diff --git "a/notebooks/hyperparamsRF.ipynb" "b/notebooks/hyperparamsRF.ipynb"
new file mode 100644--- /dev/null
+++ "b/notebooks/hyperparamsRF.ipynb"
@@ -0,0 +1,2204 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "863fa39a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%reload_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1cb3c318",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "from my_utils import load_emb,randomSearch, train_rf, randomSVM, train_svm\n",
+    "from joblib import dump, load\n",
+    "from pprint import pprint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "56b78ada",
+   "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>SwissProt_ID</th>\n",
+       "      <th>Refseq_Accession</th>\n",
+       "      <th>Other_Accession</th>\n",
+       "      <th>GramStain</th>\n",
+       "      <th>Experimental_Localization</th>\n",
+       "      <th>Phylum</th>\n",
+       "      <th>Class</th>\n",
+       "      <th>Organism</th>\n",
+       "      <th>sequence</th>\n",
+       "      <th>id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>P50307</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Gram positive</td>\n",
+       "      <td>Cytoplasmic</td>\n",
+       "      <td>Firmicutes</td>\n",
+       "      <td>Bacilli</td>\n",
+       "      <td>Staphylococcus aureus</td>\n",
+       "      <td>MLNNKRLFTSESVTEGHPDKIADQVSDAILDAILKDDPNARVACET...</td>\n",
+       "      <td>P50307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>P01552</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Gram positive</td>\n",
+       "      <td>Extracellular</td>\n",
+       "      <td>Firmicutes</td>\n",
+       "      <td>Bacilli</td>\n",
+       "      <td>Staphylococcus aureus</td>\n",
+       "      <td>MYKRLFISHVILIFALILVISTPNVLAESQPDPKPDELHKSSKFTG...</td>\n",
+       "      <td>P01552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>P09978</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Gram positive</td>\n",
+       "      <td>Extracellular</td>\n",
+       "      <td>Firmicutes</td>\n",
+       "      <td>Bacilli</td>\n",
+       "      <td>Staphylococcus aureus</td>\n",
+       "      <td>MVKKTKSNSLKKVATLALANLLLVGALTDNSAKAESKKDDTDLKLV...</td>\n",
+       "      <td>P09978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>P45723</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Gram positive</td>\n",
+       "      <td>Extracellular</td>\n",
+       "      <td>Firmicutes</td>\n",
+       "      <td>Bacilli</td>\n",
+       "      <td>Staphylococcus aureus</td>\n",
+       "      <td>MSGWYHSAHASDSLSKSPENWMSKLDDGKHLTEINIPGSHDSGSFT...</td>\n",
+       "      <td>P45723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>P81177</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Gram positive</td>\n",
+       "      <td>Extracellular</td>\n",
+       "      <td>Firmicutes</td>\n",
+       "      <td>Bacilli</td>\n",
+       "      <td>Staphylococcus aureus</td>\n",
+       "      <td>MRKFSRYAFTSMATVTLLSSLTPAALASDTNHKPATSDINFEITQK...</td>\n",
+       "      <td>P81177</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  SwissProt_ID Refseq_Accession Other_Accession      GramStain  \\\n",
+       "0       P50307              NaN             NaN  Gram positive   \n",
+       "1       P01552              NaN             NaN  Gram positive   \n",
+       "2       P09978              NaN             NaN  Gram positive   \n",
+       "3       P45723              NaN             NaN  Gram positive   \n",
+       "4       P81177              NaN             NaN  Gram positive   \n",
+       "\n",
+       "  Experimental_Localization      Phylum    Class               Organism  \\\n",
+       "0               Cytoplasmic  Firmicutes  Bacilli  Staphylococcus aureus   \n",
+       "1             Extracellular  Firmicutes  Bacilli  Staphylococcus aureus   \n",
+       "2             Extracellular  Firmicutes  Bacilli  Staphylococcus aureus   \n",
+       "3             Extracellular  Firmicutes  Bacilli  Staphylococcus aureus   \n",
+       "4             Extracellular  Firmicutes  Bacilli  Staphylococcus aureus   \n",
+       "\n",
+       "                                            sequence      id  \n",
+       "0  MLNNKRLFTSESVTEGHPDKIADQVSDAILDAILKDDPNARVACET...  P50307  \n",
+       "1  MYKRLFISHVILIFALILVISTPNVLAESQPDPKPDELHKSSKFTG...  P01552  \n",
+       "2  MVKKTKSNSLKKVATLALANLLLVGALTDNSAKAESKKDDTDLKLV...  P09978  \n",
+       "3  MSGWYHSAHASDSLSKSPENWMSKLDDGKHLTEINIPGSHDSGSFT...  P45723  \n",
+       "4  MRKFSRYAFTSMATVTLLSSLTPAALASDTNHKPATSDINFEITQK...  P81177  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "seq_df = pd.read_csv('../Data/trainingData.csv')\n",
+    "seq_df['id'] = seq_df['SwissProt_ID'].fillna(seq_df['Refseq_Accession'].fillna(seq_df['Other_Accession']))\n",
+    "seq_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9c385802",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Experimental_Localization\n",
+       "Cytoplasmic            7239\n",
+       "CytoplasmicMembrane    1969\n",
+       "Extracellular           797\n",
+       "OuterMembrane           555\n",
+       "Periplasmic             485\n",
+       "Cellwall                 95\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "drop_loc = ['Curated for secondary localization only','OuterMembrane,Extracellular','Cytoplasmic,CytoplasmicMembrane','Periplasmic,CytoplasmicMembrane','CytoplasmicMembrane,Cellwall', 'Cellwall,Extracellular', 'Periplasmic,OuterMembrane','Extracellular,Periplasmic', 'Extracellular,HostAssociated', 'Cytoplasmic,Periplasmic','Cytoplasmic,Extracellular', 'Cytoplasmic,HostAssociated','OuterMembrane,CytoplasmicMembrane','Cytoplasmic,OuterMembrane']\n",
+    "seq_df = seq_df[~seq_df['Experimental_Localization'].isin(drop_loc)]\n",
+    "\n",
+    "seq_df['Experimental_Localization'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "742f4b05",
+   "metadata": {},
+   "source": [
+    "Prost T5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "935af904",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5a29a85c08a4498ba9526c387931b85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Cargando embeddings:   0%|          | 0/11140 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dir : str = os.path.expanduser(\"~/Documentos/Tesis/datosGenerados/prost/embeddings\")\n",
+    "acc : list[str] = list(seq_df['id'])\n",
+    "\n",
+    "X = load_emb(path = dir, acc = acc)\n",
+    "y = list(seq_df['Experimental_Localization'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c4c34272",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 3 folds for each of 50 candidates, totalling 150 fits\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=40, max_features=log2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5, n_estimators=3300; total time= 1.4min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=40, max_features=log2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5, n_estimators=3300; total time= 1.4min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=40, max_features=log2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5, n_estimators=3300; total time= 1.4min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=10, n_estimators=1300; total time= 1.5min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5, n_estimators=500; total time= 2.0min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5, n_estimators=500; total time= 2.0min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5, n_estimators=500; total time= 2.1min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=10, n_estimators=1300; total time= 1.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=10, n_estimators=1300; total time= 1.6min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=8, min_samples_split=20, n_estimators=3300; total time= 3.7min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=8, min_samples_split=20, n_estimators=3300; total time= 3.8min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=8, min_samples_split=20, n_estimators=3300; total time= 3.8min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=10, n_estimators=3300; total time=12.7min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=10, n_estimators=3300; total time=12.9min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=10, n_estimators=3300; total time=13.8min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=30, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=5, n_estimators=2900; total time=16.9min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=30, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=5, n_estimators=2900; total time=16.8min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=30, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=5, n_estimators=2900; total time=17.4min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=50, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=15, n_estimators=3300; total time=20.8min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=50, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=15, n_estimators=3300; total time=21.6min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=50, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=15, n_estimators=3300; total time=22.1min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=log2, max_leaf_nodes=None, min_samples_leaf=10, min_samples_split=2, n_estimators=1700; total time=  55.5s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=log2, max_leaf_nodes=None, min_samples_leaf=10, min_samples_split=2, n_estimators=1700; total time=  55.8s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=log2, max_leaf_nodes=None, min_samples_leaf=10, min_samples_split=2, n_estimators=1700; total time=  57.1s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=50, max_features=0.3, max_leaf_nodes=100, min_samples_leaf=8, min_samples_split=5, n_estimators=3300; total time=28.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10, n_estimators=500; total time=  32.6s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=50, max_features=0.3, max_leaf_nodes=100, min_samples_leaf=8, min_samples_split=5, n_estimators=3300; total time=28.4min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10, n_estimators=500; total time=  33.0s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10, n_estimators=500; total time=  33.5s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=50, max_features=0.3, max_leaf_nodes=100, min_samples_leaf=8, min_samples_split=5, n_estimators=3300; total time=29.1min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=2100; total time= 2.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=2100; total time= 2.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2, n_estimators=2100; total time=34.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=2100; total time= 2.4min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2, n_estimators=2100; total time=35.9min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2, n_estimators=2100; total time=37.5min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=15, n_estimators=500; total time= 4.5min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=15, n_estimators=500; total time= 4.4min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=15, n_estimators=500; total time= 4.6min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5, n_estimators=1700; total time=  26.3s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5, n_estimators=1700; total time=  26.5s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5, n_estimators=1700; total time=  26.9s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=20, n_estimators=900; total time= 1.6min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.7, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=2, n_estimators=1700; total time=23.7min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.7, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=2, n_estimators=1700; total time=23.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=20, n_estimators=900; total time= 1.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=20, n_estimators=900; total time= 1.6min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.5, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=2, n_estimators=2100; total time=32.8min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=20, max_features=log2, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=2, n_estimators=2500; total time=  38.0s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=20, max_features=log2, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=2, n_estimators=2500; total time=  37.8s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=20, max_features=log2, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=2, n_estimators=2500; total time=  38.6s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.7, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=2, n_estimators=1700; total time=25.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.5, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=2, n_estimators=2100; total time=31.7min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.5, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=2, n_estimators=2100; total time=33.2min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5, n_estimators=2900; total time= 2.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=None, max_features=0.3, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=2, n_estimators=1300; total time=10.9min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=None, max_features=0.3, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=2, n_estimators=1300; total time=11.0min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=10, n_estimators=500; total time=  19.2s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=None, max_features=0.3, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=2, n_estimators=1300; total time=11.2min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5, n_estimators=2900; total time= 2.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=10, n_estimators=500; total time=  19.5s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=10, n_estimators=500; total time=  19.8s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5, n_estimators=2900; total time= 2.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=None, max_features=0.5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2, n_estimators=2100; total time=31.6min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=None, max_features=0.5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2, n_estimators=2100; total time=32.3min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=None, max_features=0.5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2, n_estimators=2100; total time=32.9min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=15, n_estimators=1300; total time= 2.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.5, max_leaf_nodes=200, min_samples_leaf=4, min_samples_split=5, n_estimators=2500; total time=61.4min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=15, n_estimators=1300; total time= 2.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=15, n_estimators=1300; total time= 2.