Spaces:
Runtime error
Runtime error
File size: 22,574 Bytes
a4b94b2 |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to\n",
"[nltk_data] C:\\Users\\kurti\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import re\n",
"import nltk\n",
"import string\n",
"import numpy as np \n",
"import pandas as pd\n",
"from nltk.corpus import stopwords\n",
"from nltk.stem import PorterStemmer\n",
"from nltk.tokenize import TweetTokenizer\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.model_selection import StratifiedKFold\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"\n",
"nltk.download(\"stopwords\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def process_tweet(tweet):\n",
" \"\"\"\n",
" Process tweet function.\n",
" Input:\n",
" tweet: a string containing a tweet\n",
" Returns:\n",
" tweets_clean: a list of words containing the processed tweet\n",
"\n",
" *Taken from Coursera NLP Specialization Course 1, week 1 programming\n",
" assignment*\n",
" \"\"\"\n",
" stemmer = PorterStemmer()\n",
" stopwords_english = stopwords.words('english')\n",
" # remove stock market tickers like $GE\n",
" tweet = re.sub(r'\\$\\w*', '', str(tweet))\n",
" # remove old style retweet text \"RT\"\n",
" tweet = re.sub(r'^RT[\\s]+', '', str(tweet))\n",
" # remove hyperlinks\n",
" tweet = re.sub(r'https?:\\/\\/.*[\\r\\n]*', '', str(tweet))\n",
" # remove hashtags\n",
" # only removing the hash # sign from the word\n",
" tweet = re.sub(r'#', '', str(tweet))\n",
" # tokenize tweets\n",
" tokenizer = TweetTokenizer(preserve_case=False, strip_handles=True,\n",
" reduce_len=True)\n",
" tweet_tokens = tokenizer.tokenize(tweet)\n",
"\n",
" tweets_clean = []\n",
" for word in tweet_tokens:\n",
" if (word not in stopwords_english and # remove stopwords\n",
" word not in string.punctuation): # remove punctuation\n",
" # tweets_clean.append(word)\n",
" stem_word = stemmer.stem(word) # stemming word\n",
" tweets_clean.append(stem_word)\n",
"\n",
" return \" \".join(tweets_clean)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>id</th>\n",
" <th>all_text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3796</td>\n",
" <td>new weapon caus un-imagin destruct destructionnon</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3185</td>\n",
" <td>f @ing thing gishwh got soak delug go pad tamp...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7769</td>\n",
" <td>dt rt 聣 没茂the col polic catch pickpocket liver...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>191</td>\n",
" <td>aftershock back school kick great want thank e...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9810</td>\n",
" <td>respons trauma children addict develop defens ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>7934</td>\n",
" <td>look like got caught rainstorm amaz disgust ti...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2538</td>\n",
" <td>favorit ladi came volunt meet hope join youth ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2611</td>\n",
" <td>ux fail emv peopl want insert remov quickli li...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9756</td>\n",
" <td>can't find ariana grand shirt fuck tragedytrag...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>6254</td>\n",
" <td>murder stori america 聣 没陋 first hijack</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id all_text\n",
"0 3796 new weapon caus un-imagin destruct destructionnon\n",
"1 3185 f @ing thing gishwh got soak delug go pad tamp...\n",
"2 7769 dt rt 聣 没茂the col polic catch pickpocket liver...\n",
"3 191 aftershock back school kick great want thank e...\n",
"4 9810 respons trauma children addict develop defens ...\n",
"5 7934 look like got caught rainstorm amaz disgust ti...\n",
"6 2538 favorit ladi came volunt meet hope join youth ...\n",
"7 2611 ux fail emv peopl want insert remov quickli li...\n",
"8 9756 can't find ariana grand shirt fuck tragedytrag...\n",
"9 6254 murder stori america 聣 没陋 first hijack"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# read train data\n",
"df = pd.read_csv(\"../inputs/train.csv\")\n",
"# shuffle data\n",
"df = df.sample(frac=1, random_state=42).reset_index(drop=True)\n",
"# create new column \"all_text\"\n",
"df[\"all_text\"] = df[\"text\"] + df[\"keyword\"].fillna(\"none\") + df[\"location\"].fillna(\"none\")\n",
"# split into features and labels\n",
"X = df.drop([\"text\", \"keyword\", \"location\", \"target\"], axis=1)\n",
"y = df[\"target\"]\n",
"\n",
"# process tweets\n",
"X[\"all_text\"] = X[\"all_text\"].apply(process_tweet)\n",
