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October 22, 2020 11:19
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NLP векторные представления текстов
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "2 Векторные представления.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [] | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "5K1WlUmN6pJN" | |
| }, | |
| "source": [ | |
| "\n", | |
| "\n", | |
| "[перейти](https://www.bigdataschool.ru/)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "vI0rSTxNfMXu", | |
| "outputId": "fdcd65b6-3e0c-4d91-a8ac-7eb04b1aa29c", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 241 | |
| } | |
| }, | |
| "source": [ | |
| "!pip install pymorphy2\n", | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "import nltk\n", | |
| "import re\n", | |
| "import csv\n", | |
| "from nltk.stem import WordNetLemmatizer\n", | |
| "import sklearn\n", | |
| "import codecs\n", | |
| "import pymorphy2\n", | |
| "import seaborn as sns\n", | |
| "sns.set_style(\"darkgrid\")\n", | |
| "from nltk.stem.snowball import SnowballStemmer\n", | |
| "\n", | |
| "from google.colab import drive\n", | |
| "drive.mount('/content/drive')" | |
| ], | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Collecting pymorphy2\n", | |
| "\u001b[?25l Downloading https://files.pythonhosted.org/packages/07/57/b2ff2fae3376d4f3c697b9886b64a54b476e1a332c67eee9f88e7f1ae8c9/pymorphy2-0.9.1-py3-none-any.whl (55kB)\n", | |
| "\u001b[K |████████████████████████████████| 61kB 1.7MB/s \n", | |
| "\u001b[?25hCollecting pymorphy2-dicts-ru<3.0,>=2.4\n", | |
| "\u001b[?25l Downloading https://files.pythonhosted.org/packages/3a/79/bea0021eeb7eeefde22ef9e96badf174068a2dd20264b9a378f2be1cdd9e/pymorphy2_dicts_ru-2.4.417127.4579844-py2.py3-none-any.whl (8.2MB)\n", | |
| "\u001b[K |████████████████████████████████| 8.2MB 5.9MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: docopt>=0.6 in /usr/local/lib/python3.6/dist-packages (from pymorphy2) (0.6.2)\n", | |
| "Collecting dawg-python>=0.7.1\n", | |
| " Downloading https://files.pythonhosted.org/packages/6a/84/ff1ce2071d4c650ec85745766c0047ccc3b5036f1d03559fd46bb38b5eeb/DAWG_Python-0.7.2-py2.py3-none-any.whl\n", | |
| "Installing collected packages: pymorphy2-dicts-ru, dawg-python, pymorphy2\n", | |
| "Successfully installed dawg-python-0.7.2 pymorphy2-0.9.1 pymorphy2-dicts-ru-2.4.417127.4579844\n", | |
| "Mounted at /content/drive\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "LBF3ntXC5TWG", | |
| "outputId": "48e8eaa1-d6f2-4213-e658-1b43c4612c29", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 68 | |
| } | |
| }, | |
| "source": [ | |
| "import nltk\n", | |
| "nltk.download('stopwords')" | |
| ], | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", | |
| "[nltk_data] Unzipping corpora/stopwords.zip.\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "True" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 2 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "NrdXlozI5Hev" | |
| }, | |
| "source": [ | |
| "### Функции" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "dRIUfW88fI6A" | |
| }, | |
| "source": [ | |
| "from nltk.corpus import stopwords\n", | |
| "stopWords = set(stopwords.words('russian'))\n", | |
| "\n", | |
| "def csv_to_list(arr):\n", | |
| " arr_list = []\n", | |
| " for row in arr:\n", | |
| " arr_list.append(list_to_str(row))\n", | |
| " return arr_list\n", | |
| "\n", | |
| "def list_to_str(arr):\n", | |
| " str_ = ''\n", | |
| " for rec in arr:\n", | |
| " str_+=rec\n", | |
| " return str_\n", | |
| "\n", | |
| "def df_preprocess(text): \n", | |
| " reg = re.compile('[^а-яА-яa-zA-Z0-9 ]') #\n", | |
| " text = text.lower().replace(\"ё\", \"е\")\n", | |
| " text = text.replace(\"ъ\", \"ь\")\n", | |
| " text = text.replace(\"й\", \"и\")\n", | |
| " text = re.sub('((www\\.[^\\s]+)|(https?://[^\\s]+))', 'сайт', text)\n", | |
| " text = re.sub('@[^\\s]+', 'пользователь', text)\n", | |
| " text = reg.sub(' ', text)\n", | |
| " \n", | |
| " # Лемматизация\n", | |
| " #morph = pymorphy2.MorphAnalyzer()\n", | |
| " #text =[morph.parse(word)[0].normal_form for word in text.split()]\n", | |
| "\n", | |
| " # Стемминг\n", | |
| " # stemmer = SnowballStemmer(\"russian\")\n", | |
| " # text =[stemmer.stem(word) for word in text.split()]\n", | |
| "\n", | |
| " # Стемминг + удаление стоп слов\n", | |
