toxic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "sample_submission = pd.read_csv(\"./data/sample_submission.csv\")\n",
    "train = pd.read_csv(\"./data/train.csv\")\n",
    "test = pd.read_csv(\"./data/test.csv\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Подробнее о метках (labels)\n",
    "Все они принимают значение -1, 0, 1\n",
    "* Insult - Оскорбление\n",
    "* Obscene - Непристойный\n",
    "* Severe toxicity - Сильная токсичность\n",
    "* Toxicity - Токсичность\n",
    "* Threat - Угроза\n",
    "* Identity hate - Ненависть к идентичности"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "                 id  toxic  severe_toxic  obscene  threat  insult  \\\n0  00001cee341fdb12    0.5           0.5      0.5     0.5     0.5   \n\n   identity_hate  \n0            0.5  ",
      "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>id</th>\n      <th>toxic</th>\n      <th>severe_toxic</th>\n      <th>obscene</th>\n      <th>threat</th>\n      <th>insult</th>\n      <th>identity_hate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00001cee341fdb12</td>\n      <td>0.5</td>\n      <td>0.5</td>\n      <td>0.5</td>\n      <td>0.5</td>\n      <td>0.5</td>\n      <td>0.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_submission.head(1)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "                 id                                       comment_text  toxic  \\\n0  0000997932d777bf  Explanation\\nWhy the edits made under my usern...      0   \n\n   severe_toxic  obscene  threat  insult  identity_hate  \n0             0        0       0       0              0  ",
      "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>id</th>\n      <th>comment_text</th>\n      <th>toxic</th>\n      <th>severe_toxic</th>\n      <th>obscene</th>\n      <th>threat</th>\n      <th>insult</th>\n      <th>identity_hate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0000997932d777bf</td>\n      <td>Explanation\\nWhy the edits made under my usern...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(1)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "                 id                                       comment_text\n0  00001cee341fdb12  Yo bitch Ja Rule is more succesful then you'll...",
      "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>id</th>\n      <th>comment_text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00001cee341fdb12</td>\n      <td>Yo bitch Ja Rule is more succesful then you'll...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(1)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "# То, что в этом выводе строки не пустые нам говорит о том, что toxic необязательно содержит в себе остальные метки.\n",
    "# train.loc[(train['toxic'] > 0) & ((train['severe_toxic'] == 0) & (train['obscene'] == 0) & (train['threat'] == 0) & (train['insult'] == 0) & (train['identity_hate'] == 0))]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "# А это говорит о том, что оскорбления, брань и прочее могут присутствовать даже в нетоксичных комментариях\n",
    "# train.loc[(train['toxic'] == 0) & ((train['severe_toxic'] != 0) | (train['obscene'] != 0) | (train['threat'] != 0) | (train['insult'] != 0) | (train['identity_hate'] != 0))]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "# просто статистика по данным, можем оценить, какие значения у нас бывают\n",
    "# cols = \"toxic severe_toxic obscene threat insult identity_hate\"\n",
    "# for i in cols.split():\n",
    "#     print(train[i].value_counts())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "# Тут мы создаём серию отфильтрованных комментариев, чтобы попробовать работать с ними, а не с сырым материалом\n",
    "\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "def preprocess_text(sen):\n",
    "    # Удаление символов пунктуации и специальных символов\n",
    "    sentence = re.sub('[^a-zA-Z0-9]', ' ', sen)\n",
    "    # Заменяем любые пробелы на один обычный\n",
    "    sentence = re.sub(r'\\s+', ' ', sentence)\n",
    "    # Делаем всё в нижнем регистре\n",
    "    sentence = sentence.lower()\n",
    "    # Удаляем стоп-слова\n",
    "    sentence = \" \".join([word for word in sentence.split() if word not in stop_words])\n",
    "    return sentence\n",
    "\n",
    "filtered_train = train['comment_text'].apply(preprocess_text)\n",
    "filtered_test = test['comment_text'].apply(preprocess_text)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "# Тут я забавы ради нарисовал облачко, но оно ничего особо не дало, потому идём дальше\n",
    "# from collections import Counter\n",
    "# from wordcloud import WordCloud, ImageColorGenerator\n",
    "# import matplotlib.pyplot as plt\n",
    "# all_words = \" \".join(filtered_train).split()\n",
    "# word_counts = Counter(all_words)\n",
    "# top_words = dict(word_counts.most_common(100))\n",
    "# wordcloud = WordCloud(background_color=\"white\")\n",
    "# wordcloud.generate_from_frequencies(top_words)\n",
    "# plt.figure(figsize=(10,10))\n",
    "# plt.axis('off')\n",
    "# plt.imshow(wordcloud)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "def make_tf_idf(docs: pd.Series, stop_words=None):\n",
    "    return TfidfVectorizer(stop_words=stop_words).fit_transform(docs)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "tfidf_train = make_tf_idf(filtered_train)\n",
    "tfidf_test = make_tf_idf(filtered_test)\n",
    "# разделение данных на обучающую и тестовую выборки\n",
    "targets_train = train.iloc[:, 2:]\n",
    "X_train, X_test, y_train, y_test = train_test_split(tfidf_train, targets_train, test_size=0.2)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
    "def rate(y_pred, y_test):\n",
    "    print('* Accuracy:', accuracy_score(y_test, y_pred))\n",
    "    print('* Precision:', precision_score(y_test, y_pred, average='weighted'))\n",
    "    print('* Recall:', recall_score(y_test, y_pred, average='weighted'))\n",
    "    print('* F1-score:', f1_score(y_test, y_pred, average='weighted'))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# Создаем LinearSVC и оборачиваем его в OneVsRestClassifier\n",
    "# создание и обучение модели\n",