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.5, max_leaf_nodes=200, min_samples_leaf=4, min_samples_split=5, n_estimators=2500; total time=59.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=50, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5, n_estimators=1300; total time=14.8min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=50, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5, n_estimators=1300; total time=14.1min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=10, n_estimators=900; total time= 8.8min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.5, max_leaf_nodes=200, min_samples_leaf=4, min_samples_split=5, n_estimators=2500; total time=62.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=log2, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=10, n_estimators=500; total time=  10.8s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=50, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5, n_estimators=1300; total time=14.7min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=20, n_estimators=2500; total time=35.1min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=log2, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=10, n_estimators=500; total time=  11.1s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=log2, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=10, n_estimators=500; total time=  10.8s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=20, n_estimators=2500; total time=36.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=10, n_estimators=900; total time= 8.5min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=log2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2, n_estimators=3300; total time=  52.8s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=log2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2, n_estimators=3300; total time=  53.2s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=log2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2, n_estimators=3300; total time=  53.5s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=20, n_estimators=2500; total time=36.1min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=20, max_features=log2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=15, n_estimators=2500; total time=  60.0s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=20, max_features=log2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=15, n_estimators=2500; total time= 1.0min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=20, max_features=log2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=15, n_estimators=2500; total time= 1.0min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.7, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=10, n_estimators=1700; total time=32.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=10, n_estimators=900; total time= 9.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.7, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=10, n_estimators=1700; total time=32.8min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.7, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=10, n_estimators=1700; total time=33.8min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.5, max_leaf_nodes=100, min_samples_leaf=10, min_samples_split=5, n_estimators=900; total time= 8.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.5, max_leaf_nodes=100, min_samples_leaf=10, min_samples_split=5, n_estimators=900; total time= 8.3min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.5, max_leaf_nodes=100, min_samples_leaf=10, min_samples_split=5, n_estimators=900; total time= 8.8min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=30, max_features=log2, max_leaf_nodes=100, min_samples_leaf=8, min_samples_split=2, n_estimators=2100; total time=  44.9s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=30, max_features=log2, max_leaf_nodes=100, min_samples_leaf=8, min_samples_split=2, n_estimators=2100; total time=  44.8s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=30, max_features=log2, max_leaf_nodes=100, min_samples_leaf=8, min_samples_split=2, n_estimators=2100; total time=  44.9s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.7, max_leaf_nodes=None, min_samples_leaf=10, min_samples_split=20, n_estimators=1300; total time=27.9min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.7, max_leaf_nodes=None, min_samples_leaf=10, min_samples_split=20, n_estimators=1300; total time=26.8min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.7, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=20, n_estimators=2100; total time=42.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=1300; total time=40.9min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.7, max_leaf_nodes=None, min_samples_leaf=10, min_samples_split=20, n_estimators=1300; total time=29.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=1300; total time=42.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.7, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=20, n_estimators=2100; total time=44.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=50, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=10, n_estimators=2900; total time=46.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.7, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=20, n_estimators=2100; total time=44.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=1300; total time=45.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=50, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=10, n_estimators=2900; total time=48.0min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=50, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=10, n_estimators=2900; total time=49.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.7, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=5, n_estimators=1300; total time=31.4min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.3, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=2, n_estimators=1700; total time=10.5min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.3, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=2, n_estimators=1700; total time=10.6min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.3, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=2, n_estimators=1700; total time=11.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.7, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=10, n_estimators=3700; total time=83.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=0.3, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=10, n_estimators=2100; total time=22.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.7, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=5, n_estimators=1300; total time=29.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=0.3, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=10, n_estimators=2100; total time=23.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.7, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=10, n_estimators=3700; total time=85.0min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.7, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=5, n_estimators=1300; total time=29.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=0.3, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=10, n_estimators=2100; total time=24.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.7, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=10, n_estimators=3700; total time=89.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=1700; total time= 1.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=15, n_estimators=1700; total time=25.0min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=15, n_estimators=1700; total time=24.8min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=1700; total time= 1.1min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=1700; total time= 1.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=20, n_estimators=2500; total time= 4.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=5, n_estimators=2500; total time=35.0min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=20, n_estimators=2500; total time= 4.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=20, n_estimators=2500; total time= 4.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=5, n_estimators=2500; total time=36.0min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=5, n_estimators=2500; total time=36.5min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=15, n_estimators=1700; total time=25.5min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.5, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=10, n_estimators=2500; total time=23.9min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.5, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=10, n_estimators=2500; total time=23.7min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.5, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=10, n_estimators=2500; total time=24.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=50, max_features=0.7, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=20, n_estimators=1300; total time=26.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=50, max_features=0.7, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=20, n_estimators=1300; total time=25.8min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=50, max_features=0.7, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=20, n_estimators=1300; total time=27.1min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.7, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10, n_estimators=1300; total time=26.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.7, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10, n_estimators=1300; total time=28.8min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.7, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10, n_estimators=1300; total time=29.1min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.7, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=5, n_estimators=3700; total time=67.6min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.7, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=5, n_estimators=3700; total time=70.4min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.7, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=5, n_estimators=3700; total time=73.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=None, max_leaf_nodes=200, min_samples_leaf=2, min_samples_split=15, n_estimators=3700; total time=84.0min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=None, max_leaf_nodes=200, min_samples_leaf=2, min_samples_split=15, n_estimators=3700; total time=88.2min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=None, max_leaf_nodes=200, min_samples_leaf=2, min_samples_split=15, n_estimators=3700; total time=90.8min\n",
+      "Best Params\n",
+      "{'bootstrap': False,\n",
+      " 'class_weight': 'balanced',\n",
+      " 'criterion': 'gini',\n",
+      " 'max_depth': 30,\n",
+      " 'max_features': 0.3,\n",
+      " 'max_leaf_nodes': 100,\n",
+      " 'min_samples_leaf': 1,\n",
+      " 'min_samples_split': 10,\n",
+      " 'n_estimators': 2100}\n"
+     ]
+    }
+   ],
+   "source": [
+    "params = randomSearch(X, y)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7033c7d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Accuracy': 0.9227631221104161,\n",
+      " 'F1': 0.9252616934022988,\n",
+      " 'Precision': 0.9311010079561661,\n",
+      " 'Recall': 0.9227631221104161}\n",
+      "                     precision    recall  f1-score   support\n",
+      "\n",
+      "           Cellwall       0.76      0.61      0.68        31\n",
+      "        Cytoplasmic       0.99      0.95      0.97      2390\n",
+      "CytoplasmicMembrane       0.88      0.90      0.89       650\n",
+      "      Extracellular       0.72      0.92      0.81       263\n",
+      "      OuterMembrane       0.94      0.86      0.90       183\n",
+      "        Periplasmic       0.66      0.81      0.73       160\n",
+      "\n",
+      "           accuracy                           0.92      3677\n",
+      "          macro avg       0.82      0.84      0.83      3677\n",
+      "       weighted avg       0.93      0.92      0.93      3677\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['../Models/rfProst.joblib']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prost_rf, prostRF_evaluation = train_rf('Random Forest Prost T5', X, y, params)\n",
+    "dump(prost_rf, '../Models/rfProst.joblib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "45aad512",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 3 folds for each of 50 candidates, totalling 150 fits\n",
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=   7.7s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=   9.0s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  10.5s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  10.2s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  10.8s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  11.0s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=1000, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  34.1s\n",
+      "[CV] END svm__C=1000, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  37.0s\n",
+      "[CV] END svm__C=1000, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  39.4s\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  11.4s\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  10.3s\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  11.7s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  11.5s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  12.2s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  12.6s\n",
+      "[CV] END svm__C=0.1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.001; total time= 2.5min\n",
+      "[CV] END svm__C=0.1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.001; total time= 2.6min\n",
+      "[CV] END svm__C=0.1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.001; total time= 2.6min\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  41.7s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  44.1s\n",
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  45.3s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  55.1s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  52.9s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 3.7min\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 3.8min\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 4.0min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.0001; total time=  38.9s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=10, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 4.0min\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  47.0s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.0001; total time=  37.7s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=10, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 4.2min\n",
+      "[CV] END svm__C=10, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 4.2min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.0001; total time=  41.1s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  52.8s\n",
