmeJYrMkoAurTUiRPQ3R3fQw5GgbTUzE9mZzumasng9nbE1021NBQkk2cABhOMvxAO/h2OPT6iPAhyDUURr4wPfQh+9jPpyMbKWEnJH0O1gdGGiDQawVIPSiibKI/au0UF1Qa2cVKVEXCBMeLGk4uYRGotG3zyt/70zeLvWa2SUbdjh1RlZKuCImNpqfZXYe//QsNyWHB7dnaqWKEoUNEMPTukfVVOLfQe5QT6wL0jVGx0/LgMkFPVfo+F8eNlIHD0KKxfH56bAsni00m6jCsyukLVGNVzyr4t2Ub9KfLu2ClEbSH7JGWUkWfkQtO5emI1fa19OI85aZrXlN6OFSk83R60gIalyjJAZIwbbP8XKZUaC7FkOhVFdHbs9mhmJKGnx3y4fCz0V4Bbg64gAZMHg8ECfX7Y0wu9fbD3ofB97Wc/k9nf//f/wpUeuq/HaacJ6+50yqumRmaaY5XMGqtCyS29UKCk0dmz5dm7c+dQY96eQz2c2HyCisaKrFRnlBI0TaNjh3R4xswRImO4CVyQxLTHH0/995YtG9rnTlYK+cYb4ZvflOb45JPyWrUKXnllKOniDQ1BvvQl+PKXU9/PVKDLS/3kJyIvddlluf09HdY6K47DDtydRZDsmUO4O9289cu36O/tx1xlZubFM4f/UhaQrC9LKXkYZILJk2HboTCR4ff4ad3WCgqMXzoehdFhPG+0GLnyoStxnXAN8QdJBE2TJLmvflX+fte75H7x3HOFl4HPFspERgmgqips7LRzp/Tl4mHES0tlC9ZGkaFJtTOfb4y/CI48KtlFQb9kvJaRFK6+Wm7eR47AX/4CN9wwdB2dyJiWrGKHv0+eBgZzmMQY7TDXy0Rzf4/o4xtyLAGk+VE8rRgDXqA2Zz8zf36YyHj3u7OzzYylpfQJ4yzK9xQ1bBFExgjEmjUySB6MdLTf42H5ctneW29FExmHD8t7RYVI62WE7s3yXjtK2mU+YR0jJF7vAejcAGOzpDtWRhklgGGrCVJcD6BqQhXHNxzHeaww2fq5QjAQHJBAsdZaBya7ooiMGDKdQ5Atmc6qSphgCflg2NC0KjwOBya/CywKfHi8JKCcejvQJOSEnsJ+8slhA6meHnlgzZwpRMa6dXD//bKe0SiExsyxMA7o74M3OsAWADxg80OwC6z5N4OdPVsmjGJVZJzYfIK3f/M2zWc0jzoiw3HEQX9vP6pZpe6kOiD5idmPf1z64zU1sB2nYigAAQAASURBVGcPfOYzw38n3r1Bl0J+4YUAa9fuY+XKaZxzjhqVjTx+vFRZ3HyzVHPcf3/ibHhFETmr73wn92oEV10lRMb//Z8owOVD5cVWLz/i6fIMs2bpQtM03vj5G/T39lM3vY7p70om0zE+kjGvP3AAfv97+J//SW6bqTzvShmTJ8M2wNcvcpCqRSXglSoAr8OLQTXkzHC92GC0GKmZHE8DfSi8XvjEJ+DPf5b/b7xRiAqjsXhk4LOB8qxoESGRacqcOTIJtmNHfCLD7RYtThihFRk+p2RkDzOZn7T5jLWx8ETFcLA2whn3gjGLZngpohjMfNKBySTaybfcAj/6Efznfw71JkypIgPCkmPG1HuMpRrHYaFaRV6KYG5JjKBfJGx8DtACWP0+CIzN2bUxf75MJG/Zkr1tZiQtFfRDzzb5O8sTxkXbNitCD7K+w4XdjxSRTDyzrf0eD8uXS1nxYJ+MSH+MjDxgNC1MZGSZYCvadplv1C8LERnrMiIyyvEcPRgp53rVKhnPxNOzT0XTWUfVBNF2T5bIKJVYeh1e8Z1QRLc9prRUpEynGqMfG3CHZTozHR8NqnxXNA2j1ofiC4YfOpYGsI8Ba0M0o75yZfztLl8uM1w9PeFXpQEMDdBxAt4+Dp4g+Dzi26Zuh09PgspG+Pu/oMsT7emxeLFkl7hcMpCurgZz5n1Z3fB7586hn+nyZn3tfWlvv1Ta5WC072gHoH5WPQajDMqSnZi97rqwx8R55wlhkMm9QVXh7LM1qqu7WbJEi9vXmjFDCIrzzpPJwXiIlP7JlQmyjtNOk8vg0CGpFPnAB3L7ewC2OrlnJKrIKNV2qWPfs/toWdeCwWTgjJvPGGij6SCRef173wv/+Af89rfw9NPhNmwwxFfYS+d5V8qYPBm8WPCoFfi9ffi9fjQ0NL9GX1sfRqvMByZjuF7q7TIeYhFlHR1CVqxdK/e4e+4RIiMSmfr1FEs8y0RGkcBoNLJcF7SOgTlzJLMjVodIhy5bUlGRWH6qZLHr52J4OeuzMO68mKsMF8eSRAFJjFKP58c/DnfdJdfN449LxyESKRMZilF8S4ypaRSWehyHRdV0CPoiFgTB0yqGi1WZZbMAcPSfsi1FBcWIohgwGU3g68wpkQFSkZEtZCQt5dghRpnmGrBnamoQRlG3zQEio3QqMpKNZypauJl0NuMZfmfNHyPohXEXSPusyl75fVG3y3yjfhkcegg63wYtKJNzKaIcz9GDkXSu9UH4FVfIJE6sCctkNJ0jUTUxeSKjlGKpZ0lbqi0oBiW+tBSEZTpjIVsynXrle+9+qaS1jmXIL6ZT+V5ZKa/B8JwhBMxFiDusowt6+6HXBSfPkt/ybwAOi3l5S4tUfIwdK52y118X3Q2QypCaGjFjuPJK8PlktjGSAKmuFhfvOJkAuix0rIoMXVe/ry09IqOU2uVgtL0j/hiNJ4fPezqEZaJ7Qyp676nEMlazi4V8SP8oilRl/OhH8MADeSIyQhUZ7q7YREYpt0sAV6uL9b9dD8DCDy1MKQN+MBKZ119+udxeenrCyy+4QOYsNE08RiH9Nj1SMHky9GFn57zVfPEX8lxa+8O1dO7qZMkNSwaq2YYzXC/1drnzHzvpOdjD1HOm0jQ/LIcZiygbO1aIsLY2aWMPPiiSUtlEMcWzTGQUCTRNo6enh5qaGpQYnSK9Q5TI8DtSViqjDMtiRe9+yUq2jIm7ynBxLEnoerbuY6L3Wj0r+vMcSmCVejyrq+FTnxKNwB/+MJrI8PnCk3lJExnmOnmliFKP47AwWOSlw9shFVRbvglta2HKNVA1Qz5LR5/ZVCU9OlMl2KegaX40xx4UnwslR+XUCxbI+7Zt0ikYXM2TKjyecMVcWkSGLitVuzCrN/iibpsVzULcpHHNFQrJxjMX2u+xsGyZvO/bJ1k6etKrXpGRMZGhWmH6f2a4kaEo6naZT3jaRMbQYALLWOjaLPdDHUk+/8vxHD0Yaec6np49SP8uVfm9hlkNnHX7WVRPHN5vppRiqZuBWuvEEC6mtFS+YW2EI4/AkX+gTbqcnob35y6Wg6vsY3UbEumE6mYKDkf41RjaXm8vvPCCEB+Rs4u/+IWUf//xj9DeHvbzqKpibv1SYAJHd7kItrox1FQNSGdVjJEEHE+3h4AvgGpKbWaylNrlYIxfOh4toDFucbhh6qTE5ZcPXT/RBG6yXheJkEoscyF1lwl0IuMf/xAPkYoc5z0OEBlxKjJKuV1qmsbrP30dv8fPmLljmHPZnLS3NVzFNQiJ0dQEH/uYvCLnIUymkeNhkAkmT5b3fSfs1E8XoqLp5CZ6j/ZiMBion16f1HZKuV0CHHvrGMc3HKdhdsMAkRGPKDtxQt7HjYPnn5dE+GyjmOJZJjKKBIFAgB07drBs2TKMxqGnRW+IiYgMPdt3RMpK+fvAHUovqjwp7mrDxbHkoOvZug6C+4RI99gnRa+TLT3bGBgJ8fzc50RH9OWX4Y03wtJshw5JZ8NqTbLTGYhTShtveeQqIyCOMRHv2A1mIR0UBdrfkFfDchj3bthyW2J9ZtUGTWfDuPPlBTDmDKiYCKYaUIxomoKLJuy2MSh+V1LnIFVMny5jTrcb9u9PgeyKA31SwWqFunTm5bt1IiO78j1F3TZtE2DFXwq9Fykh2Xjma0BcVyeyCHv2iLT4hRfKcp3EzdjoO0co6naZL0Tp2WvgOgBtL0avk+TzvxzP0YOReK4Hazq/8opo1z/2mCSp2OMnYw6BpcrCxOXJ6TuWUiwHiIxaITISVmQAaD5wHQK/SxIkcgFNk/4fEKicWZhYJusvWFMjr1ioq5OGFgwKqeFwCKlhCnnlTZggnzkc0NoKDgeTrp6IyTSBJd7XcN30IFVViGRVdTXmhQtRzSqax0v/Xx/ENnVcdKVHY2PChJVSapeDMWXVFKasGppBsXq1VJ++8EL08uEmcDPVe08llrmQussEy5ZJH+7AAXjiCZnYzCWsdVYUdWS2y+793bTvaEe1qJz+hdNRDOlP0CZjXg/w179KJcZgJOPfMhqgExmHDoWXVY6XsihnS/IeV6XcLgF6j/cCUDVekpgSEWU6VFVspXKBYopn6Z3NUQqdyNizRypmY7Ub/aY5IokMV2jWxdIQnY040qHr2ZrqwNsNBETeSJc2yqae7QjFxImiq/qnP8k45KGHZHmk0XfCbPtInd+AJ/bgwtIg640WDNI+jomak2HJ3XD8WWh9QWThTrwAju1gnzpUn9nfC33HwO+U6o7+Lhh7nsTbMkaIjAitZQUfiq8nWms5i+dAVcVvcsMGkZfKlMiIlJVKOYEh4AXnbvl7tBh9wwgtLRTkc0C8fLn0Hd56ayiRkVFFRtAP3VvkWlcT69OWkQbyqWdfRhlFjkhN59WrZeLuwAGZ5Lz11gLuWJHA1mBj0pmTqDupDo8HurtleVwyXFGhvwc0v/S7jDkYW/UdEplRgymUhJFFrc7hEAzA5lvBsQtO/312KjsNhjDZEIkYlR4qkkTw1vblbDqvmTMXOgYIEKWxEduYLrwHegi+/Ca8GZDZKR2//KUM9H//e6n0iCQ5TjkFmpowuFxCmtTXS9bNCOgv7d8v7z/8oYzdkiUlMtV7TxbZkrPKFnR5qR/8QFTRck1kNM1r4upHri54FnYuUDetjovuuQjHYcfAhHG6SLaSuq0t/me6f4vd3sGyZSeNOhIDwkRGR4dYGNnt4cn83pbeAu5Z/hAMBHG1ugCoHCdzf8kQZUeP5serp9AoExklguZmsNnC2cGxWLZIaakRh95Q7yZBNcaIhtEOtrEyIPA7wTo2/Fm29GxHMG65RYiMNWtkQm/GjBT8MXSdX58D1n9RBn3z/husEaPDHMp7FSUiYxIPekzqFsLUa+HgA+J1oRii9Zl93eBuESkqzQ8K0LgCZnwyPDIY9HuBgJ/dW7awYMECjEow5F1Sm/VzMH9+mMi47LLMtpWR0bdqgdP/AD3bwVZInYgCQnfAHiHI54B4+XIxqoz0yYg0+04bjp2w+XawjoHT/jCizk9RIfJ+GfTK/U5Rw/+XUcYog8UC3/42XH89fP/7YsDbmMLjv+2dNlq3tjJmzhjGLhw7/BdKAOMWjWPcIukf6ES12ZzAMzHgAZNdyAxvZ26IjI435b1uscgQ5hMGVeR4gz7o2QGNZ+T39xFZ6O3ba9joqeHMU6M/q/jXv+k9VknrB2/mpHOmijaQXumhZytOmSJ/9/TI4N/phEmToKmJqm3bMNx/v5ArJlPYuPyqq8DrFQfokMzVwPu4cQV9Tnfs7sBoMVI9qXrIZPiBA9JujUb49KdTq7LKJ7IhZ5VNXH21EBn//KcUCyXr45EOMqlSKAXUTKqhZlL6vhg6ik2CrFRRUyO3LodDPAPnzIGayTVMWD6BuumlIzmcCfra+tACGgaTAVuDJDTlS5q4FFAmMooEiqJgs9nistwGg3SINm4UealYRMaIlpZyhYgMe2IiY7g4ljSsISKjvwuC/SLfk2OMlHjOnw8XXwz/+pfITP3yl6IZD0lm2lsbJZtLCwAK1CwQ7f4kMVLiGIXBesSJYBsPc26ChtPhzY+Hlwdcki0HQnCYGmTSftpHh0qoRfyeEghgqPZgaHsJTjwL824Fa/ZrKE8+Wd7/+U9YuTK1cvXByMjoG6T9Na5I88vxUfRt8/izcOBv0HAqzPxkofdmWKQSz3wNiAcbfvt84faYUUWGLndWPTfrkyNF3y4Lgd69UoFRNQPMyWkD6yjHc/RgtJzra66Bu++G9evhm9+En/0s+e8efu0wOx/dyezLZickMko1lpH+GDF3PeAG514gVAXgaZWq1oAnuzsSkpWi4dTCxLJmrlT0O7YXhMiYFbI03Llz6GeL/2MxwUBQDIUVRWbu7fbo2c1YujNIH9i/dCna+edHEyA6m9fXJ+blDofIOOj41a+kE/ub30g6eCTRcdpp0vno6ZFtVVfLrHimBnER2PCHDbRtbePUz53K9HdFD76ef17eTz01vyRGOu0yUzmrbGLJEhnH7t0Ljz8uxEahUIr3y73P7KVmcg1jZsf3X00VesV1vKz5ZCuuSzGe2cbkyZJMeOhQmMg4+/azU9pGKcdRl5Wyj7UP7H+hibJiimeZyCgSqKrKokWJJUPmzAkTGZGmxTpGtLTUQEXGtISrJRPHkoVqE1ktn1PIDGvus8hGUjy/9CUhMv73f+HOO1OoyNDR3yXvBmPKEkYjKY4ZwdooEgM6PK2SWWxtkkqDQL9UaAyDgXjufFn8c1pflMqPLGLNGpkkARkPnnuu3FvvuSe9CWa9IiNtIiNHKP62qUg76Ts0/KpFgFTjmY8B8ZIlMhfR0iIERn+/yHlbLDA2k8fIgAF99ttP8bfLAsA6DiqaRWoqRZTjOXowWs61wSCZyBdcAP/zP+KHNmNGct+tmiDVB85jiXW2SymWvj4fRpsRRVEG/DGGTGTosqCe1nA1l6KITKDnuIwzsiXT2d8NzlCiSsPywsSyei4cexIcCQwmc4jZs+V9167wMlebC6/Di6IqqKo6MFGlw1Jtwd6YeCZfVVUWnHlm/BXq6uC735VST49HCI3e3nDHYtYsISmcTiE09u6VZc3NYib48MOynk6wnHKKaPR6POIsHSl1VV0tpcbDEB5Bf5DOXZ0AjJkzdNJY98bItxRKuu0yX3JWw0GXl/rud0VeKtdExtv/720693Sy9IalNMxsiPqsmO+X+nUXCccRB6/+8FWCgSAXfO+CpL2ThoOqwoc/DN/5ztDPUqm4LuZ45guRREa6KOU4DvbHgMJ79RRTPMtERpEgGAzS3t7OmDFjMMTpDOg+GbEyO2CES0s1rpTOdVXiWedk4ljSMNUKkeHryQuRMZLiec450hd/+234+c9hU2gOzuUSadphJw51g2pzfcrZxyMpjlmFfYq80GPSn9TXBuLZuApDy1PQ/irM/FQ0SZIB1qwRyZ/BHYSjR2X5Qw+lTmakLS3lc8A7PxRvjEmXZz3zvejbZkWIme9LwjmvCJBOPHM9ILbbYd482LJFDL91P9PJkzNItgx4wBnqjOTAt6Xo22UhYEw/TbUcz9GD0XSuzz8fLrpIVHRuvRUeeCC571VPlIn64YiMUorlM195BschB+d+81yOH5fxwRCjb12ms2sjbPsuWOqgZh60vgJNq+CkD2dPKrXjLelEVc0AS0NhYlkzV96du0ViKkt9xGQxuCLD1eZizXVr6Ovoi/udioYKVt+3OiGZkXQsFUV0qW226KyFc8+N/52VK0X2wekUAsThgDEh4qGvTzoSTqf8rf/Gr34lf//qV0M9PU4/HZqb6d50AEtPK0pNNdXjo49N08JERqJdywVK6RqPB53IeOIJOTVVObQS7dzTSfs77bhOuIYQGcUay1jXnaZpOI84CfQHMNlN/Ptr/+by+y4flkRMBn4/PPKI/F1VJedERyoV18Uaz3wiluE3gNfpRVEUzJXDq5OUchzdnW4g7I8B0dLEg5EPr55iimdpnc0RjGAwyL59+wgGg3HX0TM7dsRILPH5GMjAGZEVGZM+APO+KhI1CZBMHEsaupyR3wXEoGGzjJEUT0URrwyAb30rLC313/8tOvFr1gyzgf4QkWFpSLxeDIykOGYXBtJ5DA3Es2ouWOpFB7lzfVb2KBAQqZ9YWQ76sptuivZlTAZpS0t1bZZJhxMv5ETbuKjbpqdNZPT8LnAdhu6tIoehvzwJnPIKhGKNZ6S8VFb8MXq2iZmqXk2VZRRrHEsV5XiOHoy2c/3978uj8e9/hzffTO47ekVG7/FegoH4cSqlWHq6PGhBDXOVOUpaagj0ylijHapmS4KE0S6T/fYp2fMaazoL5n8dplwLFCiW1nEybgr6Q1Ja+YU+bj90SDwuvQ4vfR19GC1GzJVmgv4gQV8Qa60Va60Vo8VIX0ffkMzxwchpLO12OOkkWLgQzjwT3vMe0XsCMRa/6y7R6P3FLyTl/KtfDWdEzJsnJIjNJg69mzYNuM67/vkc83Y/wrKDD6LceCPcfLOYdwEHd3o4/dADXKr+i5XaK7B5s3RU8tBWSukaj4dFi4Q083hECjeXsNWLTr8+wRqJYo1l5HWnX2sBbwAtqGG0Gqlursbd4R72uksWv/89bN8ODQ1ibfP883DfffK+f3/yiXDFGs98IhaR8cbP32DNdWvY89SepLZRynFccN0CrnzwSuZfOz9quS5NbB1kPdXcnF6yZSoopniWKzJKCHpFRiwi4/hxmWQzGqGpKb/7VUYeEIjoMNingLFCJHUCQzsSZcSHzk4PvvcmlWnvTZ/IKGMQ4rXbVNuzYoDGVXDkMWh9CcaclvGuvfxyfF1TkPvs4cOyXipZ9GlLS+k+BDnIei9qeNrg1evkuus9IP40r1wtHio6LA2SXZplk/eRiOXL4Q9/ECLDFEpKzcgfo2uUtstCIFv3yzLKGIFYuFBkPP70J/jyl2WyaDjO39ZgQzWrBPoDuFpdUbINpQgtqA1MwllrrfGlpXS4Q0yHbRzUzBcz7rpFoPnJ2tSAaslKnywjKApUzxGvDsd2qJmT158fM0bM1ru7Yc8emFghy402I4pBwXXChcFooHZq7cB3/F5/rE0VH0wmmaltiBgTnR1fu/6IOpW2GZdx8kXNsGJsVKXH2mf6mMt25ox1YnvYJR3tyEqPn/98aKXHihVifN7VJduqqZH090IYVRQYurzUt74lVWnXXpvZ9gKB+HKntroQkdFVev0Po82I2W6m39WPu9ONwWigfkY9qkXF050dfyCnE26/Xf6+4w65PIpBgqxUEYvI0Mm03pbeGN8YeTBajRitQ5/Lq1fLnO+hQ3DbbXDeeYXz6ikUykRGCUEvUe3okOf5mAiJSX3ibcKErPpyFQdch8XY2tqUk4zkooauZ+vtCOvZgsjN6MiWnu0IRyAgCUCxoPeZb7pJ9OpjPgT6RdsVc5nISBvx2nMkUm3PTWcLkdHxhsjdqNbhv5MAeiZjttYDaV9pS0vl0IegqOFzSDsxWMQbyN8Lqlnk9UAmcb0dsl6ZyBgWekXGunXhya2MKjIGiIzFGWykjITIxf2yjDJGIL75Tbj/fnjxRTG8vfTSxOsrikLlhEp6DvTgPOoseSLD0+ORIm0FrDVhIiNmRQZEEBnjwaDCom/mYzcLg/pT5H2Yiv5cQFGkKuONN0ReauKS8GeqWQYaQX+QYCCIQR1pg/dotO510VfRSNWFK2FhtDTy0+vq+TPf4NaPAncFZTa4tzc8obF4sXS6dVPzo0dhwQL57PXX4dFHwxuz26XDc+21In/1f/8XbWpeXS2dnxE2n6ATGf/6l4SpOs1uwZo1UpUemdAV6Q+YqCKjFKCh0bWvCzQhtG31NvpdyckaJ4Mf/ABaW6Uw6ZOfzNpmRy30hKtIImPA46olsTTkSEdnZzguX/xiWDZ4NKFMZBQJFEWhpqYmoQN8RYUwk4cOSYcoFpExImWl9v2vaK3O/DRMfE/CVZOJY0lB17ONJC4GI1t6tjEwkuKZcaa9dTzUL4XKk1L+7ZEUx4yQpfYcFc+qGTJAdbdI1t3Y+BlhySBuBmOa64EMKlyu1L+Hp1WOSzFA7fzh108DRd82VZu0iUAoWyrSJyDe5G4BUazxXLAAzGbp+Opa1BlVZMy9WciMHBFsxRrHvCKLz/9yPEcPUjrXnraC9S+ziUmTZPLtBz+Ar3xFfDOMw4xwqyZUCZGRwCejVK4bPZvYUm1BMSiJpaVAjL1B+rW5wKEH5Zk97oIBAqFgsZxwsbwKhFmzhMjYtQvOiyAyDKphoCrI6/Riq7Ulvc1SaZc6+tr76GvvQzEoNMyKTgaL9Mc45xyEvKipiZ6VS+Rae9ZZcPLJQnD09EiHuzF0z3K7ZcJE74Rrmmxfr/T46U8xtLZyUn8/hrfflvKZs86SG0pHh1R76ASI1VrU5Mf8+aLcsWOHcDcf/GDq20jGH3BxnSSLxSIySqFd+np9+N1+FFWhZkp2Z36PHIG775a/v/996XNnglKIZ66hV2QcPixqGgZD2C8i2YqMUo2j1+Fl7Q/WUjWhimWfXjZk/zdulPeTTsoviVFM8SwTGUUCVVWZO3fusOvNmSNExo4d4selQ9dfH5FEhmu/vFdOHXbVZONYUrA2Dh1I7vuTGBzPvy2ng8yRFM+MM+0nXCivNDCS4pgxYrXnFDEkns3vh/4ukRDIEKtWyX306NHYPhmKIp8nGlcNhl6NUVMjyWJJQ896r5opcnI5QEm0TaMdjJWg5NeoMx0UazzNZklqfPNNOHhQlmVEZNinyCtHKNY45h1ZuF9COZ6jCUmf60j5vngoIfm+r34Vfvc7eOcdkZn62McSr7/wgwtZeP3CKBPNwSiV60YnMqyhScZhKzLGXwTVs6FqeniZ3y2Vrbbx8lm60DQ4+k/wdkLNyQNERqnEMtvQfTJ0w+9IWGutuFpdeLo9KREZpRZLS7WFs79xNr3He4dIpBw4IPMaJhOccUYaG7fb43esGxpE3wdkFrS3V176BNzy5RiOH2esXumxfz+cEqrgeeMNeOyx8LaMRjEu/9CHhBR56CHp1OtVHjU1MH16wWQxdHmpu+4Sv6BUiYzh/AF11YLX1kg79XQNlWIqhXZptBmpm1ZHwB9ANWZXg+e224Q7O/NMeP/7M99eKcQz19CVZvr74cQJSQbUKzL62vsI+AKopsTnsVTj6Dzm5MSmEziPOlE+M5Q02LBB3pcsGfJRTlFM8SwTGUWCYDDIsWPHmDBhQkIH+Dlz4Omnh3aI9EzzlGVLih0+J3ja5W/71GFXTzaOJQ/nHug7Bp1vQ0Xu2KuRFM9cZNoni5EUx2LAkHgOU6mVClRVSqivuEI67rE69T/9aWoalGnLSuXBH6Mk2qalUV4lgGKO5ymnRJvhTppUuH0ZDsUcx1JEOZ6jB0mf60j5PjXGJGqJyffV1sLXvy4SorffLsoyFQn4/5pJw6cwlsp1o08qWmutaBrDe2SMO2/osgN/FZnOsedkRmT07hUSQ7VCzYKBxQWNpaaBt03+tubXSFInMnbtGvqZtU6IDG+3F40Ync04KJV2qUM1q0w4JbZB3PPPy/upp6aY6JMqDIYw6aBj5cr4sTzvPJkhdDrDklb19fKZxyMd+507pQrE74+u9Pjxj6X8VSc4ampkdru5OezpoctdDVc6lgJ0IuPJJ8WXpbY2+e8mq1qwZY8NRVUgRjJ2KbRLg2qgYkz2E8M2bRICHeBHP8pO8U4pxDPXMBpl7Hz4sBCe48cLMWq0GvF7/LhOuKhuTqyjVqpx1KWzKsfHTrYoFJFRTPEsnbM5whEMBjly5MiwDvB6h2iw4feIlZbqDVVj2MYmlZGcbBxLHg3L5L1jXU5/ZiTFU8+0j9e5UBSZ2IuZaa9pEEhfxmYkxbEYkOt4rl4tyVaxiIevfCWBIXwc6BVzKRt9YxBfiBz6Y5Rk2+xvD5mSFh+KNZ5r1oiGfCRWrZLlKWPP76DlmbDcVw5QrHEsVZTjOXqQ8rlWbaGqt0GvWORGkeMznxH5+2PHJOEgU5TKdWNrsDHpzEmMXTCWri7w+WT52LGJvxeFxlCZf/vrEMhAM74jxJbXL5X+SwgFjeWB++D1j8Hhh/P+07q/5c6d4cQYv9tPv6sfxaCgaRq+Ph997X343cn1a0qlXSaDKFmpAiBuLK1WmTWdNQuWLYNzz4VFob54Q4OUgH3nO/CLX8jN5s47wwPM006DpUtF4srtFhZL15d97TX53n//N9x4I3zhC+HOmcsFf/sb/POf8NJLoh2zb1/sjKoYmDdPXj5fdDFJMkhWtaAzWMPVj1zNe34+NIGs2Nulft0NfiV73cWDpsEtt8j71VfL6c8Gij2e+cJgw2/d4wqS88ko1Ti6Tsg9I17VaCGJjGKJZ7kio8QwJ6ScMpjIKDppqWzp7uqyUvbUfQlGNOqXAb+Dnq1ZMTgeDUiUaa/3PeNm2gf64JVrZHC/4l4wFL/EzahD0CdeOq6DMPXajDe3erUYv7/8snTwH3kEHnwQtm9PfVtpV2TMvRmCn6WccxABbzv07gODEcwNw69fRlK6x0mTc542ydpVFGhckfV9LaOMMgoEz3GRaLRPLUkSA8BigW9/G66/Hr73Pfj4x8Ny+YMRDATZ8cgOnMecLPvUsgHz5VLEuEXjGLdIdKS2bZNldXUSjyHwtEmlTcVEMEWYnFfPkXGZpw0616V/f29/Q94bTk3v+7mA7m3Xk0YHLkPMnCnvXV3Q57dQ0VBBX0cffq9MnhqMBnz9QmRYquRzS3WsE1ea8Hv9bL1/K41zG5mwfEKUrvoQf4xShKKAzSYvHZHa34Nx9tliaOFwhCs9Ij09du+WZU6nBEhV4Ze/lM/vvlsqPSIlrc46SyZ/Ojqgp4f/uKSaW7dV8/e/m/mP/0juEIJB4U2SwYQJSjFbhcSEpVquq679XWhBDXOlGYMxelyVyXX31FPw7LMi4frd72Zjj8uIxOTJsHZttOH35JWTaTy5kYqG3MguFwMGKjJiEBl9feG54HwTGcWEMpFRYtCJjH37RC9ONxIqqoqMbOru6hUZaRgsj2jYJoQNjrs2wZgs0f8jHHqm/ec/H11C29wsJEbcyTy9LStKmcQoVvR3w7bvyjka/265x2QIVQ0PrhYtEiLjn/8U2Ya42tMxoBMZqVdkUG5vgcGGggZ5+fukSsrbEa3zXUYUktU9vuyyJOXSdLmzqlnRxutllFFGaULzg2Mn+Hrkf/dRqJxR2H3KANdcI/N969fDN78JP/tZ7PUUg8I7D76Dr8/HnA/MSUpqqhQwrKxU60uw748w9myYe0t4uaJA45lw+BFoezk9IsMTSjRQlFDCVZFA909zHZC+Q448x2LBZpOJuEOH4FCHndX3rcbrCFd497X3YaowYaqQvp6l2oK9ceQ8Wzt3d7L9oe0caDjAZf97WdRn+/eLZEza/hiliESeHmPGiC4eCLvgckV7epx+uhgF6CTIvn1SLQJS6fGPf3BDF1QD/f+y0vu7M6i84RrZxj/+ETYu19+nTePIUYX/+A947rnEu52OP2CxwN5o5wN//QBPfu5JXK0uTvnkKUxYFj0gS/e68/ulGgPgs58V4+UysovBFRkA866aV5idySN0M/NY0lJbtsgtoqkpN5LopYIykVEkMBgMNDY2Dqs1Nn48VFbKM2nvXpg7VxqyXpFRFB4Z2dTd7U2tIiPZOJY8FEXkpY78QzKnckRkjMR4Ds60Hz9eOmYJJ/H6O+XdXJ/Wb47EOBYSMeNpbRRjyZ53oO0VaL4s/gbSwMknyxji9dfhz3+GL385+e+mJS3ld+Vlorho26apWsgobwcEB8m6WccIiYsCO38CFXdDRTosUfZRbPFMVvf45ZeTzIjUDehz6NsCxRfHUkc5nqMHKZ3rgFv81nR9ftUC9ik53b9cw2CAH/wALrgA/ud/4HOfgxkxeBlFUaiaWEXn7k6cR50xiYxSuW58fT6MNiOKogxv9O0OachYY8x+NK4SIqPjrfSqvXVZqeo5YI6OZ0FjaakXbwxPKzh3Qd3ivP787NkyCbdrF6xcaY+aMK2fnvq4olTaJUDbdvEmGTN3TFQ1BoSrMXLuj5EARRtLg0EIh6qIqqlElR7nnw9LllDncPDSegdte3uYfKSRy0AqPfbsEfKjt1cmjoxGHjz7F3zyU/Cxrh9ykbGLWadU8cwb1Tio5nnO4QiTGEMbdXTj0Kr52ferUQ1WNt27mdYtrSy4bgHjFodvNEUbS8DX68Pn8mGrszHnsjlDTOfTxR//KFVwdXVw661Z2eQAijme+UQsIiMVlGoce4+HiIwYFRkbN8r7kiXZ8WNJBcUUzzKRUSQwGAxMnz58VqmiSFXGunVSUjR3LrS3ix6iohQZK6fr7sbC4ImpeJhylRhbV89KavVk4zgiUH9KmMjQU2uzjJEaz8hM+6SgV2SkmeU/UuNYKMSNZ9PZQmSceDHrRAbADTcIkfH738OXvpT8JZeytFQwIHrO5jpY+E2ZuM8RirZtWhulai+eRKG3E3b/Qt43/jcs+hbYJ+d3H2Og2OKZrO5xUutpWpjIyKFvCxRfHEsd5XiOHiR1roN+OLwG3MdAMQpxXDm9ZCWlBuP88+Gii8T09qtfFSn6WIkrleMrhcg4Fltnu1Sum2e+8gyOQw7O/ea5tLSIMUZcIsMTutnbYqxQNUOWu48LmdGUYvq15hO5qhiyUgWPZc1cITJ6duSdyJg1C555RnwyEiEYCGJQh58cKngsU0D79nYAxswZ2o/ViYxzz83jDg1CKcUyIWw2GWRMnMjJH4Wvfx0MryNERmMj3HabrKdpOFt6+frNffzsahnEdM1ZwRc+28aESgdT5jt46sHDvBqqGjqd17nG/k9WrICTXgDWmjC31tPunkvvnhbY+qxIXVVVYaiuZnp1df5nVpPAobUyCz5+2fiskRi9veGw3n67kBnZxIhpmxkiHpHR7+qnr62P2qm1Cb9finEM+AJoQUkwqRpfNeTzQvljQHHFs/BUShmAGKfs3bs3KeOUwT4Zerbl2LFhqamihLc1eQJDR+NKmPYfSU8gpxLHkkfNAtG4rV+WelyTxKiKZyLoREaamvzlOGYXcePZuBIUAzh3h7JMs4urrpKssV274JVXkv9eyhUZvXukIqO/SzIJc4iibpvWRpGNivUasxxOuQcqp0qcNv439PcUeo+LLp7JJjcktV7fEYm1ag5LdeQIxRbHUkc5nqMHSZ1rdwscf0YKMUzVUoWhBeW5o7+GyPqVFr7/fXl/6CGZKL3uOnmfOlV8gwCqJ1YD4DgamzAvlevG0+UR7fcqcxIVGaEVbDE6JIoiVRkgElGpovkyWPEXmPjeIR8VPJbVc+XdkX+fjNmz5X3Xrtift21v46kvPsUr302uY1nwWCYJTdNo3yFERuPcxkGfFYc/RqnEMhVceaW8P/MM/Pa3EudAQJa99rrC4lVV/OyBsRgMUkXwP5tXMuEz74cPf5jFv/svbun8Gg2nShnb3M+9m2t23slJv/iimA6tXk1gpvT/vK3dcPCgDIgeeADt17+m5847w7H8wQ/ga18T44hf/lLK2fVJq9ZWmcg6dkwYgSQNzdOBpmkcekVmwSevzF7C049+JFJ+06fDZz6Ttc0OYCS2zXQQi8joa+/j4Wse5smbniQYSByfUoyjalJZ/ZfVXPHAFZgrh07u6kTG4sX53S8orniWKzKKBMFgkLa2NqZMmTJsqY5OZOiZHfozoShkpeIh4BETXgBjlZSv5wCpxLHkoZrh1F/n9CdGVTwToT+zioxyHLOLuPE010imXed60YGeek1Wf7eqCq6+Gv7wB6nKSEYrNhgMZ7snTWR0b5b32gVCzOQQJd02zbWw6Duw+Q7JADUPlQbJN4otnqtWia7x0aOxx4kp6R53bZT36pPl+ZNDFFscSx3leI4eJHWu7ZNg+seFmNQC0ZVv/j7wdYOpRqrcTNV52e9sY8+e2MuPHoUrrhCCY+kEyXSMV5FRCteNFtQGPBestdbEHhlBH3hF6idmRQbAxEthwsXDS//Gg2KIOcYreCxrdCJjR86q2ONhVkhUIF5FhslmonNXJ6pZxe/1Y7Qknp4peCyThPOYk35nP6pZpW5adLp6sfhjlEosU8HWrRJXnw8++UlZ1twMK1bAww8LqTFlCtx7b+y+n6qKlO6bb4JmtqBOHAdE3C9at8LmLfQGKsJ6SppGwOFgz9q1LNI0yZReuRLa2sKm5kePgjeUePn66/D44+FtGgxw5plw/fWy/sMPR/t5VFWJxq+ipHz99hzsofdYLwaTgQnLsyNDe+wY/PCH8vd3v5ubROKR2DbTgU5ktLeLyXVFBdgabBhMBoK+IH3tfVSOHSq/pKOU46h7J0XC74fNoWmCQlRkFFM8y0RGCULP7NArMvRs36Iw+h6M3j2AKlnFphox5O3vlLL23b+CGZ+C6pnh9T1t4QGVY5eYENqnhCWqTNXpd67LKCNdDFRk5DY7vowsoOnsEJHxIky5OuuD1Y99TIiMBx8UE9HqYeZ42tul06EoiaQe2qInkk68IBmx1iZw7i3f9xLBVAWLvxdtiu5uBX/siSn5zuiJp6rCPffIxJ0+/tOhXxo//Wkcj6DB7bJ7CwT6wDq23C7LKKOUEOiHfb+HsReE+9xTroSx5wyV7zv8MBx7UjynFtxRktd4IACf/3zsz/Q5sJtugrefFiKj91hv/nYuy/A6vFJZo4hhrZ44EbO/4WmVAKhWMNXG3mC6VaCedkn2KUJZGQAqpsCkD0D1bCHvlPxNgejj9j17pG0Oft7WTKmhoqmCvtY+jm88TvNpxTigTx26rFT9zHoMxugJr+efl/fTTpOJyTKygzVrpL83OHHlyBH4+9/l7w9+EH7xC1GEigfdV2j37qGfWevEO8fT5QkvVBSw2/HVR9w/Enl6XHihsCW6cbnTCQ2hZEGvVwZP+/bJZx4PWCwy6AL49rehs1MGYPrr3e+WGe+WFujoGJC7oqoqLCu1dDwm29CJ4XRw++0yqX766RLvMnIH/VQ6nUJ+zp4tHlf2sXacR5z0tvQmJDJGGnbulEuisjK2/9doQpnIKEFESktpWrgio/iIjGAo20sDezNUzYKASyozvO3QuQHW3ywZ1LM/Cyjw6nXhSWP3MfC7ZRClZ4RZGkQ3vQQHVjmDFhTSxz4FjCND37joUDVTyDf7pELvSRnDYczpki2uWsDfKxPdWcQZZ8g9eMcOuP9++MQnEq+vE81NTZIhNQSetuj7nqaBa7+8e1th9/+U73vDIbI6wHUYnj1biI14Hk2jLJ6rV0v28ec/H2383dwsJMbq1TG+NLhd6tACsPf/wb4/jLo4llFG0SKSdAz4sfiOQG8dqEbpS+/9ozxPOjfA8l+BITT8szYOvX5P+jC0vSJ9dc2f18PIFl5+OfpeNxiaJhMim/dL/8Dd5cbv8WdNOz0fcLW58Dq89Bzuod/Vj7nKTPeBbpyHoA5osFqAQc9Ad4Q/RjKEQ7KG31oQ1t8kz92F3xLZ22KDQYXp/1mQn540SeZhvV5R4pk2LfpzRVGYeOpEdv9zN0ffPFrSRIbeLgEOv36Yflc/tgYbnXs7ASHb7I32opCVGmnQCdxEKk0NDWJQHTN5JQIzQ3x3rMo2W73MNbg7M5AftFhkYNTUNPSzxkYxItTh84HLFf7/wguF6NBJkJ6esHbWm2/CE09Eba46OAnVPIspS+okE00nP/RqjxQrPbZskc0A3H138XK3IwWKIhzVtm0iL6UTw1Xjq3AeceJscUaZzo8EbPnbFtreaWPWpbOGPA90WalFi6SQaTSjdHpsIxwGg4Hm5uakSnRmzJCG29MjEoNJSUsNzqwcjFxkVvp6pIxZMUo2GP2y3NIkhoKNZ0D3VugNZXb2HZVJE4NFPu87JoMtc6NM0Afc8rnPEXdfU4njiMHG/4ae7XDyV6DpzKxuelTGMxamXJ3R18txzC4SxtNYAaf+v5x5SyiKVGV86UsiLzUckaEbfceVlfI5ou97/l5QVDCY5V6ZxH0vE4y4tnl4jcRLUSVdxTQo5WyUxnP1arjsMpngi2V6OwSD2+VgjNI4lirK8RzBGEQ6qprGPJ8P9VWTVKZ520ExwdizYeanwyRGPNjGQd1S6Hwbjv2rYJO/mUCvShgOrd1mLv7FxVSOq4wp51Os142rzcWa69bQ19GHr89Hb0svqlnlwSsfZPl+WAocuLsC19mrsTdGkBn2yTDzU9K/SISAF7Z9F3q2wen/C6Zhsl0du8SjymiXir0YKNZY5gOqKhPDW7dKNu1gIgMYIDKOvXUMTdNQEsyOFmssI9uljoAvwOZ7N7P1b1sBqGio4AN/Xc0LL0i7LDSRUayxTAfDEbggxQovvzx83HUiY+9ekciNDI+tTvqEURUZ5DCWJhPU1ob/X748/rqXXAJnnRUmOJxOptbX03zSTJT2Nri/U3TNnE5wu8FqlbJlgG99C7q6okmOiy6CSZMIHD7G+qfaaHFV8/M/VaNq1bz/ChMrVmT3UCMxktpmpogkMnRUjpfnUm9L4orKUoxj+452Tmw8wZSzpgz5rJBG31Bc8SwTGUUCvVEkA6tVzOr27ZOs4GGlpeJlVkYim5mVpmrZXu8ByWI3mkRvNxK28TD7JkCDvsPRGT/etpAXgSbZPdZ6BnzphzG1TiWOIwbVc4TI6FyXMyKjjMxQjmN2MWw8c2yQ/eEPw1e/Kok/W7fC/Pnx19WJjGE9jFSbTAL4uoT8tdSHKwqGue9lghHXNsdfBNt/IJmk7qNSrWEeE73OKI2nqqYxaaC3y1gYpXEsRZTjOYIxiHRUALMpIPe//i5AkWz0eV+D+iRHvhMuFiLj+LMw9YM598TJNmL6Q8RZr3ZKbdzPi/W68Tq89HX0CfmigcFowGQ3Ya620hsAI36Crj68Dm80kWFtgomXDP8DqkW84QIe6Hgdxl2QeP2ON+S9/pS4RFlRxDLoh553pNqoeagheS4xa5b0F3ftgosvHvp50/wmjFYjni4PnXs6aZgZ35OvKGIZA5Ht0mgb2g78bj99HX3s2erlyBF7wf0xoHhjmQ6SJXCTWW/KFDAaRcLm6FGpKtJhq7dhMBowmAxRpFtRxNJohLo6eUUuBmgeD7fcEl44uNLjoouk0sPpDMtdBYOsWQNPfOJNlnf8C4ArQq/ptpXAh6G7W/SGdfJD10JasECy3wYzQUmiKOJZJIhl+F01PuRx1ZJASpjSjKNOzlSOG5pEsHGjvBeayCgGlImMIkEgEGDXrl3MmjULdbh6P0TaRCcyhpWWyndmpbVRSJF9f5RsrrHnwNTro9eJrACxRpQV+l0y8PKFbkoGKwMkRhJINY4jAvXL4PAjQmRk2cBuVMZzMIIBkVfIwKC+HMfsIul4+vsg2C+m0FlEUxO8732iRfv738NPfhJ/XZ1oTtro21gNVl98/eosY8S1TYNRnik+l1QF9u4He1CqW/KAERdPHc5dIitVMTk+sZFFjNg4FgjleI4ChEhHLdCPr2sHJkMAxWCSqmbVNLQ6LREaloN1jPgetK+Fsefmbr9zgFWrZEx09GhsmRVFkc9jGd1GotivG6PNiGpWsTfZMVWY0IxmfMioyWjMUBascZUkpLW+nASR8aa8jzkt7ipFEcugFzaFzImbzgJzCtdEhtDlUOIZfqsmlfGnjOfw2sMcffNoQiKjKGKZAEabEbM9Nvnp9/p5I8R7FYM/RrHHMhWkQuAOB6MRTjpJPDJ2744mMqx1Vq5ac9WQqqFijKWzxTkw4T0ESVR66J4jaO/j75xHDT1U46AKJ9331vGd98PqM/uht1cy1xwO+Tuy0uOb3xSyQyc6qquFzZw0SR5S7e3RpuYWmW8oxngWClNChQkxKzKOJ67IKLU4BgNBXK1CsA1uu5pW+IqMYopnmcgoEmiaRk9PD1oiYcMIzJkjEoCRREbSGb+xkO3MSmujECRGu2ToVE1P7nuqFazjpIIj6E9Z3z7VOI4I1JwscevvAeeeaPP0DDEq4zkYvXth/RelJH/5L9PaRDmO2UVS8Tz6OOz9PYx/l8hpZBkf+5h0cP/8Z/je9wb6nUMwrLTUYJiqw55AecDIbJsKVDSD1wqeEzIZo2lxJS+yiZEZT8DXK4RunsSAR2wcC4RyPEcPNIORAEZMBhWqZgDK0Kro4aAYpLpt/19CCUmlRWSoqswhXXFFWPpch34L++lPZb32ne3se2YfleMqOfmKk6O2UwrXjaXagqVaOiBd3bLMHK+Apv0NSeyonCYV74nQtAr23wtdG2V8EW/S330cXIekzdQtjbu5ooil0S59edchcOxISLxkG7NmyfuuXfHXmXympB2PmT0m/koUSSyHgeOIA7/HT0VTBdbqaJ+V10NERqFlpaA0YpksskXg6pgxI0xknHde5HZi9wOLLZaOIw4e//Tj1E2v48IfX4hiSK3/Gu05YsBJNU7C4zNFgZtugsv2N6F+4QvRX+wLy6txySWi6aV7ejgcUqUB8MYb8NRT0T+8ciV8+MNonZ1Y//AHKfuvqwuTIEuWyI97vXKzHwUmHXpFxsGD4WW1U2qZ8Z4ZCasqofja5XDoa+9DC2gYTAZsDdEJ6IcOifqZyQTz5hVm/4opnmUio0ShZ3a88Ua4Km5YIiPf6AvRpvbJyX9HUcHaIMbV/l4wDqPLWoZkINcvgbbXRAogi0RGGYQl2WJVM5VRvLCNF4+etrUw/ePD64KniAsvlHvu0aPw2GNw1VWx10taWqqM7MM+RSZX3C0is2KJn+FYRgJogbDpbzLGr2WUUUYBoeAxTsRSUyMVGX7X8F+JhfHvhs71MOGirFf75gOrV8NDD8lEVKRufHOzkBirV8v/fW197H1qLw2zG4YQGaUGbygnLSaRoQXhne9JkthpvwPbMMS+bbwkoTn3Qvtr0g5iQa/GqJk3vJdGMaB6bojI2J5XImO4igwQIkMnM0oZwUCQ3uO9aEENe9PQBMo3iojIGElIhcBNBjNnwr/+FdvwuxRwaK3MQ1nrrCmTGDC854imweHDMTxHVFUqK3QsWxZ/I+9/P1xwQVjKyuGA+pA8ss8nhMehQ/DOO/K5xQJLQ4Txd74ztJrjfe+T8oXDh+HECZG50l/xMu5KALGkpSrGVLD80wn8UkoUuqyUfax9CGmoV2OcfHKChIVRhDKRUaKYM0fe3wz1H+vqwJ6M2kPAFTZkS6XMPFUE/QxIQlWk0SlT1Nzu30hD/bIQkbEOpl5T6L0ZWegPERnlSdDSQu0iySDs74HuTVIZlkWoKnzkI/Dtb4u8VDwiIyVpKS0g1XEGk5izlpE5KibJ88RcK94j5M7XYcQi2C/vipFUpB7LKKOMAkFRQ9drBjDXwZLvZ2d/CoTVq+Gyy+DZZ0XJQ9Nk0kmXqQComhjS2T6WWGe7GBHwBuinH5PdhIKCN+S9a441X+XtkLGZQQVL4oz/ATSuEiKj7eUEREZoVjqPpEBGqJkLLU+Jt2AeoRMZR45IAmJSY/YShbvTjRbUMFYYMVdHz7Z5vXD8uEzCFdofYyQiWQI3GeiG37t3D/1s29+3cfSto8y5bE7Rkm+H1x4GYPLK9PYvm54jcWEwhCstBqOpiRNXXsmkZctE60vTxLREx+rV0NkZTYLoLNW6dfDkk9HbW7UKPvhBSel/+OFokqOuLlw2VoSJCzqRcfhw2pYjJQNdKiuWJFqhZaWKDWUio0hgMBiYNm1a0g7wOpERCMh70tm+Pif0HREj2VwSBQYjnPZbMYpLNYMz4E5teeTPphjHEQN9kta5K3EJeIoYtfGMhDdzIqMcx+wiqXgaVGg8UySmWl/KOpEB8J//KUTGM89IuWvk5IiOpCsyAm7wO8TTQa2Aqpnh5TnEiG2bkXEz1UjH3O+S5cEMtcMTYMTFM+CWagzNLwSbnt1dbpclhXI8Rw8UBSoqKoptHqJgUFWpoJw3T8yWN28eRGSEJgv6nf14nV4sVWEWoNivG1e7C2+3l8pxldRMrklckeEOzbZZx0r/KBk0rRK/w+4t4l9orhu6zpRrwX4SNCQmMoomltVz5d25W6p2h5PYyhLq66GhQRRmdu+GxYtjr6dpGs6jTroPdMedIC6aWMaApmm426V/UDmuEoXoG1FviC8sBn8MKO5YpgudwH35ZZlkHz9e5rBTlbOfMUPeYxEZvcd76djRgWOZY2BZMcXSecxJ9/5uFFVh4mnplcRn03MkHQyJp6KALUIdYtGi+F9+//vFvLynR/w5enrClR5er/x/6JC8ezzCrP74x/L5XXcJ26pXeVRXy0N0wgQZ1HZ1hcmXqqq8sAoTJsjh9/fDb34Dc+dKmw56fThbnFiqLDGrv6C42mUyCPgCmKvMMY2+i4HIKKZ4lomMIoHBYKCpKXkz0sZGIU+7uuT/pM3jgz55D/SHMn9zXGaWColhqpbJYm9HfM8OS0NC/fhU4zhiYGmAGZ+Qyc8UfUUSYdTGMxI6kWGuT3sT5ThmF0nHs+lsITLaX4PAjaBmtw5z2jQ491x4/nn44x/hjjuiP/f5oLVV/o5bkRF53/M5QhmT/mhN82Hue5lgxLXN4Z4jAY981vpC8t5NKWDExDNmuwyU22WJohzPUYAQuagAFgPg90ctTxs+Jxz/t2TxN52Z2bYKiKVLhchYvx7e+97wcqPViK3BhrvDjfOYE8vsaCKjaK8bTTLfCYLBbKDf1Y+3F0yAxRCDrPccl3drCrNu1iaY+B6omBJ/vFg7X17DoGhiaRsvzy2fA3r3QfXsvP307Nnw6qvikxGPyHAec/L4px/HYDIwYdkEjNahUzVFE8sY8Ll8+D1+VIuK0WKk39U/8Jnf7ccZ8uUtFlmpYo5lJlDVzGOsV2Ts3Ts0C95aJ/M77s7w86WYYqnLSo1dODaKnE4Fq1bJ3H9nZ+zPU/UcSRUZxVMnPWw2GDcu+rNx4+CLXwz/7/WGdeoB3vWucKWHwwFtbeHM6Vdfley9yN855xy45hr5ziOPhEmO6mqp9jg5JNmYQaXHP/4h7S8QgM98RpY1N8PtF2+ksmUPJ191Mos+FJvYKaZ2mQxmv3c2s987m2AgOOSzYiEyiiWeZSKjSBAIBNi6dSvz589PygFeUaQCTNeZ1C/uhF8NuMV3QgtNkvUdlcycHGdWJg1rI6y4TzqX8WCqlvXiINU4jig0v3f4dVLEqI6njixIS5XjmF0kHc/qOXK/8LRB51vQuDLr+3LDDUJk/O//wm23RXf09XJjk0ky8WIi8r539B9w5P+g6Sw46UPhdYa572WCEdc2h3uOHPuXxPjwo2CsgilxNMHSxIiJZ2QcjzwGR/9ZbpcljHI8RzAGkbeapuH2eLBZrWF95UxIxxMvwN7fQ+VUeYaWaKnH0qXw5z8LkTEYVROqBoiMSKPlYr1uLNUWjFYjwf4gikEh6A/i6fYQcIENMBmgoqFiwAQcCFdk2MbF3GZczPx0Vva5aGKpKCIv1f6GyEvlkciYNUvmARP5ZFRNqMI+zo7ruIuWDS1MOmPSkHWKJpaDYKm2EPAGRFbKasTrGJpM0t5XgRdL0RAZxRrLYsCUKaJo5PWKTJUu7wNgq5fKAE9XWOqomGI5ICuVgezVtm3Q2xv7s3Q8R1JF3uJpsUT7Z6xYEX/dyy6TDD5dysrphDGhZ6bXKxnWBw/KZ243VFbC3XfL59/4hhAmNTXhao6LLhpa6VFVJa/QMa9ZI74vg72ljx6Fn/y/Sm44BSYfj3OiKK52mQoManTFQ3t7WC4uUTFOrlFM8SwTGUUCTdNwu91JO8CvWQNbtoT/f+IJmDpVTJ6G6B9GDnL8vWFZjf4uUEM3rmxnVm6/G7xtMPVDUDsv+e9ZGzOaGEk1jmUkRjmegDeUimFOn8goxzG7SDqeiiKTr4ceFnmpHBAZH/gA1NZKv+3f/5ZEFh26rNSECcNU3ur3PYNJ/IuqZ+ekWiAWRmTbTPQcmf1f8tn+v8D+e8UANYu+QiMqnnoc7ZOhahrULii3yxJFOZ4jGIPI20DAz84tW1iwYAFGNTTMy4R0HHce7P8j9B4Axw6ZBE4HnraMEpUyhe6PGpPImFhF65bWIT4ZxXrd2BvtzL1iLsbHjUxaMYnFH10MwBWXw8ZN8KuvwnuvsGBvjJDaGCAysqSDEvTD/j+LR1/tfFASy0wUVSynXCMv+0l5/VndJ2PXrvjrKIrCxFMnsuv/dnH0zaMxiYyiimUE3J1uGmbLWOmC718QTaQBBw/AzRdY8JvtnH56AXYwBoo1lsUAoxFOOkmkpfbsGURk1AmREVmRUSyx7D3eS9feLhSDQvPpyUqWRKO7Gy6/XKSMFi0SSbhMPUdSRbHEMwp6Zl6s7Lzx4+GWW8L/+3zQ1xf+/+KLJZB6pUdHR7jSY+1aMbOKxLnnErjyGu74rw4+pq3BSRUOqnFQTTe1bNPm46SKd7YGOfnM+ERGUcYxDWzcKO8zZsS2VMkXiimeZSKjBJGImbziCjF5irqxRg5yNt0KnpDWibkWlvxQ/s72AKJnmwxaSjRzq2TRuV5kdMa9G6pnFnpvRgbql0jlknVsofekjHQw9nzxSGjMTe2vzQbXXw+//CX87nfxiYykMECaxdChLiN7mHK1TLrs+zMc+CsQFJ3v8vMqNiZfIa8yyiijOBFJ3vr9eE1dUDldZqIyhdEuMo0tz8CxJ9IjMjxt8Op1YanOWLA0yFglR2SGLuVz5IhIPkYqI1RNEElWT7dn6BeLEJqm0bq5FbPdzOzLZlM/XaRP93VDFzB1CdgHh3HAIyMNIqO/B9rWyrixMZSt69gOhx8R2bEV96Z5JAVC1YyC/KzupZuoIgMYIDKOvXUMTdPClVVFjprJNZz2udPw9HgYv2RoO1vzPPQBq4rEH6OM4TFzphAZu3fDeeeFl+sVGe6uIlH1iICt3saqr6/CcdgxhExLBsEgfOQjYfLm3/+WhLVMPUdGHUwmqb7QkYi9/MAH4Pzzw5UeDgc0NvLyy9De0k8lvUzgGNU4qKQXJ1V8iR/hpJJLvA8xfo0Pal4NS1pdcokMvo8ehbY2zC0tUvFRV5edflGO4HV4efqWp6maUMXZd5wdde8vBlmpYkPxnskyYiIQgM9/fiiJAWHpuZtuksqvqBustVH0bTWfDEpA/DIsDdIxzSb8fTJoAahIv6SvjDRw/N+SeW6qLhMZ2cKMTxR6D8rIBPZJ8sohPvYxITIefVQSTPRElaNH5T1pIqM/RGRkIGNWRpKYfCVgECPTA38LVWZcXyYzyiijjDIGY/zFQmS0vQLTbwBzzfDfiYTPISSGwQKqbejnAXfYjydHREZVlUwk79olEwIXXhj+bMaFM5hx0QxMtvwYP2eK9h3tuDvdGG1Gxi0WqShNg+MhG4zBkugATP8ouA6nN4nf9jLs/g1UzwoTGe0hbeOG5cNWY5QhiKzISCQX3zSvCZPdhLfHS8eujii5s2KGyWZi1qWz4n7+wgvyXiyyUmUMD90nY7Dh94C0VKen6Mg21azSfFoznJbe93/wA3jsMTCb4eGHw2O6crvNIYxGMSTRDclDaHkbjjOen/KFgWUGAlQglR69VPI2pxAw93LytFmYfG4hQfSJ0rVrMTzzDBPb2jA88YTII5x3Hlx9tWg1rVkTbVxeUwMLF8p3/f68kx7OFie9Lb0EfcEh11SZyBiKMpFRSESUWauaxsmTLKh9B8I9mxhVEi+/HF3aNhiaBocPy3pDbrj+XjH5BjFw87RC716oPyUrhzOAPtElxFIPpsrsbnsYqKrKnDlzCq7ZVjA0LBMio2NdtJZ5mhj18cwSynHMLlKKZx7kLJYskdeGDfCXvwjZDOGKjIkTk9yQTmRkYCyfKkZ125x8OSiq6L/3bAPHbjDEiUOidpLGs7yMxBjV7TIHKMdz9CAn57p6JlTNBOduOP6s3DvT2jlbKJlKAy0ASsQwNDhUTz/bWLpUJpHXr48mMkwVsQmMYr1ujr4pWRITT5uIapJ96+4WiXKIQ2TULZZXOmhcCXt+C45d4D4hY8iON+WzhlOT2kTRxbLjLalgH7NCxk55wPTp0i3o6ZGqoLFxCr0NRgPjl47n0MuHOPrG0SFERtHFMgloWnESGaUYy3xiRoj33LMnerm11orBaMBaZ8Xv8WOymUZELP/9b7j1Vvn7F7+AZfm5NcTESIhnphgfo4AwiEovUkUZwMg25jO2yY1jxbtpmDkoEXD1apQLLsB65IgQA04nNIbGYz6feHYcPy7kR2+vkBk/DKnV3HGHrF9VFSY6LrtMBvUHDwoREkmC2GwZJ8P1hrw+7OPsQz7TiQy9urRQKKZ2WSYyCoVBZdYKMETuLEaZtW4eOxxirqdWwPJfQn83HH9aiAxnDogM1yF5L0A1hqIo1NbW5v13iwZ1S+Um2rtPZGosmU2Ijvp4BvqBIKjWjDYz6uOYZSQdz8j7rM8pZK61SSavdWRJzuJjH4P/+i/4/e/hc5+TyzDliozm98u+WpuGXTVbGPVtc9L7xStq+93Q8lT89eK1kzSf5SUDnxPW/RdYGmHx9+MTPVnGqG+XWUY5nqMHOTvXEy6Gnbuh5UmYtDr9AXugT5KoAh6omSdjkzxh6VK4//7YPhmxUKzXzcIPLmT8kvGYK80Dy/RqjNpasGbWZR0Kc514JHVtluqMhtNEqspghPqlSW2i6GLZtUmqjAzmvBEZVqv4We7fL/JS8YgMEHmpQy8f4vjG4yz6cLSza7HF0u/x88KdLzDt/GlMPXfqEJNagL17pU9sNidWmMk3ii2WxYZ4FRmqWeWqNVdFZY0XQyz3P78fxxEHJ513EtUTUzMSOHwYrrlGpKU++lG44YYc7WSSKIZ4FhqrVokfydGjsdVoFAWUykoa6t30tvQOJTKMRpT6emrqY8yHjR8PXwhXehAMikG5jtWrRY5Kl7pyOsOGl2+8IaxXJM4+G667TljqBx4QgkM3Nq+tDRt1eb1yI4zRh+ptESKjclx0IrjLFZYkLHRFRjG1y3ItaKEQWWZtqiVorMHhMRA01oCpVpbrZdYRiMVMxkLM9QyqmHbWLRTtXJDBRLbhOijv9inZ3/Yw8Pv9vPXWW/j9/rz/dlHAXCNZcwCdb2e8uVEfz/a18PKVsOWbGW1m1Mcxy0g6npH32YAHgv3SEzLVJrzPpoPrrgOLBbZsgXXrZFnKHhmTPgAzbgBTVcb7kyzKbROomiXVMKHnMSjhNjJcO0nzWV4y8HYIKe5uyRuJAeV2mW2U4zl6kLNz3XRWqI85A/yu9LahBaBnO/jd8iz2tmd3H4dBIsPvLfdt4bmvP0fHrrCPR7FeNwbVwNiFY6mbFvbTSigr1XtAqrX7EpT0J4KnTcaNfhcc+Qcc+T/529YMfUfDcsIJUHSxrJ4j7z3b8/qzuk9GIsNvgAnLJ3DW7WdxwfcuGPJZscVy/3P7advaxra/b0MxxCY4n39e3k8rMn+MYotlsUGvyNi7V+Z5IzFY+qYYYrn78d288/d3OL7xeErf83rhyislyX7JEpELLrRaVjHEs9BQVbjnHvl78PnQ/3//F6ez5GOLo56HkUg6jgYD2CMqIU45BS64QAiNj3wEPvvZ8ATrlVfCT34Cd94JX/wifPzjcMYZ8lkgIDve0gJvvinyVQ88EN7uN74hmY9f/Sp897vS2EITBr53dlPXvY8GrUMe6n19oGls3ixdpnHj4jzj84hiapflioxCQy+z1jQCSsi/Qr8yY5RZJ8NMNjfLeglRf4pomlafnPkxDMYAkVEYf4xAIFCQ3y0a1C+T8u/OdTD+XcOvPwxGdTx1Y8osSKSN6jjmACnFU7WBbZxUiwX6wj5BkDU5i7o6uOIK+OtfpSpj+fI0pKUKhHLbDEG1QX8HuI+DzQfmkJyDBgS84D4GVdPD6/fulwmigBeUkKGgohDAOuyzvGTgDU1QWfKv0V1ul9lFOZ6jBzk516oFTvsDqObh140HRYWKiVIRHvBAf1deq7f1TMZ9+0SKKTKpsH1nOyc2naD7QDcNs8JZnaVy3eiV+DEnOdpfgwP3yZhg9udS27BedehpBdcBSZI6+g8hpVyHJOEnyarDooqlTmS49ktbzLDyOlnMng1PPTW84bfZbmbi8vidx2KJpaZp7Pw/OZhZ750V1yuhGGWldBRLLIsRU6aITYDXK9Lmk4e5XRcylq42Fx07O0CBSWek5o14882SZF9XJ74YthhWToVAuW0Kj/DQQyLbHCmv39wMP/0prF590rDbyHocFUUY2YqKoQ/d8ePhM58J/69p0N8f/v+qq6QD4nCIzmBEpYe68S1mHHqDsc9tg+3/J+ufcw4buq6liRPcUfcA/LE6LGlVWxvWP+vrk7I/Q+7rFIqlXZaJjGJBsB9DsA+oQcQpYkNnJq+4Qq6hSDJD7zv89KeDjL51dLwlkkO1C6Fmbu6IBnOdSBqVjb4Lg/plMmDp2gBBv5R+l5EedCIjj54FZeQI5gbx7/E5pTLDkMFkTBx87GNCZPztb3D33SlKS/V3S1WAZYx4KpRRGOiTGe4T8gLQ/HIv3fcnaIrIEth6F/QelCxXgxEUIwpg0uqB2jzveI6gZ0wXgMgoo4wyigzpkBiOnUIC67COE/nEzg1CAqdb3ZEG6utF2ufAAdi4MXpStWpCFcfXH8d5zJm3/UkVmqbx7FeepX5GPfOvmY+l2jLwWcKKDHeI5bAlWdYfCb3qUK2Q8Z0vFB/FKNvT/Dk3as8JrGNkfz1tkvxVtzAvP5tsRUapoOXtFpxHnZjsJqa/a3rMdSL9Mc49N3/7VkbmMBph2jRpr7t3RxMZO/+xk4MvHWTaBdOYceGMwu1kCIdfFY/WxpMbB8zIk8G998KvfiXzaH/5C5w0/Lx4GXnG6tViT3H11UI0XX65FDkUgU3D8FAUkWzQkUAbal/tcvrnzeKCr54GY4xCctTXs+HbYCDIhMlGOHFCLkanU4gUnci46y4hSHRPj5oaCdTEiZK9EenpUVMj3y102VGGKM9wFgWCKN2bsPl8oDVJpUQCDM9Mxvli++vQ8jRMvV6IjFxh7s3yHqtkpIzco2qGlP8b7ZJNm87ApQxBf4jIsDQkXq+M4ofBJBn3/j4IuHNCZJx9tnT49+2DP/1Jki0gSSKj/TXY9SsYcxrM/3rW962MJGEJPYP7jiGlGEAw1FMeTDCZG8DiFBLDYJHvBX0YgyUqIxULOpFhLRMZZZRRRgh9R8BzIrHHnqbB4Ydh/71grAAtKM9eHcYKqchwt4C5Nue7rGPJEiEy1q8fSmQARU1kdO7ppH17O137ulj0H9G+CTqREVNa2BP60JrBeEC1CQkV8Mhz0toU6lO5SrfqsHpuiMjYnjciY/ZseR+uIgMg6A+y+a+baVnfwgXfvSCuKX0hseOxHQBMf/d0jNbY00p79kiFcrH5Y5SRHGbMECJjzx44//zw8r72Pjp2dAwxoy8UDq8VImPSyuSrMTZvhk9+Uv6+7TZ4z3tysWdlZAOqChdfLESGwxEmMYKBID0He3C1uWg+rbmwO5kBgoEgtsYKAv4g9vnToCpMfmzcCMcZT/8Nn4ErIr7k84X/vu66cKWH/jKFnhlvvQXPPRf9g+ecA9dem6OjyQ/KREZRwAAGEyajFsoUHr6jojOTL78s5cTjx4ucVEJm0jtoUtbTCt1bJdMyFx24ArB8qqqycOFC1JKgaHMERYHlv8pKVveoj6e3U97NmREZoz6OWUba8VStISLDA6aarO+XwQD/+Z/w9a+LBCZIlWek5GZc6G0tz6RZuW3GgHlMWFYKZKLG1w2zboxeb+mPwLkXXrky5Idhgq6NmI0aChqJqitLBgMVGfnNtC23y+yiHM/Rg5yf667NsOlWyc4//Q+xq377u2H73dC1Uf6vmikVbr7u8KS3YpBKN18PGG3y7MtDNeLSpfDII0N9MnQiw3E0TEQX23WjZxxPWD4BoyU67gmlpTKpyIiEpUHOu5L69EGxxRIQeanWl/Lqk6FXZOzdC36/ZLzHg8Fo4PCrh+k91kvLhhYmr5R0+GKJZffBbk5sPAEKzLp0Vtz19GqM008vHskeHcUSy2JGPMNvverB3SkEdSFj2dfeR/t26a9OWhGfyAgEwnNnVVWSFOx2w0UXwe2352tvk0O5bQ7F/PnyvnVreJnf7efJzz8JwJUPXjmEUC2VOBpUAxfefeGQ5T6f+G9CjGIOU8Sc8cIEc7lXXw3vf380yRHLAD0JFFM8y0RGscBggaAv1MFPZtZLSIuUtCYHsstDDffEC5IpNfbs7BEZWnDYipJcw2zOfqZ1ySGLg8FRHc8sVmSM6jjmAGnF0xCSDYrMCs0ymprkvS1kLeDxiJTFPfckqJYDkZWCgsiYldtmlmAwQ+08MNgK/hzMGgooLVVul9lFOZ6jBzk915ZG8broOwKHHoaGZdGfuw7Cvv8VMkM1w4xPwbgL5F7ii6hWC/bLOtbQQ9NUnRdponiG39UTpd/sOu5C07QBrf9iuW40TePQK4cABia0IxFXWsrvljiDeIVlBENGz7ZiieUAdHWCQP7kzZqbZTLf7Yb9+8OTxPEw8dSJ7Hx0J0ffPBp13pONpavNhdcRv2LGUm3B3pjcvMNg7HxMykomrZiEvSn+NorZHwOKsF3q8LRF3zMHI0/3zLhERl2IyOgKj6kKFcvDrwnJO2buGCoaYrvJr1kzVM0EoLFRJKWKYF52CIq2bRYIJ4esfVtaoKMDGhrAXGnGXGWm39lP7/FeaqfWDvlescYxmfvzvuN2vF4h3jKSPbNYpLE3Zn7PKJZ4lomMQmNgQk3B1+/B1N8jpEYuJtoG9P5Dk7K6aalzb/Z+Y+/voG0tTLkWJlyUve0miUAgwLp161i2bBnGRGkuowVBvxjyqZbh142BUR1PLRhRkZHZ5PKojmMOkHI8I++nWjCsy53l++yaNeES5UgcPSq+Rg89lIDMKBCRUW6bEYjXHpJpJ6F1NE3D2XOcqqpqmQjLIWmWF5jrwTY2PNmYJ5TbZXZRjufoQU7PtacNXv8wOHaLLNT6z4MtpJ2oafIcC/RB7RKongUnfwXsoexYa2NReCjoRMaOHeByhSsmKxorMBgNBPoD9LX3YW+0F9V1072/G9dxF6pZZcKyoXqVcaWldFkpU5VIzhYIxRTLAVROg5X3SWzyBINBqjI2bRK5nmSJjGNvHUMLaigGJelYutpcrLluDX0dfXHXqWioYPV9q9MiMyavmkxfex+zL5sdd51If4xiJDKKsl2C3GtfvS48dxMLlgZYcV/O76szQvYXe/ZEL9crMjxdHqCwsQz6gpirzHFlpdaskXFYLNXztjZ48cVhks0KgKJtmwVEVZUY0B88CNu2wVlnyfLKcZV0OjtxHnMOITKKNY7J3p/9l60G7CxenBcf72FRTPEsnrM52mCqlgeQt0OqMDQfBgJSYq2b6WWzzDrQHzZo0zMrK0NEhvuoSK3oJqeZwHVQJn/LBtOFx/6/wpFHYdpHYWJZ9DFlBH0w7nxpz+a6Qu9NGelg8H0WwpMqvm55z9J9NhCQTJ9YnWRNE8W3m24SScCYWT8D0lJlY/m8I1Y7GYx47WTQdxVNQw32ofiCYXnFPEmm5ARzv1joPSijjDKKBbrxs6UR/L3STzLYwskynjYZbzSuhHlfTd4cPOAJ+QzlXo5v3DiZ7G9pkcnkFStkuUE1YB9np9/Zj6fbk3aWeq5waK1UY4xfNj6mF0HcioxsyUqNRCiGvJIYOnQiY+dOuOSSxOs2zm3EZDfR7+ynfUc7jScnP2ntdXjp6+jDaDFitA1tM363n76OPrwOb1rtffyS8Yxfkrhdlf0x0oR+rzVYxItmMAJu+dznyDmRoZNte/dCMBieTLXWybyRLi1VSMxdPZfZ75tNMBAc8lmi8RkkMT4bqSiSip9UMX9+DCJjfCWduzvpPd5b2J1LAYPvz85jTrkXj7Vjq7MN3J93vS1qPQk8wkctyrPNhYK1UVj00A0kePxFXFt+SdXE01F0s+xs3kB0iRzVHM7IMdfKpJm3E3r3Z8cA3CUdbSqGlj2XkWeoZhkcdq4rExnpQLXA7M8Vei/KyASD7rMxkaX77MsvDy1XjoSmweHDsl7MrLT+QRVzZeQPmbSTQd8N+H10vfZDKip7UOZ9VZ6zRToYKKOMMspIC6YqIWj7uyHgDBPwVdPEBPykDyZHYmgabP8RdLwOS34oGfJ5wNKl8PjjIi+lExkAF99zMaq5+GayhpOV6u+H9pAK4BAio2YezL8NDBkeVyYVi6UAPeMkD9ANv3ftGn5dg9HAhGUTOPjiQY6+eTQlIkOH0WbEbI99Pfq9/pS3lwqef17ei9EfoySg2uJXUsVLvMkyJk8WKX6vV8YxU6bIcr0iw+/24/f4Cz6raDAaMBiHpqxnPD4biSiiip9UMX++PL8jfTKqxgsh7WxxFmiv0od+f9Y0DS2gYbSE79d+r59t78h6ZSJjKMpERiERUWateR0EDTbRjNUln7KJSFmpyI5a5XQhMpx7MicyfM6wDqu9TGQUHPXLYN+foXuTZMglmxlXRhkjCXmSs9CNNtNaL+iH/h75u1yRURhk0k4iv+v3Ywz2Consd0LDKdnbxzLKKKOMYoF1rPT53SdCyUuKmECnIl+kKKD5pI/atrYgREYkipHEAJFNmbBsAsfeOsaE5UNlpVpb5d1oFM3wKJhrYMyp6f94JhWLpYC+o7DrlyI3uuyevPykbvi9c2dy6088deIAkbH4I4vT/t2ufV0YzAaqJ1YPeMCkg8OvHqZzbyezLpk1MJkdD8UsK1US0AJAECicpozRKNr8u3ZJhY1OZJhsJsxVZow2I/29/ZhrCzPP4DjioGpiVdw2ndH4bKSiiCp+UsW8efIeSWRUjq8EoLeldCoyBsPvEVLZaIment9eJjLiokxkFAnUqqlUz/soij22tl/GqJoFy38p2vCRqJwOHW9BbxZ8MlwH5d3alB2ZqjSgqirLli1DHVW1gXFgnxoefPRshfqlKW9iVMfT3ydl51loy6M6jjlARvHc/1foeAOmXgdjslfnPkSXOpX1tCBM/6iQysb8yhyU22Z2oaoqTbMuQDn2f9C9GcaeU+hdSh8db8GuX0DdUpjz+bz+dLldZhfleI4e5O1cm2rAWCETHgE3qLENVofFmJXQ9poQGVM/mJes+HiG34NRLNeNalY55eOnsPSGpTEn63RZqbFjc6ChnaXK1mKJ5RCYqqF7i/ztc+SFkEmlIgNg/CnjsdXbaJjVQMAXQDWmF0tLtYWufV14e7zUT08vaUbTNLY+sJXufd0YrUbmXTkvwbrFT2TkpV2mKuET9EPXJiGJg4egenbe/fMGY+ZMaa+7d8P554eXr/7r6oF7kqZpOY/lYHNkr8PL07c8ja3exjnfOAd7k32ITFpG47MCIi9tU6/48bbJe+RzPE8VP6li/nx537o1XEiXqCKjaJ89EQgGggS8AYAoGUCvF5y9Is2nG50XGsUUzzKRUSww1+KdeC22XNVdqubYVRJVIQenrBAZIVkp+5TMt5UB+vv7cxfHUoK3Xc6F6xAc+5cMOiORpNzJqI3n4Yfh4N+h+TKYcUPGmxu1ccwR0o6nt02k9HoPZJXIWLUKmpvF2DuWDquiyOerVsX4smqGSYVzmSu3zeyiv/JkrPyfDETzKFeRdXhahVwLuAry8+V2mV2U4zl6kLdzXT1XMoYNGWTiNiwHg0ky4/sO5WUMoRMZ27bJRIElZPHhPOZk3a/XEQwEOf/bMltXTNfNcBnHQ2SlAI78Q3zeGpaln5iTpcrWYorlAExV4p3mOgw9OzKrXkkSekXGsWPgdIqBbSKY7WYu++NlURPGycSyfWc7vj4f1lo576pFRTEo+Fw+Wre1UjmuMuV9b9vWRve+blSzyoyLZiRcd/duaZvF7o+R03aZrITPGX8FvwNOPAcnXhRvG3+veI76HEJkaIHM77dpQjf83r07evnge1IuYxnLHNnr8NLX1odqUXnkQ4/ENK/PaHxWYOTlnqmPiw1GqFkgz+Mixpw5Qth3dsKJE/Lcq55UzaKPLKJqQuybaVE+eyLQ39sPmtyjVVOYIHCHlBvnzxd5t2JBscSzCLzPh8df/vIX6uvrWbx48cDrtNNOIxAQ5qqlpYVLLrmERYsWsWDBAn79618XeI9TRyAQYPPmzQPHlDfUnAwLbocFd2a+rb5QRUYBZaUKFsdig95xOng/dL4NO34Cr1wZ/Xr1OlkvAUZ1PPVOZ1aMoEdxHHOAjOJZ0Szv7mNZ3SdVhXtCqgSD5xr0/3/60+Izkiu3zewiEAiw+YAfTVGFCPAcL/QupQ9vSHTdMibvP11ul9lFOZ6jB3k914qa+aSasSJcMdz6Sub7lAQmTRIJJr8/Wp5CNasc33Cctq1tBP3BorhuXG0ujm86HtPEVkdco++gH/b+P3jn+wX3siiGWMZF9Rx5d2zPy8/V1kJTk/w9eGI4HiInjIeLZb+rnzd+/gav/eg1XK0utIDM3lqqLIxdOBZzlRktoNFzsAdXq2sgEzgZ7HhsBwAnnX8SlipL3PUCAfjNb+TvuXOFzChG5LxdRkr4mGqHvgwWGYuvvxne/oIQj3plkLlWFDXsU2WyuWs99B3OzX4OA93we8+e+OvkOpaR5sjWWivWWitBfxCD0YB9rB2jxThgXh+JyPHZYJTHZwiRBvK8yEZic45hs4WJNf35bamycPLlJzPpjKHKNkX97AnB65Q2a66KvlG6Q5xdMclKFVM8S4LI8Pv9vOc972Hjxo0DrzfeeGOgpOXyyy/nuuuuY9OmTbz66qv88Y9/5IknnijwXqcONdgLPduEhc82jj0FBx+QjJNImKokGyobuuy2CVA7D6pmZr6tMjKD3nEy1YaYdQ0MtuiOk659WEZs9HfKu/6AL2NkwBbSl+5L4PyWJlavhocegokTo5c3N8vy1fGKLjyt4NwrPkNllDw0g0UGnyBVGaUKb4joLgCRUUYZZRQxAm7xFBj8SneyvHGlvLevzd4+JoCixJaXsjXYUM0qWlDD1VqYSrTB2PfsPp7/+vO89uPX4q6jExlDpFE8JyQFWbVK37+M2KgJaXY4duTtJ1P1yQCpxOjc04nP7Yu7zuFXD/P4px9n39P7ADBXmvF5fPS7+ul39RPwBaieVI1tjE0qO5z9vPTtl+g9Mby2vLPFydE3jgIw+32z4663Zg1MnQo//rH8v2mT/L9mTdKHOvKgS/gY7WC0SWWF0S7LFYMsU83QdBYs/AYs/kF4/Ol3gQYEfeBpF/+1PBOTOpExmHjb+8xenr7labavyQ8JCGFzZNWs4vf4MRgNVI2vipLkGYzVq+Eznxm6fNjx2aiAAWoXSjv0OXIzD5llxPLJKFX43X7cnW6C/iCqSR24V/vdfvpCl/nixQXdxaJFyUtL6YzQ9ddfD0BVVRV33XUXv/rVr3jPe95T4L1LDWOcT2LYvAOmXgsnXZ/djR9/Bhw7oWKSlNDmAs2XyauM4oHRDhUTxYDRXAVKRF1akWofFg30iowykTGyMFCRcTQnsj+rV8Nll8HLL0tJ/fjxUq6cMNOn5SmRMZt4Ccz8VFb3p4zCQKtdKNmd3ZtgwkWF3p30MHAPLBMZZZRRBrkzfm44VWQtXIfllatxSgSWLIFnnokmMhRFoXJCJT0HenAcdWBrKrx0wqFXRLZ3wilDTb51xJWW0iekbONKV+IwH6ieK+/OXZKVbMj99MjMmfDKKzKJmkw/0dXm4vk7nqdjRweLP7aYPksfXXVdqEb5khbQeOehdzjymiTpVE2sYsH1C3jprpfo6+jD0+2J2p7RYqRybCWeHg8GkwFrzfCyY7v+uQs08eyobo59ja9ZA1dcMVTC5+hRWT66J42DIvfs7ZC/6yLSrKf9h/xvDEkiedpi32uDXrmujfb07rVpQs+A37tXqm30turt8dKxs4Oqifn19wOkTWtgspswWoz0+/sTrt8Wys350Ifg4ouTHJ+NZGgawpAhZLd9ikhMuY+AMrWQezYs5s+HRx6JJjJcrS4693Zib7Kn7QGUT1iqLVQ0VIhUmgaKQSEYCEbdq7s8FXixFFVFRjGh5ImMZ599lrPPPjtq2apVq7jiiivQNC2mnqjX68XrDT8UHA7JSvf7/fj94hhvMBgwGAwEg0GCwXA5r748EAigRTyl4y1XVRVFUQa2G7kcGCjLCQQC+I0NgIbmOU5g0PpGoxFN06LKeBRFQVXVIfsYa7nB04aChmJpGHpM7iMY2tcSMFajjb84a8c03PJMjylyH/XlgUAARVEG1sn2eSrEMQ237zGXB/yogAIEbREDwshrQtMIBPxSWx/nmAKBAKqqomlaVGwKckzk9zwZPO2gaWjGWlTI6JgCgQCGkAPjiG97eTgm/beTOdYhx2QcgwFQ/C4UXw8BtSonx3T22eHlmjZwmcU8JoO7DQMQNNUSTOI6y+Z50n8ncn/KbS/9Y9KfQYGqBRgqXkGxTSzdY/K2oaERMNYNNOB8nqfIdllue8XRNxp8bGUUJ3Jmwpgl4+chMNolEcpUm7eJuXiG31UTqug50IPzmJOxS8YW1NCy53APjkMODEYDE0+bGHe9uNJSA0RGcbjYFoM5aEzYJohCgc8JvfugelZOf27NmnB1gv53c7PI38Sa5Ne9Adp3tePt9tK+sx1DpYFdFbvkvu4P4mp1MXbhWEwVJuZePpf5V89HNausvm/1EKmdwTBajRitMiWkaRp+tx9TRbQge7+rf6DKY/ZlsasxAgH4/Odj+xDoeUM33STJPsXUFPLWLt3HpQIbZOI4EHFerOPDJAbEvtcevB+O/1v8/aZ/LL17bZqYPFk0+vv74cgRmBKyMrLVC9nr7pTU8XzFMhgI4jzmjNqHRPD74emn5e/PfKa4PVsikdN4+rpFZrlikrQltULug/1d4NoXVjAoQuiG39u2hZftfmI32x/ezqz3zhpCZBTjs8feaE94f+7ogJtPs+BW7CxalOedGwbFEs+SJzKOHTvGFP1uGoLNZsNqtdLa2srYsWOHfOe73/0ud9451BNiw4YN2O3yEGlsbGT69Ons37+ftrawj0BzczPNzc3s2rWLnp6egeXTpk2jqamJrVu34naHy/3mzJlDbW0tGzZsiBqULly4ELPZzLp16waWVan1oCn4nUd4O2K5qqosX76cnp4eduwIl73abDYWLVpEe3s7+/btG1heU1PD3LlzOXbsGEeOHAEtyKzW/VjMRuyWMUOOaVrVMZra7qfb18Duo+EHYirHpAS9LFiwALOtOuqYAJYtW0Z/fz+bN2/O3jGFEO88tba25uw8FeqYUml7Ft8RFgQCGE3gcPSEJyY0qK6uRtE0+vr62L1lC15T17DH1N3dXfBjyud52r93B7M6pHz6xMEO5syfmpVjMhqNbNq0aUS3vXwd09SpU9mwYUNaxzTNqVBl8mDrO8KuFrXgxzTVuZNxJujxGNkZsZ18nSeDwRAVy3Lby/yY1u9109j4JaafNJ39e/eW3jFVVzPX24HH42Xb9sP4VVdez5PeHvX3ctsrjr6Ry1UccjtlxIfRaGT58uW5+4EsGT8PwbSPZH+bCaATGZs2gc8XNtKsnihEivOYM/exHAaH14oc8NjFYzHb45sMxJeWCn1gjeUCnl8UOpZx4WmTyWL7VJHwcR0S7xcdWZ4sTqdiQfcGsNXZ8PX60PwadRPqBhJ7/G4/bqOb6uZqzrrtLOpOqhv4rr3RHmV8PBx2PLqDXf/cxdKPLcU+Nvw9r9NL4/xGeg72YK4y42pzDdnuyy/LJHc8aBocPizrnXNO0ruUU+SvXWrgDZEY9ilgDc1P+RM8UwffayethvbXxcfAPjmvpsxGI0ybJlJou3cPJTI8XZ68XuOeHg8BbwDVomJvGr59v/46dHeLN1Ix3oZiIafxVAzgd0PAIybzhBJhjJVCZAS94n+Vp8SCVKETGVu3hknSynGVgEjgRaJonz0kvj+/tQf6gFkzobIyv/uVCMUUT0XTYvHmxYU//elP3HbbbUyePJmOjg5mzJjB1772Nc444wxuuOEGTjvtND7+8Y9HfWfy5Mm8+OKLnHTSSUO2F6siY9KkSXR0dFBdLRdsvjP0NE2jt2UDtfvuAnMDgVN/H7V+Rhl63k4Mb/4niqKgnPUoQZToY/KewPDWp9AUI4Ez/jZQUpvKMSnH/olh/x9g3AUEpkeLEOYz61DTNBwOB7W1taiqOiIyKYfb95jLe/eivnYtiqmWoFohy/o7UdzHoGauZIH0dxFYcT9UTo97TJqm4XK5qK6uTmrfR0zGq+sohnWfBtWKtuJ+VKMxo2PSNI3e3l5qa2sH2mnej2kEnSeDwUB3dzeVlZUDA7lUjkl55zsYPC0oMz5BoGZhwY/JsP7zGPoOEVzwDYI14bSLfFVkdHZ2CsEZimW57WVWkeFwOKiurkZV1dI9poAHdfu30TztBJb+fEi/INfH5Pf7B+Ko72PGxzTC214++kYOh4OGhgZ6enoG+stlRMPhcFBTU1OwGGmaRk9PDzU1NTGr0ssQBINiuux0wubNsGCBLN/37D7euOcNxi4ey7l3nVvQWD7x2SfoOdDDaZ8/jWkXTIu73rRpsH8/rF0LK1ZEfLDlLuh4C2Z9BiZcHPf7+UBRtktPG7x6XVhGMRYsDZIZnwUyIxAQr4h4k/2KIpUZ+/dHVyx07u3kwSsfxFJjoXNXJ0F/EEu9hdrJtRjNRvpd/bg73Vz54JU0zExfDjfoD/LEZ5+ga28Xxzccx1xpxloXW3KqoqGC1fetjpqE+9vf4Lrrhv+d++6Da69Nezezipy3S+deeOVKQBFvPoMJ6hYxYFPrd0lm/JkPQtX04XYWXv8IeDthwe3icZpHXHopPP44/M//wKdCKrjdB7v513/9C3OVmdV/XZ3TWOrXgbXWitlulkx2hQHT+X5XP55uD1c+eOWQjPxbb4XvfEfa3X33ZX3XcoKcts3998K+e4VQW3BHtPRg7z6R2GtYlreKn1Th84HdLu8HDgixdmLzCZ679TmqJlZx6a8vHVi3KJ89Eeg53EPluEpUU3SVw/e+B1/9Klx9Ndx/f4F2LgayFc9s9JVLoiLjiiuu4AMf+ADV1dVomsYTTzzB+973Pl599VUsFgsej2fId9xuNzZb7FIzi8WCxWIZstxoNGI0RodEH9wNRrySmnjLB2938HK/38/uI06WaWDo78SoBMX0KQKKosTcTrx9HFje1yM3KEs9GFQMoc/COz0BjDYUvxtj/3GonJr6MXlCevOm2mGPNSvHFGe53+9n9+7dLFu2LOG+p3ueklme7WMajKSOSQ3/vkFRAE3KBwNucB2QElZFwagaJc0izjH5/X527NjBsmXLCn9McfYx3eUJz5PJAhMu1L+ccN+TOSa/38/OnTvjxjHVfY+3vCja3jD7mOryWMeUKJ5JHdOC2wY6bfGKI/N6TD6pijJYx2DIwvlL5TxF3jMH72u57aV+TMFgcCCeBoMBgj4MfUcxDHquJtr3ojgmYyUs/i4KsTuKuT5PiqLEbJfltlfYvlG8YyijeBAIBBL224oavl7oeB0MVmg6M6c/ZTCIT8ZLL4m8lE5kVE2swlxlxlRhKmgsHUcd9BzoQVGVhLJSmpaMR0bhpaWKsl36HEJiGCygxpgzCLjlc58jKxN6mVYsKIqCpdZCX3ufmHMHoWGWEBeKQUExZDZJZzAauPDuC3nhrhc4+sZRPN0eNE2jZnINBmP4+eJ3++nr6MPr8EYRGUMqguIg2fXygby1S/dx0PxgapBM+IEdSMG0W1GgcSUc+Qe0rc07kRHL8FuvyOh39uPz+vISS79bEisUVdp7v6s/anks/Otf8n5xYfnclJCzttnfA0f+D1QLzL4RqmdEfz4coVYEMJlg9mypyNi6VYgMvSLDdcKFFtQG7odF+ewJQQtqPHPLMwT9QS7++cVUTQh7zWzcKO/F5o9RTPEcOuopQtjt9gGmRlEULrnkEi677DL+9a9/0dzczKFDh6LWd7vd9Pb20tTUVIjdTRtBpSLckfKcyN6G+zvlPZ5hp6IMZOXTuze933CFzoF9cnrfLyM3CLgl28PfJwMZLSD6nHq5eRmxYf3/7J13mBvltf+/M6Ou7dVl1713jGmmJ7QAAWIMIeYm994U0iCQws0vN4UACSQ3kMBNCOkhgZAQwMClhA7GYJpxN+7rsl6X7U1dM/P742hW2l1Jq/JKGu2ez/PoGe1oNPPOd86+mnnPe86pA2bfQC9m9GGmGRlqkPIyA4DN/MXJmDTwHQPeugbYeDPNbmIYhmES0/42sPNe4NCjeTlcvDoZNXNqcOXDV+LM75yZlzYk4ugH5ISoX1w/MOM4Hr29gDGfb5gjY/5/Awu+F33GY+KjOKk+gcVNaaVkhd7Hc25kgeFwymY7VzVF2UuKlDBaIhusLiuWfn4pXLUuyBYZYV8YHbs6EPaHYXPbYHPbYHHGH7w680yKKEmEJAGNjbTdmMFaBlhKAbWfHBmShSIwjJcWSK9o97jzgBnXAVM/ncNGxyeeI8NWYhtwcvm60nDKZIC9zA5N1RDsp8iLoa9wIAxXtQv2ssH95dGjwMaNZH8XXpjTJhYHhx6llFKlM4HqU5Jv62kGdv4C0EL5aVsaDK2T4aqhPksLa/C2ewvXsDToPtCNkDcE2SIPOGIMjIzPZnNkmAlzuaXSQFVVWCwWLF++HDfffPOgz9544w2cdNJJcWenmRpJohAv70FyZLgbR/5OKgTaaWlPEm5aMh3o3hZxZHw0vf3rOuBlR4apsJbR9Q500E3SwPpSCkn1HQUqFpk29yHD5AUjsWYhCVI0BmQrzYJnRg+OeppZHOoF+vZQWr9iwQz/GwzDjC1qTgV2/4pSW/iO5jySIJ4jwyypJ2Z9fBZq59VC15JngG6hcm5wOoH33qNB4oHAKnejuGfJsUDfnug9ma2C7skEZuAWEbHgKHegdn4tPD4PXNUuSBBvr5IkwV5mh7vOjb6jfQh7w+hq6oK9zD4s/UksikIFy6+8Mt4+aXnPPeYq9J1zHLXAmY8B/fuBvt1AzWnDt0mnDkvJNHoVgBmRift790bXSZIEV50LWlhLGhEhgs49nSipL4FiU/DROz4Kq3t4jRB7mX1YzYHnn6flsmVAkc1xFo+/HTjyHL2f+unk9/laGNh6C6Xgs5ZRgXkTEVsnA6CoNHe9G30tfeg72pdS7ZRC07qd6ubUzK0ZFFHX3x91GC5ZUoCGFQlF4choaWlBfX39QPjK448/jueffx533HEHxo0bh1AohL/97W+49tpr0dfXh1tuuQXf/OY3C9zq9JAkCU6nE3rtZeSxF+kQGPdRGrSWkjh2jDCyvgwiMoJdFA4uSYAzyVSMPGDoaJYHkYLhqKWcrqHewes1Fdj5M7rOFQuSO7cwhvUM9VKRK0XMbKcxq2OOyFrPUD+w5fvkMD7tQZp9VygsTmD6Z6luTQHsg21TLIP0lCSgchHQ+ibQtbm4HBn7/wIcf42KSzZcnvfDs12KhfUcOxT1tbaW0vNK1yZKnTJpZU4PZzgyNm2imhlD578VUktJklA1I3mU5urV0Vz1Ph9w7rk0I/7ee4cXjC40RWGXznE08zjcDwS7aSKgrgMHHwGmrBqcetkoEp6IOIPTRsRCS0t8/4hRI2OkiAWbywa/6s+JEyMWi8OCunl18LR6oNiVpE4Mg0svJaeab8jk/IYGcmIktMsM9BRBXuzSKNpdc3LujpEHjIiMffuo3ovhkLr0N5dCkiSoqgpnX2609Hf78f5978Nit2De1fNQv6g+5e8WY1opIEe22f4W9XEV84HKJcm3lS3AjC8B224Hmp+k7atOFNeWLJk/n5aGIwMASsaXoK+lD/1H+4FIyUkz//a0bW8DANTOH9y3bd5MvxETJpjP+WYmPYvCkfH888/jZz/72UBdi9mzZ+PVV1/F+MiUhSeffBLXXXcdfvKTn0BVVXz+85/HVVddVcgmp42iKFi8ePHIG2a0c8fIM3KMsGPf0fRnYhrRGM4Jw+p65Juc6lhsGDdOQ1l0O7D+Bpod0vwEMCnO1JkIY1bPPffT4OPMLwETL8l6d2NWxxyRtZ4WN/VbapCcGa4J4hqXLtYyoPETBTs826ZYhulZsZj6ku7NAK4pWLvSxt9K0XsFgu1SLKzn2KHor3XtGRFHxps5d2TMnk2Drv39NMt41ixav2P1Dux9fi9mfGwGFn/CnFquXg2sXDl8QLylhda/8M8Pcf7JHwLl8+hVYIrCLi2lpJXqIyeG7xig+YBjL1Pas1P+ADjrMy4SbkQsrFxJj9nxnBnJIhZiZ7w7FSdC3tCw9aKRZGlYypNkvPYaOTHq6qj49/HjFGEyKFJoKHkuuh5Lzu1SDYofG9FCNNGk8wNg7s004JwHGhupNkEwSLVeJk+m9caAZq601HUd7933HgK9AZRPKceCaxak/N1wGHjxRXp/8cXCm5ZTcqJnw+WAeypgcaU21ldzMtDwcarLsvMXwIn/SzV3TYARkbFjR9SxNufyOZh23jTUzI6m0zfrb4+u62jdRhEZdfMHeyvMnFbKTHoWRe6lz33uc9i5cyc2b96MzZs345///Cfmzo3ObJw8eTJeeOEFbN26FR9++CG+8Y1vFLC1maFpGlpbW6FpWmEa4GoATr4fOO2B9GcFew7S0gRppQquYzHgHEf5NQHKz5mEMauncTNtqxCyuzGrY47IWk9JApyR4pm+FnENK0LYNsUyTM+KRbTs3Uk5aYsFIyWlLXnUXq5guxQL6zl2KPprXXMq/Ub37aOB5BxisQDG83hseqlwIIz+o/3oOdRTEC1f+8FreOeed+Bp9cT9XFWBG2+MPxBurPvXQ+9Db/oL0PpGDluaOkVll4oTcDVSFKVzHFC1FKhYSE4MIJIuch+lXrGWA9aKwS/ZHi0SPoQVK4DHHgMmDqnfbrPR+ngRC/YyO1zVLoQDYfi7/fB1+dDf1g9fly9pbYBC8MQTtLziCuAjHwE+9SkqXJ40nVRs0fWhWo6gZ7bk1C51DfjgBmDr7ZTSRxgysP+vQNs6oGfbyJsLwmIBpkWyWsXWyTDIlZYHXj+AlndaIFtknPaN01KKDDJ4+22gpweorqbUUsVEzmyzchFQOmPk7Qym/gdFowV7yJkhMN1eNkydShMR/H6gqYnWjVsyDpNOnwRXjWtgO7P+9vQd6UOgJwDZKqNq5mDnkJkdGWbSsygcGWMBTdPQ1NQELeQHenYA7e+J2/n+vwEH/pF8dqUkkzMjWfqpRLgnU/GpysKHmw3oaIJ/LlMz7jxg6c9HzHc4ZvUccGSIGcQbszrmCCF6uiJPkd4COzK8h+mBOBx/wCLXsG2KZZiezvGAo4YGPHp2FLZx6WA4MnKQyiEV2C7FwnqOHYr+WtvKadAYANrX5fxw8epklE4oBQD0tvTmXcv+4/04tvEY9r+6H4o9/oDd2rU0IzoRug7YtKPo6EDO64ykiqntUvXRPdjQl+qnCN6ZXwYW/yi6fbgPCHbSRJj+vUCoK1LnzJ1SkfAVK4ADByh64b77aF0wCJySoO6uu9aNFQ+vwFWPXoWrHr0KV/7jSsy9ZS6u/MeVA+tWPLxiWG2AbAn7wgh6gsNeiSJANA146il6/4lMAo1ji67HvgQXXY8lp3bZ+QHgPQL0bAesAmvgyUq01kbbW+L2mwLxCn4fevMQXvzWi9j0wCbhWnrbvfjgtx8AABZ8agEqp1am9X0jrdSFFxZfbRahtulvJ0dEJig2YO5/0bJrE9C8Ovv2CEBRAGNe+7Yk/jyz/va0fUhppapnVw9zzpndkWEWPdmRYTYCx4GN/wXsvEucx/Pwk8CBv9GNWi6oXALMuRGYcGFu9s+IR5KAspnRv03iXTcFuk4PKMCINUSYIsYsERkH/wl8cBNw5PnCtoPJDZJE6aWASHqpIkDXos5ce03ybRmGYURTewb1nf7jOT+UMVAQz5HRf6Q/58cfSvO6ZgBA3cI6OMrj12k7enTk/YyvOIpAAIDDHI4MU2Ito/t8LUAR6kNfWoA+t5YNnuinqZF1Fkod5D0C9O4AkPqzlKJQpMJXvgIsX07rVicZH3TXulE1vQpV06tQOb0SrgYXKqdXDqwT6cQYGgEy9JUoAuTdd4Fjx4CyMorGyBjVS5EGqje7Eyk0Lc/QcvwFwmouDlB7Oi3b1pE95ol4Bb9D3hA6dnWg+2C38ON9+NiHCHlCqJpVhblXpl9nrljrYwin6Y/Au5/PPELP3QjM+CK971xvmnGjoQW/tbCGw+8exs6ndkI3SRsTUTuvFkv+cwlmXDg4OiYYjJ6PGR0ZZqIoamSMKeyRsNWwj4qNWUuz21/YG01nMdKgbP8B4NCjgGIHZn8tu+MyxYO/Fdh1LzDpaqDSHDnvCkqol2ZPA4AtvZkfTBFhloiMAaeZOXKOMjmg/lyKeKw+qdAtSY1gFzkzJJn7QIZh8k/d2ZRiKg/9T2xEhq4D3nYPwn6aiR7oD6B0Xym6KrugWGjGpL3MLnTQ2NPmQaA3MPD37md2I+gJonJaJTr3dcY93vgRfRM6xlUcg90O00RkmBJHLdVdSLfItK2CnPzWMqpX4DkAqAEg1EOpkNJk5Upg3Trg8ceBG25I++vCMSJAYu1yKPHs0kgrdfHFlCorYzyHaPyi50PK+qC4Rv6O2fC2AJ0byCE7IQfFGcoX0BhRqJciPioXiT9GHOJFZDgqyUnj7/TDBbHX6oTPnQCr24qp506FrKQ3//rIEWDTJroEF47lubb9TVSrT5IAVxZp4MedTw65klm0z0TE6zNzhOHI2L6dlrquY+2P1wI6MOXsKXBUCHYgCqRsYhnKVpQNW79jBxAKARUVwJQpeW9WUcGODJMgSRLKy8shWew0oBXopNyw2ToyjFmVFvfIswF0lTy11hJg1g2p1cpQ/dROV0Peik0lY0DHdOt8jGUOPwl0bQF8R4ATfzko/HVM6hk00kqVC7PpMaljDhGip1kiMgxHRoFqEbBtiiWunpWLi8tJbKSVsldllm5SAGyXYmE9xw6j4lpbXIDgAbFEzJ9PBWy7uoBdGzx4//+thrfDi+4D3dBUDV23dGFf6b4BPV3VLmFpfDxtHqxeRccDAC2koecQpf/wdnjx/n3vxz3emWcCEybQQF08yl29qC73oroagKM+63aKwLR26ajNYtBNBmxVlGrKd5ye3TNwZFx5JfCNbwBvvEHFsetHuGT50NJd607LxnU96sjIKK1ULKUzaKA02A3076eBUYvYtFkGOdPyyLO0rDqJ6qyIRraQs/foS0DbmwV1ZDirKPWXv9svXEvFqmDxpzO7f34+Euh+0klAbWGypGaFMNvc/xAta8+kWheZNwgomwusWxUdX4yHvZocxHlwZgyNyFCsCly1Lnhbveg72gdHhcO8vz0JMNJKLVmSftnifGAmPTm1lElQFAVz586FoijRm04RIdXGoGwqKXLck+iHMdQPBNpS23/PdmD9DZQaxQQM0pFJjamfoRlb/nZgz/2DPhqTegqujwGMUR1ziBA9XRMB1wSgZHpew7KHEShsRAbbplhGh54SULEAKJtTsBaMDh3NA+s5dhh11zrH9aPs9uhgyMZ3A/B2eGGxW2ArtUGxKHCVueCsdMJR4YDFboG3w5t0pno6BHqjxzNmjsoWGfYKSu2T6HiKApx6avx9ShIwruIY5s8HJHs15TU3AaPOLmMxnhfCvUgnvZTBpEk02BrrDEiGGbX88ENKN2SzZZHGRwsBWpBSdpXOitTulGjCj7cF8KeQUy1NcqJl2Acce5neN3xc3H6HUnsGLdvXURRtHjBSSzU1AWrk0clZSY6MQE8Ac2bPyVpLXdOx76V90NTszqnY00oJsc3eXUDH+zQpacqq7BsV6qVxEtlGUVPQyXlrvGQ7fZ4syk0g8+fTctcuSskEAKXjI6khj1JqSDP2lx17OnDg9QMDkxhiMXN9DMBcerIjwyRomobDhw9T4RRHxHPvP5b9jgNpODJkazTkrG9v8m0NPIdo6WpIv205YJCOTGooDmDuN+lHrvWNQfkTx6Setipg/IU000UQY1LHHCJET4sbOPm3wMIfUOG8QqD6o4M0tsI4Mtg2xZJQz1AfcPx14PhrBWlXWpTNApbcCcz7dsGawHYpFtZz7DBqrrUWBjZ/H1h3LU20ySFGeikjPYXFaYGj3AF7uR2aosHqssLmtsHizE3kucVpgc1tQ8gbgmyRUVJfkvR4XV3ASy/R++ohj3cNDcDv7jmK8eNgqrRSo8YuYzGKhEOiyYAl0yMpndOvSblyJS0ff3zkbc2opeGAOe88oDTTZBK+Fsq/399EulrLI+MSMjk4tv2Y0mALJCdaHn+NnBmuidEaabmgYhFFq7inUFqzPDBpEjmrgkGgmcr5kBNWIgdE0/bsiwDvfGon3vvf9/D6La9nXOcgFAJefJHeX5yDzF75QIht7n+QluM+Gk2pLALVR2lofccBSaFnaosbUJzijpECjY3U34TDwO7dtK5kPGUW6TvaB8Cc/WXTy014++63sfOJncM+i43IMCNm0pMdGSZhsCNDYERGurPLS6fTMlnuu1gMR4Y7i5x7AjHTP1dRUTabamQAwO5fDzw0jkk9S6cDs68HpnxK2C7HpI45ZNToGeyipeLI+82fwajR0iQk1LNnO7DjbuDgI4VpWJHBdikW1nPsMGqutWyhwUtNpRnHOWTAkbEtuq5icgVq5tVAt+vQM5hlny46dNjL7bA4LQMznBPxv/8L9PUBCxdS4e/XXgMefpiW+/cDZ61YDiz7JTD98zlvd6qMGrsE4hcJ11WahTy0SHiKXHklLV97DehIkrUFMKeWWaWVspbROEWohxyYejhG1zBFT9urKK2N4PGGnGg57iPAzC8DU67NbW4Y2QKc+idg8Y/yVs9MUYBp0+i9UfBbkiU4KhzQoePgroNZadlzqAdb/roFADD5rMkZp695+22gtxeoqQGWLcu4OQUla9vs2gJ0bSY7mXyN2MbZqij9va4Cnv1i950GkjS8TsbQiAwz9pet21oBUMHvWDSN6roA5o3IMJOehS9qwAzHyKXoExCRkU5qKYBmk+AloH9fatt7DtLSPTntpjEmY/Ingc4PgL49wK57gEW3F7pFDJN7dJ3C2QuRfmHA0VxpzkSYjDgqFtI19raQo9hRU+gWJUbX2R4Zhik8tadTwd+2t4CGy3J2GMOR8eGHwEWNwz9XAyqCwWDaxWZTIuIjkSBR8c+JyQe/e3uBe++l99/7HtX3OOecoVvZssuFziQnWZFwXQegU0HwNHLET59OM3A3bQKeegr47GcFtTUPHDoEbIjUtb4sk39TRy1wwk+BDd8CFCuw9BeUoiYWSwnVcDTqdqlBel5NVv8zUdFhf1v02qlh2EOHgf5KQLEk/16qKA5gYp7CABT7yNsIZsYMYOdOqpNx3nm0rmRcCSRFghZObXDT0+YZljJPC2t48ydvwt/jx/hl4zHt/GkZt9FIK3XhhYBciGnbsTYWD9EFseMdr/0dcqpWnwxA9D29RGOG3Zsp4lz1Akp+6loNZcECclxt2wZ88pNki0A0IsNsBPoC6D1E16p2/mAbaGqiSQp2OzCncNl9iwZ2ZJiR8nnkyRcx62DafwITLk19tm9JJCKjLwVHhq4D3khcoUvsDAmmAMgWSjH13heB/oNA1yZAdom/wTM7gQ76MbYUZoY8k0eOvwbs+S1QdSIw7+b8H99RB0z/LKX1Y0Y3FjdQMoMevLs3U5h3uuTrwWjTtykidM4381ZAkmEYZhg1y4G9vwd6d1A9qRzVklq0iAa72tqB0DggdmhU13V07etCyBuCo4rqZIii51APeg73QLbKsLlTm0xx//2UWmr27OgsfqYAxCsSfvhpoOVpYOqngbKZae/yyivJkfHYY8XlyHjySVqefjpQV5fhTvr20n1S9Uk0DjISO38O7LwHsJUnLgQer+iwv21QsWJF1zHT64XS54pO4MhjsWJhBDoB6KlPXM2CeAW/z/+f8xEOh7F+/foRv+9p82D1qtXD6gP4unzwd/ohyRJ0TYe33ZtWwflYClofY4iNxUWkjSU7nhYCmh8D2t4Qb9OyjRy2gS4g0F6wsUCjToZR8Lt0wuCIDLPR9iHVIS5tKIWjfLAj1kgrtXAhTVJgksOODJMgyzJqa2shyzLlNJ0oKK+p4gDccaYXJaJkCg1o28opv2OywdxAG+V4ly2mycM6SEcmfWQb3Qx5W4B3PwdF1zEvGISyzlbcN3jpsPWHlIN10W1AlZi4PrZLsQjTU3FRHl5fi5iGpYujDmjMJA5fHGybYkmqZ+VicmR0ZeDIyOeDkf84/Q4UKN0ZwHYpGtZz7DCqrrWjBiifA/TspPRSEy/NyWFcLmDuXODIdsDrBYwU/xIk2Gw2SG4JIU8I3uNeyBYZvg4fMD3z4+m6jj3P7sG7//sutKCGvmN9cNe7IY0wa9bjAe6+m97/939Tmpe47PsjFV6dcFHiQd48M6rsMhHBLsB3FGhbC9SdmfbXV64Evv994OWXge5uoKIi/nZm0zKrtFIGHe/RsvrkkbdVg5Q5QvUB/gCl5HaOx6BZ56ovWnQ49p5ooFixPXKPo0NWHYDNQd9P9L1U8B0Htt8BTLwEGHd+/iJbD/4TOPAQ9Y8zrsv54YyC33uHlFNN1S4DvQF4O7yw2C0DdYBCvhCCfUHIFhnucW4EegMI9AYycmS0tACbN5P8F16Y9tezZ5iNDSFFG0v5/1zQ8TLCVhtxZHQArjTGGwVipJaKdWQs/6/lKB1fCl3XTddftm0nR0bd/OFeX7MX+gbM9ftT+BYwAMgopk+fXnijUBzAGY9SbtWRZqQbaaVcDeTMMAGm0bFYCfUCmp/swFoByVYJe0k9JFslPRTJ9uiP4WglNt2PINguxSJMT6Pwmbclkg5g7MG2KZakelZGij52b07f3mIfVKwVw1+i+mYtHK3dUsD0V2yXYmE9xw6j7lrXnkHLtrdyehgjvZTPB4R9YQQ9QYS8IdgkG9y1bpQ1lEGHjrA/jDW3rUHzuuaMjhPoC2DtHWvxwW8/gBbWYHVbUTquFCFPCEFPcNAr7AsP+u7vfw+0tQFTpwKrViU4gOoHmp8Emh7IqH25YtTZZTwM50XHeir6nSZz5gDz5lGh4qefTrydmbTs6ADeeIPeZ+zICHYBfZFKvak4MhQbMOdbNBtckun7nkMU3Zxq0WHFCVjckCwlcJXWQLKUZF+s+MizVGO07c38pud0T6F7yrZ1eXmWiReRAaRvlxanBTa3DVa3FZ7jHsiKDHedG+767Jyvzz9Py5NPphoZBSNiY8NeKdpY2v/nxvGgkf3lsgC36qOJgLIFAw5A3zFa5hnDkbFvH/1+KzYFk8+cjKoZVZAkyVT9JQC0bo/Ux5g/3Klk9voYgLl+fwrfAgYAFU7Zt29ftHBKfxNw/HXy7me80zCw+z7gwD8otCxVUnVKOCdQ+Oz4Qri74zNMRyYzFCdgcUEP9SDQfwy6xZW7H0MzoYUo1yMgNDyX7VIswvR0jKOHINUfHbzNJ337KJw+gwdeUbBtiiWpnmXz6EE70Jl5FFCWD0YjEuyiB2HZQg6SAsF2KRbWc+ww6q51zXJa9mzP6e/00qVAAHb0hl0IB8Lwd/vh6/Kh53gPfF0+6LqOsgllcFQ4oAZVvHnnm3j//vehBtWUj9H2YRue/9rzaHmnBbJFxpLPLkHt/FpoYQ3+bv+wVzgQhqvaBXuZHX4/8LOf0X6+8x3AkuhRzXeUltZS00RjAKPQLuPhnkITZLQQ0PFuRrtYuZKWjz+eeBszafn001SgdvFicrBlRMd6uu8onZl6+jjZQs9p7imAZAHC/UD3FnrFsvs+4K1V0dcHN1HUfc92oGsTdOjweD3QkaUDQA0Ax16i9xNyEzmWkMolNPk00AH07sr54QxHRlMToEa6vyMfHMEL33gBz/3wuZTt0tBcgoTK6ZVwVDhQPqV8xMi0kShoWqmhaCFKjdjfRIWx0/lqJv/nWgDo3w90b4vWyRWJtYz+77QAEOoGQj1kexY3rdMC9Lk1ea0nkdTVAdXV1IXs2DH8czP1l+FAGF376D6mWCMyzKSnOabRM9A0DW1tbZg8eTJ5uJoeADo3ArO/BjjPz2ynwU7gyPP0Yz/5k+l/f6SCn66JwOSrM2tbjhimI5M5oT7AexhSKAQ9VAspjz9KBSPQSUvFRoXlBMF2KRZhesoWCkn3HQW8h3OWfzsh+/5AN5vzbgbqzsrvsSOwbYolqZ6KDSifC3RtoRRTroYMj6LT7EV7LWATbLOBdlraqwta8JvtUiys59hhVF7rmtPomcN7JHqfZpBKQd94DPneCScAXrjxjG0F3n6UitCqYRVbt27FwoULoVgoj5PVZcW+F/Zhx+M70LGrYyCbTbzitbH4unxYe/ta6JqOkgklOP2/TkfV9CrMuHBG0u/Zy+xw17rxm98AR44ADQ3AZz6T+LQGHBkmSflrMCrtciiSRPdyB/4OtK4F6s9NexcrVwK33Uazyvv6gNLS4duYSUsjrdQVV2Sxk9rlVLRayiApvLWc7lf69tIMcX1wFBNUX3SCGhDZRgV0CdBl6DoQDAbhdLoghTrJKZIJrWuAUD/grAeql2W2j0xRbED1KTQBtv0tSseXQxobAZsNCAaBPZs8qKsIoKupC0c3HkW4JIyOPR0D/aXRfxnouo7OvZ3wtnnR39qP8SeMhwQJVocV1bNoAqEaSG/AP5ZQCHgp4k+6OE/11pPi2R+xvz4g3BepRZvavXXa/+ehHur/dZUmTeViMpKjllLY5rOQ+QhIEkVlrFlD6aWWLgW6mrrQuq0VZQ1lqF1Ua5r+0mK34PI/XY6O3R1w10X/L1QVeOop4Ngx+nteCmWCCoWZfn/YkWFWHONo6c8iImMgRU5VegMS3iPAzrtplvDJ92d+fKa4sZYB9hogdBRSfxNQsbDQLco9wQz/Z5jixdVAN36+I/kvbGzMLhU9GM2YlxlfJCdpNk4z3zEg2EMP5NZyQEqUKD0DApS7FfZCxuMzDMMg89pAGXxvyRJavbvFDa3cjZoaIBwOw9XlQuX0SlhiQiCW/McS1C+qR8m4EihWBZ42Dx5f9TjVzkiAs9qJxtMa4ahwYNmXl8HqpEFbd617xDzwoRDwk5/Q+29/G7Dbk2zsj4yEmMyRMWaoPZMcGV0baWDbmt6kqAULaMb7nj3As88C11yTo3YKwOMBXnyR3mdVH8Pizm4yj2wHyudTdPVQpv3n4P8FzwHg/S8DljLA4oquD/WQM0RS0k8trOtU5B0AJlxCkd75pvZ0cmS0vQVM+2xOn2EVBZg2DTi004Pnr1sNp+5F2B9Gb0svdOh4/JrHIUWO76p2YcXDK6CrOg68fgD7X92Pzr2dCPQGIFtkBHuDsJcl69DSY906oLcXqK0FTjxR2G4zI9QNBLvpWsg2itrxHALck8UeRwvQb57qJUectYQcJiKfDWJx1JquTmqsIwMAWt5rwda/bcXUj05F7SJztdVZ5UTDqdGJbKtXAzfeCBw+HN1m/nzg3nuBFSsK0MAiYpROiRgFOOppadyQZoJxA59uihxbOdC7m2Yox85iiEXXgLa3x3Ru+bGA7poEXbICWjBaE2U0k+n/DFO8OCfQ0ns4+Xa5INbZzIwN3JOyc2JogWhaKvck8Q8qA30gOzIYhikwmdYGyuB75eXRIrZGeodkjF86HqUTaLp8oDeAzj2d8HZ4YS+1w1HhgKPCAUmRYC+1w2K3wNfhw8JrF+K0b5w24MRIlYceAg4eBOrrgc99boSNjYgMBzsyCoK7ESiZQumd299O++uSlFp6KTPwwguA308ppRbleR5QXBQHvWKxV9M1MV7OCTSorDgGp+SUZIpsMO6xjr2S+vhGz4eUrkqxAePOE3Y6aVG5lM7J30YRvzlm5kzAjgA8bVS021nlhGyRAQlwlFP/Z7Fb0NvSi1e/9yqe/sLT2Pq3reg/2g+L3QJbqQ0V0ytgK7UJbZeRVurCC4GCThbXVRojAwDnRKB8Ad1Xl0wTexzPIWDbHZHfMglwTQDK5tJvXD7RdaBrM9D01/weN4JRJ2P7dloav839x/oL0p5UWb2a+vvDQ4YgWlpo/erVhWlXscCODJMgyzIaGhqiITpOAREZwQwHZS3u6OyF/n3xt/EfB7bfAXzwNSDbvJICGaYjkxWSrEA3fnQD7TRjZTQTjKQssIl1ZLBdikWonmWzgaoTs0jzkyFhX3T2WL5TWsXAtimWnOvpbaGJBNYyQCmJ9FkCf4NtFUDFgkj4e+FguxQL6zl2GJXXWnFSqoxwP6CH0i7om2pNIaPg94YNtExVS1+nD/5uP0L9IXQd6AJ0Wtd7qBf9rf1QnORwli3pXxNVBe64g95/61uAc6RySAOppcalfaxcMirtMhHjL6RXSWZFI668kpbPPQd445RQM4uWsWmlMg4AaH4COPhPwN+a2feNosNDXyMVHY58T1I9cFp1SJIMuKcBipsGZQ/+A9j2o8QTOmM58gwt686l2jSFQLFFC6W3vZnzwxlO34CfinY7yh1QLAoUqwLZKsPmtsHipCi2zj2dgATUL6nHqd84FRfcfQHcdW4oioKQN4SgJzjoFfaFkxw5OYYjo+BppbQwAC2S4qmMnvcc9eTgMGzz6ItA16aEu0jp/7y/iTIKSAo5TKyVlFElnf8FEYT7ga0/BA49Sm3KM/Pn09KIyCgZR5Fw/Uf7TdNfamENa25bg+3/3A41qEJVKRIjnr/UWHfTTdE6NGbBLHoCnFrKNBhGMYARkeETEJGRyaBs6XS6Ge7bR0WkhuI5REtnQ2FCKBMwTEcmMyI/ehIAh90JaFVAoJWiMowZ7KMR92RgwkVA6Syhu2W7FItQPevOpFe+MZxmFtfwGWR5hG1TLCnp2fY2cPRfQNVJQMPHU995qI+cyZIFsNdRrQ2ExfZX9edmlNdbNGyXYmE9xw6j9loHOgBvc3Q2NUCFVDU/0PRnYPGPots2PUCDO4FOGkgCaDBphPvXpUuBf/5zuCNjJJxVTpROKIW/2w/Vr6Ltw7aBzxSrkpWv+ZFHgL17qZjpl76UwhcGHBnmulcftXYZj4nZFXteuhSYMgU4cIBqZQxNL2IGLUMh4JnI+H3GaaV0HTj8JP2flk4HHMOL3ybEKDoc6KAoinjEKzo85HsSAIcMIBQZ7HXU0OeKA+h4jyZszvkWUDE/cVvGXUAD1Vle96yZeClQtZTqZeQYo+C3PyK9JEuQFAkKFHTv70btXErnY3FYsPDahZhz+Ry4aiiVl6fNA1e1C94OL8KB+E4LV7Ur7ZRThw8DW7ZQJMYFF2R2XkKwllFkhGwlx0W8SaCyg2z/6L+AxpXAlFVUtzF2k0T/57E1bOvPATzNQNOf6Dih7vhtynUBbmsp2V3bWxTNNENw5MkIGI6MQ4cotVjJeHJk+Dp90IJawftLAOjc24kj7x9Bx64OzLtqHtasGR6JEYuuA83NwNq1wDnn5K2ZI2KG3x8DdmSYBFVVsXv3bsyaNQuKokRrZAS7KaeekkGIWDYpIkqmAa1vJvaqGmmG3JPS33cOGaYjkx5DbvB0XYc/EIDDbockKZRiSnHk9sewkFQuie+4yxK2S7GMCj1NklZqVGhpIlLSM9AGdEZyl6TqyJDkyGxkDbCVAJoPkCUgHKZoOWtp7h9U8gjbpVhYz7HDqL3W9irAd5juQ7UgrdPDNPN16LNK/36KepRD0eK9oV56HpITpzIxIjKM1FLpaGlxWFA9sxr9rf3wd/ohW2RUTK2As9KJoCeYyRlD04Af/5jef/3rQEkq5RaW/QrwH6XZuSZi1NplDpAkisq4+27gsceGOzLMoOWaNUB3N9UiWL48w5307yMnhuIAytOsw5hp0eEh31M1Ffv378fUqVOhyEr0e+E+4MP/oSjYQCuAyEipv234Ma1lwJR/o0Frf1vh6geUz6VXHjAcGYEYH5LVbYW3ywsloECP8d5OOWfKgBMDoLpAKx5egUBvAgcUhhcJT4Xnn6flySeT47dgpGKbsh1oeQo48jxFMfRsBebeDEBKbpv+VuDwE8D871IqeACY9mmahFnoAtzjziNHxvHXqTaNnL9h5qoqYMIE4MgRSi912ml22EpsCPYH0dPSg9Zga8F/e1q3UdRZzbwaSJKEo0dT+16q26VEvP4rlhTsxAy/PwbsyDAJuq6jp6cHuhFLZC2h8Oewh9I4ZeIwCLTTMpN8/0ZaiUSppbyRiAyTOTKG6cikx9AbPDWMnVu3YuHChbCE2sg9XDbHdEWezA7bpVhyomeoPzLTU2y+1oQYERkFTCsFsG2KJiU9KxfTsmc7zSg2Zgwno28vPeg76oEF36MHhIOPAMdeBsZ9FJh8jZgHFU0F5MIPMrFdioX1HDuM2msdr6Cv6qcBx0lXD9528lVA53uApZQGSb2HI9v2J508cMIJtNyzh2Z1ulzpaSkpEqpmVCHYF4TFYaFojCx48kngww+pfsf116f4JYtTfB52AYxau0yErgN9u6leQQYz9VeuJEfGM89QHQpHTOBuJlqqKs3sPXoUGD8eOPNMKtqcKUZaqcsuy2I/7e/SsmppZvfemRYdjvmeHg6j3d+JKe5pgCV2WKwWOPEeoPWNaJSqvw14a1U0dXc87NX0HD3Kn5ON1FLBQDQNTuW0SqAdqB5XDQnJc425a91pOypGwkgr9bGPCd1teuz+NVA6Axh3/sg2MOurQMViYPcvgZ6dwLvXUSShTlEqsq5jnNcL+YiLvJvBbvoNq1gC7P8rMPuG6L7MUIC78gR6pg10Ah3vA7Wn5fXwCxbEOjIoKqNzTyf6j/ajx1L43x4jUrNuAUWejU+xjFWq242Ivw1Ytyo6kTIeKfRfZvotZ0eGmZn1FXJm2DPsmBb+kH5sbZXpf9dwZHhbKNeexTX4cyO1lMtcjgxGALE/huEwAtYusgfL7MK2Kx/4jlIhSMtISYiZUcXm71GRsgXfA2pyH5INgP6npn+24BEZTAFwTaJaFMFuoHcX1aQYifHnU9oFxQWURabC1Z5GxUTDfZSWIVu0ELB2JbXtpF/T/QfDMIxZUFz0Mgh7AOhAxZDZ3OULAEsJYC2nfizUG3FkeJP+5tbUAJMmUXqKTZsym2kugQp8Z4uuAz+KZMv62tfImcEUEYF2YMO3aACyZnnak1ZOPhmYOJGKvr70EvDxNLJQDmX1asrFHpvGpKEBuPfe4dEeqaBpwFNP0fuM00oBlLoJiNZ2MBuKAxgfk6PI1wJ0b6KsGfZaSuUT9tHgn2yltMyBDupvCjWoHPYBR5+ne8t5386ieElyGhsBq5VKFgX8gL0EkK0y1cXIzSGTEgzS/wlQwPoY7e8CR/5FmpfNpeLyI1F3BlA6E9jxM6BzA103ew3gngxd16HKMnTFBcnfDAR7KCq7agk9P5oNWSGn36HHgeOv5N2RMX8+8OKLMXUyIo6MvmN9QIEzIemaPuDIqJ1HfcOZZ1I/3NISv06GJNHnZ4rKfh3qpf5JtsevE2aG/itNzFPcgBlO3VlUhDbTQVWLkwrYZjIYYSsHyudQJxQeUmlM12h2E2C6iAwmT/TuAj78WaSY1ShB14H11wNvXg34jhe6NUw+sUZGCHwt+TumuxFo/ARQf3b+jsmYA0kCKhbR+67NqX+vcnHUiQEA7kgh0f6m+HfB6RLooN/3cP/gwUKGYZhCkmVBX1jLaZasrXLE7w0t+J0OYV94WOHaTIvXPvccpbhyu2kQOiXa3wX23B+d6c4UDkctUDabfpvb3kr767IcLfr92GOZN2P1aoruGJqLvaWF1q9enf4+16+n75eUAB/9aIYN87fTvYskAVXLMtxJnjnyL0AN0qz5YAcQ7KKl6qWxlniDg/lGkoADD5HNefbn7DCKAkyKjNP391C/F/KEoPpUhDyhrIt2p8u6dUBfH1BXF+3D80qoD9h9H71vvDI1J4aBsx5Y8hNgQiSUxFpK9mRxQ4IGyRtJlShZqF+Zfp15JxrVRzqEjvdpslYeWRCZE2Y4MuZfNR8X/PwCTL9AwESvLOk+2I2QJwSLw0KRS6D/oXvvjb+94X+8557sIufiojgH7GvQywz9V5qwI8MkyLKMadOmmaIC/AAn/AyY/99U+CoW31GauanYorU8TIIpdSxi4uqpBoBtt1O47cFHCtc40YT76QYVEJ7uh+1SLML1dEVySXsPJ99uFMK2KZaU9TRq8XSP4MhofYMe+OPhaqBZgGEf4D+WdluHYaSjtFXnbBZfqrBdioX1HDuMqmtt1G3TAlTEdOhLCyQv6Gt8T/WS82Kk72GwIyNVLe1ldriqXQgHwvB3+4e9woFwWsVrdR24/XZ6/5WvpJHvvXsL0PIcpS00GaPKLlOlLjKVtm1tRl9fuZKW//d/NOPcIFUtVZWcYPHmORjrbrqJtksHI63Uxz42OOVVWhjRGGVzorn+C0BadtnwiWhbfcfIKSpJ5pq9rDiijqEMHGjp0DjDDi9c8PVF+z05KGfc72WDkVbqwgvJCZh39v2RHFuuicDkT6X/fdkCNK6g+/pI1KAU6oRbPUxjbhYnRW5Yywp+f54UdyNQNgtwjqd6HnlkqCOjYkoFqmdWw15iL/hvT9t2isaomVsDWYm2Y8UK4BvfGL59Q0P8+kjC0AIDKczSxUy/5ZxayiTIsoy6urrBKwOdQPdWKvJZl2Zcke8Y0Pw4dYgNl4trKEBpJ+Z9m0KPTNaZxtWRyZi4eip2YMYXqQjaoX8C1cto1lGxY+QMtJamlrM+DdguxSJcT1ck5tR3RNw+R6LnQ0Cy0k2fkumTYPawbYolZT2NOhl9u8kRES/y0tMM7PwFzcJa9kvAOWTigGwB3FMoB3ffPnpwyAbDkWGCh3K2S7GwnmOHUXWtBRX0Tfl7GO7ISEVL0cVrX3kFePddGiT+5jdT+grhi1QFzfa3IAeMKrtMldozaICzZwdNSBg6MXAEli8H6uuB48eBV18FLrqI1qeq5dq1wyMxYtF1oLmZtjvnnNTb9eSTtMwqrZTqoXvfAqeVSssuZSul/bHXAf6jlJXAVk338mai9nSgbR3Q9iYVIc/RWM3UBW785sUV+NJ5AXzle/G3yaRodyYYjoyCpJXq/AA49grpPPvG7GotKlGnj2StgGJ10W+VexI9K2j+JF82CQtvofpUeR4jnDePlsePA+3tlCoSMMdvT7A/CMWuoHb+8HuO1oi/5+qrgSuuEFPDKC5agCKHAh2UatPdCDjSv1cwg54G7MgwCaqqYtu2bViwYEG0ArxnP7DjLsA9OX1HhrcZOPI8FXzLxpGh6+Rhjp2hbnFTTj8TEldHJmMS6ll3Js2mOf46sONuYNn/FnQwVghG8TZ7qlPfUoftUizC9XROoGU+IzJ23kODDif8D1A+N3/HHQLbplhS1tNRB5RMoVQnod7hjgxdpwKAWhioXkpFvuMx+ZOArlIR3GyJjcgoMGyXYmE9xw6j7loLKOgLAGh/Bzj+Gg2cjkucD8co+L1jB9DXp6KpKTUtRRavNWpjXHcdDWSnjIkdGaPOLlPBXg2UzwO6t1NURmN6I/+KQjNy778fePzxqCMjVS2PHk3tOKluBwA7d9LLas1y0HjSVUDDFXT/UkAysksj4ivUTY4NM+Fvo3s4LQD07qGoXmOyFpDQgZsJM2YAXrixu92NqumF+x9vbga2bqVIjAsuGHl7oYQ9wO5f0fuJlwl9ntMlGX1yI0rdFSMWTzcVcSIds8LfltKkhJISYMoU4MABKvh95uka9vxrD3pbemE5yYJFSxaNaJeeNo+wCQmxLLhmAeatnActrA1aHw5TGkmAoi/PTjXbdIqaQNeB3p3AsZcoNZ6/lSbByVbKsJIBZvotZ0eGSdB1HT6fb3AFeCNtk/84GWI6ns2AgEHZUB/w3hepkz7j0ew8zHkiro5MxiTVc+aXgJ5t9OC074/ArK/mv4EiCXTSMgeDeGyXYhGupzOSWirYQ/1drnOP6nrUcVbgYt9sm2JJS88T/zfx7/rRF2gWp+IAZn458XYii9MHKPTZDBEZbJdiYT2jPPfcc7j77rvR1tYGTdNwxhln4Oc//zlcLqoLs2PHDnzpS19CT08PJEnC97//fazIWXy/ePhaJ6D/AM1SlixJHRnjx0dnwW/erMNiyY+Wqkoz4197DVizhgaKb745jR3oOj0vAhnNssw1Y9Yua88kR0Zr+o4MgOpk3H8/pXO6/37AYkldy/EpmkGq2wHRtFIf/aiAAvSyFUBhoxkytkvZRpEZZsLfBqxbRWNARuqrt/998GRUezVFqwm4z5sZKdm2Zw8tC/U//vzztDzlFKAq349UXZtJb+d4YOqnhe5a1wFVS38I0DSoQcB7iOpTZUqsTScixqYXLCBHxrZtwFlnSdj8l80I+UOYOHEi9MXJ7dLT5sHqVavh7fAm3MZV7cKKh1dk5MyQLTJky+B0TG+/DXR0AJWVwOmnp7ijVDU57SFg+4/o3gcgx4VspfFl10TqwzLATL/lhU9uxSTGXks9l+pP7nWLhwhHhqWE0lrpGuA5EF1/9CUKo1OLILyNyR0WNzD76/T+yPNU2KmYEfE/wxQnFmf0Rt+bh4Lfqjdaj8VWmfvjMeYk0ZNJoBNoeoDeT/102ukoMmagDzTZDEOGEUhJSQn++te/YsuWLdi0aRP6+vrwgx/8AADg9/tx+eWX47bbbsOmTZvwr3/9C9/5znewZcuWAreayZryObTs25V0M0mKppfauDE/o0erV9NM0nPPBW67jdbZbMB776Wxk0AH5VKXFe7DzUTt6WRUwQ4aWE6Ts8+mGikdHcAbb6T33fHjR05P0tBAaUxSxUgrdcUV6bVlEMGeLL5sAlQfXcuhL9VX2HaFeqkfkO1UQFq2AHoIsFbQS7bT5+mOKSXAcGQ0NaVfZ0UkRlqpj32sAAevXU41Zed+c1BaqKyIsS9Z85nHvtLB0wy8/Rlgyw/odylTYm3asOPY1xCbjq2TIUkSSsaXAAACHSNHHwR6A/B2eGGxW+CocAx7WewWeDu8SSM24qGpWsLPnn6alhdfTE7qlEioSRkgKVFNwv2AaxLZ5biPAnNvphRl1lK6JmbrvzKAHRlmRrFFZ+umW8gzKGBAQpKAkun0vn8fLbUwsOfXwJYfCvshZIqYykVA4xX0PsdFxXKOSWbIMwWi7mxg4sWAxZX7YxnRP9aSooh0Y3JMoHPwxIB9v6ebytKZwMRLR/5+5wfAwX9m/5vsngpULIhGKDHMKOSss87CxIlk4xaLBTfffDNefPFFAMCLL76IE044AWdH4vvHjRuHb37zm/jTn/5UsPYygiidSc81vuNAsDvppvl0ZKxeTUWdh9Yy8Hpp/erVKe7IqPHlqCdnBmMObBXAifcAp/45o2hfiyXqNHjssdS/t3EjcNZZ0QHmRPMmGhqAUIrjjC0t5FyTJODyTLNW6xrw/lco44MvzbGNQmOklNIClFZq6EsL0OeiU+uki+KkqCzZQYOWFifZnhKnFlsWNDSQwzUUAg4dErrrlAkGgZdeovcFqY8BUJ1QEbVCh9iXFOqGovVDMpt9pYJrIkWUh/ooFXm2KE56PtdDtF+LO65Nz49k2d2+nZYl4yKOjPbUnQ8WpwU2tw2SIkENqbC6rLC5bbA4M0tk9N6v3sMzX3oGzW83D/vMcGR8/OMZ7FiJ/F/LVkoN3LcH8BwCYtOQTftP4LQHgTk3UU3bYui/0oBTS5kERVEwZ86c4bnGnOPIq+Y/nl4nKWp2eck0oHMDFRIFKI2QFqZOxF749BNDSagjkxEp6Tnl0+Twqks1sZ9JqVhIyxzUK2C7FEtO9Jz+WXH7GgkTOc3YNsWStp7b76R0J/P+i2oPdW4AWt+kaMjZ19NyJPb+niKJSqcDVSdm3vip12b+XcGwXYqF9UxMZ2cnHA6q8fXyyy8PODEMzj77bNx7770Jvx8IBBAIRB+Se3vJoRgOhxEOhwFQcURZlqFpGjQtOjvPWK+q6qAw/UTrFUWBJEkD+41dD2Bg+5kzZ0LX9YHvqkOmy1osFui6Pmi9JElQFGVYGxOtz+c5pbJ+xHOCHbJjIuBtht79IZS65QnbvmSJBkDG2rUSTj11PjweKVIMWew5qSpw440K6KuDR5qNdCI33aTjkkvUgZn1Ca+T7xgUALp9HNSY45rlOhl2qSjK2LM9xyTKEQMto3NauVLGH/8IPPGEjl/8QoUs65g9ezYURYl7TmvWSLjsMh19fRIWL9bx1a9quO02GYcPR22sulpHTw/wzjsSLr5Yx2OPqSiLGb+Kd06rV8sAZJx2mo6aGhWGbGmdU+8OKKEeSLoK1VIJPY6t5vM6AcCcOXOG7T/uOVkqIZ/6EGS1n9brMecqRdouu6FbKoEhfX9ezkkNQ9F1ADokSYFeuQQ6JEAHraO/qO0C+ghAxfTpMnbskLBzp4rJk2XMmTMHuq4POi/h/0+aBlUF3nxTwiuvSOjvl1Fbq2PhwqhN5ryPOPYcULkUqn2csHOCpRI4+a+Q1D4osgJNUxHq7UW4pASSJJGN2Sug2arjXj9T9Xs6oNeeDan5cehHXgKqTs2sL9dUyLoOXdcg9e0Fgl2QXBOhOyfQ76auQ9J1WgKYMycMwIJt23SEQipKxpVAgoQKS8Ugu4x3TmqY3muqhtbtrQh6KHOCq8aFymmVA/dUalhFOBxOud87vvU4+o/1D6SVMq7H3r3Azp0WWCw6LrpISv06RbbRdR3QVUg9O8gJAQCyDboWgKTrUNUw4KyIXqeIfSHcS22XlWH9mGQth+KoTXpOsfeYmqZlbHtDzysT2JFhEiRJQkVFxfAPHPUAtqc/a8FwZGSb738gIqOJlp6DtHRPMmXCvoQ6MhmRkp6KDag/J/XCQ2al7ix65QC2S7EUvZ4D9VgK78goei1NRlp6+ttotCrsAY69TEXnFWe0H7KUprafkunkyOjfn50jw0SwXYqF9UzMb37zG3zmM58BABw5cgTnn3/+oM8bGxvR1NSU8Pt33nknbr311mHrN27cCLebZmDX1tZi+vTp2L9/P9ra2ga2aWhoQENDA3bv3o2enmi6lWnTpqGurg7btm2DzxcN958zZw4qKiqwcePGQQ/gixYtgs1mw/r16we1YdmyZQgGg4NSYymKgpNOOgk9PT3YuXPnwHqn04nFixejvb190PmWl5dj7ty5OHLkCA7HhA0U4zmN63Gi3NcN3+43MKFuecJz2rGjHUAd9u6V8MUvlkbOC7j55qM4+eTo9tme04YNZTh8eB4SoetAc7OEP/5xN5Yu7R3hOjVi7hn/wNHmfTgUo5nZrlN1dTW6u7vHnO0BAHQNFWVuzJm/OK1z+shHGlBaquLYMQV//vMuLFnSh2nTpkGSpGHntGfPQnz+824EgxKWLu3BT3+6GyUlKj78cBHee8+Ot95qQk1NCIsX92LjxjJ85ztz8dprEk47LYBf/GInqqpCCc/pb3+bD6AU553nwfr12zK6TjV9z2KiGoBz0tnYvbfJNNeptbU1Pdvbtw9tbdEc9QO2t2MHenoOFOSc7KHDmOn1Qg9aUVpRgmAoBK+X8v3Lmh929MHhdON463Ec7Ii2PZv/p5qaRgBVePXVQzjttEpUVFTg/fffz+n/05/+1IVf/GIKWlujqZz6+3X8/Of7cM45XVmfU+x1mju1BuUuCR9u2QJVo3NyhA5hVugpSBY7tlk/C79tStbnNNz2ZuHo4cM43NIJoDvmnGojtmf+fq/5WDWm9XRD73kFbfKFmL3wtLT78v3792Oc1wtZ64RF64NisUGx16G3tw+apkLWfFA0L7S+PpSVAX7/JsjyiQh0efHUnz9Ag2pD0BPEvrf3IdwQHTg/8fQTYS23YsuWLdDCGvzH/RHnHKDqKvw+P7QQDeT3HuuFYlWglCjwer3YunUrXF2ulPq9lr0taNnZAkhAsJwcI8Z1+vvfxwGYglNPDaG83IbNm1O7Tounl8Ch6+jr64WiHYct3A9dssBaORu6UoL+nlYomhd7tm5F2NGb/DoN6/c0zJ07Li+/ucakn2yQdDNU6igwvb29KC8vR09PD8pipyPkkXA4jI0bN+KEE06AJTZJ2oG/AwceBsZfAMy+IfUdvvUpINQPnPRrwN2YecN8R4F3r6M8i2c8Chz6J7Vp/PnA7K9lvt8ckVBHJiNS1tMoPORvIyearSJSxC0GgQXGig22S7HkTM+wl+w3mz4zFQ49BjT9hXJWzrkpt8caAbZNsaTdZ3oO0UQF2UoTBGJJtc807KnuDGDetzNruKYC0Om33gSwXYpFlJ5muF8WyQsvvIAbbrgBW7duhd1ux3nnnYdvf/vbg5wZmqYNzLqT4kzgiReR0djYiI6OjgGN8jmDXFVVbNq0CUuWLIHNZhtYH8uYjMjQNEjHXoK05z6gYgHkE34St+1PPilj5Up9WJQEXXodjzyi4ROf0IWc0z/+IeHTnx45SurBB1Vcc40e95wSnWvsOZnhOhl2eeKJJw5EEozUdrOfk0FKtnf0BUiHHgYmXAJ56qq0z+kzn9Hw4IMyrr9ew113hbB582YsXboUkiQNnNMf/iDhq1+VoWkSrrhCw4MPaogEmyVs+6ZNCi6+GGhrkzBjho7nnlMxderwc+rqAiZMUBAOS9i1S8O0aZldJ/mD6yH5DkOa919Qq08v+HVSVRVbtmzB4sWLBwYy0z2nZG3P6zn174Oy7hrAVgHJUgIdkX5MVyH1bgfCHkiKA9rZz0Ermy/knL71LQn33CPja1/TcNdd9D++aNGiQdGfIv+fHntMw9VXS3Gi2HRIEgb6ZyHXyd8G5d3PQAp2RNfpOuA7TDUGrGXQS2dDO/XBgfv0rCIyhqwPBoPYuHEjlixZAkVRzG17Cc5J3vz/gN6d0Kf9B5TJK9P/f+rZDfm1C4FwLyBZgJLpkOzVUdsOeyj11hmPQiqbgXA4jBPn+LF43xNYON0DpyWMvqN90CUdVVOqBkzGVePCubefi6MbjuLQ2kPQVA1n/eAsPPWZp2CvsAMaoNgV+Lv96DlAA/SuOhdki4wr/3ElKqdXptRHHHjjAN6+621UTq3ERfdeNOh6nH++jNdfl/Hzn2v4+tfTuE7eA8BbV0NXSiD17wP0MOCeAjjqAAB6iNKRqcv/AZRMF36/FwqFBu4xrVZrxrbX29uL6urqrJ4n+OnQRAztIABQqonSGYB7cno7O+VPlL7EMW7kbZPhGEf518IewNscyb0GKh5jUuLqyGRMSnoahYeCXVTIOCRTWjLjF0P1RYsxmdGRoWs0o9lenbMaCWyXYhGup+8Y8O4XKMLojMdyG3FWuQSYbgFcDbk7RhqwbYolrT7TWhGJoNQpGkOOzDBLp88cGjmZCT3bgS3fA8rnAUt+kvl+BMJ2KRbWczDNzc247rrr8Pjjj8Nup/87u90Ov98/aDufzwe73R7XiWF8x/h+LBaLZZjTKJqSYzCJUn4lWp/IGWWs13V94IEx0faSJMVdn6iN6a4XfU6prB/xnCrnA4qVakjo+rC2U6onQNeHX2tK9SThm99UsGLF4CLKmZ5TQ4q3AA0NyrBCoMV4nYwBjTFpe1YH/Z63vwlMXZX2OV11lYwHHwSefFLG3XcrA4NMlKoLuPNO4LvfpW2/8AXg/vtlKMrw/Qxt47JlwFtvARdcAOzdK+Gssyx44QVg0aLB5/T885QtacECYNYsSjGVatsH1vuOAr4WmjBRudQ010lVVciyLMQmC3pOiiXy7EL9lwQp8qeFMnz07wdC/ZC3/wjy4h8NS1meyTnNjuxi3z4ZkqRBVVUoihK3ndlfJ+DrX5cRfwo2nbPRPxunkdV10r00libbIRm1GHxHAOg0VuCcACnUCVn3DqvULOo31/gtj/2eKW0vTttlWaaJz327gNZXgUlXpv//1P4WEOykPsM9eSBlftS2pegr0sZ504Nw7fPCH7aiepwNnjYPVFWFo8IBXdXRf7wfxw4fw6v//Spsbprs4ah0wNfuG9i3rZTWl9SVQA/r6D3ci/4j/XBWO6FYUrsesiyjc2cnZElG/cL6gW0URUFXF7B2LW132WXR9SnpHjlXKdBGTgyLc9BzohTRw6JYBuxS5G+ukR7ScK4la3uycxIxSY2LfZsdVwNQfdKAly1lLE76brazKyWJokEaV9AgizfiyBg6c5RhAMDVSANxqp9u2BMUYzIdgQ4qPrfuWiS4Q2JGO/ZaGtxQg9HUfLmidAbQeAUV3mLGNhY3YC2nB82+vYCkpN9nlkyjpfcIEPYl3zYRgfbISJ115G0ZpsjxeDy44oor8KMf/QjLlkX74YaGBhwaUrW0ubkZDamOODPmxtUInP4IsPjHcScrrF07vOh2LJTqKToAkS0zZgx2iAxFkoDGRuDMM0fYka4D234M7PkNReMz5qPmVHom9zRH0zSnwfnnAyUlZJ+//a2EF1+sxpo1EkIh4Otfjzoxvvtd4Le/TW5XQ5k5k5wZCxcCx45RkXDDxlUVeP114Je/pL8vuyztpkcxiv6WzwesJVnsiEmK6qMJqLEvS2nkGd1Kzzibvg0cejzrZ94ZM2i5d6+Ado9AvvvnAYyiytCpKLJkAUpnRtYxSak7gyYIepqjY4ip0v4OcOAhem+rBCwlFM3evRXo3UF2rQ5/5pk1i5beoAWOCgfqF9ajfG45Qr4QOvZ0wNfhgxbUoFgVTD57Ms659Rxc/ufLUd5YDgAI+8IIeoIDL1u5jaI0ZECxpldnrnV7K8mwYPA47vPPU986bx4wfXp6sgwgWeiZ0V5DGSWM//U4moxWOCKDGRmjCK4WjniiYeqIDKaAyDbymPc3Ad7DgOoBnEUwADBQU6bKlLVfmDwgKxSB5m2hsGFHTaFbxIwVrCVAODL4JGdQjNlWTrOUAh2A5wBQPjf9fQTaaWlnu2dGN6qq4pprrsHHPvYxfPrTnx702fLly/Hss8/iq1/96sC6NWvWYPny5fluJpMLJIkGVRJw9Ghqu0l1u2T09gIf/zgNZhhNix1TNG5F77knhUHpUC8N+gDAtM9m3zhGPBY31bBqfxdoXQtMTS/TgsMBLFkCvPkm8LWvKQBmAgBcLiBSBgH33gt8LcOszxMmAG+8QTb55psUoXHTTcBDDw0ePP7DH4ATTwRWrMjgIIYjo/rkzBrJJMdaFr0X1ALDP5ckoGIRUHc60LkJaHoA6N4MzPt/GWcjmElmiKamgfrmOSOf/fMwvM2A/xh10vYamoAU9uTgQKMMixuY/XWacOWakPr3dB04+AgABXCOJ/sMdQNqgLJ/SDI55ySJbN4aTU00axZwHEB/P0UiWEuskMLSQPSFtcQKxabggrsvGORgsJfZ4ap2wdvhRTgw2JhtbhssdgtK6ktgLxsegRuPYH8QPQcpLVXtvMGR9U8/TcuPfzx1SQaI/T931FNWkVD34G2GaDJaYUeGSVAUZVhOwQHa3qLBtfEXUO2BkejeChxfA1TMB+rPFddISQZOuIs6c3uWRcRzRFIdmbTJSE97DXmD/ceAQBcQ7KYfG4uJZ98EI46MHNk126VYcqanq4H6Wu8RSv+UKzo30v9DyZThtWTyDNumWDLS0zGeotgc9ZlHRJRMo5va/n0ZOjIixdtM4shguxQL6xnlxhtvhNPpxO233z7ss5UrV+IHP/gB1qxZg7PPPhvHjh3DXXfdhYceeqgALc0MvtYpoqnDHMfjx6f21VS3S0QwCFx5JbBpE1BXB/zwh8AddwweMG5oICdGSgPG/mO0tFcnddQUErZLALVnkiOjbS0w5dq0Jk6tXk0OhqEYToybbsrciWFQUQG8+CLwyU/SQNtP4mSZbGsDVq4EHnssA2dG4wrAOQGoPiW7hgpkVNmlo5bqqoWSFNG1ltF93rGXgL2/pXVGBLC/beTvDkl12tgI2O1AIAAcOZJbLbPqn9M5N12nCGk9FP1cttJ6a2nespKMGtusOyP970gSsOg24PBTQP15NDEWoEH7DV+nCIT536FnnyF2OWsWsBaAxxNNCVlWVgZZkTFuyTioQRX+bj8szsHD4O5aN1Y8vAKB3jhOwAj2MjvctW607WiDvcyOsomJnQXhQBjTzp8GX4cPjgrHwPpQCPjXv+h9Ro6MVP/Pc5TK3Ux2yY4ME2EU5RvG/gdpcK1sTmqOjN7dwNEXAC2YvSPD6PjD/RQWVjabojGMXNw5/EfJlIQ6MhmRkZ6uRrpR8h0mZ4Yein/DnsFNU04I5NaRAbBdiiYnejojs0V8SWKXs0XXgW23UYTbqX8yRf/JtimWtPWUrUDprOwOOvXfgKmfpr43E0wYkcF2KRbWE+jq6sJ9992H2bNn44QTThhYL0kSnn/+edTX1+P//u//8JWvfAX9/f3QNA233norTjnFPANvqcDXOgmeQ8COuym39En3DfrozDPJgdDSkjjjyoQJKaR6SoKuA5//PPDyy4DbDTz3HM1wv+46Soly9CgNxJ15ZhrpgXyRKcjOLD0sOWbM22X1yeRo8h4BPPujaSFHwKjdkozHHwfuuiu9lFLxcDqBRx8FqqqiTpJYaGCQHCeXX57m8apOpJfJGFV26ahN7bli/AU0rmQtowvqbwPe+hQ9DydysNmraQA1Zv+yDEybBuzYAezZA0yalDstjf45UXopSaLPh/XP/jZg3arkaYPt1cCJvwR6tgHHX6Uxr0mfjPm8hrRSclNHMxGjyjaBaAeSCC0UneBnLaVnm6HUng60raPIjNLheZkmT6aKKaoG+HyA0wVIMh1TsSpQg4nrxblr3XDXJk8ZdnzLcay5dQ3sFXZc8LML4KyKnwrYVe3CKTcMv3d86y2guxuoqQFOPTXpoeKz8x56Zhx/QfYlBDLELHbJNTJMgqqqWL9+ffxijEbBbmPGzUiIml1udPxvrgRePpvqB6z9BPDmVdHXulW0nUlIqiOTNlnpqTiBkpmUC9URE04Y7AEO/B3oPxixr6sSv/JlX7GppXIA26VYcqanUXzb2yJ2v7GE+8mJAVDOzwLDtimWgulZMg0omZr5Ta3hyDBJSjW2S7GwnkRlZSV0XcfOnTuxadOmgdfGjRtRX18PAFi8eDHeeustbN68GVu3bsWqVasK3Or04Gs9ArZKmozlOQSE+gZ9pCiUngdIPNYSDgP79mV++O9+F3jwQTrWY4+RE8M49jnnAJ/6FC3TGiD2RZ4PTezIYLsE1a+sitTkaU09kf9ItQEAsbUB3n47vhPDIGe1CArAmLZL96ToBNlQL9C7C/Afp8F6a8Xgl2ynZ+U4kw+N9FK7dmk51VJRgI9+NP5nSVPxhXqp7bJ9+HlZyygiunsrsP6rQNNfyImh2IBQV8wBLHl3Yowq2/Q0A9vvALb/OPE2wR5g/Q1Ay7PJ91WxmJZdm+J+bLEC9kgARF8foOs6erp7oAuqgVo+uRzOGie8rV68dstrCHqCaX3fSCt18cUZOJ67tgDHXgH2/S4aSZ9nzGSX7MgoBpz0cAX/8dS2FzW7fKDjd1BHL1uAYC+guEf8UWPGKPEKjAEAYn48Dj0CHHgYWH890LeP6moMu7GoyK99BTtpadKUaUyecE6kpS+Hjgyjfzb6VGZsE6/PLESxtoGIjMJHCDEMw+QMa2k0V3ff7mEfr1hBDoaJEwevHz8eqK8HWluB5ctpsDddfv1r4M476f0f/gBcdFH6+4iLUb/QmPjGmJfxFwCNVwB1qYf15Ls2gPDjaWFg/99ooFzQYCIjmGBXZKJVAOjfT1k9LO7oS4k/6xyIOjL27cttjcnnniMnMEBp0GJpaEgh3ZlRtHvgnByUxcR/HAj7yDYrFgCzvwac9ldgwsX0PbPcpxczsgVoe5vq5MSLjFH9wNZbaSJh82q6HokwUj/37qCaGXFwOgELwujtCCLkCUH1qQh5Qgh6ggj7sivm4ih34CO3fwTOKid6DvTgjdvfGBbloQZVdOzpgKZqg9brOvB//0fv004rpetA05/p/fiPmXriQr5gR0YxYNyY+lKMyBiYXS5oUFZx0sCyZKEfOEke8UeNGWMYhYe0ABUcGvrSAtHCQxWLaOZ72BMpTnsQUL1UyCnFmybhiP6fYYoTdyMw4SJgYiZJK1NkwGmWm+gfpkhIp89MlaMvUcixN83UaLoGVJ1ED3DszGUYZrRTOpuWvbvifrxiBXDgAPDyyypuvXUPXn5ZRXMzsHkzsGwZ0NEBfOQjwFNPpX7IJ58Err+e3t9+O/Af/5HNCQzBiNh3plFMlSkMVScC0z+XclopIH+1W3J2vN4dwMF/AFtvw6CJbYx5sFXSs7niprR7fXuiE1xGYMYMWu7dm7vmffghRatpGvCFL1CtltdeAx5+mJb792dQs0VSaLxBttMz2eI7gSV3AuPPp/W5uE8fqzjHU+1eXQeOvzb4My0MbPsx2Zy1lGpjWJKMATnHU/S4FgZ6Phz2sb3MDmeVC1aE4evyw9/tR6g/BH83vQ8HwnBVu1Iu2h0Pd50b59x6DqxuK9q2t+Gtn701yGnRvrMdL37jRTx3/XODvrdrF/2f2GzAhRemedC2tVS7xeIEJl+TcdtHEzwdtBhwpBmRMZBaSmCKiNhBZXZgMENJp/CQo5YGzQ48BHRvplkf/fspx2/JFMBSgBuC2jMA10TAPTn/x2bMg7UMmPXV3B7DcGSw02xsk4tibcdfBbq3ARULo2nSUkGSgbnfSH17hmGYYqZsDg2mJHBkAJTy4eyzdbjdHVi2bCoUhSIyXnuNiiE/9xwNnP3qV8CXv5z8cOvW0SCcrlMtjO9+V8A5xNaY8x6myUFagCKdAVPWMGSQUW3AkWq3JKwNkCHCj9f+Li2rT6L7DcacyFaqORBoB/yttExhLMmIyNi7NzcRGR0dwGWXAb29wFlnUZ9rsVAKvrTRQwBkcmIAQOkMmtUf6h6eWrXARZVHFf42oGwu0P4e0Pw4ULGEOhJdB/b9AejcANjKgYU/pPGYZEgSUH0KjRsZ9TRicNe6Mf8HK3DPvwcwdxzw5D9UbN26FQsXLoRioetuFO3OhoopFTjre2fhtR+8hoNrDkINqFj0mUWQJAlNrzYh6AnCXm5H577OgeMZaaXOOQcoLU3jYFoI2P9Xet94JWnFsCPDLCiKgmXLlsWvAO9Mo0aGrgGBHKTJscT8s8fpNMxCUh2ZtElLz1QLjAGArEScB5No5kewk8IKPYeA8gXZNToTJl6c092zXYqlqPUMmCsio6i1NCE56zNToWQ6OTL69wFIkEi4SGC7FAvrOXbga50C5XNoaaS6SVAQI56WJSUUifGlLwF//CPwla9QvYAf/zj+bnbtohQSfj8t77svea3TlIhXvFZXgQ3fiu48TmHeQsJ2iZjr1k6pU1Tv8IHiONfNqN2ycmV07M8gaW2ADBF6PF2ndDIAFTs3GWyXQ5FoAq2/lVJNQad1STAcGfv3A0uWiNUyFAKuuorqEk2ZQkXts6oz7DtG42nOBprZL1kAxE9PBED8fXoajBrbNPo9fxvgOQB06kDXZkrtFWin2hiKHTj970DZrNT2OfNLST9ecrobXXDjgyagYpqOs6afBUVRIGX94zuYugV1WPqFpXjyM0+idVsrdj29C5Ikoe9IH8K+MHoO9aDpxSa4ql1Y8fAKPP00jaemnVbqyL8A33EaO2i4XOg5pIuZ7JLd4iYiGExQLMaIyAj20GBv0p10kzNDkqMFnERgcQOlkcLNJiehjkxG5FRPSaKc7BWLqVBtEdhXprBdiiVneqqR/LDppudJFSNiLkeF5TOBbVMsBdPTSFXRvz+976mBaAF6E8F2KRbWc+zA13oEXJOpmGvYM+JvfTwtLRbg978HfvhD+vvOOylVVDAIqCrw+uvA3/9Og24XXgh0dgKnnELrLCKmEMYrXmurpvQwJq5hOObtMva6BTrJ/iRrSrUBE9VuSak2QAYIO573cGTmtAWoWiq2kYIY83Y5FMVJ10vXYmpdJqahAbDbgVBIwr59IaFNufFGioIrKaHaAjXZJhsJdpJzTck8rVA+GRW2afR7ipPGfGQLPXNIlkgfKAPWcsA9Rdghp06lOhmBADnBcqlj9axquGpdVAS80gl7uR26qkO2yHDXuWGxW+Dt8OL4wQDeeou+k5YjQw0CBx+h95NXkQOowJjFLtmRYRJUVcWWLVviV4C3uIGFtwAn3UeFkZNhrwLO+Cdw8m/Eh2/aKgdHZpiQpDoyaZM3PSUlUmQ2t4XC4qL6AU9zSjdrGR+C7VIoOdWz+Qlg/deAQ4+J3zcA1J0NzPg85Uk2AWybYimongOOjKb0Cmo2PwGsXQHs+2Nu2pUBbJdiYT3HDnytU0BWgJrTgLqzkm6WTEtJAm65haIyFAX461+Bk04CJk0Czj0XWLWKZrQfPAiMGwc8/TTgFv0INbR4bSFqzKUI22UMiotmeUsWKhic4nWLV7slo9oAKWIcL6taBB2RtFIVi00xADcUtsshGIWtLWU0IVYNjljYWpaB6dPp/csvHxCm5a9/Ddx/P/W1f/sbsHBhljsMddG56BplFzF50e5RZ5uKE3BNiDgw+qkPLJlKqXCt6eRZiiHQSdFDQ5BlYN48er91q5ZzHS0OC6xOK2xuGyRIkGQJFocFzmonLE6avbBmDdV4WbgQmJxOJnPFBiy6FRh/ATDuvNycQBqYyS45tVSxUL0s9W0tzuRFctIlUQdv0o6fKTKG2ZFOM4TzVRCudyew+fuAexI5C5mxjVFbwHckN/svn0cvhhGNq4FmOoW9VFPLSEs5EoG2yAw1c09UYBiGEcbcbwnZzWc/S0WPr7gC2LIl/jbHjwNr1+ZowNl/HAj1UJSnyNqITG6xV0XSqnTS80eKE7ni1W7JJYqSYS0CAxOnlWJiMApbBzqo1o4xjqT56AUkLWw9cyYV5G5uFuOseuUV4Gtfo/d33kk1MjLGOLfePRQJYHEPj3riot35wXCQWSM1HhzjyZkU6k5/XwcfAfY/BEy4KG59ywULgA8+ALZvBxob09u1qtJv9tGj9Pt+5pmppdLTdR1tH7YBAKxuK6SYfv3lV2iZkS2XzgBm35DBF0c37MhgEjP0Ry0e3PEzmRLPvrQQhSDrGt3YO2pzb19GjmGRNWWY4sU1gZa5Si3FMLlCtgDuyVTstb8pDUdGOy25D2QYhkmbCy4AKiqA1uETQwe46Sbg8svF1TEAQHUWvM10z8zPYsWFtZyi0bUQ1coYLRMJYouZa0GgdzcNVNpr6N6EiyObkywLW0+LBAS/9VYF1qyRcM45mfd1e/ZQXQxVBf7t34D/+q/M9jOAoxY47W/AB1+j2fszrqPC87GwXeaP0hTrYIyEeyotuzbH/XhBpOTq9u0SLroo9d2uXk0pzQ7HDAM0NFDdoFQmIzirnfB1+OCsik4q13XgjTfofVpppUJ9mUerjAHYkWEikhZN6W8COtbTwESyUOhjrwA9O4Da07JPXZLlj1qhMEPxmdFEzvSMZ1+aCmz9Pt0IT7oKmLQy9/ZlODJyXLOA7VIsOdPTGXFkhPrE30AYRQ9tVUDpdPHp/zKEbVMsBdWzZBrdL/iPp/6dAM0e4t/y0Q3rOXbga50iuk6TFhz1lL4hDqlouXZtcieGrlNB8LVrs5zdHosWAvxHAOiArTxaT9HEsF3GItH9ZbAbCPWn7cgwpZbxitBDp/RE70eK85qsCD1gUi0LwdDC1moQ6NsFOCdSBFECVq8G/vxnev/OO5U477z0Bn5j6e6mgd6uLqor9PvfRwvMZ4UaSSNlr6RiySIzl+QQts0kVCyg52jfUXJQOeoGfRzryEhVx9WrKSXk0Oy8LS20fqT6QJIkoXJ6JcomlkFxRI/Z3w94PEB9PaWgTAnPQWDDN4DxHwOmf07QP4IYzGKX5hhFYWCxWHDSSSfBkqgKXO8uYP+DwPHXk++oazNw9IX0i30mwlFLA26JXia6EQFS0JFJi5zrOdS+ymcB0z9PYZ/t6/IzwyyY+4gMtkux5FRPxQE4IukZvC1i9x3qAbb9CNj4TZpBaQLYNsVScD2nfgY4/RGg8ROpf2cgKs08aUkKruMog/UcO/C1ToMPbgTe/wqlGI1DqloePZra4VLdbkTC/ZHo5RBgcQElM1CQGnNpwHYZB0sJLcN96X3NrFrGK0JvrSQnm0mL0JtWSzOw/Q5g038D7W8n3MQY+O3uHrzeGPhdvTr5IVQVeP114O9/p3RSn/wksGsXOUKefBJwiCqr0hapsly5tGicGGybI2BxAWWR6I44URnz59Nyzx4JixaNrKOqUiRGvBKDxrqbbqLtkiGB6mPEppXq6aHlJZdQ/Y6UaPoLORMDbaZyYpjJLtmRYRJ0XUd3dzf0RAU6HZEUESPNsszDoKyZGVFHJi0Komf9ueRVD3aTUy7XDERk5O5/hu1SLDnX0zmRlj7BjoxgJy2t5ZQGyASwbYql4HraKtJ7SAt76QWYypFRcB1HGazn2IGvdRq4IomzEzgyUtVy/PjUDpfqdklRg8DuX1FEhqQAzkZA9UcL15q0eC3bZQxGQWXJCuhhishI47qZXssiKkJvei0LSflcWvZsj/txtgO/q1cDU6YA554LrFoFnHce8OKLgM0GPPUUMC7F7KgpMeFjlFJq4iUCd5pbRp1tGv3e0Fc2v1cVi2nZPdyR0dAAlJUB4TCwfn3viDquXTs4ndRQYiMr4xH2hRH0BIe9Qr7wgCMj5bRS3VuBjvcp4mTqZ1L8Un4wk12yI8MkqKqKnTt3Jq4Ab4QM+4/H/8UwMOHMynwyoo5MWhRET9lCaaUAoPlxemjLJYHI4HIOnX9sl2LJuZ6uiCNDdERGntKYpQPbpliKTk+jPobFTdFIJqHodDQ5rOfYga91GpTNpmUCR0aqWp55Jg2aJJo0KUlUbPTMM7NpbISDDwP9B2lQ2FEHqP1UKDX2pQVMV8OQ7RLR2oBaIHqdnBMoOj2N61YUWoZ6gK5N4icECaYotCwU5ZEp7T3b4449pTrwe/fdwMaNtG0gUhLTiOSI9/1gEDhwIPvmD8JeDTR8HKhcInjHuWPU2ObQfk/k75VxPbs2D7NRSYqml3rlleNJddR14OWXUzvk0MhKe5kdrmoXwoEw/N3+YS9PTxjdQRd0mx3nn5/CAXQd2BfJ1TbhouiYhEkwk12aY0ooMzKOOvqPVAN0c2CrGL6NrkcHJXI4u5xhcs64jwKHHgH87cCxl3I7gyJovsFlpsDUnAbY6yj/pkjy4DRjGBx6jGbyTLkWqFyUfFvJQlFwkjnynTIMw+SNAUfGLnqGyjB9g6JQPviVK2kXseMpxi7vuUdQoe9JV1Hu7AXfSz7AYcIahmOeIq09mRHBTir2rYUK3RImU0pn0eTCQCfgPwY4B4eUpZoq79vfppdBWRng9SaelytJFMlx+eWC+kymsOSy3yubTfWtgt2A9xDgnjzo43nzgHXrgJdfrsbppw8vQh8OA48/DvzsZ8AHH6R2yKGRle5aN1Y8vAKB3kDc7X9zP/D4DjvOOc8NdyqlkNreBPr20OSyyZ9KrVFjFHZkFAuyhQa//O2A71h8R4bqJUcHwANlTHEjW4HGq4A999NMuVw6MiZeSgXqnOYvlMjkicoluZm1Y6SWYqcZk0v6m4CeD4G+3SM7MlwTgLnfyE+7GIZhzETJNHq+CvXGHahLhxUrqBDojTcOnmXc0EBOjHSL3ibE4gYW3iJoZ0zeGVpQeVSi8cSd0YBiA0pnAj076J5ySP+Yaqq8adMAnw9oa6OB494RSqTEpvA555zMmj6IXb+i86g/21SRx2OKXPV7spVSL9kqh2WjWb2afpMBYO3aqkFF6C+8EPjTn4Cf/zwa/eNwkJMjmZOtpiZ+ZKW71g13bXwvxTPrAC8SpJXytw128GhhSh0Z9gDjzmNH8AiwI8MkSJIEp9MJKdlsIMc4cmT4jwPlc4Z/bqQtsbgBxZ6bhpqclHRkUqageo47jzzrFfNzexwjjVUOYbsUS9HqaTgy7OZxZBStlibFFHqWTANa15JDo0gxhY6jCNZz7MDXOg1kK1AynSIyencOG6hLV8sVK2gW8dq1NFt5/Hga9Mh6VnHbOnr2a7jCVEU/04HtMgH+NmDvb2ng/8Sfp/QV02sZ6gN0lcYiLKWFbk1STK9loSmfT46M7m2ULSEGI6VeS0v8gV9Jos9376Y+UNOoKPif/wx861sjHzrViI+keJqp3ubxV4C6MwTsMH+wbaZIw+XDVhmpy4baZUsLcOWVQEkJ0N9P62pqgOuvB776VeCNN+JHVhp0d1OER6ppIltbgXfeofeXXjrkQ38bsG5VdPwWoHpXvqMAJBozOPIMRbOYyPltJrvkGhkmQVEULF68GEqyu92BOhnH4n8+MEg2NutjACnqyKRMQfVUbLl3YuQJtkux5EVPTzMNHoT6xO3ThDUy2DbFYgo9S6bRMhVHRqiPZgCZDFPoOIpgPccOfK3TpCwyMax317CPMtFSUWgW8ac+hWFpLDKiZwew825g35+Atrey3FnhYLtMgKWEUkH27aHJiilgei39rVTE3FJi+iL0ptey0MTWyRiCkVIPGO5fjZdST5aBqirgxBNTO3SqER9JMfrMyiU00beIYNvMjFSK0Pf3U6TQr38NHDwI3HILOTSMyMqJQ7I2NjQAy5ZRRNFllwHbtqXWlueeo2OecALtYxChXhoXkO2AtYJejnFAxUKKIFKc9HmylFwFwEx2yY4Mk6BpGlpbW6FpWuKNJl0NnPQrmpETj8olwJmPAgt/mIMWFgcp6cikjGn0DPYAvXtys19PMxD2it93DKbRcZSQFz0/vBPYfic9XIqi4TJgxuejDwYmgG1TLKbQs2Q6LX1HaHZPMj78H2DtCorgMBGm0HEUwXqOHfhap0nNqcDkq4G6s4Z9VHAtvS3AttsBNQhUn0z1u4qUgmtpVizO6G9274cpfcW0WlrLAGs5EI5MkJBk0xehN62WZqF8Lj23zPuvuB8nG/h97LH4KfWMSI5EE7olCWhsTH3We1LaI46M2uKKxgDYNtOiv4nqA3oOjliE3uB3vwO+/GXA5Rq8fsUKSjf12mvAww/T8sABYM0aYPlyisq46CLg0KGRj/H007SMm1bKQHGSk814WcupNrLiHPkABcBMdsmODJOgaRqampqSG4VrAqXaSZY2SnEAjrEbkZGSjkzKmELP7m3Au58DdvwPoKli9922Fnj/K8Cue8Tudwim0HEUkRc9nZG7cm+LuH1WLqEQWHejuH1mCdumWEyhp62c0pfpOtC/P/m2gTbazlqen7aliCl0HEWwnmMHvtZpUrEAmPppoHzesI8KqmWwB9j6Q4qaK50JzLsZkAs/AzJT2C6TYNhed2rTfE2rpaMWmPY5oHIp0HglcNZTwBmPDn6ZLE2KabU0CxY3PbeUzki4iTHw+/LLKm69dQ9eflnF/v2J6wKlG8mRMd4WoP8A9ZvVJ2e5s/zDtpkGhx4Fmv4CtL2dckqy1tbEn8WLrHS5yDExbx6lqLroIqCzM/E+/H7ghRfofVJHBgBAo1rHRYCZ7JIdGQzDmJvSGeSg8x0DWteI3fdAqh8uRscMwRWJAfUJdGQwTL4wZngmSy+l60AgkspiDKekZBiGKRj+NqBv3+BXz4fAB1+n97ZyKu7NRWpHLxULaBknfU/RUX8WMGUVMPVaoHT68JeJnBiMOBQFOPtsHRdc0IGzz9ZHdEJkEsmRNkZaqYrFgNXctVqYLKlYTMvuzSmnJMskdVlVFfD882SnO3ZQ3QtvAv/D668DHg8wYQKwdOkIOw50kiO7f2/6jRrDcLHvYkILA4efpIJvM74IyEMu34G/06DEhI8l9ZwzTFGhOICGTwBNDwCHHgHqz6FwZREM1JVhRwYzBOcEWoqKyAj7gK5NNGBcNlPMPhkmESVTgf59lKc6EWEPoAboPTsyGIYZq4T6qNi3xR03MiNnxCv2CdDEnbAHkBTqw7Vg/trE5B/D5jyHyBaLedDVPYlSETGjh7AXaH+b+qWp1wrb7YoVwOWXA2vXUmHv8eMpnZSw1PttxZtWikmTyogjo3cnzlzuR0ODY8Qi9JmmLmtsJGfGGWcAb78NfPKTwBNPAJYhw7JGWqlLL6X6MEkJtNFScSXfjhlE0UVk7Ny5E3a7HbfeeuvAuqNHj+KSSy7B4sWLsXDhQvzmN78pYAszQ5IklJeXJ68ALynAwb8DR56PGnws7e8AR18Egt05a6fZSUlHJmVMo+fES+jG3nsEaH1D3H6Nh8ccOzJMo+MoIS96io7I8B4Gtt8BbP+xmP0Jgm1TLKbRc/Iq4LS/UEqARBjRGNYyQLHlp10pYhodRwms59iBr3UGHHsJ2HobcPipQatzrmW8Yp/WCnIsyzYaFA73m67YZyawXSbBWhZNOdozcp0M1lIcrGUKaAFg5z00mTDsSbhZJlrGS+EjBDVI4wayFag5RdBO8wvbZho4xlFdCS0MpX97zlOXzZ9PjgqHA3jmGeCLXxzsNNH1FOtjADRRIdRHjSuCSWVmssuic2TceOON+MhHPoJQKDSw7sorr8SqVauwefNmrFu3Dg888ACee+65ArYyfRRFwdy5c5NXgJckwFFP7/3Hh38ezM+grJlJSUcmZUyjp+IAGj9B7w8+AuiC8vINpJaqErO/BJhGx1FCXvQ0IjL8bdFZ69lgRP/k2NbShW1TLKbRM5Vc6saECBPeOJtGx1EC6zl24GudAaWzadm7c9BoRN60HFrs0z0ZqD7RdPcL2cB2OQKVSygyQxpZH1NqqevA3t9T5LGoZ7Q8YEotzYatkuq06npSR5uptFRswOIfAcsfMlVx+XQwlZ5mR5KiURldm/OSuuyMM4B//IOiLf70J+D73wdUlVJK/fSnQHMzOTo++tERduQ/TpGXsgPQQuQsDHsA1Zd9I3OAmeyyqBwZjz/+OOrr63HKKVHP6pYtW6CqKq69lkLdSktLcdttt+F3v/tdoZqZEZqm4fDhwyMXTnGMo6Xv2JAdhKgwHDCmHRkp68ikhKn0nHAJYC2hme1GuGi25Mn5ZyodRwF50dNaRvYGAL4j2e9vII2ZuQYm2DbFYko948VWA6auj2FKHYsY1nPswNc6A0pnUMrSQOegNE951VL1AYg5jmTN/THzCNvlCMy4Djjhp0D1shE3NaWWPduBw/8HbL+TUmEXCabU0oyUz6dlkjouptTSUrypekypp5mpXELLrs0AokXoX3lFwy9/2YFXXtGSFqHPhMsvB4xEQD/+MVBTA5x7LvCd70S3+de/EnzZWkY1WoOd1GfKNiDUHX1pARqfMpkjzkx2WTQ1MrxeL37wgx/gpZdeGuSkePnll3H22WcP2vbMM8/EypUroet63LCXQCCAQCA6w7a3l0J2w+EwwmH68ZVlGbIsQ9O0QRfKWK+qKvSYwYFE6xVFgSRJA/uNXQ8AqqoOLJubm1FfXw9JkgbWG1gsFui6Ds1WC0nXoXuPAKoKRVGojd42yLoOyFbokgsKkLDt+TqnkdYb5xS7XpKk6DnFtDHR+qHnZOhYV1cHm802Ks5ppLbn8pxUVcXhw4dRX1+fUttze042KBMvg3Ton1A9R6DHnFdG1ynkhR6iCk2aXA4p9v9J8DkZdjlu3Djous62l+U56bqO5uZm1NbWDnxX+DlpGjDlP6ArDkCphBL5Pcn0nCRfGyRdh2yvNtV10jRtmJZse5mfUygUGtDTarUW9Jy0fX+GdPxV6FP+HXr9ucPPyVoLue5syGWzTHedYnVUFIVtzyT3RkPPjTEfxkPmuHHjII+YmJkBACh2qivUt4+iMhyUUz1vWuoq0L8HgAyUzhqVhb3ZLsVhSi2Pv0rL2tNNl6oyGabU0oyUzweOvpSSI6PgWob6aIa7rbJwbRCAafQsFioW0dJ3BFD9gOKAogBnnaXB5dqDZcuWQVHE6/iFLwCvvAI88gjQ3T34M78fWLkyQRSIoxaY/11g220Ulbn0LnJmxGIto+1MhJnssmgcGXfccQeuvfZaTJgwYdD6I0eOYPLkyYPWOZ1OOBwOtLa2or6+fti+7rzzzkE1Ngw2btwIt9sNAKitrcX06dOxf/9+tLVF61E0NDSgoaEBu3fvRk9Pz8D6adOmoa6uDtu2bYPPFw0FmjNnDioqKrBx48ZBD6WLFi2CzWbD+vXrAQC6rqO7uxuapsHn82HLli0D2yqKgpNOOgk9PT04frgPdX3d6At8gC7PiVi8eDHa29txZOcbmNzTjaBSjc49ezB37lwcOXIEhw8fHthPvs/JYNmyZQgGgwnPaefOnQPrnU7nwDk1NTUNrC8vL0/pnAwdjx49ismTJ4+KcyrkdTI6qN7eXuzZs6fw5zTzbFScfD42bN0P9Xi0/Rldp94etKmnQtH60bppe07PSdd1BINUrJFtL/tzmj9/PoLBIDZs2DDgrM7NOZVH1u7M+pzqezaj0t+DCluVqa5TSUkJenp6BmnJtpf5OTU3N6O7uxsbNmxAXV1dQc/pWNNuVHoPoNPzGjqPVCQ4p/OxuGEx2ltbTXWdNmzYMKCjJElseya5N/J4EufHZpiipmx21JFRl+fisL6jlNNdcRTVIDCTA8IecmyZbBZuUtRgNFK+/iOFbQuTG4yIjL69lG5XsRe2Pck49jLQ9Gdg4mVceH4sYasAlv6cJiXI+RviVlXgrREShdx0E0VvDMvG1PshpZOccBFQPjdXTRy1SHrs1CuTsm/fPlx66aXYuHEjHA4HfvjDHyIcDuNHP/oRPv/5z+OUU07BF77whUHfmTRpEtasWYOpU6cO21+8iIzGxkZ0dHSgrIxuHAoRkbFhwwYsW7YMFosl8Qy91nWQP7wTKJ0B/YS7ozPxjr8BeeddQPk86IvvHNOzDjds2IATTzyRIzIERWRs3LgRJ5544qDopmI+JyD/18mwy5NOOmkgoqDYzynZ+nxEZLz//vtYunRpbiIygh1QA12Dz0mOnJMaBizRGRIpR2Rsvx1S5weQ59wIfdx5prlOqqoO05JtL7uIjA0bNmDp0qWFj8g48hKk3f8LlM+HtujHRXWdDEelYZdse+a4N+rt7UV1dTV6enoG7peZwfT29qK8vLxgGoXDYaxfv37geYJJkeOvATt+DpTPAU74GYA8aNm3D3j9YkrzJ1uBsrmApST6edhDKSbOeBQonS7++HmE7TIFmv4CND8OTPokMPXahJuZTsvWN4EPf0r3xaf8cXiFXRNjOi3Niq4D7/wHpd9b/GOgctGwTUyj5YZvAb27gJlfBiZeXLh2ZIlp9Cxycq3j669TOqmReO01Kmg/CC0MdG2k0gHuRuFtywWi9BRxr1wU/xU33ngjfvSjH8HhGB5qa7fb4ff7h633+XxwOp1x92e322G3D/ckWyyWYRfEeLgbivEAmur6RBfaWC/LMurq6gYeGuNtL0kSLO6JdIMQaB1w68myDFmNVLt31A5eH6ft+TqnVNYnOtd0226sN3Q09jkazmko+TwnWZYHUnuIaLvQc+pvotowZbPjtn3Y9kPI53Uy7DLRtum2PdH60WR7BvHOSdM01NXVDQwUp9L2lM/J3wasWwUl0EEFC1UfLa2l1EaAclYuf3hQuOeI5xTqpj7aXmWq62TYZjwt2fbSPyer1TpMz0Kdk1I+k2zOexByjMNvYHt/O81gGuGcCnGd4umYaHu2vfzdG/HDtPkx7tsS3WswCSibQ8u+fXRvKVtzr2W4j+45oAPWSgASOS8MTFrsMxPYLlPAUR8pqJw4fQ9gQi2NtFL15xaVEwMwoZZmRZKA8gVA6xuA52BcR4YptPS3kRNDkoDa0wrXDgGYQs9RQK51PHo0i+1kC1B9ktD25Boz2aXpn0ief/55eL1eXHnllXE/b2howKFDhwat8/l86O/vR11dXT6aKARZljF9egqzbVwTgWW/pJudWBo+Dow/H9CCuWlgkZCyjkxKmFbPQ6uB3b8C3JOA+d8bfuOcSk5BfyvlULTXApb4Tk9RmFbHIiWneoZ6qdinHHF2e5sBWABXZKaE6qPPQ73p5a2cci3gPwa4p4hucVawbYrFVHq6GukmOeyh/s4Zc9+g68D7X6QBu1P+ADjMdb9kKh1HAazn2IGvdYY4xgFzbqIaFVJ0kllOtWx+HJAizkPFQRMehmLCYp+ZwHaZAgPpe3YNONPiYSotgz1A5wf0vj6Fackmw1Ramp2pn6Gi9LbyuB+bQsv2dbQsn1/0NTJMoWexoevAvj8CneuBhbcCzvqc6zh+fIbb6XrROX4Bc9ml6R0Z+/fvx+HDh7FkyZKBdceOHQNATo67774bN99886DvvPHGGzjppJNM4SlKFU3TsH//fkydOjV5u2UrUDIl/meKY1QWiEuHlHVkUsKUevrbgN2/BLo20c1z+9uUXzCWODPmh3H4SeDw08CklcC0f89li82pYxGTFz0VJzm4PFa62VBsgBR5qNQCyb8bj5qTxbZPEGybYjGVnrIFcE2i6LX+fYMdGaFeymsNmPJhz1Q6jgJYz7EDX+sMkSRg3EcHrcqplq1vAp2bgOplwNxvJZ7kYMJin5nAdpkCrga63qFeqkWQIGe6qbT0twLO8YDiovYXGabS0uw465N+bAot296kZe3phTm+QEyhZ7EhSUDfbsDbAnRvBpwX5FzHM88EGhqAlhYaLojXpIYG2m4A31Fg83eB+o8CU1YVlUPDTHZp+v+KL3/5y9i9ezc2bdo08PrSl76Ez3/+81i/fj3OOusshEIh/O1vfwMA9PX14ZZbbsENN9xQ4Janh6ZpaGtrG5TjmEkf1lEsptQz1AuEegBbdWS2sQ+wVkRfsj06Yz4ZgQ5a2qtz3GCT6ljE5E9PGZAjhTfV4SkMRwNsm2IxnZ5GXvX+psHrg5H+z1aRcNZnITGdjkUO6zl24GstjpxqWT4PqDqRZjmP+yj11fFeo8CJAbBdpoQkARWRqIwk6aVMpWXZTOCk+4FFPyx0SzLCVFoWOQXX0t8O9Oyk9zXLC9MGgRRcz2KlYjEtuzYByL2OigLcey+9H+qPMP6+554hhb6PvUITc/v2FJUTAzCXXZrekREPq9UKq5UevCVJwpNPPom//vWvWLhwIU455RRcffXVuOqqqwrcyhzS/i6w+z6gbV103c57gF3/S504w4wFXBNokFkLAHqYojIsbppJnwqGI8OWe0cGU8QY9hTqy3wfwS6g7e3hg8kMk2vK5tLAiH3IYJi/jZZD1zMMw4xVwl6K1N3z29wfy14FLLwFmPzJ3B+LKR7KR3ZkmA5JGhXpz5gUaF1LM8lbni10S4YzkFZqLvWvzNikcgktu7fED5HIAStWAI89BkycOHh9QwOtX7EiZqWuAcdfoffjzstL+0Yrpk8tFY/vfve7g/6ePHkyXnjhhQK1pgD07gSOPE85XGuX0z9p6xpACwOTryl06xgmP0gWyuvuOwZ4DgDOCekNyhkzkvlmh0mGrQoIdgOBSPh8JvTsBLbfQYXpl94ltHkMk5Tx59NrKIHIpAdHTX7bwzAMY2b2/Z6eqyZfDcil4vcf6IhGAksSgOKajcnkmLJ5tOzZQQNekonnnHoOUW0ZxVboljD5ItAOdG2hSV4TLyl0awZT/5HIpMaSQreEKSRlswHFTvV7PAcAR2NeDrtiBXD55cDatVTYe/x4Sic1KBIDIAeLv51stdqcaaeLhaJ0ZIxGZFlGQ0NDarnGjELffqoVglAvOTEAGnQbw6SlIzMiptfTMZ4eCtUAzTCOdWTE88L72+j/RdcBTzM9JAR7gb599HmOchGbXsciI6962qsA7yGqJxDsjqaaSodgV2Rf5ov+YdsUS9HoaTgy7OZ0ZBSNjkUC6zl24GudBRYX1RXyHAR6d0OuOkmslv5WYP31lPZk5pdpsGWMwHaZIiXTaJZu2RxAV+M6Mkyhpa4DW38IhD3Aoh9RiqkixBRaFhPlhqPtw2HFiguupbVkWJ2jYqbgehYrsgUoX0B1VLs2Q544OW86KgpwzjkjbHTsZVrWn12UTmAz2SU7MkyCYRQp4RxHS/9xWg7KdT22L2laOjIjYno9ZStQsYAG5eSYQve6Cmz+DjDhYzRDo2QKOTHWrSLHh64C/QdoItx710VvxFIpEp5JM82uY5GRFz1VX/S9pYwiMgId1M+my0AfbT5HM9umWEyrZ9gHQKMZQECMI8OcqaVMq2ORwnqOHfhaZ0nZ7IgjYyfkmlPEaanrlAY47KNCnyasTZRL2C5TRFaAOTcm3yQdLY0JXInIdAJXz3bat8UFuCen/32TwHaZJiXTyQEb6gO8zYB70sBHrKVYWM8sqFwccWRsgtx4hXl0DHuA9rfpfX1xppUyk10W3pXCAABUVcWOHTugqurIGw9EZBynG+M8Fi02O2npyIxIUegpWSkyw1YZXRf20P9F8xPA+huA9TcCzatpNpxsBxQXOf1kF30vnSLhGVAUOhYROdXTWkZ9qRYAQt30UmyUVsripPX26vTyAQc6aWnCNGZsm2IxpZ57fwe89UlKSWlQsQioP5ceSk2IKXUsYljPsQNf6ywpm0PL3p1itWx5BujeSoOAc24yd8qgHMB2KY6UtTQmcL15VeLXulXRmlnpcPxVWtaeXpSzig3YLtNEtkT7yO5tgz4qqJZ7fkvP/NnUMzQZbJtZULkEcNYDrgZz6di6ljI8uCcBpTMK3ZqMMJOeY3v6vonQdR09PT3QUylKY6+lGeRqkFKWcNHiAdLSkRkRU+sZO2N+6HpLKTDrq0DfLqDjfSqy3L2VZtnZqgBXI+CeAkCKzlAGaJA6B5haxyIkp3o6aikqR+QMtmDEkWHCiAy2TbGYUk9bJU16iC02n6h2hkkwpY5FDOs5duBrnSXGIF3fHuhqWIyW3hZg/wP0ftpnM6+3VcSwXaaBrg9EBWH8hYPS99DHKWoZ6qUxAtlONQ2GovqiE7jSuadVg0DbW/S+/tzUv2dC2C4zoHw+0LWZonImXjywumBaBruBI8/S/03t6YA1B7WNCgDbZha4pwCn/AEAoIcF/Y6LoGwOZQspmTqsXy8WzGSX7MgoRmQLOTP8rRSVYTgyuGgnMxYwZswHOhI7Hhw1QN1ZwKQraXZG6xrg0GoKMwz10sz6dGbUM2MLR23ih7pgd/wHwmQYjgyOmmMKQck0WsY6MhiGYZjhuBpogkvYA3gPZr8/TQV2/oIGfyuX0CAGwyRDCwEbvk71LysWAa4J2e1PcQ6etDXoWBlM4Op4Dwh76T65fEF2bWOKj/L5tOwdXiejILSto3aUzgQcdYVtC2MOCm2TiSiZAsz6SqFbMWpgR0ax4hxHjoxAGxCOzBzmiAxmLJDujHlrKTDxUqBsLtD5HqWiksZWbmJGEE1/AQ4/Ccy4Lr3BiIGoOfNFZDBjAMOR4WsBVD8gKZTuzF495utqMQzDDEKSgLJZNOPY1wIgzYkLQzm8GujdRQPJs2807wALYx4UG1A6iwoq92zP3pEBUG1AzyHA4qB0vNlgpJWqP5fteSxSNpues12TyRGmOEb+Ti5pj0QH1Z1R2HYw5kOL9HvMqISfYE2CLMuYNm1a6hXg53yD8vxbnDTzfNp/kjd6jJO2jkxSTKtnshnzyZBtVA8jz5hWxyKlYHraKmiGXMuzwPiLUn+Am/kVisow4Uwhtk2xmFJPWyW9gl1A/wFyZGz4BkWunfrnQrcuLqbUsYhhPccOfK2zxN8GTLgUmHwNZFgxc1wXZM9+wNAzUWrJhEWVFepzJ109piPn2S7TpHx+1JExJA1kRlr6j9LkxwCAUD9QmmF9rLAH6NpA74s8rRTAdpkRih1Y/tCwZ6C8aDm0nw31Ae3v0BiYYyJ9nsn4gAlh28wCfxuldNz8XShaADOn3pLa73iu0DWg6c9AzXJKL1XEDmAz2SU7MkyCLMuoq0tjkGtoipJCe8NNQto6MklhPcXAOoqlYHqOOw/Y/yDlLu7ZDlSkGFJv4llCbJtiMa2eJdMotV5/EznkAFNHcZpWxyKF9Rw78LXOAqM4ciSKUgYwrJe0V1NUcOwgyJDvDUPXgD33A3VnjppBtnRhu0yTivnAoUfpXnMIaWuphQDfMXovSTSpoedDwDkx/XZZ3MCy+4CuTZSGrchhu8yQOAOxOdcyXj8b6qX1ih1Y/+X4/XORwraZIbF24jkESQuh2vsFwOKKbpNvO+naBDQ/CRx7BTjtL0WdGcRMdll4VwoDgCrAb9682RQV4IsZ1lEso1JP1Uczioa+EhUPF3HI0ahjASmYnhY3UHcOvT/ybH6PnSPYNsViWj1j62QE2um93byzg02rY5HCeo4d+FpnQWxxZGsFdGs5+gIW6NZyiuaV7dHiyEm+R6+y6HvFCQTjfG8MwXaZJmVzabDYd2yYgyxtLf2t5EyzltJ+ZSvVuAj3ZdY210Rg4iWZfddksF1mSbB74G3OtYzXz2rhSO3Y+sT9c5HCtpkhsXZiq4IuW+APBEb+Hc8lx16mZd3Z1P8WMWayS47IMAm6rsPn86VeAT7YBRz4Oy21IHkWZ35lzOe7TltHJimjSs9UioTbq3NSBHxU6WgCCqrnxEuAoy8A7W9H6gyMUPfCdxTo308z19yT8tPGNGDbFItp9axcTPcLlUuAvt20zsSODNPqWKSwnmMHvtYCUJw0yBHshK6VQVfqIBkzkJMVRzaKKof7gb699JtvRL5lUlR5FMF2mSYWF+CeSpMPerZTGukIaWtpqwBkBbCUAZBov6FuSlHNdsl2mQlaCHj/q/SMs/xBwFaRPy1ji9dbnGTDrnFUD2EU2TPbZpYoToq4CPUAagC64k7td1w0oX5KfwZQZocix0x2ObZHvYsaCTjyr+ifigOYdUPhmsMwZifdIuEME4+SqUD5PArLP/o8MGVV8u071gN7f0fppeZ9Oz9tZJihVC6mFwC0r6Ml93UMwzDxiUTv2tQAJE+IZserQUD1DpqFjPZ3KULT30oDFoqN7jO1EG1n4hR+jMmpWBDXkZEysRO4FAegB4FQkD6TrTSYZ68GJBk4+E+g8UpyeCTiwD8Az36g4RNA+ZzMzokZHchWSucE0PNQ7fLCtKNkBgANgAxonsK0gTEv1lIAgKz5KQItB5NVR6T1DbofKJkSjY5nhMCOjGLFWk4/IGrEo2ivLurCMQyTFzItEs4wsUy8hG7cj78GTP5U8r432ElL2wiRGwyTL4ogtRTDMExBsZYCgXbImg8IhGmdHqZUJqo3ul3fXqD1TSo6q/oAKfJoLdsA9+T8t5sZPYy/AKhaRsVhM0FxAKf9LXkKKUspsOdXQOdGoGcbTbgxZrvHouvAsZfIYVd7BgB2ZIx5yucD/QfI0VYoRwYAzpTPJESyUnRbqAdS705yJCjO/Lbh+Cu0HHcej9UKhh0ZJkFRFMyZMweKkmQmRCySBDjGUdFZYHjx7zFK2joySWE9xcA6iqXgetYsB2Z8Aag/d+SbEiO3sUkdGQXXcpRhaj21MOA9DPTsoL9N7MgwtY5FCOs5duBrLQh7DaDrkMMBwNBSDQCqhwZ/DSoXAw1X0Ex1xR2ZpSxROh+JH7MN2C4zwD05rjMsJS11HdhyC6Wcnn0j1bVIxPiLaHJO50ZgwzeBBd8fvn3PdnJiWFxA9SkZnpD5YLvMgvL5QMuzQPc2AHnUUguTPbonA5aS3B6rgLBtCqJkOhQcBtQ++l02JoF3b6GIt9rlUeetv01s5g5PM9C7myLdjBqbRY6Z7JLvsEyCJEmoqKhI70uO+qgjg0OXAWSoI5MQ1lMMrKNYCq6nbAEaLkttWyMiY6RaGgWi4FqOMkytZ9OfgcP/R+/rz6XJECbF1DoWIazn2IGvtSgkSI66wQ/KYQ/VFrDGODIqFtAMz/0PUCHReLPZGbZLgaSkZdtbQN8eisoYySZrlwPOccC22wFvCzkzZlw32Ily6J9k/1UnAN7mUZOKl+0yC8rn09KzHwh7IFncuddS1wBPE6Xq8RwAyhfk9ngFhG1TDJLihFI+E4AOQAIQcWQceQ7wHQP2/gaoPpX6tu0/iT63x8NeTWnKU+37Au20bck0wFae5ZmYAzPZJcdimYRwOIz3338f4XB45I39bUDfPsppGfbQS1dpXd8++nyMkpaOzIiwnmJgHcViOj21JO0YSC1lTmez6bQsckypp3HPINvpfsHVADRcTjfYJr1nMKWORQzrOXbgay2ASH0MLdSP3q6j0EL91HeqvpS+N+w10vfGAGyXGdLfBOz7E9Dy3MCqEbXUwsD+v9L7xitpFvJIlEwDlv4CKJ8LBDqBtz4JvHoB8OZVwNqVwK5fAp0fAIcepXXrVpny3iFd2C6zwF5FDjBdB3p35l5LLQj4j1GfCglwThzV/SzbZpYM+x0fYicVi+l5SA1SHYttdwCdG4BgDz0vWSsGv2Q7ZVlIFrExlKoTgFP+SFFxowQz2SVHZJgIVVVH3sjfRjcPgQ76RzNyXXsOAgf/Tu/T9RaOMlLSkUkZ1lMMrKNYTKFn93aahemeCsz6SvxtAoYjozJvzUoXU2g5ijCVnrH3DGqAUkt1bQQ63o1uY9J7BlPpOApgPccOfK0zJLY4shaApOuQVS+kkBpNI2mvHl4wdMj34hLve2MMtssM8BwEmp8AymYDEy8eWJ1UyyP/AnxHyYHReEXqx7JVAIt/DGy9LXKP0EcDfeF+mjypOAHHeBoINAb0THbfkAlsl1lQPp9mtXdvB8oW505LTQX2P0hLgPpT1Tu4XpGxfhT1s2ybGZDq7/jkT1IKyb69QOtrwOGnaWJ4qJsyL5TOGr7vRL/vyZCkwVGcowCz2CU7MoqNUC/9Y8p2GhgLddN6WwUVAB9lNxcMwzCmRVeBnp1U7G7avw8P31f9kZlDMG1qKWaUE3vPYC2n2WwAIDsplzvfMzAMwxCOWnLqRmZcqmoYe7ZuxcKFC2FRIo/M8VLqDPleXEZJKh4mzxjpe/r30mQExZ58+7AXOPgPej/lWkotlQ6yFZj678CBv1EKa2sZ3TdIFvrbuM/NZECPGX3UnEY2Wbk4d8fQdWDP/UD3VqD6ZGDW9UBZnEFmgPtZJv3f8bKZ9Ko9G3jjMkAPD64hqAUpjZnFTbaYKr27KdJN5uH2XMHKFiuKkwbGHHUxxWYjmcL45oJhGCb3VCwE3I1UzOvYq0DDx4dsIAPzv0PppRRXQZrIMADonsHijhaf9ewDqk6i93zPwDAMQzhqowMc4TAC1i6gZDpgGeGROfZ7DCMKey3gqAH87UDvLqByUfLtmx+nATzXRGDc+ZkdU4oUq1ec9Le1nAbz7OZMkcoUkJpT6AUAuUo1c+wl4OgLZJfzv0s1XRgmGZn8jssWek4aWusq0A4Eu2mpa8D+vwCTriYnszEGO7RIeLgf2Hgz9aELf0iRbXx/IBx2ZJgERVGwaNGiNCvAy1SzhhkgMx2ZRLCeYmAdxWIaPSUJmHApzRQ68iww8dIYxzIAxWb6G27TaDlKML2eso0GJCwlhW5JUkyvY5HBeo4d+FqLg7UUB2uZIZJEA2b+NUDPdqByUWItdR3o2kzvp/0HIAvS2lFPr1EI26U4cqZl3TlAx/tA1Ymmf6YSCdumGLLW0V4FQKcUamEP0Pom0LmRJpPXf4Rs8r3rKLrdwEj/r9iBdz9n2hS+mWAmu+Ri3ybCZrMVugmjAtZRLKynGFhHsZhGz/pzAYsT8LYA3ZsL3ZqMMI2WowRT61k2m2Z4lkwrdEtGxNQ6FiGs59iBr7U4WEtxsJYZYqSX6tk+sCqulpIEnPA/wILvAdWn5KlxxQ/bZZZoYaBnB9CzIzdaKjZg/n8DEy4Sv2+Tw7Yphqx0lB1UWL5sDuCaANSdQc/9/lbg8BOUzi82ha+1giaMyRbAMS6zIuEmxyx2yY4Mk6CqKtavX2+a4inFCusoFtZTDKyjWEylp8VJMzIAoOXZwZ/1NwFt68jJYVJMpeUowPR6Kk6gZCrdWJsY0+tYZLCeYwe+1uJgLcXBWmaB4cjo3Qlo4eRaSjKl+pE4ZUMqsF0K4OjzwMb/gn7gYXFadm2i4t5GTYIxaM9sm2JIW0fVR5EXQ1+qj56hpv47cNpDwLybgSn/Fq1DpDgBzyHA1xJxZFgpxZ+Rom+UYCa7ZEcGwzAMw2TDhEto2fEu5ck0OP46sP1OuslnGIZhGIZhmHRwNQLWUhoQC7TF36b1TUANij1usgE9hjGIdbTpAgY3+/YC234MHPwn1cZgmHxgLaMUUFoACHUPf2kB+txaRlFCdWcBjVdEv68GqDZGsJv+tlVG6xIyOYHVLVYS3UTwzQXDMEx+cTcCjZ+gtD22yuj6YCctbVWFaRfDGPA9A8MwDMMUH5IEnHQfpSyRpOFFlbu3Ax/+lHK2n3Q/DbJlgzGgF+igwbt4GAN6zNjG30appXQdCHSgTN0A9NcASmSI0VoWvy7A0OLIA+uPky2HfUDNScC4j+a2/Qxj4KilOhbJUkAlsmeA6mFULKB+M+yhdFRMTmFHRrHBNxcMwzDmY/pnh68LRBwZ9ur8toVhDPiegWEYhmGKm9hJMrHoOtD0Z3pfdWL2Tgwg+wE9ZmzgbwPWraL7S99RSGEvpmE3lHV/iqaCilfkOPZ7sWhhwHcE0EKArRw45feUnodh8oWjNrt+TXEBLpe49jBJkXTdSD43dunt7UV5eTl6enpQVlaYh3ld16GqKhRFgTRSHsBEXmyDMXxzkZaOzIiwnmJgHcVSNHq+9yWqj7HkTpqlYUKKRssiwZR6FuE9gyl1LGJE6WmG+2WzU2iN+H9HHKylOFhLQeg6aalppGX7OmD7TyhP+ym/S+zwYOLCdpkFffuAN6+immuhfuj+I1Ts2D0FEkARv1oAOONRoHR6/O8Z9QN0FejfR9+RLIBzHHDm6sHfG2OwbYoh5zoa9mytACzu4Z+HPZSaauj/QZFipucJjsgwEcFgEE5nCgVhsvUWjnJS1pFJCdZTDKyjWEypZ9hDBb89+4F5347ONjJ5ailTalnEmE7PIr1nMJ2ORQ7rOXbgay0O1lIcrGWW7LwH6PwAWHgbgko9nHYr0PQX+qzxE+zEyBC2yyxRnORIC7RCV/2QLC4AEgDzymysAAAp4klEQVQdUL1UNyDUF90+3E+OC8UeHfjt3UmRGIoTcE/mtKcR2DbFkBcdx1AKX7PYJRf7NgmqqmLLli2mqABfzLCOYmE9xcA6isW0empB4ODfqehi9zZA9dN6u3kdGabVskhhPcXAOoqF9Rw78LUWB2spDtZSAIEOINgNrXsbtmzZAq3lX4DvKKXhafhEoVtXlLBdCsLiBiQZgbAFuhap4aIGgf4DwAdfB95aFX198HVa7zsW/b6jFpAtVGtQthfiDEwH26YYcq5jOkXCRwFmskuOyGAYhmEYEWhhoHQ20PEesOt/KUJDcVJ6KcCUKXwYhmEYhmEYE+NvA+w1dF/ZuhaOwGJI+/4BqB6g4Qqa5W4p/AxZZqwiAfYaaFqAUkOli606UsxeIRtnmGKBawoVDHZkMAzDMEy2GMXrvC306vwgctMiUe5MIH7RO4ZhGIZhGIaJx8D95WHAewRy1ybMRBUkxUuRwLu7gEP/4PtLpqDorskIBrsx4E5T7FQT4OTfDa+R8dbV5LiIRVLy1FKGEUyRpvAtdtiRYSIUhTtwEbCOYmE9xcA6isV0eoZ6KezfWg5YeyknpuIE7JEbG9VHn4d6TXezYzotixzWUwyso1hYz7EDX2txsJbiYC0zJPb+Um4DoEOT3NBLGyFBI2eGSe8viwG2S3FIcpziv5JEr9i/mZRg2xQD6ygWs+gp6bquF7oRhUZE1XSGYRhmDNO3jyIvrBVU3K5/PxW/q1hEn4c9lCvzjEcHz0xiGIYpEvh+eWRYI4ZhhBJ7f+k9CIT6gZJplGoK4PtLpjAYdinbaeLWUFQf1QcYapeZfo9hmFGDiHtlLvZtEnRdR3d3N9ivlB2so1hYTzGwjmIxvZ72alqqfiDYVdi2jIDptSwyWE8xsI5iYT3HDnytxcFaioO1FITihg5ADXSDlcwetsssGFLkWA91I+zvgD5SkeMxVhw5U9g2xcA6isVMerIjwySoqoqdO3eaogJ8McM6ioX1FAPrKBbz6ykDrgZ6a/IQavNrWVywnmJgHcXCeo4d+FqLg7UUB2spiEgURiAQMMVAUrHDdpkFRpHjMx4FzngU6ml/x9by70M97e8D6+LWbRnyvbgvrvfCtikI1lEsZtKTa2QwDMMwjGicEwBHHSDxzyzDMAzDMAyTJRY39IolCPR64Ch0WxgmtshxOIyAtQsomQ5YRnj24eLIDMNkCUdkMAzDMEwuYCcGwzAMwzAMIwrZCpg72JdhGIZhcgqPspgESZLgdDohmTwNidlhHcXCeoqBdRSLqfVUfemtLzCm1rIIYT3FwDqKhfUcO/C1FgdrKQ7WUgCR+0gJOiwIQlI9ACTT3l8WA2yX4mAtxcJ6ioF1FIuZ9JR0TrAopGo6wzAMM4bxtwHrVgGBjsTb2Ks57yvDMEUL3y+PDGvEMIxQ+P6SYRiGGUWIuFfmiAyToGka2tvbUVNTA1nmjF+ZwjqKhfUUA+soFlPqaRSvC/Um3sZaZrqHTFNqWcSwnmJgHcXCeo4d+FqLg7UUB2uZBUPuLzVNQ1dXFyorK6NamvD+shhguxQHaykW1lMMrKNYzKQnX02ToGkampqaoGlaoZtS1LCOYmE9xcA6isW0ejpqgdLpiV8mfMg0rZZFCuspBtZRLKzn2IGvtThYS3GwllkSc3+puadizzENmnuqqe8viwG2S3GwlmJhPcXAOorFTHqyI4NhGIZhGIZhGIZhGIZhGIZhGNPCjgyGYRiGYRiGYRiGYRiGYRiGYUwLOzJMgiRJKC8vN0UF+GKGdRQL6ykG1lEsrKc4WEuxsJ5iYB3FwnqOHfhai4O1FAdrKQ7WUhyspThYS7GwnmJgHcViJj0lXdf1Qjei0Iioms4wDMMwDMMwoxW+Xx4Z1ohhGIZhGIZh4iPiXpkjMkyCpmk4fPiwKQqnFDOso1hYTzGwjmJhPcXBWoqF9RQD6ygW1nPswNdaHKylOFhLcbCW4mAtxcFaioX1FAPrKBYz6cmODJNgJqMoZlhHsbCeYmAdxcJ6ioO1FAvrKQbWUSys59iBr7U4WEtxsJbiYC3FwVqKg7UUC+spBtZRLGbSkx0ZDMMwDMMwDMMwDMMwDMMwDMOYFnZkMAzDMAzDMAzDMAzDMAzDMAxjWtiRYRJkWUZtbS1kmS9JNrCOYmE9xcA6ioX1FAdrKRbWUwyso1hYz7EDX2txsJbiYC3FwVqKg7UUB2spFtZTDKyjWMykp6Trul7oRhQaEVXTGYZhGIZhGGa0wvfLI8MaMQzDMAzDMEx8RNwrF96VwgCgwin79u0zReGUYoZ1FAvrKQbWUSyspzhYS7GwnmJgHcXCeo4d+FqLg7UUB2spDtZSHKylOFhLsbCeYmAdxWImPdmRYRI0TUNbW5spjKKYYR3FwnqKgXUUC+spDtZSLKynGFhHsbCeYwe+1uJgLcXBWoqDtRQHaykO1lIsrKcYWEexmElPdmQwDMMwDMMwDMMwDMMwDMMwDGNaLIVugBkwyoT09vYWrA3hcBgejwe9vb2wWPiyZArrKBbWUwyso1hYT3GwlmJhPcXAOopFlJ7GfTKX10tMoZ8p+H9HHKylOFhLcbCW4mAtxcFaioX1FAPrKBYzPU/w1QTQ19cHAGhsbCxwSxiGYRiGYRjGvPT19aG8vLzQzTAl/EzBMAzDMAzDMMnJ5nlC0nlaFTRNw5EjR1BaWgpJkgrSht7eXjQ2NqK5uTnjyu0M6yga1lMMrKNYWE9xsJZiYT3FwDqKRZSeuq6jr68PEyZMgCxzdtp4FPqZgv93xMFaioO1FAdrKQ7WUhyspVhYTzGwjmIx0/MER2QAkGUZDQ0NhW4GAKCsrIz/yQTAOoqF9RQD6ygW1lMcrKVYWE8xsI5iEaEnR2IkxyzPFPy/Iw7WUhyspThYS3GwluJgLcXCeoqBdRSLGZ4neDoVwzAMwzAMwzAMwzAMwzAMwzCmhR0ZDMMwDMMwDMMwDMMwDMMwDMOYFnZkmAS73Y5bbrkFdru90E0palhHsbCeYmAdxcJ6ioO1FAvrKQbWUSys59iBr7U4WEtxsJbiYC3FwVqKg7UUC+spBtZRLGbSk4t9MwzDMAzDMAzDMAzDMAzDMAxjWjgig2EYhmEYhmEYhmEYhmEYhmEY08KODIZhGIZhGIZhGIZhGIZhGIZhTAs7MhiGYRiGYRiGYRiGYRiGYRiGMS3syGAYhmEYhmEYhmEYhmEYhmEYxrSwI4NhGIZhGIZhGIZhGIZhGIZhGNPCjgyGieD3+wvdBIYZxvHjxwvdBIYZBveXjBlhu2QYptBwP8SYEX6eYMwK95mMGWG7NDfsyChydF0vdBNGBbfffjtuvPHGQjeDYQZx55134vrrry90M0YV3GdmD/eX4mG7zB62S/GwXY4t+HpnD/dDjBnh5wnxcH8pBu4zxcJ2KQa2S/GItk12ZBQpTz/9NABAkiTusLLkZz/7GZ566im88MILePzxxwvdnKLmkUceQU9PT6GbMSr4n//5Hzz11FNYv349/vCHPxS6OUUP95li4P5SLGyXYmC7FAvb5diCr7cYuB8SCz9TiIGfJ8TC/aU4uM8UB9ulONguxZIr22RHRhGyfv16XH755Vi1ahUA7rCy4ac//SmeeeYZrFu3Dr/85S/R1NQEANA0rcAtKz6amprwqU99Ctdffz06OzsL3Zyi5qc//SmeffZZvPPOO3j00UexdetW+Hw+tssM4T5TDNxfioXtUgxsl2Jhuxxb8PUWA/dDYuFnCjHw84RYuL8UB/eZ4mC7FAfbpVhyaZvsyChCLBYLrrnmGqxfvx4XXXQRAO6wMuGHP/whXnrpJTzxxBOw2WywWq343e9+h3379kGW+V8jXVRVxXnnnYcDBw7gP/7jP9DV1VXoJhUlt95664BdGrz33nvYu3cv22WGcJ+ZPdxfioftMnvYLsXDdjm24OudPdwPiYefKbKHnyfEw/2lGLjPFAvbpRjYLsWTS9vkK1KE7NixA4sWLcLu3btx+PBhXHrppQC4w0qHlpYWbN68Gf/4xz9QVVUFVVVx0UUX4aqrrsJDDz0EVVXZ85omhw8fxjnnnIO1a9ciHA7jc5/7HD94pEl3dzd27NgxyC6XLVuGSy65BHfccQd8Pl+hm1iUcJ+ZHdxf5ga2y+xgu8wNbJdjC77e2cH9UG7gZ4rs4OeJ3MD9ZfZwnyketsvsYbvMDbm0TUln6y4adF2HJEkAaEbFySefDABYsGABJk+ejGeffXbYdkxiwuEwLBYLVFWFLMuQJAmPPfYYHn30UTzyyCOFbp7piWdn77zzDk499VQAwMUXXwyHw4E//vGPqKysLEQTixJVVaEoyiC73LRpE+6++2785je/gdvt5v/xFOE+UxzcX4qD7VIcbJfiYLscW/D1Fgf3Q9nDzxTi4ecJcXB/KRbuM8XAdikWtktx5MM22ZFRBPT29sLtdgMAFEWJuw13WKkxVEvjJi+W5cuX47LLLsP/+3//rxBNLBqOHTuGiooKhMNhlJSUxN2GHzxSY6iWmqYNC2H8yEc+giVLluDnP/95gVpZPHCfKQbuL8XCdikGtkuxsF2OLfh6i4H7IbHwM4UY+HlCLNxfioP7THGwXYqD7VIs+bRNdmSYnDvuuAPbtm1Df38/lixZghUrVmDJkiUDF9zwHALcYY3ESFoaHdfTTz+NNWvW4K677mINE/DjH/8Y77zzDiwWC2w2G2644QacccYZA5/H2iU/eCRnJC0Nu9ywYQN+9atf4c4770R9fX0BW2xuuM8UA/eXYmG7FAPbpVjYLscWfL3FwP2QWPiZQgz8PCEW7i/FwX2mONguxcF2KZa826bOmJa77rpLP+uss/Tjx4/rTz31lP7LX/5Sr6io0J955plB24VCoYH3ixYt0s8+++w8t9T8pKqlruv69u3b9cbGRv3FF18sQEvNzy9/+Uv99NNP17u6uvQtW7boDzzwgD5x4kT9gQce0Lu7uwe2i7XLSy+9VL/gggv0vr6+QjTZtKSqpa7renNzs37qqafqjz76aIFaa364zxQD95diYbsUA9ulWNguxxZ8vcXA/ZBY+JlCDPw8IRbuL8XBfaY42C7FwXYplkLYJjsyTMyqVav09evX67qu66qq6rqu6w899JDe2NioP/LII7qu67qmabquDzaKc845R29ubs5za81NKlqqqjrw2X333ad/6Utf0j0eT2EabGK++MUv6v/61790XY/a3dNPP62fccYZ+n333TfowSIcDg+8/9SnPqUfPnw4v401Oalqafyf/+1vf9PPPfdc/fjx44VpsMnhPlMM3F+Khe1SDGyXYmG7HFvw9RYD90Ni4WcKMfDzhFi4vxQH95niYLsUB9ulWAphmxbRISVM9miahlAohGPHjqGvrw8Ahdvouo5rr70WdrsdX/va11BWVoaLLroImqbBYrEMhOu89tprBT4D85CulqqqAgAmTpyINWvWDMsrOpbRIyFfgUAAXq930PpLL70Udrsd3//+91FWVoZ/+7d/g6ZpUBRlwC4ffvjhArbeXGSipSzLmD9/PmbOnAmn01nA1psP7jPFwP2lWNguxcB2KRa2y7EFX28xcD8kFn6mEAM/T4iF+0txcJ8pDrZLcbBdiqWgtpmR+4PJCz//+c/1lStX6m1tbbquk3fL8GQ98MADelVVlb5ly5ZCNrFoyETL1tbWvLezGPjDH/6gL126dMB7GjtD6sknn9Srq6v1t99+u1DNKyoy0bKzszOvbSwmuM8UA/eXYmG7FAPbpVjYLscWfL3FwP2QWPiZQgz8PCEW7i/FwX2mONguxcF2KZZC2Ca7lEyIHqm//olPfAITJkzAE088gf7+fsiyDE3ToOs6/v3f/x033HADXnrppUHfYQaTiZbhcBgAUFtbW7B2m5nPfvazOO+88/DrX/8a7e3tUBQFqqpC13Vcfvnl+M53voM///nPCIVCbJcjkI6Whl1yccPhcJ8pBu4vxcJ2KQa2S7GwXY4t+HqLgfuh3MDPFGLg5wkxcH8pDu4zxcF2KQ62S7EU0jbZkWFCjIrtU6ZMwZIlS/Duu+/imWeeQX9//0BYLUA3IDt37hz0HWYwmWhpsXDGtUTokRDmCy64AH19ffj9738/cMMcDAYBADNmzEAgEIDVamW7TEK6WrJdJob7TDFwfykWtksxsF2Khe1ybMHXWwzcD4mHnynEwM8T4uD+UhzcZ4qD7VIcbJdiKaRt8lUxIcYNCQD853/+J4LBIF588UW0t7fjmmuuQU1NDQCgrKwMdrt9IMcYMxzWUhyxWn7kIx9BMBjE008/jZ/85Cf4+te/jokTJwIA+vr6EAqF4Pf7Ybfb+Yc0DqylWPj/XAyso1hYTzGwjmJhPccWfL3FwDqKhe+DxcA6ioX/z8XBWoqDtRQHaymWQurJV8VkqKoKRVEAAPfeey/6+/vx3e9+FzabDevXr8fVV1+Nz33uczhy5AgefPDB/9/evQZHWZ9/GP9uCDkUAuEwtAShCqUokZMcJBBBxIpjx1JQ2gRK61iQ1kF4URFbh2nrgKk42pYqpVBSw6mAFBFx0AIWKFikFKEQEAOCFZBDMCEm5Lh7/1/wz8pJBbzDbpLrM+MM7C6ZX65l2fvxl+dZLVq0iBfXZ6Cln3P/0XnmmWd09OhR/fa3v1XDhg31+uuva8CAAXr44YdVWFio5cuXa8mSJUpISIjwqqMTLX3xOvdBR1/nDnb0vHp09FN9Kjc96w/+XfdBR1/MwT7o6IvXuR9a+mEO9kNLP9FwTBEwLqAWMdnZ2frf//6nmJgYDR48WP379w/fN3PmTC1YsEALFixQ+/btJUlHjhzR8uXLtXv3bsXGxmr8+PG66aabIrX8qEJLP/Pnz1d5ebliY2M1YMCAcDPpbMuFCxdq3rx56tChQ/j2BQsW6NChQ6qoqNCoUaPUqVOnSCw96tDS10svvSTp7IB87733qlGjRuH7eJ1fPjr6KioqUpMmTS55Hz0vHx19bdy4UYmJiTIz9enTR9KnB3H0rHuYg33Q0RdzsA86+mIO9kNLP8zBfmjpKyqPKa7uM8LxZWVlZVm/fv1s0aJF9oMf/MDGjRtnn3zyiZmZbdmyxb797W/bBx98YGZmFRUVkVxq1KOln2nTplnv3r0tOzvbMjMz7bHHHrOsrCwzMzt48KBlZGSEW1ZWVkZyqVGPlr6mTZtmaWlp9rvf/c769u1rjz76qD3//PNmZpabm2tDhgzhdX4Z6OhrxowZdvfdd9vu3bsvuu/tt9/m/ecy0dHX1KlT7dZbb7WMjAxr27at/eEPfwjfx1xU9zAH+6CjL+ZgH3T0xRzsh5Z+mIP90NJXtB5TsJERAUuXLrWBAwdaUVGRmZl9+OGHlpKSYlu3bjUzs1AoZIWFhWZmVlVVFf5zoVDo2i82ytHSz+uvv27p6enhlsXFxfbmm2/a8OHD7cknnzSzT7ud2xIXo6Wvf/3rX5aWlmbFxcVmZpafn28LFy60UaNG2aRJk8zs0468zj8bHf1lZWVZ69at7ZFHHrE9e/acdx/vP5ePjn6ee+45GzBggJWXl5vZ2YO21q1b25tvvmlm9KxrmIN90NEXc7APOvpiDvZDS1/MwX5o6SeajylifM/vwOU4efKk7rvvPiUlJUmSUlJSlJKSooYNG6qoqEiS1LRp04v+HB/MdTFa+snPz1e3bt2UlJSkiooKNWrUSP3791dycrKys7P15JNPhrtVX/cSl0ZLX8XFxfrqV7+qRo0aKRgMqkWLFrrvvvv0yCOPaP/+/Zo0aVK4Y0zMp29rvM7PR0dfwWBQBQUFeuqpp1RcXKznnntOe/fuDd8fCATUtGlTmdl5r3N6no+Ofo4fP67Dhw9r1qxZiouLU1lZmfr06aMxY8bogw8+kETPuoY52AcdfTEH+6CjL+ZgP7T0wxzsh5Z+ov2Ygo2Ma8j+/+NIPvroIx0+fFiFhYWSpFdeeUXvvfeeXnjhBXXp0kUZGRn6+c9/Lomh5LPQ0l/1P1DVv66oqFBcXJzS0tI0ceJEFRUVacOGDRFeZe1ASx/Vr/Mbb7xRcXFxOnjwoBo0aCAzU3x8vHr16qWf/exn+uijjzRnzhxJDCKXQsea0aBBA/Xo0UN33nmnZsyYccmBWaLlF6Gjn2bNmikzMzN8jdrqD3Zt1aqV/vznPysUCoUfS8/ajTnYBx1rBnOwDzr6YA72Q0t/zMF+aOkn2o8p2Mi4BuyCT3X/7ne/q9dee00jRoxQjx49dP/998vMdPvtt+upp55SZmam8vLy9Oqrr0Zy2VGJln6qW1br0qWLVq9eHR464uLidPLkSS1evFgdOnRQfHy8Nm7cGImlRj1a1ozq1/lXvvIVxcbGauXKleHbQ6GQGjRooFtuuUXp6enatWtXJJca1ehYczIyMpSSkqLGjRtrzpw5KikpueTAjM9HRx9xcXHq2rWr4uPjJX363jRkyBAlJyef91ORqJ2Yg33Q0RdzsA861gzmYD+0rBnMwX5o6SPqjylq/OJVsCNHjpiZWTAYDF877ODBg7Zjxw5buXKl/eQnP7F58+aFH19eXm6jR4+2mTNnRmS90YyWfi7Vct26dda8eXMbPXq0TZw40fr372/PPvusmZnt3bvXBg4caPn5+VxH8AK09PWPf/zDVqxYYTt27LDTp0+b2dlrsbZp08b+8pe/hB9X3e7w4cPWrl07W79+fSSWG7Xo6OvcntUfAFvdrvoDNj/55BPLzMy0MWPGXHRdVpxFR1/n9qy+jvqF7yvFxcWWnp5uR48etWAwaGZm77zzTvi6tqg9mIN90NEXc7APOvpiDvZDSz/MwX5o6as2HVPERnYbpe7LycnRxIkTtWrVKqWnpysYDCoUCun666+XJH3zm9/Ub37zG33rW9+SdPa6bnFxcfrGN76hxMTECK48+tDSz6VaVlVV6Y477tCGDRu0ZcsWNWrUSEOHDtWgQYNkZiovL1ebNm3UokWLSC8/qtDS17Rp07Rq1SolJyercePGateunZ544gn17dtX8+fP15AhQ1RVVaUxY8aEfyqoTZs2uvfee5WcnBzZxUcROvq6sOfXv/51TZkyJXzN9NjYWIVCITVu3FizZ8/WQw89pN///veaMGGCOnfuHOHVRw86+vqinpJUVVWl8vJynTlzRpWVlYqJidH8+fP1zDPPaO3atRFcPa4Uc7APOvpiDvZBR1/MwX5o6Yc52A8tfdW2YwrOMa9hxcXFuvnmmzV69GitXbv2ouuqlpeXq7S0VCUlJZLOXtdt8eLFevnll3XbbbdFYslRi5Z+LtUyEAgoGAzq5ptv1pgxY5SZmalBgwZJOnu66K5duxQfH6+ysrKLTn2uz2jpZ8aMGVqzZo3Wrl2r1atXa/z48SooKND7778vSRo0aJDeeOMN/fKXv9T06dP19ttvS5KWLFmif/7zn2rWrFkklx816OjrUj0//vhj7d+/X9Knp9rGxMQoGAyGT2U+evSocnJyVFlZGcnlRw06+rqSns2bN9fXvvY1tWzZUsuWLdMLL7ygRYsWqVWrVpH8FnCFmIN90NEXc7APOvphDvZDSz/MwX5o6atWHlNc0/M/6pHqU3AeeOABmzdvni1dutTatm1ra9asCd9ffSrOihUrLCkpyX784x/bhAkTrEePHpabmxuxtUcbWvr5opbVHS80b9486927t+3evfuarTXa0dLXyZMn7dFHH7X33nvvvNtHjRplY8eONbNPm27bts0efPBBGzBggN1zzz3WrVs3ev4/Ovq6nJ4Xqu5bUlISvkxEfUdHX1fTMzMz0+6//37r2bMnr/NahjnYBx19MQf7oKMv5mA/tPTDHOyHlr5q6zEFGxk1bOfOnbZ9+3YzM5s1a9ZFg0n19S+3bt1qc+bMsRUrVtihQ4citt5oRks/n9fywuvg5ebm2vDhwxlGPgMt/fz3v/+1/Px8M/u03bZt2ywzM9PMzKqqqsKDSGlpqRUUFNj+/fvt5MmTkVlwlKKjry/qeanrUn/W/3ioz+jo63J7BoNBq6ystLvuustat2590YEKag/mYB909MUc7IOOfpiD/dDSD3OwH1r6qo3HFGxk1LDS0tLzfj979uzPHJjx+Wjp54tanvuP/+nTp+3UqVPXdH21CS1rVm5urrVv395OnDgR/tAuXDk6+vq8ngzKl4+Ovj6v59atW/mJ8lqOOdgHHX0xB/ugY81iDvZDSz/MwX5o6Svajyn4sG9H2dnZOnHihBITE9WzZ0+lp6crISFBVVVVio09m3rs2LGSpAcffFDZ2dm68847FQqFIrnsqERLP1+mZSAQUJMmTSK5/KhCS1+X6inpvJ4dOnRQ27ZtlZCQEL5tzZo16tGjh1q2bBmxtUcTOvqipw86+rrann//+9/VtWtX9e7dO2Jrx5VjDvZBR1/MwT7o6It5ww8t/dDSDy191YVjCj7s28nTTz+tuXPnqn379srNzdXcuXP1wAMPSJJiY2NVVVUVfuzYsWM1ZcoUPfTQQ1q9erViYngazkVLP1+2ZSAQiNDKow8tfV1uz/j4eEnSsWPHJEkLFizQ5MmTVVxcHJF1Rxs6+qKnDzr6+jI9H3/8cZWVlUVk3bg6zME+6OiLOdgHHX0xb/ihpR9a+qGlrzpzTBHR80HqiH379ll6erodP37czMzKysrs8OHDdvvtt1t6enr4cReekvP888/bLbfcYiUlJZe8jlt9REs/tPRDS1+X27OsrMyCwaB1797d9u7day+99JL16tUr4qcyRgs6+qKnDzr6omf9wrzhg46+6OmDjr54f/RDSz+09ENLX3WpJxsZDvbu3WtDhgy55H39+vWzgQMHhn9/4fXZTp8+XZNLq3Vo6YeWfmjp60p6mpmNGTPGfvjDH1r//v35YMNz0NEXPX3Q0Rc96xfmDR909EVPH3T0xfujH1r6oaUfWvqqSz05b9ZBp06dFAwGtWTJkvBt1afkbN68WTExMeHrXF54qjLXuDwfLf3Q0g8tfV1JT0kKhUJasWKF5s6dq9TU1Gu+3mhFR1/09EFHX/SsX5g3fNDRFz190NEX749+aOmHln5o6asu9WQj4ypt3bpVubm52rFjhwKBgDIyMvSf//xHeXl5ks6/vtisWbNUUlKiPXv2RHLJUYuWfmjph5a+rqbnrl27JEkTJkzQ7t271alTp4itP1rQ0Rc9fdDRFz3rF+YNH3T0RU8fdPTF+6MfWvqhpR9a+qqrPWMjvYDaaOrUqVq9erWSk5NVWFiocePGacSIEfrpT3+qv/3tb/rRj36k1q1bKzY2Vmam1q1b68iRI9q1a5c6d+4c6eVHFVr6oaUfWvq62p65ubnq0qWLunXrFulvISrQ0Rc9fdDRFz3rF+YNH3T0RU8fdPTF+6MfWvqhpR9a+qrLPdnIuELPPvus1qxZo/Xr16ukpETbt2/X448/rvT0dD322GOaNGmSgsGghg0bps6dOysQCCgpKUlpaWlq0KBBpJcfVWjph5Z+aOnry/SMjeUtqhodfdHTBx190bN+Yd7wQUdf9PRBR1+8P/qhpR9a+qGlr7rek0tLXYEPP/xQ77//vl588UU1bNhQjRs3Vp8+fdS9e3ft2LFD3bp1U1ZWlnJzczVr1izNnDlT5eXlysnJ0cqVK9WjR49IfwtRg5Z+aOmHlr7o6YOOvujpg46+6Fm/8Hz7oKMvevqgoy96+qGlH1r6oaWvetHzWn+6eG1WWlpq69evt4qKCguFQuHbp0+fbkOGDLFgMGhmZgcOHLAXX3zR+vXrZ0OHDrV+/fpF3ae8Rxot/dDSDy190dMHHX3R0wcdfdGzfuH59kFHX/T0QUdf9PRDSz+09ENLX/WhJxsZV6iqqir86+q/FJs3b7Z77rnHzCz8l6LamTNnrKio6NotsBahpR9a+qGlL3r6oKMvevqgoy961i883z7o6IuePujoi55+aOmHln5o6auu9+TSUlfo3GtWBgIBSdJNN92kiooKlZSUhO/buXOnJCkxMVFJSUnXdpG1BC390NIPLX3R0wcdfdHTBx190bN+4fn2QUdf9PRBR1/09ENLP7T0Q0tfdb0nGxlfUjAYVGlpqY4eParTp08rJiZG8+fP18iRI5Wfnx/p5dUqtPRDSz+09EVPH3T0RU8fdPRFz/qF59sHHX3R0wcdfdHTDy390NIPLX3VtZ5sZDho3ry5WrVqpZSUFC1btkwzZ87U0qVL1bJly0gvrdahpR9a+qGlL3r6oKMvevqgoy961i883z7o6IuePujoi55+aOmHln5o6asu9QyYmUV6EXVBZmamkpKStH37duXk5Cg1NTXSS6q1aOmHln5o6YuePujoi54+6OiLnvULz7cPOvqipw86+qKnH1r6oaUfWvqqKz1jI72A2s7MVFFRoT179ujYsWPatGmTOnbsGOll1Uq09ENLP7T0RU8fdPRFTx909EXP+oXn2wcdfdHTBx190dMPLf3Q0g8tfdW1npyR4eStt95S06ZNa+2OVjShpR9a+qGlL3r6oKMvevqgoy961i883z7o6IuePujoi55+aOmHln5o6auu9GQjAwAAAAAAAAAARC0+7BsAAAAAAAAAAEQtNjIAAAAAAAAAAEDUYiMDAAAAAAAAAABELTYyAAAAAAAAAABA1GIjAwAAAAAAAAAARC02MgAAAAA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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result = analyze_trend('daejeon', ['hm','PM10'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result = analyze_trend('seoul', ['PM25','PM10'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Analysis_code/find_reason/utils.py b/Analysis_code/find_reason/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d51528b3f6a5618201518430239d29813227d6ef
--- /dev/null
+++ b/Analysis_code/find_reason/utils.py
@@ -0,0 +1,340 @@
+"""
+Trend 분석 유틸리티 함수 모듈
+
+지역별 데이터의 월별 trend를 분석하고 시각화하는 함수들을 제공합니다.
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+import warnings
+from typing import List, Dict, Any, Tuple, Optional
+
+warnings.filterwarnings('ignore')
+
+
+# ==========================================
+# 1. 설정 및 유틸리티 함수
+# ==========================================
+
+def setup_korean_font():
+    """한글 폰트 설정."""
+    plt.rcParams['font.family'] = 'NanumGothic'
+    plt.rcParams['axes.unicode_minus'] = False
+
+
+def load_region_data(
+    region: str,
+    features: List[str] = None,
+    years: List[int] = None
+) -> pd.DataFrame:
+    """지역별 데이터를 로드하고 필터링합니다.
+    
+    Args:
+        region: 지역명 ('seoul', 'busan', 'daegu', 'incheon', 'gwangju', 'daejeon')
+        features: 선택할 컬럼 리스트 (기본값: None - 전체 컬럼)
+        years: 필터링할 연도 리스트 (기본값: None - 전체 연도)
+    
+    Returns:
+        pd.DataFrame: 필터링된 데이터프레임
+    
+    Raises:
+        FileNotFoundError: 데이터 파일을 찾을 수 없는 경우
+    """
+    # 기본값 설정
+    if features is None:
+        features = [
+            # 대기질 변수
+            'hm', 'PM10', 'PM25', 'O3', 'NO2',
+            # 기상 변수
+            'temp_C', 'wind_speed', 'cloudcover', 'solarRad', 'precip_mm',
+            'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
+            # 기타 변수
+            'visi', 'groundtemp', 'ground_temp - temp_C',
+            # 필수 변수
+            'multi_class', 'year', 'month', 'hour'
+        ]
+    if years is None:
+        years = [2018, 2019, 2020, 2021]
+    
+    # 데이터 파일 경로
+    file_path = f"../../data/data_for_modeling/df_{region}.feather"
+    
+    try:
+        # 데이터 로드
+        df = pd.read_feather(file_path)
+        
+        # Feature 선택
+        if features:
+            # 필수 컬럼 확인
+            required_cols = ['year', 'month']
+            missing_cols = [col for col in required_cols if col not in df.columns]
+            if missing_cols:
+                raise ValueError(f"필수 컬럼이 없습니다: {missing_cols}")
+            
+            # features에 없는 필수 컬럼 추가
+            for col in required_cols:
+                if col not in features:
+                    features.append(col)
+            
+            # 존재하는 컬럼만 선택
+            available_features = [f for f in features if f in df.columns]
+            df = df[available_features]
+        
+        # 연도 필터링
+        if years:
+            df = df.loc[df['year'].isin(years), :]
+        
+        return df
+    
+    except FileNotFoundError:
+        raise FileNotFoundError(f"데이터 파일을 찾을 수 없습니다: {file_path}")
+    except Exception as e:
+        raise RuntimeError(f"데이터 로드 중 오류 발생: {e}")
+
+
+# ==========================================
+# 2. Trend 분석 함수
+# ==========================================
+
+def get_monthly_trend(df: pd.DataFrame, variable: str) -> pd.DataFrame:
+    """월별 평균, Q1, Q3를 계산합니다.
+    
+    Args:
+        df: 데이터프레임
+        variable: 분석할 변수명
+    
+    Returns:
+        pd.DataFrame: 월별 trend 데이터프레임
+            - year: 연도
+            - month: 월
+            - mean: 평균값
+            - q1: 1사분위수
+            - q3: 3사분위수
+            - date: 날짜 (월의 첫날)
+    
+    Raises:
+        ValueError: 변수가 데이터프레임에 없는 경우
+    """
+    if variable not in df.columns:
+        raise ValueError(f"변수 '{variable}'가 데이터프레임에 없습니다.")
+    
+    if 'year' not in df.columns or 'month' not in df.columns:
+        raise ValueError("데이터프레임에 'year'와 'month' 컬럼이 필요합니다.")
+    
+    # 월별 평균, Q1, Q3 계산
+    monthly = df.groupby(['year', 'month'])[variable].agg([
+        'mean',
+        lambda x: x.quantile(0.25),
+        lambda x: x.quantile(0.75)
+    ]).reset_index()
+    
+    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']
+    
+    # 날짜 컬럼 생성 (월의 첫날로)
+    monthly['date'] = pd.to_datetime(
+        monthly['year'].astype(str) + '-' + 
+        monthly['month'].astype(str).str.zfill(2) + '-01'
+    )
+    
+    # 날짜 순으로 정렬
+    monthly = monthly.sort_values('date')
+    
+    return monthly
+
+
+def calculate_trend_slope(trend_data: pd.DataFrame) -> float:
+    """선형 트렌드의 기울기를 계산합니다.
+    
+    Args:
+        trend_data: get_monthly_trend()로 생성된 데이터프레임
+    
+    Returns:
+        float: 월별 변화율 (기울기)
+    """
+    if len(trend_data) < 2:
+        return 0.0
+    
+    # 선형 회귀로 기울기 계산
+    z = np.polyfit(range(len(trend_data)), trend_data['mean'], 1)
+    slope = z[0]
+    
+    return slope
+
+
+# ==========================================
+# 3. 시각화 함수
+# ==========================================
+
+def plot_monthly_trend(
+    trend_data: pd.DataFrame,
+    variable: str,
+    ax: Optional[plt.Axes] = None,
+    title: Optional[str] = None,
+    show_slope: bool = True
+) -> plt.Axes:
+    """단일 변수의 월별 trend를 시각화합니다.
+    
+    Args:
+        trend_data: get_monthly_trend()로 생성된 데이터프레임
+        variable: 변수명 (제목에 사용)
+        ax: matplotlib Axes 객체 (None이면 새로 생성)
+        title: 그래프 제목 (None이면 자동 생성)
+        show_slope: 월별 변화율 표시 여부
+    
+    Returns:
+        plt.Axes: 생성된 또는 사용된 Axes 객체
+    """
+    if ax is None:
+        fig, ax = plt.subplots(figsize=(8, 6))
+    
+    # 평균값 플롯
+    ax.plot(trend_data['date'], trend_data['mean'], 'o-', color='blue', label='Mean')
+    
+    # Q1, Q3 플롯
+    ax.plot(trend_data['date'], trend_data['q1'], 's--', color='orange', alpha=0.7, label='Q1')
+    ax.plot(trend_data['date'], trend_data['q3'], 's--', color='purple', alpha=0.7, label='Q3')
+    
+    # 선형 트렌드선
+    if len(trend_data) > 1:
+        z = np.polyfit(range(len(trend_data)), trend_data['mean'], 1)
+        p = np.poly1d(z)
+        ax.plot(trend_data['date'], p(range(len(trend_data))), "r--", linewidth=1, alpha=0.6)
+        
+        if show_slope:
+            slope = z[0]
+            ax.text(
+                trend_data['date'].iloc[0],
+                trend_data['mean'].max(),
+                f'Monthly Change Rate: {slope:.4f}/month',
+                fontsize=10,
+                color='darkred'
+            )
+    
+    # 제목 설정
+    if title is None:
+        title = f'{variable} Monthly Trend'
+    ax.set_title(title, fontsize=14)
+    
+    # 축 레이블 설정
+    ax.set_xlabel('Date', fontsize=12)
+    ax.set_ylabel(variable, fontsize=12)
+    
+    # 그리드 및 범례
+    ax.grid(True, linestyle='--', alpha=0.7)
+    ax.legend()
+    
+    # x축 날짜 포맷
+    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
+    plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)
+    
+    return ax
+
+
+def plot_multiple_trends(
+    df: pd.DataFrame,
+    variables: List[str],
+    figsize: Tuple[int, int] = (16, 6),
+    show_slope: bool = True
+) -> plt.Figure:
+    """여러 변수의 월별 trend를 동시에 시각화합니다.
+    
+    Args:
+        df: 원본 데이터프레임
+        variables: 분석할 변수 리스트
+        figsize: 그림 크기
+        show_slope: 월별 변화율 표시 여부
+    
+    Returns:
+        plt.Figure: 생성된 Figure 객체
+    """
+    n_vars = len(variables)
+    fig, axes = plt.subplots(1, n_vars, figsize=figsize)
+    
+    # 단일 변수인 경우 axes를 리스트로 변환
+    if n_vars == 1:
+        axes = [axes]
+    
+    for i, var in enumerate(variables):
+        if var in df.columns:
+            trend = get_monthly_trend(df, var)
+            plot_monthly_trend(trend, var, ax=axes[i], show_slope=show_slope)
+        else:
+            axes[i].text(0.5, 0.5, f'{var} Column not found', ha='center', va='center', fontsize=12)
+            axes[i].set_title(f'{var} (No data)', fontsize=14)
+    
+    plt.tight_layout()
+    return fig
+
+
+# ==========================================
+# 4. 메인 분석 함수
+# ==========================================
+
+def analyze_trend(
+    region: str,
+    variables: List[str],
+    years: Optional[List[int]] = None,
+    features: Optional[List[str]] = None,
+    show_plot: bool = True,
+    figsize: Tuple[int, int] = (16, 6),
+    show_slope: bool = True
+) -> Dict[str, Any]:
+    """지역과 변수를 받아 월별 trend를 분석하고 시각화합니다.
+    
+    Args:
+        region: 지역명 ('seoul', 'busan', 'daegu', 'incheon', 'gwangju', 'daejeon')
+        variables: 분석할 변수 리스트 (예: ['hm', 'PM25', 'temp_C', 'O3'])
+            사용 가능한 변수: hm, PM10, PM25, O3, NO2, temp_C, wind_speed, 
+            cloudcover, solarRad, precip_mm, vap_pressure, dewpoint_C, 
+            loc_pressure, sea_pressure, visi, groundtemp, ground_temp - temp_C 등
+        years: 분석할 연도 리스트 (기본값: [2018, 2019, 2020, 2021, 2022, 2023])
+        features: 데이터에서 선택할 컬럼 리스트 
+            (기본값: None - 주요 변수들 자동 포함)
+        show_plot: 시각화 표시 여부
+        figsize: 그림 크기
+        show_slope: 월별 변화율 표시 여부
+    
+    Returns:
+        dict: 분석 결과
+            - data: 필터링된 데이터프레임
+            - trends: 각 변수별 trend 데이터프레임 딕셔너리
+            - slopes: 각 변수별 월별 변화율 딕셔너리
+    
+    Example:
+        >>> result = analyze_trend('seoul', ['hm', 'PM25', 'temp_C', 'O3'])
+        >>> print(result['slopes'])
+        {'hm': 0.0123, 'PM25': -0.0456, 'temp_C': 0.0234, 'O3': -0.0012}
+    """
+    # 한글 폰트 설정
+    setup_korean_font()
+    
+    # 데이터 로드 및 필터링
+    df = load_region_data(region, features=features, years=years)
+    
+    # 각 변수별 trend 계산
+    trends = {}
+    slopes = {}
+    
+    for var in variables:
+        if var in df.columns:
+            trend_data = get_monthly_trend(df, var)
+            trends[var] = trend_data
+            slopes[var] = calculate_trend_slope(trend_data)
+        else:
+            print(f"Warning: 변수 '{var}'가 데이터에 없습니다.")
+            trends[var] = None
+            slopes[var] = None
+    
+    # 시각화
+    if show_plot:
+        plot_multiple_trends(df, variables, figsize=figsize, show_slope=show_slope)
+        plt.show()
+    
+    return {
+        'data': df,
+        'trends': trends,
+        'slopes': slopes
+    }
+
diff --git a/Analysis_code/find_reason/wasserstein_distance.ipynb b/Analysis_code/find_reason/wasserstein_distance.ipynb
deleted file mode 100644
index cd92bde0d757dfec9665606fd8b36c5ae25bbedf..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/wasserstein_distance.ipynb
+++ /dev/null
@@ -1,541 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 분석에 필요한 라이브러리 임포트\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy import stats\n",
-    "from scipy.spatial import distance\n",
-    "from scipy.stats import wasserstein_distance, entropy, ks_2samp\n",
-    "from sklearn.manifold import TSNE\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "from sklearn.ensemble import RandomForestClassifier # Added\n",
-    "from sklearn.model_selection import train_test_split # Added\n",
-    "from sklearn.metrics import roc_auc_score # Added\n",
-    "from statsmodels.distributions.empirical_distribution import ECDF # Added\n",
-    "import ot\n",
-    "\n",
-    "\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seoul = pd.read_feather(\"../../data/data_for_modeling/df_seoul.feather\")\n",
-    "seoul= seoul[['datetime','hm','PM10','PM25','year','month','hour','multi_class']]\n",
-    "\n",
-    "busan = pd.read_feather(\"../../data/data_for_modeling/df_busan.feather\")\n",
-    "busan= busan[['datetime','hm','PM10','PM25','year','month','hour','multi_class']]\n",
-    "\n",
-    "incheon = pd.read_feather(\"../../data/data_for_modeling/df_incheon.feather\")\n",
-    "incheon= incheon[['datetime','hm','PM10','PM25','year','month','hour','multi_class']]\n",
-    "\n",
-    "daegu = pd.read_feather(\"../../data/data_for_modeling/df_daegu.feather\")\n",
-    "daegu= daegu[['datetime','hm','PM10','PM25','year','month','hour','multi_class']]\n",
-    "\n",
-    "daejeon = pd.read_feather(\"../../data/data_for_modeling/df_daejeon.feather\")\n",
-    "daejeon= daejeon[['datetime','hm','PM10','PM25','year','month','hour','multi_class']]\n",
-    "\n",
-    "gwangju = pd.read_feather(\"../../data/data_for_modeling/df_gwangju.feather\")\n",
-    "gwangju= gwangju[['datetime','hm','PM10','PM25','year','month','hour','multi_class']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.5920662  0.92351786]\n",
-      "[0.60414398 0.9190468 ]\n",
-      "[0.60250035 0.9391276 ]\n",
-      "[0.60112832 0.92493121]\n",
-      "[0.58469137 0.90476229]\n",
-      "[0.617718   0.93503164]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.decomposition import PCA\n",
-    "\n",
-    "# 특성 선택 (예: PM10, PM25, hm 등)\n",
-    "features = ['PM10','PM25', 'hm']\n",
-    "# 스케일링\n",
-    "scaler = StandardScaler()\n",
-    "scaled_features = scaler.fit_transform(seoul[features])\n",
-    "pca = PCA(n_components=2)\n",
-    "pca.fit(scaled_features)\n",
-    "print(pca.explained_variance_ratio_.cumsum())\n",
-    "seoul_pca = pca.transform(scaled_features)\n",
-    "seoul.drop(columns=['PM25', 'hm'], inplace=True)\n",
-    "seoul[['pca_x', 'pca_y']] = seoul_pca\n",
-    "\n",
-    "\n",
-    "scaled_features = scaler.fit_transform(busan[features])\n",
-    "pca = PCA(n_components=2)\n",
-    "pca.fit(scaled_features)\n",
-    "print(pca.explained_variance_ratio_.cumsum())\n",
-    "busan_pca = pca.transform(scaled_features)\n",
-    "busan.drop(columns=['PM25', 'hm'], inplace=True)\n",
-    "busan[['pca_x', 'pca_y']] = busan_pca\n",
-    "\n",
-    "scaled_features = scaler.fit_transform(incheon[features])   \n",
-    "pca = PCA(n_components=2)\n",
-    "pca.fit(scaled_features)\n",
-    "print(pca.explained_variance_ratio_.cumsum())\n",
-    "incheon_pca = pca.transform(scaled_features)\n",
-    "incheon.drop(columns=['PM25', 'hm'], inplace=True)\n",
-    "incheon[['pca_x', 'pca_y']] = incheon_pca\n",
-    "\n",
-    "scaled_features = scaler.fit_transform(daegu[features])\n",
-    "pca = PCA(n_components=2)\n",
-    "pca.fit(scaled_features)\n",
-    "print(pca.explained_variance_ratio_.cumsum())\n",
-    "daegu_pca = pca.transform(scaled_features)\n",
-    "daegu.drop(columns=['PM25', 'hm'], inplace=True)\n",
-    "daegu[['pca_x', 'pca_y']] = daegu_pca\n",
-    "\n",
-    "scaled_features = scaler.fit_transform(daejeon[features])\n",
-    "pca = PCA(n_components=2)\n",
-    "pca.fit(scaled_features)\n",
-    "print(pca.explained_variance_ratio_.cumsum())\n",
-    "daejeon_pca = pca.transform(scaled_features)\n",
-    "daejeon.drop(columns=['PM25', 'hm'], inplace=True)\n",
-    "daejeon[['pca_x', 'pca_y']] = daejeon_pca\n",
-    "\n",
-    "scaled_features = scaler.fit_transform(gwangju[features])\n",
-    "pca = PCA(n_components=2)\n",
-    "pca.fit(scaled_features)\n",
-    "print(pca.explained_variance_ratio_.cumsum())\n",
-    "gwangju_pca = pca.transform(scaled_features)\n",
-    "gwangju.drop(columns=['PM25', 'hm'], inplace=True)\n",
-    "gwangju[['pca_x', 'pca_y']] = gwangju_pca\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seoul_2018 = seoul[seoul['year'] == 2018]\n",
-    "seoul_2019 = seoul[seoul['year'] == 2019]\n",
-    "seoul_2020 = seoul[seoul['year'] == 2020]\n",
-    "seoul_2021 = seoul[seoul['year'] == 2021]\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "\n",
-    "\n",
-    "busan_2018 = busan[busan['year'] == 2018]\n",
-    "busan_2019 = busan[busan['year'] == 2019]\n",
-    "busan_2020 = busan[busan['year'] == 2020]\n",
-    "busan_2021 = busan[busan['year'] == 2021]\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "\n",
-    "\n",
-    "incheon_2018 = incheon[incheon['year'] == 2018]\n",
-    "incheon_2019 = incheon[incheon['year'] == 2019]\n",
-    "incheon_2020 = incheon[incheon['year'] == 2020]\n",
-    "incheon_2021 = incheon[incheon['year'] == 2021]\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "\n",
-    "\n",
-    "daegu_2018 = daegu[daegu['year'] == 2018]\n",
-    "daegu_2019 = daegu[daegu['year'] == 2019]\n",
-    "daegu_2020 = daegu[daegu['year'] == 2020]\n",
-    "daegu_2021 = daegu[daegu['year'] == 2021]\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "\n",
-    "\n",
-    "daejeon_2018 = daejeon[daejeon['year'] == 2018]\n",
-    "daejeon_2019 = daejeon[daejeon['year'] == 2019]\n",
-    "daejeon_2020 = daejeon[daejeon['year'] == 2020]\n",
-    "daejeon_2021 = daejeon[daejeon['year'] == 2021]\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "\n",
-    "\n",
-    "gwangju_2018 = gwangju[gwangju['year'] == 2018]\n",
-    "gwangju_2019 = gwangju[gwangju['year'] == 2019]\n",
-    "gwangju_2020 = gwangju[gwangju['year'] == 2020]\n",
-    "gwangju_2021 = gwangju[gwangju['year'] == 2021]\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          2018      2019      2020      2021\n",
-      "2018       0.0  0.130217  0.063132  1.081307\n",
-      "2019  0.130217       0.0  0.059051  0.830648\n",
-      "2020  0.063132  0.059051       0.0  0.039927\n",
-      "2021  1.081307  0.830648  0.039927       0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 연도별 데이터 준비\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "data_dict = {\n",
-    "    2018: seoul_2018[['pca_x', 'pca_y']].values,\n",
-    "    2019: seoul_2019[['pca_x', 'pca_y']].values,\n",
-    "    2020: seoul_2020[['pca_x', 'pca_y']].values,\n",
-    "    2021: seoul_2021[['pca_x', 'pca_y']].values\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# 결과를 저장할 데이터프레임 생성\n",
-    "result_df = pd.DataFrame(index=years, columns=years)\n",
-    "\n",
-    "for i, year1 in enumerate(years):\n",
-    "    for j, year2 in enumerate(years):\n",
-    "        if year1 == year2:\n",
-    "            result_df.iloc[i, j] = 0.0\n",
-    "        if j < i:\n",
-    "            # 이미 계산된 값 사용\n",
-    "            result_df.iloc[i, j] = result_df.iloc[j, i]\n",
-    "        else:\n",
-    "            X = data_dict[year1]\n",
-    "            Y = data_dict[year2]\n",
-    "            a = np.ones(len(X)) / len(X)\n",
-    "            b = np.ones(len(Y)) / len(Y)\n",
-    "            W = ot.emd2(a, b, ot.dist(X, Y))\n",
-    "            result_df.iloc[i, j] = W\n",
-    "            result_df.iloc[j, i] = W  # 대칭 위치에 동일 값 저장\n",
-    "\n",
-    "# 결과 출력\n",
-    "print(result_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          2018      2019      2020      2021\n",
-      "2018       0.0  0.116261   0.10445  1.424479\n",
-      "2019  0.116261       0.0   0.09933  1.164067\n",
-      "2020   0.10445   0.09933       0.0  1.075336\n",
-      "2021  1.424479  1.164067  1.075336       0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 연도별 데이터 준비\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "data_dict = {\n",
-    "    2018: busan_2018[['pca_x', 'pca_y']].values,\n",
-    "    2019: busan_2019[['pca_x', 'pca_y']].values,\n",
-    "    2020: busan_2020[['pca_x', 'pca_y']].values,\n",
-    "    2021: busan_2021[['pca_x', 'pca_y']].values\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# 결과를 저장할 데이터프레임 생성\n",
-    "result_df = pd.DataFrame(index=years, columns=years)\n",
-    "\n",
-    "for i, year1 in enumerate(years):\n",
-    "    for j, year2 in enumerate(years):\n",
-    "        if year1 == year2:\n",
-    "            result_df.iloc[i, j] = 0.0\n",
-    "        if j < i:\n",
-    "            # 이미 계산된 값 사용\n",
-    "            result_df.iloc[i, j] = result_df.iloc[j, i]\n",
-    "        else:\n",
-    "            X = data_dict[year1]\n",
-    "            Y = data_dict[year2]\n",
-    "            a = np.ones(len(X)) / len(X)\n",
-    "            b = np.ones(len(Y)) / len(Y)\n",
-    "            W = ot.emd2(a, b, ot.dist(X, Y))\n",
-    "            result_df.iloc[i, j] = W\n",
-    "            result_df.iloc[j, i] = W  # 대칭 위치에 동일 값 저장\n",
-    "\n",
-    "# 결과 출력\n",
-    "print(result_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          2018      2019      2020      2021\n",
-      "2018       0.0  0.080291  0.074071  0.449094\n",
-      "2019  0.080291       0.0  0.060171  0.384189\n",
-      "2020  0.074071  0.060171       0.0   0.04047\n",
-      "2021  0.449094  0.384189   0.04047       0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 연도별 데이터 준비\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "data_dict = {\n",
-    "    2018: incheon_2018[['pca_x', 'pca_y']].values,\n",
-    "    2019: incheon_2019[['pca_x', 'pca_y']].values,\n",
-    "    2020: incheon_2020[['pca_x', 'pca_y']].values,\n",
-    "    2021: incheon_2021[['pca_x', 'pca_y']].values\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# 결과를 저장할 데이터프레임 생성\n",
-    "result_df = pd.DataFrame(index=years, columns=years)\n",
-    "\n",
-    "for i, year1 in enumerate(years):\n",
-    "    for j, year2 in enumerate(years):\n",
-    "        if year1 == year2:\n",
-    "            result_df.iloc[i, j] = 0.0\n",
-    "        if j < i:\n",
-    "            # 이미 계산된 값 사용\n",
-    "            result_df.iloc[i, j] = result_df.iloc[j, i]\n",
-    "        else:\n",
-    "            X = data_dict[year1]\n",
-    "            Y = data_dict[year2]\n",
-    "            a = np.ones(len(X)) / len(X)\n",
-    "            b = np.ones(len(Y)) / len(Y)\n",
-    "            W = ot.emd2(a, b, ot.dist(X, Y))\n",
-    "            result_df.iloc[i, j] = W\n",
-    "            result_df.iloc[j, i] = W  # 대칭 위치에 동일 값 저장\n",
-    "\n",
-    "# 결과 출력\n",
-    "print(result_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          2018      2019      2020      2021\n",
-      "2018       0.0  0.127512  0.112157  0.731476\n",
-      "2019  0.127512       0.0  0.094651  0.647071\n",
-      "2020  0.112157  0.094651       0.0  0.041217\n",
-      "2021  0.731476  0.647071  0.041217       0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 연도별 데이터 준비\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "data_dict = {\n",
-    "    2018: daegu_2018[['pca_x', 'pca_y']].values,\n",
-    "    2019: daegu_2019[['pca_x', 'pca_y']].values,\n",
-    "    2020: daegu_2020[['pca_x', 'pca_y']].values,\n",
-    "    2021: daegu_2021[['pca_x', 'pca_y']].values\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# 결과를 저장할 데이터프레임 생성\n",
-    "result_df = pd.DataFrame(index=years, columns=years)\n",
-    "\n",
-    "for i, year1 in enumerate(years):\n",
-    "    for j, year2 in enumerate(years):\n",
-    "        if year1 == year2:\n",
-    "            result_df.iloc[i, j] = 0.0\n",
-    "        if j < i:\n",
-    "            # 이미 계산된 값 사용\n",
-    "            result_df.iloc[i, j] = result_df.iloc[j, i]\n",
-    "        else:\n",
-    "            X = data_dict[year1]\n",
-    "            Y = data_dict[year2]\n",
-    "            a = np.ones(len(X)) / len(X)\n",
-    "            b = np.ones(len(Y)) / len(Y)\n",
-    "            W = ot.emd2(a, b, ot.dist(X, Y))\n",
-    "            result_df.iloc[i, j] = W\n",
-    "            result_df.iloc[j, i] = W  # 대칭 위치에 동일 값 저장\n",
-    "\n",
-    "# 결과 출력\n",
-    "print(result_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          2018      2019      2020      2021\n",
-      "2018       0.0  0.273013  0.053969  0.877338\n",
-      "2019  0.273013       0.0  0.137817  0.780071\n",
-      "2020  0.053969  0.137817       0.0  0.042294\n",
-      "2021  0.877338  0.780071  0.042294       0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 연도별 데이터 준비\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "data_dict = {\n",
-    "    2018: daejeon_2018[['pca_x', 'pca_y']].values,\n",
-    "    2019: daejeon_2019[['pca_x', 'pca_y']].values,\n",
-    "    2020: daejeon_2020[['pca_x', 'pca_y']].values,\n",
-    "    2021: daejeon_2021[['pca_x', 'pca_y']].values\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# 결과를 저장할 데이터프레임 생성\n",
-    "result_df = pd.DataFrame(index=years, columns=years)\n",
-    "\n",
-    "for i, year1 in enumerate(years):\n",
-    "    for j, year2 in enumerate(years):\n",
-    "        if year1 == year2:\n",
-    "            result_df.iloc[i, j] = 0.0\n",
-    "        if j < i:\n",
-    "            # 이미 계산된 값 사용\n",
-    "            result_df.iloc[i, j] = result_df.iloc[j, i]\n",
-    "        else:\n",
-    "            X = data_dict[year1]\n",
-    "            Y = data_dict[year2]\n",
-    "            a = np.ones(len(X)) / len(X)\n",
-    "            b = np.ones(len(Y)) / len(Y)\n",
-    "            W = ot.emd2(a, b, ot.dist(X, Y))\n",
-    "            result_df.iloc[i, j] = W\n",
-    "            result_df.iloc[j, i] = W  # 대칭 위치에 동일 값 저장\n",
-    "\n",
-    "# 결과 출력\n",
-    "print(result_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          2018      2019      2020      2021\n",
-      "2018       0.0  0.105633   0.08202   1.00155\n",
-      "2019  0.105633       0.0  0.069322  0.892938\n",
-      "2020   0.08202  0.069322       0.0  0.480667\n",
-      "2021   1.00155  0.892938  0.480667       0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 연도별 데이터 준비\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "data_dict = {\n",
-    "    2018: gwangju_2018[['pca_x', 'pca_y']].values,\n",
-    "    2019: gwangju_2019[['pca_x', 'pca_y']].values,\n",
-    "    2020: gwangju_2020[['pca_x', 'pca_y']].values,\n",
-    "    2021: gwangju_2021[['pca_x', 'pca_y']].values\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# 결과를 저장할 데이터프레임 생성\n",
-    "result_df = pd.DataFrame(index=years, columns=years)\n",
-    "\n",
-    "for i, year1 in enumerate(years):\n",
-    "    for j, year2 in enumerate(years):\n",
-    "        if year1 == year2:\n",
-    "            result_df.iloc[i, j] = 0.0\n",
-    "        if j < i:\n",
-    "            # 이미 계산된 값 사용\n",
-    "            result_df.iloc[i, j] = result_df.iloc[j, i]\n",
-    "        else:\n",
-    "            X = data_dict[year1]\n",
-    "            Y = data_dict[year2]\n",
-    "            a = np.ones(len(X)) / len(X)\n",
-    "            b = np.ones(len(Y)) / len(Y)\n",
-    "            W = ot.emd2(a, b, ot.dist(X, Y))\n",
-    "            result_df.iloc[i, j] = W\n",
-    "            result_df.iloc[j, i] = W  # 대칭 위치에 동일 값 저장\n",
-    "\n",
-    "# 결과 출력\n",
