NLP_Intro/Lesson_2.ipynb - master | Gitverse (2024)

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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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 "kernelspec": {

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 "name": "python3",

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 "display_name": "Python 3"

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 "name": "python"

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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"

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 ],

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 "metadata": {

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 "id": "Ib6Rr8FfSEkB"

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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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 "!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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 "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,

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 "metadata": {

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 "id": "HT8T4vIlPWSz"

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 },

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 "outputs": [],

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 "source": [

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 "import pandas as pd\n",

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 "import numpy as np\n",

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 "from sklearn.metrics import *\n",

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 "from sklearn.model_selection import train_test_split\n",

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 "from sklearn.pipeline import Pipeline\n",

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 "import nltk\n",

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 "import re\n",

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 "import wget\n",

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 "from datetime import datetime\n",

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 "from bs4 import BeautifulSoup\n",

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 "import requests"

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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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 "!unzip \"/content/Lesson-2.zip\" -d \"/content/\""

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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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 "id": "KmG9sNj7TdK1",

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 "outputId": "ddd77438-5f59-42f4-a62e-abfd5c995e5d"

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 "execution_count": 4,

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 "outputs": [

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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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 "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')"

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 ],

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 "metadata": {

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 "id": "046Z2X6UqrQJ",

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 "outputId": "fc93944f-419a-42d1-9fd1-06f8728c3164"

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 "execution_count": 6,

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 "outputs": [

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 "data": {

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 "True"

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 "metadata": {},

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 "execution_count": 6

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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",

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 " 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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 "3 2 Excellent Soundtrack: I truly like this soundt...\n",

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 " "

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 "metadata": {},

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 "execution_count": 7

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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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 "x_train, x_test, y_train, y_test = train_test_split(df.text, df.category)"

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 ],

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 "metadata": {

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 "id": "De4ElJKvrXnU"

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 },

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 "execution_count": 8,

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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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 "from string import punctuation\n",

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 "from collections import Counter\n",

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 "from sklearn.linear_model import LogisticRegression\n",

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 "from sklearn.feature_extraction.text import CountVectorizer\n",

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 "from nltk import ngrams\n",

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 "import nltk\n",

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 "from nltk import collocations \n",

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 "from nltk.tokenize import word_tokenize, wordpunct_tokenize\n",

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 "from nltk.corpus import stopwords\n",

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 "nltk.download('genesis')\n",

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 "nltk.download('stopwords')\n",

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 "nltk.download('punkt')"

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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": "d1o9qGUKsOva",

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 "outputId": "8f56b848-8b0d-471b-87c7-15ae00fee6e1"

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 },

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 "execution_count": 9,

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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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 "[nltk_data] Downloading package genesis to /root/nltk_data...\n",

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 "[nltk_data] Unzipping corpora/genesis.zip.\n",

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 "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",

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 "[nltk_data] Unzipping corpora/stopwords.zip.\n",

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 "[nltk_data] Downloading package punkt to /root/nltk_data...\n",

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 "[nltk_data] Unzipping tokenizers/punkt.zip.\n"

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 ]

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 },

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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": 9

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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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 "noise = stopwords.words('english') + list(punctuation)\n",

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 "tokens = [token for sense in df.text for token in word_tokenize(sense) if token not in punctuation]"

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 ],

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 "metadata": {

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 "id": "26LthWpU5uvU"

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 },

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 "execution_count": 10,

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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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 "freq_dict = Counter(tokens)\n",

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 "freq_dict_sorted= sorted(freq_dict.items(), key=lambda x: -x[1])\n",

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 "max([el[1] for el in list(freq_dict_sorted)]), min([el[1] for el in list(freq_dict_sorted)])"

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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": "Y3heRAPa7Ow9",

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 "outputId": "86c955fb-6669-48ac-e192-7b656c2a3796"

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 },

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 "execution_count": 11,

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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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 "(35041, 1)"

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 ]

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 },

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 "metadata": {},

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 "execution_count": 11

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 }

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 ]

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 },

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 {

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 "cell_type": "markdown",

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 "source": [

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 "### Задание 1.\n",

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 "Задание: обучите три классификатора:\n",

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 "\n",

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 "1) на токенах с высокой частотой\n",

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 "\n",

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 "2) на токенах со средней частотой\n",

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 "\n",

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 "3) на токенах с низкой частотой"

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 ],

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 "metadata": {

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 "id": "--5qiaE39Pua"

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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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 "high_tokens = set()\n",

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 "med_tokens = set()\n",

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 "low_tokens = set()\n",

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 "h = 20000\n",

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 "l = 5000\n",

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 "for i in freq_dict_sorted:\n",

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 " if i[1] > h:\n",

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 " high_tokens.add(i[0])\n",

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 " elif i[1] < l:\n",

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 " low_tokens.add(i[0])\n",

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 " else:\n",

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 " med_tokens.add(i[0])\n",

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 "print(len(high_tokens),len(med_tokens),len(low_tokens))"

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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": "BINXJzoJ7odM",

