Introduction Manual Feature Engineering (한글 번역본).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction : Manual Feature Engineering (한글 번역본)\n",
    "\n",
    "본 커널은 캐글코리아에서 주관하는 'kaggle 커널 한글화 프로젝트' 의 일환으로 진행된 '**Will Koehrsen**의 Introduction to Manual Feature Engineering'의 번역입니다. 커널은 [Home credit Default Risk Competition](https://www.kaggle.com/c/home-credit-default-risk)의 데이터를 활용한 **Feature Engineering**에 관한 내용을 담고 있습니다.\n",
    "\n",
    "\n",
    "[캐글코리아 페이스북 페이지](https://www.facebook.com/groups/KaggleKoreaOpenGroup//),  [블로그](https://kaggle-kr.tistory.com/?fbclid=IwAR3wgGKC12tLtbC3bimR4XnwuT2KEtelM1yomcs887RR-LMTQk5uG_FuEWQ)\n",
    "\n",
    "[캐글코리아 커널 한글화 프로젝트](https://kaggle-kr.tistory.com/25?fbclid=IwAR3tcLF0ukqUr_wikwVqnfm1jhR4SSJHGxZydO767YEinnnoaWedDGNVONk)\n",
    "\n",
    "[Home Credit Default Risk Competition 페이지](https://www.kaggle.com/c/home-credit-default-risk)\n",
    "\n",
    "[Introduction : Manual Feature Engineering 원본 커널 페이지](https://www.kaggle.com/willkoehrsen/introduction-to-manual-feature-engineering/notebook?fbclid=IwAR0PRN0wtRI2uZKhrWccEAnqT2a0OztLPbwdOpp7l9rqB0UNKHPUMCvUXGM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 역자의 말\n",
    "\n",
    "\n",
    "번역에는 한승균이 참여하였으며, 아직 검수되지는 않았습니다. 번역은 5월 26일부터 6월 9까지 진행되었습니다.\n",
    "\n",
    "- 일부 단어 및 문장의 경우 의미 전달을 위해 번역하지 않은 채 그대로 사용하였습니다.\n",
    "- 일부 Markdown의 설명에 한하여 번역 결과를 자연스럽게 하기 위한 의역이 있습니다. (코드의 주석들은 그대로 번역하되, 원문을 항상 밑에 달아놓았습니다.) \n",
    "- 별도로 찾아본 내용이 있을 경우 링크를 첨부해놓았습니다.\n",
    "- 파파고, 카카오 i, 구글 번역 등의 어플리케이션을 적극적으로 사용했음을 미리 밝힙니다 :)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡ"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction: Manual Feature Engineering\n",
    "\n",
    "만약 해당 컴피티션 참여가 처음일 경우, 이 [커널](https://www.kaggle.com/willkoehrsen/start-here-a-gentle-introduction/)부터 시작하는 것을 권장합니다.\n",
    "\n",
    "이 커널에서는 `The Home Credit Defalut Risk Competition` 데이터를 기반으로 Feature를 생성하는 방법을 다루고 있습니다. 커널 초반 부분에는 모델을 만들기 위해 `application` 데이터만 사용하였습니다. 이 데이터를 기반으로 만들어진 가장 성능이 좋은 모델은 리더보드에서 약 0.74를 기록하였습니다. 이 스코어를 뛰어넘기 위하여, 다른 데이터프레임들을 활용하여 정보를 좀 더 모으고자 합니다. 여기서는 `bureau` 및 `bureau_balance` 데이터를 살펴봅니다. 각 데이터파일의 정의는 다음과 같습니다.\n",
    "\n",
    "* `bureau`: 'Home Credit'에 제출된 고객(Client)의 다른 금융기관에서의 과거의 대출 기록. (각각의 대출 기록은 각각의 열로 정리되어있습니다.)\n",
    "* `bureau_balance`: 과거 대출들의 월별 데이터.(각 월별 데이터는 각각의 열로 정리되어있습니다.)\n",
    "* (추가: [Competition Data 페이지](https://www.kaggle.com/c/home-credit-default-risk/data)에 있는 다이어그램을 첨부합니다.)<br /><br /> ![데이터 다이어그램](../home_credit.png)<br /><br />\n",
    "\n",
    "**수작업(Manual) Feature Engineering**은 어떻게 보면 지루한 과정일 수 있습니다. (이것은 많은 사람들이 자동화된 Feature Engineering 기능을 활용하는 주된 이유입니다.) 또한, 이 작업은 도메인 지식(domain expertise)을 필요로 하기도 합니다. 대출 및 채무 불이행의 주된 원인에 대한 지식을 갖추는데는 한계가 있기 때문에, 최종 학습용 데이터프레임에서 가능한 많은 정보들을 얻는데 주안점을 두었습니다. 즉, 이 커널은 어떤 Feature들이 중요한지를 결정하는 것에 있어서, 사람보다 모델이 고르도록하는 접근방식을 취합니다. 기본적으로 이러한 접근방식에서는 최대한 많은 Feature들을 만들고, 모델은 이러한 Feature들을 전부 활용합니다. 그리고 추후, 모델에서 얻어진 Feature Importance나 PCA를 통해 Feautre Reduction을 할 수 있습니다. \n",
    "\n",
