04. Identifying Fraudulent Activities.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import  warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import auc, roc_curve, classification_report\n",
    "\n",
    "import h2o\n",
    "from h2o.frame import H2OFrame\n",
    "from h2o.estimators.random_forest import H2ORandomForestEstimator\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>signup_time</th>\n",
       "      <th>purchase_time</th>\n",
       "      <th>purchase_value</th>\n",
       "      <th>device_id</th>\n",
       "      <th>source</th>\n",
       "      <th>browser</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22058</td>\n",
       "      <td>2015-02-24 22:55:49</td>\n",
       "      <td>2015-04-18 02:47:11</td>\n",
       "      <td>34</td>\n",
       "      <td>QVPSPJUOCKZAR</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>M</td>\n",
       "      <td>39</td>\n",
       "      <td>7.327584e+08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>333320</td>\n",
       "      <td>2015-06-07 20:39:50</td>\n",
       "      <td>2015-06-08 01:38:54</td>\n",
       "      <td>16</td>\n",
       "      <td>EOGFQPIZPYXFZ</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>F</td>\n",
       "      <td>53</td>\n",
       "      <td>3.503114e+08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1359</td>\n",
       "      <td>2015-01-01 18:52:44</td>\n",
       "      <td>2015-01-01 18:52:45</td>\n",
       "      <td>15</td>\n",
       "      <td>YSSKYOSJHPPLJ</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Opera</td>\n",
       "      <td>M</td>\n",
       "      <td>53</td>\n",
       "      <td>2.621474e+09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150084</td>\n",
       "      <td>2015-04-28 21:13:25</td>\n",
       "      <td>2015-05-04 13:54:50</td>\n",
       "      <td>44</td>\n",
       "      <td>ATGTXKYKUDUQN</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>41</td>\n",
       "      <td>3.840542e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>221365</td>\n",
       "      <td>2015-07-21 07:09:52</td>\n",
       "      <td>2015-09-09 18:40:53</td>\n",
       "      <td>39</td>\n",
       "      <td>NAUITBZFJKHWW</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>4.155831e+08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id         signup_time       purchase_time  purchase_value  \\\n",
       "0    22058 2015-02-24 22:55:49 2015-04-18 02:47:11              34   \n",
       "1   333320 2015-06-07 20:39:50 2015-06-08 01:38:54              16   \n",
       "2     1359 2015-01-01 18:52:44 2015-01-01 18:52:45              15   \n",
       "3   150084 2015-04-28 21:13:25 2015-05-04 13:54:50              44   \n",
       "4   221365 2015-07-21 07:09:52 2015-09-09 18:40:53              39   \n",
       "\n",
       "       device_id source browser sex  age    ip_address  class  \n",
       "0  QVPSPJUOCKZAR    SEO  Chrome   M   39  7.327584e+08      0  \n",
       "1  EOGFQPIZPYXFZ    Ads  Chrome   F   53  3.503114e+08      0  \n",
       "2  YSSKYOSJHPPLJ    SEO   Opera   M   53  2.621474e+09      1  \n",
       "3  ATGTXKYKUDUQN    SEO  Safari   M   41  3.840542e+09      0  \n",
       "4  NAUITBZFJKHWW    Ads  Safari   M   45  4.155831e+08      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./data/Fraud/Fraud_Data.csv', parse_dates=['signup_time', 'purchase_time'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lower_bound_ip_address</th>\n",
       "      <th>upper_bound_ip_address</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16777216.0</td>\n",
       "      <td>16777471</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16777472.0</td>\n",
       "      <td>16777727</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16777728.0</td>\n",
       "      <td>16778239</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16778240.0</td>\n",
       "      <td>16779263</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16779264.0</td>\n",
       "      <td>16781311</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   lower_bound_ip_address  upper_bound_ip_address    country\n",
       "0              16777216.0                16777471  Australia\n",
       "1              16777472.0                16777727      China\n",
       "2              16777728.0                16778239      China\n",
       "3              16778240.0                16779263  Australia\n",
       "4              16779264.0                16781311      China"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address2country = pd.read_csv('./data/Fraud/IpAddress_to_Country.csv')\n",
    "address2country.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add Country to Fraud Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>signup_time</th>\n",
       "      <th>purchase_time</th>\n",
       "      <th>purchase_value</th>\n",
       "      <th>device_id</th>\n",
       "      <th>source</th>\n",
       "      <th>browser</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>class</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22058</td>\n",
       "      <td>2015-02-24 22:55:49</td>\n",
       "      <td>2015-04-18 02:47:11</td>\n",
       "      <td>34</td>\n",
       "      <td>QVPSPJUOCKZAR</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>M</td>\n",
       "      <td>39</td>\n",
       "      <td>7.327584e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>333320</td>\n",
       "      <td>2015-06-07 20:39:50</td>\n",
       "      <td>2015-06-08 01:38:54</td>\n",
       "      <td>16</td>\n",
       "      <td>EOGFQPIZPYXFZ</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>F</td>\n",
       "      <td>53</td>\n",
       "      <td>3.503114e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1359</td>\n",
       "      <td>2015-01-01 18:52:44</td>\n",
       "      <td>2015-01-01 18:52:45</td>\n",
       "      <td>15</td>\n",
       "      <td>YSSKYOSJHPPLJ</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Opera</td>\n",
       "      <td>M</td>\n",
       "      <td>53</td>\n",
       "      <td>2.621474e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150084</td>\n",
       "      <td>2015-04-28 21:13:25</td>\n",
       "      <td>2015-05-04 13:54:50</td>\n",
       "      <td>44</td>\n",
       "      <td>ATGTXKYKUDUQN</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>41</td>\n",
       "      <td>3.840542e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>221365</td>\n",
       "      <td>2015-07-21 07:09:52</td>\n",
       "      <td>2015-09-09 18:40:53</td>\n",
       "      <td>39</td>\n",
       "      <td>NAUITBZFJKHWW</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>4.155831e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id         signup_time       purchase_time  purchase_value  \\\n",
       "0    22058 2015-02-24 22:55:49 2015-04-18 02:47:11              34   \n",
       "1   333320 2015-06-07 20:39:50 2015-06-08 01:38:54              16   \n",
       "2     1359 2015-01-01 18:52:44 2015-01-01 18:52:45              15   \n",
