Toxic_Comment_Classifier_usingXGBoost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# #!/usr/bin/python3\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_formats = [\"retina\"]\n",
    "# %load_ext autotime\n",
    "\n",
    "#The features are borrowed from Eike Dehling's kernel at  https://www.kaggle.com/eikedehling/feature-engineering\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import string\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "import xgboost as xgb\n",
    "from tqdm import tqdm\n",
    "import gc\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import scipy\n",
    "import multiprocessing\n",
    "\n",
    "# Display progress logs on stdout\n",
    "import logging\n",
    "logging.basicConfig(level=logging.INFO,\n",
    "                    format='%(asctime)s %(levelname)s %(message)s')\n",
    "\n",
    "# from nltk.corpus import stopwords\n",
    "# stop_words = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtypes = {\n",
    "    'comment_text'   : np.unicode ,\n",
    "    'toxic':         np.int16, \n",
    "    'severe_toxic': np.int16,\n",
    "    'obscene': np.int16,\n",
    "    'threat': np.int16,\n",
    "    'insult': np.int16,\n",
    "    'identity_hate': np.int16\n",
    "}\n",
    "\n",
    "train = pd.read_csv('data/train.csv', dtype=dtypes, encoding='utf-8')\n",
    "test = pd.read_csv('data/test.csv', dtype=dtypes, encoding='utf-8')\n",
    "\n",
    "train.comment_text.fillna(\"unknown\", inplace=True)\n",
    "test.comment_text.fillna(\"unknown\",  inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_mes, valid_mes, train_l, valid_l = train_test_split(train['comment_text'],\n",
    "                                                          train[['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']],\n",
    "                                                          test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Using the tokenize function from Jeremy's kernel\n",
    "import re, string\n",
    "re_tok = re.compile(u'([{string.punctuation}“”¨«»®´·º½¾¿¡§£₤‘’])')\n",
    "def tokenize(s):\n",
    "    return re_tok.sub(r' \\1 ', s).split()\n",
    "\n",
    "transform_com = TfidfVectorizer(ngram_range=(1,2), tokenizer=tokenize,\n",
    "               min_df=3, max_df=0.9, strip_accents='unicode', use_idf=1,\n",
    "               smooth_idf=1, sublinear_tf=1).fit(train['comment_text'])\n",
    "\n",
    "comments_train = transform_com.transform(train_mes)\n",
    "comments_valid = transform_com.transform(valid_mes)\n",
    "comments_test = transform_com.transform(test['comment_text'])\n",
    "gc.collect()\n",
    "\n",
    "train_mes = pd.DataFrame(train_mes)\n",
    "valid_mes = pd.DataFrame(valid_mes)\n",
    "data = [train_mes, valid_mes, test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# These features are borrowed from https://www.kaggle.com/eikedehling/feature-engineering\n",
    "for element in data:\n",
    "    element['total_length'] = element['comment_text'].apply(len)\n",
    "    element['capitals'] = element['comment_text'].apply(lambda comment: sum(1 for c in comment if c.isupper()))\n",
    "    element['caps_vs_length'] = element.apply(lambda row: float(row['capitals'])/float(row['total_length']), axis=1)\n",
    "    element['num_exclamation_marks'] = element['comment_text'].apply(lambda comment: comment.count('!'))\n",
    "    element['num_question_marks'] = element['comment_text'].apply(lambda comment: comment.count('?'))\n",
    "    element['num_punctuation'] = element['comment_text'].apply(lambda comment: sum(comment.count(w) for w in '.,;:'))\n",
    "    element['num_symbols'] = element['comment_text'].apply(lambda comment: sum(comment.count(w) for w in '*&$%'))\n",
    "    element['num_words'] = element['comment_text'].apply(lambda comment: len(comment.split()))\n",
    "    element['num_unique_words'] = element['comment_text'].apply(lambda comment: len(set(w for w in comment.split())))\n",
    "    element['words_vs_unique'] = element['num_unique_words'] / element['num_words']\n",
    "    element['num_smilies'] = element['comment_text'].apply(lambda comment: sum(comment.count(w) for w in (':-)', ':)', ';-)', ';)')))\n",
    "    \n",
    "col = ['total_length', 'capitals', 'caps_vs_length',\n",
    "       'num_exclamation_marks', 'num_question_marks', 'num_punctuation',\n",
    "       'num_symbols', 'num_words', 'num_unique_words', 'words_vs_unique',\n",