    "**수작업(Manual) feature engineering**의 각 과정은 많은 양의 Pandas 코드와 약간의 인내심, 특히 데이터 처리에 있어서 많은 인내심을 필요로 합니다. 비록 자동화된 Feature Engineering 도구들이 활용되기 시작했지만, 당분간 Feature Engineering은 여전히 전처리 작업을 필요로 합니다."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 처리을 위한 Pandas 및 Numpy\n",
    "# pandas and numpy for data manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# 시각화를 위한 matplotlib 및 seaborn\n",
    "# matplotlib and seaborn for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# pandas에서 나오는 경고문 무시\n",
    "# Suppress warnings from pandas\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 예시 : 고객의 이전 대출 수량 파악 (Counts of a client's previous loans)\n",
    "\n",
    "**Manual feature engineering**의 보편적인 방법을 설명하기 위해, 먼저 고객의 과거 타 금융기관에서의 대출 수량을 간단히 파악하고자 합니다. 이 과정은 이 커널에서 반복적으로 사용되는 아래의 Pandas 명령어들을 포함합니다. \n",
    "\n",
    "* `groupby` : Column값에 따라 데이터프레임을 그룹화. 이 과정에서는 `SK_ID_CURR` Column의 값에 따라 고객별로 데이터프레임을 그룹화 [(Link)](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html)\n",
    "* `agg` : 그룹화된 데이터의 평균 등을 계산. 'grouped_df.mean()' 을 통해 직접 평균을 계산하거나, `agg` 명령어와 리스트를 활용하여 평균, 최대값, 최소값, 합계 등을 계산 ( grouped_df.agg([mean, max, min, sum]) ) [(Link)](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.agg.html)\n",
    "* `merge` : 집계된(aggregated) 값을 해당 고객와 매칭. `SK_ID_CURR` Column을 활용하여 집계된 값을 원본 트레이닝 데이터로 병합하고,  해당값이 없을 경우에는 NaN 값을 입력.[(Link)](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html#pandas.DataFrame.merge)\n",
    "\n",
    "또한 `rename` 명령어를 통해 Column을 dict을 활용하여 변경합니다. 이러한 방식은 생성된 변수들을 계속해서 추적하는데 유용합니다.\n",
    "\n",
    "이러한 과정이 길게 느껴질 수도 있기 때문에, 추후 이러한 과정을 대신 해줄 함수(function)를 작성할 예정입니다. 우선 수작업으로 시행하는 과정을 살펴봅시다.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>SK_ID_CURR</th>\n",
       "      <th>SK_ID_BUREAU</th>\n",
       "      <th>CREDIT_ACTIVE</th>\n",
       "      <th>CREDIT_CURRENCY</th>\n",
       "      <th>DAYS_CREDIT</th>\n",
       "      <th>CREDIT_DAY_OVERDUE</th>\n",
       "      <th>DAYS_CREDIT_ENDDATE</th>\n",
       "      <th>DAYS_ENDDATE_FACT</th>\n",
       "      <th>AMT_CREDIT_MAX_OVERDUE</th>\n",
       "      <th>CNT_CREDIT_PROLONG</th>\n",
       "      <th>AMT_CREDIT_SUM</th>\n",
       "      <th>AMT_CREDIT_SUM_DEBT</th>\n",
       "      <th>AMT_CREDIT_SUM_LIMIT</th>\n",
       "      <th>AMT_CREDIT_SUM_OVERDUE</th>\n",
       "      <th>CREDIT_TYPE</th>\n",
       "      <th>DAYS_CREDIT_UPDATE</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>215354</td>\n",
       "      <td>5714462</td>\n",
       "      <td>Closed</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>-497</td>\n",
       "      <td>0</td>\n",
       "      <td>-153.0</td>\n",
       "      <td>-153.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>91323.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Consumer credit</td>\n",
       "      <td>-131</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>215354</td>\n",
       "      <td>5714463</td>\n",
       "      <td>Active</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>-208</td>\n",
       "      <td>0</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>171342.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Credit card</td>\n",
