Format: application/octet-stream
1 {
2 "cells": [
3 {
4 "cell_type": "code",
5 "execution_count": 1,
6 "id": "954e9fd9",
7 "metadata": {},
8 "outputs": [],
9 "source": [
10 "import pandas as pd\n",
11 "import numpy as np\n",
12 "import nltk\n",
13 "from nltk.corpus import stopwords\n",
14 "stopwords= stopwords.words('english')\n",
15 "stopwords.extend([',','`','?',\"'s\", \"''\", '``',\"n't\",\"(\", \")\", ';',\"'\",'re','!','--','-','*',\"'re\",':'])"
16 ]
17 },
18 {
19 "cell_type": "code",
20 "execution_count": 2,
21 "id": "5c9f32c2",
22 "metadata": {},
23 "outputs": [],
24 "source": [
25 "stopwords.extend([i.capitalize() for i in stopwords])"
26 ]
27 },
28 {
29 "cell_type": "code",
30 "execution_count": 3,
31 "id": "18a56afa",
32 "metadata": {},
33 "outputs": [],
34 "source": [
35 "s1= pd.read_csv('data/unique synopsis.tsv', sep='\\t')\n"
36 ]
37 },
38 {
39 "cell_type": "code",
40 "execution_count": 4,
41 "id": "f07f0c11",
42 "metadata": {
43 "scrolled": false
44 },
45 "outputs": [
46 {
47 "data": {
48 "text/html": [
49 "<div>\n",
50 "<style scoped>\n",
51 " .dataframe tbody tr th:only-of-type {\n",
52 " vertical-align: middle;\n",
53 " }\n",
54 "\n",
55 " .dataframe tbody tr th {\n",
56 " vertical-align: top;\n",
57 " }\n",
58 "\n",
59 " .dataframe thead th {\n",
60 " text-align: right;\n",
61 " }\n",
62 "</style>\n",
63 "<table border=\"1\" class=\"dataframe\">\n",
64 " <thead>\n",
65 " <tr style=\"text-align: right;\">\n",
66 " <th></th>\n",
67 " <th>0</th>\n",
68 " </tr>\n",
69 " </thead>\n",
70 " <tbody>\n",
71 " <tr>\n",
72 " <th>0</th>\n",
73 " <td>In 1947, Andy Dufresne (Tim Robbins), a banker...</td>\n",
74 " </tr>\n",
75 " <tr>\n",
76 " <th>1</th>\n",
77 " <td>The movie begins with a gang of men with clown...</td>\n",
78 " </tr>\n",
79 " <tr>\n",
80 " <th>2</th>\n",
81 " <td>A young man, exhausted and delirious, washes u...</td>\n",
82 " </tr>\n",
83 " <tr>\n",
84 " <th>3</th>\n",
85 " <td>We back out of the webbing of neurons and brai...</td>\n",
86 " </tr>\n",
87 " <tr>\n",
88 " <th>4</th>\n",
89 " <td>Late one morning in the Hawthorne Grill, a res...</td>\n",
90 " </tr>\n",
91 " </tbody>\n",
92 "</table>\n",
93 "</div>"
94 ],
95 "text/plain": [
96 " 0\n",
97 "0 In 1947, Andy Dufresne (Tim Robbins), a banker...\n",
98 "1 The movie begins with a gang of men with clown...\n",
99 "2 A young man, exhausted and delirious, washes u...\n",
100 "3 We back out of the webbing of neurons and brai...\n",
101 "4 Late one morning in the Hawthorne Grill, a res..."
