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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Lesson_2.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# Введение в обработку естественного языка\n",
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"## Урок 2. Создание признакового пространства"
23
],
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"metadata": {
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"id": "Ib6Rr8FfSEkB"
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}
27
},
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{
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"cell_type": "code",
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"source": [
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"!pip install wget"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Yk-uMKf0Z0ZL",
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"outputId": "d94f0240-9d6c-4a7f-aa0f-a37307324930"
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},
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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"Collecting wget\n",
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" Downloading wget-3.2.zip (10 kB)\n",
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"Building wheels for collected packages: wget\n",
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" Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9675 sha256=3727f9497cab54146ff09c7e906783002619ae30e46b61f1a9384d68ed5bba09\n",
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" Stored in directory: /root/.cache/pip/wheels/a1/b6/7c/0e63e34eb06634181c63adacca38b79ff8f35c37e3c13e3c02\n",
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"Successfully built wget\n",
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"Installing collected packages: wget\n",
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"Successfully installed wget-3.2\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
63
"metadata": {
64
"id": "HT8T4vIlPWSz"
65
},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
69
"import numpy as np\n",
70
"from sklearn.metrics import *\n",
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"from sklearn.model_selection import train_test_split\n",
72
"from sklearn.pipeline import Pipeline\n",
73
"import nltk\n",
74
"import re\n",
75
"import wget\n",
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"from datetime import datetime\n",
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"from bs4 import BeautifulSoup\n",
78
"import requests"
79
]
80
},
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{
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"cell_type": "code",
83
"source": [
84
"!unzip \"/content/Lesson-2.zip\" -d \"/content/\""
85
],
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"metadata": {
87
"colab": {
88
"base_uri": "https://localhost:8080/"
89
},
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"id": "KmG9sNj7TdK1",
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"outputId": "ddd77438-5f59-42f4-a62e-abfd5c995e5d"
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},
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"execution_count": 4,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Archive: /content/Lesson-2.zip\n",
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" creating: /content/image/\n",
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" inflating: /content/image/2D_tsne.PNG \n",
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" inflating: /content/image/CBOW_.png \n",
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" inflating: /content/image/skipgram.png \n",
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" inflating: /content/image/tf_idf.PNG \n",
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" inflating: /content/image/w2v_formula.PNG \n",
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" inflating: /content/image/word_embeddings.PNG \n",
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" inflating: /content/corpus \n",
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" inflating: /content/lesson_2.ipynb \n",
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" inflating: /content/sem2.ipynb \n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"HEADER = {\n",
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" \"User-Agent\": 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.127 Safari/537.36'\n",
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"}\n",
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"\n",
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"url = 'https://gbcdn.mrgcdn.ru/uploads/asset/2800511/attachment/aa4f455409c2e805aa4b13c4011764fa'\n",
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"\n",
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"response = requests.get(url, headers=HEADER)\n",
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"\n",
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"with open(\"d_corpus\", \"w\") as f:\n",
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" f.write(response.text)"
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],
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"metadata": {
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"id": "HSZ88PupT3g5"
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},
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"execution_count": 5,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"import filecmp\n",
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"\n",
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"filecmp.cmp('/content/corpus', '/content/d_corpus')"
140
],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "046Z2X6UqrQJ",
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"outputId": "fc93944f-419a-42d1-9fd1-06f8728c3164"
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},
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"execution_count": 6,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"True"
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]
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},
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"metadata": {},
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"execution_count": 6
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"with open('corpus', 'r') as f:\n",
166
" df = pd.DataFrame({'category': t[0], 'text': t[1]} for t in (l.split(' ', 1) for l in f.readlines()))\n",
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"df['category'] = df['category'].apply(lambda t: re.search(r'\\d+', t).group())\n",
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"df"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 424
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},
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"id": "xj9pr0C4rDZQ",