    "       'num_smilies']\n",
    "columns = ('toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now, let's Combine both the Feature Vectors and the Engineered Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_mes = scipy.sparse.csr_matrix(train_mes[col].values)\n",
    "valid_mes = scipy.sparse.csr_matrix(valid_mes[col].values)\n",
    "test = scipy.sparse.csr_matrix(test[col].values)\n",
    "\n",
    "\n",
    "comments_train = scipy.sparse.hstack([train_mes.tocsr(),comments_train.tocsr()])\n",
    "comments_valid = scipy.sparse.hstack([valid_mes,comments_valid])\n",
    "comments_test = scipy.sparse.hstack([test,comments_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def runXGB(train_X, train_y, test_X, test_y=None, feature_names=None, seed_val=2017, num_rounds=500):\n",
    "    param = {}\n",
    "    param['objective'] = 'binary:logistic'\n",
    "    param['eta'] = 0.1\n",
    "    param['max_depth'] = 6\n",
    "    param['silent'] = 1\n",
    "    param['eval_metric'] = 'auc'\n",
    "    param['min_child_weight'] = 1\n",
    "    param['subsample'] = 0.7\n",
    "    param['colsample_bytree'] = 0.7\n",
    "    param['seed'] = seed_val\n",
    "    num_rounds = num_rounds\n",
    "\n",
    "    plst = list(param.items())\n",
    "    xgtrain = xgb.DMatrix(train_X, label=train_y)\n",
    "\n",
    "    if test_y is not None:\n",
    "        xgtest = xgb.DMatrix(test_X, label=test_y)\n",
    "        watchlist = [ (xgtrain,'train'), (xgtest, 'valid') ]\n",
    "        model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=20)\n",
    "    else:\n",
    "        xgtest = xgb.DMatrix(test_X)\n",
    "        model = xgb.train(plst, xgtrain, num_rounds)\n",
    "\n",
    "    return model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit toxic\n",
      "[0]\ttrain-auc:0.77242\ttest-auc:0.765702\n",
      "Multiple eval metrics have been passed: 'test-auc' will be used for early stopping.\n",
      "\n",
      "Will train until test-auc hasn't improved in 20 rounds.\n",
      "[1]\ttrain-auc:0.815782\ttest-auc:0.809515\n",
      "[2]\ttrain-auc:0.830233\ttest-auc:0.824112\n",
      "[3]\ttrain-auc:0.842537\ttest-auc:0.833436\n",
      "[4]\ttrain-auc:0.851532\ttest-auc:0.842671\n",
      "[5]\ttrain-auc:0.853684\ttest-auc:0.845302\n",
      "[6]\ttrain-auc:0.857449\ttest-auc:0.847592\n",
      "[7]\ttrain-auc:0.862579\ttest-auc:0.849572\n",
      "[8]\ttrain-auc:0.866918\ttest-auc:0.852981\n",
      "[9]\ttrain-auc:0.869632\ttest-auc:0.855417\n",
      "[10]\ttrain-auc:0.871686\ttest-auc:0.857329\n",
      "[11]\ttrain-auc:0.874539\ttest-auc:0.860705\n",
      "[12]\ttrain-auc:0.877057\ttest-auc:0.861877\n",
      "[13]\ttrain-auc:0.878491\ttest-auc:0.861911\n",
      "[14]\ttrain-auc:0.880472\ttest-auc:0.863216\n",
      "[15]\ttrain-auc:0.882349\ttest-auc:0.864087\n",
      "[16]\ttrain-auc:0.88567\ttest-auc:0.867465\n",
      "[17]\ttrain-auc:0.889556\ttest-auc:0.869415\n",
      "[18]\ttrain-auc:0.893246\ttest-auc:0.871862\n",
      "[19]\ttrain-auc:0.894864\ttest-auc:0.872676\n",
      "[20]\ttrain-auc:0.896964\ttest-auc:0.874628\n",
      "[21]\ttrain-auc:0.900528\ttest-auc:0.876535\n",
      "[22]\ttrain-auc:0.903797\ttest-auc:0.878887\n",
      "[23]\ttrain-auc:0.906856\ttest-auc:0.880569\n",
      "[24]\ttrain-auc:0.909833\ttest-auc:0.882433\n",
      "[25]\ttrain-auc:0.912465\ttest-auc:0.883641\n",
      "[26]\ttrain-auc:0.915288\ttest-auc:0.885112\n",
      "[27]\ttrain-auc:0.917831\ttest-auc:0.887067\n",
      "[28]\ttrain-auc:0.921029\ttest-auc:0.889278\n",
      "[29]\ttrain-auc:0.922366\ttest-auc:0.890132\n",
      "[30]\ttrain-auc:0.924327\ttest-auc:0.890609\n",
      "[31]\ttrain-auc:0.926132\ttest-auc:0.891369\n",
      "[32]\ttrain-auc:0.927557\ttest-auc:0.892438\n",
      "[33]\ttrain-auc:0.928657\ttest-auc:0.893169\n",
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    {
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      "[363]\ttrain-auc:0.996209\ttest-auc:0.937133\n",
      "[364]\ttrain-auc:0.996234\ttest-auc:0.937145\n",
      "Stopping. Best iteration:\n",
      "[344]\ttrain-auc:0.99576\ttest-auc:0.937332\n",
      "\n",
      "fit severe_toxic\n",
      "[0]\ttrain-auc:0.84325\ttest-auc:0.832804\n",
      "Multiple eval metrics have been passed: 'test-auc' will be used for early stopping.\n",
      "\n",
      "Will train until test-auc hasn't improved in 20 rounds.\n",
      "[1]\ttrain-auc:0.882976\ttest-auc:0.877783\n",
