Quovo-Submiss.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test.csv\n",
      "train.csv\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \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",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "from subprocess import check_output\n",
    "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk\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 matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import word_tokenize, ngrams\n",
    "from sklearn import ensemble\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import log_loss\n",
    "import xgboost as xgb\n",
    "\n",
    "eng_stopwords = set(stopwords.words('english'))\n",
    "color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2142, 6)\n",
      "(516, 4)\n"
     ]
    }
   ],
   "source": [
    "train_df = pd.read_csv('../input/train.csv')\n",
    "test_df = pd.read_csv('../input/test.csv')\n",
    "print(train_df.shape)\n",
    "print(test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>description_x</th>\n",
       "      <th>description_y</th>\n",
       "      <th>ticker_x</th>\n",
       "      <th>ticker_y</th>\n",
       "      <th>same_security</th>\n",
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       "  </thead>\n",
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       "      <th>2</th>\n",
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       "      <td>vanguard small cap index adm</td>\n",
       "      <td>vanguard small-cap index fund inst</td>\n",
       "      <td>VSMAX</td>\n",
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       "      <td>False</td>\n",
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       "      <td>duke energy corp new com new isin #us4 sedol #...</td>\n",
       "      <td>duke energy corp new com new isin #us26441c204...</td>\n",
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       "      <td>DUK</td>\n",
       "      <td>True</td>\n",
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       "      <th>4</th>\n",
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       "      <td>visa inc class a</td>\n",
       "      <td>visa inc.</td>\n",
       "      <td>V</td>\n",
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      "text/plain": [
       "   Unnamed: 0                                      description_x  \\\n",
       "0           0                     first trust dow jones internet   \n",
       "1           1                schwab intl large company index etf   \n",
       "2           2                       vanguard small cap index adm   \n",
       "3           3  duke energy corp new com new isin #us4 sedol #...   \n",
       "4           4                                   visa inc class a   \n",
       "\n",
       "                                       description_y ticker_x ticker_y  \\\n",
       "0                        first trust dj internet idx      FDN      FDN   \n",
       "1  schwab strategic tr fundamental intl large co ...     FNDF     FNDF   \n",
       "2                 vanguard small-cap index fund inst    VSMAX    VSCIX   \n",
       "3  duke energy corp new com new isin #us26441c204...      DUK      DUK   \n",
       "4                                          visa inc.        V        V   \n",
       "\n",
       "  same_security  \n",
       "0          True  \n",
       "1          True  \n",
       "2         False  \n",
       "3          True  \n",
       "4          True  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df.rename(columns ={'description_x':'question1','description_y':'question2','same_security':'is_similar'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th>Unnamed: 0</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
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       "      <th>ticker_y</th>\n",
       "      <th>is_similar</th>\n",
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       "      <td>schwab intl large company index etf</td>\n",
       "      <td>schwab strategic tr fundamental intl large co ...</td>\n",
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       "      <td>vanguard small cap index adm</td>\n",
       "      <td>vanguard small-cap index fund inst</td>\n",
       "      <td>VSMAX</td>\n",
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      "text/plain": [
