Spaces:
Build error
Build error
File size: 42,026 Bytes
1179be6 17e1803 1179be6 17e1803 1179be6 17e1803 1179be6 17e1803 1179be6 17e1803 1179be6 17e1803 1179be6 17e1803 1179be6 17e1803 1179be6 17e1803 1179be6 17e1803 1179be6 17e1803 1179be6 17e1803 1179be6 17e1803 1179be6 17e1803 1179be6 17e1803 1179be6 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 |
{
"cells": [
{
"cell_type": "markdown",
"id": "1fecbc87",
"metadata": {},
"source": [
"## Import Statement"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "5169e3ee",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"id": "76905f72",
"metadata": {},
"source": [
"### read the data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b1043895",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"../all_port_labelled.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2e40d90a",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Index</th>\n",
" <th>Unnamed: 0.1</th>\n",
" <th>Headline</th>\n",
" <th>Details</th>\n",
" <th>Severity</th>\n",
" <th>Category</th>\n",
" <th>Region</th>\n",
" <th>Datetime</th>\n",
" <th>Year</th>\n",
" <th>...</th>\n",
" <th>IT</th>\n",
" <th>EP</th>\n",
" <th>NEW</th>\n",
" <th>CSD</th>\n",
" <th>RPE</th>\n",
" <th>MN</th>\n",
" <th>NM</th>\n",
" <th>if_labeled</th>\n",
" <th>Month</th>\n",
" <th>Week</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>34.0</td>\n",
" <td>Grasberg Mine- Grasberg mine workers extend st...</td>\n",
" <td>Media sources indicate that workers at the Gra...</td>\n",
" <td>Moderate</td>\n",
" <td>Mine Workers Strike</td>\n",
" <td>Indonesia</td>\n",
" <td>28/5/17 17:08</td>\n",
" <td>2017.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>5.0</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.0</td>\n",
" <td>10.0</td>\n",
" <td>63.0</td>\n",
" <td>Indonesia: Undersea internet cables damaged by...</td>\n",
" <td>News sources are stating that recent typhoons ...</td>\n",
" <td>Minor</td>\n",
" <td>Travel Warning</td>\n",
" <td>Indonesia</td>\n",
" <td>4/9/17 14:30</td>\n",
" <td>2017.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 46 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Index Unnamed: 0.1 \\\n",
"0 0.0 8.0 34.0 \n",
"1 1.0 10.0 63.0 \n",
"\n",
" Headline \\\n",
"0 Grasberg Mine- Grasberg mine workers extend st... \n",
"1 Indonesia: Undersea internet cables damaged by... \n",
"\n",
" Details Severity \\\n",
"0 Media sources indicate that workers at the Gra... Moderate \n",
"1 News sources are stating that recent typhoons ... Minor \n",
"\n",
" Category Region Datetime Year ... IT EP NEW \\\n",
"0 Mine Workers Strike Indonesia 28/5/17 17:08 2017.0 ... 0.0 0.0 0.0 \n",
"1 Travel Warning Indonesia 4/9/17 14:30 2017.0 ... 0.0 0.0 0.0 \n",
"\n",
" CSD RPE MN NM if_labeled Month Week \n",
"0 0.0 0.0 0.0 1.0 False 5.0 21.0 \n",
"1 0.0 0.0 1.0 0.0 False 4.0 14.0 \n",
"\n",
"[2 rows x 46 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(2)"
]
},
{
"cell_type": "markdown",
"id": "643a7e40",
"metadata": {},
"source": [
"### Clean empty data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d6ee1fd7",
"metadata": {},
"outputs": [],
"source": [
"import nltk\n",
"from nltk.corpus import stopwords\n",
"from nltk.tokenize import word_tokenize\n",
"from nltk.stem import WordNetLemmatizer\n",
"import string\n",
"\n",
"# nltk.download('punkt')\n",
"# nltk.download('stopwords')\n",
"# nltk.download('wordnet')\n",
"\n",
"\n",
"def clean_text(text):\n",
" # Lowercase\n",
" text = text.lower()\n",
" # Tokenization\n",
" tokens = word_tokenize(text)\n",
" # Removing punctuation\n",
" tokens = [word for word in tokens if word not in string.punctuation]\n",
" # Removing stop words\n",
" stop_words = set(stopwords.words(\"english\"))\n",
" tokens = [word for word in tokens if word not in stop_words]\n",
" # Lemmatization\n",
" lemmatizer = WordNetLemmatizer()\n",
" tokens = [lemmatizer.lemmatize(word) for word in tokens]\n",
"\n",