      "[2]\ttrain-auc:0.9071\ttest-auc:0.890086\n",
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      "[12]\ttrain-auc:0.943256\ttest-auc:0.913428\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[76]\ttrain-auc:0.998517\ttest-auc:0.979301\n",
      "[77]\ttrain-auc:0.998559\ttest-auc:0.979591\n",
      "[78]\ttrain-auc:0.9986\ttest-auc:0.9796\n",
      "[79]\ttrain-auc:0.998625\ttest-auc:0.979842\n",
      "[80]\ttrain-auc:0.998673\ttest-auc:0.979817\n",
      "[81]\ttrain-auc:0.998731\ttest-auc:0.97956\n",
      "[82]\ttrain-auc:0.998766\ttest-auc:0.979732\n",
      "[83]\ttrain-auc:0.99879\ttest-auc:0.980001\n",
      "[84]\ttrain-auc:0.998835\ttest-auc:0.980031\n",
      "[85]\ttrain-auc:0.998872\ttest-auc:0.980011\n",
      "[86]\ttrain-auc:0.998891\ttest-auc:0.979824\n",
      "[87]\ttrain-auc:0.998921\ttest-auc:0.979734\n",
      "[88]\ttrain-auc:0.998976\ttest-auc:0.979675\n",
      "[89]\ttrain-auc:0.999009\ttest-auc:0.97958\n",
      "[90]\ttrain-auc:0.999028\ttest-auc:0.979583\n",
      "[91]\ttrain-auc:0.999066\ttest-auc:0.979507\n",
      "[92]\ttrain-auc:0.999085\ttest-auc:0.979236\n",
      "[93]\ttrain-auc:0.999104\ttest-auc:0.979315\n",
      "[94]\ttrain-auc:0.999134\ttest-auc:0.979254\n",
      "[95]\ttrain-auc:0.999155\ttest-auc:0.979308\n",
      "[96]\ttrain-auc:0.999166\ttest-auc:0.979587\n",
      "[97]\ttrain-auc:0.999178\ttest-auc:0.979579\n",
      "[98]\ttrain-auc:0.999199\ttest-auc:0.979621\n",
      "[99]\ttrain-auc:0.999222\ttest-auc:0.979517\n",
      "[100]\ttrain-auc:0.999236\ttest-auc:0.979303\n",
      "[101]\ttrain-auc:0.999251\ttest-auc:0.979389\n",
      "[102]\ttrain-auc:0.999265\ttest-auc:0.979358\n",
      "[103]\ttrain-auc:0.999299\ttest-auc:0.979233\n",
      "[104]\ttrain-auc:0.999316\ttest-auc:0.979111\n",
      "Stopping. Best iteration:\n",
      "[84]\ttrain-auc:0.998835\ttest-auc:0.980031\n",
      "\n",
      "fit obscene\n",
      "[0]\ttrain-auc:0.795889\ttest-auc:0.805032\n",
      "Multiple eval metrics have been passed: 'test-auc' will be used for early stopping.\n",
      "\n",
      "Will train until test-auc hasn't improved in 20 rounds.\n",
      "[1]\ttrain-auc:0.809\ttest-auc:0.815286\n",
      "[2]\ttrain-auc:0.813989\ttest-auc:0.821474\n",
      "[3]\ttrain-auc:0.859749\ttest-auc:0.857059\n",
      "[4]\ttrain-auc:0.871524\ttest-auc:0.866538\n",
      "[5]\ttrain-auc:0.879237\ttest-auc:0.868625\n",
      "[6]\ttrain-auc:0.886084\ttest-auc:0.876891\n",
      "[7]\ttrain-auc:0.889267\ttest-auc:0.879809\n",
      "[8]\ttrain-auc:0.894452\ttest-auc:0.882229\n",
      "[9]\ttrain-auc:0.901389\ttest-auc:0.888961\n",
      "[10]\ttrain-auc:0.904062\ttest-auc:0.892507\n",
      "[11]\ttrain-auc:0.907749\ttest-auc:0.895535\n",
      "[12]\ttrain-auc:0.908687\ttest-auc:0.897307\n",
      "[13]\ttrain-auc:0.908781\ttest-auc:0.89833\n",
      "[14]\ttrain-auc:0.916603\ttest-auc:0.903337\n",
      "[15]\ttrain-auc:0.919044\ttest-auc:0.904386\n",
      "[16]\ttrain-auc:0.924288\ttest-auc:0.907255\n",
      "[17]\ttrain-auc:0.925875\ttest-auc:0.907768\n",
      "[18]\ttrain-auc:0.930193\ttest-auc:0.910049\n",
      "[19]\ttrain-auc:0.931485\ttest-auc:0.911027\n",
      "[20]\ttrain-auc:0.935963\ttest-auc:0.915842\n",
      "[21]\ttrain-auc:0.938198\ttest-auc:0.919777\n",
      "[22]\ttrain-auc:0.941964\ttest-auc:0.922814\n",
      "[23]\ttrain-auc:0.942242\ttest-auc:0.92285\n",
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      "[98]\ttrain-auc:0.993169\ttest-auc:0.956803\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[99]\ttrain-auc:0.993243\ttest-auc:0.95687\n",
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      "[241]\ttrain-auc:0.998925\ttest-auc:0.962143\n",
      "[242]\ttrain-auc:0.998931\ttest-auc:0.962152\n",
      "[243]\ttrain-auc:0.998944\ttest-auc:0.962156\n",
      "[244]\ttrain-auc:0.998963\ttest-auc:0.962353\n",
      "[245]\ttrain-auc:0.99897\ttest-auc:0.962233\n",
      "[246]\ttrain-auc:0.998984\ttest-auc:0.962374\n",
      "[247]\ttrain-auc:0.998992\ttest-auc:0.962378\n",
      "Stopping. Best iteration:\n",
      "[227]\ttrain-auc:0.998728\ttest-auc:0.9626\n",
      "\n",
      "fit threat\n",
      "[0]\ttrain-auc:0.583534\ttest-auc:0.579317\n",
      "Multiple eval metrics have been passed: 'test-auc' will be used for early stopping.\n",
      "\n",
      "Will train until test-auc hasn't improved in 20 rounds.\n",
      "[1]\ttrain-auc:0.609278\ttest-auc:0.596861\n",