       "   Unnamed: 0                                          question1  \\\n",
       "0           0                     first trust dow jones internet   \n",
       "1           1                schwab intl large company index etf   \n",
       "2           2                       vanguard small cap index adm   \n",
       "3           3  duke energy corp new com new isin #us4 sedol #...   \n",
       "4           4                                   visa inc class a   \n",
       "\n",
       "                                           question2 ticker_x ticker_y  \\\n",
       "0                        first trust dj internet idx      FDN      FDN   \n",
       "1  schwab strategic tr fundamental intl large co ...     FNDF     FNDF   \n",
       "2                 vanguard small-cap index fund inst    VSMAX    VSCIX   \n",
       "3  duke energy corp new com new isin #us26441c204...      DUK      DUK   \n",
       "4                                          visa inc.        V        V   \n",
       "\n",
       "  is_similar  \n",
       "0       True  \n",
       "1       True  \n",
       "2      False  \n",
       "3       True  \n",
       "4       True  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_id</th>\n",
       "      <th>description_x</th>\n",
       "      <th>description_y</th>\n",
       "      <th>same_security</th>\n",
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       "      <td>vanguard mid cap index</td>\n",
       "      <td>vanguard midcap index - a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>spdr gold trust gold shares</td>\n",
       "      <td>spdr gold trust spdr gold shares</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>vanguard total bond index adm</td>\n",
       "      <td>vanguard total bond market index</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "   test_id                       description_x  \\\n",
       "0        0                        semtech corp   \n",
       "1        1              vanguard mid cap index   \n",
       "2        2         spdr gold trust gold shares   \n",
       "3        3       vanguard total bond index adm   \n",
       "4        4  oakmark international fund class i   \n",
       "\n",
       "                      description_y  same_security  \n",
       "0               semtech corporation            NaN  \n",
       "1         vanguard midcap index - a            NaN  \n",
       "2  spdr gold trust spdr gold shares            NaN  \n",
       "3  vanguard total bond market index            NaN  \n",
       "4        oakmark international cl i            NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_df.rename(columns={'description_x':'question1','description_y':'question2','same_security':'is_similar'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_id</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_similar</th>\n",
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       "      <td>vanguard mid cap index</td>\n",
       "      <td>vanguard midcap index - a</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>spdr gold trust gold shares</td>\n",
       "      <td>spdr gold trust spdr gold shares</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>vanguard total bond index adm</td>\n",
       "      <td>vanguard total bond market index</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>oakmark international fund class i</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   test_id                           question1  \\\n",
       "0        0                        semtech corp   \n",
       "1        1              vanguard mid cap index   \n",
       "2        2         spdr gold trust gold shares   \n",
       "3        3       vanguard total bond index adm   \n",
       "4        4  oakmark international fund class i   \n",
       "\n",
       "                          question2  is_similar  \n",
       "0               semtech corporation         NaN  \n",
       "1         vanguard midcap index - a         NaN  \n",
       "2  spdr gold trust spdr gold shares         NaN  \n",
       "3  vanguard total bond market index         NaN  \n",
