Code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n  \"This module will be removed in 0.20.\", DeprecationWarning)\n/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "from datetime import datetime, date\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import grid_search\n",
    "from sklearn.metrics import make_scorer\n",
    "from IPython.display import display\n",
    "import gc\n",
    "import xgboost as xgb\n",
    "import operator\n",
    "\n",
    "# Draw inline\n",
    "%matplotlib inline\n",
    "\n",
    "# Set figure aesthetics\n",
    "sns.set_style(\"white\", {'ytick.major.size': 10.0})\n",
    "sns.set_context(\"poster\", font_scale=1.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the code\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date_account_created</th>\n",
       "      <th>timestamp_first_active</th>\n",
       "      <th>date_first_booking</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>signup_method</th>\n",
       "      <th>signup_flow</th>\n",
       "      <th>language</th>\n",
       "      <th>affiliate_channel</th>\n",
       "      <th>affiliate_provider</th>\n",
       "      <th>first_affiliate_tracked</th>\n",
       "      <th>signup_app</th>\n",
       "      <th>first_device_type</th>\n",
       "      <th>first_browser</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gxn3p5htnn</td>\n",
       "      <td>2010-06-28</td>\n",
       "      <td>20090319043255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>facebook</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>820tgsjxq7</td>\n",
       "      <td>2011-05-25</td>\n",
       "      <td>20090523174809</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MALE</td>\n",
       "      <td>38.0</td>\n",
       "      <td>facebook</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>seo</td>\n",
       "      <td>google</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4ft3gnwmtx</td>\n",
       "      <td>2010-09-28</td>\n",
       "      <td>20090609231247</td>\n",
       "      <td>2010-08-02</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>56.0</td>\n",
       "      <td>basic</td>\n",
       "      <td>3</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>IE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bjjt8pjhuk</td>\n",
       "      <td>2011-12-05</td>\n",
       "      <td>20091031060129</td>\n",
       "      <td>2012-09-08</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>42.0</td>\n",
       "      <td>facebook</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Firefox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>87mebub9p4</td>\n",
       "      <td>2010-09-14</td>\n",
       "      <td>20091208061105</td>\n",
       "      <td>2010-02-18</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>41.0</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id date_account_created  timestamp_first_active date_first_booking  \\\n",
       "0  gxn3p5htnn           2010-06-28          20090319043255                NaN   \n",
       "1  820tgsjxq7           2011-05-25          20090523174809                NaN   \n",
       "2  4ft3gnwmtx           2010-09-28          20090609231247         2010-08-02   \n",
       "3  bjjt8pjhuk           2011-12-05          20091031060129         2012-09-08   \n",
       "4  87mebub9p4           2010-09-14          20091208061105         2010-02-18   \n",
       "\n",
       "      gender   age signup_method  signup_flow language affiliate_channel  \\\n",
       "0  -unknown-   NaN      facebook            0       en            direct   \n",
       "1       MALE  38.0      facebook            0       en               seo   \n",
       "2     FEMALE  56.0         basic            3       en            direct   \n",
       "3     FEMALE  42.0      facebook            0       en            direct   \n",
       "4  -unknown-  41.0         basic            0       en            direct   \n",
       "\n",
       "  affiliate_provider first_affiliate_tracked signup_app first_device_type  \\\n",
       "0             direct               untracked        Web       Mac Desktop   \n",
       "1             google               untracked        Web       Mac Desktop   \n",
       "2             direct               untracked        Web   Windows Desktop   \n",
       "3             direct               untracked        Web       Mac Desktop   \n",
       "4             direct               untracked        Web       Mac Desktop   \n",
       "\n",
       "  first_browser  \n",
       "0        Chrome  \n",
       "1        Chrome  \n",
       "2            IE  \n",
       "3       Firefox  \n",
       "4        Chrome  "
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date_account_created</th>\n",
       "      <th>timestamp_first_active</th>\n",
       "      <th>date_first_booking</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>signup_method</th>\n",
       "      <th>signup_flow</th>\n",
       "      <th>language</th>\n",
       "      <th>affiliate_channel</th>\n",
       "      <th>affiliate_provider</th>\n",
       "      <th>first_affiliate_tracked</th>\n",
       "      <th>signup_app</th>\n",
       "      <th>first_device_type</th>\n",
       "      <th>first_browser</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5uwns89zht</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>20140701000006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>35.0</td>\n",
       "      <td>facebook</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Moweb</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>Mobile Safari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>jtl0dijy2j</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>20140701000051</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Moweb</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>Mobile Safari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>xx0ulgorjt</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>20140701000148</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>linked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6c6puo6ix0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>20140701000215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>linked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>IE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>czqhjk3yfe</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>20140701000305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Safari</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id date_account_created  timestamp_first_active  \\\n",
       "0  5uwns89zht           2014-07-01          20140701000006   \n",
       "1  jtl0dijy2j           2014-07-01          20140701000051   \n",
       "2  xx0ulgorjt           2014-07-01          20140701000148   \n",
       "3  6c6puo6ix0           2014-07-01          20140701000215   \n",
       "4  czqhjk3yfe           2014-07-01          20140701000305   \n",
       "\n",
       "   date_first_booking     gender   age signup_method  signup_flow language  \\\n",
       "0                 NaN     FEMALE  35.0      facebook            0       en   \n",
       "1                 NaN  -unknown-   NaN         basic            0       en   \n",
       "2                 NaN  -unknown-   NaN         basic            0       en   \n",
       "3                 NaN  -unknown-   NaN         basic            0       en   \n",
       "4                 NaN  -unknown-   NaN         basic            0       en   \n",
       "\n",
       "  affiliate_channel affiliate_provider first_affiliate_tracked signup_app  \\\n",
       "0            direct             direct               untracked      Moweb   \n",
       "1            direct             direct               untracked      Moweb   \n",
       "2            direct             direct                  linked        Web   \n",
       "3            direct             direct                  linked        Web   \n",
       "4            direct             direct               untracked        Web   \n",
       "\n",
       "  first_device_type  first_browser  \n",
       "0            iPhone  Mobile Safari  \n",
       "1            iPhone  Mobile Safari  \n",
       "2   Windows Desktop         Chrome  \n",
       "3   Windows Desktop             IE  \n",
       "4       Mac Desktop         Safari  "
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_users\n",
    "train_users = pd.read_csv(\"Data/train_users_2.csv\")\n",
    "target = train_users['country_destination']\n",
    "train_users = train_users.drop(['country_destination'], axis=1)\n",
    "\n",
    "# test_users\n",
    "test_users = pd.read_csv(\"Data/test_users.csv\")\n",
    "\n",
    "display(train_users.head())\n",
    "display(test_users.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combining both train and test for some exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of given users are 275547 and number of given features are 15\n"
     ]
    },
    {
     "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>date_account_created</th>\n",
       "      <th>timestamp_first_active</th>\n",
       "      <th>date_first_booking</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>signup_method</th>\n",
       "      <th>signup_flow</th>\n",
       "      <th>language</th>\n",
       "      <th>affiliate_channel</th>\n",