| " stemmer = SnowballStemmer(\"russian\")\n", | |
| " #text =[stemmer.stem(word) for word in text.split() if word not in stopWords]\n", | |
| " text = ' '.join([stemmer.stem(word) for word in text.split() if word not in stopWords])\n", | |
| "\n", | |
| " return text" | |
| ], | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "0dCTRpn55ONd" | |
| }, | |
| "source": [ | |
| "### Считываем данные\n", | |
| "\n", | |
| "Используем корпус с сайта https://study.mokoron.com/#download" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "87Chu_tOYxW2" | |
| }, | |
| "source": [ | |
| "positive_recalls = csv_to_list(csv.reader(codecs.open('/content/drive/My Drive/Colab Notebooks/NLP/positive_recalls.csv', 'rU', 'utf-8', errors='ignore')))\n", | |
| "negative_recalls = csv_to_list(csv.reader(codecs.open('/content/drive/My Drive/Colab Notebooks/NLP/negative_recalls.csv', 'rU', 'utf-8', errors='ignore')))" | |
| ], | |
| "execution_count": 5, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "wF5HZPIb957U" | |
| }, | |
| "source": [ | |
| "### Формируем датасет " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "KjrfTaD_7wo1", | |
| "outputId": "d605803a-529a-45dd-f769-3949698f333e", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 204 | |
| } | |
| }, | |
| "source": [ | |
| "df_positive_recalls = pd.DataFrame(positive_recalls, columns=['recall'])\n", | |
| "df_positive_recalls['type']=1\n", | |
| "df_positive_recalls.head()" | |
| ], | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>recall</th>\n", | |
| " <th>type</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>@first_timee хоть я и школота но поверь у нас ...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Да все-таки он немного похож на него. Но мой м...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>RT @KatiaCheh: Ну ты идиотка) я испугалась за ...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>RT @digger2912: \"Кто то в углу сидит и погибае...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>@irina_dyshkant Вот что значит страшилка :D\\nН...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " recall type\n", | |
| "0 @first_timee хоть я и школота но поверь у нас ... 1\n", | |
| "1 Да все-таки он немного похож на него. Но мой м... 1\n", | |
| "2 RT @KatiaCheh: Ну ты идиотка) я испугалась за ... 1\n", | |
| "3 RT @digger2912: \"Кто то в углу сидит и погибае... 1\n", | |
| "4 @irina_dyshkant Вот что значит страшилка :D\\nН... 1" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 6 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "C3oNsEsg-2Yb", | |
| "outputId": "1ca2222d-fa21-44a8-c104-6cc3dbb114eb", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 204 | |
| } | |
| }, | |
| "source": [ | |
| "df_negative_recalls = pd.DataFrame(negative_recalls, columns=['recall'])\n", | |
| "df_negative_recalls['type']=0\n", | |
| "df_negative_recalls.head()" | |
| ], | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>recall</th>\n", | |
| " <th>type</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>на работе был полный пиддес :| и так каждое за...</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Коллеги сидят рубятся в Urban terror а я из-за...</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>@elina_4post как говорят обещаного три года жд...</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>Желаю хорошего полёта и удачной посадкия буду ...</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>Обновил за каким-то лешим surf теперь не работ...</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " recall type\n", | |
| "0 на работе был полный пиддес :| и так каждое за... 0\n", | |
| "1 Коллеги сидят рубятся в Urban terror а я из-за... 0\n", | |
| "2 @elina_4post как говорят обещаного три года жд... 0\n", | |
| "3 Желаю хорошего полёта и удачной посадкия буду ... 0\n", | |
| "4 Обновил за каким-то лешим surf теперь не работ... 0" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "2H5O4MG1-8uT", | |
| "outputId": "73d56848-2634-4299-fa95-395514f8483b", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 204 | |
| } | |
| }, | |
| "source": [ | |
| "# Объединяем два датафрейма вместе\n", | |
| "df_recalls = pd.concat((df_negative_recalls, df_positive_recalls),axis = 0).sample(frac = 1.0) # объединяем и перемешиваем\n", | |
| "df_recalls.index = range(0,len(df_recalls))\n", | |