       "3   150084 2015-04-28 21:13:25 2015-05-04 13:54:50              44   \n",
       "4   221365 2015-07-21 07:09:52 2015-09-09 18:40:53              39   \n",
       "\n",
       "       device_id source browser sex  age    ip_address  class        country  \n",
       "0  QVPSPJUOCKZAR    SEO  Chrome   M   39  7.327584e+08      0          Japan  \n",
       "1  EOGFQPIZPYXFZ    Ads  Chrome   F   53  3.503114e+08      0  United States  \n",
       "2  YSSKYOSJHPPLJ    SEO   Opera   M   53  2.621474e+09      1  United States  \n",
       "3  ATGTXKYKUDUQN    SEO  Safari   M   41  3.840542e+09      0             NA  \n",
       "4  NAUITBZFJKHWW    Ads  Safari   M   45  4.155831e+08      0  United States  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = []\n",
    "for i in range(len(data)):\n",
    "    ip_address = data.loc[i, 'ip_address']\n",
    "    tmp = address2country[(address2country['lower_bound_ip_address'] <= ip_address) &\n",
    "                          (address2country['upper_bound_ip_address'] >= ip_address)]\n",
    "    if len(tmp) == 1:\n",
    "        countries.append(tmp['country'].values[0])\n",
    "    else:\n",
    "        countries.append('NA')\n",
    "        \n",
    "data['country'] = countries\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Time difference between sign-up time and purchase time\n",
    "* If the device id is unique or certain users are sharing the same device (many different user ids using the same device could be an indicator of fake accounts)\n",
    "* Same for the ip address. Many different users having the same ip address could be an indicator of fake accounts\n",
    "* Usual week of the year and day of the week from time variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get the time difference between purchase time and signup time\n",
    "time_diff = data['purchase_time'] - data['signup_time']\n",
    "time_diff = time_diff.apply(lambda x: x.seconds)\n",
    "data['time_diff'] = time_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Check user number for unique devices\n",
    "device_num = data[['user_id', 'device_id']].groupby('device_id').count().reset_index()\n",
    "device_num = device_num.rename(columns={'user_id': 'device_num'})\n",
    "data = data.merge(device_num, how='left', on='device_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Check user number for unique ip_address\n",
    "ip_num = data[['user_id', 'ip_address']].groupby('ip_address').count().reset_index()\n",
    "ip_num = ip_num.rename(columns={'user_id': 'ip_num'})\n",
    "data = data.merge(ip_num, how='left', on='ip_address')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Signup day and week\n",
    "data['signup_day'] = data['signup_time'].apply(lambda x: x.dayofweek)\n",
    "data['signup_week'] = data['signup_time'].apply(lambda x: x.week)\n",
    "\n",
    "# Purchase day and week\n",
    "data['purchase_day'] = data['purchase_time'].apply(lambda x: x.dayofweek)\n",
    "data['purchase_week'] = data['purchase_time'].apply(lambda x: x.week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>signup_time</th>\n",
       "      <th>purchase_time</th>\n",
       "      <th>purchase_value</th>\n",
       "      <th>device_id</th>\n",
       "      <th>source</th>\n",
       "      <th>browser</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>class</th>\n",
       "      <th>country</th>\n",
       "      <th>time_diff</th>\n",
       "      <th>device_num</th>\n",
       "      <th>ip_num</th>\n",
       "      <th>signup_day</th>\n",
       "      <th>signup_week</th>\n",
       "      <th>purchase_day</th>\n",
       "      <th>purchase_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22058</td>\n",
       "      <td>2015-02-24 22:55:49</td>\n",
       "      <td>2015-04-18 02:47:11</td>\n",
       "      <td>34</td>\n",
       "      <td>QVPSPJUOCKZAR</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>M</td>\n",
       "      <td>39</td>\n",
       "      <td>7.327584e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>13882</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>333320</td>\n",
       "      <td>2015-06-07 20:39:50</td>\n",
       "      <td>2015-06-08 01:38:54</td>\n",
       "      <td>16</td>\n",
       "      <td>EOGFQPIZPYXFZ</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>F</td>\n",
       "      <td>53</td>\n",
       "      <td>3.503114e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>17944</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1359</td>\n",
       "      <td>2015-01-01 18:52:44</td>\n",
       "      <td>2015-01-01 18:52:45</td>\n",
       "      <td>15</td>\n",
       "      <td>YSSKYOSJHPPLJ</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Opera</td>\n",
       "      <td>M</td>\n",
       "      <td>53</td>\n",
       "      <td>2.621474e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150084</td>\n",
       "      <td>2015-04-28 21:13:25</td>\n",
       "      <td>2015-05-04 13:54:50</td>\n",
       "      <td>44</td>\n",
       "      <td>ATGTXKYKUDUQN</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>41</td>\n",
       "      <td>3.840542e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>NA</td>\n",
       "      <td>60085</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>221365</td>\n",
       "      <td>2015-07-21 07:09:52</td>\n",
       "      <td>2015-09-09 18:40:53</td>\n",
       "      <td>39</td>\n",
       "      <td>NAUITBZFJKHWW</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>4.155831e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>41461</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id         signup_time       purchase_time  purchase_value  \\\n",
       "0    22058 2015-02-24 22:55:49 2015-04-18 02:47:11              34   \n",
       "1   333320 2015-06-07 20:39:50 2015-06-08 01:38:54              16   \n",
       "2     1359 2015-01-01 18:52:44 2015-01-01 18:52:45              15   \n",
       "3   150084 2015-04-28 21:13:25 2015-05-04 13:54:50              44   \n",
       "4   221365 2015-07-21 07:09:52 2015-09-09 18:40:53              39   \n",
       "\n",
       "       device_id source browser sex  age    ip_address  class        country  \\\n",
       "0  QVPSPJUOCKZAR    SEO  Chrome   M   39  7.327584e+08      0          Japan   \n",
       "1  EOGFQPIZPYXFZ    Ads  Chrome   F   53  3.503114e+08      0  United States   \n",
       "2  YSSKYOSJHPPLJ    SEO   Opera   M   53  2.621474e+09      1  United States   \n",
       "3  ATGTXKYKUDUQN    SEO  Safari   M   41  3.840542e+09      0             NA   \n",
       "4  NAUITBZFJKHWW    Ads  Safari   M   45  4.155831e+08      0  United States   \n",
       "\n",
       "   time_diff  device_num  ip_num  signup_day  signup_week  purchase_day  \\\n",
       "0      13882           1       1           1            9             5   \n",
       "1      17944           1       1           6           23             0   \n",
       "2          1          12      12           3            1             3   \n",