102 ]
103 },
104 "execution_count": 4,
105 "metadata": {},
106 "output_type": "execute_result"
107 }
108 ],
109 "source": [
110 "s1.head()"
111 ]
112 },
113 {
114 "cell_type": "code",
115 "execution_count": 5,
116 "id": "a52e3b31",
117 "metadata": {},
118 "outputs": [
119 {
120 "data": {
121 "text/plain": [
122 "9643"
123 ]
124 },
125 "execution_count": 5,
126 "metadata": {},
127 "output_type": "execute_result"
128 }
129 ],
130 "source": [
131 "len(s1)"
132 ]
133 },
134 {
135 "cell_type": "code",
136 "execution_count": 6,
137 "id": "74d6ffd7",
138 "metadata": {},
139 "outputs": [],
140 "source": [
141 "from nltk.tokenize import word_tokenize"
142 ]
143 },
144 {
145 "cell_type": "code",
146 "execution_count": 7,
147 "id": "50095664",
148 "metadata": {},
149 "outputs": [],
150 "source": [
151 "texts= s1.dropna(axis=0).values.reshape(-1,).astype('object')"
152 ]
153 },
154 {
155 "cell_type": "code",
156 "execution_count": 8,
157 "id": "d1ea9fdd",
158 "metadata": {},
159 "outputs": [],
160 "source": [
161 "texts=' '.join(texts)"
162 ]
163 },
164 {
165 "cell_type": "code",
166 "execution_count": 9,
167 "id": "10634606",
168 "metadata": {},
169 "outputs": [],
170 "source": [
171 "texts=' '.join(texts.split('.')).lower()"
172 ]
173 },
174 {
175 "cell_type": "code",
176 "execution_count": null,
177 "id": "3c3adcaf",
178 "metadata": {},
179 "outputs": [],
180 "source": []
181 },
182 {
183 "cell_type": "code",
184 "execution_count": 10,
185 "id": "cd909ad7",
186 "metadata": {},
187 "outputs": [],
188 "source": [
189 "words= np.array(word_tokenize(texts)) ### chewck"
190 ]
191 },
192 {
193 "cell_type": "code",
194 "execution_count": 11,
195 "id": "661ed7bd",
196 "metadata": {},
197 "outputs": [],
198 "source": [
199 "words=np.array([w for w in words if not w in stopwords])"
200 ]
201 },
202 {
203 "cell_type": "code",
204 "execution_count": 12,
205 "id": "2fe22ff2",
206 "metadata": {},
207 "outputs": [
208 {
209 "data": {
210 "text/plain": [
211 "5641411"
212 ]
213 },
214 "execution_count": 12,
215 "metadata": {},
216 "output_type": "execute_result"
217 }
218 ],
219 "source": [
220 "len(words)"
221 ]
222 },
223 {
224 "cell_type": "code",
225 "execution_count": 13,
226 "id": "62986f26",
227 "metadata": {},
228 "outputs": [],
229 "source": [
230 "features=np.array(list(set(words)))"
231 ]
232 },
233 {
234 "cell_type": "code",
235 "execution_count": 14,
236 "id": "0373e926",
237 "metadata": {},
238 "outputs": [
239 {
240 "data": {
241 "text/plain": [
242 "128962"
243 ]
244 },
245 "execution_count": 14,
246 "metadata": {},
247 "output_type": "execute_result"
248 }
249 ],
250 "source": [
251 "len(features)"
252 ]
253 },
254 {
255 "cell_type": "code",
256 "execution_count": null,
257 "id": "c0f901b4",
258 "metadata": {},
259 "outputs": [],
260 "source": []
261 },
262 {
263 "cell_type": "code",
264 "execution_count": 15,
265 "id": "ddc5ac8d",
266 "metadata": {},
267 "outputs": [
268 {
269 "data": {
270 "text/plain": [
271 "array([], dtype=int64)"
272 ]
273 },
274 "execution_count": 15,
275 "metadata": {},
276 "output_type": "execute_result"
277 }
278 ],
279 "source": [
280 "np.where(words== 'in')[0]"
281 ]
282 },
283 {
284 "cell_type": "code",
285 "execution_count": 16,
286 "id": "35406a83",
287 "metadata": {},
288 "outputs": [],