    "# rf = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=6, max_depth=100) # не подходит, тк ждет опр кол-во признаков, а слов может быть произвольное кол-во\n",
    "logreg_ovr = OneVsRestClassifier(LogisticRegression(multi_class='ovr'))\n",
    "catboost = CatBoostClassifier(loss_function='MultiCrossEntropy')\n",
    "svm_ovr = OneVsRestClassifier(LinearSVC(multi_class='ovr'))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "OneVsRestClassifier(estimator=LinearSVC())"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = svm_ovr\n",
    "model.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Accuracy: 0.9190976030079899\n",
      "* Precision: 0.8288784258413564\n",
      "* Recall: 0.6146920108617979\n",
      "* F1-score: 0.7014125742142919\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "rate(y_pred, y_test)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Теперь надо сделать submission."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "                 id  toxic  severe_toxic  obscene  threat  insult  \\\n0  00001cee341fdb12    0.5           0.5      0.5     0.5     0.5   \n\n   identity_hate  \n0            0.5  ",
      "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>id</th>\n      <th>toxic</th>\n      <th>severe_toxic</th>\n      <th>obscene</th>\n      <th>threat</th>\n      <th>insult</th>\n      <th>identity_hate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00001cee341fdb12</td>\n      <td>0.5</td>\n      <td>0.5</td>\n      <td>0.5</td>\n      <td>0.5</td>\n      <td>0.5</td>\n      <td>0.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# посмотрим, как он выглядит\n",
    "sample_submission.head(1)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "# Переведем в tf-idf матрицу тестовые комментарии, чтобы модель понимала данные\n",
    "tfidf_test = make_tf_idf(test.comment_text)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "X has 246855 features, but LinearSVC is expecting 182086 features as input.",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_17660\\386709711.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[0mmodel\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0msvm_ovr\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 2\u001B[1;33m \u001B[0mmodel\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mpredict\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtfidf_test\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\multiclass.py\u001B[0m in \u001B[0;36mpredict\u001B[1;34m(self, X)\u001B[0m\n\u001B[0;32m    455\u001B[0m             \u001B[0mindptr\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0marray\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0marray\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"i\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    456\u001B[0m             \u001B[1;32mfor\u001B[0m \u001B[0me\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mestimators_\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 457\u001B[1;33m                 \u001B[0mindices\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mextend\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mwhere\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0m_predict_binary\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0me\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mX\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m>\u001B[0m \u001B[0mthresh\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    458\u001B[0m                 \u001B[0mindptr\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mindices\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    459\u001B[0m             \u001B[0mdata\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mones\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mindices\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mdtype\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mint\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\multiclass.py\u001B[0m in \u001B[0;36m_predict_binary\u001B[1;34m(estimator, X)\u001B[0m\n\u001B[0;32m     98\u001B[0m         \u001B[1;32mreturn\u001B[0m \u001B[0mestimator\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mpredict\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mX\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     99\u001B[0m     \u001B[1;32mtry\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 100\u001B[1;33m         \u001B[0mscore\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mravel\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mestimator\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdecision_function\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mX\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    101\u001B[0m     \u001B[1;32mexcept\u001B[0m \u001B[1;33m(\u001B[0m\u001B[0mAttributeError\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mNotImplementedError\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    102\u001B[0m         \u001B[1;31m# probabilities of the positive class\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py\u001B[0m in \u001B[0;36mdecision_function\u001B[1;34m(self, X)\u001B[0m\n\u001B[0;32m    405\u001B[0m         \u001B[0mcheck_is_fitted\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    406\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 407\u001B[1;33m         \u001B[0mX\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_validate_data\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mX\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0maccept_sparse\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m\"csr\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreset\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    408\u001B[0m         \u001B[0mscores\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0msafe_sparse_dot\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mX\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcoef_\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mT\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mdense_output\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mTrue\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m+\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mintercept_\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    