       "4        oakmark international cl i         NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J4F+Bo8rtRwF3APcD+0TEzuVI/f2BJS2IR5KkWhqqRX9f+X5vC+q9Gtg9Iu4BbqSYZvcC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\n2WAFb5sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8947c61c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  Target Exploration\n",
    "is_sim = train_df['is_similar'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "sns.barplot(is_sim.index, is_sim.values, alpha=0.8, color=color[1])\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Is Similar', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     0.753035\n",
       "False    0.246965\n",
       "Name: is_similar, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_sim/is_sim.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_ques_df = pd.DataFrame(pd.concat([train_df['question1'], train_df['question2']]))\n",
    "all_ques_df.columns =[\"questions\"]\n",
    "\n",
    "all_ques_df[\"num_of_words\"] = all_ques_df[\"questions\"].apply(lambda x: len(str(x).split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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bgDcDMwEi4myK/9y6xYTMPHjAtt8BP4uIrph6U3oMGDjFpo/i53azkY8zpLUz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\nh5wScMITAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8947bfbc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_str = all_ques_df[\"num_of_words\"].value_counts()\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(count_str.index, count_str.values, alpha=0.8, color=color[0])\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Number of words in the question', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UP14gKUmSJA2IybYkSZI0ICbbkiRJ0oCYbEuSJEkD4gWSk1gvV5t7RbokSYuW\nkwmoCXu2JUmSpAEx2ZYkSZIGxGRbkiRJGhDHbEuShoLXqAyG7SoNlj3bkiRJ0oDYsy1J0iRhL/N8\ntoWmCpNtSZJG0c8p3kweJ1Yvr6WvkQbBYSSSJEnSgJhsS5IkSQPiMBJJ0oRzRT5JU5U925IkSdKA\n2LMtSdJC8KI6Sd3Ysy1JkiQNiD3bkiRJk5jfngw3e7YlSZKkATHZliRJkgbEZFuSJEkaEJNtSZIk\naUBMtiVJkqQBcTYSSZKmKFfmlCaeybYkSRpaTounyc5hJJIkSdKA2LM9ATwLlyRJmh7s2ZYkSZIG\nxGRbkiRJGhCHkUiSpEXK4ZSaTky2+8xpliRJ05VJtPRYJtuSJElTwESc7NjJODaTbUmSpAlisjr1\neYGkJEmSNCD2bEuSJGla6uWbhYUdnjNUyXZEfBVYDxgBPpiZV09wSJIkSdKohibZjojXAKtl5voR\nsQbwXWD9ftTteClJkqT5+nmx5WSta1EZmmQb2AT4EUBm/iEilo+IJ2XmvaM9YLq/uJIkaWJM5rxh\nsnYy9jOuydT+w3SB5ErA7Nrfs6v7JEmSpElpxsjIyETH0JOIOBI4JzPPqP6+DNg5M/80sZFJkiRJ\nnQ1Tz/bNLNiT/XTglgmKRZIkSRrTMCXbFwBbAUTE/wNuzsw5ExuSJEmSNLqhGUYCEBFfBF4NzAP2\nzMxrJzgkSZIkaVRDlWxLkiRJw2SYhpFIkiRJQ8VkW5IkSRoQk21JkiRpQKZNsh0RG/dQZvGIeHZE\ndF1ZMyKe2se4ZnTbFhEzI+Jp/dqfJEmSFp0peYFkRLyn7a4ZwKeAAwEy85iq3Ncy84PV7dcCRwG3\nAk8Dds/M8yPijcBbM3P3KmH/HjAHWBrYKzPPqR5/L3A0cGBm3j5KXK8HvkZZ/fLDwOGU+cLnALtl\n5iVVudWBQ4BnA6sCfwCeAvwS+FBm/mPhWkiSJEmLQtce3CG2H3AncA4l0QZ4IiVxrXtx22M2ysy/\nRMRKwOnA+cBngc2rMp+plVmhqv+catsvgZOB4yPir8D3gSsyc27bPjamJM4XA5tk5nUR8WzgOGCD\nqty3gV2r/QTwgczcMyLeAPwA2BAgIh4P7Ay8Fli5euzNwHnA0Zn5yFgNFRFfzMyPVb3n+1axHZ+Z\nF9XKHJaZe1W3VwDeC/w9M4+LiI8DrwISOCgz7xhlPxdl5kZt970xM39c3X4KcACwFvBb4IDMvCMi\nngxskJlMI0vDAAAc4UlEQVRnR8RywCeAF1b7+2Jmzq4e/23gO5l5zRjP98nAbsAdlNdoT2Bt4Hrg\nG5l5b0Q8DtgG2JRy4jUDmAWcVYu3b21f3R6z/cfb9lUdC7R/L21fa6+u7d/Ptq/KDbz9m7Z9dXto\nj/3J1PZVHR77k6D9B33sV48divbvpe2ruoam/Uf57PG4nt+ufTsuxjJVk+21gE8DL6H0BN8UEW/I\nzAPaytW79e/KzL8AZOatEfFwdf/jKT3PAP8EbmyVZ34iDzCSmZcCr42IdYBdgSMjYg5we2ZuBjyU\nmbcAt0TEPzPzump/N0VE/UB6QisWyov+4qrceRFRfw7HAn+m9ILfXsWzCrAlpQf+PQARsVSXtlq/\n+n0c8CPgGuAzEfEfmXlgte2Fbfu8EtggIrakvEEPAF5RbXtjRMyjvEkeqrXRyhFxY9VOz63u+wjQ\nOlgPA64Fvkk5mfge8GbgFODEqsw3gd8B+1PeEEcDb6o9j8dXb55vtL4l6OBY4OfAC4DLqp/jgZcD\n36UsnPQt4K/V/jatnsNVwE4RsUlmfpj+tj301v5jtn21z17av5e2h97av59tD31q/z63fSv+YT32\nF2nbg8d+m8na/n079qt9DnP799L2rbomXfs3+OzxuG4Wf6/HRVdTMtnOzH8Dn4yIAA6PiCvoPD59\nrYg4idJ4q0XE1pl5ckTsS0msAQ4Gfh0R/0tJsH9U1bcx8J1aXY8m3tXZ4DUAEbEy88/S7o6IzwMr\nADdUZ4/nA+sBt9Xq+m1EnAD8gvLiXlTVdRTljdGycma+s+05/Rm4NCLqb45/Au1DT0aqmFes/l4i\nM79Z7edU4NiI2C8zP8uCJxVPzMzPRhlr/sfM3KK6/+qIaL2x3gh8DDgsM0+t6vx5Ztb/wbZbMTO/\nVN3+Q0RsU91+Uma22nnlzNy2un1NRLy79vi7MnOXKENwPhgRX6O037WUk52Tq3LLZOZBVUx/yMyP\nVvdfEBE/rW6vnpm71Z7XhdUb9YKIuKoWS7/aHnpr/17aHpq3/2htD721fz/bHvrX/v1sexjuY39R\ntz147E/ksQ+L/nMfhrv9e2n7ViyTsf17bXuP6/n6eVx0NSWT7ZbMTGDziNie+T3SdVu3/X199fsW\nYNuqjh9ExI8pX208h/JC3QbslJk31x577Cgx3FLVB+XMbEfgusw8MSK2A14H3EAZrtKyO/BWYDXg\n0Mw8r7r/a8D/1crNi4i3U77OeBggIp5AORN8sFbuw8DTMvNT7fFFROvrlYerM+bTMnNe1Wbfi4gj\ngWVrD3l8RDw7S2/8B2r1vJjyLQBZxrpfDHyievN+iAW/RWh5akS0eucejIgXZxlWsyplTDyUk5Kv\nUobPXBQRWwOXUj5YbqnVNVLt+0/AnlG+knoN5Qx1dcoQn1b8zwdmAk+JiPUy88qIeAGwRFXmcVHG\n118NbAY8UD3HNzHfvKq9zuxD20Np/62AU7u0/5htX7VBL+3favsZwEOjtD301v79bHtYsP03p1n7\nb8X89l/Ytv8fejv2X8L4j/0Z9H7sbwNcwviO/dHafg1Gb/tux36nz51620Oz9h/rs2dQx/5obQ+9\ntf/CHvvd2r/bsd+v9l/Yz/1Hj/2qHZq0P0yu9u+l7WHRtP8gP3uG7bgedD7Tj3xgTFM62W7JzGPp\nkAznKF+NZObxbX/fBZw0xj6+20Mc/6JcFNn6+weUA7693AjlK5D2+69ru2t7SpL+lYhofUjNAS4E\ndqg97usRsX1ELF3FUHdB9XtnygWk5wIPZOY8YIcoJwSvrJX/KPBl4B2ZeT5ARLyN8jXUrrV9Pkj5\n+mZ14BuUg5mIWC4zW98a/JL5Jzy3UXr8oXybcFB1ewfKWLXPMv9k51bK15CPfuhQxlzV2+rhqh0u\njDIureWTwAmUi1RfA3yj+od9C+UkB+B9wJcoJzvXAe+v7l8X2Ku63Wr7L1dtP4P5bb9LLY6vR8S7\nI2KZzLyPBdXP/HeitP/ZwL/b2r81k85HGL3t96rVRWY+GBH7A89nwfZ/Rmb+ndL2WwGLVc+93vbf\nqFXVav8DKO3/OOa3/4eqMq0xljMyc6St7Z9Rq+sTlK/o7uKxbV+Pv1v7t76y69b+21dt0Gr7R4/7\niHhqlvGN/1vbX6vtz6E69qN8u/V6FvzKsVP7b025+HqnettTjv3Vqras9+K2tNofqmM/yixHX6n2\nAwu2/arVc5xH+SzboVbXY479iPhNZl5Yu7vV9ndQ2v6wiNiQMk5291q5Vts/n3Jiv3sV1yuo2pX5\nbX9w7XPnPmptX8XxmM+diNg4M3/KY9v/c9Q+eyLimKrd1qzKdGr7rSjv511qdXVq/5ksqNX2rY6T\nFaJc/P5+5rc9dP7suQM4i+orbTq3/bxWb1nNJ3js587LgL8zdvtvTElyWr2O9c/9pSj/x5en9lV7\nFcsC7R9llq1VKL2CrfZfoO0p7+/9qhhbbQ9t7V/VtVfVPtvXyrW3/+HAyyNisZw/5rb9c3/FKNct\n1Y99eGz7L0H5vDqK+cd/+1jlEUqn2cW54PVS7e1/eESsVf3d+r/V/rmzRxXXq9qeY3v7P+azp2qH\nXj5/Wp899WN/0J89nY7r9s+VXj5TOrXrWJ8rrbatf650Oq7b85n243qBdq0ZtV2Z//+0flx3el+O\nlQ+07l+XtmO/m2mRbE9VVdK0c6dtUb4C2bhWtmPPO6Vn/aDM/Bul1719Hz+IiHry+HPKGKd6mR9R\nhtcssM9q258o3y48vbrrtFaZzNyJDjJzq6qu06sPzG9VP6M+x8zcpn17TX2fl1L+cbW8rr2+6huR\nt3WIa//aPl8ObELpiTmHMjPNnPa4qmT488BXI+LcejlgI6oZcqr6Ngb+Vi9XtX/rn8HKwHoRMZvy\nIbJXp7aPiC2AQ4GlqnLvYv7Z/DFVuTOrMq34r6meY6vtW8fL5pSvJrs9zxMi4iZgqQ7PsbU/gKdS\nPvyfC3wceHunNqP8k3lZtc+HgHs6tP9LKP8EbwT2oZy0LkbpnTkTOLfqefg0sGNE7F2VWbz6IN+z\n9tK+hHJCeVat3GLAMm3lVgBeGhEXAo/WV5VbqXoemwH/DfytKvNMYEZEzAL2yMxzq7pOaStXr2uZ\nqszrq21/A95BGYe4OOXD/jrKawtwdERke13V82zt80nAk4F7qzpmUsZ9Lk+5iKjl+cAaVV2fr/ax\nePVaXAX8qWr/iym9YS2fBq6gDHtrzfbU+ge5ZURANStURBxISTJbNqaceG5dL0d5bxxRlVkdOKdW\nZ73MWsCv2vbZcjKwX+v+LDNRXVSrYwR4Vq2uZ9ceuy3wL+aPb6Uqdyvlgqlj2mJqf46PznxFOVk6\nrLq9LuW1XJVyDKxOGY8LJfk4rRbbJpS2PZAyHvV64COZuXP1fFuzaP2Z8u3ryVV8RPfZtlonsB/J\nzB3bytxCeZ+2ygC8KzPf0aGupamdzNT3WbXlmpT36A0RsXuVKN47SlwrAkvW9nlIVe5bbeV2ofQy\nnk/tG57251jbH8BWmfnyWrnnUy5yWxFodcbskZlvq5W5rBbXpZRjH+BFwMOZ+dyYP0PZv4E3UE7E\nWjOUvRH4jywXND46k1n1vvzPWl0PZeYDtTL3Uj4DWmWgvEf/WdVbnxVtGaphqtXn3Vsyc3fK586L\ngEeijNl+dOY0yv+8l2Tmm9rq2o1ygncO8P2I+Ha2zcJWxV6va1ngV5m5W1VuNeCPVZvWp0h+LnBb\nZr6tKncF82d1+wXluP49Zcz4ejnKjG7V82zN/HYB5f35qMz8QnWzVdeyVD3R1fb2Ds5zKRdGtmaR\nex2PdXVV1/vqcWXm/qPF2InJ9hCLiD26bF6lh3KtCxD6Udej5UYrE2W8WV/qahDXoPb5MUpC+E/K\nh/8FUS7CvYcFx4R9vFZu1y7lPtal3EgPZbrWRflHQK1cr3XV4x/teY47rh7KjbbPT1E+GJ9F+Qf3\nlixfRa9Y/X0uJUlplTmbMoXntbUy53Soa2HKnduhTKe4OtXVXu6ctvhHe46DjqtTXZ1me1qC+b1k\nLd1mhRppWG60Mr3scwkWnImqW130UK5e16d7KNMtrl73WX+evcyi1Uu583osAyVx68c+z++hrvP6\nWFcrrl722Wtdvc5QNla5s3ssA+XbrbH22UuZXuPvta6xyrXi72WfvczoRo/leq3rmj7W1ZXJ9nD7\nEOUrlls6bHt8w3LToa5+7vORLMOLAP4nIm4Hzo+IzVkwkaiXOzIibhtHuX7WNbIQdY32PPsZV6/7\nfDAz/wr8NSL+kfNn9rktIv49SplrO5Tpd7le4hpv/L3W1c+4OtU12mxP9etOupU7YBzl+rnPRVFX\nr8+x133Wy/Uyi1av5Xqtix7LNa1vUdXVtFy3Mu0zlM1qPYYFT/x6mcms19nOxlNXr3F1KjfeuHrd\nZ6f6epnRrddyE1FXVybbw+1twNeBD2YZq/WoKOOmmpSbDnX1c5+XRcTZwNaZ+UBmnlElIz9h/vjn\nfpebDnX1Wu62iPhwZn4lM19VvTbPoMyt+rcGZfpdbsrXlT3O9tTPctOhrgbleplFq9dy/axrIvY5\nEXW1z1B2enSeoayXmcx6ne1sPHX1GlencuONa2Ha4tHkPEef0a3XchNRV1cm20MsM38bpbev05n5\nvk3KTYe6+rnPzPxolMT70V7BLBcO/ZwyvpZ+l5sOdTUotyPz5+NteRpwE2XoS69l+l1uOtQF9DTb\nU9/LTYe6eig35ixaDcr1s66J2Ociryt7nKGsl3LToa4G5XqZ0a3XchNRV1dTcrl2SZIkaTLotNCL\nJEmSpD4w2ZYkSZIGxGRbUs8iYseIGImIHSc6lqYi4uMRcWdEPBQR7xj7EWPWd3EsOJOH+iwi9q+O\ntw0Xwb5mRcQfB72fqSIinlO9Nt+f6Fikyc5kW5oEaknsAxHx3C7lZkVZklcNRMSawBcoi1S8lzJ3\nqioRsVdEvHSi4+jgJMqFa7/rZ6VRVqDbsJ91TnUR8amIeE7trtspr81hnR8hqcXZSKTJ5YmUZXZ7\nmrtTPWstWPHNzDx6QiOZZCLiCZSVDN8H/GaCw1lAZv6eshpcv32esirexQOoe8qJiFUpq1heRjWX\ncmbeT1kJVdIY7NmWJpeLgDdFxNsnOpAp5onV7391LTU9vYQFF36a0iLiaZQVMtW7l090ANIwc+o/\naRKoxkB/j7Ks+mGU5PAFmfmvtnKzgFmZuWHb43bKzO+3lT0P2BRYNTNnVV8B3wgcBfwP8FXgpZQF\nG/4H2B9YGzik+n03ZbndD2bmQ+37o8yVui/wfOAe4FTgvzKztVJYK46dgd0pq+PNA/5U1XF4Zs6r\nytRju4TS83hrZq47Rru9Avgk8ErgSZSvtv8XOCAzZ9Xa7NltD31Me7XV+zxKT94mwHLAn4FDge/U\nYr4YWK/a7xeAdwFPrZ7HgZn5g7Y6X0dpr3WBpYG/U06u9qvPSRsR+1OWNN6Eslz9m4GPZ+bhTeqp\nyi5LWXr67cAzgNnAccBBmXlPNd52h7anv1FmXlw9fgPK/NrrA0tW+zod+EJm3l3bzyxgLmUe9KOA\nFwBPy8x7q+EprTqeRjmurgG+mJmXd2j+evyttqjHNEJZ4GgHyjG8MbAs8NuqnS7oob66nTLz+9Vz\n+DfwCkpP/5spr+0fKMf1AvVGxIspqz1uCDyZMkTpx8BnM/Mf3Z5X9fiZlPfaZpS2/S