Toxic Comment Classification Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/overview/description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "# Tweet tokenizer does not split at apostophes which is what we want\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.base import BaseEstimator,TransformerMixin\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "import Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dataset:\n",
    "\n",
    "https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000103f0d9cfb60f</td>\n",
       "      <td>D'aww! He matches this background colour I'm s...</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000113f07ec002fd</td>\n",
       "      <td>Hey man, I'm really not trying to edit war. It...</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0001b41b1c6bb37e</td>\n",
       "      <td>\"\\nMore\\nI can't make any real suggestions on ...</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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0001d958c54c6e35</td>\n",
       "      <td>You, sir, are my hero. Any chance you remember...</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>"
      ],
      "text/plain": [
       "                 id                                       comment_text  toxic  \\\n",
       "0  0000997932d777bf  Explanation\\nWhy the edits made under my usern...      0   \n",
       "1  000103f0d9cfb60f  D'aww! He matches this background colour I'm s...      0   \n",
       "2  000113f07ec002fd  Hey man, I'm really not trying to edit war. It...      0   \n",
       "3  0001b41b1c6bb37e  \"\\nMore\\nI can't make any real suggestions on ...      0   \n",
       "4  0001d958c54c6e35  You, sir, are my hero. Any chance you remember...      0   \n",
       "\n",
       "   severe_toxic  obscene  threat  insult  identity_hate  \n",
       "0             0        0       0       0              0  \n",
       "1             0        0       0       0              0  \n",
       "2             0        0       0       0              0  \n",
       "3             0        0       0       0              0  \n",
       "4             0        0       0       0              0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('Data/train.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 159571 entries, 0 to 159570\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   id             159571 non-null  object\n",
      " 1   comment_text   159571 non-null  object\n",
      " 2   toxic          159571 non-null  int64 \n",
      " 3   severe_toxic   159571 non-null  int64 \n",
      " 4   obscene        159571 non-null  int64 \n",
      " 5   threat         159571 non-null  int64 \n",
      " 6   insult         159571 non-null  int64 \n",
      " 7   identity_hate  159571 non-null  int64 \n",
      "dtypes: int64(6), object(2)\n",
      "memory usage: 9.7+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id                                       comment_text\n",
       "0  00001cee341fdb12  Yo bitch Ja Rule is more succesful then you'll...\n",
       "1  0000247867823ef7  == From RfC == \\n\\n The title is fine as it is...\n",
       "2  00013b17ad220c46  \" \\n\\n == Sources == \\n\\n * Zawe Ashton on Lap...\n",
       "3  00017563c3f7919a  :If you have a look back at the source, the in...\n",
       "4  00017695ad8997eb          I don't anonymously edit articles at all."
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('Data/test.csv')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Here are six different classes present corresoponding to each comments. so we will try to implement six different binary classifer model to predict each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['identity_hate', 'insult', 'obscene', 'severe_toxic', 'threat', 'toxic']\n"
     ]
    }
   ],
   "source": [
    "class_list =train.columns.difference(['id','comment_text']).to_list()\n",
    "print(class_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://drive.google.com/file/d/0B1yuv8YaUVlZZ1RzMFJmc1ZsQmM/view\n",
    "# Aphost lookup dict\n",
    "APPO = {\n",
    "\"aren't\" : \"are not\",\n",
    "\"can't\" : \"cannot\",\n",
    "\"couldn't\" : \"could not\",\n",
    "\"didn't\" : \"did not\",\n",
    "\"doesn't\" : \"does not\",\n",
    "\"don't\" : \"do not\",\n",
    "\"hadn't\" : \"had not\",\n",
    "\"hasn't\" : \"has not\",\n",
    "\"haven't\" : \"have not\",\n",
    "\"he'd\" : \"he would\",\n",
    "\"he'll\" : \"he will\",\n",
    "\"he's\" : \"he is\",\n",
    "\"i'd\" : \"I would\",\n",
    "\"i'd\" : \"I had\",\n",
    "\"i'll\" : \"I will\",\n",
    "\"i'm\" : \"I am\",\n",
    "\"isn't\" : \"is not\",\n",
    "\"it's\" : \"it is\",\n",
    "\"it'll\":\"it will\",\n",
    "\"i've\" : \"I have\",\n",
    "\"let's\" : \"let us\",\n",
    "\"mightn't\" : \"might not\",\n",
    "\"mustn't\" : \"must not\",\n",
    "\"shan't\" : \"shall not\",\n",
    "\"she'd\" : \"she would\",\n",
    "\"she'll\" : \"she will\",\n",
    "\"she's\" : \"she is\",\n",
    "\"shouldn't\" : \"should not\",\n",
    "\"that's\" : \"that is\",\n",
    "\"there's\" : \"there is\",\n",
    "\"they'd\" : \"they would\",\n",
    "\"they'll\" : \"they will\",\n",
    "\"they're\" : \"they are\",\n",
    "\"they've\" : \"they have\",\n",
    "\"we'd\" : \"we would\",\n",
    "\"we're\" : \"we are\",\n",
    "\"weren't\" : \"were not\",\n",
    "\"we've\" : \"we have\",\n",
    "\"what'll\" : \"what will\",\n",
    "\"what're\" : \"what are\",\n",
    "\"what's\" : \"what is\",\n",
    "\"what've\" : \"what have\",\n",
    "\"where's\" : \"where is\",\n",
    "\"who'd\" : \"who would\",\n",
    "\"who'll\" : \"who will\",\n",
    "\"who're\" : \"who are\",\n",
    "\"who's\" : \"who is\",\n",
    "\"who've\" : \"who have\",\n",
    "\"won't\" : \"will not\",\n",
    "\"wouldn't\" : \"would not\",\n",
    "\"you'd\" : \"you would\",\n",
    "\"you'll\" : \"you will\",\n",
    "\"you're\" : \"you are\",\n",
    "\"you've\" : \"you have\",\n",
    "\"'re\": \" are\",\n",
    "\"wasn't\": \"was not\",\n",
    "\"we'll\":\" will\",\n",
    "\"didn't\": \"did not\",\n",
    "\"tryin'\":\"trying\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = TweetTokenizer()\n",
    "\n",
    "lematizer = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "#### Uncomment this when running for first time\n",
    "#### it generates cleaned train set\n",
    "\n",
    "#train_x = train['comment_text'].apply(lambda x: Utils.text_clean(x,tokenizer,lematizer,APPO))\n",
    "#train_x.to_csv('Data/train_x.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = pd.read_csv('Data/train_x.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>comment_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>explanationwhy edit make username hardcore met...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>d'aww ! match background colour I am seemingly...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>hey man , I am really try edit war . it is guy...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                       comment_text\n",
       "0           0  explanationwhy edit make username hardcore met...\n",
       "1           1  d'aww ! match background colour I am seemingly...\n",
       "2           2  hey man , I am really try edit war . it is guy..."