289 "source": [
290 "def count_f(array):\n",
291 " unique, counts = np.unique(array, return_counts=True)\n",
292 " if len(counts)>1:\n",
293 " counts= counts/len(counts)\n",
294 " else:\n",
295 " counts= counts/counts\n",
296 " array=dict(zip(unique, counts))\n",
297 " array={k: v for k, v in sorted(array.items(), key=lambda item: item[1], reverse=True)} #set to true\n",
298 " #print(array)\n",
299 " return array"
300 ]
301 },
302 {
303 "cell_type": "code",
304 "execution_count": 17,
305 "id": "d7a75ae7",
306 "metadata": {},
307 "outputs": [],
308 "source": [
309 "def next_previous(feature):\n",
310 " indexs= np.where(words== feature)[0]\n",
311 " \n",
312 " next_word= count_f(words[indexs+1])\n",
313 " previous_word= count_f(words[indexs-1])\n",
314 "\n",
315 " return np.vstack((previous_word, next_word)).astype('object').T"
316 ]
317 },
318 {
319 "cell_type": "code",
320 "execution_count": null,
321 "id": "38253854",
322 "metadata": {},
323 "outputs": [],
324 "source": []
325 },
326 {
327 "cell_type": "code",
328 "execution_count": null,
329 "id": "977a0bc0",
330 "metadata": {},
331 "outputs": [],
332 "source": []
333 },
334 {
335 "cell_type": "code",
336 "execution_count": null,
337 "id": "3acfa8e6",
338 "metadata": {},
339 "outputs": [],
340 "source": []
341 },
342 {
343 "cell_type": "code",
344 "execution_count": 18,
345 "id": "7bdf69e7",
346 "metadata": {},
347 "outputs": [],
348 "source": [
349 "def word_cha(features):\n",
350 " final={}\n",
351 " for feature in features:\n",
352 " final[feature]= next_previous(feature)\n",
353 " return final"
354 ]
355 },
356 {
357 "cell_type": "code",
358 "execution_count": 27,
359 "id": "db74d8c2",
360 "metadata": {},
361 "outputs": [],
362 "source": [
363 "def find_most_probable(word,num=10):\n",
364 " try:\n",
365 " words=next_previous(word)\n",
366 " previous_words= np.array(list(words[0][0].keys())).astype('object')[:num]\n",
367 " next_words= np.array(list(words[0][1].keys())).astype('object')[:num]\n",
368 " previous_words_prob= np.array(list(words[0][0].values()))\n",
369 " prob_words=np.array([])\n",
370 " for i,previous_word in enumerate(previous_words):\n",
371 " #print(i, 'p',previous_word)\n",
372 " #c=previous_words_prob[i]\n",
373 " #print(f'c= {c}')\n",
374 " n=next_previous(previous_word)\n",
375 " #print(n)\n",
376 "\n",
377 " next_words_child= np.array(list(n[0][1].keys())).astype('object')\n",
378 " prob_words= np.append(prob_words, next_words_child)\n",
379 "\n",
380 " if i<len(next_words):\n",
381 " n=next_previous(next_words[i])\n",
382 " previous_words_child= np.array(list(n[0][0].keys())).astype('object')\n",
383 " prob_words= np.append(prob_words, previous_words_child)\n",
384 " #print(next_words_child)\n",
385 " #next_words_child_prob= np.array(list(next_previous(word)[0][1].values()))\n",
386 "\n",
387 " \n",
388 " #del prob_words[word]\n",
389 " #a=count_f(words)\n",
390 " #for key, value in prob_words.items():\n",
391 " # prob_words[key]= value/a[key]\n",
392 " # prob_words={k: v for k, v in sorted(prob_words.items(), key=lambda item: item[1], reverse=True)}\n",
393 " except:\n",
394 " pass\n",
395 " prob_words=count_f(prob_words)\n",
396 " #del prob_words[word]\n",
397 " return prob_words"
398 ]
399 },
400 {
401 "cell_type": "code",