3lPbh69bw2\nysyLa++NozNzx7Y6PgYcRNvx3Gts1fHxIWAbygnIPOAvwDHA1zJzXnWcv6Yt/NZy9I+JKyJWobTx\nG4CVKCsHXkU55n5WK7d/VW6D6jnvCzwPuJdyjO1T9Z5LQ8+ebWlyeRDYi5IY7T+gfTwDOBE4C/gA\ncCclIfsU8CNKsvoBytfFuwMf7FDH1sB/URK3vYArgPdXfz8qIg6hJF83A/8JfJiyEMDXgSNHie0A\n4HOUZHdUEbEJcCnwMkoivGu1/62Bq6p/+lRxHV7dPrzaflGXep8H/IqSwB9EGV6RwBHAl9uKjwDf\nBZ5LSW4+Slnl8tiIiFqdbwLOq57ffsDOlK/gtwN+HhHLdAhlH2AZymtwSdN6ImIJyjCJPYDjKYn7\nSZSk5sfV9sM6tM3vqse/rWqnlSlJ0e7AhZTj4ZKIWLIt3hmU1/okymvx72r4weVVWx5GOUk7mJJc\n/SQixttjunQV272UY+ogICgr2HVb1e0k5r+vTuaxx8IM4Izq90eAzwKrAadFxHKtQtVJ3pWUk9WD\nKW17IuV1uCoiVuoWfEQ8DjgX2B44jXKMnQF8C3hj12c+hoaxnUR5bS8G9gT2ppxY/jfwlarMZyht\nBaXttqac1Hba90rAL4B3U47L91LeMy8AfhoRb+jwsPcBH6O8j/aknHS8j3LCLU0JjtmWJplqtcRT\ngb0j4ujM/G2fd7EpsGFmthK4ayn/ID8LbNrqwYuIc4F/UHr4Dm6rYz0gMvOOqmyrR/otEbFmZv4u\nIl5C6TX7ZmbuWXvstyPiFGCXiDg8M39d2/Z6YIOxejwrh1F64l6dmY+ushcRv6QkEZ8C3p+ZP46I\nFavN12TmWONMDwaWAtbJzOurOo+hjFfdJyK+WusdfCJwf2a+u7b/24ATKL2FrROGNSgnJLtk5p+q\n+34QEfMoicbbaDtRoSTwL83M+kqmTerZHfh/LNjreVw1g8rHgS0z84SIWKu9bapx3N8CrgVelWUZ\ncYDvR8RvKdcV7E7pgW1ZFfh0Zn6h1hZvq9pyh3q7R8QPgB9Uz+dqmlsP+GhmPnpcRsQjlGP4jZTE\n7TEy8/cRcUn15+87HAurU3pq689hHiWZfzPzV5P7FnAHsF5m3lkrewnlJPbjdD5JbdkMWAc4LjPf\nW3v8ycD/dXlcL3qKLSKeQul9Pqft/fm9iPgq8JSImJGZl0TERtW2S2rfMHTa92eApwPbZuYJtX2f\nQDlh/SrlZLHutcAamXlPVfZEyufOlpQTTmnomWxLk9PelKT4mxHxmszs53ivWa1Eu3Jt9fuW+lfl\nmXlrlTh26ik8rZVoV2VHqkRhA2AjSu/oNtXmE+u9gpVTKP9MNwTqyfYtvSTaEfECSm/ZmfVEuxUb\nZVjL5pRe7Z5VPcObAVe2Em149Pm9B3hKVXfdoW1/X1f9fnrt8YdQhgwQETMowx4eR/nKHsoSxu3O\naEu0m9bzTuBh4Idt9R5CGR705w77bHk1ZQjAN4AnRsQTa9vOBL5Gee3qyfYM5veAtsytfr+K2sV0\nmXkbJckar7mUb0fqWkl7t57tsYxQnlvdAq9nRKxG+TblCOCRtmP7MuAuStt003ruCww1ysw/RcRP\nKUlwYw1je4RysvqCiFihnphn5niT3C0ow4ROqt+ZmX+NiAspS9A/LzPrx95RrUS7KntfRPyBMpRN\nmhJMtqVJKDP/HhEHUHpZdwC+38fqZ7Xt66Gql+qmDmUfovPFc51621sJX2t89Aur35d0KNvSfqHa\nrE6FOnjBaHFk5iMRcQOwdkQsmZkP9FgnlJ7NJZj/XOr1/pnOCeoNbX+3xpk+OswiIh5PGXazHaXH\neom2x3T6LG4/iWhaz1qUk5cF5gKvkqorOuyvrvXafZ7Rv87vdJHhrLa/T6AMH9q7GgJzFmW89UXt\ncTX0j8x8sO2+Vn0Lc7Hnre3XSfDY17PVNrtVP52MNUSzNb3n9R22/Z5xJts0iK0as/81Su/xjRFx\nNuW1Oa+XMeftqsR+ReCKzHykQ5GknACvzoLvo07vqX9jfqIpxINZmrwOBXYEvhwRZ9QvSFtI7UnK\nWPd3MqfDfa2ktpWULFv9fhflAq1Obumh3k5aY5NHm12kFcvStdu9aMX+UK8PaF08OobvUsaxXkUZ\nQ/1XSq/zxpSx3p10aosm9SwJ3NZDbJ20Xrsv8div/Vva2/XB9rbIzDsiYj3KNzXvoowX3xe4txrP\n/7nWBacNDWoxoV5ey1bbHM3oJ8FjfRO1VPW70wWAC3NRYNPY9gV+TjmWtqa8RiMR8WNg98z8W4N9\nN3lP1rkwlKY8k21pksrMuRGxB6Vn+IuM3lM1mvYL2PppqS73tZKFVrL4l8z8RZ/3f1/1u9OFhTD/\nH/p9o2wfTevCr/ZhL+NWXbC3HWUWlo3qPe31iygHUM/tjP95tF67u1pjdMcrM++iXMy5X0Q8n9K7\n+Z+UC2HnUS6GHSattrl/Idqm9do9scO2JzWop/093ii2anjaycDJEfEkygw4O1HGp18QES/KzLnd\n6qjp9T3Z6wm1NGU4G4k0iWXmpZRpuHaNiE7T4LXG9D6hw7bVBhZYubCt3fOr360hGK1V/17VXjAi\nlmkbB9xUa6GTF7VviIjFq1huHMdQhb9TxrKu2aHeF0ZZ6bN9GsGxPJsynvmKDkNaXj3AemYBK7TP\njBERT6mexyu67GvU166q46m9hbygzLwhMw+lTK/3MGXc/rAZq21m9lBHa8jWqh22vbjt727v8dX7\nFVtm3puZp2fmWyhT772ADu+DLo//J+WbqjUiYrEORVpDXP7Qa53SVGGyLU1+H6FMcfZtoP2fWGsY\nxjr1OyNiaxbuQrGxbNU2FdoM5l8Q+dPqd+tiufd3mCbuy8Dsapq9xqqZOK4DXhePXd5+O8rX6aeO\no94HgAsoCcMr2zZ/mVFmuRhDayjHc+p3Rpm6cNPqz16+hWhaT2sKu53b6tmFMs95qze8Nb62fvJz\nKaVn/E3Vxaj1/W0D3BoR244VcEQcERG/6XBi9a9qv02GLvVLp+fbs8y8gbLS5osjYoGLPKsTmFuj\nzH/dTes6hne0PX5NykXGdXdQLghdu3qftco+C3jreGOLiM0iYlZEvL5DfPdWv1uvT69tdjLl25R3\ntu17NcqF09dk5l/HqEOachxGIk1ymXl7RHyS+fMh1y8ouoKyUMkOEXEHpcf3xZSE8zzGf6HVWH4F\nXB5lUZR/Uv7prw/8sDUlXWZeGxGHUsbrXh4RR1B66TajLLJyXNusBE3tRZkT/KKI+CZlXPiLKeNP\n/0xZaGY8PkLpGTwjIg6u6t28ivuQzOx0IWk3syhTK24YEV+vbr+MMsfyDpSZQbaMiP+jbRaHhazn\nMMr47gOqnuhfUuZe/gBlHubjq3pbF2LuFRFLAZdn5lUR8f6qnosj4r8pJ3brUIYzZbW/sfyUMtfy\nz6vpE2+lzOiyPSVxO6yHOvptFmXc8nbVe+a6bgvhjGIPysWEp1VtcwPl2549KSdFP+jyWCg9x38E\n3ltNLXgZpZf7/ZS5zFsnT2Tmg9UY6jcDJ0SZknNlylCcH1HGWY8ntisoOcApEfEtysXGj6O8j98D\nXJiZf6zKto6RT0bECylzhHf61uhAyvST/xNlYZ3fAc+sntdcyntWmnbs2ZaGw7cpq+4toBom8VpK\nT9lulOTlRcDrKHPVDsoRlIUvdqJMD7cOZSq2Xdri24eywMlcyjRx36R89f3R6rHjVq1G9x+UeYn/\ni7IK5pbV7/XHe0FpZv6OMo/zJVWc36G06W6URLxpfSOUXv+zKMnv1yhDfDbOzHMoi/usTJnLedSZ\nNJrWU62+9xpKm29F6c3etnrc61pjcTPzsmrbqpSLLJ9T3X8a5cLLX1PmZv4uVSJFmae91fvZ7bmf\nSDlJub2q4xhKQjYH2Dwz2+cWH7jqor/PU8ZGf4YOQ5F6qOPnlGPkAkoS21pV9QzglWNdWFhN6fh6\nynSI76IcY1tQFnO5ssNDdqZc8LgB5bNgm2q/5443tur98QpK8r0N5fj5NuU9tT/wllq1p1BOrtah\nvI4dhxFV04GuV9W5HWWRo72r57R+Zl7VrV2kqcrl2iVJmiSiwxL1koabPduSJEnSgJhsS5IkSQNi\nsi1JkiQNiGO2JUmSpAGxZ1uSJEkaEJNtSZIkaUBMtiVJkqQBMdmWJEmSBsRkW5IkSRqQ/w+eoRBf\nex5h3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8947b89a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_ques_df[\"num_of_chars\"] = all_ques_df[\"questions\"].apply(lambda x: len(str(x)))\n",