-    "print(result_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Analysis_code/model_voting_test_best_sample/ensemble__voting_best_sample.ipynb b/Analysis_code/model_voting_test_best_sample/ensemble__voting_best_sample.ipynb
deleted file mode 100644
index bee2b13b969ca8d40d54cb1a1758b2a8bbb22ddd..0000000000000000000000000000000000000000
--- a/Analysis_code/model_voting_test_best_sample/ensemble__voting_best_sample.ipynb
+++ /dev/null
@@ -1,2642 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader, TensorDataset\n",
-    "import random\n",
-    "from collections import Counter\n",
-    "import sys\n",
-    "sys.path.append('../../../../../../../../mnt/workspace/LightGBM/python-package')\n",
-    "from lightgbm import LGBMClassifier\n",
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.inspection import permutation_importance\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "from xgboost import XGBClassifier\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "import sys\n",
-    "sys.path.append('../../')\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "import optuna\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import random\n",
-    "from models.ft_transformer import FTTransformer\n",
-    "from models.resnet_like import ResNetLike\n",
-    "from models.deepgbm import DeepGBM\n",
-    "from pytorch_tabnet.tab_model import TabNetClassifier\n",
-    "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Python 및 Numpy 시드 고정\n",
-    "seed = 42\n",
-    "random.seed(seed)\n",
-    "np.random.seed(seed)\n",
-    "\n",
-    "# PyTorch 시드 고정\n",
-    "torch.manual_seed(seed)\n",
-    "torch.cuda.manual_seed(seed)\n",
-    "torch.cuda.manual_seed_all(seed)  # Multi-GPU 환경에서 동일한 시드 적용\n",
-    "\n",
-    "# PyTorch 연산의 결정적 모드 설정\n",
-    "torch.backends.cudnn.deterministic = True  # 실행마다 동일한 결과를 보장\n",
-    "torch.backends.cudnn.benchmark = True  # 성능 최적화를 활성화 (가능한 한 빠른 연산 수행)\n",
-    "\n",
-    "# 전처리 함수\n",
-    "def preprocessing(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df['lm_cloudcover'] = df['lm_cloudcover'].astype('int')\n",
-    "    df['cloudcover'] = df['cloudcover'].astype('int')\n",
-    "    return df\n",
-    "\n",
-    "# 데이터셋 준비 함수\n",
-    "def prepare_dataset(region, data_sample='pure', target='multi', fold=3):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    return X_train, X_val, X_test, y_train, y_val, y_test, categorical_cols, numerical_cols\n",
-    "\n",
-    "# 데이터 변환 및 dataloader 생성 함수\n",
-    "def prepare_dataloader(region, data_sample='pure', target='multi', fold=3, random_state=None):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    # 연속형 변수와 범주형 변수 분리\n",
-    "    X_train_num = torch.tensor(X_train[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_train_cat = torch.tensor(X_train[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_val_num = torch.tensor(X_val[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_val_cat = torch.tensor(X_val[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_test_num = torch.tensor(X_test[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_test_cat = torch.tensor(X_test[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    # 레이블 변환\n",
-    "    if target == \"binary\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.float32)  # 이진 분류 → float32\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.float32)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32)\n",
-    "    elif target == \"multi\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.long)  # 다중 분류 → long\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.long)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.long)\n",
-    "    else:\n",
-    "        raise ValueError(\"target must be 'binary' or 'multi'\")\n",
-    "\n",
-    "    # TensorDataset 생성\n",
-    "    train_dataset = TensorDataset(X_train_num, X_train_cat, y_train_tensor)\n",
-    "    val_dataset = TensorDataset(X_val_num, X_val_cat, y_val_tensor)\n",
-    "    test_dataset = TensorDataset(X_test_num, X_test_cat, y_test_tensor)\n",
-    "\n",
-    "    # DataLoader 생성\n",
-    "    if random_state == None:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
-    "    else:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, generator=torch.Generator().manual_seed(random_state))\n",
-    "    val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
-    "    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
-    "    \n",
-    "    return X_train, categorical_cols, numerical_cols, train_loader, val_loader, test_loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "# 디바이스 설정 (CUDA 사용 가능하면 GPU로, 아니면 CPU로)\n",
-    "import glob\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
-    "def calculate_csi(Y_test, pred):\n",
-    "\n",
-    "    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경\n",
-    "    # 혼동 행렬에서 H, F, M 추출\n",
-    "    H = (cm[0, 0] + cm[1, 1])\n",
-    "    \n",
-    "    F = (cm[1, 0] + cm[2, 0] +\n",
-    "         cm[0, 1] + cm[2, 1])\n",
-    "\n",
-    "    M = (cm[0, 2] + cm[1, 2])\n",
-    "    \n",
-    "    # CSI 계산\n",
-    "    CSI = H / (H + F + M + 1e-10)\n",
-    "    return CSI\n",
-    "\n",
-    "def csi_metric(y_true, pred):\n",
-    "    y_pred_binary = np.argmax(pred, axis=1)\n",
-    "    score = calculate_csi(y_true, y_pred_binary)\n",
-    "    return 'CSI', score, True  # higher_better=True\n",
-    "\n",
-    "\n",
-    "def eval_metric_csi(y_true, pred_prob):\n",
-    "\n",
-    "    pred = np.argmax(pred_prob, axis=1)\n",
-    "    y_true = y_true\n",
-    "    y_pred = pred\n",
-    "    csi = calculate_csi(y_true, y_pred)\n",
-    "    return -1*csi\n",
-    "\n",
-    "from sklearn.metrics import matthews_corrcoef, accuracy_score\n",
-    "\n",
-    "def multiclass_mcc(y_val, y_pred):\n",
-    "    \"\"\"\n",
-    "    다중 분류에서도 sklearn의 matthews_corrcoef를 그대로 사용할 수 있음.\n",
-    "    \"\"\"\n",
-    "    return matthews_corrcoef(y_val, y_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "# 디바이스 설정 (CUDA 사용 가능하면 GPU로, 아니면 CPU로)\n",
-    "import glob\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "\n",
-    "def calculate_csi(Y_test, pred):\n",
-    "\n",
-    "    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경\n",
-    "    # 혼동 행렬에서 H, F, M 추출\n",
-    "    H = (cm[0, 0] + cm[1, 1])\n",
-    "    \n",
-    "    F = (cm[1, 0] + cm[2, 0] +\n",
-    "         cm[0, 1] + cm[2, 1])\n",
-    "\n",
-    "    M = (cm[0, 2] + cm[1, 2])\n",
-    "    \n",
-    "    # CSI 계산\n",
-    "    CSI = H / (H + F + M + 1e-10)\n",
-    "    return CSI\n",
-    "\n",
-    "# Soft Voting 앙상블\n",
-    "def get_proba(region, model_choose, data_sample, fold, target='multi'):\n",
-    "    _, _, _, _,val_loader , test_loader = prepare_dataloader(region=region, data_sample=data_sample, target=target,fold=fold ,random_state=120)\n",
-    "    _ ,_,_ ,_ , y_val,_ ,_ ,_ = prepare_dataset(region=region, data_sample=data_sample, target=target, fold=fold)\n",
-    "\n",
-    "    folder_path = f'../save_model/{model_choose}/{data_sample}'\n",
-    "    model_paths = [path for path in glob.glob(f'{folder_path}/*.pth') if f'{region}' in path]\n",
-    "\n",
-    "    model = torch.load(model_paths[fold-1], weights_only=False).to(device)\n",
-    "    model.eval()\n",
-    "\n",
-    "    val_preds = []\n",
-    "\n",
-    "\n",
-    "    with torch.no_grad():\n",
-    "        for x_num_batch, x_cat_batch, _ in val_loader:\n",
-    "            output = model(x_num_batch.to(device), x_cat_batch.to(device))\n",
-    "            output = torch.softmax(output, dim=1)\n",
-    "            val_preds.extend(output.cpu().numpy())\n",
-    "\n",
-    "\n",
-    "    return val_preds, y_val\n",
-    "\n",
-    "\n",
-    "from sklearn.metrics import matthews_corrcoef\n",
-    "\n",
-    "def multiclass_mcc(y_val, y_pred):\n",
-    "    \"\"\"\n",
-    "    다중 분류에서도 sklearn의 matthews_corrcoef를 그대로 사용할 수 있음.\n",
-    "    \"\"\"\n",
-    "    return matthews_corrcoef(y_val, y_pred)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_seoul_test = pd.read_csv(\"../../data/data_for_modeling/seoul_test.csv\")\n",
-    "\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "df_busan_test = pd.read_csv(\"../../data/data_for_modeling/busan_test.csv\")\n",
-    "\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "df_daegu_test = pd.read_csv(\"../../data/data_for_modeling/daegu_test.csv\")\n",
-    "\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "df_daejeon_test = pd.read_csv(\"../../data/data_for_modeling/daejeon_test.csv\")\n",
-    "\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "df_incheon_test = pd.read_csv(\"../../data/data_for_modeling/incheon_test.csv\")\n",
-    "\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")\n",
-    "df_gwangju_test = pd.read_csv(\"../../data/data_for_modeling/gwangju_test.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocessing_df(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df['year'] = df['year'].astype('int')\n",
-    "    df['month'] = df['month'].astype('int')\n",
-    "    df['hour'] = df['hour'].astype('int')\n",
-    "    df['binary_class'] = df['binary_class'].astype('int')\n",
-    "    df['multi_class'] = df['multi_class'].astype('int')\n",
-    "\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',\n",
-    "       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',\n",
-    "       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',\n",
-    "       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',\n",
-    "       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',\n",
-    "       'month_sin', 'month_cos','multi_class']]\n",
-    "    return df\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul_test= preprocessing_df(df_seoul_test).copy()\n",
-    "df_busan_test= preprocessing_df(df_busan_test).copy()\n",
-    "df_daegu_test= preprocessing_df(df_daegu_test).copy()\n",
-    "df_gwangju_test= preprocessing_df(df_gwangju_test).copy()\n",
-    "df_daejeon_test= preprocessing_df(df_daejeon_test).copy()\n",
-    "df_incheon_test= preprocessing_df(df_incheon_test).copy()\n",
-    "\n",
-    "df_seoul= preprocessing_df(df_seoul).copy()\n",
-    "df_busan= preprocessing_df(df_busan).copy()\n",
-    "df_daegu= preprocessing_df(df_daegu).copy()\n",
-    "df_gwangju= preprocessing_df(df_gwangju).copy()\n",
-    "df_daejeon= preprocessing_df(df_daejeon).copy()\n",
-    "df_incheon= preprocessing_df(df_incheon).copy()\n",
-    "\n",
-    "df_seoul_test.drop(columns=['year'], inplace=True)\n",
-    "df_busan_test.drop(columns=['year'], inplace=True)\n",
-    "df_daegu_test.drop(columns=['year'], inplace=True)\n",
-    "df_daejeon_test.drop(columns=['year'], inplace=True)\n",
-    "df_incheon_test.drop(columns=['year'], inplace=True)\n",
-    "df_gwangju_test.drop(columns=['year'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import joblib\n",
-    "\n",
-    "lgb_seoul= joblib.load('../save_model/LGB_optima/lgb_seoul_smote.pkl')\n",
-    "lgb_busan= joblib.load('../save_model/LGB_optima/lgb_busan_smote.pkl')\n",
-    "lgb_incheon= joblib.load('../save_model/LGB_optima/lgb_incheon_smote.pkl')\n",
-    "lgb_daegu= joblib.load('../save_model/LGB_optima/lgb_daegu_smote.pkl')\n",
-    "lgb_daejeon= joblib.load('../save_model/LGB_optima/lgb_daejeon_smote.pkl')\n",
-    "lgb_gwangju= joblib.load('../save_model/LGB_optima/lgb_gwangju_smote.pkl')\n",
-    "\n",
-    "xgb_seoul= joblib.load('../save_model/XGB_optima/xgb_seoul_smote.pkl')\n",
-    "xgb_busan= joblib.load('../save_model/XGB_optima/xgb_busan_ctgan20000.pkl')\n",
-    "xgb_incheon= joblib.load('../save_model/XGB_optima/xgb_incheon_smote.pkl')\n",
-    "xgb_daegu= joblib.load('../save_model/XGB_optima/xgb_daegu_smote.pkl')\n",
-    "xgb_daejeon= joblib.load('../save_model/XGB_optima/xgb_daejeon_smote.pkl')\n",
-    "xgb_gwangju= joblib.load('../save_model/XGB_optima/xgb_gwangju_smote.pkl')\n",
-    "\n",
-    "lgb_seoul_1= lgb_seoul[0]\n",
-    "lgb_seoul_2= lgb_seoul[1]\n",
-    "lgb_seoul_3= lgb_seoul[2]\n",
-    "\n",
-    "lgb_busan_1= lgb_busan[0]\n",
-    "lgb_busan_2= lgb_busan[1]\n",
-    "lgb_busan_3= lgb_busan[2]\n",
-    "\n",
-    "lgb_incheon_1= lgb_incheon[0]\n",
-    "lgb_incheon_2= lgb_incheon[1]\n",
-    "lgb_incheon_3= lgb_incheon[2]\n",
-    "\n",
-    "lgb_daegu_1= lgb_daegu[0]\n",
-    "lgb_daegu_2= lgb_daegu[1]\n",
-    "lgb_daegu_3= lgb_daegu[2]\n",
-    "\n",
-    "lgb_daejeon_1= lgb_daejeon[0]\n",
-    "lgb_daejeon_2= lgb_daejeon[1]\n",
-    "lgb_daejeon_3= lgb_daejeon[2]\n",
-    "\n",
-    "lgb_gwangju_1= lgb_gwangju[0]\n",
-    "lgb_gwangju_2= lgb_gwangju[1]\n",
-    "lgb_gwangju_3= lgb_gwangju[2]\n",
-    "\n",
-    "\n",
-    "xgb_seoul_1= xgb_seoul[0]\n",
-    "xgb_seoul_2= xgb_seoul[1]\n",
-    "xgb_seoul_3= xgb_seoul[2]\n",
-    "\n",
-    "xgb_busan_1= xgb_busan[0]\n",
-    "xgb_busan_2= xgb_busan[1]\n",
-    "xgb_busan_3= xgb_busan[2]\n",
-    "\n",
-    "xgb_incheon_1= xgb_incheon[0]\n",
-    "xgb_incheon_2= xgb_incheon[1]\n",
-    "xgb_incheon_3= xgb_incheon[2]\n",
-    "\n",
-    "xgb_daegu_1= xgb_daegu[0]\n",
-    "xgb_daegu_2= xgb_daegu[1]\n",
-    "xgb_daegu_3= xgb_daegu[2]\n",
-    "\n",
-    "xgb_daejeon_1= xgb_daejeon[0]\n",
-    "xgb_daejeon_2= xgb_daejeon[1]\n",
-    "xgb_daejeon_3= xgb_daejeon[2]\n",
-    "\n",
-    "xgb_gwangju_1= xgb_gwangju[0]\n",
-    "xgb_gwangju_2= xgb_gwangju[1]\n",
-    "xgb_gwangju_3= xgb_gwangju[2]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **Soft Voting**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deepgbm=[] #DeepGBM\n",
-    "ft_transformer=[] #FT-transformer\n",
-    "resnet_like=[] #ResNet-like\n",
-    "XGBoost=[] #XGBoost\n",
-    "LightGBM=[] #LightGBM\n",
-    "val= []\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "\n",
-    "# 1 Fold\n",
-    "probas = []\n",
-    "val_preds, y_val = get_proba('seoul', 'deepgbm', 'ctgan20000', 1)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('seoul', 'resnet_like', 'smote', 1)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('seoul', 'ft_transformer', 'smote', 1)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul.loc[df_seoul['year'].isin([2018, 2019]), df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "XGBoost.append(xgb_seoul_1.predict_proba(X_val))\n",
-    "LightGBM.append(lgb_seoul_1.predict_proba(X_val))\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "probas = []\n",
-    "val_preds, y_val = get_proba('seoul', 'deepgbm', 'ctgan20000', 2)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('seoul', 'resnet_like', 'smote', 2)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('seoul', 'ft_transformer', 'smote', 2)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul.loc[df_seoul['year'].isin([2018, 2020]), df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "XGBoost.append(xgb_seoul_2.predict_proba(X_val))\n",
-    "LightGBM.append(lgb_seoul_2.predict_proba(X_val))\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "# 3 Fold\n",
-    "probas = []\n",
-    "val_preds, y_val = get_proba('seoul', 'deepgbm', 'ctgan20000', 3)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('seoul', 'resnet_like', 'smote', 3)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('seoul', 'ft_transformer', 'smote', 3)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul.loc[df_seoul['year'].isin([2019, 2020]), df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "XGBoost.append(xgb_seoul_3.predict_proba(X_val))\n",
-    "LightGBM.append(lgb_seoul_3.predict_proba(X_val))\n",
-    "val.append(Y_val)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "model = [deepgbm, ft_transformer, resnet_like, XGBoost, LightGBM]\n",
-    "model_name = [\"deepgbm\", \"ft_transformer\", \"resnet_like\", \"XGBoost\", \"LightGBM\"]\n",
-    "voting = []\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        soft = []\n",
-    "        hard = []\n",
-    "        mcc= []\n",
-    "        accuracy = []\n",
-    "        for k in range(3):\n",
-    "            result = []\n",
-    "            result.append(model[i][k])\n",
-    "            result.append(model[j][k])\n",
-    "            \n",
-    "            soft.append(calculate_csi(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            hard.append(calculate_csi(val[k],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "            mcc.append(multiclass_mcc(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            accuracy.append(accuracy_score(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            \n",
-    "        # 모델 조합과 함께 평균 CSI 저장\n",
-    "        voting.append((f\"{model_name[i]}+{model_name[j]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "        #voting.append((f\"{model_name[i]}+{model_name[j]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            soft = []\n",
-    "            hard = []\n",
-    "            mcc= []\n",
-    "            accuracy = []\n",
-    "            for l in range(3):\n",
-    "                result = []\n",
-    "                result.append(model[i][l])\n",
-    "                result.append(model[j][l])\n",
-    "                result.append(model[k][l])\n",
-    "\n",
-    "                soft.append(calculate_csi(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                hard.append(calculate_csi(val[l],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                mcc.append(multiclass_mcc(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                accuracy.append(accuracy_score(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                \n",
-    "            # 모델 조합과 함께 평균 CSI 저장\n",
-    "            voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "            #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                soft = []\n",
-    "                hard = []\n",
-    "                mcc= []\n",
-    "                accuracy = []\n",
-    "                for m in range(3):\n",
-    "                    result = []\n",
-    "                    result.append(model[i][m])\n",
-    "                    result.append(model[j][m])\n",
-    "                    result.append(model[k][m])\n",
-    "                    result.append(model[l][m])\n",
-    "               \n",
-    "                    soft.append(calculate_csi(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    hard.append(calculate_csi(val[m],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                    mcc.append(multiclass_mcc(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    accuracy.append(accuracy_score(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    \n",
-    "                # 모델 조합과 함께 평균 CSI 저장\n",
-    "                voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                for m in range(l+1, len(model)):\n",
-    "                    soft = []\n",
-    "                    hard = []\n",
-    "                    mcc= []\n",
-    "                    accuracy = []\n",
-    "                    for n in range(3):\n",
-    "                        result = []\n",
-    "                        result.append(model[i][n])\n",
-    "                        result.append(model[j][n])\n",
-    "                        result.append(model[k][n])\n",
-    "                        result.append(model[l][n])\n",
-    "                        result.append(model[m][n])\n",
-    "\n",
-    "                        soft.append(calculate_csi(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        hard.append(calculate_csi(val[n],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                        mcc.append(multiclass_mcc(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        accuracy.append(accuracy_score(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        \n",
-    "                    # 모델 조합과 함께 평균 CSI 저장\n",
-    "                    voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                    # voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "       \n",
-    "\n",
-    "            "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "      <th>region</th>\n",
-       "      <th>data_sample</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>deepgbm+ft_transformer</td>\n",
-       "      <td>0.699242</td>\n",
-       "      <td>0.804978</td>\n",
-       "      <td>0.961705</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>deepgbm+resnet_like</td>\n",
-       "      <td>0.709072</td>\n",
-       "      <td>0.813500</td>\n",
-       "      <td>0.963958</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>deepgbm+XGBoost</td>\n",
-       "      <td>0.669227</td>\n",
-       "      <td>0.782838</td>\n",
-       "      <td>0.957995</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>deepgbm+LightGBM</td>\n",
-       "      <td>0.693515</td>\n",
-       "      <td>0.801572</td>\n",
-       "      <td>0.962094</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ft_transformer+resnet_like</td>\n",
-       "      <td>0.625273</td>\n",
-       "      <td>0.750936</td>\n",
-       "      <td>0.948804</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>ft_transformer+XGBoost</td>\n",
-       "      <td>0.614284</td>\n",
-       "      <td>0.740312</td>\n",
-       "      <td>0.947094</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>ft_transformer+LightGBM</td>\n",
-       "      <td>0.608569</td>\n",
-       "      <td>0.735933</td>\n",
-       "      <td>0.946637</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>resnet_like+XGBoost</td>\n",
-       "      <td>0.619427</td>\n",
-       "      <td>0.744657</td>\n",
-       "      <td>0.947822</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>resnet_like+LightGBM</td>\n",
-       "      <td>0.613685</td>\n",
-       "      <td>0.741096</td>\n",
-       "      <td>0.945773</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>XGBoost+LightGBM</td>\n",
-       "      <td>0.586111</td>\n",
-       "      <td>0.714906</td>\n",
-       "      <td>0.942197</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like</td>\n",
-       "      <td>0.682169</td>\n",
-       "      <td>0.794663</td>\n",
-       "      <td>0.959626</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost</td>\n",
-       "      <td>0.673033</td>\n",
-       "      <td>0.786757</td>\n",
-       "      <td>0.958448</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>deepgbm+ft_transformer+LightGBM</td>\n",
-       "      <td>0.676476</td>\n",
-       "      <td>0.790202</td>\n",
-       "      <td>0.959055</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>deepgbm+resnet_like+XGBoost</td>\n",
-       "      <td>0.678438</td>\n",
-       "      <td>0.791505</td>\n",
-       "      <td>0.959138</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>deepgbm+resnet_like+LightGBM</td>\n",
-       "      <td>0.677563</td>\n",
-       "      <td>0.791321</td>\n",
-       "      <td>0.959024</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>deepgbm+XGBoost+LightGBM</td>\n",
-       "      <td>0.632538</td>\n",
-       "      <td>0.754727</td>\n",
-       "      <td>0.951200</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>0.633231</td>\n",
-       "      <td>0.756134</td>\n",
-       "      <td>0.950061</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>0.630655</td>\n",
-       "      <td>0.754589</td>\n",
-       "      <td>0.949681</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>0.605288</td>\n",
-       "      <td>0.731968</td>\n",
-       "      <td>0.945577</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.610349</td>\n",
-       "      <td>0.736946</td>\n",
-       "      <td>0.946037</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>0.672997</td>\n",
-       "      <td>0.787585</td>\n",
-       "      <td>0.958111</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>0.671956</td>\n",
-       "      <td>0.787372</td>\n",
-       "      <td>0.958034</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>0.653134</td>\n",
-       "      <td>0.771641</td>\n",
-       "      <td>0.954997</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>deepgbm+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.649528</td>\n",
-       "      <td>0.769203</td>\n",
-       "      <td>0.953974</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.623331</td>\n",
-       "      <td>0.747511</td>\n",
-       "      <td>0.948277</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost+Lig...</td>\n",
-       "      <td>0.656912</td>\n",
-       "      <td>0.775223</td>\n",
-       "      <td>0.955378</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                model       CSI       MCC  \\\n",
-       "0                              deepgbm+ft_transformer  0.699242  0.804978   \n",
-       "1                                 deepgbm+resnet_like  0.709072  0.813500   \n",
-       "2                                     deepgbm+XGBoost  0.669227  0.782838   \n",
-       "3                                    deepgbm+LightGBM  0.693515  0.801572   \n",
-       "4                          ft_transformer+resnet_like  0.625273  0.750936   \n",
-       "5                              ft_transformer+XGBoost  0.614284  0.740312   \n",
-       "6                             ft_transformer+LightGBM  0.608569  0.735933   \n",
-       "7                                 resnet_like+XGBoost  0.619427  0.744657   \n",
-       "8                                resnet_like+LightGBM  0.613685  0.741096   \n",
-       "9                                    XGBoost+LightGBM  0.586111  0.714906   \n",
-       "10                 deepgbm+ft_transformer+resnet_like  0.682169  0.794663   \n",
-       "11                     deepgbm+ft_transformer+XGBoost  0.673033  0.786757   \n",
-       "12                    deepgbm+ft_transformer+LightGBM  0.676476  0.790202   \n",
-       "13                        deepgbm+resnet_like+XGBoost  0.678438  0.791505   \n",
-       "14                       deepgbm+resnet_like+LightGBM  0.677563  0.791321   \n",
-       "15                           deepgbm+XGBoost+LightGBM  0.632538  0.754727   \n",
-       "16                 ft_transformer+resnet_like+XGBoost  0.633231  0.756134   \n",
-       "17                ft_transformer+resnet_like+LightGBM  0.630655  0.754589   \n",
-       "18                    ft_transformer+XGBoost+LightGBM  0.605288  0.731968   \n",
-       "19                       resnet_like+XGBoost+LightGBM  0.610349  0.736946   \n",
-       "20         deepgbm+ft_transformer+resnet_like+XGBoost  0.672997  0.787585   \n",
-       "21        deepgbm+ft_transformer+resnet_like+LightGBM  0.671956  0.787372   \n",
-       "22            deepgbm+ft_transformer+XGBoost+LightGBM  0.653134  0.771641   \n",
-       "23               deepgbm+resnet_like+XGBoost+LightGBM  0.649528  0.769203   \n",
-       "24        ft_transformer+resnet_like+XGBoost+LightGBM  0.623331  0.747511   \n",
-       "25  deepgbm+ft_transformer+resnet_like+XGBoost+Lig...  0.656912  0.775223   \n",
-       "\n",
-       "    Accuracy region data_sample  \n",
-       "0   0.961705  seoul        best  \n",
-       "1   0.963958  seoul        best  \n",
-       "2   0.957995  seoul        best  \n",
-       "3   0.962094  seoul        best  \n",
-       "4   0.948804  seoul        best  \n",
-       "5   0.947094  seoul        best  \n",
-       "6   0.946637  seoul        best  \n",
-       "7   0.947822  seoul        best  \n",
-       "8   0.945773  seoul        best  \n",
-       "9   0.942197  seoul        best  \n",
-       "10  0.959626  seoul        best  \n",
-       "11  0.958448  seoul        best  \n",
-       "12  0.959055  seoul        best  \n",
-       "13  0.959138  seoul        best  \n",
-       "14  0.959024  seoul        best  \n",
-       "15  0.951200  seoul        best  \n",
-       "16  0.950061  seoul        best  \n",
-       "17  0.949681  seoul        best  \n",
-       "18  0.945577  seoul        best  \n",
-       "19  0.946037  seoul        best  \n",
-       "20  0.958111  seoul        best  \n",
-       "21  0.958034  seoul        best  \n",
-       "22  0.954997  seoul        best  \n",
-       "23  0.953974  seoul        best  \n",
-       "24  0.948277  seoul        best  \n",
-       "25  0.955378  seoul        best  "
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df= pd.DataFrame(voting, columns=['model', 'CSI', 'MCC','Accuracy'])\n",
-    "df['region'] = 'seoul'\n",
-    "df['data_sample'] = 'best'\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deepgbm=[] #DeepGBM\n",
-    "ft_transformer=[] #FT-transformer\n",
-    "resnet_like=[] #ResNet-like\n",
-    "XGBoost=[] #XGBoost\n",
-    "LightGBM=[] #LightGBM\n",
-    "val= []\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba('busan', 'deepgbm', 'pure', 1)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('busan', 'resnet_like', 'ctgan10000', 1)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('busan', 'ft_transformer', 'pure', 1)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan.loc[df_busan['year'].isin([2018, 2019]), df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "LightGBM.append(lgb_busan_1.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_busan_1.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba('busan', 'deepgbm', 'pure', 2)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('busan', 'resnet_like', 'ctgan10000', 2)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('busan', 'ft_transformer', 'pure', 2)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan.loc[df_busan['year'].isin([2018, 2020]), df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "LightGBM.append(lgb_busan_2.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_busan_2.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba('busan', 'deepgbm', 'pure', 3)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('busan', 'resnet_like', 'ctgan10000', 3)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('busan', 'ft_transformer', 'pure', 3)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan.loc[df_busan['year'].isin([2019, 2020]), df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "LightGBM.append(lgb_busan_3.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_busan_3.predict_proba(X_val))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "model = [deepgbm, ft_transformer, resnet_like, XGBoost, LightGBM]\n",
-    "model_name = [\"deepgbm\", \"ft_transformer\", \"resnet_like\", \"XGBoost\", \"LightGBM\"]\n",
-    "voting = []\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        soft = []\n",
-    "        hard = []\n",
-    "        mcc= []\n",
-    "        accuracy= []\n",
-    "        for k in range(3):\n",
-    "            result = []\n",
-    "            result.append(model[i][k])\n",
-    "            result.append(model[j][k])\n",
-    "            \n",
-    "            soft.append(calculate_csi(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            hard.append(calculate_csi(val[k],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "            mcc.append(multiclass_mcc(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            accuracy.append(accuracy_score(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "        # 모델 조합과 함께 평균 CSI 저장\n",
-    "        voting.append((f\"{model_name[i]}+{model_name[j]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "        #voting.append((f\"{model_name[i]}+{model_name[j]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            soft = []\n",
-    "            hard = []\n",
-    "            mcc= []\n",
-    "            accuracy= []\n",
-    "            for l in range(3):\n",
-    "                result = []\n",
-    "                result.append(model[i][l])\n",
-    "                result.append(model[j][l])\n",
-    "                result.append(model[k][l])\n",
-    "\n",
-    "                soft.append(calculate_csi(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                hard.append(calculate_csi(val[l],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                mcc.append(multiclass_mcc(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                accuracy.append(accuracy_score(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            # 모델 조합과 함께 평균 CSI 저장\n",
-    "            voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "            #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                soft = []\n",
-    "                hard = []\n",
-    "                mcc= []\n",
-    "                accuracy= []\n",
-    "                for m in range(3):\n",
-    "                    result = []\n",
-    "                    result.append(model[i][m])\n",
-    "                    result.append(model[j][m])\n",
-    "                    result.append(model[k][m])\n",
-    "                    result.append(model[l][m])\n",
-    "               \n",
-    "                    soft.append(calculate_csi(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    hard.append(calculate_csi(val[m],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                    mcc.append(multiclass_mcc(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    accuracy.append(accuracy_score(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                # 모델 조합과 함께 평균 CSI 저장\n",
-    "                voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                for m in range(l+1, len(model)):\n",
-    "                    soft = []\n",
-    "                    hard = []\n",
-    "                    mcc= []\n",
-    "                    accuracy= []\n",
-    "                    for n in range(3):\n",
-    "                        result = []\n",
-    "                        result.append(model[i][n])\n",
-    "                        result.append(model[j][n])\n",
-    "                        result.append(model[k][n])\n",
-    "                        result.append(model[l][n])\n",
-    "                        result.append(model[m][n])\n",
-    "\n",
-    "                        soft.append(calculate_csi(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        hard.append(calculate_csi(val[n],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                        mcc.append(multiclass_mcc(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        accuracy.append(accuracy_score(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    # 모델 조합과 함께 평균 CSI 저장\n",
-    "                    voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                    # voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "       \n",
-    "\n",
-    "\n",
-    "\n",
-    "tm_df= pd.DataFrame(voting, columns=['model', 'CSI', 'MCC','Accuracy'])\n",
-    "tm_df['region'] = 'busan'\n",
-    "tm_df['data_sample'] = 'best'\n",
-    "\n",
-    "df = pd.concat([df, tm_df], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deepgbm=[] #DeepGBM\n",
-    "ft_transformer=[] #FT-transformer\n",
-    "resnet_like=[] #ResNet-like\n",
-    "XGBoost=[] #XGBoost\n",
-    "LightGBM=[] #LightGBM\n",
-    "val= []\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "\n",
-    "# 1 Fold\n",
-    "\n",
-    "val_preds, y_val = get_proba('incheon', 'deepgbm', 'pure', 1)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('incheon', 'resnet_like', 'smote', 1)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('incheon', 'ft_transformer', 'pure', 1)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon.loc[df_incheon['year'].isin([2018, 2019]), df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "LightGBM.append(lgb_incheon_1.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_incheon_1.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "\n",
-    "val_preds, y_val = get_proba('incheon', 'deepgbm', 'pure', 2)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('incheon', 'resnet_like', 'smote', 2)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('incheon', 'ft_transformer', 'pure', 2)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon.loc[df_incheon['year'].isin([2018, 2020]), df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "LightGBM.append(lgb_incheon_2.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_incheon_2.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "# 3 Fold\n",
-    "\n",
-    "val_preds, y_val = get_proba('incheon', 'deepgbm', 'pure', 3)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('incheon', 'resnet_like', 'smote', 3)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('incheon', 'ft_transformer', 'pure', 3)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon.loc[df_incheon['year'].isin([2019, 2020]), df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "LightGBM.append(lgb_incheon_3.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_incheon_3.predict_proba(X_val))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "model = [deepgbm, ft_transformer, resnet_like, XGBoost, LightGBM]\n",
-    "model_name = [\"deepgbm\", \"ft_transformer\", \"resnet_like\", \"XGBoost\", \"LightGBM\"]\n",
-    "voting = []\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        soft = []\n",
-    "        hard = []\n",
-    "        mcc= []\n",
-    "        accuracy = []\n",
-    "        for k in range(3):\n",
-    "            result = []\n",
-    "            result.append(model[i][k])\n",
-    "            result.append(model[j][k])\n",
-    "            \n",
-    "            soft.append(calculate_csi(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            hard.append(calculate_csi(val[k],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "            mcc.append(multiclass_mcc(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            accuracy.append(accuracy_score(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "        # 모델 조합과 함께 평균 CSI 저장\n",
-    "        voting.append((f\"{model_name[i]}+{model_name[j]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "        #voting.append((f\"{model_name[i]}+{model_name[j]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            soft = []\n",
-    "            hard = []\n",
-    "            mcc= []\n",
-    "            accuracy = []\n",
-    "            for l in range(3):\n",
-    "                result = []\n",
-    "                result.append(model[i][l])\n",
-    "                result.append(model[j][l])\n",
-    "                result.append(model[k][l])\n",
-    "\n",
-    "                soft.append(calculate_csi(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                hard.append(calculate_csi(val[l],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                mcc.append(multiclass_mcc(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                accuracy.append(accuracy_score(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            # 모델 조합과 함께 평균 CSI 저장\n",
-    "            voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "            #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                soft = []\n",
-    "                hard = []\n",
-    "                mcc= []\n",
-    "                accuracy = []\n",
-    "                for m in range(3):\n",
-    "                    result = []\n",
-    "                    result.append(model[i][m])\n",
-    "                    result.append(model[j][m])\n",
-    "                    result.append(model[k][m])\n",
-    "                    result.append(model[l][m])\n",
-    "               \n",
-    "                    soft.append(calculate_csi(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    hard.append(calculate_csi(val[m],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                    mcc.append(multiclass_mcc(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    accuracy.append(accuracy_score(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                # 모델 조합과 함께 평균 CSI 저장\n",
-    "                voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                for m in range(l+1, len(model)):\n",
-    "                    soft = []\n",
-    "                    hard = []\n",
-    "                    mcc= []\n",
-    "                    accuracy = []\n",
-    "                    for n in range(3):\n",
-    "                        result = []\n",
-    "                        result.append(model[i][n])\n",
-    "                        result.append(model[j][n])\n",
-    "                        result.append(model[k][n])\n",
-    "                        result.append(model[l][n])\n",
-    "                        result.append(model[m][n])\n",
-    "\n",
-    "                        soft.append(calculate_csi(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        hard.append(calculate_csi(val[n],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                        mcc.append(multiclass_mcc(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        accuracy.append(accuracy_score(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    # 모델 조합과 함께 평균 CSI 저장\n",
-    "                    voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                    # voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "       \n",
-    "\n",
-    "\n",
-    "\n",
-    "tm_df= pd.DataFrame(voting, columns=['model', 'CSI', 'MCC','Accuracy'])\n",
-    "tm_df['region'] = 'incheon'\n",
-    "tm_df['data_sample'] = 'best'\n",
-    "\n",
-    "df = pd.concat([df, tm_df], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deepgbm=[] #DeepGBM\n",
-    "ft_transformer=[] #FT-transformer\n",
-    "resnet_like=[] #ResNet-like\n",
-    "XGBoost=[] #XGBoost\n",
-    "LightGBM=[] #LightGBM\n",
-    "val= []\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba('daegu', 'deepgbm', 'smote', 1)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daegu', 'resnet_like', 'smote', 1)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daegu', 'ft_transformer', 'pure', 1)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu.loc[df_daegu['year'].isin([2018, 2019]), df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "LightGBM.append(lgb_daegu_1.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_daegu_1.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba('daegu', 'deepgbm', 'smote', 2)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daegu', 'resnet_like', 'smote', 2)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daegu', 'ft_transformer', 'pure', 2)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu.loc[df_daegu['year'].isin([2018, 2020]), df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "\n",
-    "LightGBM.append(lgb_daegu_2.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_daegu_2.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba('daegu', 'deepgbm', 'smote', 3)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daegu', 'resnet_like', 'smote', 3)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daegu', 'ft_transformer', 'pure', 3)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu.loc[df_daegu['year'].isin([2019, 2020]), df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "\n",
-    "LightGBM.append(lgb_daegu_3.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_daegu_3.predict_proba(X_val))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "model = [deepgbm, ft_transformer, resnet_like, XGBoost, LightGBM]\n",
-    "model_name = [\"deepgbm\", \"ft_transformer\", \"resnet_like\", \"XGBoost\", \"LightGBM\"]\n",
-    "voting = []\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        soft = []\n",
-    "        hard = []\n",
-    "        mcc= []\n",
-    "        accuracy= []\n",
-    "        for k in range(3):\n",
-    "            result = []\n",
-    "            result.append(model[i][k])\n",
-    "            result.append(model[j][k])\n",
-    "            \n",
-    "            soft.append(calculate_csi(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            hard.append(calculate_csi(val[k],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "            mcc.append(multiclass_mcc(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            accuracy.append(accuracy_score(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "        # 모델 조합과 함께 평균 CSI 저장\n",
-    "        voting.append((f\"{model_name[i]}+{model_name[j]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "        #voting.append((f\"{model_name[i]}+{model_name[j]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            soft = []\n",
-    "            hard = []\n",
-    "            mcc= []\n",
-    "            accuracy= []\n",
-    "            for l in range(3):\n",
-    "                result = []\n",
-    "                result.append(model[i][l])\n",
-    "                result.append(model[j][l])\n",
-    "                result.append(model[k][l])\n",
-    "\n",
-    "                soft.append(calculate_csi(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                hard.append(calculate_csi(val[l],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                mcc.append(multiclass_mcc(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                accuracy.append(accuracy_score(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            # 모델 조합과 함께 평균 CSI 저장\n",
-    "            voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "            #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                soft = []\n",
-    "                hard = []\n",
-    "                mcc= []\n",
-    "                accuracy= []\n",
-    "                for m in range(3):\n",
-    "                    result = []\n",
-    "                    result.append(model[i][m])\n",
-    "                    result.append(model[j][m])\n",
-    "                    result.append(model[k][m])\n",
-    "                    result.append(model[l][m])\n",
-    "               \n",
-    "                    soft.append(calculate_csi(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    hard.append(calculate_csi(val[m],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                    mcc.append(multiclass_mcc(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    accuracy.append(accuracy_score(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                # 모델 조합과 함께 평균 CSI 저장\n",
-    "                voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                for m in range(l+1, len(model)):\n",
-    "                    soft = []\n",
-    "                    hard = []\n",
-    "                    mcc= []\n",
-    "                    accuracy= []\n",
-    "                    for n in range(3):\n",
-    "                        result = []\n",
-    "                        result.append(model[i][n])\n",
-    "                        result.append(model[j][n])\n",
-    "                        result.append(model[k][n])\n",
-    "                        result.append(model[l][n])\n",
-    "                        result.append(model[m][n])\n",
-    "\n",
-    "                        soft.append(calculate_csi(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        hard.append(calculate_csi(val[n],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                        mcc.append(multiclass_mcc(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        accuracy.append(accuracy_score(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    # 모델 조합과 함께 평균 CSI 저장\n",
-    "                    voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                    # voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "       \n",
-    "\n",
-    "\n",
-    "\n",
-    "tm_df= pd.DataFrame(voting, columns=['model', 'CSI', 'MCC','Accuracy'])\n",
-    "tm_df['region'] = 'daegu'\n",
-    "tm_df['data_sample'] = 'best'\n",
-    "\n",
-    "df = pd.concat([df, tm_df], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deepgbm=[] #DeepGBM\n",
-    "ft_transformer=[] #FT-transformer\n",
-    "resnet_like=[] #ResNet-like\n",
-    "XGBoost=[] #XGBoost\n",
-    "LightGBM=[] #LightGBM\n",
-    "val= []\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "\n",
-    "# 1 Fold\n",
-    "\n",
-    "val_preds, y_val = get_proba('daejeon', 'deepgbm', 'pure', 1)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daejeon', 'resnet_like', 'pure', 1)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daejeon', 'ft_transformer', 'pure', 1)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon.loc[df_daejeon['year'].isin([2018, 2019]), df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "LightGBM.append(lgb_daejeon_1.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_daejeon_1.predict_proba(X_val))\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "# 2 Fold\n",
-    "\n",
-    "val_preds, y_val = get_proba('daejeon', 'deepgbm', 'pure', 2)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daejeon', 'resnet_like', 'pure', 2)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daejeon', 'ft_transformer', 'pure', 2)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon.loc[df_daejeon['year'].isin([2018, 2020]), df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "LightGBM.append(lgb_daejeon_2.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_daejeon_2.predict_proba(X_val))\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "# 3 Fold\n",
-    "\n",
-    "val_preds, y_val = get_proba('daejeon', 'deepgbm', 'pure', 3)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daejeon', 'resnet_like', 'pure', 3)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daejeon', 'ft_transformer', 'pure', 3)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon.loc[df_daejeon['year'].isin([2019, 2020]), df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "LightGBM.append(lgb_daejeon_3.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_daejeon_3.predict_proba(X_val))\n",
-    "val.append(Y_val)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "model = [deepgbm, ft_transformer, resnet_like, XGBoost, LightGBM]\n",
-    "model_name = [\"deepgbm\", \"ft_transformer\", \"resnet_like\", \"XGBoost\", \"LightGBM\"]\n",
-    "voting = []\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        soft = []\n",
-    "        hard = []\n",
-    "        mcc= []\n",
-    "        accuracy= []\n",
-    "        for k in range(3):\n",
-    "            result = []\n",
-    "            result.append(model[i][k])\n",
-    "            result.append(model[j][k])\n",
-    "            \n",
-    "            soft.append(calculate_csi(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            hard.append(calculate_csi(val[k],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "            mcc.append(multiclass_mcc(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            accuracy.append(accuracy_score(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "        # 모델 조합과 함께 평균 CSI 저장\n",
-    "        voting.append((f\"{model_name[i]}+{model_name[j]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "        #voting.append((f\"{model_name[i]}+{model_name[j]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            soft = []\n",
-    "            hard = []\n",
-    "            mcc= []\n",
-    "            accuracy= []\n",
-    "            for l in range(3):\n",
-    "                result = []\n",
-    "                result.append(model[i][l])\n",
-    "                result.append(model[j][l])\n",
-    "                result.append(model[k][l])\n",
-    "\n",
-    "                soft.append(calculate_csi(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                hard.append(calculate_csi(val[l],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                mcc.append(multiclass_mcc(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                accuracy.append(accuracy_score(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            # 모델 조합과 함께 평균 CSI 저장\n",
-    "            voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "            #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                soft = []\n",
-    "                hard = []\n",
-    "                mcc= []\n",
-    "                accuracy= []\n",
-    "                for m in range(3):\n",
-    "                    result = []\n",
-    "                    result.append(model[i][m])\n",
-    "                    result.append(model[j][m])\n",
-    "                    result.append(model[k][m])\n",
-    "                    result.append(model[l][m])\n",
-    "               \n",
-    "                    soft.append(calculate_csi(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    hard.append(calculate_csi(val[m],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                    mcc.append(multiclass_mcc(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    accuracy.append(accuracy_score(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                # 모델 조합과 함께 평균 CSI 저장\n",
-    "                voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                for m in range(l+1, len(model)):\n",
-    "                    soft = []\n",
-    "                    hard = []\n",
-    "                    mcc= []\n",
-    "                    accuracy= []\n",
-    "                    for n in range(3):\n",
-    "                        result = []\n",
-    "                        result.append(model[i][n])\n",
-    "                        result.append(model[j][n])\n",
-    "                        result.append(model[k][n])\n",
-    "                        result.append(model[l][n])\n",
-    "                        result.append(model[m][n])\n",
-    "\n",
-    "                        soft.append(calculate_csi(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        hard.append(calculate_csi(val[n],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                        mcc.append(multiclass_mcc(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        accuracy.append(accuracy_score(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    # 모델 조합과 함께 평균 CSI 저장\n",
-    "                    voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                    # voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "       \n",
-    "\n",
-    "\n",
-    "tm_df= pd.DataFrame(voting, columns=['model', 'CSI', 'MCC','Accuracy'])\n",
-    "tm_df['region'] = 'daejeon'\n",
-    "tm_df['data_sample'] = 'best'\n",
-    "\n",
-    "df = pd.concat([df, tm_df], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba('gwangju', 'deepgbm', 'pure', 1)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('gwangju', 'resnet_like', 'pure', 1)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('gwangju', 'ft_transformer', 'pure', 1)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju.loc[df_gwangju['year'].isin([2018, 2019]), df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "LightGBM.append(lgb_gwangju_1.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_gwangju_1.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba('gwangju', 'deepgbm', 'pure', 2)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('gwangju', 'resnet_like', 'pure', 2)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('gwangju', 'ft_transformer', 'pure', 2)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju.loc[df_gwangju['year'].isin([2018, 2020]), df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "LightGBM.append(lgb_gwangju_2.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_gwangju_2.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba('gwangju', 'deepgbm', 'pure', 3)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('gwangju', 'resnet_like', 'pure', 3)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('gwangju', 'ft_transformer', 'pure', 3)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju.loc[df_gwangju['year'].isin([2019, 2020]), df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "LightGBM.append(lgb_gwangju_3.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_gwangju_3.predict_proba(X_val))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "model = [deepgbm, ft_transformer, resnet_like, XGBoost, LightGBM]\n",
-    "model_name = [\"deepgbm\", \"ft_transformer\", \"resnet_like\", \"XGBoost\", \"LightGBM\"]\n",
-    "voting = []\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        soft = []\n",
-    "        hard = []\n",
-    "        for k in range(3):\n",
-    "            result = []\n",
-    "            result.append(model[i][k])\n",
-    "            result.append(model[j][k])\n",
-    "            \n",
-    "            soft.append(calculate_csi(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            hard.append(calculate_csi(val[k],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "            mcc.append(multiclass_mcc(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            accuracy.append(accuracy_score(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "        # 모델 조합과 함께 평균 CSI 저장\n",
-    "        voting.append((f\"{model_name[i]}+{model_name[j]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "        #voting.append((f\"{model_name[i]}+{model_name[j]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            soft = []\n",
-    "            hard = []\n",
-    "            mcc= []\n",
-    "            accuracy = []\n",
-    "            for l in range(3):\n",
-    "                result = []\n",
-    "                result.append(model[i][l])\n",
-    "                result.append(model[j][l])\n",
-    "                result.append(model[k][l])\n",
-    "\n",
-    "                soft.append(calculate_csi(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                hard.append(calculate_csi(val[l],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                mcc.append(multiclass_mcc(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                accuracy.append(accuracy_score(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            # 모델 조합과 함께 평균 CSI 저장\n",
-    "            voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "            #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                soft = []\n",
-    "                hard = []\n",
-    "                mcc= []\n",
-    "                accuracy = []\n",
-    "                for m in range(3):\n",
-    "                    result = []\n",
-    "                    result.append(model[i][m])\n",
-    "                    result.append(model[j][m])\n",
-    "                    result.append(model[k][m])\n",
-    "                    result.append(model[l][m])\n",
-    "               \n",
-    "                    soft.append(calculate_csi(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    hard.append(calculate_csi(val[m],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                    mcc.append(multiclass_mcc(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    accuracy.append(accuracy_score(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                # 모델 조합과 함께 평균 CSI 저장\n",
-    "                voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                for m in range(l+1, len(model)):\n",
-    "                    soft = []\n",
-    "                    hard = []\n",
-    "                    mcc= []\n",
-    "                    accuracy = []\n",
-    "                    for n in range(3):\n",
-    "                        result = []\n",
-    "                        result.append(model[i][n])\n",
-    "                        result.append(model[j][n])\n",
-    "                        result.append(model[k][n])\n",
-    "                        result.append(model[l][n])\n",
-    "                        result.append(model[m][n])\n",
-    "\n",
-    "                        soft.append(calculate_csi(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        hard.append(calculate_csi(val[n],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                        mcc.append(multiclass_mcc(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        accuracy.append(accuracy_score(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    # 모델 조합과 함께 평균 CSI 저장\n",
-    "                    voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                    # voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "       \n",
-    "\n",
-    "\n",
-    "\n",
-    "tm_df= pd.DataFrame(voting, columns=['model', 'CSI', 'MCC','Accuracy'])\n",
-    "tm_df['region'] = 'gwangju'\n",
-    "tm_df['data_sample'] = 'best'\n",
-    "\n",
-    "df = pd.concat([df, tm_df], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "      <th>region</th>\n",
-       "      <th>data_sample</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>deepgbm+XGBoost</td>\n",
-       "      <td>0.657407</td>\n",
-       "      <td>0.773279</td>\n",
-       "      <td>0.954158</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>deepgbm+LightGBM</td>\n",
-       "      <td>0.661543</td>\n",
-       "      <td>0.776346</td>\n",
-       "      <td>0.955109</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ft_transformer+resnet_like</td>\n",
-       "      <td>0.587478</td>\n",
-       "      <td>0.712493</td>\n",
-       "      <td>0.944881</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>ft_transformer+XGBoost</td>\n",
-       "      <td>0.581913</td>\n",
-       "      <td>0.707980</td>\n",
-       "      <td>0.943359</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>ft_transformer+LightGBM</td>\n",
-       "      <td>0.580533</td>\n",
-       "      <td>0.707099</td>\n",
-       "      <td>0.943739</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>resnet_like+XGBoost</td>\n",
-       "      <td>0.562109</td>\n",
-       "      <td>0.692599</td>\n",
-       "      <td>0.938989</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>resnet_like+LightGBM</td>\n",
-       "      <td>0.555271</td>\n",
-       "      <td>0.686253</td>\n",
-       "      <td>0.938609</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>XGBoost+LightGBM</td>\n",
-       "      <td>0.528844</td>\n",
-       "      <td>0.663474</td>\n",
-       "      <td>0.932714</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like</td>\n",
-       "      <td>0.660600</td>\n",
-       "      <td>0.774370</td>\n",
-       "      <td>0.955334</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost</td>\n",
-       "      <td>0.649524</td>\n",
-       "      <td>0.765199</td>\n",
-       "      <td>0.953663</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>deepgbm+ft_transformer+LightGBM</td>\n",
-       "      <td>0.655685</td>\n",
-       "      <td>0.769959</td>\n",
-       "      <td>0.955032</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>deepgbm+resnet_like+XGBoost</td>\n",
-       "      <td>0.646377</td>\n",
-       "      <td>0.764436</td>\n",
-       "      <td>0.952637</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>deepgbm+resnet_like+LightGBM</td>\n",
-       "      <td>0.644172</td>\n",
-       "      <td>0.762363</td>\n",
-       "      <td>0.952752</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>deepgbm+XGBoost+LightGBM</td>\n",
-       "      <td>0.610494</td>\n",
-       "      <td>0.735516</td>\n",
-       "      <td>0.947010</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>0.592672</td>\n",
-       "      <td>0.716951</td>\n",
-       "      <td>0.945338</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>0.586850</td>\n",
-       "      <td>0.712067</td>\n",
