File size: 25,133 Bytes
e9e9f7f |
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 |
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BlYwantP4fpF",
"outputId": "32e70ec1-c239-452f-8cb2-c6646522ac62"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting top2vec\n",
" Downloading top2vec-1.0.29-py3-none-any.whl (26 kB)\n",
"Requirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.10/dist-packages (from top2vec) (1.23.5)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from top2vec) (1.5.3)\n",
"Requirement already satisfied: scikit-learn>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from top2vec) (1.2.2)\n",
"Requirement already satisfied: gensim>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from top2vec) (4.3.2)\n",
"Collecting umap-learn>=0.5.1 (from top2vec)\n",
" Downloading umap-learn-0.5.4.tar.gz (90 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.8/90.8 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Collecting hdbscan>=0.8.27 (from top2vec)\n",
" Downloading hdbscan-0.8.33.tar.gz (5.2 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m53.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: wordcloud in /usr/local/lib/python3.10/dist-packages (from top2vec) (1.9.2)\n",
"Requirement already satisfied: scipy>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from gensim>=4.0.0->top2vec) (1.11.3)\n",
"Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim>=4.0.0->top2vec) (6.4.0)\n",
"Collecting cython<3,>=0.27 (from hdbscan>=0.8.27->top2vec)\n",
" Using cached Cython-0.29.36-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (1.9 MB)\n",
"Requirement already satisfied: joblib>=1.0 in /usr/local/lib/python3.10/dist-packages (from hdbscan>=0.8.27->top2vec) (1.3.2)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.2.0->top2vec) (3.2.0)\n",
"Requirement already satisfied: numba>=0.51.2 in /usr/local/lib/python3.10/dist-packages (from umap-learn>=0.5.1->top2vec) (0.56.4)\n",
"Collecting pynndescent>=0.5 (from umap-learn>=0.5.1->top2vec)\n",
" Downloading pynndescent-0.5.10.tar.gz (1.1 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m45.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from umap-learn>=0.5.1->top2vec) (4.66.1)\n",
"Requirement already satisfied: tbb>=2019.0 in /usr/local/lib/python3.10/dist-packages (from umap-learn>=0.5.1->top2vec) (2021.10.0)\n",
"Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->top2vec) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->top2vec) (2023.3.post1)\n",
"Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from wordcloud->top2vec) (9.4.0)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from wordcloud->top2vec) (3.7.1)\n",
"Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.51.2->umap-learn>=0.5.1->top2vec) (0.39.1)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from numba>=0.51.2->umap-learn>=0.5.1->top2vec) (67.7.2)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->top2vec) (1.16.0)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud->top2vec) (1.1.1)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud->top2vec) (0.12.1)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud->top2vec) (4.43.1)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud->top2vec) (1.4.5)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud->top2vec) (23.2)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud->top2vec) (3.1.1)\n",
"Building wheels for collected packages: hdbscan, umap-learn, pynndescent\n",
" Building wheel for hdbscan (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for hdbscan: filename=hdbscan-0.8.33-cp310-cp310-linux_x86_64.whl size=3039150 sha256=9f003292f1f8cb7ad7c75405b0ae9c5dd34781ed3d3e84b67c79cbb555901c90\n",
" Stored in directory: /root/.cache/pip/wheels/75/0b/3b/dc4f60b7cc455efaefb62883a7483e76f09d06ca81cf87d610\n",