" return \" \".join(tokens)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9e35b49a",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package omw-1.4 to\n",
"[nltk_data] C:\\Users\\ellay\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package omw-1.4 is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import nltk\n",
"\n",
"nltk.download(\"omw-1.4\")"
]
},
{
"cell_type": "markdown",
"id": "ca331c4b",
"metadata": {},
"source": [
"### The Details column has an issue\n",
"\n",
"some of the data are of the type float and none of the text processing functions can be applied to it therefore we have to process it"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2438c58f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5782 entries, 0 to 5781\n",
"Data columns (total 2 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Details 5781 non-null object\n",
" 1 maritime_label 5781 non-null object\n",
"dtypes: object(2)\n",
"memory usage: 90.5+ KB\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5782 entries, 0 to 5781\n",
"Data columns (total 3 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Details 5781 non-null object\n",
" 1 maritime_label 5781 non-null object\n",
" 2 Details_cleaned 5781 non-null object\n",
"dtypes: object(3)\n",
"memory usage: 135.6+ KB\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-6-254c36cdfdd3>:3: 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",
" text_df['Details_cleaned'] = text_df['Details'].apply(lambda x: clean_text(x) if not isinstance(x, float) else None)\n"
]
}
],
"source": [
"text_df = df[[\"Details\", \"maritime_label\"]]\n",
"text_df.info()\n",
"text_df[\"Details_cleaned\"] = text_df[\"Details\"].apply(\n",
" lambda x: clean_text(x) if not isinstance(x, float) else None\n",
")\n",
"# no_nan_df[no_nan_df[\"Details\"].apply(lambda x: print(type(x)))]\n",
"# cleaned_df = text_df[text_df[\"Details\"].apply(lambda x: clean_text(x))]\n",
"# cleaned_df = df['Details'][1:2]\n",
"# type(no_nan_df[\"Details\"][0])\n",
"# print(clean_text(no_nan_df[\"Details\"][0]))\n",
"text_df.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4d3b0011",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Details</th>\n",
" <th>maritime_label</th>\n",
" <th>Details_cleaned</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Media sources indicate that workers at the Gra...</td>\n",
" <td>FALSE</td>\n",
" <td>medium source indicate worker grasberg mine ex...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>News sources are stating that recent typhoons ...</td>\n",
" <td>FALSE</td>\n",
" <td>news source stating recent typhoon impact hong...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>The persisting port congestion at Shanghai’s Y...</td>\n",
" <td>TRUE</td>\n",
" <td>persisting port congestion shanghai ’ yangshan...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Updated local media sources from Jakarta indic...</td>\n",
" <td>TRUE</td>\n",
" <td>updated local medium source jakarta indicate e...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>According to local police in Jakarta, two expl...</td>\n",
" <td>TRUE</td>\n",
" <td>according local police jakarta two explosion c...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Details maritime_label \\\n",
"0 Media sources indicate that workers at the Gra... FALSE \n",
"1 News sources are stating that recent typhoons ... FALSE \n",
"2 The persisting port congestion at Shanghai’s Y... TRUE \n",
"3 Updated local media sources from Jakarta indic... TRUE \n",
"4 According to local police in Jakarta, two expl... TRUE \n",
"\n",
" Details_cleaned \n",
"0 medium source indicate worker grasberg mine ex... \n",
"1 news source stating recent typhoon impact hong... \n",
"2 persisting port congestion shanghai ’ yangshan... \n",
"3 updated local medium source jakarta indicate e... \n",
"4 according local police jakarta two explosion c... "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"processed_data = text_df.dropna()\n",
"processed_data.head()"