    "count_str = all_ques_df[\"num_of_chars\"].value_counts()\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(count_str.index, count_str.values, alpha=0.8, color=color[3])\n",
    "plt.ylabel('Number of Occurrences', fontsize=20)\n",
    "plt.xlabel('Number of characters in the question', fontsize=20)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()      \n",
    "\n",
    "# del all_ques_df     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_unigrams(que):\n",
    "    return [word for word in word_tokenize(que.lower()) if word not in eng_stopwords]\n",
    "\n",
    "## Finding the intersection between two series in pandas and return len.\n",
    "def get_common_unigrams(row):\n",
    "    return len( set(row[\"unigrams_ques1\"]).intersection(set(row[\"unigrams_ques2\"])) ) \n",
    "\n",
    "def get_common_unigram_ratio(row):\n",
    "    return float(row[\"unigrams_common_count\"]) / max(len( set(row[\"unigrams_ques1\"]).union(set(row[\"unigrams_ques2\"])) ),1)\n",
    "\n",
    "train_df[\"unigrams_ques1\"] = train_df['question1'].apply(lambda x: get_unigrams(str(x)))\n",
    "train_df[\"unigrams_ques2\"] = train_df['question2'].apply(lambda x: get_unigrams(str(x)))\n",
    "train_df[\"unigrams_common_count\"] = train_df.apply(lambda row: get_common_unigrams(row), axis=1)\n",
    "train_df[\"unigrams_common_ratio\"] = train_df.apply(lambda row: get_common_unigram_ratio(row),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KG0TERsC5wObA04DTKG5Jfj4wFVgCHJaZKyPiUOA4YDWwMDPPqXoikiRJUqdVuWnLoxGx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\nNtkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8947bdce80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_str = train_df[\"unigrams_common_count\"].value_counts()\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(count_str.index, count_str.values, alpha=0.8)\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Common unigrams count', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RZtarnuI801p7yMSTAADAiPUsjfCpqrr7xJMAAMCI9Yw4fynJpVX14STfXHphtXOcAQBg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\n6KA4AwBAh/8PEpvOUvEeaxsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8944737da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.boxplot(x=\"is_similar\", y=\"unigrams_common_count\", data=train_df)\n",
    "plt.xlabel('Is similar', fontsize=12)\n",
    "plt.ylabel('Common unigrams count', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DDQDAwWacAv0rE08BAADrxH6XcIx2Ivz7JBuS3HZ0+O/sUAgAwMFonNvYPSLJG5NckeRr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\nDKBAAwDAAAo0AAAMoEADAMAA/x+fKaQTkyJhkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8943eacfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.boxplot(x=\"is_similar\", y=\"unigrams_common_ratio\", data=train_df)\n",
    "plt.xlabel('Is similar', fontsize=12)\n",
    "plt.ylabel('Common unigrams ratio', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_bigrams(que):\n",
    "    return [ i for i in ngrams(que,2)]\n",
    "\n",
    "def get_common_bigrams(row):\n",
    "    return len( set(row['bigrams_ques1']).intersection(set(row['bigrams_ques2'])) )\n",
    "\n",
    "def get_common_bigram_ratio(row):\n",
    "    return float(row[\"bigrams_common_count\"]) / max(len( set(row[\"bigrams_ques1\"]).union(set(row[\"bigrams_ques2\"])) ),1)\n",
    "\n",
    "train_df[\"bigrams_ques1\"] = train_df[\"unigrams_ques1\"].apply(lambda x: get_bigrams(x))\n",
    "train_df[\"bigrams_ques2\"] = train_df[\"unigrams_ques2\"].apply(lambda x: get_bigrams(x))\n",
    "train_df[\"bigrams_common_count\"] = train_df.apply(lambda row: get_common_bigrams(row), axis=1)\n",
