Query Wellformedness.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "from tqdm import tqdm\n",
    "from collections import Counter\n",
    "import ast\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import matplotlib\n",
    "from textblob import TextBlob\n",
    "import re\n",
    "from sklearn.model_selection import train_test_split\n",
    "matplotlib.rcParams['figure.figsize'] = (10.0, 6.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = ['query','wellformedness']\n",
    "df = pd.read_csv('well_formedness_train.tsv', delimiter='\\t', header = None, names = column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>query</th>\n",
       "      <th>wellformedness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The European Union includes how many ?</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>What are Mia Hamms accomplishment ?</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Which form of government is still in place in ...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>When was the canal de panama built ?</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What color is the black box on commercial aero...</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               query  wellformedness\n",
       "0             The European Union includes how many ?             0.2\n",
       "1                What are Mia Hamms accomplishment ?             0.4\n",
       "2  Which form of government is still in place in ...             1.0\n",
       "3               When was the canal de panama built ?             0.8\n",
       "4  What color is the black box on commercial aero...             0.6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17500 entries, 0 to 17499\n",
      "Data columns (total 2 columns):\n",
      "query             17500 non-null object\n",
      "wellformedness    17500 non-null float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 273.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.000000    4189\n",
       "0.000000    3773\n",
       "0.200000    2779\n",
       "0.800000    2578\n",
       "0.400000    2081\n",
       "0.600000    2002\n",
       "0.833333      32\n",
       "0.666667      22\n",
       "0.166667      19\n",
       "0.500000      13\n",
       "0.333333      12\n",
       "Name: wellformedness, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.wellformedness.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    17500.000000\n",
       "mean         0.508331\n",
       "std          0.379398\n",
       "min          0.000000\n",
       "25%          0.200000\n",
       "50%          0.600000\n",
       "75%          0.800000\n",
       "max          1.000000\n",
       "Name: wellformedness, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.wellformedness.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the 7289\n",
      "what 6989\n",
      "is 5562\n",
      "of 4460\n",
      "how 4242\n",
      "in 3632\n",
      "are 2133\n",
      "do 1915\n",
      "you 1686\n",
      "many 1479\n",
      "to 1296\n",
      "where 1247\n",
      "does 1200\n",
      "and 1082\n",
      "for 1020\n",
      "did 959\n",
      "on 947\n",
      "much 892\n",
      "was 774\n",
      "can 760\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_top_n_words(corpus, n=None):\n",
    "    vec = CountVectorizer().fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]\n",
    "common_words = get_top_n_words(df['query'], 20)\n",
    "for word, freq in common_words:\n",
    "    print(word, freq)\n",
    "pd.DataFrame(common_words, columns = ['query' , 'count']).plot(x = 'query', kind='bar', title =\"Top 20 words in query before removing stop words\", legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "does 1200\n",
      "did 959\n",
      "make 272\n",
      "year 229\n",
      "located 221\n",
      "people 195\n",
      "world 189\n",
      "live 180\n",
      "worth 172\n",
      "use 170\n",
      "cost 168\n",
      "value 166\n",
      "population 164\n",
      "money 156\n",
      "invented 148\n",
      "calories 147\n",
      "change 147\n",
      "country 146\n",
      "kind 145\n",
      "used 138\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_top_n_words(corpus, n=None):\n",
    "    vec = CountVectorizer(stop_words = 'english').fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]\n",
    "common_words = get_top_n_words(df['query'], 20)\n",
    "for word, freq in common_words:\n",
    "    print(word, freq)\n",
    "pd.DataFrame(common_words, columns = ['query' , 'count']).plot(x = 'query', kind='bar', title =\"Top 20 words in query after removing stop words\", legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['query_len'] = df['query'].astype(str).apply(len)\n",
    "df['word_count'] = df['query'].apply(lambda x: len(str(x).split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>query</th>\n",
       "      <th>wellformedness</th>\n",
       "      <th>query_len</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The European Union includes how many ?</td>\n",
       "      <td>0.2</td>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>What are Mia Hamms accomplishment ?</td>\n",
       "      <td>0.4</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Which form of government is still in place in ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>When was the canal de panama built ?</td>\n",
       "      <td>0.8</td>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What color is the black box on commercial aero...</td>\n",
       "      <td>0.6</td>\n",
       "      <td>53</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               query  wellformedness  \\\n",
       "0             The European Union includes how many ?             0.2   \n",
       "1                What are Mia Hamms accomplishment ?             0.4   \n",
       "2  Which form of government is still in place in ...             1.0   \n",
       "3               When was the canal de panama built ?             0.8   \n",
       "4  What color is the black box on commercial aero...             0.6   \n",
       "\n",
       "   query_len  word_count  \n",
       "0         38           7  \n",
       "1         35           6  \n",
       "2         54          11  \n",
       "3         36           8  \n",
       "4         53          10  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['query_len'])\n",
    "plt.title('Query lengths distribution');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['word_count'])\n",
    "plt.title('Word count distribution');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blob = TextBlob(str(df['query']))\n",