      "[2]\ttrain-auc:0.654003\ttest-auc:0.630623\n",
      "[3]\ttrain-auc:0.699488\ttest-auc:0.64727\n",
      "[4]\ttrain-auc:0.764947\ttest-auc:0.675419\n",
      "[5]\ttrain-auc:0.769141\ttest-auc:0.675243\n",
      "[6]\ttrain-auc:0.775128\ttest-auc:0.67496\n",
      "[7]\ttrain-auc:0.799112\ttest-auc:0.682396\n",
      "[8]\ttrain-auc:0.807298\ttest-auc:0.69988\n",
      "[9]\ttrain-auc:0.811305\ttest-auc:0.700336\n",
      "[10]\ttrain-auc:0.817408\ttest-auc:0.699985\n",
      "[11]\ttrain-auc:0.845098\ttest-auc:0.772196\n",
      "[12]\ttrain-auc:0.845239\ttest-auc:0.772775\n",
      "[13]\ttrain-auc:0.851694\ttest-auc:0.773048\n",
      "[14]\ttrain-auc:0.864278\ttest-auc:0.772952\n",
      "[15]\ttrain-auc:0.870133\ttest-auc:0.77291\n",
      "[16]\ttrain-auc:0.896738\ttest-auc:0.782794\n",
      "[17]\ttrain-auc:0.898894\ttest-auc:0.784967\n",
      "[18]\ttrain-auc:0.904769\ttest-auc:0.786457\n",
      "[19]\ttrain-auc:0.9069\ttest-auc:0.786131\n",
      "[20]\ttrain-auc:0.90861\ttest-auc:0.785947\n",
      "[21]\ttrain-auc:0.909471\ttest-auc:0.79141\n",
      "[22]\ttrain-auc:0.90944\ttest-auc:0.791164\n",
      "[23]\ttrain-auc:0.909182\ttest-auc:0.790662\n",
      "[24]\ttrain-auc:0.910939\ttest-auc:0.790004\n",
      "[25]\ttrain-auc:0.91779\ttest-auc:0.828968\n",
      "[26]\ttrain-auc:0.943681\ttest-auc:0.827037\n",
      "[27]\ttrain-auc:0.943249\ttest-auc:0.830611\n",
      "[28]\ttrain-auc:0.947659\ttest-auc:0.83004\n",
      "[29]\ttrain-auc:0.957726\ttest-auc:0.855445\n",
      "[30]\ttrain-auc:0.961197\ttest-auc:0.864465\n",
      "[31]\ttrain-auc:0.963279\ttest-auc:0.862716\n",
      "[32]\ttrain-auc:0.966482\ttest-auc:0.878436\n",
      "[33]\ttrain-auc:0.966409\ttest-auc:0.884118\n",
      "[34]\ttrain-auc:0.96691\ttest-auc:0.893201\n",
      "[35]\ttrain-auc:0.970877\ttest-auc:0.89139\n",
      "[36]\ttrain-auc:0.973473\ttest-auc:0.889123\n",
      "[37]\ttrain-auc:0.974047\ttest-auc:0.894597\n",
      "[38]\ttrain-auc:0.976056\ttest-auc:0.894215\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[39]\ttrain-auc:0.980066\ttest-auc:0.913364\n",
      "[40]\ttrain-auc:0.980064\ttest-auc:0.910812\n",
      "[41]\ttrain-auc:0.980647\ttest-auc:0.919733\n",
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      "[64]\ttrain-auc:0.998865\ttest-auc:0.949788\n",
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      "[103]\ttrain-auc:0.999969\ttest-auc:0.95937\n",
      "[104]\ttrain-auc:0.999972\ttest-auc:0.960162\n",
      "[105]\ttrain-auc:0.999972\ttest-auc:0.959889\n",
      "[106]\ttrain-auc:0.999974\ttest-auc:0.96009\n",
      "[107]\ttrain-auc:0.999977\ttest-auc:0.958888\n",
      "[108]\ttrain-auc:0.999978\ttest-auc:0.96003\n",
      "[109]\ttrain-auc:0.99998\ttest-auc:0.959777\n",
      "[110]\ttrain-auc:0.99998\ttest-auc:0.960426\n",
      "[111]\ttrain-auc:0.999981\ttest-auc:0.959704\n",
      "[112]\ttrain-auc:0.999981\ttest-auc:0.95885\n",
      "[113]\ttrain-auc:0.999982\ttest-auc:0.958854\n",
      "[114]\ttrain-auc:0.999984\ttest-auc:0.959003\n",
      "[115]\ttrain-auc:0.999986\ttest-auc:0.959338\n",
      "[116]\ttrain-auc:0.999987\ttest-auc:0.959708\n",
      "[117]\ttrain-auc:0.999988\ttest-auc:0.960307\n",
      "[118]\ttrain-auc:0.999988\ttest-auc:0.959789\n",
      "[119]\ttrain-auc:0.999989\ttest-auc:0.958941\n",
      "[120]\ttrain-auc:0.99999\ttest-auc:0.959474\n",
      "[121]\ttrain-auc:0.999991\ttest-auc:0.95951\n",
      "[122]\ttrain-auc:0.999993\ttest-auc:0.958866\n",
      "[123]\ttrain-auc:0.999993\ttest-auc:0.958304\n",
      "[124]\ttrain-auc:0.999993\ttest-auc:0.957611\n",
      "[125]\ttrain-auc:0.999994\ttest-auc:0.956689\n",
      "[126]\ttrain-auc:0.999994\ttest-auc:0.957452\n",
      "[127]\ttrain-auc:0.999994\ttest-auc:0.958173\n",
      "[128]\ttrain-auc:0.999995\ttest-auc:0.957963\n",
      "[129]\ttrain-auc:0.999995\ttest-auc:0.957354\n",
      "[130]\ttrain-auc:0.999996\ttest-auc:0.957819\n",
      "Stopping. Best iteration:\n",
      "[110]\ttrain-auc:0.99998\ttest-auc:0.960426\n",
      "\n",
      "fit insult\n",
      "[0]\ttrain-auc:0.801134\ttest-auc:0.796103\n",
      "Multiple eval metrics have been passed: 'test-auc' will be used for early stopping.\n",
      "\n",
      "Will train until test-auc hasn't improved in 20 rounds.\n",
      "[1]\ttrain-auc:0.825379\ttest-auc:0.818993\n",
      "[2]\ttrain-auc:0.838477\ttest-auc:0.829135\n",
      "[3]\ttrain-auc:0.860698\ttest-auc:0.848287\n",