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"outputId": "3f5bae9a-37c4-40ae-ab15-d762df1e10ef"
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},
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"execution_count": 7,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" category text\n",
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"0 2 Stuning even for the non-gamer: This sound tra...\n",
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"3 2 Excellent Soundtrack: I truly like this soundt...\n",
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342
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343
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344
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345
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346
"\n",
347
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
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]
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},
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"metadata": {},
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"execution_count": 7
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}
366
]
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},
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{
369
"cell_type": "code",
370
"source": [
371
"x_train, x_test, y_train, y_test = train_test_split(df.text, df.category)"
372
],
373
"metadata": {
374
"id": "De4ElJKvrXnU"
375
},
376
"execution_count": 8,
377
"outputs": []
378
},
379
{
380
"cell_type": "code",
381
"source": [
382
"from string import punctuation\n",
383
"from collections import Counter\n",
384
"from sklearn.linear_model import LogisticRegression\n",
385
"from sklearn.feature_extraction.text import CountVectorizer\n",
386
"from nltk import ngrams\n",
387
"import nltk\n",
388
"from nltk import collocations \n",
389
"from nltk.tokenize import word_tokenize, wordpunct_tokenize\n",
390
"from nltk.corpus import stopwords\n",
391
"nltk.download('genesis')\n",
392
"nltk.download('stopwords')\n",
393
"nltk.download('punkt')"
394
],
395
"metadata": {
396
"colab": {
397
"base_uri": "https://localhost:8080/"
398
},
399
"id": "d1o9qGUKsOva",
400
"outputId": "8f56b848-8b0d-471b-87c7-15ae00fee6e1"
401
},
402
"execution_count": 9,
403
"outputs": [
404
{
405
"output_type": "stream",
406
"name": "stdout",
407
"text": [
408
"[nltk_data] Downloading package genesis to /root/nltk_data...\n",
409
"[nltk_data] Unzipping corpora/genesis.zip.\n",
410
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
411
"[nltk_data] Unzipping corpora/stopwords.zip.\n",
412
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
413
"[nltk_data] Unzipping tokenizers/punkt.zip.\n"
414
]
415
},
416
{
417
"output_type": "execute_result",
418
"data": {
419
"text/plain": [
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"True"
421
]
422
},
423
"metadata": {},
424
"execution_count": 9
425
}
426
]
427
},
428
{
429
"cell_type": "code",
430
"source": [
431
"noise = stopwords.words('english') + list(punctuation)\n",
432
"tokens = [token for sense in df.text for token in word_tokenize(sense) if token not in punctuation]"
433
],
434
"metadata": {
435
"id": "26LthWpU5uvU"
436
},
437
"execution_count": 10,
438
"outputs": []
439
},
440
{
441
"cell_type": "code",
442
"source": [
443
"freq_dict = Counter(tokens)\n",
444
"freq_dict_sorted= sorted(freq_dict.items(), key=lambda x: -x[1])\n",
445
"max([el[1] for el in list(freq_dict_sorted)]), min([el[1] for el in list(freq_dict_sorted)])"
446
],
447
"metadata": {
448
"colab": {
449
"base_uri": "https://localhost:8080/"
450
},
451
"id": "Y3heRAPa7Ow9",
452
"outputId": "86c955fb-6669-48ac-e192-7b656c2a3796"
453
},
454
"execution_count": 11,
455
"outputs": [
456
{
457
"output_type": "execute_result",
458
"data": {
459
"text/plain": [
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"(35041, 1)"
461
]
462
},
463
"metadata": {},
464
"execution_count": 11
465
}
466
]
467
},
468
{
469
"cell_type": "markdown",
470
"source": [
471
"### Задание 1.\n",
472
"Задание: обучите три классификатора:\n",
473
"\n",
474
"1) на токенах с высокой частотой\n",
475
"\n",
476
"2) на токенах со средней частотой\n",
477
"\n",
478
"3) на токенах с низкой частотой"
479
],
480
"metadata": {
481
"id": "--5qiaE39Pua"
482
}
483
},
484
{
485
"cell_type": "code",
486
"source": [
487
"high_tokens = set()\n",
488
"med_tokens = set()\n",
489
"low_tokens = set()\n",
490
"h = 20000\n",
491
"l = 5000\n",
492
"for i in freq_dict_sorted:\n",
493
" if i[1] > h:\n",
494
" high_tokens.add(i[0])\n",
495
" elif i[1] < l:\n",
496
" low_tokens.add(i[0])\n",
497
" else:\n",
498
" med_tokens.add(i[0])\n",
499
"print(len(high_tokens),len(med_tokens),len(low_tokens))"
500
],
501
"metadata": {
502
"colab": {
503
"base_uri": "https://localhost:8080/"
504
},
505
"id": "BINXJzoJ7odM",
506
"outputId": "6a8d611e-5b14-4a68-d505-6d7620f3f994"
507
},
508
"execution_count": 12,
509
"outputs": [
510
{
511
"output_type": "stream",
512
"name": "stdout",
513
"text": [
514
"3 19 47469\n"
515
]
516
}
517
]
518
},
519
{
520
"cell_type": "code",
521
"source": [
522
"sw = noise + list(low_tokens) + list(med_tokens)"
523
],
524
"metadata": {
525
"id": "XB1WweYo-ySa"
526
},
527
"execution_count": 13,
528
"outputs": []
529
},
530
{
531
"cell_type": "code",
532
"source": [
533
"vec = CountVectorizer(ngram_range=(1, 1), tokenizer=word_tokenize, stop_words=sw)\n",
534
"bow = vec.fit_transform(x_train)\n",
535
"clf = LogisticRegression(random_state=42)\n",
536
"clf.fit(bow, y_train)\n",
537
"pred = clf.predict(vec.transform(x_test))\n",
538
"print(classification_report(pred, y_test))"
539
],
540
"metadata": {
541
"colab": {
542
"base_uri": "https://localhost:8080/"
543
},
544
"id": "d5y71You_KZQ",
545
"outputId": "a336c1b1-4aaa-47db-cfdf-eae6688eeb1b"
546
},
547
"execution_count": 14,
548
"outputs": [
549
{
550
"output_type": "stream",