       "3      60085           1       1           1           18             0   \n",
       "4      41461           1       1           1           30             2   \n",
       "\n",
       "   purchase_week  \n",
       "0             16  \n",
       "1             24  \n",
       "2              1  \n",
       "3             19  \n",
       "4             37  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>signup_day</th>\n",
       "      <th>signup_week</th>\n",
       "      <th>purchase_day</th>\n",
       "      <th>purchase_week</th>\n",
       "      <th>purchase_value</th>\n",
       "      <th>source</th>\n",
       "      <th>browser</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>country</th>\n",
       "      <th>time_diff</th>\n",
       "      <th>device_num</th>\n",
       "      <th>ip_num</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>34</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>M</td>\n",
       "      <td>39</td>\n",
       "      <td>Japan</td>\n",
       "      <td>13882</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>F</td>\n",
       "      <td>53</td>\n",
       "      <td>United States</td>\n",
       "      <td>17944</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Opera</td>\n",
       "      <td>M</td>\n",
       "      <td>53</td>\n",
       "      <td>United States</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>44</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>41</td>\n",
       "      <td>NA</td>\n",
       "      <td>60085</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>39</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>United States</td>\n",
       "      <td>41461</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   signup_day  signup_week  purchase_day  purchase_week  purchase_value  \\\n",
       "0           1            9             5             16              34   \n",
       "1           6           23             0             24              16   \n",
       "2           3            1             3              1              15   \n",
       "3           1           18             0             19              44   \n",
       "4           1           30             2             37              39   \n",
       "\n",
       "  source browser sex  age        country  time_diff  device_num  ip_num  class  \n",
       "0    SEO  Chrome   M   39          Japan      13882           1       1      0  \n",
       "1    Ads  Chrome   F   53  United States      17944           1       1      0  \n",
       "2    SEO   Opera   M   53  United States          1          12      12      1  \n",
       "3    SEO  Safari   M   41             NA      60085           1       1      0  \n",
       "4    Ads  Safari   M   45  United States      41461           1       1      0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define features and target to be used\n",
    "columns = ['signup_day', 'signup_week', 'purchase_day', 'purchase_week', 'purchase_value', 'source', \n",
    "           'browser', 'sex', 'age', 'country', 'time_diff', 'device_num', 'ip_num', 'class']\n",
    "data = data[columns]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fraudulent Activity Identification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "  Java Version: openjdk version \"1.8.0_121\"; OpenJDK Runtime Environment (Zulu 8.20.0.5-macosx) (build 1.8.0_121-b15); OpenJDK 64-Bit Server VM (Zulu 8.20.0.5-macosx) (build 25.121-b15, mixed mode)\n",
      "  Starting server from /anaconda/lib/python3.5/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /var/folders/gx/b15jqbt1567grsfr7l_znn2h0000gn/T/tmpq14c42t4\n",
      "  JVM stdout: /var/folders/gx/b15jqbt1567grsfr7l_znn2h0000gn/T/tmpq14c42t4/h2o_jifu_started_from_python.out\n",
      "  JVM stderr: /var/folders/gx/b15jqbt1567grsfr7l_znn2h0000gn/T/tmpq14c42t4/h2o_jifu_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>03 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>America/Chicago</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.18.0.2</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>3 months </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_jifu_3z98p9</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>3.556 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O API Extensions:</td>\n",
       "<td>XGBoost, Algos, AutoML, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.5.4 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ----------------------------------------\n",
       "H2O cluster uptime:         03 secs\n",
       "H2O cluster timezone:       America/Chicago\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.18.0.2\n",
       "H2O cluster version age:    3 months\n",
       "H2O cluster name:           H2O_from_python_jifu_3z98p9\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    3.556 Gb\n",
       "H2O cluster total cores:    8\n",
       "H2O cluster allowed cores:  8\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         XGBoost, Algos, AutoML, Core V3, Core V4\n",
       "Python version:             3.5.4 final\n",
       "--------------------------  ----------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize H2O cluster\n",
    "h2o.init()\n",
    "h2o.remove_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr><th>       </th><th>signup_day  </th><th>signup_week       </th><th>purchase_day  </th><th>purchase_week     </th><th>purchase_value   </th><th>source  </th><th>browser  </th><th>sex  </th><th>age              </th><th>country      </th><th>time_diff         </th><th>device_num        </th><th>ip_num            </th><th>class  </th></tr>\n",
       "<tr><td>type   </td><td>enum        </td><td>int               </td><td>enum          </td><td>int               </td><td>int              </td><td>enum    </td><td>enum     </td><td>enum </td><td>int              </td><td>enum         </td><td>int               </td><td>int               </td><td>int               </td><td>enum   </td></tr>\n",
       "<tr><td>mins   </td><td>            </td><td>1.0               </td><td>              </td><td>1.0               </td><td>9.0              </td><td>        </td><td>         </td><td>     </td><td>18.0             </td><td>             </td><td>1.0               </td><td>1.0               </td><td>1.0               </td><td>       </td></tr>\n",
       "<tr><td>mean   </td><td>            </td><td>16.501740430938508</td><td>              </td><td>24.658571126052173</td><td>36.93537243898567</td><td>        </td><td>         </td><td>     </td><td>33.14070358409671</td><td>             </td><td>40942.584427444956</td><td>1.6843665625496433</td><td>1.6027185134205137</td><td>       </td></tr>\n",
       "<tr><td>maxs   </td><td>            </td><td>34.0              </td><td>              </td><td>51.0              </td><td>154.0            </td><td>        </td><td>         </td><td>     </td><td>76.0             </td><td>             </td><td>86399.0           </td><td>20.0              </td><td>20.0              </td><td>       </td></tr>\n",