       "      <th>affiliate_provider</th>\n",
       "      <th>first_affiliate_tracked</th>\n",
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       "      <th>first_device_type</th>\n",
       "      <th>first_browser</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gxn3p5htnn</td>\n",
       "      <td>2010-06-28</td>\n",
       "      <td>20090319043255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>facebook</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>820tgsjxq7</td>\n",
       "      <td>2011-05-25</td>\n",
       "      <td>20090523174809</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MALE</td>\n",
       "      <td>38.0</td>\n",
       "      <td>facebook</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>seo</td>\n",
       "      <td>google</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4ft3gnwmtx</td>\n",
       "      <td>2010-09-28</td>\n",
       "      <td>20090609231247</td>\n",
       "      <td>2010-08-02</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>56.0</td>\n",
       "      <td>basic</td>\n",
       "      <td>3</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>IE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bjjt8pjhuk</td>\n",
       "      <td>2011-12-05</td>\n",
       "      <td>20091031060129</td>\n",
       "      <td>2012-09-08</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>42.0</td>\n",
       "      <td>facebook</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Firefox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>87mebub9p4</td>\n",
       "      <td>2010-09-14</td>\n",
       "      <td>20091208061105</td>\n",
       "      <td>2010-02-18</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>41.0</td>\n",
       "      <td>basic</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>untracked</td>\n",
       "      <td>Web</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Chrome</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id date_account_created  timestamp_first_active date_first_booking  \\\n",
       "0  gxn3p5htnn           2010-06-28          20090319043255                NaN   \n",
       "1  820tgsjxq7           2011-05-25          20090523174809                NaN   \n",
       "2  4ft3gnwmtx           2010-09-28          20090609231247         2010-08-02   \n",
       "3  bjjt8pjhuk           2011-12-05          20091031060129         2012-09-08   \n",
       "4  87mebub9p4           2010-09-14          20091208061105         2010-02-18   \n",
       "\n",
       "      gender   age signup_method  signup_flow language affiliate_channel  \\\n",
       "0  -unknown-   NaN      facebook            0       en            direct   \n",
       "1       MALE  38.0      facebook            0       en               seo   \n",
       "2     FEMALE  56.0         basic            3       en            direct   \n",
       "3     FEMALE  42.0      facebook            0       en            direct   \n",
       "4  -unknown-  41.0         basic            0       en            direct   \n",
       "\n",
       "  affiliate_provider first_affiliate_tracked signup_app first_device_type  \\\n",
       "0             direct               untracked        Web       Mac Desktop   \n",
       "1             google               untracked        Web       Mac Desktop   \n",
       "2             direct               untracked        Web   Windows Desktop   \n",
       "3             direct               untracked        Web       Mac Desktop   \n",
       "4             direct               untracked        Web       Mac Desktop   \n",
       "\n",
       "  first_browser  \n",
       "0        Chrome  \n",
       "1        Chrome  \n",
       "2            IE  \n",
       "3       Firefox  \n",
       "4        Chrome  "
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users = pd.concat((train_users, test_users), axis = 0, ignore_index = True)\n",
    "print(\"Number of given users are {} and number of given features are {}\".format(users.shape[0],users.shape[1]))\n",
    "display(users.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x116bc2630>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CAAAAwBgECgAAAABjECgAAAAAjEGgAAAAADAGgQIAAADAGAQKAAAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1064b5588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = users[\"gender\"].value_counts(dropna=True)\n",
    "counts.plot(kind=\"bar\",rot=0)\n",
    "plt.xlabel('Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x11563e748>, <matplotlib.text.Text at 0x116bd2a58>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bq47ZbK72XM7mnL0mAAAAgNpX56u63YiXl5ckqaSkxGFfSUmJfHx8bLny8nKVl5fb3UUq\nLi6WJLtcxbbKKue8vb3ttl17zYqcs7PVxKZNm6rNZGdnKzw8vMbnBgAAAH7O6vUdn4CAAEn/eYen\nssrv61Tk8vLyHDKSbLkWLVo4ZK7N+fj46Pbbb79uztfXVw0aNHB6NgAAAAB1r14Xn7Zt28rDw0OH\nDh2y256bm6u8vDzbimodO3aUJB08eNAuV/F3xQptISEhysnJ0cWLF+1yWVlZ8vLyUps2bWznu/aa\nFbmKazo7GwAAAIC6V6+Lj4eHh/r27av333/frqykpaXJbDZr8ODBkqTu3bvL19dXa9eutWUsFovW\nrl2roKAgde7cWZI0YMAAWSwWrV+/3pbLz8/Xtm3bNGjQINvqcgMHDlRmZqb27dtny+3Zs0cnTpxQ\nZGRkjWYDAAAAUPfq9Ts+kvTkk08qJiZGY8aM0WOPPaZjx44pLS1No0aNUlBQkKSrq8NNmzZNs2fP\n1pQpU9SvXz99+OGH2rt3rxYuXGgrNB06dFB0dLQWLFig/Px83XXXXUpNTZXVatXkyZNt1xw5cqRS\nU1M1adIkJSYmymKxaPny5QoNDVVUVFSNZgMAAABQ9+p98QkODtbKlSuVkpKi5ORkNWvWTFOmTNGk\nSZPscjExMbJYLFq1apV2796tVq1aaf78+YqIiLDLvfTSS/L19dWWLVtUVFSkzp07KyUlRa1atbJl\nPDw8tGbNGiUnJ+v111+Xh4eHHnroIc2cOVPu7u41ng0AAABA3TJZnVm/GfVGxapuO3fuVGBgYI2P\nH5K0uRamql76vKF1cl0AAAD8PFT37+R6/Y4PAAAAALgCxQcAAACA4VF8AAAAABgexQcAAACA4VF8\nAAAAABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcAAACA\n4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcA\nAACA4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAAABge\nxQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAA\nABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8\nAAAAABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcAAACA\n4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcAAACA4VF8AAAAABgexQcA\nAACA4d0yxefrr79WXFycunTpoh49eujpp59Wbm6uXeabb75RXFycwsLC1KdPHy1cuFDl5eUO50pP\nT1dUVJS6dOmiqKgopaenO2RKS0u1YMEC9evXT127dlVcXJwOHDjgkMvLy1NSUpJ69uypHj16KCkp\nSWfPnnXdBwcAAADwo90SxScnJ0fjx4/Xd999p6eeekrjxo3Txx9/rLi4OBUXF0uSjh07pnHjxunC\nhQuaNm2aBg4cqCVLlmjOnDl250pPT9f06dMVGBioWbNmKSAgQNOnT9fWrVvtcnPmzNHSpUsVHh6u\nGTNm6OzZsxo7dqy+++47W6a4uFgTJkzQ7t27NX78eCUmJmrXrl1KSEhQaWlp7X8xAAAAAJziVtcD\nOOPNN99UUVGRNm7cqLZt20qS2rVrp9/97ndKT09XTEyMFi1aJE9PT6WmpqpRo0aSJG9vby1btkzx\n8fFq3bq1ysrKlJKSot69e2vx4sUymUyKjY1VXFyc5s6dq4iICJnNZh09elQbNmzQ008/rYkTJ0qS\nBg8erIiICL322mtKSUmRJG3atEnffvut1q1bp27dukmSOnfurPj4eG3evFkxMTF18G0BAAAAuNYt\nccfnxIkTat68ua30SFLv3r0lSYcPH1Zpaal27NihyMhIW+mRpNGjR8tisWj79u2SpK+++kpnzpxR\nbGysTCaTJMlsNmvUqFHKycnR/v37JUkZGRkymUwaOXKk7VxNmzZVRESEduzYYbubk5GRoeDgYFvp\nkaRevXqpTZs22rZtWy19GwAAAABq6pYoPkFBQfrhhx906dIl27bs7GxJkp+fn44cOaKysjKFhITY\nHefv7y8/Pz9lZmZKkrKysiTJIVfxd8X+zMxMtWzZUk2aNLHLhYaGqrCw0Pa4W1ZWlkJDQx3mDQ0N\ntV0TAAAAQN27JYpPYmKifH19NX36dB09elQHDhzQs88+K19fXz3yyCO2RQ6aN2/ucKy/v79Onz4t\nSdfN+fn5SZJOnTply/n7+zucq3KuoKBAly9frvKafn5+OnfunEpKSm72IwMAAABwoVviHZ+WLVsq\nMTFRycnJ+uSTTyRJt912m9asWSM/Pz8VFBRIkjw9PR2O9fDw0OXLlyVJBQUFcnNzU8OGDe0yFccV\nFRXZco0bN3Y4V+VcddeUpMLCQtt/O2P48OHVZlg0AQAAAKi5W6L4/OlPf9LSpUvVu3dvjRgxQoWF\nhVq9erUmTJigNWvWyGq13vD4ivd5nM1Vx2w2V3uuihwAAACAulfvi8/Fixe1evVqde/eXStWrLCV\nk4iICEVGRurFF1/UpEmTJKnKR8tKSkrk4+MjSfLy8lJ5ebnKy8vl5vafj16xJHblXMW2yirnvL29\n7bZde83K53PWpk2bqs1kZ2crPDy8RucFAAAAfu7q/S2JkydPqrS0VA8//LDdHRkfHx+Fh4frm2++\nUdOmTSXJ4QdNK7ZVvK8TEBAg6eqPjl6bkWTLtWjRwiFzbc7Hx0e33377dXO+vr5q0KBBjT8vAAAA\nANer98XH3d1dkmSxWBz2XblyRVarVR06dJCHh4cOHTpktz83N1d5eXm2Vds6duwoSTp48KBdruLv\nihXaQkJClJOTo4sXL9rlsrKy5OXlpTZt2tjOd+01K3LXrhwHAAAAoO7U++LTrl07+fn5adOmTSor\nK7NtP3funHbs2KF77rlHt912m/r27av333/frqykpaXJbDZr8ODBkqTu3bvL19dXa9eutWUsFovW\nrl2roKAgde7cWZI0YMAAWSwWrV+/3pbLz8/Xtm3bNGjQINu7OwMHDlRmZqb27dtny+3Zs0cnTpxQ\nZGRk7XwhAAAAAGqs3r/j06BBAz377LN6+umnFRsbq+HDh6uoqEjr1q3TxYsXtWjRIknSk08+qZiY\nGI0ZM0aPPfaYjh07prS0NI0aNUpBQUGSJDc3N02bNk2zZ8/WlClT1K9fP3344Yfau3evFi5caCs0\nHTp0UHR0tBYsWKD8/HzdddddSk1NldVq1eTJk22zjRw5UqmpqZo0aZISExNlsVi0fPlyhYaGKioq\n6qf/sgAAAABUqd4XH0mKjIyUj4+P3njjDaWkpMhsNqtbt25asGCB7rnnHklScHCwVq5cqZSUFCUn\nJ6tZs2aaMmWKbeGDCjExMbJYLFq1apV2796tVq1aaf78+YqIiLDLvfTSS/L19dWWLVtUVFSkzp07\nKyUlRa1atbJlPDw8tGbNGiUnJ+v111+Xh4eHHnroIc2cOdP2iB4AAACAumeyOrMuM+qNilXddu7c\nqcDAwBofPyRpcy1MVb30eUPr5LoAAAD4eaju38n1/h0fAAAAAPixKD4AAAAADI/iAwAAAMDwKD4A\nAAAADI/iAwAAAMDwnC4+0dHRWrNmjfLz82tzHgAAAABwOaeLT0FBgV599VX16dNHU6ZM0Y4dO1Re\nXl6bswEAAACASzj9A6Y7d+7UP/7xD7333nvavn27Pv74Y91xxx0aMmSIHnnkEXXs2LE25wQAAACA\nm1ajd3x69Oihl19+WZ999plSUlLUqVMnrV27VsOHD9ewYcP01ltv6dy5c7U1KwAAAADclJta3MDD\nw0NRUVFavny5du3apaFDh+rQoUN65ZVX1LdvXz3zzDM6duyYq2cFAAAAgJvi9KNulVmtVn3++edK\nT0/Xzp07dfHiRdtjbyaTSVu2bNG2bdv0pz/9Sf3793f1zAAAAABQIzUqPgcPHtSWLVv0wQcfKC8v\nT2azWQ888IBGjBihX/3qV2rYsKEkafLkyfr1r3+tV199leIDAAAAoM45XXyGDBmio0ePymq1qlWr\nVpo2bZqGDRsmf39/h2yTJk3UrVs3ffrppy4dFgAAAABuhtPFJzs7W4888ohGjBih7t27V5sfNGiQ\noqOjf9RwAAAAAOAKThefPXv26LbbblNhYaGsVqtMJpMk6fjx4/L395ePj49dnkfcAAAAANQXTq/q\ndtttt2nx4sV64IEH9P3339u2L126VL169VJaWlqtDAgAAAAAP5bTxefPf/6zFi5cqPbt29sWMZCk\nyMhI3XPPPXrppZe0Y8eOWhkSAAAAAH4Mp4vP2rVr1atXL61bt04tW7a0be/bt6/efvtt9ejRQytW\nrKiVIQEAAADgx3C6+Jw4cUKDBg2yvdtTmclkUkREhI4ePerS4QAAAADAFWr0jk9eXt5191+4cKHK\nUgQAAAAAdc3p4hMWFqb169fr7NmzDvvOnTund955R2FhYS4dDgAAAABcwenlrCdOnKhRo0YpOjpa\n0dHRatOmjUwmk06cOKH3339f+fn5WrhwYW3OCgAAAAA3xenic8899+iNN97QCy+8oNWrV9vta9Gi\nhV5//XV16dLF5QMCAAAAwI/ldPGRpD59+uijjz5SVlaWsrOzVV5ersDAQIWGhqpBgwa1NSMAAAAA\n/Cg1Kj4VQkJCFBIS4upZAAAAAKBW1Kj4XLx4Udu3b9cPP/ygK1euOOw3mUx64oknXDYcAAAAALiC\n08Vn3759SkhIUFFRkaxWa5UZig8AAACA+sjp4jN//nyVl5dr6tSp6tSpk9zd3WtzLgAAAABwGaeL\nz/79+xUfH69JkybV5jwAAAAA4HJO/4Cpu7u7WrZsWZuzAAAAAECtcLr49OzZU//3f/9Xm7MAAAAA\nQK1w+lG3Z555RqNGjdJLL72kIUOGyNfXV2azY2/irhAAAACA+sbp4jNkyBCVl5crLS1NaWlpVWZM\nJpOysrJcNhwAAAAAuILTxWfQoEEymUy1OQsAAAAA1Aqni8+rr75am3MAAAAAQK1xenGDyn744Qd9\n/fXXunTpkkpLS6/7g6YAAAAAUB/UqPgcPnxYjz32mB588EHFxsbqwIED+vLLLxUeHq5PP/20tmYE\nAAAAgB/F6eLz3XffadSoUTpy5Ij69+9v296wYUOdO3dOTzzxhPbt21crQwIAAADAj+F08fnf//1f\nubm5KT09XX/4wx9sj7fde++9Sk9PV5MmTbRkyZJaGxQAAAAAbpbTxWfPnj169NFHFRAQ4LC6W2Bg\noB599FEdOHDA5QMCAAAAwI/ldPG5fPnyDX+ctGnTprp48aJLhgIAAAAAV3K6+Nx55503vKPzxRdf\nKCAgwCVDAQAAAIArOV18Hn74YW3atEl//etfbdtMJpMsFotWrlyp7du3KyIiolaGBAAAAIAfw+kf\nMH388cf12WefaerUqWrUqJFMJpNeeOEFnTt3ThcuXFBwcLAmTpxYm7MCAAAAwE1x+o6Ph4eH3nrr\nLU2bNk0BAQHy8PDQv//9b/n6+mrSpElat26dvLy8anNWAAAAALgpTt/xkSR3d3c9/vjjevzxx2tr\nHgAAAABwOafv+AAAAADArcrpOz7h4eHVZkwmk3bs2PGjBgIAAAAAV3O6+FitVodtV65c0fnz51VS\nUqKgoCAFBwe7dDgAAAAAcAWni89HH31U5fby8nJt27ZNv//975WYmOiywQAAAADAVX70Oz5ubm6K\niorS8OHD9ac//ckVMwEAAACAS7lscYPg4GAdOHDAVacDAAAAAJdxWfH5+9//Lg8PD1edDgAAAABc\nxul3fF577bUqt5eUlOjAgQP629/+piFDhrhsMAAAAABwlR9dfCp0795dzzzzzI8eCAAAAABczeni\n89Zbb1W5vUGDBvL391dQUJDLhgIAAAAAV3K6+Pzyl7+szTkAlxuStLlOrps+b2idXBcAAADX53Tx\n+fe//32opVcGAAAgAElEQVRTF2jZsuVNHQcAAAAAruJ08fnVr34lk8lUo5ObTCZlZWXVeKiq5OXl\nae7cufrkk0905coVhYWFadasWWrbtq0ts3v3bi1YsEBHjx5Vs2bNFBcXp3Hjxjmc680331RaWprO\nnDmjX/ziF5o2bZp69+5tl7l8+bLmzZunDz/8UAUFBfrlL3+p5557TnfddZdd7uTJk3r11Ve1d+9e\nNWzYUIMHD1ZSUpK8vb1d8rkBAAAA/HhOF58ZM2Zow4YN+v7779WnTx+1a9dOnp6eOnHihD788EOZ\nzWYNGjRIZrPLVsi2uXTpksaMGaPz589r/Pjxcnd31+rVqxUXF6f09HT5+vrq73//uyZOnKiuXbtq\nxowZ2rdvn5KTk1VSUqLHH3/cdq4lS5Zo/vz5GjZsmLp27ar33ntPEydO1FtvvaXu3btLkqxWq6ZO\nnap//OMfio+PV5MmTbRq1SqNGTNGW7Zs0R133CFJOnv2rMaOHSs3NzdNnjxZFy9e1KpVq/T9999r\nxYoVLv8eAAAAANwcp4uPu7u7cnNztX79enXp0sVu37/+9S/Fxsaqbdu2diXDVZYuXap//etfeued\nd9S5c2dJUu/evRUdHa133nlHU6ZMUUpKiu6++26tXr1a7u7uGj16tKxWqxYvXqxHH31UjRo10rlz\n57R48WLFxsbqxRdflCQ98sgjGjp0qObNm6e1a9dKknbt2qXdu3dr3rx5ioqKkiQ9+OCDio6O1po1\na/TUU09JklatWqVz584pIyNDgYGBkqTAwEDNnj1bn3/+ue6//36XfxcAAAAAas7p2zNvvfWW4uLi\nHEqPJAUFBWn06NFav369S4eTrt592bx5swYMGGArPZLUvn17JSUlqV27dsrOztbXX3+t4cOHy93d\n3ZYZM2aMCgsLtWvXLknSxx9/rOLiYsXGxtoynp6eGjFihL788kudOXNGkpSRkaFGjRopMjLSlmvb\ntq169uypjIwM27aMjAw98MADttIjXS1SXl5edjkAAAAAdcvp4pObmyt/f//r7vf29tbZs2ddMlRl\n2dnZys3NVa9evSRJFotFhYWFkqTHH39cAwYMsL1HFBISYndsxd+ZmZmSpKysLDVo0EDt27evMnfw\n4EFbvkOHDg6P7YWGhurkyZMqKCjQ+fPnlZOTo9DQULuMm5ub2rdvb7smAAAAgLrndPFp1aqVPvjg\nA1ksFod9paWleu+99xQcHOzS4aSriwdI0h133KEXX3xR3bt3V1hYmKKjo7Vv3z5JV0uZJDVv3tzu\nWHd3d91xxx06ffq0Lefr6ys3N/sn/Pz8/CRJp06dsuWqKnkVudOnT1/3mhW5imsCAAAAqHtOv+Mz\natQovfDCC4qLi9P48eN11113yWq16vjx41qxYoW+/fZbLVq0yOUDXrp0SZI0d+5c+fj46IUXXlBp\naakWL16s+Ph4/fnPf1ZBQYEk6bbbbnM43sPDQ0VFRZKkgoKCKjOenp6SVKNcWVmZ3bZrr1lxV6om\nhg8fXm2mtLS0xucFAAAAfu6cLj6PPvqoTp48qTfffFNfffWVbbvVapWbm5uee+459e/f3+UDVvxD\nv7i4WO+9955tmeiePXtq8ODBWrx4sX7xi1/c8BwVy3BbrdYb5pxdkc5sNrvsXAAAAABqn9PFR5Jm\nzZql2NhY7dy5Uzk5OZKku+++WwMHDqzykS9XqLjzMmjQILvfxgkKClK3bt30j3/8Q/fcc4+kq+Xo\nWiUlJfLx8ZEkeXl5VZmp2FZx/upyPj4+tmJTUlJyw2vWxKZNm6rNZGdnKzw8vMbnBgAAAH7OalR8\nJKlNmzZKTEysjVmqVFGofH19HfY1bdpUWVlZCggIkHT1R05btWpl219aWqrz58/b3tdp0aKFPvnk\nE1ksFrs7MhXv61TO5eXlOVwvNzdXZrNZvr6+aty4sd2x1+ZutBAEAAAAgJ9WjZ7Hslgs2rhxo37z\nm9/o4Ycf1j//+U8dOnRICxYs0MWLF2tlwHbt2qlhw4Y6fvy4w76cnBwFBASoY8eOkv6zKluFitXe\nKlZeCwkJUVlZmY4cOWKXqziucu7QoUMOj7NlZWWpdevW8vb2VuPGjXXnnXfq0KFDdpny8nIdPnzY\nYYU5AAAAAHXH6eJTVlam3/zmN/rv//5v7dmzR8ePH1dxcbFOnDihJUuWaPTo0Tp//rzLB/T29lbf\nvn3117/+Vf/6179s2/fv36+vv/5a4eHhCgwMVGhoqDZu3GhbdECSUlNT5e3trT59+kiSHnroIbm7\nuystLc2WKS4u1l/+8hf98pe/VLNmzSRJAwcO1Pnz57V161Zb7tixY/rb3/5m99s+AwcO1Keffqrs\n7GzbtnfffVeFhYV2OQAAAAB1y+lH3ZYvX67PPvtMzz77rPr3769f/epXkqQBAwbomWee0dy5c7Vs\n2TI988wzLh9yxowZ2rt3r0aPHq2xY8eqrKxMq1ev1p133qkJEyZIkpKSkpSYmKj4+HhFR0dr7969\nSk9P18yZM23v2zRt2lQTJkzQ4sWLVV5eri5duujdd99VTk6OXn31Vdv1+vXrp3vvvVfPPfecjh8/\nrttvv12rV69W8+bNNWbMGFsuMTFR7733nsaOHavx48crPz9fK1euVL9+/XTfffe5/HsAAAAAcHOc\nvuOTnp6uhx9+WGPHjrVb6tnNzU0JCQkaNmyYdu7cWStDtm7dWuvWrVOnTp30xhtvaNWqVerVq5fW\nrl2r22+/XZL0wAMPaNGiRbpw4YLmzJmj/fv3a/bs2UpISLA71+9+9zs99dRT2rNnj5KTk3XlyhUt\nX75cYWFhtozJZNIbb7yhwYMH66233tLrr7+ukJAQrVmzRk2aNLHlmjVrprffflutWrXSvHnz9Oc/\n/1mxsbGaN29erXwPAAAAAG6O03d8srOzNW7cuOvu79q1qz744AOXDFWVu+++W2+88cYNM/379692\nSW2z2azJkydr8uTJN8w1btxYr7zyil555ZUb5tq1a6fVq1ffMAMAAACgbjl9x8fHx0f5+fnX3Z+T\nkyMvLy+XDAUAAAAAruR08bnvvvu0YcMGXbp0yWFfdna21q1bpx49erh0OAAAAABwBacfdXvyySf1\n61//WkOHDlXfvn1lMpm0c+dO7dy5U++++67KysqqfXwMAAAAAOqC03d82rZtq9WrV8vT01Pr1q2T\n1WpVamqqUlNT1bhxYy1evNj2ezoAAAAAUJ84fcfnypUr6tq1q7Zu3apDhw7pxIkTslqtCgwMVKdO\nnWQ21+i3UAEAAADgJ+N08Rk2bJhiYmI0duxYdejQQR06dKjNuQAAAADAZZy+TXPy5El5enrW5iwA\nAAAAUCucLj7t27fX/v37a3MWAAAAAKgVTj/qFh8fr+eff16nTp1Sz5495evrqwYNGjjkhg0b5tIB\nAQAAAODHcrr4JCUlSZL27NmjPXv2SJJMJpNtv9VqlclkovgAAAAAqHeuW3xGjBihyZMnq3///pKk\nV155RT/88IOaNWv2kw0HAAAAAK5w3eJz+PBh5efn2/5+9tln9cc//lFDhgz5SQYDAAAAAFe5bvHx\n8/PTypUrVVpaKh8fH1mtVu3du1dXrly54Ql51A0AAABAfXPd4jNhwgTNmTNHL7/8sqSr7/Ns2LBB\n77zzjkPWZDLxjg8AAACAeuu6xWf06NHq0aOHDh8+rNLSUj377LOKjY1V165df8r5AAAAAOBHu+Gq\nbsHBwQoODpYkvfbaa+rTp4/Cw8N/ksEAAAAAwFWcXs76o48+qs05AAAAAKDWmOt6AAAAAACobRQf\nAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABg\neBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEA\nAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH\n8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAA\nAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQf\nAAAAAIZH8QEAAABgeLdc8XnvvffUvn17ffHFF3bbv/nmG8XFxSksLEx9+vTRwoULVV5e7nB8enq6\noqKi1KVLF0VFRSk9Pd0hU1paqgULFqhfv37q2rWr4uLidODAAYdcXl6ekpKS1LNnT/Xo0UNJSUk6\ne/as6z4sAAAAAJe4pYpPfn6+XnnlFYftx44d07hx43ThwgVNmzZNAwcO1JIlSzRnzhy7XHp6uqZP\nn67AwEDNmjVLAQEBmj59urZu3WqXmzNnjpYuXarw8HDNmDFDZ8+e1dixY/Xdd9/ZMsXFxZowYYJ2\n796t8ePHKzExUbt27VJCQoJKS0tr5wsAAAAAcFPc6nqAmnj55ZdVUFDgsH3RokXy9PRUamqqGjVq\nJEny9vbWsmXLFB8fr9atW6usrEwpKSnq3bu3Fi9eLJPJpNjYWMXFxWnu3LmKiIiQ2WzW0aNHtWHD\nBj399NOaOHGiJGnw4MGKiIjQa6+9ppSUFEnSpk2b9O2332rdunXq1q2bJKlz586Kj4/X5s2bFRMT\n8xN9KwAAAACqc8vc8dm1a5e2bdumhIQEu+2lpaXasWOHIiMjbaVHkkaPHi2LxaLt27dLkr766iud\nOXNGsbGxMplMkiSz2axRo0YpJydH+/fvlyRlZGTIZDJp5MiRtnM1bdpUERER2rFjh+1uTkZGhoKD\ng22lR5J69eqlNm3aaNu2bbXzJQAAAAC4KbdE8SkoKNALL7yg+Ph4tW/f3m7fkSNHVFZWppCQELvt\n/v7+8vPzU2ZmpiQpKytLkhxyFX9X7M/MzFTLli3VpEkTu1xoaKgKCwttj7tlZWUpNDTUYdbQ0FDb\nNQEAAADUD7dE8VmwYIHMZrN++9vfOuzLzc2VJDVv3txhn7+/v06fPn3DnJ+fnyTp1KlTtpy/v7/D\nuSrnCgoKdPny5Sqv6efnp3PnzqmkpMTpzwcAAACgdtX7d3y+/vprpaWladmyZfL09HTYX/HOT1X7\nPDw8dPnyZVvOzc1NDRs2tMtUHFdUVGTLNW7c2OFclXPVXVOSCgsLbf/trOHDh1ebYeEEAAAAoObq\n9R2fsrIyPffcc4qMjFTv3r2rzFit1hueo+J9Hmdz1TGbzdWeqyIHAAAAoH6o13d8Vq5cqezsbC1Y\nsED5+fmS/nOH59KlS8rPz5eXl5ckVfloWUlJiXx8fCRJXl5eKi8vV3l5udzc/vOxi4uLJckuV7Gt\nsso5b29vu23XXrPy+Wpi06ZN1Ways7MVHh5e43MDAAAAP2f1uvh89tlnKiwsVGRkpMO+J554QpL0\n7rvvSvrPOzyV5ebm6t5775UkBQQESLr6o6MV/135uIr3elq0aKEjR45Uea6KnI+Pj26//Xbl5eVV\nmfP19VWDBg2c/6AAAAAAalW9Lj4zZ87UxYsX7bZ98cUXWrJkiWbOnKkOHTqobdu28vDw0KFDh+xy\nubm5ysvLs63a1rFjR0nSwYMH7YrPwYMHJcm2QltISIg++eQTXbx40W557KysLHl5ealNmza28117\nzYrctSvHAQAAAKhb9fpFlE6dOqlXr152/wsODpZ0taj06tVLHh4e6tu3r95//327kpSWliaz2azB\ngwdLkrp37y5fX1+tXbvWlrFYLFq7dq2CgoLUuXNnSdKAAQNksVi0fv16Wy4/P1/btm3ToEGDbO/u\nDBw4UJmZmdq3b58tt2fPHp04caLKO1QAAAAA6k69vuPjrCeffFIxMTEaM2aMHnvsMR07dkxpaWka\nNWqUgoKCJElubm6aNm2aZs+erSlTpqhfv3768MMPtXfvXi1cuNBWaDp06KDo6Gjbe0V33XWXUlNT\nZbVaNXnyZNs1R44cqdTUVE2aNEmJiYmyWCxavny5QkNDFRUVVSffAwAAAICqGaL4BAcHa+XKlUpJ\nSVFycrKaNWumKVOmaNKkSXa5mJgYWSwWrVq1Srt371arVq00f/58RURE2OVeeukl+fr6asuWLSoq\nKlLnzp2VkpKiVq1a2TIeHh5as2aNkpOT9frrr8vDw0MPPfSQZs6cKXd395/kcwMAAABwjsnqzNrM\nqDcqVnXbuXOnAgMDa3z8kKTNtTBV9dLnDf3Jr/lz+qwAAAA/d9X9O7lev+MDAAAAAK5A8QEAAABg\neBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEA\nAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH\n8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAA\nAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQf\nAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABg\neBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEA\nAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH8QEAAABgeBQfAAAAAIZH\n8QEAAABgeBQfAAAAAIZH8QEAAABgeLdM8dmxY4diYmJ0zz336N5779XkyZN1/Phxu8zu3bv161//\nWl27dlX//v315ptvVnmuN998UwMHDlSXLl00YsQI7d692yFz+fJl/eEPf1Dv3r0VFhamiRMn6vvv\nv3fInTx5UpMmTdK9996r+++/Xy+++KIKCgpc86EBAAAAuMQtUXw+/fRTPfHEE5Kk6dOnKyEhQf/8\n5z/12GOP6d///rck6e9//7smTpwoDw8PzZgxQ2FhYUpOTtayZcvszrVkyRIlJycrLCxMs2bNkpub\nmyZOnKgvv/zSlrFarZo6dar+8pe/aMSIEZo6daoOHjyoMWPG6Pz587bc2bNnNXbsWB0+fFiTJ0/W\nyJEjtXHjRk2dOvUn+FYAAAAAOMutrgdwxiuvvKLg4GCtW7dObm5XRx4wYICGDRumFStW6Pnnn1dK\nSoruvvturV69Wu7u7ho9erSsVqsWL16sRx99VI0aNdK5c+e0ePFixcbG6sUXX5QkPfLIIxo6dKjm\nzZuntWvXSpJ27dql3bt3a968eYqKipIkPfjgg4qOjtaaNWv01FNPSZJWrVqlc+fOKSMjQ4GBgZKk\nwMBAzZ49W59//rnuv//+n/qrAgAAAFCFen/HJy8vTydOnNDDDz9sKz2S1K5dO7Vr10779u1Tdna2\nvv76aw0fPlzu7u62zJgxY1RYWKhdu3ZJkj7++GMVFxcrNjbWlvH09NSIESP05Zdf6syZM5KkjIwM\nNWrUSJGRkbZc27Zt1bNnT2VkZNi2ZWRk6IEHHrCVHulqkfLy8rLLAQAAAKhb9b74NGnSRNu2bVNM\nTIzDvvPnz6tBgwbKysqSJIWEhNjtr/g7MzNTkpSVlaUGDRqoffv2VeYOHjxoy3fo0EFms/3XExoa\nqpMnT6qgoEDnz59XTk6OQkND7TJubm5q37697ZoAAAAA6l69Lz5ubm5q06aNfH197bZ//PHHOnXq\nlMLCwpSbmytJat68uV3G3d1dd9xxh06fPi1Jys3Nla+vr92dI0ny8/OTJJ06dcqW8/f3d5ilInf6\n9OnrXrMiV3FNAAAAAHXvlnjH51p5eXl64YUX5Onpqbi4OG3dulWSdNtttzlkPTw8VFRUJEkqKCio\nMuPp6SlJNcqVlZXZbbv2moWFhTX+XMOHD682U1paWuPzAgAAAD939f6Oz7XOnz+vxMREnT59WrNn\nz1ZQUJCsVusNjzGZTJJUbe7aR9tulHPVuQAAAADUvlvqjk9eXp4SEhJ0+PBhPfnkk7b3fry8vCRJ\nxcXFDseUlJTIx8fHlqsqU7HN29vbqZyPj4+t2JSUlNzwmjWxadOmajPZ2dkKDw+v8bkBAACAn7Nb\n5rbE6dOnNWbMGB0+fFi/+93v9Nvf/ta2LyAgQNLVYlRZaWmpzp8/b3tfp0WLFsrPz5fFYrHLVbyv\nUzl37bkqcmazWb6+vrZrVhx7ba6qd4QAAAAA1I1bovhcvHhRCQkJOnnypJKSkmw/ZlqhY8eOkv6z\nKluFitXeKlZeCwkJUVlZmY4cOWKXqziucu7QoUMOj7NlZWWpdevW8vb2VuPGjXXnnXfq0KFDdpny\n8nIdPnzYYYU5AAAAAHXnlig+v//973Xs2DFNnTpVjz/+uMP+wMBAhYaGauPGjbZFByQpNTVV3t7e\n6tOnjyTpoYcekru7u9LS0myZ4uJi/eUv/9/evcfleP9/AH9dbkoRhkixmFPIaMJDbFSKIuccUg21\nyWHMchiaOUxMC9sMo1DOTIehUoochhG2Vc6Hjcipk6LT3f37w6/761YodXd1X72ej8cej3Vdn/u+\n3re4r+t1fa7P57MP3bp1Q8OGDQEAtra2SEtLU06aAAA3btzA6dOnVdb2sbW1RWxsLO7evavcFhwc\njGfPnqm0IyIiIiIicVX6MT6XLl1CWFgY9PX1YWhoiNDQUJX9enp6sLKygqenJ9zd3TF+/HgMGjQI\n586dw/79+zFnzhzleJv69evDzc0N69atQ35+Pjp16oTg4GAkJSVh+fLlyve0tLSEubk55s+fj5s3\nb0JPTw+bN29G48aN4ezsrGzn7u6OkJAQuLq6Yty4cUhJSYG/vz8sLS3RvXv3ivkDIiIiIiKit6r0\nwefMmTMAXozfmTNnTpH9LVq0gJWVFXr27Imff/4ZP/74I5YsWQJDQ0N4eXnBxcVFpf20adOgra2N\n3bt34+DBg2jTpg02btwIMzMzZRtBELB27VosX74cgYGBAABzc3PMnTsX7733nrJdw4YNsXXrVnh7\ne8PX1xd6enoYNWoUZsyYoY4/CiIiIiIiekeC4m3zMlOlUjirW3R0NJo2bVrq1zt4hr69kRrs9x1c\n4cesSp+ViIiIqKp723WyRozxISIiIiIiKgsGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIi\nIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIi\nkjwGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikjwG\nHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIi\nIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikjwGHyIiIiIikrzqYhdAROXD\nwTNUlOPu9x0synGJiIiISoM9PkREREREJHkMPkREREREJHkMPkREREREJHkMPkREREREJHkMPkRE\nREREJHkMPkREREREJHkMPkREREREJHkMPkREREREJHkMPkREREREJHkMPkREREREJHkMPkRERERE\nJHkMPkREREREJHkMPkREREREJHkMPkREREREJHkMPkREREREJHkMPkREREREJHkMPkREREREJHnV\nxS6AiKi0HDxDRTnuft/BohyXiIiIyo49PkREREREJHkMPkREREREJHkMPkREREREJHkMPkRERERE\nJHkMPkREREREJHkMPkREREREJHkMPkREREREJHlcx4eIqJLjukVERERlxx4fIiIiIiKSPPb4EBFR\npSJGD5dYvVvszSMiqjjs8SEiIiIiIslj8CkHt2/fhoeHB8zNzdGjRw8sXrwYWVlZYpdFRERERET/\nj4+6ldGTJ0/g6uqK6tWrY9KkScjIyMCmTZvw33//wc/PT+zyiIiIKo2q9BgjEVU+DD5ltGnTJqSm\npiI8PBxNmzYFADRt2hReXl44deoUevToIXKFRERERETE4FNG4eHh6NmzpzL0AMDQoUPh7e2N8PBw\nBh8iIqIqiBNXEFU+DD5lkJaWhqSkJAwdOlRle/Xq1dG2bVskJCSIVBkRERFRxeFjjKQJGHzK4OHD\nhwCAxo0bF9mnr6+P8+fPl+r9hg0b9tY2OTk5AIDk5ORSvXehvGcp7/S6srp7926FH7MqfVagan3e\nqvRZAX7eilCVPitQtT5vVfqsQNX6vGJ9VvelUaIc12++jSjH1SSF18dyubzY/YJCoVBUZEFScuHC\nBYwePRo+Pj4YNGiQyr6ZM2ciOjoaFy5cKPH7lST4ZGVl4fbt26UtlYiIiIioSti+fTvMzc2LbGeP\nTxm8LTNWq1a62cKDgoLe2iY7Oxvx8fHQ19eHTCYr1fsTEREREUmVXC7Ho0ePYGpqWux+Bp8y0NXV\nBfC/x89elpOTg9q1a5f7MWvWrFlsgiUiIiIiquqMjY1fu48LmJZBkyZNAPxvrM/LHj58iEaNGlV0\nSUREREREVAwGnzKoW7cujIyMcPnyZZXt+fn5uHr1Ktq3by9SZURERERE9DIGnzKytbVFbGysyswi\nwcHBePbsGezt7UWsjIiIiIiICnFWtzJ6/PgxBg4cCF1dXYwbNw4pKSnw9/dHz549sX79erHLIyIi\nIiIiMPiUi2vXrsHb2xvnz5+Hnp4e+vfvjxkzZqBWrVpil0ZERERERGDwISIiIiKiKoBjfIiIiIiI\nSPIYfIiIiIiISPIYfIiIiIiISPIYfIiIiIiISPIYfIiqkMTERBQUFIhdBhHRO8vNzUVYWJjYZRCR\nBiLr6MgAACAASURBVOKsbkRVSI8ePTBixAh4enqKXUqlkpmZidq1a4tdRqndu3cP9evXR82aNcUu\nRXQPHjzAP//8A0EQYGZmhvr164tdEpWzuLg4hISE4NChQ3j69CkuXbokdklESiYmJhAE4bX7tbW1\noaenhzZt2sDGxgaOjo6QyWQVWGH508RzUHWxC6DKYe7cuRg9ejQ6deokdikVTqFQFPmyunLlCuLi\n4qCjo4NevXpBX19fpOrKV25uLgwMDMQuo0Lk5eXh1q1bUCgUaNGiBbS0tIptd+jQISxduhTHjh2r\n4ArLztraGitWrICDg4PYpVSIe/fuYevWrbhx4wbef/99fPrpp2jWrBl++uknbNiwAXK5HAAgk8kw\nbtw4zJw5U+SKy0dycnKRf7cxMTHK76g+ffrA1NRUpOrU686dOwgNDcXvv/+OO3fuQKFQoE6dOhg7\ndqzYpb2zkJAQmJubo2nTpiVqf/bsWQQEBGDNmjVqrqxyWLVqFQ4ePIjDhw+LXUqpDBky5I3BJz8/\nHxkZGbh69SoWLlyI8PBwbNy48bXnJk2giecgBh8CAAQHB8PCwkIl+GRlZWHOnDmYNm0a2rRpI2J1\n6pGQkIBly5YhISEBFy5cUG5ftmwZAgMDAbwIRTVq1MCcOXPg7OwsVqnlZurUqfD390fTpk3RpUsX\njezlKIkNGzbA398fGRkZAAAdHR1MnDgREydOVLZ58OABFi9ejJiYGFSvrplfhVWpw/7KlStwcXFB\nZmYmDAwMcPbsWYSGhmLy5MlYu3YtunfvDhsbGxQUFCAiIgL+/v4wNDSEk5OT2KW/s6NHj2LJkiV4\n8OAB4uPjAbz4nU+fPh1RUVHK3/8vv/yCcePGYc6cOWKWW24yMzMRHh6OkJAQnD9/Xrm9ffv2cHFx\ngZ2dHbS1tUWssGzmzp2LFStWqASfjIwMDB06FD/88APMzMxU2t+/fx/R0dEVXaZo0tLSkJSUJHYZ\npbZ8+fISt42MjISnpye2bt0KNzc3NValXpp4DtLMsz1ViNzcXBw+fFij76y9zr///gtnZ2fIZDLY\n2NhALpdDJpMhLCwMAQEBqFOnDpYsWYKWLVti7969WLp0KVq2bIkePXqIXXqZhIaGIjU1FR4eHgCA\n6tWro1o11aF+giDg4sWLYpRXLgICArBy5Uo0bNgQo0aNgo6ODk6cOIHVq1ejdu3aGDt2LA4fPox5\n8+YhIyMDH330EZYsWSJ22fQWq1evRr169bBv3z40a9YM2dnZmD17NlasWAErKyusXbtW2dbFxQVO\nTk4ICgrS2OATHx+PKVOmoHHjxvjss89QUFCAatWqYdeuXYiMjISBgQHWrFmj/I5avnw5PvzwQ9jZ\n2Yld+jspKCjA8ePHERoaipiYGOTk5EChUMDU1BRdunRBYGAgJk+eDGtra7FLLbPiLhblcjmSkpKQ\nnZ0tQkVU0WxtbTFo0CD8/vvvGh18NBGDD1VJ69evh46ODvbt24cmTZoot2/atAmCIGDatGno168f\ngBd3565cuYJNmzZpfPBp164d2rVrJ3YZahUcHAxjY2Ps27dP2aM1e/ZszJo1C5s2bULDhg0xY8YM\n6OrqYvHixRg5cqTIFZfNmx6tkJILFy5gwoQJaNasGQCgZs2amDx5MiIjI4tc7AuCgEGDBsHHx0eM\nUsuFn58fDAwMEBoaqtIzu337dgiCgC+//FL5eJurqyvOnDmDHTt2aGTwWb58OQ4cOIAnT55AJpOh\nW7dusLa2Rt++fdG4cWMkJSUhICBA7DKJylXnzp0REREhdhllpmnnIAYfqpJOnz4NR0dHldDz6NEj\nxMfHQyaTYeDAgSrtX72jrKmWLVsmdglqd+fOHUycOFHlYlEQBIwbNw4HDhzA/PnzYWZmBl9fX0mM\nd/L29saqVatK3F4QBI17dh4A0tPTi4y1K/y5QYMGRdrXqlULz58/r5Da1CEuLg5jxoxR+Xt89+5d\nXL9+HTVq1ICtra1KewsLC6xevbqiyywXW7Zsga6uLtzd3eHm5oZ69eqJXRKR2uXn52v85AaA5p2D\nGHyoSnr8+LHyznGhU6dOAQBMTU2LnHh1dXXx7NmzCqtP3e7cuYNjx44hOTkZI0aMgI6ODu7cuYMu\nXbqIXVqZZWVlqQTaQoXbWrVqhYCAAI0d1/OqevXqFXvhLzUKhaLI76zwMU1Nu+NYEmlpaWjUqJHK\ntsLvqM6dO0NXV1dln5aWFvLy8iqsvvI0cuRIREZGYuPGjdi+fTu6dOkCKysrWFtbF/kzIJKKM2fO\n4P333xe7jDLTtHOQNM78VC5ed/EgxYsKPT09pKWlqWw7ceIEBEFAr169irS/deuWRv3DfpOVK1fC\n398fcrkcgiDAwsICmZmZ+OKLL9CvXz/4+Pho9Cwzr1N4kezq6iqZ0AMAkyZN0qgZdahk6tWrh8eP\nH6tsO3bsGARBwCeffFKk/ZUrV9CwYcOKKq9cLV68GAsWLMDJkydx4MABxMTE4Pjx41iyZAk6duwI\nMzMzSZ6Hqoq5c+eWqv3Lkw1JkVwuR0BAAA4dOoR58+aJXU6Zado5SDpnfyqzdevWYc+ePcqf8/Pz\nIQgCli9fjjp16hRpLwiCxj53bWZmhvDwcLi5uUEQBKSmpiq7Xvv376/SNjMzE6GhoejWrZsYpZar\n7du3Y8OGDZgwYQKsrKyUM9WZm5vD2dkZ27ZtQ9u2bTF58mSRK1Wf9957T+wS6B1FRUXh33//Vf6c\nnZ0NQRAQGhqKuLg4lbZXr16t6PLKVffu3RESEoLx48dDW1sbd+/exdGjRyEIQpFxPA8ePEBoaChs\nbGxEqrbsqlevjt69e6N3797IyclBTEwMDh48iOPHj+Ovv/4C8GKCi6SkJDg4OGj8v+OqdKMxODhY\n7BIqhKur6xv3y+VyZGVl4d9//8Xz58/Rt29fScwWq2kYfAgAYGhoiOfPn+Pu3bsq25s0aYKMjAzl\ntMBS8fnnn8PJyQljx45F9+7dERUVhWfPnqFfv35o3bo1gBcXVRcuXICvry9SUlLe+qWmCbZv347+\n/ftj9uzZSE1NVW5/77334OXlhadPnyqnCNZkb7p4kOKFRVURGRmJyMjIIttDQkKKba/Jv+spU6Zg\n2LBhGDhwIMzMzPDHH38gLy8PY8aMUU6DnJycjOPHj2P9+vXIycmRzOxQ2trasLOzg52dHTIzMxEV\nFYUDBw7gzJkz8Pb2ho+PD/r06YOff/5Z7FLf2avjIgpneps5c2aRqbo1eawaAFy+fFnsEirEq9dP\nr9LW1kbdunVhb2+vfJSTKh6DDwF4sRheVdKpUyesXbsW3t7eWLduHWQyGezt7bF48WJlm1WrVinH\ngixatAidO3cWseLycefOnTcGuC5dukhilplXey+BN/dgamrvpaGhYZGxHlJVldYxAYAWLVpgx44d\n8PHxQVRUFGrVqoXPPvsM06dPV7YJCAjA5s2bUa9ePfz8889o2bKliBWrR+3atTF06FAMHToUKSkp\niIiIwIEDBzT670PXrl2L3W5kZFTBlVSMsLCwUr+muJ7Nyq6011EXL15ESEgIFi5cqJ6CKoAmnoME\nhSauPkRUjlJTU1GzZk3o6OiobD916hSuX78OW1tbNG7cWKTqylefPn3g4OAAT09PpKamokePHti8\nebNymu6FCxciNjYWR44cEbnSd2dlZfVOr9PE8P/333+/0+s+/PDDcq6kYt26dQstWrQA8KJn1s/P\nr0ib4cOHFzvJhZQkJiYiOTkZPXv21OgFPQudO3cOe/bswYoVK4rsW7RoEW7duoWJEyeiR48eSE5O\nlsSsjFWBiYkJBEF462KXr/bQXrp0SZ1lieLevXsIDQ1FSEgI/vvvPwCa/znPnz+PtWvX4uLFi5DL\n5Wjfvj0mTJhQaXu02ONDRaSmpuLMmTO4fPky0tLSIAgC6tevDxMTE1hYWKBWrVpil1hmZ8+efe0+\nQRCgpaWFJk2aoHv37kUW+NRkdnZ22L59O/r06YMPPvgAwP9ONiEhIfjtt980dsHHQpoYYN7VyJEj\nS/U4l0KhgCAIGnuivXXrFry8vHD+/Hn89ttv6NChA54/f441a9aotBMEAXFxcdi8ebNIlZbdmjVr\nYGtrizZt2ry2Tfv27dG+fXsAQGxsLFavXq2x4yk2btwIX19fyGQyTJo0SRlsC2VlZeHs2bM4c+YM\npkyZgqlTp4pUafk6cuQIjh8/Xuz51srKSuPXjgOAwMDAt7YpHPB/9OhRAEXH2mqyrKwsREREICQk\nBHFxccoAaGFhgVGjRolcXdn8+eefmDBhAuRyOVq3bg2ZTIb4+HhMnToV3377LUaPHi12iUWwx4eU\nsrOz8f333yMoKAi5ublF7s4UBoJRo0bhq6++Qs2aNUWqtOwK70C9Tc2aNWFpaQlPT09JPIbw/Plz\nuLu748KFCzAwMMD9+/fRqlUrZGRk4MGDB2jXrh22bt2qsnaIpmnXrh18fHyKrMWUmZkJXV1dSQXZ\noKCgdxrHMnToUDVUo14pKSlwcHBAfn4+3NzcMGLECNSvX1/Zc/njjz8qp2P/6aefsHfvXo1edNjE\nxAQ+Pj4qsyWlpqbCwsKi2M/1+++/Y86cORoZag8fPoypU6eid+/eWLBgwWu/a5OTkzF79mycPXsW\nGzZswMcff1zBlZafGzdu4Msvv8T169ehUCigo6ODOnXqID8/HxkZGcjLy4MgCGjXrh1++OEH5Y0q\nKYqLi8OiRYtw9epVNG/eHAsWLICFhYXYZZWJQqHAyZMnERISgujoaGRnZytvPA0ZMgRTp06VxDXF\n+PHjcfv2bfj5+SkftX348CE8PDxw//59/PHHH5VurCV7fAjAi9Dj5OSExMREWFhYYODAgWjdujXq\n1q2LvLw8pKWlITExEREREQgMDMTFixexdetWjX284m0LeRaefK5cuYLo6GhcuHABu3fv1vg1JXR0\ndBAYGIigoCBER0dDR0cHeXl5aNmyJdzc3DB69GiNn8paoVAUCe1vumDUZMOGDRO7hAqzceNGPHv2\nDEFBQUV6AwCgTp06yumcv/76a0RHRyM4OFhSv28Ab31cSBMFBATAxMQE69evf+NFkoGBATZu3IiB\nAwdiy5YtGht8kpKSMGbMGOTm5mLatGlwcHBQTlgBAAUFBbh+/TrCw8MREBAAFxcXBAUFSeaR60Ip\nKSnw8fFBSEgItLW1MX36dLi7u6NGjRpil/bOrl+/juDgYOzfvx+PHj2CQqGAsbExrK2tYWpqiq++\n+gp9+/aVROgBgISEBHh4eKiML2zUqBG++uorfPbZZ7h582alG3vI4EMAAD8/PyQmJmLJkiVwdHQs\ntk2XLl3g4uKCkJAQzJ07FwEBAfj8888ruNLyUZo73klJSXB0dMSGDRvg5eWlxqoqhkwmg6Oj42t/\nz1IlxQvGquTIkSMYNmxYsaHnVbq6urC3t9foAfBVSWJiIiZPnlyiO8Pa2toYPHgwtmzZov7C1GT9\n+vXIy8vDrl27YGJiUmR/tWrV0KZNG7Rp0wYDBgyAo6MjNm7cKInzT6GdO3di9erVSE9Ph6WlJby8\nvDQ+DAwbNkzZ42pqagonJydYW1srZ4pNSkoSszy1yMrKQv369Ytsb9WqFRQKhcrssZUFgw8BACIi\nItCvX78SXQwPGTIEMTExiIiI0NjgUxpGRkYYPnw4Dh06JHYp5SI/Px/x8fF4/PgxcnNzi21jb29f\nwVURvdn9+/fRrl27Ittr1KiBTp06QU9PT2V7mzZtsHv37ooqj8pAoVCUauyovr4+5HK5GitSr5Mn\nT2L48OHFhp5XtWrVCkOGDMHx48croDL1i4+Px8KFC5GQkABDQ0MsX74clpaWYpdVLhITE6Grq4sx\nY8bA1tYWHTp0kNRi2cWRy+WQyWRFthc+DZSXl1fRJb2VtH8jVGJJSUmlGoTWtWtXnDhxQo0VVS4t\nWrTAgwcPxC6jzK5du4bPP/8cycnJr+0BEQSBwYcqHV1dXeTk5BTZXrt27WIDTmZmZrELL1Pl06xZ\ns1KNTbp06RIMDQ3VWJF6PX78WNkLUBJt27bFvn371FiR+j19+hS+vr7Yu3cvqlWrhokTJ2LSpEka\n+7h8cXx9fXHw4EEEBgZi06ZN0NXVxSeffIK+ffuid+/eYpdH/4/BhwC8GOPz6h3TN6lTp47GL6pW\nGhkZGRo94L/Q0qVLkZqaimnTpsHU1FRSJx2SNmNjY/z5558YO3ZsidqfOHGi0j1bTsWzt7fHunXr\nMH78eDRv3vyNbW/fvo3Q0FCMHDmyYopTg9zc3FKtfaKrq1sp75yXVHBwMH744QekpKTAwsICCxYs\ngLGxsdhllbsBAwZgwIABePr0KSIiInDw4EFERkYiIiIC1atXR4cOHSAIAvLz88UutVxp2oLhDD4E\n4MWjBqWZ7aoy/mVWl4KCAoSFhZXosYTK7q+//oKbmxsmTZokdilqlZaWhnv37il/Tk9PB/BiMO3L\n21+myXeQq4KBAwdi2bJlOHfuHMzNzd/YNjY2FqdOncLSpUsrqDr1eN33rNS+f52cnLBnzx44Oztj\n3rx5sLOzK/IZCwoKEBERge+//x5aWloYP368SNVSac2dO1f5/+fOncOgQYPe+hpBEHDx4kV1lqU2\nenp6ynG0jx8/RlhYGMLCwpSfx9PTE/v378ewYcPQu3fvYh8V0ySzZs3CrFmzit1X3L9TQRCQmJio\n7rJei8GHlKR2Mi2LgoICZGVl4dq1a/Dz88M///yDdevWiV1WmdWqVQsNGjQQuwy18/b2hre3d5Ht\nM2fOfO1rNHEa4Kpk+PDh2L17Nz7//HN4eHjA1dW1yJT6OTk52LNnD1atWoWOHTuqTAWtiV53QSG1\ni349PT2sX78eU6ZMgaenJ7799lt06NABDRo0QEFBAZ48eYKEhAQ8e/YMBgYG8Pf31/jFS6vS+XbI\nkCFV6vO+rGHDhnB1dYWrqyuSkpJw8OBBHDx4EIcPH0Z0dDQaNGig0cMGNHFpBK7jQwBerBnRsmXL\nEl8UP3nyBDdv3tTYi8WSruMDvBikN2PGDHz66adqrkr9vv/+e5w5cwa7du3S+GmrX+fVxSxLSioL\nIkrZvXv38MUXXyAhIQFaWlowNTWFgYEBFAoFHj9+jPj4eDx//hwdO3bEL7/8otHTz798l7w03jZV\nf2WWnZ2Nbdu2ITw8HFeuXFE+ElSjRg107twZtra2GDVqlMZ/d5Xm/PMyTT3fUlE3btzA/v37ER4e\nLpmJkzQFgw8BAKysrN7pdTExMeVcScX4+uuv33ji0dLSQt26ddGyZUv07NlTuT6Iptm4caPKz7m5\nufDz84O+vj4sLS3RoEGDIn8OgiDA3d29IsskKjGFQoGQkBAcOHAAcXFxyM7OBvC/2d0cHBwwYsQI\njX98pKornApXJpOhbt26YpdTrqpiqCWqLBh8iCTsXcYlCYLAO4ukMdLT01FQUID33ntP7FKIiKiS\nY/AhAEBYWFipXyMIAuzs7NRQDZWXd10wTdMXkiMiIiJ6FYMPAfjfM8dv++vw6mNR7BnQLCEhITA3\nN0fTpk2L3X/jxg1ER0dXiYVpiYiIqGrhrG4EAAgMDHxrG7lcjoCAABw9ehQA0L9/fzVXReVt7ty5\n8PHxeW3wOXnyJNasWcPgQ0RERJLDHh8qkbi4OCxatAhXr15F8+bNsWDBAlhYWIhdFr3FnTt3MGnS\nJBQUFAAAbt68iUaNGhW7GGtBQQGSkpJgZGSEiIiIii6ViIiISK3Y40NvlJKSAh8fH4SEhEBbWxvT\np0+Hu7s7atSoIXZpVALNmjWDnZ0dTp8+DeBF8Kldu3ax05bLZDK0b98eEyZMqOgyiYiIiNSOPT70\nWjt37sTq1auRnp4OS0tLeHl5cdC7hrOyssL8+fNhbW2N9PR03L9/HzVq1ECjRo2gp6cndnlERERE\nasPgQ0XEx8dj4cKFSEhIgKGhIby8vGBpaSl2WVROLl++jO+++w7nz59XTmYhCAI++ugjzJs3D+3b\ntxe5QiIiIqLyx+BDSk+fPoWvry/27t2LatWqwc3NDZMmTYK2trbYpVE5uXr1KkaPHg2FQoHBgwej\nZcuWkMvluHnzJvbv3w8A2LNnD1q3bi1ypURERETli8GHAADBwcH44YcfkJKSAgsLCyxYsADGxsZi\nl0XlzMPDAwkJCdi7dy8MDAxU9iUnJ8PR0RFmZmb46aefRKqQiIiISD0YfAjAi3V8CpW0h0cQBFy8\neFFdJZEamJubw93dHR4eHsXuX7duHbZs2YIzZ85UcGVERERE6sVZ3QgAMGTIkCKLk5L05Ofno2bN\nmq/dr6Ojg9zc3AqsiIiIiKhisMeHqApxdnZGRkYGfvvtN2hpaansy87OhqOjI/T09LBjxw6RKiQi\nIiJSDwYfoirk1KlTcHNzg7GxMZydndG8eXMAwK1bt7Bt2zb8999/2LBhA3r16iVuoURERETljMGH\nqIqJiorC4sWL8ejRI+XjjQqFAg0bNsT8+fNhZ2cncoVERERE5Y/Bh6gKksvlSEhIwN27dwEARkZG\n6NChA6pX57A/IiIikiYGHyIiIiIikrxqYhdARERERESkbgw+REREREQkeQw+RERE5SwjIwMpKSkl\natu2bVu4uLiouSIiImLwISIiKkfHjh2Dra0trl27VqL2K1asgIeHh5qrIiIiTuFERERUjv766y+k\npqaWuP3gwYPVWA0RERVijw8REREREUkegw8REWmUW7duYdasWejVqxc6deqEAQMGwN/fH/n5+co2\nwcHBGDFiBD788EOYmZnB2dkZMTExKu8TFBSEtm3bIigoqMgxXh138/XXX8PMzAx37tzBtGnT0LVr\nV3Tq1Amurq64cOGCsp2LiwvWrFkDAHB1dYWVlRUA4Oeff0bbtm1x9OhR9O/fH6amphg/fnyxxwKA\n9PR0LFu2DFZWVjA1NcXHH3+Mb775Bg8fPlRp9+zZM3z33Xfo168fOnbsiB49euCLL77A5cuX3+WP\nlohI0vioGxERaYzLly/DyckJCoUCY8aMwfvvv48//vgDK1aswI0bN+Dt7Y1vv/0Wu3btQqdOnTBj\nxgzk5uYiKCgIkyZNwqxZs+Du7v5Ox87Ly4OTkxM6duyIr776Cvfv38fmzZsxYcIEHDlyBPXq1YOH\nhwfq1q2LqKgoeHh4oGPHjirv4enpiZEjR6Jp06bQ0dEp9jhpaWkYNWoUkpOTMXLkSLRs2RI3b97E\n7t27ERsbi71796Jx48YAgC+//BKnT5+Gs7MzWrRogeTkZAQGBuLkyZMIDw9XtiMiIgYfIiLSIN99\n9x3y8vIQFBSE1q1bAwBGjx6N6dOnY9++fRgyZAh27dqFjz/+GL/++itkMhmAFz0xjo6OWLlyJWxs\nbGBsbFzqY+fl5cHGxgYLFixQbtPR0cHq1asRGRmJkSNHomfPnjh//jyioqJgYWGB7t27q7xHv379\nMGfOnDceZ+XKlbh79y62bdsGMzMz5XYHBweMGjUKK1asgK+vL1JSUhAbGwsnJyfMnj1b2c7ExASr\nVq1CQkICgw8R0Uv4qBsREWmElJQUnDt3Dp988oky9BTy8vLCgQMHEBYWBgCYPHmyMvQAgK6uLiZO\nnAi5XI5Dhw69cw0ODg4qP5uamgIAHj16VKLX9+jR4437FQoFIiIi8MEHH8DY2BgpKSnK/4yMjNCu\nXTvExMQgPz8ftWvXRu3atREeHo7du3fjyZMnAAAbGxuEhYUpH7MjIqIX2ONDREQa4d69e1AoFPjg\ngw+K7NPX14e+vj6SkpIAAK1atSrSpjAs3b17951raNiwocrPWlpaAICCgoJ3ev2rUlJSkJ6ejvT0\n9DeGpNTUVOjr62Pp0qWYP38+FixYgG+//RYmJib45JNPMHToULRo0aJENRERVRUMPkREpBEKJy8Q\nBOG1bRQKxWv3FYaTwrDytuMU503HLolq1d78oEVhjV26dMHUqVNf265OnToAgP79++Pjjz9GbGws\nTpw4gdOnT+PXX3+Fn58fVq5cif79+5epXiIiKWHwISIijWBkZAQAuH37dpF9//zzD7Zs2YKcnBwA\nwPXr1/HRRx+ptClcUNTQ0BAAlI/CZWdnq7R7/PhxudZdGvXr14euri6ePn0KCwuLIvuPHTsGbW1t\naGlpITMzE1evXoWRkRHs7e1hb28PAPjzzz8xfvx4bNiwgcGHiOglHONDREQaQV9fH507d0ZsbCz+\n/fdflX2BgYE4cOCA8kJ/7dq1kMvlyv3Pnz+Hn58fqlWrBmtrawBAo0aNAADx8fEq7xUaGlqmOgt7\ndUr6+NvLZDIZrK2tcfXqVYSHh6vs+/vvvzFx4kSsWrUKgiDg+vXrGDNmDNatW6fSrkOHDtDS0kL1\n6ry3SUT0Mn4rEhGRxvjmm2/g4uKCESNGwMnJCU2aNMHJkycRGRmJcePGYezYsUhMTMRvv/2GMWPG\nwN7eHrm5uQgODsbNmzcxY8YM5Yxu3bp1g5GREYKCgqCjowMTExOcP38eMTExbx2L8yaFr925cyce\nPnyIwYMHl+r1s2bNwp9//glPT0+cOHECHTt2xL1797Bjxw7UrFkT8+fPBwB07twZvXr1ws6dO/H0\n6VN07doVOTk5CA0NxfPnz5XrBBER0QsMPkREpDFMTU2xd+9e/PTTT9i1axdycnLQvHlzLFmyBCNG\njADwYsrrDz/8ELt378bKlSuhpaWFDh06YPbs2bC0tFS+l0wmg5+fH3x8fBAcHAyFQoGuXbti+/bt\n8PT0fOcaBwwYgKioKBw9ehSnTp2CjY1NqV7fuHFj7Nu3D+vWrcORI0cQGhqK+vXro1evXpg8eTLa\ntGmjbPvjjz/C398f4eHhiI6OhkwmQ4cOHfDrr7+id+/e7/wZiIikSFC8aSQoERERERGRBHCMDxER\nERERSR6DDxERERERSR6DDxERERERSR6DDxERERERSR6DDxERERERSR6DDxERERERSR6DDxERetVU\nwQAAACZJREFUERERSR6DDxERERERSR6DDxERERERSR6DDxERERERSd7/ASWMFHjP7/mnAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1064b5b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target.value_counts().plot(kind='bar').set(xlabel='countries', ylabel='frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "\n",
    "1. Replacing missing data with -1\n",
    "2. Dropping the \"date_first_booking\" column\n",
    "3. Removing erroneous ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                          0.000000\n",
       "date_account_created        0.000000\n",
       "timestamp_first_active      0.000000\n",
       "date_first_booking         67.733998\n",
       "gender                      0.000000\n",
       "age                        42.412365\n",
       "signup_method               0.000000\n",
       "signup_flow                 0.000000\n",
       "language                    0.000000\n",
       "affiliate_channel           0.000000\n",
       "affiliate_provider          0.000000\n",
       "first_affiliate_tracked     2.208335\n",
       "signup_app                  0.000000\n",
       "first_device_type           0.000000\n",
       "first_browser               0.000000\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "id                          0.000000\n",
       "date_account_created        0.000000\n",
       "timestamp_first_active      0.000000\n",
       "date_first_booking         58.347349\n",
       "gender                      0.000000\n",
       "age                        41.222576\n",
       "signup_method               0.000000\n",
       "signup_flow                 0.000000\n",
       "language                    0.000000\n",
       "affiliate_channel           0.000000\n",
       "affiliate_provider          0.000000\n",
       "first_affiliate_tracked     2.841402\n",
       "signup_app                  0.000000\n",
       "first_device_type           0.000000\n",
       "first_browser               0.000000\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "id                           0.000000\n",
       "date_account_created         0.000000\n",
       "timestamp_first_active       0.000000\n",
       "date_first_booking         100.000000\n",
       "gender                       0.000000\n",
       "age                         46.502190\n",
       "signup_method                0.000000\n",
       "signup_flow                  0.000000\n",
       "language                     0.000000\n",
       "affiliate_channel            0.000000\n",
       "affiliate_provider           0.000000\n",
       "first_affiliate_tracked      0.032208\n",
       "signup_app                   0.000000\n",
       "first_device_type            0.000000\n",
       "first_browser                0.000000\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating % of missing values\n",
    "display((users.isnull().sum()/users.shape[0])*100)\n",
    "display((train_users.isnull().sum()/train_users.shape[0])*100)\n",
    "display((test_users.isnull().sum()/test_users.shape[0])*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "users.index = users.id\n",
    "users = users.fillna(-1)\n",
    "users = users.replace('-unknown-', -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Date of first booking is missing in the test dataset and as it is inconsistent with the training set data, I have decided to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "users = users.drop(['date_first_booking'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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73XfzuMlk0oIFCwK+p7CwUKdOnVJpaamSk5MlScnJySooKNCuXbs0evRonzFN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\nuaEoyRERERERkRuKkhwREREREbmh/H8nO6hqEAp7PgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fcb9940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(users.age.dropna())\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Distribution')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/indexing.py:141: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "users.age.loc[users.age < 15] = np.nan\n",
    "users.age.loc[users.age > 100] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v9pkAAIArQTABAoCN7xdlpvnuM+GgRQAA0DcEEyAAugSTMF7KJUnxMZEaHhcl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\nwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114088f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(users.age.dropna())\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Distribution')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# One-hot encoding the age according the below intervals)\n",
    "interv =  [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 100]\n",
    "def get_interv_value(age):\n",