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"/usr/local/lib/python3.7/dist-packages/sklearn/feature_extraction/text.py:401: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens [\"'`living\", \"'accelerator\", \"'adoption\", \"'anno\", \"'arular\", \"'ascension\", \"'ashes\", \"'away\", \"'bertha\", \"'big\", \"'brave\", \"'bubbabrain\", \"'bubbabyte\", \"'bucky\", \"'buffy\", \"'cade\", \"'canon\", \"'catch\", \"'centerview\", \"'cf\", \"'china\", \"'clan\", \"'clothes\", \"'collector\", \"'comeback'i\", \"'coyote\", \"'craig\", \"'d.c.rip\", \"'dark\", \"'deliverance\", \"'diary\", \"'did\", \"'disney\", \"'distillation\", \"'dressing\", \"'duh\", \"'eject\", \"'em.anyways\", \"'error\", \"'estate\", \"'even\", \"'eye\", \"'fahreneheit\", \"'fahrenheit\", \"'fame\", \"'fillers'.an\", \"'flashdance\", \"'flashdance'and\", \"'forlane\", \"'foundation\", \"'fsol\", \"'funny\", \"'galang\", \"'gladiator\", \"'go\", \"'golden\", \"'heart\", \"'hell\", \"'helter\", \"'herland\", \"'highwayman\", \"'holt\", \"'how\", \"'i\", \"'if\", \"'incesticide\", \"'inferno\", \"'it\", \"'japanese\", \"'kuch\", \"'lab\", \"'life\", \"'lifeforms\", \"'lightning\", \"'lights\", \"'m.i.a\", \"'magic\", \"'masquerade\", \"'merry\", \"'milk\", \"'milk'is\", \"'mona\", \"'murican\", \"'muswell\", \"'newer\", \"'nursery\", \"'october\", \"'on\", \"'papua\", \"'persephone\", \"'phantom\", \"'pull\", \"'qr\", \"'r\", \"'rape\", \"'rebel\", \"'red\", \"'remove\", \"'roman\", \"'romeo\", \"'rumors\", \"'run\", \"'sailors\", \"'sappy\", \"'scream\", \"'seal\", \"'shell\", \"'silver\", \"'son\", \"'sound\", \"'spank\", \"'sports\", \"'starman\", \"'stopped\", \"'streetz\", \"'sunshowers\", \"'that\", \"'till\", \"'time\", \"'tombstone\", \"'trekkie\", \"'troy\", \"'unreliable\", \"'v\", \"'walk\", \"'war\", \"'website\", \"'wee\", \"'when\", \"'whore\", \"'with\", \"'wrong\", \"'you\", \"'your\", \"'zero\", '******it', '****this', '***spoiler', '***very', '***you', '**great', '**note', '**this', '*also', '*america*', '*anything*.well', '*dvd', '*ep*', '*graphics', '*great', '*how', '*l*', '*percy', '*quite', '*r', '*t', 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'2gb', '2k', '2mhz', '2nd-coming', '3-dimensional', '300k', '30f', '33x', '34b', '36-inch', '3m', '4-h', '4.the', '402-vlz3', '40d', '40f', '40g', '451.the', '451at', '451fahrenheit', '4608kbps', '46d', '47lw6500', '4am', '4gb', '4shady', '5-6.it', '5.5/10the', '500megas', '5mb', '5mb/s', '5mutant', '60gb', '633hs.it', '64mb', '64x2,4', '6620ldg', \"70's.i\", \"70's.the\", '75k', '7am', '7r', '8-movie', '8.addl', '8.in', '8/10i', '80.slow', '8500dv', '8x10', '9/30/01.to', '90-day', '960cse', '98/100this', '99centsand', ':1', '\\\\i', '_*the', '_foundation_', '_heroes', '_the', '`big', '`do', '`easy', '`kittenz', '`oceania', '`professional', '`requiem', '`romeo', '`silver', '`the', '`travels', 'a*teens', 'a+', 'a+++', 'a++++++++++++', 'a+.thank', 'a-1', 'a-b', 'a-bomb', 'a-m', 'a-pix', 'a.a.brill', 'a.d', 'a/b', 'a/v', 'a1c', 'a201', 'a2dp', 'a40', 'a80', 'aa', 'aaa', 'aaaarrrggggghhhhhh', 'aaargh', 'aaas', 'aaf', 'aas', 'abarca', 'abated.like', 'abba', 'abba10', 'abba11', 'abba12', 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'tucson', 'tudo', 'tuesday', 'tugging', 'tugs', 'tumbiumbi', 'tumpthumping', 'tune.but', 'tunisia', 'tupac', 'turchin', 'turistas', 'turkey.since', 'turkish', 'turturro', 'tuscany', 'tuscany.ho-hum', 'tusk', 'tutu', 'tv-mix', 'tv-out', 'tv-program', 'tv-special', 'tv.it', 'tv.on', 'tv/monitor', 'tv/multimedia', 'tv/vhs/dvd', 'tvc15', 'tvs', 'twain', 'tweeezerman', 'twenty-first', 'twian', 'twinxl', 'twist.story', 'tx', 'tyan', 'tybalt', 'tyco', 'tycoon.idid', 'tykes', 'tykwer', 'tyler', 'tymers', 'tynan', 'tyra', 'tyson', 't|c', 'tú', 'u-boat', 'u-boats', 'u-haul', 'u.f.o', 'u.k', 'u.r.a.q.t', 'u.s', 'u.s.a', 'u.s.aspider-man', 'u2', 'u6', 'ubuntu', 'uc', 'uckkk', 'ucl-l', 'ucla', 'ucs-200', 'ufc', 'uffizi', 'ufo-logy', 'ufos', 'ugly-american', 'ugolin', 'uhe1e102mhd6', 'uhh', 'uk', 'ukraine', 'ul', 'ulead', 'ulmer', 'ulob', 'ulta', 'ultra-bad', 'ultra-overrated', 'ulysses', 'ulysses.again', 'uma', 'umbra', 'umbria', 'umc', 'umff', 'un-catchy.and', 'un-christianity', 'unappetizing.i', 'unasked.after', 'unattractive.i', 'unavoidably', 'unbearable.i', 'unbeliavable', 'unbox', 'unbroken', 'uncomfortable.i', 'uncomprehendable', 'unconvincing.one', 'uncured', 'undaunted', 'undelivery', 'underappreciated.i', 'undercover', 'undergroud', 'underrated..so', 'understand..he', 'understand.unfortunately', 'understanding.avoid', 'understood.with', 'undertaken', 'undertaker', 'underwhelmed.asimov', 'underwhelming', 'underwood.while', 'undestanding', 'undomestic', 'undone.there', 'undressing', 'undubbed', 'unearthing', 'unemployed', 'unexpectedly.btw', 'unfaithful', 'unfortionately', 'unfortunate.please', 'unfortunetly', 'unforunately', 'unfound', 'unhappily', 'unicorn', 'unida', 'unified', 'union.the', 'unit.if', 'unit.observations', 'unit.the', 'units.i', 'units.while', 'univ', 'univeral', 'universe.i', 'universe.ignoring', 'univesity', 'unmasked', 'unnecessary.obs', 'unnessecary', 'unnessisary', 'unpacking.but', 'unpredictable.the', 'unpredictably', 'unqiue', 'unrealistic.note', 'unrelaxing', 'unremarkable', 'unremarkable.if', 'unrestrained', 'unrevised', 'unscrupulous', 'unsentimental', 'unsharpened', 'unstealable', 'unstolen', 'unsupported', 'unsuspected', 'untitled', 'untouchables', 'unusable.i', 'unwatchably', 'up-beatconfidant', 'up.add', 'up.as', 'up.butthis', 'up.cons', 'up.i', 'up.in', 'up.it', 'up.noting', 'up.pictures', 'up.strong', 'up.you', 'update.this', 'upgrades', 'upn', 'upon.atea', 'upyr', 'url', 'urn', 'ursula', 'us.i', 'us.our', 'us.shortly', 'us.the', 'us/canadian', 'usa.for', 'usaaf', 'usable.due', 'usable.unfortunately', 'usb2.0', 'usbav-700', 'usd', 'use.-beware', 'use.by', 'use.i', 'use.my', 'use.not', 'use.positive', 'use.update', 'used-', 'used.a', 'used.as', 'used.we', 'useful.mine', 'useful.now', 'useful.there', 'useing', 'useless.and', 'usgs', 'usher', 'usher..this', 'using.the', 'usps', 'uss', 'ussr', 'usual.this', 'utley', 'utopias', 'uv', 'uwe', 'uyanik', 'v', 'v190', 'v3', 'v323', 'v325', 'v360', 'v3c', 'v3i', 'v3m', 'v8', 'va912b', 'vacation.it', 'vai', 'vaio', 'vaiopc', 'val', 'vala', 'valdemar', 'valdez', 'valentine', 'valgar', 'valley.disk', 'value-2k3', 'value-conscious', 'value.mr', 'value.negatives', 'van-damme', 'vanbebber', 'vance', 'vancouver', 'vandamme', 'vanen', 'vanessa', 'vanessa-mae', 'vangelis', 'vanilli', 'var', 'varese', 'varg', 'vargas', 'variedad', 'vas', 'vasquez', 'vasquez-figueroa', 'vaultreports', 'vause', 'vb', 'vbac', 'vcd', 'vcd/horrible', 'vcr', 'vcr/dvd', 'vct-tk1', 'veclo', 'vedic', 'vee-jay', 'veena', \"veg'art\", 'vegas', 'velocd', 'velveteen', 'venius', 'venker', 'venom-like', 'ventaires', 'venus', 'venus.femalien', 'venusian', 'verdad', 'verdi', 'verizon', 'vermeulen', 'vermont', 'verne', 'vernes', 'vernon', 'veronica', 'verrinder', 'verrrrrry', 'verrrrryyyyyyy', 'version.get', 'version.the', 'version/director', 'vertigo', 'verto', 'vespera', 'vets', 'vexx', 'vh-1', 'vhs-like', 'viacord', 'viao', 'viceversa.that', 'vickery', 'victims.if', 'victor', 'victoria', 'victorian-era', 'video.and', 'video/dvd', 'videos.to', 'videos/dvds.despite', 'videostudio', 'videostuio', 'vienna', 'viet', 'vietnam', 'view.this', 'viewers.this', 'viewing.if', 'vig', 'vigilante', 'vii', 'vikernes', 'viking', 'vimes', 'vimes/watch', 'vince', 'vincent', 'vinci', 'vindication', 'vinny', 'violetta', 'violins.this', 'virgil', 'virgilian', 'virgina', 'virginia', 'virginian', 'virigin', 'viruses.then', 'visioneers', 'visor', 'visuals.after', 'visualy', 'vitaminless', 'vito', 'vittorio', 'viva', 'vivendi', 'vivicam', 'vivitar', 'vivo', 'vmc-il4615', 'voce', 'voice.after', 'voice.it', 'voice.the', 'voice.this', 'voivod', 