       "      <td>-20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>215354</td>\n",
       "      <td>5714464</td>\n",
       "      <td>Active</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>-203</td>\n",
       "      <td>0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>464323.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Consumer credit</td>\n",
       "      <td>-16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>215354</td>\n",
       "      <td>5714465</td>\n",
       "      <td>Active</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>-203</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Credit card</td>\n",
       "      <td>-16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>215354</td>\n",
       "      <td>5714466</td>\n",
       "      <td>Active</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>-629</td>\n",
       "      <td>0</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77674.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2700000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Consumer credit</td>\n",
       "      <td>-21</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  SK_ID_BUREAU CREDIT_ACTIVE CREDIT_CURRENCY  DAYS_CREDIT  \\\n",
       "0      215354       5714462        Closed      currency 1         -497   \n",
       "1      215354       5714463        Active      currency 1         -208   \n",
       "2      215354       5714464        Active      currency 1         -203   \n",
       "3      215354       5714465        Active      currency 1         -203   \n",
       "4      215354       5714466        Active      currency 1         -629   \n",
       "\n",
       "   CREDIT_DAY_OVERDUE  DAYS_CREDIT_ENDDATE  DAYS_ENDDATE_FACT  \\\n",
       "0                   0               -153.0             -153.0   \n",
       "1                   0               1075.0                NaN   \n",
       "2                   0                528.0                NaN   \n",
       "3                   0                  NaN                NaN   \n",
       "4                   0               1197.0                NaN   \n",
       "\n",
       "   AMT_CREDIT_MAX_OVERDUE  CNT_CREDIT_PROLONG  AMT_CREDIT_SUM  \\\n",
       "0                     NaN                   0         91323.0   \n",
       "1                     NaN                   0        225000.0   \n",
       "2                     NaN                   0        464323.5   \n",
       "3                     NaN                   0         90000.0   \n",
       "4                 77674.5                   0       2700000.0   \n",
       "\n",
       "   AMT_CREDIT_SUM_DEBT  AMT_CREDIT_SUM_LIMIT  AMT_CREDIT_SUM_OVERDUE  \\\n",
       "0                  0.0                   NaN                     0.0   \n",
       "1             171342.0                   NaN                     0.0   \n",
       "2                  NaN                   NaN                     0.0   \n",
       "3                  NaN                   NaN                     0.0   \n",
       "4                  NaN                   NaN                     0.0   \n",
       "\n",