       "<tr><td>sigma  </td><td>            </td><td>9.814287461798854 </td><td>              </td><td>11.651556782719474</td><td>18.32276214866212</td><td>        </td><td>         </td><td>     </td><td>8.617733490961495</td><td>             </td><td>26049.661902118358</td><td>2.6169536028041693</td><td>2.596239527375835 </td><td>       </td></tr>\n",
       "<tr><td>zeros  </td><td>            </td><td>0                 </td><td>              </td><td>0                 </td><td>0                </td><td>        </td><td>         </td><td>     </td><td>0                </td><td>             </td><td>0                 </td><td>0                 </td><td>0                 </td><td>       </td></tr>\n",
       "<tr><td>missing</td><td>0           </td><td>0                 </td><td>0             </td><td>0                 </td><td>0                </td><td>0       </td><td>0        </td><td>0    </td><td>0                </td><td>0            </td><td>0                 </td><td>0                 </td><td>0                 </td><td>0      </td></tr>\n",
       "<tr><td>0      </td><td>1           </td><td>9.0               </td><td>5             </td><td>16.0              </td><td>34.0             </td><td>SEO     </td><td>Chrome   </td><td>M    </td><td>39.0             </td><td>Japan        </td><td>13882.0           </td><td>1.0               </td><td>1.0               </td><td>0      </td></tr>\n",
       "<tr><td>1      </td><td>6           </td><td>23.0              </td><td>0             </td><td>24.0              </td><td>16.0             </td><td>Ads     </td><td>Chrome   </td><td>F    </td><td>53.0             </td><td>United States</td><td>17944.0           </td><td>1.0               </td><td>1.0               </td><td>0      </td></tr>\n",
       "<tr><td>2      </td><td>3           </td><td>1.0               </td><td>3             </td><td>1.0               </td><td>15.0             </td><td>SEO     </td><td>Opera    </td><td>M    </td><td>53.0             </td><td>United States</td><td>1.0               </td><td>12.0              </td><td>12.0              </td><td>1      </td></tr>\n",
       "<tr><td>3      </td><td>1           </td><td>18.0              </td><td>0             </td><td>19.0              </td><td>44.0             </td><td>SEO     </td><td>Safari   </td><td>M    </td><td>41.0             </td><td>NA           </td><td>60085.0           </td><td>1.0               </td><td>1.0               </td><td>0      </td></tr>\n",
       "<tr><td>4      </td><td>1           </td><td>30.0              </td><td>2             </td><td>37.0              </td><td>39.0             </td><td>Ads     </td><td>Safari   </td><td>M    </td><td>45.0             </td><td>United States</td><td>41461.0           </td><td>1.0               </td><td>1.0               </td><td>0      </td></tr>\n",
       "<tr><td>5      </td><td>3           </td><td>21.0              </td><td>3             </td><td>28.0              </td><td>42.0             </td><td>Ads     </td><td>Chrome   </td><td>M    </td><td>18.0             </td><td>Canada       </td><td>7331.0            </td><td>1.0               </td><td>1.0               </td><td>0      </td></tr>\n",
       "<tr><td>6      </td><td>5           </td><td>31.0              </td><td>3             </td><td>35.0              </td><td>11.0             </td><td>Ads     </td><td>Chrome   </td><td>F    </td><td>19.0             </td><td>NA           </td><td>17825.0           </td><td>1.0               </td><td>1.0               </td><td>0      </td></tr>\n",
       "<tr><td>7      </td><td>0           </td><td>15.0              </td><td>0             </td><td>22.0              </td><td>27.0             </td><td>Ads     </td><td>Opera    </td><td>M    </td><td>34.0             </td><td>United States</td><td>35129.0           </td><td>1.0               </td><td>1.0               </td><td>0      </td></tr>\n",
       "<tr><td>8      </td><td>1           </td><td>17.0              </td><td>1             </td><td>23.0              </td><td>30.0             </td><td>SEO     </td><td>IE       </td><td>F    </td><td>43.0             </td><td>China        </td><td>51800.0           </td><td>1.0               </td><td>1.0               </td><td>0      </td></tr>\n",
       "<tr><td>9      </td><td>6           </td><td>4.0               </td><td>0             </td><td>13.0              </td><td>62.0             </td><td>Ads     </td><td>IE       </td><td>M    </td><td>31.0             </td><td>United States</td><td>18953.0           </td><td>1.0               </td><td>1.0               </td><td>0      </td></tr>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transform to H2O Frame, and make sure the target variable is categorical\n",
    "h2o_df = H2OFrame(data)\n",
    "\n",
    "for name in ['signup_day', 'purchase_day', 'source', 'browser', 'sex', 'country', 'class']:\n",
    "    h2o_df[name] = h2o_df[name].asfactor()\n",
    "\n",
    "h2o_df.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Split into 70% training and 30% test dataset\n",
    "strat_split = h2o_df['class'].stratified_split(test_frac=0.3, seed=42)\n",
    "\n",
    "train = h2o_df[strat_split == 'train']\n",
    "test = h2o_df[strat_split == 'test']\n",
    "\n",
    "# Define features and target\n",
    "feature = ['signup_day', 'signup_week', 'purchase_day', 'purchase_week', 'purchase_value', \n",
    "           'source', 'browser', 'sex', 'age', 'country', 'time_diff', 'device_num', 'ip_num']\n",
    "target = 'class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drf Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# Build random forest model \n",
    "model = H2ORandomForestEstimator(balance_classes=True, ntrees=100, mtries=-1, stopping_rounds=5, \n",
    "                                 stopping_metric='auc', score_each_iteration=True, seed=42)\n",
    "model.train(x=feature, y=target, training_frame=train, validation_frame=test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>duration</th>\n",
       "      <th>number_of_trees</th>\n",
       "      <th>training_rmse</th>\n",
       "      <th>training_logloss</th>\n",
       "      <th>training_auc</th>\n",
       "      <th>training_lift</th>\n",
       "      <th>training_classification_error</th>\n",
       "      <th>validation_rmse</th>\n",
       "      <th>validation_logloss</th>\n",
       "      <th>validation_auc</th>\n",
       "      <th>validation_lift</th>\n",
       "      <th>validation_classification_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:44</td>\n",
       "      <td>0.100 sec</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:45</td>\n",
       "      <td>1.181 sec</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.329237</td>\n",
       "      <td>0.763404</td>\n",
       "      <td>0.956487</td>\n",
       "      <td>1.930543</td>\n",
       "      <td>0.103585</td>\n",
       "      <td>0.257295</td>\n",
       "      <td>1.705777</td>\n",
       "      <td>0.780964</td>\n",
       "      <td>7.318324</td>\n",