"X.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# create a dictionary mapping predictions to the tweet idx\n",
"pred_idx_dict = {}\n",
"# initialize kfold\n",
"skf = StratifiedKFold(n_splits=5, shuffle=False)\n",
"for fold, (train_idx, val_idx) in enumerate(skf.split(X=X, y=y)):\n",
" X_train, X_val = X.loc[train_idx, :], X.loc[val_idx, :]\n",
" y_train, y_val = y[train_idx], y[val_idx]\n",
"\n",
" # vectorize text and store model\n",
" count_vect = CountVectorizer()\n",
" X_train_vect = count_vect.fit_transform(X_train[\"all_text\"].values)\n",
" X_val_vect = count_vect.transform(X_val[\"all_text\"].values)\n",
" \n",
" # classify predictions\n",
" clf = MultinomialNB()\n",
" clf.fit(X_train_vect, y_train)\n",
" y_preds = clf.predict(X_val_vect)\n",
" \n",
" # idx of tweet mapping to prediction of model\n",
" for idx, key in enumerate(val_idx):\n",
" pred_idx_dict[key] = y_preds[idx]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# create df with actual and prediction\n",
"error_df = X.copy()\n",
"error_df.rename(columns={\"all_text\":\"processed_all_text\"}, inplace=True)\n",
"error_df[\"all_text\"] = df[df[\"id\"] == error_df[\"id\"].values][\"all_text\"]\n",
"error_df[\"actual\"] = y.copy()\n",
"error_df[\"predictions\"] = pred_idx_dict.values()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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>id</th>\n",
" <th>processed_all_text</th>\n",
" <th>all_text</th>\n",
" <th>actual</th>\n",
" <th>predictions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3796</td>\n",
" <td>new weapon caus un-imagin destruct destructionnon</td>\n",
" <td>So you have a new weapon that can cause un-ima...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3185</td>\n",
" <td>f @ing thing gishwh got soak delug go pad tamp...</td>\n",
" <td>The f$&amp;@ing things I do for #GISHWHES Just...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7769</td>\n",
" <td>dt rt 聣 没茂the col polic catch pickpocket liver...</td>\n",
" <td>DT @georgegalloway: RT @Galloway4Mayor: 聣脹脧The...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>191</td>\n",
" <td>aftershock back school kick great want thank e...</td>\n",
" <td>Aftershock back to school kick off was great. ...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9810</td>\n",
" <td>respons trauma children addict develop defens ...</td>\n",
" <td>in response to trauma Children of Addicts deve...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7608</th>\n",
" <td>7470</td>\n",
" <td>mani obliter server alway like play :D obliter...</td>\n",
" <td>@Eganator2000 There aren't many Obliteration s...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7609</th>\n",
" <td>7691</td>\n",
" <td>panic attack bc enough money drug alcohol want...</td>\n",
" <td>just had a panic attack bc I don't have enough...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7610</th>\n",
" <td>1242</td>\n",
" <td>omron hem 712c automat blood pressur monitor s...</td>\n",
" <td>Omron HEM-712C Automatic Blood Pressure Monito...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7611</th>\n",
" <td>10862</td>\n",
" <td>offici say quarantin place alabama home possib...</td>\n",
" <td>Officials say a quarantine is in place at an A...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7612</th>\n",
" <td>10409</td>\n",
" <td>move england five year ago today whirlwind time</td>\n",
" <td>I moved to England five years ago today. What ...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>7613 rows 脳 5 columns</p>\n",
"</div>"
],
"text/plain": [
" id processed_all_text \\\n",
"0 3796 new weapon caus un-imagin destruct destructionnon \n",
"1 3185 f @ing thing gishwh got soak delug go pad tamp... \n",
"2 7769 dt rt 聣 没茂the col polic catch pickpocket liver... \n",
"3 191 aftershock back school kick great want thank e... \n",
"4 9810 respons trauma children addict develop defens ... \n",
"... ... ... \n",
"7608 7470 mani obliter server alway like play :D obliter... \n",
"7609 7691 panic attack bc enough money drug alcohol want... \n",
"7610 1242 omron hem 712c automat blood pressur monitor s... \n",
"7611 10862 offici say quarantin place alabama home possib... \n",
"7612 10409 move england five year ago today whirlwind time \n",
"\n",
" all_text actual predictions \n",
"0 So you have a new weapon that can cause un-ima... 1 0 \n",
"1 The f$&@ing things I do for #GISHWHES Just... 0 0 \n",
"2 DT @georgegalloway: RT @Galloway4Mayor: 聣脹脧The... 1 0 \n",
"3 Aftershock back to school kick off was great. ... 0 0 \n",