| "df_recalls.head()" | |
| ], | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>recall</th>\n", | |
| " <th>type</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Сегодня у всех парней обострениеговно изо рта ...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Я никогда не туплю. Я просто делаю все в своем...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>RT @Sugar_Kroshka: Опять сердце ..и голова кру...</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>RT @DoDFavorit: @Olga_Wholock Мадам да Вы смущ...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>RT @SachihiroB: 20k Years Into Space — очень з...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " recall type\n", | |
| "0 Сегодня у всех парней обострениеговно изо рта ... 1\n", | |
| "1 Я никогда не туплю. Я просто делаю все в своем... 1\n", | |
| "2 RT @Sugar_Kroshka: Опять сердце ..и голова кру... 0\n", | |
| "3 RT @DoDFavorit: @Olga_Wholock Мадам да Вы смущ... 1\n", | |
| "4 RT @SachihiroB: 20k Years Into Space — очень з... 1" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 8 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "qYV7zbB7_MC-" | |
| }, | |
| "source": [ | |
| "### Удалим стоп слова" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "AZ_qod5R5LpL" | |
| }, | |
| "source": [ | |
| "stopWords = set(stopwords.words('russian'))" | |
| ], | |
| "execution_count": 9, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "54lNMQdG5QHC", | |
| "outputId": "8a9c7b8e-f69c-41a3-9c53-c89705c74dd4", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| } | |
| }, | |
| "source": [ | |
| "stopWords" | |
| ], | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "{'а',\n", | |
| " 'без',\n", | |
| " 'более',\n", | |
| " 'больше',\n", | |
| " 'будет',\n", | |
| " 'будто',\n", | |
| " 'бы',\n", | |
| " 'был',\n", | |
| " 'была',\n", | |
| " 'были',\n", | |
| " 'было',\n", | |
| " 'быть',\n", | |
| " 'в',\n", | |
| " 'вам',\n", | |
| " 'вас',\n", | |
| " 'вдруг',\n", | |
| " 'ведь',\n", | |
| " 'во',\n", | |
| " 'вот',\n", | |
| " 'впрочем',\n", | |
| " 'все',\n", | |
| " 'всегда',\n", | |
| " 'всего',\n", | |
| " 'всех',\n", | |
| " 'всю',\n", | |
| " 'вы',\n", | |
| " 'где',\n", | |
| " 'да',\n", | |
| " 'даже',\n", | |
| " 'два',\n", | |
| " 'для',\n", | |
| " 'до',\n", | |
| " 'другой',\n", | |
| " 'его',\n", | |
| " 'ее',\n", | |
| " 'ей',\n", | |
| " 'ему',\n", | |
| " 'если',\n", | |
| " 'есть',\n", | |
| " 'еще',\n", | |
| " 'ж',\n", | |
| " 'же',\n", | |
| " 'за',\n", | |
| " 'зачем',\n", | |
| " 'здесь',\n", | |
| " 'и',\n", | |
| " 'из',\n", | |
| " 'или',\n", | |
| " 'им',\n", | |
| " 'иногда',\n", | |
| " 'их',\n", | |
| " 'к',\n", | |
| " 'как',\n", | |
| " 'какая',\n", | |
| " 'какой',\n", | |
| " 'когда',\n", | |
| " 'конечно',\n", | |
| " 'кто',\n", | |
| " 'куда',\n", | |
| " 'ли',\n", | |
| " 'лучше',\n", | |
| " 'между',\n", | |
| " 'меня',\n", | |
| " 'мне',\n", | |
| " 'много',\n", | |
| " 'может',\n", | |
| " 'можно',\n", | |
| " 'мой',\n", | |
| " 'моя',\n", | |
| " 'мы',\n", | |
| " 'на',\n", | |
| " 'над',\n", | |
| " 'надо',\n", | |
| " 'наконец',\n", | |
| " 'нас',\n", | |
| " 'не',\n", | |
| " 'него',\n", | |
| " 'нее',\n", | |
| " 'ней',\n", | |
| " 'нельзя',\n", | |
| " 'нет',\n", | |
| " 'ни',\n", | |
| " 'нибудь',\n", | |
| " 'никогда',\n", | |
| " 'ним',\n", | |
| " 'них',\n", | |
| " 'ничего',\n", | |
| " 'но',\n", | |
| " 'ну',\n", | |
| " 'о',\n", | |
| " 'об',\n", | |
| " 'один',\n", | |
| " 'он',\n", | |
| " 'она',\n", | |
| " 'они',\n", | |
| " 'опять',\n", | |
| " 'от',\n", | |
| " 'перед',\n", | |
| " 'по',\n", | |
| " 'под',\n", | |
| " 'после',\n", | |
| " 'потом',\n", | |
| " 'потому',\n", | |
| " 'почти',\n", | |
| " 'при',\n", | |
| " 'про',\n", | |
| " 'раз',\n", | |
| " 'разве',\n", | |
| " 'с',\n", | |
| " 'сам',\n", | |
| " 'свою',\n", | |
| " 'себе',\n", | |
| " 'себя',\n", | |
| " 'сейчас',\n", | |
| " 'со',\n", | |
| " 'совсем',\n", | |
| " 'так',\n", | |
| " 'такой',\n", | |
| " 'там',\n", | |
| " 'тебя',\n", | |
| " 'тем',\n", | |
| " 'теперь',\n", | |
| " 'то',\n", | |
| " 'тогда',\n", | |
| " 'того',\n", | |
| " 'тоже',\n", | |
| " 'только',\n", | |
| " 'том',\n", | |
| " 'тот',\n", | |
| " 'три',\n", | |
| " 'тут',\n", | |
| " 'ты',\n", | |
| " 'у',\n", | |
| " 'уж',\n", | |
| " 'уже',\n", | |
| " 'хорошо',\n", | |
| " 'хоть',\n", | |
| " 'чего',\n", | |