       "      <td>0.064986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:46</td>\n",
       "      <td>1.837 sec</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.337703</td>\n",
       "      <td>0.666988</td>\n",
       "      <td>0.958739</td>\n",
       "      <td>1.945962</td>\n",
       "      <td>0.100437</td>\n",
       "      <td>0.211104</td>\n",
       "      <td>0.612876</td>\n",
       "      <td>0.808146</td>\n",
       "      <td>10.238311</td>\n",
       "      <td>0.045618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:47</td>\n",
       "      <td>2.924 sec</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.342474</td>\n",
       "      <td>0.585470</td>\n",
       "      <td>0.962420</td>\n",
       "      <td>1.959389</td>\n",
       "      <td>0.096241</td>\n",
       "      <td>0.205624</td>\n",
       "      <td>0.416203</td>\n",
       "      <td>0.820663</td>\n",
       "      <td>10.566838</td>\n",
       "      <td>0.044361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:48</td>\n",
       "      <td>4.475 sec</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.341220</td>\n",
       "      <td>0.530667</td>\n",
       "      <td>0.967584</td>\n",
       "      <td>1.967190</td>\n",
       "      <td>0.088710</td>\n",
       "      <td>0.204276</td>\n",
       "      <td>0.340733</td>\n",
       "      <td>0.823580</td>\n",
       "      <td>10.641350</td>\n",
       "      <td>0.044140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:49</td>\n",
       "      <td>4.799 sec</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.339991</td>\n",
       "      <td>0.494876</td>\n",
       "      <td>0.970585</td>\n",
       "      <td>1.973005</td>\n",
       "      <td>0.085154</td>\n",
       "      <td>0.203786</td>\n",
       "      <td>0.294007</td>\n",
       "      <td>0.825970</td>\n",
       "      <td>10.669638</td>\n",
       "      <td>0.044162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:49</td>\n",
       "      <td>5.173 sec</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.338576</td>\n",
       "      <td>0.454248</td>\n",
       "      <td>0.974636</td>\n",
       "      <td>1.979192</td>\n",
       "      <td>0.081914</td>\n",
       "      <td>0.203566</td>\n",
       "      <td>0.268536</td>\n",
       "      <td>0.828691</td>\n",
       "      <td>10.674389</td>\n",
       "      <td>0.044096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:49</td>\n",
       "      <td>5.534 sec</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.337354</td>\n",
       "      <td>0.427348</td>\n",
       "      <td>0.977835</td>\n",
       "      <td>1.983172</td>\n",
       "      <td>0.076219</td>\n",
       "      <td>0.203257</td>\n",
       "      <td>0.251817</td>\n",
       "      <td>0.830573</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:50</td>\n",
       "      <td>5.923 sec</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.337933</td>\n",
       "      <td>0.405616</td>\n",
       "      <td>0.980248</td>\n",
       "      <td>1.987241</td>\n",
       "      <td>0.072625</td>\n",
       "      <td>0.203124</td>\n",
       "      <td>0.243703</td>\n",
       "      <td>0.831204</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:50</td>\n",
       "      <td>6.339 sec</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.336345</td>\n",
       "      <td>0.385657</td>\n",
       "      <td>0.982926</td>\n",
       "      <td>1.990128</td>\n",
       "      <td>0.067415</td>\n",
       "      <td>0.203066</td>\n",
       "      <td>0.231357</td>\n",
       "      <td>0.832550</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:51</td>\n",
       "      <td>6.749 sec</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.334526</td>\n",
       "      <td>0.366322</td>\n",
       "      <td>0.985589</td>\n",
       "      <td>1.992683</td>\n",
       "      <td>0.061423</td>\n",
       "      <td>0.203097</td>\n",
       "      <td>0.227298</td>\n",
       "      <td>0.832316</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:51</td>\n",
       "      <td>7.162 sec</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.335901</td>\n",
       "      <td>0.357655</td>\n",
       "      <td>0.986884</td>\n",
       "      <td>1.994899</td>\n",
       "      <td>0.058755</td>\n",
       "      <td>0.203096</td>\n",
       "      <td>0.220205</td>\n",
       "      <td>0.833527</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:51</td>\n",
       "      <td>7.571 sec</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.332650</td>\n",
       "      <td>0.341858</td>\n",
       "      <td>0.989303</td>\n",
       "      <td>1.996165</td>\n",
       "      <td>0.052590</td>\n",
       "      <td>0.203156</td>\n",
       "      <td>0.218058</td>\n",
       "      <td>0.833289</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:52</td>\n",
       "      <td>8.018 sec</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.332947</td>\n",
       "      <td>0.337632</td>\n",
       "      <td>0.990264</td>\n",
       "      <td>1.996937</td>\n",
       "      <td>0.050032</td>\n",
       "      <td>0.203230</td>\n",
       "      <td>0.213603</td>\n",
       "      <td>0.833152</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:52</td>\n",
       "      <td>8.467 sec</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.334967</td>\n",
       "      <td>0.338206</td>\n",
       "      <td>0.990769</td>\n",
       "      <td>1.997604</td>\n",
       "      <td>0.049137</td>\n",
       "      <td>0.203201</td>\n",
       "      <td>0.209188</td>\n",
       "      <td>0.834405</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:53</td>\n",
       "      <td>8.945 sec</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.334957</td>\n",
       "      <td>0.335644</td>\n",
       "      <td>0.991332</td>\n",
       "      <td>1.998106</td>\n",
       "      <td>0.047514</td>\n",
       "      <td>0.203163</td>\n",
       "      <td>0.207753</td>\n",
       "      <td>0.834603</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:53</td>\n",
       "      <td>9.403 sec</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.334266</td>\n",
       "      <td>0.331837</td>\n",
       "      <td>0.992059</td>\n",
       "      <td>1.998392</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.203135</td>\n",
       "      <td>0.204570</td>\n",
       "      <td>0.835777</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:54</td>\n",
       "      <td>9.872 sec</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.332295</td>\n",
       "      <td>0.324349</td>\n",
       "      <td>0.993148</td>\n",
       "      <td>1.998720</td>\n",
       "      <td>0.041518</td>\n",
       "      <td>0.203177</td>\n",
       "      <td>0.203516</td>\n",
       "      <td>0.836050</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:54</td>\n",
       "      <td>10.358 sec</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.331662</td>\n",
