Refactored VAV

physLSTM/lstm_vav_rtu1.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,7 +17,8 @@
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
     "from keras.callbacks import ModelCheckpoint\n",
-    "import tensorflow as tf"
+    "import tensorflow as tf\n",
+    "import joblib"
    ]
   },
   {
@@ -31,46 +32,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
-    "zones = [69, 68,67, 66,65, 64, 42,41,40,39,38,37,36]\n",
-    "rtus = [1]\n",
+    "zones = [69, 68, 67, 66, 65, 64, 42, 41, 40, 39, 38, 37, 36]\n",
+    "rtu = 1\n",
     "cols = []\n",
     "\n",
     "for zone in zones:\n",
-    "        for column in merged.columns:\n",
-    "            if f\"zone_0{zone}\" in column and 'co2' not in column and \"hw_valve\" not in column and \"cooling_sp\" not in column and \"heating_sp\" not in column:\n",
-    "                cols.append(column)\n",
+    "    for column in merged.columns:\n",
+    "        if (\n",
+    "            f\"zone_0{zone}\" in column\n",
+    "            and \"co2\" not in column\n",
+    "            and \"hw_valve\" not in column\n",
+    "            and \"cooling_sp\" not in column\n",
+    "            and \"heating_sp\" not in column\n",
+    "        ):\n",
+    "            cols.append(column)\n",
+    "\n",
     "\n",
-    "for zone in zones:\n",
-    "        for column in merged.columns:\n",
-    "              if f\"zone_0{zone}\" in column: \n",
-    "                if \"cooling_sp\" in column or \"heating_sp\" in column:\n",
-    "                  cols.append(column)\n",
     "# for rtu in rtus:\n",
     "#     for column in merged.columns:\n",
-    "#         if f\"rtu_00{rtu}_fltrd_sa\" in column:\n",
+    "#         if f\"rtu_00{rtu}_fltrd_sa\" or f\"rtu_00{rtu}_sa_temp\" in column:\n",
     "#                 cols.append(column)\n",
-    "cols =['date'] + cols + ['air_temp_set_1',\n",
-    " 'air_temp_set_2',\n",
-    " 'dew_point_temperature_set_1d',\n",
-    " 'relative_humidity_set_1',\n",
-    " 'solar_radiation_set_1']\n",
+    "\n",
+    "cols = (\n",
+    "    [\"date\"]\n",
+    "    + cols\n",
+    "    + [\n",
+    "        f\"rtu_00{rtu}_fltrd_sa_flow_tn\",\n",
+    "        f\"rtu_00{rtu}_sa_temp\", \n",
+    "        \"air_temp_set_1\",\n",
+    "        \"air_temp_set_2\",\n",
+    "        \"dew_point_temperature_set_1d\",\n",
+    "        \"relative_humidity_set_1\",\n",
+    "        \"solar_radiation_set_1\",\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "for zone in zones:\n",
+    "    for column in merged.columns:\n",
+    "        if f\"zone_0{zone}\" in column:\n",
+    "            if \"cooling_sp\" in column or \"heating_sp\" in column:\n",
+    "                cols.append(column)\n",
+    "                \n",
     "input_dataset = merged[cols]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\arbal\\AppData\\Local\\Temp\\ipykernel_16740\\4293840618.py:1: SettingWithCopyWarning: \n",
+      "C:\\Users\\arbal\\AppData\\Local\\Temp\\ipykernel_29192\\4293840618.py:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -96,430 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>zone_069_temp</th>\n",
-       "      <th>zone_069_fan_spd</th>\n",
-       "      <th>zone_068_temp</th>\n",
-       "      <th>zone_068_fan_spd</th>\n",
-       "      <th>zone_067_temp</th>\n",
-       "      <th>zone_067_fan_spd</th>\n",
-       "      <th>zone_066_temp</th>\n",
-       "      <th>zone_066_fan_spd</th>\n",
-       "      <th>zone_042_temp</th>\n",
-       "      <th>...</th>\n",
-       "      <th>zone_038_heating_sp</th>\n",
-       "      <th>zone_037_cooling_sp</th>\n",
-       "      <th>zone_037_heating_sp</th>\n",
-       "      <th>zone_036_cooling_sp</th>\n",
-       "      <th>zone_036_heating_sp</th>\n",
-       "      <th>air_temp_set_1</th>\n",
-       "      <th>air_temp_set_2</th>\n",
-       "      <th>dew_point_temperature_set_1d</th>\n",
-       "      <th>relative_humidity_set_1</th>\n",
-       "      <th>solar_radiation_set_1</th>\n",
-       "    </tr>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>72.3</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>73.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>12.850</td>\n",
-       "      <td>12.930</td>\n",
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-       "      <td>48.7</td>\n",
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-       "      <td>2019-01-08 20:56:00</td>\n",
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-       "      <td>75.0</td>\n",
-       "      <td>72.0</td>\n",
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-       "      <td>12.930</td>\n",
-       "      <td>9.10</td>\n",
-       "      <td>78.15</td>\n",
-       "      <td>48.7</td>\n",
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-       "      <th>438787</th>\n",
-       "      <td>2019-01-08 20:57:00</td>\n",
-       "      <td>70.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>72.4</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>70.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>70.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>72.3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>73.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>75.0</td>\n",
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-       "      <td>48.7</td>\n",
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-       "    <tr>\n",
-       "      <th>438788</th>\n",
-       "      <td>2019-01-08 20:58:00</td>\n",
-       "      <td>70.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>72.4</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>70.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>70.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>72.3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>73.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>12.850</td>\n",
-       "      <td>12.930</td>\n",
-       "      <td>9.10</td>\n",
-       "      <td>78.15</td>\n",
-       "      <td>48.7</td>\n",
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-       "      <td>35.0</td>\n",
-       "      <td>71.4</td>\n",
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-       "      <td>71.0</td>\n",