'voivodian', 'voivodians', 'voltaire', 'volverme', 'volvo', 'voyager', 'vs.net', 'w/kindred', 'w/plug.i', 'waaaaay', 'wabash', 'wacken', 'waded', 'waechtersbach', 'wagner', 'wagnerian', 'wah', 'wailer', 'wailers', 'waistband', 'waitin', 'waitley', 'waits-', 'wake.for', 'wal*mart', 'wal-mart', 'walden', 'wales', 'walk.i', 'wall.please', 'wallace', 'walley', 'wallhanger', 'walnut', 'walt', 'walter', 'walton', 'waltons', 'waltz', 'wang', 'wanker.com', 'want.i', 'want/need.the', 'wanted.it', 'war.gypsies', 'war.henry', 'war.i', 'ward', 'wardell', 'wardh', 'warfare', 'warhammer', 'warhol', 'wario', 'warlord', 'warner', 'warners', 'warnes', 'warning.many', 'warrack', 'warranties', 'warren', 'warrick', 'warrickbrown', 'wart', 'warts', 'warwick', 'was.all', 'was.i', 'was.in', 'was.sho', 'was.stars', 'washington', 'washington/gabriel', 'wasit', 'waslife', 'wasp', 'watch.the', 'watched.the', 'watched.why', 'watching.the', 'watchmaker', 'waterfall', 'waterfront', 'waterless', 'waterloo', 'waterworld', 'watson', 'watters', 'waugh', 'waverunner', 'wavery-voiced', 'way.claus', 'way.i', 'way.in', 'way.it', 'way.like', 'way.one', 'way.the', 'waylon', 'wayne/john', 'ways..first', 'wbap-tv', 'wcj', 'wcw', 'wd', 'weak.i', 'weak/gentle', 'weapons.there', 'wear.as', 'wearings.they', 'weathertop', 'webb', 'webber', 'webcam', 'weber', 'website.a', 'website.i', 'website.the', 'webster', 'wed', 'wednesday', 'week.the', 'week.we', 'weekly/star/tmz', 'weeks.i', 'weetzie', 'wehrmacht-devotee', 'weight.the', 'weight.this', 'weill', 'weinreich', 'weinstock', 'weird.do', 'weird.i', 'weiss', 'welch', 'welcome.all', 'welden', 'well-acted', 'well.as', 'well.asimov', 'well.despite', 'well.each', 'well.fair', 'well.guy', 'well.i', 'well.if', 'well.jean', 'well.my', 'well.no', 'well.not', 'well.the', 'well.this', 'well.with', 'well.would', 'welsh', 'wenders', 'wendie', 'wendigo', 'wendingo', 'wendy', 'went.i', 'wenzel', 'wep200', 'were.unless', 'werner', 'wes', 'wesley', 'west-european', 'westbound', 'westend', 'westerners', 'westone', 'wet.i', 'whaddup', \"what'a\", 'what-so-ever.thank', 'whati', 'whats-his-name', 'whatsoever.i', 'whatsoever.secondly', 'whatsoever.the', 'whatsoeveri', 'whe', 'wheat-free', 'wheelconcluction', 'whereareyougoing', 'whew', 'whibal', 'which.i', 'while.even', 'while.if', 'whinstone', 'whiplash', 'whisky', 'whisper', 'whistle', 'whistler', 'white-wash', 'whitechapel', 'whiteheadinterview', 'whitehouse', 'whitey', 'whitney', 'whom.hulk', 'whomever', 'whoohoo', 'whoop', 'whoot', 'whql', 'why-ever', 'why.another', 'why.you', 'wiccan/pagan', 'wiccans', 'wick', 'wicker', 'wickham', 'wickiup', 'widowing', 'wierd.if', 'wife.its', 'wigan', 'wiggles', 'wilbur', 'wild.most', 'wilde', 'wilder', 'wilding/bonus', 'wiley', 'wilife', 'will.better', 'willam', 'willard', 'williams', 'williams.the', 'willie', 'willin', 'willows', 'willy', 'wilmington', 'wilson/beach', 'wilsons', 'win.in', 'win95', 'win98', 'win98se', 'window.her', 'windowsme', 'windowsxp', 'winged', 'winn', 'winnegago', 'winnie', 'winslet', 'winston', \"winston's.very\", 'winstone', 'winstons', 'winteriziing', 'winthrop', 'winxp', 'wipeout', 'wisconsin', 'wisconsin.when', 'wisefield', 'wish.a', 'wishmaster', 'wishy-washy', 'wiston', 'wit.i', 'witchlight', 'witchy', 'with..', 'with.if', 'with.occasionally', 'with.simply', 'withen', 'withers', 'withing', 'without.the', 'withstand.it', 'withstands', 'witless', 'witted', 'witwicky', \"witwicky's..\", 'wli2pcig54s', 'wm', 'wma', 'wmp', 'wmp54g', 'woburn', 'woderful', 'wolfe', 'wolfgang', 'wolfman', 'wolfram', 'wolitzer', 'wolters', 'woltzer', 'woman-ms', 'woman.clan', 'woman.do', 'womanolopy', 'wonder.thanks', 'wonderful.as', 'wonderful.bertulluci', 'wonderful.i', 'wonderfulfor', 'wonen', 'woo-hoo', 'woodbine', 'woodhouse', 'woodlands', 'woodstock', 'woody', 'woogie', 'woohoo', 'word-by-word.the', 'worddi', 'words.buy', 'words.everthing', 'words.i', 'wordsworth', 'work.i', 'work.is', 'work.newer', 'work.nicotetta', 'work.oh', 'work.the', 'work.this', 'work.to', 'work.yet', 'workbook.this', 'worked.it', 'workers.while', 'workinglibrary.jack', 'works.for', 'works.foundation', 'works.hurry', 'works.i', 'works.if', 'world.after', 'world.i', 'world.kerouac', 'world.the', 'world.while', 'worldwide.his', 'worms.hideous', 'worries.when', 'worrior', 'worry-free', 'worse.i', 'wort', 'worth.save', 'worthi', 'worthless.why', 'worthless.yes', 'woth', 'wouk', 'would.so', 'wow-', 'wow-factor', 'wow..', 'wow..thats', 'wp', 'wpa', 'wrapi', 'wrestle.expecially', 'wrestlecrap', 'wrestlemania', 'wrestling.sexxxy', 'writer.count', 'writers.although', 'writing.thank', 'writingwords', 'written.i', 'written.jul', 'wrong..i', 'wrong.a', 'wrong.enjoy', 'wrong.if', 'wrong.please', 'wrong.their', 'wrong.this', 'wrong.time', 'wrt54g', 'wtffollow', 'wto', 'wunderland', 'wuzzup', 'wv', 'ww', 'ww1', 'ww2', 'wwii', 'www.crucialconfrontations.comthere', 'wyatt', 'wyo', 'würde', \"x'mas\", 'x-er', 'x-files', 'x-l', 'xanax', 'xavier', 'xc-2', 'xenogears', 'xenote', 'xerox', 'xgvxhqws', 'xibalbá', 'xl', 'xl..', 'xml', 'xo', 'xp-home', 'xps', 'xps-t450', 'xs', 'xtasy', 'xtc', 'xtras', 'xtremm', 'xuela', 'xv', 'xx', 'xxl', 'xxxxx-large', 'xy', 'yahoo', 'yaiza', 'yale', 'yallah', 'yamakura', 'yang', 'yank', 'yankee', 'yankees', 'yanks', 'yanni', 'yarber', 'yardbirds', 'yasunori', 'yawn.i', 'yazawa', 'yazoo', 'year.edit', 'year.go', 'year.i', 'year.manipulative', 'years.i', 'years.nash', 'years.so', 'years.upon', 'yeats', 'yech', 'yedidot', 'yee', 'yeh', 'yello', 'yellowstone', 'yerby', 'yes..', 'yesterday.the', 'yet.good', 'yet.i', 'yet.oddly', 'yiddish', 'yikes', 'yin', 'yitzhak', 'yoko', 'yorkinos', 'yoruba', 'yosemite', 'you.also', 'you.and', 'you.as', 'you.i', 'you.if', 'you.it', 'you.of', 'youkeith', 'youloveme', 'young.or', 'younker', 'yourcenar', 'yournenar', 'yourself.although', 'yourself.p.s', 'yourself.very', 'yousincerelybar', 'youtry', 'youtube.if', 'youtube.taosguy', 'yu', 'yu-gi-oh', 'yugoslav', 'yuki', 'yum', 'yup', 'yves', 'yyyyaaaawwwwnnnn', 'z6tv', 'zach', 'zachary', 'zaller', 'zamatin', \"zapruder'ssalvaged\", 'zara.awaiting', 'ze', 'zealand', 'zealander', 'zebra', 'zebra.bonus', 'zebraskin', 'zefferelli', 'zeffirelli', 'zeitgeist', 'zelbessdisk', 'zelda', 'zelia', 'zemel', 'zeno', 'zeta', 'zeta-jones', 'zhao', 'zhivago', 'ziemke', 'ziggy', 'ziggy/bowie', 'zimbler', 'zimmer', 'zina', 'zinn', 'zippers', 'zirconia', 'zita', 'znowhite', 'zoe', 'zoete', 'zoey', 'zola', 'zonder', 'zoot', 'zoot.i', 'zorro', 'zort', 'zr', 'zr20', 'zr45mc', 'zr50mc', 'zu', 'zuez', 'zulkeh', 'zydeco', 'zzzz', 'zzzzz', 'zzzzzzz', 'zzzzzzzzzz', 'zzzzzzzzzzzz', 'zzzzzzzzzzzzz', 'zzzzzzzzzzzzzzzzzz', 'zzzzzzzzzzzzzzzzzzzzz', '~acclaim~', '~bewildered', '~ondine', '¡y', '¡¡good', 'ángel', 'única'] not in stop_words.\n",
554
" % sorted(inconsistent)\n"
555
]
556
},
557
{
558
"output_type": "stream",
559
"name": "stdout",
560
"text": [
561