       "       CREDIT_TYPE  DAYS_CREDIT_UPDATE  AMT_ANNUITY  \n",
       "0  Consumer credit                -131          NaN  \n",
       "1      Credit card                 -20          NaN  \n",
       "2  Consumer credit                 -16          NaN  \n",
       "3      Credit card                 -16          NaN  \n",
       "4  Consumer credit                 -21          NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bureau 파일 읽기 \n",
    "# Read in bureau\n",
    "bureau = pd.read_csv('../input/bureau.csv')\n",
    "bureau.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>previous_loan_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   SK_ID_CURR  previous_loan_counts\n",
       "0      100001                     7\n",
       "1      100002                     8\n",
       "2      100003                     4\n",
       "3      100004                     2\n",
       "4      100005                     3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 고객 id (SK_ID_CURR)를 기준으로 groupby 실행, 이전 대출 갯수를 파악하고, Column 이름을 변경\n",
    "# Groupby the client id (SK_ID_CURR), count the number of previous loans, and rename the column\n",
    "previous_loan_counts=bureau.groupby('SK_ID_CURR', as_index=False)['SK_ID_BUREAU'].count().rename(columns = {'SK_ID_BUREAU': 'previous_loan_counts'})\n",
    "previous_loan_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "      <th>previous_loan_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 123 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "   ...  FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "0  ...                 0                0                0   \n",
       "1  ...                 0                0                0   \n",
       "2  ...                 0                0                0   \n",
       "3  ...                 0                0                0   \n",
       "4  ...                 0                0                0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                        0.0                       0.0   \n",
       "1                        0.0                       0.0   \n",
       "2                        0.0                       0.0   \n",
       "3                        NaN                       NaN   \n",
       "4                        0.0                       0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        0.0   \n",
       "3                        NaN                        NaN   \n",
       "4                        0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  previous_loan_counts  \n",
       "0                        0.0                         1.0                   8.0  \n",
       "1                        0.0                         0.0                   4.0  \n",
       "2                        0.0                         0.0                   2.0  \n",
       "3                        NaN                         NaN                   0.0  \n",
       "4                        0.0                         0.0                   1.0  \n",
       "\n",
       "[5 rows x 123 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 훈련용 데이터프레임과 병합(Join)\n",
