{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "from datetime import datetime \n",
    "from datetime import date\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense\n",
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = pd.read_csv(r'../data/long_merge.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "zones = [72, 71, 63, 62, 60, 59, 58,57, 50, 49, 44, 43, 35, 34, 33, 32, 31, 30, 29, 28, ]\n",
    "rtus = [2]\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",
    "\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",
    "#                 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",
    "input_dataset = merged[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arbal\\AppData\\Local\\Temp\\ipykernel_38868\\1855433847.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",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  input_dataset['date'] = pd.to_datetime(input_dataset['date'], format = \"%Y-%m-%d %H:%M:%S\")\n"
     ]
    }
   ],
   "source": [
    "input_dataset['date'] = pd.to_datetime(input_dataset['date'], format = \"%Y-%m-%d %H:%M:%S\")\n",
    "df_filtered = input_dataset[ (input_dataset.date.dt.date >date(2019, 3, 1)) & (input_dataset.date.dt.date< date(2021, 1, 1))]\n",
    "\n",
    "if df_filtered.isna().any().any():\n",
    "    print(\"There are NA values in the DataFrame columns.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>date</th>\n",
       "      <th>zone_072_temp</th>\n",
       "      <th>zone_072_fan_spd</th>\n",
       "      <th>zone_071_temp</th>\n",
       "      <th>zone_071_fan_spd</th>\n",
       "      <th>zone_063_temp</th>\n",
       "      <th>zone_063_fan_spd</th>\n",
       "      <th>zone_062_temp</th>\n",
       "      <th>zone_062_fan_spd</th>\n",
       "      <th>zone_059_temp</th>\n",
       "      <th>...</th>\n",
       "      <th>zone_035_heating_sp</th>\n",
       "      <th>zone_032_cooling_sp</th>\n",
       "      <th>zone_032_heating_sp</th>\n",
       "      <th>zone_030_cooling_sp</th>\n",
       "      <th>zone_030_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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>511205</th>\n",
       "      <td>2019-03-02 00:00:00</td>\n",
       "      <td>71.2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>72.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>72.9</td>\n",
       "      <td>55.0</td>\n",
       "      <td>71.9</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>11.590</td>\n",
       "      <td>11.130</td>\n",
       "      <td>3.00</td>\n",
       "      <td>55.87</td>\n",
       "      <td>120.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511206</th>\n",
       "      <td>2019-03-02 00:01:00</td>\n",
       "      <td>71.2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>72.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>72.9</td>\n",
       "      <td>55.0</td>\n",
       "      <td>71.9</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>11.590</td>\n",
       "      <td>11.130</td>\n",
       "      <td>3.00</td>\n",
       "      <td>55.87</td>\n",
       "      <td>120.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511207</th>\n",
       "      <td>2019-03-02 00:02:00</td>\n",
       "      <td>71.2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>72.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>72.6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>71.9</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>11.590</td>\n",
       "      <td>11.130</td>\n",
       "      <td>3.00</td>\n",
       "      <td>55.87</td>\n",
       "      <td>120.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511208</th>\n",
       "      <td>2019-03-02 00:03:00</td>\n",
       "      <td>71.2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>72.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>72.9</td>\n",
       "      <td>55.0</td>\n",
       "      <td>71.9</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>11.590</td>\n",
       "      <td>11.130</td>\n",
       "      <td>3.00</td>\n",
       "      <td>55.87</td>\n",
       "      <td>120.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511209</th>\n",
       "      <td>2019-03-02 00:04:00</td>\n",
       "      <td>71.2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>71.4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>72.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>72.9</td>\n",
       "      <td>55.0</td>\n",
       "      <td>71.9</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>11.590</td>\n",
       "      <td>11.130</td>\n",
       "      <td>3.00</td>\n",
       "      <td>55.87</td>\n",
       "      <td>120.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2072148</th>\n",
       "      <td>2020-12-31 23:57:00</td>\n",
       "      <td>69.5</td>\n",
       "      <td>40.0</td>\n",