    "    iv = 20\n",
    "    for i in range(len(interv)):\n",
    "        if age < interv[i]:\n",
    "            iv = i \n",
    "            break\n",
    "    return iv\n",
    "users['age_interv'] = users.age.apply(lambda x: get_interv_value(x))\n",
    "users_ai = pd.get_dummies(users.age_interv, prefix='age_interv')\n",
    "users = users.drop(['age_interv'], axis=1)\n",
    "users = pd.concat((users, users_ai), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Splitting the \"date_account_created\" column into year, data, month, weeknumber and weekday\n",
    "dac = np.vstack(users.date_account_created.astype(str).apply(lambda x: list(map(int, x.split('-')))).values)\n",
    "users['dac_y'] = dac[:,0]\n",
    "users['dac_m'] = dac[:,1]\n",
    "users['dac_d'] = dac[:,2]\n",
    "dac_dates = [datetime(x[0],x[1],x[2]) for x in dac]\n",
    "users['dac_wn'] = np.array([d.isocalendar()[1] for d in dac_dates])\n",
    "users['dac_w'] = np.array([d.weekday() for d in dac_dates])\n",
    "users_wd = pd.get_dummies(users.dac_w, prefix='dac_w')\n",
    "users = users.drop(['date_account_created', 'dac_w'], axis=1)\n",
    "users = pd.concat((users, users_wd), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the \"timestamp_first_active \" column into year, data, month, hour, weeknumber and weekday\n",
    "tfa = np.vstack(users.timestamp_first_active.astype(str).apply(lambda x: list(map(int, [x[:4],x[4:6],x[6:8],x[8:10],x[10:12],x[12:14]]))).values)\n",
    "users['tfa_y'] = tfa[:,0]\n",
    "users['tfa_m'] = tfa[:,1]\n",
    "users['tfa_d'] = tfa[:,2]\n",
    "users['tfa_h'] = tfa[:,3]\n",
    "tfa_dates = [datetime(x[0],x[1],x[2],x[3],x[4],x[5]) for x in tfa]\n",
    "users['tfa_wn'] = np.array([d.isocalendar()[1] for d in tfa_dates])\n",
    "users['tfa_w'] = np.array([d.weekday() for d in tfa_dates])\n",
    "users_wd = pd.get_dummies(users.tfa_w, prefix='tfa_w')\n",
    "users = users.drop(['timestamp_first_active', 'tfa_w'], axis=1)\n",
    "users = pd.concat((users, users_wd), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating season each from \"date_account_created\" and \"timestamp_first_active\"\n",
    "\n",
    "Y = 2000 # dummy leap year to allow input X-02-29 (leap day)\n",
    "seasons = [(0, (date(Y,  1,  1),  date(Y,  3, 20))),  #'winter'\n",
    "           (1, (date(Y,  3, 21),  date(Y,  6, 20))),  #'spring'\n",
    "           (2, (date(Y,  6, 21),  date(Y,  9, 22))),  #'summer'\n",
    "           (3, (date(Y,  9, 23),  date(Y, 12, 20))),  #'autumn'\n",
    "           (0, (date(Y, 12, 21),  date(Y, 12, 31)))]  #'winter'\n",
    "def get_season(dt):\n",
    "    dt = dt.date()\n",
    "    dt = dt.replace(year=Y)\n",
    "    return next(season for season, (start, end) in seasons\n",
    "                if start <= dt <= end)\n",
    "users['season_dac'] = np.array([get_season(dt) for dt in dac_dates])\n",
    "users['season_tfa'] = np.array([get_season(dt) for dt in tfa_dates])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#One-hot-encoding features\n",
    "ohe_feats = ['gender', 'signup_method', 'signup_flow', 'language', 'affiliate_channel', 'affiliate_provider', 'first_affiliate_tracked', 'signup_app', 'first_device_type', 'first_browser']\n",
    "for f in ohe_feats:\n",
    "    users_dummy = pd.get_dummies(users[f], prefix=f)\n",
    "    users = users.drop([f], axis=1)\n",
    "    users = pd.concat((users, users_dummy), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>age_interv_4</th>\n",
       "      <th>age_interv_5</th>\n",
       "      <th>age_interv_6</th>\n",
       "      <th>age_interv_7</th>\n",
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       "      <th>age_interv_10</th>\n",
       "      <th>age_interv_11</th>\n",
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       "</table>\n",
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       "</div>"
      ],
      "text/plain": [
       "                    id   age  age_interv_4  age_interv_5  age_interv_6  \\\n",
       "id                                                                       \n",
       "gxn3p5htnn  gxn3p5htnn   NaN             0             0             0   \n",
       "820tgsjxq7  820tgsjxq7  38.0             0             0             0   \n",
       "4ft3gnwmtx  4ft3gnwmtx  56.0             0             0             0   \n",
       "bjjt8pjhuk  bjjt8pjhuk  42.0             0             0             0   \n",
       "87mebub9p4  87mebub9p4  41.0             0             0             0   \n",
       "\n",
       "            age_interv_7  age_interv_8  age_interv_9  age_interv_10  \\\n",
       "id                                                                    \n",
       "gxn3p5htnn             0             0             0              0   \n",
       "820tgsjxq7             0             1             0              0   \n",
       "4ft3gnwmtx             0             0             0              0   \n",
       "bjjt8pjhuk             0             0             1              0   \n",
       "87mebub9p4             0             0             1              0   \n",
       "\n",
       "            age_interv_11            ...             first_browser_Silk  \\\n",
       "id                                   ...                                  \n",
       "gxn3p5htnn              0            ...                              0   \n",
       "820tgsjxq7              0            ...                              0   \n",
       "4ft3gnwmtx              0            ...                              0   \n",
       "bjjt8pjhuk              0            ...                              0   \n",
       "87mebub9p4              0            ...                              0   \n",
       "\n",
       "            first_browser_SiteKiosk  first_browser_SlimBrowser  \\\n",
       "id                                                               \n",
       "gxn3p5htnn                        0                          0   \n",
       "820tgsjxq7                        0                          0   \n",
       "4ft3gnwmtx                        0                          0   \n",
       "bjjt8pjhuk                        0                          0   \n",
       "87mebub9p4                        0                          0   \n",
       "\n",
       "            first_browser_Sogou Explorer  first_browser_Stainless  \\\n",
       "id                                                                  \n",
       "gxn3p5htnn                             0                        0   \n",
       "820tgsjxq7                             0                        0   \n",
       "4ft3gnwmtx                             0                        0   \n",
       "bjjt8pjhuk                             0                        0   \n",
       "87mebub9p4                             0                        0   \n",
       "\n",
       "            first_browser_TenFourFox  first_browser_TheWorld Browser  \\\n",
       "id                                                                     \n",
       "gxn3p5htnn                         0                               0   \n",
       "820tgsjxq7                         0                               0   \n",
       "4ft3gnwmtx                         0                               0   \n",
       "bjjt8pjhuk                         0                               0   \n",
       "87mebub9p4                         0                               0   \n",
       "\n",
       "            first_browser_UC Browser  first_browser_Yandex.Browser  \\\n",
       "id                                                                   \n",
       "gxn3p5htnn                         0                             0   \n",
       "820tgsjxq7                         0                             0   \n",
       "4ft3gnwmtx                         0                             0   \n",
       "bjjt8pjhuk                         0                             0   \n",
       "87mebub9p4                         0                             0   \n",
       "\n",
       "            first_browser_wOSBrowser  \n",
       "id                                    \n",
       "gxn3p5htnn                         0  \n",
       "820tgsjxq7                         0  \n",
       "4ft3gnwmtx                         0  \n",
       "bjjt8pjhuk                         0  \n",
       "87mebub9p4                         0  \n",
       "\n",
       "[5 rows x 198 columns]"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of given users are 275547 and number of given features are 198\n"
     ]
    }
   ],
   "source": [
    "# Displaying the dataset after creating new features\n",
    "display(users.head())\n",
    "print(\"Number of given users are {} and number of given features are {}\".format(users.shape[0],users.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "users = users.drop(['id'], axis=1)\n",
    "users = users.fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <th>age_interv_12</th>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gxn3p5htnn</th>\n",
       "      <td>-1.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>820tgsjxq7</th>\n",
       "      <td>38.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4ft3gnwmtx</th>\n",
