toxic_text_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "46e0a923",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "57a476d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(\"train.csv\")\n",
    "test_df = pd.read_csv(\"test.csv\")\n",
    "test_labels = pd.read_csv(\"test_labels.csv\")\n",
    "sample_submissions_df = pd.read_csv(\"sample_submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5e9b03a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[144277.,      0.,      0.,      0.,      0.,      0.,      0.,\n",
       "              0.,      0.,  15294.],\n",
       "        [157976.,      0.,      0.,      0.,      0.,      0.,      0.,\n",
       "              0.,      0.,   1595.],\n",
       "        [151122.,      0.,      0.,      0.,      0.,      0.,      0.,\n",
       "              0.,      0.,   8449.],\n",
       "        [159093.,      0.,      0.,      0.,      0.,      0.,      0.,\n",
       "              0.,      0.,    478.],\n",
       "        [151694.,      0.,      0.,      0.,      0.,      0.,      0.,\n",
       "              0.,      0.,   7877.],\n",
       "        [158166.,      0.,      0.,      0.,      0.,      0.,      0.,\n",
       "              0.,      0.,   1405.]]),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <a list of 6 BarContainer objects>)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([train_df.toxic, train_df.severe_toxic, train_df.obscene, train_df.threat, train_df.insult, train_df.identity_hate])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4a4036a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(max_features=5000, stop_words='english')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vect = TfidfVectorizer(max_features=5000,stop_words='english')\n",
    "vect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "68655c22",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<159571x5000 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 3228494 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_dtm = vect.fit_transform(train_df.comment_text.values.astype('U'))\n",
    "X_dtm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c1166373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<153164x5000 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 2668361 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x_dtm = vect.fit_transform(test_df.comment_text.values.astype('U'))\n",
    "test_x_dtm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6fa11035",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf1 = XGBClassifier(use_label_encoder= False, eval_metric='logloss', objective= 'binary:logistic')\n",
    "clf2 = LGBMClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a17d4963",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_target = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "299c29ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df_lgbm = test_df\n",
    "test_df_xgb = test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "080be0e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy is : 0.9605128751464865\n",
      "Training accuracy is : 0.9941718733353805\n",
      "Training accuracy is : 0.9848656710805849\n",
      "Training accuracy is : 0.9986275701725251\n",
      "Training accuracy is : 0.9772891064165794\n",
      "Training accuracy is : 0.9949238896792023\n"
     ]
    }
   ],
   "source": [
    "for label in cols_target:\n",
    "    clf1.fit(X_dtm, train_df[label])\n",
    "    train_pred = clf1.predict(X_dtm)\n",
    "    test_acc = accuracy_score(train_df[label], train_pred)\n",
    "    print(f\"Training accuracy is : {test_acc}\")\n",
    "    test_pred = clf1.predict(test_x_dtm)\n",
    "    test_df_xgb[label] = test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "2539c2ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df_xgb.drop(columns=['comment_text']).to_csv('result_xgb.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "adf93bb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy is : 0.9949238896792023\n",
      "Training accuracy is : 0.9949238896792023\n",
      "Training accuracy is : 0.9949238896792023\n",
      "Training accuracy is : 0.9949238896792023\n",
      "Training accuracy is : 0.9949238896792023\n",
      "Training accuracy is : 0.9949238896792023\n"
     ]
    }
   ],
   "source": [
    "for label in cols_target:\n",
    "    clf2.fit(X_dtm, train_df[label])\n",
    "    train_pred = clf2.predict(X_dtm)\n",
    "    train_acc = accuracy_score(train_df[label], train_pred)\n",
    "    print(f\"Training accuracy is : {test_acc}\")\n",
    "    test_pred = clf2.predict(test_x_dtm)\n",
    "    test_df_lgbm[label] = test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "bf843e8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df_lgbm.drop(columns=['comment_text']).to_csv('result_lgbm.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cc5ddb3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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