]
},
{
"cell_type": "markdown",
"id": "3c4be609",
"metadata": {},
"source": [
"## Naive Bayes Model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5c660011",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"\n",
"# from sklearn.feature_extraction.text import CountVectorizer\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.metrics import accuracy_score, classification_report"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8f009a65",
"metadata": {},
"outputs": [],
"source": [
"X = processed_data[\"Details_cleaned\"]\n",
"y = processed_data[\"maritime_label\"]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0185a967",
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.2, random_state=42\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d3c2de6b",
"metadata": {},
"outputs": [],
"source": [
"# vectorizer = CountVectorizer()\n",
"# X_train_vec = vectorizer.fit_transform(X_train)\n",
"# X_test_vec = vectorizer.transform(X_test)\n",
"\n",
"tfidf_vectorizer = TfidfVectorizer(max_features=1000)\n",
"X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
"X_test_tfidf = tfidf_vectorizer.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ead2fc7a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"MultinomialNB()"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"naive_bayes = MultinomialNB()\n",
"naive_bayes.fit(X_train_tfidf, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "74c5df68",
"metadata": {},
"outputs": [],
"source": [
"predictions = naive_bayes.predict(X_test_tfidf)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "109e9456",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of Naive Bayes model: 0.8582541054451167\n",
" precision recall f1-score support\n",
"\n",
" FALSE 0.88 0.94 0.91 847\n",
" TRUE 0.79 0.65 0.71 310\n",
"\n",
" accuracy 0.86 1157\n",
" macro avg 0.83 0.79 0.81 1157\n",
"weighted avg 0.85 0.86 0.85 1157\n",
"\n"
]
}
],
"source": [
"accuracy = accuracy_score(y_test, predictions)\n",
"print(\"Accuracy of Naive Bayes model:\", accuracy)\n",
"print(classification_report(y_test, predictions))"
]
},
{
"cell_type": "markdown",
"id": "9518614a",
"metadata": {},
"source": [
"## Logistic Regression model"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "912ad7a6",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "03eac734",
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.2, random_state=42\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "e84ff87c",
"metadata": {},
"outputs": [],
"source": [
"tfidf_vectorizer = TfidfVectorizer(max_features=1000)\n",
"X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
"X_test_tfidf = tfidf_vectorizer.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "cedb263c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression()"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = LogisticRegression()\n",
"model.fit(X_train_tfidf, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "6f49fddb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of Logistic Regression Model: 0.9317199654278306\n",
" precision recall f1-score support\n",
"\n",
" FALSE 0.92 0.99 0.96 847\n",
" TRUE 0.98 0.76 0.86 310\n",
"\n",
" accuracy 0.93 1157\n",
" macro avg 0.95 0.88 0.91 1157\n",
"weighted avg 0.93 0.93 0.93 1157\n",
"\n"
]
}
],
"source": [
"y_pred = model.predict(X_test_tfidf)\n",
"\n",
"accuracy = accuracy_score(y_test, y_pred)\n",
"print(\"Accuracy of Logistic Regression Model:\", accuracy)\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"id": "613c0cdf",
"metadata": {},
"source": [
"## Support Vector Machine (SVM) model"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "706302c1",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.svm import SVC\n",
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "b0988ca4",
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.2, random_state=42\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "4f682c60",
"metadata": {},
"outputs": [],
"source": [