    "train_df[\"bigrams_common_ratio\"] = train_df.apply(lambda row: get_common_bigram_ratio(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6Xma+obmKmhURxwJvAbYAp2bmFxouqRERcTywtKXp8Zm5Z1P1NCki9gTOBwaB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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8943e142e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_str = train_df['bigrams_common_count'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(count_str.index, count_str.values, alpha=0.8)\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Common bigrams count', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Oee0gjrfPsz7eyTfcOqj1P/2y3Z71Mde1r9w0uEvgR/bY9Fkfc/pNizte9+A9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\nzMw6wIG4mZmZmVkHOBA3MzMzM+uAvwCWbSSRPGhhDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f89443e9438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_str = train_df['bigrams_common_ratio'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(count_str.index, count_str.values, alpha=0.8)\n",
    "plt.ylabel('Number of Occurrences', fontsize=10)\n",
    "plt.xlabel('Common bigrams ratio', fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LSU5JclpVPbq7/2qy8QAAYDaMTNV4QZKzu/uDBw5U1f2S/EqSfzupYAAAMEtGVtU4aWlp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f89443e9da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.boxplot(x=\"is_similar\", y=\"bigrams_common_count\", data=train_df)\n",
    "plt.xlabel('Is similar', fontsize=12)\n",
    "plt.ylabel('Common bigrams count', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JPlu7+xXd/fAkj87iGubH78MYv5fkM0m+e7Tk4+/34XsD7LcUaIA5U1Xrq+pD\nVXXEaInEJ5Is7GS/N1bVj42efiHJv+xsv7vgXkn+rru3jT7n+CSH78P3B9gvKdAAc6a7N2bxyhlX\nVtVnk/xJktN2sut/T/Jbo+UVn03y10k+vA+j/Nck51fVZ5KcnOTcJOdW1WP24WcA7HdWLCzsy8kK\nAAA4sJmBBgCAARRoAAAYQIEGAIABFGgAABhAgQYAgAEUaAAAGECBBgCAARRoAAAYQIEGAIAB/h+h\n7bLkJjw9yAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8943eb3fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.boxplot(x=\"is_similar\", y=\"bigrams_common_ratio\", data=train_df)\n",
    "plt.xlabel('Is similar', fontsize=12)\n",
    "plt.ylabel('Common bigrams ratio', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def feature_extraction(row):\n",
    "    que1 = str(row['question1'])\n",
    "    que2 = str(row['question2'])\n",
    "    out_list = []\n",
    "    # get unigram features #\n",
    "    unigrams_que1 = [word for word in que1.lower().split() if word not in eng_stopwords]\n",
    "    unigrams_que2 = [word for word in que2.lower().split() if word not in eng_stopwords]\n",
    "    common_unigrams_len = len(set(unigrams_que1).intersection(set(unigrams_que2)))\n",
    "    common_unigrams_ratio = float(common_unigrams_len) / max(len(set(unigrams_que1).union(set(unigrams_que2))),1)\n",
    "    out_list.extend([common_unigrams_len, common_unigrams_ratio])\n",
    "    \n",
    "    # get bigram features #\n",
    "    bigrams_que1 = [i for i in ngrams(unigrams_que1, 2)]\n",
    "    bigrams_que2 = [i for i in ngrams(unigrams_que2, 2)]\n",
    "    common_bigrams_len = len(set(bigrams_que1).intersection(set(bigrams_que2)))\n",
    "    common_bigrams_ratio = float(common_bigrams_len) / max(len(set(bigrams_que1).union(set(bigrams_que2))),1)\n",
    "    out_list.extend([common_bigrams_len, common_bigrams_ratio])\n",
    "    \n",
    "    # get trigram features #\n",
    "    trigrams_que1 = [i for i in ngrams(unigrams_que1, 3)]\n",
    "    trigrams_que2 = [i for i in ngrams(unigrams_que2, 3)]\n",
    "    common_trigrams_len = len(set(trigrams_que1).intersection(set(trigrams_que2)))\n",
    "    common_trigrams_ratio = float(common_trigrams_len) / max(len(set(trigrams_que1).union(set(trigrams_que2))),1)\n",
    "    out_list.extend([common_trigrams_len, common_trigrams_ratio])\n",
    "    return out_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def runXGB(train_X, train_y, test_X, test_y=None, feature_names=None, seed_val=0):\n",
    "        params = {}\n",
    "        params[\"objective\"] = \"binary:logistic\"\n",
    "        params['eval_metric'] = 'logloss'\n",
    "        params[\"eta\"] = 0.02\n",
    "        params[\"subsample\"] = 0.7\n",
    "        params[\"min_child_weight\"] = 1\n",
    "        params[\"colsample_bytree\"] = 0.7\n",
    "        params[\"max_depth\"] = 4\n",
    "        params[\"silent\"] = 1\n",
    "        params[\"seed\"] = seed_val\n",
    "        num_rounds = 300 \n",
    "        plst = list(params.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, 'test') ]\n",
    "                model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=100, verbose_eval=10)\n",
    "        else:\n",
    "                xgtest = xgb.DMatrix(test_X)\n",
    "                model = xgb.train(plst, xgtrain, num_rounds)\n",
    "                \n",