    "# Join to the training dataframe\n",
    "\n",
    "train = pd.read_csv('../input/application_train.csv')\n",
    "train = train.merge(previous_loan_counts, on='SK_ID_CURR', how = 'left')\n",
    "\n",
    "# NaN 값에 0을 대입\n",
    "# Fill the missing values with 0 \n",
    "train['previous_loan_counts'] = train['previous_loan_counts'].fillna(0)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "스크롤을 우측 끝까지 움직여 새롭게 만들어진 Column(previous_loan_counts)을 확인해보세요"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## R Value를 활용한 변수 유용성 평가 (Assessing Usefulness of New Variable with r value)\n",
    "\n",
    "새롭게 생성된 Column의 변수들이 유용한지 판단하기 위해, 우선 목표값(target)과 해당 변수간의 [피어슨 상관계수 (Pearson Correlation Coefficient, r-value)](https://ko.wikipedia.org/wiki/%ED%94%BC%EC%96%B4%EC%8A%A8_%EC%83%81%EA%B4%80_%EA%B3%84%EC%88%98)를 계산하고자 합니다. 두 변수 사이의 선형관계(linear relationship)는 -1(완벽하게 음의 선형관계)에서부터 +1(완벽히 양의 선형관계) 사이의 값으로 표현됩니다. r-value가 변수의 유용성을 평가하기 위한 최선의 방식은 아니지만, 머신러닝 모델을 발전시키는데 효과가 있을 지에 대한 대략적인 정보를 줄 수 있습니다. 목표값에 대한 r-value가 커질 수록, 해당 변수가 목표값에 영향을 끼칠 가능성이 높아집니다. 그러므로 목표값에 대해 가장 큰 r-value의 절댓값을 가지는 변수를 찾고자 합니다.\n",
    "\n",
    "또한 우리는 커널밀도추정그래프(Kernal Density Estimate Plot)을 활용하여 목표값과의 상관관계를 시각적으로 살펴볼 것입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 커널밀도추정그래프(Kernal Density Estimate Plots)\n",
    "\n",
    "커널밀도추정그래프는 단일 변수의 분포를 보여줍니다.(히스토그램을 부드럽게한(smoothed) 것으로 생각해보면 될 것입니다.) 범주형 변수(Categorical Variable)의 값 차이에 따른 분포를 차이를 보기위해, 카테고리에 따라 색을 다르게 칠하도록 하겠습니다. 예를 들어, `TARGET` 값이 0인지 1인지에 따라 색을 다르게 칠한 `previous_loan_count`의 커널밀도추정그래프를 그릴 수 있습니다. 이러한 그래프는 대출을 상환한 그룹(`TARGET==0`)과 그렇지 못한 그룹(`TARGET==1`)의 분포에 있어 차이점들을 보여줄 것입니다. 이는 변수들이 머신러닝 모델과 관련성을 가지는 지를 보여줄 수있는 지표로 활용될 수 있습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 변수의 분포에 대한 그래프 target값에 따라 색을 달리하여 작성 \n",
    "# Plots the disribution of a variable colored by value of the target\n",
    "def kde_target(var_name, df):\n",
    "    '''\n",
    "    Args\n",
    "    \n",
    "    input:\n",
    "    \n",
    "    var_name = str, 변수가 되는 Column\n",
    "    df : DataFrame, 대상 데이터프레임\n",
    "    \n",
    "    \n",
    "    return: None\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    # 새롭게 생성된 변수와 target간의 상관계수를 계산\n",
    "    # Calculate the correlation coefficient between the new variable and the target\n",
    "    corr = df['TARGET'].corr(df[var_name])\n",
    "    \n",
    "    # 대출의 상환한 그룹과 그렇지 않은 그룹의 중간값(median)계산\n",
    "    # Calculate medians for repaid vs not repaid\n",
    "    avg_repaid = df.ix[df['TARGET']==0,var_name].median()\n",
    "    avg_not_repaid = df.ix[df['TARGET']==1,var_name].median()\n",
    "    \n",
    "    plt.figure(figsize = (12,6))\n",
    "    \n",
    "    # target값에 따라 색을 달리하여 그래프 작성\n",
    "    # Plot the distribution for target == 0 and target == 1\n",
    "    sns.kdeplot(df.ix[df['TARGET']==0,var_name],label = 'TARGET == 0')\n",
    "    sns.kdeplot(df.ix[df['TARGET']==1,var_name],label = 'TARGET == 1')\n",
    "    \n",
    "    # 그래프 라벨링\n",
    "    # label the plot\n",
    "    plt.xlabel(var_name); plt.ylabel('Density'); plt.title('%s Distribution' % var_name)\n",