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-       "      <td>68.0</td>\n",
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-       "      <td>51.61</td>\n",
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-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1633368 rows × 46 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                       date  zone_069_temp  zone_069_fan_spd  zone_068_temp  \\\n",
-       "438785  2019-01-08 20:55:00           70.9               NaN           72.4   \n",
-       "438786  2019-01-08 20:56:00           70.9               NaN           72.4   \n",
-       "438787  2019-01-08 20:57:00           70.9               NaN           72.4   \n",
-       "438788  2019-01-08 20:58:00           70.9               NaN           72.4   \n",
-       "438789  2019-01-08 20:59:00           70.9               NaN           72.4   \n",
-       "...                     ...            ...               ...            ...   \n",
-       "2072148 2020-12-31 23:57:00           68.8              20.0           71.7   \n",
-       "2072149 2020-12-31 23:58:00           68.8              20.0           71.7   \n",
-       "2072150 2020-12-31 23:58:00           68.8              20.0           71.7   \n",
-       "2072151 2020-12-31 23:59:00           68.8              20.0           71.7   \n",
-       "2072152 2020-12-31 23:59:00           68.8              20.0           71.7   \n",
-       "\n",
-       "         zone_068_fan_spd  zone_067_temp  zone_067_fan_spd  zone_066_temp  \\\n",
-       "438785               20.0           70.2               NaN           70.9   \n",
-       "438786               20.0           70.2               NaN           70.9   \n",
-       "438787               20.0           70.2               NaN           70.9   \n",
-       "438788               20.0           70.2               NaN           70.9   \n",
-       "438789               20.0           70.2               NaN           70.9   \n",
-       "...                   ...            ...               ...            ...   \n",
-       "2072148              20.0           70.4              20.0           68.6   \n",
-       "2072149              20.0           70.4              20.0           68.6   \n",
-       "2072150              20.0           70.4              20.0           68.6   \n",
-       "2072151              20.0           70.4              20.0           68.6   \n",
-       "2072152              20.0           70.4              20.0           68.6   \n",
-       "\n",
-       "         zone_066_fan_spd  zone_042_temp  ...  zone_038_heating_sp  \\\n",
-       "438785                NaN           72.3  ...                 72.0   \n",
-       "438786                NaN           72.3  ...                 72.0   \n",
-       "438787                NaN           72.3  ...                 72.0   \n",
-       "438788                NaN           72.3  ...                 72.0   \n",
-       "438789                NaN           72.3  ...                 72.0   \n",
-       "...                   ...            ...  ...                  ...   \n",
-       "2072148              35.0           71.4  ...                 71.0   \n",
-       "2072149              35.0           71.4  ...                 71.0   \n",
-       "2072150              35.0           71.4  ...                 71.0   \n",
-       "2072151              35.0           71.4  ...                 71.0   \n",
-       "2072152              35.0           71.4  ...                 71.0   \n",
-       "\n",
-       "         zone_037_cooling_sp  zone_037_heating_sp  zone_036_cooling_sp  \\\n",
-       "438785                  73.0                 70.0                 75.0   \n",
-       "438786                  73.0                 70.0                 75.0   \n",
-       "438787                  73.0                 70.0                 75.0   \n",
-       "438788                  73.0                 70.0                 75.0   \n",
-       "438789                  73.0                 70.0                 75.0   \n",
-       "...                      ...                  ...                  ...   \n",
-       "2072148                 74.0                 68.0                 74.0   \n",
-       "2072149                 74.0                 68.0                 74.0   \n",
-       "2072150                 74.0                 68.0                 74.0   \n",
-       "2072151                 74.0                 68.0                 74.0   \n",
-       "2072152                 74.0                 68.0                 74.0   \n",
-       "\n",
-       "         zone_036_heating_sp  air_temp_set_1  air_temp_set_2  \\\n",
-       "438785                  72.0          12.850          12.930   \n",
-       "438786                  72.0          12.850          12.930   \n",
-       "438787                  72.0          12.850          12.930   \n",
-       "438788                  72.0          12.850          12.930   \n",
-       "438789                  72.0          12.850          12.930   \n",
-       "...                      ...             ...             ...   \n",
-       "2072148                 68.0          13.994          13.528   \n",
-       "2072149                 68.0          13.994          13.528   \n",
-       "2072150                 68.0          13.994          13.528   \n",
-       "2072151                 68.0          13.994          13.528   \n",
-       "2072152                 68.0          13.994          13.528   \n",
-       "\n",
-       "         dew_point_temperature_set_1d  relative_humidity_set_1  \\\n",
-       "438785                           9.10                    78.15   \n",
-       "438786                           9.10                    78.15   \n",
-       "438787                           9.10                    78.15   \n",
-       "438788                           9.10                    78.15   \n",
-       "438789                           9.10                    78.15   \n",
-       "...                               ...                      ...   \n",
-       "2072148                          4.11                    51.61   \n",
-       "2072149                          4.11                    51.61   \n",
