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October 26, 2020 20:26
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NLP grid search, feature selection, grid search
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "3 Тюнинг модели отбор фичей.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "toc_visible": true | |
| }, | |
| "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": "457c44b1-a5c3-4189-ea73-1a12138e6117", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 122 | |
| } | |
| }, | |
| "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": [ | |
| "Requirement already satisfied: pymorphy2 in /usr/local/lib/python3.6/dist-packages (0.9.1)\n", | |
| "Requirement already satisfied: dawg-python>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from pymorphy2) (0.7.2)\n", | |
| "Requirement already satisfied: docopt>=0.6 in /usr/local/lib/python3.6/dist-packages (from pymorphy2) (0.6.2)\n", | |
| "Requirement already satisfied: pymorphy2-dicts-ru<3.0,>=2.4 in /usr/local/lib/python3.6/dist-packages (from pymorphy2) (2.4.417127.4579844)\n", | |
| "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "LBF3ntXC5TWG", | |
| "outputId": "c128d7ff-7e77-464c-cb02-ed770f628981", | |
| "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] Package stopwords is already up-to-date!\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": 3, | |
| "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": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "wF5HZPIb957U" | |
| }, | |
| "source": [ | |
| "### Формируем датасет " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "KjrfTaD_7wo1", | |
| "outputId": "6fb12b40-5ed6-4ffc-8244-fb73821a8ae7", | |
| "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": 5, | |
| "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": 5 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "C3oNsEsg-2Yb", | |
| "outputId": "e350344a-d31c-415e-c3af-117fe157bd91", | |
| "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": 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>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": 6 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "2H5O4MG1-8uT", | |
| "outputId": "371c897f-d089-4c7d-eef5-09349c967ae6", | |
| "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": 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>AAAW!!! МНЕ НРАВИТСЯ МОЙ ГОЛОС СЕЙЧАС! НЕМНОГО...</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>@krissmurr аххаха;D\\nОбрадовала блин меня;(</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>@Doll_perian @RastaPushka в четверг ему будет ...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>@JulikaKing у меня с телефона норм, а вот с ко...</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>RT @locorti69: Как романтично писал Вольтер да...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " recall type\n", | |
| "0 AAAW!!! МНЕ НРАВИТСЯ МОЙ ГОЛОС СЕЙЧАС! НЕМНОГО... 0\n", | |
| "1 @krissmurr аххаха;D\\nОбрадовала блин меня;( 0\n", | |
| "2 @Doll_perian @RastaPushka в четверг ему будет ... 1\n", | |
| "3 @JulikaKing у меня с телефона норм, а вот с ко... 0\n", | |
| "4 RT @locorti69: Как романтично писал Вольтер да... 1" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "-mTrgM0E_eAW" | |
| }, | |
| "source": [ | |
| "### Очитска текста приведение слов к стандартному виду" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "gHI2wqsn_kMB", | |
| "outputId": "fe638ba9-79dc-44c6-f82b-c75138c71e0b", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 51 | |
| } | |
| }, | |
| "source": [ | |
| "%time df_recalls['recall'] = df_recalls['recall'].apply(df_preprocess)" | |
| ], | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "CPU times: user 1min 40s, sys: 107 ms, total: 1min 40s\n", | |
| "Wall time: 1min 40s\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Kca4OwK8BRPt", | |
| "outputId": "367707c4-9ee6-451a-e262-1318d874ebc7", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 204 | |
| } | |
| }, | |
| "source": [ | |
| "df_recalls.head()" | |
| ], | |
| "execution_count": 9, | |
| "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>aaaw нрав мо голос сеичас немн хриплы кароч вл...</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>пользовател аххах d обрадова блин</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>пользовател пользовател четверг некогд</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>пользовател телефон норм комп ел</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>rt пользовател романтичн писа вольтер дам сво ...</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " recall type\n", | |
| "0 aaaw нрав мо голос сеичас немн хриплы кароч вл... 0\n", | |