+      "[CV] END svm__C=0.1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.0001; total time= 4.7min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  55.2s\n",
+      "[CV] END svm__C=0.1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.0001; total time= 4.6min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  51.6s\n",
+      "[CV] END svm__C=0.1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.0001; total time= 4.7min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=True, svm__tol=0.001; total time= 4.8min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time= 1.0min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  55.9s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  59.1s\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=1e-05; total time=  12.1s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=1e-05; total time=  11.6s\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=1e-05; total time=  12.2s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=0.1, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  28.0s\n",
+      "[CV] END svm__C=0.1, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  29.7s\n",
+      "[CV] END svm__C=0.1, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  28.2s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=True, svm__tol=0.001; total time= 4.6min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=True, svm__tol=0.001; total time= 4.7min\n",
+      "[CV] END svm__C=1000, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.01; total time=  48.3s\n",
+      "[CV] END svm__C=1000, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.01; total time=  46.4s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=True, svm__tol=0.001; total time= 1.0min\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=True, svm__tol=0.001; total time= 1.0min\n",
+      "[CV] END svm__C=1000, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.01; total time=  47.3s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=True, svm__tol=0.001; total time= 1.0min\n",
+      "[CV] END svm__C=0.1, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 2.4min\n",
+      "[CV] END svm__C=0.1, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 2.4min\n",
+      "[CV] END svm__C=0.1, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 2.4min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.01; total time= 4.4min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.01; total time= 4.5min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.5min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.01; total time= 4.7min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.4min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.7min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  56.0s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  55.2s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  50.3s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.5min\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  36.0s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.4min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.5min\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  39.9s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.4min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.5min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.5min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.6min\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  41.7s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  57.3s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  56.7s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  57.6s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  58.3s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  59.3s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=False, svm__tol=0.001; total time=  52.2s\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=0.001; total time=  10.3s\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=0.001; total time=  10.0s\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=0.001; total time=  10.9s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time= 1.0min\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=False, svm__tol=0.001; total time=  46.6s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.5min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.5min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.5min\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=False, svm__tol=0.001; total time=  49.3s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  44.7s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  47.3s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  49.0s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.4min\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  50.5s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  52.2s\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 4.0min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 5.0min\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 3.9min\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  47.0s\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 4.1min\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 2.7min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  51.7s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 2.9min\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 2.9min\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 3.6min\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 3.6min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  49.1s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 3.7min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  50.6s\n",
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time=  46.1s\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  10.6s\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  11.4s\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  11.4s\n",
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time=  51.2s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  14.0s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  13.1s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  13.4s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=10, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.1min\n",
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time=  46.6s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=10, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 3.9min\n",
+      "[CV] END svm__C=10, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=False, svm__tol=0.001; total time=  44.9s\n",
+      "[CV] END svm__C=10, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=False, svm__tol=0.001; total time=  46.3s\n",
+      "[CV] END svm__C=10, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=False, svm__tol=0.001; total time=  47.8s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=10, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.2min\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=True, svm__tol=1e-05; total time=  35.9s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=True, svm__tol=1e-05; total time=  35.5s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=True, svm__tol=1e-05; total time=  35.4s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  52.3s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  58.1s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  56.4s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.0001; total time= 4.0min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  47.1s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.0001; total time= 4.0min\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 3.8min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  45.6s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.0001; total time= 4.1min\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 3.9min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  49.7s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 3.8min\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 1.9min\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 1.8min\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 1.8min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 1.2min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time=  48.9s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time=  41.9s\n",
+      "{'svm__C': 10,\n",
+      " 'svm__cache_size': 200,\n",
+      " 'svm__class_weight': 'balanced',\n",
+      " 'svm__decision_function_shape': 'ovo',\n",
+      " 'svm__gamma': 'auto',\n",
+      " 'svm__kernel': 'rbf',\n",
+      " 'svm__max_iter': 1000,\n",
+      " 'svm__probability': False,\n",
+      " 'svm__shrinking': False,\n",
+      " 'svm__tol': 1e-05}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "params = randomSVM(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "53cb01f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Accuracy': 0.9586619526788143,\n",
+      " 'F1': 0.9581524274127878,\n",
+      " 'Precision': 0.9584147861484424,\n",
+      " 'Recall': 0.9586619526788143}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                     precision    recall  f1-score   support\n",
+      "\n",
+      "           Cellwall       0.86      0.58      0.69        31\n",
+      "        Cytoplasmic       0.98      0.99      0.98      2390\n",
+      "CytoplasmicMembrane       0.96      0.93      0.95       650\n",
+      "      Extracellular       0.89      0.92      0.91       263\n",
+      "      OuterMembrane       0.95      0.90      0.92       183\n",
+      "        Periplasmic       0.82      0.82      0.82       160\n",
+      "\n",
+      "           accuracy                           0.96      3677\n",
+      "          macro avg       0.91      0.86      0.88      3677\n",
+      "       weighted avg       0.96      0.96      0.96      3677\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['../Models/svmProst.joblib']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svm_prost, prostSVM_evaluation = train_svm('Prost T5', X, y, params = params)\n",
+    "dump(svm_prost, '../Models/svmProst.joblib')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4918a186",
+   "metadata": {},
+   "source": [
+    "ESM 300m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3492a01b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73ba0b957f7b4e4e92d1a6d19f2155f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Cargando embeddings:   0%|          | 0/11140 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dir : str = os.path.expanduser(\"~/Documentos/Tesis/datosGenerados/esm300m/embeddings\")\n",
+    "X = load_emb(path = dir, acc = acc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7f3021d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 3 folds for each of 50 candidates, totalling 150 fits\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=15, n_estimators=3700; total time=  30.8s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=15, n_estimators=3700; total time=  30.8s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=15, n_estimators=3700; total time=  31.1s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=log2, max_leaf_nodes=None, min_samples_leaf=10, min_samples_split=20, n_estimators=2500; total time=  47.9s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=log2, max_leaf_nodes=None, min_samples_leaf=10, min_samples_split=20, n_estimators=2500; total time=  47.2s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=log2, max_leaf_nodes=None, min_samples_leaf=10, min_samples_split=20, n_estimators=2500; total time=  49.3s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=8, min_samples_split=5, n_estimators=2100; total time= 1.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=5, n_estimators=3700; total time= 3.9min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=5, n_estimators=3700; total time= 3.9min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=5, n_estimators=3700; total time= 4.0min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=8, min_samples_split=5, n_estimators=2100; total time= 1.4min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=8, min_samples_split=5, n_estimators=2100; total time= 1.4min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=30, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=2, n_estimators=900; total time= 3.2min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=30, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=2, n_estimators=900; total time= 3.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=30, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=2, n_estimators=900; total time= 3.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=2, min_samples_split=5, n_estimators=2100; total time= 2.6min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=2, min_samples_split=5, n_estimators=2100; total time= 2.6min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=2, min_samples_split=5, n_estimators=2100; total time= 2.6min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=2, n_estimators=2100; total time=18.7min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=2, n_estimators=2100; total time=18.6min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.3, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=2, n_estimators=2100; total time=19.3min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=None, max_leaf_nodes=50, min_samples_leaf=10, min_samples_split=20, n_estimators=1300; total time=23.2min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=None, max_leaf_nodes=50, min_samples_leaf=10, min_samples_split=20, n_estimators=1300; total time=23.9min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=None, max_leaf_nodes=50, min_samples_leaf=10, min_samples_split=20, n_estimators=1300; total time=25.2min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=log2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5, n_estimators=2100; total time=  32.4s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=log2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5, n_estimators=2100; total time=  32.4s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=log2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5, n_estimators=2100; total time=  31.9s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.3, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=10, n_estimators=3300; total time=46.8min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.3, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=10, n_estimators=3300; total time=48.0min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.3, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=10, n_estimators=3300; total time=49.5min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=30, max_features=log2, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=20, n_estimators=500; total time=   6.0s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=30, max_features=log2, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=20, n_estimators=500; total time=   6.2s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=30, max_features=log2, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=20, n_estimators=500; total time=   6.1s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.3, max_leaf_nodes=200, min_samples_leaf=2, min_samples_split=15, n_estimators=3300; total time=29.9min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=8, min_samples_split=2, n_estimators=900; total time=  53.0s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=8, min_samples_split=2, n_estimators=900; total time=  53.0s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=8, min_samples_split=2, n_estimators=900; total time=  53.7s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.7, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=20, n_estimators=3700; total time=52.7min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.3, max_leaf_nodes=200, min_samples_leaf=2, min_samples_split=15, n_estimators=3300; total time=29.5min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.3, max_leaf_nodes=200, min_samples_leaf=2, min_samples_split=15, n_estimators=3300; total time=30.7min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.7, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=20, n_estimators=3700; total time=55.0min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=5, n_estimators=2900; total time= 3.