" precision recall f1-score support\n",
562
"\n",
563
" 1 0.85 0.53 0.66 1987\n",
564
" 2 0.26 0.64 0.37 513\n",
565
"\n",
566
" accuracy 0.56 2500\n",
567
" macro avg 0.56 0.59 0.51 2500\n",
568
"weighted avg 0.73 0.56 0.60 2500\n",
569
"\n"
570
]
571
}
572
]
573
},
574
{
575
"cell_type": "code",
576
"source": [
577
"sw = noise + list(high_tokens) + list(low_tokens)\n",
578
"\n",
579
"vec = CountVectorizer(ngram_range=(1, 1), tokenizer=word_tokenize, stop_words=sw)\n",
580
"bow = vec.fit_transform(x_train)\n",
581
"clf = LogisticRegression(random_state=42)\n",
582
"clf.fit(bow, y_train)\n",
583
"pred = clf.predict(vec.transform(x_test))\n",
584
"print(classification_report(pred, y_test))"
585
],
586
"metadata": {
587
"colab": {
588
"base_uri": "https://localhost:8080/"
589
},
590
"id": "7dn43JLd_RtE",
591
"outputId": "d901edde-8f02-409c-b60c-fe4f23068feb"
592
},
593
"execution_count": 15,
594
"outputs": [
595
{
596
"output_type": "stream",
597
"name": "stderr",
598
"text": [
599
"/usr/local/lib/python3.7/dist-packages/sklearn/feature_extraction/text.py:401: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens [\"'`living\", \"'accelerator\", \"'adoption\", \"'anno\", \"'arular\", \"'ascension\", \"'ashes\", \"'away\", \"'bertha\", \"'big\", \"'brave\", \"'bubbabrain\", \"'bubbabyte\", \"'bucky\", \"'buffy\", \"'cade\", \"'canon\", \"'catch\", \"'centerview\", \"'cf\", \"'china\", \"'clan\", \"'clothes\", \"'collector\", \"'comeback'i\", \"'coyote\", \"'craig\", \"'d.c.rip\", \"'dark\", \"'deliverance\", \"'diary\", \"'did\", \"'disney\", \"'distillation\", \"'dressing\", \"'duh\", \"'eject\", \"'em.anyways\", \"'error\", \"'estate\", \"'even\", \"'eye\", \"'fahreneheit\", \"'fahrenheit\", \"'fame\", \"'fillers'.an\", \"'flashdance\", \"'flashdance'and\", \"'forlane\", \"'foundation\", \"'fsol\", \"'funny\", \"'galang\", \"'gladiator\", \"'go\", \"'golden\", \"'heart\", \"'hell\", \"'helter\", \"'herland\", \"'highwayman\", \"'holt\", \"'how\", \"'i\", \"'if\", \"'incesticide\", \"'inferno\", \"'it\", \"'japanese\", \"'kuch\", \"'lab\", \"'life\", \"'lifeforms\", \"'lightning\", \"'lights\", \"'m.i.a\", \"'magic\", \"'masquerade\", \"'merry\", \"'milk\", \"'milk'is\", \"'mona\", \"'murican\", \"'muswell\", \"'newer\", \"'nursery\", \"'october\", \"'on\", \"'papua\", \"'persephone\", \"'phantom\", \"'pull\", \"'qr\", \"'r\", \"'rape\", \"'rebel\", \"'red\", \"'remove\", \"'roman\", \"'romeo\", \"'rumors\", \"'run\", \"'s\", \"'sailors\", \"'sappy\", \"'scream\", \"'seal\", \"'shell\", \"'silver\", \"'son\", \"'sound\", \"'spank\", \"'sports\", \"'starman\", \"'stopped\", \"'streetz\", \"'sunshowers\", \"'that\", \"'till\", \"'time\", \"'tombstone\", \"'trekkie\", \"'troy\", \"'unreliable\", \"'v\", \"'walk\", \"'war\", \"'website\", \"'wee\", \"'when\", \"'whore\", \"'with\", \"'wrong\", \"'you\", \"'your\", \"'zero\", '******it', '****this', '***spoiler', '***very', '***you', '**great', '**note', '**this', '*also', '*america*', '*anything*.well', '*dvd', '*ep*', '*graphics', '*great', '*how', '*l*', '*percy', '*quite', '*r', 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'tucson', 'tudo', 'tuesday', 'tugging', 'tugs', 'tumbiumbi', 'tumpthumping', 'tune.but', 'tunisia', 'tupac', 'turchin', 'turistas', 'turkey.since', 'turkish', 'turturro', 'tuscany', 'tuscany.ho-hum', 'tusk', 'tutu', 'tv-mix', 'tv-out', 'tv-program', 'tv-special', 'tv.it', 'tv.on', 'tv/monitor', 'tv/multimedia', 'tv/vhs/dvd', 'tvc15', 'tvs', 'twain', 'tweeezerman', 'twenty-first', 'twian', 'twinxl', 'twist.story', 'tx', 'tyan', 'tybalt', 'tyco', 'tycoon.idid', 'tykes', 'tykwer', 'tyler', 'tymers', 'tynan', 'tyra', 'tyson', 't|c', 'tú', 'u-boat', 'u-boats', 'u-haul', 'u.f.o', 'u.k', 'u.r.a.q.t', 'u.s', 'u.s.a', 'u.s.aspider-man', 'u2', 'u6', 'ubuntu', 'uc', 'uckkk', 'ucl-l', 'ucla', 'ucs-200', 'ufc', 'uffizi', 'ufo-logy', 'ufos', 'ugly-american', 'ugolin', 'uhe1e102mhd6', 'uhh', 'uk', 'ukraine', 'ul', 'ulead', 'ulmer', 'ulob', 'ulta', 'ultra-bad', 'ultra-overrated', 'ulysses', 'ulysses.again', 'uma', 'umbra', 'umbria', 'umc', 'umff', 'un-catchy.and', 'un-christianity', 'unappetizing.i', 'unasked.after', 'unattractive.i', 'unavoidably', 'unbearable.i', 'unbeliavable', 'unbox', 'unbroken', 'uncomfortable.i', 'uncomprehendable', 'unconvincing.one', 'uncured', 'undaunted', 'undelivery', 'underappreciated.i', 'undercover', 'undergroud', 'underrated..so', 'understand..he', 'understand.unfortunately', 'understanding.avoid', 'understood.with', 'undertaken', 'undertaker', 'underwhelmed.asimov', 'underwhelming', 'underwood.while', 'undestanding', 'undomestic', 'undone.there', 'undressing', 'undubbed', 'unearthing', 'unemployed', 'unexpectedly.btw', 'unfaithful', 'unfortionately', 'unfortunate.please', 'unfortunetly', 'unforunately', 'unfound', 'unhappily', 'unicorn', 'unida', 'unified', 'union.the', 'unit.if', 'unit.observations', 'unit.the', 'units.i', 'units.while', 'univ', 'univeral', 'universe.i', 'universe.ignoring', 'univesity', 'unmasked', 'unnecessary.obs', 'unnessecary', 'unnessisary', 'unpacking.but', 'unpredictable.the', 'unpredictably', 'unqiue', 'unrealistic.note', 'unrelaxing', 'unremarkable', 'unremarkable.if', 'unrestrained', 'unrevised', 'unscrupulous', 'unsentimental', 'unsharpened', 'unstealable', 'unstolen', 'unsupported', 'unsuspected', 'untitled', 'untouchables', 'unusable.i', 'unwatchably', 'up-beatconfidant', 'up.add', 'up.as', 'up.butthis', 'up.cons', 'up.i', 'up.in', 'up.it', 'up.noting', 'up.pictures', 'up.strong', 'up.you', 'update.this', 'upgrades', 'upn', 'upon.atea', 'upyr', 'url', 'urn', 'ursula', 'us.i', 'us.our', 'us.shortly', 'us.the', 'us/canadian', 'usa.for', 'usaaf', 'usable.due', 'usable.unfortunately', 'usb2.0', 'usbav-700', 'usd', 'use.-beware', 'use.by', 'use.i', 'use.my', 'use.not', 'use.positive', 'use.update', 'used-', 'used.a', 'used.as', 'used.we', 'useful.mine', 'useful.now', 'useful.there', 'useing', 'useless.and', 'usgs', 'usher', 'usher..this', 'using.the', 'usps', 'uss', 'ussr', 'usual.this', 'utley', 'utopias', 'uv', 'uwe', 'uyanik', 'v', 'v190', 'v3', 'v323', 'v325', 'v360', 'v3c', 'v3i', 'v3m', 'v8', 'va912b', 'vacation.it', 'vai', 'vaio', 'vaiopc', 'val', 'vala', 'valdemar', 'valdez', 'valentine', 'valgar', 'valley.disk', 'value-2k3', 'value-conscious', 'value.mr', 'value.negatives', 'van-damme', 'vanbebber', 'vance', 'vancouver', 'vandamme', 'vanen', 'vanessa', 'vanessa-mae', 'vangelis', 'vanilli', 'var', 'varese', 'varg', 'vargas', 'variedad', 'vas', 'vasquez', 'vasquez-figueroa', 'vaultreports', 'vause', 'vb', 'vbac', 'vcd', 'vcd/horrible', 'vcr', 'vcr/dvd', 'vct-tk1', 'veclo', 'vedic', 'vee-jay', 'veena', \"veg'art\", 'vegas', 'velocd', 'velveteen', 'venius', 'venker', 'venom-like', 'ventaires', 'venus', 'venus.femalien', 'venusian', 'verdad', 'verdi', 'verizon', 'vermeulen', 'vermont', 'verne', 'vernes', 'vernon', 'veronica', 'verrinder', 'verrrrrry', 'verrrrryyyyyyy', 