Spaces:
Sleeping
Sleeping
File size: 38,173 Bytes
e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf e8d4213 e3c6dcf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 35,
"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\n",
"import joblib"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"merged = pd.read_csv(r'../data/long_merge.csv')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"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 (\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 rtu in rtus:\n",
"# for column in merged.columns:\n",
"# if f\"rtu_00{rtu}_fltrd_sa\" or f\"rtu_00{rtu}_sa_temp\" in column:\n",
"# cols.append(column)\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": 32,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"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",
"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": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 36,
"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": 37,
"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": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1073512, 391818)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(traindataset), len(testdataset)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['scaler_vav_1.pkl']"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"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)\n",
"\n",
"joblib.dump(scaler, 'scaler_vav_1.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 51,
"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,0:26])\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": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((1073481, 30, 55), (1073481, 26))"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.shape, y_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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"
]
},
{
"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[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: "
]
}
],
"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.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=3, batch_size=128, verbose=1, callbacks=[checkpoint_callback])"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"model.load_weights(checkpoint_path)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m12244/12244\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m110s\u001b[0m 9ms/step\n"
]
}
],
"source": [
"test_predict1 = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{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": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"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": 84,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib qt\n",
"plt.figure()\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]) > 0.5)\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": 86,
"metadata": {},
"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",
"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 = 2\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",
"\n",
"joblib.dump(kmeans, 'kmeans_vav_1.pkl')"
]
},
{
"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
}
|