| " 'чем',\n", | |
| " 'через',\n", | |
| " 'что',\n", | |
| " 'чтоб',\n", | |
| " 'чтобы',\n", | |
| " 'чуть',\n", | |
| " 'эти',\n", | |
| " 'этого',\n", | |
| " 'этой',\n", | |
| " 'этом',\n", | |
| " 'этот',\n", | |
| " 'эту',\n", | |
| " 'я'}" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 10 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "-mTrgM0E_eAW" | |
| }, | |
| "source": [ | |
| "### Очитска текста приведение слов к стандартному виду" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "gHI2wqsn_kMB", | |
| "outputId": "a553bb84-2503-4387-c85a-9afdd6d4b871", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 51 | |
| } | |
| }, | |
| "source": [ | |
| "%time df_recalls['recall'] = df_recalls['recall'].apply(df_preprocess)" | |
| ], | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "CPU times: user 1min 40s, sys: 79.8 ms, total: 1min 40s\n", | |
| "Wall time: 1min 40s\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Kca4OwK8BRPt", | |
| "outputId": "f51ad583-e159-4f84-a810-45bb64cf185a", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 204 | |
| } | |
| }, | |
| "source": [ | |
| "df_recalls.head()" | |
| ], | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>recall</th>\n", | |
| " <th>type</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>сегодн парн обострениеговн из рта льет</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>тупл прост дела сво стил d сайт</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>rt пользовател сердц голов круж</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>rt пользовател пользовател мад смуща деиствите...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>rt пользовател 20k years int spac очен занимат...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " recall type\n", | |
| "0 сегодн парн обострениеговн из рта льет 1\n", | |
| "1 тупл прост дела сво стил d сайт 1\n", | |
| "2 rt пользовател сердц голов круж 0\n", | |
| "3 rt пользовател пользовател мад смуща деиствите... 1\n", | |
| "4 rt пользовател 20k years int spac очен занимат... 1" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 12 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "O2NIMMwd_Cpi" | |
| }, | |
| "source": [ | |
| "### Train/test split" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "_kkes5A7_CAl" | |
| }, | |
| "source": [ | |
| "from sklearn.model_selection import train_test_split\n", | |
| "X_train, X_test, y_train, y_test = train_test_split(df_recalls['recall'], df_recalls['type'], test_size=.15, random_state=42)\n" | |
| ], | |
| "execution_count": 13, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ImvDPxubh_Su", | |
| "outputId": "cb002f7c-caf9-488e-8374-1f46696a7f1b", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "type(X_train)" | |
| ], | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "pandas.core.series.Series" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 14 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "J9nRJ3Z5iEjQ", | |
| "outputId": "34b6cfdf-94e5-449a-8524-4cf47f8d1925", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "print(len(X_train), len(X_test))" | |
| ], | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "192808 34026\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ipEpIuJwiM0g", | |
| "outputId": "5eb9cf10-1d9e-4188-bce5-110655681d3b", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| } | |
| }, | |
| "source": [ | |
| "X_train[100]" | |
| ], | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| }, | |
| "text/plain": [ | |
| "'добав стен потеря музык сайт'" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 16 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "hTYYSDy5jx_o", | |
| "outputId": "7fb84c2e-4e70-4a75-9af3-c574b5762e90", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "y_train[100]" | |
| ], | |
| "execution_count": 17, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "1" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 17 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ZBy6f8q6-xzv" | |
| }, | |
| "source": [ | |
| "### BOW" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "1PdMo6PY_BXL" | |
| }, | |
| "source": [ | |
| "from sklearn.feature_extraction.text import CountVectorizer\n", | |
| "vectorizer = CountVectorizer()\n", | |
| "\n", | |
| "X_train_BOW = vectorizer.fit_transform(X_train)\n", | |