402 "execution_count": null,
403 "id": "735f09df",
404 "metadata": {},
405 "outputs": [],
406 "source": []
407 },
408 {
409 "cell_type": "code",
410 "execution_count": 28,
411 "id": "0253d5f9",
412 "metadata": {},
413 "outputs": [
414 {
415 "data": {
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599 " 'jacob': 0.0006124636349716736,\n",
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601 " 'jesse': 0.0006124636349716736,\n",
602 " 'julie': 0.0006124636349716736,\n",
603 " 'junior': 0.0006124636349716736,\n",
604 " 'kate': 0.0006124636349716736,\n",
605 " 'kevin': 0.0006124636349716736,\n",
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611 " 'leaving': 0.0006124636349716736,\n",
612 " 'left': 0.0006124636349716736,\n",
613 " 'look': 0.0006124636349716736,\n",
614 " 'lot': 0.0006124636349716736,\n",
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1391 " 'christopher': 0.00042106874904302555,\n",
1392 " 'claim': 0.00042106874904302555,\n",
1393 " 'claims': 0.00042106874904302555,\n",
1394 " 'classes': 0.00042106874904302555,\n",
1395 " 'clean': 0.00042106874904302555,\n",
1396 " 'cliff': 0.00042106874904302555,\n",
1397 " 'close': 0.00042106874904302555,\n",
1398 " 'coat': 0.00042106874904302555,\n",
1399 " 'coffee': 0.00042106874904302555,\n",
1400 " 'coin': 0.00042106874904302555,\n",
1401 " 'college': 0.00042106874904302555,\n",
1402 " 'complete': 0.00042106874904302555,\n",
1403 " 'continue': 0.00042106874904302555,\n",
1404 " 'convince': 0.00042106874904302555,\n",
1405 " 'cop': 0.00042106874904302555,\n",
1406 " 'danger': 0.00042106874904302555,\n",
1407 " 'darkness': 0.00042106874904302555,\n",
1408 " 'decided': 0.00042106874904302555,\n",
1409 " 'decision': 0.00042106874904302555,\n",
1410 " 'derek': 0.00042106874904302555,\n",
1411 " 'detective': 0.00042106874904302555,\n",
1412 " 'discovers': 0.00042106874904302555,\n",
1413 " 'donna': 0.00042106874904302555,\n",
1414 " 'donnie': 0.00042106874904302555,\n",
1415 " 'downstairs': 0.00042106874904302555,\n",
1416 " 'dracula': 0.00042106874904302555,\n",
1417 " ...}"
1418 ]
1419 },
1420 "execution_count": 28,
1421 "metadata": {},
1422 "output_type": "execute_result"
1423 }
1424 ],
1425 "source": [
1426 "find_most_probable('money')"
1427 ]
1428 },
1429 {
1430 "cell_type": "code",
1431 "execution_count": null,
1432 "id": "898438a3",
1433 "metadata": {},
1434 "outputs": [],
1435 "source": [
1436 "14/20"
1437 ]
1438 },
1439 {
1440 "cell_type": "code",
1441 "execution_count": null,
1442 "id": "b82f46fe",
1443 "metadata": {},
1444 "outputs": [],
1445 "source": [
1446 "a=count_f(words)"
1447 ]
1448 },
1449 {
1450 "cell_type": "code",
1451 "execution_count": null,
1452 "id": "ce8ff4e2",
1453 "metadata": {},
1454 "outputs": [],
1455 "source": []
1456 }
1457 ],
1458 "metadata": {
1459 "kernelspec": {
1460 "display_name": "Python 3",
1461 "language": "python",
1462 "name": "python3"
1463 },
1464 "language_info": {
1465 "codemirror_mode": {
1466 "name": "ipython",
1467 "version": 3
1468 },
1469 "file_extension": ".py",
1470 "mimetype": "text/x-python",
1471 "name": "python",
1472 "nbconvert_exporter": "python",
1473 "pygments_lexer": "ipython3",
1474 "version": "3.8.8"
1475 }
1476 },
1477 "nbformat": 4,
1478 "nbformat_minor": 5
1479 }
1480