       "      <td>71.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>67.6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>67.5</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>72.714138</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>13.994</td>\n",
       "      <td>13.528</td>\n",
       "      <td>4.11</td>\n",
       "      <td>51.61</td>\n",
       "      <td>188.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2072149</th>\n",
       "      <td>2020-12-31 23:58:00</td>\n",
       "      <td>69.5</td>\n",
       "      <td>40.0</td>\n",
       "      <td>71.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>67.6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>67.5</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>72.714138</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>13.994</td>\n",
       "      <td>13.528</td>\n",
       "      <td>4.11</td>\n",
       "      <td>51.61</td>\n",
       "      <td>188.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2072150</th>\n",
       "      <td>2020-12-31 23:58:00</td>\n",
       "      <td>69.5</td>\n",
       "      <td>40.0</td>\n",
       "      <td>71.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>67.6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>67.5</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>72.714138</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>13.994</td>\n",
       "      <td>13.528</td>\n",
       "      <td>4.11</td>\n",
       "      <td>51.61</td>\n",
       "      <td>188.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2072151</th>\n",
       "      <td>2020-12-31 23:59:00</td>\n",
       "      <td>69.5</td>\n",
       "      <td>40.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>67.6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>67.5</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>72.714138</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>13.994</td>\n",
       "      <td>13.528</td>\n",
       "      <td>4.11</td>\n",
       "      <td>51.61</td>\n",
       "      <td>188.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2072152</th>\n",
       "      <td>2020-12-31 23:59:00</td>\n",
       "      <td>69.5</td>\n",
       "      <td>40.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>67.6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>67.5</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>72.714138</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>13.994</td>\n",
       "      <td>13.528</td>\n",
       "      <td>4.11</td>\n",
       "      <td>51.61</td>\n",
       "      <td>188.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1560948 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       date  zone_072_temp  zone_072_fan_spd  zone_071_temp  \\\n",
       "511205  2019-03-02 00:00:00           71.2              40.0           71.5   \n",
       "511206  2019-03-02 00:01:00           71.2              40.0           71.5   \n",
       "511207  2019-03-02 00:02:00           71.2              40.0           71.5   \n",
       "511208  2019-03-02 00:03:00           71.2              40.0           71.5   \n",
       "511209  2019-03-02 00:04:00           71.2              40.0           71.4   \n",
       "...                     ...            ...               ...            ...   \n",
       "2072148 2020-12-31 23:57:00           69.5              40.0           71.2   \n",
       "2072149 2020-12-31 23:58:00           69.5              40.0           71.3   \n",
       "2072150 2020-12-31 23:58:00           69.5              40.0           71.3   \n",
       "2072151 2020-12-31 23:59:00           69.5              40.0           71.5   \n",
       "2072152 2020-12-31 23:59:00           69.5              40.0           71.5   \n",
       "\n",
       "         zone_071_fan_spd  zone_063_temp  zone_063_fan_spd  zone_062_temp  \\\n",
       "511205               20.0           72.3              20.0           72.9   \n",
       "511206               20.0           72.3              20.0           72.9   \n",
       "511207               20.0           72.3              20.0           72.6   \n",
       "511208               20.0           72.3              20.0           72.9   \n",
       "511209               20.0           72.3              20.0           72.9   \n",
       "...                   ...            ...               ...            ...   \n",
       "2072148              20.0           68.0              20.0           67.6   \n",
       "2072149              20.0           68.0              20.0           67.6   \n",
       "2072150              20.0           68.0              20.0           67.6   \n",
       "2072151              20.0           68.0              20.0           67.6   \n",
       "2072152              20.0           68.0              20.0           67.6   \n",
       "\n",
       "         zone_062_fan_spd  zone_059_temp  ...  zone_035_heating_sp  \\\n",