'version.get', 'version.the', 'version/director', 'vertigo', 'verto', 'vespera', 'vets', 'vexx', 'vh-1', 'vhs-like', 'viacord', 'viao', 'viceversa.that', 'vickery', 'victims.if', 'victor', 'victoria', 'victorian-era', 'video.and', 'video/dvd', 'videos.to', 'videos/dvds.despite', 'videostudio', 'videostuio', 'vienna', 'viet', 'vietnam', 'view.this', 'viewers.this', 'viewing.if', 'vig', 'vigilante', 'vii', 'vikernes', 'viking', 'vimes', 'vimes/watch', 'vince', 'vincent', 'vinci', 'vindication', 'vinny', 'violetta', 'violins.this', 'virgil', 'virgilian', 'virgina', 'virginia', 'virginian', 'virigin', 'viruses.then', 'visioneers', 'visor', 'visuals.after', 'visualy', 'vitaminless', 'vito', 'vittorio', 'viva', 'vivendi', 'vivicam', 'vivitar', 'vivo', 'vmc-il4615', 'voce', 'voice.after', 'voice.it', 'voice.the', 'voice.this', 'voivod', 'voivodian', 'voivodians', 'voltaire', 'volverme', 'volvo', 'voyager', 'vs.net', 'w/kindred', 'w/plug.i', 'waaaaay', 'wabash', 'wacken', 'waded', 'waechtersbach', 'wagner', 'wagnerian', 'wah', 'wailer', 'wailers', 'waistband', 'waitin', 'waitley', 'waits-', 'wake.for', 'wal*mart', 'wal-mart', 'walden', 'wales', 'walk.i', 'wall.please', 'wallace', 'walley', 'wallhanger', 'walnut', 'walt', 'walter', 'walton', 'waltons', 'waltz', 'wang', 'wanker.com', 'want.i', 'want/need.the', 'wanted.it', 'war.gypsies', 'war.henry', 'war.i', 'ward', 'wardell', 'wardh', 'warfare', 'warhammer', 'warhol', 'wario', 'warlord', 'warner', 'warners', 'warnes', 'warning.many', 'warrack', 'warranties', 'warren', 'warrick', 'warrickbrown', 'wart', 'warts', 'warwick', 'was.all', 'was.i', 'was.in', 'was.sho', 'was.stars', 'washington', 'washington/gabriel', 'wasit', 'waslife', 'wasp', 'watch.the', 'watched.the', 'watched.why', 'watching.the', 'watchmaker', 'waterfall', 'waterfront', 'waterless', 'waterloo', 'waterworld', 'watson', 'watters', 'waugh', 'waverunner', 'wavery-voiced', 'way.claus', 'way.i', 'way.in', 'way.it', 'way.like', 'way.one', 'way.the', 'waylon', 'wayne/john', 'ways..first', 'wbap-tv', 'wcj', 'wcw', 'wd', 'weak.i', 'weak/gentle', 'weapons.there', 'wear.as', 'wearings.they', 'weathertop', 'webb', 'webber', 'webcam', 'weber', 'website.a', 'website.i', 'website.the', 'webster', 'wed', 'wednesday', 'week.the', 'week.we', 'weekly/star/tmz', 'weeks.i', 'weetzie', 'wehrmacht-devotee', 'weight.the', 'weight.this', 'weill', 'weinreich', 'weinstock', 'weird.do', 'weird.i', 'weiss', 'welch', 'welcome.all', 'welden', 'well-acted', 'well.as', 'well.asimov', 'well.despite', 'well.each', 'well.fair', 'well.guy', 'well.i', 'well.if', 'well.jean', 'well.my', 'well.no', 'well.not', 'well.the', 'well.this', 'well.with', 'well.would', 'welsh', 'wenders', 'wendie', 'wendigo', 'wendingo', 'wendy', 'went.i', 'wenzel', 'wep200', 'were.unless', 'werner', 'wes', 'wesley', 'west-european', 'westbound', 'westend', 'westerners', 'westone', 'wet.i', 'whaddup', \"what'a\", 'what-so-ever.thank', 'whati', 'whats-his-name', 'whatsoever.i', 'whatsoever.secondly', 'whatsoever.the', 'whatsoeveri', 'whe', 'wheat-free', 'wheelconcluction', 'whereareyougoing', 'whew', 'whibal', 'which.i', 'while.even', 'while.if', 'whinstone', 'whiplash', 'whisky', 'whisper', 'whistle', 'whistler', 'white-wash', 'whitechapel', 'whiteheadinterview', 'whitehouse', 'whitey', 'whitney', 'whom.hulk', 'whomever', 'whoohoo', 'whoop', 'whoot', 'whql', 'why-ever', 'why.another', 'why.you', 'wiccan/pagan', 'wiccans', 'wick', 'wicker', 'wickham', 'wickiup', 'widowing', 'wierd.if', 'wife.its', 'wigan', 'wiggles', 'wilbur', 'wild.most', 'wilde', 'wilder', 'wilding/bonus', 'wiley', 'wilife', 'will.better', 'willam', 'willard', 'williams', 'williams.the', 'willie', 'willin', 'willows', 'willy', 'wilmington', 'wilson/beach', 'wilsons', 'win.in', 'win95', 'win98', 'win98se', 'window.her', 'windowsme', 'windowsxp', 'winged', 'winn', 'winnegago', 'winnie', 'winslet', 'winston', \"winston's.very\", 'winstone', 'winstons', 'winteriziing', 'winthrop', 'winxp', 'wipeout', 'wisconsin', 'wisconsin.when', 'wisefield', 'wish.a', 'wishmaster', 'wishy-washy', 'wiston', 'wit.i', 'witchlight', 'witchy', 'with..', 'with.if', 'with.occasionally', 'with.simply', 'withen', 'withers', 'withing', 'without.the', 'withstand.it', 'withstands', 'witless', 'witted', 'witwicky', \"witwicky's..\", 'wli2pcig54s', 'wm', 'wma', 'wmp', 'wmp54g', 'woburn', 'woderful', 'wolfe', 'wolfgang', 'wolfman', 'wolfram', 'wolitzer', 'wolters', 'woltzer', 'woman-ms', 'woman.clan', 'woman.do', 'womanolopy', 'wonder.thanks', 'wonderful.as', 'wonderful.bertulluci', 'wonderful.i', 'wonderfulfor', 'wonen', 'woo-hoo', 'woodbine', 'woodhouse', 'woodlands', 'woodstock', 'woody', 'woogie', 'woohoo', 'word-by-word.the', 'worddi', 'words.buy', 'words.everthing', 'words.i', 'wordsworth', 'work.i', 'work.is', 'work.newer', 'work.nicotetta', 'work.oh', 'work.the', 'work.this', 'work.to', 'work.yet', 'workbook.this', 'worked.it', 'workers.while', 'workinglibrary.jack', 'works.for', 'works.foundation', 'works.hurry', 'works.i', 'works.if', 'world.after', 'world.i', 'world.kerouac', 'world.the', 'world.while', 'worldwide.his', 'worms.hideous', 'worries.when', 'worrior', 'worry-free', 'worse.i', 'wort', 'worth.save', 'worthi', 'worthless.why', 'worthless.yes', 'woth', 'wouk', 'would.so', 'wow-', 'wow-factor', 'wow..', 'wow..thats', 'wp', 'wpa', 'wrapi', 'wrestle.expecially', 'wrestlecrap', 'wrestlemania', 'wrestling.sexxxy', 'writer.count', 'writers.although', 'writing.thank', 'writingwords', 'written.i', 'written.jul', 'wrong..i', 'wrong.a', 'wrong.enjoy', 'wrong.if', 'wrong.please', 'wrong.their', 'wrong.this', 'wrong.time', 'wrt54g', 'wtffollow', 'wto', 'wunderland', 'wuzzup', 'wv', 'ww', 'ww1', 'ww2', 'wwii', 'www.crucialconfrontations.comthere', 'wyatt', 'wyo', 'würde', \"x'mas\", 'x-er', 'x-files', 'x-l', 'xanax', 'xavier', 'xc-2', 'xenogears', 'xenote', 'xerox', 'xgvxhqws', 'xibalbá', 'xl', 'xl..', 'xml', 'xo', 'xp-home', 'xps', 'xps-t450', 'xs', 'xtasy', 'xtc', 'xtras', 'xtremm', 'xuela', 'xv', 'xx', 'xxl', 'xxxxx-large', 'xy', 'yahoo', 'yaiza', 'yale', 'yallah', 'yamakura', 'yang', 'yank', 'yankee', 'yankees', 'yanks', 'yanni', 'yarber', 'yardbirds', 'yasunori', 'yawn.i', 'yazawa', 'yazoo', 'year.edit', 'year.go', 'year.i', 'year.manipulative', 'years.i', 'years.nash', 'years.so', 'years.upon', 'yeats', 'yech', 'yedidot', 'yee', 'yeh', 'yello', 'yellowstone', 'yerby', 'yes..', 'yesterday.the', 'yet.good', 'yet.i', 'yet.oddly', 'yiddish', 'yikes', 'yin', 'yitzhak', 'yoko', 'yorkinos', 'yoruba', 'yosemite', 'you.also', 'you.and', 'you.as', 'you.i', 'you.if', 'you.it', 'you.of', 'youkeith', 'youloveme', 'young.or', 'younker', 'yourcenar', 'yournenar', 'yourself.although', 'yourself.p.s', 'yourself.very', 'yousincerelybar', 'youtry', 'youtube.if', 'youtube.taosguy', 'yu', 'yu-gi-oh', 'yugoslav', 'yuki', 'yum', 'yup', 'yves', 'yyyyaaaawwwwnnnn', 'z6tv', 'zach', 'zachary', 'zaller', 'zamatin', \"zapruder'ssalvaged\", 'zara.awaiting', 'ze', 'zealand', 'zealander', 'zebra', 'zebra.bonus', 'zebraskin', 'zefferelli', 'zeffirelli', 'zeitgeist', 'zelbessdisk', 'zelda', 'zelia', 'zemel', 'zeno', 'zeta', 'zeta-jones', 'zhao', 'zhivago', 'ziemke', 'ziggy', 'ziggy/bowie', 'zimbler', 'zimmer', 'zina', 'zinn', 'zippers', 'zirconia', 'zita', 'znowhite', 'zoe', 'zoete', 'zoey', 'zola', 'zonder', 'zoot', 'zoot.i', 'zorro', 'zort', 'zr', 'zr20', 'zr45mc', 'zr50mc', 'zu', 'zuez', 'zulkeh', 'zydeco', 'zzzz', 'zzzzz', 'zzzzzzz', 'zzzzzzzzzz', 'zzzzzzzzzzzz', 'zzzzzzzzzzzzz', 'zzzzzzzzzzzzzzzzzz', 'zzzzzzzzzzzzzzzzzzzzz', '~acclaim~', '~bewildered', '~ondine', '¡y', '¡¡good', 'ángel', 'única'] not in stop_words.\n",