-       "      <td>0.944805</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>0.569775</td>\n",
-       "      <td>0.697854</td>\n",
-       "      <td>0.940813</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.556842</td>\n",
-       "      <td>0.687964</td>\n",
-       "      <td>0.938342</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>0.646448</td>\n",
-       "      <td>0.762862</td>\n",
-       "      <td>0.953283</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>0.643276</td>\n",
-       "      <td>0.760303</td>\n",
-       "      <td>0.953017</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>0.626859</td>\n",
-       "      <td>0.747025</td>\n",
-       "      <td>0.950128</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>deepgbm+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.617990</td>\n",
-       "      <td>0.741269</td>\n",
-       "      <td>0.948493</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.577877</td>\n",
-       "      <td>0.704685</td>\n",
-       "      <td>0.942678</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost+Lig...</td>\n",
-       "      <td>0.625171</td>\n",
-       "      <td>0.745410</td>\n",
-       "      <td>0.950090</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>deepgbm+ft_transformer</td>\n",
-       "      <td>0.679848</td>\n",
-       "      <td>0.767237</td>\n",
-       "      <td>0.954138</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>deepgbm+resnet_like</td>\n",
-       "      <td>0.665680</td>\n",
-       "      <td>0.771460</td>\n",
-       "      <td>0.954373</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>deepgbm+XGBoost</td>\n",
-       "      <td>0.657407</td>\n",
-       "      <td>0.771914</td>\n",
-       "      <td>0.954319</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>deepgbm+LightGBM</td>\n",
-       "      <td>0.661543</td>\n",
-       "      <td>0.772801</td>\n",
-       "      <td>0.954477</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ft_transformer+resnet_like</td>\n",
-       "      <td>0.587478</td>\n",
-       "      <td>0.762750</td>\n",
-       "      <td>0.952878</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>ft_transformer+XGBoost</td>\n",
-       "      <td>0.581913</td>\n",
-       "      <td>0.754925</td>\n",
-       "      <td>0.951518</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>ft_transformer+LightGBM</td>\n",
-       "      <td>0.580533</td>\n",
-       "      <td>0.748947</td>\n",
-       "      <td>0.950546</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>resnet_like+XGBoost</td>\n",
-       "      <td>0.562109</td>\n",
-       "      <td>0.742686</td>\n",
-       "      <td>0.949262</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>resnet_like+LightGBM</td>\n",
-       "      <td>0.555271</td>\n",
-       "      <td>0.737043</td>\n",
-       "      <td>0.948196</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>XGBoost+LightGBM</td>\n",
-       "      <td>0.528844</td>\n",
-       "      <td>0.730355</td>\n",
-       "      <td>0.946789</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like</td>\n",
-       "      <td>0.660600</td>\n",
-       "      <td>0.774370</td>\n",
-       "      <td>0.955334</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost</td>\n",
-       "      <td>0.649524</td>\n",
-       "      <td>0.765199</td>\n",
-       "      <td>0.953663</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>deepgbm+ft_transformer+LightGBM</td>\n",
-       "      <td>0.655685</td>\n",
-       "      <td>0.769959</td>\n",
-       "      <td>0.955032</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>deepgbm+resnet_like+XGBoost</td>\n",
-       "      <td>0.646377</td>\n",
-       "      <td>0.764436</td>\n",
-       "      <td>0.952637</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>deepgbm+resnet_like+LightGBM</td>\n",
-       "      <td>0.644172</td>\n",
-       "      <td>0.762363</td>\n",
-       "      <td>0.952752</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>deepgbm+XGBoost+LightGBM</td>\n",
-       "      <td>0.610494</td>\n",
-       "      <td>0.735516</td>\n",
-       "      <td>0.947010</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>0.592672</td>\n",
-       "      <td>0.716951</td>\n",
-       "      <td>0.945338</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>0.586850</td>\n",
-       "      <td>0.712067</td>\n",
-       "      <td>0.944805</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>0.569775</td>\n",
-       "      <td>0.697854</td>\n",
-       "      <td>0.940813</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.556842</td>\n",
-       "      <td>0.687964</td>\n",
-       "      <td>0.938342</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>0.646448</td>\n",
-       "      <td>0.762862</td>\n",
-       "      <td>0.953283</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>0.643276</td>\n",
-       "      <td>0.760303</td>\n",
-       "      <td>0.953017</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>0.626859</td>\n",
-       "      <td>0.747025</td>\n",
-       "      <td>0.950128</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>deepgbm+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.617990</td>\n",
-       "      <td>0.741269</td>\n",
-       "      <td>0.948493</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.577877</td>\n",
-       "      <td>0.704685</td>\n",
-       "      <td>0.942678</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost+Lig...</td>\n",
-       "      <td>0.625171</td>\n",
-       "      <td>0.745410</td>\n",
-       "      <td>0.950090</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                model       CSI       MCC  \\\n",
-       "2                                     deepgbm+XGBoost  0.657407  0.773279   \n",
-       "3                                    deepgbm+LightGBM  0.661543  0.776346   \n",
-       "4                          ft_transformer+resnet_like  0.587478  0.712493   \n",
-       "5                              ft_transformer+XGBoost  0.581913  0.707980   \n",
-       "6                             ft_transformer+LightGBM  0.580533  0.707099   \n",
-       "7                                 resnet_like+XGBoost  0.562109  0.692599   \n",
-       "8                                resnet_like+LightGBM  0.555271  0.686253   \n",
-       "9                                    XGBoost+LightGBM  0.528844  0.663474   \n",
-       "10                 deepgbm+ft_transformer+resnet_like  0.660600  0.774370   \n",
-       "11                     deepgbm+ft_transformer+XGBoost  0.649524  0.765199   \n",
-       "12                    deepgbm+ft_transformer+LightGBM  0.655685  0.769959   \n",
-       "13                        deepgbm+resnet_like+XGBoost  0.646377  0.764436   \n",
-       "14                       deepgbm+resnet_like+LightGBM  0.644172  0.762363   \n",
-       "15                           deepgbm+XGBoost+LightGBM  0.610494  0.735516   \n",
-       "16                 ft_transformer+resnet_like+XGBoost  0.592672  0.716951   \n",
-       "17                ft_transformer+resnet_like+LightGBM  0.586850  0.712067   \n",
-       "18                    ft_transformer+XGBoost+LightGBM  0.569775  0.697854   \n",
-       "19                       resnet_like+XGBoost+LightGBM  0.556842  0.687964   \n",
-       "20         deepgbm+ft_transformer+resnet_like+XGBoost  0.646448  0.762862   \n",
-       "21        deepgbm+ft_transformer+resnet_like+LightGBM  0.643276  0.760303   \n",
-       "22            deepgbm+ft_transformer+XGBoost+LightGBM  0.626859  0.747025   \n",
-       "23               deepgbm+resnet_like+XGBoost+LightGBM  0.617990  0.741269   \n",
-       "24        ft_transformer+resnet_like+XGBoost+LightGBM  0.577877  0.704685   \n",
-       "25  deepgbm+ft_transformer+resnet_like+XGBoost+Lig...  0.625171  0.745410   \n",
-       "0                              deepgbm+ft_transformer  0.679848  0.767237   \n",
-       "1                                 deepgbm+resnet_like  0.665680  0.771460   \n",
-       "2                                     deepgbm+XGBoost  0.657407  0.771914   \n",
-       "3                                    deepgbm+LightGBM  0.661543  0.772801   \n",
-       "4                          ft_transformer+resnet_like  0.587478  0.762750   \n",
-       "5                              ft_transformer+XGBoost  0.581913  0.754925   \n",
-       "6                             ft_transformer+LightGBM  0.580533  0.748947   \n",
-       "7                                 resnet_like+XGBoost  0.562109  0.742686   \n",
-       "8                                resnet_like+LightGBM  0.555271  0.737043   \n",
-       "9                                    XGBoost+LightGBM  0.528844  0.730355   \n",
-       "10                 deepgbm+ft_transformer+resnet_like  0.660600  0.774370   \n",
-       "11                     deepgbm+ft_transformer+XGBoost  0.649524  0.765199   \n",
-       "12                    deepgbm+ft_transformer+LightGBM  0.655685  0.769959   \n",
-       "13                        deepgbm+resnet_like+XGBoost  0.646377  0.764436   \n",
-       "14                       deepgbm+resnet_like+LightGBM  0.644172  0.762363   \n",
-       "15                           deepgbm+XGBoost+LightGBM  0.610494  0.735516   \n",
-       "16                 ft_transformer+resnet_like+XGBoost  0.592672  0.716951   \n",
-       "17                ft_transformer+resnet_like+LightGBM  0.586850  0.712067   \n",
-       "18                    ft_transformer+XGBoost+LightGBM  0.569775  0.697854   \n",
-       "19                       resnet_like+XGBoost+LightGBM  0.556842  0.687964   \n",
-       "20         deepgbm+ft_transformer+resnet_like+XGBoost  0.646448  0.762862   \n",
-       "21        deepgbm+ft_transformer+resnet_like+LightGBM  0.643276  0.760303   \n",
-       "22            deepgbm+ft_transformer+XGBoost+LightGBM  0.626859  0.747025   \n",
-       "23               deepgbm+resnet_like+XGBoost+LightGBM  0.617990  0.741269   \n",
-       "24        ft_transformer+resnet_like+XGBoost+LightGBM  0.577877  0.704685   \n",
-       "25  deepgbm+ft_transformer+resnet_like+XGBoost+Lig...  0.625171  0.745410   \n",
-       "\n",
-       "    Accuracy   region data_sample  \n",
-       "2   0.954158  daejeon        best  \n",
-       "3   0.955109  daejeon        best  \n",
-       "4   0.944881  daejeon        best  \n",
-       "5   0.943359  daejeon        best  \n",
-       "6   0.943739  daejeon        best  \n",
-       "7   0.938989  daejeon        best  \n",
-       "8   0.938609  daejeon        best  \n",
-       "9   0.932714  daejeon        best  \n",
-       "10  0.955334  daejeon        best  \n",
-       "11  0.953663  daejeon        best  \n",
-       "12  0.955032  daejeon        best  \n",
-       "13  0.952637  daejeon        best  \n",
-       "14  0.952752  daejeon        best  \n",
-       "15  0.947010  daejeon        best  \n",
-       "16  0.945338  daejeon        best  \n",
-       "17  0.944805  daejeon        best  \n",
-       "18  0.940813  daejeon        best  \n",
-       "19  0.938342  daejeon        best  \n",
-       "20  0.953283  daejeon        best  \n",
-       "21  0.953017  daejeon        best  \n",
-       "22  0.950128  daejeon        best  \n",
-       "23  0.948493  daejeon        best  \n",
-       "24  0.942678  daejeon        best  \n",
-       "25  0.950090  daejeon        best  \n",
-       "0   0.954138  gwangju        best  \n",
-       "1   0.954373  gwangju        best  \n",
-       "2   0.954319  gwangju        best  \n",
-       "3   0.954477  gwangju        best  \n",
-       "4   0.952878  gwangju        best  \n",
-       "5   0.951518  gwangju        best  \n",
-       "6   0.950546  gwangju        best  \n",
-       "7   0.949262  gwangju        best  \n",
-       "8   0.948196  gwangju        best  \n",
-       "9   0.946789  gwangju        best  \n",
-       "10  0.955334  gwangju        best  \n",
-       "11  0.953663  gwangju        best  \n",
-       "12  0.955032  gwangju        best  \n",
-       "13  0.952637  gwangju        best  \n",
-       "14  0.952752  gwangju        best  \n",
-       "15  0.947010  gwangju        best  \n",
-       "16  0.945338  gwangju        best  \n",
-       "17  0.944805  gwangju        best  \n",
-       "18  0.940813  gwangju        best  \n",
-       "19  0.938342  gwangju        best  \n",
-       "20  0.953283  gwangju        best  \n",
-       "21  0.953017  gwangju        best  \n",
-       "22  0.950128  gwangju        best  \n",
-       "23  0.948493  gwangju        best  \n",
-       "24  0.942678  gwangju        best  \n",
-       "25  0.950090  gwangju        best  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.tail(50)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv(\"../model_result/best_sample/ensemble_best_sample.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Analysis_code/plots/model_performance_ranking_by_region_and_data_sample.txt b/Analysis_code/plots/model_performance_ranking_by_region_and_data_sample.txt
new file mode 100644
index 0000000000000000000000000000000000000000..185098c814664d9557e6fb5641938d96558f28c4
--- /dev/null
+++ b/Analysis_code/plots/model_performance_ranking_by_region_and_data_sample.txt
@@ -0,0 +1,179 @@
+====================================================================================================
+지역별, 모델별 data_sample 성능 순위 (CSI 기준 내림차순)
+====================================================================================================
+
+📍 BUSAN
+----------------------------------------------------------------------------------------------------
+
+  🔹 LightGBM:
+
+  순위     Data Sample               CSI        MCC        Accuracy  
+  ----------------------------------------------------------------------
+  1      ctgan10000                0.467663   0.631435   0.959516  
+  2      ctgan7000                 0.466374   0.634410   0.960050  
+  3      ctgan20000                0.466346   0.626520   0.957503  
+  4      smote                     0.466021   0.631880   0.950198  
+  5      smotenc_ctgan10000        0.456299   0.624299   0.957919  
+  6      smotenc_ctgan7000         0.448540   0.617030   0.957082  
+  7      smotenc_ctgan20000        0.433627   0.608014   0.956400  
+  8      pure                      0.430188   0.600801   0.956971  
+
+  🔹 XGBoost:
+
+  순위     Data Sample               CSI        MCC        Accuracy  
+  ----------------------------------------------------------------------
+  1      smotenc_ctgan20000        0.467128   0.630099   0.958033  
+  2      smote                     0.466966   0.629257   0.952444  
+  3      ctgan7000                 0.464988   0.633134   0.959059  
+  4      ctgan20000                0.463713   0.629221   0.958679  
+  5      ctgan10000                0.462157   0.623260   0.957997  
+  6      pure                      0.460244   0.626085   0.959745  
+  7      smotenc_ctgan10000        0.459860   0.625067   0.957994  
+  8      smotenc_ctgan7000         0.459674   0.624993   0.957958  
+
+📍 DAEGU
+----------------------------------------------------------------------------------------------------
+
+  🔹 LightGBM:
+
+  순위     Data Sample               CSI        MCC        Accuracy  
+  ----------------------------------------------------------------------
+  1      smote                     0.447354   0.616921   0.963730  
+  2      ctgan20000                0.440419   0.608625   0.967447  
+  3      ctgan7000                 0.426128   0.599566   0.968853  
+  4      smotenc_ctgan20000        0.422337   0.591076   0.963494  
+  5      smotenc_ctgan10000        0.411417   0.589803   0.967411  
+  6      ctgan10000                0.406700   0.578623   0.966763  
+  7      smotenc_ctgan7000         0.402243   0.583238   0.966424  
+  8      pure                      0.292340   0.481989   0.956964  
+
+  🔹 XGBoost:
+
+  순위     Data Sample               CSI        MCC        Accuracy  
+  ----------------------------------------------------------------------
+  1      smote                     0.454066   0.620948   0.964908  
+  2      ctgan7000                 0.437178   0.609142   0.970487  
+  3      ctgan20000                0.422417   0.595599   0.966613  
+  4      ctgan10000                0.422093   0.598276   0.967489  
+  5      smotenc_ctgan10000        0.416966   0.592889   0.966614  
+  6      pure                      0.416651   0.600957   0.968324  
+  7      smotenc_ctgan7000         0.410981   0.591435   0.965780  
+  8      smotenc_ctgan20000        0.408470   0.583915   0.965550  
+
+📍 DAEJEON
+----------------------------------------------------------------------------------------------------
+
+  🔹 LightGBM:
+
+  순위     Data Sample               CSI        MCC        Accuracy  
+  ----------------------------------------------------------------------
+  1      smote                     0.521335   0.656321   0.930621  
+  2      smotenc_ctgan20000        0.482738   0.627963   0.931189  
+  3      ctgan20000                0.480839   0.625832   0.931760  
+  4      pure                      0.478437   0.625244   0.932748  
+  5      ctgan10000                0.478241   0.626177   0.932976  
+  6      smotenc_ctgan7000         0.476309   0.625184   0.932141  
+  7      ctgan7000                 0.470145   0.619110   0.931494  
+  8      smotenc_ctgan10000        0.468342   0.620789   0.931759  
+
+  🔹 XGBoost:
+
+  순위     Data Sample               CSI        MCC        Accuracy  
+  ----------------------------------------------------------------------
+  1      smote                     0.516494   0.651203   0.931456  
+  2      pure                      0.504450   0.647193   0.935752  
+  3      ctgan10000                0.499208   0.644695   0.935751  
+  4      smotenc_ctgan7000         0.497847   0.641809   0.934080  
+  5      ctgan7000                 0.490323   0.635876   0.933508  
+  6      smotenc_ctgan20000        0.489587   0.636099   0.933698  
+  7      ctgan20000                0.486799   0.632363   0.932787  
+  8      smotenc_ctgan10000        0.486735   0.634073   0.933356  
+
+📍 GWANGJU
+----------------------------------------------------------------------------------------------------
+
+  🔹 LightGBM:
+
+  순위     Data Sample               CSI        MCC        Accuracy  
+  ----------------------------------------------------------------------
+  1      smote                     0.522731   0.660423   0.936850  
+  2      ctgan20000                0.493713   0.637313   0.936783  
+  3      ctgan7000                 0.486034   0.634803   0.942667  
+  4      smotenc_ctgan10000        0.485951   0.638436   0.942780  
+  5      pure                      0.482777   0.636815   0.943236  
+  6      ctgan10000                0.481417   0.630243   0.941869  
+  7      smotenc_ctgan20000        0.475348   0.618830   0.934692  
+  8      smotenc_ctgan7000         0.471552   0.623710   0.940309  
+
+  🔹 XGBoost:
+
+  순위     Data Sample               CSI        MCC        Accuracy  
+  ----------------------------------------------------------------------
+  1      smote                     0.512112   0.652238   0.935221  
+  2      ctgan7000                 0.497729   0.642781   0.942971  
+  3      ctgan10000                0.494866   0.639831   0.941983  
+  4      smotenc_ctgan10000        0.492042   0.640042   0.942248  
+  5      pure                      0.490036   0.637981   0.943429  
+  6      smotenc_ctgan20000        0.487791   0.635238   0.941375  
+  7      smotenc_ctgan7000         0.484990   0.631557   0.940424  
+  8      ctgan20000                0.483538   0.630085   0.941072  
+
+📍 INCHEON
+----------------------------------------------------------------------------------------------------
+
+  🔹 LightGBM:
+
+  순위     Data Sample               CSI        MCC        Accuracy  
+  ----------------------------------------------------------------------
+  1      smote                     0.583560   0.706137   0.910464  
+  2      ctgan20000                0.566681   0.688934   0.907626  
+  3      smotenc_ctgan20000        0.564798   0.688999   0.909293  
+  4      pure                      0.554663   0.687951   0.911954  
+  5      ctgan10000                0.553099   0.687651   0.912108  
+  6      smotenc_ctgan7000         0.551851   0.686446   0.911078  
+  7      smotenc_ctgan10000        0.547445   0.682151   0.910282  
+  8      ctgan7000                 0.540065   0.677611   0.909713  
+
+  🔹 XGBoost:
+
+  순위     Data Sample               CSI        MCC        Accuracy  
+  ----------------------------------------------------------------------
+  1      smote                     0.589146   0.709143   0.912136  
+  2      smotenc_ctgan10000        0.563561   0.693149   0.912638  
+  3      ctgan7000                 0.561770   0.692611   0.913133  
+  4      smotenc_ctgan7000         0.561186   0.692362   0.912675  
+  5      ctgan10000                0.558826   0.690054   0.912600  
+  6      pure                      0.558144   0.689218   0.913058  
+  7      smotenc_ctgan20000        0.557241   0.687968   0.911421  
+  8      ctgan20000                0.553268   0.685576   0.911232  
+
+📍 SEOUL
+----------------------------------------------------------------------------------------------------
+
+  🔹 LightGBM:
+
+  순위     Data Sample               CSI        MCC        Accuracy  
+  ----------------------------------------------------------------------
+  1      smote                     0.578939   0.708499   0.939995  
+  2      ctgan10000                0.548902   0.686531   0.943140  
+  3      ctgan7000                 0.548318   0.687390   0.943140  
+  4      ctgan20000                0.543010   0.678072   0.940934  
+  5      smotenc_ctgan10000        0.539145   0.680238   0.941963  
+  6      smotenc_ctgan20000        0.535881   0.678979   0.941392  
+  7      smotenc_ctgan7000         0.535260   0.678219   0.941240  
+  8      pure                      0.505041   0.646992   0.936174  
+
+  🔹 XGBoost:
+
+  순위     Data Sample               CSI        MCC        Accuracy  
+  ----------------------------------------------------------------------
+  1      smote                     0.582266   0.710502   0.942040  
+  2      ctgan10000                0.572645   0.705046   0.945992  
+  3      pure                      0.565012   0.700483   0.945572  
+  4      ctgan20000                0.561291   0.696774   0.944776  
+  5      ctgan7000                 0.560659   0.697024   0.944889  
+  6      smotenc_ctgan10000        0.558459   0.696414   0.944813  
+  7      smotenc_ctgan20000        0.557444   0.695379   0.944244  
+  8      smotenc_ctgan7000         0.556605   0.695629   0.944015  
+
+====================================================================================================
\ No newline at end of file
diff --git a/Analysis_code/visualization/model_visualize.ipynb b/Analysis_code/visualization/model_visualize.ipynb
deleted file mode 100644
index c340b733a91b3f52f4d1b39a8d665d20a05fb1d0..0000000000000000000000000000000000000000
--- a/Analysis_code/visualization/model_visualize.ipynb
+++ /dev/null
@@ -1,2418 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using device: cpu\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "device = torch.device(\"cuda:1\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(f\"Using device: {device}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd \n",
-    "import numpy as np \n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a= pd.read_csv(\"./model_result/deepgbm_sampled_data_test.csv\")\n",
-    "b= pd.read_csv(\"./model_result/ft_transformer_sampled_data_test.csv\")\n",
-    "c= pd.read_csv(\"./model_result/lightgbm_sampled_data_test.csv\")\n",
-    "d= pd.read_csv(\"./model_result/xgboost_sampled_data_test.csv\")\n",
-    "e= pd.read_csv(\"./model_result/resnet_like_sampled_data_test.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "models= pd.concat([a,b,c,d,e], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "region\n",
-       "seoul      25\n",
-       "busan      25\n",
-       "incheon    25\n",
-       "daegu      25\n",
-       "daejeon    25\n",
-       "gwangju    25\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "models['region'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "data_sample\n",
-       "pure          30\n",
-       "smote         30\n",
-       "ctgan20000    30\n",
-       "ctgan10000    30\n",
-       "ctgan7000     30\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "models['data_sample'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "model\n",
-       "deepgbm           30\n",
-       "ft_transformer    30\n",
-       "LightGBM          30\n",
-       "XGBoost           30\n",
-       "resnet_like       30\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "models['model'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "models['model_data'] = models['model'] + '_' + models['data_sample']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "      <th>model_data</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.644251</td>\n",
-       "      <td>0.766097</td>\n",
-       "      <td>0.957526</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.681864</td>\n",
-       "      <td>0.804999</td>\n",
-       "      <td>0.974908</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.567129</td>\n",
-       "      <td>0.688707</td>\n",
-       "      <td>0.909057</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.576840</td>\n",
-       "      <td>0.715856</td>\n",
-       "      <td>0.975236</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.687967</td>\n",
-       "      <td>0.797628</td>\n",
-       "      <td>0.957317</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.460507</td>\n",
-       "      <td>0.638240</td>\n",
-       "      <td>0.947905</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.534332</td>\n",
-       "      <td>0.664453</td>\n",
-       "      <td>0.899439</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.445301</td>\n",
-       "      <td>0.617498</td>\n",
-       "      <td>0.967371</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.509262</td>\n",
-       "      <td>0.649690</td>\n",
-       "      <td>0.918171</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.492702</td>\n",
-       "      <td>0.637537</td>\n",
-       "      <td>0.927253</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>150 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     region        model data_sample       CSI       MCC  Accuracy  \\\n",
-       "0     seoul      deepgbm        pure  0.644251  0.766097  0.957526   \n",
-       "1     busan      deepgbm        pure  0.681864  0.804999  0.974908   \n",
-       "2   incheon      deepgbm        pure  0.567129  0.688707  0.909057   \n",
-       "3     daegu      deepgbm        pure  0.576840  0.715856  0.975236   \n",
-       "4   daejeon      deepgbm        pure  0.687967  0.797628  0.957317   \n",
-       "..      ...          ...         ...       ...       ...       ...   \n",
-       "25    busan  resnet_like   ctgan7000  0.460507  0.638240  0.947905   \n",
-       "26  incheon  resnet_like   ctgan7000  0.534332  0.664453  0.899439   \n",
-       "27    daegu  resnet_like   ctgan7000  0.445301  0.617498  0.967371   \n",
-       "28  daejeon  resnet_like   ctgan7000  0.509262  0.649690  0.918171   \n",
-       "29  gwangju  resnet_like   ctgan7000  0.492702  0.637537  0.927253   \n",
-       "\n",
-       "               model_data  \n",
-       "0            deepgbm_pure  \n",
-       "1            deepgbm_pure  \n",
-       "2            deepgbm_pure  \n",
-       "3            deepgbm_pure  \n",
-       "4            deepgbm_pure  \n",
-       "..                    ...  \n",
-       "25  resnet_like_ctgan7000  \n",
-       "26  resnet_like_ctgan7000  \n",
-       "27  resnet_like_ctgan7000  \n",
-       "28  resnet_like_ctgan7000  \n",
-       "29  resnet_like_ctgan7000  \n",
-       "\n",
-       "[150 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **단일 모델 성능 비교 by 원데이터**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plt.figure(figsize=(20, 10))\n",
-    "# sns.barplot(x='region', y='CSI', hue='model', data=models.loc[models['data_sample']=='pure',:], ci=None)\n",
-    "# plt.title('Single Model Using Pure', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "# plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
-    "# plt.ylabel('csi', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "# plt.xlabel('', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "# plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **증강데이터에 대한 각 모델별 성능 확인**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def model_filter(model_name, df):\n",
-    "    return df.loc[df['model'].str.contains(model_name), :]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **증강데이터에 따른 XGBoost 모델 성능 비교**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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/M0LuO1BBY9q6davsezn/o2nTppLf+3KvYUGPvN/F1L1mR48eFczNzUv0mpXm9+5nz54Jnp6eatvXqVNHuH//foHHqFQanx1EREREJcFACtFHJiQkpEh/NPTt27fAfuzt7SVtRo0aVSpjLGogJTIyUtDU1BTV09LSUntBedSoUcX6A1NTU1M4cOCAbF+//vprsfoCIKxYsULSz8CBA4vVx4cQSFm/fr3g7Oys+lmhUAgxMTGqNvkDQ2fOnCnS6ykIgvDHH38ICoWiWMc1ZcoU2b5mz55daFtjY2PB1tZWUi4XSAkKChI0NDSKPC4TExPZi7pFCaTcunWrWEEUoGSBlCNHjkj6Wbt2rahO7969JXW+/fZbUZ0ff/xRUuf69euiOu8zkGJiYiJUrFix0D7WrFlT7OdMqSSBlN27d8uOIy4uTlTvQwmkJCcni97rRXnMmjVL9tjlxlytWjXZPvIGUsryfWdpaVlof1paWqL+yjqQEhwcLNvPvXv3Cm3r4+Mjade0aVPV9lu3bkm2Dx48WLavyZMnF3gOCoIgnD17VtDX1y/yc6GtrS0cO3ZMsi+5C7iVK1dW+xl49+5dITs7W3B0dCzWa/GugRTlMZw9e1bSj9z5VZTPoHHjxglNmzYttN7Ro0eLtE8bG5si/Q5dvXq1pL+yDqSYm5tLvrPJPUJDQyX7zMzMFOrXr19oWwcHh3d+DxZ2rAcPHhRMTExUPxsaGgrPnj0TBEEQsrKyBGtra9U2DQ0N4dSpU0Ue0+LFi4t1PlatWlVITExUtS/tQIqVlVWR3uNyr5kglN737tzcXKF9+/aFtq9Tp06BxygIQql9dhARERGVBHOkEH1kvL29MWjQIGzcuFFtnUqVKmHZsmVqtyclJUmWcQCAtm3blsYQZaWmpmL8+PEAAEEQ8PDhQxw8eFCyPM8PP/yAatWqSdpv3boVq1evlpTXqFEDXl5eePHiBYKDg/H06VPVtpycHPTr1w8xMTGoXLmyqjwiIkJ2GRUbGxu0b98eubm5CA0NlSx/NG7cODRv3hz169cHAGRlZWHnzp2iOrq6umjbti1sbW2Rk5ODx48fIzo6WjZJdIcOHWBmZoYHDx4gKChItK1WrVro0KGDqOyTTz6R9FESCoUCo0aNwqRJkwC8fT1Wr16N3377DX/++ScuXLigqluvXj14enoWqd+7d+9iyJAhkuUqLCws8Omnn0JPTw8nTpzAnTt3RNvnz58PLy8vdOrUSVV2584dzJo1S7KPChUqoHPnztDV1cWRI0dw//79QpdeA4CEhAQMGDBAsnyWo6MjmjVrBi0tLZw5cwaxsbGqbenp6ejVqxeuX78OLa3i/brcsWOHZGkfFxcXNGvWDEZGRkhPT0dMTAyuX7/+TktOeHp6QltbW7SviIgIDB06VPRzfvnL8v9saWmJOnXqFHkc1apVw7hx4wAA69evl7wmym1FlZ6eDgAwNjZGly5doKuri927d6vKlZYvXy5Z8rAseXl5yZZfuHBBdqkodYYMGYLU1FScP38ekZGRom3t27dH7dq1VT83bdoUFhYWcHNzw99//42jR4+K6jdp0gRNmzZV/Zz3c8LHx0d0TgOAvr4+OnToACsrK8TGxuLEiROi9+yMGTPQqlUrtGjRotDjUCZJb9SoERo1aoTnz5+LPj/K+n2nXB7QxsYGHTt2REpKimTJnOzsbKxZs0b1O7Fp06YYN24c0tPTRUsYAkDVqlUlS9o5OzsX+jzkdeXKFUmZpaUl7OzsCm3buHFjybJoeZcdql69Ojw8PHDu3DlV2Z49e7B69WrJ8kP5l5Syt7cXnb/Pnz9Hjx49JEt2ValSBV5eXjA0NERUVBSuXbum2paVlYW+ffvi9u3bMDMzK/BY/vnnHwCAlpYW2rRpA0dHRyQlJeHkyZMAgHPnziEuLk7UxsLCAm3atIGlpSVevXqF+Ph4XLt2rUjLQNrb28PZ2RkVK1ZExYoVIQgCHj16hIiICFH7rKwsTJ8+HSdOnCi0T+VSYI0aNULDhg1x9uxZ/PXXX6I6S5cuBfB2ibbOnTvD3NwcBw8elCwj5u/vj3bt2hW6z/v37wN4+/uidevWSE9Px6FDhySfqZMmTcLnn3+OSpUqFdpnaUlNTQXw9nXq3Lkz3rx5g927d0uWZFq+fLnk+8vChQtF55JS3bp10bx5czx69AjBwcGy35VKm6GhIQYOHIjly5cDAF68eIFNmzZhzJgx2Ldvn2iZw86dO8PW1rZI/V64cAETJ06UlNetW1eVyzAsLAwPHz5UbXv48CEGDhyo+lyvXbu26vel8txSMjY2xpAhQ0Rl5ubmBY5JuZxsSV6z0vzevWXLFsnvLgBwcnJC27ZtVed5/veXnNL+7CAiIiIqlvKM4hBRyaSmpgpVqlRRe+fV9u3bC2wfHR0t2+7y5cuSuhkZGYXe6ZX/Tsbi3lEHvL1rOP+d9HlVr15d0mbIkCFCdna2qs4///wjW2/y5MmivuTuivP29hZevXolOm65JQi++OILVZ2HDx9Kth88eFB2/A8ePBDWrFkjrFy5UrKtqEtFlYTcnfEbNmwQUlJSREuumJubC69evRKGDBkiqqscb/4+5GakDB8+XFLPzc1NSE1NVdV58+aN8MUXX0jqNW7cWNTXxIkTJXUcHR2FR48eqeq8fPlSaNOmTZHOya+//lpSZ8qUKaKlobKysoTBgwdL6m3cuFHUV1Fer/zPRZMmTUTnat7n4/Tp08LYsWNLvKxes2bNRPuqXr26apvcHarK91tGRobquA0NDUXbe/bsKdlPQTNSSlIvL7kxWltbi17HS5cuyd7hnvf8Ko6SzEgRBEF0J7PykX9JrMJmpBQ0hvzLYuVV1DvOBUEQzp8/L6lbp04d4fHjx6J6oaGhkqWoWrduLelP7nVVKBSy433+/LkgCGX/vgMgtG3bVnj58qWqnr+/v6SOq6urZIzqlsF5VxMmTJD027BhwyK13bdvn+wxKt+rgiAIa9askWzPv4znuXPnJHV++uknUZ158+ZJ6vTv3194/fq1qN5PP/0kqTdz5kxRHXWfMxUrVhQuXbokqpuZmSm8efNG+N///ieqq6+vLyQlJck+L9HR0cLPP/8su2zQqVOnhNjYWLXP6fPnzwUPDw/JeZv/c0Pd+TVs2DAhNzdXEIS3v3PkZvMCELZu3arq69KlS5LtZmZmkrGp22ffvn2FrKws0fMr951vzpw5ov7KekYKAKFu3bpCSkqKqt6BAwcKPdbs7GzZ8Q8ePFj0WXDmzBnZZfpKe0bKyZMnJd+D69SpIwiCIPlOERwcXOTPik8//VRSL//vhpcvXwodO3aU1JNbNi9/nYKWVBWE0n3NBKF0v3c3atRIUuezzz4T3rx5Ixq/3HmS/7hL67ODiIiIqCQYSCH6SOVf41n5MDQ0LPTCotxSTQBEyzspva9AivKPJblAxPXr1yV1jY2NRReXlPbu3Sup6+LiotqelpYmWZpCoVDILrty5coV2edX+Ydfenq6ZHtJlhoqj0CKIEiXJVuyZIlgYGCg+tnIyEhIT08XBKHwP+hzc3Nll9mRuzjw5MkT2YslDx8+VNWRW0f9jz/+kPT1119/FXpO5ubmStbSrlKlimx+DbngWNeuXUV1ivJ65b+YWqdOnRJf8C/M999/LxmPMiizZcsW0RhMTU1VPyuXmrlw4YKkvVyujPcdSJEbg1xw888//yzO06VS0kCKXK6P/Bc0P4RAitzSTsHBwbJ18weXFQqFkJycLKoj97qqW1ZKEN7P+w6AcO3aNVG9169fSz7jTUxMJPssq0DKsGHDJP02b968SG2PHTsme4wPHjxQ1UlLS5PkHenXr5+on/Hjx0tezzt37ojqNG7cWFRHV1dXePr0qWRMWVlZkqWB6tWrJ6qj7gJu/mBYXvv37xfV1dHRkbyWxZGRkSEcPnxYWLJkifDjjz8KEyZMEMaNGyeMGzdONo/ZiRMnRO3lzi8dHR3J57bccp6ffPKJZDxyS1QVJXijo6Mjee8JgiCsWLGi0PPqfQRS5JZtkvtMzHuscnlP9PT0ZG8ekPvsLItAiiAIQsuWLUXl+YOUDg4OQk5OTpE+K549eyYJSMudF4IgCBEREZL+xo4dK6mXv05JAyklec1K83v3kydPJNs1NDRklzOVO8/zH3dpf3YQERERFYcGiOijc/LkSWzbtk1224sXLzBhwoQC25uamsqWP3/+/J3H9i7i4+PRs2dPyTJDUVFRkrpeXl4wMjKSlHfs2BGampqispiYGKSlpQEA/vzzT8lyYnXq1JFddsXV1RVVq1YVlb148QJ///03gLfLLDRu3Fi0feTIkahatSo6dOiAMWPGYPny5Th79izevHmj5qjLz9dffy36efLkyXj58qXq56+++grGxsZF6uvevXuSJRSMjY1llweytLSUPG/A/7/Ob968wY0bNyTbO3bsKCmrXbs2bGxsCh3bkydPRGWPHj2CpqYmFAqF6JH/9QYgWkanqPIvk/fXX3/BysoKDRs2RN++feHn54cdO3aIlvgoqVatWknKzp49CwA4c+aMqqxly5bw8PBQ/ax8n8kt/SXX5/vWr18/SVmVKlUkZUVZ2q00ye1P3Wdqecq/ZBgAdOrUSXLOKxQKyZIrgiDg/Pnzhe5j0KBBare9j/ddvXr1UK9ePVGZrq4uKlSoICp7n+eIiYmJpCzv52pB1C3zl/f8MjU1Rffu3UXb9+/fr1qiSxAEyZKTLVq0gKOjo+rnnJwcXLp0SVQnMzMTFSpUkLw22trakuW/oqOjC/2+oKenhz59+qjd7unpCT09PdXPb968gaurK2rUqIHu3btjypQpWL9+Pf766y/JcpF5PXnyBEOHDoWFhQW8vb0xfvx4/Pzzz1i0aBGWLl2KpUuXipabU0pOTi5w/ADg7u4uOZesrKwk9eSW7JKrV5Tz8JNPPkHFihUl5d7e3pIyuWXkypKpqSk6d+4sKS/sc1lunM2bN4elpaWkPP/SemUp/3egMWPGiH4eOXIkNDSK9qfypUuXkJ2dLSq7cOGC7Oet3HKpJfmeURQlfc1K83u33Otfp04d2WV85c7z/Errs4OIiIioJBhIIfrIvHz5EsOGDSvwj4PAwECEhoaq3a5uTW25tal1dHQwbtw41aOoF9bzs7Ozg/B2FhwEQUBSUhJ2794tuQiemZmJH3/8UVQmt8axuovnenp6sn+cK/soTl/qtuW9OLhkyRLo6+uLticmJuLo0aNYsWIFxo4dC09PT1SqVAnjxo0TrSVd3po2bYoGDRqofs6f02P06NFF7kvuea1WrRoUCoVs/YKe19TUVMn5ra+vr3YtcLk/xgsbW3GkpKRILpAUplOnTpKLnW/evMHly5exbds2zJw5E3369EG1atXQpEkTHDp0qMTjU+ZJyUsuSOLp6YnmzZurflYGWfIGW4Di50cpC0ZGRrKvd96LJ0r582+UpeTkZEmeFgCynznl7V3Pe+Xa+gVR5osqi/0X5X2nLu9I/vPkfV5Ms7CwkJQpc8kURi6wqqurK7l4mT+A9fz5c9VnSHh4uKQfHx8f0c8pKSnv9L4RBEGVA0Wd6tWrQ1dXV+12c3Nz/PLLL5J+b9++jX379mHBggUYOnQo6tatCwcHByxZskRyE8TTp0/h6emJ9evXS3I+FKYowS2531Nyn0Fy9fLnrAGK9lml7vuI3O+558+fF/u434WNjY3s7/TCPpfz54tR9qVuH+9Lz549RXk88n4H0tXVFeUaK8z7+LwtiZK+ZqX5vVvu9Vf3va2w73NA6Xx2EBEREZUUAylEH5lp06ZJkizKJeQdMWKE2rsfK1WqJHsBSpkENi8dHR0sWbJE9SgssWVRWVhY4PPPP8fatWtlx/EhBRwK0qxZM1y5cgUDBw6UvRNZ6dmzZ1i2bBnatGkjCViUp/x3ZCp5enpK7vT+rxIEoUQJ4YOCgrB69Wq4ubkVWO/ChQvo2rWrJDl0URkaGsLd3V1UFhERgbS0NFHiVk9PT9GdsOfPn0dOTo5kRkrLli3VBsDeF7k7sgFI7np938LCwmTL8yZ4/7coygzFwhKOv4uivO8+xPMkb3Ba6cmTJ6ok4gWRuwtcrr/27dvD2tpaVLZ9+3bRv0oGBgZlcpd/YedHUc4NX19fHDlyBB06dJAEg/OKj4+Hr68vvv32W1H5L7/8gtjY2CKNN7+iBNfkLjYX9aL0v1Fpvt/y34Ci9D5/92hra6sNlvTq1Us2KFpWympG+If4GVka3vWzg4iIiKikpFdfieiDde7cOfj7+4vKFAoFgoODMWbMGNy+fVtVnpCQgO+++w4rV66U7atDhw4ICAgQlW3evBk///zze12mplmzZrLl8fHxqiU15O50U3dRKjMzU/ZOOmUfxelL3bb8M3qqV6+OjRs3IicnB9HR0bhx4wbu3LmDv/76CyEhIarlDYC3Sxzs2rULffv2VbvP96lfv36YNGmS5C57dQEWdeSe1wcPHkAQBNkLIwU9r+bm5lAoFKILXa9evUJqaqpsIK+wu73lZmBVq1YNPXv2LLBdXnJ3FxdGQ0MDI0eOxMiRI5GUlISrV6/i9u3biImJwdmzZ0XLzQiCgB9//BG9e/cu9n6At0tu5F0a5NKlSzh58qTqDtOqVavC3t4elStXhra2NrKysvD8+XMEBQVJ7oT9EJb1+lAtW7ZMUubs7Ax7e/v3P5hCVKpUSbJE3pAhQ4o8q7CwACBQ8EXP8nrflbfmzZur3mN5bd26FVOmTFHb7tWrV9i3b5+kvHXr1pIyTU1NfPXVV5g/f76q7NChQ3j27Bl27dolqtuzZ0/Ja16xYkVoaGiI7kA3MTHB4MGDCz64PAq7yFzUC+Lt27dH+/bt8eLFC1y9ehW3bt1CbGwsLl++jGPHjomex9WrV2PatGmqpeDkni8fHx9MnDgRjo6OMDAwAAB8//33+PXXX4t6aOVK3fcRud9zRkZGBc76+VDInSvqlrUsSsCxNI0YMQK//vqrZLZQcb8DyX3e1a5dG+3bty9Se3WBpfJSmt+75YI56r63FXX2HvBunx1EREREJcVACtFHIjMzE0OGDJH8sffNN9+gffv2WL9+PVq2bCnavnr1avTp0wdeXl6S/saMGYO1a9eKLlanpaVh+PDh2LZtW5HXhX5X+dfQV8q7pItcPo3Tp0/jxYsXMDQ0FJWHhoZKpvC7uLio7o5t2LAhNDU1RXX++usvxMfHS2bpXLt2TfLHvqGhIWrXri07Zk1NTbi6usLV1VVVFhsbCxcXF1G9yMhIUSBF7s7A97UMgaGhIQYOHIjly5eryiwtLYt9B7O9vT0sLS1Ff0xnZGTgzJkzkjwpycnJsndeK19nHR0d1KpVS5WLRunIkSP48ssvRWU3btwo9MKLnZ2dZGzPnj3D3Llzi3TxIicn553v3rS0tES7du1Ea+l/9dVX+N///qf6+fbt20hLSyvRXf6tWrUSXSjMzMzEkiVLVD8rZ6Lo6+ujYcOGqvwZeS/E5u3rXag7nz/2O2AXLFiA8PBwSfm4cePKYTSFa9y4MU6dOiUqa9OmDfr3719o29J4vT6E911Byupz18jICH369MGWLVtE5fPnz8egQYNESwnlNWfOHKSmpkrGqC644ePjI3r/vnr1Cr6+vpLfqfmX9VL227BhQ1y8eFFVlpGRgYkTJxZpaaWyeG0MDQ3RrFkz0c0Va9euxfDhw1U/5+bmIioqSnUxNCEhQdSHiYkJ1q1bJ/n+Ipcv6EMVFRUle9OA3HKtRQl2fgjkxhkeHo7MzExJICgoKOg9jeotOzs7dOrUCQcPHlSV1a9fXzaPSUEaNGgg+W6ZnZ2NxYsXFymoKPfZkz/Y+T6XpyrN791yr/9ff/2Fhw8fSgIbBS1LrE5JPjuIiIiISopLexF9JGbOnImbN2+Kyuzs7DB37lwAby+Ujh07VrRdEAQMGzZMkigWePuHotwFtZ07d6Jr164lXi6jOHJycvDzzz9LyhUKhSioUbduXVSvXl1UJz09HePGjRP98fbkyRNMnjxZ0l/eXBWmpqZo06aNaLsgCBg1ahRev36tKnvx4gW++eYbSV+dOnUSLSMwePBg7NixA8+ePZM9Rrl15POvaS6XvFMu2XpZ+frrr6GnpwddXV3o6upi2LBhxb7LVaFQSHKCAMC3334rmpGTlZWF0aNH482bN6J6jRs3Fi1V8+mnn0r6+uGHH0QXCV+/fl2k5RoUCgU+//xzUVlGRgZ69+4tuXCplJycjI0bN8LLy0tyQbQo9u3bh+nTp+PatWuy23Nzc2WTHZd0vfvmzZtLlvg7ffq06v95Lwrl/X/+hNOlkR9F7nzOHxT7mPzzzz8YPXq07GyC6tWriy7WfEh69OghKRs3bpzapMZZWVk4efIkRowYIfteLq7yeN8Vh9x5GhsbWypLL06bNk2y3ExKSgo6duyImJgYUXlubi5+++03yZr/wNsZg/kD8Uq1atWSXOzcsGGD6Gc7OzvZGS2A9PwQBAG9evVSO1MgPT0dO3fuRJcuXWTHWlwJCQkYMmQITp48qfY5L+z3Z/4ZS8+fP8edO3dUPwuCgIULF8ouW/qhyszMxJgxY0Q3kyQkJGD27NmSunK/Jz9Ebm5ukqXoUlNT8f3334vKzp07J7q54H355ptvVN9/dHV1i5UfTsnU1BRt27YVld2+fRsjR45Uu0ThgwcPsHLlSjRq1Eg2SJ//M+qff/5R+9lZ2krze7elpSUaNWok2p6bm4uxY8eK3vvqzvP8SuOzg4iIiKikOCOF6CPw559/YsGCBZLygIAA0R9av/zyCw4ePCi6kBAbG4sffvgBCxculLRftWoVrl+/jqtXr4rKg4ODcfjwYTRs2BB16tSBkZERnj59iosXLyI+Pr5Ex5Camorx48erfk5JScG5c+dEY1Vq2rSpZFkBPz8/9OvXT1S2bt06nDlzBl5eXnj58iUOHTokya1ibGyMiRMnispmzJiBY8eOiWbjHD58GNWrV0eHDh2Qm5uL0NBQJCYmitppaWnhhx9+EJUdPXoUgYGB0NLSQu3atVG9enXVsinx8fE4ceKE5Pjy/3Hq5OQkWcrq0qVL8PT0hKurq+piUVHvFi6u2rVrywbbiuv777/H5s2bRQGpK1euwMXFBZ06dYKuri5OnDgh+5r7+fmJfh41ahSWLVsm+iP5zp07qFWrFjp37gxdXV0cOXJEckeyOtOmTcPmzZtFx3nw4EFUq1YNXl5eqoSsKSkp+Pvvv3Hr1i3VnaDFWe5GKSkpCb/88gt++eUXWFpaol69erC1tYWRkREyMjJw7tw50VJ8wNucAiVNWq7Mk3L+/HnZ7XmDJ82bN8eiRYtk65VGfhQXFxdJAKljx47w9vZW5RFq0aJFsZZ4el9iY2Mxfvx4CIKAZ8+e4fbt24iKipJNem5sbIy9e/d+sEvreHh44NNPP0VISIiqLCUlBc2aNUOjRo1Qs2ZNmJmZ4dmzZ4iLi8O1a9dU6/TLzWIsiff9viuOChUqoGLFiqJEyI8fP0bjxo3RtGlTVd6LwYMHi2YZFkWtWrWwePFijBkzRlR+9epV1K5dG15eXnBxccHz589x6tQp2Vl1Li4ukqU88/Px8ZGd3ac0YMAAte/nMWPGYOnSpaILjhcuXIC9vT28vLxgZ2cHHR0dpKam4ubNm7hx44bq8zh/TqaSePPmDTZs2IANGzbA0NAQ9erVg5OTE0xNTfHmzRv8/fffskG/vL8/GzVqJMpblJubC3d3d3Tq1AmGhoa4cOECrl+//s5jfd+2bt2KS5cuoVWrVsjIyMChQ4cky28aGhoWKxl6edLU1MSYMWMwbdo0UfnixYtx8uRJNG3aFI8fP8ahQ4fKJYect7e36HtLSc2cORPHjh0TzSIJCAjAjh070LJlS1hbWyM3Nxf//PMP/vrrL9nvQnm5uLiIbnbIyspCkyZN0KpVK9WskM8++0xtsPRdleb37m+//RaDBg0Sle3Zswe1a9dGmzZt1J7nckrjs4OIiIioxAQi+qBlZWUJrq6uAgDRY8iQIbL1T548KSgUClFdDQ0N4fz587L1ExMTBU9PT0n/xXncvXtX1OeGDRtK3Je2trZw5swZ2bGOGjWqWH1pamoK+/fvl+3r119/LfbYVqxYIemnatWqxerD1NRUePTokaSfFi1aFNo2KipK9lgKMmjQIEk/GzZsKHY/+fuws7OTrffHH39Izr/CHlOmTJHta+bMmYW21dHRESpXrlzoOSkIgrBr1y5BQ0Oj2K97/ufr5MmTkjqDBg0S1QkICCj2fr777rtivy55fffdd7L9GhkZCdnZ2ap6//zzj9ox+Pv7q+3fzs5OUl/OunXrCj3Wb775RtSmqOeX3Pl88uTJYj9XgiAIM2bMKPZrpHw4ODgIf/75p9q+izpOuTEU9P6U+2ydMWOG2vpPnjwRnJycin18Xl5ekr6K+vrn9z7fd8Ud64ABAwodx86dO4t0nHJmzZpV7M9DAELNmjWFmJiYQvtPSUkRdHV11fYTGxtbYPuIiAhBT0+v2OPLf87dvXu3SOdQXjExMcXeb9OmTUV9BAUFFdpGS0tLaNOmTamdX0V9z3p5eUnq5f+9JLfP6tWrC1paWoUel9z3kaJ+PhRlbMV5TYvSX2ZmplC/fv1Cj6t27drFPpcKIje24v7OKM5zsWjRomKf1+rG9OOPPxbabsGCBSUaZ1FeM0Eove/dubm5su/D/A8bGxtJWf7vA6Xx2UFERERUUlzai+gDN3fuXMmMkSpVqsjOMAHe5jfInyQzNzcXQ4YMkSynpOwrLCwMs2fPlk0IWRAXFxf8+uuvqFKlSrHaqVO5cmXs2bNH7drUK1euxC+//FKkO8Ctra1x5MgRdO3aVXb7d999h/Xr1xcp8bKZmRm2bt0qu9xDce7et7CwwJ49e2BlZSXZNm/ePNUd0B+zvn374uDBg7KJV/PT19fH4sWLMW/ePNntP/74Y4EJXw0MDLBt2zbUrFmzSGPr2bMnjh49Cltb2yLVBwBHR0e1S+sUpLizOvr164eZM2cWez95qctt0qRJE1E+g0qVKqk9ptJINP/VV1/Jrq/+b2BsbIxvv/0Wly5dQoMGDcp7OIWytLTE+fPn8dlnnxW5jYGBgSSv0bt4n++74poxYwYqVKhQZv3/+OOPOHDgAGrVqlWk+tra2hgxYgTOnz8PZ2fnQuubm5ur/R3XvHlzODk5Fdi+WbNmOHfuXLGW86tSpUqxZ+jIKe5npJubG3bs2CEq69GjByZNmqS2jY6ODtatW1eq53NZ8/DwQGBgYIHfB6ZPn16i5afKk46ODg4fPqw2xxzwdubk5s2bJeUf6qw/Ob6+vti2bVuxvk/Xq1dPsvQZAIwfPx729valOLriK63v3QqFAkFBQWjatKna9nZ2djh69Gih+ymNzw4iIiKikuLSXkQfsL///lt2veBVq1YVmJB63rx5OHTokGgZrr///huzZs2S7U9LSwvTp0/H+PHjsXv3bpw8eRJRUVF48uQJnj59Ci0tLRgbG6Nq1aqoWbMmGjdujPbt26Nu3bolPjYNDQ0YGRnBysoK9erVg7e3N/r06VNgYEOhUOD777/H4MGDsXbtWhw/fhw3b95EamoqtLS0VOswd+7cGf379y/0D7/Bgwfj888/x/r163H06FFcv35dtcyLhYUF6tevj44dO2Lw4MFqx3X16lUcPXoUZ8+exZUrV3Dv3j0kJSUhMzMTenp6qFSpEmrXrg1vb28MGjRItbRRfh4eHrhw4YIqofWjR48+2vWcO3XqhHv37mHz5s0IDg7G5cuXkZycjJycHJibm6NWrVpo27Ythg8fXuBSVgqFAitXrkSXLl2wfPlyXLhwAc+fP4e1tTXat2+PyZMnw9nZGUuXLi3y2Nq0aYPY2FgEBQXh4MGDiIqKwj///IPnz5/DwMAAlSpVQs2aNdGkSRN06NABTZo0KdFzMGTIELi6uuLEiROIiorCzZs38fDhQ2RkZEChUMDY2BgODg5o0qQJ+vXrV+zEtnKUeVLyL0Ml13fz5s0luRpKIz8K8PaC2cmTJ7Fo0SLs3bsXMTExeP78uWjpug+ZhoYGtLW1YWBgAHNzc1StWhXVq1dHs2bN0LNnT7Xv4Q+VhYUF9u7diytXrmDz5s2IiIjA3bt3kZaWBg0NDZiZmcHJyQmurq5o27YtOnToIJs/5F28r/ddcTk5OeHPP//E/PnzcezYMTx48KBUljnMq3Pnzvj0008RHByMkJAQRERE4PHjx0hNTYWenh4qVqyIWrVqoU2bNujdu3exAk4AMGjQIOzatUtSLpdkXo6bmxuuX7+OQ4cOYc+ePYiMjERiYiLS09Ohp6cHCwsLVK9eXfV7v0WLFqWSaN7JyQl37tzB0aNHcf78eURHR+P+/ftIS0tDdnY2DA0NUbVqVbi5ueHzzz9Hjx49ZPe7YMECeHl5wd/fH1FRUXj58iWsrKzQqlUr+Pr6wtXVVbJ05Ieuf//+aNiwIRYuXIhjx47h8ePHMDU1hYeHB3x9fUtt6b33rUqVKrh06RKWLl2KrVu3IiYmBtra2qhZsyYGDBiAUaNGISIiQtKupEtelpc+ffqga9eu+OOPPxAaGopLly4hOTkZL1++hKGhIaytrVGzZk14enqiY8eOqFevnmw/5ubmqu+FISEhuHv3rtp8K2WlNL93m5mZITw8HGvWrMHGjRtVuQAdHBzQo0cPTJw4sUg3N5XWZwcRERFRSSiEj+XKBhERERER0b9AWFiYJL/FoEGDEBgYWD4D+gBMmDABixcvFpXNnz9fNqE5EREREdH7xqW9iIiIiIiIqMwsW7YMO3fulE3sLggCNm3aBH9/f1G5QqFAly5d3tcQiYiIiIgKxKW9iIiIiIiIqMz8+eef2LhxIwwNDdG4cWM4OTlBX18fKSkpOH/+PO7evStp8+WXXxY5xxARERERUVljIIWIiIiIiIjK3IsXLxAWFoawsLAC69WtWxcrV658P4MiIiIiIioCLu1FRERERERE5U6hUOCrr75CeHg4zMzMyns4REREREQqnJFCREREREREZWbhwoVo0aIFjh8/jps3b+Kff/5BcnIytLS0UKFCBdSsWRMeHh4YOHAgXFxcynu4REREREQSCkEQhPIeBBERERERERERERER0YeIM1JKIDc3F4mJiTA2NoZCoSjv4RARERERERERUTkRBAEZGRmwtraGhgZX0Sci+jdiIKUEEhMTYWNjU97DICIiIiIiIiKiD8T9+/dRrVq18h4GERGVAQZSSsDY2BjA21+QJiYm5TwaIiIiIiIiIiIqL+np6bCxsVFdLyIion8fBlJKQLmcl4mJCQMpRERERERERETE5d+JiP7FuHAjERERERERERERERGRGgykEBERERERERERERERqcFAChERERERERERERERkRoMpBAREREREREREREREanBQAoREREREREREREREZEaDKQQERERERERERERERGpwUAKERERERERERERERGRGlrlPQAiIiIiIiIiIiL698rKykJOTk55D4OICACgoaEBbW1tKBSKIrdhIIWIiIiIiIiIiIhKXXp6OpKTk5GZmVneQyEiEtHU1ISBgQEqVaoEHR2dQuszkEJERERERERERESlKj09HQ8fPoSRkREsLCyKffc3EVFZEAQBOTk5ePXqFZ49e4Z79+6hWrVqMDAwKLAdAylERERERERERERUqpKTk2FkZIRq1aoxgEJEHxwjIyOYm5sjPj4eycnJsLW1LbD+R5tsftu2bWjYsCH09fVhbm6OXr164c6dO4W2u3v3Lnx8fFClShXo6OigcuXK6Ny5M549e/YeRk1ERERERERERPTvlpWVhczMTJiamjKIQkQfLE1NTZibm+PFixfIzs4usO5HOSNl3bp1GDZsGADAwcEBKSkpCAoKQnh4OK5evQorKyvZdrdv30azZs2QkpICAwMD1KpVC2/evMHRo0eRkZEBU1PT93kYRERERERERERE/zrKxPLa2trlPBIiooLp6uoCALKzs6GlpT5c8tHNSHnz5g2mTp0KAOjZsyfi4uJw48YNGBsb48mTJ/jll1/Utv3222+RkpKC1q1b4+HDh7h69Spu3LiBZ8+eqQ2+EBERERERERERUfFxNgoRfeiK+jn10QVSoqKikJycDOBtIAUArK2t0bRpUwDA4cOHZds9ffoUR44cAQBUqFAB7u7uMDY2RtOmTXHmzJkCo01ERERERERERERERPTf9NEFUu7fv6/6f6VKlVT/r1y5MgAgISFBtl1MTAwEQQAA7N69G7m5udDT00NkZCQ+/fRTREZGqt1nZmYm0tPTRQ8iIiIiIiIiIiIiIvr3++gCKeoogyTq5E0W065dO9y5cwexsbEwNzdHTk4OVq1apbbt3LlzYWpqqnrY2NiU2riJiIiIiIiIiIjo323Tpk1QKBSoV68esrKyZOucP38empqasLCwQFJSkmibIAgICgpCnz59YG9vDwMDA+jr68Pe3h7du3dHQECA7M3fPj4+UCgUkoepqSmaNm0Kf3//QpNsE9FHmGw+bxDjyZMnkv/b2trKtqtatarq/+7u7qoPjOrVq+P8+fO4d++e2n1+//33mDBhgurn9PR0BlOIiIiIiIiIiIhKqNHkTeU9hCK5tGBgqfQzcOBAbN68GceOHcP8+fMxffp00fasrCwMHz4cubm5WLhwISwtLVXbEhMT0aNHD0RGRkKhUMDV1RXu7u7Q1NTEgwcPEBISgn379mHq1KmIiIhAzZo1Jfv39PSEs7MzgLc3nMfHx+Ps2bOIjIxESEgIDh069EHntLl37x4cHBzg5eWFsLCw8h4O/Qd9dDNSGjdujIoVKwIAgoKCALz9MDl//jwAwNvbGwBQs2ZN1KxZE8uXLwcA2NnZwcXFBQBw6dIlCIKA9PR03L59GwBU2+To6urCxMRE9CAiIiIiIiIiIiIqqjVr1kBfXx+zZ89GTEyMaNu8efMQHR2Ndu3aYdCgQary9PR0eHl5ITIyEp06dUJsbCwuX76MXbt2Yfv27YiIiEBKSgqWLFkCDQ0NVW7p/IYNG4bAwEAEBgZiy5YtCA8PR2RkJPT19RESEoI9e/aU6bETfew+ukCKjo4OfvnlFwBvAymOjo6oVasWMjIyYGFhgalTpwIAbt26hVu3bok+PH799VcoFAocPXoUzs7OcHZ2RmpqKgwNDUUzToiIiIiIiIiIiIhKk6OjI2bOnInXr19jxIgRqvJbt25h9uzZ0NfXx+rVq0VtpkyZgtjYWHTs2BH79++Ho6OjpF8jIyOMGzcO169fl92ujru7O3r16gUAOH36dAmPiui/4aMLpADAiBEjsGXLFri5uSExMREKhQI9evTA2bNnYW1trbZdjx49sHfvXjRu3BiJiYnQ0NBA9+7dcfHiRdSqVes9HgERERERERERERH91/j6+qJBgwYICwvDunXrIAgCRowYgczMTPj5+cHJyUlVNzk5GYGBgVAoFFi2bBk0NTUL7NvKyqrAa6NyKlWqBACyeVKys7Ph7++PRo0awcjICEZGRvjkk0+watUq5OTkyPaXkpKCyZMnw8XFBXp6ejA3N4e3tzeOHDkiWz8+Ph5ff/01qlevDgMDA5ibm6NOnToYOXIkbt26BQDw8/ODg4MDAODUqVOiXC8+Pj7FOl5lfwqFAoGBgYiMjETHjh1hZmYGExMTtG/fXrXyUV7K18HPz0+2z1atWkGhUIjSR9y7dw8KhQKtWrVCeno6JkyYAAcHB2hra2P8+PGqeqmpqfj+++9Ru3Zt6Ovrw9TUFG3atMHBgweLfWxUdj66HClK/fv3R//+/dVuV5d8vlu3bujWrVtZDYuIiIiIiIiIiIhIlpaWFgICAtCkSRNMnjwZiYmJOH36NNzc3CQr5pw8eRKZmZlo1KgRqlevXibjuXjxIgBIbjLPycnBZ599huDgYFWAQRAEnDhxAqNHj8bRo0exa9cuaGj8/336Dx8+RMuWLREXFwdbW1t0794dSUlJOHbsGEJDQ7Fo0SL4+vqq6t+/fx8NGzZEamoqXFxc0KlTJ+Tk5CA+Ph4BAQHw8PBAjRo14Obmhp49eyIoKAiVK1dWpXYAgObNm5f42M+ePYuRI0fC2dkZn376KWJjY3Hs2DGcPn0aBw4cQIcOHUrcd16vXr2Cl5cX4uPj4eXlhYYNG6JChQoAgNu3b6Ndu3a4f/8+7O3t0bFjR2RkZOD8+fPo2rUrFixYgEmTJpXKOOjdfLSBFCIiIiIiIiIiIqKPTaNGjTB+/HgsXLgQP/30EzQ1NREQEAAtLfGl2qtXrwIAGjRoUKr7z87ORkJCApYvX45Tp07BxsYGAwYMENVZsmQJgoODUadOHRw/fhyVK1cGADx69AitW7fGnj17sHLlSowZM0bVZtSoUYiLi0O/fv2wYcMG6OjoAADOnDmDjh07YvLkyWjdujXc3NwAAGvXrkVqairGjBkDf39/0f4TEhKQlZUFAOjevTvc3NwQFBSEmjVrIjAwsFSeh4CAAEyfPh0///wzFAoFAGDVqlUYPXo0fHx8cOfOHejr67/zfi5cuAAPDw/ExcXBzMxMVZ6Tk4NevXrh/v37mD9/PiZOnKgKTMXGxqJDhw6YOnUqvL29Ubdu3XceB72bj3JpLyIiIiIiIiIiIqKP1YQJE1QX7wcNGgR3d3dJnZSUFACAhYWFbB8LFiyAj4+P6LF27VrZuoMHD1Yth6WtrQ0nJycsXrwY/fr1w7lz52BiYiKqv2zZMgDAokWLVEEUAKhSpQoWLFgAAFi6dKmqPC4uDgcPHoSRkRH8/f1VQRTg7ayRUaNGIScnBytWrFCVJyUlAQDatWsnGa+tra1ombOyYGdnp1rmS+nrr79GkyZN8OjRIwQFBZXavpYtWyYKogDAgQMHcP36dfTs2ROTJ08Wze5xdnbGwoULkZOTg4CAgFIbB5UcZ6QQERERERERERERvUczZsxQpSYIDQ1FRkYGjI2Ni9VHaGgojh8/LikfNmyYpMzT0xPOzs4A3qZEePz4MS5evIgdO3agQoUKWLp0qSoHS0JCAhISEmBpaSm7vFWXLl1gZmaG2NhYPH78GFZWVjhz5gwAwNvbG+bm5pI2AwYMwKJFixAeHq4qa9SoEQBg2rRp0NTURLt27aCnp1es5+Bd9OzZUzILCAD69u2LyMhIhIeH46uvvnrn/VSpUkU2UKbMG9OjRw/Zdi1atADwdkYLlT/OSCEiIiIiIiIiIiJ6T06fPo1169ahSpUq6N69Ox4+fIjp06dL6lWsWBHA26Tzco4dOwZBECAIArZu3VrgPocNG4bAwEAEBgZi48aNCA0Nxb1799ChQwesWLECM2fOVNVNTEwE8HbGhhyFQqHa9vDhQ1Ebe3t72TbKcmV9APDx8UHv3r3x999/o2vXrqhQoQJatmyJX375BY8fPy7weEqDuuNTjlV5TO/K1tZWtlyZmL5///6q2UJ5H5aWlgDUv/70fnFGChEREREREREREdF7kJmZiREjRkAQBPj7+6N58+Y4deoUVqxYgQEDBqBx48aquq6urgCAy5cvl8lYjI2NMX/+fAQHB8Pf3x+zZs0qctu8y2GVtL6mpia2b9+OqVOnYt++fThx4oRqJsivv/6Kw4cPo1mzZsXaT3nJzc1Vu03dLBtlG29vb9HyafmpW9qN3i8GUoiIiAjBkQnltu9OTeTvziEiIiIiIvq3mT17Nm7duoVu3bqhZ8+eAID58+dj+PDhGD58OC5evKhabqp169bQ1dXFn3/+iZiYGLi4uJT6eBwcHAAAaWlpSEpKgqWlJaytrQEA8fHxatspt1WtWhUACm2jnH2hrJ9XgwYN0KBBA/j5+SE9PR1+fn5YvHgxxo8fX6bLWqkbq7JceUwAVDlfnj9/Ltvm/v37xd5/tWrVALydLaQ8F+jDxaW9iIiIiIiIiIiIiMpYdHQ05s2bB2NjY1HS9aFDh6JFixa4evUqFi9erCq3sLCAj48PBEHA2LFjkZOTU+pjiouLA/B2xoiBgQGAt0tR2draIikpSTYHy6FDh/D06VM4OzvDysoKwNuE8gBw+PBhpKWlSdps2bIFwP/n/VDHxMQEc+fOhUKhQHR0tKpcGcjIzs4u5hGqt3v3btnndNu2bQD+/5iAt3lOAOD27duS+rdv30ZCQvFvTmzfvj0AYM+ePcVuS+8fAylEREREREREREREZSg3NxfDhw9HVlYW5syZo5qNALwNYqxZswY6Ojrw8/NTzd4AgHnz5sHJyQmhoaHo1q2bKvCR15s3b3Dx4sVijykjIwNTpkwBAHh5ecHQ0FC1bezYsQCACRMmICkpSVX++PFjTJ48GQAwbtw4VbmjoyM6d+6MjIwMjBs3DllZWapt586dw6pVq6CpqYlvvvlGVb5582ZRsEQpJCQEgiDAxsZGVWZhYQFtbW3cuXOn1AJK9+7dE+WGAYDff/8d586dQ+XKlUWzRBo3bgwDAwOEhITg0qVLqvLk5GQMGzaswKW91OnZsydq166N//3vf/j555+RmZkp2i4IAiIiIhAREVHsvqn0cWkvIiIiIiIiIiIiojK0cuVKnD9/Hk2aNBEFE5Rq1aqFqVOnYtasWRg9ejSCg4MBAKampjh9+jR69OiB4OBghISEwNXVFc7OztDQ0EBiYiKuX7+OZ8+eoUKFCujUqZPs/teuXYuwsDAAby/Q//PPP4iKikJqaiosLCxEM2QAwNfXFydOnEBISAhcXFzQpk0bCIKA48ePIyMjA927d8fo0aNFbdasWYMWLVpg06ZNOHXqFDw8PJCUlISwsDDk5ORg4cKFcHNzU9UPCgrCwIED4eTkhHr16kFfXx93795FZGQkNDQ0MHv2bFVdHR0deHt748CBA3B1dUXDhg2ho6MDT09PDB48uCQvCYYPH45ff/0Vu3fvRv369REbG4uoqChoa2sjMDBQNUMHAIyMjDBp0iTMmjULzZs3h5eXFxQKBSIjI1GrVi14eHjg3Llzxdq/lpYW9u7di44dO+Knn37C8uXLUb9+fVSqVAnJycm4cuUKnjx5gsWLF8PT07NEx0ilh4EUIiIiIiIiIiIiojLy4MEDTJs2DVpaWggICICGhvwiQdOmTcO2bdsQEhKC7du3o0+fPgDe5uo4d+4cdu/ejW3btiEyMhI3b94EAFhaWqJly5bo1KkT+vbtC1NTU9m+889s0NfXh4ODAwYPHoxJkyapluhS0tTUxP79+7Fy5UoEBgYiNDQUAFC7dm0MHjwYI0eOlBxH1apVERUVhblz52Lv3r3YvXs3DAwM0LZtW0ycOBEdOnQQ1Z8wYQKqVauGiIgIhIeH48WLF7C2tkafPn0wceJEuLu7i+qvXbsWkyZNwtGjR/HHH38gJycH2dnZJQ6kNGvWDIMHD8aPP/6IgwcPQhAEtG3bFrNmzZJNcu/n5wdjY2OsWbMGJ06cQOXKlTFkyBDMmjVLbQCrMC4uLrh8+TKWL1+O3bt34/z588jOzoaVlRUaNGiAbt26oXfv3iXqm0qXQhAEobwH8bFJT0+Hqakpnj17BhMTk/IezjthcmEiIgL4+4CIiIiIqKT+TdeJSsvr169x9+5dODg4QE9Pr7yHQyTi5+eHmTNnYsOGDfDx8Snv4VA5K+rnFXOkEBERERERERERERERqcFAChERERERERERERERkRrMkUJEREREREREREREH53k5GRMmjSpSHVr1qyJqVOnlvGI6N+KgRQiIiIiIiIiIiIi+ug8f/4cGzduLFJdLy8vTJ06FX5+fvDz8yvbgdG/DgMpRERERERERERERPTRsbe3hyAI5T0M+g9gjhQiIiIiIiIiIiIiIiI1GEghIiIiIiIiIiIiIiJSg4EUIiIiIiIiIiIiIiIiNRhIISIiIiIiIiIiIiIiUoOBFCIiIiIiIiIiIiIiIjUYSCEiIiIiIiIiIiIiIlKDgRQiIiIiIiIiIiIiIiI1GEghIiIiIiIiIiIiIiJSg4EUIiIiIiIiIiIiIiIiNRhIISIiIiIiIiIiIipDmzZtgkKhQL169ZCVlSVb5/z589DU1ISFhQWSkpJE2wRBQFBQEPr06QN7e3sYGBhAX18f9vb26N69OwICApCeni7p08fHBwqFQvIwNTVF06ZN4e/vj+zs7DI5ZqJ/EwZSiIiIiIiIiIiIiMrQwIED0a5dO0RHR2P+/PmS7VlZWRg+fDhyc3OxcOFCWFpaqrYlJibCw8MDvXr1ws6dO1GhQgV06tQJ3bp1Q9WqVRESEoIRI0bAwcEBN2/elN2/p6cnBg0ahEGDBqF///6oX78+oqKi8O2336Jbt24QBKHMjr003Lt3DwqFAq1atSrvobx3ymBYWFhYeQ/lP02rvAdARERERERERERE/y3BkQnlPYQi6dTEttT6WrNmDerWrYvZs2ejd+/ecHFxUW2bN28eoqOj0a5dOwwaNEhVnp6eDi8vL8TGxqJTp07w9/eHo6OjqN/nz59j3bp1mD17NpKTk2X3PWzYMPj4+IjKLl68iJYtWyIkJAR79uxBjx49Su1Yif5tGEih/6Snx6SR//elQrsp5bZvIiIiIiIiIiIqH46Ojpg5cyamTJmCESNG4OTJkwCAW7duYfbs2dDX18fq1atFbaZMmYLY2Fh07NgR+/fvh6ampqRfIyMjjBs3Dn369EFubm6Rx+Pu7o5evXph8+bNOH36NAMpRAVgIOUD0GjypnLb98+9WpXbvomIiIiIiIiIiP5LfH19sXXrVoSFhWHdunUYMmQIRowYgczMTMybNw9OTk6qusnJyQgMDIRCocCyZctkgyh5WVlZFXs8lSpVAgDZPCnZ2dlYtWoVAgMDcevWLQBA7dq1MXjwYIwYMUJ2PCkpKfj111+xd+9e3L9/HwYGBvjkk08wYcIEdOjQQVI/Pj4ev/76K44fP44HDx5AT08PVapUQfPmzTFhwgTUqFEDfn5+mDlzJgDg1KlTUCgUqvaDBg1CYGBgsY5ZEAT88ccfWLVqFWJiYvDs2TNYWlqiRo0a+Pzzz/HNN9+o6vr4+GDjxo04efIksrOzMWvWLFy+fBn6+vr47LPP8Ntvv8HU1BRPnjzBjz/+iIMHDyIlJQX169fH/Pnz1S5FtnnzZqxZswbXrl1DdnY2nJ2d0bdvX/j6+kJPT09VL++xtm7dWtTH3bt3YW9vr/r58OHD8Pf3x4ULF5Ceno6qVauie/fumD59OipWrFis54ikGEghes88/T3Lbd8RYyPKbd9ERERERERERP91WlpaCAgIQJMmTTB58mQkJibi9OnTcHNzw4QJE0R1T548iczMTDRq1AjVq1cvk/FcvHgRAFCrVi1ReU5ODj777DMEBwfDxMQE7du3hyAIOHHiBEaPHo2jR49i165d0ND4/xTcDx8+RMuWLREXFwdbW1t0794dSUlJOHbsGEJDQ7Fo0SL4+vqq6t+/fx8NGzZEamoqXFxc0KlTJ+Tk5CA+Ph4BAQHw8PBAjRo14Obmhp49eyIoKAiVK1eGt7e3qo/mzZsX+5inTJmC3377Dbq6umjZsiUsLCzw+PFjXLt2DbGxsaJAitKePXuwYsUKeHh4wNvbG+fPn8fatWsRExODXbt2wcPDAzk5OWjRogXu3buHyMhIeHt7IyoqCvXq1RP1NXLkSPz+++/Q09NDmzZtYGBggLCwMEybNg0HDhzAsWPHYGBgAOBtoOjMmTO4c+cOOnbsKAqWGRkZqf4/depUzJs3Dzo6OmjcuDGqVKmCq1evYvHixdi/fz8iIiJQuXLlYj9X9P8YSCEiIiIiIiIiIiJ6Txo1aoTx48dj4cKF+Omnn6CpqYmAgABoaYkv1V69ehUA0KBBg1Ldf3Z2NhISErB8+XKcOnUKNjY2GDBggKjOkiVLEBwcjDp16uD48eOqi/CPHj1C69atsWfPHqxcuRJjxoxRtRk1ahTi4uLQr18/bNiwATo6OgCAM2fOoGPHjpg8eTJat24NNzc3AMDatWuRmpqKMWPGwN/fX7T/hIQEZGVlAQC6d+8ONzc3BAUFoWbNmsWegZLX69ev4e/vD2NjY1y9ehUODg6i5+XcuXOy7ZYvX479+/ejc+fOAICMjAw0a9YMp06dgpeXF5o1a4b169dDW1sbAPDjjz9i9uzZ+O2337Bx40ZVP0FBQfj9999hbW2NsLAwVZ6cZ8+eoUuXLjhz5gx++ukn/PbbbwCAwMBA+Pj44M6dO5g6darsDJedO3di3rx5qFu3Lvbs2QNnZ2cAb2fe+Pn5YdasWRg3bhy2bdtW4ueNAI3CqxARERERERERERFRaZkwYYJq2aZBgwbB3d1dUiclJQUAYGFhIdvHggUL4OPjI3qsXbtWtu7gwYOhUCigUCigra0NJycnLF68GP369cO5c+dgYmIiqr9s2TIAwKJFi0QzGapUqYIFCxYAAJYuXaoqj4uLw8GDB2FkZAR/f39VEAV4O2tk1KhRyMnJwYoVK1TlSUlJAIB27dpJxmtrayta5qy0pKenIzMzE05OTqIgCvB2tlCLFi1k2/Xr108VRAEAY2NjDB8+HADw4MEDLFu2TBVEAYBJkyZBoVDg1KlTon6Uz+uMGTNUQRQAMDU1xYoVK6BQKLBmzRq8fv26yMc0Z84cAMDWrVtVQRTg7bJgfn5+cHNzw65du5CcnFzkPkmKM1KIiIiIiIiIiIiI3qMZM2ZAEAQAQGhoKDIyMmBsbFysPkJDQ3H8+HFJ+bBhwyRlnp6eopkKjx8/xsWLF7Fjxw5UqFABS5cuVeU8SUhIQEJCAiwtLWXzmnTp0gVmZmaIjY3F48ePYWVlhTNnzgAAvL29YW5uLmkzYMAALFq0COHh4aqyRo0aAQCmTZsGTU1NtGvXTpQfpCxUqlQJ1apVw5UrVzB16lSMGDECjo6OhbaTex6U7dzd3VGhQgXRNlNTU5ibm+PRo0eqsqysLJw/fx4A0L9/f0l/9evXR/369XH16lVcuXIFTZs2LXRcT548wdWrV+Hi4oK6detKtisUCnh6euLKlSu4dOkSOnbsWGifJI+BFCIiIqJywJxZRERERET/TadPn8a6detQpUoVNGnSBHv37sX06dNVsxWUlAnC1c0kOHbsmOr/27ZtQ9++fdXuc9iwYfDx8RGVZWRk4Msvv8SKFStgbm6OWbNmAQASExMBAHZ2drJ9KRQK2NnZIS0tDQ8fPoSVlZWqTd7k53kpyx8+fKgq8/HxwZEjR7Bjxw507doVenp6aNy4Mby9vTFkyBBRPpDStHHjRnz55ZeYN28e5s2bBzs7O3h5eeHLL7/Ep59+KtumatWqkjJljhK5bcrtyllFwNsZRm/evIGFhQUMDQ1l29jb2+Pq1aui56kg9+7dAwDExMSIEtPL4YyUd8NAChER0Qei0eRN5bbvn3u1Krd9ExERERER/VdkZmZixIgREAQB/v7+aN68OU6dOoUVK1ZgwIABaNy4saquq6srAODy5ctlMhZjY2PMnz8fwcHB8Pf3VwVSiqKwi/ZFqa+pqYnt27dj6tSp2LdvH06cOIHIyEiEh4fj119/xeHDh9GsWbNi7aco2rRpg9jYWBw8eBCHDx9GWFgYNm3ahE2bNqFnz57YtWuXpI2GhvoMGQVtK67iPq+5ubkAACsrq0Jnm6gLjFHRMJBCRERE/1lPj80v7yEQEREREdF/yOzZs3Hr1i1069YNPXv2BADMnz8fw4cPx/Dhw3Hx4kVV0vnWrVtDV1cXf/75J2JiYkQ5NUqLMk9IWloakpKSYGlpCWtrawBAfHy82nbKbcrZGIW1Uc6ckJu90aBBAzRo0AB+fn5IT0+Hn58fFi9ejPHjx+PChQslO7BCmJiYoF+/fujXrx8A4Pz58/jiiy8QFBSE4OBgdOrUqdT3WbFiRejo6CA5ORkvXryQnZVS0PMkp1q1agDe5tEJDAwsraGSDAZSqNwkzKpXbvs2bjag3PZNRERERERERET/PdHR0Zg3bx6MjY1FSdeHDh2KTZs2ITw8HIsXL8bkyZMBvL047uPjgzVr1mDs2LE4dOiQKo9JaYmLiwPwdiaEgYEBgLeJ3m1tbZGQkIDjx4+jbdu2ojaHDh3C06dP4ezsrFp+q3nz5gCAw4cPIy0tDWZmZqI2W7ZsAQC1ydyVTExMMHfuXCxZsgTR0dGqcmXy+uzs7BIeacGaNm2KAQMGYO7cuYiOji6TQIq2tjaaNm2K06dPY9u2bRg6dKhoe3R0NK5evQojIyO4ubmpygs69mrVqqFmzZr4+++/cfv2bVSvXr3Ux01vld68IyIiIiIiIiIiIiKSyM3NxfDhw5GVlYU5c+aoZhIAb4MYa9asgY6ODvz8/FSzEgBg3rx5cHJyQmhoKLp166YKfOT15s0bXLx4sdhjysjIwJQpUwAAXl5eohkSY8eOBQBMmDABSUlJqvLHjx+rAj3jxo1TlTs6OqJz587IyMjAuHHjkJWVpdp27tw5rFq1Cpqamvjmm29U5Zs3bxYFS5RCQkIgCAJsbGxUZRYWFtDW1sadO3eQk5NT7GNVSkhIQGBgIF6+fCkqf/36NU6ePAkAov2WNuXz6ufnJ3otMzIyMGbMGAiCgJEjR0JPT0+1TTnb59atW7J9/vjjj8jNzUXPnj1x5coVyfaUlBQEBASU4lH8N3FGChEREREREREREVEZWrlyJc6fP48mTZqIgglKtWrVwtSpUzFr1iyMHj0awcHBAABTU1OcPn0aPXr0QHBwMEJCQuDq6gpnZ2doaGggMTER169fx7Nnz1ChQgW1MynWrl2LsLAwAIAgCPjnn38QFRWF1NRUWFhYiGbIAICvry9OnDiBkJAQuLi4oE2bNhAEAcePH0dGRga6d++O0aNHi9qsWbMGLVq0wKZNm3Dq1Cl4eHggKSkJYWFhyMnJwcKFC0UzLYKCgjBw4EA4OTmhXr160NfXx927dxEZGQkNDQ3Mnj1bVVdHRwfe3t44cOAAXF1d0bBhQ+jo6MDT0xODBw8u8uuQmpqKwYMH45tvvoG7uzuqVauGFy9e4OzZs0hKSoK7uzt69OhR5P6Kq1evXhgxYgR+//131K1bF23atIGBgQHCwsKQlJSEpk2bSnLVdO3aFbNmzcKkSZNw9OhRWFhYAHgbZKtYsSL69euHv/76C7/88gsaNWoENzc3ODk5QRAE3LlzB9euXYORkRGGDx9eZsf1X8BAChEREREREREREVEZefDgAaZNmwYtLS0EBASoTU4+bdo0bNu2DSEhIdi+fTv69OkD4O2MhHPnzmH37t3Ytm0bIiMjcfPmTQCApaUlWrZsiU6dOqFv374wNTWV7TsiIgIRERGqn/X19eHg4IDBgwdj0qRJqiW6lDQ1NbF//36sXLkSgYGBCA0NBQDUrl0bgwcPxsiRIyXHUbVqVURFRWHu3LnYu3cvdu/eDQMDA7Rt2xYTJ05Ehw4dRPUnTJiAatWqISIiAuHh4Xjx4gWsra3Rp08fTJw4Ee7u7qL6a9euVQUT/vjjD+Tk5CA7O7tYgRQnJycsXLgQx48fx99//40LFy7A0NAQDg4OmDZtGkaMGAFdXd0i91cSa9asQfPmzbF69WqcOnUK2dnZcHJywvjx4+Hr6wt9fX1R/UaNGmHLli1YuHAhjhw5glevXgEAfvjhB1SsWBEAMGfOHHTs2BHLly9HREQErl+/DhMTE1StWhVff/01vvjiizI9pv8ChSAIQnkP4mOTnp4OU1NTPHv2DCYmJu/cX6PJm0phVCXzc69W5bbvuqGdy23f5ZkjpcuNfeW274ixEYVXIqJyw98H7x9/HxARERHRuyrt60T/Bq9fv8bdu3fh4OAgWqKIiOhDU9TPK+ZIISIiIiIiIiIiIiIiUoOBFCIiIiIiIiIiIiIiIjWYI4WIiIiIiIiIiIiIPjrJycmYNGlSkerWrFkTU6dOLeMR0b8VAylERERERERERERE9NF5/vw5Nm7cWKS6Xl5eDKRQiTGQQkREREREREREREQfHXt7ewiCUN7DoP8A5kghIiIiIiIiIiIiIiJSg4EUIiIiIiIiIiIiIiIiNRhIISIiIiIiIiIiIiIiUoOBFCIiIiIiIiIiIiIiIjUYSCEiIiIiIiIiIiIiIlKDgRQiIiIiIiIiIiIiIiI1GEghIiIiIiIiIiIiIiJSg4EUIiIiIiIiIiIiIiIiNRhIISIiIiIiIiIiIiIiUoOBFCIiIiIiIiIiIiIiIjUYSCEiIiIiIiIiIiIqQ5s2bYJCoUC9evWQlZUlW+f8+fPQ1NSEhYUFkpKSRNsEQUBQUBD69OkDe3t7GBgYQF9fH/b29ujevTsCAgKQnp4u6dPHxwcKhULyMDU1RdOmTeHv74/s7OwyOWYqf5cuXYKfnx+aNWsGMzMz6OjowMbGBl999RWuXbtWYNu//voLX3zxBSwtLaGvr4969ephyZIlyM3NVdvm6dOnGDduHOzs7KCrqws7OzuMHz8eaWlpatvk5ORg8eLFqFevHvT19WFpaYnevXvjxo0bBY7vwIED8PLygomJCUxMTNCqVSscOnSowDbvQqvMeiYiIiIiIiIiIiKSkTCrXnkPoUhsf7peKv0MHDgQmzdvxrFjxzB//nxMnz5dtD0rKwvDhw9Hbm4uFi5cCEtLS9W2xMRE9OjRA5GRkVAoFHB1dYW7uzs0NTXx4MEDhISEYN++fZg6dSoiIiJQs2ZNyf49PT3h7OwMAMjOzkZ8fDzOnj2LyMhIhISE4NChQ1AoFKVyrGXh3r17cHBwgJeXF8LCwt7rvhUKBezs7HDv3r33ut93lZ2dDXd3dwCAubk5mjVrBkNDQ1y+fBn/+9//sHPnTvzvf/9Dr169JG3PnTuHtm3b4tWrV/jkk09gb2+P06dPw9fXF2fPnsX27dsl50tycjI8PDwQGxsLR0dHdO/eHX/99ReWLl2KkJAQnDt3Dubm5qI2ubm5+OKLL7Bnzx6YmZmhc+fOSE5Oxq5du3Do0CGcPHkSn3zyiWR8S5Ysga+vL7S0tNCuXTvo6uriyJEj6NKlC/z9/TFmzJhSfCbf4owUIiIiIiIiIiIiojK2Zs0a6OvrY/bs2YiJiRFtmzdvHqKjo9GuXTsMGjRIVZ6eng4vLy9ERkaiU6dOiI2NxeXLl7Fr1y5s374dERERSElJwZIlS6ChoYHk5GTZfQ8bNgyBgYEIDAzEli1bEB4ejsjISOjr6yMkJAR79uwp02On8tG4cWPs3bsXT548QXBwMHbu3Inbt29j+vTpePPmDYYMGSI5Z7KystC/f3+8evUKixYtQmRkJLZv346YmBh4eHhg586d2Lhxo2Rf48ePR2xsLHr06IFbt25h+/btiI6OxtixY3H79m1MmDBB0mb9+vXYs2cPXFxccPPmTezatQthYWHYuXMnXr58if79+0tmTN26dQuTJk2Crq4uTp8+jZCQEOzduxdXrlxBxYoV4evri9jY2NJ9IsFAChEREREREREREVGZc3R0xMyZM/H69WuMGDFCVX7r1i3Mnj0b+vr6WL16tajNlClTEBsbi44dO2L//v1wdHSU9GtkZIRx48bh+vXrstvVcXd3V81GOH36dAmPij5UWlpauHDhAj777DNoamqqyjU0NPDzzz+jRo0ayMjIkCyHtWfPHty9exeurq7w9fVVlRsZGWH58uUAgIULF4raPHr0CFu3boWOjg5WrlwJLa3/XwhrwYIFsLS0xJYtW/DkyRNRu0WLFgEA5s+fj8qVK6vKe/bsiW7duiE2Nhb79u0TtVm6dClycnIwatQoeHh4qMqrV6+O6dOnIzs7G0uXLi3Wc1UUDKQQERERERERERERvQe+vr5o0KABwsLCsG7dOgiCgBEjRiAzMxN+fn5wcnJS1U1OTkZgYCAUCgWWLVsmuhgux8rKCtbW1sUaT6VKlQBANk9KdnY2/P390ahRIxgZGcHIyAiffPIJVq1ahZycHNn+UlJSMHnyZLi4uEBPTw/m5ubw9vbGkSNHZOvHx8fj66+/RvXq1WFgYABzc3PUqVMHI0eOxK1btwAAfn5+cHBwAACcOnVKlOvFx8enWMeb1+HDh9GtWzdUrlwZurq6sLGxQZcuXRAUFAQAqudeOc68+23VqpWor2vXrqFr164wMzODsbExWrZsiaNHjyIsLEx2nI8ePcL8+fPh5eWFqlWrQkdHB1ZWVujRoweioqJkx2tvb68az9q1a1G/fn3o6+vDysoKI0eOLDAPSX4KhQL169cH8HbpuLyUgRW5Jb8aNmwIR0dHREdHi5Y6O3z4MHJzc9GiRQtRQAQAdHV10bVrV+Tk5CA4OFhVfvfuXdy4cQP6+vro3LmzZF/K/R84cKDI41PXpjQwRwoRERERERERERHRe6ClpYWAgAA0adIEkydPRmJiIk6fPg03NzfJ0kcnT55EZmYmGjVqhOrVq5fJeC5evAgAqFWrlqg8JycHn332GYKDg2FiYoL27dtDEAScOHECo0ePxtGjR7Fr1y5oaPz/ffoPHz5Ey5YtERcXB1tbW3Tv3h1JSUk4duwYQkNDsWjRItEMh/v376Nhw4ZITU2Fi4sLOnXqhJycHMTHxyMgIAAeHh6oUaMG3Nzc0LNnTwQFBaFy5crw9vZW9dG8efMSHffEiROxaNEiaGhowMPDA7a2tkhMTERERAQePHiAnj17wtnZGYMGDcLGjRthaGgounCfNw/NuXPn0K5dO7x8+RL169dH7dq1cefOHXh7e+Obb76R3f++ffvw3XffoUaNGqhfvz5MTEwQExODPXv24ODBgzh48CA6dOgg23bKlClYunQpWrVqBWdnZ0REROD333/HjRs3VIGmooiLiwPwNgCX19WrVwG8DZrIadiwIeLi4nDt2jXY29sXuc369etFCe6VberWrQttbW3ZNgBEbdLS0pCQkAAAaNCggaSNjY0NLCwsEB8fj/T0dJiYmMiOpyQYSCEiIiIiIiIiIiJ6Txo1aoTx48dj4cKF+Omnn6CpqYmAgADRckjA/19olrtg/C6ys7ORkJCA5cuX49SpU7CxscGAAQNEdZYsWYLg4GDUqVMHx48fV80yePToEVq3bo09e/Zg5cqVoqTeo0aNQlxcHPr164cNGzZAR0cHAHDmzBl07NgRkydPRuvWreHm5gbg7ayK1NRUjBkzBv7+/qL9JyQkICsrCwDQvXt3uLm5ISgoCDVr1kRgYOA7Hf+WLVuwaNEiWFtb49ChQ6rxAMCrV69w5swZAG+DNM2bN8fGjRthYWEhu9/c3Fz4+Pjg5cuXmDNnDqZNm6batm7dOgwbNkx2DJ6enoiOjkadOnVE5aGhoejWrRtGjx6NmJgY2aDI5s2bce3aNdSoUQPA/yd5Dw8Px8mTJ9GmTZtCn4MzZ87g0qVL0NHREQWmAKgCFdWqVZNtqyyPj48vtzYVKlSAoaGh2nbJycmIj49HvXr1ZOuUBJf2IiIiIiIiIiIiInqPJkyYoLpIPmjQILi7u0vqpKSkAAAsLCxk+1iwYAF8fHxEj7Vr18rWHTx4sGpZKm1tbTg5OWHx4sXo168fzp07J7lzf9myZQDe5rDIu1RTlSpVsGDBAgAQ5aGIi4vDwYMHYWRkBH9/f1UQBXgbkBg1ahRycnKwYsUKVXlSUhIAoF27dpLx2traipY5K02//PILgLfHljeIAgD6+vpo3759kfs6ceIEbt++DRcXF0ydOlW0bejQofD09JRtV69ePUkQBQA6duyIL774Anfu3EF0dLRsW2V+EyULCwuMGjUKQNFy3aSnp2PIkCEA3i41V6VKFdH258+fAwAMDAxk2ysDGBkZGR9cG3XtSgNnpBARERERERERERG9RzNmzIAgCADezkLIyMiAsbFxsfoIDQ3F8ePHJeVysyA8PT3h7OwMABAEAY8fP8bFixexY8cOVKhQAUuXLlXlYElISEBCQgIsLS1ll5fq0qULzMzMEBsbi8ePH8PKyko1i8Pb2xvm5uaSNgMGDMCiRYsQHh6uKmvUqBEAYNq0adDU1ES7du2gp6dXrOeguBITE3Hjxg2YmZmhd+/e79xfREQEgLfJ0fMuc6bUp08fVZ38MjMzcfjwYVy4cAFJSUl48+YNAOD69esAgJiYGNkZFXKviXLpt0ePHhU43pycHPTv3x8xMTH45JNPMGvWrALr0/9jIIWIiIiIiIiIiIjoPTl9+jTWrVuHKlWqoEmTJti7dy+mT5+umgWiVLFiRQBvl26Sc+zYMdX/t23bhr59+6rd57BhwyQJzzMyMvDll19ixYoVMDc3V11UVyYft7Ozk+1LoVDAzs4OaWlpePjwIaysrFRtlDkz8lOWP3z4UFXm4+ODI0eOYMeOHejatSv09PTQuHFjeHt7Y8iQIZLcHaXh/v37AABHR8ci5xIpiDJwYWNjI7vd1tZWtvz69evo1q2bKGF7fupmVMgthaUMwmVmZhY0XHz99dc4ePAgatSogUOHDolmDikZGRnh6dOnePnypWwfL168EO1T2QZAubdR1640cGkvIiIiIiIiIiIiovcgMzMTI0aMgCAI8Pf3x+rVq1GhQgWsWLECUVFRorqurq4AgMuXL5fJWIyNjTF//nwAkOQoKUxxgxBy9TU1NbF9+3b8+eefmDFjBho3bozIyEhMnz4d1atXx9mzZ4u1j4+FIAjo3bs37t27h1GjRuHKlStIT09Hbm4uBEHA999/r6onR27mS1FMnToVAQEBsLGxwdGjR9UuGacM/jx48EB2u7I8b6Dtfbd5+vSpKmBSlHalgYEUIiIiIiIiIiIiovdg9uzZuHXrFrp164aePXuicuXKmD9/PnJzczF8+HBkZ2er6rZu3Rq6urr4888/ERMTUybjcXBwAACkpaWpcpZYW1sDECf5zk+5rWrVqkVqo5x5oayfV4MGDeDn54fTp08jKSkJvr6+yMjIwPjx44t/QIVQzhyJi4tTG6goDmV+EeVMl/zkym/evImbN2/C3d0dq1atgqurK4yNjVXBpri4uHceV37z58/HvHnzUKlSJRw9elTtDBrg/wN4f/75p+x2ZXn9+vVLpU10dDSysrKK1MbMzEwVTJELMN6/fx/Jycmws7OT5P15VwykEBEREREREREREZWx6OhozJs3D8bGxqKk60OHDkWLFi1w9epVLF68WFVuYWEBHx8fCIKAsWPHIicnp9THpLxor1AoVAm8bW1tYWtri6SkJNkcLIcOHcLTp0/h7OysWn6refPmAIDDhw8jLS1N0mbLli0AgBYtWhQ4HhMTE8ydOxcKhUKUbF25BFXeQFNJWFtbo1atWkhLS8POnTuL1EZbW1vtfpXJ5Pfs2SMbmNmxY4ek7OnTpwDkl+h6+vQpjh49WqRxFVVAQAC+++47mJmZITQ0VJSoXk7nzp0BALt27ZJsu3z5MuLi4lC3bl3RMm7e3t7Q0NBAeHg4njx5ImqTmZmJAwcOQFNTE506dVKVOzg4oFatWnj16hUOHTok2Zdy/127di3y+NS1KQ0MpBARERERERERERGVIeWMk6ysLMyZM0d0EV2hUGDNmjXQ0dGBn5+fKG/GvHnz4OTkhNDQUHTr1k12tsKbN29w8eLFYo8pIyMDU6ZMAQB4eXnB0NBQtW3s2LEAgAkTJqhmqgDA48ePMXnyZADAuHHjVOWOjo7o3LkzMjIyMG7cONEMg3PnzmHVqlXQ1NTEN998oyrfvHmzKFiiFBISAkEQRLMmLCwsoK2tjTt37rxzQGnq1KmqY7t27Zpo2+vXryWBDGtra/zzzz+yAaI2bdrAxcUFt27dUi2TphQYGIjw8HBJG2dnZ2hoaODEiROimUavX7/GqFGjkJqaWtJDk9i1axdGjRoFIyMjBAcHw83NrdA2n3/+ORwcHCSBvRcvXqhev4kTJ4raVKlSBX379sWbN28wevRoUeBpypQpSEpKwldffYVKlSqJ2k2YMEFVJ28AZvfu3di/fz+cnZ3x2WefidqMGzcOmpqaWL16Nc6fP68qj4mJwZw5c6ClpSU6N0sLk80TERERERERERERlaGVK1fi/PnzaNKkiSiYoFSrVi1MnToVs2bNwujRoxEcHAwAMDU1xenTp9GjRw8EBwcjJCQErq6uqovxiYmJuH79Op49e4YKFSqI7vjPa+3atQgLCwPwNvfGP//8g6ioKKSmpsLCwkI0QwYAfH19ceLECYSEhMDFxQVt2rSBIAg4fvw4MjIy0L17d4wePVrUZs2aNWjRogU2bdqEU6dOwcPDA0lJSQgLC0NOTg4WLlwoupAfFBSEgQMHwsnJCfXq1YO+vj7u3r2LyMhIaGhoYPbs2aq6Ojo68Pb2xoEDB+Dq6oqGDRtCR0cHnp6eGDx4cLFei4EDB+LixYvw9/dHw4YN4eHhARsbGzx69AhXrlyBnZ0drly5oqrfrVs3Vd1mzZpBT08PNWrUwOTJk6GhoYGNGzeiXbt2mDp1KrZu3YratWvjzp07iIqKwjfffIMVK1aIkrpXqlQJQ4cORUBAAFxdXdGmTRvo6+sjPDwcOTk58PHxQWBgYLGOSc6TJ0/Qv39/5ObmwsHBAWvWrMGaNWsk9bp3747u3burftbW1saWLVvQrl07TJgwAdu3b4ednR3Cw8Px6NEj9OrVC4MGDZL0s2TJEpw/fx5BQUGoWbMm3N3d8ddffyE6OhouLi5YtGiRpM2QIUMQHByMPXv2oGbNmmjbti2Sk5Nx6tQp6OvrY8uWLdDSEocwatSogQULFmDChAlo0aIF2rdvDx0dHRw5cgSvXr3CsmXL4Ozs/M7PX34MpBARERERERERERGVkQcPHmDatGnQ0tJCQECA2mTh06ZNw7Zt2xASEoLt27ejT58+AN7OiDh37hx2796Nbdu2ITIyEjdv3gQAWFpaomXLlujUqRP69u0LU1NT2b4jIiIQERGh+llfXx8ODg4YPHgwJk2apFqiS0lTUxP79+/HypUrERgYiNDQUABA7dq1MXjwYIwcOVJyHFWrVkVUVBTmzp2LvXv3Yvfu3TAwMEDbtm0xceJEdOjQQVR/woQJqFatGiIiIhAeHo4XL17A2toaffr0wcSJE+Hu7i6qv3btWkyaNAlHjx7FH3/8gZycHGRnZxc7kAIAy5YtQ7t27bBy5UpERUXhwoULqFSpEpo3b44hQ4aI6s6dOxeCIGDfvn3Yvn07srOz4eXlpZqZ4+HhgbNnz+KHH37A6dOnERsbCzc3NwQHByMlJQUrVqxAxYoVRX2uWrUKNWvWxLp163D8+HGYmpqiXbt2mDNnDjZs2FDs45Hz8uVLvHnzBgBw/fp1XL9+Xbaevb29KJACAM2aNUNUVBRmzJiBsLAwXL16FU5OTpg8eTLGjRunyueSl4WFBS5cuAA/Pz/s3bsXe/bsQeXKlfHtt99i5syZMDMzk7TR0NDAzp07sXTpUqxfvx4HDx6EoaEhevbsiZkzZ6J27dqyY/b19YWzszMWLFigmvXj7u6OKVOmoEuXLsV4lopOIZRGVp3/mPT0dJiamuLZs2elkrSm0eRNpTCqkvm5V6ty23fd0M7ltm/jZgPKbd9dbuwrt31HjI0ovBIRlRv+Pnj/+PuAiIiIiN5VaV8n+jd4/fo17t69CwcHB+jp6ZX3cIjKzahRo7BmzRps27ZNFRijD0tRP6+YI4WIiIiIiIiIiIiIqARSU1NFeW2Utm/fjrVr18LMzKzMZknQ+8OlvYiIiIiIiAjBkQnltu9OTWzLbd9ERERE7+L27dvw8PBA/fr14ejoCAC4ceMGbt26BU1NTaxZswaGhoblPEp6VwykEBEREREREREREdFHJzk5GZMmTSpS3Zo1a2Lq1KmlPgZHR0d88803OHHiBE6ePIkXL17AwsICPXr0wKRJk+Dh4VHq+6T3j4EUIiIiIiIiIiIiIvroPH/+HBs3bixSXS8vrzIJpFSqVAnLly8v9X7pw8JAChERERERERERERF9dOzt7SEIQnkPg/4DmGyeiIiIiIiIiIiIiIhIDQZSiIiIiIiIiIiIiIiI1GAghYiIiIiIiIiIiIiISA0GUoiIiIiIiIiIiIiIiNRgIIWIiIiIiIiIiIiIiEgNrfIeABERERERERHRhyA4MqHc9t2piW257ZuIiIgKxhkpREREREREREREREREajCQQkREREREREREREREpAYDKURERERERERERERERGowkEJERERERERERERUhjZt2gSFQoF69eohKytLts758+ehqakJCwsLJCUlibYJgoCgoCD06dMH9vb2MDAwgL6+Puzt7dG9e3cEBAQgPT1d0qePjw8UCoXkYWpqiqZNm8Lf3x/Z2dllcsxU/pKTk7Fu3TqMGDECbm5u0NLSgkKhQGBgYKFt//rrL3zxxRewtLSEvr4+6tWrhyVLliA3N1dtm6dPn2LcuHGws7ODrq4u7OzsMH78eKSlpaltk5OTg8WLF6NevXrQ19eHpaUlevfujRs3bhQ4vgMHDsDLywsmJiYwMTFBq1atcOjQoUKPq6Q+2mTz27Ztw/z583Hjxg3o6+ujTZs2mDdvHpycnNS28fHxwcaNGyXlVatWxYMHD8pyuERERERERIVqNHlTue37516tym3fRERE/3YDBw7E5s2bcezYMcyfPx/Tp08Xbc/KysLw4cORm5uLhQsXwtLSUrUtMTERPXr0QGRkJBQKBVxdXeHu7g5NTU08ePAAISEh2LdvH6ZOnYqIiAjUrFlTsn9PT084OzsDALKzsxEfH4+zZ88iMjISISEhOHToEBQKRdk+Ce/g3r17cHBwgJeXF8LCwt7rvhUKBezs7HDv3r33ut/ScObMGQwbNqzY7c6dO4e2bdvi1atX+OSTT2Bvb4/Tp0/D19cXZ8+exfbt2yXnS3JyMjw8PBAbGwtHR0d0794df/31F5YuXYqQkBCcO3cO5ubmoja5ubn44osvsGfPHpiZmaFz585ITk7Grl27cOjQIZw8eRKffPKJZHxLliyBr68vtLS00K5dO+jq6uLIkSPo0qUL/P39MWbMmGIfc2E+ykDKunXrVCeAg4MDUlJSEBQUhPDwcFy9ehVWVlYFtq9atSqqVaum+rlSpUplOl4iIiIiIiIiIiL6f0+PzS/vIRRJhXZTSq2vNWvWoG7dupg9ezZ69+4NFxcX1bZ58+YhOjoa7dq1w6BBg1Tl6enp8PLyQmxsLDp16gR/f384OjqK+n3+/DnWrVuH2bNnIzk5WXbfw4YNg4+Pj6js4sWLaNmyJUJCQrBnzx706NGj1I6VPgyVK1fG6NGj4e7ujsaNG2PZsmUICAgosE1WVhb69++PV69eYdGiRfD19QXw9jzr0KEDdu7ciU6dOknOp/HjxyM2NhY9evTA9u3boaX1NvTw7bffwt/fHxMmTJDMhFm/fj327NkDFxcXhIeHo3LlygCAoKAg9OrVC/3798eNGzdUfQHArVu3MGnSJOjq6uLkyZPw8PAAANy+fRvNmjWDr68vvL29VYHD0vLRLe315s0bTJ06FQDQs2dPxMXF4caNGzA2NsaTJ0/wyy+/FNrHsGHDcP78edVj//79ZT1sIiIiIiIiIiIi+g9zdHTEzJkz8fr1a4wYMUJVfuvWLcyePRv6+vpYvXq1qM2UKVMQGxuLjh07Yv/+/ZIgCgAYGRlh3LhxuH79uux2ddzd3dGrVy8AwOnTp0t4VPQh8/DwwIoVKzB48GDUrVsXGhqFhwP27NmDu3fvwtXVVRVEAd6eZ8uXLwcALFy4UNTm0aNH2Lp1K3R0dLBy5UpR4GPBggWwtLTEli1b8OTJE1G7RYsWAQDmz5+vCqIAb6/7d+vWDbGxsdi3b5+ozdKlS5GTk4NRo0apgigAUL16dUyfPh3Z2dlYunRpocdZXB9dICUqKkoVWe3ZsycAwNraGk2bNgUAHD58uNA+lixZAl1dXdjY2ODLL7/EnTt3CqyfmZmJ9PR00YOIiIiIiIiIqLQkzKpXbg8ien98fX3RoEEDhIWFYd26dRAEASNGjEBmZib8/PxEaQuSk5MRGBgIhUKBZcuWQVNTs8C+raysYG1tXazxKFfqkcuTkp2dDX9/fzRq1AhGRkYwMjLCJ598glWrViEnJ0e2v5SUFEyePBkuLi7Q09ODubk5vL29ceTIEdn68fHx+Prrr1G9enUYGBjA3NwcderUwciRI3Hr1i0AgJ+fHxwcHAAAp06dEuV6yT8rojgOHz6Mbt26oXLlyqprxV26dEFQUBAAqJ575Tjz7rdVq1aivq5du4auXbvCzMwMxsbGaNmyJY4ePYqwsDDZcT569Ajz58+Hl5cXqlatCh0dHVhZWaFHjx6IioqSHa+9vb1qPGvXrkX9+vWhr68PKysrjBw5ssA8JMWhzDOiDLLl1bBhQzg6OiI6Olq01Nnhw4eRm5uLFi1aiAIiAKCrq4uuXbsiJycHwcHBqvK7d++q0nZ07txZsi/l/g8cOFDk8alrUxo+uqW97t+/r/p/3iW5lC9QQkJCge11dHRQpUoVvHnzBnFxcdi+fTuOHDmC69evo2rVqrJt5s6di5kzZ5bC6ImIiIiIiIiIiOi/SktLCwEBAWjSpAkmT56MxMREnD59Gm5ubpgwYYKo7smTJ5GZmYlGjRqhevXqZTKeixcvAgBq1aolKs/JycFnn32G4OBgmJiYoH379hAEASdOnMDo0aNx9OhR7Nq1SzTD4eHDh2jZsiXi4uJga2uL7t27IykpCceOHUNoaKhomSjg7XXehg0bIjU1FS4uLujUqRNycnIQHx+PgIAAeHh4oEaNGnBzc0PPnj0RFBSEypUrw9vbW9VH8+bNS3TcEydOxKJFi6ChoQEPDw/Y2toiMTERERERePDgAXr27AlnZ2cMGjQIGzduhKGhoejCfd48NOfOnUO7du3w8uVL1K9fH7Vr18adO3fg7e2Nb775Rnb/+/btw3fffYcaNWqgfv36MDExQUxMDPbs2YODBw/i4MGD6NChg2zbKVOmYOnSpWjVqhWcnZ0RERGB33//HTdu3FAFmt7F1atXAbwNmshp2LAh4uLicO3aNdjb2xe5zfr163Ht2jXJfurWrQttbW3ZNgBEbdLS0lTX/xs0aCBpY2NjAwsLC8THxyM9PR0mJiYFHmtxfHSBFHUEQSi0zqRJk7B8+XIYGRkBeLsu4ahRo/D06VNs2LABP/zwg2y777//XvRBlp6eDhsbm9IZOBEREREREREREf1nNGrUCOPHj8fChQvx008/QVNTEwEBAaLlkID/v9Asd8H4XWRnZyMhIQHLly/HqVOnYGNjgwEDBojqLFmyBMHBwahTpw6OHz+uuon90aNHaN26Nfbs2YOVK1eKknqPGjUKcXFx6NevHzZs2AAdHR0AbxOed+zYEZMnT0br1q3h5uYG4O2sitTUVIwZMwb+/v6i/SckJCArKwsA0L17d7i5uSEoKAg1a9aU5Nkori1btmDRokWwtrbGoUOHVOMBgFevXuHMmTMA3gZpmjdvjo0bN8LCwkJ2v7m5ufDx8cHLly8xZ84cTJs2TbUtb57v/Dw9PREdHY06deqIykNDQ9GtWzeMHj0aMTExskGRzZs349q1a6hRowaA/0/yHh4ejpMnT6JNmzbFfUpElIGKvDnG81KWx8fHl1ubChUqwNDQUG275ORkxMfHo1690pt1+dEt7ZU3gJF3TTXl/21tbdW2rVu3riqIAgD9+/dX/b+gmSy6urowMTERPYiIiIiIiIiIiIhKYsKECaqL5IMGDYK7u7ukTkpKCgDAwsJCto8FCxbAx8dH9Fi7dq1s3cGDB6uWpdLW1oaTkxMWL16Mfv364dy5c5LrncuWLQPwNodF3qWaqlSpggULFgCAKA9FXFwcDh48CCMjI/j7+6uCKMDbgMSoUaOQk5ODFStWqMqTkpIAAO3atZOM19bWVrTMWWlS5thetGiRKIgCAPr6+mjfvn2R+zpx4gRu374NFxcXVV5vpaFDh8LT01O2Xb169SRBFADo2LEjvvjiC9y5cwfR0dGybX/++WdVEAV4e36MGjUKQOnkunn+/DkAwMDAQHa7MoCRkZHxwbVR1640fHQzUho3boyKFSsiJSUFQUFB6Nu3LxITE3H+/HkAUE3tUk6vGjNmjCoyOmPGDIwZMwaWlpYAgG3btqn6VU5DIiIiIiIiIiIiIipLM2bMUK2wExoaioyMDBgbGxerj9DQUBw/flxSLjcLwtPTE87OzgDeruzz+PFjXLx4ETt27ECFChWwdOlSVQ6WhIQEJCQkwNLSUnZ5qS5dusDMzAyxsbF4/PgxrKysVLM4vL29YW5uLmkzYMAALFq0COHh4aqyRo0aAQCmTZsGTU1NtGvXDnp6esV6DoorMTERN27cgJmZGXr37v3O/UVERAB4m8tbLpF7nz59VHXyy8zMxOHDh3HhwgUkJSXhzZs3AIDr168DAGJiYmRnVMi9Jsql3x49elSyA6FCfXSBFB0dHfzyyy8YOXIkgoKC4OjoiJSUFGRkZMDCwkIV+VMmI1ImpgeAWbNmYfbs2XB0dIQgCKok81ZWVmqnWRERERERERERERGVltOnT2PdunWoUqUKmjRpgr1792L69OmqWSBKFStWBCC+vpnXsWPHVP/ftm0b+vbtq3afw4YNkyQ8z8jIwJdffokVK1bA3Nwcs2bNAvA22AAAdnZ2sn0pFArY2dkhLS0NDx8+hJWVlaqNupvVleUPHz5Ulfn4+ODIkSPYsWMHunbtCj09PTRu3Bje3t4YMmQIrKys1B5PSSnzbzs6Or5zLhHg/wMX6tJAqFs96fr16+jWrZsoYXt+6mZUyC2FpQzCZWZmFjTcIjEyMsLTp0/x8uVL2e0vXrwQ7VPZBkC5t1HXrjR8dIEUABgxYgQMDQ3x22+/4caNG9DT00OPHj3w66+/wtraWm27OXPmICQkBLdv30Z6ejqcnZ3Rrl07/PDDD6LE9UREREREREREVPY8/eWXvXkfIsbK3yVOVJYyMzMxYsQICIIAf39/NG/eHKdOncKKFSswYMAANG7cWFXX1dUVAHD58uUyGYuxsTHmz5+P4OBg+Pv7qwIpRVHcIIRcfU1NTWzfvh1Tp07Fvn37cOLECURGRiI8PBy//vorDh8+jGbNmhVrPx8DQRDQu3dv3Lt3D6NGjcKoUaPg6OgIIyMjKBQKTJs2DXPnzlWbE1xu5ktpsrW1xdOnT/HgwQPUr19fsv3BgwcAxIE2ZcBIua2s2zx9+hQvXryQzZMi1640fHQ5UpT69++Py5cv4/Xr10hLS0NQUBBcXFxU2wVBgCAI8PPzU5VNmzYN4eHh+Oeff/Dq1SvExMRg1apVqFq1ajkcAREREREREREREf2XzJ49G7du3UK3bt3Qs2dPVK5cGfPnz0dubi6GDx+O7OxsVd3WrVtDV1cXf/75J2JiYspkPA4ODgCAtLQ0Vc4S5Y3qeZN856fcpryuWlgb5cwLueuwDRo0gJ+fH06fPo2kpCT4+voiIyMD48ePL/4BFUI5cyQuLk5toKI4qlSpAuD/Z7rkJ1d+8+ZN3Lx5E+7u7li1ahVcXV1hbGysCjbFxcW987jehTKA9+eff8puV5bnDbK8S5vo6GhkZWUVqY2ZmZkqmCIXYLx//z6Sk5NhZ2dX6nnOP9pAChEREREREREREdHHIjo6GvPmzYOxsbEo6frQoUPRokULXL16FYsXL1aVW1hYwMfHB4IgYOzYscjJySn1MSkv2isUClUCb1tbW9ja2iIpKUk2B8uhQ4fw9OlTODs7q5bfat68OQDg8OHDSEtLk7TZsmULAKBFixYFjsfExARz586FQqEQJVtXJq/PG2gqCWtra9SqVQtpaWnYuXNnkdpoa2ur3a8ymfyePXtkAzM7duyQlD19+hSA/BJdT58+xdGjR4s0rrLSuXNnAMCuXbsk2y5fvoy4uDjUrVtXtIybt7c3NDQ0EB4ejidPnojaZGZm4sCBA9DU1ESnTp1U5Q4ODqhVqxZevXqFQ4cOSfal3H/Xrl2LPD51bUrDR7m0FxEREREREf17JMySJlJ9X2x/ul5u+yYiov8O5YyTrKwsLFy4UHQRXaFQYM2aNXBzc4Ofnx+++OIL1UXqefPm4dixYwgNDUW3bt3g7+8PR0dHUd9v3rzBxYsXiz2mjIwMTJkyBQDg5eUlWiZp7NixmDx5MiZMmIBjx47B0tISAPD48WNMnjwZADBu3DhVfUdHR3Tu3BmHDh3CuHHjsHbtWmhrawMAzp07h1WrVkFTUxPffPONqs3mzZvRoEED1K1bVzSukJAQCIIgyjtiYWEBbW1t3LlzBzk5OdDU1Cz28SpNnToVgwYNwoQJE1CzZk3RjIfXr18jPDwc7du3V5VZW1vj4cOHSEtLg5mZmaivNm3awMXFBbdu3cL8+fPx3XffqbYFBgYiPDxcsn9nZ2doaGjgxIkTiImJUa2y9Pr1a4waNQqpqaklPrbS8Pnnn8PBwUEV2PP19QXwNveI8vWbOHGiqE2VKlXQt29f/O9//8Po0aOxbds2aGm9DT1MmTIFSUlJGDRokCS9xoQJEzB8+HBMmTIFzZo1U23fvXs39u/fD2dnZ3z22WeiNuPGjcPvv/+O1atX48svv0TTpk0BADExMZgzZw60tLRE52ZpYSCFiIiIiIiIiIiIqAytXLkS58+fR5MmTUTBBKVatWph6tSpmDVrFkaPHo3g4GAAgKmpKU6fPo0ePXogODgYISEhcHV1VV2MT0xMxPXr1/Hs2TNUqFBBdMd/XmvXrkVYWBiAtykR/vnnH0RFRSE1NRUWFhaiGTIA4OvrixMnTiAkJAQuLi5o06YNBEHA8ePHkZGRge7du2P06NGiNmvWrEGLFi2wadMmnDp1Ch4eHkhKSkJYWBhycnKwcOFCuLm5qeoHBQVh4MCBcHJyQr169aCvr4+7d+8iMjISGhoamD17tqqujo4OvL29ceDAAbi6uqJhw4bQ0dGBp6cnBg8eXKzXYuDAgbh48SL8/f3RsGFDeHh4wMbGBo8ePcKVK1dgZ2eHK1euqOorA1gNGzZEs2bNoKenhxo1amDy5MnQ0NDAxo0b0a5dO0ydOhVbt25F7dr/196dx2lVFf4D/wz7jrIqKSCC4o64IuIGFLkHmKmZa+ovUxOX0Ba1xdLSNJc0NbWotCRyQ1xREBewFDfEDcWERNxANER4fn/w4vnyOFx0ZBlH3+/Xa17Mc88995zL3Dt3Zj7POWfjvPDCC5k0aVKOPfbYXHLJJeURNUnSoUOHHHHEEbniiiuyxRZbZLfddkvTpk0zfvz4LFy4MIceemiuueaaGp3T8iwJGpJk2rRpSZKf/vSnueyyy5IkvXv3zqWXXlrep2HDhhkxYkQGDBiQYcOG5frrr0+XLl0yfvz4zJw5M0OHDs0hhxxSrZ0LLrggDz30UEaOHJmePXtm6623zlNPPZUnn3wyPXr0yPnnn1+tzuGHH57Ro0dn1KhR6dmzZ/r375/Zs2fnvvvuS9OmTTNixIhyILPEhhtumF/96lcZNmxY+vXrl4EDB6ZRo0a544478v777+e3v/1tunfvvlL+75YmSAEAAAA+M7Y65Y+11vZPh+5Sa23XprfuOre2uwCfa//5z39y+umnp0GDBrniiisKFws//fTTc9111+W2227L9ddfn/333z/J4hERDz74YP7xj3/kuuuuy8MPP5xnnnkmSdK+ffvstNNO2X333XPAAQekdevWyzz2hAkTMmHChPLrpk2bZr311sthhx2Wk08+uTxF1xL169fPTTfdlEsvvTTXXHNNbr/99iTJxhtvnMMOOyxHH310tfP40pe+lEmTJuUXv/hF/vnPf+Yf//hHmjVrlv79++ekk07Kl7/85Yr9hw0blnXWWScTJkzI+PHjM2/evHTq1Cn7779/TjrppGy99dYV+1955ZU5+eSTc+edd+Yvf/lLFi5cmA8//LDGQUqS/Pa3v82AAQNy6aWXZtKkSZk4cWI6dOiQHXfcMYcffnjFvksWfr/xxhtz/fXX58MPP8zOO+9cHpnTp0+fPPDAA/nhD3+YcePG5fnnn0+vXr0yevTovPHGG7nkkkvStm3bimP+7ne/S8+ePXPVVVfl7rvvTuvWrTNgwID8/Oc/z9VXX13j81mehx9+uNq2F198sTytW5MmTaqV77DDDpk0aVLOOOOM3HvvvZk8eXLWX3/9nHLKKTnhhBPK67ksrV27dpk4cWLOPPPM/POf/8yoUaPSsWPHHH/88TnrrLOqjeZJknr16uXvf/97LrzwwvzhD3/ILbfckubNm2fIkCE566yzsvHGGy/znE488cR07949v/rVr8qjfrbeeuuceuqp2XPPPWvy3/OJVZVWxqo6XzBz5sxJ69at884776yURWu+qD8kbnr7HrXWdssdDq61tveccmOttT3huAkfvxNQazwPVj/PA+CzxrNg9TO112eP+2D18zPRilnZfyf6PPjf//6XadOmZb311lvmH2nhi+KYY47J5Zdfnuuuu64cjPHZ8km/XxmRAgAAALWg70V9a61toToArBxvvvlm5syZU7H4epJcf/31ufLKK7PGGmusslESrD6CFAAAAAAA+BSeffbZ9OnTJ5tvvnm6deuWJJkyZUqmTp2a+vXr5/LLL0/z5s1ruZesKEEKAAAAAAB1zuzZs3PyySd/on179uyZ4cOHr/Q+dOvWLccee2zuueeejB07NvPmzUu7du0yePDgnHzyyenTp89Kb5PVT5ACAAAAAECd8+677+baa6/9RPvuvPPOqyRI6dChQy6++OKVflw+WwQpAAAAfGG9dde5td0FAOBT6tq1a0qlUm13gy+AerXdAQAAAAAAgM8qQQoAAAAAAEABQQoAAAAAAEABQQoAAAAAAEABQQoAAAAAAEABQQoAAAAAAEABQQoAAAAAAEABQQoAAAAAAEABQQoAAAAAAEABQQoAAAAAAEABQQoAAAAAwCr0xz/+MVVVVdlss82yYMGCZe7z0EMPpX79+mnXrl1ef/31irJSqZSRI0dm//33T9euXdOsWbM0bdo0Xbt2zb777psrrrgic+bMqXbMQw89NFVVVdU+Wrdune233z4XXXRRPvzww1VyztS+rl27LvPrv/RHt27dlln3qaeeyn777Zf27dunadOm2WyzzXLBBRdk0aJFhe299dZbOeGEE9KlS5c0btw4Xbp0yfe+9728/fbbhXUWLlyY3/zmN9lss83StGnTtG/fPl//+tczZcqUFT39lapBbXcAAAAAAPhi6XtR39ruwicy4bgJK+U43/rWt/KnP/0pd911V84999z84Ac/qChfsGBBvv3tb2fRokU577zz0r59+3LZjBkzMnjw4Dz88MOpqqrKFltska233jr169fPf/7zn9x222258cYbM3z48EyYMCE9e/as1n7fvn3TvXv3JMmHH36Yl19+OQ888EAefvjh3Hbbbbn11ltTVVW1Us51VXjppZey3nrrZeedd8699967WtuuqqpKly5d8tJLL63WdleGoUOHZvbs2cssu++++/LSSy+lX79+1coefPDB9O/fP++//3623XbbdO3aNePGjcuJJ56YBx54INdff32162X27Nnp06dPnn/++XTr1i377rtvnnrqqVx44YW57bbb8uCDD6ZNmzYVdRYtWpT99tsvo0aNyhprrJE99tgjs2fPzg033JBbb701Y8eOzbbbbrvy/kNWgCAFAAAAAGAVu/zyy7PpppvmZz/7Wb7+9a+nR48e5bJzzjknTz75ZAYMGJBDDjmkvH3OnDnZeeed8/zzz2f33XfPRRddVG0EwbvvvpurrroqP/vZzwr/aH7kkUfm0EMPrdj2yCOPZKeddsptt92WUaNGZfDgwSvvZPlM+PWvf73M7YsWLco666yTJDn44IMryhYsWJCDDjoo77//fs4///yceOKJSRZfZ1/+8pfz97//Pbvvvnu16+l73/tenn/++QwePDjXX399GjRYHD0cf/zxueiiizJs2LBcc801FXX+8Ic/ZNSoUenRo0fGjx+fjh07JklGjhyZoUOH5qCDDsqUKVPKx6pNpvYCAAAAAFjFunXrlrPOOiv/+9//ctRRR5W3T506NT/72c/StGnTXHbZZRV1Tj311Dz//PP5yle+kptuummZ0zC1aNEiJ5xwQp544onCaZqWZeutt87QoUOTJOPGjfuUZ0VddPfdd2fmzJn50pe+lN12262ibNSoUZk2bVq22GKLcoiSLL7OLr744iTJeeedV1Fn5syZ+etf/5pGjRrl0ksvrQg+fvWrX6V9+/YZMWJEZs2aVVHv/PPPT5Kce+655RAlSYYMGZK99947zz//fG688caVc9IrSJACAAAAALAanHjiidlyyy1z77335qqrrkqpVMpRRx2V+fPn58wzz8z6669f3nf27Nm55pprUlVVld/+9repX7/+co+91lprpVOnTjXqT4cOHZJkmeukfPjhh7nooouy1VZbpUWLFmnRokW23Xbb/O53v8vChQuXebw33ngjp5xySnr06JEmTZqkTZs2GTRoUO64445l7v/yyy/n//2//5cNNtggzZo1S5s2bbLJJpvk6KOPztSpU5MkZ555ZtZbb70ki6ejWnp9j4+OiqiJMWPGZO+9907Hjh3TuHHjrLvuutlzzz0zcuTIJCn/3y/p59Lt7rLLLhXHevzxx7PXXntljTXWSMuWLbPTTjvlzjvvzL333rvMfs6cOTPnnntudt5553zpS19Ko0aNstZaa2Xw4MGZNGnSMvu7ZL2TJLnyyiuz+eabp2nTpllrrbVy9NFHL3cdko8aMWJEkuTAAw9MvXqVEcGtt96aJOWQbWm9e/dOt27d8uSTT1ZMdTZmzJgsWrQo/fr1qwhEkqRx48bZa6+9snDhwowePbq8fdq0aZkyZUqaNm2aPfbYo1pbS9q/+eabP/F5rUq1PyYGAAAAAOALoEGDBrniiiuy3Xbb5ZRTTsmMGTMybty49OrVK8OGDavYd+zYsZk/f3622mqrbLDBBqukP4888kiSZKONNqrYvnDhwuyzzz4ZPXp0WrVqlYEDB6ZUKuWee+7Jd77zndx555254YYbKv4I/+qrr2annXbKiy++mM6dO2fffffN66+/nrvuuiu33357xTRRSfLKK6+kd+/eefPNN9OjR4/svvvuWbhwYV5++eVcccUV6dOnTzbccMP06tUrQ4YMyciRI9OxY8cMGjSofIwdd9zxU533SSedlPPPPz/16tVLnz590rlz58yYMSMTJkzIf/7znwwZMiTdu3fPIYcckmuvvTbNmzevCBaWXofmwQcfzIABA/Lee+9l8803z8Ybb5wXXnghgwYNyrHHHrvM9m+88cZ8//vfz4YbbpjNN988rVq1ynPPPZdRo0bllltuyS233JIvf/nLy6x76qmn5sILL8wuu+yS7t27Z8KECfn973+fKVOmlIOm5Xn//fczatSoJMk3v/nNauWTJ09Osjg0WZbevXvnxRdfzOOPP56uXbt+4jp/+MMf8vjjj1drZ9NNN03Dhg2XWSdJRZ3aJEgBqEXTf7JZrbXdcoeDP36nVWTNAafWWtsAAABQm7baaqt873vfy3nnnZcf//jHqV+/fq644opq60As+UPzlltuuVLb//DDDzN9+vRcfPHFue+++7LuuutWWyfjggsuyOjRo7PJJpvk7rvvLo8ymDlzZnbdddeMGjUql156ab773e+W6xxzzDF58cUXc+CBB+bqq69Oo0aNkiT3339/vvKVr+SUU07Jrrvuml69eiVZPKrizTffzHe/+91cdNFFFe1Pnz49CxYsSJLsu+++6dWrV0aOHJmePXtWW2ejpkaMGJHzzz8/nTp1yq233lruT7I4ZLj//vuTLA5pdtxxx1x77bVp167dMttdtGhRDj300Lz33nv5+c9/ntNPP71cdtVVV+XII49cZh/69u2bJ598MptssknF9ttvvz177713vvOd7+S5555bZijypz/9KY8//ng23HDDJP+3yPv48eMzduzYalN1fdQ///nPzJ07N5tvvnk233zzauXTp09PkvIaKh+1ZPvLL7+82uvUJlN7AQAAAACsRsOGDSv/kfyQQw7J1ltvXW2fN954I0nSrl27ZR7jV7/6VQ499NCKjyuvvHKZ+x522GHlaakaNmyY9ddfP7/5zW9y4IEH5sEHH0yrVq0q9v/tb3+bZPEaFktP1bT22mvnV7/6VZLkwgsvLG9/8cUXc8stt6RFixa56KKLyiFKsjiQOOaYY7Jw4cJccskl5e2vv/56kmTAgAHV+tu5c+eKac5WprPPPjvJ4nNbOkRJkqZNm2bgwIGf+Fj33HNPnn322fTo0SPDhw+vKDviiCPSt2/fZdbbbLPNqoUoSfKVr3wl++23X1544YU8+eSTy6z705/+tByiJIuvj2OOOSbJJ1vr5k9/+lOS6ovML/Huu+8mSZo1a7bM8ubNmydJ5s6du9rr1CYjUgAAAAAAVqMzzjgjpVIpyeJRCHPnzk3Lli1rdIzbb789d999d7XtyxoF0bdv33Tv3j1JUiqV8t///jePPPJI/va3v2XNNdfMhRdeWF6DZfr06Zk+fXrat2+/zOml9txzz6yxxhp5/vnn89///jdrrbVWeRTHoEGD0qZNm2p1Dj744Jx//vkZP358edtWW22VJDn99NNTv379DBgwIE2aNKnR/0FNzZgxI1OmTMkaa6yRr3/96yt8vAkTJiRZvDj6R9caSZL999+/vM9HzZ8/P2PGjMnEiRPz+uuv54MPPkiSPPHEE0mS5557LpttVn0mk2V9TZZM/TZz5szl9nfWrFm58847U69evRx44IHL3ZdKghTgC2/0w9Nrre1Na61lAAAAoDaMGzcuV111VdZee+1st912+ec//5kf/OAH5VEgS7Rt2zbJ4qmbluWuu+4qf37dddflgAMOKGzzyCOPrLbg+dy5c/ONb3wjl1xySdq0aZOf/OQnSRaHDUnSpUuXZR6rqqoqXbp0ydtvv51XX301a621VrnOkjUzPmrJ9ldffbW87dBDD80dd9yRv/3tb9lrr73SpEmTbLPNNhk0aFAOP/zwrLXWWoXn82m98sorSZJu3bp97Foin8SS4GLdddddZnnnzp2Xuf2JJ57I3nvvXbFg+0cVjcRY1lRYS0K4+fPnL6+7ue666/Lhhx9m4MCB6dSp0zL3adGiRd5666289957yyyfN29eRZtL6iRZ5XVqk6m9AAAAAABWg/nz5+eoo45KqVTKRRddlMsuuyxrrrlmLrnkkkyaNKli3y222CJJ8uijj66SvrRs2TLnnntuklRbo+Tj1DSEWNb+9evXz/XXX59///vfOeOMM7LNNtvk4Ycfzg9+8INssMEGeeCBB2rURl1RKpXy9a9/PS+99FKOOeaYPPbYY5kzZ04WLVqUUqmU0047rbzfsixr5MsnNWLEiCTLXmR+iSXhz3/+859lli/ZvnTQtrrq1CZBCgAAAADAavCzn/0sU6dOzd57750hQ4akY8eOOffcc7No0aJ8+9vfzocffljed9ddd03jxo3z73//O88999wq6c96662XJHn77bfLa5YsGamwvEW+l5R96Utf+kR1loy8WLL/0rbccsuceeaZGTduXF5//fWceOKJmTt3br73ve/V/IQ+xpKRIy+++GJhUFETa6+9dpL/G+nyUcva/swzz+SZZ57J1ltvnd/97nfZYost0rJly3LY9OKLL65wv5bl2WefzaRJk9KsWbMMHjy4cL8lAd6///3vZZYv2b70QvUrUufJJ5/MggULPlGd2mRqLwBWu74XLXuxtdVhwnHLnpsUoDanetx9u2UP+QcA4PPjySefzDnnnJOWLVtWLLp+xBFH5I9//GPGjx+f3/zmNznllFOSLF5E/NBDD83ll1+e4447Lrfeemt5HZOVZckf7auqqsqLfnfu3DmdO3fO9OnTc/fdd6d///4VdW699da89dZb6d69e3n6rR133DFJMmbMmLz99ttZY401KuosGQnRr1+/5fanVatW+cUvfpELLrigYrH1JYvXLx00fRqdOnXKRhttlClTpuTvf//7J1onpWHDhoXtLllMftSoUTn77LOrjbz529/+Vq3OW2+9lWTZU3S99dZbufPOOz+2T5/Gkq/B1772tfK0Wsuyxx575I9//GNuuOGG/PCHP6woe/TRR/Piiy9m0003rZjGbdCgQalXr17Gjx+fWbNmpUOHDuWy+fPn5+abb079+vWz++67l7evt9565a/Frbfemn333beirRtuuCFJstdee33aU16pBCnAZ8JWp/yx1tr+6dBdaq1tAAAA4PNvyYiTBQsW5Lzzzqv4I3pVVVUuv/zy9OrVK2eeeWb222+/8h+pzznnnNx11125/fbbs/fee+eiiy5Kt27dKo79wQcf5JFHHqlxn+bOnZtTTz01SbLzzjunefPm5bLjjjsup5xySoYNG5a77ror7du3T5L897//LQc9J5xwQnn/bt26ZY899sitt96aE044IVdeeWUaNmyYJHnwwQfzu9/9LvXr18+xxx5brvOnP/0pW265ZTbdtHIF2dtuuy2lUqli3ZF27dqlYcOGeeGFF7Jw4cIVCpSGDx+eQw45JMOGDUvPnj0rRjz873//y/jx4zNw4MDytk6dOuXVV19dZkC02267pUePHpk6dWrOPffcfP/73y+XXXPNNRk/fny19rt375569erlnnvuyXPPPZcePXqU2z7mmGPy5ptvfupzW54///nPSZKDDz54uft97Wtfy3rrrZfJkyfnN7/5TU488cQki9csWfL1O+mkkyrqrL322jnggAPy5z//Od/5zndy3XXXpUGDxdHDqaeemtdffz2HHHJIRcCSJMOGDcu3v/3tnHrqqdlhhx3K5f/4xz9y0003pXv37tlnn31W/ORXAkEKAAAAAMAqdOmll+ahhx7KdtttVxEmLLHRRhtl+PDh+clPfpLvfOc7GT16dJKkdevWGTduXAYPHpzRo0fntttuyxZbbFH+Y/yMGTPyxBNP5J133smaa65Z8Y7/pV155ZW59957kyxee+O1117LpEmT8uabb6Zdu3YVI2SS5MQTT8w999yT2267LT169Mhuu+2WUqmUu+++O3Pnzs2+++6b73znOxV1Lr/88vTr1y9//OMfc99996VPnz55/fXXc++992bhwoU577zz0qtXr/L+I0eOzLe+9a2sv/762WyzzdK0adNMmzYtDz/8cOrVq5ef/exn5X0bNWqUQYMG5eabb84WW2yR3r17p1GjRunbt28OO+ywGn0tvvWtb+WRRx7JRRddlN69e6dPnz5Zd911M3PmzDz22GPp0qVLHnvssfL+SwKs3r17Z4cddkiTJk2y4YYb5pRTTkm9evVy7bXXZsCAARk+fHj++te/ZuONN84LL7yQSZMm5dhjj80ll1xSHlGTJB06dMgRRxyRK664IltssUV22223NG3aNOPHj8/ChQtz6KGH5pprrqnROX2cBx54IC+++GLWWmutDBgwYLn7NmzYMCNGjMiAAQMybNiwXH/99enSpUvGjx+fmTNnZujQoTnkkEOq1bvgggvy0EMPZeTIkenZs2e23nrrPPXUU3nyySfTo0ePnH/++dXqHH744Rk9enRGjRqVnj17pn///pk9e3buu+++NG3aNCNGjCgHMrXNGikAAAAAAKvIf/7zn5x++ulp0KBBrrjiisLFwk8//fRssMEGue2223L99deXt3fq1CkPPvhgbrjhhgwZMiRvvPFGbrnlltx00015+eWXs9NOO+V3v/tdpk2bVjhV1YQJE3Lttdfm2muvzR//+MeMGzcua621Vk466aQ88cQT2XjjjSv2r1+/fm666aZceOGF6datW26//fbccccd2XDDDXPJJZfkhhtuqHYeX/rSlzJp0qScdNJJadCgQf7xj3/kX//6V/r375/bb789w4YNq9h/2LBhOfbYY9OyZcuMHz8+o0aNyqxZs7L//vvn4Ycfzn777Vex/5VXXpmDDz44b7zxRv7yl7/kqquuyn333feJvw5L++1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",
-      "text/plain": [
-       "<Figure size 1500x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "plt.figure(figsize=(15, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(model_filter(\"XGBoost\", models)))  # 더 많은 색상 지원\n",
-    "\n",
-    "sns.barplot(x='region', y='CSI', hue='model_data', ci=None, data=model_filter(\"XGBoost\", models), palette=palette)\n",
-    "\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=15)\n",
-    "plt.title('XGBoost Models with Different Oversampling Methods', fontsize=20, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('', fontsize=20)  # x축 라벨 제거\n",
-    "\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **증강데이터에 따른 LightGBM 모델 성능 비교**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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lS5fSEUccUev7+dixYz/2fblr166lyZMn12jvV7/61Qo/d7Vp06Z0++2316j385//vM7PTsOGDavzurQ61ywav8ZwfH/02nT++edXlLseUF+nn356rf/f6667bunLX/5yjeN+wYIFpebNm5eXnXfeeeVt3XDDDRXr//rXvy6XXXzxxeXlVVVVpVmzZpVKpVJp0aJFH3sONWvWrPSnP/2pot+r+zmtvvu9zIquE64hAHyWGenCarnkkkuyaNGiJEmTJk0ycODAHHjggenfv386duxYY/3/+q//yh133FF+3bt37+y///7ZZpttysseffTRHH300RX1rrzyyopvQTdv3jyDBg3KDjvskKqqqiTJBx98kOHDh+ehhx4qchdhpVx11VWpqqrKnnvumc9//vMVZWeeeWaeeeaZ1W7jwgsvLD9v0aJF9txzz/L507Zt2zrrtWrVKtttt1322WefHHzwwRk0aFBat25dLr/88strjJBZ3rPPPptnnnkmm266afbYY480a9asXHbPPffkvvvuW809ozEaM2ZM5s2bl5122in9+/evKLvkkksyf/788utnnnkmQ4cOzTvvvFNe1qlTp+y1114ZPHhwOnTosMK2nnjiiTzyyCMrdQy+99572W+//fLiiy+Wl22zzTbZf//906tXr/Kyq666qsbommOOOSb33ntv+XWHDh0yePDgbLLJJuVlb775Zg444IDMnj27zv7+6U9/SqtWrbLnnntW3BNnyZIl+e53v7vCfaXxuOOOO2p8Y3j77bfPoEGDMnv27Nx0000rrN+7d+/y56Z99903m266ablsxowZOeWUUyrWv/POO/P//X//X/lzV6tWrbL33ntn8ODBadGiRZJk/vz5OfzwwzNlypRyvWeffTannXZaxbY222yz7LnnnmnSpEluuOGG+u88a73GcnyviOsB9XXXXXflxz/+ccWy7bbbLrvuumvmz5+fW265pUadVq1aZYcddii/Xv530Y/+Xvrggw/W+nyrrbaq8btzly5dsuOOO2bfffct/37dpEmTJMmiRYtywgknZMGCBXXuS30+p63KfgMAH6OhUx8at379+pW/mXLhhRdWlH344Yelhx9+uPSHP/yhVCqVSk8//XTFN1m++c1vVnyr7Ic//GFF+UMPPVQqlZZ+26dLly4V38p89NFHy/WuvvrqinqDBw8ulxnpwiflo8dHmzZtSs8++2y5/KyzzqooP+6440ql0up946tFixbl5TfccENFvUWLFpXGjRtXuu222yqWP/vss6WFCxfW6P+MGTNKbdq0KW/vjDPOqChffpRBktIpp5xSLrv++utX+K1SGreVHemSpDR69OhyveHDh9d5bB922GEVZcOGDSvNnz+/XL5w4cLS73//+4p+rOox+NFv9N90003lssWLF5cOOuigcln79u1L7777bqlUKpUmTpxYUa9v376l6dOnl0qlUmnJkiWlr3/96xXly1/zPnped+7cufwt7nfffbe05ZZbVnw7tbZzksZn8ODBFf/vv/jFL8pl48aNq/Ht9mXv57NmzSpNnTq11m2effbZ5fXXWWed0pw5c8pl/fv3L5f17t279Oabb5bLpkyZUmrbtm25/L/+67/KZccdd1xFP/77v/+7XDZx4sRSq1atfEuZGhrL8b2ikS6uB9TXF77whYr/v+VHq9911111Hvff/va3K34nWLx4calUKpW22mqrUrJ0ZFOS0kYbbVQqlZb+ntyxY8dyneVHnX/44YeliRMn1jqS/I477qho/29/+1u5bHU+p63qfpdKRroAQF2MdGG1LP9tr+uvvz6/+MUvMm7cuLz55pupqqrKTjvtlCOOOCJJcvvtt1fUff7553PYYYfl0EMPzaGHHpq77767onzZiJjHH388b731Vnn5oYceWvFtomOOOabi23P/+Mc/8v777xe3k7AShg0bls0337z8+jvf+U6qq6vLr8eNG7fabSx/vl1xxRW56qqr8uCDD2bmzJlp2rRp9thjjxxwwAEVdXr16pWf//zn2X333ctzQldVVaVr164V33B74YUX6my3VatW+f73v19+ve+++1aUv/nmm6u7azRCu+66aw455JDy67qOiyVLllSMcOzYsWN+9atfVYy2at68eY466qg626rPMbj8taZJkyb585//XL7OfOUrX8lrr71WLp8zZ07Gjx+fJBV9TJLTTz89Xbt2TZKss846GTVqVEX5mDFj6uzvCSecUL4utWzZsuIGs4sWLcqsWbPqrEvjsHjx4jzwwAPl1xtuuGGOP/748us99tgjgwcPrrVux44d88Ybb+TrX/96+vXrlzZt2mSdddZJVVVVLrnkkvJ6H374Yfkb/dOnT8/jjz9eLmvSpElOOumk8rF91llnpWnTpuXy5Y/n5b+tX11dXfHt+q222ipf/epXV+VHwFqsMR3fK+J6QH0sXrw4999/f/n1snvHLTN48ODsvffetdZd/v91/vz5efLJJzN37tzySPfDDjssSfLKK69k2rRpee655yruCbR8/aqqqnTo0CFnnXVW+vfvnw4dOqRp06apqqqq8flnRZ/fV/Zz2ursNwBQt6YfvwrUbcSIEbnjjjuyZMmSTJo0KSeeeGK5rHv37jnwwANzzjnnpGfPnjVujLf8HwFq8+qrr1b8u8wWW2xRY90tttii/KFz0aJFefPNN7PRRhutyi7BKlk+cEmStm3bpmfPnuUpLZb/xX5Vffvb386RRx6ZJJkwYUImTJhQLttoo40ydOjQjBgxIp06dUqSzJs3L7vssstKTW02d+7cOss+97nPpV27duXX7du3ryhfuHBhvfaDtcO2225b8bqu42LWrFkVAd9WW22VNm3a1Kut+hyDy19rlixZktGjR69w2yt7rVl//fXToUOH8hRpH11/eSv7s6HxmjlzZsUXPPr165d11qn8LtMWW2xR682yf/e732X48OEplUof286y9+aPHm9TpkxZ4RRLb7zxRpYsWZImTZpU3PS8Z8+eFedSkhpTYkJjOr5XxPWA+pg1a1bFcb/55pvXOO4///nP1/iiYJLssssuad68eT744IMkS6cOe+utt/Lhhx8mSc4+++zylHwPPvhgxXSrVVVV2W233cqvn3zyyeyxxx6ZM2fOx/Z5RZ/f6/M5bVX3GwCom9CF1TJw4MA89thjufLKKzNu3Li88sor5V+ypk2bll//+te58847M3HixHpv+9133y26u1myZEnF6+nTpxfeBtRHfY7JI444Iuutt15+85vf5IEHHqj4Q9orr7ySSy+9NPfff38eeuihNG3aNFdeeWVF4NK6desMGDAg6667bpKl3+Rcdp6t6I8jH51j+uP+yMFnw6d5XHySbX0S1xrnzGfPsvvLfZyFCxfm1FNPrXjP3WyzzdKnT580a9Ysr776av75z3+Wy1bmD9e1KZVKee+992oEnCvbz7r4HPXZ1FiO79XlesDy6vN+uey+Lsvu4fLQQw9l5syZSZaOOt9mm23St2/fTJ48uUbo8tH7uYwYMaIicFl//fWzzTbbpFWrVnn33XcrRmJ9Ep/fV/c6sSKuIQB8lphejNW27bbb5qqrrsqUKVPy7rvv5umnn66YuuLf//53br/99oobVibJfffdl1KpVOfj5ptvTrL0W5nLe+6552r0YfllzZo1y/rrr59k6ZQ1y1v+A26SipECsDo+elzOnz+/YnTLhhtumGT1j8k99tgjN954Y1577bXMnz8///rXv/LNb36zXP7oo4+W/6CxbJqMZOmUMpMnT87dd9+dm2++OX/84x8/0V+qYJlOnTpV/GHsqaeeqhj5UrTlrzWtWrXKe++9t8JrzUknnZTk4681b775ZsX5+tH1+Wzp3Llz+ebeydIpUz/6x69JkybVqPfss89WfDP5xBNPzHPPPZdbb701N998c4YOHVprex893r7xjW+s8LgulUrl8275uq+99lqNPyyvaDSkz1GfTY3p+F4R1wPqo3PnzmnZsmX59eTJk2sc9yt6v1x+irCHHnooDz74YJKlX1Jc/t8HH3ywXPbReknl5/dtt922/Lv0zTffnPPOO69+O7USVne/V8Q1BIDPMqELq+Waa67JvffeW/7WSosWLfL5z38+w4YNq1jv1VdfrTGP7Nlnn11jHuPFixfn3nvvzVe/+tXyt/i33377dO7cubzOzTffXDHv83XXXZfJkyeXX+++++7lXxTXW2+9iu3fdNNNWbBgQZLkj3/8Y/7617+u0n7DR91www0Vv5SPGjWqYqj+sl+oPnpMXnvtteXz59JLL13hqLCf/exnefjhh8u/CLVu3TrbbrttxXzNyf9Nc7Fo0aLysnXWWad8XpRKpXz3u98tnwvwSWrSpEmGDBlSfv3222/nhBNOqDj+Fi9enBtvvLGQ9pa/1rz77rs544wzytN9LDNv3rz84Q9/qLiPzEevUT/+8Y/L31L98MMP853vfKeifPl94rOnadOm2XXXXcuvp06dmt/85jfl1w888ECtUy8t/76cpOLeRq+++mquuOKKWttbb731KqaK+cMf/pB77rmnxnovvfRSvv/97+dXv/pVedlee+1Vfv7ee+/loosuKr9+6qmnVnjurc41i8arMR3fK+J6QH00adIku+++e/n1q6++mt/97nfl12PHjq31uFxm+fBk2rRpNUKXZefUxIkTK6a++2josvx5VF1dnWbNmiVZegyff/759duplbC6+70iriEAfKaVYDXsv//+pSSlddddtzRw4MDSwQcfXPrCF75QateuXSlJ+XHzzTeXSqVS6bDDDqtY3qpVq9Juu+1WOuigg0o777xzqU2bNuWyV155pdzO5ZdfXlGvurq6tNtuu5V23HHHUlVVVXn5OuusU7r//vsr+tizZ8+Kuq1bty5169atYtmyx7hx48r1xo0bV1F2zTXXfAo/URqLjx4fy47nvfbaq7TllltWLG/atGnpqaeeKpVKpdLcuXNLrVq1qijv0KFDqWPHjrUek8ufB1tssUUpSalLly6l3XffvfSlL32ptOeee5ZatGhRUeef//xnqVQqlc4999yK5V27di3tv//+pb59+5aSVJw7u+++e8X+9erVq86yUqlUsd2vfe1rn9BPmYZQ13vfK6+8UrH8/PPPX6l6pVKp9NRTT9U4Tjt16lTae++9S1/84hdLnTt3LrVv375ie6t6DC5YsKDUu3fvGsf+4MGDSwcccEBp6623LjVr1qyUpNSrV6+KbX75y1+uqNexY8fSF77whVKfPn1qbG/WrFnletdcc02d15JSqVQ6//zz6zyvabz+/ve/13jP3mGHHUq77bZbqXnz5rW+n8+bN6/UsmXLiuU777xzaZ999im1atWq4n35o8fS3/72txrlW265ZenAAw8s7bPPPqUNN9yw1vPz2WefLR/zy9fbc889a/Tlo22uzjWLxq2xHN8ruja5HlBfd911V8X/T1VVVWmHHXYo7brrrqWmTZuu8D1vwYIFtZ4bTz75ZKlUKpVeeOGFGmVVVVUVx0+pVCoNGjSoYp2+ffuW9ttvv9J6661X4xxZ2XOhVFrx57TV2e8VHfOuIQB8lhnpQiFmz56dhx56KLfeemvuuuuuiqkFBg0alIMPPjjJ0ptrfvGLXyyXvfvuu7n//vvz17/+NRMmTKiYcmb5eWdPOeWUnHrqqeXXCxcuzP33359HH320/K3/Zs2a5de//nUGDRpU0bcLLrig4vWCBQsyffr0tGzZMkcfffTq7zwk+cpXvpJFixbl3nvvzdNPP11RdvHFF2fLLbdMkrRt2zZnnHFGRfk777yTt99+O507d64xaqU2b731Vu6777785S9/ybhx4ypG1BxxxBHp379/kuTkk0/OBhtsUC6bMWNG/v73v2fy5MkZPny46TD41Gy55Zb585//XHET11mzZuWee+7JnXfeWf4GcRFatWqV22+/PZ/73OfKy2bMmJGxY8fmb3/7WyZOnFj+FulH5ze/9tprK77t+fbbb+euu+7Kiy++WF7WrVu33HbbbTXmSuezZ7/99suJJ55Yseyxxx7L/fffnxYtWuTAAw+sUadNmzY599xzK5ZNmDAhd999d5o2bVrj+rC8/fffP1dccUX5W89J8vTTT+e2227L3XffXTGl5fLH9uabb54f//jHFdt6+umnM27cuJRKpXzta1+rKFt+Opgirlk0To3l+F4R1wPqa/DgwTn99NPLr0ulUh577LE8+OCDad++fQ499NCK9Zd/v1x2X5fltWvXrvw7QJ8+fdK1a9eK8o/ezyVJfvCDH6Rp0/+79e7kyZNz++23Z/r06RVTeBdpdfZ7RVxDAPgsE7qwWs4777yce+652XPPPdO7d++0adMmTZs2TefOnbPbbrvl8ssvz9ixY8sfHNu0aZM77rgj//u//5svf/nL6dGjR6qrq9O8efP06NEjgwcPzoUXXpgXXnihfA+MZX7yk5/k/vvvz1e/+tX06tUr1dXVadmyZTbddNOccMIJmThxYoYPH16jj9/4xjdy/fXXZ+utt07z5s3TsWPHHHroofnXv/5VMeUGrI4hQ4bk4YcfzgEHHJAOHTqkZcuW2WmnnTJ69OiKX2KSpUHgT3/60/Tt2zfNmzdPt27d8o1vfCNPPvlk+Rez2lx++eU566yzMnDgwGy44YZp2bJlmjVrlvXWWy9f+MIXcs011+SGG24or9+lS5dMmDAhRx55ZDp27JgWLVpkiy22yGWXXVYxTQh8Gg444IBMmjQp3/nOd7L99tunffv2adq0abp165ZBgwblnHPOKaytzTbbLBMnTszPfvaz7LnnnuncuXOaNm2aVq1aZZNNNsnQoUPzq1/9Ko8++mhFvXbt2uWee+7J73//+wwZMiRdu3ZN06ZN07Zt22y//fa54IIL8uyzz2bHHXcsrK80bj//+c/zm9/8JltttVWqq6vTuXPnfOUrX8njjz+e7bbbrtY6I0eOzFVXXZXPf/7zadasWTp16pShQ4fmsccey+abb77C9r75zW/m6aefzimnnJItt9wybdu2TZMmTdKhQ4f0798/J554Yu64444a59PJJ5+cv/71r9l5553TsmXLdOjQIQcddFAeffTRGgH8R6eDWdVrFo1fYzi+Pzpd2PKhTeJ6QP1deuml+d3vfpdtttkm1dXV6dKlS7761a/m8ccfr7j3SZMmTSqmwE5qThU2YMCArLPO//3JZdlUY3WtnyS77bZbxo0blz322COtWrVKmzZtMmjQoNx+++2f6BcGV3W/P+4cdA0B4LOqqlT6yF3SAACAtcYbb7yR9dZbr8a3+adPn57+/fvnjTfeSLL0huDL7gsGjcHf/va3ilE3V111VY499tgG7BGN3dSpU2sdDT5p0qTsuOOO5ZkZdtttt9x3332fdvc+Mau632eccUbFaMqXXnqpYnQZAHxWNf34VQAAgMbq+9//fv73f/83e+21V3r06JHmzZvn1VdfzV//+teKqV3PO++8BuwlrLxx48bl3nvvzXXXXVdeVlVVlT333LMBe8Xa4Ljjjstzzz2X3XbbrTxN75QpU3LbbbeVR3VUVVXlO9/5TkN2s3D13e//+Z//yeOPP57f/va35W306tVL4AIA/39CFwAAWMu99dZb+eMf/1hr2TrrrJNvf/vbRgjQaNx66625/PLLK5Ydf/zx2XjjjRuoR6xNXnvttYope5dXXV2dyy+/PIMHD/6Ue/XJq89+/+QnP8nEiRMr1rn44os/8T4CQGMhdAEAgLXY0UcfnVKplAkTJuTNN9/M7Nmz06pVq/Tu3TuDBg3Kf/3Xf2Xrrbdu6G5CvTVr1iwbb7xxjjnmmBo37IZVccopp6Rbt2559NFHM3369MybNy9t2rTJJptskj333DPHH3/8WjmaY1X3u2XLltl6660zcuTIHHTQQQ3QcwBYM7mnCwAAAAAAQAHWaegOAAAAAAAArA2ELgAAAAAAAAUQugAAAAAAABRA6AIAAAAAAFAAoQsAAAAAAEABhC4AAAAAAAAFELoAAAAAAAAUQOgCAAAAAABQAKELAAAAAABAAf5/o9Ru80vFiVoAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1500x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(15, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(model_filter(\"LightGBM\", models)))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model_data', ci=None, data=model_filter(\"LightGBM\", models), palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=15)\n",
-    "plt.title('LightGBM Models with Different Oversampling Methods', fontsize=20, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **증강데이터에 따른 DeepGBM 모델 성능 비교**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(15, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(model_filter(\"deepgbm\", models)))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model_data', ci=None, data=model_filter(\"deepgbm\", models), palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=15)\n",
-    "plt.title('DeepGBM Models with Different Oversampling Methods', fontsize=20, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table= model_filter(\"deepgbm\", models).copy()\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **증강데이터에 따른 Resnet-like 모델 성능 비교**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table= model_filter(\"resnet_like\", models).copy()\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(15, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(model_filter(\"resnet_like\", models)))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model_data', ci=None, data=model_filter(\"resnet_like\", models), palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=15)\n",
-    "plt.title('ResNet-like Models with Different Oversampling Methods', fontsize=20, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **증강데이터에 따른 FT-transformer 모델 성능 비교**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table= model_filter(\"ft_transformer\", models).copy()\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(15, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(model_filter(\"ft_transformer\", models)))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model_data', ci=None, data=model_filter(\"ft_transformer\", models), palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=15)\n",
-    "plt.title('FT-Transformer Models with Different Oversampling Methods', fontsize=20, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "      <th>model_data</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.644251</td>\n",
-       "      <td>0.766097</td>\n",
-       "      <td>0.957526</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.681864</td>\n",
-       "      <td>0.804999</td>\n",
-       "      <td>0.974908</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.567129</td>\n",
-       "      <td>0.688707</td>\n",
-       "      <td>0.909057</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.576840</td>\n",
-       "      <td>0.715856</td>\n",
-       "      <td>0.975236</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.687967</td>\n",
-       "      <td>0.797628</td>\n",
-       "      <td>0.957317</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.460507</td>\n",
-       "      <td>0.638240</td>\n",
-       "      <td>0.947905</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.534332</td>\n",
-       "      <td>0.664453</td>\n",
-       "      <td>0.899439</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.445301</td>\n",
-       "      <td>0.617498</td>\n",
-       "      <td>0.967371</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.509262</td>\n",
-       "      <td>0.649690</td>\n",
-       "      <td>0.918171</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.492702</td>\n",
-       "      <td>0.637537</td>\n",
-       "      <td>0.927253</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>150 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     region        model data_sample       CSI       MCC  Accuracy  \\\n",
-       "0     seoul      deepgbm        pure  0.644251  0.766097  0.957526   \n",
-       "1     busan      deepgbm        pure  0.681864  0.804999  0.974908   \n",
-       "2   incheon      deepgbm        pure  0.567129  0.688707  0.909057   \n",
-       "3     daegu      deepgbm        pure  0.576840  0.715856  0.975236   \n",
-       "4   daejeon      deepgbm        pure  0.687967  0.797628  0.957317   \n",
-       "..      ...          ...         ...       ...       ...       ...   \n",
-       "25    busan  resnet_like   ctgan7000  0.460507  0.638240  0.947905   \n",
-       "26  incheon  resnet_like   ctgan7000  0.534332  0.664453  0.899439   \n",
-       "27    daegu  resnet_like   ctgan7000  0.445301  0.617498  0.967371   \n",
-       "28  daejeon  resnet_like   ctgan7000  0.509262  0.649690  0.918171   \n",
-       "29  gwangju  resnet_like   ctgan7000  0.492702  0.637537  0.927253   \n",
-       "\n",
-       "               model_data  \n",
-       "0            deepgbm_pure  \n",
-       "1            deepgbm_pure  \n",
-       "2            deepgbm_pure  \n",
-       "3            deepgbm_pure  \n",
-       "4            deepgbm_pure  \n",
-       "..                    ...  \n",
-       "25  resnet_like_ctgan7000  \n",
-       "26  resnet_like_ctgan7000  \n",
-       "27  resnet_like_ctgan7000  \n",
-       "28  resnet_like_ctgan7000  \n",
-       "29  resnet_like_ctgan7000  \n",
-       "\n",
-       "[150 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "models['region'] = models['region'].apply(lambda x: x[0].upper() + x[1:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_plot(df, region):\n",
-    "    df = df.loc[df['region'] == region, :]\n",
-    "    df.sort_values(by=['CSI'], ascending=False, inplace=True)\n",
-    "\n",
-    "    plt.figure(figsize=(8, 8))\n",
-    "    palette = sns.color_palette(\"tab20\", n_colors=df['model_data'].shape[0])  # 더 많은 색상 지원\n",
-    "    ax = sns.barplot(x='region', y='CSI', hue='model_data', ci=None, data=df, palette=palette)\n",
-    "\n",
-    "    # 바 내부에 model_data 값 표시 (너무 아래로 내려가지 않도록 조정)\n",
-    "    for p, label in zip(ax.patches, df['model_data']):\n",
-    "        ax.annotate(f'{label}', \n",
-    "                    (p.get_x() + p.get_width() / 2., p.get_height() * 0.4),  # 높이를 20% 정도로 조정\n",
-    "                    ha='center', va='center', fontsize=10 ,rotation=90, color='black', fontweight='bold')  \n",
-    "\n",
-    "    # 범례 제거\n",
-    "    ax.get_legend().remove()\n",
-    "\n",
-    "    plt.ylabel('CSI', fontsize=20)  # y축 라벨 변경 및 스타일 조정\n",
-    "    plt.xlabel('', fontsize=20)  # x축 라벨 변경 및 스타일 조정\n",
-    "    plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "    plt.yticks(fontsize=10, fontweight='bold')  # y축(지역명) 폰트 크기 증가\n",
-    "\n",
-    "    plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "make_plot(models, 'Seoul')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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PHiQ3N5dWrVoxbNgwXnnlFdUaFrEh6xbuatD+BlzdtUH7ExGRC+eSDMNgWu97+prfM9W0oe6ll17ipZdeOp/DEhEbNPH2Gxq0v5d/mtmg/YmISO0uuWUSIiIiIiINRWFYRERERGyWwrCIiIiI2CyFYRERERGxWQrDIiIiImKzFIZFRERExGYpDIuIiIiIzVIYFhERERGbpTAsIiIiIjZLYVhEREREbJbCsIiIiIjYLIVhEREREbFZCsMiIiIiYrMUhkVERETEZikMi4iIiIjNUhgWEREREZulMCwiIiIiNkthWERERERslsKwiIiIiNgshWERERERsVkOjT0AERExt+KHrxu0vyvveqBB+xMR+V+iMCwi8j8uauLyBu2v/ctDGrQ/EZHGpDAsIiJWyV6xt0H787myU4P2JyJiDYVhERFpVG+88cZF3Z+I/G9TGBYRkf8pGzdubLC++vbt22B9icjFSdUkRERERMRmaWZYRESkFr/93rtB+7vpxs0N2p+I1J/CsIiIyAWye/cXDdrfHQebN2h/L7Zv1aD93dKxZYP2J3I+aJmEiIiIiNgszQyLiIjIedHz2R8btL9t/3dng/YnApoZFhEREREbpjAsIiIiIjZLYVhEREREbJbCsIiIiIjYLG2gExERkUvCwk2xDdbX1X0iG6wvubRpZlhEREREbJbCsIiIiIjYLIVhEREREbFZCsMiIiIiYrMUhkVERETEZikMi4iIiIjNUhgWEREREZulMCwiIiIiNkthWERERERslsKwiIiIiNgshWERERERsVkKwyIiIiJisxSGRURERMRmKQyLiIiIiM1yaOwBiIiIiFxosW91btD+Il/b06D9yYWjmWERERERsVkKwyIiIiJisxSGRURERMRmKQyLiIiIiM1SGBYRERERm6UwLCIiIiI2S2FYRERERGyWwrCIiIiI2CyFYRERERGxWQrDIiIiImKzFIZFRERExGYpDIuIiIiIzVIYFhERERGbpTAsIiIiIjZLYVhEREREbJZDYw9ARERE5FKXtew/Ddqf77DnGrQ/qZ1mhkVERETEZikMi4iIiIjNUhgWEREREZulMCwiIiIiNkthWERERERslsKwiIiIiNgshWERERERsVkKwyIiIiJisxSGRURERMRmXbJheMaMGfTo0QNXV1f8/Py44YYbOHLkiEXXVlRU0L9/fwwGAwaDgRdeeOE8j1ZERERELkaXZBieOnUqt956Kzt27CA0NJSKigpmzZpF//79SU5OPuf1b731Fhs2bLgAIxURERGRi9klF4ZLS0urZnLHjx/P0aNHiYqKwtPTk9TUVN55552zXr9+/XomTpzITTfddCGGKyIiIiIXsUsuDG/ZsoX09HTAFIYBwsLC6Nu3LwCLFy+u9drc3Fxuv/12wsLC+PLLLy3+nCUlJeTm5pq9iYiIiMil75ILw3FxcVXvBwUFVb0fHBwMQGxsbK3X/utf/+L48eP89NNP+Pj4WPw53333Xby9vaveIiIirB+4iIiIiFx0LrkwXBuj0XjW9jlz5vDTTz/x0ksvccUVV1jV94svvkhOTk7V2+mBXEREREQuXZdcGD59VjY1NbXa+5GRkTVet2vXLgA+/PBDPDw88PDwqGr78MMPadKkSa2f09nZGS8vL7M3EREREbn0XXJhuFevXvj7+wMwa9YsABITE9m4cSMAI0eOBKBdu3a0a9eOyZMnm11fWFhIQUEBBQUFVY+VlZWRn59/IYYvIiIiIheRSy4MOzk5VVWMmDVrFi1atKB9+/bk5eUREBBQVWkiOjqa6Ojoqs12b7zxBkaj0eztpOeff57s7OwL/rWIiIiISOO65MIwwIMPPshPP/1Et27dSExMxGAwMG7cONavX09YWFhjD09ERERELhEOjT2AupowYQITJkyotf1cG+osfY6IiIiI/O+6JGeGRUREREQagsKwiIiIiNisS3aZhIiIiMj/qgGfDmjQ/tY9uq5B+/tfoplhEREREbFZCsMiIiIiYrMUhkVERETEZikMi4iIiIjNUhgWEREREZulMCwiIiIiNkthWERERERslsKwiIiIiNgshWERERERsVkKwyIiIiJis6w+jvmtt95q8EG89tprDd6niIiIiMi5WB2G33jjDQwGQ4MOQmFYRERERBqD1WEYwGg0NtgAGjpYi4iIiIhYyuowvGLFivMxDhERERGRC87qMDxo0KDzMQ4RERERkQtO1SRERERExGYpDIuIiIiIzarTBjprFBQUsGzZMg4fPozBYKBFixYMGzYMDw+P8/2pRURERETOyuowbDQaWbp0KQARERG0b9++1uf+8MMPPP3002RlZZk97u7uzsSJE3n00Uet/fQiIiIiIg3G6jC8efNmRo4cicFgYObMmbWG4WnTpnHPPfdgMBiqlWLLz8/niSeeoKysjKeeeqpuIxcRERERqSer1wwvW7YMgKCgIMaOHVvjc7Kysnj88ccB00xyq1atePXVV5kyZQr33XcfDg4OGI1GXn31VRISEuo+ehERERGRerA6DG/evBmDwcCYMWNqPTDjhx9+IDs7G4PBwMCBA9m5cydvvvkmDz30EF9//TV//vkndnZ2FBcXM23atHp/ESIiIiIidWF1GD548CAAAwYMqPU5c+bMqXr/448/xs3Nzax9+PDh3HjjjRiNRh3iISIiIiKNxuownJiYCEDr1q1rbC8rK6uaPW7dujXdu3ev8XnXXXcdAPv377d2CCIiIiIiDcLqMFxYWAiYKkLUZNeuXZSUlAAwcODAWvtp1aoVQLVKEyIiIiIiF4rVYfjkkofaQuymTZuq3u/Zs2et/Tg4mApZlJWVWTsEEREREZEGYXUYbtKkCQBbtmypsX3VqlVV7/ft27fWfjIyMgDw9PS0dggiIiIiIg3C6jDct29fjEYjX3/9dbVZ3fT0dP78808AAgIC6NatW6397N27F4CmTZtaOwQRERERkQZhdRi+8847ATh8+DBjx47lwIEDlJWVsXv3bsaNG0dRUREGg4HbbrvtrP2sWrUKg8FAp06d6jZyEREREZF6svoEukGDBnHdddcxd+5cFi9ezOLFi6s9x8PDg2effbbWPrKysli4cCFw9k12IiIiIiLnk9UzwwA//fQTI0aMwGg0Vntzc3Nj+vTphIWF1Xr9F198QWlpKQAjR46s28hFREREROrJ6plhMJVVW7x4MQsXLmTu3LnExsbi5OREjx49uO+++6o22dUmNjaW8ePHEx4efs7nioiIiIicL3UKwyddffXVXH311VZfN2XKlPp8WhERERGRBlGnZRIiIiIiIv8L6hSGFy1aRI8ePejRowfTp0+36trp06dXXbts2bK6fHoRERERkQZhdRg2Go08+eST7Nq1i8DAwHOWUDvTrbfeSkBAADt37uTpp5+29tOLiIiIiDQYq8Pw8uXLOXjwIHZ2dnz00UdWf0KDwcDHH3+Mvb09e/fuNTuxTkRERETkQrI6DM+aNQuA4cOH06FDhzp90g4dOnDVVVcBMHPmzDr1ISIiIiJSX1aH4c2bN2MwGLj22mvr9YlHjx6N0Whk48aN9epHRERERKSurA7Dx48fB6Bt27b1+sRt2rQBICYmpl79iIiIiIjUldVhOCcnBwA/P796feKT1+fm5tarHxERERGRurI6DHt5eQGQnZ1dr0988npPT8969SMiIiIiUldWh+HAwEAA9u/fX69PHBUVBUBQUFC9+hERERERqSurw3Dv3r0xGo3Mnz+/Xp947ty5GAwGevXqVa9+RERERETqyuowPGrUKAD++usv1q5dW6dPunr1av766y+z/uTSV1FaQn7iYQqSjjT2UEREREQs4mDtBePHj6dZs2bExMRw4403snr1alq3bm3x9QcPHuSmm27CYDDQrFkzbrjhBmuHIBehpA1zSdn8J5XlpbiHtiSoxwgS1vxO2OXj8Wvfr7GHJyIiIlIjq2eGHR0dmTRpEgCpqan07NmTTz75hIKCgrNel5+fz8cff8xll11GamoqAB988AEODlbncbnIpO1cTtK6OVSWlYLR9Jhn046U5mWSdUB1pEVEROTiVackOm7cON58801ef/11CgoKeOqpp3j11VcZOHAgPXv2JCgoCHd3dwoKCkhJSWH79u2sWbOGgoICjEZTWnrzzTcZO3ZsQ34t0kjSti8Fg4EmV95K/PLpADi4euDk4UtRalwjj05ERESkdnWeln311Vdp0qQJjz76KIWFheTn57N48WIWL15c4/NPhmA3NzcmT57M3XffXddPLReZkpxUXAPCCeoxoioMA9i7uFOckdiIIxMRERE5u3qtUbjnnnu46qqr+PDDD/nxxx9JT0+v9bkBAQHcddddPPnkk4SFhdXn08pFxt7JlbL8bCrLS6seKy8uoCQrGXtn10Ycmcm+pCJWHc4nIacMgHBvRwa18qBjaOOPTURERBpXvRfshoWFMWnSJCZNmsS+ffvYtWsXGRkZ5OXl4enpib+/P127dqVjx44NMV65CHlEtCP70Faif34LgJLsVKJ/fpPK8lK8W3RrtHFVVBp5bm4Cs3dlV2v7v79TGNvFh0ljw7G3M1z4wYmIiMhFoUF3r3Xs2FGh1waFDRhH3vF9FKXFgwHKi/IoL8zD3tmV0P5jreqrtKSYhXN+ZtvGVaQlJwAQGBLOZf0GM/K623B2cbG4r8mr05hVQxA+6Y/d2TTzc+Lxwf87B7+UlZSx4rc17Fm7n8ykLAD8Qn3pfHlHBt84ACcXp0YeoYiIyMVFpRyk3lz8w2h7+xskb5xHYfIxANxCmhPS51pc/EIs7ic7M50X/3UL8cdNdYpPrjNPiD3Kzs1rWDj7J96f8hs+fgEW9TdrVzb2Bnh1ZCijOngR6O6AEUgvKGfhvlzeXpLEzJ1ZVofhPYcT+XtrNPGp2QA0CfJh6GVt6dyqcZf/5Gbk8f69H5N0LMX0wInKHkkxKezbcICVv63hhe+fxMtfR6CLiIicpDAs9WKsKCfzwEbAQNOR92EwWF2tr8r3U94nLuYwBoOB0CbNTKHXaCQ7K4Ok+BgS447x/ZT3eeLl/7Oov6ScMpr5O3N3H3+zx4M9Hbmnrz8/bc0kLqu0lqurq6io5LEPZvLr39urtf37uyXcOKQ7k5+5EXv7un8P6mPmx3NJOpoCBgiODMTL3xOjEfIy80iJTSP5eCozP57LvW/f3ijjExERuRgpDEu9GOwdiP3rO5y9g/DvOKBefW1ZuxxnF1c++HoOzVq1M2s7diiKpx+4ni1rl1vcn7+7PXFZpaw4lMeVrc1nQ5cfzCM2sxR/d3uL+/tg+nJmLKsehE/6ffkOmof589wdwyzusyHtWrUXJxdHXv75GSLahJu1xUUn8O/bJ7Fr1d5GGZuIiMjFSmFY6s01MIKy/Ox691NQkEdwaJNqQRigeev2BAaHkZIUb3F/V3fw5ttNGdz783FcHO3wdTUF38zCckrKjVXPsdSvy7Zjb2dg4j+u5dqBnQjy9cBohLTsfOat2cPLUxYwY+m2RgvDhflFBIT7VwvCABFtw/EL8SU9IaMRRiYiInLxUhiWegvudQ0xC78kZuGXBHYfhoObNwbDqQoNTl7+Z7n6lNDwSOKPH+Hbye/Sf/BIfPxM12VnZrB+xSISYo8S0ayVxeN6ekgQ+5KL2HS8kKKySorKKs3ae0W68fQQy9cLJ6Rl0yI8gAfG9jd7PMTfiwfHDuDb+Rs5npRpcX8NLSgigKRjKfz24R/0HNYVLz/TbHhuZh7blu4kJSaV0BbBjTY+ERGRi5HCsNTbsfmfgQEyozaQGbXhjFYDPZ7+zqJ+Rl0/ga8+epM5079izvSvqrUbDAZGXT/B4nG5O9sz4+7mLI7KZdXhfBJP1BkOO1Fn+Kp2XthZUVYtwMeD40mZLN18gOG9zWevl246QExSBgE+Hhb319CuvGkg09+byeLvl7H4+2XVn2AwPUdEREROURiWhmGs5XFDbQ3VjbnpHnKyMpj505dUlJeZtdnbOzBuwoOMuekeq4ZlMBgY1cGbUVYsh6h1fAM78+Wcddz66ve4Ojni6+UGQGZuAcWl5VXPaSzDJgwmNzOPRd8uo6K8wqzNzt6OUfcMY9iEwY0zOBERkYuUwrDUW+ubX2iwvu546BmuvfFudm5ZS1pKEgCBwaF0vWwAvv6BVveXnl/OZ2vSWHU4z+wEusGtPPnH5QEEeTpa3NdLd49gz+FE1u85RmFJGYVpOWbtfTs146W7R1g9xoY07tFrGXbbYPZtPEBm8ok6wyG+dOjTFu8Ar0Ydm4iIyMVIYVjqzTOi+oa3+vDxC6DLZf1JS04EIDAkrE5BODazlHFTj5JRUG42cX00o5RjGRnM25vDrPua09TP2aL+PFydmTfpQRas3cffW6NJSMsGIDzQVGf4mgEdsLNrnLJqp/Py96RDnzZknDh0wz/UV0FYRESkFgrDUm9J6/84a7s1p9Dt2LyWqZ9O5PiRA2aPN2vVjnv/9RLd+1i+5vXdpcmkF5Tj4WRH9yZuBHg4YDQaySioYEd8IekF5by/LIXPb4q0uE+DwcC1Aztx7cBOFl9zIe3bcIBfJ80m/lCi2eMRbcK56amxdOzfvpFGJiIicnFSGJZ6S1r/B5xlH5qlYXjnlrW88dTdVFZWVJ0+d9KxQ1G88fQ9vPnR93TrdblF/a0/VoCnsx3LHmlN8BnLIVJyyxj22SHWHi2wqK+T0rLy+fCX5SzfetD8BLpebXn85sEE+zXe6W77Nx7go39+TmVlZbU13HHRCXz08BSe+uJhOvRt2Jl8ERGRS5nCsNTbmaXTKkqLqCguBIPB4rJqANO/+ZiKinLaduxGn4HD8fELwGg0kpOVwaY1S4net5PpUz+xOAwXl1cS7OlYLQgDBHs54uNqT2p+ucXji0nKYNQTU0jLzuf0rH44Pp0jCenMXrGTRR8/TPMwy7/mhvTH5wuprKikRZdmdBvc2XTsstFUWm3nyj0c3R3D3CkLFYZFREROozAs9dbpwQ+qPZZ7fB9H//iE0P7XW9zP4ei9BASF8n9fza629nb8hIe4d9zlHD6wx+L+WgU4sz+5mH/9FsvIDt5Vp81lFFSwaH8OcdlldAxxsbi/N75eRGpWPh6uTlzWPpIgX0+MRiNp2flsjYolLbuAt6Yu4rtXG+e44+NRcfgG+/DStKeqff9G3TOMZ696jZj9cY0yNhERkYuVwrCcF15NO+Ie2pLkTfMtPqbZ3t6BstJSSktLcHFxNWsrKyulrKwUe3vL/8s+NCCAx2bFs3B/Lgv351ZrNwAPDgiwuL81O4/g5e7Chm+eIsTffENaUnou/e7/gFU7DlvcX0Ozt7ejvLScspJynF2dzNrKS8spLyvH3r7xN/iJiIhcTBSGpd7y4sw3u2E0UpyVTEHiYTBYfqhFu8492Ll5Df+aMIIeva/A+8QJdDmZGWzfvJq8nCy69bZ8A92Yzj4Ullbyn79TyCw0r7vr62rPc0ODua6zj8X9FZeWEeLvVS0IA4QGeOHn5UZKZp7F/TW0ll2bs2/DAV69fiKd+rfD88T65bzMPPauP0B+dgEd+2mJxIWQX1zMqr3RHEpKJqegCABvd1fahIUwsENbPF0tvyMhIiLnl8Kw1NuhX9+reQOdETyatLW4nzv/8Sz7d20hJTGOxXOnm3dlNOLk7MKd/3jWqrHd0tOP8d182ZVQRFKuqc5wqJcjXcNdcbS3PKgDtIkMYs+RRO7998+MvrwTgSdOm0vLzmf+mj0cT86iS6swq/psSOMfG8Oh7UdIT8hg5cx15o1GcHRxZPxjYxpncDYkMy+fr/5aRUFJsdlGxozcfDbkHWZ3TBwPjBiMv2fjnVYoIiKnKAxLw6jhoDn3sJZEXnWvxV20bteZD76eww9f/B+7tq6jtKQYACdnF7peNoA7H3qG5q2tLw1mAOwMp/L66e9b45Ebr+DBd2cwb80e5q2pvnbZYIBHbriiDj03jGYdI3n552eY/d/57N8UTVmxKfw7ujjSoU9bxj16LRFtwxttfLZiyc69FBQX4+ToQESAHx4uLhiNUFBSTFx6JgXFJSzduZdbBvZt7KGKiAgKw9IAOj4wyexjg8GAg5sndg5OtVxRu2at2vH6pKlUVFSQm50JgJePH/b29nUa2+87snhvaXK1ZRJ+bvY8NyyEm3v4WtzX+Cu7UVhcytvfLiYjp9C8Py83Xrn3KsYP6VancTaUiDbhPD75H1RWVJKXlQ+Ap68HdlorfMEcTU7D2cmRx0cPx9PVfO17bmER/12wlCPJqY00OhEROZPCsNSbs/epTWjlRaYAVpcgfNKRg/vYtnEVackJAASGhHNZv8G0aN3Bqn4W7M3h2bkJNbZlFFbwwrwE3JzsuLaTt8V93jGqN7cM78n26DgSThzHHB7oTY+2ETg61C2wN7TYA3HsWRtFRpLpxYR/qB+dL+9AZLsmjTwy21BeUYGnq0u1IAzg5eaKq7MT+UXFjTAyERGpicKwNIjU7UtJ2bSAskJTQHR08ya4z2iCegy3uI+Kigo+mfgcKxbPrtY27Yv/Y/BVY3nilUkWzxJPWZsGwKj2Xozs4EWAuwNGIKOgnEX7c1kclcuX69KsCsNgWg5hZzBU7Q08/f3GVFlRybev/cT6+Zurtc367zz6XdOL+/59h2aJz7NAb0+SsrKZsWYTHSPCcHcxHfddUFzCvrgEsvMLCPXzadxB2qBVq1axceNGUlJSAAgODqZv374MGjSokUcmIo3tkg3DM2bM4D//+Q9RUVG4uroyZMgQ3n//fVq2bFnrNS+++CJ//PEHCQkJlJaWEhwczNChQ3n99ddp2rTpBRz9/5bEdbNJ3jjPbN1wWUEO8St+prwoj7AB4yzq59fvJ7N80axa21cu+YPQJs247b7HLervcHoJkb5OTLm5+nHL13X2YeDH0RxKK7Gor5OmL9nKm98sIiPX/OQ6fy93Xr1vJLeP7GVVfw1p/leLWT+vehA+acOfWwiKDOS6f159AUdley5v34bf121mX1w8++Liqz/BAAPat7nwA7NRRUVFPPvss+zYsaNa27x58+jWrRuTJk3CtYaZfBGxDZfkFNHUqVO59dZb2bFjB6GhoVRUVDBr1iz69+9PcnJyrdctWbKEgoICWrduTUREBLGxsXz33XdcddVVF3D0/3vSdy4HwKNJG5oMmUCTIRPwiGgLxlNtlli+aBZ2dvY89NQb/Dh/E/PWHWXu2iP8OH8TDz75OhgM/L1wpsX9uTjYkVVYTloNp8yl5pWRXVSBs4PlPwJzVu3i0Q9mkp5TgNGI2Vt6TgFPfDSL2St3WdxfQ1s/bxN29gZue/EGPlw+kW92/Zdvdv6XD5dP5LYXbsBgMLBu7qZGG5+t6NIsguv69MDNydn0AvG0N1cnJ8b07kHXZhGNPErb8dVXX7F9+3aMRiOOjo4EBgYSEBCAo6MjRqORnTt38tVXXzX2MEWkEV1yM8OlpaW88MILAIwfP56ZM2eSmJhIu3btSE1N5Z133uG///1vjdeuX78eF5dT9T3vuOMOfvrpJ6Kjo8nIyMDfv3GO0b3UVVaU4ejhS+ubXsBw4uSzwG5D2Pv1M1SUFFncT1pKEmERzbj2xrvNHvcLCGbMTfewcPZPJCdafoJav+buLI7KZcinB+nexI0Ad9N/9/SCcnbEF5JfUsnI9tVrBtfmv7+uAuDayztx7eWdCPT1MAXh7Hzmrd3DgrX7mPzbKsYN7mpxnw0pMzmb4Mgght022Oxxn0Bvhk0YzIpf15Aan94oY7M1l7VqTvcWTUnIyCKnwLTZ0tvdjXB/X+ztLsk5iEvW8uXLcXR0ZOLEiQwYMADDiTVNRqORtWvX8vLLL7N8+XIef9yyO04i8r/nkgvDW7ZsIT3d9Ad9/PjxAISFhdG3b1+WLl3K4sWLa73WxcWFzz//nB9++IHMzEwOHzadFtahQwf8/Pxqva6kpISSklO303Nzq59mZsu8W3YnPz76jHplpg98Wve0uB8fX3+SE+PYun4Fl/W/0qxty/rlJCXE4uNr+QuWF4cHs+V4ARmFFaw5km/WZsRUUeL5YcEW93cwNpVmoX58/1r145bHD+lGjzvfJzq27lUCcg7mkBeTh4ObAwE9A6goqsDR0xE7R8vCk6efB6nx6exes48uAzuate1avZfUuHQ8/VTb9kIyGKg6eOZiWFdui7KysggPD+fyyy83e9xgMDBw4EDCw8NJTExspNGJyMXgkgvDcXGnZgaDgoKq3g8ONoWa2NjYs14fGxvL5s2n1lV2796dBQsWVM0W1OTdd9/lzTffrOuQ/+e5BTcj+9BWDv36Pj5tLgMg+9A2KkoKcQtuRsa+tVXP9e94eW3dMGDI1cz79VvefOZenJxd8PQ2lT3Lzc6krLSk6jmWaurnzKJ/tuKzNWmsOpxPYo6p7m6YtyODWnnw8OWBBHs5Wtyfi5MjmbmFpGblEeTradaWkplHZm4hzk7W/0hVlFZw4OsD5Bw0bT70bOqJo6cj0d9GEzk6kibDLasC0WtEd5b+tJKP/zUFJ2dHPHzcAcjLLqCspKzqOXL+bT8Sw5KdeyksMV+T7ubszIhunejZslnjDMwGBQUFERcXx+zZsxk8eDC+vqbfK1lZWaxYsYK4uDhCQkIaeZQi0pguuTBcG6OxhlMfavDee+8xceJEDh8+zD//+U9WrFjBhAkTWLZsWa1VCl588UWeeuqpqo9zc3OJiNCav5MSVs4AA+QnRJOfEG3WFr/i59M+Mpw1DN/x4NMcjd7H3p2bKCkuoqTYfIlFh669uOPBp60aW5CnI29e3TCnwl3erQUL1u6j9z2T6NUh0uwEui37Y8kvKmH0gE5W9xv7Z2xVED7Jt4Mvdg52ZO/PtjgMX//oaI5HxXNw22FKi8vITM42a2/dowXXPzra6vGJdfYcj2fOxm01thUWl/DHpm042dvTWeuGL4hRo0YxdepUPvjgAz744INanyMituuSC8Onh9DU1NRq70dGVq8ccCZ7e3vatm3LE088wYoVK1i5ciV///03I0aMqPH5zs7OODs713Pk/+Msei1y9ie5urnz7ucz2LBqCVs3rCQ9xXTrMiA4jJ59B9Fv0FXY1WG95ZKoXFYdziPhxMxwuLcjg1t7MqKd5euFAd64/2o27okhPaeAFdsOmbUZjeDv7cZr9420enwZOzKwc7Sj85Od2fUf0wY8O0c7nH2dKUq1fM21i5sLz3/3ONuW7WLvuv1kJmcB4BfiS6cBHegxtEudvn8AiUVFROXl4Wxvx+X+Aee+wIat2W96QdghMpwOEeF4nCitll9cwv7YBPbHJbAm6qDC8AVy1113ER8fz5IlS2psHz58OHffffeFHZSIXFQuuTDcq1cv/P39ycjIYNasWdx6660kJiayceNGAEaONIWRdu3aAfDII4/wyCOPcOjQIaKiohg9ejR2dnZUVlaarS8uKCio/snEIj2e+b7B+jIYDPQfPJL+g60PlWcqLK3k3unH2RRT/d/2l21Z9G7qzncTmuLmZFlAbB7mz+ovn+CjX5bz95aDJKRlAxAe6MPQXm14/OYrCQ2wLmADlOWV4Rbihnu4u9njBnsD5UXVK2GcjcFg4LLh3bhseDerx1GTCqORDw4dYnGKqUpLe08vCssreO9gNI+0aMm4cB3vfKa0nDx8Pdy5tYbjlrs2i+CDuYtJy8lrhJHZJgcHB15//XVuueUWNmzYUDVxEhQURL9+/Wjbtm0jj1BEGtslF4adnJx45513eOihh5g1axYtWrQgIyODvLw8AgICqipNREebZmdObrZLSEjguuuuw8PDgxYtWpCSklJVfL1JkyYMHTq0cb4gG3Fk7n8pSo2l0xlHN59u+tRP8PMPYvDIsbi41L/m56TlKWw8EYSdHQz4uTlgNBrJKqqgpNzI5uMFTFqewmsjQy3uM9jPk/f+dV29x3Y6J28nitKKKE47dSpZQXwBhSmFOPtad0ciNyOPBV8vYc+6/WSeOIHOL9SPLpd34Or7huMdYN0BIz/HxbIoxbxc4eUBAdgfOsi6zAyF4Ro42NtTWFpKflExHq4uZm15RcUUlZTioINPLri2bdvSpk0bsrJMd0x8fX3PuldERGzHJReGAR588EHc3d2ZNGkSUVFRuLi4MG7cON577z3CwmpeHxoZGcnYsWPZtm0b0dHRGI1GWrZsybBhw3jllVfw8rJ+Rk8sV56fTWnu2ct6Tf/mIwwGAz9MeZ+rx9/BtTfchY9f3W/JL9yXg5O9gSk3RTCkjadZSaW/D+bxz1/jWLgvx6owDPDnun38vSWa+NRsAJoE+TCsd1uu7t/x7BfWwq+zH0mrk9j53k4AChIK2P3BbjCa2iyVGpfOO3d8QG5mntmKlJSYVJYeT2Xjwq28PO1pgiIDLe5zcUoKDgYDb7TvwCv79wHgZm9PoLMzxwsLLe7HlrQIDmR/XAIfz/+LiAA/sxPo4tIzKSkvo0OEXkRcSEePHuXLL79ky5YtVZWBnJ2d6dWrFw888ACtWrVq5BGKSGO6JMMwwIQJE5gwYUKt7WduqGvRogVz5sw538OSejIajeTlZvPb95OZ8/NXXDlqHNffej9NmtZ+smBtMgoqiPRzYmhb8xc6BoOBYW29iPRzIi6r1OL+CopKue2171m3+2i1th8XbaZ/5+b88vY9uLs6WTXOiKsjyD2cS0GiaRa7srwSALcwNyJGWb6u9PeP/iA3Iw8Xd2dadGmOt78nRqOR3Mx8ju4+Rl5mPjM/mcvDH9xvcZ9pJSU0dXNjwBk1uN3s7Ukrse70PlsxonsnYtLSKSwu4XByinmj8VRFCbkwoqOjefjhhykuLjb7u1BcXMyaNWvYsmULU6ZM0XIJERt2yYZh+d/UpGlLul42gGV//k5JcRF/zZvBX/N/pfeAoYyf8CAdulp+3HGIlwMxGSVM25zByA7eBLibqoWkF1SwaH8OxzJKCPe2vLTaOz8sYe0uUxB2cXLAz8sNI5CVW0hxaTnr9xzjnR+WMPEf11r1NTu4OtD5mc6kb0sn/7ipHrJHpAcBPQOws+KEvKhNB3H1dGHi3FfxCTRfDpGVms3L1/2b/Ruia7m6Zt6OjiQVF5NTVlb1WEpxMccLC/F2tPx7dykpLStl6cqF7DuwB39ff64ZcT3JqYm0aNoKL89zLzPx9/TgkauHsWrfAQ4lppBzYgbd282N1mHBXNGhLV5uOvr3Qvniiy8oKioiNDSUXr164efnZ1oulZXFli1bSEpK4osvvuCjjz5q7KGKSCNRGJaLirunF/985i1uf+ApFsz6gT9nTSM7M53Na5exee0y2nTsxgdfWzbDP76rLx+vSuW1hUm8tjCp1udYau7qPTg52PP9a7czok87s2UXSzZGcffbPzF39R6rw3Dq5lQcPRwJ6h1EUO9TtbOLM4qpLK3ELdTNon7KSkrxCfSuFoQBfIN88PB2Jyc9p4Yra9fL15fFKSncu91UKiymsIAHdmynwmikt6/lSzguFTm52Tz6wv3ExJle9HRo04lO7bvx7BuPcNctD3DfhH9a1I+nqwujL+t2HkcqltqzZw8+Pj5MmzYNNzfzn6WCggJuvPFG9uzZ00ijE5GLgcKwXJQ8vX249d7HueGOf/L3n7OY88vXJMQe5eC+nRb38cgVgcRklvDHnpoD4JhO3jw6yPL1s+nZ+TQL9eeqvu3NHjcYDIzs14Fmof4cP7FpzRqHfz6MZ1NPfDuYB/ODPxwkPzaf/h/3t6if0BYhxB6I5/Onp9JzWDe8/E0Hg+Rm5LFt2Q7SEzKIbG9ZzeKT7m/WjG3Z2VVLIgorKgAIcHLi3qZNrerrUvD5tx9zLPYIzk7OlJw46OWybn1wdnZh49Z1FodhgP1xCdVmhtuEhdA+omHqXotlKioqcHJywtW1+my8q6srjo6OFBVZXsJQRP73KAzLBWHZkSjVOTo6MXLsrYwceysbVy9lzi9fW3ytg72Bj8dHcH//AFYeyiMpx1SmLNTbgUGtPOkcZt2t6rAAb44mpPPt/A2MvryT2aEb89fs5Uh8OhHBPlb1eTblheVWfeNG3T2ML5//nq1Ld7B16Y7qTzDAyLuHWTUGfydnvunegzmJiUTlm8qBtfPwZGxYGD7/g8sk1m9ZjbubBz9/MYexdw4HTHXJQ4JCSUpOsKiP0vJypq1cT0xqWrW2rUeO0SwokDsG98fJQb9+L4Q2bdqwd+9eHnroIQYOHGh2At2aNWtIT0+nc+fOjTxKEWlM+m0sF0TLsY9RWW5dzdwz9b1iOH2vGG71dZ1CXekUWv81mjcP78F/pv3Nc5Pn8tzkubU+x1Lb3jx1SllBQoHZx5WllZTll+HgbvmPaJ+rL6OkqJSZn8wlP8u8trK7jzs3PD6GvldfZnF/5ZWV/BwXh53BwJ2RkTZRhio/P49mkS3wP6OKSWVFJYVFltUiX7ZrHzEppiDsYG+Pm7MTRqCopJTyigpiUtNYtmsfV/fs2tDDlxrcd999PPXUU+zbt499+/aZtRmNRuzs7Lj33nsbaXTyv66m6kNDe7XlmgF1qz4k54fCsNSbsbKSjL2ryYuNorww12zHtsFgoPVNz+Po7nPOfr6dvRYHJ+sqMdTH47PiSM0r55e7m1v0/KdvG8KxhAx+X76zxvbxV3blmdssr1ddknmqGkNleaXZxyf5d/Wv9tjZXDG+P/3H9OHY3uNmJ9A179QUB8eajxuvjYOdHT/HxxHu4sIdFpzs+L8gOCiUY7FH2LXv1Mz62k2riE2IISLcsmUhe2MTsLez45aBfWkbHmK2tjw6IYlf1mxib2yCwvAF0rt3byZNmsTkyZM5etS8EkyLFi3417/+Re/evRtpdPK/6nxVH5LzQ2FY6i1u+U+k71pu+uDM2/pWTCYGhVq3nrW+dsYXEWtFaTUHe3u+eOEWHr5hIMs2R5OQZlqLHB7ozdBebena2rrasREjTWXT4hbH4eTjRHDf4Ko2Oyc7XINc8e1k+Qa/qnE62tO6ewurr6tJJ08vjhYWUFZZiWMdj3K+lAwfNJLvZ3zNoy/ch8FgYP/Bvbz07ydN5fgGWXYqYkFxCX4e7rRrYl6/2mAw0K5JGH4e7mTl68TLC6lv37707duXtLQ0sxPoAgMt3zMgYo3zVX1Izg+FYam3rOhNAHiEt8bJO+gczz634qJCfv/xc3ZuXUd2Znq1meaps9ZY1M/0rWffzJZfUlGn8XVpFU6XVvU/NOFkDeGcQzm4hVhXU7gunr/6DdITMpi661OLrxkaFMiewzk8v3cv14aG4uvoyOmrJbp6+zT8QBvRnTc/wIFD+9m4bZ3Z47179OeOG++zqA8vN1cy8vLZdPAIHSPCzQ7d2BeXQEZePt7ullUIkYaTlZXFzp07q04eDQ4O5rLLLqtaQyzSkM5X9SE5PxSGpd7sHJxw8PWkzS0vN0h/k99/iVV/mdbknnl4ijXrVl9akHjWiWkjVk1cA5BfVMLqHYdxcnRgUPdWODqcWnrw0+ItJKbl8Nwd1m1S6/SY6QCGnEM55MedqDMc4YF3a+uOTrbEmd/Pc5l06BAGYGdONjtzsqu1Lx94RcMM7CLh6OjI/705mZ17t7E/ei8A7dt0pHtny9dad28eyYo9USzYupMFW3fW+hy5cL755humTZtG+Rn7FhwcHJgwYQIPPvhgI41M/ledr+pDcn4oDEu9hfa7jri/fyLzwEa8W3TD3smlXv1tWW9actGybSeaNG2JvX3d/5vWtYpFTY4nZXL1k1NIyTJVVWge6s/0t++mdYTpVuuPCzezPTrO6jBcWVbJga8PkB2dbfa4Tzsf2t3fDjtHy5YnfPHcd2dtz83Is2pcJ9X2Pfxf3E733fQvCQwIZvSIsXTr1LPq8b1Ru8jLz6Vfr4Hn7GNQp3Zk5BWwOya2xvbOzSIY3Kl9jW3S8GbOnMm3335bY1tZWRk//PADvr6+3HjjjRd4ZObKyyvYv/84SUmmtf6hob506NAUBwfr1vqfzmg0UpFrWs5l7+VtE5tgLxYXuvqQ1I/CsNSbT6uepG5bQsyfX9TQaqDH02cPaWdycnLGM8yHj7+bX69x+bnZ4+PqwI93VN/4ZDTCLd8fIzGnrIYra/afactIzjwVKI8mZjD2ua9Y/PHDRATX/VZr3OK4akEYIPtANvFL4okcbdks4uZF286eUOswFf5LL9vaWPTt9C/o2LYzo0eMNXv8028+4MDBfayav63mC09jb2fHjQN6MaB9aw4mJpN7os6wl5sbbcKCCfO7OG7LRyUdZu3BLSTlmNbQhnoHcXmbXrQPbVWn/vYcPsCyzWuJT00GoElQCMN6X07nVu0abMx1MXv2bAwGAzfffDNXXnml2Ql0K1asYMaMGcyZM6dRw/DcuRv4+OM5ZGfnmz3u4+PBY4+N5frrLas1flJxXAzJv/xA/u4dVJaa9kXYOTnh0aUHwTffgWtTyzYNS901dPUhOb8UhqXeYhZ9RXFmUs1TiAbr52ZHXncbc3/9lsyMVPz8674GuXOYK+uPFhDo4YBzDcca29tZlwzX7j6KwQAPXT+Afp2a8/2fm1ix7RA3vvQtSz55uM7jTN+eDgZoNrYZgT1Ns8xpW9OI+SOG9O3pFodhAEdnR7z8PGtsy0rJprKy0qqxhbjUb5b/bFbv28+Ww4fxcXfnxv79ySksIMjbG+eLrH5xSUkxGZlpGK28zxDm50OYn8/5GVQ9VFRW8OqcSczbuaxa2yfLvmV016FMHPcs9naWzUhWVFTw6KTX+HXpgmpt//72U24ceg2fPfc29vZ1n+Gsj4SEBJo0acJjjz1m9niTJk3o3Lkz69atIyHBshrS58OSJdt4/fVpNbZlZeXz1ls/4erqxMiRli3VKTp2mCOvPUtlSQmn/1KuLC0hd+sG8ndvp8Vb/4dbi9YNMXypRUNXH5LzS2FY6i0v7gBgwK9DX5y8AjDUs+pASlIcJSXF/OPmoXTp2Q8PT69TjQYDT7z8fxb186+BgQxq6UFxmRHnGv6nvzQixKpNdKmZeTQN8ava8HB1/w7c/vqP/LX5ALe//iOlZXWro1yaXYprsCthg0+dTBZ2ZRgpG1IoTiu2uJ+AcD8MBgPvL3qzxvbnr36DtPh0q8b2/sHoWtsMwHNt2lrVH0BRaSm3fPAhq/btB+CyVi0J9Pbizk8+5fWbb+LJa0db3Wd+US6rdiwgJikaP69ABnW/lrjUI7Ru0hk/r3NXDLjiWtMMzckKEic/Pp2vj2Vl7mZv2Iqnqwu927TA2+3i2yj35cqfmbtzaa3tC3b9TaRfGA8PudOi/j74+Stm/FX7XZzf//6TFuERPHen5af3NSR3d3fS0tI4evQoLVqYV1k5cuQIqampuLu7N8rYAL77bgkAw4Z1Z+jQbvideDGbmZnHsmU7+Pvvnfzww1KLw3Dy9O+pLCnGKTAYjy7dcfD2BYyU52STv3sHpWkppPzyA81f/vf5+pIaRUV5OYcO7CYtORGAwJAwWrfrgn0jHW7T0NWH5PxSGJZ6c/ELwVhRTrOrH2qQ/pYvMt3WLCzIY9OaU3+0jUYjBivCcO+m7vRuWvsfuZHtvWptq4mvpytOp9XqtbOz45uXb2P0U1+wYe8xjEaoy5I8exd7SrNKKc0pxcnbVHOyNKeU0qxS7F0sn01r1rEpW5fuoCCnEHfvGkKYlZvnABanpNS4suLkiou6hOG3f5vJyhNB+KSrunXDycGBJTt2Wh2GM3JS+GjG8+QWZgPQLKQNRSUF/Lz4E4ZcNpaxV9xzzj5Obiw0GAy1bjIcM3KcRePZcfQ4GGBt1CE6N23CgPatCfX1sejaC2HuzqXYG+x4/uqHGdFxIP7uvhgxklmQzZK9q3l/0efM3fGXxWF4xl/zsbezZ+LDzzLmiuEE+fpjNBpJy85k3qq/eOnz/+OXJfMaLQz379+fhQsXcuedd9KkSROzE+ji4+MxGo0MHdp4M3THjiXTpEkAkyY9UK1t1KheXHPNqxw9mmRxfwXR+3Hw8qb1pCnYn3EEdUVRIQceuYeC6P21XH1pWrrgd77/7D1yc8w3pHl5+3HXw88x4tqbG2lkpupDnVuGkZ5tKqcY4OOutdsXIYVhqbeQPmM4vvhrkjctMG2gczb/BezkZd3BEZ269bkod2e1ighkw54YEtKyCQ/0AcDNxYlf/n0PIx77rOqEIWt5tfQic08mOybuwKulKaDnHsmlorQCv3Z+Fvcz4cUbGP3AVTi51rzM4IUfnqSi3Lpycl28vc3+KQrKKzhaWABGI12861btYs6mTbg6ObLsjdcZ8NIrADg7OhIREMDh5GSr+5u75ntyC7Lw8fQnOy8DgJbhHXBxdiP6+E6L+njpCdNs+jsfv054aBPuuvlUMHF2dqFpRHNaNrPitrIRKo2V7IqJZVdMLC1Dgri8fRtahQaf+9rzLDknjUj/cCb0HWv2eKCnP7f3u54Zm+cRn2V5+EpIS6Zlk0gevP42s8dD/AN5cNwEvp3/GzGJ8Q0x9Dp5+OGH2bNnD3FxccTGxhIXFwecegEUERHBww/XfZlTfTk7O5GdnU9GRi7+/uYv0NPTc8jJKcDJyfKlQ8bKCgwOjtjVsMTJztkFO0dHykssv+N0sVuzbAGfTHy2xrac7Aw+ffcFXFzcuGL4hS9hFhWTzMTvlrBq+2GKSk37U1ydHBncszUv3jWcDs1Dz9GDXCgKw1Jvx+Z/BgZIXDuTxLUzz2i1fgPde1N+bbjBAYWllXy+Jo11x/JJzy83W/lpANY8Ydns5og+7YlNyeK3ZTt48tYrqx4P9vNkxr/v5q43p1FWbt2aXIDIayLJic6hoqSCrP1ZVY/bO9sTebXl64W9A7zxDqg9oPoG+Vg9tk+6VD8l7XhhIY/s2kk/P+te5JyUlptLu/BwOp1xqp2jvT05JzabWSP6+C7cXb14+e7PefbTUzNAvp6BZOSmWtTHqGFjANi+ewvhYZFVH9dViK83XZtHsv7AYfIKiziSnMqR5FRCfLwZ0L4NXZpGYGflmvWG4u/uQ3xWEqsPbuKKNn3M2lZFbyIuMwl/C06MPCnAx4+YxHiWblrD8D7m1Tb+2riaY4lxBPhY/qKuofn5+fHDDz8wZ84cNm7caFZnuG/fvowdOxZX1/of115XvXq14e+/d3LddW/QpUtzs2USu3cfo6CgmKFDu1vcn2uzlhQePMCRV5/G67K+OJyoBV6ek03u1o2UZWbi1rbxq5kUph4n99geSnNNL2CdvPzxat4ZtyDLTno86fdpUwDof+Uo+g8eia9fAEajkeysDNavWMT6lYuZ9fOXFzwM7z6cwOinvqCwpMzsplxhSRkL1+9n5bZDLPjwH1oucZFQGJaGUWv9rYYsblY3L81PYO4e03qtehyQxyM3XsEjN9ZcV7d9sxA2f1fz7MS5uIW60fmZziQsTSA/9kSd4UgPwoeH4xZc9zWnZaVl5Gbkc+ZX7R9av2DS1M2NVu7uzE5M4OYm1p8aGOLjw+HkJI6eCCUAu2OOE52YSIS/9QG7rLyUQN9QnB3NZ8JKy4opr7C8WgjAy0+9TVlZGYuWzePAIdOt5HatOzBs0CgcrdjY52Bvz+Xt29CvbSt2x8Sx/sAhkrNySM7KYdaGLSzduY9nrx9l1dgayohOVzBtw2wenvYKzo5O+LiaZiOzC3MpKS+teo6lrrtiOF/M/plbXn4EVydnfL18AMjMzaa4tKTqOY3JxcWFW2+9lVtvvbVRx1GTJ564nu3bD5OVlc+GDVFmbUYj+Pi489hj11ncX/BNt3PsnVcpPHiAwoMHzmg1reUKvmFCA4y8boyVlRxfMpXM/euqtSWunYlf+340HfmAxXtP4mMOExIeyUvvTKnWNnjEddw3fiBxxw7Ve9zWevvbxRQUlxEZ7Mvgnq0I9PHEiJH07HxWbjtMbEoW//5uMb+/Y9lhPnJ+KQxLvTUdeX+D9ldeXsaPX0xi9bL5ZKalYDSeNttqMDB/XfWz3s9m+SFTwOwU6krLACccGmlG7mzcgt1ofXvD7O5Ojknh29d+5vDO6t8ng8Fg1Ql0S04LrACVRiNxRUXszs3FpY4bJa/u2YMv/1pK3+dfxIApCF/52usYjUau7ml9qaEAnxCSMuLYsn8FAOUVZazasYCMnBTCAptZ1VduXi6Pvng/x44fNnv81z9+4tP3vsHTw7p15vZ2dnRv0ZTuLZpyOCmFNfsPcjQ5ldwi62fAG8qjQ+8hKvEwW4/vprishOSyNLP2HpGdeHTouddZn/TSvY+y+/AB1u/eRmFJMYVp5ktd+nbqzkv3PtogY6+rkpKSGmeG+/Xrx3XXXYeLlVVTSksrWbo0lV07c0hPN72ACAhwolt3b4YNC8LJyfKfjYiIQH7//WW++WYx69btJyUl68T4fBkwoAP33nsVQVbc1fHs0oPmL7xJ0rRvKI47btbmEtGU0Nvvx7Nr45X0St44j8x91YPwSZlRG3D2CSa0/1iL+nNydiE3J4usjDR8/c03y2ZmpJKXk42js3N9hlwnm/cdJ8DbnTVfPYGHq/nnzyssoedd77N53/FarpYLTWFY6s2/0+UN2t+M7z5l9s9f1thWlxjr7GDAx9eJ+Q+1rN/ATigoKuXjX1ewesdh0rLyzW6BGQyw/cfnre6zvLCc/OP5lOaVVmsL6m1debnv35jO4R01v2CwtjzYeweja91A17WOa4ZfueEG1h+IZk+s6VCKkhOngnWKiOCl8eOt7q9/5xHMXjmVaUs+AYOB+LRjxK/8BgwG+na07gCUr6dN5miMaRbJxdkUkIpLijl6/DBfT/uMp/75otXjO6lVaDCtQoNJzsphbdTBOvdTX+7Ornx/3wcs27+WtYfOqDPcuhdD2w/AzooXOh6ubsz/8FsWrPmbv7esJf5EGG4SGMLQXpdzzeVDrOqvoWVmZvLII49w/LgpeJxcKxwbG8uWLVuYM2cOn3/+OX5+lt0xyckp4603D5CQYL7uNjGxmN27c1m6NI033miHt7fldxICArx54YWG2+Tl2e0yPLtdRllmBmUZphc7jv6BONZxaVNDyty/DuzsaDL4Vnzb9MLB3QuMUF6YS9bBLcSvmE7GvrUWh+EuPfuxfuViHrp5CO06dcfHLwCA7Mx0DuzdQVFhPv0GjzyPX1HNKiorcXK0x93FqVqbu4sjTo4OFBZX/30vjUNhWBpEYWosKZv/pCjdtFHGNTCC4F5X4xZk/bGzq/6ah8FgYPBVY1mxeA4BQaE0a9WO6L07uOYGy3a4n+62nr58uzGD1LwygjzrX8P26U9mM3PFTqB6gYa6bBLO3J3JoWmHqCitYXObwfowHLMvFoOdgeG3DyasRSh2NdRYtsaZ8dnH0ZGePj483KJuLy683FxZ8dYb/L5hI9uOHAGgR4sW3Ni/H051KIM0qPu1pGQlsG73klP/IAYD/TuPYHAP69YJrtu0Cgd7B9564T8M7GdaF756w3Jee+851m5aVa8wfFKIrzc39O9V737qw2AwMLzjQIZ3PPeJepb2d+0Vw7j2CutefFwIU6ZMISYmBoPBQJMmTcwO3YiPjycuLo4pU6bw8suWHSf/y/T4qiAcEuKCt48DGCEnp5zk5GKSEov5ZXo8//indQdbLF++k3Xr9pOcnHmibz8uv7wjV15Zfd2+JcpzsimI2kNpuikMOwUE4tGpW9Ua4sZSmpeJi08wQT1OWzpjAEcPH4J6DCd953JKctJq7+AM9/zrRfbt3EJOdgY7Nq8xazMajXj5+HH3P62foKivzi3D2RJ1nFFPTGFUvw4E+JgqG6VnF7Bow36SM3Lp3cG69dFy/igMS71lHdzCsQVTwFhZlZyKMxLIit5M89H/xLeNdX/401IS8Q8K5enXP2LF4jn4B4Xw2v9N5Z6x/SktKbF6fHFZZRSXGRk6+RD9mnvg5XIqHBqA/xtr3brXvzab1uF1bRVO64hA7O3rFzZj5sbUHIShTudJ+4b4Ymdn4JZnrZ9lPdOKgZavHbVEWXk5j3/7Hc4ODnx4z93cNrD+dxUMBgM3D/0nwy4bR2yKaXlDRHBLArxDrO4rMzuDyPBmVUEY4Ip+Q4gMb0ZcgmW3NN+eUP/v+/mWkZ/FV6umV5sZHtimN/cNvIVAT+vWladlZfDhz9+YZobPOIHu8VvvI/jEbF1jWLduHS4uLnz11Ve0amV+ut7hw4d54IEHWLeu9tv2Z9q+PRsnJzv+PbE9kZHma/qPHy/k1Vei2L492+L+CgtLeOyxz9m2rfq61tmz19KjR2s+/fRh3Nwsv9Wf/Ns00v74DeMZ1WMMDvYEjrmRkFusn1RoKA5uXpTkpJFzdBfeLcyDfs7RXZTkpOLgZvlypNAmTfn0p0X89v1nbNu4ivRUU53hgKAwevYdxI13Pox/4IWv4vL8ncO46aVv2Xoglq0HzI9mNxrBzmDg2dt16MbFQmFY6i1x9e9QWYm9ixueEaZdynlxUVQUF5K45nerw7C9vT1e3qZaoA6OTmRnpmNnZ4e9gwNLF/zGvY9YNzs3e3c2BiCvxMjSA7lVj5+slWttGHZxdMA3xI+/P2uYdZCluaU4ejrS+YnOuATU/8S3cY+O5qsXfmDX6r10vaJTA4wQCsvLiSsqAiDC1RW3Ohayd3Rw4I9Nm2kWFNhgtTZ/XvIJAT6hXNXnJvy9T/3R23loPbkF2VzR7WqL+/L29CEhOZ5DR6Np3cJUZeTQkQPEJ8XhdWJj2KUuLjORCV89TmZBttmymZiMeI5vSODP3cv5+YH/EukfdpZeTolJjGPkY3eSlp1pVqP5cPxxjiTEMmv5Ihb/dxrNwyMa/GuxREFBASEhIdWCMECrVq0ICgoi2YqSfoWFFQQGOlcLwgBNm7rh7+9EWprlL9o/+2w+W7eagrCzswM+Ph4YjZCTk09JSTnbtx/is8/m8+yzN1jUX/rieaTOnF5jm7G8nNTZM3Dw9iFgVP0qptSVb5vLSN22lCNzPsLOwQkHFw8AyovyqDyx4dW3jWUHjJzk5x/EP56u+bChxjK4R2t+eftuXv96IVEx5nsv2jcL5o0HrubKnm0aaXRyJoVhqbfS/EzsnV3pcM+7OLqb1pGWFeSy/9vnKc3LOsfV1Xn7+pOdabpNFhQSTlJ8DA/dPITUpHg8PK1fp9qnqRsNWbj4zmv68MXstaRk5hFcy9HH1vDv7E92dDaOHg1zDPFvH/wBRvjkkS9w83DFzev0slEG/rPY8j8aZZWVfB0Twx9JiZSfOMrZwc6OsaGhPNCsOY51WAt6ZadOrNm/n9zCIrzc6l/SatO+5TQLbctVfW4ye/zvrXM4nnzIqjB8Wfe+/LXiT+5/YgKRTUy3MGPjj2M0VtK7e1+rxpVfXMyibXs4mpJKfrH5+lIDBt66zbJDPBraB0u+JqMgC3cnN7pGtMffwxej0XToxq64KDILsvlo6Td8dMtrFvX3xlcfkZqVgYebO73adyHwxKEb6dmZbInaTVp2Jm998zHfvf7Bef7KahYeHs7x48f57LPPGDx4sNmhGytWrCAuLo5mzZpZ3F9wsDMJCcX8/FMcvfv4Vq0NzskpY9OmLJKSigkPt/xF7dKl23F0tGfSpAe54opOVS8SjUYjq1bt4Zlnvmbp0u0Wh+GMJQsAAwGjr8e7zwAcfHzBaDqBLmfjWtL/nEPGXwsaLQyHDhhPYUos+fHRVJaVUlpmflCGR5PWhA6w/u7KhlVL2LZxFWnJpqO1A0PC6dlvMP2uGNEg466Lob3aMrRXW5LSc0lIywYgPNCH0ADrNuLK+acwLPXmHtKSssKcqiAM4OjuhYO7t9ljlmreqj2b1iwlLuYwA64cxe8/fk5CrGlDWN86lGj69Z4W536SFWKTMykuLaPPvZO4oltLvDxOBTqDAT59+kar+mtxUwt2f7ib7W9vx7OFJw4up/1YGqDVbdVntM4mI/HUH5fCvCIK84rM+rPGZ0ePMC8pyWy1RlllJTMTEiitrOSJVtZXwOjduhV/7drFsDfe5LaBlxPo7W02S2zp0onM3FPrCssrysw+Li0rJjM3zeqXQA/e+Qjbdm0iIzOdmNhTmxD9/QJ44M5HrOprzsZtHExMrnGpi7ERSw5uOroDT2d35j/+LYGe5huqUnPTufa/97LhyHaL+1u9czNe7h5s/G4uIWfs5k9KT6XvPWNZuX1jg4y9Lq6//no++ugjpk+fzvTp1WdMDQYD119/vcX9DRsexA/fxzJ/fjLz59c8ozxsuOXr/DMz84iICGDQoM7VxjV4cBciIgKIt+IY9dKUJJxDwwi70/xEO+eQMNzbdiBv+2ZKUyw/VKWh2Tu50PrmF8g+tI3cmN2U5Zp+Xzl6+eHVrAs+rXtgMFj+Iru4qJA3n7mXvTs2VWtbMvcXOnbrzRsffIeLa+McjZ6enc/6PUdJOHEoU3iQD4O6tyLAx6NRxiM1UxiWOjlZKB0gpM9ojs6bTOLamfi2Nc2eZUVvoiw/i4gh1tezfP7tTykrK8XFxY07HnoGZxdXovftpHmrdtx0t3WB5Hz4ddkODAZTeZyFG04da3ryOGZrw3DSmiSKUkyBNXN3ZrV2a8PwmH82XP3apamm9aRDAwMZEmj6A788LY2/01JZmppapzD82oxfMQAHExN549ffzNoMBoPFYfiNqQ+cvIj4tGOnPj6Nr6d1a1WDA0P47tNfmT1/xqk6w206Mm70zfh6W7eONibFFGDaR4QR5O2F3UVyBGtJeSmBnv7VgjBAkFcAPq5epOVX/39Ym+KSEkL8A6sFYYDQgCD8vLxJybQ8zDW0G2+8kczMTH7++WfKT1QuOcne3p4JEyZw442W/8yOGhVMbk4Z8+YlU1Fh/qLGzs7AmDEhjBpl+RrV4GAfYmNT+e23VQwd2t3s0I1ly3Zw/HgqoVbUBrd3c6csI53iuBhcIpqZtRXHxlCakYa9W+3H1F8IBoMB3zaXWb0coibTvpzEnhMvtpycnPHyMW2QzMvJorS0hH07NzPty0k88IRldzoa0ns/LuWTGSspqzBfu+1ob8+jNw3ipbsbb9ZazCkMS53s/frpao8lb1pA8qYFpx4wwuFZH1p9Ap2TswtOzqduM95yT/3W5pZVGJn0dwrz9+aQkldG5Rml0I6+bt262v6dm9epakRtEpebNnwY7A04ejhiqGcd5LEPX9MQwwLA3mAgxMWFV9qdOrGqv78/+/NyyT8jWFijtnlR45nlOc7ayanKEdXKemAKOiN6W3Zr+XS+3n7cd3v9j+d1dXbC096F267oV+++GlKLwEgOJB3hqRlvM7zj5fidOG0usyCbv/atIT47mfahlr8AaxPZnD1Horn3rWcYPXAYgSdOm0vLzmT+6qUcT06gS6t25+NLsdhDDz3EjTfeyJYtW0g98QIvKCiIXr16WVxS7XQ339KEkaOC2bMnl4wTdYb9A5zo1MkLHx/rljtde20fvvhiIe+++yvvvlvz6ZvXXtunxsdr4tmjN1krl3LwmYdxDg3H4cRa9/LcbEqSEsAInv0HWTXGhlZWkEvypvnkxpifQOfdvDPBva/B0YoTENcuX4iDoxMvvTOFXgOGmC0z2bz2b9556Z+sXb7wgofhb+au5/9++rvGttLyCj78ZTmBPh48MLb/BR2X1ExhWOrG4sxi/e3g6VM/qbXN2dmZFq070r2P5SWhPl2Vypfra5mZqsPd6vkfPGT9RWdjAGdfZ7q91A17J/sG6zYtPp3s1BwqK82PiG57meWzuUMCg1iZnkZxRQUu9qaxFVVUUFhRwcjguu3Qzvnpxzpdd6ZHb5wIGPn091cI8Y/gxiH/qGpzcnQmwDsEd1fr13THxsewY89Wss7YEAZwz22W/9sP7NCGxdv3kJKdQ7BP3Woynw/3Xn4Tz/7+Dn/tW81f+1ZXazdg4J4BN9VwZc0evfkeHpj4PHNXL2Xu6qXV+zMYeOSmu+sz5Abh5+fHVVdd1WD9eXs70qmTF+npps1yAQHOVgdhgPvvH0VsbBoLF26psX3UqMt44AHL172HTriXwuj9lCQlUJIYT0liwokW0/9lp5AwQm6z/FCVhlaSnUr0L/+mvDDX7PdvSWYyqVnJZEZtou1tL+PsY9nvl+ysDELDI+l9uXllBoPBQJ+BwwgNjyQ5Ma4hvwSLTJ2/AYMB/jnucq4d2JkgX9PGyLTsfOat3sOU2Wv5dsEGheGLhMKw1Enrm184b31P/+ajc1Ya6NS9D29++L3ZDHJt5u3NwQCM7eLDnN3ZhHo50i7YhR3xhdzZq35HEzeE8CHhxC+Np7ygvEHCcE56Dv997CuO7a1eCszaE+jc7O0prKjggR3b6XeiYP/GzAxKKytxtrPnh+OnPsddTS9szczWEaYZ/ZH9bsHXw7/q4/r4Y9FMPprynvmph6exJgzvjU2g0mjks4V/E+zjjYvTqaBkAO4d1rBl6yx1dZchFJUW89HSqWQV5pi1+bh68cTw+7im6xCL+xs/ZBSFxUW89c0nZOSYb5j18/Lh1fse44ahloe58yEpKYnVq1fj6OjIlVdeWbWJDuC7774jMTHR4jrDALt35/DTtDhiY4vMHm/a1I0JtzehSxfLX/w4ONjzzjv3cMcdw1i3bi/JyabvYUiILwMGdKR9e+tqtTt4+9D6P5PJ+OtP8nZuMzt0w7NbT/yHX4OdlSfuNaSE1b9RXpCLnZML7mEtcTxRRq2sMJeCxCOUF+aSsPp3WoyxbElcQFAIiXEx/DlrGv0HjzQ7dGPdikUkxB0jKCT8vH09tYlJzKRleABvPzTa7PHmYf707tCUpZsPEJNo+XIkOb8UhqVOPCPO/23Ps90y37tjEzOnfcFt9z9xzn4Sc8oI9XLko3FNmLM7mxAvB6beGkn/j6IpKbd+arisvIKJ3y1h9spdJGfkUnnaOA0YSFvyrlX9Ze3PwlhuZPvb23ELdcPe5VQgNhgMdHyko1X9/f7RXI7tqbkmrrUn0P0SH4cBiC8qYmZC/Ik+TGHu5zjz2pmWhuHRE9+ptc1gMDD/JetK513d71YKinJZuOEX4k7UGY4Mbs0V3a7G3dW6XdvTfp1KZWUFTk7O+Hr71qv8W0zKqQ19yVnZ5o31XGZTVFLC0bh47OzsaN/CusMdAMZfdjVjuo9gb0I0ySfqDId4B9EpvC2O9tb/Wbjj6nHcMuJath/YR8KJE+jCA0Po0a4jjg4NUyWlrg4fPsxDDz1E8YmKHt988w3vvvsuXbuaatyuX7+e/fv3WxyG9+zJ5b13D1FZWf1n6fjxQt579xAvvtSGzp2t+7/Xvn0E7do1ITMzDwA/P886//+zc3Yh8NrxBF578dW8zovdf6r6kIePWVtpfhZR371EXuz+mi+uwdBR45k+9WO++OA1vvig5qUQQ0dd+O+Dl7sLiWk5RMUk076Zec3z/ceSSUjLxsu98V6UiDmFYam3pPV/1Npm5+CEa1AkXs0sn7V7f8rvvPnMvdz/+CsMHGp6Vb162Xym/nciz731X/Jys/nwradY8/cCi8KwvZ0BXzdTwHSyN5CeX46dnQEHewO/7cjixRHWHc4w6ee/+fT36reXAahDlYDcI6dqHxfEF1h9/Zn2bTiAwc7AXa/dyvdvTCesZQj9ru3N4u+Wcedrt1jVV5CzcwMWpTNZE3UAA+YrVE5+XJfPlZWXxoe/PE9OwalZln3HtrFh7188ect/rNpEV1CYR3BgCNOmzMbVpX5l37q1OD8z5dPm/ckvfy6mtLSUdi2bc8OIYXz9+2zuHXcdQ/tZvrbUgOHEpj7Td93OYMBQj39tg8GAnZ2haj296f3G3zQ4depUiopOzeBmZ2fz9NNP89lnn9G2bVur+5v5ewKVlUZatXKn52U++Hg7YsRUWm3b1mwOHy5g5swEq8Lw4cOJTJ48j40bD1BSYlqD7OzsRN++7fjXv66ldWvrZjYrS0vJWPoneTu2UnbiBDrHgEC8uvfCb9go7JwtP8CjoVWWl+Hk4VMtCAM4efhi7+JOWUFO9QtrcfPdj5AYH8PKJX/U2D5o+Bhurue+k7oY3qcdv/y1jSse+oQW4f4EnqgekZadz9GEDIwYGTvI+tMFK8sqSV6bTHZUNiVZpiU6zr7O+HTwIbh/cIMutbMlCsNSb0nr/zhnivFo0o5W45/CzqH6Oe1nmvLBawQEhTDi2purHrtqzC3MnTGV76f8h8nTFrFozs8cPrDHovH5u9uTlm/a7BXu7UhMZilDPj1EfHYZ3i7W/+KYvWIXBgPcOKQ7v/29g7AALzq2CGVrVCz3jbF+s5RXy4atOZmXmU9IsyCuGN+f79+YjrObM9fcN4L1czexedE2eo3oYXFfv/a2PFxZ6tbLB5iFpNzCItZERZFfXMz4vtbV8gWYv3YaOfkZGAwGgvxMoSE1M4Hs/EwWrJvGHSOftLivUcPGsGjZfHLzcuodhsf3q/9O+TPNW76K7+fMM3use4d2pGZmsnzTFovD8Jzti/lwyddkFeaaPe7r5sUTI+5nfE/rKpJMX/wHb3z9ERk52WaP+3v78Nr9T3D7KMtLlzW0ffv2YTAYePzxx+natSuzZ89m/vz5PPPMM3zzzTdW93fsWCF+fk689XZ77M7Y7DpmTCiPPrKLY0cLLe4vKiqOe+/9gOLiUrN9oMXFpaxcuZuNGw/w3XdPWbxcojwnmyNvPEfJiTs5J192liTGk797BxlL/6Tlm//XaMcyu/iHUpQay9H5n+HT+jKzZRLZB7dQmpOOa7DlS0PsHRx45o2Puf7W+9m6YSXpJ8rGBQSH0rPvIFq163yOHs6P1+8bxZb9xzkcn87h+HSOJJj2rZz8N24Z7s9r9420qs/SvFL2fbqvqvrQSUWpRWRHZ5OyLoWOj3XEyfPcf2fFnMKwNJyzTIrmxx8gZfOfhPY/9x/FhNgjGI2wdcNKLus3GIAdm9eSGH+8atbJw8sHg4UHPrQPdmFpdB6H00oY1cGLz9emczTD9Ip6eDvrN1jFp2YTFuDNlOdvNoXhQG+mv3UXXW5/j5JS6yssdHqsYU6JO8nZzQn7E5vdnF2dSItPJyc9l9ysfDLXRTXo5wJ4eOcODuTlsdzCo5u/+Ef1dbcZeXn0e+Elwv18a7ji7A4c34mjgxNP3PIeEUEtAYhLOcJHvz5PVMwOq/r6x92Ps2XHRm59YAzNm7bC/bQSVAaDgU/e+crq8ZVXVFJYUmK2nAbAx936uqdzli3HYDDw8K038tl0U1k6bw8PAnx8ORIXf46rTRbtWckrcybV2JZZmMPrf3yIm5MLozpfWeNzqo1p5WIe+b+ab0+nZ2fx+Adv4ObiyrgrrfvD31BycnKIjIzkpptMmwJffPFFDAYD8+bN46mnnrKuggmmGe/y8krKyipxdjZ/MV1WVklZmbFaSD6bTz/9g6KiUsLC/Ojbtz3+/l6mQ1Ay89i4MYrExEw+/XQun39u2exm0s/fUpIQBxhwCgnF0cd0qEp5TjalyUmUJCaQ9PO3RDz8lDVfdoMJ7nU1MQu+IPvgFrIP1rBp0ADBl1lfHrJl2060aNORnCxTdQpvX/9GvTMR6OvBis8f47sFG/l7y0GzQzeG9mrD3df0xd3VutAaOz+2Kgi7BLrg5OmE0WikLL+M4rRiilKLiJ0fa3U5TlEYlgbQ5paXODLnI8IH34pv296Aqc5wwsoZNLvmH1QUFxCz6CuyojdbFIZbtO5A9L6dvPn0PTi7uAIGSopNMy1tOnYD4PjRaIs3RXx6QwSlFUbcHO14Zkgwro527Ewool2wC48MrF4b9Vwc7O3w9zaFJCcHe9Ky8rGzs8PR3p6fl2zlDSt2ftck73geeUfzcAtzw6etj9XX+wb5kHliE05wsyBiD8Tz5JCXAPAJvHiqGpzO39OTFsHB/LxmDW/ccvO5LzhNYXE+Qb7hVUEYICK4JQHeIaRlJ1rV11c/fMrxuGMAHDxieuFgMBgwGo1W/2EtKStjzsbtRMUnUnnGhry6nkCXmJpGs/Awxg0fWhWGATw93DieYNlBClPXzABgeIeBDO8w0HQCHUYy87NZun81S/ev5ds1v1kchj/55VsAxgwcxuiBwwjy9ceIkbSsTOavWcb8Ncv49NfvGi0M+/j4VKsv/Oyzz5KSksKmTaaDGqz5t23Txp3du3N59pl9dOnihdeJE+hyc8rYvTuXvLxyunSx/G7Prl1H8fX1YObMV3BzM19DWlBQzDXXvMquXUdrubq6vO2bsXNypuXEj3Btar6evOj4UQ6//CR52zdb3F9D82vXl8qyEhJX/055Ub5Zm4OrB2EDb8CvvXV32I4fPciPX/wfO7espbTEtDbcydmF7r0HcvsDT9GskUr7ubk48a8bruBfNzTMZtmsfVnYOdrR+anOuIeZ14ouSChgz0d7yNpn/amvojAsDSDu72k4evgS0PnUD3xA50Gkbl1C4pqZtL/rbdJ2raAwJcai/h55/l1ef+puMtNTKC46dbvRPzCER194j6T44zRv1Z7OPSy7pe7iaIfLaXt4Hh1k+elQNQnw8SD1xCaXiGAfjiZm0OfeScSmZOHjYf2t9UPTDpG2Na1qhnjfp/uqZqta3tKS4H7WlTDrOqgTe9dFkXA4kRG3X8k3r0yrmrUffrtlAed8em/2HLOPKyorOZyczIaDB/Fxt/4wAC93H9KyE9hzZDOdW5pejO05sonUrES83K2baV7w1x8YDAYC/YMJDgrB3q7u6++W7drHvtiaZ2vregKdu6srGdnZlJaWVT2WX1BIfHIqHhYebX00LZYmvqF8fOvr1dqu6TqEqz64nSNpNW/ArMnB2GM0C23C9298WK3thqFX033CKKKPWx7mznTs2DHi4+NxdXWlc+fOFBcX4+7ujoODZX++mjdvzpYtWzh+/DhNT2zytLe3Z+LEifzjH//g8OHDVo3nllubcODAAVJTS1i2LK1au5OTHbfc2sTi/srLK/HwcMDVtfo6XldXJ5ycHCkqKrW4v4rCApwCg6sFYQDXpi1w8g+kNC3F4v7Oh4DOg/DvMICC5GOU5pnW+jt5+uEe0hyDlRs4j0Tv5fl/3kRJcZHZLH9JcREbV//Fjs1reH/Kb42yXKK4tKxqZjj+xAl0TYJ8GNarLXdd0wdXZ+s2l5YXlePi51ItCAO4h7vj7ONMcWZxDVfKuSgMS70VZyWBEXKO7ca7eRcAco/voyQ7tWotsYOLu8WzL81bt+ebmatY+ddcYo8eBKBpy7YMHnEdjk6mPxivvG/57epPVqbW2ubsYKBjqCsDW1p+NGbHFiEs2hDFwdhUrr28Mx//upLDJ45LHdW/g8X9nJQfm4+9sz2eLTw5+ttRjEYjrkGuFKUWkbwm2eowfOOTY7nxybEAhLcKI6CJP8f2HKdJm3A69mvcww8A3p09p9Yl5iO7d7O6v04terN21yK+nvcOTg6m/x+l5aZlMCfDsaXc3dzx9fFjxtfzzv3kc4iKTwIDDOrYjlV7D+Dn6U7LkGD2xsYztIv1/08AurRtzdptO3j4bVPFkqTUNB5+6x1KS0vp19WyP/bODk7kFOaSnp9JgId5acG0vEyyi3JxsmBt/0kuzs5k5maTmplOkJ/5ZsWUzHQyc3NwdrJ+DWNZWRkzZszg2DHTTH14eDju7u78/vvvDBkyhMsvt+ykwuHDh1NWVsamTZuqwjCAm5sbH3zwARMnTqSsrOwsPZhr0cKdf09sz4xfEti7N5fSUtOsv5OTHZ06eXHzLeE0bWr5Eph27SLYvfsod989iUGDupidQLdq1W7S0rLp2tXyI+WdgkMpSYgn6aepePcZYHboRs7GtZQkJeAcHmFxf+eNwbTB0lD1oYG6nGb0w5T/UFxUSFBoE7r3HmgqrWY0kp2VwY7Na0hNiufHL/6Ptz5umPrmlkrLymfMs19yKM70gulkTj8cn8bK7Yf4bsFGFnzwDwJ9Lf/b4xLgQlFKETFzY/Dv6o+jpylMl+WVkbErg6K0IlyD67fXwVYpDEu9uQVGUpB0lCOzP8TOwRkMUFlmCiPuoaZb10Xp8Th5VT/+tSZ/L5yFt4+f2QY6gJTEOEpKiohs3saq8X20MvWce+T7NHPn+wlNcXE89zrkb16+jdKyctxcnHj5nhG4uTix7UAsHVuE8uSt1s+8lmaX4uzvjMFgoCC+ANcQV7q/2J1tb26jOL3+r/Lb9GhFmx4XzxqyCH9/s795BgwEeHkxuFNHnhpzrdX9jR5wO4fj95GcEUtp2anvV2hAJNf0t+448AfufIQPp7zL3gO76dSui9VjOV1eUTF+Hu4M69qRVXsP4ObszJje3TmUlEJiZnad+rx3/Fi27YviWHwCBiAnL5/svHzcXV25a6xl37s+LbqxdP9arvn4brpGdMD/xGlfGQXZ7IrbT0FJEcM6WBY0AQZ26838NcvoddcYenXsQqCP6ec8LTuDLft2k19UwLVnHIhgieXLl3P0qPmMcuvWrbG3t+fQoUMWh+FrrrmGa66p+VTGwMBAPv74Y6vHFhnpxnPPt6ay0khurmkJhpeXg1VrhU/65z+v4V//mszu3cfYvfuYWZvRaFqj/NBDlp8q6T/iGhK/+4K0ebNImzerhmcY8B/RcKdU1kXG3jWmesNFeWaPO7h6EjbwRrO7jOcStWc73j7+fPbTElzPOGa6sCCf+2+4gqg92xtk3NZ469tFHIxNw2CAFmH+BPp6YjQaSc/O52hiBkcS0nnr20V8+rTlR4GHDAjh2OxjJC5PrDq5tKbniPUUhqXeIkbczZFZH1KWn10VggEcPX2JHH4PJdkpuAZG4BnR/iy9nPLR20/TtlN3LutvHiz/89qjHIzazfx1dbvlerYb05tiCvhibTpPXHnuJRQuTo5mByg8PcHyAwpqHduJesfFacX4tPMBwMHFgbI8y2esTnr/3tpP8HNydiSyXROGTRiEd0DjrB/e+8lHDdqfm4sHz074kG0HVhF7ss5wSGt6tr3C6hq33/48hYqKCh5+9m48PbxwczXfQPfb1AVnudqcg50dTic+v4O9PTmFRVRUVlJRUcm+2ASu79vTqrEBRIaG8MUbL/PT/IVEH4sBoG3zZtw2ehQRIZbdQXhqxINsi9lDZmEO6w9vM2szYsTXzZsnR9xv8ZjeeOBJNuzZRnp2Fiu2bjDvz2jE39uX1x54wuL+Ttq/fz+Ojo7cd999fPHFFwA4ODjg7e1NRkaG1f01tJiYQnbuzDE7ga5bN2+aNbNuY2Tfvu3573//xUcfzebwYfOA06pVGE8+eT39+ln2uxMgYNR1lOdkkzZ3JsYK87XSBjt7AsfcQMCo66waY0PKOrCJ44un1thWXphH7F/fYufojF87yyqjVFSU4+jkiYtr9e+7i6sbjo5OZsvtLpS/Nh7AzdmRJf99mA7NQ83a9h1N4qrHP+OvjQes6jN0UChl+WUkLEvAeEada4OdgbChYYQOCq3lajkbhWGpN7fASDre/x8yozZQnG76Ze4aEI5v+37YnQgDLcc+Xu/Pk5ebA1bu/Ab4/Z7m3Dv9OK9cFcrojqaNLfP35jDxr2T+e0ME2UUVPDU7ngX7ciwKw/+ZtqzWNhdnRzq3DOXKnpbPXrsEuFCQWMCOiTsoLyrHPcIUwEpzS3Hytv72cvSWQzWXujvxrduzbj9r527klZ+fxT/U+uoNNXVblxWwh5KS2H+iAkKHJk1oHVb3X+KODo707TSMvp2G1bkPgOTUU5vQcvNyyM07Ve/U2g10Hq4u5Baadn77ebiTmpvLuzMXUFJWhptL3eq8pmRk4OriwvP3312n6wEi/cOY/cjXfLVqOmsPbSY5x3QbN8Q7kMtb9+aBK24hyMvy2szNwyNY8/UsPvz5a/7eso6E1BOHbgSFMLTXAJ649T5CA6xfp19QUEBgYCDBZxz7bW9vX3WAhjUyMzP59NNP2bp1K5mZ5id/GQwG1q5da1E/lZVGvphyjNWrqwfyGb/Ec/lAfx5+uLlVs8QDBnRgwIAOpKZmm51AFxTkY3Efpwu55S4CRl1H3u4dZifQeXTuhqNP/X/m6yN5y58A+LS5DJ/WPXF09zZVuyjMJfvgVrIPbSN1y0KLw3DLNh05sHc7zz44nj4Dh+Nz4qTM7MwMNq1ZSmZ6Cu07W//Cs75yC4qJDPGtFoQBOrYIJTzQh9hk6ze7RV4TScgVIeRE55jVGfZu442Tl0qq1ZXCsDQIOwcnAjoPqtod7OBq+Tqok+4dd+q259GD+8w+LikuJjc7A09v63+Rv7YwiRAvR27uceraW3r6MXVjBv9ZlsKif7bi5y2Z7EkqOksvp7w/bdk5l7YN6NKCXyfeYzaDXJvQK0M5/PNhilKLcHBzILBXIAWJBZTll+HdxvrZ2zY9W3F8fyxlpeVEtDFV3Ig7mICjkwOhLUJIOJxITnou875YyD1v1r6MoLyykju3bcXd3oGvunevNQxO6dbdqvHlFBbyyFffMH/rVrPHR1/Wk8kP3F+nTXQHju/gYNwe8gqyOfM4jwlXPWZxPyOHjG6wckwRAX4cSEgiOSuH7i2bsmT7HkpOrE3t3ty6I3ZPuu2Zl+jQsgWfvvK82eMvfPAJh47HMuu/H1jUT6CnHy+Ptuy4W0sE+wXw/qPWnRx4Lh4eHmRkZJgF1+TkZNLS0vD2tv7n4p133mHDhg1Wl1I70+zZiTUG4ZPWrskgJMSZG26w7qCMzMw8tm07ZBaG+/RpV7WG2FoO3j74Dmz8DbNnKs5IwtknsMbjlv3a92Pv189QlGF5FZjb7n+S15+8i+h9O4jeZ15K0Wg0YrCz49Z76z8ZY61mYX4cikvjja8XMvryTmaHbsxfu5fD8em0ibS+mhGAk6cT3m28zcKwgnD9KAxLg0jdvpSUTQsoKzTNpDm6eRPcZzRBPYZb3keSaZbQYDBQVlpa9fHp+g+2vjzTkRO3MVceymNwa9MflrVH8jmeeWqHto+r/YnTuCx3tr+p63Yf5ZNfV/L8Hef++oN6B+Ee7k5xejGezT1Nv9SM0OHhDrgEWH9cZ59RPTkeFcfbc14mtLlpVi3paDJv3fIfLh/bl04DOvD6+HfYt/7sNYcd7OworKjA1d6+Qet1PvHtd8w7IwgDLNi6DUcHB7575F9W9bdw/XQWb/qteoPRCAbrwvDLT71t1ec+mxv696p6P8TXGw8XF+IzMgnx8aZny2Z17remI7WzcvPIycuv4dm1W7Z/LWsPbSHpxHHMod5BDGzTm6HtB9RpXH+u/ZtlW9YRf+LQgybBoQzvfTlXD6jbMqK2bduyefNmpkyZgsFgIDk5ma+//rqqzVo7dpiC0qBBg2jWrJnF1SjOtHpVBnZ2Bu68K4I+fXzxPlFaLSenjI0bs/jxh1hWr8qwKgxPmbKAb79dQnl5hdnjDg723H33CP71L8vX0h9543kcff3wHzUG9zaWL6+4UOwcHCkvyqesIAdHd/MXNWUF2VQUF1TdUbRE996X8/oH3/Ltp+9w/Gi0WVvTFm2555EX6d5nYIOM3Rr3jO7Li5/PZ/LM1UyeWf3EUoPB9BxrZUdnE/NHDIWJ5ks/3MPcaXpd06pldmIdhWGpt8R1s0neOM9sQq6sIIf4FT9TXpRH2ADL6qnedt8TAEyf+jEBQaFmG+icXVxo0rQVveuwEadDiAs7E4q45+fjuDraYTBA4Ykd4N2amHbeRqcWE+5t2S/gPz98iFte+Z5//2M0YweZNlnNWbmLV7/8k69fupXsvCL++Z9f+WPVbovCMJjK4riHn5oRdfJ2MlsiceCbAxQkFNDz9XPf7vvzmyX4BftUBWGA0BYh+IX4snDqXwy55QpadW/Bgc2HztnXyOBgZiUkcLSggBZ1mLGtyeLtOzAAT147mhv6meqJztq4kQ/mzWfxdusOyQBYt+cvMBrx9QrEzyuoXkcK5xfkkV+Qj6+3L87OLqxct4yde7fRqnlbRo8YW+d+Abo1j6RbHWeE/zP1+6r3k1LTzD4uLinlSFw8rhYuvSgsLeJf015hS8zuam0zty7ksmad+fyOibg5WbYrvaCokFtffpR1u6u/wPnxz1n079KTGRMn417Dms6zGTJkCLGxsSQnm5ZdnKwVHBwczJVXWj/j6eXlRUBAAO+++67V154uI6OUkBBnRo40X77h6+vEqFHBLP0rldTUklqurm7GjJV8+eXCGtvKyir45ptF+Pl5cuutgy3qr2D/bsBA9rpVuLfrQMC14/HuZf3JmOeLZ0R7sg9tY/+3L+Ae2hKHEyfQlRfmUpB0hIrSInxaW3d6Y8++g+jZdxAZaSmkpZhmlQODw/APtK4ST0N6cOwA0rLz+fS3VZSVm9cZd7C349Ebr+DBsda98MyOzibqi6hq64UBChILiPoyivb/aF+n+vS2TmFY6i1953IAPJq0waeNaTYs+9BW8uOiSd+53PIwfP8TAOzetoGmLdtUfVxf714bzt0/x5CSV05h2alfSiGeDrx3bTjHM0toH+xC32aWhb3nPp1LWKA3t488NfN3x6jeTJm9lrenLmb1l0/w3YKN7DqU0CDjB9P64ZJMy/7A5mUXkJmSze8f/VF19PK2v3eRdCwFp9MKLju5njv8Z5aaZs//sXMH3b298XVyOlUKCXiujfUzdB6uLjTx9+f1m2+qeqxjZATztmwlt9D6jS6VlRUE+ITw2r1fWn3tmf5v8r9ZsXYpX334E+mZabz67rNVs+I5uVlMuOEei/uavaF6ODzJ0d6eED8fujWPxNH+3LWMl6zdUPV9z8nL56+1pzaqnfyz2L6lZeW3Pl32HZtjdgGmMms+bt6mUlRFuZSUl7I1Zg+fLvuO569+2KL+3vluMmt3mU4Sc3Fyxs/bB6PRSFZuDsWlJazfvY13vpvMxIefs6i/k5ydnbn//vvZu3cvCQmmn6WwsDA6d+5cdcKiNW6//XYmT57MkSNHaNmy5bkvqIW3twOpqSXs2JFN9+4+Zm07tmeTklKCt7flf1p//XU1BgPcfvtQhg3rjr+/J0ajadnE0qXb+emn5fz222qLw7CJ6X9FwYF9FBzYj3NoGAFjxuN7xTDsHK3bVHq64sJCYg8ewM7enladu9apj/BBN5GfcJDywjxyj++tNmzTwRuWV1g4KScrg707N5GWfCIMh4TR7bIBePtaVsXofHj57qt48LoBrNpxiIRU013T8CBvrujeiiBf65e/xC2Kw1hpxKOpB36d/MxKq2XuzST/eD7xi+MVhutAYVjqrbKiDEcPX1rf9ELVEcmB3Yaw9+tnqCixbB3u6Z558xPycrLIz8vBw9N0Gy0/L4e05ES8fPysfrXfPsSFVY+1Ye6eHA6mmjbetA124brO3jg7mMb71a1Nz9aFmcPxaRiNsGxLNMN6mcLgyu2HOJaQUbWW2NfTzeplFw2l6xWd2PrXDhZ9t4xF35lv9us6qDNlpWXE7I8jtPm5S/AsTTWVpTMajWzOyqoKZEbqHobvGTKELxYvIT03lwAv06xQWk4OKdnZPDHa+pJPg7qPZumWWRxLPEDzsPrVUY4+vB8Pd0/ate7AxA9fxWAw0Kt7PzZvX8+iv+dbFYZ3HD1e80bG02w4cIgHhg/G1fns6/26tGmNwQC7og/h6uJC66an6sQ6OzkRGRrKTaMsuwuxZO9qHO0d+PiW1xnUtm9V2DcajayM3sATv7zFkr2rLQ7Df6z6CycHR75/40Ou6nuFWX+LN6zi7jef4o9Vf1kdhnft2oWbmxtdu3ala9dTwSs7O5uysjICA61bb7l8+XIqKiq46667aNmyJZ6e5mFk8uTJFvXTp48fixal8P57h3ByssPT0/RnNC+vvKrmcJ8+fmfrwkx8fBqRkUE8/fR4s8cjIgLp2rUFa9bsJT6++uEeZ+PWqi2BY24gbd5MCg9HU5KUQMKXn5Lyy4/4jxqD/4jROHhaF8Zmf/kZ8775ktKSYlp17sqoO+5mxseTuOnRJxlwzRiL+3H2Cab9XW+TvHEBuTG7zQ7d8GrWheA+1+DkYd3ekJ+//ojfp02hoty8+o69gyPjb3+IOx582qr+GlKgrwc3DLFuX0VtCuILcPJxovMTnTGcsUEzfGg4297cRn6cdculxERhWOrNu2V38uOjz/jDb/rAp7X1u3gnvf44UXu28c2s1VVhuLiokCfuuZb2XXry3ue/WtXfrJ1Z+Lk7mG2gA4jLKqWorJI2Qdaty+3UMoxtB+K45ZXvcHN2BIOBwmLTDGrPdqaQEhWTTJNgH6v6bSh3v34rlRWVbP97l9njPYd15a7XbiEvM59rHxxJkzZh5+yri7d3PRYd1Ox4WhrFZWX0fPZ5rmhvWtO4JiqKyspKjqSk8PBXpnWhBuCzBx84Z39Deo5lW/RqPvr1Bdyc3XFxPnU73oCB1++z/ICW9Iw0IsJNL4yOxBymdYt2fPDWZ0x46HpS0pKt+CrB292N/OJiKioqq8JuUUkpDvb2ODk4UFhSQlpuHiv2RnF1z7PPsn304jMADL3nIZqGh/LhC89YNZbTZRRkE+EXyuB25rfODQYDV7brT4RfKPFZlh3tDJCenUnzsAhG9htUrb9R/QfTPCyCmMSaT+I7mz/++IMmTZrQunVrs8dnzZpFQkICr732mlX9nVwzDHDokPkSIWvWxN98SzgxMYVEReVRWlpJRob56XBt23lw8y2Wrxf28HAlJSWbw4cTadXK/Gfy0KEEkpOz8LD2ZEuDAe++l+Pd93IKovaSNm8muds2U56bTcqv00j74zc6TZtz7n5OWPrrdGZO/tjssU59+5ORnMT6RQusCsMAju4+RAy93aprarNg5g/88m3N5STLy0r57fvJ+Pj6c+2NdzfI52soY575kvjUbLb/+Py5n3yCwc6AsdxIZXkl9k7md0cqyyupLK+sFpLFMgrDUm9uwc3IPrSVQ7++j08b01qv7EPbqCgpxC24GRn7TpUs8u947kL5Rw/vJyyiGYHBp/4wBASFEhbRjKOH9ls9vqf/SKB7E1eubG0+E/LozDh2JxZx9PVOVvX30RPjuOnlb0nOyKOg+NRMRGiAFx89MZ5jiRl0bBHKgC6WnxrVkNy83Hjk4wdIjUsn8Ygp1IS3CiWwSUBV+4g7LFtz+UmXut0KPZsZa9dhAErKy6sqSpy81T999Zqqjy0NwzOWfUZKZgIYjRQW51NYfNrMiJWz8w4OjuQX5FFaVkp8YiwD+5m+Tw6OjtgZzn0gy+mu7tmFmeu3cMfQgbQMMZUWO5Kcyk8r1zOmd3c8XJz59u81RCcknTMMn/T3d6alIKWlZcQkmm4HNwsLw8mCqiUnhXgFEJuRwIxN8xjW8XL8TxxZnVGQxdJ9azieEU+Yt+V3X8ICgjkSH8vUub9y7cChBJ64LZ2WlcG8Ncs4HHeciOCGq31aVGT93SaAUaNGNchGUBcXe157vS1btmSzc0dOVRj293eiazcvevXytaqs2sCBnZg3byM33TSRyMggsxPoYmNTMRqNXHVV3UuDubfvhHv7TpQkxpM2bxZZa5ZTWWL5mmaAJT//gMHOjjuefYkf3/83AJ4+vvgGBRMbbV2t3Ly4Azi6++Di1zCHQ/w5axoGg4HrbrmPAVeOwscv8MQJdOmsW76QP2ZMZeHsny66MJyYlkNsinWl1TybeZIdnc3Od3fi087HbJlE9oFsygvKtUSijhSGpd4SVs4AA+QnRJOfYL6bN37Fz6d9ZLAoDJeVllCQn1ft8YL8XMpKrfslfjY5RRV1KVtMxxahbPvhOWYu38mBmBQA2jcL5oYh3XF2Mv1ITXvjzgYbp7WmvjKN4MhARj84kqCIU/Vity7dSW5GLkNusfx0p5MSi4qIysvD2d6Oy/0tr0FbkwHt2tZrk9uZdh3eCEDz8Pb4ewVhZ2f9etKTmkU0Z//BvYyZMITikiI6tDW9UEpLTyHQylq5f+3Yi4+7e1UQBmgZEoSvhztLd+7liTFX0TTIn9hU6w6QmL5gET/NX0jpifXcTk5O3H7t1dw2epRF14/pPoLPV/zIvxd8yr8XfFrrcyx1y4gxvP/jFJ779B2e+/SdWp9jqU8+OTXLl5ycbPZxWVkZhYWFuLpaf+Tsq6++avU1tTEYDPTu7Uvv3vWv2fv442PZtesox4+nEhOTwvHjpt8pJ383RUYG8vjjY+v9eZzDmtDkH48TcutdpC+eb9W1KXGxNGnZmpG331UVhgE8vL1JOHLYqr4O/foeGMCraSeCe1+NZ2TdjiY/KSkhlrCI5tz/2Ctmj4c2aUr7zj3Zsn4FSQmx9focdfH21MVnbc/Ms35/ROToSHKP5lKSWULK+pRq7XaOdkSOrtsmXVunMCwNw6JQaVnyDA6NICH2KF999CY33PEPAGb99CWZ6alENLP8WOHLPz4VzPclFZt9XFxmJKOgHF8364PTjKXb8Pd2N9tABxCbnEnh/7N33uFR1N3fvmeT3c2m956QQui9F+lIkSIKCKgoYkdU7AX7g70CUiyogFKk995Beg0QQnrvbTdtd5Ps+8eQhCVtZhN/+rxP7uviujIzuyeTsJn5zPme8zl6I21a/A0dzDJE+4ktpwnpFMTYp8xt6Hb/tp/Y8HhZYrjcZOLrqCh2Z4glAm0dHCkuK+ezm5HMDgnlfj95XqoAO9+Z2+BrMvLzMZSVNfg6ACc7V6wUVrw05TPZ53Inj059krkfv0JRcRF+Pv6MGjqWqzeuoCvUMqCPPAeD/OJiKipMnLkZS4cW4u/pelIq2VodVrdq65UKK6yspGecdx09zrINm8326Q0GftmwGVcnJ0YN6NdgjKcHPURiTgrbrxyo9fg9HYfyzGDpY6xfefhJYlMSWXdgR63HJw4dzasPPyU5Xn5+PiAKzrKysqrt22nb1nLLMKPRSF5eHhUV5h3+3t7SMpXr16fg4qzirgGuqNWWP3hV4ubmyJo1b7Nu3VFOnLhOxq1soZeXC/37t2PSpAHY2kof0qJ098Dape6aZWsnZ7ynTJd1jrYODuRlZWC4LaNcpNWSFh+Pxt4CH2QTaBOuok24isYzEK8eo3Fp0xtB5uoLgJ2dA9mZaSTE3qRFiPmwo/iYSLIyUrGzs8yr+XbKyk1YW0l/iP9u7eF6F6ZuOT/Kwj7Qno4vdyRxeyIFNwuouNUQrlAqcGrlROCYQDNXomak0yyGm2k03V79rUnjDR45gd9//Jpt635j27rq2IIgMGTUfZLjJOeLJQwCYCg3VW3fzqi2jrLP77kv19GjbQB39zJv1nrik9VcjEwma0/jrJtqo82Tbai4w57nTnLSqocTlBnLzLb1xQZyUnNlLxP/kZTIrgzzWtm73N2xirrJidwci8SwFB78dj4XYmPJW7m8wddOGvIkv+74kvM3jtIhpCdqiZZgtdG35wA2Lt9DRlY6wS1CUSlVhASGsuanrTg6yBv0EOTpTkxaJtvOXWTbudss40zisQqTibS8Alztpd+8Nh04BMBd3bowpLf4MHbw9FlOXLjEpv0HJYlhaysrPp/8Fo/2n8Sxm2dI196aQOfowYBWPWnnK316ohjPmh/e/pTnJj/CvjPHzSbQDe/Zn86t5GX+Bg0Sa4+PHDmCo6MjXbtWNx8plUrc3d1p1UreOYI40e7TTz/l6NGjlJeb+/nKmUC3fp1YnrJ6dTIjRngycpRnldewpWg0Kh55ZDiPPNK4CYoAbRevaHSMGjF79OLM/r28O01s8stISuSdafdj0JfSbZB8mzulvTPWGntKspIpyUgkfucPpB5bj2f3Ebh1HIyVSrr479l/CPt3rGf29FH4+gfh7CquXOXnZpOaHA8mEwOHj5V9jgCn4ov45mAGl1JK6OCj4eWhnmy+ks/Ubq70CGzYKrCR811qxc7XjrZPtcVUYcJYKN7TlPbK5lrhRtIshptpUoyF+ZgqylE5Wm5nM2n6M0RevcjZvw6a7e/ZfxgTH3pacpw5g8Tl6e+OZOLjqGRK1+olTRulQEsPNcNaNT5jUEm+rrjWgQgNYaowkXkqk4KoAow6o9mELEEQaD+7vaTpQq+NvNVQJEDijeTq7dtw9Za3rLs7IwNrQeCDtu145/o1AGytrPBQq0mwwAZNDlInhS3dLA7KWL7rmxrHBGD+S5tlfV9nJxecb5t0aGtrh61ttWB9+pVHuHHzGke2na83zoTe3Vl19CRpuflm+31dXZjQpxv5RcV0aOGHv5t054HE1HS83d348Plnq/YN7tWDB199i8Q06U1vAO18w2jnG9bwCyXSKawtncIaP+Rh8ODBAMTHx+Ph4VG13Vh+/PFHDh482PALJVJYWMbGjals25bOgIFujB3rha+v5Q9iOl0JVlaKGlngGzeSKCoqpXt3y/6vKowGygpqjrJXeUgv+5k8+yXCT54gKSoSBAFdfh66vFxsHRyYOEv6UJuq7+3oRusH36Ug7gqZZ3ehS4zAoM0h+fBq0k5uwb3TYPwGPtBwIGDGrDeJCL9ASmIsKYmxpCbFAdXXD9+AYB59VnqTWiUn4wqZvjKBslueviZM+DkpWX8pH6BBMezt5oCjnQ0nf67dyaLHo18Qn55b67GGKEouIi8iz2wCnUs7l+ascCNoFsPNNAk510+QdmwDhsJc7HxC8eo1hszze/HqORqnEHlNWNbWSt7/+heuXjpTNV6zdfuudOjSS1acOUPEi/3J+EJaedpUbVtK1+mfV30dHp1qtl2iN5BdUISro7zBAgBx6+NIPyHPqaBWKu91ArWWVVhZWzH2yZGyQmbp9bSwtaW/m/nDja2VFVkym3D+NuoRzaa/yd5OykOPs50ts0YPIyY9k8wCLQBeTo6E3FZDLLVxrhIrKwUGo5Hy8vIqr92ysjIMRiMKhfQl5iJ9CadiL6CyUtIntBtKq+pbwYbzu8goyGLW0Kape3/qkzfJyMlmy9c/y3rfjBkzAFEUp95qFvT19SUoKMii8zh69CiCIPDII4+wfPly/Pz86NWrFwcOHOCpp6SXcYjnYUOHjo4cPpSNwVDBwQNZHDqYRbduzowb702bNtIfsouLS3n99WWcOHENhUJg1KiezJ07rUoUf/zxaq5dS+DChUWyzlGfmkzSkm8pjqxl0qQAndbWPuijNvxCQvl47SY2/7iEmKvisJbQDp2494ln8AkKlnVet+MU3Amn4E4UZyaQcXYX+ZFnKC8tJuPsTsli2NnVnfnLd7Br4++cP3XEbOhG9z6DGH3fQ9jIHPgC8M2hTMorTIxs48ieG+Lfb7CbGnc7a84nNpwI6NY6gN0nIygs0WOvqZnptiRpbKowEb0qmqyzNa32Ercn4tHDg5YPtWzOEltAsxhuptHk3TxLws6fzPbZegVRmHwDpa2jbDFcSYcuveoUwPPeeIq46AiWbTjWYJz5EwPIKy6noKQcJ40oIApKykktMOJqa4WXo7QlzsrOX0EAvbG81k7gsf3lOVMAZF/MBsAxxBG1m7pBb9q6eP2XF8Fk4ovHF+Ab6s3Dc6sn+KlsVHgGumPvJC9z4KRUklZaSoGxusQko7SUhOJinBph3t+UjOo79Z8+hVo5GB6Bk62G7qFBZk10iVk5lBgMtPaT77DQMjCAa1ExvPjplwzoLg5UOXb+AvlaHe3DpNXTJ+em8fBPL5JdKH5+A1x9WPzwxwR7iLaA68/tIDw5ssnE8PmIcOJrGa3eEGVlZaxZs4bY2Fiz/aGhoUyZMkX2OOWcnBx8fX15+umnWb58Oc7Ozrz22mucPn2ayMjIhgPchp2dFTNntuCBB/zYszuTvXszKSgwcv58PufP5xMWZsd/5kkrD/n1130cPy6uupSXm9i58wxpabksWfI8arX4NyZ1leR2kpfOpziyDvcdk/SLTJnRyF87tyMI8NRHn8p66JKKrWcLgsc8g2HAZDLP7yE7vOb44vqwsdFw34NPct+DDbvPSOVKagkBLip+mBpI0AfVw0E8HayJu8NOrzbmPjaSaSO61/ms/uNb0yjV1yzdq4/kPcm1CuFKss5lYeNuQ8DogDpf00ztNIvhZhpN+ultIIBntxFknt8LiAbqSnsXitJjG3i3ZeTlZJIp8Qb74oYkzieVcPTFsCoxXGyoYNyPMXQPsGXtY9IyG69PF0dBf7HyAL7ujjw8urqBTqNW0SrAg5F95C8TK1QKNPYaOrwoX0jfTpue4jLq+GdH4+rtUrXdGHq6uLA7I4OZF8SSgPjiIp68eIFyk4le9TTp/F9yT99p//Qp1MqhK9fxd3ele2iQ2f5dF66QkpPHRw9Km8x4Ow+MHsF7UUu4ERPHjZhby8GIz09T75GW9V98aAVZhdXLs4m5qcz89VV+f3IBfi7ymz9/276u3uO6YsuGABw5coSYmJga+2NiYjh69ChDhw6VFU+pVGJrK2YIVSoVmZmZYlbdYODgwYO89dZbss/R3t6aiZN8uXeCN0cO57B9RzppqaVERRVJjrFnzzkEATp2DKZ9+xbs33+RixejeeONZXz33TOyz6mSktgoEATc75mAjX8gWDC1D0RbwZ8+eBuvgEAGjJfes2EJKkc3/Ic8iE+/pvs+29b9hk5bwIOPvyjrfdYKocZDSEWFiQytESl9dG1aeNXbTF3pSS+HrLNZCIJA0P1BuHV2M7NWy7mUQ9ymODLPZDaLYQtoFsPNNJrSnFRsXHzwH/JglRgGsNY4UJqb+g+emcj19FKCXFX4OlXX3fo4KQlyVXE9Xbpn6RvTxQlfxy/F0jbIq2q7sQSMDCB2fSzZ57Nx6eCCVSM71MO6hpIak0ZmUnaVtVpmYhaXj17FN9SH9n2lT2l7IiiI8/n5VSURxbcaj9xVKma2kD617+8kuyCdfF023m6B2GscOXBuEzEp1/BzD2ZUnylYWf17LnPGsnJ0JaUW1ZYD9O/ahTeffIxfNmwhM1cUtJ6urjw+cQJ9u3SSFONM3GUEBKb3vY/uQZ348+x2TkSf4+kVb7Lqqdqt1urj5W//U29jpslkssjf99q1awiCwIgRI+jYsSMA4eHh7N27l6tXr8oWw25ubmRliVk1Pz8/4uPjGT16NEVFRTg7O8s+v9uxtlYwbLgHw4Z7cO5cHtu31bS9qouMjDycne1ZtuwllEprnnhiFDNmfM2RI1f44os/LT4npZs7KBT4PiqvBKQ2WrRuS16W9J+p3lijnsDatv4yEiu15bXXd7J17a+kpybKFsPtvW04m1jMG1vEUeC5ReU8vz6JnOJy+gT9M7W5+jw9Nh42+Aw0X1VSOanwGeRD+vF0SnNK/5Fz+2/n33OXaOa/FoWVknJDCSZTtdtBRZkRQ0EWCuuGG7/+bvRlJnT68hr7taXl6Mvki5If3ppKnraYgsISnG5NhiooLCE5Mx9XRzt83OU5VLh2ciX1cCo3V9yseVCAft817BBwO2u/2khWUjYDJ/av2ufk4cTGBdvwDPTgw/XSM2BuKjU/d+3GxtRUbtzyfm5j78AEX1+c/8YyCS9nJwLcpTVhbjr8C+GxZ3j7kYVExF9gy9HfALgae46yijLuHfDo33aetfHuqg3iFwIk5+RWb9+GvY28qYe3c3e/Ptzdrw/5WvH/w9lRXhNodmEufi7eVeOWh7bpx/Or3uNI5Gme/+M9DGXylm7BsmX8htBqtbi7u9OnT5+qfX369OHChQvk5spvPOrQoQPHjx8nOjqaMWPG8P3331NUJGZwR4+W5tEshR49XOjRQ3qjqkajxtPTGaVSvB27uTmyePFspk//gtWrD2NtbdnDsdfUR0la+AXaC2dw7Cav3+JOxs18kkVvvsLit15lxIPTcXJzN3vAcfdpeJplJW4dGvaal0NKYv2rj2USLRrv5NkBHpz9I4E/L4pj6BPzDCTmGRCAp/tL91rPyivk3R+2c/RiNJn55qskAoIs9yGlg5LSnFLyrufh0s78M5Z3LY/S7NKqbHEz8mgWw800Gjvflmjjw4nZIHbzG3S5RK37gnJDCY7B0rJVfycBLipis/V8uCuNZ+4SL2I/nMgms7CMlu7SLXwqefrTNZy5nsD55a9XieHCEj1Dn1tI7/Yt2PqVdMcLgKjfoyjJqCNDbYHGyEjMwivAA7Wm+kFErVHh4e9ORkKm7HiOSiUz/oYscFxmJueiY7BVqRjTw3zC1qqX5kiOk5wVi73GEW+3AHafWouVlRV92g/nr/C9XI76q8nFsMlkql/8NdDICNCjpeVNRwAGoxGD0YgJExk51UM7vNwafoBw0jiisq6+YSoUCr564B0e+fklzieEY8IkayiKm5MLro5OrPtsaS1HTYx7+XGSM+U5XYBYyqDVatHpdDg4iIJfp9NRUFCAWi3/7/b28c0tW7bEzc2Na9eu0bJlS8aNGyc5zsLvO6FUNl3drI+PK7GxaRQX66ua5gICPJg//1meeuo79Poy2X60AGkrfwYTxH/2AVa2tljZ2VcfFATafP+r5FjzX3kBBIHjO7ZyfMdWs2OCIPDHZXk115VoE66hS7xOWZEW8z8WgRajHpcU4+kpQ/+WlYkhYQ4smBTAZ/vSSSkQHxD9nJS8PtyrxjTT+njhm/XsO3Oj9tphQd4F3q2LG2lH0oj4IQKFUoG1nSjhyorKqjyH3bpY7uT0v0yzGG6m0fj0m4Au8TrahKsggLEwD6MuD8HKCp++9/7Tp8eEjk58fSiT307n8NvpauEgAPd1cpYd72psGiF+bvh7Vr/Xz8OZED83wmPk3/S1UVoQwKObB2o3daM7gRWCQE56LvpiPepbN9fS4lJy0uX7DAMkFhdzuaCAPKOhxgX9UQtEcnlFBS8u+4U/jh7DZDLRo2Uo2pISnv3hRz6b/jDPjJQ+/QxAV5SPt5tYI5eWk0CAZ0umDJ9FbGoE2fnSXTrKyow8+PR92NvZs2z+6jp/Vz9+s7LeOPf1FUeSbzp5DlcHOwZ1qK4jV1lZ4e7ogLeLPM/iShJT0/jylxVExNSSDRME9v9SmyA1J9jdn/MJ4aQXZOHt5AGARmXD4ukfM+2H2aQVyHtg6tKqHUcvnsbL1R21quZKkJWFDVctWrTgxo0bLFq0iMBAcapWYmIiRqOR0NBQi2LezsiRIxk5Up67CoCHh3whXh9du4YSEZHE+vXHzHyGO3cOYd68Gbz2mjwXjkqM2dX/j+XFRZQX317HbME1po4HQEvXBNJObSXtxMbaAwpIFsPiqTXtykRFhYk0rZFu/hqOz2lFXrG4suhqJ18ynbgi/q2O6d+e1i08sZYxZOdOAscEUpRchDZGS4WxAkO+eSOfY4gjgWOaJ9BZQrMYbqbR2PmEEvbAG6Qe30BxutjUY+sdjG//+7HzafxNqzbkXPueucuDi8klHIwyH/E8rJWDrOWuSvQGI9qimnVZ2sJS9Ab5S8waTw0V5RWEPdI0nq/+rfyIuRLH188sqpo2d2jtUUoKS2nZWV5GcmtaKvNjYuq82Vgihr/espWVR8y7xcf17MHzPy9j14ULssWwSqmmoCiPgsJcsvLT6NFGHNpgMpmwtpK+ZGhtraS4pAiNxtaih4ZKuoWIv5O4jCzcHOyrtpuCL39ZwfXahDAgSPyjGNS6Dyl5GWy9tI+nBj1Ytd/DwZUl0z/hxdXvU1Zes6yoLl568AmG9exPib6kVjH80dOvWNREN2TIEGJjY9Hr9URHiyN/TSYTarWaIUPkD3qYN29encfUajWtWrVi1KhRkrPOpaXlbNmcxtWrWgoKysyuSYIACxZKWxWbPXs8Dz88rMo54nbuvrsb+/Z9itEof6nfa7L0KYIN8c4vvzdZrEqyLx0EEwhWVljbOlo0fQ5AbaPBycWNBx+fU+vxXxd9hjZf3shzgAHzb+Jpb82pV9pYJIIrcXGwxcfNkRXvy5v6VxtWaivaP9+e3Cu55Efkm/kMO7d1xrWja7OtmoU0i+FmmgR7v1a0miK/G9tS3vniR8oMDdvbACitBH55qAVnEoq4mCyWI3T119CrhWVNEIHerkQnZ/HW4q28OGUwAAvXHSE9V0erQA/Z8fxG+BH9RzTJ+5Jxae+CtY35n6XaVV4mavhDg4m5HEf0pViiL8XWOCaHP5KSqDCZUCkUOCuVlrq+mfH70WMoraxY8cLzTPv2O0CsofVzcyUyRX7DpZ9HMFHJV3n3p5kAhPi2pcJUQZ4uG3cnee4I9wwfz/qtq4mNjyZExujv2ujXpiX5RcVoi0twtBXLabTFJaTk5uFiZ2dRdjgmKRkbtYrZD07Bx8PDouXzx+56gMfuqt3DNcwriJ1zGp76dzv9OnWnX6fudR4fO2CYrHiVeHp68uSTT3L8+HEzn+G77roLd3f5D7E7d+5s8CFn7dq1LF26FEfHhuv+f/4pgePH5YusO7G1tcHWtu4acg8Py1YRvCY/bOkp1aBdz95NFquSckMJ1rYOtHvsU6w19g2/oQ5CwtoRFx3BsHsm1vr/u/bXhbLFsEIh4OekRNmILG4lLzwwiPd/2sH1uHTaBUsb+V0fgiDg1tkNt87N5RBNSbMYbqZJqCgzkhdxkqK0GKztnHHvOBB9QRYad3/ZF7ry8nL2bfuTKxf+Ij83u8ZEtk++X42rm/wBGr1a2NUpgJ9ak0hEegnH5rRuMM7koV35ZPleftryFz9t+eu2c4MHhnWTfV43fxMb5xK3J5K4PdH8oAUNdL1HdycvI48ti3eiLxEfGNQaFROeG0OvUXWLltooLCvDU63mt+490FhozXQnqbm5tPHz457u5r8rBxsNKTnyG6PG3TWdpZs+ori0kGDfNnRvM4iopHD0hhKCfeVZ3eXkiTfNJ156iG6deuDq7Eal4hQQeGvOB5JjbTl9gfT8Al6/756qfdZWVqw9fhofF2eeHik/u9ky0J/cAi2jBzZtE9K/FXd3dyZMmNAksby8vMjNzcVoNFaJXa1Wi0qlwtbWlvz8fBISEvj111958cWGnQcuXMgHIDjEFj9fDQopflsNcPr0DU6fvkFOjq7Gde/DDy3LLBoy0jHm5WCqMB/nbt+uo+QYG5bU7zIy8dnnZZ+XU2hXCpNuYKWWPxDjdobeM5HzJw+Tl5tV632h7+BRFOTJf2h5aYgnr25OYfW5XKb1sNxGcsvRK5SVVzD42fm0C/au6jMBsVhl85fy3D4MOgMpe1NqZobbOeM33E/StNJmatIshptpNGUlhdxc+ymlOaIFjZ1PKPa+LYne+DXefe7Ft788z8gfvnmfXZv+AGrWgjVm+bo+MnVGkvOllTi8MGUQ524ksvf0DbP9I3u34fkHBjbtiVlYCjdqxnCGTh1ISrRYw+zX0geVTfVFsiBbS5mxDDef+i/yo7y82ZOZga7M2GRi2M3BgYSsLHJ01WUrSdnZRKam4u4gfzx2kE9rPn32d4pLC7HTiO9vHdiZ7+ZsRKEQz1lblEdZeRmujvVn7vce2oEgiP6ip8//VfV5q2zCkSOGswp0uDnYY3vbsrutWoWbgz2Z+VqZP6XIqzMf5fUvv+XNbxbQu1MH7DTmFlQj+veVHKvYUMLPR9dwKuYCOYV5d7QvwZ5X5C2NF5UU893qZRy5cJqsvBwz+zgBgYt/7JIVD6CkpITU1FQKC2uWWXTuLG+Yz4svvshHH33E119/TY8eYl332bNnef3113nttddwdXXl+eef5/jx45LEsEqlwN7emk8/bS/rPOrip592sXjxthr7TSbxeUyuGDbm5ZLw5UcUR9fuUiNnAt2GxQuobxnCEjFs6xVE/s2z3Fz7CS6te9UQxW7tpT3wjZ7wIKMnPFjn8ZmzLVux/PpgJtYKgbe3p/LRnnRcba2qfgUCSEqcAJy4Elf19Z09JXJvZ6XZpYR/F45RZ36vKskqoeRICdnns+k4pyM2Hpa71fyv0iyGm2k0KUfWUpqdgsJaScUtWyaHFu1RWKvRxl2RLYaP7d8OQNtOPfD2DfzbBLClKK2tWP2fGZwMj+PcDTGT26NNIH07WuYQ0PLBxi3H14XKRkVwh9rrVRe++CNxVxNYdrn+jM9TwcGcz8/jobNnCbazw+42QSwg8E0n+W4hwzp15I+jx+j75tsARKakMmDuuxjLyhje2TL3EUEQqoRwJZVCGOCnrZ+QmB7F/Jc21xunc4duspwU6qPCZKKwRE95RUVVE1l5RQWFJXoqLGz4ScvKoqikhHPh1zgXfs38oCDIEsMfbvmOHVcOAjXHS1vyO3j52/+w/qAosJriIfbGjRts2rQJo7H2h1S5YnjJkiX4+PhUCWGAnj174uvry9KlS1m7di2dO3fmypUrkuING+bBzp0Z5OcbcXZuvJ3Vn38exWQCa2srXF0dsGrkEn36H79QHF2Hy4OMCXQAbj6+Zv+HxTodxTotgkIhy1btdlIOrwEBilKjKUqNvuOoIFkM/11UOkgAlBgrSCmozqzL+e1NHd7N4qmid5KwNQGjzoiV2gqHIAfRRs0ExkIjungdxkIjCdsSaD1TmlBvpppmMdxMoymIvYSVWkO7mZ8SvmQOAIJCgcrRDUNB3aMj60Jto8HR2ZUvltY/2eqfpm/H4DoF8CMfrOBqbBoXVrzRYBzP3vJLPpoCKR3YP8fHkVBcDEDUrexcpWOYpdf39yZP5vDVa6Tc8orVloh13L4uLsydNNHCqA0jRX5+/9myJvt+7o4OpOfn8+eJM/RvIzZH/nUjmmK9Hh9XZ4tifv/HWkpK9bX+LFIb6Co5cvM0AO18wwh2D8BK0bjM/97TYlNk57C2hAUEY93IlYR9+/ZhkNgXIIX09HTKy8vZuHEjw4aJdcxHjhwhISGharSzSqVCKdE/OzNTj8FQwUtzwmnfwQE72+rbqSDAM8/KezguKirFxcWeTZvew9nZ8hraSnRXLoIg4P/UCyT/MB+1fyAuA4eRtXU9fk/Ky+Qu3Hukxr7wkyf4+oVnmTTrBctPss6PrGUPi5fOHufyub/Iy83izo7GOXO/lBVrzqCmuS4ver32+nxLKLhZgJWNFV3f7orKybwcwlBg4OInFym4WdBk3+9/iWYx3EyjKdcXY+Pmi9LO2Wy/yVRBuUH+NJypj73A0m/e58i+rfTqPwyN7T8z7acxpOfqSMzIk/z6opQiUvanUJwqCk9bX1v8hvth5/fP/uw709MRAA+1Gk+1GqsmyNJ7uzhz/JN5/Lh3H+dvOSN0CwnmqRF3Y2eBf+zfQUpaMtcjw7FR2zCgr/zaXoAeLYPYfvYS15NSuJ6UUn1AgB6hlq0i5OQXYG9ny3uznsLL3a1RAlZtrcLJxYE/n11scYzbsVGpcXFw4uCSNU0Sr7CwEHt7e2bOnImLi/QhFnXRtWtXzpw5w9dff83XX39dtd9kMtG1a1cqKiqIiorC11dapvPYMbEOtaSknHNn82sclyuGBw3qxLlzN3FwaFwNbSXl2gLUvv64DhtF8g/zsbLR4DnhAfIO76fgxBGc+w5oVPyOffvTslNnNv+01KIxzd1e/a1R3/9O1vy6kD9++qbG/soSJ9lieEjTJikMxjKyC4qouKN2299T+me7wliByklVQwiDOIXO2tYao1a+o1EzzWK4mSZA5ehGaXYKhcnVtWn5MRfR56ajdpXfPdt38Ei2rF3GV+/XUrcnCGw7Uf/Eof82ci7lcPO3m2aZ2uL0YnIu5tBqRqt/1ETd1soKF5WK33v0bLKYX2/Zyiv3jufN+81voPlFRYz/9HP2vv9uk30vuZSXl/Pl9/PYtX8rJky0a9WBopIiPvn2PV548jUmjZ8mOVbvVqFkFeg4HRVjNoijT6tQerUKsej8BvboxqWIG3Ru3QqrRmZeJ/cYw8qTG8jS5eLhYHmDUCWPjpnEkg2/k5GbjZerfLeHO2nTpg0xMTHY2jaNOHzrrbd48803iYw0Lx1o06YNb731Funp6QwdOpT27aXVALdt69Bky99ivAD27bvAzJnfMGJENxwdzX/uceP61PHO2lHY2CDc+owo1DboM9Iw5udRpi1Ad/m8rFgR586YbVdUlJMWH0/0lUs06S+hEezc+DsmkwkrayXOLm6N/vs4HV9U7/HeEkcy64r1vPjNenb+dY2ycnMhLHcCncZLQ1FKEZG/RuLW2Q2lvbiKYSw0knMpB32u/h9PoPy30iyGm2k0Lm36kH5yKzfXfiLWgKXFELt5Pgjg2ka+Jc83H75EckLt3rb/jstu05KwLUH0xNVY4xh2q8s9SktZSRkJ2xL+UTH8RFAw38VEc02rpb0EuykpfLRuPdZWVrw4dkzVvsyCAiZ89jnXk5Kb5HtYysp1v7Bj32azfQP7DuXz+R9y/PRhWWIYYGzPLvRvG0ZKrrhK4Ofqgou95TcrF0cH8nWFPP3+PLp3aFejge6Re8dKjpWSl0ap0cDY+TPoHdIVB5vqpXkBmHf/a7LOLSE9hVJDKb0eHc/Arr1wsq+u4RYEgYWvfSQr3j333MOyZctYuHAhgYGBNfx/771X3kAfLy8vfv31V86dO0dcnNjUFBISQvfu1Q4rc+bMkRzv/Q/ayPr+DfHNNxsRBLhyJZYrV8wf+AVBkC2GrV3dMGaLZWpqHz9K4mOJeEr0Hla6ynv4+c9jD9Xe7WUy0aa75Q/KBbGXybtxGmNRvpnbhSAIhD3QcInZ7RQXFeLk7MaSNftxdGr8SsKU3+LqvN8IAsS+30FSnI9/3cOWo+F1BJJXDuI3zI+bK26ScymHnEu1O2T4DrOshvt/nWYx3Eyj8e4znuL0eLRx5o0njkEd8OotfcxpJeEXToEgMHjEvXj5+GNl9fd/TH+cGoihvGmnGEnFkG/AysaKLm93qbLFMegMXJx3scaEof9rfk2Ip9xk4vnLl7C3tsbO+ra6SGBVz16yY1opFLy/Zi0KQeD5MfcQl5nJhM8+Jz4zC2e7f7gsZN8WrK2s+c9bX/LWvJcAsNXY4unhTUJSXAPvrh0Xe7s6BfAPew6RkpPHRw/eLynWn7v3IQDxKanE1+LJLEcMb728HwGBQn0xByOqLQIrxzHLFcNr921DEAR0xYXs/OtQdbxby9RyxfDZs2fJyspCEARu3Kh2bqmMJ1cML1u2DE9PT8aNG2fWRBceHo5Op6NfP3kWhn8HdZV9WzJhzbFbb3SXz1OaFI/7mAkkLfqGyiUK93smNMnJhXXuylMffSI/FpB7/S/id/1Yy/fBoqxH7wHDCb9wCjv7pnlorzwVeQdqsuvkdQQBXpo2hG9WHSLYx5XB3cPYfOQKb8+QN2DIvbs75YZyErYlUFZkPojF2taaFuNa4NFdvtd9M81iuJkmQGFlTcuJL6NLukFxupjRsPUOwSHAssyJX4sQyoxGXv3guyY5v/IKE39eyOOv+CKyC8tqWEitnhGMp0Pju8Etxb6FPUad0cwfUuWgQuWoEruF/wac3BwbtFUDyNDrq77WlZWhK6u+AFuapV/+/GxmLlrMu6vXkJ6fz/qTp0jPz8fP1ZWNr8sTYFJxtHXBxaHhpfus7AyCAkO4q89gs/22Glsys6SPdpbDnU4ODb++aejRopNFQzvqol+n7k3q/HLy5EkAFAoFdnZ2KCwc61zJsmXLaN++PePGmT+gL1iwgIiICI4fPy4rXllZBWvXpPDXX7nk5RmpqLjdFxhWr5GXMb10qWlqtyvxeXgmPg+Lg2hsAoJQeXpTHB2JTYtgHDrJ80Ofv+ew2bYgCDi6uqFqRI1/5oW9YAK1iyf6vEwUKhusVGoqyoxoPOSPFG7ZugPHD+7kzWcfYMCwsdg5mIviYffIa849PqeV2bautILt1wpYejyLBZMCJMfJyNUS5O3K3Bkj+WbVIVyd7Pjqhfs4eO4ml6NSGg5wB159vfDo5UFhQmFVskTlrMK+hT2KJhgS8r9KsxhuxiJurv2s3uMFsVdIw7LlrikzZvPtf17lzxWL6dV/GLZ25p3Vnt5+suK9vzONP86JzgV3Com/q+yioUyOPrdaZPrd7UfkL5Ekbk/ErZtYEpFzMQdDvoHg+y1rtALITMomNjwelY2SbkPNbaieXyDN6H2Ep1eTCiYQRy+vemkO0+fPZ9Gu3ZiA9gH+bHj9NXwa0SiVnZ9OfHokKms1nVqaLyk/ee/bkmI4OTmTlpFCgTa/al96ZhoJSXE4N8HSa2M58OsPTRZr+RM1m40aw/Zvf23SeADOzs7MmjVLssODXEpLS8nOzrYo87pxYxrbttX+gGShc97fil3bDti1lba0fycevtXXXF2+WPLTGCEMUJqTipXGjraPfsyl755E4+5H6P0vc+3n13DrIL+5b9nCj8VVhKsXuHH1gvlBQZAthv2dazaptfW24XR8Eb+dzmFMe2mTAVVKa+xtxd+Vjcqa1OwCjGXl6I1lbDkazvyXJ8k6LwCFlQLHkKbLgDfTLIabsZDCpBvVHluV3CmaLFzu+mzucwiCwMqlX7Jy6R0dwBY00G2/JlrN9Ai0JdBF9X9Sd/z7h49iMJbVefz8hzUbWJL3JZO8z7xm9voP12VPoKsor+C3D1dzYsspTCYTIR2DKC0s5ed3VvLgG5Mkj2SuMJmY2UL0KfZUqxuV9Vt1rGbWbWKfPvx+9BgONjZMHzSIQ1dF39wHB8jzF62oKGfN/sWcvnYAExDk3YpSQwm/75nPxMFPMKir9NKBXt36sWv/Vh55bjIA8UmxPP7iNMrKy+jdvb+s8/q/oFSvJyk9A19Pjxr1w//t9OvXj+PHj1NcXIyTk2UjiQH69xf/3wRB4Pr161Xbt+Mqs4YW4K8TYs3mXQPcOH4sB1dXFS1aaIiKKmLESMucCI4fv8ru3efIyiqgvNy8hvann+bIihXzQd1JCIVKhSYoFLfR41G6SPvZ96xaweafllKQkw2Ak5s79z7xDKMeekTWeVViqqjAxtEdhbUSFAoqjHqsbexQ2jmTfnIzbu3l/73V+VDTBE8nJpOJ2BwDCXkGtKXlkt/n5eJAarZ4DwrycSMyMYOwSR+hK9bj7iS/LEwXryP7QjYKawUePT2w9alutIz9M5aSzBLaz26aQTD/SzSL4WYswt6/tZnQLU6Px1RuROMhLh+VZCWBlRV2PqEWxa/romaJHNMoFbjaWrNupmUd/HdSXl7B73vOcvxSLFl5uhrPA5u/fAovV/mT1GrFgmv49p/3cHzTSbN93YZ3xur9VVw8FC5ZDANMO3sGN5WKdb3lNe/cybM//Fjr/50AFJaW8tbv4sRBQRBki+G9Z9Zz6up+s32dW/Zh9b6FhMecliWGn3pkNucunSYrOwOAomKxo9zDzZMnHn5W1nn9HazdtZfTV8J5Zsok7G1tefGTL8gr0GKjVvPpy8/TsVWY5FjG8jIW7P+FnVcOkaXLMRsEIggQ/tE+WedmLDMy75eFbDy4m/ScLCpMtw8pEMjef0lWvOjoaMrKyli4cCGenp5mDXSCIPDII9JEWOW1pHKyYG3IrT8GyM424OamYvbsEI4fy8HNTclrr4cx+7nLGA0VDQe4gx07zvDOO7/V2F85gU4uRdevUPsVU/wd6C6dJ/fwPlp+8h0q9/rrTNd9/x2bflxsJioLsrNY8fk8dHm5TJ49R/b5WdvYUVYq/n0pbR0oyU4hcd9vlOamobCWP1J4+8l42e+pj+APrtZ5LMRdela8Z7tAdp2M4HpcGtNGdOf9n3aiKxZXBqfcLa9cpSC6gOuLrmO6VZKTdiSNsOlhVU3WhcmFFCbUnNbYTMM0i+FmLKLV1OoRl1mXD1GcEU/bGZ9gc8tKrTQ3nRsr38e5ZVfZsee881WTnSfACwM9eH9XGlvD8xnWygE7deMsd95ctJVfd5wCaiYcpN60/s4n9+ObTmFlbcWsbx5n4Qtig4qNrQ2u3s6kxUmve1UIAt42Nlg3UZ2EFF1vyXL16WsHsLKyYubYN/hpi9jMo1ZpcLZ3JyNXnjuFu6sHvy1cy/ptq7lxU8xUt2nVnvvHTPlXlEkcPnOWuORUAn28Wbl1B7kF4ljnEr2eFVu28+VrL0mOtfTw7/xy/M/aD8qcUAbw1e8/snDtb7UftOAjFB8fXyVg09LEMbaV23JWKebOnQvAxx9/jJ+fHzNmzKg6ZmNjQ4sWLWjZUv4USIVCwMFBvIVaWwsUFJShUAhYWSk4dCibhx6WXlcK8McfBzGZICDAg6SkLOzs1Gg0agyGMlq1klcaBmJZRElsNCajEZsWYrlVaUIcglKJ2i+A0qQEyvLzyFz/B/7PzKk31v4/VwHQunsPet89GoCz+/cQce4M+/9cZZEYtnHzRZccibFYi31AW/IiTpF95TCYwM6naRIXjaGuK5GdSsHcEdItQxe/PqXq63bBPni5OnD+RiLtgn2YPlpeXXnK3pQqIQxQUVbBzRU3aatui3NbZ1mxmjGnWQw302gyTm1DZe9aJYQBbFy9UTm4knFuN57dR8qKN3yM/Bqq+hjZ1pFlp3J4cUNNYSTHIqeSTUcuA9C7fRBB3q4W3eidwixf9m2IvIx8fEO96TrEfLSxjZ0NuenymsBmBLbgs5uRbE9LY6yPj8XnVPD7Covf2xD5hdl4uwbQMdTcxs9GpSE9N1t2PEcHJ2Y++ExTnV69mEzIyv6nZWbj6eaKjVpNRGwc7i7OLHl/Lk+88yExifKE/84rBxEQGNt5GNsu78fL0YPW3sFcTopgWm/5mdINB3chCAKTh43hz/3b8fXwon1IK85dv8ITE6bKjteiRYsmacgbM0a08Lt48SL+/v5V243FyUlJfr444MDdXU16eikvvRROVpYeOzv5D9yxsek4OdmyYcM79Or1IqGhvnz//SzGjHmPe++V73Th3H8wJXExhH29BBs/UZiXpiQR/eYLuAy5G4cuPYh6dRa6yxcaiARGvR5XTy/eXfY7ilv+vXdPeZAXRg6mpMiyTKTf4GkYtNlgAv/BD1JWrKUoLQaNRwABd8+wKOa5vw5xdP82crIyqKioLmUQBIFPvl8tK9ZXE8wfQATAzc6arv62OGksT6hMHtaVycPkJ4lAHM6EAG0eb4NjqCNpx9JI2plE5G+RdHjBsnrwZkSaxXAzjaasREdFYS4px9bjEiZ6duZHn7+13GVZ40ts1HXWr1xCfIxokB8c2oaJ058hJKyd7FgvbUomJrv2EbaWlCFo1ErcnOzY8U3TCKakXUl1HlOoFNj52eHcxllyPHsXO7JScijMr75J5aTlkhqbjoOrvDGvvyTEYyUIfBMdxfexMTgrlVUCxVJrtabGzsaRHG0mRSXaqn252izSc5Ox18h/6EhMjudi+Dny8nNrZKofe/BpSTHKKyr4bttebJRKZo0eWqeoe2aUvOl2pQYDnipxCTk5PYOwFoG4Ojni6eZaq9VafaQVZOLl6M5nk95k2+X9eDu6s+iheQz/6kEMZfIt/ZIz0vB192LpW5+IYtjdi9XzFtJx2ghKDfqGA9zB7RncpmDKlCmkp6eTlZWFh4dYFpCZmUlkZCQ+Pj6ys8OBgRrOn88nJaWE3r1d2LIljbRUceJmjx7yVxHKy8vx9fVCpVJiZSVQUqLH0dEODw8nfvhhB+PGyfNsz9y0FqWre5UQBrDxC0Dp5kHW5nW4jxyHbet2FF27Uk8UkW6Dh4mDN277HFd+pnsNl5fsqMTWMxBbz2rXiLDJr1sUp5JDuzfxzUcv19gvdyWhkkldmmYlaPZX6+o8plEp6dDShynDu2GjavheWV5ajq23La4dxTrvgJEBUAFJu5OI+DGi2U2iETSL4WYajWNIZ/JvniPjzHYyzmyvcUwuJw7u5PN3n8dkqqgSI0lxURw7sIM3/rOQ/kPvkRXvVHwRggD3dnDC31mFtaJx2aZXHx7Gm99vZcOhS4zs0xZ7TeO6qpN21y2GK3Fs6Ui7Z9qhUDZ8sevQry0ntpzm3fvEkoHU2HQ+mPwZ5WXldOgv72Hidms1fUWF2bac3+LYjz+hjb8/Xz36CGM/rtuXVBAEtr39Vp3Ha6NtUFdOXzvIJyteACA9N4kvfp9DeUU5bYPk1eRt3rWeb5d8hslUe82nVDFspVBgMJahtrZuUrsxN2cn4lNS+ea3leTmFxB6lyh08nU6nB3kPehYKaxwsRMfFpRW1uQU5qFQKLC2smbj+d28MlKa40gl1lZWuDk5A6CyVpKVl4NCoUBpbc0fuzbz4VM1hYocUlJSSExMxMvLi5AQ+cvon332GdHR0WzdurVqn1qt5p133qFVq1b89NNPsuK9OCeUsrIK1Gorpkz1Q22jIDqqiMAWGu67T/4qipOTHVqtOI7dxcWBmJg05s1bRXx8Bmq1/KRCma4AU042ab//gtOt0cvaMyfQpyahUFVfsxQSXCFC2nfgzP49zJv5ML1HjALgzP69lBTqCG7XnqNbNlW9duC90kYz65Ju1HlMYa3Cxs0XK5WNpFgAW//8FZPJhI9/EGnJ8Whs7dHY2mLQ6wkOays5zu1cTy9hyfFsIjPEh5w2XjY83d+d9j7Sm1VX7z3fYPnc0o3H2fXtszg3MIpb6aCkrMS8MTtgdACluaVkncmSfE7N1KRZDDfTaAJHPAYmE/lR5g4JTmHdxGMy+W3x51RUlGPn4Einbn0BcRBHoa6A35Z8IVsMh7ipMZab+G6ivBq+uhjbvwNLNx7n6c/W1Dgmd7ymVLTRWlL2pxAwuuGfYeKL47l+KpK8jHwASgvFC7mzpxP3zZa3RPxoYAvZ51obxyJuUGo0Vn19pxFJJZbIxrH9pxOZeJl8ndjdX6oXBYWTvRtj+j0oK9bKtcuoqChHpVLj4uTSKCHbNaQFpyKjycgvwMu5acpiBvfqwZqde9hx5DiCIDCoV3ey8/LJzs2jVyd5y6Rudi5k60TLQV9nLxJzUhk7/zFS8tNxtJEnrAHcXVzJzBX/DwK8fIlNTaT3jPEkpqfi7CDfBmrTpk2Eh4czY8YMTCYTK1asqHo4HjduHF27yltqjo+PJyAgwMyZwsnJiYCAAGJj5Y94V6kUqFTVD6f339+4yV/Bwd5cuBBFbq6Onj1bs2vXWTZsOI7JBB07BsmO59itNwWnjpG1dT1ZW9ebHXPo3psKo4GS2GjUvv4Nxvr9y09BELhx4Rw3LpwzO7b8s3lVXwuCIFkMR639rN4/eMHKGq+eY/DtLy1eYlwUDo7OLP5jD/cNak1gSBgffP0bT0wawN1jH5AU43Z2Xi/g+XVJVJiqr1VRWXp2XCtg4eQA7mkn7W/a39OZrDwdemM5Lg6iiM7TlWCjssZOoyKnoJiopCy+/OMAHz9T/5AqOz87cq/mUphYiH1g9d9oy6ktMeQZKIgqkP1zNiPyXyuG16xZwxdffEFERAQajYahQ4fy+eefExpat3vBm2++yZEjR4iJiUGr1eLr68uYMWN499138fS0zAqnGbErOOTe59HnZ1KSI5qIa9x8UTt7WRQvOysNW3sHlq4+gIubuJyZn5vNU1OGkJOZJjve7IEevLo5hcXHshjWygF7tXl21a8WP8n6ePbztUQlZdXu1iNzvCZAhxc6EPFjBEETgnDvJg6GyL6QTfzmeFo92oqyojKifo8i+2K2JDHs7OHEhxve4sCqI8SFJwAQ1CGQYdMGYWMrL4s9o0XTiOFpd/Un1Nu76uumzJY62bvyxsPzOXppBwnpNwEI9A5jYJcxqJXSM0sARcU6vDy8WblkIxqbxlmVFZaKDyFLdh8kxMsDOxub6nu/IHB/n+51vrcuHp84ATdnJ1IysujTpSOhAf7EJiXz4Nh76NRaupMEQCvvYA7dOElsViJ3txvAz8fWEJctrlIMbSvf1qp9SCt2/XWYm4mxjBs4nO9WLyMqKR6Ae/rJKwcBSE1NRaVSERAQwI4dO6ioqMDd3Z3s7GzOnDkjWwyXl5eTm5tLWVkZ1rcmKZaVlZGbm0tFhXz3h/Xr6x6YoFIpCAqypVMn6Q9Br746idTUHEwmE6++OpHcXC3h4fGEhfnxzjvyHuoA/J9+AVNFOdozf5ntd+rVH/+nnqdMW4DXxGnYBAZJCyihuVV2A2w9LzeVlZF+agtqZw/c2jfsMFNeXo6njz9KlRqFwgp9SQkOjk64uXuxatl3DB0tbcpjJZ/vy6DcBI42VvQNEi3QTsUXUVBazhf7MySL4Y+fGcuzn69l0+ePMbCrWIpz5EI0D73/G1+9cB+eLg5MeP1H9pyMaFAMe/b1xMrWiqLUIjMxLFgJtH68NQlbEqgol/9Zbua/VAwvW7aMJ554AoDg4GBycnLYsGEDx44d4/Lly3h7197p+fnnn2NlZUXbtm1RKpXExcXx/fffc/jwYS5fvtzoCUf/66idPVE7N/6honW7ruTlZlUJYQBnV3dcXD3M9knluXVJCMCXBzL48kCG2TFLGuhOXIlFQGDS0M4EeLtg3cg6rdj1saicVXj1rX548OrrRerhVBK2JdDljS6kn0inKKlIUrztP+1h7JMjufdZ8wx6sbaYL59YwNsrX5F1fgVGI5tSU4ks1AHQxt6BCb6+OMkYhLD0madr/bop2HtmHSN6TWZ0X/MmreLSQr5f/y4vTf1ccqzRw8eza/82tLqCRovhy3GJYuarAqJSM6qzYLf8ty0RwwqFgvvvHma2LyTAn5CA6uzeyq07SM/K5rXHH6031tcPvIuh3IBGqeHF4TPRqDSEJ0fQyiuEpwbJF1/L3v0SvcGAnUbDOzOfx9ZGw/mIcNqHhPHyQ0/KjqfVanFxEbPz6enpeHh4MGvWLBYsWEBeXp7seC1atCA6Opr33nuPadOmAbB27Vry8/Np1apVA++uyfp1Dddot2vnwJtvtTLLINdF69b+tG5d/f/4ww8vyj6n27Gysyfo1XfRZ6ShTxIfim0CglB5eVcddx8zQVKsVeFRjTqX2gi65ykS9y3HOawbLq3Feui8yNPkR13Ab/BUijPiyLlylOzLhySJYQdHJwp1YmbU2dWNhLibLPp8LkkJMajV8h6KAdK0RhzUCg7MDsPDXpRK2YVlDFl4kzStUXKcj5btIsDbpUoIAwzq1pJAb1fm/bKbM7++Rt8OwZy6Gt9gLNcOrrh2qN0X2lpjTehUy6xMm/kvFMMGg4E333wTgIkTJ7J+/XpSU1Np06YNmZmZfPLJJyxYsKDW986dO5cXX3wRDw8PysvLmTJlChs2bODq1atcvnxZdqahmaYjM706yzL50Vl8+vazrFj6JQPvHg/AsVsdwk+//IFF8ZtixnwlLf09MJSVsfRN+R3ytVGaWYrJZCLveh4u7cSmjfzIfEqzSqsyqNa21pJrCDYu2IaVlYLRM++u2leQreXrp74nOVpek1WmvpTnLl0ix1DdUHU6N5cdGel837kLnhKnUCVlS3d1CHBveGzy7Ww//jsKwYrhPaszP9qiPBZteJ+0nERZsZ6Z8SJnL55i2pPjCW7REjvbalN8QRCY/8mPkmO18HRv0gy4VE5fDudGbFyDYlitVKFWVq+KPDP4oUZ9XxuVGpvbalFffVhezXFtlN0a/52Tk1PV4KZWqykslO9gMH78eL7++muOHDnCkSNHqvYLgsD48eMbfa61cf26ji1b0pg8uWFrtPPn6xacarWSkBBvbG2li7qkxd+g9vbF8/6pqL2qa5gLTh/HmJ+H+8j6s5By+ebFWSRERjB/9yFJr8+NOIXS3omge6ofjp1Cu3Dt59cpiL5Ay4mvUJQSTUm2NJeUgKCWXL14hoK8HDp268uRvVvYvWUVJpOJVu26yP55OvlqyC0urxLCAO721njYK832NURyZj5l5RX8uu0U9w7qCMD249eITspCaS26UqhV1qiU8h0qKowVGAuNNe5jatfG9bH8L/JfJ4bPnj1L9q0b68SJ4nhFX19f+vTpw759+9i9e3ed7503r7q2ycrKin79+rFhwwYAM0P3O9Hr9ehvaxzSarV1vrYZy3j8/ppP/utWLGbdisVV2yaTifdfniF7At2dFjmN5eUHhzD7y3V8t+YQI3q3xdHO/LPj7ymvC9nWz5bChEIifohAoVIgIFBuEG2B7FuIS2HFacWSL3AKK4F1321BUAiMmjGczKRsvn5qIVnJOdg61t+gcSc/xceTbTAgAIG24nsTi4vJ1utZFh/HW63bSIrTcY605ilBEMhbuVzWOQoKBVuPr0AQBIb1uI/s/HQWbXiPnIIMNDJrX39cvpCEpDgAbsZEVJ2TJR3pT9w9SNbr/69ZfLBuuzu1Uk1bn1D6tewhOd4XK5bUecxGZUPHlq0Z0kO6RZirqysZGRksWrQIvV6Pzy1rP51Oh4OD/KE2EydOJD4+no0bN5oN4pg0aRL33y9vCR3ggw/b8PlnUTzySAB9+4nZur/+ymXliiReeDGEwsJyFn0fy6mTuZLE8OOPf1tvo5VSac1jj43g2WelDZHJO7wP27A2eN5v/tCetWU9xdE3m1wM52dnkpVad+nIneiSIlBYWWMs0qK0E2vKy4p1lJUWYkgSM/9qFy/0BZmS4j3x4rtkpCVjMpl48sV3yc/NJvLaJYJbtmH2G3U37dbFM3e5M+vPJL48kMH4DmJJxLarBWTojHww2puU/OoEQX2ldn07BnP4QjSvfb+Z177fXLXfZIJBnYKpqKggPCaVIB83yedWkllC9KpodHG6mgcFZE8tbea/UAwnJVV33t9e5+vlJS4xJyZKywQVFRWxYoV4M+jfvz/t2tXdZf/pp5/y4YcfWnK6zUhEcq2ZBUMZmsoip5KZ81YhCDDv1z3M+3WP2TFLGuhCp4QSsTQCg9ZAxW2Tq1ROKkKmhlCaVYqdnx2OLaU1IT371eMsff1X/vxmM/lZWk7vOkdBlhYXL2de/uE5Wed2Li8PtULBws5dCLMXheXNQh3PX77MGRlL1ZL/1yz4/5055nV+2/kVW44tR1uUx/kbR9EW5eHs4Maz938gK9b2vZsRBAEPNy+8PL2xUjRuQAtArq6QpJw8VFZWtA1oXJNVU7Lo0AqEBpYbegZ1Yukjn5plkOvis+VLGnxg6N+pB39+ttgsg1wXffr0YcuWLWRnZ6PRaOjUqRMZGRkUFRVZ5CYB8Morr/Dggw8SESE+6LRt27ZKZMvll2UJuLqpGDK0unRr6FAPduzIYPWqZL74sgP792USG1ssOWZ9H3+DoYwff9yJv78748bVPRHSkFUtHk1Go9l2hb4UQ3amZZ2qTYzK3hl9QRbXf3kDOz+xTKUoNYpyfTFqJ/F3atDloNRIu+6FhLUzs978eOEfjTq/J1eLWmLxsSwWHzN3anj094SqrxsqtZv/8iQe+WAFl+9YlesS5sd3L00kKSOfewd2onsb6Q3eMatjahfCYNFqZzP/hWK4LuQU7mdlZTFu3DguX75MmzZtWLeubh9AgLfeeouXX67ObGm1WgICmsaZoBmRTxfVdGZoSmqzyHnmLnfaeVtWF1rnx82CBjo7Pzu6vdeNrHNZFKeLN05bH1s8untUWam1eUJaBhag+/AuvLDgKb5/6Wf2rjwomtqH+fLS0lm4eDrLOjddWRkBGk2VEAZoZe+Aj40NKSUlkuPsmCvPLk0OncP68uT4t1m27TMOXdgKJhM+7i2Ydf/7ONlLz7YA2Nna4eLsypqftjb84gaoqDCx5cwFLsYmYMKEv5srpUYjG0+d457unenbWv7Us78DUz13z7PxV/j52BqeGypt9DHUfy0+ceUc81f/whuPNjzaunPnznh7e5Obm0tAQAD29vaYTCamT5+Oi4vlD7g+Pj51CuAnn3ySiIgIjh8/3mCc1FuewpcuFdCli5g5vHKlgIz00qrX2NtbS55K+fHHM5g3bxVDh3ZhxAixnnzPnnMcOnSZl1+eSEREIhs3nmD9+mP1iuEbz8249ZVASXzsbdvVKGWWIv0d+A6YTNz2JZTrS9DGiYOMxHp6Ad+BD1Cal4GhIBvnltLsEa9ePF3nMZXahoCglmhuK3tqCKlX8oakh7+nMwcXv8DRi9HcSBB7VtoGeTOgS3V97yfPysvSFyYVggA+g3yw9bZFaKRdaDP/hWL4dhGamZlZ4+vAwMAa77mdyMhI7rnnHmJjY+nTpw/btm3DvYELg1qtrreMopnG07Fb3Rf3xtJUFjmVfP/q5CY/R4VSgVdfL4xFYmOG0k6er+iJLTVvBL1Hdef45lPY2KkZcH9frp8UB5j0v1e6eb+rSkVySQl/5eTQz00UlidyckguKcFNJd2F46628nw+v9i0mYSsLBY9VXvj1enrB2vs69Z6AKevHUCt0tC3w93cSLw1KbDdUMnf98lHZvPNkk+5euMKHdp0avgN9XDk2g0uxMSb7WsX4Mfm0xe4kZz2j4vhFY9/y6zf5/L66GcZ1WEwALvDD/HF7qV8+cBcCop1vLXhc3ZfPSxJDO/47jemvj2bj2e9xoTB4iCGTYd2886Sr/j5nc/J0xXw7Gdz2Xx4jyQxDOKKX+WqH4CDg4NZicTatWvJyMjghRdekPGT14/UxEpQkC3R0UV89ulN1GoFggClpeLKTliYKLqSkkrw8JD2d7Jr11nc3Z34+OMZVfsGDerIuHHvcfjwZRYtms2lSzFERTVU9195/rUbGApW1nje1zT9Do3BpXUv1C5eZJ7bfZsLkT+ePUdh6yHexzvPXlxfCDPenDWl3pUJa6WSSQ8/y0NPShtZvmZGsOTvXR9frNyPr4cTD4/qadZEd+Z6AgW6Eu7uLT3RUYnaWQ0KCL6vac6xmf9CMdyzZ0/c3NyqHCSmTZtGamoqp06dAmDUKNEQvE0b8QM2e/ZsZs+eDcDRo0e57777yM3NZdKkSaxcuRIbG/ldps38vaxaNr/OY2q1mpCw9nTtPUByvKayyKlk2gj5LgANkXY0jeS9yRh1t8SwgxL/u/3xGSRtCXfZOytrX/oUoLRYz+ovxNp4QRBkieF+rq5sSUvjnevXUN8aw6ovL791TF7WVQ57Ll3mfExMnWL4j93zqTXlJgjojaVsPLJM3ESeGP7ljyWUl5cz67UZONg7Yqsxb6D7c9n2et5tzoXYBBQKBVMH9GbVkZMAqJXWONlqyCqoY4mzCZAq5uZtX4iXowcTu4+u2jexxz0s/2sD3+5dxqbZP7L27Daup0pzEnh9wSf4enjx8OhqX9jp99zP4vUr+ejn+Rz7aT2/blvH5ZvX5f1A9VBYWEh+fn6TxZPDk08F8dmnN8nLM6LXV5c3uboqefKpINLTSwkM1NCuvbT65rNnI1EqrcnJ0eLmJpYG5OUVkp9fRGbmLcvAQE+Sk+tvRg15/3PAROyHb6L2D8Tv8erSKIVajcrLF2sLaq6bGoM2B6Wds1kDXWOp77NvNBhY8+sCvP0CGXbPxAZj9QlqOIu84EgmiXmGel/7+cr99GgbwMOjeprtf2fpdi5GJlvkSx8wJoColVHkXcvDpX3TlgH+r/JfJ4ZVKhWffPIJTz/9NBs2bCAkJIScnBx0Oh3u7u5VThORkWIWLPu2Lva7774bg8GAIAgkJiYyePDgqmPvvvtuk82sb6ZxrPr52wZrDzt07c2H3/yGSoJlTlNZ5NzO1ZhU5q89wvW4dADah3jzwgOD6BAqvyY0cWciyXvMO6aNOiNxG+MwFhkJvKf+1Y4qJGgguT6gj7cI4nJBAfHFxZTeEsEAwbZ2zAwKkhWryZHieyozZPptPtZaXQFaXbWJvdwGOm1xCZ5ODrT1N/9MqJXWFBT/fWJ4+r1jKdA1HD8+OwkTJo7dPMOAVuJY7b+iz5OYm1JVS+ykcZT8c0cnxWMymdh/5jjDe4kNsYfPnyQuJbEqhouDI4p/wGHj76BFC1vmL+jEieM5JCWLJUMBARruussN5a3ypldfk+797OHhTEpKNvfe+wFdb2UQL1+OpbCwBD8/cfUyPT0XV9f6hax9e3FFw2vyQyjdPKq2/27kXluu/vgKdr6htH7wXbP90eu/ojgzgU6zFsqK98r737Loi7n0HTSSAcPEJsOj+7dx6uheHn9+LlE3wtm7dQ27Nv0hSQxL4eBNHZdTSvhqQsODS26nRG8kI1dXb4lSfSRsSQATRPwYgbXGGiuNeW9D9/ebPmHz/zv/dWIY4KmnnsLOzo6vvvqKiIgIbGxsuP/++/nss8/w9a1bjBhu2UOZTCbOnDljdiwrq3mU4b+N+i6uVy+eZv3KpTz4xJwG43T115BV2HiLnEq2HgvniY9XUWEyVemxyMQMNh+5ws9zH2T8gI6y4qUfFwW1Y4gjbl3EbGvO5Ry0MVrSj6dLEsO/hH8v74eQiINSyQ9du3EgK5PIWwKrtYMDwzw8Uf2DvtwLXt7yt8QdNXRsk9mh2apV5BUWU3ybE01+UTFZBTrsZJZdbT98VNLrxg4eSJ/O0j5/bXxCuZJ8g2dXzsVGqUYQBEoMYr1rJ39xZS06Iw5ficNzOrZszbmIcKa8/Ry2ahsEQaCoVBSJ3duI5xQRF02A17+nibAxHDmSjaOjtVkDHUBmph6DoQJ/f3n9CC+8cC9vvvkLhYWlHD9+FRCf9xQKgTlzJpCYmElKSg5DhkgbcW/buh365ET0GWlV1mr69FR0F86g9g/EoZP0UeUblizE1cubIfebl4jdvHSBIq2WrgMH88qCpRhvs2C0FGOJlrIS+Q+Lh/duwcXNk1fe/7ZqX6+7hvHkpEGcOrqXD79dzo3w88THRDb6HKXgPlJMzAkCnL+RVLV9O57O8ic9Aujzqq8pZSVlNUY0NyOf/0oxDPDQQw/x0EN1+2LWJqRkT8dp5h/h8yXr+PDVmTzx4jtmT/jLFnzM6x8tQKfN55uPXubYge11iuHbbW9mDfDg2bV1W+TI5aOfd1FeYcLJ3oa7OotNECcux5JfWMJ/lu2WLYZNRhMqZxXtn29f1QjhfZc35z88T3lpeQPvbnqmnT1DmJ09H7Vrx0tXLhNka8eLLVsy2kv+7+q/iYqKCh5/eBYAXh7ejRbFYT5eXIxNYOGO/QBkFehYvOsA5aYKwnzlTWf8dvkfDRsACAJjBw+UHPODe1/imRVvk6nLocRY3fTl5eDOhxNeJjEnlVbeIfQKlia+vnnpPaa8NYu0nKwqEQzg4+7Jd6+8T1xKEu1DWnFXl571RPnvYcniOMLC7Oja1dls/4L5McTEFLF6jbyfc8SI7gQGerJy5QFiYsS64JYtfXnkkeG0aiVmHo8d+1pyvLQVP2PISMN12KiqfUoXV9JXL0fl7YvDlzLE8OIFtOzUpYYY/v3LT4i5Gs4flyNxdpc2EClh989VX+vzM822y416SjKTUMicHAkQfv4k1ioV+bnZOLuKmfSC/Fy02jxyLogJB9+AYNJSEuoL02RUyg1BqHsR65Ex0kvWbidgVHMDf1PzXyuGm/n/lyVfv4e7pzcjxk2p2jdy/FS2rFnGb0u+4PuVu9i16Q+ib4TXGeOu727W2HenRY4JmPFHguwJdKnZBTja2XBq2St4uohLlll5hfSa+RWp2fJnw7t0cEEbc4d39S3l49q59mlDd/L5zPn4tfTh4bcf4POZdddcCwK8vqz+yVbppaU435oud6mgAEPFv+8hcsG6ufi4BTJ56NMsWDe3ztcJCDw/eV6dx+9kyuNjcXN1Z+PyPQ2/uAHu7tKemPRMtMWiMNQbxZIcB1sNwzrVbeVYH/X9TwgyH/Zbe4ey+6WVbL9ygOjMeADCPIMZ23koKmux6WvhQx9JjtchtDXnf9/J+gM7uREfDUDboJZMGjYG9a1my9//U/dn89+AyWRqdNKksLDcEodA0tJy8fAwb6BrDPr0FNTevihuKyVTqG1QeXljSJPuB1wXhtJS8rKyZP++cq4er7q+lZXoyLl2m3PHrVB2vvInqbl6eJGRmsRTDwyhfWfxQSQi/DzFhTq8fEXxmJmegpPL/42TRmWj9eyv1hHs48orD1VPjtSolbQK9KBdsGW2fgGjm8VwU9Mshpv515GSGIPJBOdOHqZH38EAXDxznNTkhKqeKXtHZ4R6lumbyhanNrq3CSQzT1clhAE8XOzxdLHHq4F6vtqwD7Qn90ou176/ZlYmUV5Sjn2APZlnql1TPHvVPu468mwURr2x6us6Gskl+Ys6WFsTVVjIvBuiF2tqaQmf36y5tCgAr7dq3XBAC2joBhuddJWyMmPV13WmX2RkdxUKBV6ePiit5Tl51IWDRsNz9wznVGQ0KTmiJ7Ofmwu9W4ViZyPfncYEWFtZMahnd8YPHYR7I+zFALZc3IuLnZNZAx1ASl46JcZSWnoGyYq3Zu9W3JxczBroABLTUyguLaVNUNOPipUqxMrKypg6dSp2dnb89ttvdWb9f/7551r3387zs69UfR0XV2y2rdeXo9WW4eAg/9Y6evQ7dO4czPLlr5ntnzVrIRERSRw69IWseIKgwJidRUVpKYpbjeLlJSUYs7Mk/1082KlVZTCiwy9Xb9+Gk5s8cWnv3xoEKEyKxEqlQeNZXQamsFZj4+qDV8/R9USonUeffYMv3nue4iId506KU/BMJhOCQsFjs94kNSmejLQk+g4cITu2JVQ2Wh+/HEOIr/vf0nhdmlOKocCA6Y6EhVNLeU3hzTSL4Wb+hYSEtSPy2iU+fOUx1DYaQEBfKvrvtmrfBYCE2Eg8veue6tRUtjiVJGdWD5iYM3Uwj/3ndz7+dQ/3DRaXkDcduUxajpZPZ8kf6xq/OR4AbYy2RoY4bkNc9YZQtxjuN74XXoGeVV83Zom/s5MTx3NyOJiVhQAUGI3sycgwe40Jy8XwiYgbOGg0dApqYbZfbzRSXlGBrVrNG/dPIFtbd91gz3ZD8HTxrfq6qep8Zz70DJ9++z5bd29g/KjGN9nYqlUMtTALfDvL5r3Ppv2H2H/yFAdOneHwmXPc1b0r9w0fQsdW0pu0buftjV/Q2b8tA1uZL9W++uc8rqZEEv7RPlnxZn3+Dj3aduLuO5xenpj3OhduXCN7/yVZ8Y4cOYKjoyNdu3Y125+UlERpaSlhYWFMnTq1amRzfVhbW1NcXIxGo2n0ZyUr67Z6zTKT2XYlvXpZ9qBSm7bPzdWRny9//LRNi2CKb94g9uO5uI0Uy81y9+6gvLgY21YS7bwkrPUPmzSl1v110Wqq6Dl+4asZ2Lj50mpK03iQDxg2Bt+AIDav/pmEWHFlMCi0NfdNe5LgMNHace3eK/WF+Ft49v67SMrIJy1bi4+76BKSml3A5ZspBHq70D5EfnbYoDVw46cbFCbW8rlonkBnEc1iuJl/HbPf+JT3X55BbnYGpSXV05vcPLx5/s3PSEtOILhl23q9iaXY4sihy/TPa+z7ds0hvl1zqGrbZIIpc3+1yCpHEvUkwZ74+JFav7aEV8Ja4alOJL64iAv5+dhaWZkN3Wgs93z8Cb1atmTfB++Z75/3CRdiY8lbuZyRXbrUG2P6qDm1ft1Ylv2+GCsrK75a9DELf/oKZycXKtPpcq3VALK0OuIzsigs1df47xvaUbr3cpCfLy89+hBPPXA/O48eZ+vBIxw9e56jZ88TEuDPwnfeqCpFaCwFJTqLVkzqIk+rtahr/vDhw/j7+9cQw3v37iUlJYX33nsPexmfy3vuuYd169YRExNDaKjlWeqJk8SHsA3rU3F1VTFkaHVmVK1W4OeroVt36Zm5996rHoudlJRltl1SYuDmzRRsbeWvJLiPvpfEmxEUR4r/zI7dM0FSjKfnide9H955A6+AQCY8XW3TpraxwTc4lEALV4e6vfqbRe+ri8z0FFzvaKD7u3lhkCe5xfU/jM35biPXY9O4uvrtqn0alZLHP/6DDqG+7F0gbyooQMLWhNqFMDRPoLOQZjHczL+O4LC2/Lz+CIf2bCYpTvQ4bRHamsEj7kV5a4zrO5//KDne/MN1z7ZXWwu099EwILT+m6rkadEWXIn6zW/8U3xOWq7k17r51F+H7KxU8vwtsTDk2FGCbO34rpO0Jiqp1PZ7KtbrJS9752qlu7+4Okpr7AFza7VSfanZttyM4pmoWLafvVTnZ0KOGK7ETqPh3iGD0ajV/LB2A8WlpcQmJaM3GCWL4RFfP1z1dURatNl2ibGUvKICnG2ljcAF6PJQdYNWePQNs+2S0lKyC/JwdXSWHK8+jEYjOgm2cbWRk5MDwMyZM+nevTuurtV/B4IgMHdu3bXntzN5srgidf2ajoAATdW2pWzdeqqqaiE/v5Bt205VHav8c+jYUf5Kl3P/QRhzs8n483cq9GKDpEJtg9cDD+PcT1qj5aB77wfg+plTeAW2qNpuCuJ2LEUbH07Y5DewvVUqUZyZSNSfn+EY3IngMc/Iijfzvv606dCNr37aaLb/vTmPEBN5jT92nZcUZ9U5adfSB3u4MrRVw2VxNxMzCfFzx9WxOkHj4mhLiJ971UQ6uRREFoAAoVNCiVkTg8Zbg0cPD1IPphLygGWjyv/XaRbDzfwrUaltGDl+KtoCsTzB0cny+shvD2c2WCrbO8iO3x5qgY2y9jrkrV8+ZfH3l0Nl/ZfaRV4m6LWR7zX8IsSb/rLL0v07Dw2oedPUGY04KOXX1Y79+JOqryNTUs22i/R6ricn42RrKynWB8tqH8ZxJwIw/6XNks/xsQebbgDAkauRmEwmrK2sLKoRvpP07By2HjzMzqPHKSwSV0x6dmjHhOFDcbSXvhKSki921gsIGMqNVdu3M7zdXZLjJaaLzgeCIKA3Gqq2b2fsgGE19tXFRx99VBUvJSWlavt27Ozkr/zs2bMHQRAwmUycOnWq6uHGZDLJEsOVPP9CCDpdGUVFZdjZibfSoqIysrMNODhY4+oq7eGke/eWgMD581HY2dnQunV1c5RGoyIoyItHH71b1rlV4jFuIm4jxlKaLDoo2Pi3QHGbrZ8xPw+T0YjKo/byq0oeeeMdSooKMZSWorKx4fS+3dw4d5bA1m1qOExIRZdwHSulTZUQBrD1DMRKbYsu0bLBLLU9eObn5aAtkJ4seHt7aoP3C0EQxbAUyssryMzTUVZejvWtwUXGsnIy83RUVFQ08O7aMRYa0Xhq8OrrRcyaGKxUVvjf7U/WmSyyL2Tj3vWfH7f930azGG7mX8m2db/x5/JF5OeKQ1OcXd2Z/Mgsxj/wmMUx68s5no4vYunxbOYMqf2m0L/z3/u0nXU2i4TtCRjyDTi0cMDvbj9SD6fiN9RP2oShvylzvTcjg4sF+Uz288dZqeTV8HDii4vwUKv5tH0HQmSIkmMRNxAQBaq2pIRjETdqvGZIh/bSgknMIJtkZnNnPigvG1UfeqMRJztbXhh7Nyrrxl1q352/iFNXwjFVmNDYqJkwfCj3DR+Cn1f9IqY2Zg0Ry2gWH1pRYwKdjVJNiEcAg1v3lRzvjUfE0cqfr1iCr4cX00dXZw81ahvCAoMZ1XeQ5HiVqwOVwrU2uneX34zUpUuXJqstB1i4IJbIyEIWLOxYJYZLSyt4683rtG5jz/vvS6vLXbbs5VvnN4uQEB+WLZM2LlgqCrUa29CajW8ACV98SHHMTTqt3Vn/OX70Lqf27mLe6o3kZWUw/+Xnq5rwdPl5jJ8pP1lQri/Cyqbmqo1gZU15YV4t76id7+a9WvV1enKi2XZpSQlx0RHYaOQ/PNV7hZFxGQ0L8OBqbBpPfLyaWZPEevqlG4+TU1BMp5aWeW4rVIoqG06FSiE20mkNGAuN6G/UrGFvpmGaxXAz/zp+/+kb1v660OxGmJeTxU/ffYS2II+Hn3xZVrx1jwUzc1UC74z0YWx7cfl329UCPt6bzoJJAeSXlPPyxmS2XyuoUwzfzhcr99d5zEatpGOoD0O6137zqY2cSzlE/W4+8tYuwA5ttBalg1KSGH79l/rt0ixla3oaN3Q6ZoWEsjElhbjiIgAy9Xp+SYhnXjuJ4hV4cICYbVx17DjuDg6M6FJdeqFRqWjl68v0QdKWb5+f/LGMn0IeBdp8Nmxfw40oMTvVNqw994+dgpPMpf6uIS24GJdAsd7QaDH81yWx8cfayoqOrVqRr9Px66atZq8RgLnPPNFgrOeGimL4TOwlwryCqrYt5Y1HRTF87NIZ2ga1rNq2lHvvvReALVu24OrqyoAB1Q15SqUSd3d3vLzk+TQDLF68uFHndScJCcX4+Khxd6/OtLq5qfDxUZMQX1zPO2vn0qWmPT/JSBB2cdevYufgSEj7DiyZuwIEgU797uLKiWMc3bLRIjFsbeuIPi+dvJvncGnVA4D8qHPoc9NROkhfCdy/Y33VQ462IJcDOzdUHau8h7Tp0LXW99aFCVBZCYxu58jDPV3xcbTcYWb66F68/v0Wtp+4yvYTV6v2CwI8MrqXRTFVzioMt7z0NR4ailKKOPfuOfGYU9P0Dvyv0SyGm/nXsXPj7wC079KL/kPErNVfh3dz9eJpdm78XbYYfm9nGt6OSqZ0q77ATu3uyrJTOXyxP4Ndz7bkj7O5hKeV1BOlms9X7m/Qmah/pxDWfvwYNqqGL6LJe8VRzD6DfEg7Itaoqp3VqJxUFCZI6yJv01Oeo8DWH3aRnZzDzP88XO/rkktK8FSrcbC25qpOi5NSySft2vPa1XCu1+P2UBtLnhZvmMeuR9AlOKhq2xLCAuR5Q+8+tZacggweGvlCva/LyErn2VcfJTu3uib51LnjbNu7iaVfLcfTXboIG9G1AzHpGXy3dQ+ezo6obystEYCZw6UPyKh8T3l5OWeu1PTXrnT3kCKGK/nigbfIL9aiLSnEUSPWzGtLCkkryMDF1glPR3lLrT++/Rm52nwKCrU42YsPnQWFWpIy0nBzcsHHXVoWu8ut5sn4+HhcXV2rtpuKlJQUrl27ho2NDQMHyvs/uB2DoYLi4ppDcYqKyjEY5C9/v/XWr/z11zV+/HEOrVuLQzYiI5N58snv6N+/PZ9+avmqWGPJy8rEu0UQAElRkQS1acebS3/hlXEjyUlLq//NdeAY3JGcK0eJ2/Y9qbemHOrzM0AApxDpPQoduvQGQZxKqrG1J6RVtXuL2kZDQItQ7n9I+rVm76yWLD+dy6Yr+WwJL2DHNS0j2zgwo7cbPVvIzzA/Pr4vNxMzWbbtpJk5xxPj+/HYuLqbwOvDtb0r+RH5FKcV4zPYh+g/oquO+QyyzLv4f51mMdzMvw6DXo+bhzeffL8aq1s1VmPun87M+++iuEi+xVBMtrhsdDhKx+AwseHheEwhCbnVU+qcNVYoZC6h1rdSf+JKLPPXHuaN6Q3X+pVklKDx1BB8f3CVGAZQ2ispyZAm0OVy5eg1YsPjGxTDRWVleN6qMUwsLqaVvT3tHB3x02iIKyqy6HtfnS92exeVlnI1KQkrhYIejejul8K1uHMkpEc1KIZ/XL6QrJxMFIKCAH/R+i0pOYHsnEx+XLGQd16WPsBj36WrZBWIDwxpufnizkr/Z5mr9Z6urnIskyXx+p+fcjHpGnteWlklhosNJUxeMotuge357fFvZMV76pM3OXPtEhd+31UlhgtLihn67FR6d+jKtm9+kRVv1KhR6PV6jEYjSqWS69evk5CQgLe3dw2HCSmUl5fz+eefs3PnTkwmE+3ataOoqIh58+YxZ84cJk+WV/vq6akmNbWU335L5N57xemM27amk59vxM9P/gS106dvYGtrUyWEAVq39sfe3oYzZ2qWFP1fYq1UUqzTYTToSU+Ip8fQu6v2Vy7Xy8W3/0R08dcwaHPQ51U3kqmc3PHpJ71R77MlawEY2zeIgOCWfLZ4rUXnU0krTxs+HufLm3d7sfZCHivP5rLzupad17W08bJh0xMhdfaW1MXns+9l9uSBXIhMAqBb6wACvCzvg2kxvgUtxovXJ1sfW2zcbChMKMTWzxbn1s4Wx/1fplkMN/Ovo/ddw7h66Yx5fd+tr/sNHlXHu+qmnbcNl1JKeOyPBDRKBYIAxbcyN138NQBEZpbi5yRtKWzHN08z9Z3fmPfMWCYM6gTApsOXefeHHfz09jTydSU8+8VaNh+5IkkMC9YC5aXlZsbpFcYKSnNKUci86DY1LioVCcXF/JGUSJZezxAPscZPV1aGowVNdJV8sWkz327bTonBQI+WocwaNYoP1qzlncmTeKD/P+eRefbiKdQqNYu//I1WoWLNZ2R0BLNef4wz50/KinU+Jh4EcNTY4mynQVHPkJiGWP1109v1RabH0MLNDx/n6oytt5MHLdz8uJEWIzve1ZhIQv1a4O9ZPbbbz8ObUL8WhEfLF3Pbt2/n+vXrPPHEE+h0OtatW1d1TSguLqZ///6y4q1YsYLt282t8QYNGsSnn37KsWPHZIvhuwa48efaFHbvymD3rowax+Si1Rbj51fzfUqlNZmZ8idbNiW+waFEh1/mmUF9KC0poeUtd5ncjHRcLRzTrrRzos30D8m6dICiW583O59QPLoMw1oj38px+8l4i86jLhxsrHi4pyu2KgWf7ktHp6/gRkYppcYK2WIYIMDLpU4BPOKFRVyMTLbYltMx1BHHUOkOMM3UpFkMN/Ovo2XbTvx1ZDdvPzeN/kOryySKCnW0bNPBrCZs2D0ND0b4dJwfM/6IJ0NXRrGxevnS28Gaz8b5kZCrp62XjWRv4tcXbsHXw4mHR/Ws2jd9dC+WbDzOf5bt5ugPc/h1+ykuR0kbeeoQ7EB+RD4RS0UvUH2BnmuLrlFeWo5Lu8ZNGWssfVxc2ZaexrL4eAD6ubqhNRrJ0uvp5GTZlKNl+w/w8QZz+6NB7duRnJvLhlOn/lExrC0sINAvqEoIA7Ru2RZfbz+SUxJlxVIrldjb2PDS+JFNfZpNgr7MQGFpzey+rqQQfZmhlnc0EM9gQFtUs3SmoFCH3iA/XlpaGjY2Nvj6+rJ582YEQSA0NJTo6GguX74sWwzv2LEDa2trPv74Y9544w0AbG1t8fT0JP7W51sO48d7E3WzkIsXzYVqt27OjB8vf6nazc2BxMRM9u+/yPDhYub7wIFLJCRk4uXlLDteU3Lf08/x7ZxZlBQW4ukfwF3jJhB1+SJF2gJ6DB1ucVxrjT0+fe+t83jayS0YCrJpMerxBmN9+f6LXDh1hI+/X0VImFgqERt1nbefm0b3voN57UPpo8CT8w2sPJPL2ot5FJSIpTADQ+2Z0dsNZ9u/RzbJaW6+uvBqnccUSgV2/nb4DPRB5dhcPyyVZjHczL+OZQvmIQgC1y6f4drlM2bHfvz2w+oNQZAkhtt623DkhVZsvpJP1K1pUa29bLi3oxNqa/EJ/8dpLeoLYUZ0chYmE+w/G8nwnqLh/OELUcSl5FQtZbs42EouuwgYFUBBZAH5kfkAGPINGPINCAoB/5H+9b/5b+bZkBDUVgpSSkro5+pGJycnbuh0DPHwoI+LNGuhO1m6Zy8KQeCThx7kzd//AMDNwQFfFxeuJsgTnE2Nq4s7SSkJHD99hLt6iw4Ix08dJiklATcX6X7FAHd3bs+2s5dIys4hwF1+pvBOsvLyuBEbj6+nB6EB/py7ep2VW3dgMBoZ0L0rD46VN8LWz8Wb+OxkPt2xiCcGTgXgl2NrySrMJcQjsIF316SFjx9RSfG89f3nvDhtJgAL1/5Kem4WrQPlu7HodDrc3MTfW2ZmJt7e3jz00EMsWrSIggL5mdLMzEyCgoLMGvJAFMSZmXV7kdeFtbWCN95sRUSEjugosXyrZZg9bdvKH8kO0L9/ezZuPMFrr/1EQID4WUtKykIQ4K675NXIS8Xa2RWle8Of664DB/P9gePkpKXi3zIMpUqNf8swvt2xH3vnv++BXRt7maL0WEli+NLZE2hs7aqEMIjTTG3tHbh87oTk7/nk6gQO3tRRYQI7lYIZvd14tJcrQW6Nt0dsKrTR2nqP50fkk3U6i44vd5Rt0/m/SrMYbuZfiaThCzLGZNkoFUzt7krerWlBLo14uu8Q6sv5G0lMfedXbNVKEASKS8XMV/c2okdoRHw6/hKzOQ5BDrR/vj2J2xOrpgrZB9oTOCYQhyDLbqxNhcbKiudCzOt52zg4MLe1xHGutRCXmUlbfz+eHTWySgwDuNjbcSOlpkft/yX9ew1k8851vD3vJdRqse5Tf2tgQaU4lsqBK9epMFXw497DaFQq1MrbP3MCr9wrveTnYsQN5n63CMOtDOsjE8bxx7adlJeXYwKi4kUfWTmCeGzn4Sw88Ct/nNrMH6c233ZmAuM6y8/2TRo2hk9+/Z4fN6/ix82rquMJApPvHis7npWVFaWlpZSVlZGTk0ObNm2q9ltikebk5ERaWpqZkE5PTychIQFnZ2fZ8Spp29ahTgH81VdRJCaUsGBhpwbjPPfcOE6evE5aWh6JidUNnL6+rsyaJf/3V4k+I43iqBso1DY49TS3zAt6XZo/OYCjiyv64mIuHBGnboa064BXoPQkwt9Noa4Ae4eAGvuVShU5WdKHW+yLFFc3VFYCvVrYkV1UxteHzB+WBGDBpJrf6/8Kx1BHCpMKMZWZsPUVvdmLU4sRrAVsvWwpTivGoDWQtDuJltNa/mPn+d9Esxhu5l9HU9d+Afx2OodFx7LILhTFsLu9NbPu8uCxPvIzdt/OuZ8H5v5Ceo6OolJj1X4fd0e+nTORuNQc2of40L+T9GyYY4gjHV74e7I/tSF10huI9cE3dFryDMYaC3kjLbC4crTVkJaXT+ltS+f5RUVEp6XjqNHIjicJiT/vk9Nnc/nqBeISYygtrW5eDGnRkiemz5L1LQuKqu21SvQGSvS3lQrI1HIrt+4wKzVYsXkbJsTGOoDM3FwOnDotSww/PmAKV5Kuc+TmabP9g1v3YeZdU+SdIPDi1Mc4d/0Ke08fNds/ss8gXpgyQ3Y8d3d3UlJS+OqrrzAajfj5iZPetFotjo7y6yN79+7Nzp07efhhsWk0Pj6eGTNmUFZWRp8+lnX1N0R+npHMTGm+r25ujqxZ8zZr1hwhPDwOECfPTZ06CCcnC3xyy8tJ/nEBeYf3gQlsw1pTUVxE0qJv8H3sadxH112ecCcVFRX88p/3OLRxnZkP9JD7H+Dx9z5qUv9mS3FxdSc1KY4Th3aZuRClJMbi7imvbEUAjOUmDkXVLPup7H/9J8Wwezd3ipKK6PxmZzRe4jWzOKOYK19dwaO3By5tXbj0+SXyb+T/Y+f430azGG7mX01udgbl5eV4eFlmTg7wzcEMFh7NMhNyWYVlfLQ7jbziMl4eKk/QtQ/x4fzy11l34CKRCWLGoG2QF5OGdkWtEv+kVn4gz7u1wlhB1vksCuMLUToq8erjRWluKbY+tijt5DWqRZ6LQmNvQ2Ab84u10WCkotyEWqNi/DOj0eU17MxxMjeHeTduUFJe00IKLBPDd7Vpw9az5xj6/gcAxGVkMuS9DygxGBjZtYvseNHJV7FR2eLvaf7wYSwzYjJVoFKqGdVnKoUlDS+tOzo48vP8Vew/vIuIqGuA6DM8fPBoVEp59XddQpouaxabmIxKac0rMx4hJTOTFVu242Rvz/LPPgITTH3lTVIzpY+oBlBaWbN4+seci7/ClWSxwa2Tfxt6BDWcxaw1nrWSNZ98z19XznM+QrR/6962I/06yR+QATBw4EDWrl2LXq/HxcWFzp07k5ycTElJCa1bt5Yd75lnnuHcuXNVJRFFt9xQPDw8ePJJaRMN/26cnOx4+ul76jz+4487SUnJ4cMPpzcYK3PzWvIO7TXb59irP8LS+WjPnpIlhncu/4WD681dGkwmEwc3rMW7RQvGzpBu6fd30a3PIPZuXcNnc2fh4x8EQFpyPIIg0KPfEMlxpDZS/5Mk70tG5ayqEsIAtl62qJ3VpOxPwWeAD47BjhRE/bONl/9NNIvhZv6VHNy1kRU/fEVOZhqt2ndh8vRZbFm7jPsfeoqe/YbKivX7rVnzvQJtGd1ObPraHaHldEIRv5/LlS2GAWxUSqaP7kWuVryh3j53Xi7GIiPXFlyjOF3MJDq0cMAh2IGIpRH4j/Qn8B559ZufPzaf0M7BzP39lRr7464msOzyQjoPlJaFXhIbS3EdQtjSXNC7kydz6Oo1riUlIwA5Oh3ZOh1OGg1vT5RuqVTJgj/nEuTbmpenfmG+f93bJKZHMf+lzbQP6VHn+yfPHEPrlm2Z9/ZXvPDWkwQHhvLSs29yz93SxcKdVJhMDOsk1i462WoanTkrLCmhVVAgw/v1pryighVbtuPr6YHqlqOHj4c7kXHxFsXuEdSpTgH8wqr3iUyLYc8rv0uO169T9zoF8PT35nA1JpKLf+xqME5YWBgvv/wyBQUFeHh4YG1tjYeHB88//zy2Esd23467uzvLly9n/fr1XL8uDlRp164dEydObFSZxP8lx45d5erVeEliOO/QPgQrawJffpuEL8WR1lYaDUo3d/QpSbK+7+FN60AQGPXQI/S/ZzwAJ3ZuZffvyzm8af2/QgxPf+oVLp4+RlZGCqlJcVX7PX38ZXnTn3hJ/oNWYzGZTHKq/igrKsOQbyBhawJuXcTVzdwruZRklpg5EClU/6wb0X8TzWK4mX8dJw7u5JuPzC9eLdt05OrF0zi7uMsWw/oyE96OSlbPCMbqlifm9J6u3PVdJIV6y2bD/7T5L75dfZDMfDG76ulsz5xpQ3hqgrwOd4CELQkUpxejUCqouOV24dzaGYVKQX5EvmwxDLWXQeiL9bLKIwAy9HrUCgXvtWlLC1tbrJpgOTTM14fD//mIr7Zs4UJsLADdQkJ4efw4wnwsNIyv5ccyGEsl9WenZ6bi7CQ2AV0MP4fB0DTjTL/Zsht7jQ2v31d3pk8qJpOJ8vIKMnNyq7rOjWVlVdvG8rJGf4/ayNLlkJIvvd6yITJys0jMkF4Xbmtri8Fg4ObNmwD4+vri6mpZ4yaIdcOPP95wM9b/DxhzslH7B9aoE1ZobNHnyGtUzUxOwqdFEI+88U7VvtCOnbh07AiZSX9f06ucq5WLmwcLlu9g+4blRF67BEDr9l0YO/FRHJyc/47TaxBjWTm9Z36Fo50Nhxa/UOdD8b6Fs2XFdWnvQs6lHFIOpJBywNy1yKWDCxXGCgqTCs0yx83UT7MYbuZfx9rlixAEgfEPPMaWtaJRv7unN67uXtyMuCw73rBWDpxJKDLLZFZek0a1lV97+OnyvXy96qDZk3xGXiFvL9lGTkERbz06Qla8vGt5WNlY0XVu16qRmoJCQO2qpjS7VHKcz2dWWwelxqabbRtK9KREp2HrIO/i2NrenjyjkX5ujXdDuJ1Qb69GTaADWLBubtXX6blJZtsGo5607EQ06oYz9g72jkTF3uDDL98CICU9mU++e7/G6wQE3przgaRzUwgCTna2WDfCW/hOYhKTePDVt26di/n2/4+YTCa2b9/OxYsXzfZ369aNMWPGWJRtT0hI4OLFi+Tm5tZ4MPz/TSRbOThiyEynTFftPGDIykSfkoi1ozxbRKVajTYvj5KiQjR2twa0FOrQ5eWhspE/YEQqPn3vpaxY+qRLBydnps2sezT9ml8WkJ6WxJy5X9YbJ11r5FJKCS1cVLT1tuFYTCELjmSiLzMxso0jzw2U5iyjtLaisESPnUbdpHXVoVNCMVWYyL2Sa7bftZMroQ+EYiw04j/SHztfy1cs/9doFsPN/OtIio/CLzCEJ+e8VyWGAZycXUlKiK7nnbXTyVfD7ggt05bHmZVJ6PQVdPDVsOFSXtVrJ3Zp2Cbo1+2nAOjbIYjxAzsCsP34VU5ciePX7adki+Gy4jJsvW1rekJWQLm+9hKF2og8GyWqJAFKC0vF7Tto11feEuAU/wA+iLjO0rhY7vb0xN7K/JLhZeGNML+oiPMxsWQWFNTI/jw44C5JMaKTropPNYJAqb5Y3L6D1i0aHuvapUN3jp06xIGjexAEgQJtPrsPbDN7jclkQhCki2GAoR3bsunUec5Gx9GzZbDk99VFQ1myf76FqWk5efIkFy5cqLH/woULuLq60q+fPD/qTZs28fXXX9e5OvJ3iGF56zBNi0OX7uQd3sfNV54BoDQ5kag3ZmMqK8e+i7w67pYdOxN+6i/enDiOLgNEV5VLx45QpNPSsa+81bDsy4ckvc698xBZY5mlcPavg9y8frleMfxXXCGPr0qk9NYq3ZzBniw6loWxXFyTCU8VG2ulCuJpI3rw46YTXI9Lp12wZQNK7sTa1po2j7ehNLuU4jSxvM7WV5xEV3ncd7DlfTb/izSL4Wb+dShVaoqLCqmoqC5hMBr0pKcloVbLX/aZtzcdATiTUMyZhGKzYx/uqh5/LAjSxHCpoQwfN0e2fPkUVlZi5m/muD50mf45uiLpmdxK1K5qitOL0cZUZ3Byw8X6L42H9J+3/729ATix5TQOLvZ0Gti+6pjKRoVPsBcD7utb19tr5Z3r1xCAP5OT+TM5ucbxgwMGyooHsPviJZ5cvARdSc1R04IgSBbDvdqL5TJnrh3E3taRdsHVdcEqazVerv706dCwRdjrz7+Ll6c3cQkxnL98BjtbO8JCLLeOq+TAlesoBIGtZy6w6/xlbNXq20Yqy7NWe+Rey621/lu5ePEigiDQq1cvOnYUHzrDw8M5ffo0ly5dki2GV6xYQUVFBSqVChcXl0Zn6tavT8HNVcWQoeai6ObNQooKy+jazZnXXgvDaLSsFKuxeE97lMIrFzHmZgNQUSJe+5SubnhPkdfge98zs7l25jRZKcnsW3vLNs9kwsramonPPC8rVuK+5RKe3ATcO0tvemtKFhzJouS2/7PvDmdiorqxLqXAyJbwfMliODNXzGwPn72QuzqH4uFiX/XZEwRY+Iq8yYcA0X9EY+Nhg/8If2zcqxMSOZdzMGgN+AywsNzsf5hmMdzMv462Hbpx4fRR3n95BgA5menMff4hSooK6d53sEUxpWRopJbTjurTlr/C47j9XircurqPtcAc3727O8l7krm6QMxs6hJ03Pj5RtUxqTw+T2yqiThzk6B2gVXbjaWuX4ulUuKdVavR1iKExW8mPZf28EhxOTQqKZwAz9Cqbbk4O7nw4lOvAzBgbFeCAkJY+NnPFsW6ndut1Yxl5RSU3fYgJvOX9+iEcY0+n/828vLycHV1ZdSo6ocGPz8/oqOjyc3NreedtVNYWIiXlxerVq1C0wQWfuvXpRIWZldDDK9YnkhMTBGr1/TE2blpnQnk1PwrXdwI+3IxObu3UhwdCYBty1a4jRyHwkbez9+mWw/e/vFX1i2eT9w18ToV0r4jk557kVZdu8mKVUV9P4rwz+XUI9JLUVsLfDbej4QcA98dycTV1oqDz4dhMkG/byNJyJM+UfHPAxcRBPHSduDczar7hslkuRjOPJOJQwsH/EeYD2VKOZBCYUJhsxi2gGYx3My/jmmPz+HSub+4dOYYgiCQk5VOdmYaVtZKpj72gux48R80rX9vl1b+bD9+lXtf+4nxA26VSZy4iraolC5h/qzZd77qtVPvbng50n+EP4WJheRH5Jvtd27jjN/dfrLP76u9/wHEhrmkmykorBSEdAySHQfg246W2WzVR1J2NrYqFb/MnkVrPz+sFVaNivfhE6Jw1RtLSc2KRxAUBPm0sijWse0Xa+zT6rQ4OsivLR/Ssa1F5yCFy5E3a+xrGxJc5S7RVMjst2zSeNbW1hQXF6PX61GrxSlaer2e4uJilBb8nPfccw+7du1Cq9U2iRiuDYOhgvx8Y5P/3ip5+ukx5OVJq6HN3LQGz/um4jX5IbP95UWFxH70Ji3nfSPre7fr1Yf3ezWRH7MJBCtrnFv1wKPLUJT2ljdFNjU6fTkdfTTc18mZ8goT3x3JJNBFVTWtNNBFxeWUOh7ma6Ffx2CaqlxYn1vd3FtRVmG2XW4oF7f/f6uX+j+iWQw386+jTYeufPr9Klb88BVREVcACGvbiYefeoU2Hbo2KnaGzkh5hQlfJ8tntr/7ww4EAU5ejePk1TizY28t2Vr1tYAgSQwrrBW0e6YdBdEFFCbcmkDXwh6nlvKaXG5n6w+72PnzXgx6IyEdgxgxfQjrv9vCfc+Po++YnpLjdPkbLKe6BgeTpdUyupuFGaVa2H1qLfvObsBYZiDIuxWDu41n6/HljO33MD3aSp8ct/vgdi5cOcuUCQ/j4uTKS+88Q1xiDB5uXnz5wUJCgqRPcxraqV3DL5LI3hMnWb55G09Ovp/BvXrw8mdf17jnvfXUTIb17S055uKDK/By8mBid/NBHZcSr1FQUsig1r1Z+NBHGMqMdUQw54sVS/D18Obh0feZ7T9z7RL5Oi0j+gzk9//MNxseUh/+/v7ExsaydOlSwsLCAIiKiqK0tJSQEPnjnWfNmsXZs2d54IEHCAkJwc7OvLno+++/lxRn6pSzVV9HRRWZbVciJyO8fv0xSa+bNGkAAwZIf7BPX70cFFZ43ludeTTm5xE3721KExMkx6lEl5/HnlUribsmekiHdOjEiGkP4yBzHHPbGR+TdXEfuddPkhdxivzIMziFdcez693Y+1v2ENuUVJigrMJEaoGh6qHGWF69bSiX96Sz7eunm+zczn9YnWgpSiky266kefyyZTSL4Wb+lbTr3JPPFq9t+IUS2Xg5n68OZJCmNdLFX8OsuzxYdiqHp/q5M7SV/JHHkqZF17MOeHVhzWav28m7nkcSSQiCQPvZ7et97Z0cWnuMzd/vMNvXrk9rctPzOLPrvCwxDJBnMHAqN5dsg4GKO37wR1vIHyzxwph7eGTBQt5dtZopd/XH6Q7P2AB36aUhAMcv72LnX6vM9rUO7ESeLpvzkcdkieEtu9YTcfMqzz/xCuu3rib2VsNmZnY6P/2+iE/f+VbWuRXr9ZyKjCElV2zS9HNzpU+rEGzV8m5YR86eJzM3l65tqxsg7/x0HT13QZYYXnRoBZ3929YQw5/vWsLVlEjCP9qHh4P0jN1ny5fQo22nGmL4nSVfcuHGNbL3X8LLVfr/7cCBA4mLiyM/P59z50SXFZPJhJWVFYMGyRuNDbB06VLi4+MBiIwUywYEQahqjmxKhg2TVk8KMG/e6gYzh4IgMGnSAHknoVCQ/sevWk2ONgAAfvtJREFUCAoFHuMmos9II+4/b2PITMfqliOEVHLSUnnv4QfIy6oeS3zx2BEObfiTD3//Ezdv6cvyGnc/Au+egd/AKWSHHyH70kHyb54l/+ZZNB4BtJ72LgqZA26kIjVjfz29lP7fiqsvwh3blhKflsP5iCQ0Nkru6Sfvmi4VQSFYtJrYTLMYbuZfitGg5/DeLdy4ehFXN0/uHvcAmWnJtAhpLdszcuf1Al7eZN781dFXw+n4ItztrGWL4Zy9n8l6fW1oo7UNv8hC9v9xGEEhMPW1+1n9+QYA7J3tcfZ0JimyZhNcfdzQ6Xgl/EqdE+gsEcPTvv0OAVi4cxcLd5oPXxAEgbyVy2XFO3JxO4IgcN+gx9l4WCyZsNM44mzvRkpWXAPvNicpJQEvDx8c7B0Jj7iMk6Mzn7+3gFfee5brN8JlxcovKubHvYfNGgVvpqZzPjqOp0YOrvEQUB/xKam4OTnh5FD9WQ308WbWtAcwmUx88sMy4pJT6okgjVKjnixdbpMt85foS0nPya73wbAuAgMDmT59OocPHyY1VfQm9vX1ZfDgwQQEyB+Fu23bNgRBwNPTEy8vL6ysLCvPefZZ0RlkyZI4vLzU3H9/dde+Sq3Az8+GwED5Q0Hq/53L//21eOltEud/RtrKZRjzcsk/fpiy/FyUru4Ez50nK9aa+V+Tl5mBoFDgEyT+/GnxceRmZrB2wTfM+qR+q7LasFJr8OgyFCulmpSjf1KuL6EkK4mKMsPfJoanPf4CBXkN15s3pXNLeXkFL323kdV7z2PCRPc2AeiK9Dz31Z988uw4Wd70lYmRa99fQ+OtIWRS9QqJQqXAxt1G9sTSZkSaxXAz/zq0BXm8OWsKSXGiNVir9l1o07EbH7w8g6mPvcBDT74kK96io1kIwGN93PjlVA4A3o5KvBysuZxSXP+bGyA9R0tZeQX+ns6y3ucYal6DWphUiKnMhK2veBMtTi1GUAjYB8nL4ABkJmXjF+rD3Q8PqRLDAHZOdqTGpNXzzposS4hv8gl0UM/NxgIVll2QhrdbIIO7jasSwwC2Nvak58ibtFVcXISXh5jlSkyOo3XLdrRv0xE/n0DiZNr67bt0DV1xCYIg4O4o/j9mawvRlpSw79I1JvWTnqHPLSggwLvalinE34+WgQH07CjeHL3c3UjOkDYco8O7dwNiGc+V5BtV27fjZi996dttuGh/JQgC52+EV23fjqeLZT7VQUFBzJgxw6L33omdnR2urq78+eefjYozaLCY3b52TYuXt03VdmMwmUCptGL48G488MBAvLycGx3TqXd/gl57n/iv/0P29k2ACZvAIILfnofSVd7/R/jJE6hsbHh/xRqC24qfubjrV/ngkalcOSGtzON29AXZZF86QPbVo5SXilM8HYM64NHtbqw18q55uzavavhFwOgJD0oa2DRnkKes798Q3645xB97zpntG3NXe178VsGuk9dliWGnMLF0LmBUACpnVdV2M42nWQw386/jl+8/ITH2Jiq1DQa9aFXWpeddqG00nDt5WLYYjsrSE+Ku5r1RPlViGMDVzproLMumjf25/wLzft1DanYB3dsEMGfKEJZuOs7sSQO5u3fDtlwdXqiu/Us/kU5RUhGd3+qMxlNs7CnJLOHyl5dx7SC/sUTjYENeVgFGfXWtZ7G2mIz4DGzt5TUO3dDpUCkU/NKtOw+fO0s7B0eeCwnhnevX+bS9ZUt9O+Y27aAIG5Ud2qJcjGXVtajFpYVk5qWiUcvL0Dk7uxKfFMvKP5eRmZ3B0AGiZ7RWV4CDg7wbT0x6BtZWVjw5YjC+rs4ApObm8dPeI0SnyZvqZqWwIi0ru2r7p/+8Z3Y8M1d6NrcySysg1JmxndxjjORzq3Q4qCw5qI1Hx0ySHO92iouLOXPmDGlp4kOcr68vPXv2tGgc89NPP83XX3/N1atX6dCh8U21j84IpKSkHIOhApVKwelTuURE6GjRwraGw0R9bNjwLqtXH2LHjjPs2nWWvXvPM3RoF6ZNG0y3btJr1AHyjuyvsc+53yDyDu9DYaPBdehICsPFJlGXQQ3bDlZSWFCAb3BwlRAGCG7XAU//ANIT4mWdY8zm+RTEXgZTBQqlDR5d78aj63BsXLxkxalk0edvN1zmIgiMnvCgpHhzhjStGF615xxKawW/vvMwD3+wAgB7jRo/D2duJmY28O7acQhxoCS9hNLs0iprtdKsUvKu56Hx1uDc2rmpTv9/hmYx3My/jrPHD2Jr78DSNQd4ZGwvAKysrPD09iM9Vf7oT7W1QKG+nIqK6hu1vqyCpDwDGqX8CWFbj4Xz7Bfm2aXOrfw4cSUWd2c7SWL4dpL3JqNyVlUJYQCNpwa1s5rUQ6n4DpFnnt66Rxjn91/iP9PEpcuspGw+mvYlBr2RzoM6yopVUl5OsK0dfhoNAlBuMtHO0REXlZLvYqJZ0kV+Q+NdbZvWZaGlf3suR5/iq1WvAmKm+KtVr2IsM9AhRF59dL+eA9iyaz0/rVwEQP/eg9HqCsjKyaBze3mDCkoMBtwdHKqEMICvqwsu9nbk6AplxfLz8iQmMYl1u/cxeZR5JnfHkWNoC4sI9pdWK/jxfa8BMHfTlwS4+vLMoGq3ARuVDSHuAbTylt6gtuh10b3kuS/eJdg3gFcfrp4sqFHbEBYYTPsQ+Y1RBQUF/PLLL+h01e4JUVFRXLhwgccffxxHR3kOHz///DNlZWU8/fTTODg4mDXQCYLA+vXrZcX76acETp3M5ZNP25GXa+Tbb2Oqjml1Zdx7r7Q62tBQH95550HmzLmPTZv+4s8/j7B//wX2779AWJgfK1a8ho2NtLKBpEVfU/uajUBFaSmpv/1YuSlLDDu7u5MWH8f5wwfoPngYAOcO7SctPh4XD+nCH6AgWhTjgpU19v6tKSvWknZi4x2nKxA85hlZceuznWvMKtbp+KIa+7r4a6rcJRoiNbuA1oFejO5n3lBrr1GTkpVv0TnFb46nNLsUz77Vwl3ppCRhewIadw3ObzhbFPd/mWYx3My/jsJCLYHBLXF1M39CLy8vp6S45oWpIboF2HI0upAZf4gd1OnaMh5aHk+hvoLBYfKb575dfQhBgKfv68/SjScA8HV3wtvNkYsya3IByorKMOQbSNiegFsncfky94o4dENhgVi///lxXDt5g+SoVBBAl1+ILq8QjYMNE2bdIyuWnZU1hlvDT+ytrYkvLuJgViYpJSUWT9fad/kK52NimNi3D97Ozjy+aDF/3YikQ2Agy56bhZ+bvGz42P4PE5l4mbTsBBAECkt0FBZrsVHbMrrvNFmxnnv8ZdQqNcmpSfTvPZDO7bsScfMqwwaMpG9PeQ1M9jY2ZOsKuZGcSht/8YEmIjmVHJ0Oe5k+r/26diY6MYkf1q7nSuRNOrVphUJQcDUqmmPnLyIA/btKm9Y1odtIAM7EXSLQ1a9q21KmjbwXgGOXzhDiG1i13VgOHjyIVqsVy0xuNVVmZ2ej0+k4ePAgEyZMkBUvPT296mutVotWW123b0kDXVxsEXZ2VoSE2LF4VywAnTs7cflyAUePZEsWw5XY22t44IGBaDQqvvtuE4WFpURFpaDXGyWLYRFJ3b2y6DZ4KPvXruLrF55Ffeuzqy8Va+G7DRkmLxiAAKaKMrRxl2s/NwFZYthkMmGtVNF/yGjG3P8w7p6W++xuuJTHt4czeXO4N2M7ODHlt7gaYvrb+/2Z0MlZUjw3RzsS0nPJ1Vbfu5Iz87iZmImbk2XjkkuzxIywlaq67t1KZYWNmw0lWdJt35qpplkMN/Ovw9Pbj8TYKK5dqrYsOn1sPymJsfgFyrdUmjPYk79iizgWU4iAOHc+TWtEqRB4YZC8rAZAZEIGLf09+PiZcVViGMDdyc6iZS+Xdi7kXM4hZV8KKftSahyTi0+wF++vfZ3tP+4h7qr4ABDcoQVjnhiBd5C8pUgfGxvii4vQV1QQZm/Phfx85t0QB4K0sGCpGmDBjh0cj7jBzGFD+fXgQfZcEm+Ip27e5P21a/l51rOy4nm5+vPag1+z58w6EtPFOvNA7zBG9JqEp4u8zmqNjYbnn3zVbF/bVh1499WPZcUBaOPnw5moWP44ehLlrTHWxvIyMaa/vJv1xBHD2HXsBFm5eZy8dIWTl65UHTMBnq6uTB5Zs/a3Pt685zkK9UWUGvXYKNXsvXaUc/FXaO0dWsNhQgqfPvcG2qIiSvSlaNQ2bD26j7+unKdDaOsaDhNSiImJQalU8thjj+HjI/6+0tLS+OWXX4iOlj+WffTo0U3qGpGXZ8THR1yiTkwsITjYlrfebsVLL4WTnS19KANAamoOa9ceZfPmE2i1Yh9Dv35tmTZtCE4yBFOnP3c1/CILeOD5l7lx/izJ0VHoS6r7LPxbtuKB2fLK1lSOltWP18XiVXvZ9udyDu3ZxJG9Wzh+YAd9B41k3OQZtO8ib2UIYOd1LWkFRvoFV//e73x22HVdK1kMD+nRitV7z3PXk6ITTWRCJoOfXYCxvJxhPVo38O46EECfp6dcX46VWhTE5fpy9HnNPsOW0iyGm/nXMWjEeNb8soA3Zz2AIAjcvHaJeW88iSAIDLp7vOx4Xf1tWTUjiK8OZHLl1lz5Tr4aXhnqSVd/+YJOrVKiKy41GxetN5SRkJ6HRi2/Czp0aigmk4ncK+Zdzq4dXQmdGio7HoBXoGeTTKCb6OdLpK6QbL2e/9feXcc3db0PHP/cSlJ3N9rS0uLuNmAwZIbMDcbG9p3BFLYxYBsy4wdzhkyYsQ0dw4YOd6dGS93d0jZtk98fgZTQluaG4Of9evEi997ck9M2bZ577jnP82xwCG+cPkV5TQ02lpb8z4RcrwAx6en4u7nh5ezM7ugYnGxtmf/0OF5ctIjdUdEmtenp6mdyBbpLlZaVEB13moKignq3XocNMr4S3J3tW5OUk0dOcQnVNTX6/V4uTgxqLy8HsYOdHfOmvMGsBYuJPmeYIaNV81DeeW48Dvby3svv/z2PTad38sfzX5NTmseryz7QV1IsUhUzvu/Dstp7dd6HrPnvX7Z+8xuZebmMff91ffCZX1zIxIefltVeRUUFHh4e+kAYwNfXF1dXV9kV6DQaDc8++ywA3t7eZgmKrawkystrqK7WkJVVSZeurvr9ctqfNGkBu3adQqPRYmen5JFH7uDhh+8gKMi8c1evhIOzM7P+WMXe9f+QcFp3Ida8TTt6Db8ba4W8NIFtJsw1a9+CQlrw4uRZjHtpCpv+/oP1K35mz/b17Nm+nuCwSD5buAqljU3TDZ0Xm1OJl6M1bvZ14VGYh5L3hvqg1cLEFWnE5lQa3d7UcXfx39GzZOTp7kSUqnTrVHw9nHj7qSFGt3Mxez97SpNKifo2Cp++uoW12buzqa2sxTFE/t1OQQTDwg3oobEvcTb6JEf27TDY36l7Px546gWT2uwaZM8f40LM0Dvo2jKIbUfiePDdHwDIyC1m5ORFlKoqubOr/Ct9KzsrIsdHUplXiSpTN+pi52OHjafxf8AvpSpRce5UMiX5JfUWVvW+z/hctIO9vBnsVTea/Fe37qRWqPC1scXRyrQ/H0XlKloF6sqIxmVk0jEkhNE9e/DFuvVEpcmfZgK6BXPJWWcpVRXVWxTWvVXTK8gv2HtwJx989g6qivpZRiQkWcGwrVLB/4YN5GRSKun5F/IMu9IuOBArE9J6+Xp68NV7U0hMzyA5XZdqLNjfj2B/eXPKLziTcRZHG3ta+7fg1xWrkJDoFdaZPfGHWX3sX9nB8Im4KJwdHOnQojULV72LJEkM6NKTbYf2smzT37KDYQcHB/Lz84mNjSUiQvd7FRsbS35+Po6O8j/wx4wZg7u7O2vWrJF9bkP8/W04e7acCc8ep7JSQ3iYbiSxIF+Nu7vx6a127NAFl9bWlnTqFE5BQSnffLPW4DmSJDFnTtPfv4QZk7EJbIb/+BdImDG50edJkkTodHkpIq0VSvrfP5r+94+Wdd61YmfvyIhRj2Nja8cPX81BVV5KUnwM6qpKWcFwblkNoe51AX5Lbxta+djQP0z3ngtwsSYx3/iRfx93J3YumMTCNXv00+g6RgTwzL29TJ4m4dvfl9KkUkoTdf8MjvUTpZhNIYJh4YZjba3g/f/7kdPHDhAbdRyAiFYdaNPR+CDuUlU1GtacKuZYqgovRyse7OhKWlE1EV5KXOzk/Rq89cSd7Dwez46jZ5EkyMwvISOvBGsrC954zIT5c+fZeNjoVwZfiRP/nea7KT9SWV5/9EKSJKOD4RqNhiF7duNsbc3K7j2QJAlbS0taOFzZyIOrvT0JmVn8tXcfKXl53NWxAwDFKpWs3LsXnD53iKUb/o9Kdf25chLyguGvl8yjvLF56UYM9s1dswE/V1ce6deDJVt24u3sxN1dO9CpebDRfbicpWv+wdPVhWH9+hjsPxOfQGm5ih7tjV8gmVuSTzOP8xcl2Ym09A1j4VMfcffn48gskj/dJys/l+YBurzTUefO0i4skuUfLaD72HtJy5GX0g+gRYsWHD58mD/++ENffrm6ulp/TA4LCwt8fHywMvECriGjRvkxd248FRW1eHkp6dvPg7i4MsrLa+naVd70JkmCmppadu+uX4xHq9UdNyYYLo86ibZarX+se9M2NEHY+JHranUVaQnxuPv44uTqRkpcLP/8sAi1uoqug4bQe7jxF4gXqEsLUGWdQ+HihZ1nECVJp8navwZNTTUu4Z3x6S6vzZzMNP5Z8TOb1/5BWWkxoBs8uefBsbLz0ltZSKQV1QW7G/5nmNEjo7ha9noJVyc7Jj8hbxrT5Xh08qCqqIrUDalo1Lo7lBYKC4KGBeHR6cpT/d2ORDAs3BDefvHyo1CH924HdMHc7K9+l9V2oaqGh35I5Oz5NGodAmzpFGjH2F+SeaW/J68OkDePtkvLIFZ/MoHZP24yuNJ/+6khdGkZJKutq+GPz1ZSWdbwbTw5xQ+sLCxwUyhwsLIy61zLvq1asnzffp795lsABrVtS3llJekFBXQKlT96v3rnD1RWNZwvWiuz31m5mdgobZj+1keEBIXKLsxQVKbC/nx1uaTsXGoaydFsqp9Wr6Vl85B6wfC3v/9FTGISW75fYHRb1pZWlFaUoa5Rk5yfzqCWvfX7LST5CzetrawoLiulSq3mXHoKw3sPPL/fGslCfnsDBw4kJSWFnJwc1BeVcPby8mLgQOMvcC4YP348s2bNYs2aNdx335Uv8uvYyYVvvm1PXp6awEBbrK0tCAy0Zf7nbXF0NP6j1dfXFXNN9HTtNwiFr7/+cZOl7ZqQFBPFR889TUlhAdYKBU9PfZ+fP5mFqkyXDeXg5k1Ulpcz6AHj7yKUpkSTsGoemvOpEH173k/WgX/QampAC6rsJACjA+IP33qWQ3u2odVqsLG1554HxnL3mKfwCwyW9bVeEOymICqrkkV783i2l2Fg+fuRAgoraon0kjdocTY1lz0nz5FbWFrvTt1bTxif1eNi/gP98enjQ0WWbhDA1sfWYEGdukSNtkaL0k2UZzaGCIaFG8Kpo/vr5Sm9NAAztWzq7H+ziMutwsZKorJG136fUAdsrS3YcbZMdjAM0KNNMH9/Zr6a8+aUn1mAwsaa5z99Gr9QHyyMTAHUkDH+/ixKSuJQYQFdXeXnPG7InMcfo1JdzbnsbIZ16siQDu3ZHxtHp9AQRvfoIbu9gpIcrK2VjB3+Bj7ugVhamFZZDCAyrBWFxQX06S6/3C+AjUJBZmERf+45qOtbWTkr9x2u/0RJYlQPeanaGlOlVpNfVCy7YEmoZxAn02Lo+9EYKtSVtAvQpQTMKs7F21n+6FKLoFCOxJyixeg7KK+soHNL3Sh1Rl42/h7yf8dsbW159tlnOX36NOnpuoWl/v7+tGnTxqQR3kWLFmFpacknn3zC559/jouLi/7viSmp1QCcnKyprNRw9EgRACGh9vj4yAuUNmyQvzizMYEvvdHgY1P9+cU8Sgp0udmrq6pYOP0dtBoNChtbQIu6spKtfy2TFQxn7luDplp90fZq0NYtrFOX5FMQvd/oYPjArs0AWFkraNOhG0WFefyyyHBesoTEmx98YVR7gyOcOJNVyex/sziYXE73ZvZYSHA4VcXG6BIkYHCk8XfHfvxnP299taZeKfsLTA2GQZdBwiGo4SIlMYtjKEspo9f8Xia3fzsRwbBwQ2jTobvB4Eh8zCmq1WqCw3Qf0EnxMVhaWhLRRn5e221xpTgqLdj6Ujjd5sYCYGkh4e9iTUqhvFXfF1Spa1i+7RiHY1LwdnPi8aFdSMkqpGWwD65OpmVZMJfg1kGUFJTR4Q55OYUbsr+gAAtg8unTBNra4aqw1v+YJCT+r1072W16OTvz66uGi916RLRg07T3TOpjkHcYpapi2jbvZtL5F3tk1JO8N+dNvvl+HkMGjMDB3vBDz6eJlE0h3h5Ep2ZwKjkVJFBVVXEsMdnwSedTR8kJhu8cp7vwkoCYhET99sVcneXl3X3ujseZ+Pt0yqpUBLj6cm+HwZxIjaKkskw/SizHG49P4Mnpr1KqKiPYN4CHBt/DoagTFJWWMKK3/JFcACsrKzp06ECHDh1MOv9iF6dWq6ysNNg25SJbo9GyeHEy27fl6q9DJAkGDvTkmWebmfVuirHUucZPb1F4Nr1AL+H0SSwsLRn9v1fIzUhjx8q/sHN0ZN76rWi1Wl4bcSeZSfJKnlfkpiBZWdNsyDiqirLJ3LsGK1sHWj39EaDl9HevUSVzmo4kSdTWVHN43/Z6xy4MohgbDD/d050/jxWSUVLNlthStsTWzcnVAv7O1jzT0/iLxf/7fTu1Gi02Cis8XByudLBeHjOVVb8diGBYuCF89O0f+scbVv1KfMxpvvntX30qtfSUc0wcezfd+8qfd1VSqSHMU4mXo+GilloNlKs1jZzVuIKScu55fSGxKboqYp0jA+naMoiHpv7AG48NYsqT5psbZoqhY+/km9eW8OfcVfS8uxt2ToY5bd19jR/hPVFcrH+cUqEi5aJpuVfyN72qupq/9u7jUHw8Xs7OPHlHf1Jy82gZGICbg7xyrIO6jOT7fz5m9c4f6NryDmyVhue7ORmfPu/tma8iSRLLVv3MslU/GxyTkPhv7ZHLnn9ft04429mRU1zCuawclNbW+F5UdMNUFz7TGpsBCjCiv7w8yP0jurP9zT/ILM4mzCsYhZWCMK9gNkxaioudvMAaYEiPfpz5cwtp2ZlEBoehVCiIDA7jyNJ1uDnLq95XU1NDbm4uzs7O2NnZkZ2dzd69e6mpqSEyMpK2beVf6I0fP172OZez7p8stm3NNdin1cLWrbn4+Npwzz0+jZxZX3Z2EadPJxEY6EGLFgHs2xfNwoXrqaqqZtCgDowfP9SodmJeHGvcC0rQ7o/1TT6tvKSYoBaRjHzuBWqq1exY+Rc+zUJwdNHNifZpFsK5M6eMe83zaqsqsPMOxq1VL7QaDZl716B08cLCSvf3WeHihSrrnNHteXr7mzXAdLKxZNm4EF5ZnsrxdMN1CJ0C7Jg/OgBnW+PvPpWUVxLg5cLeRa9hbys/25BwbYhgWLjh/PHT13h4+RjkFPYPCsXDy5dVvy3i/oflfaj5u1hzNreKQ8l1C6O2xJZwLr+KUHf5f5ymL1pPTHI2tkorKqp0KbPu6BSOndKaLYdir3sw/OUrC0GCjT9tZeNPWw2OSZLEkhNfGt3WEC9vs49k5JeWMmLmbGLO3/ruEtac7i3CGfPJZ7w18n7eGT1KVnuL1swGSWLbkTVsO2KYKUACPn91taz2Gq1kZcT3wd5GyYguuuIX7/26Ak9nR8bf2U/W6zfkrfFPAfDJkp/w8/Lk8XvqiqcoFQqCfH0IPZ+hQw5Xe2dU6gp2xO4HoLVfC4LcTctOAeDu7Ep5hYpN+/8DoEOLVoT4B8pqIysri19++QWVSoWlpSUjRoxg06ZNVFXp5vxHR0ejVqvp3FneNBNzB8Pbt+vKYw8b5k3vProLzD27C9iwIZsd23ONDoYPHozllVe+papKd5fquedGsGTJRmpqatFqITo65Xz/jQmIja3Jbdwvtaa2FqvzixetrHV/Ky0umv9tYWHCHwetFq22FnVJft3raGr029ramsbObNAPq/c0/SSZglwVrH62OXE5lfq1JuGeSlrInCsM8MiQzizbfJTCUpUIhm9gIhgWbjglRQXkq6v46dtP6DVAl/x/346NpCUnoFDK/2N0bxtnvtiZy4M/6CoJHU+r4NnfU5CAe9u4yG7v3/0xONnbsH/J67R6WDffz9LSggBvV5Iz5eU/vWoa+UyUs4AO4O0IE5PCX8a035cRnZ6OrcKaCrUuO8CANm2wUyrZfOKE7GAYaHS+rNwFdF/MWST/tRvx4WP1U1BVVKlNykV9Vx/dvL/j0bH4eXvpt6+ERqPh/b/ns/LoBoPb/KM7D2f6vZNk3+bXaDS8Nv9Dflm/Sv8+k5B4YsQo/m/Se0a3t23bNsrLdReuNTU1/P3332i1WoOMEkeOHJEdDIOuxPPy5cuJjtbls27ZsiVjxozBWebINUBOThW+vjY8NbZu0WxYmAPHjhWTnV1ldDsLF66nsrJuutZ3361Dq72wsA4yMwtZv/6QUcFw6PSPZXwFxok/dYJH253P3iFJhtsmqshJ4fSi18+3ecn2DeLzHTn4OFnzUCfDzCBHUlUUV9QysIVx84anPzOMHUfP0nXsp7QM9sbRvu4zTAJWfzqh8ZOFa0YEw8INp2vvgezZtp7lP3/L8p+/rXdMrpf6eXIyo4Id8WUG+/s1d+CFvvIXChWXVxAR5I23m+Efw9paDWUq4z8Er5a3vjdP8QmARw4dJNzegQ9aGRaJWJyUSHpFJdNbtpTd5sZjx3G2teXQpx/T4qVXALC0sCDQw52knNwmzq7v5QfMtwCpY9suZmvr2LlkErNz6RUZjoONkh+27SKnuAQnWzueHNALbxf5AdjkZ8cBoK6upqiktN7Fjbe78dW9fty7nOVHDG+Va7Ww/PB6mrn7M67Pg7L69vVfS1m6boVhe2hZum4Fzf2b8dKDTxnVTkZGBhYWFvTv35+ioiKOHTuGjY0NL7/8MgBffvkl+fn5TbRSX3Z2NhMmTCAvL0+/b9++faxdu5aFCxfi5SWvyIW1tQWlpTVUVNRie/62uUpVS2lpNQqF8YtW4+LSUSqtmDbtcVJTc1iwYD0uLvasWTMDrRbuuusd0tKM+71waC1vDn/2it9QZ2cR+MJrjT+pqYWZptw6auqaXGaTeTlZxEUdx9e/GSHhLTl2YBe///AF1eoqeva/iwefelF2F+ftyKFjgG29YPjDjZmczKjg3PQ2RrXz4fcbiUvR/fxOxOvyg0tSXco84cYggmHhhvPylDloamvZ998mg/09+g3h5SlzZLensLLgx8eDOZBUrp8D1sHflu7BpiU8D/RyJSY5m/2n6xaObNwXRXxaHmEB1z/HY2TXcLO1lVVZiZt1/ZHMI0VFxJaWAvKD4WKVikh/f7xdXAz212q0lFUaX9npgvBA4z6UjFVYVMDeQzvJy881qDIIMO5R4zOIHIpPJD2/gGGd27EvNoGcIl0FqhKVii0nonisf0/ZfUvLyubT73/izNmE+gclSVZqtZVHNiAh8XjPkYxop7vIXHdyGz/vW8nKoxtlB8O/blyFJEk8N/JRxgwaAcDyretYsPJXft2wyuhguKKiAm9vb/r160dtbS3Hjh3D3d0du/M5qN3d3cnIyJDVN4AFCxaQm5uLhYUFQUG60dyUlBRyc3NZsGAB06ZNk9VeWLg9p06W8NabZ+jYUXdhc+xYMeXltbRrZ/yc67KyClq2DGLEiG7U1mpYsGA9gYGeKBS6kfCAAA/OnEluohXTlB45iCo+rtFguN99JtylaYJvrytPa3exE0f28sEb41FX6f52PDJ+En/+9DW1NdVotVriY3Rzmk0JiC9VWa0ht6xGVuKWXzYcQpLAz8OZAC8XrCxNz+4jh8JJgdJVpFUzlgiGhRuOg6Mz7370HZnpKaSciwMgKCQc3/MJ/Y3x8I+XX+G8/axuhbAE/D5WXm7bUQPa89mv27j79e+QJDgSk8rjM5YiSbpj19up3Wc4dyqZ7sM64+zpzHdv/UDckXgCI/x57uNxuPk0XRBgU3a2/nFRtdpgu7K2lmSVCisTcscCBHp4EJ2Wxr7YWP2+DUePcjYzkzAf4xcdXRCVeITkrLN0juyLk70rP62fS3zaGfw9g3lq+Bu4Ohp/gRIdd5pXpz7fYAU6kBcM55eU4Wxnh61CQWpuPnZKJY/f0Yuftu0mNc+06TSf/bCU0w0FwoAkM7VaWmEmzdz9mTK8rqpj24BIdsYdILVAfrCZlJFGc/8gZr9YV/msU2QbNh/YTWJGqtHtaDQafX7nC/9fPMXC1CwNBw8eRKlUsmDBAoOKds899xwHDhyQ3d7o0X6cOV1Kbm4V//5bl/3A0lJi9Bjj511rNFpqa2vJyirQB1nV1TX67epq8+aqluP5meafduHba6RZ21v2/RdUVdYtdPt9yXy0Wi2e3rp8y7nZ6ez4d43RwXDIDF3hkwtT6i5sX8zDwfjQydHeBi9XRw79+KbR5xirMq+S0uRSLK0tcWtnuDA68plIs7/erUwEw8INy9c/CF9/04pY7E8qr7fy/tKPUG0D+4zx+qMDOR6XzpZDsQb7B3YJ59WHB5jQonlt+GELsYfjueOBPuz4czcnd54B4Oyxc/w1bw3PfTy2yTY+iotFQvf9yays5OM4w69VC4TamzayPqZnDz5ZvYZhH85CAg7HJ/DI/81HOn9Mrq2HVxGfdpre7e5iz8lNnDmny+t7LiOGv3f9xFPDjZ+LuOjnbxqtQCc3CKuqrsbZXpfJI7ekFD83FwI93HBztNePEssVl5SCJEmMHjyIZn6+WF7BKJPSSkGhqpjyKhX2St2oa1llOYXlxdhYyR9RUiqU5BcXUaoqx9FO994oKS8jv6QQG6W89tLT0/nggw8A3ff94m1TlZSUEBQUpA+EASIiIvD39yc11fhg/YLISEfendqCv/7M4Nw53XumeXN7HnjQj4gIeVUaY2PTGDZsKqC7dX7x9o0kL7P+RZKbt4/Bojq5SlNj6u2z922uzy7RlMSz0SgUSl5++yMy05L5bcl8nFzcWPjnNrRaLWPv70VWmvEj68Zkbnm0s/EVBqeOu4u3vlzNoahkurYyfkDncrQaLQnLEsg5mANacGzmSE1lDfG/xhMyKgTf/qIks1wiGBZuSd2b2XFxqHsqowJ1rZZIb92Hckx2FZYW0DFAfk5ghbUVf8wax96T5zgSq/sQ7RwRSK92oU2ceW1kJGTh6u2Cs4cTsYfPYuNgw1PTHuH7ab8Qe/is0e1cuFi49ANBaWFBkJ0dL4c2N6l/b95/H8cTk/j3xAmD/YPatuX1++6V3V5WQSoujh442bsSn3YaG6UdD935Ar9t+oL4tPqjOpcTHXcahULJ0q//4uFn76V1RFtefvZN3pn5Kh9PNy5P6QX2Nkpyi0v573QMxSoVbYJ02R4q1KYtogPwdteN/vzvkQdMOv9i7QJasi/hKCO/mkC/FroczTvjDlJaWU6vMPmL07q0bMuOo/vp++xoBnfTpXnbfHAXxWWlDOgsb0pIoxk9zjNldNjd3Z3U1FR27dpF3766/u3atYuUlBQ8PEyb3tS6tROt35efhu5SV2Narjkc2bGV9Ut/YNgTY+ky4E5eGdK/Xmdem/81XQYan0En/8xuMveuxr/vA7hGdufsHx/VG5UIHv4cbi2Ne8+Ul5cSFtGWAUNHUltby29L5uPrH4S1Qve33scviLPRJ5popc5n9+tGlN9YnU4zVwUv969LzWhrbUFzDyWR3sYv5P5o6WZqajUMe/VbXBxscbS7aAGdBEeXTr7M2Q1L25xGzgHDXMzu7d1J+D2BgtMFIhg2gQiGhVvSH+PqAtNfDxVwOrOCf18II9RD9wfyXF4Vd3+XwGAjR3Due3PhZY9vOagbOb0RVgeXl6gICNfdps1MzCakdRDdh3Vm4w9bSIs37vb39r66dGADdu2klaMTX5uh6AFAdU0Noz/5FBtrBevffYcj53S3/DuFhtLHhMV4AKrKMvw8dCMu2QVpBHqH0TmiL1sPryIzT95cy4pKFaHNwvD3DdQl8tfU0jqyLS4ubsz9ZjaL5v1idFsR/j4cOpvIlpO6kfnIAF9UVWpKVBU08zIt+Hr2gVF8+O1C9p84RY/2V1ZU5fk7HudA4nHSi7JYdnAtoFvwZmVhxf8GPCG7vTeeeI5dxw+SkpXB92v/1LWn1WJtZcWbTzxvdDvmKLDRkN69e7Nq1SqmTJmCjY0uIKk8P0e9T58+lzu1UaWlNWzcmM25hPMjw2H23HWXt6xyzM8/P7zpJ10nu9auJubIIV6Y/Wndzksi9/2bNsgKhoviDqEuLcAx6KJFudpLn3PY6GBYq9FQW1tDbnaG/iKqurpav11TLa+w0pgOulHffYnlNHNT6LdNlZpdpH9cWFpBYWndlA5TL3Jy9ucgWUhEPB1BzGLdyLql0hKlq1JfnlmQRwTDwi3v6125+DhZ6wNhgFAPJb7O1izam8d4I6oJ7T5xTr8C+IJL/5DdKKuDHZztyU7OYf/6w+Rn5NO+n26BmaqsAjtH2ybONjSvbTvsmyh9uzQlmazKSt5q0XQaNmsrK04kJhHo4UHvlpH0bnnl89rsbBzJKczgcMx/5Jfk0DpElxGisqocW6W8qRwOdo6o1boPTwd7RxKTE9i6cxPpGSmyizkN7dQOK0tLCkrLiQjwJdjLg7T8AtoEBRLhb/zc6MfefMdgW6PRMnX+V9jb2eJgZ3hn49dPZxvdbufgtix+6mO+2vYTZ9J1c/Pb+Efw0qCn6BjU2uh2LujZthMrP1nInB+/5nhcFAAdI1rz9tgX6N6mg9Ht3HefeRdYXfDcc89x/PhxEhMTqaioCxhCQ0OZMEH+BWxeXhXT3oumoKBav+/YsWK2bc3jw5ktcTcyh/nzz98t+7WvleToKJzdPXD3qRtp9AoI5O5xzwLw62cfkRR9RlabFXnpWNs7Y2VXNxBh4+5LwB2PokVL0roFVOTJm7aSeDaKp0fqqiZKkmSwbaq5I3V3cqpqNOSX11805+9i3M/34Ts7XVmFogaoi9TY+djh1tZwnrCl0pKqwuuf0ehmJIJh4ZZXoKqhqkTLJ1uyGdZKd0tzY3QJCXlV2FgZ91eqV9sQg0D3eFwaVdW1tA7RBTVnErOwsrSgS0vT5jibU2S3cA6sP8LCKT8C0KZ3S6pUVRRkFRLSRt6ctQ6XZHxoyP6CAmJKS40KhgHu7tqFVfsPkFlYiK/rlY26ALQIbMuRmJ0s3TAPgMjgTlRVV1JYmkeQj7zMGr4+fiQmJ1ClrqJF80iOnDjI+5++DUBwoLxpMAorK4Z3NlxQGeDuxgO9ja8ACJCV13AasTJVBWWqi0aZZLWq0y20A0tDO5hwZsP6dOjKuvk/mq090OUGvpSTk5PsqRJOTk788MMPbN68magoXbDeqlUrBg8ejEIhf9rKst/TKSio1mUK8NONNGdkVFJQoGbZ72m8+JJp06aOHKk/lalt22B9dgljlUWdwtLWDtsQw+lMmmo1aDRYKG3wGvMoNSX1v78XFObm4hscrN+2dXDAOzCIOx98BIBty/8gO1Xe3Zfq8mJs3OouBm09A7H1CsIpRHenQ+HsQVVBdmOnN+hqTKtJzK/izdXpHEmtv5hWkjAqtZpGo+HtsbpRc39PF7OV6LZ2sKayoJLq8roLsaqCKlTZKqwd5L1PBB0RDAu3vIEtHFkfVcK3u3P5dnduvWPGWDu3LovAj//s5/jZdPYsepWwAN18svi0XAa+8AVDe5p2q9+cHn5zNOrKanJSculwR1va9W3N2aMJhLRpRvdh8ueCmpu7gwM1Gg193pnKvd264nVJYDNllLzV5iP7P426poq8okzahHajdUhnzqVHEeQTTucIeSWKH7j3UWLORpGXn8NzT73Mq1P/R7mqDBsbW14cf5lcrI2oUKtJyy+krKJ+yriOocZdmDx539UbOSxSFfPr/jWcSddN82njH8GjPe7DxU5+DmSAguIiFq3+nWOxutHCTpGteea+R3BzdjG6jdjYWPbv30+PHj2IiIhg/vz59YKIBx98kMjIpu8qjB49moiICGbPns1LL71ESEgIr7/+OiNGjJD1dTXk5MliFAoL3v8gkpAQ3R2Ic+fKmT4thhMnGg8wL7V27X6+/fYfJk4cyV13dWb8+Hn17jDNmjWW4cO7yerfuRlvYdeiJWEz/89w//S3UCXE0e6P9Th1arrN/KxM/eMl+44ZHCvMzaGmuvrSUy5LsrBAXVyX67nlUx8aHK8uKcDoSnrAo+MnyXp9Y01ek87hBgJhQE736PjEJ3i7O3L6t3eafrKRXCJdyDmYw/E5xwFQZas48ekJtLVaXFq6mO11biciGBZueXPu8adWA5tiDFfwD4l0Ys49/rLb+7/ft+Pn4awPhAHCAjzx83Th6+W7+N8oeQGYuTl7OPHy54a3fcM7NeedpfKDuavhi/UbkIC80lJ+2Lqt3nG5wbCTvSvP3mv4QRPq34pXH/pIdt+GDBjBkAG6QMnfN5BVS/8lJS0JPx9/HB3kLZSKTc/krz2HqKqpHyxISEYHw0/df4+s1zVWZlEOjy18hZzSupHnnXEHWX5kA79N+AIfZ8/LnF1fWk4Wd738OFn5dRecmw/uYun6lWz68mf8PY2bGnLy5EmSk5MZObLufXDpyF9UVJRRwXBmZiYu5+9uHD16VF/S2RzKymrx87PRB8IAoaH2eHkryco0Pl/25s1Hyc4upFu3ujsrlw50btlyTHYw3GBDgKaq0uhgzisggLSEePasX0vv4YbvwyM7tlKcl4tvsLzUlEpXbypyUsg+vBHvLoZV9fJO7qCmogxbT+NLiz/6zCRZr2+sU5mVWEgwrrs74Z5KrCzlj+paWFgQ6O2CtZWlWfsWdHcQRXFFqIt0U7pqK3Xp9xTOCoJGXP+7kzcjEQwLtzxnW0u+eziIlAI1cbm6D6lwTyXN3ExLSF5QUk5GXjEzv9/I3X10t8rW7TnD2dRcbJU3xq9Utbqa/esOk3AyEWd3J/qN7kVeej7+4X44OJuWEs1cAt3dzT63urqmmiMx/5GUGYujvSs92wymoCQbX/dm2NsaN/pfU1PNwJHdcXZy4e9ftiJJErY2tkSEmTbav/HoKaoaGTXTSnJnIOuciI1r9JjSWkEzPx9sbYxb6T5/8xKyS/OwkCSCPQIBSMpLJackj883L2HOmCmy+vbh4s/JzMvBQrIgPDAYgLOpSWTm5TBzyZd8O8W4SoFZWVk4ODjg5FR38eHq6kqvXroS1Js3byYzM7Ox0w04OjoSFxfH9OnTAV3KtpkzZ9Z7niRJvPvuu0a1eYGLizWZmZUcOVxE5y4uABw+XEhmRiWursbfqo6Pz8DDwxlXVwf9vpAQH954Ywyg5e23f+DsWePzPifMqMtOUJmWYrCtqaqkMjUZSzvj/ga069WXtPizLHj3LRJOnaBl525IFhKxx46yedkvIEl06Nvf6L4BuDTvSEV2Cun/LaMsLRaHgAgkyYKy9DiKzh4BCZybd5LV5sVOH6ufMzqidQd9dglj+TlbIwHvDb2yzAyTnxjMS5/9xU/rD/DU8O5X1NYFCmcF7d9qT9bOLMpSdJVVHYIc8Onrg6XSvIH37eLG+OQWhGsgyE1BkJtpKa0uNrhbJH/vOs38P3Yw/48d9Y5db2VFZXw07nMyEnQBQ2jbYMI6hjLvf99w73PDuP/FK79FfCVOfz7PrO2VV5Tw+Z/vklWgW3QT7NOCUL9Ivl31AUO7P8jwXo8a1Y6VlTXurh44ODiaZW5fUbkKaytLHuzdDU9nJyzM0OZrH8297NxgKysrHh5+F2NHNp2ibm/CEWyslPz87Hxa+enmVkdlxPH4oknsjj8su287juzDVmnDhs9/ol247gLiRFwUwyY+xbZDe4xup7S0FPeLykorlUrc3Nzo0kW3MPLo0aMUFBhXtKRjx47s3LmTLVu2IEkSxcXFbNiwweA5Wq3WpGC4c2cXNm/O4dNPz6JU6vLsVlXpKhZeCI6NkZ9fQrNm3vrtFi38iYgIoHdvXbYFPz83kpNzGju9nvKok3A+S7imQnV+25BDu45GtTVi7Hj+W7OS8pJiNv66lI2/Lq07qNVi7+zCiLHPGN03AK/Od5F/ahfq0gKKE45RnHDR1AstKJzc8epyl9HtbV2/gl8Xz2PcC1Poe+fdTHnhoXq/v69Pn8cdd90vq59vD/bmxb9S2RZXavR0uobMWfovVpYWvP75KqZ++w/uLvZI53+LTU6t9m8aAUMCCBwWaLC/RlXDma/P0HbSlWWauR2JYFgQZJr/6mhqNVrW7TFcRT2iVyvmvzr6OvWqzp9zV5MRn4m1jTXVlbqRydY9IlHaKDi1+4zZg2HTxjjNZ/XOH8nKT8HaSkF1je62YURQexRWSqKSjhodDAM8cN+jfPfTVxw8updunXpdUb/83Fwpr6okMsD4amTGuNz3u7qmhl/+XoeflydDel8+NVVxRSkhHoH6QBiglV8LAlx9Sc5Pl92vwtJiwgKD9YEwQPsWrWjmG8C5dHmLrEpK6qY0TZliOEJdWlpKba1xVdmmTJmCt7c3iYmJHD58GHt7e1q0aCGrL4156GF/oqNLSUur0AfBAIGBtjz0kPHTrywtLcnIqJuq8uefhkF5VlahrPK/rv3vBKDwvy1YOTnj2LGr/piFUonSPxC3AcYFm66eXkz+djGfv/4K+ZcU3PDw82fiZ1/g5uXdyNkNs1TaEf7Q2ySt+5byzHMGx+z9mhM8/HmsbIy/e7V723rysjNp16Xu9/XSaTV7tm8wKhjuM/+S4kJaGP9bMo42ljjZ1BUWkYBdk4xbMHxxajVVVTWqi7ZNvT5OWZeCZCnhP6jufaYuURP1TRSqzEbmOQuXJYJhQZDJ2cGWpdOfICkzn5gk3arniGbehPi5N3HmtXHiv9PYOtow++/3eHWA7oPVwtICdz83ctIazkzQaFvFRdhZWhHu4GCwX63RoNFqsbG05KmgZhTJzOVpTmcSD2GjtGPq2G+Y+t1YACwsLHFz8iS/OEtWW/sO7cbSwoI3pr9EkH8wri5u+lEmSZL4fPbl801frE+rcJbtOsDGo6foEBKEzSXZAFzs5Rd8efvZp5m39Bf6dOrIHd10I6XbDxxiz7HjPP/QGOKSUli/czdrt+9sMhj2cHAjKS+N7TF7GRCpCyS2Re8lKT8NTwd5GS8AvNw8SEhLZsPeHQzrdQcA6/dsJyEtCW834+cfu7q6kpuby6lTp2jb1nCEKzY2lrKyMoOR48txcXHh1VdfBaBXr14EBwfz9ddfG92Xy3FwsGLOR63Ys6eAhPi6PMO9e7thbW18RbagIE9iY9NYunQLTz55p8GxlSt3U1RUTni48RdUgS/qKi6WnTmBbWi4ftsUlapywtq2Z966zZzcu4f0hHgAAsLCaduzN1bW1uRlZuDhK++CT+niScRj06jIS6MyXxdk27j7Y+shfw1H8rlY3Dy8cHape88GBofxzMT30Gq1fDZ9IkkJsZdpoU5aUcPTmkoqaymprLsAkxPDvvXEIBnPNo4kSST/nQwS+A/0pzKvkqhvoqjMr8TKVoR1phDfNUEwUbCvO8G+N0YAfDFVaQV+zX1w9jDMCKCp1VBZbvzCHoBJJ0/S2smJr9p3uGT/CWJKS9nWtx893OQHTuZUUVWOj1sgTvaGado0Wg2VankJ6I+fPqJ/nJyWSHJaon5b7tSJ3/7bBxLsiYljT4zhXF8JiQ8eHSWrPYCt+w/g7uzM2xOe1u/r2aEdT0yeyp5jJ/jotVc4E59AYlrTI7sDInuw7OBaXv51OjbWuvmUldW6BWYDWsofFR/asz/f//0nj0+biJ1SN29ZVaV7v10Ijo3RvHlzcnJyWLNmDenp6TRr1gxJkkhNTeXQoUNIkkR4uLyUeQB79+6tt6+kpMRgbrJc1tYW3HGHB3fcYVoRFYA77mhHTEwa8+at5OjReDp3DsfCQuL48QS2bj2OJMEdd7RvuqFLtPxGN6VBU1lJRfI5JAtL7MKNG8284K2RI3hh9qdEdu5Kp/4D6NTfsNz89pV/8cuns+tlmTBG5t7VWDu64dG2n8H+sox4aivLcQ417msuys/Fv1ldGrvgsJaEhreicw/dXGYv3wAyUhMbO93ApP5eRvbeeJOfML4gibFajGtB3E9xJK9JprqkmrwjeahL1ChcFLR6vlXTDQj1iGBYEG4x7n5upMdnEnc0Xr/v+I5TZCXl4B0s/499Q7doK2s19XdeJ66OXmTmp5KQHqXfdyrhIDkF6Xi6yhuxGjrwbrPlAgUandNg6gK64zGxWFtZU1hSguv5IK64tJSSsjLyCgsB8Pf2IiMn93LNAPDKnU9zOOkU8TlJVFTXXSSFewXzyqBxsvs29emX2XvyCDFJCZRX1l2EtAwJ492nXzK6nZ49e3L8+HEqKys5ePAgBw8e1B/TarXY2trqF9PJsWHDBo4cOcLDDz+Mm5sbr7zyComJiXh5efHZZ5/RvLnx5cWrqzWkpVXg7q7AycmalBQVf/+dRbVaQ7durvTuY/xF8mOPDWL16n1kZRXy338n+e+/ujm+Wi34+rryhImji9krfiN31Z9o1GrswiPwGDGSrF+/x/vhp3DtO6DJ8/My0vnw6ccZ/sQ4Hpr4GlbWujUXJQX5LJrxLkd21M8GY6zMvaux92teLxhO3/4b5VmJdHr9B6PasbC0IjsjTb/91c+Gc8IvrkzXlEkDzB8Mg27R9aI1ezkep7tI7dgigGfu64mbk2mLmd3buxP5TCSx38eSsV03sm7na0er/7VC4Xzl62JuRyIYFoRbTPdhnVn73UY+GjsfJDh3KokvXvkOJIzOM/zqyRP6x0mqcoPtSo2GRFU5Dk1UprtWOkf2Y9P+P/j8z3dAkkjKimPR37NBkugc2a/pBi7y7msfNv0kIz19p7zXNoa7iwtZuXk8Ofk92rY4v+gtPoFyVQU+nrrRyZz8Alycml7w42zryF//+4Z1J7dx+qI8wyPaDURhJf8D1cXRme3f/sGKbes5GnsagE4RbRg9cDhKGUUtHB0deeyxx/jrr7/qFdxwcXFhzJgxODrKX9C0evVqoqKimDhxIn/++Sfnzunmq2ZnZ7Nw4UI+/vhjo9pJSlIxe1YsJSU1WFtbMP6ZZiz9KQWVSncb/cCBQioqa7nzTuMCK0dHWxYvfpW33/6eU6eSDI61bx/CrFnjcHKSP6Um/991ZP/xs8E+h7YdUOfnUrRnh1HBcPO27Ug4dZJ1S7/nxJ5dvDDnU3LT01n8/lRKiwpBq6XP3earGKipVlNdXoyclQh+gcEkno1i1W+LGPnoswbHNq35ndLiQoKby1/YfCCpvNFjNtYSYR5K7I3I3JCWU8TQSd+QlV83D37zwRh+3nCQjZ+/gL+ni1H9yTlYfxGlRycPcg7kYKm0xLunN0WxRQB4dbs6Qf2t7Mb4NBMEwWzueW4oyVGpnNxluMCvTe+W3P2scQtnjhcX6+fFqWprOd5AFbDORlSnuxbu6v4AqdnxRCUeMdgfGdyRId3GyGrrgadHEBHWkpnvfGawf+HSr0jLSOGDKZ8Y3VaIt7w8vcZ4ZsxIZi5YjKqykoMnTwG6sEGSJCY8MIr07Byy8vLo3amDUe0prBSM7DSUkZ2GNv1kIygVCh4dej+PDr3f5DbUajX+/v68/PLLJCQkkJurG+X28vIiNDQUS0tLiouLcXaWVxgkNTUVb29vHB0dOXXqFC4uLnz66adMmjSJM2eMLyn8x7I0SkpqAN0I8XcLEtFqQaGwON9/DVu35BodDAMEBHjw889vER+fwblzuiwwzZv70by56Wm98tavAUnC76kJZPz4HQBWjk5Yu7lTmWzctIH3f/mLNYsWsHLBl6QlnGXqw6PRaGpBq8XBxYXx731A9yHDZPXr6NyxugcSlGcm1G1fxFpG0ZcefQdzLu4M3381m9PHD9KmY3csLCyIOnmYvTs2IkkS3fvJn6rw0I+Jl50bbG0p8b8+Hrw64PILCGd+v5HMvBIsJImwQN0Fa3xqHpn5Jcz6YRPfvPWQUf2J/zW+0WO1VbUkrjz/M5VEMGwKEQwLwi2kprqW/3v+axRKBZN/mMi5U7pV/CFtmhHZ1bh5ltmVlXRxdcVdoWBTdjYu1tZ0v2hesI2FBUG2dgzzMa6IwtVUW1vDtyvfx9pKwSsPziI5S1fKNsg7nPDApsulXiorJwN31/q3uA8d209sfFQDZ1xeWWUlselZlFZUornkVu3AtvLzF9/RrQsB3t78tWkzSem626MhAf48MHQwzQN1hQrWfD2/yXbUNWric5LxdfbC1d6ZuKxzfL/7T6pq1NzZsg8j2g+U1a8qtZrY5AT8vXxwd3blzLk4vvzjR6rUVYzoM4gxg4Yb3da3337L/fffT7NmzWjRokW97A/Hjh1j06ZN9bJMNKW8vBxvb13gkpycTEREBK1btyYgIEA/SmyMhIRyLCwkxjzgR25OFdu352FnZ8nnX7RDq9UyaeIpMjLkzc0HWLBgHd7erowcaTgF5MSJc5SUqOjbV977WZ2diU1gMzyG368PhgEsHRypSksxqg0LCwtGPvcC7Xr14YNxj1KtVoNWi6d/AO//8hcuHibMlb7wayDR6ACwe7s7jG7uvoef5t9//iQvO4ODu7dwcPeWupfSavH09mfkI/LSv13a1Yaoa7V88V8uQa4KRndovKz8jqNnsVVasX7e/2gXplsgeOJsOsNf/ZZthxvPG26y653e5yYlgmFBuIVYWVuSHJ2Ku68bEV3Ciegif6HRw4cO0trJiSktItiUnU2tVsuUFvIW3lwrlpZWpOYk4ObkRVhAG8IC5AfAABu2rtU/LiopNNiurKwgOTURKyvjCykApOUX8OPW3Q1WoAPTgmGAsGaBBgvo5IrOjGfCT1MoLC9GYWXNe/e8wsfrv6WsSpeSafOZXZSrVTzY1bgy0KfiYxgz+XnyigtRWiv4bOK7vPPNJ5SqdLeZ/961hbKKcsbe/YBR7RUVFfHTTz/Ro0cPBg0ahKWl7lZ0eXk5a9euJS7OtADC1dWVxMREfvrpJ3Jychg0SDcPV+4iurKyWpo1s2XUKD9qajRs356Hr68Njo66j1NfXxsSEhq/xd6YBQvW0a5dSL1g+LPPlnPmTDJHj8rLgmFhZ0d1QQEadV2ml9ryMtQZ6UYX3QBIPRvHovff1QfCALkZ6Sya8Q7PzpgtOyBuNlQXmCZvXIzSxQufHnU5sS2sFdi4+WLrGdjY6fXYOzjx0TfL+HTaK8SeOW5wLLJNJ954fz4OjvLLi88bFcC7azO4q6UTd7fWvT/Wni7m35hS3h3iw6nMCpYdLeTXw4WXDYYLSysIC/DUB8IA7cP9aebrxrn0vEbPu1Svz68s1aNweSIYFoRbTKeB7Tm06SiFOUW4ernIPl8Cymtq9NuBtvLnK15L7cN6cDRuD8Vl+Tg7mJbdY/a8aUiShCRJZGSlM2f+dIPjWq2W5sHyLiy2nohqtAKdnNxM/+7Zh7OjA93bteXfPfsu+9ym0qkBfLHlewrKiwCoqlEzbfVcXZo8ayVoobKmir8OrTM6GJ75/ZfkFumKYFSqq3jlsxlotBrslDZogYqqSn76Z7nRwbC/vz/p6ens37+fhIQERo4cSVFREf/88w8qlQqtVku7du2MautivXr1YvXq1SxcqEuP17dvX4qLi8nJyaFDhw5Gt6PRaLGy0v0Arax0UyMsLsqkZs71l5WVavLyio1eAHYxh1ZtKT6wl/h3JgK6keKzb09Eo1bj2Nm4Smh/f7+Q5V9/To1ajYWlFSOfe4Gs5CT2rPubYzt38NbIYbKnSri36QNAaWo0Shcv/faV8PELYu7i1SSfiyMlUXd3qFloOEEhpueUXnOyCC9HK+aNqisNPSjCif6fx/FvbAk/PR7MkVQVsTmXvwvg5epAQnouG/dFMbSnLtPDhr1RJKTl4e1mejEPwbxEMCwItxhHVwdqazXMGPMRnQd3wMndsKLaff+7/C1rN4WCZJWKMQf2A3C2vIxHDh2s9zwJ+K1rN7P23RT2tk5oNLV8/Msk2of3wtHORV/hCWBYz4eNaudCJbJLAw+lQklQQAiTnn9LVr/S8guwsrTkpRF3Mv/vTQR4uDG8c3t+/W8fT9xh/CjPx4t/pFXzULq3a8vHi39sPI6WJKOC4dNpsVhKFrww8CkyCrNYcXQDjkp7Nr62FK0Whs17ksS8VKP7dyz2NJYWlkx+6n+kZmXw84aVONk7cPTn9WjR0vmJEcSnJhnd3vjx49m1axc7d+4kNzeXRYsWodVq0Wq12NnZMWLECFq1kp8+6uWXX0apVJKWlkafPn1o3749UVFR3HnnnbKzU5w9W87DDx1qdFuOjh1fAHRB9KlTifrti7m7y0//5v3wU5SePEZlShIgUVNSAiXFWNrZ4/3g40a1sWzepwD4hTbnxTmfEdJKd+ely6DBfP/hNEoLC/nizUn8KnPeMEDwMN1iN01NNTWqknrHFU7yLmx/W/I5Hl4+DLnHcA5u9KkjlJUW07WXvKk/+5LKUVhK5JXV4OGgC5UKymsorKgl6/ziuhA3BckFl8+xPrRHK77/Zz+Pz1iKnVJ3d0lVpbtIHtbT+Pfx6S9PY+drR+iYUE5/ebrR50mSROuXWhvdrqAjgmFBuMVs/HErSFBaWMaOv3bXO95UMDzI04s/09PIV6uRgGqNhqzK+qMfZhwAuyLbDq8GSaJMVcKek5vqHTcmGN71jy5Pat+7O9I6oi0L5i5t4oymqWtq8HJ2xt3RASTQaLUEerjhYKNk7aFjPD/U+A9n7UUTARsbI5SMHD0sriglwqc5z9/xGOqaalYc3UAzjwBczi9aauYRwJl046ciFJaU0KZ5C954fALq6mp+3rCSsIBg3JxdAAgLCOZYnPEL1CRJol+/fjRv3pwff/yR2tpatFotLi4ujB8/HodLCsAYy9bWlokTJxrsa9WqFdOnT2/kjGvjwo9NkhpOYwgwerT80VMb/0DCP/qSnJW/o0rQ/TztmrfAa+RDKP0CmjgbfaeGPTGWhye+jrVCqd/dffBQIjt1ZeG0KRzb9Z/svgFUFmaRvHEJ5RlnG3pho1OrXfDb4nlEtOlYLxhe/PmHxEWfZO0e4+eFA3g7WpNaqGbAl3F0DdJNKzmSqqK0spZAV112lPTiajzsLx9GvTtuCHtPJRKTnE15Zd2dopbB3rwzdojR/SmJL0FbrdU/FsxLBMOCcItx83W9oly5/wsNpY2TE4mqcn5ITsZTqWS49/VfLNcYVyfzZW34Ys4i7O0uH2z9uGwRmVnpvD1pxmWfp7S2puZ82WAbawU5RSWcSkolv7RMVp+2/vBdg48v0Gg0rNqy3ejb87VaDdaWuj/9ivPzoC2luvv8FjLfO7WaWqzPt6Ow1v1vcdG8AQsL+e/FnJwc/vnnH30gDFBcXMzatWu55557TA6IS0tLiYqKoqCgoN4dgOHDjVvk16+/6QU2GvLBB08AMG3azwQGevDss3WjrDY2CkJCfAgPl1+ZDUDp63dFFeimLl5Kq249Gjzm7O7Om18vYvuKP01qO2XT95SnNxQIAybm4b5UVWUlBXm5jV9lXMbkO715eXkqpVUatp8tBXQXohYSTBnsTVJ+FalF1QyJvPxUBxdHO7Z9/TIrth/nWKwuH3LHiABGD+iAUmF8CObZ1RNbT1v94xtmNOIWIYJhQbjFfPbvlefK7evhQV88OFJURIidPWObNTNDz66O959ZbLa2Orbt0uRz9h3aRXTc6SaDYVcHe3KKSqiurcXPzYVzWTn8uVc33cTrCqqeXaqmtpZvfv8TSZIYNdi44gwn02Jo854u3ZSEZLBtiiMxp3C/U1cxTJIkg2259uzZw44dO6ipqcHCwoL+/ftTUFDAyZMnOXv2LN9++61JUyX27NnDjBkzUKlU9Y5JkmR0MPzCCyGyXrcp996rm9py6FAcgYGe+m1zqC0vQxUfS01RYb1jrv3vbOAMQ40FwhcbMPpBk/qmyk4CScKr0xBsPPyQpKZz9jbknl66n4ckScSdOa7fvpiLm/wLmBGtnQl2U7B4Xz5x5+cFR3jZ8GwvD1r66CosnpzS8CLYjk98TLtwP36a9gT3vbmQyGbefPzSfTx6V9N/XxoT/nh4g48F8xDBsCAIjfq8nWkBjQA9I8LIKCikRFXB4A6t+XFbIVXqaqytrBjaqa35X1DG6Je2ifxLksxhp6YWeMm5U7Fliy41lqenJyNHjsTXV5drNyIignXr1qFSqVi+fDnTpk2T1ccvv/yS8nL5WR6akpdXVW+fm5tC9oj4hx8+BYBaXU1BQWm9H6evr7yy5yVHDpDyxSdoKhooSS4ZFwxfTQpHd5AgYMAjV9TOhfdeQ/P9Lxh636Mmtd3a19ZgAZ2xUrILcXfRTa3YfeIclepGFtLKUFVQ/33WGKWbsuknCQZEMCwIgnAVdAgJokNIkH77rZHDySspxdXBHlsZFdnM7b4Oxs9TNMYjQ+5t+kkySJJE9+7dGTRoEFYXVTls1aoVzZo14++//+bs2UZur19GVlYWNjY2vP/++4SEhOhTtsl15HAR69ZlMXyEN126uPLSiyfrPef1N8Lo2rXxdFsNSU7OYfr0pZw4UX9uqyRJslOrZf68GE1F/VFwALTX/x67X/8HSVz7DcXnTuAcavpF96SpugI582e+ga9/Mx4a97L+mNLGlsBmzQkOM64C3YrjhbjZWzEg3JEVx+uPpl/scunUXBxsORWfwYQ5vwOQlFHAS5/9Ve95kgRfvm5clpUj7x9p+kkAEvSaL9KwySWCYUEQBDOr1WiYsWwVdkolU0aNQJIkFFZW+LnJC5Cuhtmj5WXFaMrXk2eatb0nn3yS4ODgBo/Z29vzyCOPcPToUdnttmzZkoKCAvr27XtF/du5M4/o6FJefCm00efs31cgOxh+//1fOH68sUVe8ue8qnNzsFAoCZo0BWVAEJKJwb85nV70huEOrZaEVfOwVNphqTRM4djmWcMqkI25c4SuyuTJI/vwC2im3zbF66vT6RRgx4BwR15fnd7o/RFJunww3LtdCOv2RrFyxwkkCfJLylm22TCY1WrlBcNGE0U3TCKCYUEQBDOztLDA0cYWG4X1FS1mvOCxN99p9JgpOWgvllGUXW+fj5OnwSI4OVKzM+vt8/f0Nrq9xgLhi3Xq1Elut3j00Ud59913+eqrrxg6dGi9RXg+RlZUTEpS4exsjbt73ei+l5eSe+7Vnf/Lz6kkJjYyInsZUVEpWFhIPProAEJDffU5jE1l1zycmuIinLo0Pe/3WlEXN1xkorZSRW3lRd8zE35lXps2F4BqdRVFhfn1fi+8fIxbhGhM5pamAs55r47G38uFmORsdh5LwNFOSbswP6NevzEiXdrVJYJhQRAEGYwNPntGNmfz8TOczcwm3Nf7il4zKy//is6/2PaYvSzds4Ineo1mYMteDJ77WL05wp8/OoNBLXsb1d6GvTv4dvnPPD/6cYb3HkD7R++qdwHw8/vzGN5bXp5Xc5s8eTKSJPH777/z+++/GxyTJIndu+unIWxIYWE1vn42+m1bW0u8fZQMHuwFwLatuWRlGT+/8wJfX1dA4o03TB/ZvJjHPaNJ+b/ZZP68GJd+A7G8JEuKwtPLLK8jh2+v+65a2+kpiXw+602iTzUwnUCSjEqtljSjTYOPL9BotPx4IL/JC1x3Z3vmvKCbPuQ+ZAoRzbz4+7Pnmnz9y3EOl1dFL3VTKlX5VYQ9GnZFr3u7EMGwIAjCecdPH8He1p7w5oZzDNXVajS1tdjY2DLukQkUFV9+PiFAXEY2FpLE0u278XB0xMG2LoCSgKfv7Gd0v9q1CDdbZbO/j2/hcPJJ5oyZot936YK6Taf/MzoY/nPzWvaePMK3b8+qa++SC4bV//173YNhuPJR9Avy8+oKLfzwo+EodWFhNTU18l9n4sSRTJ68mF27TtO3r2llxS+W/OkHgETu2pXkrl1peFCCdn+sv+LXkMu318ir1vYXcyYTdfJwg8fMNUO6WqPlg01ZWEgwrodxRUHy//2o3r6iUhUujle3smfhmULKkstEMGwkEQwLgiCc9/KUZ2gT2Y5vP/up3v6YuDP8t/YIPbsaN+c0KTtX/zivpJS8ktK6gzI/nee9/UbTTzJSdGY87vau+DjX5WcOcPXl6T66FFmfbvyOqAYLITTsZHwMXq7u+HvWTTMI9g3g5YfGAjBtwVxOno02T+evwFdffWWWdry8lKSlVbBndz69+xgGREcOF1FUVI2vr00jZxsaPnyqwbZGo+WVV77BwcEWR0db/X5Jkli3zpSUiY0E5TfAArrS1JhGj1lYKbBx98NSYdz3ESA+5hSShQX3PTiOwJBwLC2vXngj55rqj81H2XUigRdG98HDxYFRkxcTk5yNn4czy2aOo1XIjZvD/XYigmFBEISLNDR6WFlZ0WQ6skt1CL0xczPnluYT7F6XLspBaUeQmx8PdbsHgOWH15NSkG50e9n5uTQPCNZvO9o5EOIXyLh7dMH10nUrOJdufHnnq8WUecYNad/eibS0Cr75JpH4+HJatnJEkiA2tox/N+UA0LGjcbe0MzIKGtxfWlpBaWldSjRT7gqETv9Y/knX0Nk/PrrsRaFkaYV31xH49TZuNNnT2w9Jknhm4ntm6qF5/LjuAEdjU5n1/N18t2oP0Um6OfrpucXM+elffp7x5HXuoQAiGBYEQeCVt5/VP05KPWewXVFZwbnkeBzsL19p6lKje5qeYP9qyyquG7U+MPVvg2M5pflU19bIai89N0v/OHntXsPXys+luubK86yaQ0FBAXv37iUvL4/a89UBLxg/frxRbdx9jw///ZdPWVkNGzZks2GD4QJEBwcr7r7HuNG+5583rtCHKRxat7tqbZvNZa4vtTU1ZO1fg9LFE/fWTZejHvfi23w89UUO7d1G117Xf0rOBQnpuQR4ueDsYMvBqGTcne347YOxjHl7CYejU65394TzRDAsCMJt79ipw/pFMeWqco6dqj/3sEuH7rLanLtmA36urjzSz3A1/+bjZ8gvLePhvvLaM5cAV18ScpJZd2IbI9obBg3bY/aSV1ZgMHLclCBff2KTz7F863rGDDIM7jbs3UF2QR5hAdd/lDwqKoqJEyc2WIEOjA+GXV0VTHk7nPnzEsi7aO4wgIeHgkmvNsfNzbg80s8/f7dRzzNF6bHDqOJjcendHytXN1I+/5jyqFPYBocSOHEyCnfzlTE3RfDwCaRs/gmX8E64Ruh+FwpjD1B09ij+dzyMKjuR/JM7yTuxvdFg+OlRhvs1Wi0fvDEeOwdH7B3qqjxKksSSFbuM6lef+bGNHjNlynlpeRUBni4AnE3NpX24P11aBhHq564fJRauv5s2GF62bBmffPIJ0dHR2NraMnDgQD7++GOaN2/e6DkrV67k66+/5vDhw5SUlACwYcMGhg4deq26LQjCDSYrJ5OuHXvi4ebBhq1rcXF2pWeXug9ZpdKGZgEhjBhyv6x2i8pUONjUn/OYkJVNekEhcH2C4d5hXYjPSeKdlZ9wKj2GLsHtkCSJY8mn+f3A30hI9GvRzej2BnXpTUxSAi9+MpWjMafo1a4LFhYS+08fZ8maZUiSxODuV5bb1xwWLlzYaAU6OenvKitrCQtzYP7nbTl5soS0NN10hsAAW9q2c8LKyoK8vCo8PORVATtypPF52kqlNaGhPtjZGT+HNvfv5ZRFncRt8HAKNq+n9KiuFHh5TBRZv35P0CuTZfXP3Aqi92Pt4Ezw8LosC87NO3Bm8VsUxx8lbPTrlKfHU5GX1mgbOZkNHysvLaG8tES/Lefnm1Zk3rsYHi72xKbkMO/37aTnFnF/f92IfWFpBa4XzQs3O5FvWJabMhhesmQJzzzzDAAhISHk5+ezYsUKdu3axYkTJxrNF7lz50727NlDQECAPhgWBOH29sDTw2kT2Y53Xv2GDVvXUltbyzuvfmBye8fOJesfq6qqDLbVNTXkFpdiaWIOX3MY1+cBVh/bRElFGb/sW8Uv+1bpj2nR4mzryLjzi+mM8eKDT/HbpjUUlZXw3arf+G7Vb3XtabW4Ojrz0oNjzfklmCQqKgqFQsEvv/zCgw8+SOvWrZk0aRKTJ0/ms8+MK/AA8Mbrp3nxpVBatnSkUycXOnVyMTi+fVsuS5em1ssy0ZTx4+dddm6wtbUV48YN4X//M240uTItGWt3D6xdXCmLOoWFrR0BE14h9dv/o/zMKVl9uxpKU6OxsLSiurwEa3vdKG6NqpSayjLUqbpsLUpXb6qKcxpt49Hxk8zer+7N7DBf/gkY0r0lP647wKwfNwEwrGdLCktUZOQV06ttiEltFscXY2VjhX2AvcF+TbUGrVaLpcKSwKGBVJfdGNOTbgY3XTCsVquZMkWXEmj06NEsX76cjIwMIiMjycnJYfbs2XzxxRcNnvv222/zySefsHfvXgYMGHAtuy0Iwg1KkiTKVXUjhkH+V3ZLf+W+w7rPUgkKyspZuf+SKRda8HaVlzPUnDwd3Vnw5BxeW/YBmZcEGn7O3vzfw+/h5eRhdHs+7p78Oecbxn3wBmk5hgU3Ar39+GHaZ/h6XPuctpeqqKggNDSUgIAAJEmitraW1q1b4+rqyqeffsqSJUuMaicvT80H78cwYoQPDz/iry+OUVJSzXcLkjhypMjkPl7uNrxaXcPChesJCPDgnnuaLqRRW16GTVAwAFXpqdg1D8eld39y/15OZWqSyX00F4WDC1XFuUR9Pxl7/xYAlGecpbZKhfJ8phN1aT7Wtk6NtvHoM5PM3q8/xjVeWdAUH0wYgY3SisT0fO7q2ZIebUI4GpvKyP7tGNzduDLRlzrz5Rkcgx1p+2pbg/2nvzxNWUoZveb3wrX19a92eTO56YLhQ4cOkZenq2IzevRoAPz8/OjRowebN29m48aNjZ7r7W1a4vuqqiqqquqSqItRZUG4dbi5upOUeo6RTw4BIO5cLA88PaLe8yRJ4s8l/xjXqBZdQHxJcGNlaYmnsyMjOre/sk5fgfKqCtoFRLJ+0k/sSzhCfI5u5DrMK5heYZ2xtrQioygbPxfj/l6WVajo3LIth5f+w/Yj+4hNSgAgMrg5A7r0xNrKmtTsTAK9fa/a12QMBwcH1Gq1/nFiYiJbtmwhLS1NVv7h5mH2JMSX888/WZw4UcyLL4WSm1PFokVJlJToFh726WtcDtqLzZo1lpkzf2PgwA4MGdIZgE2bDrN9+wlee2000dEprFy5h+XLdxkVDFs6OFKVkU7h7u2oc7Nx6qSb+lKrKq9XgON68Ov7AIn/fEttVQUliSd0O7WAJOHX70EqC7NRF+fhEmbcCPvpYwcaPaZQ2hAYHIatnX2jz7la7G0VzHr+HoN9nSICWTDl4StruIG3rEatEdMjTHTTBcOpqXUpery86kYbLgS6KSnmX505Z84c3n//fbO3KwjC9Te4/zCWrfqZvIJcJEmiulpNVk5GvecZO+/ww8d0F+nv/bqCAA83nrvrxroLdf+XzzBnzGS6BLejf0QP+kcYBlYrjmzgkw3f1ssy0Zje40fx7ZRZ9GrXmbt69OOuHobFRH7ZsIp3v/m0XpaJa83X15fExESqqqqIiIjg8OHDTJ8+HTCuBPQFH37YktWrM1mxPIPU1AreeTsKjUYXgTg4WPHMs83o0cNNdv82bDiEh4czs2aN1e/r378t99wzjR07TvD11y9x/HgCZ8/Wf282xKF1e4r27CD1i0912x06o6mspDo/F9vmLWT3z9xcI7qhdPUm5/BGKvJ1qfxs3QPw6joUO88gANq/9I3R7U154aHL/o5aWVsz5vH/8dizr15Zx01QXFbBkZhUcgvL6qVofHhwZ6PbOf3laf1jVbbKYFuj1qDKVGFle9OFdTeEW+a7Zq7KQg15++23ee211/TbJSUlBAYGXrXXEwTh2nlx/Gu0bdWBc8kJLPnlGzw9vLlb5mK5hjx9Zz+U1taXfc72U9EUlqsY1cP4D8QrlVGczbjvX+fJXmOYeOfTKKx0fSwoL2La6rnsiNkvq73U7AzufW08/xvzBO+NfwXF+a85r6iAiXNnsHHff2b/Gkzx0EMPER0dTW5uLs8//zyTJk2irKwMW1tbXn75ZaPbsbCQGDXKj3btnHh/RizV1RoAPD2VfDizJS4ul/+ZN+bQoVisra3Izy/B3V03NaCwsIyionJycuIACAryIi0tz6j2fJ+agEZdhTorA6fOPXDq2JXymDPYNm+BS+/+JvXR3Oy8mhksoLtSl4sDqtVqlv3wBT7+QQwaPtpsr9mUfw9EM2HOMsoq6pfolpBkBcMl8XV3pWsraw22L3Bucf2mYN3Mbrpg+OIgNCcnp97joKAgs7+mUqlEqZS3MlgQhJtHv54D6ddzIIeP7ye0WRhPP/r8FbcZ4t106qq4jCzS8guuaTDc1j+SU+kx/LRnOXvOHmLOmCmkF2bx/pp5FKpK0KLlnvZ3Gt1e58i2HIk5xTfLl7Lt8B4WTJlNSlY6r877gPziIrRaLQ/eefVSiBnrrrvu4q677tJvr1mzhpSUFPz8/HB0lJdDOiVFxaKFyfpAGCA3t4qF3yUx4blgkwJiT08X0tPzuO++GXTsqCuhe+LEOcrKKvD3183hzsoqwM3NuL5au7gS/OY0g332ka0J+3Cu7L6ZS/6Z3VjZOuIc2p78M7sv+1xjcgtf7PXp8/j6k3fp2f8u+g7Svd92blnL/p3/Mv7ldzkbc4p//17GhlW/XtNg+L3v1lGqqh8IAyDJG8Tz7Kb7m5J7MBdrB2tcWrnoj1laW2LrbYtXj+s/P/9mdNMFw127dsXd3V2fQeKRRx4hIyOD/ft1oxkX0qRFRuompr/00ku89NJL162/giDcPL76yLhFVDez3yZ8wcKdv/Pt9p+Jz0nmoQUvotFo0KLFxdaJafdO5K42xo8cbvryZ/7vt8V8+vMCYpISGPTCI9RqdKva3ZxcmDtpKvf1H3IVv6Km1dTU0L9/f5ydnVm3bh2SJGFra0tERITsttasyeSvP9OpqdHqRolH+5KVWcXu3fkcPVrEG6+fNmmqxCuv3MeUKd9TVlbJ7t26299arW4ketKk+0lJySE9PZ8BA4yfb66pVlO0ewequGisXNxwG3gX6txsbAKDsZJ5AWAOyRsWY+/XHOfQ9iRvWHyZpA2S7GB4x79rcHX34vXp8/T7uvUZxLNj+rN/57+8P+8nYk4dISmh8TzCV0NaThF2SmsWvfMIEc28sbI0PZNM+GPhAJScLcE+0F6/LVy5my4YVigUzJ49m+eee44VK1YQGhpKfn4+paWleHh46DNNxMbq3vAXFtsBfPHFF3zxxRdUVNSVuXz66aexs7Nj9OjRfPzxjV2+UhAE4UpZWFjw/B2P0TusM08teQ11TTVatPi7+PDbc1/g4SAviLOwsOCNxycwsEsv7n51HFXVarRaLUE+fvz75S94uRmfmeJqsbKywt3dHUdHR1k5Zxvy+2+63Lb+/ja8+FIooaG6RVldu7qweHEypaU1fD4/gR7L5H0fhwzpTFCQFz//vJWEBN284LAwP5588k5atNAVQdm1y/hR3ZrSEs5Nf4vKNN06GrvwCOwjWpI4expeYx7B58EnZPXvqmhsYFTmiCnAqSP7sFIoKCrIw+X8e664qICSkkLyj+oqJPoFhpCZnny5ZsyuQ4sA8orKGNqzldna7DxDdyeptqoWVYYKLMCx2bW/uLmV3HTBMMCECROwt7fns88+Izo6GhsbG0aNGsVHH32En59fo+cVFBSQkJBgsC8zU5cKKDtbVIIRBOH2cDY7kelr5ukDYYCMomymr/4/3r//NdkBcVTiWSb93/v6QBggNTuTiXPf5/PXp98QAfGDDz7IggULOHDgAN27X1nBk+EjvHnkkQCsretG+br3cCOypSMLvk3k2LFik9qNjAw0WEB3JTJ/XkxlWjKSQoH2QhaNth2xUCopPXb4ugTDnd74scHHF2i1GnKPbjEpza+bpzfZGalMeHAArdt3BSD61BFUZaV4++mmV+ZkpePsem3fiy+N6ce4mb8wfdF6HhzUEWcHw8IpAV6mpUBL3ZRK+uZ0NNUaHJs54nuHL8lrkwkaEYRnl+tbXfBmdFMGwwCPPfYYjz32WKPHG5pIP2PGDGbMmHEVeyUIgnBjW7JrGV9t/Ql1bTWWFpY83/8xkvPT+efkVv6LPcB9Xzwja6rE58u+56Mfv6GqWo2VpSVvPD6BhLQU/tq6jn8P7KTX+FE3xFSJffv2YWlpyWuvvUZQUBBubm4Go8RfffWVUe28Ny2C1q0bzn3r7GzN5Ckt2LYt16i21q7dj6urA336tGHt2ssvXDQmndrFSo8exNLOnhbzFhL9nO6zUrK0xNrTC3V2lqy2rhVtbS1p238DScKrk7z3y1P/m8wn015GVV7K4X3bde1ptUgWFox7YQoZqUlkZ6bSs9+1fR8+PmMpkgRfL9/J18t3GhyTkMjdNEd2m1m7s0hdn2qwzznCGfVSNXlH80QwbIKbNhgWBEG42V3FJDiN+r9/FwMQ6hnEx2Om0MpPl2ZrUKvefPD35xSqinnjz5lGB8PvL5oPQERQKAvenk37FrrbwSP6DOT1+TPJLy5k/IdvXfdg+NixY/rHycnJJCfX3S6XM3WisUD4YgMHGheMvPfeUtq3D6FPnza8997SRivQSZIkOxiuLS9HGRCEteslo/waDZpKlay2rj35vxh9B43ALzCY1b8vJvmcLvtGcPMIRj7yLCHhLQH449+TZu2lsRr9PTdhOghA5n+ZIEHIyBASVyYCYG1vjcJFQXl6wyXHhcsTwbAgCMJVkJidi421Nb5uLgb7a2pr0Wi1KKysGNA2kvIq9TXtl4TEE71G8erg8SisFPr9Q1r3o3Oztry36jN2xh00vj1J4n+jH+e98RNRKurau7ffYHq27cTLn05n88FdZv0aTDFs2LArni98NVwcKDV+cSQ/aLL29KIqNZny6LpctCWH91OVkYbS1192ezeD5i1aGyyguxH8/ekEs7dZmVeJnY8dvv199cEwgJWdFaqsG/1C58YkgmFBEISr4PstOwn0cGfCXXcY7F+yZSfp+YV88OgoIvyvfVW278d9SrfQDg0ec3dw5ZsnZrHi8Hqj21vz2WL6dOja4DFPV3eWzf6Kn9evNKWrZvXee+9d7y7Uc/z4Nw0+vkCj0fD77ztMatul9x3krPiNhOlvARKqs7EkffIBIOHS5w6T2rzRbF2/AmcXN7r0GsDW9Ssu+9xrmU7tYr3bm7e8M4ClrSXqYjWai1L71ahqqMipEEU3TCS+a4IgCFfJpdWmANQ1NQ3uv1YaC4QvNrrLcKPbaywQvtgTw0cZ3d7VMnr0aCIiIpg9e7bB/u+++47U1FRmzpx5nXrWuJqaWj79dDkWFhKPPTZQ1rleox6m4txZSo8dMtjv2L4znvc/ZM5uynJ60RuNH5Q5b2jeh68T2aYTXXoNYN6Hrzc+8i9J1y0YBsgtLOPfA9Fk5ZdSq9EYHHvrCeNzel/gHOZM/ol8Ts7VTfuozK/k5NyTaKo1uLYxbUHe7U4Ew4IgCGa0ZEvdIpnc4lKD7eqaGnKKSrBRmFalTDBdZmYmbm71s2QcPHiQmJiY69Aj48mtsKqtqSFp9ntICgWhMz6hIl6XatS2eQscWre7Gl00mrrYuAp6xrr4wrKx79P1nBxzNDaVUZMXN1iBDkwLhoNGBFEUW4QqUzclorqsmuqyaixtLAkcJqrjmkIEw4IgCGaUlJ2r//Stqq7WbV+iuY+oEnWtrF9fN+WjqKjIYLuyspKkpCSsrG6tj0LJyoqKxHisPbxwaNUWh1Ztr3eX9BwCIswWnf6zL6nBxxdoNBrW/vUj0nUMh2f/+G+jFehMncJu621LuzfakfZvGmUpZQA4BDkQMDgAWy9bU7t6W7u1/gIIgiBcR0XlKsJ8vXGwteH4uWTsbJS08PPRH1dYWuLh7Ejn5sHXr5O3mZkzZyJJEpIkkZGRwaxZswyOa7VawsLCrlPvrh6nbr0o3ruT6oJ8rN3cr3d39Fo8/PY1e63ammoWzf8AycKCex8ad81e92JHY1OxUVixe+GrdBn7KV1aBjLr+Xt4YsZSfv9wrMnt2nraigp0ZiSCYUEQBDOZu3oDgR7uPDWwC8fPJaPRaBnds8v17tZtT6vVIklSvdvoSqWSZs2a8eqrr16nnsHw4VMbPXYlqfesHJ3Qamo5+9aLOHfvg5WLCxcPyXo/0Hie/lvO9chheF55hZqWwd6E+LkjSVBbq6FLyyA8XBx448vVbPnyJZParVHVUJZchrq0fjYar27izpNcIhgWBEEwF0k3NeICDyeH69gZAWDv3r0A9OrVi9atW7No0aLr3CNDGRkFV6Xd3LUrAImakmLyN9fPDnJbBcPXkZO9DZXqGgCc7W2JSc5m5Y4TJGbkmRyjF5wp4OzSs9RW1tY/KIlg2BQiGBYEQTATBxsbckpK+GSlLvjILCxi7poNDTxT4vX7hl7bzt3mvvrqK+zt7S/7nB9++IGMjAzefffda9Qr6Nw5jKuxxMvaw/OqtCvI08zHlZjkbCrV1bQL92PnsQQmzPkdgIggb5PaTF6d3HAgDKakpBYQwbAgCILZtA8OZE/0WUorKuD8LdGisgaS4IsY5Zrr1KlTk8/Zu3cvUVFR1zQYXrLktavSbstvll6Vdm80T4/q0/jB6zg94oLnRvbhWFwamXklvPf0UEZPWUJJeSX2NtZ8MMH4FIYXqyqowsLaghZPtcDOxw4szdzp25AIhgVBEMxkaKd2BHm6k11UwraTUTjZ2YrFcoJwFeVkpl3vLlzWA4M68sCgjgCE4M7p394hPi2XYF83nB1My/zgEORAdWk1bm3rpwoUTCOCYUEQBDNqFehPq0B/ErJy8HZxYmC7Vte7S4Jwy2rTofsNe6eluqYW3xHv4u5kT8yfU5EkCXtbBe3Dr6wctt9AP2K/jyVpTRKeXTzrVZ1TuimvqP3bkQiGBUEQroJnBve/3l0QhFveR9/+cb270ChrK0u83RxxdrBtvDqeCWIW64rEZGzLIGNbhuFBCXrN72W217pdWFzvDgiCIAiCINyKnhvZh/jUXLYdjrs2L3j9p0nflMTIsCAIgiAgv+yxIDRly8EYLC0sePDd7wkL8MTL1UE/SiwBqz+dILvN1i+1NnMvBREMC4IgCLe8Y8eOYW9vT4sWLQz2q9VqNBoNNjY2PP300xQWFl6nHgq3oj0nE/WPz6bmcja1rjy7qTMnnMOdr7RbwiVEMCwIgiDc8l588UXatGnDwoUL6+2Pjo5m9+7d9Ool5loK5vXwnZ3MvsCvMKqQsuQyPDp7oHBSEPdTHCUJJdj72xP+ZDhKV7GATi4RDAuCIAi3hYamQVRUVIjpEcJV8/VbD5q9zYxtGRTHF+Pd25usPVkURunuZpScKyF5bTItnmzRRAvCpUQwLAiCINyyXnrpJf3jpKQkg+2KigrOnTuHg4Momy1cHR2f+Jh24X78NO0Jg/2zfthEQnoe30+VXxZblaVC6aJE4aSgJL4ESxtLmj/UnPjf4ik5W2Kurt9WRDAsCIIg3LKOHj2qX7BUXl7O0aNH6z2na9eu17pbwm0iJbsQL7f6F1vbj57leJxpBUNqVDXY+dkBUJFTgUOgAx6dPEjfmo4qs4GKl0KTRDAsCIIg3JKysrLo1q0bHh4erF+/HhcXF4N5wUqlkuDgYO6+++7r2EvhVrRs8xH94/yicoNtVaWauJQcFFam1VG2srOiMqeS3CO5VOVX4drKFYDaytp6BTgE44jvmiAIgnBLGjVqFG3atGHq1KmsX7+e2tpapk6der27JdwGXvz0LyRJlzEiKauAlz77y+C4VgutQ3xMats53Jm8o3mcXXoWAJeWLtRW1VJVWIVDkJjyYwoRDAuCIAi3JEmSKC8v128HBQVdx94ItxutVhcMX7o+01ZpRXigF3NeuNekdoNHBqOp1lCZV4lrG1dcW7lScq4EhyDddAlBPhEMC4IgCLckNzc3kpKSuPdeXdARFxfH6NGj6z1PkiSWL19+rbsn3MLy//0IAPchU+jSMpBNn79otrYVTgoin4k02OcU6kTbSW3N9hq3GxEMC4IgCLekIUOG8Pvvv5OXl4ckSVRXV5OZmVnveZKp1Q8EoQl/fzoBR/vL5/397NetJGcV8OXrDxjdrqZaQ+6RXMqSyrB2ssa7hzeVBZXY+dphbW99pd2+7YhgWBAEQbglvfzyy7Rr145z586xaNEivLy8uOeee653t4TbSO/2oU0+598DMRyNTTU6GK4ur+bMF2dQZekyRzg2c8QxxJHoBdEE3BVA0HAxHUguEQwLgiAIt6z+/fvTv39/Dh06RGhoKOPHj7/eXRKEK5K8JhlVlgoLaws01RoAXCJcsFBYUBRdJIJhE4hgWBAEQbjlffPNN9e7C4JgFoVnCrG0saTjux05/N5hACQLCaWbksq8yuvcu5uTxfXugCAIgiAIgmCcGlUNNm42KJwUhgc0UFtVe306dZMTwbAgCIIgCMJNQummRJWloiShrvRywakCKnIqsHGzuY49u3mJYFgQBEEQBOE60V6aiLgJHp090Gq0nP7iNAClyaXELI7RHxPkE3OGBUEQBEEQroK9J8/haGdD2zA/g/1V6hpqNRrsbBS89fid5BWXN9JCfQFDAihPLacwqtBgv0ukC/6D/c3S79uNCIYFQRAEQRCugnveWEjXlkFs/PyFS/Z/x7HYNHI3zWFw98hGzq5PU6shekE0FtYWtH65NWXJZQA4BDngHO5s1r7fTkQwLAiCIAiCcJVoqT8NQlWpbnB/UywsLShLLUPppsQ5zBnnMBEAm4MIhgVBEARBEMzovjcX6h/HJucYbJdXqolOysbZwbTFbu7t3Mk7loe6WI3CWdH0CUKTRDAsCIIgCIJgRrtPnONCle9SVRW7T5yr95z+HcNMatvKwQqtRsuJT07g1t4NhaNhQBw4LNCkdm9nIhgWBEEQBEEwk7ScQgZ0DsfbzZFlm4/i4WzPnd0i9MftlArCgzx57K6uJrWfsS0DgOqyarL3ZNc7LoJh+UQwLAiCIAiCYCbtH/+Yri2DWD5nPMs2H6WmVsPXbz5otvaVrkqztSXoiGBYEARBEATBTCRJNzXigrBA8+b+7Tyjs1nbE0QwLAiCIAiCYDbero7EpmTT+pFZAJyKz6DjEx/Xe54kwdGlk69194QGiGBYEARBEATBTEYP7MDXy3eRlV+KJEFVdS0p2YX1nndhgZ1w/YlgWBAEQRAEwUw+mDCC7q2DiU7KYs5Pm/HzcOLxYaYtlhOuDREMC4IgCIIgmNGI3q0Z0bs1/x2Np2WwN5OfGHy9uyRchgiGBUEQBEEQroK1c5+73l0QjGBxvTsgCIIgCIIgCNeLCIYFQRAEQRCE25YIhgVBEARBEITblgiGBUEQBEEQhNuWCIYFQRAEQRCE25YIhgVBEARBEITblgiGBUEQBEEQhNuWCIYFQRAEQRCE25YIhgVBEARBEITblgiGBUEQBEEQhNuWCIYFQRAEQRCE25YIhgVBEARBEITblgiGBUEQBEEQhNuWCIYFQRAEQRCE25YIhgVBEARBEITblgiGBUEQBEEQhNuWCIYFQRAEQRCE25YIhgVBEARBEITblgiGBUEQBEEQhNuWCIYFQRAEQRCE25YIhgVBEARBEITblgiGBUEQBEEQhNuWCIYFQRAEQRCE25YIhgVBEARBEITblgiGBUEQBEEQhNuWCIYFQRAEQRCE25YIhgVBEARBEITblgiGBUEQBEEQhNuWCIYFQRAEQRCE25YIhgVBEARBEITblgiGBUEQBEEQhNvWTRsML1u2jE6dOmFra4ubmxtjxowhISGhyfO+/PJLWrVqhVKpxMvLi6effprs7Oxr0GNBEARBEAThRnNTBsNLlizhkUce4dixY/j6+lJbW8uKFSvo1asXWVlZjZ733nvv8corrxAdHU2zZs0oKyvjhx9+4I477kClUl3Dr0AQBEEQBEG4EVhd7w7IpVarmTJlCgCjR49m+fLlZGRkEBkZSU5ODrNnz+aLL76od152djYff/wxAK+//jqfffYZJ0+epEOHDsTExLBgwQJee+21Bl+zqqqKqqoq/XZxcTEAJSUl5v7ybni1VRVmbU9VXmrW9kora83Wlra80mxtAdRU1Ji1vYoy8/4symvM278SlXn7V1Flbba2ylVlZmsLoLK62qztlVeY93tXVllu1vZKys37/bv476s5lJeb7+tVqcz3NwWgzMy/t7Uq8/5sVWXm/Zt8I39mmPPzAm78z4zbMWa58DVrtdrLP1F7k9m9e7cW0ALa3377Tb9/8ODBWkAbHh7e4Hm//PKL/ry9e/fq94eHh2sB7eDBgxt9zenTp+vPFf/EP/FP/BP/xD/xT/wT/26ef6mpqZeNLW+6keHU1FT9Yy8vL/1jb29vAFJSUmSfd/bs2UbPA3j77bcNRo01Gg0FBQW4u7sjSZL8L0IQBOEGU1JSQmBgIKmpqTg5OV3v7giCIFwxrVZLaWkpfn5+l33eTRcMN0bb1BD4FZynVCpRKpUG+1xcXEx6PUEQhBuZk5OTCIYFQbhlODs7N/mcm24BXWBgoP5xTk5OvcdBQUFmPU8QBEEQBEG4dd10wXDXrl1xd3cHYMWKFQBkZGSwf/9+AIYOHQpAZGQkkZGRfPXVVwAMGjQIKysrg/NOnjxJfHy8wXmCIAiCIAjC7eOmC4YVCgWzZ88GdEFtaGgoLVu2pLS0FA8PD32midjYWGJjY8nLywPAx8eHN998E4C5c+cSERFBjx490Gq1hIeH89xzz12fL0gQBOEGoFQqmT59er0pYYIgCLe6my4YBpgwYQK//PILHTp0ICMjA0mSGDVqFHv37r3sJOlZs2Yxf/58IiMjSUxMxN7enqeeeoqdO3dib29/Db8CQRCEG4tSqWTGjBkiGBYE4bYjaU1deSYIgiAIgiAIN7mbcmRYEARBEARBEMxBBMOCIAiCIAjCbUsEw4IgCIIgCMJtSwTDgiAI11lSUhKSJDX4z9bWlsDAQIYOHcrChQtRq9XXu7uCIAi3FLGAThAE4TpLSkoiJCTEqOf26NGD7du3Y2Njc5V7JQiCcHsQI8OCIAg3GA8PD0aPHs3o0aPp27cvkiTpj+3fv5+FCxdex94JgiDcWkQwLAiCcINp3bo1y5cvZ/ny5ezcuZOvv/7a4PiOHTuuT8cEQRBuQSIYFgRBuMH179/fYLuqqkr/eMaMGQZzjJOSkgyeO3bsWIPjl/rrr78YPnw4vr6+KBQKHBwcCAkJYfDgwbz33nucOXPG4PmLFi3iySefpH379vj6+qJUKrGzs6N58+Y8+uij7Nq1q8Gv4eI+jB07lry8PCZNmkSzZs1QKpWEhIQwbdo0ampqTPwuCYIgmMbqendAEARBuLz//vvPYLtjx45maff9999nxowZBvuqq6spLy8nKSmJLVu2oFQqad26tcE56enp9do6d+4c586dY9myZcydO5dXX3210deNj4+nY8eOpKWl6fclJSXx4Ycfkp6ezpIlS678ixMEQTCSCIYFQRBuMGfOnGHMmDEA5ObmGoy2hoSEMHHixCt+DbVazSeffKLfdnR0pEePHlhZWZGamkpCQgIVFRUNnuvs7Ex4eDhubm7Y2NiQm5vL0aNHqaqqQqvVMnnyZMaMGUNgYGCD5+/ZsweA9u3b4+joyJ49e7iwlvuHH35g6tSpRi8oFARBuFIiGBYEQbjB5OXlsWLFinr77ezs+P777/H09Lzi1ygsLESlUum3N27cSK9evfTblZWVbNu2DQcHB4PzNm7cSMuWLbG0tDTYHxMTQ8uWLQHd6PLff//Niy++2Ojrz507l9deew2AWbNmMXXqVAC0Wi3//fefCIYFQbhmxJxhQRCEm4RKpWLQoEH8+eefV9yWl5cXjo6O+u2ZM2eydOlSDhw4QHFxMTY2NgwfPpx+/foZnOfr68vMmTPp2bMn7u7uWFtbI0mSPhC+IC4urtHXDgoKYtKkSfrtYcOGGRzPzMy8gq9MEARBHhEMC4Ig3GD69++PVqtFq9VSXFzMsmXLUCqVAGg0Gl544QWDUV1TSJLEu+++q9/esGEDTz31FD169MDFxYVWrVrx4YcfUl5ern9OWloaHTp0YMaMGezfv5+CgoJGF7yVlJQ0+trt27fHwqLu48fZ2dng+MULBAVBEK42EQwLgiDcwJycnHjooYd47LHH9Pvy8/PZv39/g8+vra012M7Ozm607cmTJ7Nq1Sruu+8+vL29DY5FR0czbdo0nnzySf2+mTNnGix6c3V15a677tLnRL7Y5eo5ubm5GWxfOuVCEAThWhJzhgVBEG4CTk5OBtsXglyFQmGwv7CwUP+4qqqKo0ePXrbd+++/n/vvvx+A4uJioqOjmTt3LsuXLwdg5cqVZGVl4ePjw969e/Xn+fn5ER0dre9XVlZWg/OcBUEQbnRiZFgQBOEGl5+fz+rVqw32+fj4GPx/wYW0ZLW1tbz55pvk5OQ02u6cOXM4deqUftvZ2ZkePXowdOhQg+clJycDuoVxF1hZWekD8ZqaGt5++22ZX5UgCMKNQYwMC4Ig3GAuTq1WWlqqX9R2gY+Pjz7zw6UFORYsWMCKFSuoqKigrKzssq8za9Ys3nnnHfz9/YmIiMDJyYm8vDyDKRgWFhb6zA5du3YlJiYGgJSUFCIiImjbti2nT58mJSXlyr9wQRCE60AEw4IgCDeYxlKrAdja2vLTTz/pF9Q1b96cJ598kqVLl+qfk5ubqz8WHBzM1q1bL/t66enpDRbSAHjrrbfw8vIC4N1332XNmjX6xXEpKSn6IHjmzJn69GiCIAg3EzFNQhAE4QZmYWGBk5MTHTt25PXXXycqKoohQ4YYPGfx4sVMnTqV4OBgrK2tCQgIYOLEiRw+fJiAgIBG2/7555955ZVX6NatG/7+/iiVShQKBQEBAdx7772sWrWKOXPm6J8fERHBnj17uPvuu3F0dMTOzo7OnTvzyy+/GGSmEARBuJlI2sst+RUEQRAEQRCEW5gYGRYEQRAEQRBuWyIYFgRBEARBEG5bIhgWBEEQBEEQblsiGBYEQRAEQRBuWyIYFgRBEARBEG5bIhgWBEEQBEEQblsiGBYEQRAEQRBuWyIYFgRBEARBEG5bIhgWBEEQBEEQblsiGBYEQRAEQRBuWyIYFgRBEARBEG5bIhgWBEEQBEEQblv/D4fFtEmOcKHaAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "make_plot(models, 'Busan')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "make_plot(models, 'Incheon')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "make_plot(models, 'Daegu')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "make_plot(models, 'Daejeon')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "make_plot(models, 'Gwangju')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "file_path = \"./model_result/\"\n",
-    "file_list = os.listdir(file_path)\n",
-    "file_list = [file for file in file_list if file.endswith(\".csv\")]\n",
-    "\n",
-    "\n",
-    "models= pd.DataFrame()\n",
-    "for i in file_list:\n",
-    "    models = pd.concat([models, pd.read_csv(file_path + i)], axis=0)\n",
-    "\n",
-    "models = models.reset_index(drop=True)\n",
-    "models['model_data'] = models['model'] + '_' + models['data_sample']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df= models.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = df.loc[df['data_sample']=='pure', :].copy()\n",
-    "df.sort_values(by='CSI', ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **각 지역별 앙상블 모델 성능 비교 by 원데이터**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(15, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(df['model'].unique()))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model', ci=None, data=df.loc[(df['data_sample'] == 'pure'),:], palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=15)\n",
-    "plt.title('CSI Score Comparison of Models Averaged Over 3-Fold Cross-Validation', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "file_path = \"./model_result/\"\n",
-    "file_path_1 = \"./model_result/best_sample/\"\n",
-    "file_list = os.listdir(file_path)\n",
-    "file_list_1 = os.listdir(file_path_1)\n",
-    "file_list = [file for file in file_list if file.endswith(\".csv\")]\n",
-    "file_list_1 = [file for file in file_list_1 if file.endswith(\".csv\")]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "models= pd.DataFrame()\n",
-    "for i in file_list:\n",
-    "    models = pd.concat([models, pd.read_csv(file_path + i)], axis=0)\n",
-    "    \n",
-    "for i in file_list_1:\n",
-    "    models = pd.concat([models, pd.read_csv(file_path_1 + i)], axis=0)\n",
-    "\n",
-    "\n",
-    "models = models.reset_index(drop=True)\n",
-    "models = models.reset_index(drop=True)\n",
-    "models['model_data'] = models['model'] + '_' + models['data_sample']\n",
-    "df= models.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df= df.loc[df['model'] !=(\"deepgbm\"),:]\n",
-    "df= df.loc[df['model'] != (\"XGBoost\"),:]\n",
-    "df= df.loc[df['model']!=(\"LightGBM\"),:]\n",
-    "df= df.loc[df['model']!=(\"ft_transformer\"),:]\n",
-    "df= df.loc[df['model']!= (\"resnet_like\"),:]\n",
-    "\n",
-    "df.sort_values(by='CSI', ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table= df.loc[(df['region']=='seoul'),:'Accuracy']\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(df['model'].unique()))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model', ci=None, data=df.loc[(df['region']=='seoul'),:], palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=11)\n",
-    "plt.title('Soft Voting Ensemble: Model Performance Comparison in Seoul', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('')  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table = df.loc[(df['region']=='busan'),:'Accuracy']\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(df['model'].unique()))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model', ci=None, data=df.loc[(df['region']=='busan'),:], palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=11)\n",
-    "plt.title('Soft Voting Ensemble: Model Performance Comparison in Busan', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('')  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table = df.loc[(df['region']=='incheon'),:'Accuracy']\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(df['model'].unique()))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model', ci=None, data=df.loc[(df['region']=='incheon'),:], palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=11)\n",
-    "plt.title('Soft Voting Ensemble: Model Performance Comparison in Incheon', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('')  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table = df.loc[(df['region']=='daegu'),:'Accuracy']\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(df['model'].unique()))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model', ci=None, data=df.loc[(df['region']=='daegu'),:], palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=11)\n",
-    "plt.title('Soft Voting Ensemble: Model Performance Comparison in Daegu', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('')  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>254</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.680</td>\n",
-       "      <td>0.789</td>\n",
-       "      <td>0.958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>255</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+resnet_like</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.666</td>\n",
-       "      <td>0.780</td>\n",
-       "      <td>0.955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>257</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.662</td>\n",
-       "      <td>0.776</td>\n",
-       "      <td>0.955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>264</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.661</td>\n",
-       "      <td>0.774</td>\n",
-       "      <td>0.955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>256</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.657</td>\n",
-       "      <td>0.773</td>\n",
-       "      <td>0.954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>266</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.656</td>\n",
-       "      <td>0.770</td>\n",
-       "      <td>0.955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>265</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.650</td>\n",
-       "      <td>0.765</td>\n",
-       "      <td>0.954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>274</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.646</td>\n",
-       "      <td>0.763</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>267</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.646</td>\n",
-       "      <td>0.764</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>268</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.644</td>\n",
-       "      <td>0.762</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>275</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.643</td>\n",
-       "      <td>0.760</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>276</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.627</td>\n",
-       "      <td>0.747</td>\n",
-       "      <td>0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>279</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost+Lig...</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>0.745</td>\n",
-       "      <td>0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>277</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.618</td>\n",
-       "      <td>0.741</td>\n",
-       "      <td>0.948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>269</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.610</td>\n",
-       "      <td>0.736</td>\n",
-       "      <td>0.947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>270</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.593</td>\n",
-       "      <td>0.717</td>\n",
-       "      <td>0.945</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>258</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer+resnet_like</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.587</td>\n",
-       "      <td>0.712</td>\n",
-       "      <td>0.945</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>271</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.587</td>\n",
-       "      <td>0.712</td>\n",
-       "      <td>0.945</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>259</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.582</td>\n",
-       "      <td>0.708</td>\n",
-       "      <td>0.943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>260</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.581</td>\n",
-       "      <td>0.707</td>\n",
-       "      <td>0.944</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>278</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.578</td>\n",
-       "      <td>0.705</td>\n",
-       "      <td>0.943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>272</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.570</td>\n",
-       "      <td>0.698</td>\n",
-       "      <td>0.941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>261</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.562</td>\n",
-       "      <td>0.693</td>\n",
-       "      <td>0.939</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>273</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.557</td>\n",
-       "      <td>0.688</td>\n",
-       "      <td>0.938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>262</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.555</td>\n",
-       "      <td>0.686</td>\n",
-       "      <td>0.939</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>263</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.529</td>\n",
-       "      <td>0.663</td>\n",
-       "      <td>0.933</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      region                                              model data_sample  \\\n",
-       "254  daejeon                             deepgbm+ft_transformer        best   \n",
-       "255  daejeon                                deepgbm+resnet_like        best   \n",
-       "257  daejeon                                   deepgbm+LightGBM        best   \n",
-       "264  daejeon                 deepgbm+ft_transformer+resnet_like        best   \n",
-       "256  daejeon                                    deepgbm+XGBoost        best   \n",
-       "266  daejeon                    deepgbm+ft_transformer+LightGBM        best   \n",
-       "265  daejeon                     deepgbm+ft_transformer+XGBoost        best   \n",
-       "274  daejeon         deepgbm+ft_transformer+resnet_like+XGBoost        best   \n",
-       "267  daejeon                        deepgbm+resnet_like+XGBoost        best   \n",
-       "268  daejeon                       deepgbm+resnet_like+LightGBM        best   \n",
-       "275  daejeon        deepgbm+ft_transformer+resnet_like+LightGBM        best   \n",
-       "276  daejeon            deepgbm+ft_transformer+XGBoost+LightGBM        best   \n",
-       "279  daejeon  deepgbm+ft_transformer+resnet_like+XGBoost+Lig...        best   \n",
-       "277  daejeon               deepgbm+resnet_like+XGBoost+LightGBM        best   \n",
-       "269  daejeon                           deepgbm+XGBoost+LightGBM        best   \n",
-       "270  daejeon                 ft_transformer+resnet_like+XGBoost        best   \n",
-       "258  daejeon                         ft_transformer+resnet_like        best   \n",
-       "271  daejeon                ft_transformer+resnet_like+LightGBM        best   \n",
-       "259  daejeon                             ft_transformer+XGBoost        best   \n",
-       "260  daejeon                            ft_transformer+LightGBM        best   \n",
-       "278  daejeon        ft_transformer+resnet_like+XGBoost+LightGBM        best   \n",
-       "272  daejeon                    ft_transformer+XGBoost+LightGBM        best   \n",
-       "261  daejeon                                resnet_like+XGBoost        best   \n",
-       "273  daejeon                       resnet_like+XGBoost+LightGBM        best   \n",
-       "262  daejeon                               resnet_like+LightGBM        best   \n",
-       "263  daejeon                                   XGBoost+LightGBM        best   \n",
-       "\n",
-       "       CSI    MCC  Accuracy  \n",
-       "254  0.680  0.789     0.958  \n",
-       "255  0.666  0.780     0.955  \n",
-       "257  0.662  0.776     0.955  \n",
-       "264  0.661  0.774     0.955  \n",
-       "256  0.657  0.773     0.954  \n",
-       "266  0.656  0.770     0.955  \n",
-       "265  0.650  0.765     0.954  \n",
-       "274  0.646  0.763     0.953  \n",
-       "267  0.646  0.764     0.953  \n",
-       "268  0.644  0.762     0.953  \n",
-       "275  0.643  0.760     0.953  \n",
-       "276  0.627  0.747     0.950  \n",
-       "279  0.625  0.745     0.950  \n",
-       "277  0.618  0.741     0.948  \n",
-       "269  0.610  0.736     0.947  \n",
-       "270  0.593  0.717     0.945  \n",
-       "258  0.587  0.712     0.945  \n",
-       "271  0.587  0.712     0.945  \n",
-       "259  0.582  0.708     0.943  \n",
-       "260  0.581  0.707     0.944  \n",
-       "278  0.578  0.705     0.943  \n",
-       "272  0.570  0.698     0.941  \n",
-       "261  0.562  0.693     0.939  \n",
-       "273  0.557  0.688     0.938  \n",
-       "262  0.555  0.686     0.939  \n",
-       "263  0.529  0.663     0.933  "
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table= df.loc[(df['region']=='daejeon'),:'Accuracy']\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)\n",
-    "table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>280</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.680</td>\n",
-       "      <td>0.767</td>\n",
-       "      <td>0.954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>281</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+resnet_like</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.666</td>\n",
-       "      <td>0.771</td>\n",
-       "      <td>0.954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>283</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.662</td>\n",
-       "      <td>0.773</td>\n",
-       "      <td>0.954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>290</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.661</td>\n",
-       "      <td>0.774</td>\n",
-       "      <td>0.955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>282</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.657</td>\n",
-       "      <td>0.772</td>\n",
-       "      <td>0.954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>292</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.656</td>\n",
-       "      <td>0.770</td>\n",
-       "      <td>0.955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>291</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.650</td>\n",
-       "      <td>0.765</td>\n",
-       "      <td>0.954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.646</td>\n",
-       "      <td>0.763</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>293</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.646</td>\n",
-       "      <td>0.764</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>294</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.644</td>\n",
-       "      <td>0.762</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>301</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.643</td>\n",
-       "      <td>0.760</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>302</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.627</td>\n",
-       "      <td>0.747</td>\n",
-       "      <td>0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>305</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost+Lig...</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>0.745</td>\n",
-       "      <td>0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>303</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.618</td>\n",
-       "      <td>0.741</td>\n",
-       "      <td>0.948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>295</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.610</td>\n",
-       "      <td>0.736</td>\n",
-       "      <td>0.947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>296</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.593</td>\n",
-       "      <td>0.717</td>\n",
-       "      <td>0.945</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>284</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer+resnet_like</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.587</td>\n",
-       "      <td>0.763</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>297</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.587</td>\n",
-       "      <td>0.712</td>\n",
-       "      <td>0.945</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>285</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.582</td>\n",
-       "      <td>0.755</td>\n",
-       "      <td>0.952</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>286</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.581</td>\n",
-       "      <td>0.749</td>\n",
-       "      <td>0.951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>304</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.578</td>\n",
-       "      <td>0.705</td>\n",
-       "      <td>0.943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>298</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.570</td>\n",
-       "      <td>0.698</td>\n",
-       "      <td>0.941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>287</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.562</td>\n",
-       "      <td>0.743</td>\n",
-       "      <td>0.949</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>299</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.557</td>\n",
-       "      <td>0.688</td>\n",
-       "      <td>0.938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>288</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.555</td>\n",
-       "      <td>0.737</td>\n",
-       "      <td>0.948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>289</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.529</td>\n",
-       "      <td>0.730</td>\n",
-       "      <td>0.947</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      region                                              model data_sample  \\\n",
-       "280  gwangju                             deepgbm+ft_transformer        best   \n",
-       "281  gwangju                                deepgbm+resnet_like        best   \n",
-       "283  gwangju                                   deepgbm+LightGBM        best   \n",
-       "290  gwangju                 deepgbm+ft_transformer+resnet_like        best   \n",
-       "282  gwangju                                    deepgbm+XGBoost        best   \n",
-       "292  gwangju                    deepgbm+ft_transformer+LightGBM        best   \n",
-       "291  gwangju                     deepgbm+ft_transformer+XGBoost        best   \n",
-       "300  gwangju         deepgbm+ft_transformer+resnet_like+XGBoost        best   \n",
-       "293  gwangju                        deepgbm+resnet_like+XGBoost        best   \n",
-       "294  gwangju                       deepgbm+resnet_like+LightGBM        best   \n",
-       "301  gwangju        deepgbm+ft_transformer+resnet_like+LightGBM        best   \n",
-       "302  gwangju            deepgbm+ft_transformer+XGBoost+LightGBM        best   \n",
-       "305  gwangju  deepgbm+ft_transformer+resnet_like+XGBoost+Lig...        best   \n",
-       "303  gwangju               deepgbm+resnet_like+XGBoost+LightGBM        best   \n",
-       "295  gwangju                           deepgbm+XGBoost+LightGBM        best   \n",
-       "296  gwangju                 ft_transformer+resnet_like+XGBoost        best   \n",
-       "284  gwangju                         ft_transformer+resnet_like        best   \n",
-       "297  gwangju                ft_transformer+resnet_like+LightGBM        best   \n",
-       "285  gwangju                             ft_transformer+XGBoost        best   \n",
-       "286  gwangju                            ft_transformer+LightGBM        best   \n",
-       "304  gwangju        ft_transformer+resnet_like+XGBoost+LightGBM        best   \n",
-       "298  gwangju                    ft_transformer+XGBoost+LightGBM        best   \n",
-       "287  gwangju                                resnet_like+XGBoost        best   \n",
-       "299  gwangju                       resnet_like+XGBoost+LightGBM        best   \n",
-       "288  gwangju                               resnet_like+LightGBM        best   \n",
-       "289  gwangju                                   XGBoost+LightGBM        best   \n",
-       "\n",
-       "       CSI    MCC  Accuracy  \n",
-       "280  0.680  0.767     0.954  \n",
-       "281  0.666  0.771     0.954  \n",
-       "283  0.662  0.773     0.954  \n",
-       "290  0.661  0.774     0.955  \n",
-       "282  0.657  0.772     0.954  \n",
-       "292  0.656  0.770     0.955  \n",
-       "291  0.650  0.765     0.954  \n",
-       "300  0.646  0.763     0.953  \n",
-       "293  0.646  0.764     0.953  \n",
-       "294  0.644  0.762     0.953  \n",
-       "301  0.643  0.760     0.953  \n",
-       "302  0.627  0.747     0.950  \n",
-       "305  0.625  0.745     0.950  \n",
-       "303  0.618  0.741     0.948  \n",
-       "295  0.610  0.736     0.947  \n",
-       "296  0.593  0.717     0.945  \n",
-       "284  0.587  0.763     0.953  \n",
-       "297  0.587  0.712     0.945  \n",
-       "285  0.582  0.755     0.952  \n",
-       "286  0.581  0.749     0.951  \n",
-       "304  0.578  0.705     0.943  \n",
-       "298  0.570  0.698     0.941  \n",
-       "287  0.562  0.743     0.949  \n",
-       "299  0.557  0.688     0.938  \n",
-       "288  0.555  0.737     0.948  \n",
-       "289  0.529  0.730     0.947  "
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table= df.loc[(df['region']=='gwangju'),:'Accuracy']\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)\n",
-    "table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(df['model'].unique()))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model', ci=None, data=df.loc[(df['region']=='daejeon'),:], palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=11)\n",
-    "plt.title('Soft Voting Ensemble: Model Performance Comparison in Daejeon', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('')  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(df['model'].unique()))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model', ci=None, data=df.loc[(df['region']=='gwangju'),:], palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=11)\n",
-    "plt.title('Soft Voting Ensemble: Model Performance Comparison in Gwangju', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('')  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "6it [00:00, 8513.47it/s]\n"
-     ]
-    },
-    {
-     "data": {
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-       "            var tile_layer_74ac5c839b13ec41df020714e3cf6fe9 = L.tileLayer(\n",
-       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
-       "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var circle_marker_47460d79c6805e06f6b7ef1d5cb00331 = L.circleMarker(\n",
-       "                [37.57142, 126.9658],\n",
-       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.7, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var marker_2b2a90f64813c20a23ff74007db864a8 = L.marker(\n",
-       "                [37.57142, 126.9658],\n",
-       "                {}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var div_icon_0cd42476bb19dacb43454ee26a97f1d8 = L.divIcon({&quot;className&quot;: &quot;empty&quot;, &quot;html&quot;: &quot;\\u003cdiv style=\\&quot;font-size: 18pt; color: black;\\&quot;\\u003e108\\u003c/div\\u003e&quot;, &quot;iconAnchor&quot;: [0, 0], &quot;iconSize&quot;: [120, 30]});\n",
-       "            marker_2b2a90f64813c20a23ff74007db864a8.setIcon(div_icon_0cd42476bb19dacb43454ee26a97f1d8);\n",
-       "        \n",
-       "    \n",
-       "            var circle_marker_a2166893c44ea5420ad169c4fbc0b914 = L.circleMarker(\n",
-       "                [37.47772, 126.6249],\n",
-       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.7, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var marker_957f167293bbd1898ca7a19665469078 = L.marker(\n",
-       "                [37.47772, 126.6249],\n",
-       "                {}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var div_icon_c2ac2f46664cf0eae9bb0bed704472b3 = L.divIcon({&quot;className&quot;: &quot;empty&quot;, &quot;html&quot;: &quot;\\u003cdiv style=\\&quot;font-size: 18pt; color: black;\\&quot;\\u003e112\\u003c/div\\u003e&quot;, &quot;iconAnchor&quot;: [0, 0], &quot;iconSize&quot;: [120, 30]});\n",
-       "            marker_957f167293bbd1898ca7a19665469078.setIcon(div_icon_c2ac2f46664cf0eae9bb0bed704472b3);\n",
-       "        \n",
-       "    \n",
-       "            var circle_marker_09090fa39081d25b100002b6135ab510 = L.circleMarker(\n",
-       "                [36.37199, 127.3721],\n",
-       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.7, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var marker_b677f20e38ce7e599e8e1e6a496c7699 = L.marker(\n",
-       "                [36.37199, 127.3721],\n",
-       "                {}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var div_icon_7355463f8fff8ed2274886afc19ca14c = L.divIcon({&quot;className&quot;: &quot;empty&quot;, &quot;html&quot;: &quot;\\u003cdiv style=\\&quot;font-size: 18pt; color: black;\\&quot;\\u003e133\\u003c/div\\u003e&quot;, &quot;iconAnchor&quot;: [0, 0], &quot;iconSize&quot;: [120, 30]});\n",
-       "            marker_b677f20e38ce7e599e8e1e6a496c7699.setIcon(div_icon_7355463f8fff8ed2274886afc19ca14c);\n",
-       "        \n",
-       "    \n",
-       "            var circle_marker_22f52f6a1a9cb22da7d67d6848d17a40 = L.circleMarker(\n",
-       "                [35.87797, 128.65296],\n",
-       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.7, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var marker_4bdb88fe6ed4e708f5c5840cb66a1d92 = L.marker(\n",
-       "                [35.87797, 128.65296],\n",
-       "                {}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var div_icon_1ca6fdfb04d8b606e332defbd9dd6520 = L.divIcon({&quot;className&quot;: &quot;empty&quot;, &quot;html&quot;: &quot;\\u003cdiv style=\\&quot;font-size: 18pt; color: black;\\&quot;\\u003e143\\u003c/div\\u003e&quot;, &quot;iconAnchor&quot;: [0, 0], &quot;iconSize&quot;: [120, 30]});\n",
-       "            marker_4bdb88fe6ed4e708f5c5840cb66a1d92.setIcon(div_icon_1ca6fdfb04d8b606e332defbd9dd6520);\n",
-       "        \n",
-       "    \n",
-       "            var circle_marker_7f255e624ee41e5f1aa1724b6019aca0 = L.circleMarker(\n",
-       "                [35.17294, 126.89156],\n",
-       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.7, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var marker_ea0d3cdd91747084562f9e85e1cf11af = L.marker(\n",
-       "                [35.17294, 126.89156],\n",
-       "                {}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var div_icon_7e9d7d058db195bcab1ea6f0c84dac40 = L.divIcon({&quot;className&quot;: &quot;empty&quot;, &quot;html&quot;: &quot;\\u003cdiv style=\\&quot;font-size: 18pt; color: black;\\&quot;\\u003e156\\u003c/div\\u003e&quot;, &quot;iconAnchor&quot;: [0, 0], &quot;iconSize&quot;: [120, 30]});\n",
-       "            marker_ea0d3cdd91747084562f9e85e1cf11af.setIcon(div_icon_7e9d7d058db195bcab1ea6f0c84dac40);\n",
-       "        \n",
-       "    \n",
-       "            var circle_marker_dd7c80c7a7ab624c17cc06d75a4ef4a4 = L.circleMarker(\n",
-       "                [35.10468, 129.03203],\n",
-       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.7, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var marker_d725b8fb90e957c948e41a25f2ed9214 = L.marker(\n",
-       "                [35.10468, 129.03203],\n",
-       "                {}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var div_icon_96bd66015b5d69d4a86c9d5ad1ce6181 = L.divIcon({&quot;className&quot;: &quot;empty&quot;, &quot;html&quot;: &quot;\\u003cdiv style=\\&quot;font-size: 18pt; color: black;\\&quot;\\u003e159\\u003c/div\\u003e&quot;, &quot;iconAnchor&quot;: [0, 0], &quot;iconSize&quot;: [120, 30]});\n",
-       "            marker_d725b8fb90e957c948e41a25f2ed9214.setIcon(div_icon_96bd66015b5d69d4a86c9d5ad1ce6181);\n",
-       "        \n",
-       "&lt;/script&gt;\n",
-       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
-      ],
-      "text/plain": [
-       "<folium.folium.Map at 0x7fbe820554c0>"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "df = pd.read_feather(\"../data/asos_air_merge.feather\") # asos와 air 병합한 데이터 불러오기\n",
-    "\n",
-    "df['지역_1'] = df['지역'].str.split(' ').str[0] # 도,시,광역시 이름 저장\n",
-    "df['지역_2'] = df['지역'].str.split(' ').str[1] # 시,군,구 이름 저장\n",
-    "data = df.loc[df['거리차이(km)'] <= 5,:].drop_duplicates(subset=['지점']).copy() # 5km 이내 데이터만 추출\n",
-    "# 데이터 필터링 (6개 지점만 선택)\n",
-    "data = data.loc[data['지점'].isin([112, 108, 133, 159, 143, 156]), :].copy()\n",
-    "\n",
-    "\n",
-    "import folium\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "# 지도 중심 설정 (첫 번째 지점 기준)\n",
-    "map_center = [data['위도'].iloc[0], data['경도'].iloc[0]]\n",
-    "\n",
-    "m = folium.Map(location=map_center, zoom_start=10, tiles=\"OpenStreetMap\")\n",
-    "\n",
-    "# CircleMarker + 텍스트 추가\n",
-    "for index, row in tqdm(data.iterrows()):\n",
-    "    # 원형 마커 추가\n",
-    "    folium.CircleMarker(\n",
-    "        location=[row['위도'], row['경도']],\n",
-    "        radius=6,  # 마커 크기\n",
-    "        color='blue',  \n",
-    "        fill=True,\n",
-    "        fill_color='blue',\n",
-    "        fill_opacity=0.7,\n",
-    "    ).add_to(m)\n",
-    "\n",
-    "    # 텍스트 항상 표시 (DivIcon 사용)\n",
-    "    folium.map.Marker(\n",
-    "        [row['위도'], row['경도']], \n",
-    "        icon=folium.DivIcon(\n",
-    "            icon_size=(120, 30),\n",
-    "            icon_anchor=(0, 0),\n",
-    "            html=f'<div style=\"font-size: 18pt; color: black;\">{row[\"지점\"]}</div>'\n",
-    "        )\n",
-    "    ).add_to(m)\n",
-    "\n",
-    "m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import Counter\n",
-    "\n",
-    "seoul= pd.read_feather(\"../data/data_for_modeling/df_seoul.feather\")\n",
-    "busan= pd.read_feather(\"../data/data_for_modeling/df_busan.feather\")\n",
-    "incheon= pd.read_feather(\"../data/data_for_modeling/df_incheon.feather\")\n",
-    "daegu = pd.read_feather(\"../data/data_for_modeling/df_daegu.feather\")\n",
-    "daejeon = pd.read_feather(\"../data/data_for_modeling/df_daejeon.feather\")\n",
-    "gwangju = pd.read_feather(\"../data/data_for_modeling/df_gwangju.feather\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seoul = seoul.loc[seoul['year'].isin([2018, 2019, 2020, 2021]), :]\n",
-    "busan = busan.loc[busan['year'].isin([2018, 2019, 2020, 2021]), :]\n",
-    "incheon = incheon.loc[incheon['year'].isin([2018, 2019, 2020, 2021]), :]\n",
-    "daegu = daegu.loc[daegu['year'].isin([2018, 2019, 2020, 2021]), :]\n",
-    "daejeon = daejeon.loc[daejeon['year'].isin([2018, 2019, 2020, 2021]), :]\n",
-    "gwangju = gwangju.loc[gwangju['year'].isin([2018, 2019, 2020, 2021]), :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "seoul :\n",
-      "multi_class\n",
-      "2    91.12\n",
-      "1     8.73\n",
-      "0     0.15\n",
-      "Name: count, dtype: float64\n",
-      "\n",
-      "busan :\n",
-      "multi_class\n",
-      "2    94.54\n",
-      "1     5.12\n",
-      "0     0.34\n",
-      "Name: count, dtype: float64\n",
-      "\n",
-      "incheon :\n",
-      "multi_class\n",
-      "2    83.46\n",
-      "1    14.54\n",
-      "0     2.00\n",
-      "Name: count, dtype: float64\n",
-      "\n",
-      "daegu :\n",
-      "multi_class\n",
-      "2    96.34\n",
-      "1     3.52\n",
-      "0     0.14\n",
-      "Name: count, dtype: float64\n",
-      "\n",
-      "daejeon :\n",
-      "multi_class\n",
-      "2    90.01\n",
-      "1     9.35\n",
-      "0     0.64\n",
-      "Name: count, dtype: float64\n",
-      "\n",
-      "gwangju :\n",
-      "multi_class\n",
-      "2    90.93\n",
-      "1     8.71\n",
-      "0     0.36\n",
-      "Name: count, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"seoul :\")\n",
-    "print(np.round(seoul['multi_class'].value_counts()*100 / seoul.shape[0],2))\n",
-    "print()\n",
-    "print('busan :')\n",
-    "print(np.round(busan['multi_class'].value_counts()*100 / busan.shape[0],2))\n",
-    "print()\n",
-    "print('incheon :')\n",
-    "print(np.round(incheon['multi_class'].value_counts()*100 / incheon.shape[0],2))\n",
-    "print()\n",
-    "print('daegu :')\n",
-    "print(np.round(daegu['multi_class'].value_counts()*100 / daegu.shape[0],2))\n",
-    "print()\n",
-    "print('daejeon :')\n",
-    "print(np.round(daejeon['multi_class'].value_counts()*100 / daejeon.shape[0],2))\n",
-    "print()\n",
-    "print('gwangju :')\n",
-    "print(np.round(gwangju['multi_class'].value_counts()*100 / gwangju.shape[0],2))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(35064, 31)"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "seoul.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "year\n",
-       "2020    8784\n",
-       "2018    8760\n",
-       "2019    8760\n",
-       "2021    8760\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "seoul['year'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/README.md b/README.md
index b84b9154771fa7188594f6d69c442d471db90efe..dee76b5618135fd3afde97d7dcc7f935542fdd98 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 ### 가시거리(Visibility) 예측 모델링 프로젝트
 