       "511205               55.0           71.9  ...                 70.0   \n",
       "511206               55.0           71.9  ...                 70.0   \n",
       "511207               55.0           71.9  ...                 70.0   \n",
       "511208               55.0           71.9  ...                 70.0   \n",
       "511209               55.0           71.9  ...                 70.0   \n",
       "...                   ...            ...  ...                  ...   \n",
       "2072148              40.0           67.5  ...                 68.0   \n",
       "2072149              40.0           67.5  ...                 68.0   \n",
       "2072150              40.0           67.5  ...                 68.0   \n",
       "2072151              40.0           67.5  ...                 68.0   \n",
       "2072152              40.0           67.5  ...                 68.0   \n",
       "\n",
       "         zone_032_cooling_sp  zone_032_heating_sp  zone_030_cooling_sp  \\\n",
       "511205             74.000000                 68.0                 73.0   \n",
       "511206             74.000000                 68.0                 73.0   \n",
       "511207             74.000000                 68.0                 73.0   \n",
       "511208             74.000000                 68.0                 73.0   \n",
       "511209             74.000000                 68.0                 73.0   \n",
       "...                      ...                  ...                  ...   \n",
       "2072148            72.714138                 71.0                 71.0   \n",
       "2072149            72.714138                 71.0                 71.0   \n",
       "2072150            72.714138                 71.0                 71.0   \n",
       "2072151            72.714138                 71.0                 71.0   \n",
       "2072152            72.714138                 71.0                 71.0   \n",
       "\n",
       "         zone_030_heating_sp  air_temp_set_1  air_temp_set_2  \\\n",
       "511205                  67.0          11.590          11.130   \n",
       "511206                  67.0          11.590          11.130   \n",
       "511207                  67.0          11.590          11.130   \n",
       "511208                  67.0          11.590          11.130   \n",
       "511209                  67.0          11.590          11.130   \n",
       "...                      ...             ...             ...   \n",
       "2072148                 70.0          13.994          13.528   \n",
       "2072149                 70.0          13.994          13.528   \n",
       "2072150                 70.0          13.994          13.528   \n",
       "2072151                 70.0          13.994          13.528   \n",
       "2072152                 70.0          13.994          13.528   \n",
       "\n",
       "         dew_point_temperature_set_1d  relative_humidity_set_1  \\\n",
       "511205                           3.00                    55.87   \n",
       "511206                           3.00                    55.87   \n",
       "511207                           3.00                    55.87   \n",
       "511208                           3.00                    55.87   \n",
       "511209                           3.00                    55.87   \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",
       "511205                   120.3  \n",
       "511206                   120.3  \n",
       "511207                   120.3  \n",
       "511208                   120.3  \n",
       "511209                   120.3  \n",
       "...                        ...  \n",
       "2072148                  188.8  \n",
       "2072149                  188.8  \n",
       "2072150                  188.8  \n",
       "2072151                  188.8  \n",
       "2072152                  188.8  \n",
       "\n",
       "[1560948 rows x 56 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdataset_df = df_filtered[(df_filtered.date.dt.date >date(2020, 3, 1)) & (df_filtered.date.dt.date <date(2020,7, 1))]\n",
    "\n",
    "# traindataset_df = df_filtered[ (df_filtered.date.dt.date >date(2019, 11, 8))]\n",
    "\n",
    "traindataset_df = df_filtered[(df_filtered.date.dt.date >date(2019, 3, 1)) & (df_filtered.date.dt.date <date(2020, 3, 1)) | (df_filtered.date.dt.date >date(2020, 7, 1)) & (df_filtered.date.dt.date <date(2020, 12, 1))]\n",
    "testdataset = testdataset_df.drop(columns=[\"date\"]).values\n",
    "traindataset = traindataset_df.drop(columns=[\"date\"]).values\n",
    "\n",
    "columns_with_na = traindataset_df.columns[traindataset_df.isna().any()].tolist()\n",
    "columns_with_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'zone_072_temp', 'zone_072_fan_spd', 'zone_071_temp',\n",
       "       'zone_071_fan_spd', 'zone_063_temp', 'zone_063_fan_spd',\n",