" Building wheel for umap-learn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for umap-learn: filename=umap_learn-0.5.4-py3-none-any.whl size=86770 sha256=32645d482bfb04f8b261441c153ed2211fcacdb89e09dd301d22767f4bb4e117\n",
" Stored in directory: /root/.cache/pip/wheels/fb/66/29/199acf5784d0f7b8add6d466175ab45506c96e386ed5dd0633\n",
" Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pynndescent: filename=pynndescent-0.5.10-py3-none-any.whl size=55615 sha256=34156dbda65a71550da9cc4bf7d24d226a288d55fc81e4d3db3a8a182871d238\n",
" Stored in directory: /root/.cache/pip/wheels/4a/38/5d/f60a40a66a9512b7e5e83517ebc2d1b42d857be97d135f1096\n",
"Successfully built hdbscan umap-learn pynndescent\n",
"Installing collected packages: cython, pynndescent, hdbscan, umap-learn, top2vec\n",
" Attempting uninstall: cython\n",
" Found existing installation: Cython 3.0.4\n",
" Uninstalling Cython-3.0.4:\n",
" Successfully uninstalled Cython-3.0.4\n",
"Successfully installed cython-0.29.36 hdbscan-0.8.33 pynndescent-0.5.10 top2vec-1.0.29 umap-learn-0.5.4\n"
]
}
],
"source": [
"!pip install top2vec"
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"from top2vec import Top2Vec\n",
"import nltk\n",
"import re"
],
"metadata": {
"id": "PnJXA9RQ9ACj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df = pd.read_csv('all_bills.csv')"
],
"metadata": {
"id": "ei_AkGL74iks"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df['DocumentText']"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "g9VfgRw9PDgQ",
"outputId": "6df29022-8d82-4fb2-9d4a-03bf045d9022"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 NaN\n",
"1 \\tParagraph (c) of Section 91 of chapter 32 of...\n",
"2 \\tSECTION 1. Chapter 20 Section 23 of the Gene...\n",
"3 \\tSECTION 1. Chapter 26 of the General Laws is...\n",
"4 \\tSECTION 1. Chapter 32A of the General Laws i...\n",
" ... \n",
"6591 \\tSECTION 1. Section 3 of chapter 258B, as app...\n",
"6592 \\tSECTION 1. Section 222 of chapter 6 of the G...\n",
"6593 \\tSECTION 1. Chapter 12 of the General Laws, a...\n",
"6594 \\tSECTION 1. Section 172(a)(3) of chapter 6, a...\n",
"6595 \\tSection 127I of Chapter 111 of the General L...\n",
"Name: DocumentText, Length: 6596, dtype: object"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"nltk.download('punkt')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rM2quh8P9Q8p",
"outputId": "c9d4dfe6-3bc4-4b17-f955-b41c59f4ce7e"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Unzipping tokenizers/punkt.zip.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"documents = df['DocumentText'].tolist()"
],
"metadata": {
"id": "5AJtHmk35Pjz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from nltk.corpus import stopwords\n",
"\n",
"nltk.download('stopwords')\n",
"stop_words = set(stopwords.words('english'))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_woW4lDxPTa1",
"outputId": "6dbebac9-3dc8-420c-abf0-d1978b9e978a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def tokenize(text):\n",
" if not isinstance(text, str): # Check if the input is a string, could be null\n",
" return []\n",
"\n",
" wordstoremove = []\n",
"\n",
" all_stopwords = stop_words.union(wordstoremove) # remove stopwords\n",
"\n",
" text = re.sub(r'(section|chapter)\\s+(\\d+|\\d+[a-zA-Z])', r'\\1_\\2', text, flags=re.IGNORECASE) #combine \"section\" and \"chapter\" followed by a number into one token\n",
"\n",
" tokens = [word for word in nltk.word_tokenize(text) if word.lower() not in all_stopwords and word.isalnum()] #only alphanumeric\n",
" tokens = map(str.lower, tokens)\n",
"\n",
" return tokens\n",
"\n",
"tokenized_documents = [\" \".join(tokenize(doc)) for doc in documents]\n"
],
"metadata": {
"id": "W9_54EGS9W8C"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"len(tokenized_documents)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cne6GU2k6OJI",
"outputId": "b2a8a635-f35c-436c-a899-dd52d10b3199"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"6596"
]
},
"metadata": {},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"tokenized_documents[5]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 160
},
"id": "E7FVyM1bSySo",
"outputId": "023d7323-6202-4703-a3d3-632edcee0ce0"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'general laws appearing 2020 official edition hereby amended striking first sentence second paragraph inserting words commission shall provide child adult 26 years age younger active retired employee commonwealth insured group insurance commission coverage cost 1 hearing aid per ear hearing aid defined every 36 months upon written statement child treating physician hearing aids necessary regardless etiology general laws appearing 2020 official edition hereby amended striking first sentence second paragraph inserting words individual group health maintenance contract except contracts providing supplemental coverage medicare governmental programs shall provide coverage benefits children 21 years age younger insured contracts expenses incurred cost 1 hearing aid per hearing impaired ear hearing aid defined every 36 months upon written statement child treating physician hearing aids necessary regardless etiology'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 31
}
]
},
{
"cell_type": "code",
"source": [
"model = Top2Vec(tokenized_documents)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WcNxyEeA4_Al",
"outputId": "da47e569-f7b1-4370-dfe0-e44a7a5295b3"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"2023-10-26 18:48:40,257 - top2vec - INFO - Pre-processing documents for training\n",
"INFO:top2vec:Pre-processing documents for training\n",
"2023-10-26 18:48:50,371 - top2vec - INFO - Creating joint document/word embedding\n",
"INFO:top2vec:Creating joint document/word embedding\n",
"2023-10-26 19:01:10,148 - top2vec - INFO - Creating lower dimension embedding of documents\n",
"INFO:top2vec:Creating lower dimension embedding of documents\n",
"2023-10-26 19:01:54,142 - top2vec - INFO - Finding dense areas of documents\n",
"INFO:top2vec:Finding dense areas of documents\n",
"2023-10-26 19:01:54,586 - top2vec - INFO - Finding topics\n",
"INFO:top2vec:Finding topics\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# 86 topics found\n",
"model.get_num_topics()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "f6K7dLRr5HzO",
"outputId": "ad6600c3-f4c2-49c9-8340-5aa5cb45a634"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"86"
]
},
"metadata": {},
"execution_count": 33
}
]
},
{
"cell_type": "code",
"source": [
"topic_sizes, topic_nums = model.get_topic_sizes()"
],
"metadata": {
"id": "nBOvsphU6TsK"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"topic_sizes, topic_nums"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_ficy58j6Val",
"outputId": "c3aa223b-78b8-4bec-a7d2-8419d038abd4"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([269, 222, 184, 179, 168, 161, 157, 149, 149, 144, 138, 128, 122,\n",
" 118, 105, 101, 97, 95, 95, 90, 90, 89, 88, 88, 84, 83,\n",
" 83, 80, 79, 77, 76, 75, 74, 73, 73, 73, 72, 71, 69,\n",
" 69, 69, 68, 66, 65, 65, 64, 64, 62, 62, 62, 61, 60,\n",
" 59, 55, 54, 53, 52, 50, 49, 49, 49, 48, 48, 48, 48,\n",
" 47, 46, 44, 44, 41, 40, 37, 37, 36, 36, 35, 35, 33,\n",
" 32, 30, 29, 29, 25, 25, 24, 24]),\n",
" array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
" 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
" 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
" 85]))"
]
},
"metadata": {},
"execution_count": 35
}
]
},
{
"cell_type": "code",
"source": [
"topic_words, word_scores, topic_nums = model.get_topics(86)\n"
],
"metadata": {
"id": "incHyPTC6Wky"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"topic_words"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "z9iNedwx6bIO",