600
" % sorted(inconsistent)\n",
601
"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
602
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
603
"\n",
604
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
605
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
606
"Please also refer to the documentation for alternative solver options:\n",
607
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
608
" extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
609
]
610
},
611
{
612
"output_type": "stream",
613
"name": "stdout",
614
"text": [
615
" precision recall f1-score support\n",
616
"\n",
617
" 1 0.48 0.62 0.54 970\n",
618
" 2 0.70 0.58 0.63 1530\n",
619
"\n",
620
" accuracy 0.59 2500\n",
621
" macro avg 0.59 0.60 0.59 2500\n",
622
"weighted avg 0.62 0.59 0.60 2500\n",
623
"\n"
624
]
625
}
626
]
627
},
628
{
629
"cell_type": "code",
630
"source": [
631
"sw = noise + list(high_tokens) + list(med_tokens)\n",
632
"\n",
633
"vec = CountVectorizer(ngram_range=(1, 1), tokenizer=word_tokenize, stop_words=sw)\n",
634
"bow = vec.fit_transform(x_train)\n",
635
"clf = LogisticRegression(random_state=42)\n",
636
"clf.fit(bow, y_train)\n",
637
"pred = clf.predict(vec.transform(x_test))\n",
638
"print(classification_report(pred, y_test))"
639
],
640
"metadata": {
641
"colab": {
642
"base_uri": "https://localhost:8080/"
643
},
644
"id": "qgvDua7U_i-j",
645
"outputId": "5f3738a3-453a-4667-c496-4d3252b76d3f"
646
},
647
"execution_count": 16,
648
"outputs": [
649
{
650
"output_type": "stream",
651
"name": "stderr",
652
"text": [
653
"/usr/local/lib/python3.7/dist-packages/sklearn/feature_extraction/text.py:401: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens [\"'d\", \"'ll\", \"'re\", \"'ve\", '``', 'could', 'might', 'must', 'need', 'sha', 'wo', 'would'] not in stop_words.\n",
654
" % sorted(inconsistent)\n",
655
"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
656
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
657
"\n",
658
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
659
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
660
"Please also refer to the documentation for alternative solver options:\n",
661
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
662
" extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
663
]
664
},
665
{
666
"output_type": "stream",
667
"name": "stdout",
668
"text": [
669
" precision recall f1-score support\n",
670
"\n",
671
" 1 0.83 0.85 0.84 1212\n",
672
" 2 0.85 0.83 0.84 1288\n",
673
"\n",
674
" accuracy 0.84 2500\n",
675
" macro avg 0.84 0.84 0.84 2500\n",
676
"weighted avg 0.84 0.84 0.84 2500\n",
677
"\n"
678
]
679
}
680
]
681
},
682
{
683
"cell_type": "markdown",
684
"source": [
685
"Сравните полученные результаты, оцените какие токены наиболее важные для классификации. \n",
686
" \n",
687
"Лучшие показатели продемонстрировали токены с низкой частотой, но надо учитывать, что токены с низкой частотой были выбраны вручную. В целом \"черные лебеди\" более непредсказуемы, значит их последствия могут быть более значимы, а значит и токены с низкой частотой могут иметь схожее положение."
688
],
689
"metadata": {
690
"id": "aywQY2a5_3nY"
691
}
692
},
693
{
694
"cell_type": "markdown",
695
"source": [
696
"### Задание 2. \n",
697
"\n",
698
"Найти фичи с наибольшей значимостью, и вывести их"
699
],
700
"metadata": {
701
"id": "_AmegZ79A3tT"
702
}
703
},
704
{
705
"cell_type": "code",
706
"source": [
707
"f = []\n",
708
"conv_to_int = [2 if el == '2' else 1 for el in y_test]\n",
709
"for i in tokens:\n",
710
" cool_token = i\n",
711
" pred = [2 if cool_token in sense else 1 for sense in x_test]\n",
712
" f.append((i,accuracy_score(pred, conv_to_int)))\n",
713
"print(sorted(f)[:10])"
714
],
715
"metadata": {
716
"colab": {
717
"base_uri": "https://localhost:8080/"
718
},
719
"id": "2-OadT12_x2m",
720
"outputId": "3f181367-17e6-421e-d9a4-fc8b674e30eb"
721
},
722
"execution_count": 17,
723
"outputs": [
724
{
725
"output_type": "stream",
726
"name": "stdout",
727
"text": [
728
"[(\"''\", 0.4968), (\"''\", 0.4968), (\"''\", 0.4968), (\"''\", 0.4968), (\"''\", 0.4968), (\"''\", 0.4968), (\"''\", 0.4968), (\"''\", 0.4968), (\"''\", 0.4968), (\"''\", 0.4968)]\n"
729
]
730
}
731
]
732
},
733
{
734
"cell_type": "code",
735
"source": [
736
"print(sorted(f, reverse=True)[:10])\n",
737
"print(sorted(set(f))[:10])"
738
],
739
"metadata": {
740
"colab": {
741
"base_uri": "https://localhost:8080/"
742
},
743
"id": "Bea7oWk9c-Tm",
744
"outputId": "26a76ed9-8de5-4b6b-e55a-7d2321ee2a43"
745
},
746
"execution_count": 20,
747
"outputs": [
748
{
749
"output_type": "stream",
750
"name": "stdout",
751
"text": [
752
"[('éviter', 0.4964), ('étre', 0.4968), ('étai', 0.4968), ('émouvantes', 0.4972), ('è', 0.4972), ('è', 0.4972), ('è', 0.4972), ('è', 0.4972), ('à', 0.4964), ('à', 0.4964)]\n",
753
"[(\"''\", 0.4968), (\"'0\", 0.4972), (\"'01\", 0.4968), (\"'02\", 0.4968), (\"'100\", 0.4968), (\"'16\", 0.4968), (\"'1984\", 0.4972), (\"'50\", 0.4972), (\"'50's-'70\", 0.4972), (\"'50s\", 0.4968)]\n"
754
]
755
}
756
]
757
},
758
{
759
"cell_type": "markdown",
760
"source": [
761
"### Задание 3.\n",
762
"1) сравнить count/tf-idf/hashing векторайзеры/полносвязанную сетку (построить classification_report)\n",
763
"\n",
764
"2) подобрать оптимальный размер для hashing векторайзера\n",
765
"\n",
766
"3) убедиться что для сетки нет переобучения"
767
],
768
"metadata": {
769
"id": "Ip7RFED_FjNs"
770
}
771
},
772
{
773
"cell_type": "code",
774
"source": [
775
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
776
"from sklearn.feature_extraction.text import HashingVectorizer"
777
],
778
"metadata": {
779
"id": "V-0wpSzDGwvS"
780
},
781
"execution_count": 18,
782
"outputs": []
783
},
784
{
785
"cell_type": "code",
786
"source": [
787
"c_filter = stopwords.words('english') + list(punctuation)\n",
788
"\n",
789
"count_vect = CountVectorizer(ngram_range=(1, 1), tokenizer=word_tokenize, stop_words=c_filter)\n",
790
"bow = count_vect.fit_transform(x_train)\n",
791
"clf = LogisticRegression(random_state=42)\n",
792
"clf.fit(bow, y_train)\n",
793
"pred = clf.predict(count_vect.transform(x_test))\n",
794
"print(classification_report(pred, y_test))\n",
795