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 "outputId": "6a8d611e-5b14-4a68-d505-6d7620f3f994"

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 },

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 "execution_count": 12,

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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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 "3 19 47469\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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 "sw = noise + list(low_tokens) + list(med_tokens)"

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 ],

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 "metadata": {

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 "id": "XB1WweYo-ySa"

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 },

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 "execution_count": 13,

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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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 "vec = CountVectorizer(ngram_range=(1, 1), tokenizer=word_tokenize, stop_words=sw)\n",

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 "bow = vec.fit_transform(x_train)\n",

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 "clf = LogisticRegression(random_state=42)\n",

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 "clf.fit(bow, y_train)\n",

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 "pred = clf.predict(vec.transform(x_test))\n",

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 "print(classification_report(pred, y_test))"

539

 ],

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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": "d5y71You_KZQ",

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 "outputId": "a336c1b1-4aaa-47db-cfdf-eae6688eeb1b"

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 },

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 "execution_count": 14,

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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', '*thou', '*very', '*want', '*why', '*you', '++must', '+jawbone', '+the', '-absolutely', '-bad', '-buy', \"-c'mon\", '-excellent', '-i', '-j', '-jan', '-maybe', '-my', '-rant', '-then', '-what', '-wo', '-wrong..', '..actually', '..all', '..buy', '..diane', '..garmin', '..i', '..is', '..john', '..la', '..marino', '..now', '..other', '..so', '..therefore', '..two', '..would', '.abe', '.again', '.ahora', '.also', '.an', '.anyone', '.avoid', '.btw', '.buy', '.close', '.david', '.despite', '.dont', '.el', '.elvis', '.etcstrong', '.except', '.find', '.first', '.forget', '.formating', '.from', '.gameplay-', '.give', '.go', '.granted', '.he', '.highly', '.hope', '.hostel', '.how', '.if', '.imo', '.inconsistent', '.instead', '.its', '.lead', '.marquez', '.none', '.now', '.oh', '.once', '.ordered', '.overall', '.probably', '.regarding', '.ridiculously', '.rock', '.rounding', '.sadly', '.say', '.seems', '.should', '.sometime', '.spare', '.to', '.tom', '.tragedies', '.trevor', '.try', '.two', '.unfortunately', '.weber', '.well', '.wenzel', '.what', '.while', '.why', '.worth', '.yikes', '.yitzak', '//www.amazon.com/gp/product/b00005yvx9/ref=cm_cr_rev_prod_title', '/no', '/sighnote', '0-394-40619-2though', '09/24/2011this', '0s9', '0sx', '1.00.i', '10.curt', '10.the', '10.this', '1000uf', '1030pm', '10bm/s', '10gameplay', '10gb', '10mb/s', '10overall', '10sound', '1115.if', '11i', '128mb', '12th.it', '12x', '13.00now', '13w', '150.and', '150.personally', '15sci_fi_researcher', '16+mph', '16gb', '17+mph', '1757-may', '179.mp3', '18/20movement', '19.95.why', '1924-p', \"1930's.finally\", '1958.author', '1970.it', \"1980's.title\", '1991.took', '1f4t', '1gb', '2,2006.i', '2.8gb', '2.the', '2/06rate', '20/20battle', '20/20fun', '20/20overall', '20/20sound', '2004cover', '2012.well', '20gb', '20mb/s', '20mph', '222ms', '23669mlane27716', '24-bitlpcm', '24-bitspider-man', '240v', '256mb', '25k', '28f', '28k', '29.all', '2:01:11movie', '2:07:25movie', '2:15:46movie', '2:19:10movie', '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', 'abba13', 'abba14', 'abba2', 'abba3', 'abba4', 'abba5', 'abba6', 'abba7', 'abba8', 'abba9', 'abbath', 'abbey', 'abbott', 'abby', 'abc', 'abcfam', 'abcs', 'abductees', 'aberaham', 'abfab', 'ability.colt', 'abner', 'abore', 'about.furthermore', 'about.i', 'about.the', 'about.this', 'about.too', 'about.well', 'about.while', 'aboutplan', 'above.most', 'above.the', 'abraham', 'abreast', 'absentia', 'abstinance', 'abstinence', 'abstracting', 'aburrido', 'ac-3', 'ac/dc', 'ac/dc.in', 'acc', 'accadio', 'accelorator.accelorator', 'access.remember', 'account.jean', 'accuse.nothing', 'acessory', 'acetic', 'ached', 'achète', 'acrobat', 'act.this', 'act.yes', 'actin', 'acting.poor', 'acting.the', 'acting.was', 'acting.what', 'action.i', 'action.the', 'action/rpg/lotr', 'activision', 'actor.the', 'acts-', 'acuarelas', 'ad.my', 'ada', 'adair', \"adam'sjoyce\", 'adam.i', 'adams', 'adanced', 'adaptor.after', 'additionally', 'addonics', 'addressed.with', 'addy', 'adiemus', 'adios', 'adj.do', 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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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