"tfidf_vectorizer = TfidfVectorizer(max_features=1000)\n",
"X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
"X_test_tfidf = tfidf_vectorizer.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "71ae91d9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SVC(kernel='linear')"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svm_model = SVC(kernel=\"linear\")\n",
"svm_model.fit(X_train_tfidf, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "2dc1b193",
"metadata": {},
"outputs": [],
"source": [
"y_pred = svm_model.predict(X_test_tfidf)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "92801e61",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of SVM model: 0.9498703543647364\n",
" precision recall f1-score support\n",
"\n",
" FALSE 0.94 1.00 0.97 847\n",
" TRUE 0.99 0.82 0.90 310\n",
"\n",
" accuracy 0.95 1157\n",
" macro avg 0.96 0.91 0.93 1157\n",
"weighted avg 0.95 0.95 0.95 1157\n",
"\n"
]
}
],
"source": [
"accuracy = accuracy_score(y_test, y_pred)\n",
"print(\"Accuracy of SVM model:\", accuracy)\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"id": "1d1f6ebd",
"metadata": {},
"source": [
"## Random Forest Model"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "9170c174",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "2092ca05",
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.2, random_state=42\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "206296ce",
"metadata": {},
"outputs": [],
"source": [
"tfidf_vectorizer = TfidfVectorizer(max_features=1000)\n",
"X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
"X_test_tfidf = tfidf_vectorizer.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "258bd78f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(random_state=42)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
"rf_model.fit(X_train_tfidf, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "0e2910f6",
"metadata": {},
"outputs": [],
"source": [
"y_pred = rf_model.predict(X_test_tfidf)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "f06900d3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of Random Forest Model: 0.9636992221261884\n",
" precision recall f1-score support\n",
"\n",
" FALSE 0.96 0.99 0.98 847\n",
" TRUE 0.98 0.88 0.93 310\n",
"\n",
" accuracy 0.96 1157\n",
" macro avg 0.97 0.94 0.95 1157\n",
"weighted avg 0.96 0.96 0.96 1157\n",
"\n"
]
}
],
"source": [
"accuracy = accuracy_score(y_test, y_pred)\n",
"print(\"Accuracy of Random Forest Model:\", accuracy)\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"id": "64673d66",
"metadata": {},
"source": [
"## Simpe Neural Network"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b8b103b0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting tensorflow\n",
" Obtaining dependency information for tensorflow from https://files.pythonhosted.org/packages/f9/14/67e9b2b2379cb530c0412123a674d045eca387dfcfa7db1c0028857b0a66/tensorflow-2.16.1-cp311-cp311-macosx_12_0_arm64.whl.metadata\n",
" Downloading tensorflow-2.16.1-cp311-cp311-macosx_12_0_arm64.whl.metadata (4.1 kB)\n",
"Collecting absl-py>=1.0.0 (from tensorflow)\n",
" Obtaining dependency information for absl-py>=1.0.0 from https://files.pythonhosted.org/packages/a2/ad/e0d3c824784ff121c03cc031f944bc7e139a8f1870ffd2845cc2dd76f6c4/absl_py-2.1.0-py3-none-any.whl.metadata\n",
" Downloading absl_py-2.1.0-py3-none-any.whl.metadata (2.3 kB)\n",
"Collecting astunparse>=1.6.0 (from tensorflow)\n",
" Obtaining dependency information for astunparse>=1.6.0 from https://files.pythonhosted.org/packages/2b/03/13dde6512ad7b4557eb792fbcf0c653af6076b81e5941d36ec61f7ce6028/astunparse-1.6.3-py2.py3-none-any.whl.metadata\n",
" Downloading astunparse-1.6.3-py2.py3-none-any.whl.metadata (4.4 kB)\n",
"Collecting flatbuffers>=23.5.26 (from tensorflow)\n",
" Obtaining dependency information for flatbuffers>=23.5.26 from https://files.pythonhosted.org/packages/bf/45/c961e3cb6ddad76b325c163d730562bb6deb1ace5acbed0306f5fbefb90e/flatbuffers-24.3.7-py2.py3-none-any.whl.metadata\n",