| "X_test_BOW = vectorizer.transform(X_test)" | |
| ], | |
| "execution_count": 18, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "URXczudRj9p2", | |
| "outputId": "147ddf3c-d72b-4d2b-891e-c67b86dd1775", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "print(X_train_BOW.shape, X_test_BOW.shape)" | |
| ], | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "(192808, 110125) (34026, 110125)\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "6D2JImLqZ-bW", | |
| "outputId": "a94654ee-d1b5-457f-a0c2-10dfc4d90048", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| } | |
| }, | |
| "source": [ | |
| "X_train.iloc[200]" | |
| ], | |
| "execution_count": 20, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| }, | |
| "text/plain": [ | |
| "'пользовател иогурт эрмигурт очен широк горлышк пит неудобн бутылк убира губ царапа'" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 20 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "hb9w4Mgqm2s7", | |
| "outputId": "ba29e657-8dcb-46d9-ee8d-1d86cf8e310d", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 51 | |
| } | |
| }, | |
| "source": [ | |
| "# Векторное представление\n", | |
| "X_train_BOW[200]" | |
| ], | |
| "execution_count": 21, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<1x110125 sparse matrix of type '<class 'numpy.int64'>'\n", | |
| "\twith 12 stored elements in Compressed Sparse Row format>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 21 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "pmOeNxCznybd" | |
| }, | |
| "source": [ | |
| "### TF-IDF" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "T3mKWWnwn0im" | |
| }, | |
| "source": [ | |
| "from sklearn.feature_extraction.text import TfidfVectorizer\n", | |
| "vectorizer = TfidfVectorizer()\n", | |
| "\n", | |
| "X_train_TFIDF = vectorizer.fit_transform(X_train)\n", | |
| "X_test_TFIDF = vectorizer.transform(X_test)" | |
| ], | |
| "execution_count": 22, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "NpFtdBSYoBgT", | |
| "outputId": "abc70af0-d17a-4cb1-8734-7fb7ef259986", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "print(X_train_TFIDF.shape, X_test_TFIDF.shape)" | |
| ], | |
| "execution_count": 23, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "(192808, 110125) (34026, 110125)\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "E6ArOkpioIOs", | |
| "outputId": "bfc6ee5b-e1c1-4093-d47d-02d20cb2e433", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 51 | |
| } | |
| }, | |
| "source": [ | |
| "# Векторное представление\n", | |
| "X_train_TFIDF[200]" | |
| ], | |
| "execution_count": 24, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<1x110125 sparse matrix of type '<class 'numpy.float64'>'\n", | |
| "\twith 12 stored elements in Compressed Sparse Row format>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 24 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "DVF6O7dsaRjO" | |
| }, | |
| "source": [ | |
| "### Строим простейшую модель" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "hd5OlzOpbxD_" | |
| }, | |
| "source": [ | |
| "#### На данных BOW" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "4q3FF9T5dgZZ" | |
| }, | |
| "source": [ | |
| "from sklearn.linear_model import LogisticRegression\n", | |
| "from sklearn.metrics import accuracy_score\n" | |
| ], | |
| "execution_count": 25, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "2hIL5d9Wa3WV", | |
| "outputId": "ae2b187c-4134-4881-a9e6-5cfae750a1d8", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 207 | |
| } | |
| }, | |
| "source": [ | |
| "# обучаем классификатор\n", | |
| "%time clf = LogisticRegression(random_state=0).fit(X_train_BOW, y_train)" | |
| ], | |
| "execution_count": 26, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "CPU times: user 6.83 s, sys: 4.54 s, total: 11.4 s\n", | |
| "Wall time: 5.86 s\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", | |
| "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", | |
| "\n", | |
| "Increase the number of iterations (max_iter) or scale the data as shown in:\n", | |
| " https://scikit-learn.org/stable/modules/preprocessing.html\n", | |
| "Please also refer to the documentation for alternative solver options:\n", | |
| " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", | |
| " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" | |
| ], | |