       "      <td>0.320754</td>\n",
       "      <td>0.993694</td>\n",
       "      <td>1.999123</td>\n",
       "      <td>0.039879</td>\n",
       "      <td>0.203168</td>\n",
       "      <td>0.202761</td>\n",
       "      <td>0.836510</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:55</td>\n",
       "      <td>10.920 sec</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.330554</td>\n",
       "      <td>0.316733</td>\n",
       "      <td>0.994235</td>\n",
       "      <td>1.999381</td>\n",
       "      <td>0.038212</td>\n",
       "      <td>0.203183</td>\n",
       "      <td>0.202061</td>\n",
       "      <td>0.836516</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:55</td>\n",
       "      <td>11.517 sec</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.331997</td>\n",
       "      <td>0.319453</td>\n",
       "      <td>0.994307</td>\n",
       "      <td>1.999455</td>\n",
       "      <td>0.037777</td>\n",
       "      <td>0.203136</td>\n",
       "      <td>0.198964</td>\n",
       "      <td>0.837179</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:56</td>\n",
       "      <td>12.045 sec</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.332174</td>\n",
       "      <td>0.319514</td>\n",
       "      <td>0.994558</td>\n",
       "      <td>1.999454</td>\n",
       "      <td>0.036471</td>\n",
       "      <td>0.203140</td>\n",
       "      <td>0.198079</td>\n",
       "      <td>0.837936</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:56</td>\n",
       "      <td>12.578 sec</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.331975</td>\n",
       "      <td>0.318617</td>\n",
       "      <td>0.994821</td>\n",
       "      <td>1.999529</td>\n",
       "      <td>0.035568</td>\n",
       "      <td>0.203130</td>\n",
       "      <td>0.196660</td>\n",
       "      <td>0.838474</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:57</td>\n",
       "      <td>13.145 sec</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.331260</td>\n",
       "      <td>0.316874</td>\n",
       "      <td>0.995103</td>\n",
       "      <td>1.999529</td>\n",
       "      <td>0.034353</td>\n",
       "      <td>0.203161</td>\n",
       "      <td>0.196128</td>\n",
       "      <td>0.838535</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:58</td>\n",
       "      <td>13.706 sec</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.333191</td>\n",
       "      <td>0.320829</td>\n",
       "      <td>0.995035</td>\n",
       "      <td>1.999566</td>\n",
       "      <td>0.034467</td>\n",
       "      <td>0.203116</td>\n",
       "      <td>0.194919</td>\n",
       "      <td>0.839078</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:58</td>\n",
       "      <td>14.287 sec</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.333100</td>\n",
       "      <td>0.320371</td>\n",
       "      <td>0.995231</td>\n",
       "      <td>1.999566</td>\n",
       "      <td>0.033742</td>\n",
       "      <td>0.203085</td>\n",
       "      <td>0.194157</td>\n",
       "      <td>0.839023</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:59</td>\n",
       "      <td>14.897 sec</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.334370</td>\n",
       "      <td>0.322707</td>\n",
       "      <td>0.995213</td>\n",
       "      <td>1.999566</td>\n",
       "      <td>0.033783</td>\n",
       "      <td>0.203029</td>\n",
       "      <td>0.192694</td>\n",
       "      <td>0.838821</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:25:59</td>\n",
       "      <td>15.503 sec</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.334643</td>\n",
       "      <td>0.322935</td>\n",
       "      <td>0.995278</td>\n",
       "      <td>1.999566</td>\n",
       "      <td>0.033309</td>\n",
       "      <td>0.202999</td>\n",
       "      <td>0.189930</td>\n",
       "      <td>0.839291</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:26:00</td>\n",
       "      <td>16.160 sec</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.334961</td>\n",
       "      <td>0.323930</td>\n",
       "      <td>0.995309</td>\n",
       "      <td>1.999566</td>\n",
       "      <td>0.033121</td>\n",
       "      <td>0.202994</td>\n",
       "      <td>0.189183</td>\n",
       "      <td>0.839264</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:26:01</td>\n",
       "      <td>16.805 sec</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.335004</td>\n",
       "      <td>0.323465</td>\n",
       "      <td>0.995424</td>\n",
       "      <td>1.999566</td>\n",
       "      <td>0.032500</td>\n",
       "      <td>0.202957</td>\n",
       "      <td>0.188449</td>\n",
       "      <td>0.839421</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td></td>\n",
       "      <td>2018-05-20 20:26:01</td>\n",
       "      <td>17.456 sec</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.334927</td>\n",
       "      <td>0.323080</td>\n",
       "      <td>0.995487</td>\n",
       "      <td>1.999604</td>\n",
       "      <td>0.032255</td>\n",
       "      <td>0.202957</td>\n",
       "      <td>0.187168</td>\n",
       "      <td>0.839391</td>\n",
       "      <td>10.679152</td>\n",
       "      <td>0.044692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp    duration  number_of_trees  training_rmse  \\\n",
       "0     2018-05-20 20:25:44   0.100 sec              0.0            NaN   \n",
       "1     2018-05-20 20:25:45   1.181 sec              1.0       0.329237   \n",
       "2     2018-05-20 20:25:46   1.837 sec              2.0       0.337703   \n",
       "3     2018-05-20 20:25:47   2.924 sec              3.0       0.342474   \n",
       "4     2018-05-20 20:25:48   4.475 sec              4.0       0.341220   \n",
       "5     2018-05-20 20:25:49   4.799 sec              5.0       0.339991   \n",
       "6     2018-05-20 20:25:49   5.173 sec              6.0       0.338576   \n",
       "7     2018-05-20 20:25:49   5.534 sec              7.0       0.337354   \n",
       "8     2018-05-20 20:25:50   5.923 sec              8.0       0.337933   \n",
       "9     2018-05-20 20:25:50   6.339 sec              9.0       0.336345   \n",
       "10    2018-05-20 20:25:51   6.749 sec             10.0       0.334526   \n",
       "11    2018-05-20 20:25:51   7.162 sec             11.0       0.335901   \n",
       "12    2018-05-20 20:25:51   7.571 sec             12.0       0.332650   \n",
       "13    2018-05-20 20:25:52   8.018 sec             13.0       0.332947   \n",
       "14    2018-05-20 20:25:52   8.467 sec             14.0       0.334967   \n",
       "15    2018-05-20 20:25:53   8.945 sec             15.0       0.334957   \n",
       "16    2018-05-20 20:25:53   9.403 sec             16.0       0.334266   \n",
       "17    2018-05-20 20:25:54   9.872 sec             17.0       0.332295   \n",
       "18    2018-05-20 20:25:54  10.358 sec             18.0       0.331662   \n",
       "19    2018-05-20 20:25:55  10.920 sec             19.0       0.330554   \n",