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=5, n_estimators=2900; total time= 3.2min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=0.7, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=20, n_estimators=3700; total time=58.0min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=5, n_estimators=2900; total time= 3.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=10, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=15, n_estimators=2500; total time= 2.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=5, n_estimators=500; total time=  46.0s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=5, n_estimators=500; total time=  44.4s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=5, n_estimators=500; total time=  46.7s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=10, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=15, n_estimators=2500; total time= 2.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=10, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=15, n_estimators=2500; total time= 2.6min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=100, min_samples_leaf=10, min_samples_split=20, n_estimators=1700; total time=  21.2s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=100, min_samples_leaf=10, min_samples_split=20, n_estimators=1700; total time=  21.9s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=100, min_samples_leaf=10, min_samples_split=20, n_estimators=1700; total time=  21.7s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=10, min_samples_split=15, n_estimators=500; total time=  30.6s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=10, min_samples_split=15, n_estimators=500; total time=  30.1s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_samples_leaf=10, min_samples_split=15, n_estimators=500; total time=  30.6s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=10, n_estimators=900; total time= 5.4min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=10, n_estimators=900; total time= 5.6min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=10, n_estimators=900; total time= 5.7min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=2, n_estimators=900; total time= 7.7min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=2, n_estimators=900; total time= 7.6min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=10, min_samples_split=2, n_estimators=900; total time= 8.6min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=4, min_samples_split=10, n_estimators=1700; total time= 1.2min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=4, min_samples_split=10, n_estimators=1700; total time= 1.2min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=4, min_samples_split=10, n_estimators=1700; total time= 1.2min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=None, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=2, n_estimators=3700; total time=67.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.3, max_leaf_nodes=200, min_samples_leaf=4, min_samples_split=10, n_estimators=3300; total time=47.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.3, max_leaf_nodes=200, min_samples_leaf=4, min_samples_split=10, n_estimators=3300; total time=48.0min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=30, max_features=log2, max_leaf_nodes=200, min_samples_leaf=8, min_samples_split=15, n_estimators=900; total time=  11.9s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=30, max_features=log2, max_leaf_nodes=200, min_samples_leaf=8, min_samples_split=15, n_estimators=900; total time=  11.9s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=30, max_features=log2, max_leaf_nodes=200, min_samples_leaf=8, min_samples_split=15, n_estimators=900; total time=  11.8s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.3, max_leaf_nodes=200, min_samples_leaf=4, min_samples_split=10, n_estimators=3300; total time=49.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10, n_estimators=3300; total time= 3.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10, n_estimators=3300; total time= 3.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10, n_estimators=3300; total time= 3.5min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=None, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=2, n_estimators=3700; total time=64.6min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=None, max_leaf_nodes=50, min_samples_leaf=10, min_samples_split=2, n_estimators=3700; total time=99.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5, n_estimators=1700; total time= 1.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=0.7, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=5, n_estimators=3300; total time=111.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5, n_estimators=1700; total time= 1.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=log2, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=20, n_estimators=2900; total time=  35.0s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=None, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=2, n_estimators=3700; total time=69.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=0.7, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=5, n_estimators=3300; total time=110.4min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5, n_estimators=1700; total time= 1.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=log2, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=20, n_estimators=2900; total time=  34.7s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=None, max_leaf_nodes=50, min_samples_leaf=10, min_samples_split=2, n_estimators=3700; total time=98.7min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=log2, max_leaf_nodes=10, min_samples_leaf=8, min_samples_split=20, n_estimators=2900; total time=  34.9s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=1300; total time=  47.6s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=1300; total time=  48.0s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=1300; total time=  49.7s\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=None, max_leaf_nodes=50, min_samples_leaf=10, min_samples_split=15, n_estimators=2100; total time=56.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=None, max_leaf_nodes=50, min_samples_leaf=10, min_samples_split=2, n_estimators=3700; total time=102.8min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=0.7, max_leaf_nodes=100, min_samples_leaf=1, min_samples_split=5, n_estimators=3300; total time=116.2min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10, n_estimators=1700; total time= 5.9min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10, n_estimators=1700; total time= 6.0min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.7, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=10, n_estimators=900; total time=18.5min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10, n_estimators=1700; total time= 6.3min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.7, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=10, n_estimators=900; total time=18.0min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=40, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=5, n_estimators=2900; total time= 2.8min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=None, max_leaf_nodes=50, min_samples_leaf=10, min_samples_split=15, n_estimators=2100; total time=54.2min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=40, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=5, n_estimators=2900; total time= 2.7min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=40, max_features=sqrt, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=5, n_estimators=2900; total time= 2.8min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5, n_estimators=2900; total time= 3.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5, n_estimators=2900; total time= 3.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5, n_estimators=2900; total time= 3.6min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.7, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=10, n_estimators=900; total time=18.5min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2, n_estimators=900; total time=13.5min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2, n_estimators=900; total time=13.3min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10, n_estimators=500; total time=  12.3s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10, n_estimators=500; total time=  12.1s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10, n_estimators=500; total time=  12.7s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=5, n_estimators=2900; total time=  36.6s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=5, n_estimators=2900; total time=  36.6s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=15, n_estimators=500; total time=   6.0s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=15, n_estimators=500; total time=   6.0s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=15, n_estimators=500; total time=   6.1s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=50, max_features=log2, max_leaf_nodes=200, min_samples_leaf=10, min_samples_split=5, n_estimators=2900; total time=  37.5s\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=0.5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2, n_estimators=900; total time=14.1min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=20, n_estimators=3300; total time= 3.0min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=20, n_estimators=3300; total time= 3.0min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=900; total time=10.3min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=900; total time=10.0min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=None, max_features=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=20, n_estimators=900; total time=10.7min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=20, n_estimators=3300; total time= 3.1min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=15, n_estimators=3300; total time= 3.2min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=15, n_estimators=3300; total time= 3.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=None, max_features=None, max_leaf_nodes=50, min_samples_leaf=10, min_samples_split=15, n_estimators=2100; total time=69.7min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=2, min_samples_split=15, n_estimators=3300; total time= 3.3min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=10, min_samples_split=20, n_estimators=2900; total time= 2.8min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.5, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=15, n_estimators=500; total time=11.2min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.5, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=15, n_estimators=500; total time=11.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=30, max_features=0.5, max_leaf_nodes=50, min_samples_leaf=4, min_samples_split=15, n_estimators=500; total time=11.4min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=10, min_samples_split=20, n_estimators=2900; total time= 2.7min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=entropy, max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_samples_leaf=10, min_samples_split=20, n_estimators=2900; total time= 2.8min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.7, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=15, n_estimators=900; total time=29.4min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.7, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=15, n_estimators=900; total time=29.4min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=20, max_features=0.7, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=15, n_estimators=900; total time=29.8min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.5, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=5, n_estimators=2900; total time=28.7min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.5, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=5, n_estimators=2900; total time=29.6min\n",
+      "[CV] END bootstrap=True, class_weight=balanced, criterion=gini, max_depth=40, max_features=0.5, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=5, n_estimators=2900; total time=31.0min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.7, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=15, n_estimators=3700; total time=83.6min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10, n_estimators=1700; total time=23.0min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10, n_estimators=1700; total time=25.0min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=entropy, max_depth=40, max_features=0.5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10, n_estimators=1700; total time=23.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.7, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=15, n_estimators=3700; total time=74.3min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=20, max_features=0.7, max_leaf_nodes=100, min_samples_leaf=4, min_samples_split=15, n_estimators=3700; total time=91.4min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=None, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=15, n_estimators=3300; total time=67.1min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=None, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=15, n_estimators=3300; total time=69.5min\n",
+      "[CV] END bootstrap=False, class_weight=balanced, criterion=gini, max_depth=30, max_features=None, max_leaf_nodes=50, min_samples_leaf=1, min_samples_split=15, n_estimators=3300; total time=86.2min\n",
+      "Best Params\n",
+      "{'bootstrap': True,\n",