-       "2072150                          4.11                    51.61   \n",
-       "2072151                          4.11                    51.61   \n",
-       "2072152                          4.11                    51.61   \n",
-       "\n",
-       "         solar_radiation_set_1  \n",
-       "438785                    48.7  \n",
-       "438786                    48.7  \n",
-       "438787                    48.7  \n",
-       "438788                    48.7  \n",
-       "438789                    48.7  \n",
-       "...                        ...  \n",
-       "2072148                  188.8  \n",
-       "2072149                  188.8  \n",
-       "2072150                  188.8  \n",
-       "2072151                  188.8  \n",
-       "2072152                  188.8  \n",
-       "\n",
-       "[1633368 rows x 46 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_filtered"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -528,7 +124,7 @@
        "[]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -548,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -565,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -574,7 +170,7 @@
        "(1073512, 391818)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -585,41 +181,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "traindataset = traindataset.astype('float32')\n",
-    "testdataset = testdataset.astype('float32')\n",
-    "\n",
-    "scaler = StandardScaler()\n",
-    "traindataset = scaler.fit_transform(traindataset)\n",
-    "testdataset = scaler.transform(testdataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(1073512, 45)"
+       "['scaler_vav_1.pkl']"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "traindataset.shape"
+    "traindataset = traindataset.astype('float32')\n",
+    "testdataset = testdataset.astype('float32')\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "traindataset = scaler.fit_transform(traindataset)\n",
+    "testdataset = scaler.transform(testdataset)\n",
+    "\n",
+    "joblib.dump(scaler, 'scaler_vav_1.pkl')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -630,7 +219,7 @@
     "    Y = []\n",
     "    for i in range(len(dataset) - time_step - 1):\n",
     "        x.append(dataset[i:(i+time_step),:])\n",
-    "        Y.append(dataset[i+time_step,0:23])\n",
+    "        Y.append(dataset[i+time_step,0:26])\n",
     "    x= np.array(x)\n",
     "    Y = np.array(Y)\n",
     "    return x,Y\n",
@@ -642,16 +231,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "((1073481, 30, 45), (1073481, 23))"
+       "((1073481, 30, 55), (1073481, 26))"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -662,51 +251,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/5\n",
-      "8387/8387 [==============================] - ETA: 0s - loss: 0.0178\n",
-      "Epoch 1: val_loss improved from inf to 0.42313, saving model to lstm_vav_01.tf\n",
-      "INFO:tensorflow:Assets written to: lstm_vav_01.tf\\assets\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Assets written to: lstm_vav_01.tf\\assets\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8387/8387 [==============================] - 307s 36ms/step - loss: 0.0178 - val_loss: 0.4231\n",
-      "Epoch 2/5\n",
-      "8387/8387 [==============================] - ETA: 0s - loss: 0.0032\n",
-      "Epoch 2: val_loss improved from 0.42313 to 0.40364, saving model to lstm_vav_01.tf\n",
-      "INFO:tensorflow:Assets written to: lstm_vav_01.tf\\assets\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Assets written to: lstm_vav_01.tf\\assets\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8387/8387 [==============================] - 274s 33ms/step - loss: 0.0032 - val_loss: 0.4036\n",
-      "Epoch 3/5\n",
-      " 259/8387 [..............................] - ETA: 4:02 - loss: 0.0028"
+      "Epoch 1/3\n",
+      "\u001b[1m8387/8387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0696\n",
+      "Epoch 1: val_loss improved from inf to 0.65445, saving model to lstm_vav_01.keras\n",
+      "\u001b[1m8387/8387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m589s\u001b[0m 69ms/step - loss: 0.0696 - val_loss: 0.6544\n",
+      "Epoch 2/3\n",
+      "\u001b[1m 449/8387\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m7:16\u001b[0m 55ms/step - loss: 0.0033"
      ]
     },
     {
@@ -716,17 +273,18 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[13], line 11\u001b[0m\n\u001b[0;32m      9\u001b[0m checkpoint_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlstm_vav_01.tf\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     10\u001b[0m checkpoint_callback \u001b[38;5;241m=\u001b[39m ModelCheckpoint(filepath\u001b[38;5;241m=\u001b[39mcheckpoint_path, monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, save_best_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 11\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m128\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
-      "File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\keras\\src\\engine\\training.py:1742\u001b[0m, in \u001b[0;36mModel.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1734\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[0;32m   1735\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1736\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1739\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m   1740\u001b[0m ):\n\u001b[0;32m   1741\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m-> 1742\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1743\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[0;32m   1744\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
-      "File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
-      "File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:825\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    822\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    824\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 825\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    827\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    828\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
