toxic_comment_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.layers import RNN, GRU, LSTM, Dense, Input, Embedding, Dropout, Activation, concatenate\n",
    "from keras.layers import Bidirectional, GlobalAveragePooling1D, GlobalMaxPooling1D\n",
    "from keras.models import Model\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras import initializers, regularizers, constraints, optimizers, layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('train.csv')\n",
    "test_data = pd.read_csv('test.csv')\n",
    "embedding_file = 'glove.6B.300d.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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>count</th>\n",
       "      <td>159571.000000</td>\n",
       "      <td>159571.000000</td>\n",
       "      <td>159571.000000</td>\n",
       "      <td>159571.000000</td>\n",
       "      <td>159571.000000</td>\n",
       "      <td>159571.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.095844</td>\n",
       "      <td>0.009996</td>\n",
       "      <td>0.052948</td>\n",
       "      <td>0.002996</td>\n",
       "      <td>0.049364</td>\n",
       "      <td>0.008805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.294379</td>\n",
       "      <td>0.099477</td>\n",
       "      <td>0.223931</td>\n",
       "      <td>0.054650</td>\n",
       "      <td>0.216627</td>\n",
       "      <td>0.093420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               toxic   severe_toxic        obscene         threat  \\\n",
       "count  159571.000000  159571.000000  159571.000000  159571.000000   \n",
       "mean        0.095844       0.009996       0.052948       0.002996   \n",
       "std         0.294379       0.099477       0.223931       0.054650   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "              insult  identity_hate  \n",
       "count  159571.000000  159571.000000  \n",
       "mean        0.049364       0.008805  \n",
       "std         0.216627       0.093420  \n",
       "min         0.000000       0.000000  \n",
       "25%         0.000000       0.000000  \n",
       "50%         0.000000       0.000000  \n",
       "75%         0.000000       0.000000  \n",
       "max         1.000000       1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>153164</td>\n",
       "      <td>153164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>153164</td>\n",
       "      <td>153164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>ed9fbe02c99358ff</td>\n",
       "      <td>== Seattle Pilots Template == \\n\\n Yes, thanks...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "                      id                                       comment_text\n",
       "count             153164                                             153164\n",
       "unique            153164                                             153164\n",
       "top     ed9fbe02c99358ff  == Seattle Pilots Template == \\n\\n Yes, thanks...\n",
       "freq                   1                                                  1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id               False\n",
       "comment_text     False\n",
       "toxic            False\n",
       "severe_toxic     False\n",
       "obscene          False\n",
       "threat           False\n",
       "insult           False\n",
       "identity_hate    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id              False\n",
       "comment_text    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Furhter actions on any columns is not required, because no columns has any missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = [\"toxic\", \"severe_toxic\", \"obscene\", \"threat\", \"insult\", \"identity_hate\"]\n",
    "y = train_data[classes].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sentences = train_data[\"comment_text\"].fillna(\"fillna\").str.lower()\n",
    "test_sentences = test_data[\"comment_text\"].fillna(\"fillna\").str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_features = 100000\n",
    "max_len = 150\n",
    "embed_size = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer(max_features)\n",
    "tokenizer.fit_on_texts(list(train_sentences))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized_train_sentences = tokenizer.texts_to_sequences(train_sentences)\n",
    "tokenized_test_sentences = tokenizer.texts_to_sequences(test_sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"d'aww! he matches this background colour i'm seemingly stuck with. thanks.  (talk) 21:51, january 11, 2016 (utc)\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sentences[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[96145,\n",
       " 52,\n",
       " 2635,\n",
       " 13,\n",
       " 555,\n",
       " 3809,\n",
       " 73,\n",
       " 4556,\n",
       " 2706,\n",
       " 21,\n",
       " 94,\n",
       " 38,\n",
       " 803,\n",
       " 2679,\n",
       " 992,\n",
       " 589,\n",
       " 8377,\n",
       " 182]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_train_sentences[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\"\\n\\ncongratulations from me as well, use the tools well. \\xa0· talk \"'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sentences[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2671, 31, 35, 17, 100, 81, 1, 2228, 100, 13229, 38]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_train_sentences[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_padding = pad_sequences(tokenized_train_sentences, max_len)\n",
    "test_padding = pad_sequences(tokenized_test_sentences, max_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_coefs(word, *arr): return word, np.asarray(arr, dtype='float32')\n",
    "embeddings_index = dict(get_coefs(*o.rstrip().rsplit(' ')) for o in open(embedding_file))\n",
    "\n",
    "word_index = tokenizer.word_index\n",
    "nb_words = min(max_features, len(word_index))\n",
    "embedding_matrix = np.zeros((nb_words, embed_size))\n",
    "for word, i in word_index.items():\n",
    "    if i >= max_features:\n",
    "        continue\n",
    "    embedding_vector = embeddings_index.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        embedding_matrix[i] = embedding_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_input = Input(shape=(max_len, ))\n",
    "X = Embedding(max_features, embed_size, weights=[embedding_matrix])(image_input)\n",
    "X = Bidirectional(GRU(64, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))(X)\n",
    "# Dropout and R-Dropout sequence, inspired by Deep Learning with Python - Francois Chollet\n",
    "avg_pl = GlobalAveragePooling1D()(X)\n",
    "max_pl = GlobalMaxPooling1D()(X)\n",
    "conc = concatenate([avg_pl, max_pl])\n",
    "X = Dense(6, activation=\"sigmoid\")(conc)\n",
    "model = Model(inputs=image_input, outputs=X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "saved_model = \"weights_base.best.hdf5\"\n",
    "checkpoint = ModelCheckpoint(saved_model, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
    "early = EarlyStopping(monitor=\"val_acc\", mode=\"max\", patience=5)\n",
    "callbacks_list = [checkpoint, early]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 143613 samples, validate on 15958 samples\n",
      "Epoch 1/2\n",
      "143613/143613 [==============================] - 2111s 15ms/step - loss: 0.0519 - acc: 0.9813 - val_loss: 0.0444 - val_acc: 0.9831\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.98307, saving model to weights_base.best.hdf5\n",
      "Epoch 2/2\n",
      "143613/143613 [==============================] - 2154s 15ms/step - loss: 0.0364 - acc: 0.9856 - val_loss: 0.0445 - val_acc: 0.9834\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.98307 to 0.98341, saving model to weights_base.best.hdf5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f3a2d5eb630>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_sz = 32\n",
    "epoch = 2\n",
    "model.fit(train_padding, y, batch_size=batch_sz, epochs=epoch, validation_split=0.1, callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "153164/153164 [==============================] - 211s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "test_values = model.predict([test_padding], batch_size=1024, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_submission = pd.read_csv('sample_submission.csv')\n",
    "sample_submission[classes] = test_values\n",
    "sample_submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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