" Downloading flatbuffers-24.3.7-py2.py3-none-any.whl.metadata (849 bytes)\n",
"Collecting gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 (from tensorflow)\n",
" Obtaining dependency information for gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 from https://files.pythonhosted.org/packages/fa/39/5aae571e5a5f4de9c3445dae08a530498e5c53b0e74410eeeb0991c79047/gast-0.5.4-py3-none-any.whl.metadata\n",
" Downloading gast-0.5.4-py3-none-any.whl.metadata (1.3 kB)\n",
"Collecting google-pasta>=0.1.1 (from tensorflow)\n",
" Obtaining dependency information for google-pasta>=0.1.1 from https://files.pythonhosted.org/packages/a3/de/c648ef6835192e6e2cc03f40b19eeda4382c49b5bafb43d88b931c4c74ac/google_pasta-0.2.0-py3-none-any.whl.metadata\n",
" Downloading google_pasta-0.2.0-py3-none-any.whl.metadata (814 bytes)\n",
"Collecting h5py>=3.10.0 (from tensorflow)\n",
" Obtaining dependency information for h5py>=3.10.0 from https://files.pythonhosted.org/packages/8d/70/2b0b99507287f66e71a6b2e66c5ad2ec2461ef2c534668eef96c3b48eb6d/h5py-3.10.0-cp311-cp311-macosx_11_0_arm64.whl.metadata\n",
" Downloading h5py-3.10.0-cp311-cp311-macosx_11_0_arm64.whl.metadata (2.5 kB)\n",
"Collecting libclang>=13.0.0 (from tensorflow)\n",
" Obtaining dependency information for libclang>=13.0.0 from https://files.pythonhosted.org/packages/db/ed/1df62b44db2583375f6a8a5e2ca5432bbdc3edb477942b9b7c848c720055/libclang-18.1.1-py2.py3-none-macosx_11_0_arm64.whl.metadata\n",
" Downloading libclang-18.1.1-py2.py3-none-macosx_11_0_arm64.whl.metadata (5.2 kB)\n",
"Collecting ml-dtypes~=0.3.1 (from tensorflow)\n",
" Obtaining dependency information for ml-dtypes~=0.3.1 from https://files.pythonhosted.org/packages/6e/a4/6aabb78f1569550fd77c74d2c1d008b502c8ce72776bd88b14ea6c182c9e/ml_dtypes-0.3.2-cp311-cp311-macosx_10_9_universal2.whl.metadata\n",
" Downloading ml_dtypes-0.3.2-cp311-cp311-macosx_10_9_universal2.whl.metadata (20 kB)\n",
"Collecting opt-einsum>=2.3.2 (from tensorflow)\n",
" Obtaining dependency information for opt-einsum>=2.3.2 from https://files.pythonhosted.org/packages/bc/19/404708a7e54ad2798907210462fd950c3442ea51acc8790f3da48d2bee8b/opt_einsum-3.3.0-py3-none-any.whl.metadata\n",
" Downloading opt_einsum-3.3.0-py3-none-any.whl.metadata (6.5 kB)\n",
"Requirement already satisfied: packaging in /Users/barebear/anaconda3/lib/python3.11/site-packages (from tensorflow) (23.0)\n",
"Collecting protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 (from tensorflow)\n",
" Obtaining dependency information for protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 from https://files.pythonhosted.org/packages/f3/bf/26deba06a4c910a85f78245cac7698f67cedd7efe00d04f6b3e1b3506a59/protobuf-4.25.3-cp37-abi3-macosx_10_9_universal2.whl.metadata\n",
" Downloading protobuf-4.25.3-cp37-abi3-macosx_10_9_universal2.whl.metadata (541 bytes)\n",
"Requirement already satisfied: requests<3,>=2.21.0 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from tensorflow) (2.31.0)\n",
"Requirement already satisfied: setuptools in /Users/barebear/anaconda3/lib/python3.11/site-packages (from tensorflow) (68.0.0)\n",
"Requirement already satisfied: six>=1.12.0 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from tensorflow) (1.16.0)\n",
"Collecting termcolor>=1.1.0 (from tensorflow)\n",
" Obtaining dependency information for termcolor>=1.1.0 from https://files.pythonhosted.org/packages/d9/5f/8c716e47b3a50cbd7c146f45881e11d9414def768b7cd9c5e6650ec2a80a/termcolor-2.4.0-py3-none-any.whl.metadata\n",
" Downloading termcolor-2.4.0-py3-none-any.whl.metadata (6.1 kB)\n",
"Requirement already satisfied: typing-extensions>=3.6.6 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from tensorflow) (4.7.1)\n",
"Requirement already satisfied: wrapt>=1.11.0 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from tensorflow) (1.14.1)\n",
"Collecting grpcio<2.0,>=1.24.3 (from tensorflow)\n",
