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仕事ではじめる機械学習 第9章
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uplift_modeling.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Table of Contents"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [data description](#data-description)\n",
"* [data preprocessing](#data-preprocessing)\n",
"* [training](#training)\n",
"* [scoreing and visualization](#scoring-and-visualization)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# data description"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.utils import shuffle\n",
"\n",
"csv_path = '~/downloads/kevin_hillstrom_minethatdata_e-mailanalytics_dataminingchallenge_2008.03.20.csv'\n",
"df = pd.read_csv(csv_path)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>recency</th>\n",
" <th>history_segment</th>\n",
" <th>history</th>\n",
" <th>mens</th>\n",
" <th>womens</th>\n",
" <th>zip_code</th>\n",
" <th>newbie</th>\n",
" <th>channel</th>\n",
" <th>segment</th>\n",
" <th>visit</th>\n",
" <th>conversion</th>\n",
" <th>spend</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10</td>\n",
" <td>2) $100 - $200</td>\n",
" <td>142.44</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Surburban</td>\n",
" <td>0</td>\n",
" <td>Phone</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>6</td>\n",
" <td>3) $200 - $350</td>\n",
" <td>329.08</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Rural</td>\n",
" <td>1</td>\n",
" <td>Web</td>\n",
" <td>No E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7</td>\n",
" <td>2) $100 - $200</td>\n",
" <td>180.65</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Surburban</td>\n",
" <td>1</td>\n",
" <td>Web</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>9</td>\n",
" <td>5) $500 - $750</td>\n",
" <td>675.83</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Rural</td>\n",
" <td>1</td>\n",
" <td>Web</td>\n",
" <td>Mens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>1) $0 - $100</td>\n",
" <td>45.34</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Urban</td>\n",
" <td>0</td>\n",
" <td>Web</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>2) $100 - $200</td>\n",
" <td>134.83</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Surburban</td>\n",
" <td>0</td>\n",
" <td>Phone</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>9</td>\n",
" <td>3) $200 - $350</td>\n",
" <td>280.20</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Surburban</td>\n",
" <td>1</td>\n",
" <td>Phone</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>9</td>\n",
" <td>1) $0 - $100</td>\n",
" <td>46.42</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Urban</td>\n",
" <td>0</td>\n",
" <td>Phone</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>5) $500 - $750</td>\n",
" <td>675.07</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Rural</td>\n",
" <td>1</td>\n",
" <td>Phone</td>\n",
" <td>Mens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>1) $0 - $100</td>\n",
" <td>32.84</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Urban</td>\n",
" <td>1</td>\n",
" <td>Web</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" recency history_segment history mens womens zip_code newbie channel \\\n",
"0 10 2) $100 - $200 142.44 1 0 Surburban 0 Phone \n",
"1 6 3) $200 - $350 329.08 1 1 Rural 1 Web \n",
"2 7 2) $100 - $200 180.65 0 1 Surburban 1 Web \n",
"3 9 5) $500 - $750 675.83 1 0 Rural 1 Web \n",
"4 2 1) $0 - $100 45.34 1 0 Urban 0 Web \n",
"5 6 2) $100 - $200 134.83 0 1 Surburban 0 Phone \n",
"6 9 3) $200 - $350 280.20 1 0 Surburban 1 Phone \n",
"7 9 1) $0 - $100 46.42 0 1 Urban 0 Phone \n",
"8 9 5) $500 - $750 675.07 1 1 Rural 1 Phone \n",
"9 10 1) $0 - $100 32.84 0 1 Urban 1 Web \n",
"\n",
" segment visit conversion spend \n",
"0 Womens E-Mail 0 0 0.0 \n",
"1 No E-Mail 0 0 0.0 \n",
"2 Womens E-Mail 0 0 0.0 \n",
"3 Mens E-Mail 0 0 0.0 \n",
"4 Womens E-Mail 0 0 0.0 \n",
"5 Womens E-Mail 1 0 0.0 \n",
"6 Womens E-Mail 0 0 0.0 \n",
"7 Womens E-Mail 0 0 0.0 \n",
"8 Mens E-Mail 0 0 0.0 \n",
"9 Womens E-Mail 0 0 0.0 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(n=10)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(64000, 12)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>zip_code</th>\n",
" </tr>\n",
" <tr>\n",
" <th>zip_code</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Rural</th>\n",
" <td>9563</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Surburban</th>\n",
" <td>28776</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Urban</th>\n",
" <td>25661</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" zip_code\n",
"zip_code \n",
"Rural 9563\n",
"Surburban 28776\n",
"Urban 25661"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('zip_code')[['zip_code']].count()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>channel</th>\n",
" </tr>\n",
" <tr>\n",
" <th>channel</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Multichannel</th>\n",
" <td>7762</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Phone</th>\n",
" <td>28021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Web</th>\n",
" <td>28217</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" channel\n",
"channel \n",
"Multichannel 7762\n",
"Phone 28021\n",
"Web 28217"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('channel')[['channel']].count()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>segment</th>\n",
" </tr>\n",