    "        pred_test_y = model.predict(xgtest)\n",
    "\n",
    "        loss = 1\n",
    "        if test_y is not None:\n",
    "                loss = log_loss(test_y, pred_test_y)\n",
    "                return pred_test_y, loss, model\n",
    "        else:\n",
    "            return pred_test_y, loss, modelv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/bin/ipython:21: DeprecationWarning: generator 'ngrams' raised StopIteration\n",
      "/opt/conda/bin/ipython:20: DeprecationWarning: generator 'ngrams' raised StopIteration\n"
     ]
    }
   ],
   "source": [
    "train_X = np.vstack( np.array(train_df.apply(lambda row: feature_extraction(row), axis=1)) ) \n",
    "test_X = np.vstack( np.array(test_df.apply(lambda row: feature_extraction(row), axis=1)) )\n",
    "train_y = np.array(train_df[\"is_similar\"])\n",
    "test_id = np.array(test_df[\"test_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean target rate :  0.5040625\n"
     ]
    }
   ],
   "source": [
    "train_X_similar = train_X[train_y==1]\n",
    "train_X_non_similar = train_X[train_y==0]\n",
    "\n",
    "train_X = np.vstack([train_X_non_similar, train_X_similar, train_X_non_similar, train_X_non_similar])\n",
    "train_y = np.array([0]*train_X_non_similar.shape[0] + [1]*train_X_similar.shape[0] + [0]*train_X_non_similar.shape[0] + [0]*train_X_non_similar.shape[0])\n",
    "del train_X_similar\n",
    "del train_X_non_similar\n",
    "print(\"Mean target rate : \",train_y.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-logloss:0.691483\ttest-logloss:0.691145\n",
      "Multiple eval metrics have been passed: 'test-logloss' will be used for early stopping.\n",
      "\n",
      "Will train until test-logloss hasn't improved in 100 rounds.\n",
      "[10]\ttrain-logloss:0.66482\ttest-logloss:0.665453\n",
      "[20]\ttrain-logloss:0.643468\ttest-logloss:0.645061\n",
      "[30]\ttrain-logloss:0.624858\ttest-logloss:0.629388\n",
      "[40]\ttrain-logloss:0.612544\ttest-logloss:0.618438\n",
      "[50]\ttrain-logloss:0.601257\ttest-logloss:0.608913\n",
      "[60]\ttrain-logloss:0.59035\ttest-logloss:0.599993\n",
      "[70]\ttrain-logloss:0.580631\ttest-logloss:0.592579\n",
      "[80]\ttrain-logloss:0.574185\ttest-logloss:0.587856\n",
      "[90]\ttrain-logloss:0.56935\ttest-logloss:0.584218\n",
      "[100]\ttrain-logloss:0.563939\ttest-logloss:0.580435\n",
      "[110]\ttrain-logloss:0.558397\ttest-logloss:0.577037\n",
      "[120]\ttrain-logloss:0.554203\ttest-logloss:0.574323\n",
      "[130]\ttrain-logloss:0.551484\ttest-logloss:0.572361\n",
      "[140]\ttrain-logloss:0.548075\ttest-logloss:0.570161\n",
      "[150]\ttrain-logloss:0.54526\ttest-logloss:0.56847\n",
      "[160]\ttrain-logloss:0.54311\ttest-logloss:0.567252\n",
      "[170]\ttrain-logloss:0.540439\ttest-logloss:0.566306\n",
      "[180]\ttrain-logloss:0.538168\ttest-logloss:0.565591\n",
      "[190]\ttrain-logloss:0.536289\ttest-logloss:0.56485\n",
      "[200]\ttrain-logloss:0.534846\ttest-logloss:0.564355\n",
      "[210]\ttrain-logloss:0.533176\ttest-logloss:0.564028\n",
      "[220]\ttrain-logloss:0.531807\ttest-logloss:0.563818\n",
      "[230]\ttrain-logloss:0.529944\ttest-logloss:0.563415\n",
      "[240]\ttrain-logloss:0.528607\ttest-logloss:0.563328\n",
      "[250]\ttrain-logloss:0.527557\ttest-logloss:0.563269\n",
      "[260]\ttrain-logloss:0.526324\ttest-logloss:0.56295\n",
      "[270]\ttrain-logloss:0.525165\ttest-logloss:0.562971\n",
      "[280]\ttrain-logloss:0.524215\ttest-logloss:0.562899\n",
      "[290]\ttrain-logloss:0.523127\ttest-logloss:0.562644\n",
      "[299]\ttrain-logloss:0.5224\ttest-logloss:0.562841\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state=2016)\n",
    "for dev_index, val_index in kf.split(range(train_X.shape[0])):\n",
    "    dev_X, val_X = train_X[dev_index,:], train_X[val_index,:]\n",
    "    dev_y, val_y = train_y[dev_index], train_y[val_index]\n",
    "    preds, lloss, model = runXGB(dev_X, dev_y, val_X, val_y)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xgtest = xgb.DMatrix(test_X)\n",
    "preds = model.predict(xgtest)\n",
    "\n",
    "out_df = pd.DataFrame({\"test_id\":test_id, \"is_similar\":preds})\n",
    "out_df.to_csv(\"issimilar_predicted.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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