    "pos_df = pd.DataFrame(blob.tags, columns = ['word' , 'pos'] )\n",
    "pos_df.pos.value_counts()[:20].plot(x = 'pos', kind='bar')\n",
    "plt.title('Top 20 Part-of-speech tagging for query corpus')\n",
    "plt.xlabel('POS');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['wellformedness'])\n",
    "plt.title('Wellformedness distribution');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>query</th>\n",
       "      <th>wellformedness</th>\n",
       "      <th>query_len</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The European Union includes how many ?</td>\n",
       "      <td>0.2</td>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>What are Mia Hamms accomplishment ?</td>\n",
       "      <td>0.4</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Which form of government is still in place in ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>When was the canal de panama built ?</td>\n",
       "      <td>0.8</td>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What color is the black box on commercial aero...</td>\n",
       "      <td>0.6</td>\n",
       "      <td>53</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               query  wellformedness  \\\n",
       "0             The European Union includes how many ?             0.2   \n",
       "1                What are Mia Hamms accomplishment ?             0.4   \n",
       "2  Which form of government is still in place in ...             1.0   \n",
       "3               When was the canal de panama built ?             0.8   \n",
       "4  What color is the black box on commercial aero...             0.6   \n",
       "\n",
       "   query_len  word_count  \n",
       "0         38           7  \n",
       "1         35           6  \n",
       "2         54          11  \n",
       "3         36           8  \n",
       "4         53          10  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['label'] = np.where(df['wellformedness']>=0.8, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>query</th>\n",
       "      <th>wellformedness</th>\n",
       "      <th>query_len</th>\n",
       "      <th>word_count</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The European Union includes how many ?</td>\n",
       "      <td>0.2</td>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>What are Mia Hamms accomplishment ?</td>\n",
       "      <td>0.4</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Which form of government is still in place in ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>When was the canal de panama built ?</td>\n",
       "      <td>0.8</td>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What color is the black box on commercial aero...</td>\n",
       "      <td>0.6</td>\n",
       "      <td>53</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               query  wellformedness  \\\n",
       "0             The European Union includes how many ?             0.2   \n",
       "1                What are Mia Hamms accomplishment ?             0.4   \n",
       "2  Which form of government is still in place in ...             1.0   \n",
       "3               When was the canal de panama built ?             0.8   \n",
       "4  What color is the black box on commercial aero...             0.6   \n",
       "\n",
       "   query_len  word_count  label  \n",
       "0         38           7      0  \n",
       "1         35           6      0  \n",
       "2         54          11      1  \n",
       "3         36           8      1  \n",
       "4         53          10      0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10701\n",
       "1     6799\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_0 = df.loc[df['label'] == 0]\n",
    "target_1 = df.loc[df['label'] == 1]\n",
    "\n",
    "sns.distplot(target_0[['query_len']], hist=False, rug=True, label = 'Not wellformed')\n",
    "sns.distplot(target_1[['query_len']], hist=False, rug=True, label = 'Wellformed')\n",
    "plt.legend()\n",
    "plt.title('Distribution of query lengths');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(target_0[['word_count']], hist=False, rug=True, label = 'Not wellformed')\n",
    "sns.distplot(target_1[['word_count']], hist=False, rug=True, label = 'Wellformed')\n",
    "plt.legend()\n",
    "plt.title('Distribution of word count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems in general, wellformed queries are longer than thoes not wellformed queries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['query', 'label']]\n",
    "df['query'] = df['query'].apply(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = ['query','wellformedness']\n",
    "valid = pd.read_csv('wellformedness_dev.tsv', delimiter='\\t', header = None, names = column_names)\n",
    "valid['label'] = np.where(valid['wellformedness']>=0.8, 1, 0)\n",
    "valid = valid[['query', 'label']]\n",
    "valid['query'] = valid['query'].apply(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = pd.concat((df, valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "import xgboost as xgb\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = all_data[:df.shape[0]]\n",
    "X_test = all_data[df.shape[0]:]\n",
    "y_train = df.label\n",
    "y_test = valid.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl_enc = preprocessing.LabelEncoder()\n",
    "y_train = lbl_enc.fit_transform(df.label.values)\n",
    "y_test = lbl_enc.fit_transform(valid.label.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_names = lbl_enc.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "countvec = CountVectorizer(analyzer='word',token_pattern=r'\\w{1,}',\n",
    "            ngram_range=(1, 3), stop_words = 'english', binary=True)\n",
    "countvec.fit(list(X_train) + list(X_test))\n",
    "X_train_countvec =  countvec.transform(X_train) \n",
    "X_test_countvec = countvec.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---Test Set Results---\n",
      "Accuracy with Xgboost: 0.6168\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.62      0.99      0.76      2293\n",
      "          1       0.63      0.03      0.06      1457\n",
      "\n",
      "avg / total       0.62      0.62      0.49      3750\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = xgb.XGBClassifier(n_jobs=-1)\n",
    "clf.fit(X_train_countvec.tocsc(), y_train)\n",
    "y_pred = clf.predict(X_test_countvec.tocsc())\n",
    "print(\"---Test Set Results---\")\n",
    "print(\"Accuracy with Xgboost: {}\".format(accuracy_score(y_test, y_pred)))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}