"outputId": "4b3ecf45-bfe7-4763-8360-ae52d75df083"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([['senate', 'representatives', 'recommendations', ...,\n",
" 'massachusetts', 'member', 'vice'],\n",
" ['taxable', 'taxpayer', 'tax', ..., 'deducted', 'percent',\n",
" 'assessors'],\n",
" ['ballot', 'election', 'voters', ..., 'names', 'choice', 'papers'],\n",
" ...,\n",
" ['pesticide', 'pesticides', 'applicator', ..., 'contain',\n",
" 'application', 'polyfluoroalkyl'],\n",
" ['affordable', 'real', 'transfers', ..., 'respect', 'excise',\n",
" 'barnstable'],\n",
" ['detail', 'police', 'superannuation', ..., 'eeo', 'overtime',\n",
" 'passage']], dtype='<U15')"
]
},
"metadata": {},
"execution_count": 41
}
]
},
{
"cell_type": "code",
"source": [
"# example of a topic\n",
"topic_words[15]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2rnYtcMsXeFj",
"outputId": "8f52f099-c97a-4023-c003-d768ba546278"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['assessors', 'exemption', 'valuation', 'qualify', 'domicile',\n",
" 'exemptions', 'levy', 'taxes', 'taxation', 'real', 'occupied',\n",
" 'town', 'assessed', 'abatement', 'tax', 'deferral', 'city',\n",
" 'deadline', 'excessive', 'parcel', 'property', 'domiciled',\n",
" 'senior', 'surcharge', 'condominium', 'seeks', 'raised', 'estate',\n",
" 'clause', 'assessor', 'homeowners', 'voters', 'bears', 'income',\n",
" 'owned', 'shift', 'estates', 'residential', 'deed', 'filed',\n",
" 'eighteenth', 'lien', 'decedent', 'certifies', 'year', 'find',\n",
" 'qualifies', 'nonpayment', 'mayor', 'question'], dtype='<U15')"
]
},
"metadata": {},
"execution_count": 53
}
]
},
{
"cell_type": "code",
"source": [
"word_scores"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "z4l9d3jT6b7b",
"outputId": "f4f2a0f0-fd81-4da3-f4a8-6f3c00069e73"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0.8107485 , 0.76834613, 0.75448215, 0.7522937 , 0.7377519 ,\n",
" 0.73589534, 0.6997856 , 0.6921088 , 0.6573265 , 0.63992095,\n",
" 0.63835424, 0.63353044, 0.6259784 , 0.6161628 , 0.6136363 ,\n",
" 0.61158836, 0.5917918 , 0.59075636, 0.58117676, 0.56409115,\n",
" 0.5382238 , 0.53799826, 0.53545713, 0.5059549 , 0.4826639 ,\n",
" 0.47157508, 0.47056037, 0.44637784, 0.42377606, 0.42022526,\n",
" 0.4182046 , 0.41616178, 0.41021925, 0.40735632, 0.38740572,\n",
" 0.3687165 , 0.36273247, 0.360592 , 0.3579704 , 0.35582146,\n",
" 0.35553864, 0.3502974 , 0.34837744, 0.34191263, 0.34068382,\n",
" 0.33285785, 0.3286861 , 0.30501026, 0.30333635, 0.29198554],\n",
" [0.6651959 , 0.65478694, 0.6534573 , 0.6209365 , 0.6145715 ,\n",
" 0.60944825, 0.578282 , 0.5713501 , 0.56624013, 0.5658411 ,\n",
" 0.5317777 , 0.5303422 , 0.52419686, 0.48169795, 0.47910136,\n",
" 0.4783927 , 0.46999556, 0.46702635, 0.45967853, 0.4526093 ,\n",
" 0.43824282, 0.4347589 , 0.43464437, 0.4327737 , 0.4292552 ,\n",
" 0.42650297, 0.42139018, 0.41712484, 0.4164454 , 0.40693063,\n",
" 0.3967685 , 0.3945102 , 0.39216924, 0.390456 , 0.39004803,\n",
" 0.37376937, 0.36957765, 0.3680184 , 0.36738494, 0.35837635,\n",
" 0.33738944, 0.3371164 , 0.33683145, 0.3082167 , 0.30309388,\n",
" 0.30301207, 0.28206518, 0.27416578, 0.2736898 , 0.27321362]],\n",
" dtype=float32)"
]
},
"metadata": {},
"execution_count": 40
}
]
},
{
"cell_type": "code",
"source": [
"def save_topics_to_txt(topic_words, word_scores, topic_nums, filename=\"bills_topics.txt\"):\n",
" with open(filename, 'w') as file:\n",
" for topic_num, words, scores in zip(topic_nums, topic_words, word_scores):\n",
" file.write(f\"Topic {topic_num}\\n\")\n",
" for word, score in zip(words, scores):\n",
" file.write(f\"{word}: {score}\\n\")\n",
" file.write(\"\\n\")\n",
"\n",
"save_topics_to_txt(topic_words, word_scores, topic_nums, filename=\"bills_topics.txt\")\n"
],
"metadata": {
"id": "2vKfMzeA667n"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "VbuUEU2OYlVW"
},
"execution_count": null,
"outputs": []
}
]
} |