| "1 пользовател аххах d обрадова блин 0\n", | |
| "2 пользовател пользовател четверг некогд 1\n", | |
| "3 пользовател телефон норм комп ел 0\n", | |
| "4 rt пользовател романтичн писа вольтер дам сво ... 1" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 9 | |
| } | |
| ] | |
| }, | |
| { | |
| "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": 10, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ZBy6f8q6-xzv" | |
| }, | |
| "source": [ | |
| "### Формируем векторное представление (BOW)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "oeXBgN5q9gH5" | |
| }, | |
| "source": [ | |
| "from sklearn.feature_extraction.text import CountVectorizer\n", | |
| "from sklearn.linear_model import LogisticRegression\n", | |
| "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n" | |
| ], | |
| "execution_count": 11, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "hDFo5A8wcinG", | |
| "outputId": "4187c6fc-d53f-4562-8439-5e4372b56b6d", | |
| "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": 12, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "(192808, 893869) (34026, 893869)\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": [ | |
| "0.753071180861694" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 12 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "YnHStNfuBBEG" | |
| }, | |
| "source": [ | |
| "### Подбор гиперпараметров / отбор фичей / grid search / cross-validation" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "rGXGKrGNUps_" | |
| }, | |
| "source": [ | |
| "#### Отбор фичей" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "CVdqq4pdBLn6", | |
| "outputId": "e25b0869-7981-43e4-c398-fbd9a67db487", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 139 | |
| } | |
| }, | |
| "source": [ | |
| "from sklearn.svm import LinearSVC\n", | |
| "from sklearn.feature_selection import SelectFromModel\n", | |
| "from sklearn.model_selection import cross_val_score\n", | |
| "\n", | |
| "lsvc = LinearSVC(C = .5) # C = 0.5\n", | |
| "selective_model = SelectFromModel(lsvc, max_features = None)\n", | |
| "\n", | |
| "X_train_BOW_bi_select_features = selective_model.fit_transform(X_train_BOW_bi,y_train)\n", | |
| "X_test_BOW_bi_select_features = selective_model.transform(X_test_BOW_bi)\n", | |
| "print('\\nNew shapes: ', X_train_BOW_bi.shape, X_test_BOW_bi.shape)\n", | |
| "print('\\nNew shapes: ', X_train_BOW_bi_select_features.shape, X_test_BOW_bi_select_features.shape)\n" | |
| ], | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "New shapes: (192808, 893869) (34026, 893869)\n", | |
| "\n", | |
| "New shapes: (192808, 361229) (34026, 361229)\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", | |
| " \"the number of iterations.\", ConvergenceWarning)\n" | |
| ], | |
| "name": "stderr" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ZHzMnCWl_QwY", | |
| "outputId": "241f134c-de6e-4089-84c8-eb2c5e46bb64", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 547 | |
| } | |
| }, | |
| "source": [ | |
| "clf = LogisticRegression(random_state=0)\n", | |
| "scores = cross_val_score(clf, X_train_BOW_bi_select_features, y_train, cv=3, scoring='accuracy')\n", | |
| "print(clf,'\\n Cross-validate: ', scores)" | |
| ], | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "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", | |
| "/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": "stream", | |
| "text": [ | |
| "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", | |
| " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", | |
| " multi_class='auto', n_jobs=None, penalty='l2',\n", | |
| " random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n", | |
| " warm_start=False) \n", | |
| " Cross-validate: [0.77186868 0.76962455 0.77066704]\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": "0MlQnqAHVve3", | |
| "outputId": "829e889e-26bb-4775-8b95-f42067590cd8", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 190 | |
| } | |
| }, | |
| "source": [ | |
| "#-----------------------------------------------\n", | |
| "clf = LogisticRegression(random_state=0).fit(X_train_BOW_bi_select_features, y_train)\n", | |
| "#-----------------------------------------------\n", | |
| "y_predict_BOW_bi = clf.predict(X_test_BOW_bi_select_features)\n", | |
| "#-----------------------------------------------\n", | |
| "accuracy_score(y_predict_BOW_bi, y_test)" | |
| ], | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "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": [ | |
| "0.7490448480573679" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 15 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "FpYQg7MBUst-" | |
| }, | |
| "source": [ | |
| "#### GridSearch" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Z8p1o4C5I6LY" | |