"\n",
796
"# vec = CountVectorizer(ngram_range=(1, 1), tokenizer=word_tokenize, stop_words=sw)\n",
797
"# bow = vec.fit_transform(x_train)\n",
798
"# clf = LogisticRegression(random_state=42)\n",
799
"# clf.fit(bow, y_train)\n",
800
"# pred = clf.predict(vec.transform(x_test))\n",
801
"# print(classification_report(pred, y_test))"
802
],
803
"metadata": {
804
"id": "UeGMi2bDAjs9",
805
"colab": {
806
"base_uri": "https://localhost:8080/"
807
},
808
"outputId": "a7c8f70d-1f68-47d5-d5df-0dd8ad89effe"
809
},
810
"execution_count": 31,
811
"outputs": [
812
{
813
"output_type": "stream",
814
"name": "stderr",
815
"text": [
816
"/usr/local/lib/python3.7/dist-packages/sklearn/feature_extraction/text.py:401: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens [\"'d\", \"'ll\", \"'re\", \"'s\", \"'ve\", '``', 'could', 'might', 'must', \"n't\", 'need', 'sha', 'wo', 'would'] not in stop_words.\n",
817
" % sorted(inconsistent)\n",
818
"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
819
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
820
"\n",
821
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
822
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
823
"Please also refer to the documentation for alternative solver options:\n",
824
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
825
" extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
826
]
827
},
828
{
829
"output_type": "stream",
830
"name": "stdout",
831
"text": [
832
" precision recall f1-score support\n",
833
"\n",
834
" 1 0.86 0.85 0.86 1255\n",
835
" 2 0.85 0.86 0.86 1245\n",
836
"\n",
837
" accuracy 0.86 2500\n",
838
" macro avg 0.86 0.86 0.86 2500\n",
839
"weighted avg 0.86 0.86 0.86 2500\n",
840
"\n"
841
]
842
}
843
]
844
},
845
{
846
"cell_type": "code",
847
"source": [
848
"o_filter = stopwords.words('english')\n",
849
"tf_vect = TfidfVectorizer(ngram_range=(1, 1), tokenizer=word_tokenize, stop_words= o_filter)\n",
850
"bow = tf_vect.fit_transform(x_train)\n",
851
"clf = LogisticRegression(random_state=42)\n",
852
"clf.fit(bow, y_train)\n",
853
"pred = clf.predict(tf_vect.transform(x_test))\n",
854
"print(classification_report(pred, y_test))"
855
],
856
"metadata": {
857
"id": "TUO8iBMdBv3E",
858
"colab": {
859
"base_uri": "https://localhost:8080/"
860
},
861
"outputId": "81c3d43a-af74-4449-ecfb-f6527a5d476f"
862
},
863
"execution_count": 32,
864
"outputs": [
865
{
866
"output_type": "stream",
867
"name": "stderr",
868
"text": [
869
"/usr/local/lib/python3.7/dist-packages/sklearn/feature_extraction/text.py:401: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens [\"'d\", \"'ll\", \"'re\", \"'s\", \"'ve\", 'could', 'might', 'must', \"n't\", 'need', 'sha', 'wo', 'would'] not in stop_words.\n",
870
" % sorted(inconsistent)\n"
871
]
872
},
873
{
874
"output_type": "stream",
875
"name": "stdout",
876
"text": [
877
" precision recall f1-score support\n",
878
"\n",
879
" 1 0.88 0.86 0.87 1267\n",
880
" 2 0.86 0.87 0.87 1233\n",
881
"\n",
882
" accuracy 0.87 2500\n",
883
" macro avg 0.87 0.87 0.87 2500\n",
884
"weighted avg 0.87 0.87 0.87 2500\n",
885
"\n"
886
]
887
}
888
]
889
},
890
{
891
"cell_type": "code",
892
"source": [
893
"vectorizer = HashingVectorizer(n_features=2**4,)\n",
894
"bow = vectorizer.fit_transform(x_train)\n",
895
"clf = LogisticRegression(random_state=42)\n",
896
"clf.fit(bow, y_train)\n",
897
"pred = clf.predict(vectorizer.transform(x_test))\n",
898
"print(classification_report(pred, y_test))"
899
],
900
"metadata": {
901
"id": "yqWTfX4bHBR7",
902
"colab": {
903
"base_uri": "https://localhost:8080/"
904
},
905
"outputId": "6a5cfc9c-e457-459b-df2c-f845eb13118f"
906
},
907
"execution_count": 33,
908
"outputs": [
909
{
910
"output_type": "stream",
911
"name": "stdout",
912
"text": [
913
" precision recall f1-score support\n",
914
"\n",
915
" 1 0.63 0.57 0.60 1370\n",
916
" 2 0.53 0.59 0.56 1130\n",
917
"\n",
918
" accuracy 0.58 2500\n",
919
" macro avg 0.58 0.58 0.58 2500\n",
920
"weighted avg 0.59 0.58 0.58 2500\n",
921
"\n"
922
]
923
}
924
]
925
},
926
{
927
"cell_type": "code",
928
"source": [
929
"vectorizer = HashingVectorizer(n_features=2**8,)\n",
930
"bow = vectorizer.fit_transform(x_train)\n",
931
"clf = LogisticRegression(random_state=42)\n",
932
"clf.fit(bow, y_train)\n",
933
"pred = clf.predict(vectorizer.transform(x_test))\n",
934
"print(classification_report(pred, y_test))"
935
],
936
"metadata": {
937
"id": "raJWvKeJHCk_",
938
"colab": {
939
"base_uri": "https://localhost:8080/"
940
},
941
"outputId": "1a2a3838-668f-4550-eb2a-564fc5403d04"
942
},
943
"execution_count": 34,
944
"outputs": [
945
{
946
"output_type": "stream",
947
"name": "stdout",
948
"text": [
949
" precision recall f1-score support\n",
950
"\n",
951
" 1 0.75 0.74 0.75 1277\n",
952
" 2 0.73 0.75 0.74 1223\n",
953
"\n",
954
" accuracy 0.74 2500\n",
955
" macro avg 0.74 0.74 0.74 2500\n",
956
"weighted avg 0.74 0.74 0.74 2500\n",
957
"\n"
958
]
959
}
960
]
961
},
962
{
963
"cell_type": "code",
964
"source": [
965
"vectorizer = HashingVectorizer(n_features=2**10,)\n",
966
"bow = vectorizer.fit_transform(x_train)\n",
967
"clf = LogisticRegression(random_state=42)\n",
968
"clf.fit(bow, y_train)\n",
969
"pred = clf.predict(vectorizer.transform(x_test))\n",
970
"print(classification_report(pred, y_test))"
971
],
972
"metadata": {
973
"id": "j8Yuwjl-HNFT",
974
"colab": {
975
"base_uri": "https://localhost:8080/"
976
},
977
"outputId": "b33cf815-716e-4e79-85e6-591976a30aff"
978
},
979
"execution_count": 35,
980
"outputs": [
981
{
982
"output_type": "stream",
983
"name": "stdout",
984
"text": [
985
" precision recall f1-score support\n",
986
"\n",
987
" 1 0.83 0.80 0.81 1284\n",
988
" 2 0.80 0.82 0.81 1216\n",
989
"\n",
990
" accuracy 0.81 2500\n",
991
" macro avg 0.81 0.81 0.81 2500\n",
992
"weighted avg 0.81 0.81 0.81 2500\n",
993
"\n"
994
]
995
}
996
]
997
},
998
{
999
"cell_type": "code",
1000
"source": [
1001
"vectorizer = HashingVectorizer(n_features=2**12,)\n",
1002
"bow = vectorizer.fit_transform(x_train)\n",
1003
"clf = LogisticRegression(random_state=42)\n",
1004
"clf.fit(bow, y_train)\n",
1005
"pred = clf.predict(vectorizer.transform(x_test))\n",
1006
"print(classification_report(pred, y_test))"
1007
],
1008
"metadata": {
1009
"id": "gG7w52p3HQsR",
1010
"colab": {
1011
"base_uri": "https://localhost:8080/"
1012
},
1013
"outputId": "430aaa66-e99b-4cd3-d738-671cfdd09d5d"
1014
},
1015
"execution_count": 36,
1016
"outputs": [
1017
{
1018
"output_type": "stream",
1019
"name": "stdout",
1020
"text": [
1021
" precision recall f1-score support\n",
1022
"\n",
1023
" 1 0.86 0.83 0.84 1292\n",
1024
" 2 0.82 0.85 0.84 1208\n",
1025
"\n",
1026
" accuracy 0.84 2500\n",
1027
" macro avg 0.84 0.84 0.84 2500\n",
1028
"weighted avg 0.84 0.84 0.84 2500\n",
1029
"\n"
1030
]
1031
}
1032
]
1033
}
1034
]
1035
}
1036
NLP_Intro/Lesson_2.ipynb - master | Gitverse (2024)
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