+      " 'class_weight': 'balanced',\n",
+      " 'criterion': 'entropy',\n",
+      " 'max_depth': 10,\n",
+      " 'max_features': 0.3,\n",
+      " 'max_leaf_nodes': 200,\n",
+      " 'min_samples_leaf': 2,\n",
+      " 'min_samples_split': 15,\n",
+      " 'n_estimators': 3300}\n"
+     ]
+    }
+   ],
+   "source": [
+    "paramsRF = randomSearch(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d1099389",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Accuracy': 0.9464237149850422,\n",
+      " 'F1': 0.9458912537157523,\n",
+      " 'Precision': 0.9476226292547676,\n",
+      " 'Recall': 0.9464237149850422}\n",
+      "                     precision    recall  f1-score   support\n",
+      "\n",
+      "           Cellwall       0.79      0.61      0.69        31\n",
+      "        Cytoplasmic       0.97      0.99      0.98      2390\n",
+      "CytoplasmicMembrane       0.97      0.85      0.90       650\n",
+      "      Extracellular       0.85      0.89      0.87       263\n",
+      "      OuterMembrane       0.93      0.86      0.90       183\n",
+      "        Periplasmic       0.76      0.87      0.81       160\n",
+      "\n",
+      "           accuracy                           0.95      3677\n",
+      "          macro avg       0.88      0.85      0.86      3677\n",
+      "       weighted avg       0.95      0.95      0.95      3677\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['../Models/rfESM300.joblib']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "esm300_rf, esm300RF_evaluation = train_rf('Random Forest ESM 300m', X, y, paramsRF)\n",
+    "dump(esm300_rf, '../Models/rfESM300.joblib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2178ac2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 3 folds for each of 50 candidates, totalling 150 fits\n",
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=   8.8s\n",
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=   9.8s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  12.2s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  11.9s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  11.8s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  12.8s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=1000, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  37.5s\n",
+      "[CV] END svm__C=1000, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  39.5s\n",
+      "[CV] END svm__C=1000, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  41.0s\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  13.6s\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  13.4s\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  11.9s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  11.6s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  11.1s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  13.1s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=0.1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.001; total time= 2.8min\n",
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  37.8s\n",
+      "[CV] END svm__C=0.1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.001; total time= 2.8min\n",
+      "[CV] END svm__C=0.1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.001; total time= 2.8min\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  41.6s\n",
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  40.0s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  52.2s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  53.3s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 3.9min\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 4.0min\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 4.1min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.0001; total time=  42.9s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.0001; total time=  38.0s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=0.0001; total time=  39.9s\n",
+      "[CV] END svm__C=10, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 4.4min\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time= 1.0min\n",
+      "[CV] END svm__C=10, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 4.5min\n",
+      "[CV] END svm__C=10, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 4.6min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  53.3s\n",
+      "[CV] END svm__C=0.1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.0001; total time= 4.8min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  58.1s\n",
+      "[CV] END svm__C=0.1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.0001; total time= 4.9min\n",
+      "[CV] END svm__C=0.1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.0001; total time= 4.9min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  56.7s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  53.3s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=True, svm__tol=0.001; total time= 4.9min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  56.5s\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=1e-05; total time=  13.3s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  56.2s\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=1e-05; total time=  13.9s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=1e-05; total time=  14.6s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=0.1, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  30.3s\n",
+      "[CV] END svm__C=0.1, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  30.0s\n",
+      "[CV] END svm__C=0.1, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  34.1s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=True, svm__tol=0.001; total time= 4.7min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=True, svm__tol=0.001; total time= 5.0min\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=True, svm__tol=0.001; total time= 1.1min\n",
+      "[CV] END svm__C=1000, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.01; total time=  57.9s\n",
+      "[CV] END svm__C=1000, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.01; total time=  56.8s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=True, svm__tol=0.001; total time= 1.1min\n",
+      "[CV] END svm__C=1000, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.01; total time=  59.5s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=True, svm__tol=0.001; total time= 1.2min\n",
+      "[CV] END svm__C=0.1, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 2.7min\n",
+      "[CV] END svm__C=0.1, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 2.8min\n",
+      "[CV] END svm__C=0.1, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=1e-05; total time= 2.8min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.01; total time= 4.7min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.6min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.01; total time= 4.8min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.01; total time= 4.8min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.9min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.8min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time= 1.0min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  59.3s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.8min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time= 1.1min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.7min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.8min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.7min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.9min\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  45.3s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.9min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.9min\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  47.5s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  53.4s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  59.2s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  58.7s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  59.6s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time= 1.0min\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=False, svm__tol=0.001; total time=  52.9s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time= 1.0min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  56.7s\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=0.001; total time=  10.4s\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=0.001; total time=  12.6s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.7min\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=0.001; total time=  13.1s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.8min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.9min\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=False, svm__tol=0.001; total time=  55.8s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=False, svm__tol=0.001; total time=  52.8s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  49.9s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  51.3s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=True, svm__tol=0.01; total time=  54.9s\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 4.2min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.9min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.9min\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  49.7s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  51.6s\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 4.3min\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=10, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 4.4min\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  52.6s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  56.0s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 3.7min\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 3.8min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  51.6s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=1000, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 4.1min\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.2min\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.1min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=False, svm__tol=1e-05; total time=  50.0s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time= 4.2min\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  10.9s\n",
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time=  47.6s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=10, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.1min\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  11.5s\n",
+      "[CV] END svm__C=1000, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=-1, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  10.9s\n",
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time=  42.7s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=10, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.2min\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  13.6s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  12.5s\n",
+      "[CV] END svm__C=1, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.001, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=True, svm__tol=0.001; total time=  13.1s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=10, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 4.2min\n",
+      "[CV] END svm__C=100, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=True, svm__tol=0.01; total time=  52.8s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=10, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=False, svm__tol=0.001; total time=  50.0s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n",
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END svm__C=10, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=False, svm__tol=0.001; total time=  53.5s\n",
+      "[CV] END svm__C=10, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=5000, svm__probability=True, svm__shrinking=False, svm__tol=0.001; total time=  52.9s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=True, svm__tol=1e-05; total time=  33.5s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=True, svm__tol=1e-05; total time=  35.7s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.0001; total time= 4.2min\n",
+      "[CV] END svm__C=0.01, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=scale, svm__kernel=rbf, svm__max_iter=1000, svm__probability=False, svm__shrinking=True, svm__tol=1e-05; total time=  37.2s\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 4.0min\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.0001; total time= 4.3min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  49.2s\n",
+      "[CV] END svm__C=1, svm__cache_size=600, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=True, svm__tol=0.0001; total time= 4.4min\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 4.0min\n",
+      "[CV] END svm__C=10, svm__cache_size=200, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.1, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=True, svm__tol=1e-05; total time= 4.0min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  42.8s\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovo, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=10000, svm__probability=False, svm__shrinking=True, svm__tol=0.0001; total time=  47.3s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  43.2s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  42.3s\n",
+      "[CV] END svm__C=0.01, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=1, svm__kernel=rbf, svm__max_iter=5000, svm__probability=False, svm__shrinking=False, svm__tol=0.01; total time=  41.8s\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 1.9min\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 2.0min\n",
+      "[CV] END svm__C=100, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=0.01, svm__kernel=rbf, svm__max_iter=-1, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 1.9min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 1.3min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 1.3min\n",
+      "[CV] END svm__C=0.001, svm__cache_size=400, svm__class_weight=balanced, svm__decision_function_shape=ovr, svm__gamma=auto, svm__kernel=rbf, svm__max_iter=10000, svm__probability=True, svm__shrinking=False, svm__tol=0.0001; total time= 1.1min\n",
+      "{'svm__C': 10,\n",
+      " 'svm__cache_size': 200,\n",
+      " 'svm__class_weight': 'balanced',\n",
+      " 'svm__decision_function_shape': 'ovo',\n",
+      " 'svm__gamma': 0.001,\n",
+      " 'svm__kernel': 'rbf',\n",
+      " 'svm__max_iter': 5000,\n",
+      " 'svm__probability': False,\n",
+      " 'svm__shrinking': True,\n",
+      " 'svm__tol': 0.01}\n"
+     ]
+    }
+   ],
+   "source": [
+    "params_SVM = randomSVM(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "218a7103",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jpuglia/miniconda3/envs/tesisEnv/lib/python3.10/site-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Accuracy': 0.9621974435681262,\n",
+      " 'F1': 0.961806189217868,\n",
+      " 'Precision': 0.9622014817178194,\n",