" Obtaining dependency information for grpcio<2.0,>=1.24.3 from https://files.pythonhosted.org/packages/c1/0a/a8c0f403b2189f5d3e490778ead51924b56fa30a35f6e444b3702e28c8c8/grpcio-1.62.1-cp311-cp311-macosx_10_10_universal2.whl.metadata\n",
" Downloading grpcio-1.62.1-cp311-cp311-macosx_10_10_universal2.whl.metadata (4.0 kB)\n",
"Collecting tensorboard<2.17,>=2.16 (from tensorflow)\n",
" Obtaining dependency information for tensorboard<2.17,>=2.16 from https://files.pythonhosted.org/packages/3a/d0/b97889ffa769e2d1fdebb632084d5e8b53fc299d43a537acee7ec0c021a3/tensorboard-2.16.2-py3-none-any.whl.metadata\n",
" Downloading tensorboard-2.16.2-py3-none-any.whl.metadata (1.6 kB)\n",
"Collecting keras>=3.0.0 (from tensorflow)\n",
" Obtaining dependency information for keras>=3.0.0 from https://files.pythonhosted.org/packages/59/a8/d94e8acb59d678d908fe1db0c7ad89dfa2c2e2e529eeb3c2b3cc218a758d/keras-3.1.1-py3-none-any.whl.metadata\n",
" Downloading keras-3.1.1-py3-none-any.whl.metadata (5.6 kB)\n",
"Collecting tensorflow-io-gcs-filesystem>=0.23.1 (from tensorflow)\n",
" Obtaining dependency information for tensorflow-io-gcs-filesystem>=0.23.1 from https://files.pythonhosted.org/packages/3e/56/1b7ef816e448464a93da70296db237129910b4452d6b4582d5e23fb07880/tensorflow_io_gcs_filesystem-0.36.0-cp311-cp311-macosx_12_0_arm64.whl.metadata\n",
" Downloading tensorflow_io_gcs_filesystem-0.36.0-cp311-cp311-macosx_12_0_arm64.whl.metadata (14 kB)\n",
"Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from tensorflow) (1.24.3)\n",
"Requirement already satisfied: wheel<1.0,>=0.23.0 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from astunparse>=1.6.0->tensorflow) (0.38.4)\n",
"Collecting rich (from keras>=3.0.0->tensorflow)\n",
" Obtaining dependency information for rich from https://files.pythonhosted.org/packages/87/67/a37f6214d0e9fe57f6ae54b2956d550ca8365857f42a1ce0392bb21d9410/rich-13.7.1-py3-none-any.whl.metadata\n",
" Downloading rich-13.7.1-py3-none-any.whl.metadata (18 kB)\n",
"Collecting namex (from keras>=3.0.0->tensorflow)\n",
" Obtaining dependency information for namex from https://files.pythonhosted.org/packages/cd/43/b971880e2eb45c0bee2093710ae8044764a89afe9620df34a231c6f0ecd2/namex-0.0.7-py3-none-any.whl.metadata\n",
" Downloading namex-0.0.7-py3-none-any.whl.metadata (246 bytes)\n",
"Collecting optree (from keras>=3.0.0->tensorflow)\n",
" Obtaining dependency information for optree from https://files.pythonhosted.org/packages/14/8a/a7a152dedfc700de64efa68c3ae7ccf079ba5a5bc8ff1706a671b374cfcd/optree-0.10.0-cp311-cp311-macosx_11_0_arm64.whl.metadata\n",
" Downloading optree-0.10.0-cp311-cp311-macosx_11_0_arm64.whl.metadata (45 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.3/45.3 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorflow) (2.0.4)\n",
"Requirement already satisfied: idna<4,>=2.5 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorflow) (3.4)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorflow) (1.26.16)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorflow) (2024.2.2)\n",
"Requirement already satisfied: markdown>=2.6.8 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (3.4.1)\n",
"Collecting tensorboard-data-server<0.8.0,>=0.7.0 (from tensorboard<2.17,>=2.16->tensorflow)\n",
" Obtaining dependency information for tensorboard-data-server<0.8.0,>=0.7.0 from https://files.pythonhosted.org/packages/7a/13/e503968fefabd4c6b2650af21e110aa8466fe21432cd7c43a84577a89438/tensorboard_data_server-0.7.2-py3-none-any.whl.metadata\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Downloading tensorboard_data_server-0.7.2-py3-none-any.whl.metadata (1.1 kB)\n",
"Requirement already satisfied: werkzeug>=1.0.1 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (2.2.3)\n",
"Requirement already satisfied: MarkupSafe>=2.1.1 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow) (2.1.1)\n",
"Requirement already satisfied: markdown-it-py>=2.2.0 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from rich->keras>=3.0.0->tensorflow) (2.2.0)\n",