       "       'zone_062_temp', 'zone_062_fan_spd', 'zone_059_temp',\n",
       "       'zone_059_fan_spd', 'zone_058_temp', 'zone_058_fan_spd',\n",
       "       'zone_057_temp', 'zone_057_fan_spd', 'zone_049_temp',\n",
       "       'zone_049_fan_spd', 'zone_044_temp', 'zone_044_fan_spd',\n",
       "       'zone_043_temp', 'zone_043_fan_spd', 'zone_035_temp',\n",
       "       'zone_035_fan_spd', 'zone_033_temp', 'zone_033_fan_spd',\n",
       "       'zone_032_temp', 'zone_032_fan_spd', 'zone_030_temp',\n",
       "       'zone_030_fan_spd', 'zone_028_temp', 'zone_028_fan_spd'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindataset_df.columns[0:31]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0\n"
     ]
    }
   ],
   "source": [
    "print(traindataset_df.isna().sum().sum(), testdataset_df.isna().sum().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1073512, 391818)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(traindataset), len(testdataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1073512, 55)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train,test = traindataset,testdataset\n",
    "\n",
    "def create_dataset(dataset,time_step):\n",
    "    x = []\n",
    "    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,0:31])\n",
    "    x= np.array(x)\n",
    "    Y = np.array(Y)\n",
    "    return x,Y\n",
    "time_step = 30\n",
    "X_train, y_train = create_dataset(train, time_step)\n",
    "X_test, y_test = create_dataset(test, time_step)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1073481, 30, 55), (1073481, 31))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
    "model.add(LSTM(units=50, return_sequences=True))\n",
    "model.add(LSTM(units=30))\n",
    "model.add(Dense(units=y_train.shape[1]))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "checkpoint_path = \"lstm_vav_02.tf\"\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])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x2b142b76250>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_weights(checkpoint_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12244/12244 [==============================] - 61s 5ms/step\n"
     ]
    }
   ],
   "source": [
    "test_predict1 = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib qt\n",
    "plt.figure()\n",
    "var = 2\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",
    "plt.scatter(anomalies,test_predict1[anomalies,var], color='black',marker =\"o\",s=100 )\n",
    "\n",
    "\n",
    "plt.title('Testing Data - Predicted vs Actual')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Value')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1254/33547 [>.............................] - ETA: 3:00"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[18], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmatplotlib\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mqt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m test_predict2 \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\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:2554\u001b[0m, in \u001b[0;36mModel.predict\u001b[1;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   2552\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step \u001b[38;5;129;01min\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39msteps():\n\u001b[0;32m   2553\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_predict_batch_begin(step)\n\u001b[1;32m-> 2554\u001b[0m     tmp_batch_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2555\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[0;32m   2556\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:864\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\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\u001b[0;32m    862\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    863\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--> 864\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_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\n\u001b[0;32m    865\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ALLOW_DYNAMIC_VARIABLE_CREATION:\n\u001b[0;32m    866\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    867\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:\\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",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%matplotlib qt\n",
    "test_predict2 = model.predict(X_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "var = 3\n",
    "plt.plot(y_train[:,var], label='Original Training Data', color='blue')\n",
    "plt.plot(test_predict2[:,var], label='Predicted Training Data', color='red',alpha=0.8)\n",
    "anomalies = np.where(abs(test_predict2[:,var] - y_train[:,var]) > 0.38)\n",
    "plt.scatter(anomalies,test_predict2[anomalies,var], color='black',marker =\"o\",s=100 )\n",
    "\n",
    "\n",