-      "File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:857\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    854\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m    855\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[0;32m    856\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[1;32m--> 857\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_no_variable_creation_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[0;32m    858\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    859\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[0;32m    860\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[0;32m    861\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
-      "File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compiler.py:148\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    145\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m    146\u001b[0m   (concrete_function,\n\u001b[0;32m    147\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[1;32m--> 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    149\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\monomorphic_function.py:1349\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, args, captured_inputs)\u001b[0m\n\u001b[0;32m   1345\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1347\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1348\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1349\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m   1350\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1351\u001b[0m     args,\n\u001b[0;32m   1352\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1353\u001b[0m     executing_eagerly)\n\u001b[0;32m   1354\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
-      "File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:196\u001b[0m, in \u001b[0;36mAtomicFunction.__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    194\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[0;32m    195\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[1;32m--> 196\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    197\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    198\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    199\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    200\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    201\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    202\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mlist\u001b[39m(args))\n",
-      "File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\tensorflow\\python\\eager\\context.py:1457\u001b[0m, in \u001b[0;36mContext.call_function\u001b[1;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[0;32m   1455\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[0;32m   1456\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1457\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1458\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1459\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1460\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1461\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1462\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1463\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1464\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1465\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m   1466\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1467\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1471\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[0;32m   1472\u001b[0m   )\n",
-      "File \u001b[1;32md:\\Programs\\minconda3\\envs\\smartbuildings\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "Cell \u001b[1;32mIn[54], line 11\u001b[0m\n\u001b[0;32m      9\u001b[0m checkpoint_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlstm_vav_01.keras\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     10\u001b[0m checkpoint_callback \u001b[38;5;241m=\u001b[39m ModelCheckpoint(filepath\u001b[38;5;241m=\u001b[39mcheckpoint_path, monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, save_best_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 11\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m128\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:314\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[0;32m    312\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[0;32m    313\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m--> 314\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    315\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pythonify_logs(logs)\n\u001b[0;32m    316\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[0;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[1;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[0;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[1;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[0;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[0;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[0;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[0;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1324\u001b[0m     args,\n\u001b[0;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1326\u001b[0m     executing_eagerly)\n\u001b[0;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[0;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[1;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[0;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[0;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[0;32m    261\u001b[0m     )\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\context.py:1500\u001b[0m, in \u001b[0;36mContext.call_function\u001b[1;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[0;32m   1498\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[0;32m   1499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1500\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1501\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1502\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1503\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1504\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1505\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1506\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1507\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1508\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m   1509\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1510\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1514\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[0;32m   1515\u001b[0m   )\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
