gakuba/train-test-sprit.ipynb

## train-test-sprit.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ast import literal_eval\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(filename):\n",
    "    data = pd.read_csv(filename, encoding = \"ISO-8859-1\")\n",
    "    #data['tags'] = data['tags'].apply(literal_eval)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = read_data('data/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>comment_text</th>\n",
       "      <th>toxic</th>\n",
       "      <th>severe_toxic</th>\n",
       "      <th>obscene</th>\n",
       "      <th>threat</th>\n",
       "      <th>insult</th>\n",
       "      <th>identity_hate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000997932d777bf</td>\n",
       "      <td>Explanation\\nWhy the edits made under my usern...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(143613, 8)\n",
      "(15958, 8)\n"
     ]
    }
   ],
   "source": [
    "categories = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']\n",
    "train, test = train_test_split(train, random_state=42, test_size=0.1, shuffle=True)\n",
    "#X_train = train.comment_text\n",
    "#X_test = test.comment_text\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_labels_per_category(df):\n",
    "    \n",
    "    df_toxic = df.drop(['id', 'comment_text'], axis=1)\n",
    "    counts = []\n",
    "    categories = list(df_toxic.columns.values)\n",
    "    for i in categories:\n",
    "        counts.append((i, df_toxic[i].sum()))\n",
    "    df_stats = pd.DataFrame(counts, columns=['category', 'number_of_comments'])\n",
    "    return df_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>number_of_comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>toxic</td>\n",
       "      <td>13814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>severe_toxic</td>\n",
       "      <td>1447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>obscene</td>\n",
       "      <td>7613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>threat</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>insult</td>\n",
       "      <td>7086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>identity_hate</td>\n",
       "      <td>1258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        category  number_of_comments\n",
       "0          toxic               13814\n",
       "1   severe_toxic                1447\n",
       "2        obscene                7613\n",
       "3         threat                 441\n",
       "4         insult                7086\n",
       "5  identity_hate                1258"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stats_train = count_labels_per_category(train)\n",
    "df_stats_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>number_of_comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>toxic</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>severe_toxic</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>obscene</td>\n",
       "      <td>836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>threat</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>insult</td>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>identity_hate</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        category  number_of_comments\n",
       "0          toxic                1480\n",
       "1   severe_toxic                 148\n",
       "2        obscene                 836\n",
       "3         threat                  37\n",
       "4         insult                 791\n",
       "5  identity_hate                 147"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stats_test = count_labels_per_category(test)\n",
    "df_stats_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_count_labels_per_category(df_stats):\n",
    "    df_stats.plot(x='category', y='number_of_comments', kind='bar', legend=False, grid=True, figsize=(8, 5))\n",
    "    plt.title(\"Number of comments per category\")\n",
    "    plt.ylabel('# of Occurrences', fontsize=12)\n",
    "    plt.xlabel('category', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OA07rovwq4Kouyh8hG81gZmZmNfIMiWZmZlbg5MDMzMwKnByYmZlZgZMDMzMzK3ByYGZm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\n//NGwGq2slFjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29c9fa4860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count_labels_per_category(df_stats_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zM7OKchJgZmZWUU4CzMzMKqoUSYCkQZLulnRF2l5L0m2Spku6QNISqXzJtD09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\nOQkwMzOrqP8PnNOhQgfQnbUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29c9e90128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count_labels_per_category(df_stats_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_pickle('./data/train.pkl')\n",
    "test.to_pickle('./data/test.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}