      "[4]\ttrain-auc:0.865432\ttest-auc:0.851676\n",
      "[5]\ttrain-auc:0.871599\ttest-auc:0.856414\n",
      "[6]\ttrain-auc:0.877602\ttest-auc:0.86082\n",
      "[7]\ttrain-auc:0.887218\ttest-auc:0.869176\n",
      "[8]\ttrain-auc:0.891709\ttest-auc:0.870259\n",
      "[9]\ttrain-auc:0.893751\ttest-auc:0.8747\n",
      "[10]\ttrain-auc:0.894413\ttest-auc:0.876403\n",
      "[11]\ttrain-auc:0.898043\ttest-auc:0.879523\n",
      "[12]\ttrain-auc:0.900349\ttest-auc:0.879966\n",
      "[13]\ttrain-auc:0.902684\ttest-auc:0.882657\n",
      "[14]\ttrain-auc:0.906571\ttest-auc:0.886196\n",
      "[15]\ttrain-auc:0.908324\ttest-auc:0.887684\n",
      "[16]\ttrain-auc:0.913152\ttest-auc:0.891127\n",
      "[17]\ttrain-auc:0.916134\ttest-auc:0.892016\n",
      "[18]\ttrain-auc:0.922052\ttest-auc:0.894375\n",
      "[19]\ttrain-auc:0.92401\ttest-auc:0.895355\n",
      "[20]\ttrain-auc:0.926199\ttest-auc:0.898044\n",
      "[21]\ttrain-auc:0.930001\ttest-auc:0.90087\n",
      "[22]\ttrain-auc:0.933089\ttest-auc:0.903537\n",
      "[23]\ttrain-auc:0.936524\ttest-auc:0.90572\n",
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      "[25]\ttrain-auc:0.939681\ttest-auc:0.908244\n",
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      "[48]\ttrain-auc:0.970346\ttest-auc:0.92857\n",
      "[49]\ttrain-auc:0.971471\ttest-auc:0.929804\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[99]\ttrain-auc:0.988678\ttest-auc:0.944324\n",
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      "[182]\ttrain-auc:0.995948\ttest-auc:0.950682\n",
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      "[184]\ttrain-auc:0.996\ttest-auc:0.951005\n",
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      "[190]\ttrain-auc:0.996218\ttest-auc:0.950906\n",
      "[191]\ttrain-auc:0.996277\ttest-auc:0.950816\n",
      "[192]\ttrain-auc:0.996304\ttest-auc:0.950801\n",
      "[193]\ttrain-auc:0.996335\ttest-auc:0.950805\n",
      "[194]\ttrain-auc:0.996382\ttest-auc:0.950844\n",
      "[195]\ttrain-auc:0.99642\ttest-auc:0.95085\n",
      "[196]\ttrain-auc:0.996435\ttest-auc:0.950867\n",
      "[197]\ttrain-auc:0.996446\ttest-auc:0.950912\n",
      "[198]\ttrain-auc:0.996493\ttest-auc:0.950926\n",
      "[199]\ttrain-auc:0.996536\ttest-auc:0.951016\n",
      "[200]\ttrain-auc:0.996567\ttest-auc:0.950971\n",
      "[201]\ttrain-auc:0.996576\ttest-auc:0.951005\n",
      "[202]\ttrain-auc:0.996618\ttest-auc:0.951072\n",
      "[203]\ttrain-auc:0.99664\ttest-auc:0.951068\n",
      "[204]\ttrain-auc:0.996683\ttest-auc:0.951015\n",
      "[205]\ttrain-auc:0.996707\ttest-auc:0.951002\n",
      "Stopping. Best iteration:\n",
      "[185]\ttrain-auc:0.996024\ttest-auc:0.951079\n",
      "\n",
      "fit identity_hate\n",
      "[0]\ttrain-auc:0.733864\ttest-auc:0.680108\n",
      "Multiple eval metrics have been passed: 'test-auc' will be used for early stopping.\n",
      "\n",
      "Will train until test-auc hasn't improved in 20 rounds.\n",
      "[1]\ttrain-auc:0.752436\ttest-auc:0.696049\n",
      "[2]\ttrain-auc:0.790836\ttest-auc:0.746416\n",
      "[3]\ttrain-auc:0.809421\ttest-auc:0.759418\n",
      "[4]\ttrain-auc:0.812107\ttest-auc:0.76219\n",
      "[5]\ttrain-auc:0.8239\ttest-auc:0.772258\n",
      "[6]\ttrain-auc:0.825692\ttest-auc:0.784618\n",
      "[7]\ttrain-auc:0.827478\ttest-auc:0.784937\n",
      "[8]\ttrain-auc:0.844905\ttest-auc:0.795134\n",
      "[9]\ttrain-auc:0.8543\ttest-auc:0.811925\n",
      "[10]\ttrain-auc:0.876262\ttest-auc:0.81936\n",
      "[11]\ttrain-auc:0.880463\ttest-auc:0.819794\n",
      "[12]\ttrain-auc:0.886477\ttest-auc:0.825863\n",
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      "[14]\ttrain-auc:0.89074\ttest-auc:0.823493\n",
      "[15]\ttrain-auc:0.90349\ttest-auc:0.836811\n",
      "[16]\ttrain-auc:0.905959\ttest-auc:0.839217\n",
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      "[23]\ttrain-auc:0.919273\ttest-auc:0.845914\n",
      "[24]\ttrain-auc:0.91895\ttest-auc:0.844624\n",
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      "[31]\ttrain-auc:0.934797\ttest-auc:0.857537\n",
      "[32]\ttrain-auc:0.935476\ttest-auc:0.855999\n",
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      "[58]\ttrain-auc:0.991913\ttest-auc:0.925908\n",
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      "[61]\ttrain-auc:0.993523\ttest-auc:0.926466\n",
      "[62]\ttrain-auc:0.993939\ttest-auc:0.930182\n",
