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{
"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 = [69, 68,67, 66,65, 64, 42,41,40,39,38,37,36]\n",
"rtus = [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",
"\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_16740\\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",
"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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are NA values in the DataFrame columns.\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, 1, 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": {
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"</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_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",
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" <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",
" </thead>\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>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",
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" <td>12.930</td>\n",
" <td>9.10</td>\n",
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],
"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,
"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": [
{
"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": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1073512, 391818)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(traindataset), len(testdataset)"
]
},
{
"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,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1073512, 45)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"traindataset.shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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,0:23])\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": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((1073481, 30, 45), (1073481, 23))"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.shape, y_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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"
]
},
{
"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[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",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"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_01.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": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x2a4b2344610>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.load_weights(checkpoint_path)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"12244/12244 [==============================] - 58s 5ms/step\n"
]
}
],
"source": [
"test_predict1 = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"traindataset_df.columns"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib qt\n",
"plt.figure()\n",
"var = 1\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": [],
"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": null,
"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",
"\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": 19,
"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": null,
"metadata": {},
"outputs": [],
"source": [
"X = processed_data\n",
"\n",
"kmeans = KMeans(n_clusters=3, 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": 21,
"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": 38,
"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": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"329810"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(labels==0)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "tensorflow",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|