+      " 'Recall': 0.9621974435681262}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                     precision    recall  f1-score   support\n",
+      "\n",
+      "           Cellwall       0.83      0.61      0.70        31\n",
+      "        Cytoplasmic       0.98      1.00      0.99      2390\n",
+      "CytoplasmicMembrane       0.97      0.93      0.95       650\n",
+      "      Extracellular       0.90      0.89      0.90       263\n",
+      "      OuterMembrane       0.94      0.87      0.90       183\n",
+      "        Periplasmic       0.82      0.88      0.85       160\n",
+      "\n",
+      "           accuracy                           0.96      3677\n",
+      "          macro avg       0.91      0.86      0.88      3677\n",
+      "       weighted avg       0.96      0.96      0.96      3677\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['../Models/svm300.joblib']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svm_300, svm300_evaluation = train_svm('SVM ESM 300m', X, y, params)\n",
+    "dump(svm_300, '../Models/svm300.joblib')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94e718f9",
+   "metadata": {},
+   "source": [
+    "ESM 600m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f4e4353e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26babb7ae94c43759810a862e84f7895",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Cargando embeddings:   0%|          | 0/11140 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dir : str = os.path.expanduser(\"~/Documentos/Tesis/datosGenerados/esm600m/embeddings\")\n",
+    "X = load_emb(path = dir, acc = acc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "918622f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Accuracy': 0.9496872450367148,\n",
+      " 'F1': 0.9489932463603546,\n",
+      " 'Precision': 0.9503881906612863,\n",
+      " 'Recall': 0.9496872450367148}\n",
+      "                     precision    recall  f1-score   support\n",
+      "\n",
+      "           Cellwall       0.78      0.58      0.67        31\n",
+      "        Cytoplasmic       0.97      0.99      0.98      2390\n",
+      "CytoplasmicMembrane       0.97      0.86      0.92       650\n",
+      "      Extracellular       0.87      0.90      0.89       263\n",
+      "      OuterMembrane       0.96      0.86      0.91       183\n",
+      "        Periplasmic       0.79      0.86      0.82       160\n",
+      "\n",
+      "           accuracy                           0.95      3677\n",
+      "          macro avg       0.89      0.85      0.86      3677\n",
+      "       weighted avg       0.95      0.95      0.95      3677\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['../Models/rfESM600.joblib']"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "esm600_rf, rf600_evaluation = train_rf('Random Forest ESM 600m', X, y, paramsRF)\n",
+    "dump(esm600_rf, '../Models/rfESM600.joblib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "79f4ea9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Accuracy': 0.9600217568670112,\n",
+      " 'F1': 0.95930764868715,\n",
+      " 'Precision': 0.9593448919036438,\n",
+      " 'Recall': 0.9600217568670112}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                     precision    recall  f1-score   support\n",
+      "\n",
+      "           Cellwall       0.86      0.58      0.69        31\n",
+      "        Cytoplasmic       0.98      1.00      0.99      2390\n",
+      "CytoplasmicMembrane       0.96      0.93      0.94       650\n",
+      "      Extracellular       0.89      0.89      0.89       263\n",
+      "      OuterMembrane       0.95      0.89      0.92       183\n",
+      "        Periplasmic       0.85      0.83      0.84       160\n",
+      "\n",
+      "           accuracy                           0.96      3677\n",
+      "          macro avg       0.91      0.85      0.88      3677\n",
+      "       weighted avg       0.96      0.96      0.96      3677\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['../Models/svmESM600.joblib']"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svm_600, svm600_evaluation = train_svm('SVM ESM 600m', X, y, params = params_SVM)\n",
+    "dump(svm_600, '../Models/svmESM600.joblib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8d0fe239",
+   "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>metric</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest Prost T5</td>\n",
+       "      <td>Accuracy</td>\n",
+       "      <td>92.276312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SVM Prost T5</td>\n",
+       "      <td>Accuracy</td>\n",
+       "      <td>95.866195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Random Forest ESM 300m</td>\n",
+       "      <td>Accuracy</td>\n",
+       "      <td>94.642371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>SVM ESM 300m</td>\n",
+       "      <td>Accuracy</td>\n",
+       "      <td>96.219744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Random Forest ESM 600m</td>\n",
+       "      <td>Accuracy</td>\n",
+       "      <td>94.968725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>SVM ESM 600m</td>\n",
+       "      <td>Accuracy</td>\n",
+       "      <td>96.002176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Random Forest Prost T5</td>\n",
+       "      <td>Recall</td>\n",
+       "      <td>92.276312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>SVM Prost T5</td>\n",
+       "      <td>Recall</td>\n",
+       "      <td>95.866195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Random Forest ESM 300m</td>\n",
+       "      <td>Recall</td>\n",
+       "      <td>94.642371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>SVM ESM 300m</td>\n",
+       "      <td>Recall</td>\n",
+       "      <td>96.219744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Random Forest ESM 600m</td>\n",
+       "      <td>Recall</td>\n",
+       "      <td>94.968725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>SVM ESM 600m</td>\n",
+       "      <td>Recall</td>\n",
+       "      <td>96.002176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Random Forest Prost T5</td>\n",
+       "      <td>Precision</td>\n",
+       "      <td>93.110101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>SVM Prost T5</td>\n",
+       "      <td>Precision</td>\n",
+       "      <td>95.841479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Random Forest ESM 300m</td>\n",
+       "      <td>Precision</td>\n",
+       "      <td>94.762263</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>SVM ESM 300m</td>\n",
+       "      <td>Precision</td>\n",
+       "      <td>96.220148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Random Forest ESM 600m</td>\n",
+       "      <td>Precision</td>\n",
+       "      <td>95.038819</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>SVM ESM 600m</td>\n",
+       "      <td>Precision</td>\n",
+       "      <td>95.934489</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Random Forest Prost T5</td>\n",
+       "      <td>F1</td>\n",
+       "      <td>92.526169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>SVM Prost T5</td>\n",
+       "      <td>F1</td>\n",
+       "      <td>95.815243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Random Forest ESM 300m</td>\n",
+       "      <td>F1</td>\n",
+       "      <td>94.589125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>SVM ESM 300m</td>\n",
+       "      <td>F1</td>\n",
+       "      <td>96.180619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Random Forest ESM 600m</td>\n",
+       "      <td>F1</td>\n",
+       "      <td>94.899325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>SVM ESM 600m</td>\n",
+       "      <td>F1</td>\n",
+       "      <td>95.930765</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     model     metric      value\n",
+       "0   Random Forest Prost T5   Accuracy  92.276312\n",
+       "1             SVM Prost T5   Accuracy  95.866195\n",
+       "2   Random Forest ESM 300m   Accuracy  94.642371\n",
+       "3             SVM ESM 300m   Accuracy  96.219744\n",
+       "4   Random Forest ESM 600m   Accuracy  94.968725\n",
+       "5             SVM ESM 600m   Accuracy  96.002176\n",
+       "6   Random Forest Prost T5     Recall  92.276312\n",
+       "7             SVM Prost T5     Recall  95.866195\n",
+       "8   Random Forest ESM 300m     Recall  94.642371\n",
+       "9             SVM ESM 300m     Recall  96.219744\n",
+       "10  Random Forest ESM 600m     Recall  94.968725\n",
+       "11            SVM ESM 600m     Recall  96.002176\n",
+       "12  Random Forest Prost T5  Precision  93.110101\n",
+       "13            SVM Prost T5  Precision  95.841479\n",
+       "14  Random Forest ESM 300m  Precision  94.762263\n",
+       "15            SVM ESM 300m  Precision  96.220148\n",
+       "16  Random Forest ESM 600m  Precision  95.038819\n",
+       "17            SVM ESM 600m  Precision  95.934489\n",
+       "18  Random Forest Prost T5         F1  92.526169\n",
+       "19            SVM Prost T5         F1  95.815243\n",
+       "20  Random Forest ESM 300m         F1  94.589125\n",
+       "21            SVM ESM 300m         F1  96.180619\n",
+       "22  Random Forest ESM 600m         F1  94.899325\n",
+       "23            SVM ESM 600m         F1  95.930765"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = {\n",
+    "    'Random Forest Prost T5' : prostRF_evaluation,\n",
+    "    'SVM Prost T5' : prostSVM_evaluation,\n",
+    "    'Random Forest ESM 300m' : esm300RF_evaluation,\n",
+    "    'SVM ESM 300m' : svm300_evaluation,\n",
+    "    'Random Forest ESM 600m' : rf600_evaluation,\n",
+    "    'SVM ESM 600m' : svm600_evaluation\n",
+    "}\n",
+    "\n",
+    "results_df = pd.DataFrame(results).T.rename_axis('model').reset_index()\n",
+    "\n",
+    "plot_df = results_df.melt(id_vars='model', var_name='metric', value_name='value')\n",
+    "plot_df['value'] = plot_df['value']*100\n",
+    "\n",
+    "plot_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "34cd39ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UqZOf/vSntZZ/8L2dPHnycrNzr6rV2c/ddtst8+fPX+6XNMtmOt9tt90+1hjWlG7duqVdu3a55ZZbau3LggUL8sc//rFmRvOV+frXv56iKPLaa6+t8M+yR48eyz2noqIi2223XS655JJssskmefLJJ2seq1+//kdeQdKqVascddRR+eY3v5kpU6Ysdwk8AKvPmW4AVss//vGP5WZrTpKtttoqm222Wc39o48+OhdeeGEOO+ywNGzYcLlZvPfee+/86le/ymGHHZbjjz8+b7/9di6++OIVntH7oNatW6d///45//zz07x583Ts2DF33313br/99lrrNW3aNLvuumsuuuiibLrpptlyyy1z33335brrrssmm2xSa91zzz03//d//5ddd901P/7xj9OjR4/Mnj07f/3rX/ODH/wgW2+99QrHcv7552f33XdPv379ctppp6VevXoZOXJk/vGPf+TWW29dpRmwP8pGG22Un//85zn22GPzjW98I8cdd1xmz56doUOHLnfZ9aGHHpr//u//zte+9rV873vfy5e//OVUVlbm1VdfzT333JP99tsv3/jGN1Z7DF27ds3xxx+fkSNH5oEHHsguu+ySr3/96/n5z3+eIUOGpE+fPpkyZUrOPffcdOrUaYV/R9bkfh5xxBG58sorc+SRR+bFF19Mjx498sADD+S8887L1772tfTv33+1X39N2mijjTJ8+PAcfvjh+frXv54TTjghVVVVueiiizJ79uxccMEFH7mNr3zlKzn++OMzaNCgPP7449l1113TqFGjvPHGG3nggQfSo0ePfOc738kdd9yRkSNHZuDAgencuXOKosjtt9+e2bNnZ/fdd6/ZXo8ePXLvvffmz3/+c9q0aZMmTZqkW7du6d27d77+9a+nZ8+ead68eZ599tncdNNNq/SLAQBWwdqawQ2A9cvKZmtOUvzmN79Z7jk777xzkaQ4/PDDV7jNUaNGFd26dSvq169fdO7cuTj//POL6667rkhSTJ8+vWa9D85eXhRF8cYbbxQHHnhg0aJFi6JZs2bFt771rZpZnN8/u/irr75aHHDAAUXz5s2LJk2aFHvttVfxj3/8o+jYseNyMzm/8sorxdFHH120bt26qKysLNq2bVscfPDBxb///e+iKD58dvQJEyYUX/3qV4tGjRoVDRs2LHbcccfiz3/+8wrfv8cee6zW8g+biX1Frr322qJr165FvXr1is997nPFqFGjiiOPPLLWrN5FURTV1dXFxRdfXGy33XZFgwYNisaNGxdbb711ccIJJxTPP//8Sl9j2UzaM2fOXO6xf//730Xjxo2Lfv36FUVRFFVVVcVpp51WtGvXrmjQoEHRq1evYsyYMcuNadn7dtFFFy23zSTFkCFDPtZ+vv3228WJJ55YtGnTpqhbt27RsWPH4qyzzirefffd5V7jpJNOqrXsw8a07M/jD3/4w0rfpw/78/ygMWPGFL179y4aNGhQNGrUqNhtt92KBx98sNY6K3vPi+K946R37941f7+22mqr4ogjjigef/zxoiiK4l//+lfxzW9+s9hqq62Khg0bFs2aNSu+/OUvFzfccEOt7UycOLH4yle+Umy88cZFkppj6kc/+lGxww47FM2bN685Fr///e8Xb7311kr3DYBVU1EU77vmCQAAAFhjfKYbAAAASiK6AQAAoCSiGwAAAEqyVqP7/vvvzz777JO2bdumoqJiua/9KIoiQ4cOTdu2bdOwYcP07ds3zzzzTK11qqqqcvLJJ2fTTTdNo0aNsu+++67296wCAABAGdZqdC9YsCDbbbddrrjiihU+Pnz48PzqV7/KFVdckcceeyytW7fO7rvvnnnz5tWsc+qpp2b06NH53e9+lwceeCDz58/P17/+9eW+fxUAAAA+bevM7OUVFRUZPXp0Bg4cmOS9s9xt27bNqaeemjPPPDPJe2e1W7VqlQsvvDAnnHBC5syZk8022yw33XRTzfe/vv766+nQoUP+8pe/ZM8991xbuwMAAACpu7YH8GGmT5+eGTNmZI899qhZVr9+/fTp0ycPPfRQTjjhhDzxxBOprq6utU7btm3z+c9/Pg899NCHRndVVVWqqqpq7i9dujTvvPNOWrZsmYqKivJ2CgAAgM+Eoigyb968tG3bNhtt9OEXka+z0T1jxowkSatWrWotb9WqVV566aWaderVq5fmzZsvt86y56/I+eefn3POOWcNjxgAAIANzSuvvJL27dt/6OPrbHQv88Ezz0VRfOTZ6I9a56yzzsoPfvCDmvtz5szJFltskVdeeSVNmzb9ZAMGAADgM2/u3Lnp0KFDmjRpstL11tnobt26dZL3zma3adOmZvmbb75Zc/a7devWWbRoUWbNmlXrbPebb76ZnXfe+UO3Xb9+/dSvX3+55U2bNhXdAAAArLKPOim8zn5Pd6dOndK6deuMHz++ZtmiRYty33331QT1F7/4xVRWVtZa54033sg//vGPlUY3AAAAfBrW6pnu+fPnZ+rUqTX3p0+fnokTJ6ZFixbZYostcuqpp+a8885L165d07Vr15x33nnZeOONc9hhhyVJmjVrlmOOOSY//OEP07Jly7Ro0SKnnXZaevTokf79+6+t3QIAAIAkazm6H3/88fTr16/m/rLPWR955JG54YYbcsYZZ2ThwoUZPHhwZs2ald69e+fOO++sdc38JZdckrp16+bggw/OwoULs9tuu+WGG25InTp1PvX9AQAAgPdbZ76ne22aO3dumjVrljlz5vhMNwAAAB9pVTtynf1MNwAAAKzvRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQEnW+eieN29eTj311HTs2DENGzbMzjvvnMcee6zm8fnz5+e73/1u2rdvn4YNG6Z79+656qqr1uKIAQAA4D111/YAPsqxxx6bf/zjH7npppvStm3b3Hzzzenfv3/++c9/pl27dvn+97+fe+65JzfffHO23HLL3HnnnRk8eHDatm2b/fbbb20PHwAAgA3YOn2me+HChfnjH/+Y4cOHZ9ddd02XLl0ydOjQdOrUqeZs9sMPP5wjjzwyffv2zZZbbpnjjz8+2223XR5//PG1PHoAAAA2dOt0dC9evDhLlixJgwYNai1v2LBhHnjggSTJLrvskrFjx+a1115LURS555578txzz2XPPff80O1WVVVl7ty5tW4AAACwpq3T0d2kSZPstNNO+fnPf57XX389S5Ysyc0335xHHnkkb7zxRpLksssuyzbbbJP27dunXr162WuvvTJy5MjssssuH7rd888/P82aNau5dejQ4dPaJQAAADYg63R0J8lNN92UoijSrl271K9fP5dddlkOO+yw1KlTJ8l70f33v/89Y8eOzRNPPJFf/vKXGTx4cO66664P3eZZZ52VOXPm1NxeeeWVT2t3AAAA2IBUFEVRrO1BrIoFCxZk7ty5adOmTQ455JDMnz8///M//5NmzZpl9OjR2XvvvWvWPfbYY/Pqq6/mr3/96ypte+7cuWnWrFnmzJmTpk2blrULAAAAfEasakeu82e6l2nUqFHatGmTWbNmZdy4cdlvv/1SXV2d6urqbLRR7d2oU6dOli5dupZGCgAAAO9Z578ybNy4cSmKIt26dcvUqVNz+umnp1u3bhk0aFAqKyvTp0+fnH766WnYsGE6duyY++67L7/97W/zq1/9am0PHQAAgA3cOh/dc+bMyVlnnZVXX301LVq0yAEHHJBf/OIXqaysTJL87ne/y1lnnZXDDz8877zzTjp27Jhf/OIXOfHEE9fyyAEAANjQrTef6S6Tz3QDAACwOj5zn+kGAACA9Y3oBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkqzz0T1v3ryceuqp6dixYxo2bJidd945jz32WK11nn322ey7775p1qxZmjRpkh133DEvv/zyWhoxAAAAvGedj+5jjz0248ePz0033ZSnn346e+yxR/r375/XXnstSTJt2rTssssu2XrrrXPvvfdm0qRJ+dnPfpYGDRqs5ZEDAACwoasoiqJY24P4MAsXLkyTJk3ypz/9KXvvvXfN8u233z5f//rXM2zYsBx66KGprKzMTTfdtMrbraqqSlVVVc39uXPnpkOHDpkzZ06aNm26RvcBAACAz565c+emWbNmH9mR6/SZ7sWLF2fJkiXLnbVu2LBhHnjggSxdujT/+7//m8997nPZc889s/nmm6d3794ZM2bMSrd7/vnnp1mzZjW3Dh06lLgXAAAAbKjW6ehu0qRJdtppp/z85z/P66+/niVLluTmm2/OI488kjfeeCNvvvlm5s+fnwsuuCB77bVX7rzzznzjG9/I/vvvn/vuu+9Dt3vWWWdlzpw5NbdXXnnlU9wrAAAANhR11/YAPspNN92Uo48+Ou3atUudOnXSq1evHHbYYXnyySezdOnSJMl+++2X73//+0neu/T8oYceytVXX50+ffqscJv169dP/fr1P7V9AAAAYMO0Tp/pTpKtttoq9913X+bPn59XXnkljz76aKqrq9OpU6dsuummqVu3brbZZptaz+nevbvZywEAAFjr1vnoXqZRo0Zp06ZNZs2alXHjxmW//fZLvXr18qUvfSlTpkypte5zzz2Xjh07rqWRAgAAwHvW+cvLx40bl6Io0q1bt0ydOjWnn356unXrlkGDBiVJTj/99BxyyCHZdddd069fv/z1r3/Nn//859x7771rd+AAAABs8Nb5M91z5szJSSedlK233jpHHHFEdtlll9x5552prKxMknzjG9/I1VdfneHDh6dHjx659tpr88c//jG77LLLWh45AAAAG7p1+nu6Py2r+v1qAAAAkHxGvqcbAAAA1meiGwAAAEoiugEAAKAkohsAAABKIroBAACgJKIbAAAASiK6AQAAoCSiGwAAAEoiugEAAKAkohsAAABKIroBAACgJKIbAAAASiK6AQAAoCSiGwAAAEoiugEAAKAkohsAAABKIroBAACgJKIbAAAASiK6AQAAoCSiGwAAAEoiugEAAKAkohsAAABKIroBAACgJKIbAAAASiK6AQAAoCSiGwAAAEoiugEAAKAkohsAAABKIroBAACgJKIbAAAASiK6AQAAoCSiGwAAAEoiugEAAKAkohsAAABKIroBAACgJKIbAAAASiK6AQAAoCSiGwAAAEoiugEAAKAkohsAAABKIroBAACgJKIbAAAASiK6AQAAoCSiGwAAAEoiugEAAKAkohsAAABKIroBAACgJKIbAAAASiK6AQAAoCSiGwAAAEoiugEAAKAkohsAAABKIroBAACgJKIbAAAASiK6AQAAoCSiGwAAAErysaN76tSpGTduXBYuXJgkKYpijQ0KAAAAPgtWO7rffvvt9O/fP5/73Ofyta99LW+88UaS5Nhjj80Pf/jDNT5AAAAAWF+tdnR///vfT926dfPyyy9n4403rll+yCGH5K9//esaHRwAABueadOmZcCAAWnevHnatWuX4cOH1zx21FFHpV69emncuHHN7eGHH/7QbVVXV+e73/1uWrRokRYtWuTkk0/O4sWLax4fMWJENt9883Tp0iX3339/zfLZs2dn2223zcyZM8vZSWCDsdrRfeedd+bCCy9M+/btay3v2rVrXnrppTU2MAAANjxLlizJvvvum169euXNN9/M3/72t1xxxRW55ZZbatYZPHhw5s+fX3PbaaedPnR7w4YNywMPPJBnnnkmzzzzTCZMmJDzzjsvSTJjxowMGzYskyZNyogRI3LSSSfVPO/MM8/Maaedls0226y8nQU2CKsd3QsWLKh1hnuZt956K/Xr118jgwIAYMM0ZcqUTJkyJUOGDEllZWW6deuWY445Jr/+9a8/1vZGjRqVn/70p2nTpk3atGmTn/zkJ7nuuuuSJC+99FK6du2aNm3apH///pk2bVqS5MEHH8zUqVMzaNCgNbZfwIZrtaN71113zW9/+9ua+xUVFVm6dGkuuuii9OvXb40ODgCADcvSpUuT1J6kd+nSpZk8eXLN/d/+9rdp0aJFtt122/zyl7+sec4HzZo1K6+++mq23377mmXbb799Xn755cyZMyddu3bN9OnT8+qrr2b8+PHp0aNHqqurc8opp+Sqq64qZweBDU7d1X3CRRddlL59++bxxx/PokWLcsYZZ+SZZ57JO++8kwcffLCMMQIAsIHo1q1bOnXqlLPPPjvnnntupk6dmlGjRmXu3LlJklNOOSUXXXRRWrRokcceeywHH3xwNtpoo3z/+99fblvz589PkmyyySY1y5b997x589K+fftcfvnlGThwYJo2bZprr702F154YQYOHJjq6uoMGDAgCxcuzPe+97184xvfKH3fgc+miuJjfNfXjBkzctVVV+WJJ57I0qVL06tXr5x00klp06ZNGWMs3dy5c9OsWbPMmTMnTZs2XdvDAQDYoD377LM59dRT8+STT6Zdu3bZd999c8011+Tf//73cuuOHDkyv/3tb/P3v/99ucdmzZqVFi1aZOrUqdlqq62SvPe1t127ds3s2bPTrFmzWus///zzOfTQQ/Pwww9n1113zfDhw9OjR4/07NkzkydPTvPmzcvZYWC9tKod+bG+p7t169Y555xzcscdd+Qvf/lLhg0btt4GN6wtZmaFdYfjEdYt3bt3z7hx4zJz5sxMnDgxVVVV6dOnzwrX3WijD//nbPPmzdO+fftMnDixZtnEiRPToUOH5YI7eW+CtksvvTT16tXLpEmT0rt375ptPP/88594v4AN02pfXv7+fyCsyK677vqxBwMbimUzsw4cODBjx47NCy+8kN133z3t27fPYYcdluS9H/wjRoxYpe29f2bWJBkwYEDOO++8nH322TUzsz799NN54oknctJJJ+Xpp59OYmZWSByPsC6aPHlyttpqq1RWVuaOO+7IqFGjcvfddydJbrvttuy1115p0qRJnnjiiVxwwQW1Zh3/oEGDBuUXv/hFvvKVryRJzjvvvBx77LHLrXfjjTemc+fO2WWXXZIknTt3zvjx49OrV68899xz6dixYwl7CmwQitVUUVGx3G2jjTaqua2P5syZUyQp5syZs7aHwgbimWeeKerUqVNUVVXVLBs6dGjRp0+foiiK4sgjjyy+973vrfL22rdvX/zhD3+ouX/bbbcVW2yxRVEURfH3v/+92HHHHYuiKIqFCxcWDRs2LIqiKB544IHiq1/96ifcE1j/OR5h3fOTn/ykaN68ebHxxhsXO+20U/HAAw/UPPZf//VfRbNmzYpGjRoVn/vc54oLL7ywWLJkSc3jJ5xwQnHCCSfU3F+0aFExePDgYpNNNik22WST4qSTTiqqq6trvd5bb71VfP7zny9mzZpVs+yee+4pOnXqVGy22WbFZZddVt7OAuutVe3I1T7TPWvWrFr3q6ur89RTT+VnP/tZfvGLX6yhXwXAZ9uqzsz629/+Nm3atMnRRx+d73//+yu8hG51ZmZ96qmnas3Meuutt5a3k7CecDzCumfYsGEZNmzYCh/7qKsur7766lr3Kysrc+WVV+bKK6/80Oe0bNmy5qqTZfr27ZsXXnhhFUcM8OFW+zPdzZo1q3XbdNNNs/vuu2f48OE544wzyhgjfOa8f2bWqqqqPPPMM8vNzDplypTMnDkz1113XS699NJceumlK9zWR83M2qJFi5qZWS+55JIVzszat2/fjB49utR9hnWV4xEAKNPHmr18RZ599tl86UtfqvkHx/rE7OWsDWZmhXWH4xHK9ZXLv7K2h/Cpe/BkX6XLZ8O0adPy3e9+N3//+9+z8cYb53vf+95yJ1sXLlyYHj165K233srs2bM/9rZGjBiR8847L02bNs2oUaNq5gubPXt2vvKVr+Tee+9dp+Y+KW328smTJ9e6TZo0KX/961/zne98J9ttt90nGjRsSMzMCusOxyMALG/ZZKO9evXKm2++mb/97W+54oorcsstt9Ra7+yzz0779u0/0baWTTY6adKkjBgxotYEiev7ZKOrHd3bb799vvCFL2T77bev+e+vfe1rWbRoUa677royxgifSZMnT86CBQuyaNGi3H777Rk1alR++tOfJnlvZta5c+emKIo8/vjjueCCC3LAAQd86LaWzcw6Y8aMzJgxY7VnZn399dfNzMoGzfEIAMubMmVKpkyZkiFDhqSysjLdunXLMccck1//+tc16zz55JP5y1/+krPOOusTbeull15K165d06ZNm/Tv3z/Tpk1Lkjz44IOZOnVqBg0aVN6Olmy1J1KbPn16rfsbbbRRNttsszRo0GCNDQo2BLfddltGjhyZqqqqbLfddhkzZkx69uyZJLniiity/PHHZ/HixWnXrl0GDx6cH/7whzXPPfHEE5P8v8lifvazn+Xtt99O9+7dkySHH354fvzjH9d6vbfffjsXX3xxJkyYULPsyiuvzNFHH5358+dn6NChadWqVan7DOsqxyMALO+jJhtdvHhxjjvuuJVOVLiq2/osTza6xj7TvT7zmW4AgPL4TDesn6qrq7PNNttk//33z7nnnpupU6dmr732yhtvvJHFixfnwgsvzL/+9a9cf/31uffeezNw4MAP/Uz3R20rSf7whz/kwgsvTNOmTXPppZfmT3/6U4qiyP7775/TTjstCxcuzPe+97184xvf+BTfhQ+3qh25Sme6L7vsslV+4VNOOWWV1wUAAGDdVFlZmbFjx+bUU09N+/bt065duwwaNCjXXHNNpk2bliuvvDJPPfXUJ97WMgcddFAOOuigJO9NNjp69OgVTjbat2/f9Wqy0VWK7ksuuWSVNlZRUSG62WD5LT6sOxyPALBmLJtsdJkzzzwzffr0yYQJEzJz5sxsu+22SZJFixZl7ty5ad26dcaOHZsvf/nLq7ytFVnRZKP169evmWx0RdtfV61SdH/wc9wAAAB89k2ePDlbbbVVKisrc8cdd2TUqFG5++6707Vr1+y111416z300EMZNGhQJk6cmJYtW67Wtj7owyYb7dWr13o52ehqT6QGAADAhmFlk402bNiwZr0WLVqkoqIirVu3rln2wclGV7atZT6Lk41+rInUXn311YwdOzYvv/xyFi1aVOuxX/3qV2tscJ8WE6mxJricFdYdjkdYtzgmgc+iNTqR2vvdfffd2XfffdOpU6dMmTIln//85/Piiy+mKIr06tXrEw0aAAAAPks2Wt0nnHXWWfnhD3+Yf/zjH2nQoEH++Mc/5pVXXkmfPn1qZpoDAGDNmDZtWgYMGJDmzZunXbt2GT58+HLrLFy4MF26dMkmm2yy0m299tprGThwYFq2bJlNN900Bx10UP7973/XPD5ixIhsvvnm6dKlS+6///6a5bNnz862226bmTNnrrH9AthQrPaZ7meffbbmy8nr1q2bhQsXpnHjxjn33HOz33775Tvf+c4aHyQAwIZoyZIl2XfffTNw4MCMHTs2L7zwQnbfffe0b98+hx12WM16Z599dtq3b5+33nprpdsbPHhwKioq8tJLL6Uoihx++OH53ve+l9/97neZMWNGhg0blqeffjpPPPFETjrppDz99NNJ3pth+LTTTstmm21W6v4C5fNxj0/fap/pbtSoUaqqqpIkbdu2zbRp02oe+6j/0QMAsOqmTJmSKVOmZMiQIamsrEy3bt1yzDHH5Ne//nXNOk8++WT+8pe/5KyzzvrI7U2fPj0HH3xwGjdunCZNmuSQQw7JP/7xjyTJSy+9lK5du6ZNmzbp379/zb/xHnzwwUydOjWDBg0qZycBPuNW+0z3jjvumAcffDDbbLNN9t577/zwhz/M008/ndtvvz077rhjGWMEANggLV26NEny/nlvly5dmsmTJydJFi9enOOOOy5XXnnlKm3vBz/4Qf7whz9k7733TlEUufXWW7P33nsnSbp27Zrp06fn1VdfzVNPPZUePXqkuro6p5xySs1VjgCsvtU+0/2rX/0qvXv3TpIMHTo0u+++e37/+9+nY8eOue6669b4AAEANlTdunVLp06dcvbZZ6eqqirPPPNMRo0alblz5yZJfvnLX6Znz57p27fvKm3vK1/5St588800b948LVq0yDvvvJOf/vSnSd77up/LL788AwcOzCWXXJJrr702F154YQYOHJjq6uoMGDAgffv2zejRo8vaXYDPpNU+0/3zn/883/rWt1IURTbeeOOMHDmyjHEBAGzwKisrM3bs2Jx66qlp37592rVrl0GDBuWaa67JtGnTcuWVV+app55apW0tXbo0u+++ew4++OCMHz8+yXsnUPbcc8889NBDSZKDDjqoZmLc559/PqNHj87DDz+cXXfdNcOHD0+PHj1qIr958+bl7DTAZ8xqn+l+++23s/fee6d9+/b54Q9/mIkTJ5YwLAAAkqR79+4ZN25cZs6cmYkTJ6aqqip9+vTJhAkTMnPmzGy77bZp3bp19t9//8ydOzetW7fOo48+utx23nnnnbz00ks55ZRTsvHGG2fjjTfOySefnIcffniF8/IMHjw4l156aerVq5dJkyald+/ead68edq3b5/nn3/+09h1gM+E1Y7usWPHZsaMGRkyZEieeOKJfPGLX8w222yT8847Ly+++GIJQwQA2HBNnjw5CxYsyKJFi3L77bdn1KhR+elPf5pDDjkk06dPz8SJEzNx4sRce+21adKkSSZOnJgvfOELy21n0003TZcuXXLllVfm3Xffzbvvvpsrr7wy7du3z6abblpr3RtvvDGdO3fOLrvskiTp3Llzxo8fn9dffz3PPfdcOnbs+KnsO8BnwWpfXp4km2yySY4//vgcf/zxefXVV3Prrbdm1KhROfvss7N48eI1PUYAgA3WbbfdlpEjR6aqqirbbbddxowZk549eyZJGjZsWLNeixYtUlFRkdatW9csO/HEE5MkV199dZLkT3/6U77//e+nXbt2Wbp0ab7whS9k7NixtV7v7bffzsUXX5wJEybULLvyyitz9NFHZ/78+Rk6dGhatWpV2v4CfNZ8rOheprq6Oo8//ngeeeSRvPjii/4HDACwhg0bNizDhg37yPX69u2b2bNn11q2LLaX2WabbTJu3LiVbqdly5Y138/9/m2/8MILqzZgAGpZ7cvLk+See+7Jcccdl1atWuXII49MkyZN8uc//zmvvPLKmh4fG4hp06ZlwIABad68edq1a5fhw4cvt87ChQvTpUuXbLLJJh+5vbFjx2b77bdPo0aN0rZt21r/6BgxYkQ233zzdOnSJffff3/N8tmzZ2fbbbfNzJkz18g+wfrK8QgAsOas9pnu9u3b5+23386ee+6Za665Jvvss08aNGhQxtjYQCxZsiT77rtvBg4cmLFjx+aFF17I7rvvnvbt2+ewww6rWe/ss89O+/btVzjZy/v99a9/zeDBg3PzzTfnv/7rvzJ37tz8+9//TpLMmDEjw4YNy9NPP50nnngiJ510Us1v888888ycdtpp2WyzzcrbWVjHOR4BANas1Y7us88+OwcddJCviWCNmTJlSqZMmZIhQ4aksrIy3bp1yzHHHJNf//rXNf/If/LJJ/OXv/wlv/rVr3LIIYesdHs/+9nPcvbZZ9d8Z2nz5s1r/r6+9NJL6dq1a9q0aZP+/fvn4IMPTpI8+OCDmTp1aq655prydhTWA45HKN/L5/ZY20P49DVvurZHALDWrPbl5ccff7zgZo1aunRpkqQoilrLJk+enCRZvHhxjjvuuFx55ZWpX7/+Sre1YMGCPPHEE5k7d2623nrrtG7dOoccckhmzJiRJOnatWumT5+eV199NePHj0+PHj1SXV2dU045JVdddVVJewjrD8cjAMCa9bE+0w1rUrdu3dKpU6ecffbZqaqqyjPPPJNRo0Zl7ty5SZJf/vKX6dmzZ82ZspWZNWtWiqLITTfdlHHjxmXq1KmprKzMt7/97STvzex6+eWXZ+DAgbnkkkty7bXX5sILL8zAgQNTXV2dAQMGpG/fvhk9enSZuwzrLMcjAHw4857wcXyi2cthTaisrMzYsWNz6qmnpn379mnXrl0GDRqUa665JtOmTcuVV16Zp556apW21bhx4yTJKaecUvMdouecc066du2aBQsWpFGjRjnooINy0EEHJUmef/75jB49Og8//HB23XXXDB8+PD169KiJCld1sKFxPALAipn3hI/LmW7WCd27d8+4ceMyc+bMTJw4MVVVVenTp08mTJiQmTNnZtttt03r1q2z//77Z+7cuWndunUeffTR5bazySabZIsttkhFRcVyj73/ctllBg8enEsvvTT16tXLpEmT0rt37zRv3jzt27fP888/X8q+wrrO8QgAy1vZvCfLLJv35KyzzvrI7b1/3pM6deqkefPm2XrrrZMsP+/JtGnTkvy/eU8GDRpUzk5SCme6WSdMnjw5W221VSorK3PHHXdk1KhRufvuu9O1a9fstddeNes99NBDGTRoUCZOnJiWLVuucFvHH398Lrvssuy5555p0aJFzj333Oy22241Z92WufHGG9O5c+fssssuSZLOnTtn/Pjx6dWrV5577rmaM3OwoXE8AsDyVmfek4+ybN6TQw45JFtvvXVmz56dPn365NJLL03r1q1rzXvy1FNP1Zr35NZbby1nBymN6GadcNttt2XkyJGpqqrKdtttlzFjxqRnz55JkoYNG9as16JFi1RUVKR169Y1y0488cQkqfkMzI9+9KO888472W677ZIk/fr1y0033VTr9d5+++1cfPHFmTBhQs2yK6+8MkcffXTmz5+foUOHplWrVuXsLKzjHI8AsLz3z3ty7rnnZurUqR8678m999670m19cN6Tli1b5sQTT8y3v/3tjB8/vta8J02bNl3hvCcLFy7M9773vXzjG9/4FPaeT6KiWNE1fhuYuXPnplmzZpkzZ06aNvWVFnw8X7n8K2t7CJ+6B09+cG0PAVbI8ci6bEP8yrBvboBfGeaY/Gx69tlnc+qpp+bJJ59Mu3btsu++++aaa67JQw89lH79+uWpp55Ky5Ytc++992bgwIGZPXv2Crcze/bsNG/ePNdee22OOeaYJO9N0ta1a9fMmzcvjRo1qrX+888/n0MPPXSF855Mnjx5teY98TNyzVnVjvSZbgAAgFVg3hM+DpeXU4oN8bf42QB/i8/6wfEIAGuGeU/4OEQ3AADAKjDvCR+Hz3THZ7rLsCGeWfN5NdZVjscNg+Nx/eGY3DA4JllX+Uz3muMz3QAAALCWiW4AAAAoic90AwAAG6QN8eMeJhv99DnTDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUJJ1PrrnzZuXU089NR07dkzDhg2z884757HHHlvhuieccEIqKioyYsSIT3eQAAAAsALrfHQfe+yxGT9+fG666aY8/fTT2WOPPdK/f/+89tprtdYbM2ZMHnnkkbRt23YtjRQAAABqW6eje+HChfnjH/+Y4cOHZ9ddd02XLl0ydOjQdOrUKVdddVXNeq+99lq++93v5r//+79TWVm5FkcMAAAA/0/dtT2AlVm8eHGWLFmSBg0a1FresGHDPPDAA0mSpUuX5tvf/nZOP/30bLvttqu03aqqqlRVVdXcnzt37pobNAAAAPz/1ukz3U2aNMlOO+2Un//853n99dezZMmS3HzzzXnkkUfyxhtvJEkuvPDC1K1bN6eccsoqb/f8889Ps2bNam4dOnQoaxcAAADYgK3T0Z0kN910U4qiSLt27VK/fv1cdtllOeyww1KnTp088cQTufTSS3PDDTekoqJilbd51llnZc6cOTW3V155pcQ9AAAAYEO1zkf3Vlttlfvuuy/z58/PK6+8kkcffTTV1dXp1KlTJkyYkDfffDNbbLFF6tatm7p16+all17KD3/4w2y55ZYfus369eunadOmtW4AAACwpq3Tn+l+v0aNGqVRo0aZNWtWxo0bl+HDh+eAAw5I//79a62355575tvf/nYGDRq0lkYKAAAA71nno3vcuHEpiiLdunXL1KlTc/rpp6dbt24ZNGhQKisr07Jly1rrV1ZWpnXr1unWrdtaGjEAAAC8Z52/vHzOnDk56aSTsvXWW+eII47ILrvskjvvvNNXgwEAALDOW+fPdB988ME5+OCDV3n9F198sbzBAAAAwGpY5890AwAAwPpKdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwDrjWnTpmXAgAFp3rx52rVrl+HDh9c8dvLJJ6dDhw5p2rRp2rVrl1NPPTWLFi360G1dccUV2WGHHVK/fv0MHDhwucdHjBiRzTffPF26dMn9999fs3z27NnZdtttM3PmzDW6bwB8NoluAGC9sGTJkuy7777p1atX3nzzzfztb3/LFVdckVtuuSVJMnjw4PzrX//K3LlzM3HixEyaNKlWlH9Q27Zt89Of/jTHHXfcco/NmDEjw4YNy6RJkzJixIicdNJJNY+deeaZOe2007LZZput+Z0E4DNHdAMA64UpU6ZkypQpGTJkSCorK9OtW7ccc8wx+fWvf50k6d69exo1alSz/kYbbZTnn3/+Q7e3//77Z+DAgdl0002Xe+yll15K165d06ZNm/Tv3z/Tpk1Lkjz44IOZOnVqBg0atIb3DoDPKtENAKwXli5dmiQpiqLWssmTJ9fcv+CCC9KkSZNsvvnmmTRpUk4++eSP9Vpdu3bN9OnT8+qrr2b8+PHp0aNHqqurc8opp+Sqq676ZDsCwAZFdAMA64Vu3bqlU6dOOfvss1NVVZVnnnkmo0aNyty5c2vW+dGPfpR58+bln//8Z0488cS0bt36Y71WixYtcvnll2fgwIG55JJLcu211+bCCy/MwIEDU11dnQEDBqRv374ZPXr0mto9AD6jRDcAsF6orKzM2LFjM3HixLRv3z6HH354Bg0alJYtWy63bvfu3bPddtvlqKOO+tivd9BBB+Xxxx/P3/72tzRo0CCjR4/OmWeemWOOOSZnnXVWRo8enVNOOSWzZs36BHsFwGed6AYA1hvdu3fPuHHjMnPmzEycODFVVVXp06fPCtetrq5e6We6V8fgwYNz6aWXpl69epk0aVJ69+6d5s2bp3379mvsNQD4bBLdAMB6Y/LkyVmwYEEWLVqU22+/PaNGjcpPf/rTzJ8/P9dff31mz56doijy9NNPZ9iwYdlzzz0/dFuLFy/Ou+++m8WLF2fp0qV59913V/gVYzfeeGM6d+6cXXbZJUnSuXPnjB8/Pq+//nqee+65dOzYsbT9BWD9V3dtDwAAYFXddtttGTlyZKqqqrLddttlzJgx6dmzZxYsWJBbbrklp512WqqqqrL55pvngAMOyDnnnFPz3BNPPDFJcvXVVydJhg0bVuvxhg0bpk+fPrn33ntrlr399tu5+OKLM2HChJplV155ZY4++ujMnz8/Q4cOTatWrUreawDWZxXF+6cA3UDNnTs3zZo1y5w5c9K0adO1PZzPhJfP7bG2h/Cp+2bzDe/vzoMnP7i2h8AqcDxuGByP6w/H5IbBMbl+cDxuGMo6Hle1I11eDgAAACVxeTkAsNZ88fTfru0hfOpGN1nbIwDg0+RMNwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAH8u0adMyYMCANG/ePO3atcvw4cOTJFVVVTnuuOPSqVOnNGnSJFtvvXVGjRq10m0dddRRqVevXho3blxze/jhh2seHzFiRDbffPN06dIl999/f83y2bNnZ9ttt83MmTPL2Un4hOqu7QEAAADrnyVLlmTffffNwIEDM3bs2LzwwgvZfffd0759++y3335p06ZN7rrrrnTu3DmPPPJIBgwYkPbt22ePPfb40G0OHjw4I0aMWG75jBkzMmzYsDz99NN54oknctJJJ+Xpp59Okpx55pk57bTTstlmm5W1q/CJONMNAACstilTpmTKlCkZMmRIKisr061btxxzzDH59a9/nUaNGuXcc8/NVlttlYqKiuy4447p169fHnjggY/1Wi+99FK6du2aNm3apH///pk2bVqS5MEHH8zUqVMzaNCgNblrsEaJbgAAYLUtXbo0SVIURa1lkydPXm7dd999N48++mh69uy50m3+9re/TYsWLbLtttvml7/8Zc1rdO3aNdOnT8+rr76a8ePHp0ePHqmurs4pp5ySq666ag3uFax5ohsAAFht3bp1S6dOnXL22WenqqoqzzzzTEaNGpW5c+fWWq8oihx77LHp2rVr9t9//w/d3imnnJIpU6Zk5syZue6663LppZfm0ksvTZK0aNEil19+eQYOHJhLLrkk1157bS688MIMHDgw1dXVGTBgQPr27ZvRo0eXus/wcYhuAABgtVVWVmbs2LGZOHFi2rdvn8MPPzyDBg1Ky5Yta9YpiiLf+c53MmXKlIwZMyYbbfTh+dGrV69sttlmqVOnTnbcccf86Ec/yu9///uaxw866KA8/vjj+dvf/pYGDRpk9OjROfPMM3PMMcfkrLPOyujRo3PKKadk1qxZpe43rC7RDQAAfCzdu3fPuHHjMnPmzEycODFVVVXp06dPkveC+6STTsqjjz6aO++8M82aNVutba8s0AcPHpxLL7009erVy6RJk9K7d+80b9487du3z/PPP/+J9gnWNNENAAB8LJMnT86CBQuyaNGi3H777Rk1alR++tOfJkm++93v5sEHH8z48ePTvHnzj9zWbbfdlrlz56Yoijz++OO54IILcsABByy33o033pjOnTtnl112SZJ07tw548ePz+uvv57nnnsuHTt2XLM7CZ+QrwwDAAA+lttuuy0jR45MVVVVtttuu4wZMyY9e/bMSy+9lJEjR6Z+/fq1Ivhb3/pWrr766iTJiSeemCQ196+44oocf/zxWbx4cdq1a5fBgwfnhz/8Ya3Xe/vtt3PxxRdnwoQJNcuuvPLKHH300Zk/f36GDh2aVq1alb3bsFpENwAA8LEMGzYsw4YNW255x44da81qviLLYnuZ+++//yNfr2XLljXfz71M375988ILL6zCaGHtcHk5AAAAlMSZbgAAIF88/bdrewifutFN1vYI2BA40w0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtG9Dpo2bVoGDBiQ5s2bp127dhk+fHiSpKqqKscdd1w6deqUJk2aZOutt86oUaNWuq2TTz45HTp0SNOmTdOuXbuceuqpWbRoUc3jI0aMyOabb54uXbrk/vvvr1k+e/bsbLvttpk5c2Y5OwnrCccjAACfhOhexyxZsiT77rtvevXqlTfffDN/+9vfcsUVV+SWW27J4sWL06ZNm9x1112ZO3dubrjhhvzwhz/MnXfe+aHbGzx4cP71r39l7ty5mThxYiZNmlQTDTNmzMiwYcMyadKkjBgxIieddFLN884888ycdtpp2WyzzUrfZ1hXOR4BAPikRPc6ZsqUKZkyZUqGDBmSysrKdOvWLcccc0x+/etfp1GjRjn33HOz1VZbpaKiIjvuuGP69euXBx544EO317179zRq1Kjm/kYbbZTnn38+SfLSSy+la9euadOmTfr3759p06YlSR588MFMnTo1gwYNKndnYR3neAQA4JMS3euYpUuXJkmKoqi1bPLkycut++677+bRRx9Nz549V7rNCy64IE2aNMnmm2+eSZMm5eSTT06SdO3aNdOnT8+rr76a8ePHp0ePHqmurs4pp5ySq666ag3uFayfHI8AAHxSonsd061bt3Tq1Clnn312qqqq8swzz2TUqFGZO3durfWKosixxx6brl27Zv/991/pNn/0ox9l3rx5+ec//5kTTzwxrVu3TpK0aNEil19+eQYOHJhLLrkk1157bS688MIMHDgw1dXVGTBgQPr27ZvRo0eXtr+wLnM8AgDwSdVd2wOgtsrKyowdOzannnpq2rdvn3bt2mXQoEG55ppratYpiiLf+c53MmXKlNx1113ZaKNV+91J9+7ds9122+Woo47KXXfdlSQ56KCDctBBByVJnn/++YwePToPP/xwdt111wwfPjw9evRIz54907dv3zRv3nzN7zCswxyPAAB8Us50r4O6d++ecePGZebMmZk4cWKqqqrSp0+fJO/9A/+kk07Ko48+mjvvvDPNmjVbrW1XV1fXfIb0gwYPHpxLL7009erVy6RJk9K7d+80b9487du3/9DnwGed4xEAgE9CdK+DJk+enAULFmTRokW5/fbbM2rUqPz0pz9Nknz3u9/Ngw8+mPHjx3/kma758+fn+uuvz+zZs1MURZ5++ukMGzYse+6553Lr3njjjencuXN22WWXJEnnzp0zfvz4vP7663nuuefSsWPHNb+jsB5wPAIA8Em4vHwddNttt2XkyJGpqqrKdtttlzFjxqRnz5556aWXMnLkyNSvX7/WP7q/9a1v5eqrr06SnHjiiUmSq6++OhUVFbnlllty2mmnpaqqKptvvnkOOOCAnHPOObVe7+23387FF1+cCRMm1Cy78sorc/TRR2f+/PkZOnRoWrVq9SnsOax7HI8AAHwSFcX7p+XdQM2dOzfNmjXLnDlz0rRp07U9nM+El8/tsbaH8Kn7ZvMN7+/Ogyc/uLaHwCpwPG4Y1tfj8Yun/3ZtD+FTN7rJRWt7CJ86x+T6wfG4YXA8rjmr2pEuLwcAAICSuLz8U7Bh/tZwbY8AVszxCADAp8mZbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKs89E9b968nHrqqenYsWMaNmyYnXfeOY899liSpLq6OmeeeWZ69OiRRo0apW3btjniiCPy+uuvr+VRAwAAwHoQ3ccee2zGjx+fm266KU8//XT22GOP9O/fP6+99lr+85//5Mknn8zPfvazPPnkk7n99tvz3HPPZd99913bwwYAAIDUXdsDWJmFCxfmj3/8Y/70pz9l1113TZIMHTo0Y8aMyVVXXZVhw4Zl/PjxtZ5z+eWX58tf/nJefvnlbLHFFivcblVVVaqqqmruz507t7ydAAAAYIO1Tp/pXrx4cZYsWZIGDRrUWt6wYcM88MADK3zOnDlzUlFRkU022eRDt3v++eenWbNmNbcOHTqsyWEDAABAknU8ups0aZKddtopP//5z/P6669nyZIlufnmm/PII4/kjTfeWG79d999Nz/60Y9y2GGHpWnTph+63bPOOitz5sypub3yyitl7gYAAAAbqHU6upPkpptuSlEUadeuXerXr5/LLrsshx12WOrUqVNrverq6hx66KFZunRpRo4cudJt1q9fP02bNq11AwAAgDVtnY/urbbaKvfdd1/mz5+fV155JY8++miqq6vTqVOnmnWqq6tz8MEHZ/r06Rk/fryIBgAAYJ2wzkf3Mo0aNUqbNm0ya9asjBs3Lvvtt1+S/xfczz//fO666660bNlyLY8UAAAA3rNOz16eJOPGjUtRFOnWrVumTp2a008/Pd26dcugQYOyePHiHHjggXnyySdzxx13ZMmSJZkxY0aSpEWLFqlXr95aHj0AAAAbsnU+uufMmZOzzjorr776alq0aJEDDjggv/jFL1JZWZkXX3wxY8eOTZJsv/32tZ53zz33pG/fvp/+gAEAAOD/t85H98EHH5yDDz54hY9tueWWKYriUx4RAAAArJr15jPdAAAAsL4R3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJRHdAAAAUBLRDQAAACUR3QAAAFAS0Q0AAAAlEd0AAABQEtENAAAAJVnno3vevHk59dRT07FjxzRs2DA777xzHnvssZrHi6LI0KFD07Zt2zRs2DB9+/bNM888sxZHDAAAAO9Z56P72GOPzfjx43PTTTfl6aefzh577JH+/fvntddeS5IMHz48v/rVr3LFFVfkscceS+vWrbP77rtn3rx5a3nkAAAAbOjW6eheuHBh/vjHP2b48OHZdddd06VLlwwdOjSdOnXKVVddlaIoMmLEiPzkJz/J/vvvn89//vO58cYb85///Ce33HLL2h4+AAAAG7i6a3sAK7N48eIsWbIkDRo0qLW8YcOGeeCBBzJ9+vTMmDEje+yxR81j9evXT58+ffLQQw/lhBNOWOF2q6qqUlVVVXN/zpw5SZK5c+eWsBfJkqqFpWx3XTavcsnaHsKnbvHCxWt7CJ+6so6ZMjkeNwyOx/WHY3LD4JhcPzgeNwyOxzW/3aIoVr5isY7baaedij59+hSvvfZasXjx4uKmm24qKioqis997nPFgw8+WCQpXnvttVrPOe6444o99tjjQ7c5ZMiQIombm5ubm5ubm5ubm5ub2ye6vfLKKytt2nX6THeS3HTTTTn66KPTrl271KlTJ7169cphhx2WJ598smadioqKWs8pimK5Ze931lln5Qc/+EHN/aVLl+add95Jy5YtV/o81m1z585Nhw4d8sorr6Rp06ZreziwwXNMwrrD8QjrFsfkZ0NRFJk3b17atm270vXW+ejeaqutct9992XBggWZO3du2rRpk0MOOSSdOnVK69atkyQzZsxImzZtap7z5ptvplWrVh+6zfr166d+/fq1lm2yySaljJ9PX9OmTf3PC9YhjklYdzgeYd3imFz/NWvW7CPXWacnUnu/Ro0apU2bNpk1a1bGjRuX/fbbrya8x48fX7PeokWLct9992XnnXdei6MFAACA9eBM97hx41IURbp165apU6fm9NNPT7du3TJo0KBUVFTk1FNPzXnnnZeuXbuma9euOe+887LxxhvnsMMOW9tDBwAAYAO3zkf3nDlzctZZZ+XVV19NixYtcsABB+QXv/hFKisrkyRnnHFGFi5cmMGDB2fWrFnp3bt37rzzzjRp0mQtj5xPW/369TNkyJDlPjoArB2OSVh3OB5h3eKY3LBUFMVHzW8OAAAAfBzrzWe6AQAAYH0jugEAAKAkohsAAABKIroBWKdsueWWGTFiRM39ioqKjBkzZq2NBz4rPnhsral1AVg50U2pHnroodSpUyd77bXX2h4KsAqOOuqoVFRUpKKiInXr1s0WW2yR73znO5k1a9baHhp8prz/WKusrEznzp1z2mmnZcGCBaW95mOPPZbjjz9+ja8LfLj3H+vvv02dOjX3339/9tlnn7Rt29YvmD/jRDelGjVqVE4++eQ88MADefnll9faOKqrq9faa8P6Zq+99sobb7yRF198Mddee23+/Oc/Z/DgwWt7WPCZs+xYe+GFFzJs2LCMHDkyp5122nLrramfYZtttlk23njjNb4usHLLjvX33zp16pQFCxZku+22yxVXXLG2h0jJRDelWbBgQW677bZ85zvfyde//vXccMMNtR4fO3ZsdthhhzRo0CCbbrpp9t9//5rHqqqqcsYZZ6RDhw6pX79+unbtmuuuuy5JcsMNN2STTTapta0xY8akoqKi5v7QoUOz/fbbZ9SoUencuXPq16+foijy17/+Nbvssks22WSTtGzZMl//+tczbdq0Wtt69dVXc+ihh6ZFixZp1KhRdthhhzzyyCN58cUXs9FGG+Xxxx+vtf7ll1+ejh07xrfv8VlRv379tG7dOu3bt88ee+yRQw45JHfeeWfN49dff326d++eBg0aZOutt87IkSNrPf/DjqEkmTZtWvbbb7+0atUqjRs3zpe+9KXcddddn+r+wbpi2bHWoUOHHHbYYTn88MMzZsyYD/0ZNmfOnBx//PHZfPPN07Rp03z1q1/NpEmTam1zZT9bP3jJ+NChQ7PFFlukfv36adu2bU455ZQPXffll1/Ofvvtl8aNG6dp06Y5+OCD8+9//7vWtrbffvvcdNNN2XLLLdOsWbMceuihmTdv3pp/42A9s+xYf/+tTp06GTBgQIYNG1brOOWzSXRTmt///vfp1q1bunXrlm9961u5/vrra8L0f//3f7P//vtn7733zlNPPZW77747O+ywQ81zjzjiiPzud7/LZZddlmeffTZXX311GjduvFqvP3Xq1Nx222354x//mIkTJyZ57xcBP/jBD/LYY4/l7rvvzkYbbZRvfOMbWbp0aZJk/vz56dOnT15//fWMHTs2kyZNyhlnnJGlS5dmyy23TP/+/XP99dfXep3rr7++5tIh+Kx54YUX8te//jWVlZVJkt/85jf5yU9+kl/84hd59tlnc9555+VnP/tZbrzxxiQrP4aWPf61r30td911V5566qnsueee2WeffdbqlTCwrmjYsGHNWe0V/Qzbe++9M2PGjPzlL3/JE088kV69emW33XbLO++8k+Sjf7a+3//8z//kkksuyTXXXJPnn38+Y8aMSY8ePVa4blEUGThwYN55553cd999GT9+fKZNm5ZDDjmk1nrTpk3LmDFjcscdd+SOO+7IfffdlwsuuGANvTsA67ECSrLzzjsXI0aMKIqiKKqrq4tNN920GD9+fFEURbHTTjsVhx9++AqfN2XKlCJJzbofdP311xfNmjWrtWz06NHF+/86DxkypKisrCzefPPNlY7xzTffLJIUTz/9dFEURXHNNdcUTZo0Kd5+++0Vrv/73/++aN68efHuu+8WRVEUEydOLCoqKorp06ev9HVgfXHkkUcWderUKRo1alQ0aNCgSFIkKX71q18VRVEUHTp0KG655ZZaz/n5z39e7LTTTkVRfPQxtCLbbLNNcfnll9fc79ixY3HJJZfU3E9SjB49+uPvFKyDjjzyyGK//faruf/II48ULVu2LA4++OAV/gy7++67i6ZNm9b8/Flmq622Kq655pqiKFb+s7Uoah9bv/zlL4vPfe5zxaJFiz5y3TvvvLOoU6dO8fLLL9c8/swzzxRJikcffbQoivd+7m688cbF3Llza9Y5/fTTi969e3/0mwGfYe//ubrsduCBBy63np91n23OdFOKKVOm5NFHH82hhx6aJKlbt24OOeSQjBo1KkkyceLE7Lbbbit87sSJE1OnTp306dPnE42hY8eO2WyzzWotmzZtWg477LB07tw5TZs2TadOnZKk5izbxIkT84UvfCEtWrRY4TYHDhyYunXrZvTo0Une+8x6v379suWWW36iscK6pF+/fpk4cWIeeeSRnHzyydlzzz1z8sknZ+bMmXnllVdyzDHHpHHjxjW3YcOG1XxM46OOoQULFuSMM87INttsk0022SSNGzfOv/71L2e62SDdcccdady4cRo0aJCddtopu+66ay6//PIky/8Me+KJJzJ//vy0bNmy1vE3ffr0Wsffh/1s/aCDDjooCxcuTOfOnXPcccdl9OjRWbx48QrXffbZZ9OhQ4d06NChZtmyY/jZZ5+tWbblllumSZMmNffbtGmTN998c9XfEPiMWvZzddntsssuW9tD4lNWd20PgM+m6667LosXL067du1qlhVFkcrKysyaNSsNGzb80Oeu7LEk2WijjZb7/PSKJplp1KjRcsv22WefdOjQIb/5zW/Stm3bLF26NJ///OezaNGiVXrtevXq5dvf/nauv/767L///rnlllt8pQqfOY0aNUqXLl2SJJdddln69euXc845J9/97neTvHeJee/evWs9p06dOkk++hg6/fTTM27cuFx88cXp0qVLGjZsmAMPPLDmGIQNSb9+/XLVVVelsrIybdu2rfkYR7L8z7ClS5emTZs2uffee5fbzrJ5Tj7q+Hu/Dh06ZMqUKRk/fnzuuuuuDB48OBdddFHuu+++WuNI3vv5vaKPUH1w+QefV1FRUfPREtiQvf/nKhsmZ7pZ4xYvXpzf/va3+eUvf1nrt3qTJk1Kx44d89///d/p2bNn7r777hU+v0ePHlm6dGnuu+++FT6+2WabZd68ebW+VmXZ591W5u23386zzz6bn/70p9ltt93SvXv35b4GqWfPnpk4cWLN5+NW5Nhjj81dd92VkSNHprq62uQXfOYNGTIkF198cZYsWZJ27drlhRdeSJcuXWrdll018lHH0IQJE3LUUUflG9/4Rnr06JHWrVvnxRdf/BT3BtYdy/4h3rFjx+WC9YN69eqVGTNmpG7dussdf5tuummSrPRn64o0bNgw++67by677LLce++9efjhh/P0008vt94222yTl19+Oa+88krNsn/+85+ZM2dOunfvvsqvB7ChcqabNe6OO+7IrFmzcswxx6RZs2a1HjvwwANz3XXX5ZJLLsluu+2WrbbaKoceemgWL16c//u//8sZZ5yRLbfcMkceeWSOPvroXHbZZdluu+3y0ksv5c0338zBBx+c3r17Z+ONN86Pf/zjnHzyyXn00UeXmxl9RZo3b56WLVvm17/+ddq0aZOXX345P/rRj2qt881vfjPnnXdeBg4cmPPPPz9t2rTJU089lbZt22annXZKknTv3j077rhjzjzzzBx99NGrdWYB1kd9+/bNtttum/POOy9Dhw7NKaeckqZNm2bAgAGpqqrK448/nlmzZuUHP/jBRx5DXbp0ye2335599tknFRUV+dnPfuZMGKyC/v37Z6eddsrAgQNz4YUXplu3bnn99dfzl7/8JQMHDswOO+yQIUOGfOjP1g+64YYbsmTJkpqfqTfddFMaNmyYjh07rvC1e/bsmcMPPzwjRozI4sWLM3jw4PTp0+dDJ2oDPtr8+fMzderUmvvTp0/PxIkT06JFi2yxxRZrcWSsac50s8Zdd9116d+//3LBnSQHHHBAJk6cmKZNm+YPf/hDxo4dm+233z5f/epXa75SKEmuuuqqHHjggRk8eHC23nrrHHfccTVntlu0aJGbb745f/nLX9KjR4/ceuutGTp06EeOa6ONNsrvfve7PPHEE/n85z+f73//+7noootqrVOvXr3ceeed2XzzzfO1r30tPXr0yAUXXFBz6ewyxxxzTBYtWpSjjz76Y7xDsP75wQ9+kN/85jfZc889c+211+aGG25Ijx490qdPn9xwww01Z7o/6hi65JJL0rx58+y8887ZZ599sueee6ZXr15rc9dgvVBRUZG//OUv2XXXXXP00Ufnc5/7XA499NC8+OKLadWqVZL3fkG2sp+t77fJJpvkN7/5Tb7yla/UnCH/85//nJYtW67wtceMGZPmzZtn1113Tf/+/dO5c+f8/ve/L3Wf4bPu8ccfzxe+8IV84QtfSPLez9ovfOELOfvss9fyyFjTKooPfjgW+Ei/+MUv8rvf/W6Fl+EBAAAs40w3rIb58+fnsccey+WXX55TTjllbQ8HAABYx4luWA3f/e53s8suu6RPnz4uLQcAAD6Sy8sBAACgJM50AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDwAZo6dKlueiiizJp0qS1PRQA+EwT3QCwAfrFL36R++67L5///OfX9lAA4DNNdAPAOuioo45KRUVFKioqUllZmc6dO+e0007LggULPvG2J0yYkDvuuCO///3vU6dOnTUw2o+25ZZbZsSIEZ/KawHAuqTu2h4AALBie+21V66//vpUV1dnwoQJOfbYY7NgwYJcddVVq7WdoiiyZMmS1K373o/9//qv/8ojjzxSxpA/kSVLlqSioiIbbeScAACfHX6qAcA6qn79+mndunU6dOiQww47LIcffnjGjBmTm2++OTvssEOaNGmS1q1b57DDDsubb75Z87x77703FRUVGTduXHbYYYfUr18/EyZMyLRp07LffvulVatWady4cb70pS/lrrvuqvWaW265ZYYNG5YjjjgijRs3TseOHfOnP/0pM2fOzH777ZfGjRunR48eefzxx2s976GHHsquu+6ahg0bpkOHDjnllFNqzsr37ds3L730Ur7//e/XnL1PkhtuuCGbbLJJ7rjjjmyzzTapX79+XnrppcyaNStHHHFEmjdvno033jgDBgzI888/X/K7DQDlEN0AsJ5o2LBhqqurs2jRovz85z/PpEmTMmbMmEyfPj1HHXXUcuufccYZOf/88/Pss8+mZ8+emTdvXgYMGJC77rorTz75ZPr375999tknL7/8cq3nXXLJJfnKV76Sp556KnvvvXe+/e1v54gjjsi3vvWtPPnkk+nSpUuOOOKIFEWRJHn66aez5557Zv/998/kyZPz+9//Pg888EC++93vJkluv/32tG/fPueee27eeOONvPHGGzWv9Z///Cfnn39+rr322jzzzDPZfPPNc9RRR+Xxxx/P2LFj8/DDD6coinzta19LdXV1eW8uAJSlAADWOUceeWSx33771dx/5JFHipYtWxYHH3zwcus++uijRZJi3rx5RVEUxT333FMkKcaMGfORr7P11lsXl19+ec39jh07Ft/61rdq7r/xxhtFkuJnP/tZzbKHH364SFK88cYbRVEUxbe//e3i+OOPr7XdCRMmFBtttFGxcOHCmu1ecskltda5/vrriyTFxIkTa5Y999xzRZLiwQcfrFn21ltvFQ0bNixuu+22j9wfAFjX+Ew3AKyj7rjjjjRu3DiLFy9OdXV19ttvv1x++eV56qmnMnTo0EycODHvvPNOli5dmiR5+eWXs80229Q8f4cddqi1vYULF+bCCy/M//7v/+aNN97I4sWL88477yx3prtnz541/92qVaskSY8ePZZb9uabb6Z169Z54oknMnXq1Pz3f/93zTpFUWTp0qWZPn16unfv/qH7WK9evVqv9+yzz6Zu3brp3bt3zbKWLVumW7duefbZZz/6TQOAdYzoBoB1VL9+/XLVVVelsrIybdu2TWVlZRYsWJA99tgje+yxR26++eZsttlmefnll7Pnnntm0aJFtZ7fqFGjWvfPPPPMjBs3LldccUW6dOmShg0b5mtf+9pyz6usrKz572Wfv17RsmWxv3Tp0pxwwgk55ZRTltuHLbbYYqX72LBhw5rtJam5ZP2DiqKotR4ArC9ENwCsoxo1apQuXbrUWvavf/0rb731Vi644IJ06NAhSZab1OzD3HPPPfn2t7+d3XffPUkyb968TJs2LbvuuusnGmevXr3yzDPPLDfW96tXr16WLFnykdvaZpttsnjx4jzyyCPZeeedkyRvv/12nnvuuZWeMQeAdZWJ1ABgPbLFFlukXr16ufzyy/PCCy9k7Nix+fnPf75Kz91qq63yP//zP5k4cWImTpyYb37zmx96Znl1nHnmmXn44Ydz0kknZeLEiXn++eczduzYnHzyyTXrbLnllrn//vvz2muv5a233vrQbXXt2jX77bdfjjvuuDzwwAOZNGlSvvWtb6Vdu3bZb7/9PvFYAeDTJroBYD2y2Wab5YYbbsgf/vCHbLPNNrngggty8cUXr9JzL7nkkrRo0SI777xz9t133wwYMCC9evX6xGPq2bNn7rvvvjz//PP5r//6r/x/7d15VFd1/sfx1xcXBkGFwQVQUkYGDFxiwJWTSqKS4uDJSXNUxNSmJDWXn6amhcclW9UcmeMgYDOWmanHPC644r6ggyEgKGs2LpVoiwiy/P7gcIevaIp6K/P5OOf7x/dzP997P9+Lb+993e3r5+enWbNmydXV1egzZ84c5ebmqlWrVmrcuPFPzi8uLk7+/v4KDQ1Vly5dVF5ers2bN1td4g4AwMPCUv4gDnEDAAAAAIBqONMNAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN3Ab0TLli21aNGiX3oYAG6B+gR++6hzALdT+5ceAHC3/P/vw591ecffDq9R/4iICK1cuVKSVKtWLbm5ualfv36aP3++nJyczBjir8Ibb7yhqKioau3bt29XcHDwLzCiijFt2LBBycnJd+xXOXYbGxu5ubmpT58+WrBggRo3bmzK2Pbs2aOgoCAVFBTI0dHxln2q/lu6nfLy8luu+6ZNm+rChQsParh3LX9O2591eY/NTqlRf+rT2sNWn1V5e3vr9OnTkqTs7GzNnDlTiYmJunz5sho1aiR/f3+9/fbb8vLykiRZLBZJ0qFDh9S5c2djPkVFRXJzc9Ply5e1e/du9ejR45bjiI6OVnR0tHJzcyVJvr6+mj17tp5++mmjT3l5uaKiorR8+XIVFBSoU6dO+vvf/y5fX1+r5U2ZMkUff/yxCgsL1bNnTy1btkzNmze/4zr7tQj8IPBnXd6BcQdq1J86t0ad332dS9JXX32ladOmacuWLSosLJSXl5dWrFghf39/SY9OncMcnOkGHqCQkBCdP39eubm5iomJ0eeff66xY8f+0sMyna+vr86fP2/16tat2z3Nq7i4+AGP7qdVjj0/P1/R0dH6/PPPFR5+6wMupaWlKisrM31MixcvtlqXkhQXF1etrer4K18pKTULo48S6vPhrc+qr/379xtj6dWrl7777jutW7dOGRkZ+uSTT9SmTRtdvXrVaj7u7u6Ki4uzalu/fr0cHBzuOIbmzZvrzTffVFJSkpKSkvTUU08pLCxMqampRp+33npL7733npYuXapjx47JxcVFvXr10vfff2/0eeWVV7R+/XqtXr1a+/fv1w8//KDQ0FCVlpbezyrCTahz6ryqu63zgoICBQYGqk6dOtqyZYvS0tL07rvvWh0cp85xPwjdwANka2srFxcXNW/eXL1799bgwYOVkJBgTC8tLdWoUaPk4eEhOzs7eXt7a/HixVbziIiI0IABA/TOO+/I1dVVzs7OioyM1I0bN4w+ly5dUv/+/WVnZycPDw+tWrWq2ljy8/MVFhYmBwcHNWjQQIMGDdLFixeN6W+88YaeeOIJxcbG6rHHHpODg4NeeukllZaW6q233pKLi4uaNGmiefPm3fF7165dWy4uLlavunXrSpJSUlL01FNPyc7OTs7OznrhhRf0ww8/VPu+CxYskJubm3HU+quvvtLgwYPl5OQkZ2dnhYWFGWeapIozxh07dpS9vb0cHR0VGBiovLw8xcfHKyoqSidPnpTFYpHFYlF8fPwdx96sWTOFhoZq/PjxSkhIUGFhoeLj4+Xo6KhNmzbJx8dHtra2ysvLU0FBgcLDw+Xk5KR69erp6aef1pkzZ4x55uXlqX///nJycpK9vb18fX21efNm5ebmKigoSJLk5OQki8WiiIiIamNq2LCh1bqUJEdHx2ptt1r3Zp2h/y2gPh/e+qz6atSokSQpLS1N2dnZWrZsmTp37qwWLVooMDBQ8+bNU4cOHazmM2LECK1evVqFhYVGW2xsrEaMGHHH9de/f3/17dtXXl5e8vLy0rx58+Tg4KDDhw9Lqjj7tWjRIs2cOVPPPPOM2rRpo5UrV+ratWv66KOPJElXr17VihUr9O677yo4OFh+fn7697//rZSUFO3YsUOSlJubK4vFojVr1ujJJ5+UnZ2dOnTooMzMTB07dkwBAQFycHBQSEiIvv766zuO+1FFnVPn91LnCxcuNEJ7x44d1bJlS/Xs2VOtWrWSRJ3j/hG6AZNkZ2dr69atqlOnjtFWVlam5s2ba82aNUpLS9Ps2bM1Y8YMrVmzxuqzu3fvVlZWlnbv3q2VK1cqPj7eaoMVERGh3Nxc7dq1S2vXrtWyZct06dIlY3p5ebkGDBigy5cvKzExUdu3b1dWVpYGDx5stZysrCxt2bJFW7du1ccff6zY2Fj169dP586dU2JiohYuXKjXXnvN2LmsqWvXrikkJEROTk46duyYPv30U+3YsUMvv/yyVb+dO3cqPT1d27dv16ZNm3Tt2jUFBQXJwcFBe/fu1f79+42NUHFxsUpKSjRgwAB1795dX3zxhQ4dOqQXXnhBFotFgwcP1uTJk62OnN/8vX+KnZ2dysrKVFJSYnyHBQsWKCYmRqmpqWrSpIkiIiKUlJSkjRs36tChQyovL1ffvn2NHbLIyEgVFRVp7969SklJ0cKFC+Xg4CB3d3d99tlnkqSMjAydP3++2s5eTZ05c0Zubm7y8PDQc889p+zs7Pua36OC+nw467Oqxo0by8bGRmvXrr3jWSR/f395eHgY9ffll19q7969Gj58eI2WWVpaqtWrV+vHH39Uly5dJEk5OTm6cOGCevfubfSztbVV9+7ddfDgQUnS8ePHdePGDas+bm5uatOmjdGn0uuvv67XXntNJ06cUO3atTVkyBBNnTpVixcv1r59+5SVlaXZs2fXaNyPKuqcOr/bOt+4caMCAgL07LPPqkmTJvLz89M///lPYzp1jvvFPd3AA7Rp0yY5ODiotLRU169flyS99957xvQ6depY3bvk4eGhgwcPas2aNRo0aJDR7uTkpKVLl6pWrVpq3bq1+vXrp507d2rMmDHKzMzUli1bdPjwYXXq1EmStGLFCj3++OPG53fs2KEvvvhCOTk5cnd3lyT961//kq+vr44dO2YcHS4rK1NsbKzq168vHx8fBQUFKSMjQ5s3b5aNjY28vb21cOFC7dmzx+oeqZulpKRYXb7l4+Ojo0ePatWqVSosLNSHH34oe3t7SdLSpUvVv39/LVy4UE2bNpUk2dvbKyYmxjgqHxsbKxsbG8XExBj3acXFxcnR0VF79uxRQECArl69qtDQUOModNXv7+DgYBw5r4nTp08rOjpaHTt2VP369SVJN27c0LJly9S+fXtJFSF348aNOnDggLp27SpJWrVqldzd3bVhwwY9++yzys/P18CBA9W2bcV9zn/4wx+MZfz+97+XJDVp0uS293TfrU6dOunDDz+Ul5eXLl68qLlz56pr165KTU2Vs7Pzfc37t4j6rPAw1efNY5ek5557TjExMWrWrJmWLFmiqVOnKioqSgEBAQoKCtLQoUOtaq7SyJEjFRsbq2HDhikuLk59+/a96ytDUlJS1KVLF12/fl0ODg5av369fHx8JMl4hkLl+qrUtGlT5eXlGX3q1q1b7b7iWz2DYcqUKerTp48kacKECRoyZIh27typwMCK+6lHjRr1k2cNH3XUeQXqvGZ1np2drejoaE2aNEkzZszQ0aNHNX78eNna2io8PJw6x30jdAMPUFBQkKKjo3Xt2jXFxMQoMzNT48aNs+rzj3/8QzExMcrLy1NhYaGKi4v1xBNPWPXx9fVVrVq1jPeurq7Gvbrp6emqXbu2AgICjOmtW7e2CnDp6elyd3c3NvRSxQbY0dFR6enpxsa+ZcuWRriUKjYMtWrVko2NjVVb1aP3t+Lt7a2NGzca721tbY1xtG/f3tjQS1JgYKDKysqUkZFhbLzatm1rbOiliqPFZ8+etRqbJF2/fl1ZWVnq3bu3IiIi1KdPH/Xq1UvBwcEaNGiQXF1df3Kct1K5sS8tLVVRUZF69Oih5cuXG9Pr1q2rdu3aGe8r13/ljpYkOTs7y9vbW+np6ZKk8ePH66WXXlJCQoKCg4M1cOBAq3k8KFUf5NS2bVt16dJFrVq10sqVKzVp0qQHvryHHfVZ4WGqz5vHLslquZGRkQoPD9fu3bt15MgRffrpp5o/f742btyoXr16WX1u2LBhevXVV5Wdna34+HgtWbKkRuNITk7WlStX9Nlnn2nEiBFKTEw0grf0vwc5VSovL6/WdrNb9an6f0XVv0HVtjv9zR9l1HkF6rxmdV5WVqaAgADNnz9fkuTn56fU1FRFR0dbPeeFOse94vJy4AGyt7eXp6en2rVrpyVLlqioqMjqiPqaNWs0ceJEPf/880pISFBycrJGjhxZ7aElVS+Fkyr+k698gFd5ebnRdju32wjc3H6r5fzUsm+nbt268vT0NF6VOxk/tTGq2l51Z0Cq2Pj5+/srOTnZ6pWZmam//vWvkiqOuB86dEhdu3bVJ598Ii8vr3u6/K5yZzotLU2FhYXatWuXPD09jel2dnZWY61c/zer+l1Hjx6t7OxsDR8+XCkpKQoICNAHH3xQ47HVlL29vdq2bWt1fzn+h/p8+Orz5rF7enpWO9NUv359/fnPf9a8efN08uRJPfnkk5o7d261eTk7Oys0NFSjRo3S9evXrQ5a3e04AgICtGDBArVv3964NaTyTN7NZ7IuXbpkjNXFxUXFxcUqKCi4bZ9KVf/GlX+Hm9t+jgc6Pqyoc+r8Xurc1dXV6iCaVHHmPj8/XxJ1jvtH6AZM9Prrr+udd97Rf//7X0nSvn371LVrV40dO1Z+fn7y9PRUVlZWjeb5+OOPq6SkRElJSUZbRkaGrly5Yrz38fFRfn6+vvzyS6MtLS1NV69etbr8y2w+Pj5KTk7Wjz/+aLQdOHBANjY2xoNabuVPf/qTzpw5oyZNmlTbEDds2NDo5+fnp+nTp+vgwYNq06aN8TCTunXr3vWTQis39h4eHsaZgTt9p5KSEh05csRo+/bbb5WZmWm1bt3d3fXiiy9q3bp1mjx5snFvWOWZBDOeZFpUVKT09PR7OtPwKKI+f/31WVMWi0WtW7e2+k5VPf/889qzZ4/Cw8OtzmLWVHl5uYqKiiRVXJ7s4uKi7du3G9OLi4uVmJho3ILi7++vOnXqWPU5f/68Tp06ZfSBOahz6vxuBAYGKiMjw6otMzNTLVq0kESd4/4RugET9ejRQ76+vsblSp6enkpKStK2bduUmZmpWbNm6dixYzWap7e3t0JCQjRmzBgdOXJEx48f1+jRo2VnZ2f0CQ4OVrt27TR06FCdOHFCR48eVXh4uLp37251OZzZhg4dqt/97ncaMWKETp06pd27d2vcuHEaPnx4taO+N3+uUaNGCgsL0759+5STk6PExERNmDBB586dU05OjqZPn65Dhw4pLy9PCQkJVqG3ZcuWysnJUXJysr755htj5/hB+OMf/6iwsDCNGTNG+/fv18mTJzVs2DA1a9ZMYWFhkip+MmTbtm3KycnRiRMntGvXLmNsLVq0kMVi0aZNm/T1119bPUG2pqZMmaLExETl5OToyJEj+stf/qLvvvvurp7UCurzYajPkpISXbhwwepV+fTn5ORkhYWFae3atUpLS9PZs2e1YsUKxcbGGrV4s8onAs+ZM+eu19OMGTO0b98+5ebmKiUlRTNnztSePXs0dOhQSRUB4JVXXtH8+fO1fv16nTp1ShEREapXr55xRrBhw4YaNWqUJk+erJ07d+o///mPhg0bprZt2/5iv6P8qKDOqfO7MXHiRB0+fFjz58/X2bNn9dFHH2n58uWKjIyURJ3j/nFPNx4ax9++9W8n/9pNmjRJI0eO1LRp0/Tiiy8qOTlZgwcPlsVi0ZAhQzR27Fht2bKlRvOMi4vT6NGj1b17dzVt2lRz587VrFmzjOkWi0UbNmzQuHHj1K1bN9nY2CgkJORnucS5qnr16mnbtm2aMGGCOnTooHr16mngwIFWD7W53ef27t2radOm6ZlnntH333+vZs2aqWfPnmrQoIEKCwt1+vRprVy5Ut9++61cXV318ssv629/+5skaeDAgVq3bp2CgoJ05coVxcXF3fKnue5VXFycJkyYoNDQUBUXF6tbt27avHmzcWlYaWmpIiMjde7cOTVo0EAhISF6//33JUnNmjVTVFSUXn31VY0cOVLh4eH3/LCUc+fOaciQIfrmm2/UuHFjde7cWYcPHzaOzP+cHpv9cP4+OPX5667P1NTUaldu2Nra6vr162revLlatmypqKgo42d4Kt9PnDjxlvOzWCzGTxHdrYsXL2r48OE6f/68GjZsqHbt2mnr1q1W95JOnTpVhYWFGjt2rAoKCtSpUyclJCRY3Zf6/vvvq3bt2ho0aJAKCwvVs2dPxcfH39cZ95/bgXEHfukh3BPqnDq/kw4dOmj9+vWaPn265syZIw8PDy1atMg4uCY9OnUOc1jKb3eDIgAAAAAAuC9cXg4AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJ/h+eTVxtjwt4ZwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "rf_df = plot_df[plot_df['model'].str.contains('Random Forest')]\n",
+    "svm_df = plot_df[plot_df['model'].str.contains('SVM')]\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(10,10))\n",
+    "ax = sns.barplot(rf_df, x = 'metric', y = 'value', hue = 'model', hue_order=['Random Forest Prost T5', 'Random Forest ESM 300m', 'Random Forest ESM 600m'])\n",
+    "plt.title('Evaluación de Random Forests')\n",
+    "plt.ylim(90,100)\n",
+    "plt.xlabel('Parámetro')\n",
+    "ax.legend(loc = 'upper center',\n",
+    "          bbox_to_anchor = (0.5, -0.15),\n",
+    "          ncol = 3,\n",
+    "          frameon = False)\n",
+    "\n",
+    "for p in ax.patches:\n",
+    "    h = p.get_height()\n",
+    "    ax.annotate(f\"{h:.1f}%\", \n",
+    "                (p.get_x() + p.get_width()/2, h),\n",
+    "                ha='center', va='bottom', fontsize=9)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "17114185",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,10))\n",
+    "ax = sns.barplot(svm_df, x = 'metric', y = 'value', hue = 'model', hue_order=['SVM Prost T5', 'SVM ESM 300m', 'SVM ESM 600m'])\n",
+    "plt.title('Evaluación de Support Vector Machine')\n",
+    "plt.ylim(94,98)\n",
+    "plt.xlabel('Parámetro')\n",
+    "ax.legend(loc = 'upper center',\n",
+    "          bbox_to_anchor = (0.5, -0.15),\n",
+    "          ncol = 3,\n",
+    "          frameon = False)\n",
+    "\n",
+    "for p in ax.patches:\n",
+    "    h = p.get_height()\n",
+    "    ax.annotate(f\"{h:.1f}%\", \n",
+    "                (p.get_x() + p.get_width()/2, h),\n",
+    "                ha='center', va='bottom', fontsize=9)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tesisEnv",
+   "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": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}