      "[63]\ttrain-auc:0.993965\ttest-auc:0.930764\n",
      "[64]\ttrain-auc:0.994263\ttest-auc:0.930308\n",
      "[65]\ttrain-auc:0.994622\ttest-auc:0.931508\n",
      "[66]\ttrain-auc:0.99491\ttest-auc:0.931409\n",
      "[67]\ttrain-auc:0.995103\ttest-auc:0.931494\n",
      "[68]\ttrain-auc:0.995407\ttest-auc:0.930759\n",
      "[69]\ttrain-auc:0.995602\ttest-auc:0.930854\n",
      "[70]\ttrain-auc:0.995813\ttest-auc:0.93133\n",
      "[71]\ttrain-auc:0.99599\ttest-auc:0.932414\n",
      "[72]\ttrain-auc:0.996284\ttest-auc:0.933986\n",
      "[73]\ttrain-auc:0.996609\ttest-auc:0.93494\n",
      "[74]\ttrain-auc:0.996679\ttest-auc:0.935192\n",
      "[75]\ttrain-auc:0.996841\ttest-auc:0.935611\n",
      "[76]\ttrain-auc:0.996913\ttest-auc:0.935647\n",
      "[77]\ttrain-auc:0.997009\ttest-auc:0.933976\n",
      "[78]\ttrain-auc:0.99723\ttest-auc:0.933564\n",
      "[79]\ttrain-auc:0.997443\ttest-auc:0.932823\n",
      "[80]\ttrain-auc:0.997436\ttest-auc:0.933443\n",
      "[81]\ttrain-auc:0.99769\ttest-auc:0.932669\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[82]\ttrain-auc:0.997723\ttest-auc:0.933482\n",
      "[83]\ttrain-auc:0.997914\ttest-auc:0.934327\n",
      "[84]\ttrain-auc:0.998101\ttest-auc:0.933809\n",
      "[85]\ttrain-auc:0.998151\ttest-auc:0.93438\n",
      "[86]\ttrain-auc:0.998202\ttest-auc:0.934004\n",
      "[87]\ttrain-auc:0.998284\ttest-auc:0.933203\n",
      "[88]\ttrain-auc:0.998333\ttest-auc:0.933148\n",
      "[89]\ttrain-auc:0.998551\ttest-auc:0.934569\n",
      "[90]\ttrain-auc:0.998641\ttest-auc:0.933932\n",
      "[91]\ttrain-auc:0.998687\ttest-auc:0.934429\n",
      "[92]\ttrain-auc:0.99879\ttest-auc:0.934636\n",
      "[93]\ttrain-auc:0.998886\ttest-auc:0.93564\n",
      "[94]\ttrain-auc:0.998937\ttest-auc:0.935705\n",
      "[95]\ttrain-auc:0.998961\ttest-auc:0.936943\n",
      "[96]\ttrain-auc:0.998993\ttest-auc:0.936804\n",
      "[97]\ttrain-auc:0.999022\ttest-auc:0.937946\n",
      "[98]\ttrain-auc:0.999087\ttest-auc:0.937456\n",
      "[99]\ttrain-auc:0.999144\ttest-auc:0.937387\n",
      "[100]\ttrain-auc:0.999206\ttest-auc:0.937433\n",
      "[101]\ttrain-auc:0.999232\ttest-auc:0.937461\n",
      "[102]\ttrain-auc:0.999271\ttest-auc:0.937041\n",
      "[103]\ttrain-auc:0.999324\ttest-auc:0.935862\n",
      "[104]\ttrain-auc:0.999332\ttest-auc:0.935584\n",
      "[105]\ttrain-auc:0.999368\ttest-auc:0.934916\n",
      "[106]\ttrain-auc:0.999415\ttest-auc:0.935483\n",
      "[107]\ttrain-auc:0.999459\ttest-auc:0.934931\n",
      "[108]\ttrain-auc:0.999469\ttest-auc:0.933971\n",
      "[109]\ttrain-auc:0.999498\ttest-auc:0.934127\n",
      "[110]\ttrain-auc:0.999525\ttest-auc:0.93475\n",
      "[111]\ttrain-auc:0.999548\ttest-auc:0.934144\n",
      "[112]\ttrain-auc:0.999576\ttest-auc:0.93526\n",
      "[113]\ttrain-auc:0.999587\ttest-auc:0.935858\n",
      "[114]\ttrain-auc:0.999601\ttest-auc:0.935574\n",
      "[115]\ttrain-auc:0.999612\ttest-auc:0.936586\n",
      "[116]\ttrain-auc:0.999625\ttest-auc:0.936864\n",
      "[117]\ttrain-auc:0.999647\ttest-auc:0.93673\n",
      "Stopping. Best iteration:\n",
      "[97]\ttrain-auc:0.999022\ttest-auc:0.937946\n",
      "\n"
     ]
    }
   ],
   "source": [
    "col = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']\n",
    "preds = np.zeros((test.shape[0], len(col)))\n",
    "\n",
    "for i, j in enumerate(col):\n",
    "    print('fit '+j)\n",
    "    model = runXGB(comments_train, train_l[j], comments_valid, valid_l[j])\n",
    "    preds[:,i] = model.predict(xgb.DMatrix(comments_test), ntree_limit = model.best_ntree_limit)\n",
    "    gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "subm = pd.read_csv('data/sample_submission.csv')    \n",
    "submid = pd.DataFrame({'id': subm[\"id\"].values.astype(str)}, dtype=np.str)\n",
    "submission = pd.concat([submid, pd.DataFrame(preds, columns = col)], axis=1)\n",
    "submission.to_csv('submissions/sample_submission_xgb.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "     ### got AUC of train-auc:0.999022\tvalid-auc:0.937946\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try GloVe Word Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_mes, valid_mes, train_l, valid_l = train_test_split(train['comment_text'],\n",
    "                                                          train[['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']],\n",
    "                                                          test_size=0.2, random_state=42)\n",