@@ -741,41 +299,30 @@
     "\n",
     "model.compile(optimizer='adam', loss='mean_squared_error')\n",
     "\n",
-    "checkpoint_path = \"lstm_vav_01.tf\"\n",
+    "checkpoint_path = \"lstm_vav_01.keras\"\n",
     "checkpoint_callback = ModelCheckpoint(filepath=checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
-    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=128, verbose=1, callbacks=[checkpoint_callback])"
+    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=3, batch_size=128, verbose=1, callbacks=[checkpoint_callback])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 55,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x2a4b2344610>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.load_weights(checkpoint_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12244/12244 [==============================] - 58s 5ms/step\n"
+      "\u001b[1m12244/12244\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m110s\u001b[0m 9ms/step\n"
      ]
     }
    ],
@@ -785,52 +332,91 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Index(['date', 'zone_069_temp', 'zone_069_fan_spd', 'zone_068_temp',\n",
-       "       'zone_068_fan_spd', 'zone_067_temp', 'zone_067_fan_spd',\n",
-       "       'zone_066_temp', 'zone_066_fan_spd', 'zone_042_temp',\n",
-       "       'zone_042_fan_spd', 'zone_041_temp', 'zone_041_fan_spd',\n",
-       "       'zone_040_temp', 'zone_040_fan_spd', 'zone_039_temp',\n",
-       "       'zone_039_fan_spd', 'zone_038_temp', 'zone_038_fan_spd',\n",
-       "       'zone_037_temp', 'zone_037_fan_spd', 'zone_036_temp',\n",
-       "       'zone_036_fan_spd', 'zone_069_cooling_sp', 'zone_069_heating_sp',\n",
-       "       'zone_067_cooling_sp', 'zone_067_heating_sp', 'zone_066_cooling_sp',\n",
-       "       'zone_066_heating_sp', 'zone_042_cooling_sp', 'zone_042_heating_sp',\n",
-       "       'zone_041_cooling_sp', 'zone_041_heating_sp', 'zone_039_cooling_sp',\n",
-       "       'zone_039_heating_sp', 'zone_038_cooling_sp', 'zone_038_heating_sp',\n",
-       "       'zone_037_cooling_sp', 'zone_037_heating_sp', 'zone_036_cooling_sp',\n",
-       "       'zone_036_heating_sp', 'air_temp_set_1', 'air_temp_set_2',\n",
-       "       'dew_point_temperature_set_1d', 'relative_humidity_set_1',\n",
-       "       'solar_radiation_set_1'],\n",
-       "      dtype='object')"
+       "{0: 'zone_069_temp',\n",
+       " 1: 'zone_069_fan_spd',\n",
+       " 2: 'zone_068_temp',\n",
+       " 3: 'zone_068_fan_spd',\n",
+       " 4: 'zone_067_temp',\n",
+       " 5: 'zone_067_fan_spd',\n",
+       " 6: 'zone_066_temp',\n",
+       " 7: 'zone_066_fan_spd',\n",
+       " 8: 'zone_065_temp',\n",
+       " 9: 'zone_065_fan_spd',\n",
+       " 10: 'zone_064_temp',\n",
+       " 11: 'zone_064_fan_spd',\n",
+       " 12: 'zone_042_temp',\n",
+       " 13: 'zone_042_fan_spd',\n",
+       " 14: 'zone_041_temp',\n",
+       " 15: 'zone_041_fan_spd',\n",
+       " 16: 'zone_040_temp',\n",
+       " 17: 'zone_040_fan_spd',\n",
+       " 18: 'zone_039_temp',\n",
+       " 19: 'zone_039_fan_spd',\n",
+       " 20: 'zone_038_temp',\n",
+       " 21: 'zone_038_fan_spd',\n",
+       " 22: 'zone_037_temp',\n",
+       " 23: 'zone_037_fan_spd',\n",
+       " 24: 'zone_036_temp',\n",
+       " 25: 'zone_036_fan_spd',\n",
+       " 26: 'rtu_001_fltrd_sa_flow_tn',\n",
+       " 27: 'rtu_001_sa_temp',\n",
+       " 28: 'air_temp_set_1',\n",
+       " 29: 'air_temp_set_2',\n",
+       " 30: 'dew_point_temperature_set_1d',\n",
+       " 31: 'relative_humidity_set_1',\n",
+       " 32: 'solar_radiation_set_1',\n",
+       " 33: 'zone_069_cooling_sp',\n",
+       " 34: 'zone_069_heating_sp',\n",
+       " 35: 'zone_067_cooling_sp',\n",
+       " 36: 'zone_067_heating_sp',\n",
+       " 37: 'zone_066_cooling_sp',\n",
+       " 38: 'zone_066_heating_sp',\n",
+       " 39: 'zone_065_cooling_sp',\n",
+       " 40: 'zone_065_heating_sp',\n",
+       " 41: 'zone_064_cooling_sp',\n",
+       " 42: 'zone_064_heating_sp',\n",
+       " 43: 'zone_042_cooling_sp',\n",
+       " 44: 'zone_042_heating_sp',\n",
+       " 45: 'zone_041_cooling_sp',\n",
+       " 46: 'zone_041_heating_sp',\n",
+       " 47: 'zone_039_cooling_sp',\n",
+       " 48: 'zone_039_heating_sp',\n",
+       " 49: 'zone_038_cooling_sp',\n",
+       " 50: 'zone_038_heating_sp',\n",
+       " 51: 'zone_037_cooling_sp',\n",
+       " 52: 'zone_037_heating_sp',\n",
+       " 53: 'zone_036_cooling_sp',\n",
+       " 54: 'zone_036_heating_sp'}"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "traindataset_df.columns"
+    "idx_to_col = {i:col for i,col in enumerate(traindataset_df.drop(columns = ['date']).columns)}\n",
+    "idx_to_col"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib qt\n",
     "plt.figure()\n",
-    "var = 1\n",
+    "var = 10\n",
     "plt.plot(y_test[:,var], label='Original Testing Data', color='blue')\n",
     "plt.plot(test_predict1[:,var], label='Predicted Testing Data', color='red',alpha=0.8)\n",
-    "anomalies = np.where(abs(test_predict1[:,var] - y_test[:,var]) > 0.38)\n",
+    "anomalies = np.where(abs(test_predict1[:,var] - y_test[:,var]) > 0.5)\n",
     "plt.scatter(anomalies,test_predict1[anomalies,var], color='black',marker =\"o\",s=100 )\n",
     "\n",
     "\n",
@@ -881,9 +467,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 86,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['kmeans_vav_1.pkl']"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.cluster import KMeans\n",
     "import numpy as np\n",
@@ -894,7 +491,7 @@
     "np.random.seed(0)\n",
     "X = (test_predict1 - y_test)\n",
     "\n",
-    "k = 6\n",
+    "k = 2\n",
     "\n",
     "kmeans = KMeans(n_clusters=k)\n",
     "\n",
@@ -917,7 +514,9 @@
     "plt.title('KMeans Clustering')\n",
     "plt.xlabel('Feature 1')\n",
     "plt.ylabel('Feature 2')\n",
-    "plt.show()\n"
+    "plt.show()\n",
+    "\n",
+    "joblib.dump(kmeans, 'kmeans_vav_1.pkl')"
    ]
   },
   {