| }, | |
| "source": [ | |
| "from sklearn.model_selection import GridSearchCV" | |
| ], | |
| "execution_count": 17, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "7EI_0sL5UwaV", | |
| "outputId": "ac6f04d3-cde4-4515-8113-6df8dec60079", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| } | |
| }, | |
| "source": [ | |
| "grid={\n", | |
| " \"C\":np.logspace(-3,3,4), \n", | |
| " \"penalty\":[\"l2\"],\n", | |
| " \"solver\":['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n", | |
| " }\n", | |
| "\n", | |
| "clf=LogisticRegression()\n", | |
| "logreg_cv=GridSearchCV(clf,grid,cv=3)\n", | |
| "%time logreg_cv.fit(X_train_BOW_bi_select_features, y_train)\n", | |
| "\n", | |
| "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\n", | |
| "print(\"accuracy :\",logreg_cv.best_score_)" | |
| ], | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "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", | |
| "/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", | |
| "/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", | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", | |
| " \"the coef_ did not converge\", ConvergenceWarning)\n", | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", | |
| " \"the coef_ did not converge\", ConvergenceWarning)\n", | |
| "/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", | |
| "/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", | |
| "/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", | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", | |
| " \"the coef_ did not converge\", ConvergenceWarning)\n", | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", | |
| " \"the coef_ did not converge\", ConvergenceWarning)\n", | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", | |
| " \"the coef_ did not converge\", ConvergenceWarning)\n", | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", | |
| " \"the coef_ did not converge\", ConvergenceWarning)\n", | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", | |
| " \"the coef_ did not converge\", ConvergenceWarning)\n", | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", | |
| " \"the coef_ did not converge\", ConvergenceWarning)\n", | |
| "/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", | |
| "/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", | |
| "/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", | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", | |
| " \"the coef_ did not converge\", ConvergenceWarning)\n", | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", | |
| " \"the coef_ did not converge\", ConvergenceWarning)\n", | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", | |
| " \"the coef_ did not converge\", ConvergenceWarning)\n", | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", | |
| " \"the coef_ did not converge\", ConvergenceWarning)\n", | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", | |
| " \"the coef_ did not converge\", ConvergenceWarning)\n", | |
| "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", | |
| " \"the coef_ did not converge\", ConvergenceWarning)\n" | |
| ], | |
| "name": "stderr" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "CPU times: user 13min 38s, sys: 7min 25s, total: 21min 3s\n", | |
| "Wall time: 12min 24s\n", | |
| "tuned hpyerparameters :(best parameters) {'C': 10.0, 'penalty': 'l2', 'solver': 'liblinear'}\n", | |
| "accuracy : 0.7846717905762045\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "edmsQBeZKFsF", | |
| "outputId": "4c1d2ff7-49d0-4d1e-9d33-ca7e5040ab73", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "logreg_cv.best_params_" | |
| ], | |
| "execution_count": 20, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "{'C': 10.0, 'penalty': 'l2', 'solver': 'liblinear'}" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 20 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "cal6cdTaKBCJ", | |
| "outputId": "0eb1cca9-80db-488f-f79c-a6022e5092ef", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "#-----------------------------------------------\n", | |
| "clf = LogisticRegression(C=10.0, penalty='l2', solver='liblinear').fit(X_train_BOW_bi_select_features, y_train)\n", | |
| "#-----------------------------------------------\n", | |
| "y_predict_BOW_bi = clf.predict(X_test_BOW_bi_select_features)\n", | |
| "#-----------------------------------------------\n", | |
| "accuracy_score(y_predict_BOW_bi, y_test)" | |
| ], | |
| "execution_count": 22, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0.7483982836654323" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 22 | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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