-기상·대기오염·항공정보(ASOS, DataOn, TAF)를 통합해 가시거리(`visi`)를 예측합니다. 불균형 데이터를 SMOTENC/CTGAN으로 보강하고, GBDT(LightGBM/XGBoost)와 탭울러 딥러닝(ResNet-like, FT-Transformer, DeepGBM)을 결합해 다중/이진 분류를 수행합니다.
+기상·대기오염 정보(ASOS, DataOn)를 통합해 가시거리(`visi`)를 예측합니다. 불균형 데이터를 SMOTENC/CTGAN으로 보강하고, GBDT(LightGBM/XGBoost)와 탭울러 딥러닝(ResNet-like, FT-Transformer, DeepGBM)을 결합해 다중/이진 분류를 수행합니다.
 
 ### 기술 스택(Tech Stack)
 
@@ -14,13 +14,13 @@
 
 ### 시스템 아키텍처(파이프라인)
 
-1) 데이터 수집/적재: `data/ASOS`, `data/dataon`, `data/data_for_TAF`
-2) 병합/전처리: `0.air_data_merge.ipynb` → `1.data_merge.ipynb` → `2.eda_preproccesing.ipynb`
-3) 데이터 증강(불균형 처리): `Analysis_code/make_oversample_data/` 내 `SMOTENC` → `CTGAN(+Optuna)` → 규칙 기반 필터링
+1) 데이터 수집/적재: `data/ASOS`, `data/dataon`
+2) 병합/전처리: `1.data_preprocessing/0.air_data_merge.ipynb` → `1.data_preprocessing/1.data_merge.ipynb` → `1.data_preprocessing/2.eda_preproccesing.ipynb` → `1.data_preprocessing/3.make_train_test.ipynb`
+3) 데이터 증강(불균형 처리): `2.make_oversample_data/` 내 `SMOTENC` → `CTGAN(+Optuna)` → 규칙 기반 필터링
 4) 데이터 분할: 지역별(`*_train.csv`, `*_test.csv`), 연도 기반 3-Fold 홀드아웃
-5) 학습: GBDT(`optima/*.py`)와 딥러닝 노트북(`deeplearning_model_*`)
-6) 평가/분석: 사용자 정의 `CSI` + F1/Accuracy, `model_visualize.ipynb`, `find_reason/*`(트렌드, 분포 비교)
-7) 앙상블/최종: `model_voting_test_best_sample/*`, `final_test/final.ipynb`
+5) 학습: GBDT(`5.optima/*/`)와 딥러닝 노트북
+6) 평가/분석: 사용자 정의 `CSI` + F1/Accuracy, `visualization/model_visualize.ipynb`, `find_reason/*`(트렌드, 분포 비교)
+7) 앙상블/최종: `model_voting_test_best_sample/ensemble__voting_best_sample.ipynb`, `final_test/final.ipynb`
 
 ### TL;DR (빠른 시작)
 
@@ -28,29 +28,42 @@
 
 ```bash
 pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
-pip install pandas numpy scikit-learn matplotlib seaborn imbalanced-learn optuna ctgan xgboost lightgbm joblib
+pip install pandas numpy scikit-learn matplotlib seaborn imbalanced-learn optuna ctgan xgboost lightgbm joblib hyperopt
 ```
 
 2) 데이터 배치
 - 원천/중간 산출물을 `data/` 하위에 배치. 학습용 CSV/feather는 `data/data_for_modeling/` 참고.
-
-3) 오버샘플링 수행(SMOTE/CTGAN)
-
+- 또는 Hugging Face 저장소에서 `data/` 폴더를 다운로드:
 ```bash
-cd Analysis_code/make_oversample_data
-python smote_sample_1.py
-python oversampling_code.py
+git clone https://huggingface.co/bong9513/visibility_prediction
+# 클론 후 visibility_prediction/data/ 폴더를 프로젝트의 data/ 위치로 복사하거나 사용
 ```
 
-4) GBDT 최적화/학습 예시(서울시)
+3) 오버샘플링 수행(SMOTE/CTGAN)
 
 ```bash
-cd ../optima
-python LGB_smote_seoul.py
-python XGB_smote_seoul.py
+cd Analysis_code/2.make_oversample_data
+# SMOTE만 사용하는 경우
+python smote_only/smote_sample_1.py
+# SMOTENC + CTGAN 사용하는 경우
+python smotenc_ctgan/smotenc_ctgan_sample_10000_1.py
 ```
 
-5) 딥러닝 모델 학습/평가: 노트북 실행(`Analysis_code/` 내 `.ipynb`)
+4) 모델 학습 또는 다운로드
+   - **옵션 A: 직접 모델 생성**
+     - GBDT 최적화/학습 예시(서울시):
+     ```bash
+     cd Analysis_code/5.optima
+     python lgb_smote/LGB_smote_seoul.py
+     python xgb_smote/XGB_smote_seoul.py
+     ```
+     - 딥러닝 모델 학습/평가: 노트북 실행(`Analysis_code/` 내 `.ipynb`)
+   - **옵션 B: 사전 학습된 모델 사용**
+     - Hugging Face 저장소에서 사전 학습된 모델 다운로드:
+     ```bash
+     git clone https://huggingface.co/bong9513/visibility_prediction
+     # 클론 후 visibility_prediction/save_model/ 폴더를 Analysis_code/save_model/ 위치로 복사
+     ```
 
 ---
 
@@ -59,44 +72,65 @@ python XGB_smote_seoul.py
 ```
 visibility_prediction/
 ├── Analysis_code/
-│   ├── 0.air_data_merge.ipynb
-│   ├── 1.data_merge.ipynb
-│   ├── 2.eda_preproccesing.ipynb
-│   ├── 3.oversampling.ipynb
-│   ├── deeplearning_model_binary.ipynb
-│   ├── deeplearning_model_multi.ipynb
-│   ├── make_train_test.ipynb
-│   ├── model_visualize.ipynb
-│   ├── final_test/
-│   │   └── final.ipynb
+│   ├── 1.data_preprocessing/       # 데이터 병합 및 전처리
+│   │   ├── 0.air_data_merge.ipynb
+│   │   ├── 1.data_merge.ipynb
+│   │   ├── 2.eda_preproccesing.ipynb
+│   │   └── 3.make_train_test.ipynb
+│   ├── 2.make_oversample_data/     # 오버샘플링 (SMOTE/CTGAN)
+│   │   ├── smote_only/            # SMOTE만 사용
+│   │   ├── only_ctgan/            # CTGAN만 사용
+│   │   └── smotenc_ctgan/         # SMOTENC + CTGAN 조합
+│   ├── 3.sampled_data_analysis/    # 샘플링 데이터 분석
+│   ├── 4.sampling_data_test/       # 샘플링 데이터 성능 테스트
+│   ├── 5.optima/                   # 모델 최적화 및 학습
+│   │   ├── lgb_smote/             # LightGBM (SMOTE)
+│   │   ├── lgb_pure/              # LightGBM (원본 데이터)
+│   │   ├── lgb_ctgan10000/        # LightGBM (CTGAN 10000)
+│   │   ├── lgb_smotenc_ctgan20000/ # LightGBM (SMOTENC+CTGAN 20000)
+│   │   ├── xgb_smote/             # XGBoost (SMOTE)
+│   │   ├── xgb_pure/              # XGBoost (원본 데이터)
+│   │   ├── xgb_ctgan10000/        # XGBoost (CTGAN 10000)
+│   │   ├── xgb_smotenc_ctgan20000/ # XGBoost (SMOTENC+CTGAN 20000)
+│   │   ├── resnet_like_smote/     # ResNet-like (SMOTE)
+│   │   ├── resnet_like_pure/      # ResNet-like (원본 데이터)
+│   │   ├── resnet_like_ctgan10000/ # ResNet-like (CTGAN 10000)
+│   │   ├── resnet_like_smotenc_ctgan20000/ # ResNet-like (SMOTENC+CTGAN 20000)
+│   │   ├── ft_transformer_smote/  # FT-Transformer (SMOTE)
+│   │   ├── ft_transformer_pure/   # FT-Transformer (원본 데이터)
+│   │   ├── ft_transformer_ctgan10000/ # FT-Transformer (CTGAN 10000)
+│   │   ├── ft_transformer_smotenc_ctgan20000/ # FT-Transformer (SMOTENC+CTGAN 20000)
+│   │   ├── deepgbm_smote/         # DeepGBM (SMOTE)
+│   │   ├── deepgbm_pure/         # DeepGBM (원본 데이터)
+│   │   ├── deepgbm_ctgan10000/    # DeepGBM (CTGAN 10000)
+│   │   └── deepgbm_smotenc_ctgan20000/ # DeepGBM (SMOTENC+CTGAN 20000)
+│   ├── 6.optima_models_analysis/   # 최적화된 모델 분석
+│   ├── models/                     # 딥러닝 모델 정의 및 저장
+│   │   ├── deepgbm.py
+│   │   ├── ft_transformer.py
+│   │   ├── resnet_like.py
+│   │   ├── best_resnet_model.pth
+│   │   └── tabnet_model.zip
+│   ├── save_model/                 # 학습된 모델 저장 (Hugging Face에서 다운로드 가능)
+│   ├── optimization_history/       # 최적화 히스토리 (Hugging Face에서 다운로드 가능)
+│   ├── visualization/              # 모델 시각화
+│   │   └── model_visualize.ipynb
 │   ├── find_reason/                # 지역별 트렌드/원인 분석 노트북
-│   ├── sampling_data_test/         # 샘플링 데이터 성능 테스트 노트북
 │   ├── model_voting_test_best_sample/
 │   │   └── ensemble__voting_best_sample.ipynb
-│   ├── make_oversample_data/
-│   │   ├── oversampling_code.py    # SMOTENC+CTGAN 파이프라인
-│   │   ├── smote_sample_1.py       # 연도/전처리 포함 SMOTE 샘플
-│   │   └── (gan_sample_*.py 등)
-│   ├── optima/                     # GBDT 하이퍼파라미터 탐색/학습 스크립트
-│   │   ├── LGB_smote_seoul.py
-│   │   └── XGB_smote_seoul.py
-│   ├── models/
-│   │   ├── best_resnet_model.pth
-│   │   └── tabnet_model.zip
-│   ├── deepgbm.py
-│   ├── ft_transformer.py
-│   └── resnet_like.py
+│   └── final_test/
+│       └── final.ipynb
 ├── data/
-│   ├── ASOS/                       # 기상
-│   ├── dataon/                     # 대기오염(대용량 일자별 CSV)
+│   ├── ASOS/                       # 기상 데이터
+│   ├── dataon/                     # 대기오염 데이터(대용량 일자별 CSV)
 │   ├── data_for_modeling/          # 지역별 train/test CSV 및 feather
 │   ├── data_for_demo/
-│   ├── data_for_TAF/               # 공항 TAF(항공기상) CSV
-│   └── data_oversampled/
-│       ├── smote/
-│       ├── ctgan7000/
-│       ├── ctgan10000/
-│       └── ctgan20000/
+│   ├── data_oversampled/           # 오버샘플링된 데이터
+│   │   ├── smote/
+│   │   ├── ctgan7000/
+│   │   ├── ctgan10000/
+│   │   └── ctgan20000/
+│   └── oversampled_data_test_for_model/ # 테스트용 오버샘플링 데이터
 └── README.md
 ```
 
@@ -137,8 +171,8 @@ visibility_prediction/
   - 차분형 파생: `ground_temp - temp_C`
 
 - 분포/트렌드 분석
-  - 지역별 시계열 트렌드: `find_reason/*_trend.ipynb`
-  - 분포 비교/변화 감지: `find_reason/wasserstein_distance.ipynb`(Wasserstein 거리 기반 분포 차이 정량화)
+  - 지역별 시계열 트렌드: `Analysis_code/find_reason/*_trend.ipynb` (seoul, incheon, busan, daegu, daejeon, gwangju)
+  - 분포 비교/변화 감지: `Analysis_code/find_reason/wasserstein_distance.ipynb`(Wasserstein 거리 기반 분포 차이 정량화)
 
 - 데이터 분할
   - 지역 단위 데이터셋(`*_train.csv`, `*_test.csv`)
@@ -157,25 +191,124 @@ visibility_prediction/
   - 최종 합본 후 파생/보조 피처(`binary_class`, 주기/차분 항목) 복구
 
 - 산출물
-  - `data/data_oversampled/smote/`, `ctgan7000/`, `ctgan10000/`, `ctgan20000/` 하위에 지역별 CSV 저장
+  - `data/data_oversampled/smote/`, `data/data_oversampled/ctgan7000/`, `data/data_oversampled/ctgan10000/`, `data/data_oversampled/ctgan20000/` 하위에 지역별 CSV 저장
 
 ---
 
 ### 모델 아키텍처(상세)
 
 - 딥러닝(tabular)
-  - `resnet_like.py`
+  - `Analysis_code/models/resnet_like.py`
     - 입력: `x_num [B, N_num]`, `x_cat [B, N_cat]` → concat → 입력선형(`d_main=128`) → 잔차블록(`n_blocks=4`, `d_hidden=64`, `dropout_first=0.25`) → 출력층
     - 출력: `num_classes == 2 → 1 로짓`, `> 2 → K 로짓`
-  - `ft_transformer.py`
+  - `Analysis_code/models/ft_transformer.py`
     - 수치: Linear(`num_features → d_token=192`), 범주: `cat_cardinalities`별 `nn.Embedding(d_token)` 후 합성
     - 인코더: `TransformerEncoderLayer(d_model=d_token, nhead=8, dropout≈0.2)` × `n_blocks=6` → 평균 풀링 → 분류 헤드
-  - `deepgbm.py`
+  - `Analysis_code/models/deepgbm.py`
     - 수치 Linear(`d_main=128`) + 범주 임베딩 합산 → 잔차 MLP 블록(`n_blocks=4`, `d_hidden=64`, `dropout≈0.2`) → 분류 헤드
 
 - GBDT
-  - LightGBM(`optima/LGB_smote_seoul.py`): `objective='multiclassova'`, `n_estimators≈4000`, 조기종료, GPU 옵션 예시 존재, `hyperopt`로 `max_depth, min_child_weight, num_leaves, subsample, learning_rate` 탐색
-  - XGBoost(`optima/XGB_smote_seoul.py`): `objective='multi:softprob'`, `tree_method='hist'`, `enable_categorical=True`, GPU 옵션, `hyperopt`로 핵심 하이퍼파라미터 탐색, `eval_metric=CSI`
+  - LightGBM(`5.optima/lgb_smote/LGB_smote_seoul.py`): `objective='multiclassova'`, `n_estimators≈4000`, 조기종료, GPU 옵션 예시 존재, `hyperopt`로 `max_depth, min_child_weight, num_leaves, subsample, learning_rate` 탐색
+  - XGBoost(`5.optima/xgb_smote/XGB_smote_seoul.py`): `objective='multi:softprob'`, `tree_method='hist'`, `enable_categorical=True`, GPU 옵션, `hyperopt`로 핵심 하이퍼파라미터 탐색, `eval_metric=CSI`
+
+---
+
+### 하이퍼파라미터 최적화
+
+모든 모델은 CSI(Critical Success Index) 점수를 최대화하는 방향으로 하이퍼파라미터를 최적화합니다. GBDT 모델은 `hyperopt`(TPE 알고리즘)를, 딥러닝 모델은 `Optuna`(TPE sampler)를 사용합니다.
+
+#### LightGBM 하이퍼파라미터 탐색 범위
+
+- **최적화 라이브러리**: `hyperopt` (TPE 알고리즘)
+- **최적화 시도 횟수**: `max_evals=100`
+- **평가 지표**: CSI (3-Fold 교차 검증 평균)
+- **탐색 범위**:
+  - `learning_rate`: `hp.loguniform('learning_rate', np.log(0.01), np.log(0.2))` - 로그 균등 분포, 범위 [0.01, 0.2]
+  - `max_depth`: `hp.quniform('max_depth', 3, 15, 1)` - 정수형 균등 분포, 범위 [3, 15]
+  - `num_leaves`: `hp.quniform('num_leaves', 20, 150, 1)` - 정수형 균등 분포, 범위 [20, 150] (2^max_depth보다 작게 설정)
+  - `min_child_weight`: `hp.quniform('min_child_weight', 1, 20, 1)` - 정수형 균등 분포, 범위 [1, 20]
+  - `subsample`: `hp.uniform('subsample', 0.6, 1.0)` - 균등 분포, 범위 [0.6, 1.0]
+  - `colsample_bytree`: `hp.uniform('colsample_bytree', 0.6, 1.0)` - 균등 분포, 범위 [0.6, 1.0]
+  - `reg_alpha`: `hp.uniform('reg_alpha', 0.0, 1.0)` - 균등 분포, 범위 [0.0, 1.0] (L1 정규화)
+  - `reg_lambda`: `hp.uniform('reg_lambda', 0.0, 1.0)` - 균등 분포, 범위 [0.0, 1.0] (L2 정규화)
+- **고정 파라미터**: `n_estimators=4000`, `early_stopping_rounds=400`, `device='gpu'`, `objective='multiclassova'`, `random_state=42`
+
+#### XGBoost 하이퍼파라미터 탐색 범위
+
+- **최적화 라이브러리**: `hyperopt` (TPE 알고리즘)
+- **최적화 시도 횟수**: `max_evals=100`
+- **평가 지표**: CSI (3-Fold 교차 검증 평균, 사용자 정의 `eval_metric_csi` 함수 사용)
+- **탐색 범위**:
+  - `learning_rate`: `hp.loguniform('learning_rate', np.log(0.01), np.log(0.2))` - 로그 균등 분포, 범위 [0.01, 0.2]
+  - `max_depth`: `hp.quniform('max_depth', 3, 12, 1)` - 정수형 균등 분포, 범위 [3, 12]
+  - `min_child_weight`: `hp.quniform('min_child_weight', 1, 20, 1)` - 정수형 균등 분포, 범위 [1, 20]
+  - `gamma`: `hp.uniform('gamma', 0, 5)` - 균등 분포, 범위 [0, 5] (트리 분할을 위한 최소 손실 감소 값)
+  - `subsample`: `hp.uniform('subsample', 0.6, 1.0)` - 균등 분포, 범위 [0.6, 1.0]
+  - `colsample_bytree`: `hp.uniform('colsample_bytree', 0.6, 1.0)` - 균등 분포, 범위 [0.6, 1.0]
+  - `reg_alpha`: `hp.uniform('reg_alpha', 0.0, 1.0)` - 균등 분포, 범위 [0.0, 1.0] (L1 정규화)
+  - `reg_lambda`: `hp.uniform('reg_lambda', 0.0, 1.0)` - 균등 분포, 범위 [0.0, 1.0] (L2 정규화)
+- **고정 파라미터**: `n_estimators=4000`, `early_stopping_rounds=400`, `tree_method='hist'`, `device='cuda'`, `enable_categorical=True`, `objective='multi:softprob'`, `random_state=42`
+
+#### FT-Transformer 하이퍼파라미터 탐색 범위
+
+- **최적화 라이브러리**: `Optuna` (TPE sampler)
+- **최적화 시도 횟수**: `n_trials=100`
+- **Pruning**: `MedianPruner(n_warmup_steps=10)` - 초반 10 에폭은 관찰 후 이후 가지치기
+- **평가 지표**: CSI (3-Fold 교차 검증 평균)
+- **탐색 범위**:
+  - `d_token`: `trial.suggest_int("d_token", 64, 256, step=32)` - 정수형, 범위 [64, 256], 32 단위 증가 (64, 96, 128, 160, 192, 224, 256)
+  - `n_blocks`: `trial.suggest_int("n_blocks", 2, 6)` - 정수형, 범위 [2, 6] (깊이 축소로 과적합 방지)
+  - `n_heads`: `trial.suggest_categorical("n_heads", [4, 8])` - 범주형, 선택지 [4, 8]
+  - `attention_dropout`: `trial.suggest_float("attention_dropout", 0.1, 0.4)` - 실수형, 범위 [0.1, 0.4]
+  - `ffn_dropout`: `trial.suggest_float("ffn_dropout", 0.1, 0.4)` - 실수형, 범위 [0.1, 0.4]
+  - `lr` (learning_rate): `trial.suggest_float("lr", 1e-5, 1e-2, log=True)` - 로그 스케일 실수형, 범위 [1e-5, 1e-2]
+  - `weight_decay`: `trial.suggest_float("weight_decay", 1e-4, 1e-1, log=True)` - 로그 스케일 실수형, 범위 [1e-4, 1e-1]
+  - `batch_size`: `trial.suggest_categorical("batch_size", [32, 64, 128, 256])` - 범주형, 선택지 [32, 64, 128, 256]
+- **구조적 제약**: `d_token`은 `n_heads`의 배수여야 함 (코드에서 자동 조정)
+- **고정 파라미터**: `num_classes=3`, `optimizer='AdamW'`, `epochs=200`, `patience=12`, `scheduler='ReduceLROnPlateau'` (factor=0.5, patience=3), `random_state=42`
+
+#### ResNet-like 하이퍼파라미터 탐색 범위
+
+- **최적화 라이브러리**: `Optuna` (TPE sampler)
+- **최적화 시도 횟수**: `n_trials=100`
+- **Pruning**: `MedianPruner(n_warmup_steps=10)` - 초반 10 에폭은 관찰 후 이후 가지치기
+- **평가 지표**: CSI (3-Fold 교차 검증 평균)
+- **탐색 범위**:
+  - `d_main`: `trial.suggest_int("d_main", 64, 256, step=32)` - 정수형, 범위 [64, 256], 32 단위 증가 (64, 96, 128, 160, 192, 224, 256)
+  - `d_hidden`: `trial.suggest_int("d_hidden", 64, 512, step=64)` - 정수형, 범위 [64, 512], 64 단위 증가 (64, 128, 192, 256, 320, 384, 448, 512)
+  - `n_blocks`: `trial.suggest_int("n_blocks", 2, 5)` - 정수형, 범위 [2, 5] (너무 깊지 않게 조절)
+  - `dropout_first`: `trial.suggest_float("dropout_first", 0.1, 0.4)` - 실수형, 범위 [0.1, 0.4]
+  - `dropout_second`: `trial.suggest_float("dropout_second", 0.0, 0.2)` - 실수형, 범위 [0.0, 0.2]
+  - `lr` (learning_rate): `trial.suggest_float("lr", 1e-5, 1e-2, log=True)` - 로그 스케일 실수형, 범위 [1e-5, 1e-2]
+  - `weight_decay`: `trial.suggest_float("weight_decay", 1e-4, 1e-1, log=True)` - 로그 스케일 실수형, 범위 [1e-4, 1e-1]
+  - `batch_size`: `trial.suggest_categorical("batch_size", [32, 64, 128, 256])` - 범주형, 선택지 [32, 64, 128, 256]
+- **고정 파라미터**: `num_classes=3`, `optimizer='AdamW'`, `epochs=200`, `patience=12`, `scheduler='ReduceLROnPlateau'` (factor=0.5, patience=3), `random_state=42`
+
+#### DeepGBM 하이퍼파라미터 탐색 범위
+
+- **최적화 라이브러리**: `Optuna` (TPE sampler)
+- **최적화 시도 횟수**: `n_trials=100`
+- **Pruning**: `MedianPruner(n_warmup_steps=10)` - 초반 10 에폭은 관찰 후 이후 가지치기
+- **평가 지표**: CSI (3-Fold 교차 검증 평균)
+- **탐색 범위**:
+  - `d_main`: `trial.suggest_int("d_main", 64, 256, step=32)` - 정수형, 범위 [64, 256], 32 단위 증가 (64, 96, 128, 160, 192, 224, 256)
+  - `d_hidden`: `trial.suggest_int("d_hidden", 64, 256, step=64)` - 정수형, 범위 [64, 256], 64 단위 증가 (64, 128, 192, 256)
+  - `n_blocks`: `trial.suggest_int("n_blocks", 2, 6)` - 정수형, 범위 [2, 6]
+  - `dropout`: `trial.suggest_float("dropout", 0.1, 0.4)` - 실수형, 범위 [0.1, 0.4]
+  - `lr` (learning_rate): `trial.suggest_float("lr", 1e-5, 1e-2, log=True)` - 로그 스케일 실수형, 범위 [1e-5, 1e-2]
+  - `weight_decay`: `trial.suggest_float("weight_decay", 1e-4, 1e-1, log=True)` - 로그 스케일 실수형, 범위 [1e-4, 1e-1]
+  - `batch_size`: `trial.suggest_categorical("batch_size", [32, 64, 128, 256])` - 범주형, 선택지 [32, 64, 128, 256]
+- **고정 파라미터**: `num_classes=3`, `optimizer='AdamW'`, `epochs=200`, `patience=12`, `scheduler='ReduceLROnPlateau'` (factor=0.5, patience=3), `random_state=42`
+
+#### 공통 최적화 설정
+
+- **교차 검증**: 모든 모델은 연도 기반 3-Fold 홀드아웃 교차 검증 사용
+  - Fold 1: Train [2018, 2019] → Val 2020
+  - Fold 2: Train [2018, 2020] → Val 2019
+  - Fold 3: Train [2019, 2020] → Val 2018
+- **평가 지표**: CSI (Critical Success Index) - 모든 fold의 평균 CSI를 최적화 목표로 사용
+- **최적화 알고리즘**: TPE (Tree-structured Parzen Estimator)
+- **재현성**: `random_state=42` 고정
 
 ---
 
@@ -214,24 +347,46 @@ CSI = H / (H + F + M + 1e-10)
   - `data/ASOS/`: 연도별 기상 원천
   - `data/dataon/`: 대기오염 일자별 CSV(대용량)
   - `data/data_for_modeling/`: 지역별 학습/평가 세트(`*_train.csv`, `*_test.csv`, `df_*.feather`)
-  - `data/data_for_TAF/`: 공항별 TAF(항공기상)
+  - **Hugging Face에서 다운로드**: 전체 `data/` 폴더를 [Hugging Face 저장소](https://huggingface.co/bong9513/visibility_prediction/tree/main/data)에서 다운로드 가능
+    ```bash
+    git clone https://huggingface.co/bong9513/visibility_prediction
+    # 클론 후 visibility_prediction/data/ 폴더를 프로젝트의 data/ 위치로 복사
+    ```
 
 - 전처리/탐색
-  - `Analysis_code/0.air_data_merge.ipynb` → `1.data_merge.ipynb` → `2.eda_preproccesing.ipynb`
+  - `Analysis_code/1.data_preprocessing/0.air_data_merge.ipynb` → `1.data_preprocessing/1.data_merge.ipynb` → `1.data_preprocessing/2.eda_preproccesing.ipynb` → `1.data_preprocessing/3.make_train_test.ipynb`
 
 - 오버샘플링
-  - `Analysis_code/make_oversample_data/`에서 스크립트 실행(상단 TL;DR 참조)
+  - `Analysis_code/2.make_oversample_data/`에서 스크립트 실행(상단 TL;DR 참조)
 
 - GBDT 최적화/학습
-  - `Analysis_code/optima/LGB_smote_seoul.py`, `XGB_smote_seoul.py` 실행
-  - 산출 모델은 `Analysis_code/save_model/` 하위에 `.pkl`로 저장
+  - **옵션 1: 직접 모델 생성**
+    - `Analysis_code/5.optima/lgb_smote/LGB_smote_seoul.py`, `5.optima/xgb_smote/XGB_smote_seoul.py` 실행하여 모델 학습
+    - 산출 모델은 `Analysis_code/save_model/` 하위에 `.pkl`로 저장
+    - 각 모델별로 지역별 스크립트가 존재 (seoul, incheon, busan, daegu, daejeon, gwangju)
+  - **옵션 2: 사전 학습된 모델 사용**
+    - Hugging Face 저장소에서 사전 학습된 모델과 최적화 히스토리 다운로드 가능
+    - `save_model/`: [Hugging Face 저장소](https://huggingface.co/bong9513/visibility_prediction/tree/main/save_model)에서 사전 학습된 모델 다운로드
+    - `optimization_history/`: [Hugging Face 저장소](https://huggingface.co/bong9513/visibility_prediction/tree/main/optimization_history)에서 최적화 히스토리 파일 다운로드
+    ```bash
+    git clone https://huggingface.co/bong9513/visibility_prediction
+    # 클론 후 visibility_prediction/save_model/ 및 visibility_prediction/optimization_history/ 폴더를 
+    # 각각 Analysis_code/save_model/ 및 Analysis_code/optimization_history/ 위치로 복사
+    ```
 
 - 딥러닝 학습
-  - `deeplearning_model_*` 노트북에서 탭울러 모델 학습/평가, `model_visualize.ipynb`로 시각화
+  - **옵션 1: 직접 모델 생성**
+    - `Analysis_code/5.optima/` 하위의 각 모델 폴더(`resnet_like_*`, `ft_transformer_*`, `deepgbm_*`)에서 지역별 스크립트 실행
+    - 예: `5.optima/resnet_like_smote/resnet_like_smote_seoul.py`
+    - 모델 정의는 `Analysis_code/models/` 폴더에 있음 (`deepgbm.py`, `ft_transformer.py`, `resnet_like.py`)
+    - 시각화: `Analysis_code/visualization/model_visualize.ipynb`로 시각화
+  - **옵션 2: 사전 학습된 모델 사용**
+    - Hugging Face 저장소의 `save_model/` 폴더에서 사전 학습된 딥러닝 모델 다운로드 가능
+    - [Hugging Face 저장소](https://huggingface.co/bong9513/visibility_prediction/tree/main/save_model)에서 해당 모델 파일 다운로드 후 사용
 
 - 앙상블/최종 평가
-  - `model_voting_test_best_sample/ensemble__voting_best_sample.ipynb`
-  - `final_test/final.ipynb`
+  - `Analysis_code/model_voting_test_best_sample/ensemble__voting_best_sample.ipynb`
+  - `Analysis_code/final_test/final.ipynb`
 
 ---
 
@@ -252,8 +407,8 @@ CSI = H / (H + F + M + 1e-10)
 
 ### 주의/트러블슈팅
 
-- `optima/LGB_smote_seoul.py`의 `sys.path.append(...)`는 환경 의존적 경로입니다. 일반 환경에서는 제거해도 `from lightgbm import LGBMClassifier`가 동작해야 합니다.
-- 스크립트는 상대 경로를 가정합니다. 실행 전 현재 작업 디렉터리가 `Analysis_code/*` 하위인지 확인하세요.
+- `5.optima/lgb_smote/LGB_smote_seoul.py`의 `sys.path.append(...)`는 환경 의존적 경로입니다. 일반 환경에서는 제거해도 `from lightgbm import LGBMClassifier`가 동작해야 합니다.
+- 스크립트는 상대 경로를 가정합니다. 실행 전 현재 작업 디렉터리가 `Analysis_code/5.optima/` 하위인지 확인하세요.
 - `wind_dir`의 `'정온'` 값 치환/형변환이 누락되면 GBDT/XGB에서 오류가 발생할 수 있습니다.
 - `dataon/`은 매우 대용량입니다. 메모리 여유를 확보하거나 연도/지역 단위로 처리하세요.
 
@@ -262,11 +417,11 @@ CSI = H / (H + F + M + 1e-10)
 ### 의존성
 
 - Python 3.8+
-- PyTorch, pandas, numpy, scikit-learn, imbalanced-learn, optuna, ctgan, xgboost, lightgbm, joblib, matplotlib, seaborn
+- PyTorch, pandas, numpy, scikit-learn, imbalanced-learn, optuna, ctgan, xgboost, lightgbm, joblib, matplotlib, seaborn, hyperopt
 
 ---
 
 ### 라이선스/인용
 
 - 라이선스: 추후 업데이트 예정
-- 본 프로젝트/결과물을 인용 시 `visibility_prediction` 저장소와 사용된 데이터 소스(ASOS, DataOn, TAF)를 명시해 주세요.
+- 본 프로젝트/결과물을 인용 시 `visibility_prediction` 저장소와 사용된 데이터 소스(ASOS, DataOn)를 명시해 주세요.