"4 in response to trauma Children of Addicts deve... 0 1 \n",
"... ... ... ... \n",
"7608 @Eganator2000 There aren't many Obliteration s... 0 0 \n",
"7609 just had a panic attack bc I don't have enough... 0 0 \n",
"7610 Omron HEM-712C Automatic Blood Pressure Monito... 0 1 \n",
"7611 Officials say a quarantine is in place at an A... 1 1 \n",
"7612 I moved to England five years ago today. What ... 1 1 \n",
"\n",
"[7613 rows x 5 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"error_df"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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>id</th>\n",
" <th>processed_all_text</th>\n",
" <th>all_text</th>\n",
" <th>actual</th>\n",
" <th>predictions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>1061</td>\n",
" <td>ye i'm bleed heart liberal.bleedingl oak tx</td>\n",
" <td>@KatRamsland Yes I'm a bleeding heart liberal....</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>518</th>\n",
" <td>8946</td>\n",
" <td>storm came . . fuck coolstormnon</td>\n",
" <td>So this storm just came out of no where. .fuck...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3161</th>\n",
" <td>143</td>\n",
" <td>car even week got fuck car accid .. mf can't f...</td>\n",
" <td>only had a car for not even a week and got in ...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6624</th>\n",
" <td>9044</td>\n",
" <td>spacex founder musk structur failur took falcon 9</td>\n",
" <td>SpaceX Founder Musk: Structural Failure Took D...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>881</th>\n",
" <td>1458</td>\n",
" <td>anoth one anoth one still ain't done shit one ...</td>\n",
" <td>'I did another one I did another one. You stil...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4314</th>\n",
" <td>10364</td>\n",
" <td>router one latest ddo attack weapon</td>\n",
" <td>Your Router is One of the Latest DDoS Attack W...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5399</th>\n",
" <td>6188</td>\n",
" <td>gov brown allow parol 1976 chowchilla school b...</td>\n",
" <td>Gov. Brown allows parole for 1976 Chowchilla s...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4266</th>\n",
" <td>4911</td>\n",
" <td>chick masturb guy get explod face</td>\n",
" <td>Chick masturbates a guy until she gets explode...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3959</th>\n",
" <td>2112</td>\n",
" <td>borrow concern possibl interest rate rise coul...</td>\n",
" <td>#Borrowers concerned at possible #interest rat...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6445</th>\n",
" <td>7926</td>\n",
" <td>stuck rainstorm stay toward middl road street ...</td>\n",
" <td>Stuck in a rainstorm? Stay toward the middle o...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id processed_all_text \\\n",
"149 1061 ye i'm bleed heart liberal.bleedingl oak tx \n",
"518 8946 storm came . . fuck coolstormnon \n",
"3161 143 car even week got fuck car accid .. mf can't f... \n",
"6624 9044 spacex founder musk structur failur took falcon 9 \n",
"881 1458 anoth one anoth one still ain't done shit one ... \n",
"4314 10364 router one latest ddo attack weapon \n",
"5399 6188 gov brown allow parol 1976 chowchilla school b... \n",
"4266 4911 chick masturb guy get explod face \n",
"3959 2112 borrow concern possibl interest rate rise coul... \n",
"6445 7926 stuck rainstorm stay toward middl road street ... \n",
"\n",
" all_text actual predictions \n",
"149 @KatRamsland Yes I'm a bleeding heart liberal.... 1 0 \n",
"518 So this storm just came out of no where. .fuck... 1 0 \n",
"3161 only had a car for not even a week and got in ... 1 0 \n",
"6624 SpaceX Founder Musk: Structural Failure Took D... 1 0 \n",
"881 'I did another one I did another one. You stil... 1 0 \n",
"4314 Your Router is One of the Latest DDoS Attack W... 0 1 \n",
"5399 Gov. Brown allows parole for 1976 Chowchilla s... 0 1 \n",
"4266 Chick masturbates a guy until she gets explode... 1 0 \n",
"3959 #Borrowers concerned at possible #interest rat... 0 1 \n",
"6445 Stuck in a rainstorm? Stay toward the middle o... 0 1 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# store only the misclassified instances\n",
"misclassified_df = error_df[error_df[\"actual\"].values != error_df[\"predictions\"]]\n",
"# keep only 100 of the misclassfied instances\n",
"misclassified_100 = misclassified_df.sample(n=100, random_state=42)\n",
"misclassified_100.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"misclassified_100.to_csv(\"misclassified_data.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|