    "train_mes = pd.DataFrame(train_mes)\n",
    "valid_mes = pd.DataFrame(valid_mes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2196017it [04:09, 8808.55it/s] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2195884 word vectors.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# load the GloVe vectors in a dictionary:\n",
    "\n",
    "embeddings_index = {}\n",
    "f = open('../Sentiment_Model_Template/glove.840B.300d.txt', encoding='utf-8')\n",
    "for line in tqdm(f):\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    try:\n",
    "        coefs = np.asarray(values[1:], dtype='float32')\n",
    "    except:\n",
    "        continue\n",
    "    embeddings_index[word] = coefs\n",
    "f.close()\n",
    "\n",
    "print('Found %s word vectors.' % len(embeddings_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# this function creates a normalized vector for the whole sentence\n",
    "def sent2vec(s, embeddings_index):\n",
    "    words = str(s).lower()\n",
    "    words = tokenize(words)\n",
    "#     words = [w for w in words if not w in stop_words]\n",
    "    words = [w for w in words if w.isalpha()]\n",
    "    M = []\n",
    "    for w in words:\n",
    "        try:\n",
    "            M.append(embeddings_index[w])\n",
    "        except:\n",
    "            continue\n",
    "    M = np.array(M)\n",
    "    v = M.sum(axis=0)\n",
    "    if type(v) != np.ndarray:\n",
    "        return np.zeros(300)\n",
    "    return v / np.sqrt((v ** 2).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect() # To free up some Memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# create sentence vectors using the above function for training and validation set\n",
    "xtrain_glove = [sent2vec(x, embeddings_index) for x in tqdm(train_mes['comment_text'])]\n",
    "xvalid_glove = [sent2vec(x, embeddings_index) for x in tqdm(valid_mes['comment_text'])]\n",
    "\n",
    "xtrain_glove = np.array(xtrain_glove)\n",
    "xvalid_glove = np.array(xvalid_glove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76680, 300)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain_glove.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19171, 300)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xvalid_glove.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit toxic\n",
      "CV score for class toxic is 0.7815914773233411\n",
      "fit severe_toxic\n",
      "CV score for class severe_toxic is 0.9074135380000475\n",
      "fit obscene\n",
      "CV score for class obscene is 0.8259538580137147\n",
      "fit threat\n",
      "CV score for class threat is 0.8624474536468689\n",
      "fit insult\n",
      "CV score for class insult is 0.8230832481127066\n",
      "fit identity_hate\n",
      "CV score for class identity_hate is 0.8288065779076506\n",
      "Total CV score is 0.8382160255007216\n"
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "col = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']\n",
    "preds = np.zeros((valid_mes.shape[0], len(col)))\n",
    "\n",
    "for i, class_name in enumerate(col):\n",
    "    print('fit '+ class_name)\n",
    "#     classifier = LogisticRegression(C=0.1, solver='sag')\n",
    "    classifier = SGDClassifier(loss='log', max_iter=1000, epsilon=0.001, n_jobs=-1)\n",
    "#     classifier = SVC(C=1.0, probability=True)\n",
    "\n",
    "    cv_score = np.mean(cross_val_score(classifier, xtrain_glove, train_l[class_name], cv=5, scoring='roc_auc'))\n",
    "    scores.append(cv_score)\n",
    "    print('CV score for class {} is {}'.format(class_name, cv_score))\n",
    "\n",
    "    classifier.fit(xtrain_glove, train_l[class_name])\n",
    "    preds[:,i] = classifier.predict_proba(xvalid_glove) \n",
    "    \n",
    "print('Total CV score is {}'.format(np.mean(scores)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate Word vectors of test data\n",
    "xtest_glove = [sent2vec(x, embeddings_index) for x in tqdm(test['comment_text'])]\n",
    "xtest_glove = np.array(xtest_glove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit toxic\n",
      "fit severe_toxic\n",
      "fit obscene\n",
      "fit threat\n",
      "fit insult\n",
      "fit identity_hate\n"
     ]
    }
   ],
   "source": [
    "testpreds_glove = np.zeros((test.shape[0], len(col)))\n",
    "for i, class_name in enumerate(col):\n",
    "    print('fit '+ class_name)\n",