    "plt.title('Training Data - Predicted vs Actual')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Value')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.mixture import GaussianMixture\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "\n",
    "# Generating random data for demonstration\n",
    "np.random.seed(0)\n",
    "X =  test_predict1 - y_test\n",
    "\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "X = pca.fit_transform(X)\n",
    "\n",
    "\n",
    "# Creating the GMM instance with desired number of clusters\n",
    "gmm = GaussianMixture(n_components=2)\n",
    "\n",
    "# Fitting the model to the data\n",
    "gmm.fit(X)\n",
    "\n",
    "# Getting the cluster labels\n",
    "labels = gmm.predict(X)\n",
    "\n",
    "# Plotting the data points with colors representing different clusters\n",
    "plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
    "plt.title('GMM Clustering')\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Generating random data for demonstration\n",
    "np.random.seed(0)\n",
    "X = (test_predict1 - y_test)\n",
    "\n",
    "k = 6\n",
    "\n",
    "kmeans = KMeans(n_clusters=k)\n",
    "\n",
    "kmeans.fit(X)\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "X = pca.fit_transform(X)\n",
    "\n",
    "\n",
    "\n",
    "# Getting the cluster centers and labels\n",
    "centroids = kmeans.cluster_centers_\n",
    "centroids = pca.transform(centroids)\n",
    "labels = kmeans.labels_\n",
    "\n",
    "# Plotting the data points and cluster centers\n",
    "plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', c='red', s=200, linewidths=2)\n",
    "plt.title('KMeans Clustering')\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 60\n",
    "X= test_predict1 - y_test\n",
    "processed_data = []\n",
    "feat_df = pd.DataFrame(columns=[\"mean\",\"std\",])\n",
    "for i in range(0,len(X), 60):\n",
    "    mean = X[i:i+k].mean(axis = 0)\n",
    "    std = X[i:i+k].std(axis = 0)\n",
    "    max = X[i:i+k].max(axis = 0)\n",
    "    min = X[i:i+k].min(axis = 0)\n",
    "    iqr = np.percentile(X[i:i+k], 75, axis=0) - np.percentile(X[i:i+k], 25,axis=0)\n",
    "    data = np.concatenate([mean, std, max, min, iqr])\n",
    "    processed_data.append([data])\n",
    "processed_data = np.concatenate(processed_data,axis=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = processed_data\n",
    "\n",
    "kmeans = KMeans(n_clusters=2, algorithm='elkan', max_iter=1000, n_init = 5)\n",
    "\n",
    "kmeans.fit(X)\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "X = pca.fit_transform(X)\n",
    "\n",
    "\n",
    "# Getting the cluster centers and labels\n",
    "centroids = kmeans.cluster_centers_\n",
    "centroids = pca.transform(centroids)\n",
    "labels = kmeans.labels_\n",
    "\n",
    "# Plotting the data points and cluster centers\n",
    "plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', c='red', s=200, linewidths=2)\n",
    "plt.title('KMeans Clustering')\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.mixture import GaussianMixture\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Generating random data for demonstration\n",
    "np.random.seed(0)\n",
    "X = processed_data\n",
    "\n",
    "# Creating the GMM instance with desired number of clusters\n",
    "gmm = GaussianMixture(n_components=2, init_params='k-means++')\n",
    "\n",
    "# Fitting the model to the data\n",
    "gmm.fit(X)\n",
    "labels = gmm.predict(X)\n",
    "\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "X = pca.fit_transform(X)\n",
    "\n",
    "\n",
    "# Getting the cluster labels\n",
    "\n",
    "# Plotting the data points with colors representing different clusters\n",
    "plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
    "plt.title('GMM Clustering')\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# Generating random data for demonstration\n",
    "np.random.seed(0)\n",
    "X = test_predict1 - y_test \n",
    "\n",
    "kmeans = KMeans(n_clusters=2)\n",
    "\n",
    "kmeans.fit(X)\n",
    "\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "X = pca.fit_transform(X)\n",
    "\n",
    "\n",
    "\n",
    "# Getting the cluster centers and labels\n",
    "centroids = kmeans.cluster_centers_\n",
    "centroids = pca.transform(centroids)\n",
    "labels = kmeans.labels_\n",
    "\n",
    "# Plotting the data points and cluster centers\n",
    "plt.figure()\n",
    "plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', c='red', s=200, linewidths=2)\n",
    "plt.text(centroids[0,0], centroids[0,1], 'Normal', fontsize=12, color='red')\n",
    "plt.text(centroids[1,0], centroids[1,1], 'Anomaly', fontsize=12, color='red')\n",
    "plt.title('KMeans Clustering')\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "329763"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(labels==0)"
   ]
  }
 ],
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