409\u001B[0m         \u001B[1;32mreturn\u001B[0m \u001B[0mscores\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mravel\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mscores\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mshape\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m==\u001B[0m \u001B[1;36m1\u001B[0m \u001B[1;32melse\u001B[0m \u001B[0mscores\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py\u001B[0m in \u001B[0;36m_validate_data\u001B[1;34m(self, X, y, reset, validate_separately, **check_params)\u001B[0m\n\u001B[0;32m    583\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    584\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[0mno_val_X\u001B[0m \u001B[1;32mand\u001B[0m \u001B[0mcheck_params\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"ensure_2d\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;32mTrue\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 585\u001B[1;33m             \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_check_n_features\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mX\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreset\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mreset\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    586\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    587\u001B[0m         \u001B[1;32mreturn\u001B[0m \u001B[0mout\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py\u001B[0m in \u001B[0;36m_check_n_features\u001B[1;34m(self, X, reset)\u001B[0m\n\u001B[0;32m    398\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    399\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0mn_features\u001B[0m \u001B[1;33m!=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mn_features_in_\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 400\u001B[1;33m             raise ValueError(\n\u001B[0m\u001B[0;32m    401\u001B[0m                 \u001B[1;34mf\"X has {n_features} features, but {self.__class__.__name__} \"\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    402\u001B[0m                 \u001B[1;34mf\"is expecting {self.n_features_in_} features as input.\"\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mValueError\u001B[0m: X has 246855 features, but LinearSVC is expecting 182086 features as input."
     ]
    }
   ],
   "source": [
    "model = svm_ovr\n",
    "model.predict(tfidf_test)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'catboost' has no attribute 'predict'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_40400\\526693133.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mhstack\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcatboost\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mpredict\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtest\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0marray\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtest\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mid\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mreshape\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m-\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;31mAttributeError\u001B[0m: module 'catboost' has no attribute 'predict'"
     ]
    }
   ],
   "source": [
    "np.hstack((catboost.predict(test), np.array(test.id).reshape(-1, 1)))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "data": {
      "text/plain": "                      id                                       comment_text\n0       00001cee341fdb12  Yo bitch Ja Rule is more succesful then you'll...\n1       0000247867823ef7  == From RfC == \\n\\n The title is fine as it is...\n2       00013b17ad220c46  \" \\n\\n == Sources == \\n\\n * Zawe Ashton on Lap...\n3       00017563c3f7919a  :If you have a look back at the source, the in...\n4       00017695ad8997eb          I don't anonymously edit articles at all.\n...                  ...                                                ...\n153159  fffcd0960ee309b5  . \\n i totally agree, this stuff is nothing bu...\n153160  fffd7a9a6eb32c16  == Throw from out field to home plate. == \\n\\n...\n153161  fffda9e8d6fafa9e  \" \\n\\n == Okinotorishima categories == \\n\\n I ...\n153162  fffe8f1340a79fc2  \" \\n\\n == \"\"One of the founding nations of the...\n153163  ffffce3fb183ee80  \" \\n :::Stop already. Your bullshit is not wel...\n\n[153164 rows x 2 columns]",
      "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>id</th>\n      <th>comment_text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00001cee341fdb12</td>\n      <td>Yo bitch Ja Rule is more succesful then you'll...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0000247867823ef7</td>\n      <td>== From RfC == \\n\\n The title is fine as it is...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00013b17ad220c46</td>\n      <td>\" \\n\\n == Sources == \\n\\n * Zawe Ashton on Lap...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>00017563c3f7919a</td>\n      <td>:If you have a look back at the source, the in...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>00017695ad8997eb</td>\n      <td>I don't anonymously edit articles at all.</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>153159</th>\n      <td>fffcd0960ee309b5</td>\n      <td>. \\n i totally agree, this stuff is nothing bu...</td>\n    </tr>\n    <tr>\n      <th>153160</th>\n      <td>fffd7a9a6eb32c16</td>\n      <td>== Throw from out field to home plate. == \\n\\n...</td>\n    </tr>\n    <tr>\n      <th>153161</th>\n      <td>fffda9e8d6fafa9e</td>\n      <td>\" \\n\\n == Okinotorishima categories == \\n\\n I ...</td>\n    </tr>\n    <tr>\n      <th>153162</th>\n      <td>fffe8f1340a79fc2</td>\n      <td>\" \\n\\n == \"\"One of the founding nations of the...</td>\n    </tr>\n    <tr>\n      <th>153163</th>\n      <td>ffffce3fb183ee80</td>\n      <td>\" \\n :::Stop already. Your bullshit is not wel...</td>\n    </tr>\n  </tbody>\n</table>\n<p>153164 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
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