src/main.py

@@ -1,6 +1,7 @@
 import json
 from rtu.RTUAnomalizer import RTUAnomalizer
 from rtu.RTUPipeline import RTUPipeline
+from vav.VAVPipeline import VAVPipeline
 import paho.mqtt.client as mqtt
 
 
@@ -19,6 +20,12 @@ def main():
         num_outputs=rtu_data_pipeline.num_outputs,
     )
 
+    # vav_pipeline = VAVPipeline(rtu_id=1)
+
+    # print(vav_pipeline.input_col_names)
+
+    # print(len(vav_pipeline.output_col_names))
+
     def on_message(client, userdata, message):
         # print(json.loads(message.payload.decode()))
         df_new, df_trans = rtu_data_pipeline.fit(message)

src/rtu/RTUAnomalizer.py

@@ -4,6 +4,10 @@ import joblib
 
 
 class RTUAnomalizer:
+    """
+    Class for performing anomaly detection on RTU (Roof Top Unit) data.
+    """
+
     model = None
     kmeans_models = []
 
@@ -14,31 +18,84 @@ class RTUAnomalizer:
         num_inputs=None,
         num_outputs=None,
     ):
+        """
+        Initialize the RTUAnomalizer object.
 
+        Args:
+            prediction_model_path (str): Path to the prediction model file.
+            clustering_model_paths (list): List of paths to the clustering model files.
+            num_inputs (int): Number of input features.
+            num_outputs (int): Number of output features.
+        """
         self.num_inputs = num_inputs
         self.num_outputs = num_outputs
-        if not prediction_model_path is None and not clustering_model_paths is None:
+        if prediction_model_path is not None and clustering_model_paths is not None:
             self.load_models(prediction_model_path, clustering_model_paths)
 
     def initialize_lists(self, size=30):
+        """
+        Initialize lists for storing actual, predicted, and residual values.
+
+        Args:
+            size (int): Size of the lists.
+
+        Returns:
+            tuple: A tuple containing three lists initialized with zeros.
+        """
         initial_values = [0] * size
         return initial_values.copy(), initial_values.copy(), initial_values.copy()
 
     def load_models(self, prediction_model_path, clustering_model_paths):
+        """
+        Load the prediction and clustering models.
+
+        Args:
+            prediction_model_path (str): Path to the prediction model file.
+            clustering_model_paths (list): List of paths to the clustering model files.
+        """
         self.model = load_model(prediction_model_path)
 
         for path in clustering_model_paths:
             self.kmeans_models.append(joblib.load(path))
 
     def predict(self, df_new):
+        """
+        Make predictions using the prediction model.
+
+        Args:
+            df_new (DataFrame): Input data for prediction.
+
+        Returns:
+            array: Predicted values.
+        """
         return self.model.predict(df_new)
 
     def calculate_residuals(self, df_trans, pred):
+        """
+        Calculate the residuals between actual and predicted values.
+
+        Args:
+            df_trans (DataFrame): Transformed input data.
+            pred (array): Predicted values.
+
+        Returns:
+            tuple: A tuple containing the actual values and residuals.
+        """
         actual = df_trans[30, : self.num_outputs]
         resid = actual - pred
         return actual, resid
 
     def resize_prediction(self, pred, df_trans):
+        """
+        Resize the predicted values to match the shape of the transformed input data.
+
+        Args:
+            pred (array): Predicted values.
+            df_trans (DataFrame): Transformed input data.
+
+        Returns:
+            array: Resized predicted values.
+        """
         pred = np.resize(
             pred, (pred.shape[0], pred.shape[1] + len(df_trans[30, self.num_outputs :]))
         )
@@ -48,11 +105,36 @@ class RTUAnomalizer:
         return pred
 
     def inverse_transform(self, scaler, pred, df_trans):
+        """
+        Inverse transform the predicted and actual values.
+
+        Args:
+            scaler (object): Scaler object for inverse transformation.
+            pred (array): Predicted values.
+            df_trans (DataFrame): Transformed input data.
+
+        Returns:
+            tuple: A tuple containing the actual and predicted values after inverse transformation.
+        """
         pred = scaler.inverse_transform(np.array(pred))
         actual = scaler.inverse_transform(np.array([df_trans[30, :]]))
         return actual, pred
 
     def update_lists(self, actual_list, pred_list, resid_list, actual, pred, resid):
+        """
+        Update the lists of actual, predicted, and residual values.
+
+        Args:
+            actual_list (list): List of actual values.
+            pred_list (list): List of predicted values.
+            resid_list (list): List of residual values.
+            actual (array): Actual values.
+            pred (array): Predicted values.
+            resid (array): Residual values.
+
+        Returns:
+            tuple: A tuple containing the updated lists of actual, predicted, and residual values.
+        """
         actual_list.pop(0)
         pred_list.pop(0)
         resid_list.pop(0)
@@ -62,6 +144,15 @@ class RTUAnomalizer:
         return actual_list, pred_list, resid_list
 