       "20    2018-05-20 20:25:55  11.517 sec             20.0       0.331997   \n",
       "21    2018-05-20 20:25:56  12.045 sec             21.0       0.332174   \n",
       "22    2018-05-20 20:25:56  12.578 sec             22.0       0.331975   \n",
       "23    2018-05-20 20:25:57  13.145 sec             23.0       0.331260   \n",
       "24    2018-05-20 20:25:58  13.706 sec             24.0       0.333191   \n",
       "25    2018-05-20 20:25:58  14.287 sec             25.0       0.333100   \n",
       "26    2018-05-20 20:25:59  14.897 sec             26.0       0.334370   \n",
       "27    2018-05-20 20:25:59  15.503 sec             27.0       0.334643   \n",
       "28    2018-05-20 20:26:00  16.160 sec             28.0       0.334961   \n",
       "29    2018-05-20 20:26:01  16.805 sec             29.0       0.335004   \n",
       "30    2018-05-20 20:26:01  17.456 sec             30.0       0.334927   \n",
       "\n",
       "    training_logloss  training_auc  training_lift  \\\n",
       "0                NaN           NaN            NaN   \n",
       "1           0.763404      0.956487       1.930543   \n",
       "2           0.666988      0.958739       1.945962   \n",
       "3           0.585470      0.962420       1.959389   \n",
       "4           0.530667      0.967584       1.967190   \n",
       "5           0.494876      0.970585       1.973005   \n",
       "6           0.454248      0.974636       1.979192   \n",
       "7           0.427348      0.977835       1.983172   \n",
       "8           0.405616      0.980248       1.987241   \n",
       "9           0.385657      0.982926       1.990128   \n",
       "10          0.366322      0.985589       1.992683   \n",
       "11          0.357655      0.986884       1.994899   \n",
       "12          0.341858      0.989303       1.996165   \n",
       "13          0.337632      0.990264       1.996937   \n",
       "14          0.338206      0.990769       1.997604   \n",
       "15          0.335644      0.991332       1.998106   \n",
       "16          0.331837      0.992059       1.998392   \n",
       "17          0.324349      0.993148       1.998720   \n",
       "18          0.320754      0.993694       1.999123   \n",
       "19          0.316733      0.994235       1.999381   \n",
       "20          0.319453      0.994307       1.999455   \n",
       "21          0.319514      0.994558       1.999454   \n",
       "22          0.318617      0.994821       1.999529   \n",
       "23          0.316874      0.995103       1.999529   \n",
       "24          0.320829      0.995035       1.999566   \n",
       "25          0.320371      0.995231       1.999566   \n",
       "26          0.322707      0.995213       1.999566   \n",
       "27          0.322935      0.995278       1.999566   \n",
       "28          0.323930      0.995309       1.999566   \n",
       "29          0.323465      0.995424       1.999566   \n",
       "30          0.323080      0.995487       1.999604   \n",
       "\n",
       "    training_classification_error  validation_rmse  validation_logloss  \\\n",
       "0                             NaN              NaN                 NaN   \n",
       "1                        0.103585         0.257295            1.705777   \n",
       "2                        0.100437         0.211104            0.612876   \n",
       "3                        0.096241         0.205624            0.416203   \n",
       "4                        0.088710         0.204276            0.340733   \n",
       "5                        0.085154         0.203786            0.294007   \n",
       "6                        0.081914         0.203566            0.268536   \n",
       "7                        0.076219         0.203257            0.251817   \n",
       "8                        0.072625         0.203124            0.243703   \n",
       "9                        0.067415         0.203066            0.231357   \n",
       "10                       0.061423         0.203097            0.227298   \n",
       "11                       0.058755         0.203096            0.220205   \n",
       "12                       0.052590         0.203156            0.218058   \n",
       "13                       0.050032         0.203230            0.213603   \n",
       "14                       0.049137         0.203201            0.209188   \n",
       "15                       0.047514         0.203163            0.207753   \n",
       "16                       0.045455         0.203135            0.204570   \n",
       "17                       0.041518         0.203177            0.203516   \n",
       "18                       0.039879         0.203168            0.202761   \n",
       "19                       0.038212         0.203183            0.202061   \n",
       "20                       0.037777         0.203136            0.198964   \n",
       "21                       0.036471         0.203140            0.198079   \n",
       "22                       0.035568         0.203130            0.196660   \n",
       "23                       0.034353         0.203161            0.196128   \n",
       "24                       0.034467         0.203116            0.194919   \n",
       "25                       0.033742         0.203085            0.194157   \n",
       "26                       0.033783         0.203029            0.192694   \n",
       "27                       0.033309         0.202999            0.189930   \n",
       "28                       0.033121         0.202994            0.189183   \n",
       "29                       0.032500         0.202957            0.188449   \n",
       "30                       0.032255         0.202957            0.187168   \n",
       "\n",
       "    validation_auc  validation_lift  validation_classification_error  \n",
       "0              NaN              NaN                              NaN  \n",
       "1         0.780964         7.318324                         0.064986  \n",
       "2         0.808146        10.238311                         0.045618  \n",
       "3         0.820663        10.566838                         0.044361  \n",
       "4         0.823580        10.641350                         0.044140  \n",
       "5         0.825970        10.669638                         0.044162  \n",
       "6         0.828691        10.674389                         0.044096  \n",
       "7         0.830573        10.679152                         0.044096  \n",
       "8         0.831204        10.679152                         0.044118  \n",
       "9         0.832550        10.679152                         0.044118  \n",
       "10        0.832316        10.679152                         0.044427  \n",
       "11        0.833527        10.679152                         0.044228  \n",
       "12        0.833289        10.679152                         0.044162  \n",
       "13        0.833152        10.679152                         0.044118  \n",
       "14        0.834405        10.679152                         0.044471  \n",