" <tr>\n",
" <th>segment</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Mens E-Mail</th>\n",
" <td>21307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>No E-Mail</th>\n",
" <td>21306</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Womens E-Mail</th>\n",
" <td>21387</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" segment\n",
"segment \n",
"Mens E-Mail 21307\n",
"No E-Mail 21306\n",
"Womens E-Mail 21387"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('segment')[['segment']].count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# data preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>recency</th>\n",
" <th>history_segment</th>\n",
" <th>history</th>\n",
" <th>mens</th>\n",
" <th>womens</th>\n",
" <th>zip_code</th>\n",
" <th>newbie</th>\n",
" <th>channel</th>\n",
" <th>segment</th>\n",
" <th>visit</th>\n",
" <th>conversion</th>\n",
" <th>spend</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10</td>\n",
" <td>2) $100 - $200</td>\n",
" <td>142.44</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Surburban</td>\n",
" <td>0</td>\n",
" <td>Phone</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7</td>\n",
" <td>2) $100 - $200</td>\n",
" <td>180.65</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Surburban</td>\n",
" <td>1</td>\n",
" <td>Web</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>9</td>\n",
" <td>5) $500 - $750</td>\n",
" <td>675.83</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Rural</td>\n",
" <td>1</td>\n",
" <td>Web</td>\n",
" <td>Mens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>1) $0 - $100</td>\n",
" <td>45.34</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Urban</td>\n",
" <td>0</td>\n",
" <td>Web</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6</td>\n",
" <td>2) $100 - $200</td>\n",
" <td>134.83</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Surburban</td>\n",
" <td>0</td>\n",
" <td>Phone</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>9</td>\n",
" <td>3) $200 - $350</td>\n",
" <td>280.20</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Surburban</td>\n",
" <td>1</td>\n",
" <td>Phone</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>9</td>\n",
" <td>1) $0 - $100</td>\n",
" <td>46.42</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Urban</td>\n",
" <td>0</td>\n",
" <td>Phone</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>9</td>\n",
" <td>5) $500 - $750</td>\n",
" <td>675.07</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Rural</td>\n",
" <td>1</td>\n",
" <td>Phone</td>\n",
" <td>Mens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>10</td>\n",
" <td>1) $0 - $100</td>\n",
" <td>32.84</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Urban</td>\n",
" <td>1</td>\n",
" <td>Web</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>7</td>\n",
" <td>5) $500 - $750</td>\n",
" <td>548.91</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Urban</td>\n",
" <td>1</td>\n",
" <td>Phone</td>\n",
" <td>Womens E-Mail</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" recency history_segment history mens womens zip_code newbie channel \\\n",
"0 10 2) $100 - $200 142.44 1 0 Surburban 0 Phone \n",
"1 7 2) $100 - $200 180.65 0 1 Surburban 1 Web \n",
"2 9 5) $500 - $750 675.83 1 0 Rural 1 Web \n",
"3 2 1) $0 - $100 45.34 1 0 Urban 0 Web \n",
"4 6 2) $100 - $200 134.83 0 1 Surburban 0 Phone \n",
"5 9 3) $200 - $350 280.20 1 0 Surburban 1 Phone \n",
"6 9 1) $0 - $100 46.42 0 1 Urban 0 Phone \n",
"7 9 5) $500 - $750 675.07 1 1 Rural 1 Phone \n",
"8 10 1) $0 - $100 32.84 0 1 Urban 1 Web \n",
"9 7 5) $500 - $750 548.91 0 1 Urban 1 Phone \n",
"\n",
" segment visit conversion spend \n",
"0 Womens E-Mail 0 0 0.0 \n",
"1 Womens E-Mail 0 0 0.0 \n",
"2 Mens E-Mail 0 0 0.0 \n",
"3 Womens E-Mail 0 0 0.0 \n",
"4 Womens E-Mail 1 0 0.0 \n",
"5 Womens E-Mail 0 0 0.0 \n",
"6 Womens E-Mail 0 0 0.0 \n",
"7 Mens E-Mail 0 0 0.0 \n",
"8 Womens E-Mail 0 0 0.0 \n",
"9 Womens E-Mail 1 0 0.0 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Exclude rows whose segment is 'No E-mail'\n",
"mailed_df = df.query(\"segment != 'No E-Mail'\")\n",
"mailed_df.reset_index(drop=True, inplace=True)\n",
"mailed_df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(42694, 12)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mailed_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>zip_code=Surburban</th>\n",
" <th>zip_code=Urban</th>\n",
" <th>channel=Phone</th>\n",
" <th>channel=Web</th>\n",
" </tr>\n",
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" <tr>\n",
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" <td>1</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" zip_code=Surburban zip_code=Urban channel=Phone channel=Web\n",
"0 1 0 1 0\n",
"1 1 0 0 1\n",
"2 0 0 0 1\n",
"3 0 1 0 1\n",
"4 1 0 1 0\n",
"5 1 0 1 0\n",
"6 0 1 1 0\n",
"7 0 0 1 0\n",
"8 0 1 0 1\n",
"9 0 1 1 0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Both zip_code and channel are categorical, so convert them to dummy variables\n",
"dummied_df = pd.get_dummies(mailed_df[['zip_code', 'channel']], prefix_sep='=', drop_first=True)\n",
"dummied_df.head(n=10)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>recency</th>\n",
" <th>history</th>\n",
" <th>mens</th>\n",
" <th>womens</th>\n",
" <th>newbie</th>\n",
" <th>zip_code=Surburban</th>\n",
" <th>zip_code=Urban</th>\n",
" <th>channel=Phone</th>\n",