     def calculate_distances(self, resid):
+        """
+        Calculate the distances between residuals and cluster centers.
+
+        Args:
+            resid (array): Residual values.
+
+        Returns:
+            array: Array of distances.
+        """
         dist = []
         for i, model in enumerate(self.kmeans_models):
             dist.append(
@@ -75,6 +166,17 @@ class RTUAnomalizer:
         return np.array(dist)
 
     def pipeline(self, df_new, df_trans, scaler):
+        """
+        Perform the anomaly detection pipeline.
+
+        Args:
+            df_new (DataFrame): Input data for prediction.
+            df_trans (DataFrame): Transformed input data.
+            scaler (object): Scaler object for inverse transformation.
+
+        Returns:
+            tuple: A tuple containing the lists of actual, predicted, and residual values, and the distances.
+        """
         actual_list, pred_list, resid_list = self.initialize_lists()
         pred = self.predict(df_new)
         actual, resid = self.calculate_residuals(df_trans, pred)

src/vav/VAVAnomalizer.py

@@ -0,0 +1,176 @@
+import numpy as np
+from tensorflow.keras.models import load_model
+import joblib
+
+
+class VAVAnomalizer:
+    def __init__(
+        self,
+        rtu_id,
+        prediction_model_path,
+        clustering_model_path,
+        num_inputs,
+        num_outputs,
+    ):
+        """
+        Initializes a VAVAnomalizer object.
+
+        Args:
+            rtu_id (int): The ID of the RTU (Roof Top Unit) associated with the VAV (Variable Air Volume) system.
+            prediction_model_path (str): The file path to the prediction model.
+            clustering_model_path (str): The file path to the clustering model.
+            num_inputs (int): The number of input features for the prediction model.
+            num_outputs (int): The number of output features for the prediction model.
+        """
+        self.rtu_id = rtu_id
+        self.num_inputs = num_inputs
+        self.num_outputs = num_outputs
+        self.load_models(prediction_model_path, clustering_model_path)
+
+    def load_models(self, prediction_model_path, clustering_model_path):
+        """
+        Loads the prediction model and clustering model.
+
+        Args:
+            prediction_model_path (str): The file path to the prediction model.
+            clustering_model_path (str): The file path to the clustering model.
+        """
+        self.model = load_model(prediction_model_path)
+        self.kmeans_model = joblib.load(clustering_model_path)
+
+    def initialize_lists(self, size=30):
+        """
+        Initialize lists for storing actual, predicted, and residual values.
+
+        Args:
+            size (int): Size of the lists.
+
+        Returns:
+            tuple: A tuple containing three lists initialized with zeros.
+        """
+        initial_values = [0] * size
+        return initial_values.copy(), initial_values.copy(), initial_values.copy()
+
+    def predict(self, df_new):
+        """
+        Makes predictions using the prediction model.
+
+        Args:
+            df_new (numpy.ndarray): The new data for prediction.
+
+        Returns:
+            numpy.ndarray: The predicted values.
+        """
+        return self.model.predict(df_new)
+
+    def calculate_residuals(self, df_trans, pred):
+        """
+        Calculates the residuals between the actual values and the predicted values.
+
+        Args:
+            df_trans (numpy.ndarray): The transformed data.
+            pred (numpy.ndarray): The predicted values.
+
+        Returns:
+            numpy.ndarray: The actual values.
+            numpy.ndarray: The residuals.
+        """
+        actual = df_trans[30, : self.num_outputs]
+        resid = actual - pred
+        return actual, resid
+
+    def calculate_distances(self, resid):
+        """
+        Calculate the distances between residuals and cluster centers.
+
+        Args:
+            resid (array): Residual values.
+
+        Returns:
+            array: Array of distances.
+        """
+        dist = []
+        dist.append(np.linalg.norm(resid - self.kmeans_model.cluster_centers_[0]))
+
+        return np.array(dist)
+
+    def resize_prediction(self, pred, df_trans):
+        """
+        Resize the predicted values to match the shape of the transformed input data.
+
+        Args:
+            pred (array): Predicted values.
+            df_trans (DataFrame): Transformed input data.
+
+        Returns:
+            array: Resized predicted values.
+        """
+        pred = np.resize(
+            pred, (pred.shape[0], pred.shape[1] + len(df_trans[30, self.num_outputs :]))
+        )
+        pred[:, -len(df_trans[30, self.num_outputs :]) :] = df_trans[
+            30, self.num_outputs :
+        ]
+        return pred
+
+    def inverse_transform(self, scaler, pred, df_trans):
+        """
+        Inverse transform the predicted and actual values.
+
+        Args:
+            scaler (object): Scaler object for inverse transformation.
+            pred (array): Predicted values.
+            df_trans (DataFrame): Transformed input data.
+
+        Returns:
+            tuple: A tuple containing the actual and predicted values after inverse transformation.
+        """
+        pred = scaler.inverse_transform(np.array(pred))
+        actual = scaler.inverse_transform(np.array([df_trans[30, :]]))
+        return actual, pred
+
+    def update_lists(self, actual_list, pred_list, resid_list, actual, pred, resid):
+        """
+        Update the lists of actual, predicted, and residual values.
+
+        Args:
+            actual_list (list): List of actual values.
+            pred_list (list): List of predicted values.
+            resid_list (list): List of residual values.
+            actual (array): Actual values.
+            pred (array): Predicted values.
+            resid (array): Residual values.
+
+        Returns:
+            tuple: A tuple containing the updated lists of actual, predicted, and residual values.
+        """
+        actual_list.pop(0)
+        pred_list.pop(0)
+        resid_list.pop(0)
+        actual_list.append(actual[0, 1])
+        pred_list.append(pred[0, 1])
+        resid_list.append(resid[0, 1])
+        return actual_list, pred_list, resid_list
+
+    def pipeline(self, df_new, df_trans, scaler):
+        """
+        Perform the anomaly detection pipeline.
+
+        Args:
+            df_new (DataFrame): Input data for prediction.
+            df_trans (DataFrame): Transformed input data.
+            scaler (object): Scaler object for inverse transformation.
+
+        Returns:
+            tuple: A tuple containing the lists of actual, predicted, and residual values, and the distances.
+        """
+        actual_list, pred_list, resid_list = self.initialize_lists()
+        pred = self.predict(df_new)
+        actual, resid = self.calculate_residuals(df_trans, pred)
+        pred = self.resize_prediction(pred, df_trans)
+        actual, pred = self.inverse_transform(scaler, pred, df_trans)
+        actual_list, pred_list, resid_list = self.update_lists(
+            actual_list, pred_list, resid_list, actual, pred, resid
+        )
+        dist = self.calculate_distances(resid)
+        return actual_list, pred_list, resid_list, dist