       "15        0.834603        10.679152                         0.044361  \n",
       "16        0.835777        10.679152                         0.044118  \n",
       "17        0.836050        10.679152                         0.044228  \n",
       "18        0.836510        10.679152                         0.044625  \n",
       "19        0.836516        10.679152                         0.044493  \n",
       "20        0.837179        10.679152                         0.044846  \n",
       "21        0.837936        10.679152                         0.044272  \n",
       "22        0.838474        10.679152                         0.044272  \n",
       "23        0.838535        10.679152                         0.044383  \n",
       "24        0.839078        10.679152                         0.044780  \n",
       "25        0.839023        10.679152                         0.044493  \n",
       "26        0.838821        10.679152                         0.044493  \n",
       "27        0.839291        10.679152                         0.044603  \n",
       "28        0.839264        10.679152                         0.044427  \n",
       "29        0.839421        10.679152                         0.044449  \n",
       "30        0.839391        10.679152                         0.044692  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score_history()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qi2G0JVU1Y/RekiRJk9tUfoBJkiRJLTOMSpIkqTWGUUmSJLXGMCpJkqTWGEYlSZLUGp+m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\nkiSpmS+/CLSy5DQ9LwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature importance\n",
    "importance = model.varimp(use_pandas=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.barplot(x='scaled_importance', y='variable', data=importance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drf prediction progress: |████████████████████████████████████████████████| 100%\n",
      "drf prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# Make predictions \n",
    "train_true = train.as_data_frame()['class'].values\n",
    "test_true = test.as_data_frame()['class'].values\n",
    "train_pred = model.predict(train).as_data_frame()['p1'].values\n",
    "test_pred = model.predict(test).as_data_frame()['p1'].values\n",
    "\n",
    "train_fpr, train_tpr, _ = roc_curve(train_true, train_pred)\n",
    "test_fpr, test_tpr, _ = roc_curve(test_true, test_pred)\n",
    "train_auc = np.round(auc(train_fpr, train_tpr), 3)\n",
    "test_auc = np.round(auc(test_fpr, test_tpr), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      1.00      0.98     41088\n",
      "          1       1.00      0.53      0.69      4245\n",
      "\n",
      "avg / total       0.96      0.96      0.95     45333\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report\n",
    "print(classification_report(y_true=test_true, y_pred=(test_pred > 0.5).astype(int)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MjiVcnBR4e/rpKWOVtooNofUzUO8RuQwvhIvbunUrffr04dixY8TExBAUFCTF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\nxhhjSqH/D5/tOD2LjwAAAAJJREFUIta0+Yx7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_fpr = np.insert(train_fpr, 0, 0)\n",
    "train_tpr = np.insert(train_tpr, 0, 0)\n",
    "test_fpr = np.insert(test_fpr, 0, 0)\n",
    "test_tpr = np.insert(test_tpr, 0, 0)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.plot(train_fpr, train_tpr, label='Train AUC: ' + str(train_auc))\n",
    "ax.plot(test_fpr, test_tpr, label='Test AUC: ' + str(test_auc))\n",
    "ax.plot(train_fpr, train_fpr, 'k--', label='Chance Curve')\n",
    "ax.set_xlabel('False Positive Rate', fontsize=12)\n",
    "ax.set_ylabel('True Positive Rate', fontsize=12)\n",
    "ax.grid(True)\n",
    "ax.legend(fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the ROC, if we care about minimizing false positive, we would choose a cut-off that would give us true positive rate of ~0.5 and false positive rate almost zero (this was essentially the random forest output). However, if we care about maximizing true positive, we will have to decrease the cut-off. This way we will classify more events as “1”: some will be true ones (so true positive goes up) and many, unfortunately, will be false ones (so false positive will also go up)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PartialDependencePlot progress: |█████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "image/png": 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kfy4gfSbVLc8fB/izz+qX59ftadOWfPypU+uXZ3+uIb23qVNrl6+1Vu755Mn1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\nAAAAAAA0IgqLABpcSYn3YIytv7508sm1zz9ihLT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+7IjLRvvmHwbflydl31Xa2/+opdZ1evjn3d6NEMMK1axasyVVYyiOm98QYDY//4B7v2xvvgAwYM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\niIiIiIiItL1czFgUERERERERE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DvYCT4wQOXwgh9Akh9APuBO7dzsUUERERERERERGRODKZsbg7MCuEMCeEsAF4CTgm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bu0BlNKLmxeeOHnsBfEa9bIQPEHD5b927XXAh988OPxCV9CpaXJ4O9r18aeWDxyJAUjRljv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\nk7ijcQ2guEREkCVhURERWQScmfO8uKes6m2YYbqne2a+n+uqq7vrr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      "text/plain": [
       "<Figure size 1296x1440 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['device_num', 'time_diff', 'purchase_week', 'country', 'ip_num']\n",
    "_ = model.partial_plot(data=train, cols=cols, nbins=200, figsize=(18, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H2O session _sid_a179 closed.\n"
     ]
    }
   ],
   "source": [
    "# Shutdown h2o instance\n",
    "h2o.cluster().shutdown()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regarding “how to use this from a product perspective”: you now have a model that assigns to each user a probability of committing a fraud. You want to think about creating different experiences based on that. For instance:\n",
    "\n",
    "1. If predicted fraud probability < X, the user has the normal experience (the high majority should fall here)\n",
    "2. If X <= predicted fraud probability < Z (so the user is at risk, but not too much), you can create an additional verification step, like verify your phone number via a code sent by SMS or log in via Facebook.\n",
    "3. If predicted fraud probability >= Z (so here is really likely the user is trying to commit a fraud), you can tell the user his session has been put on hold, send this user info to someone who reviews it manually and either blocks the user or decides it is not a fraud so the session is resumed.\n",
    "\n",
    "This is just an example and there are many different ways to build products around some fraud score. However, it is important because it highlights that a ML model is often really useful when it is combined with a product which is able to take advantage of its strengths and minimize its possible drawbacks (like false positives)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}