    "    testpreds_google[:,i] = classifier.predict_proba(xtest_glove)[:,1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.concat([submid, pd.DataFrame(testpreds_glove, columns = col)], axis=1)\n",
    "submission.to_csv('submissions/sample_submission_glove_sgd.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    ### got total AUC of 0.838 on validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try Google Word Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3000001it [03:00, 16636.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3000000 word vectors.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# load the GloVe vectors in a dictionary:\n",
    "embeddings_index_google = {}\n",
    "f = open('../Sentiment_Model_Template/GoogleNews-vectors-negative300.txt')\n",
    "for line in tqdm(f):\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    try:\n",
    "        coefs = np.asarray(values[1:], dtype='float32')\n",
    "    except:\n",
    "        continue\n",
    "    embeddings_index_google[word] = coefs\n",
    "f.close()\n",
    "\n",
    "print('Found %s word vectors.' % len(embeddings_index_google))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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    },
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     ]
    }
   ],
   "source": [
    "# create sentence vectors using the above function for training and validation set\n",
    "xtrain_google = [sent2vec(x, embeddings_index_google) for x in tqdm(train_mes['comment_text'])]\n",
    "xvalid_google = [sent2vec(x, embeddings_index_google) for x in tqdm(valid_mes['comment_text'])]\n",
    "\n",
    "xtrain_google = np.array(xtrain_google)\n",
    "xvalid_google = np.array(xvalid_google)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76680, 300)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain_google.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19171, 300)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xvalid_google.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit toxic\n",
      "CV score for class toxic is 0.8010249179465745\n",
      "fit severe_toxic\n",
      "CV score for class severe_toxic is 0.9250436307925302\n",
      "fit obscene\n",
      "CV score for class obscene is 0.8458443652900014\n",
      "fit threat\n",
      "CV score for class threat is 0.8794754857125302\n",
      "fit insult\n",
      "CV score for class insult is 0.8432770129647181\n",
      "fit identity_hate\n",
      "CV score for class identity_hate is 0.8365719828043383\n",
      "Total CV score is 0.8552062325851154\n"
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "col = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']\n",
    "preds_google = np.zeros((valid_mes.shape[0], len(col)))\n",
    "\n",
    "for i, class_name in enumerate(col):\n",
    "    print('fit '+ class_name)\n",
    "    classifier = SGDClassifier(alpha= 1e-05, max_iter= 10000, loss='log', penalty= 'l2', n_jobs=-1, random_state=42)\n",
    "\n",
    "    cv_score = np.mean(cross_val_score(classifier, xtrain_google, train_l[class_name], cv=5, scoring='roc_auc'))\n",
    "    scores.append(cv_score)\n",
    "    print('CV score for class {} is {}'.format(class_name, cv_score))\n",
    "\n",
    "    classifier.fit(xtrain_google, train_l[class_name])\n",
    "    preds_google[:,i] = classifier.predict_proba(xvalid_google)[:,1] \n",
    "    \n",
    "print('Total CV score is {}'.format(np.mean(scores)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     ]
    }
   ],
   "source": [
    "# Generate Word vectors of test data\n",
    "xtest_google = [sent2vec(x, embeddings_index_google) for x in tqdm(test['comment_text'])]\n",
    "xtest_google = np.array(xtest_google)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit toxic\n",
      "fit severe_toxic\n",
      "fit obscene\n",
      "fit threat\n",
      "fit insult\n",
      "fit identity_hate\n"
     ]
    }
   ],
   "source": [
    "testpreds_google = np.zeros((test.shape[0], len(col)))\n",
    "for i, class_name in enumerate(col):\n",
    "    print('fit '+ class_name)\n",
    "    testpreds_google[:,i] = classifier.predict_proba(xtest_google)[:,1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.concat([submid, pd.DataFrame(testpreds_google, columns = col)], axis=1)\n",
    "submission.to_csv('submissions/sample_submission_google_sgd.csv', index=False)"
   ]
  }
 ],
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