src/vav/VAVPipeline.py

@@ -0,0 +1,100 @@
+import json
+from sklearn.preprocessing import StandardScaler
+from pickle import load
+import numpy as np
+
+
+class VAVPipeline:
+
+    def __init__(self, rtu_id, scaler_path=None, window_size=30):
+
+        self.window_size = window_size
+
+        if rtu_id == 1:
+            self.zones = [69, 68, 67, 66, 65, 64, 42, 41, 40, 39, 38, 37, 36]
+        if rtu_id == 2:
+            self.zones = [
+                72,
+                71,
+                63,
+                62,
+                60,
+                59,
+                58,
+                57,
+                50,
+                49,
+                44,
+                43,
+                35,
+                34,
+                33,
+                32,
+                31,
+                30,
+                29,
+                28,
+            ]
+
+        outputs = ["temp", "fan_speed"]
+        inputs = ["cooling_sp", "heating_sp"]
+        self.output_col_names = []
+        self.input_col_names = [
+            f"rtu_00{rtu_id}_fltrd_sa_flow_tn",
+            f"rtu_00{rtu_id}_sa_temp",
+            "air_temp_set_1",
+            "air_temp_set_2",
+            "dew_point_temperature_set_1d",
+            "relative_humidity_set_1",
+            "solar_radiation_set_1",
+        ]
+        for zone in self.zones:
+            for output in outputs:
+                self.output_col_names.append(f"zone_0{zone}_{output}")
+            for input in inputs:
+                self.input_col_names.append(f"zone_0{zone}_{input}")
+
+        self.column_names = self.output_col_names + self.input_col_names
+
+        if scaler_path:
+            self.scaler = self.get_scaler(scaler_path)
+
+    def get_scaler(self, scaler_path):
+        return load(scaler_path)
+
+    def get_window(self, df):
+        len_df = len(df)
+        if len_df > self.window_size:
+            return df[len_df - (self.window_size + 1) : len_df].astype("float32")
+        else:
+            return None
+
+    def transform_window(self, df_window):
+        return self.scaler.transform(df_window)
+
+    def prepare_input(self, df_trans):
+        return df_trans[: self.window_size, :].reshape(
+            (1, self.window_size, len(self.column_names))
+        )
+
+    def extract_data_from_message(self, message):
+        payload = json.loads(message.payload.decode())
+
+        len_df = len(self.df)
+
+        k = {}
+        for col in self.column_names:
+            k[col] = payload[col]
+        self.df.loc[len_df] = k
+        return self.df
+
+    def fit(self, message):
+        df = self.extract_data_from_message(message)
+        df_window = self.get_window(df)
+        if df_window is not None:
+            df_trans = self.transform_window(df_window)
+            df_new = self.prepare_input(df_trans)
+        else:
+            df_new = None
+            df_trans = None
+        return df_new, df_trans

src/vav/models/kmeans_vav_1.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:086d45b9d2c98baaea5b0588cd6d228d84eb141b707fe845b11316b3ddc58774
+size 1568153

src/vav/models/scaler_vav_1.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:293ee3c9082e7104dfc96425cecad2a44e5914bbd1f43c25a0fd8c36507b103a
+size 1925