" <th>channel=Web</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10</td>\n",
" <td>142.44</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7</td>\n",
" <td>180.65</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>9</td>\n",
" <td>675.83</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>45.34</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6</td>\n",
" <td>134.83</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>9</td>\n",
" <td>280.20</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>9</td>\n",
" <td>46.42</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>9</td>\n",
" <td>675.07</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>10</td>\n",
" <td>32.84</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>7</td>\n",
" <td>548.91</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" recency history mens womens newbie zip_code=Surburban zip_code=Urban \\\n",
"0 10 142.44 1 0 0 1 0 \n",
"1 7 180.65 0 1 1 1 0 \n",
"2 9 675.83 1 0 1 0 0 \n",
"3 2 45.34 1 0 0 0 1 \n",
"4 6 134.83 0 1 0 1 0 \n",
"5 9 280.20 1 0 1 1 0 \n",
"6 9 46.42 0 1 0 0 1 \n",
"7 9 675.07 1 1 1 0 0 \n",
"8 10 32.84 0 1 1 0 1 \n",
"9 7 548.91 0 1 1 0 1 \n",
"\n",
" channel=Phone channel=Web \n",
"0 1 0 \n",
"1 0 1 \n",
"2 0 1 \n",
"3 0 1 \n",
"4 1 0 \n",
"5 1 0 \n",
"6 1 0 \n",
"7 1 0 \n",
"8 0 1 \n",
"9 1 0 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create dataframe for feature vectors\n",
"features_to_use = ('recency', 'history', 'mens', 'womens', 'newbie')\n",
"fv_df = mailed_df.filter(features_to_use).join(dummied_df)\n",
"fv_df.head(n=10)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# target values\n",
"is_treat = mailed_df['segment'] == 'Mens E-Mail'\n",
"is_visit = mailed_df['visit'] == 1"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Divide samples into training set and test set\n",
"tr_fv_df, te_fv_df, tr_is_visit, te_is_visit, tr_is_treat, te_is_treat = \\\n",
" train_test_split(fv_df, is_visit, is_treat, test_size=0.5, random_state=42)\n",
"tr_fv_df.reset_index(drop=True, inplace=True)\n",
"te_fv_df.reset_index(drop=True, inplace=True)\n",
"tr_is_visit.reset_index(drop=True, inplace=True)\n",
"te_is_visit.reset_index(drop=True, inplace=True)\n",
"tr_is_treat.reset_index(drop=True, inplace=True)\n",
"te_is_treat.reset_index(drop=True, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Divide training set into treatment group and control group\n",
"tr_fv_df_treat = tr_fv_df[tr_is_treat]\n",
"tr_fv_df_control = tr_fv_df[tr_is_treat == False]\n",
"tr_is_visit_treat = tr_is_visit[tr_is_treat]\n",
"tr_is_visit_control = tr_is_visit[tr_is_treat == False]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# training"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ -7.29635378e-02 2.29129687e-04 1.13947058e-01 9.15730471e-02\n",
" -4.50810868e-01 -2.96988854e-01 -2.88396878e-01 -2.79273906e-01\n",
" -1.03704567e-01]] [-0.63008274]\n",
"[[ -7.40559186e-02 2.39160259e-04 -1.71160227e-01 1.95843032e-01\n",
" -4.40594690e-01 -3.09871077e-01 -3.27310983e-01 -3.34007322e-01\n",
" -9.70680904e-02]] [-0.73972984]\n"
]
}
],
"source": [
"# training\n",
"treat_model = LogisticRegression(C=0.01)\n",
"control_model = LogisticRegression(C=0.01)\n",
"treat_model.fit(tr_fv_df_treat, tr_is_visit_treat)\n",
"control_model.fit(tr_fv_df_control, tr_is_visit_control)\n",
"print(treat_model.coef_, treat_model.intercept_)\n",
"print(control_model.coef_, control_model.intercept_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# scoring and visualization"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"treat_score = treat_model.predict_proba(te_fv_df)\n",
"control_score = control_model.predict_proba(te_fv_df)\n",
"scores = treat_score[:, 1] / control_score[:, 1]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1133483c8>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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JUo0Z1iRJkmrMsCZJklRjhjVJkqQaM6xJkiTV2LJVFyBpwYZPuGKJ1psxcfcmVyJJqopH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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b56d908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"result = list(zip(te_is_visit, te_is_treat, scores))\n",
"result.sort(key=lambda row: row[2], reverse=True)\n",
"\n",
"qdf = pd.DataFrame(columns=('treat_cvr', 'control_cvr'))\n",
"for n in range(10):\n",
" start = int(n * len(result) / 10)\n",
" end = int((n + 1) * len(result) / 10) - 1\n",
" quantiled_result = result[start:end]\n",
" # count number of users in treatment group and control group\n",
" treat_uu = list( map(lambda item: item[1], quantiled_result) ).count(True)\n",
" control_uu = list( map(lambda item: item[1], quantiled_result) ).count(False)\n",
" # count number of cv in treatment group and control group\n",
" treat_cv = [item[0] for item in quantiled_result if bool(item[1]) is True].count(True)\n",
" control_cv = [item[0] for item in quantiled_result if bool(item[1]) is False].count(True)\n",
" # calculate cvr of treatment group and control group\n",
" treat_cvr = treat_cv / treat_uu\n",
" control_cvr = control_cv / control_uu\n",
" label = '{}%~{}%'.format(n * 10, (n + 1) * 10)\n",
" qdf.loc[label] = [treat_cvr, control_cvr]\n",
"qdf.plot.bar(figsize=(10,7))\n",
"plt.xlabel('percentile')\n",
"plt.ylabel('conversion rate')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"treat_uu = 0\n",
"control_uu = 0\n",
"treat_cv = 0\n",