| "name": "stderr" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "1CEVfbAKbYga" | |
| }, | |
| "source": [ | |
| "# вычисляем предсказания\n", | |
| "y_predict_BOW = clf.predict(X_test_BOW)" | |
| ], | |
| "execution_count": 27, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "cXxx3qZKboL8", | |
| "outputId": "aff5779e-817f-4d14-ce09-5fd0544eebc0", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "# вычисляем метрику accuracy\n", | |
| "accuracy_score(y_predict_BOW, y_test)" | |
| ], | |
| "execution_count": 28, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0.7360253923470288" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 28 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ccgCm6NKb0Wy" | |
| }, | |
| "source": [ | |
| "#### На данных TF-IDF" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "-Lpnp-YQb20w", | |
| "outputId": "c3e068b2-4d00-441f-f3b4-cb318f18deb8", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 207 | |
| } | |
| }, | |
| "source": [ | |
| "%time clf = LogisticRegression(random_state=43).fit(X_train_TFIDF, y_train)" | |
| ], | |
| "execution_count": 29, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "CPU times: user 7.34 s, sys: 4.94 s, total: 12.3 s\n", | |
| "Wall time: 6.3 s\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", | |
| "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", | |
| "\n", | |
| "Increase the number of iterations (max_iter) or scale the data as shown in:\n", | |
| " https://scikit-learn.org/stable/modules/preprocessing.html\n", | |
| "Please also refer to the documentation for alternative solver options:\n", | |
| " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", | |
| " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" | |
| ], | |
| "name": "stderr" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ERtxqj6qb7i0" | |
| }, | |
| "source": [ | |
| "y_predict_TFIDF = clf.predict(X_test_TFIDF)" | |
| ], | |
| "execution_count": 30, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "wFvxSEK7cBB7", | |
| "outputId": "52a9241a-514d-4da6-dc89-ead417ed644d", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "accuracy_score(y_predict_TFIDF, y_test)" | |
| ], | |
| "execution_count": 31, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0.7341444777523071" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 31 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "VQxum79ockEI" | |
| }, | |
| "source": [ | |
| "#### На данных BOW с биграммами" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "hDFo5A8wcinG", | |
| "outputId": "7a4639e2-1deb-419d-b689-95721daedb00", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 207 | |
| } | |
| }, | |
| "source": [ | |
| "#-----------------------------------------------\n", | |
| "vectorizer = CountVectorizer(ngram_range=(1, 2))\n", | |
| "#-----------------------------------------------\n", | |
| "X_train_BOW_bi = vectorizer.fit_transform(X_train)\n", | |
| "X_test_BOW_bi = vectorizer.transform(X_test)\n", | |
| "#-----------------------------------------------\n", | |
| "print(X_train_BOW_bi.shape, X_test_BOW_bi.shape)\n", | |
| "#-----------------------------------------------\n", | |
| "clf = LogisticRegression(random_state=0).fit(X_train_BOW_bi, y_train)\n", | |
| "#-----------------------------------------------\n", | |
| "y_predict_BOW_bi = clf.predict(X_test_BOW_bi)\n", | |
| "#-----------------------------------------------\n", | |
| "accuracy_score(y_predict_BOW_bi, y_test)" | |
| ], | |
| "execution_count": 32, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "(192808, 893652) (34026, 893652)\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", | |
| "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", | |
| "\n", | |
| "Increase the number of iterations (max_iter) or scale the data as shown in:\n", | |
| " https://scikit-learn.org/stable/modules/preprocessing.html\n", | |
| "Please also refer to the documentation for alternative solver options:\n", | |
| " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", | |
| " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" | |
| ], | |
| "name": "stderr" | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0.7493681302533357" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 32 | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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