"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from rich->keras>=3.0.0->tensorflow) (2.15.1)\n",
"Requirement already satisfied: mdurl~=0.1 in /Users/barebear/anaconda3/lib/python3.11/site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow) (0.1.0)\n",
"Downloading tensorflow-2.16.1-cp311-cp311-macosx_12_0_arm64.whl (227.0 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.0/227.0 MB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:02\u001b[0m\n",
"\u001b[?25hDownloading absl_py-2.1.0-py3-none-any.whl (133 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.7/133.7 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hDownloading astunparse-1.6.3-py2.py3-none-any.whl (12 kB)\n",
"Downloading flatbuffers-24.3.7-py2.py3-none-any.whl (26 kB)\n",
"Downloading gast-0.5.4-py3-none-any.whl (19 kB)\n",
"Downloading google_pasta-0.2.0-py3-none-any.whl (57 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.5/57.5 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading grpcio-1.62.1-cp311-cp311-macosx_10_10_universal2.whl (10.0 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/10.0 MB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m0:01\u001b[0m\n",
"\u001b[?25hDownloading h5py-3.10.0-cp311-cp311-macosx_11_0_arm64.whl (2.6 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
"\u001b[?25hDownloading keras-3.1.1-py3-none-any.whl (1.1 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hDownloading libclang-18.1.1-py2.py3-none-macosx_11_0_arm64.whl (26.4 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m26.4/26.4 MB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
"\u001b[?25hDownloading ml_dtypes-0.3.2-cp311-cp311-macosx_10_9_universal2.whl (389 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m389.8/389.8 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
"\u001b[?25hDownloading opt_einsum-3.3.0-py3-none-any.whl (65 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.5/65.5 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading protobuf-4.25.3-cp37-abi3-macosx_10_9_universal2.whl (394 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m394.2/394.2 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hDownloading tensorboard-2.16.2-py3-none-any.whl (5.5 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
"\u001b[?25hDownloading tensorflow_io_gcs_filesystem-0.36.0-cp311-cp311-macosx_12_0_arm64.whl (3.4 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
"\u001b[?25hDownloading termcolor-2.4.0-py3-none-any.whl (7.7 kB)\n",
"Downloading tensorboard_data_server-0.7.2-py3-none-any.whl (2.4 kB)\n",
"Downloading namex-0.0.7-py3-none-any.whl (5.8 kB)\n",
"Downloading optree-0.10.0-cp311-cp311-macosx_11_0_arm64.whl (250 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m250.2/250.2 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hDownloading rich-13.7.1-py3-none-any.whl (240 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m240.7/240.7 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hInstalling collected packages: namex, libclang, flatbuffers, termcolor, tensorflow-io-gcs-filesystem, tensorboard-data-server, protobuf, optree, opt-einsum, ml-dtypes, h5py, grpcio, google-pasta, gast, astunparse, absl-py, tensorboard, rich, keras, tensorflow\n",
" Attempting uninstall: h5py\n",
" Found existing installation: h5py 3.7.0\n",
" Uninstalling h5py-3.7.0:\n",
" Successfully uninstalled h5py-3.7.0\n",
"Successfully installed absl-py-2.1.0 astunparse-1.6.3 flatbuffers-24.3.7 gast-0.5.4 google-pasta-0.2.0 grpcio-1.62.1 h5py-3.10.0 keras-3.1.1 libclang-18.1.1 ml-dtypes-0.3.2 namex-0.0.7 opt-einsum-3.3.0 optree-0.10.0 protobuf-4.25.3 rich-13.7.1 tensorboard-2.16.2 tensorboard-data-server-0.7.2 tensorflow-2.16.1 tensorflow-io-gcs-filesystem-0.36.0 termcolor-2.4.0\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install tensorflow"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e6dd2304",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|