"control_cv = 0\n",
"treat_cvr = 0.0\n",
"control_cvr = 0.0\n",
"lift = 0.0\n",
"\n",
"stat_data = []\n",
"for is_cv, is_treat, score in result:\n",
" if is_treat:\n",
" treat_uu += 1\n",
" if is_cv:\n",
" treat_cv += 1\n",
" treat_cvr = treat_cv / treat_uu\n",
" else:\n",
" control_uu += 1\n",
" if is_cv:\n",
" control_cv += 1\n",
" control_cvr = control_cv / control_uu\n",
" # コンバージョンレートの差に実験群の人数を掛けることでliftを算出\n",
" # CVRの差なので、実験群と統制群の大きさが異なっていても算出可能\n",
" # lift is the amount of cv increment compared to \n",
" # liftは あるす\n",
" lift = (treat_cvr - control_cvr) * treat_uu\n",
" stat_data.append([\n",
" is_cv, is_treat, score,\n",
" treat_uu, control_uu, treat_cv, control_cv,\n",
" treat_cvr, control_cvr, lift\n",
" ])\n",
"\n",
"# 統計データを、DataFrameに変換する\n",
"columns = [\n",
" 'is_cv', 'is_treat', 'score',\n",
" 'treat_uu', 'control_uu', 'treat_cv', 'control_cv',\n",
" 'treat_cvr', 'control_cvr', 'lift'\n",
"]\n",
"stat_df = pd.DataFrame(stat_data, columns=columns)\n",
"\n",
"# ベースラインを書き加える\n",
"stat_df['baseline'] = stat_df.index * stat_df['lift'][len(stat_df.index) - 1] / len(stat_df.index)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11341bd68>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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Nhgb93E6U46m4iYhIjvfEt+uZujoKgOeur8nQtlVdTpQDxEbChH5Oaev6ukpb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\nREQkJJS4iYiIiISEEjcRERGRkPj//xLYPGuBMx0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c85d0b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stat_df.plot(y=['treat_cv', 'control_cv'], figsize=(10,7))\n",
"plt.xlabel('uplift score rank')\n",
"plt.ylabel('conversion count')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1140d8358>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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2pOexMe2g88h2bw5BAQG4reVXA9ox+oTWFJW42ZmZz0ld4wkNDiQyJLBOj2rr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\nREREypiCNREREZEy9v9h6QUSbws3JQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1140b16d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stat_df.plot(y=['treat_cvr', 'control_cvr'], figsize=(10,7))\n",
"plt.xlabel('uplift score rank')\n",
"plt.ylabel('conversion rate')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11452c160>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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joJy7/WFgpA9jE5FC5Exisuc4ONDfwUhECqbv7+nI9/d0TNM++V7vtk0v9Pc6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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11454d1d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stat_df.plot(y=['lift', 'baseline'], figsize=(10,7))\n",
"plt.xlabel('uplift score rank')\n",
"plt.ylabel('conversion lift')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1133bcf28>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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A1DG7+KPlZNJWTyTGpGa7LxND2pFILIZMDPnpo1NxJiJFxlrLhBW7vYUZwJAx\nJ7ZqWvHixW7EEpFiJuv3nVP5YOp6dh06xhWta9I6rjwA/574N4u3HsSQSb/wKTwe8S0Rm8I50qI/\nYw/HcdbZbYlv0QoiSxIWEUXUybtQvuBb75mKMxEpMrd/Pp/Jq/dka/tlsfNI855u2nZJRIpG1TJR\n7D6ccsrr6/Yk8tpvqwH4fNZmNg3tQ2am5Z3J62hmNvFS5HDahK1jdmZTOt35JeUqNfQu4FoYNFtT\nRIrMyYVZVkeS0095TUSkMGWcZt3Ynv+elu18/d4j9HlnOgPDxzO6xNPEmT08nHoPzZ+cDpUKfxKT\nijMR8bubhs2h7uCx2dq+GNiByY+ezwM9nPEeZWMi3YgmIsXQuAfPzdf9X8zcRL997/Bs5Jf8kdmG\nHilvMDLzPGKj/fN9S481RcRvDh1Lo9ULE3O0/3JfF+JrlwPg7m4NOJyczt16rCkiRSS/S1wkz/2M\nWyJ/54v0C5lS//84vCaBGzrG+SmdijMR8aM1uxOznfdpWZ03r21FdGS4t61kiQiev/zsoo4mIgJA\nmyG/s/CZC73nD327KPt1s4aXIobzZ0ZLhqTfxNqBnfyeScWZiPjN2KU7vcdP9W7KnV3ru5hGRCSn\n/UmppKZnsn7vEcYv2+mdpAQQRiaPR37LAUpzf9oDpBVR2aTiTET8JtOzX2bZmEiublvL5TQiIrn7\ndckOHv1hSY72+R2mUmHpah5Pu5NEStKnRfUiyaPiTET8Ys3uRL6YtRmAhc9cSHg+VscWESlKuRVm\nrcw6yi8dxhfpF/JdRneaVCvN+wPaFEkezdYUkUKXkWm56K0TU9FVmIlIoCkTnXf/1DcNp2BjyjM0\nvT8Aq3cl5nl/YVJxJiKFbtTi7W5HEBHJ0/9ubHvKay3MBkpumUJYp3s5ypltXl4QKs5EpFB8P28r\n2w4c5feVu3nk+xOPCN6/oWgeA4iI5Mc5Z1VixJ0dc732bctFUKI0dLyriFM5NOZMRApsc0IS//xp\naY72TUP7uJBGRMQ3LWuVy9FWlf2UWjsa2g2E6DLe9uUv9CqyXCrORKRADien0fvt6Tnar2pd04U0\nIiK+i8my5uJx90f8AljofB8AK17ohTHOmoxFRY81RaRA/vXbapJSM7K1PdarMW9c28qlRCIivgkP\nMwy9qoX3vAb7uD5yOrTqB+XrAFAqKqJICzNQcSYiBbQ/KTXbeZNqpbmv+1mEaYamiASBWO+sTcvU\n1lMpEWZVxJ56AAAgAElEQVTh/MddzaTHmiJyRg4eTeXDaRsYt2wXoPFlIhKcPp+5CYALwhZSYtXP\nTmFWzn/7ZvpCxZmI5FtSSjrxL/7udgwRkQLrG1+TlZt2MCTyU6h4FnR9zO1IeqwpIvk3ZMzKbOdf\nDOzgUhIRkYK5rFUN+oVPoYbZD33/C+GRbkdScSYi+ZOekcm387Z6z7+5sxNdG1V2MZGIyJkrZY9y\nd8SvzM5sCnG5r3tW1PRYU0TyZcv+o97jd/u3pnODii6mEREpmIhl31HZHCKi35duR/FSz5mI5Mux\ntBPLZlzWqoaLSURECigjDaa9DrU7Ub5JV7fTeKk4E5F8SUxOB+DL2zXOTESC3PrJkLQHzn0ITOAs\n/6PiTER8Nmt9Av0+mg1AyRI5V9YWEQkqy36AqLLQ4AK3k2Sj4kxEfNb/49ne42bVy7qYRESkgJIP\nw6pfocU1EFHC7TTZqDgTkTMSo54zEQlma36D9GRnq6YAo+JMRPLt0QsbuR1BRKRg1v0BMeWhZju3\nk+Sg4kxEfLJxX5L3uGXtci4mEREpoJQj8Pc4aNwbwgKvFAq8RCISkN75Yy0AbeuUp2vDSi6nEREp\ngPWTIeVwQD7SBC1CKyI+sNYyctF2AH665xyX04iIFNC8T6BkpYB8pAnqORMRHxxNzTj9TSIiwWDt\n77DxTzjvUShR0u00uVJxJiKndfZzEwD458WNXU4iIlIA6Skw4UkoXw/a3+F2mlNScSYiOaSkZ/DN\n3C1kZlo27D3ibW9Xp4KLqURECmjyS7BvDfR6JeDWNstKY85EJIdmz04gI9MC8MTPywCIiQynQz0V\nZyISpOZ+DDPfgZb9oElvt9PkST1nIpLNrPUJOQozgK/v7OhWJBGRgtk8C8Y/Do0uhsvfdTvNaak4\nE5FsBn0xP9f22uUDc+CsiEieEnfDz4OgbE246qOAfpx5nN+KM2NMbWPMFGPMSmPMCmPMg572540x\n240xiz0fvbO85gljzDpjzN/GmF7+yiYip5ZpbY62cQ+cR+XSUS6kEREpAGth7CNwZDdc8xlEB8ee\nwP4cc5YOPGqtXWiMKQ0sMMb87rn2lrX2jaw3G2OaAf2As4EawCRjTCNrrebwixSBo6npNHt2Qq7X\nmtUoU8RpREQKwYqRsHoMdHsSarV1O43P/FacWWt3Ajs9x4nGmFVAzTxe0hf41lqbAmw0xqwDOgCz\n/JVRRJwFZo0x/DB/W7b2TUP78PaktdStpMeZIhKEEnfBhKegagvo+n9up8mXIhlzZoypC7QG5nia\n7jfGLDXGDDfGlPe01QS2ZnnZNvIu5kSkgF4Zt4p6T4xj5KJtPDd6hbe9ZIlwAB7s2ZC+8fpnKCJB\n6Pfn4GgC9H0PwsLdTpMvfi/OjDGxwE/AQ9baw8AHQAMgHqdn7c18vt8gY8x8Y8z8vXv3FnpekVA2\nZfUenhy5jJ2HjgHw0bQNADz83ZJs913asnqRZxMRKRSZmTDxGVj6LXS6G2rEu50o3/y6zpkxJhKn\nMPvaWvszgLV2d5brHwNjPKfbgdpZXl7L05aNtfYj4COAdu3a5Ry5LCKndNtn8wAYMWcLa166JNd7\nnr20GTd0jCvKWCIihee3wTD3Q2h3O/R41u00Z8SfszUNMAxYZa39d5b2rL+SXwks9xyPBvoZY6KM\nMfWAhsBcf+UTKW6Or112XKOnx+d6X5ezKhEdGVyPAEREsBZ+f9YpzDrdC33ehPDgXGvfn6m7ADcB\ny4wxiz1tTwL9jTHxgAU2AXcBWGtXGGO+B1bizPS8TzM1RQpPcppv/5yqlY32cxIRkUJmLYz/J8z9\nCNoNhAuHgDFupzpj/pytOQPI7W9mXB6veRl42V+ZRIqzv9btA6Bx1dL8vTvR277s+Ys4kJRGhdgS\nxEYF52+ZIlLMTXvDKcw63gMXvxrUhRlob02RkDdj7T6eHLmMGuWcHrHtB4/RqX4FZm/YD0BsVASl\noyPdjCgicuaW/wxTX4VmfZ0NzYO8MAMVZyIh78Zhzgo2W/YfBWDOkxdQKiqCRk+Pp33d8pgQ+EYm\nIsXU5pnw0+1QqwP0eQvCQmNXShVnIiFg+fZDPPDNIm7sVIdbzqlLeJhTcE1csSvbfW3iylHK8+jy\nVLM1RUSCQuIu+P5mKF8PbvgWYsqf/jVBIjRKTJFi7tJ3Z7BhXxIvjllJ3/dnADB+2U4Gfbkg230p\n6ZluxBMRKVyZmTDqPkhJhOu/DKnCDNRzJhJylm8/zAdT1/Pab6tzXLu2bS0XEomIFLIVP8O6SXDJ\nv6Dq2W6nKXQqzkSCXGZmzrWYTy7Mxj94HnUrliI6Up3lIhLkMjPhr/9ApcbQ/k630/iFijORIPfe\nlHUA1KlYks0JR3Nc3zS0T1FHEhHxnyXfwK5lcMX/QmYCwMlC808lUoz8+/c1APzvxraUjtbvWyIS\nwlKPwqTnndmZLa93O43f6Du5SIhoUq00y57vxYGkVMqVjGT4X5toVye0BsmKSDE3+31I2gNXfRiy\nvWag4kwkJERHhnnXKytfqgQAt59bz81IIiKFa+dSmPqas9hs/e5up/Gr0C07RYqBhVsOAHBL57ru\nBhER8Sdr4Y8XITIG+vw7JHYByIuKM5EgdtV/ZwLwy+LtLicREfGjBZ/Cut+h22AoVcntNH6n4kwk\nBAy7pb3bEURE/CPtGEx5Fep0cTY2LwY05kwkSK3bc8R73LxmWReTiIj40Z+vOZMArhkW0pMAsioe\nf0qRELTvSAoAMZHhLicREfGTpASY9V9o2Q/qdXU7TZFRcSYSpB7/aSkA5zSo6HISERE/sBZ+exwy\nUuHch9xOU6RUnIkEqXDPbKUBneJcTiIi4gerx8KyH6D7k1ClqdtpipSKM5EgtWFfEgA9mlR1OYmI\nSCFL2gcTn4ZycXDuI26nKXKaECAiIiKBIz0Ffr4TEnfCzaMgvPiVKuo5EwlQW/cf5UhKOpNW7uZI\nSnqu91zZumYRpxIR8aPMTPh2AKyfDBe9BHGd3E7kiuJXjooEgeS0DM7715RsbZuG9vEeH9/svFrZ\n6CLNJSLiN9bCyLucxWZ7vwEd7nQ7kWtUnIkEoD9W7cnRlpFpafrMb1SKLcGOQ8kA7D+SWtTRRET8\nY/5wWPY9nPswtL/D7TSu0mNNkQB034iFOdpuHj6H1IxMb2EGULdSqaKMJSLiH5tnwrjHnLXMLngu\n5PfOPB0VZyJB4q91CdnOz2lQkZs713EpjYhIITmwCX4eBGVqwnVfFPvCDFSciQScpCyD/8f849xT\n3jfizk6UitLIBBEJYrtXwMc9IPkQXPYfiCnvdqKAoOJMJMB8M3eL97h5zbIseubCHPeUKxlZlJFE\nRArfhqkw/GIIi4A7p8BZF7idKGDo126RADNsxsZs5+VLlfAej76/C78u2cE/LmhY1LFERArHkT0w\neQgs/ALK14Vbx0LZWm6nCigqzkQCzM4sA/6Pq1w6ir2JKZSJjuSpPs1cSCUiUkDHDsDCL+HP1yA9\nGc75B5w/GKJi3U4WcFSciQSoyY+e7z3udXZVvpq9hZJR4S4mEhE5Awc2w/Q3YMl3kJEC9bs565hV\n0hOAU1FxJhKg6lc+8dvkC5c356ZOdalSWovOikiQsBYWfAq/PQEYiO8PbW+DGvFuJwt4Ks5EAkh6\nRmau7eFhhsbVShdxGhGRApj2Bkx5Cep3h77vQ1ltN+er087WNMa85kubiBTc+r1JANx+bj2Xk4iI\nFMDGaTD1FWhxLdz4swqzfPJlKY2c8/jhksIOIiLOnpoAnetXdDmJiMgZSkqAH26DCg3g0v9AmFbt\nyq9TPtY0xtwD3As0MMYszXKpNPCXv4OJFEdHU53irGQJDfwXkSBkLYx92FlU9uZfNBPzDOU15mw2\nMB54FRicpT3RWrvfr6lEiqnVuw4DEKPiTESC0dLvYeUoZ3/Mai3cThO08irOPrHWtjXGVLHWbi6y\nRCLF1F/r9vHCrysBKFlCc3VEJMgkH4Y/XoTq8dDlQbfTBLW8fgKEGWOeBBoZYx45+aK19t/+iyVS\n/Az4ZI73ODpSYzREJMiMeRgSd8K1n0KYev8LIq+fAP2ADJwCrnQuHyJSiIw5cVwhy5ZNIiIBb+s8\nWP4jdH0MandwO03QO2XPmbX2b+A1Y8xSa+34IswkUiz1alaN31bs4u1+8ZSO1sbmIhIkrIVJz0HJ\nStD5PrfThIS8ZmveaK39CmhmjGl68nU91hQpPOkZmSzYcoA2ceXoG6/1gEQkiMx6Dzb/BZe9DdFl\n3E4TEvIac1bK8/mM5sEaY2oDXwBVAQt8ZK192xhTAfgOqAtsAq6z1h4wxhjgbaA3cBS41Vq78Ey+\ntkgwqTt4rPc4POuzTRGRQJew3pkE0LgPtLnF7TQhI6/Hmh96Pr9whu+dDjxqrV1ojCkNLDDG/A7c\nCvxhrR1qjBmMs0zH4zgL2zb0fHQEPvB8FglZJ2/XtOtwsktJRETOwJ+vQUYa9P5X9oGzUiB5PdZ8\nJ68XWmsfOM31ncBOz3GiMWYVUBPoC3Tz3PY5MBWnOOsLfGGttcBsY0w5Y0x1z/uIhKQhY1a6HUFE\n5MxsWwBLv4PO90PZWm6nCSl5PdZcUFhfxBhTF2gNzAGqZim4duE89gSncNua5WXbPG3ZijNjzCBg\nEEBcXFxhRRRxRYVSUdnOH+vV2KUkIiL5kHwIfrgVytSC8x51O03Iyeux5ueF8QWMMbHAT8BD1trD\nJku3p7XWGmNsft7PWvsR8BFAu3bt8vVakUBTvWx0tvP7up/lUhIRER9lZsIv98Lh7TDwNyhZwe1E\nIcevy5AbYyJxCrOvrbU/e5p3H39caYypDuzxtG8Hamd5eS1Pm0jIOpqaDkD5kpG0raNvcCISBKa9\nDqvHQK9XtaaZn/itOPPMvhwGrDpp2Y3RwC3AUM/nUVna7zfGfIszEeCQxptJKEtOy+B5z3ZNMwdf\noP00RSTwLfkOpr4CrW6ATve4nSZk+bPnrAtwE7DMGLPY0/YkTlH2vTHmdmAzcJ3n2jicZTTW4Syl\ncZsfs4m4btKq3d5jbdckIgFv5SgYOQjqngeXvqXZmX502uLMGFMZuBNnXTLv/dbagXm9zlo7AzjV\nf7kLcrnfAlpaWIqNYTM2eo+NvsmJSCDbvhBG/QOqnA0DfoTI6NO/Rs6YLz1no4DpwCScvTZFpBAs\n2nLQ7QgiIqeXmQljHoISJaH/NyrMioAvxVlJa+3jfk8iUky1qFnW7QgiIqe28HPYuQSu+hjK13E7\nTbHgy0CXMcaY3n5PIlKM7E9K9R6Puq+Li0lERPKwdw38NhjqdYXm17idptjwpTh7EKdASzbGJHo+\nDvs7mEgo+3nhNu9xWJjGm4lIAEpPcR5nWuv0moVp4lJROe1jTWtt6aIIIlKclI2JBGD0/eo1E5EA\nlHwYvukHm/+CKz+C0tXcTlSs+LSUhjHmcqCr53SqtXaM/yKJhL7EZGfx2aplNLBWRAJM0j746irY\nvcLpMWt53elfI4XKl6U0hgLtga89TQ8aY7pYa5/wazKREFN38FgAPrutPV/O3gyc6EETEQkISfvg\n095wcDP0GwGNermdqFjypeesNxBvrc0EMMZ8DiwCVJyJ+CgpJd17fOun87zH0ZHaFUBEAkRSAnx/\nCySsg5t+hvrd3E5UbPm6Q0A5YL/nWPP+RfJp56FjbkcQETm11CT4pAcc3gmXv6vCzGW+FGevAouM\nMVNwVvzvCgz2ayqRELPvSOrpbxIRccPR/TDiOjiwCa77Eppd7naiYs+X2ZrfGGOm4ow7A3jcWrvL\nr6lEQszwLFs1HfdAj7NcSCIikkVmBoz+B+xYDNd9ocIsQJyyODPGNLHWrjbGtPE0HV+YqYYxpoa1\ndqH/44mEhvmbDwDwdr945m7cT6ta5biufW2XU4lIsWYtjHsMVo+BC1+EZn3dTiQeefWcPQIMAt7M\n5ZoFevglkUiIycy03h0B+rSoTt/4mi4nEhEBpr0B84fBOQ9AlwfdTiNZnLI4s9YO8nzuXnRxREKH\ntZaU9EwOJ6d52yLCtcK2iASAjdNhykvQ8nro+YLbaeQkvqxzdi3wm7U20RjzNNAGGGKtXeT3dCJB\n7Nt5W3ni52VuxxARye7AZhh1L5StDZe+pW2ZApAv/0We8RRm5wI9gWHA//wbSyT4zVi3L9v5de1q\nuZRERMTjyB74/DI4dgiuHgYlSrmdSHLhS3GW4fncB/jIWjsWKOG/SCKhoUx09o7pf/Ro6FISERGc\nwuybfpC4C24aCXEd3U4kp+BLcbbdGPMhcD0wzhgT5ePrRIq1UiVOFGfPXtqM2hVKuphGRIq1xN3w\n6SXOfplXfgC12rqdSPLgS5F1HTAB6GWtPQhUAB7zayqREBAebrzHt3Wp614QESneDmyCTy6AQ9vh\npl+g+dVuJ5LTyHNCgDEmHFhorW1yvM1auxPY6e9gIsFu5Y7D3mNjTB53ioj4ScJ6+PxySE2EW36F\n2u1P/xpxXZ49Z9baDOBvY0xcEeURCQnWWqav3Xf6G0VE/CVhvTP4P/0Y3DJGhVkQ8WVvzfLACmPM\nXCDpeKO1Vns8iJzCbZ/N8x6/elULF5OISLG0fSGMuB5sBtw8Cqrp+1Aw8aU4e8bvKURCTKZ1Ppcr\nGUn/Dup4FpEitGUOfHklxJSDm8ZA5cZuJ5J88mXj8z+NMXWAhtbaScaYkkC4/6OJBKdDx9KYtmYv\nAFMe7eZuGBEpXlaNgZ9uh1JV4NZfoXxdtxPJGTjtbE1jzJ3Aj8CHnqaawC/+DCUSzGatPzHWrGSU\nfo8RkSKy4HP4/iao2hwGTVVhFsR8WUrjPqALcBjAWrsWqOLPUCLB7P9+WOo9jopQcSYiRWDxCPj1\nAajfHW4ZDaUqup1ICsCXMWcp1trU40sBGGMiAOvXVCJB7EhKOgBv94t3OYmIFAsbp8GYRyDuHOj/\nDUREuZ1ICsiXnrM/jTFPAjHGmAuBH4Bf/RtLJDgdOpbmPe4bX9PFJCJSLBzc6szKLBcH132uwixE\n+FKcDQb2AsuAu4BxwNP+DCUSrFq9MNHtCCJSXKyf7Kz8bzNhwA8QqxFHocKXx5pXAF9Yaz/2dxiR\nUDH2gXPdjiAioWzlaPj5TqfHrP83UL6O24mkEPnSc3YZsMYY86Ux5lLPmDMROYm1J4Zinl2jrItJ\nRCRkZWbC6nHOchlVmztbMtXUJuahxpd1zm4zxkQClwD9gfeNMb9ba+/wezqRINF2yO8kJKUC8MQl\nTU5zt4hIPmSkwb61sGGKMytz93Ko1Aiu+wJKV3M7nfiBT71g1to0Y8x4nFmaMTiPOlWciXgcL8wA\ndh9OcTGJiI8y0iHlMBw7AGnHID3F2YMxPdk5Tj0KaUmQlgxpRyEl0fmcmeFsCZSZ4Yx1spkntR3/\nbLO32UynDfBO+Le5TPzP1pbbfYXQlq39TNoK4/18zW3h8E7nvw1A5SZw2TvQqp8G/4ew0xZnxphL\ngOuBbsBU4BPgOr+mEgkiw2dszHZeNibSpSRS7Bw7AAkb4MguSD7sFFvJhyH5ICQfcj6OHXCKqrRj\nzkdqklNoHf9h76uwCIgsBWFhYMIhLBzM8eOsbVmvhZ1oO35+nGd5JjB5tBmnLcstvr/2dG1Z2gut\nLZ9fw9fcZ1WEas2h7rlQoX7Orychx5ees5uB74C7rLXqEhDJ4tJ3p7N8++FsbXedr2+eUsiSEmDn\nIti5FPb+DfvXQ8J6OLY/9/sjS0F02Swf5aB0dYiMgRKlIKo0lCjtfI4pDyVKQkSM0xMTefxzSefe\nyJgT13IrRESk0Pky5qx/UQQRCUZZC7Pht7ajXqVYoiO1K4CcocxM2L8BdiyE7Qth5xLYuzp7EVa6\nBlRsAM0uhwoNnOMyNSCqzIliLFy9tyLBzJfHmlcBr+Fs2WQ8H9ZaW8bP2UQCSnpGJmc9NR6ATUP7\nkJqeme16jyZV3YglwSw9xekN2zYXNvwJW2ZDyiHnWkQMVGsBTfo4g7+rt3I+Ysq5m1lE/M6Xx5r/\nAi6z1q7ydxiRQPbPn07smfnl7M2kpGW4mEaCTka60xO2cSrsXgkHt8DOxZDhmUxSvi40v9JZFqFG\nG2fgd7hWLhIpjnz5l79bhZkIjFm603v8zC/LOa9hJe/5Qz0buhFJAlnibqf42r4QNv8F2xc4A/PB\nWTi0TE1ofyfEdYRaHaBMdXfzikjA8KU4m2+M+Q74BfBOCLDW/uy3VCIBqHb5GNbvTfKeT1+7D4C5\nT11AxVKa0l7sZWY660+tGg2rfnXGioEzQ7HK2dDmZqjV3plxp7WpRCQPvhRnZYCjwEVZ2iyQZ3Fm\njBkOXArssdY297Q9D9yJs1cnwJPW2nGea08AtwMZwAPW2gm+/zFE/C9rYZZVldLRRZxEAoK1zozJ\njX96PqY7A/dNGNTpAvEDnEeU1Vs6syJFRHzk0w4BZ/jenwHvAV+c1P6WtfaNrA3GmGZAP+BsoAYw\nyRjTyFqrQT0ScEbc2ZEbPp7jdgxxQ2YmbJsHS76BtRPh8HanvUxNaHQx1O3ifC5VKe/3ERHJgy+z\nNWsB7wJdPE3TgQettdvyep21dpoxpq6POfoC33rWUdtojFkHdABm+fh6Eb/KOjOzU72K3uOHezZy\nI44Utd0rnIJs2Y+QuBMioqFRL6j3KNTv5iwMqjXARKSQ+PJY81NgBHCt5/xGT9uFZ/g17zfG3AzM\nBx611h4AagKzs9yzzdOWgzFmEDAIIC4u7gwjiOTP4q0HvcdhYYb5T/ck01o90gxVKYmweaYzkH/9\nFNi11Fkh/6wL4cIh0OgiZz0xERE/8KU4q2yt/TTL+WfGmIfO8Ot9AAzBGbM2BHgTGJifN7DWfgR8\nBNCuXbtcNmYTKXw/LtgKwJvXtgKgUqwmAISMzEzYs9JZa2z7Amd25d7Vzl6QYZHOuLGLXoJWN0Cp\niqd/PxGRAvKlOEswxtwIfOM57w8knMkXs9buPn5sjPkYGOM53Q7UznJrLU+biGt2HjrGuj1HOHQs\nje/nO0/xm1TXwO6gZy0c3OwM4F/zG2ya4exFCRBTwSnGml4OcZ2cj8gYd/OKSLHjS3E2EGfM2Vs4\nPV4zgTOaJGCMqW6tPb5Y1JXAcs/xaGCEMebfOBMCGgJzz+RriOTHA98sYvSSHcx/umeO3rDOr07O\ncX+z6toYIygd2QubpsHaSc7MyqwD+Zte5syujOsI5etp7JiIuM6X2Zqbgcvz+8bGmG+AbkAlY8w2\n4DmgmzEmHqfI2wTc5fkaK4wx3wMrgXTgPs3UFH/7ZPoGRi/ZAUC7lyax7uVLyLCWqIhT741p9IM7\n8Fnr7E+5dQ6sGuOsPXZws3MtpgLU6wp1H3bWG6vUGMLC3M0rInISX2Zrfo4zO/Og57w88Ka1Ns+x\nYqfYMH1YHve/DLx8ujwiheWlsdk3vrj103nMWLePamWimf549xz3n3uWlkcISKlJzv6UG6Y42yNt\nXwhJe5xr0eWcXrG2t0Cdc6FWOwjTxvQiEth8eazZ8nhhBmCtPWCMae3HTCJFrlP9CsxY56z4v+tw\nMg09G5xn9dUdHYs6lmSVfAj2rXMG6x/Y6PSO7VwCCeuc6ybM2Y+yQQ/nEWXtjs65ijERCTK+FGdh\nxpjyniUvMMZU8PF1IgFr16HkPM9P9uPdnf0ZR3KTmgTr/nAG7G/+y3k86WWgbG1n9f3m10C1Fs7g\nfS3+KiIhwJci601gljHmB8/5tejxowS5D6au8x63rVOeBZsP5Hl/u7oV/B1JAI7uhy2zYe6HTlGW\nmQ4RMVC7A3R/Cqqe7Sz4WvEsCI90O62IiF/4MiHgC2PMfKCHp+kqa+1K/8YS8a/PZzkDxD+9rT23\nfTrvlPd9MKAN+5JSiypW8ZOwHnYscgqxTdNPPKKMrQqd7/c8ouwMESXczSkiUoR8ejzpKcZUkEnI\nKREexuwnLqDTq38AMOyWdlzQtCrP/LKcr+Zs5pIW1V1OGGKshV3LnPXFVo+FnYud9shSzizK+Bug\nZjunp0zri4lIMaWxY1Ksda5fkbCwE8tjtKpdDoAhVzRnyBXN3YoVWpISYMssZ8PwVb/C/vWAgRrx\nzlZIDbo7A/f1mFJEBFBxJsVUnYolia9dzluYnVUllnV7jlChpB6fFVh6qlOMbZkNayc4S1tgnb0p\n4zpDp3ug2RUQW9ntpCIiAUnFmRRLmxOOkpF5YmvWSY+c72KaIJd82LMn5XzY9Jez+GvaUedatZbQ\n/Umod74zs1KPKkVETkvFmRQr8zft55r/zQJg24FjLqcJYjuXwNLvnUH8u5Y5m4QDVGkGrW+C+uc7\ni7/GlHM3p4hIEFJxJsXK8cIMoFKsHmH6LD3FKcI2/wWLRzgLwZpwqHMOdH3MWWOsRhsVYyIihUDF\nmRRb7/Zv43aEwJaZ4WwSvuQ7WD0GUo847TVaQ+83oMU1EFPe3YwiIiFIxZkUC0kp6bw+4e9sbZ0b\nVHQpTYA7uh+WfANzPnQ2DI8qC82vggYXOD1kpau5nVBEJKSpOJNiYcScLXw2c5P3fPWQi90LE4is\ndQb1zxsGK36G9GRnb8qez0Pj3hAZ7XZCEZFiQ8WZFAsrdx7Odh4dqc2wAWf/ygWfwcx3IXEnlIiF\n+AHQbiBU0zpvIiJuUHEmxcLoJTu8x+o1A44dhHmfwKz34NgBZ3X+7k/B2VdAVGm304mIFGsqzqRY\nOL6m2aahfVxO4rKkBJj3Mcz6L6QccsaRnf84xHV0O5mIiHioOJOQt18bl8P+Dc7jy3nDITURGveB\nboOdhWFFRCSgqDiTkPfHqt0ANKhcyuUkRcxa2DoXpr/pbKNkwqFJH+j2BFRt5nY6ERE5BRVnEvK2\n7ne2Enrj2lYuJylCW+fCxGdg62yILAXnD4a2t0KZ6m4nExGR01BxJiHvncnrAIirUNLlJEVg/0aY\n9BysHAWxVZ3FYlv10yB/EZEgouJMio2KsVFuR/Cf5MOwajRMfslZRLbrP6HLgxAV63YyERHJJxVn\nIsLrCZUAAB98SURBVMHMWlj4OUx42hnoX6kx3PC9BvqLiAQxFWciwerofvj+Ztg03VmnrMczUKs9\nGON2MhERKQAVZxLStiQcdTtC4UtNgsUjYNrrzgKyl74FbW6FsDC3k4mISCFQcSYhbc3uRLcjFK5t\nC+CbfpC0B2p3gv7fQM22bqcSEZFCpOJMQtrxPTV/uLuzy0kKKOUI/DkUZr4HMeXhpl+gfjc9whQR\nCUEqziSk/fv3NQDUCeZlNNZMgLH/B4e2QOsboeeLUKqi26lERMRPVJxJsVC5dBAuo5GRBuMfh/nD\noHJTuG081DnH7VQiIuJnKs4k5NWvXAoTbI//UhLh6+tgy0zodC/0fAEiSridSkREioCKMwlZR1LS\nAdiwN8nlJPl0ZC/8eBtsnQNXfuis8C8iIsWGijMJSdZamj83AYDr2tVyOU0+rB4Hv9wDacfgig+g\n1fVuJxIRkSKm4kxC0nfztnqPa5UPkskAq8Y4i8pWa+H0mFVp4nYiERFxgYozCUm7D6d4j68Nhp6z\nFSPh50FQozXc/Is2KhcRKcZUnElIGjF3MwDDbmlH9bIxLqfJQ9oxZ7PyWe85Wy8N+EGFmYhIMaf9\nXiQkzFqfwE3D5pCWkQmc6DmrHcjrmx3ZA19f6xRmbW+FW8c6C8yKiEixpp4zCXqJyWn0/3g2ADPX\nJ3B+o8reaw0qx7oVK2+bZsAP/9/enYdHVd1/HH9/s5CwhV1AdgRBVoWwiaCgIIIIKi4oRYVCW2qp\nbbXan1atomKpWrUtCoiKqKjgQgUUioBQoLIrIkFEVtlBAmFJMnN+f8wYEtYAmbk3k8/refJwt7n3\ny+k0fDz33nPugiPp0OufocFlRUREUM+ZFHKHMgM0fXR6zvqXm34EoE7FktQ7rxTxcT4b38w5WP42\njOsNxcvC4NkKZiIikod6zqRQ2XXgCKWTE0hKiAfg0uEz8+yvUCo0E8D3u3w4tlnWIZj8G/jqPajd\nAW55Q7cxRUTkOApnUqikDvsPDauUZmS/lnT62+zj9u8+cISt+w5Fv7DTObIfXr8OflgKqQPhmqch\nPtHrqkRExIcidlvTzMaa2Q4zW5lrW3kzm2Fm34b/LBfebmb2gpmtNbMvzaxFpOqSwutgZmjE/9Xb\n9tNvzP/y7Fv12NUAPDNjDdO/3g7AE9c3iW6BJ7PrW3i9J2xdATe9Btc+q2AmIiInFclnzl4Duh2z\n7QFgpnOuPjAzvA5wDVA//DMYGBnBuqSQ6jv6aCDb8mPe3rHiifE5y49M/hog59anp9ZMh5HtYe96\nuPl1aHy91xWJiIjPRSycOec+B/Ycs7kX8Hp4+XWgd67t41zIQqCsmVWNVG1SOFVJSTrpPjOjb+ua\nebbd2KJapEs6tfXzYMJtUOlC+MVcuKint/WIiEihEO23NSs757aGl7cBlcPL1YBNuY7bHN4mAkAg\n6Pj06+20qFn2uH1dGoW+Rk/d0JTG56fkbDfz8E3N5W/BuF5QqjLcPgnK1vCuFhERKVQ8G0rDOecA\nd6afM7PBZrbYzBbv3LkzApWJHw2bsgqAjXvy3s6c9Kt2vNyvZc76iD7NAah/nkfjmzkH854LTV5e\n69LQUBmlK5/uUyIiIjmiHc62/3S7MvznjvD2LUDuroXq4W3Hcc6Ncs6lOudSK1WqdKJDJAYt3bAX\ngBuOuVXZslZ54nKNZdbo/BRe6teCD3/dPqr1AXBob2ji8v88Co16w23vQSl9R0VE5MxEO5xNBu4I\nL98BfJRre//wW5ttgX25bn9KEbdm+35WbN4HwK871WP5w11ofH4K8+7vdMLjuzWpSsmkKI8Sk/YJ\nvNACVn8MXYeF3spMTI5uDSIiEhMi9i+Ymb0NXAFUNLPNwCPAcOBdMxsIbABuDh8+FegOrAUOAndF\nqi4pPHbuP8KwKav4aPkPOdtKFIsnMT6OKUM7eFhZLs7B5yNg1pNQtRn0mgxVmnpdlYiIFGIRC2fO\nub4n2XXlCY51wK8jVYsUTs/PXJMnmAEkxvtoxrGdaTDjEVgzDZr0getehGI+nmhdREQKBc0QIL6V\nfMw4ZasfP3bYPI8EsmDO0zD3WUhIhk4PQYc/QJyPgqOIiBRaCmfiW1mBYJ715EQfDCq7dz1MHAhb\nFkPz20LPl5Ws4HVVIiISQxTOxLdeX7AhZ7lHMx+MSbxuNrzzM8Cgz6vQ5AavKxIRkRikcCaey8wO\nkhUI5nnDMnevWd2KJXmit8fzZG5YABMHQOmqcPt7UK6Wt/WIiEjMUjgTz1340DQA0oZ1y5kPc/Tc\ndTn7P7v3Ci/KCsnOhHnPwtxnoEx1uOUNBTMREYkoPcEsvnHLywtzlv/6SRoAN7Ws7lU5sH87vN4T\nZj8F9bvCwP9ApQbe1SMiIkWCes7EN5Zv+pHsQJCEXMNlDL2yvjfFHNgBY7uG/uwzFprc6E0dIiJS\n5KjnTHyl3oPT8qzXKO/BuGHZR0IP/u/fDnf8W8FMRESiSj1n4qlDmYHjts1K23GCI6PkwA548ybY\nuhxuGAPVU72rRUREiiT1nImn5qzZCUCdiiVztt316iIAHu3ZKLrF7FgNY6+GHavgpteh2U3Rvb6I\niAgKZ+Khhet288vxSwAYN6A1f7mucZ79d7avE71ivp0BozvBob2hMcwa947etUVERHLRbU3xzK2j\njr6dmZKcSO1cvWdRtfwtmPwbKH8B3DEZSlfxpg4REREUzsQjuw4cybNepkRinufP1g/vEfkigkGY\nNSw0hlmdy+HmcVC8bOSvKyIicgoKZ+KJzXsPHbftigaVAHipX4vIF5CZAe/eAWtnQIv+0P0ZSCgW\n+euKiIichsKZRMWyjXsZOmEZEwa3o1rZ4mzbdxiAF/peQtNqZYDQxOZR6THLOhwaKmPdLOj+N2j1\nczCL/HVFRETyQS8ESFRc/6/5bNpziPbDPwNg3trQW5pXN66c503NiNv+NYzuDN/NhK5PQOtBCmYi\nIuIr6jkTT4xfuBEgZy7NiHMOvhgF0x+C5LJw23twYdfoXFtEROQMKJxJ1L23eBMADauUjs4F928P\nvY357adwwZVwwygoWTE61xYRETlDCmcScRt3H8yzft/ELwHIyMyO/MW3r4K3boGMHXDNX6HVIIjT\n3XwREfEv/SslEddxxCwArmmSd/wwI8LPem1eAuN6QfZhuONjaPMLBTMREfE9/UslUTNt5TYeuKZh\nzvq/f3NZ5C624F8wpjPExcOdU6BGq8hdS0REpADptqZE1OGsowPLjumfylWNKjO4Q12CzpEQH4H/\nNjicDp88AMvfhNodQnNklqxQ8NcRERGJEPWcSYE6nBWg9gNTeHnOdwBs2hN63ux3V13IVY0qAxAX\nZ5EJZnvWwZirQtMxXToU+k1SMBMRkUJHPWdSoJ6ZngbAU9NW89dP0wgEHQDZwWBkL3xoL7x5Mxzc\nFZofs07HyF5PREQkQtRzJgVq9Nzvc5Z/CmYAAy+rE7mLbvsKxl4De9dDn1cVzEREpFBTOJOoKFsi\nAvNWBgOhSctfvhwydsLt78EFnQr+OiIiIlGk25oSEdXKFmfLj8dPbl5gDu6BST8PTcPUqBf0fB6K\nl4vc9URERKJE4UwKzA+5wtiDPS5iyJtLmXPfFVQqnVSwF9q3JTSw7M5v4NrnoOVdmh9TRERihsKZ\nFJhLw5Oa929Xi+5Nq7J+eI+Cv8jGhfBu/9CQGbe+rfkxRUQk5iicSYG7rvn5BX/SjN0w8y+w7A0o\nUwMGvgdVmxf8dURERDymcCZnJRh07DpwhPNSkgE4kn10sNmWtQr42a+NC2HiADiwA1r0hy6PQ3JK\nwV5DRETEJxTO5Kw0f2w6+w9n8/dbLqb3JdVo8NAnAAzqUAcrqOe/juyHWU/BF6OgVGUY+ClUa1kw\n5xYREfEpDaUhZyzjSDb7D2cDcM87y/Psu6VVjYK5SNah0EP/C/8FTW+CwbMVzEREpEhQz5mcsVVb\n0/Osb9x9MGe5Uqnkczt5IBvSpobmx0zfErqF2X7ouZ1TRESkEFE4kzOWnBCfZ73jiFk5y2VKJJ79\niTN2wzv9YON8KFMTbnwFmvY5+/OJiIgUQrqtKWdsd8YRAP5yXeOCO+mWJTDqcvhhKVz3Dxi6VMFM\nRESKJPWcyRlL27YfgAsrl86zfdpvO5zdCVdOgg9+CaWqwF1T9WyZiIgUaQpnkm8fLtvCPe8sp3vT\nKgBcUrNszr6ujSpzUdUzGN7COdj0RWhuzG8/hSrNoP9HUKJ8QZctIiJSqCicSb799Gbm1K+2AZCc\nGM+4Aa2pVq44dSuWzN9JDqfDigmw5FXYsQqSUqDzn6HtEChWIlKli4iIFBoKZ5Ivs9N2nHB7xwsr\n5f8k6+bAR3fDvo1w/iXQ8wVociMklSqgKkVERAo/T8KZma0H9gMBINs5l2pm5YF3gNrAeuBm59xe\nL+qTvJxz3PnqorM/QTAA0+6HRaNDUy/dPgnqX1VwBYqIiMQQL9/W7OScu9g5lxpefwCY6ZyrD8wM\nr4sPrNuVkbP8zE1nOJ/lxv/B+BtDwaztELh7sYKZiIjIKfjptmYv4Irw8uvAbOB+r4qRo/qMnJ+z\n3LxGGQAaVil9ssNDnIPJd8Oy8VC8HHQbDm1/FckyRUREYoJX4cwB083MAS8750YBlZ1zW8P7twGV\nPapNjlGzQkn2HvyRyXe3p955pVk/vMepP7AzDSYPhU0LIXUAdH1CD/uLiIjkk1fh7DLn3BYzOw+Y\nYWarc+90zrlwcDuOmQ0GBgPUrFkz8pUWUVmBIGnb9lPvvFKs2PQjJYrF06x62dN/cNEroamXipUM\nPfDfoj8U1EToIiIiRYAn4cw5tyX85w4z+wBoDWw3s6rOua1mVhU44euB4V62UQCpqaknDHBybjbs\nzuDyEbPzbDuYGTj1hwJZMPMxmP8C1O4QmnqptDo/RUREzlTUXwgws5JmVvqnZaArsBKYDNwRPuwO\n4KNo1yaQHQgeF8xOyzn4cEgomLW4A372oYKZiIjIWfKi56wy8IGFbnUlAG855z4xs0XAu2Y2ENgA\n3OxBbUXe+0u3nHD7uAGtT/yBHath5l8gbSp0/CN0fjCC1YmIiMS+qIcz59w64LjxGJxzu4Ero12P\n5PXHSV8C8M/bWtCwammufGYOd3eqd+LBZr//HN66FeLiofNDcNkfolytiIhI7PHTUBrisWlfbc1Z\n7takCvFxxndPdic+7pgH+oMBmPcszHoSylSHOz6GcrWiXK2IiEhsUjiTHE9M/SZn+adAdlww278N\nJv8Gvp0OF10HPZ/XZOUiIiIFyMsZAsRnLqtX8eQ7g0GYMwKeaxIKZlc+AjePUzATEREpYOo5kxyB\noKNqmWQW/OmYR/+2roAp98LmL6BJH+j0f1DhAm+KFBERiXEKZ5LjvSWbj9/43+dhxsNQogL0HgnN\n+2pQWRERkQhSOBMAdqQfPrqSnQmrP4bFY2H9XKiWCv0mQfF8zBAgIiIi50ThTAC4b2JoCI3f10iD\n5+6BjB1Quip0eSw0P2bSaSY6FxERkQKhcFbE/W/dbkbPXcfiNRsZnvAGt+6cDVWawnUvQv0uoTHM\nREREJGoUzooo5xy7DmQyaNRMboufyfCkqVS0dGh/D1zxACQW97pEERGRIknhrAhyztHuT28wOGEK\n85NmU8oOMzfQhIzUu+nWpa/X5YmIiBRpCmdFyPSvt5G4fTlH5v6Dz5PmAzAl2JbR2T1468+DKFui\nmMcVioiIiMJZEZG1ZwMb3n6AQQlTOeCSGR/owtjANTz3i+t4pVxxBTMRERGfUDiLZZkZsHISLBtP\nwqYvGBgPHwUu5aGsAQzt3pJ5Het6XaGIiIgcQ+EsVn3/OXw4BPZtgooNeCWxL6/ub8MWKgEwSMFM\nRETElxTOYs2PG2H6Q7DqIyh/ATPbvMLAOcnA0VH9Z/yuo3f1iYiIyCkpnMWKQBbM+SvMfxEsDtr+\nmvR29zLwqQV5Dlv25y6UK6nny0RERPxK4cynnHP8dsJy4gz+fuslpz5411p4fxD8sBQadIeuw9iT\nXIMWj8/Ic9gHQy5VMBMREfE5hTOfGvvf9Uxe8QMAw65vSqmkE/xP5RysmABTfg8JSYyp8jDDVjTg\n7dZl2bhn29Fz3ZlK54aVo1W6iIiInIM4rwuQvHakH2bBd7t5/ONVOdveWbTp+AO3ryJ97A3w4S9J\nL9cYfrWAYesbAkbf0Qt57N9HP9+xfqUoVC4iIiIFQT1nPhIMOlo/OTNnvVHVFFZtTefxj1cx8LI6\noY3ZmbB8PEy7H7ITeCL7NsZuvIbfL8nIc66MzAAAacO6kRCvDC4iIlJY6F9tH/lmW3qe9cl3tweg\n2E/h6tsZ8MLF8PHvoHJjhpYbyejAtQSIZ8SnaSc8Z1KCJi4XEREpTBTOfOJwVoAeL8wDoHPD81jw\np845PV6Vg1txU+6FN/tAsVLQbxI/3DSV2dsST3nOuX/sFPG6RUREpGDptqZPjJm77uhy/1Ti4gyc\no2/8TB5JGEdgUZCEJn3g2ucIFivNpf839bhzPN6rMW3rViD9cBYta5WPZvkiIiJSQBTOfGL6qu05\ny3FxBnu+hzlP81Ti2ywP1uWXR37H9Gv7kpKcSMbhrJxjJwxuS5s65Uk/nE2Z4qfuSRMRERH/021N\nn/hy8z4AGthGeH9w6NmyFW9Di/78IvFJtlGBRyd/zY8HM2k//DMA+rauQdu6FTAzBTMREZEYoZ4z\njwWDjl+/tRQjyG8T3ueehPdhZQK0Hgzt7oZytRjTYh89/zGP95du4f2lW3I+e3XjKh5WLiIiIpGg\ncOahdxZt5P5JX1HddvJusX/SKm4NNLsVrn4SSlbIOa5p9TJ0alCJWWk783y+bd0Kx55SRERECjnd\n1vSAc47sQJD7J31Fr7h5TC72IM1sHVk9nofrX8oTzH7Sr22tPOuLH7qK5EQNkyEiIhJr1HN2DoJB\nx7y1u2hRq9yJp1c6iT9PWsKhpe8xsdhnpMat4ZtgDSbWfJg/t7r5pJ+5osF5Ocuf3NOBiqWSzql2\nERER8Sdzznldw1lLTU11ixcv9uz6Q95cwtSvQnNYPt6rMT9rV/u0n0lb/l/i3h9E/bgtfB+szBuB\nrtzzx8dIKaOhL0RERGKZmS1xzqWe7jjd1jxL+w5l5QQzgBXhty0BAkHH1n2HCASPBt9g0PHG6Gep\n80FPUiyDuzLvo1Pms6yscbuCmYiIiOTQbc2z1Pwv0/OsT1yymaGd69NxxKycbc91KcP1Zb8je/18\ntn67jJ8dTuMbV4PbMh9k7JBujKyaQlKC8rGIiIgcpXB2hv6zajs/H3f0VmrLWuVYsmEvAB1HzCKF\nDHrEL6Rb3CIun/slAHtdCuuCtfnMdWF0oAd7SeGSmuU8qV9ERET8TeEsn5xz1PlT3imTqpUtzluD\n2jDrf0tYMHU8XeIW0yZuNYkWYIurwMjsnkwMdOQ7dz4liyWQkRWgVoUSfPv7yz36W4iIiIjfKZzl\nQ2Z2kKv//nmebdVLGXMuXU78mEfotu1LuiVCVrn6ZNcfQmKz3jw8I8jMXOOSffHgVZQ8gzc6RURE\npGhSWjiFzXsPUqFkEvdNXMH3uzIowWFevTqRNkcWwpcT4LPdcH4L6PIYNOhBYsV6/DSJ0gu3ZZOR\nmU2Hp2dxY8vqCmYiIiKSL0oMJ7Fpz0E6/HUWdUsHaHlwLq8kLuKyuJUkzckCDBp0h1YDoN5VJ/x8\nyaQESiYlkDbsmugWLiIiIoWawlkur/33e4b/exmtim3gokAaE4otJzUzjYTEIJtdRZLaDYK6naBK\nE0g53+tyRUREJAYV6XDW+ZnZrN+5n8aJW7m6zCbq75vPl0nLKGYBiIM1wWq8FOjJ54FmbC/Xgjnd\nOntdsoiIiMS4IhHOHv5oJd9sTWf8z9uQlBDPoSPZfD17Anf/+DZXJi2ljB2EA7AnrhRvBq6iY7eb\nOFDpEspXOp/bkhKwLzby8w51vP5riIiISBHgu+mbzKwb8DwQD4xxzg0/2bGXtGzp3pj82XHb48y4\nsHIpEuLj2J5+mPZPfsrFtpZL41bRqcR3XBDcQEpgDwdcMlMDbbjhhltJqNUGytcFs8j95URERKTI\nyu/0Tb7qOTOzeOCfQBdgM7DIzCY751ad6Pg12/Zz7Yvz8p6DIBVJp3O1bPrUT2DP2i+YVWwKNeJ2\nEnTGN0dqMtNdxJLghZRqN5B+7S8goVyJiP/dRERERPLDV+EMaA2sdc6tAzCzCUAv4IThrHQwnYeT\n36Fr3eIkZO0n6dBWUvasJD6YCbsJ/QCrqcGqS5+jXrtezFv6I0s37uXTr7ez4sqGlCmeeKJTi4iI\niHjCb+GsGrAp1/pmoM3JDq5uO7krfhq2vQwkpUByCjS/hQPlG7EtWJ6n5+/j+8yy1K9Xl5FdWwHw\ni8srAKER/023MEVERMRn/BbOTsvMBgODAepWr4w9+APE5Z08vBRQDxh9ilmSFMxERETEj+JOf0hU\nbQFq5FqvHt6Wwzk3yjmX6pxLLVe5+nHBTERERKQw81uyWQTUN7M6ZlYMuBWY7HFNIiIiIlHjq9ua\nzrlsM7sb+JTQUBpjnXNfe1yWiIiISNT4KpwBOOemAlO9rkNERETEC367rSkiIiJSpCmciYiIiPiI\nwpmIiIiIjyiciYiIiPiIwpmIiIiIjyiciYiIiPiIwpmIiIiIjyiciYiIiPiIwpmIiIiIjyiciYiI\niPiIwpmIiIiIjyiciYiIiPiIOee8ruGsmdlOYIPXdRQiFYFdXhdRhKi9o09tHn1q8+hTm0dXQbZ3\nLedcpdMdVKjDmZwZM1vsnEv1uo6iQu0dfWrz6FObR5/aPLq8aG/d1hQRERHxEYUzERERER9ROCta\nRnldQBGj9o4+tXn0qc2jT20eXVFvbz1zJiIiIuIj6jkTERER8RGFsxhjZmPNbIeZrTzJ/ivMbJ+Z\nLQ//PBztGmPN6do813GtzCzbzPpEq7ZYlI/veC8z+zL8/V5sZpdFu8ZYk482vz3c5l+Z2Xwzax7t\nGmNNPtq8oZktMLMjZnZvtOuLRfloczOzF8xsbfj73iJStSicxZ7XgG6nOWauc+7i8M9jUagp1r3G\nadrczOKBp4Hp0Sgoxr3Gqdt7JtDcOXcxMAAYE42iYtxrnLrNvwcud841BR5Hz0QVhNc4dZvvAYYC\nf4tKNUXDa5y6za8B6od/BgMjI1WIwlmMcc59Tuj/tBIl+Wzz3wCTgB2Rryi2na69nXMH3NGHaUsC\nerD2HOWjzec75/aGVxcC1aNSWAzLR5vvcM4tArKiV1Vsy8fv8l7AOBeyEChrZlUjUYvCWdHUzsxW\nmNk0M2vsdTGxzsyqAdcTwf/KkrzM7HozWw1MIdR7JtEzEJjmdREiEVAN2JRrfXN4W4FTOCt6lhKa\nPqI58CLwocf1FAV/B+53zgW9LqSocM594JxrCPQmdJtNosDMOhEKZ/d7XYtIYaZwVsQ459KdcwfC\ny1OBRDOr6HFZsS4VmGBm64E+wL/MrLe3JRUN4dsUdfUdjzwza0bo+b5ezrndXtcjEgFbgBq51quH\ntxU4hbMixsyqmJmFl1sT+g7oF2kEOefqOOdqO+dqAxOBIc459VhGiJnVy/UdbwEkoe94RJlZTeB9\n4GfOuTVe1yMSIZOB/uG3NtsC+5xzWyNxoYRInFS8Y2ZvA1cAFc1sM/AIkAjgnHuJUM/Nr8wsGzgE\n3Jrr4Wk5C/locylA+WjvGwn9As0i9B2/Rd/xc5OPNn8YqECoVxggWxNzn5vTtbmZVQEWAylA0Mzu\nARo559I9KrnQy8f3fCrQHVgLHATuilgt+p0lIiIi4h+6rSkiIiLiIwpnIiIiIj6icCYiIiLiIwpn\nIiIiIj6icCYiIiLiIwpnIhKTzOxOM/tHePmXZtY/vNzQzJab2TIza2lmQ7ytVEQkL4UzEYl5zrmX\nnHPjwqu9gYnOuUsIDU4blXBmZhpXUkTyRb8sRMT3zKw28LFzrkl4/V6glHPuUTObDawALif0O22A\nc+6LYz7/KHAAWAXcAwTM7EpgO3CBmS0HZjjn7sv1mZLAu4SmaIkHHnfOvWNmrYDngZLAEeBKIIvQ\nxPapQDbwe+fcLDO7E7gBKBU+x+Vmdh9wM6GZCz5wzj1ScC0lIrFA4UxEYkEJ59zFZtYRGAs0OdFB\nzrmpZvYScMA597dw6GvinLv4BId3A35wzvUAMLMyZlYMeIfQrAOLzCyF0CwEvw2d3jU1s4bAdDO7\nMHyeFkAz59weM+sK1AdaAwZMNrOO4TlARUQA3dYUkdjwNuRMdJ5iZmUL4JxfAV3M7Gkz6+Cc2wc0\nALY65xaFr5funMsGLgPGh7etBjYAP4WzGc65PeHlruGfZcBSoCGhsCYikkM9ZyJSGGST9z8mk4/Z\nf+w8dOc8L51zbk144vTuwDAzmwl8cBanysi1bMBTzrmXz7U+EYld6jkTkcJgO3CemVUwsyTg2mP2\n3wJgZpcB+8K9XPmxHyh9oh1mdj5w0Dk3HhhB6PZkGlA1/NwZZlY6/KD/XOD28LYLgZrhY4/1KTDA\nzEqFj61mZufls1YRKSLUcyYivuecyzKzx4AvgC3A6mMOOWxmy4BEYMAZnHe3mf3XzFYC03K/EAA0\nBUaYWZDQA/+/cs5lmtktwItmVpzQ82ZXAf8CRprZV4R6+e50zh0xs2OvN93MLgIWhPcdAPoBO/Jb\ns4jEPnPunHv/RUQ8E35b817n3GKvaxERKQi6rSkiIiLiI+o5ExEREfER9ZyJiIiI+IjCmYiIiIiP\nKJyJiIiI+IjCmYiIiIiPKJyJiIiI+IjCmYiIiIiP/D/FKcNy0fc8BQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114474390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stat_df.plot(x='score', y=['lift','baseline'], figsize=(10,7))\n",
"plt.xlabel('uplift score')\n",
"plt.ylabel('conversion lift')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AUUC: 77.1989102489\n"
]
}
],
"source": [
"auuc = (stat_df['lift'] - stat_df['baseline']).sum() / len(stat_df['lift'])\n",
"print('AUUC:', auuc)"
]
}
],
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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