       "      <td>56.0</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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bjjt8pjhuk</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>87mebub9p4</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 197 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             age  age_interv_4  age_interv_5  age_interv_6  age_interv_7  \\\n",
       "id                                                                         \n",
       "gxn3p5htnn  -1.0             0             0             0             0   \n",
       "820tgsjxq7  38.0             0             0             0             0   \n",
       "4ft3gnwmtx  56.0             0             0             0             0   \n",
       "bjjt8pjhuk  42.0             0             0             0             0   \n",
       "87mebub9p4  41.0             0             0             0             0   \n",
       "\n",
       "            age_interv_8  age_interv_9  age_interv_10  age_interv_11  \\\n",
       "id                                                                     \n",
       "gxn3p5htnn             0             0              0              0   \n",
       "820tgsjxq7             1             0              0              0   \n",
       "4ft3gnwmtx             0             0              0              0   \n",
       "bjjt8pjhuk             0             1              0              0   \n",
       "87mebub9p4             0             1              0              0   \n",
       "\n",
       "            age_interv_12            ...             first_browser_Silk  \\\n",
       "id                                   ...                                  \n",
       "gxn3p5htnn              0            ...                              0   \n",
       "820tgsjxq7              0            ...                              0   \n",
       "4ft3gnwmtx              1            ...                              0   \n",
       "bjjt8pjhuk              0            ...                              0   \n",
       "87mebub9p4              0            ...                              0   \n",
       "\n",
       "            first_browser_SiteKiosk  first_browser_SlimBrowser  \\\n",
       "id                                                               \n",
       "gxn3p5htnn                        0                          0   \n",
       "820tgsjxq7                        0                          0   \n",
       "4ft3gnwmtx                        0                          0   \n",
       "bjjt8pjhuk                        0                          0   \n",
       "87mebub9p4                        0                          0   \n",
       "\n",
       "            first_browser_Sogou Explorer  first_browser_Stainless  \\\n",
       "id                                                                  \n",
       "gxn3p5htnn                             0                        0   \n",
       "820tgsjxq7                             0                        0   \n",
       "4ft3gnwmtx                             0                        0   \n",
       "bjjt8pjhuk                             0                        0   \n",
       "87mebub9p4                             0                        0   \n",
       "\n",
       "            first_browser_TenFourFox  first_browser_TheWorld Browser  \\\n",
       "id                                                                     \n",
       "gxn3p5htnn                         0                               0   \n",
       "820tgsjxq7                         0                               0   \n",
       "4ft3gnwmtx                         0                               0   \n",
       "bjjt8pjhuk                         0                               0   \n",
       "87mebub9p4                         0                               0   \n",
       "\n",
       "            first_browser_UC Browser  first_browser_Yandex.Browser  \\\n",
       "id                                                                   \n",
       "gxn3p5htnn                         0                             0   \n",
       "820tgsjxq7                         0                             0   \n",
       "4ft3gnwmtx                         0                             0   \n",
       "bjjt8pjhuk                         0                             0   \n",
       "87mebub9p4                         0                             0   \n",
       "\n",
       "            first_browser_wOSBrowser  \n",
       "id                                    \n",
       "gxn3p5htnn                         0  \n",
       "820tgsjxq7                         0  \n",
       "4ft3gnwmtx                         0  \n",
       "bjjt8pjhuk                         0  \n",
       "87mebub9p4                         0  \n",
       "\n",
       "[5 rows x 197 columns]"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(users.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save as\n",
    "users.to_pickle('newUserBookings_checkpoint1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load from \"newUserBookings_checkpoint1\"\n",
    "users = pd.read_pickle('newUserBookings_checkpoint1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def dcg_score(y_true, y_score, k=5):\n",
    "    \"\"\"Discounted cumulative gain (DCG) at rank K.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    y_true : array, shape = [n_samples]\n",
    "        Ground truth (true relevance labels).\n",
    "    y_score : array, shape = [n_samples, n_classes]\n",
    "        Predicted scores.\n",
    "    k : int\n",
    "        Rank.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    score : float\n",
    "    \n",
    "    Example\n",
    "    -------\n",
    "    >>> ground_truth = [0 0 0 0 0 0 0 1 0 0 0 0]\n",
    "    >>> y_score = [ 0.0017368   0.00538507  0.07079092  0.00823662  0.03216999  0.00871099\n",
    "                    0.01175533  0.61346039  0.00279277  0.0009836   0.20804779  0.03592973]\n",
    "    >>> score = ndcg_score(ground_truth, predictions, k=2)\n",
    "    1.0\n",
    "    >>> predictions = [ 0.00265752  0.0074776   0.03245427  0.01112108  0.03771622  0.01119133\n",
    "                        0.0147063   0.49428478  0.00356924  0.00104084  0.33389187  0.04988896]\n",
    "    >>> score = ndcg_score(ground_truth, predictions, k=2)\n",
    "    0.630929\n",
    "    \n",
    "    \"\"\"\n",
    "    order = np.argsort(y_score)[::-1]\n",
    "    y_true = np.take(y_true, order[:k])\n",
    "\n",
    "    gain = 2 ** y_true - 1\n",
    "\n",
    "    discounts = np.log2(np.arange(len(y_true)) + 2)\n",
    "    return np.sum(gain / discounts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ndcg_score(ground_truth, predictions, k=5):\n",
    "    \"\"\"Normalized discounted cumulative gain (NDCG) at rank K.\n",
    "\n",
    "    Normalized Discounted Cumulative Gain (NDCG) measures the performance of a\n",
    "    recommendation system based on the graded relevance of the recommended\n",
    "    entities. It varies from 0.0 to 1.0, with 1.0 representing the ideal\n",
    "    ranking of the entities.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ground_truth : array, shape = [n_samples]\n",
    "        Ground truth (true labels represended as integers).\n",
    "    predictions : array, shape = [n_samples, n_classes]\n",
    "        Predicted probabilities.\n",
    "    k : int\n",
    "        Rank.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    score : float\n",
    "\n",
    "    Example\n",
    "    -------\n",
    "    >>> ground_truth = [1, 0, 2]\n",
    "    >>> predictions = [[0.15, 0.55, 0.2], [0.7, 0.2, 0.1], [0.06, 0.04, 0.9]]\n",
    "    >>> score = ndcg_score(ground_truth, predictions, k=2)\n",
    "    1.0\n",
    "    >>> predictions = [[0.9, 0.5, 0.8], [0.7, 0.2, 0.1], [0.06, 0.04, 0.9]]\n",
    "    >>> score = ndcg_score(ground_truth, predictions, k=2)\n",
    "    0.6666666666\n",
    "    \"\"\"\n",
    "    lb = LabelBinarizer()\n",
    "\n",
    "    T = lb.fit_transform(ground_truth) \n",
    "\n",
    "    scores = []\n",
    "\n",
    "    # Iterate over each y_true and compute the DCG score\n",
    "    for y_true, y_score in zip(T, predictions):\n",
    "        actual = dcg_score(y_true, y_score, k)\n",
    "        best = dcg_score(y_true, y_true, k)\n",
    "        score = float(actual) / float(best)\n",
    "        scores.append(score)\n",
    "\n",
    "    return np.mean(scores)\n",
    "\n",
    "# NDCG Scorer function\n",
    "ndcg_scorer = make_scorer(ndcg_score, needs_proba=True, k=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "1. Random Forest\n",
    "2. XGBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Uncomment to Load from Checkpoint\n",
    "users = pd.read_pickle('newUserBookings_checkpoint1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_split_function(users, target, is_test_train_split):\n",
    "    \n",
    "    \"\"\"Splits the combined data into test and train.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    users : pd.DataFrame\n",
    "      one row for each observation, one column for each prediction.\n",
    "    target : array, shape = [n_samples, 1]\n",
    "        target labels.\n",
    "    is_test_train_split : bool\n",
    "        Signifies whether to use split ratio.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "\n",
    "    X_train, X_test, y_train, y_test, test_set : pd.DataFrame\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    #Split data back into train and test sets\n",