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#### Uncomment this when running for first time\n",
    "#### it generates cleaned test set\n",
    "#test_x = test['comment_text'].apply(lambda x: Utils.text_clean(x,tokenizer,lematizer,APPO))\n",
    "#test_x.to_csv('Data/test_x.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_x = pd.read_csv('Data/test_x.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "      <th>comment_text</th>\n",
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       "      <td>yo bitch ja rule succesful you will ever whats...</td>\n",
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       "      <td>1</td>\n",
       "      <td>= = rfc = = title fine , imo .</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>\" = = source = = * zawe ashton lapland — / \"</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   Unnamed: 0                                       comment_text\n",
       "0           0  yo bitch ja rule succesful you will ever whats...\n",
       "1           1                     = = rfc = = title fine , imo .\n",
       "2           2       \" = = source = = * zawe ashton lapland — / \""
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                 id\n",
       "0  00001cee341fdb12\n",
       "1  0000247867823ef7\n",
       "2  00013b17ad220c46"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Submission = pd.DataFrame()\n",
    "Submission['id'] = test['id']\n",
    "\n",
    "Submission.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Classifier to predict toxic labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pipe = Pipeline(steps = [('lemmatize',Lemmatizer()),('tfidf',TfidfVectorizer(max_features=2500)),('logreg',LogisticRegression(solver='liblinear'))])\n",
    "        \n",
    "#cross_val_score(pipe,x,y,cv=3,scoring='roc_auc').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "y=train['toxic']\n",
    "\n",
    "model = Utils.build_pipeline(train_x, y, cv=5, scoring='roc_auc')\n",
    "\n",
    "Submission = Utils.make_prediction(model,test_x,Submission,'toxic')\n",
    "\n",
    "with open('Models/toxic.pkl','wb') as model_pkl:\n",
    "    pickle.dump(model,model_pkl,protocol=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Classifier to predict severe_toxic labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time \n",
    "y=train['severe_toxic']\n",
    "\n",
    "model = Utils.build_pipeline(train_x, y, cv=5, scoring='roc_auc')\n",
    "\n",
    "Submission = Utils.make_prediction(model,test_x,Submission,'severe_toxic')\n",
    "\n",
    "with open('Models/severe_toxic.pkl','wb') as model_pkl:\n",
    "    pickle.dump(model,model_pkl,protocol=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Classifier to predict obscene labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time \n",
    "y=train['obscene']\n",
    "\n",
    "model = Utils.build_pipeline(train_x, y, cv=5, scoring='roc_auc')\n",
    "\n",
    "Submission = Utils.make_prediction(model,test_x,Submission,'obscene')\n",
    "\n",
    "with open('Models/obscene.pkl','wb') as model_pkl:\n",
    "    pickle.dump(model,model_pkl,protocol=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Classifier to predict threat labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time \n",
    "y=train['threat']\n",
    "\n",
    "model = Utils.build_pipeline(train_x, y, cv=5, scoring='roc_auc')\n",
    "\n",
    "Submission = Utils.make_prediction(model,test_x,Submission,'threat')\n",
    "\n",
    "with open('Models/threat.pkl','wb') as model_pkl:\n",
    "    pickle.dump(model,model_pkl,protocol=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Classifier to predict insult labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time \n",
    "y=train['insult']\n",
    "\n",
    "model = Utils.build_pipeline(train_x, y, cv=5, scoring='roc_auc')\n",
    "\n",
    "Submission = Utils.make_prediction(model,test_x,Submission,'insult')\n",
    "\n",
    "with open('Models/insult.pkl','wb') as model_pkl:\n",
    "    pickle.dump(model,model_pkl,protocol=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Classifier to predict identity_hate labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time \n",
    "y=train['identity_hate']\n",
    "\n",
    "model = Utils.build_pipeline(train_x, y, cv=5, scoring='roc_auc')\n",
    "\n",
    "Submission = Utils.make_prediction(model,test_x,Submission,'identity_hate')\n",
    "\n",
    "with open('Models/identity_hate.pkl','wb') as model_pkl:\n",
    "    pickle.dump(model,model_pkl,protocol=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submit Test Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file='Submission/submission.csv'\n",
    "\n",
    "Submission.to_csv(file,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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