    "    vals = users.values\n",
    "    piv_train = len(target)\n",
    "    X = vals[:piv_train]\n",
    "    test_set = vals[piv_train:]\n",
    "\n",
    "    # Encoding 12 country/destination/target variables\n",
    "    labels = target.values\n",
    "    le = LabelEncoder()\n",
    "    y = le.fit_transform(labels)\n",
    "    \n",
    "    if is_test_train_split:\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "        return X_train, X_test, y_train, y_test, test_set, le\n",
    "    \n",
    "    return X, y, test_set, le\n",
    "    \n",
    "X,y, X_test, le = train_test_split_function(users, target, is_test_train_split = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=25, n_jobs=1, oob_score=False, random_state=101,\n",
       "            verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'min_samples_split': [2, 20], 'max_depth': [6, 8]},\n",
       "       pre_dispatch='2*n_jobs', refit=True,\n",
       "       scoring=make_scorer(ndcg_score, needs_proba=True, k=5), verbose=0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=25, random_state=101)\n",
    "parameters = {'min_samples_split': [2, 20],\n",
    "              'max_depth': [6, 8]\n",
    "             }\n",
    "\n",
    "reg = grid_search.GridSearchCV(clf, parameters, scoring=ndcg_scorer, cv=3)\n",
    "\n",
    "reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=6, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=20, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=25, n_jobs=1, oob_score=False, random_state=101,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = reg.best_estimator_\n",
    "est"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Score for Random Forest: 0.817564277726\n"
     ]
    }
   ],
   "source": [
    "y_pred = est.predict_proba(X)\n",
    "\n",
    "def validation_prediction(y_pred, y, le, print_msg):\n",
    "\n",
    "    truth = pd.Series(le.inverse_transform(y))\n",
    "    print(print_msg + str(ndcg_score(truth, y_pred)))\n",
    "    \n",
    "print_msg =  \"Validation Score for Random Forest: \"\n",
    "validation_prediction(y_pred, y, le, print_msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Making predictions on the test set\n",
    "ypred = est.predict_proba(X_test)\n",
    "\n",
    "def test_prediction(ypred, file_name):\n",
    "    id_test = test_users['id']\n",
    "    # Showing top 5 classes with highest probabilities\n",
    "    ids = []  #list of ids\n",
    "    cts = []  #list of countries\n",
    "    for i in range(len(id_test)):\n",
    "        idx = id_test[i]\n",
    "        ids += [idx] * 5\n",
    "        cts += le.inverse_transform(np.argsort(y_pred[i])[::-1])[:5].tolist()\n",
    "\n",
    "    sub = pd.DataFrame(np.column_stack((ids, cts)), columns=['id', 'country'])\n",
    "    sub.to_csv(file_name,index=False)\n",
    "file_name = 'random_forest.csv'\n",
    "test_prediction(ypred, file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y, test_set, le = train_test_split_function(users, target, is_test_train_split=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# XGB Classifier Parameters\n",
    "params = {'eta': 0.2,\n",
    "          'max_depth': 6,\n",
    "          'subsample': 0.5,\n",
    "          'colsample_bytree': 0.5,\n",
    "          'objective': 'multi:softprob',\n",
    "          'num_class': 12}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age_interv_8',\n",
       " 'affiliate_channel_content',\n",
       " 'age_interv_11',\n",
       " 'affiliate_channel_sem-brand',\n",
       " 'first_browser_-1',\n",
       " 'signup_method_facebook',\n",
       " 'dac_w_6',\n",
       " 'signup_flow_0',\n",
       " 'gender_MALE',\n",
       " 'signup_flow_1',\n",
       " 'first_browser_Chrome',\n",
       " 'affiliate_channel_direct',\n",
       " 'first_device_type_Windows Desktop',\n",
       " 'gender_-1',\n",
       " 'signup_app_Web',\n",
       " 'first_device_type_Mac Desktop',\n",
       " 'first_affiliate_tracked_untracked',\n",
       " 'season_dac',\n",
       " 'gender_FEMALE',\n",
       " 'first_affiliate_tracked_-1',\n",
       " 'dac_m',\n",
       " 'dac_y',\n",
       " 'tfa_y',\n",
       " 'signup_flow_3',\n",
       " 'tfa_wn',\n",
       " 'tfa_d',\n",
       " 'dac_wn',\n",
       " 'age',\n",
       " 'tfa_h',\n",
       " 'dac_d']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First train on whole training set for feature importances\n",
    "num_boost_round = 1\n",
    "dtrain = xgb.DMatrix(X, y)\n",
    "clf1 = xgb.train(params=params, dtrain=dtrain, num_boost_round=num_boost_round)\n",
    "\n",
    "# Get feature scores and store in DataFrame\n",
    "importance = clf1.get_fscore()\n",
    "importance_df = pd.DataFrame(\n",
    "    sorted(importance.items(), key=operator.itemgetter(1)), \n",
    "    columns=['feature','fscore']\n",
    "    )\n",
    "\n",
    "top_features = [users.columns[int(i.split('f')[1])] for i in importance_df.iloc[-30:,:].feature.values]\n",
    "\n",
    "display(top_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113fbd7f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o16+fqf3IkSPlHjMwMJA1a9Zw6NAh6tWrR/Pmzalbty4AO3fu5NixY0RERFQ4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TenzieSdOnCA5OZn3339fK66qVavy3nvvcejQIa3kw/MFNHv06IGfnx979+5Vkg+7d++mevXq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\n+g93S8Zw8CH+VwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1156f0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance_df.feature = [users.columns[int(i.split('f')[1])] for i in importance_df.feature.values]\n",
    "# Plot feature importance of top 30\n",
    "importance_df.iloc[-30:,:].plot(x='feature',y='fscore',kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utilizing Top Features for modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "users_clf2 = users[top_features]\n",
    "X_train, X_test, y_train, y_test, test_set, le = train_test_split_function(users_clf2, target, is_test_train_split = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Train the XGB Classifier\n",
    "dtrain = xgb.DMatrix(X_train, y_train)\n",
    "clf2 = xgb.train(params=params, dtrain=dtrain, num_boost_round=num_boost_round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Score for XGBoost using top 30 features: 0.813677612442\n"
     ]
    }
   ],
   "source": [
    "# Predicting on Validation Set\n",
    "\n",
    "y_pred = clf2.predict(xgb.DMatrix(X_test))\n",
    "print_msg = \"Validation Score for XGBoost using top 30 features: \"\n",
    "validation_prediction(y_pred, y_test, le, print_msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting on test set\n",
    "y_pred = clf2.predict(xgb.DMatrix(test_set))\n",
    "file_name = 'xgb_feature_importance.csv'\n",
    "test_prediction(ypred, file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from xgboost.sklearn import XGBClassifier\n",
    "\n",
    "clf_rp = XGBClassifier(\n",
    "        max_depth=7,\n",
    "        learning_rate=0.18,\n",
    "        n_estimators=80,\n",
    "        objective=\"rank:pairwise\",\n",
    "        gamma=0,\n",
    "        min_child_weight=1,\n",
    "        max_delta_step=0,\n",
    "        subsample=1,\n",
    "        colsample_bytree=1,\n",
    "        colsample_bylevel=1,\n",
    "        reg_alpha=0,\n",
    "        reg_lambda=1,\n",
    "        scale_pos_weight=1,\n",
    "        base_score=0.5,\n",
    "        missing=None,\n",
    "        silent=True,\n",
    "        nthread=-1,\n",
    "        seed=111\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.18, max_delta_step=0,\n",
       "       max_depth=7, min_child_weight=1, missing=None, n_estimators=80,\n",
       "       n_jobs=1, nthread=-1, objective='multi:softprob', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=111,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the XGB Classifier with objective function as Rank pairwise. \n",
    "# This makes the model to predict the relative score for each document to a specific query,  \n",
    "# instead of classifying the classes\n",
    "users_clf2_rp = users[top_features]\n",
    "X_train, X_test, y_train, y_test, test_set, le = train_test_split_function(users_clf2_rp, target, is_test_train_split = True)\n",
    "\n",
    "clf_rp.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Score for XGBoost(RankPairwise) using top 30 features: 0.823843007379\n"
     ]
    }
   ],
   "source": [
    "# Predicting on Validation Set\n",
    "y_pred_rp = clf_rp.predict_proba(X_test)\n",
    "\n",
    "print_msg = \"Validation Score for XGBoost(RankPairwise) using top 30 features: \"\n",
    "validation_prediction(y_pred_rp, y_test, le, print_msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting on test set\n",
    "y_pred = clf_rp.predict_proba(test_set)\n",
    "file_name = 'xgb_hyperPtuned_rp.csv'\n",
    "test_prediction(ypred, file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1140687b8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zzz8/06ZNy4MPPpjko09X5s+fn2nTpmXAgAFJkq9+9asZOHBg7rnnnlx99dVJ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b069a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the predicted countries count\n",
    "results = pd.read_csv(\"xgb_hyperPtuned_rp.csv\")\n",
    "results[\"country\"].value_counts().plot(kind=\"bar\",rot=0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}