    "    plt.legend();\n",
    "    \n",
    "    # 상관계수 출력\n",
    "    # print out the correlation\n",
    "    print('The correlation between %s and the TARGET is %0.4f' % (var_name, corr))\n",
    "    \n",
    "    # 중간값 출력\n",
    "    # Print out average values\n",
    "    print('Median value for loan that was not repaid = %0.4f' % avg_not_repaid)\n",
    "    print('Median value for loan that was repaid =     %0.4f' % avg_repaid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest 및 Gradient Boosting Machine에 의해 가장 중요한 변수로 판명된 `EXT_SOURCE_3`를 활용하여 테스트하도록 하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between EXT_SOURCE_3 and the TARGET is -0.1789\n",
      "Median value for loan that was not repaid = 0.3791\n",
      "Median value for loan that was repaid =     0.5460\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kde_target('EXT_SOURCE_3', train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제는 방금 새롭게 만들었던, `previous_loan_counts`를 살펴보도록 하겠습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between previous_loan_counts and the TARGET is -0.0100\n",
      "Median value for loan that was not repaid = 3.0000\n",
      "Median value for loan that was repaid =     4.0000\n"
     ]
    },
    {
     "data": {
      "image/png": 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xfR4nAUoejX/5d7IjhyRJJbvqVPbqc4otuCjPowIAAEBfrVy5Utdcc43uuuuuZN+iRYv0/PPPa+LEzpSxCRMm6JZbbtGdd96pRYsW6TOf+YwWLlyohQsXJutQDh8+3C9j6u8ZlEsvvVSf/vSn9bnPfU719fV69913ddZZZ/V1mAQo+TRk59ZA2/buzNNIAAAAisfR1oz0p+XLl6fNOCxdulTLly/XF7/4xUD/kiVLdPvtt+u5557Tueeeq3vuuUe33HKL6uvrNX78eI0dO1Zf/epXk+en1qBI0r333qsTTzwxt28og9mzZ+sjH/mI5s+fr7KyMn3nO9/p8wpekmS5yh2LUmNj48B7E4m4hvz5pSptb0t2tV18pdqu7X0+IfKvtrZW1dXV+R4G+gH3sjhwH4sH97J45PJeNjY2atSoUTl5bqRraWlRVVVVr67p6h6NGjXKUtssM5wntrs+EJxIkh1syNNoAAAAgMJAgJInJXUb0/rs4IE8jAQAAAAoHAQoeVK67d20Pmvcn4eRAAAAAIWDACVPSralz6CUkOIFAACAQY4AJU8ypng1H5RisTyMBgAAACgMBCj50HJYtntHxkPWRB0KAAAABi8ClDwo2b5ZlmV5ZztIHQoAAMBA0dDQoJqaGtXU1GjmzJmaPXt2st3W1qaHH35Yo0eP1jvvvJO8ZsuWLZowYYJqamo0f/583XDDDWpvb08eX716tRYvXqwzzzxT559/vq6++mqtW7dOkreRYupr1NTU6MEHH0w+njhxoubOnauamhrdcMMNOX/vS5Ys0cSJE/WVr3yl356XjRrzIFP9SQdrpA4FAABgoBg7dmxyd/bbbrtNw4cP1xe+0Lmv3YoVK7RgwQKtWLFCN998c7J/6tSpevbZZxWPx3X55Zfr17/+ta6++mrt3r1b119/ve6+++7kTvLPP/+8Nm3apJNPPlmSdOONNwZeQ5KuuOIKSdLixYv17W9/W2eccUZO37ckVVZW6u/+7u+0fv16rV+/vt+elxmUPMhUf9KBpYYBAACKQ3Nzs1588UV9//vf14MPPpjxnNLSUp111lmqr6+XJP3oRz/SsmXLksGJJC1YsEBLliyJZMy9MWzYMC1YsECVlZX9+rzMoORBpiWGO5DiBQAA0DfDr/tAvz5f88+ePKrrHnnkES1cuFAzZszQmDFjtGbNGp1++umBc1paWvTyyy/r9ttvlyS99dZbWrZsWZfP+8Mf/lD333+/JGn06NH6zW9+06PxNDU16dJLL8147O6779asWbN0880365lnnkk7fuWVV+pLX/pSj16nryILUMzsEkn/LKlU0t3OudtDx8+XdJek0yRd45xbHjo+UtJ6Sb92zn0+mlHngHMq2bYp62FSvAAAAIrDihUr9NnPflaSl4K1YsWKZICyadMm1dTUaOPGjVq6dKlOOeWUjM+xcOFCNTU16YILLtAdd9whKXOKV0+MGDEimY6WzW233dbr5+1vkQQoZlYq6QeSLpZUJ+klM3vIOfdmymlbJV0v6a+zPM23JD2Vy3FGwfbvlR06mP04MygAAAADXkNDg55++mm9+eabMjMlEgmZmW699VZJnTUoO3fu1JIlS7Rq1SotWrRIs2bN0tq1a7V48WJJ0hNPPKGVK1fq0Ucf7fOYmEEJmidpg3NuoySZ2X2SLpOUDFCcc5v9Y4nwxWZ2lqRjJT0qaW4E482Zkl11XR5nN3kAAICBb+XKlbrmmmt01113JfsWLVqk559/XhMnTkz2TZgwQbfccovuvPNOLVq0SJ/5zGe0cOFCLVy4MFmHcvjw4X4ZEzMoQRMlbUtp10man+XcADMrkfRdSZ+QtLD/hxaxttZAMzFitEpS9j5hBgUAAKBvjrZmpD8tX748bcZh6dKlWr58ub74xS8G+pcsWaLbb79dzz33nM4991zdc889uuWWW1RfX6/x48dr7Nix+upXv5o8P7UGRZLuvfdenXjiibl9Q1mceuqpampqUnt7ux555BE9+OCDmjVrVp+e01yW/Tj6k5ldJelDzrlP++1PSJrnnEtLnjOzn0r6TUcNipl9XtJQ59w/mtn1kuaGa1AaGxuTb6K2tjZn76M/jHrrFU1b/q/J9uEJJ2jozq3JdvvwUXrjL7+Tj6EBAAAMSFVVVRo/fny+h4Eu7NmzRy0tLcl2dXV18vGoUaMs9dyoZlDqJE1OaU+SlHkr9XQLJJ1nZjdKGi6pwsyanXM3ZTo59c0WorKGbYF2xXGTpZQApexwk6qnT5dKWAF6IKmtrS34zx56hntZHLiPxYN7WTxyeS8bGxtVVVWVk+dGupaWll7/vkeOHKnJkyd3f6KiC1BeklRtZlMlbZd0jaSP9eRC59zHOx6nzKBkDE4GhJRdQiXJDRkqN3SY7PAhSZIlElLzQWnk6HyMDgAAAMirSP5M75yLSfq8pMfkLRX8gHNunZndamZLJcnMzjazOklXSfp3M1sXxdgiFwsGKCorlxs5NtBVcpClhgEAADA4RbYPinNulaRVob6vpzx+SV7qV1fP8VNJP83B8CJjGQOUMdLOztQva9zfzW8CAAAAKE4UOkQtFKC4sjK5UWMCfazkBQAA0HMlJSVqa2vL9zCQRVtbm0p6UV8d2QwKfGkzKBVKjCRAAQAAOFrDhw9Xc3Ozjhw5ku+hDAoHDx7UyJEje3x+SUmJhg8f3uPzCVCiFiqSV3m5XNWQQBebNQIAAPScmWnEiBH5HsagsXv37h6vyHU0SPGKmMVjgbYrLfNqUFLPYQYFAAAAgxQBStTaQ/mR5RVyoSWFCVAAAAAwWBGgRC00g6KyMrlRwWWGrZFlhgEAADA4EaBEzMIbNXYsM5x6DjMoAAAAGKQIUKIWC6V4lVVkWGb4gORchIMCAAAACgMBStRi6SleqhwiV1mV7LJYu3SoKeKBAQAAAPlHgBKx8E7yrrzc++fo9wT6Sw7si2xMAAAAQKEgQIlaeKPGUj9AGTMu0G0EKAAAABiECFCilmGjRklKhGZQ7MDeqEYEAAAAFAwClIhZPBSglHWkeIVmUPYzgwIAAIDBhwAlahmWGZYkNyY0g7J/T2RDAgAAAAoFAUrUwjUoWQIUiuQBAAAwGBGgRC1LgEINCgAAAECAErnsywxTgwIAAAAQoEQtvFFjaZmkDAFK4z4pkYhqVAAAAEBBIECJWqwt2C6v8P5ZWSU3dHiy2+JxWXNjhAMDAAAA8o8AJWIWmkFxZWXJx2l1KPupQwEAAMDgQoAStfBGjX6RvMRu8gAAAAABStTSNmqsSD50zKAAAABgkCNAiVIiIYvHg32lpcmH6Zs1EqAAAABgcCFAiVJoieFEaZlklmyHV/Jis0YAAAAMNgQoUQrvgVJaFmgnwjMobNYIAACAQYYAJULhTRoTZcEAhc0aAQAAMNgRoESpmxmUtBoUZlAAAAAwyBCgRKm9mwBl1NhA2w7ul+KhnecBAACAIkaAEqVQsBEOUFRWrsSI0cmmOSdrbIhiZAAAAEBBIECJkLW3BdppAYoybNZIHQoAAAAGEQKUKHVTJC9Jbsz4QJs6FAAAAAwmBChRioVSvEoyBCjhlbzYCwUAAACDCAFKhCwWSvHKNIMyOriSVwm7yQMAAGAQiSxAMbNLzOxtM9tgZjdlOH6+mb1iZjEz+2hK/+lm9ryZrTOz18zsT6Iac78Lz6BkqEFJhGtQmEEBAADAIBJJgGJmpZJ+IOlSSXMkLTOzOaHTtkq6XtIvQ/2HJX3SOXeypEsk3WVmozUQhZYZTmQskg/thbKnPqdDAgAAAApJ+jfk3JgnaYNzbqMkmdl9ki6T9GbHCc65zf6xROqFzrl3Uh7vMLPdksZLOpD7Yfcvi4f2QcmQ4pWYcEKgXbJ9cy6HBAAAABSUqFK8JkraltKu8/t6xczmSaqQ9G4/jSta4Y0aMxXJH3OcXHlFsl3SdMDbsBEAAAAYBKKaQbEMfa5XT2B2nKSfS7rOOZfIdl5tbW0vhxadcdvrlDo/kigryzjek8ZN0NCdW5PtHS8+q+YpsyIYIfqikD976B3uZXHgPhYP7mXx4F4Wj77ey+rq6qzHogpQ6iRNTmlPkrSjpxeb2UhJj0j6mnPuha7O7erN5lv5ljcCbVdalnG85dNmSSkByglqV3sBvy94/5IW8mcPPce9LA7cx+LBvSwe3Mviket7GVWK10uSqs1sqplVSLpG0kM9udA//9eS/tM59185HGPuhTZqzLSKlyQlJk0JtEu2b8rViAAAAICCEkmA4pyLSfq8pMckrZf0gHNunZndamZLJcnMzjazOklXSfp3M1vnX361pPMlXW9ma/yf06MYd79LC1BKM56WmDg10C6pI0ABAADA4BBVipecc6skrQr1fT3l8UvyUr/C1/1C0i9yPsAopC0zXJ7xtMTEKYF2yfbNknOSZSrlAQAAAIoHO8lHyOKhjRozLDMsSW7csXJVQzqvO9wsY0d5AAAADAIEKFFqbws0s9WgqKQk8ywKAAAAUOQIUKIUnkHJFqAoQx0KhfIAAAAYBAhQImRpNSiZi+SlTAHK5lwMCQAAACgoBChRioVTvDIXyUsZCuVZyQsAAACDAAFKlGLhFK8uZlAmhWZQdmyWEolcjAoAAAAoGAQoEbLwPihl2WdQ3OhxckOHd17bckS2b1fOxgYAAAAUAgKUKMXCNShdbENjphgbNgIAAGCQIUCJUnvPdpKXpDf3t+tXbccF+javeUNHYi4nQwMAAAAKAQFKhCweDlCyp3h9/41m/W7ItEBf3drXNO2X9fqTx/fpt1uP5GSMAAAAQD4RoESplzMoL46cEeibd/BdtcTiemxbi5Y90aCVmwlSAAAAUFwIUKIUrkHpoki+rjmu2iETtK+ss1B+VPyIZh3ekWzft+Fw/48RAAAAyCMClCiFV/HKMoNyOJbQvtaEZJY2i3LOwQ3Jx2v3tYUvBQAAAAY0ApQIpS0znGUVr+2H4snH4QDl3IO1ycc7Die0+0hcAAAAQLEgQIlSaKPGRJYi+brm7AHKeYfeDbTX7A0GPQAAAMBARoASpVgwJStbite2lBmUP46croQs2Z52sE4jY521J2tI8wIAAEARIUCJkIVmULLtJJ+a4nWwbKh2j52cbJfI6eyDG5PtNfuYQQEAAEDxIECJUg+XGa47FKwr2Tt5VqA9P6UOZS0pXgAAACgiBChRCm3UmMhSJJ9agyJJrVNnB9oLmjpX8tp+OK49FMoDAACgSBCgRCWRkMU7AwlnJpVkm0EJpoKVzzw50F7Q9K7kXLJNmhcAAACKBQFKVEJLDKusTDJLO805l5biNXbaNLkhw5Lt0W1NmnFkV7K9Zi+F8gAAACgOBChRSQtQKjKetrclodaU+GREuWlUVani07LXoTCDAgAAgGJBgBKR8CaNKstSfxKaPZk0rFRmpsT0OYH++YEd5QlQAAAAUBwIUKIS3kU+yxLD25rTAxRJiocClAUpAUrdobj2tVAoDwAAgIGPACUq7eEZlCy7yIdnUIb7AcqMYIBy6qGtGhpvSbZJ8wIAAEAxIECJSjy4Mle2AGV7WoqXnwo2fJQSx07qvNwldFbTpmR7DfuhAAAAoAgQoETE2oMrbbnybDMowUCmYwZFSk/zOiclzWvNPlbyAgAAwMBHgBKVcJF8aZYAJUsNipSe5jU/EKAwgwIAAICBjwAlKrFQilfWGZRggDIxJUBJzAhu2Dj/4Ibkho3bmuNqoFAeAAAAAxwBSkQsFkrxylCD0hp32nUk0XmNpOOHpgQok6bKVVQm28e1HdAJrXuTbWZRAAAAMNARoEQlPIOSIUDZEZo9mTC0RBWlKbvNl5YpMTW4YeM5jaR5AQAAoHgQoEQlbZnh9I0at2XYpDEsXIdyTuqO8nsplAcAAMDARoASEYuHN2qsSDunrjm0gtew9CAmPi0YoMxt2ph8zAwKAAAABrrIAhQzu8TM3jazDWZ2U4bj55vZK2YWM7OPho5dZ2a1/s91UY25X/Vgo8ZsmzSmSkw9KdB+b/NWlTivbmVrc1z7WxNp1wAAAAADRSQBipmVSvqBpEslzZG0zMzmhE7bKul6Sb8MXTtW0i2S5kuaJ+kWMxuT6zH3u/AywxlSvNI3aUwPUNzY8UqMGJ1sD0uKgUMZAAAgAElEQVS06qTDO5LtteyHAgAAgAEsqhmUeZI2OOc2OufaJN0n6bLUE5xzm51zr0kKTwF8SNL/OucanHP7Jf2vpEuiGHR/slCA4sozpHj1IECRmRJTqgNdZ7KjPAAAAIpEVAHKREnbUtp1fl+ury0caRs1ps+gpG3SmCHFS5ISU4JpXmc2b04+pg4FAAAAA1n6t+TcsAx9LhfX1tbWZjuUV8fu3KnjU9r7m5sldY7XOWlr0xClvt22XVtU25D+XKMqhmtaSjt1BuWlnYdUW7uvH0eOnirUzx56j3tZHLiPxYN7WTy4l8Wjr/eyuro667GoApQ6SZNT2pMk7chybqZrPxC69slsJ3f1ZvOp/I0/BNpjxh+jHeoc7/7WhI78oT55fEipae7sGTJLj89s9HBpxb8l26c3b5G5hJyVaHtLiYYePy2wAz1yr7a2tmA/e+gd7mVx4D4WD+5l8eBeFo9c38uoUrxeklRtZlPNrELSNZIe6uG1j0n6oJmN8YvjP+j3DSgWDy4hHN5Jflt4ieHhpRmDE0ly75kgN2xksj0i3qKZh3cm20/uaOnrcAEAAIC8iCRAcc7FJH1eXmCxXtIDzrl1ZnarmS2VJDM728zqJF0l6d/NbJ1/bYOkb8kLcl6SdKvfN7C0h1bXCgUoPSqQ72CmeKhQ/ozmzjSvJ3e0Ht0YAQAAgDyLKsVLzrlVklaF+r6e8vgleelbma79iaSf5HSAuRaaQUkLUMIF8t2kaCWmzJTWrU62z2rapPuOfZ8kL0BJOKeSLDMwAAAAQKFiJ/mIWHt4meFuZlCyrODVIR5ayevsQ5uTj/e0JLRufyggAgAAAAYAApSoxEIpXqXBAKUnmzSmSpwYTvHyCuU7PLmdOhQAAAAMPAQoUYmFZjS6m0EZ1nX2nTvmeLmhw5PtYe2HNf3I7mT799ShAAAAYAAiQIlI2k7yZcEApLc1KF6h/MxAV2qh/HO7WtUS6+lWMwAAAEBhIECJSngn+bKK5MP2hFP9kWCAcnwP9jFJnDAj0D63dXvycUtcenE3sygAAAAYWAhQotIeDlA6U7zqD8eVSJnsGF9VoiFl3a/AlZg8LdCuaa8LtFluGAAAAAMNAUpELB4OUDpTvNLSu7pZwatDYvL0QLu6cUugTR0KAAAABhoClKikLTPcmeLVq00aUySOO0GupPMWDm/crZGxw8n22n3tamiJZ7oUAAAAKEgEKFEJ16CUpsygHGWAoopKJSacEOhaWlKffOwkPVXPLAoAAAAGDgKUqIQDlJRlhtNTvLpeYjhVuA5liW0PtEnzAgAAwEBCgBKR9GWGOwOU7YeCe6T0eAZF6XUoc49sC7R/v6NVzrHcMAAAAAYGApSohDdqTAlQtoVSvCb3KkAJzqBM3LdFlSmXb2uOa+NB6lAAAAAwMBCgRCXWFmynBCjhGpSJfZhBKdu+UQvGB3ep//2Olh4/HwAAAJBPBCgRsdAMSsdO8s3tCR1s60zBKi+Rxg/p+W1xY8fLDR3e+TotR3T5sMbAOdShAAAAYKAgQIlK2k7y3izHkViwPmREeYlKrPtNGpPM0mZRLkwEC+WfqW9VLEEdCgAAAAofAUpU4qE6ED9AaY0HA4fKnmd3dT51qA5l2oEtGlfZeWsPtju9urc9fBkAAABQcAhQouBc2ipeHfugtCWC3RUlvZg98aXVodRt1PnHVQb6Xt0bqoEBAAAAChABShRCsyeutFTy07jSZ1COJkAJzqCUbNuo098TLJR/Yz8zKAAAACh8BChRiIdnTzqDh34JUCZNlUupW7FddXrvkOCMyboGAhQAAAAUPgKUKKTtgdK5U3x/1KCocojccSckm+acTm/eHDjlzf0xxSmUBwAAQIEjQImAxUNLDJemBCj9UIMiSfFpswLt8dvfDhTKH4k7bWqKhS8DAAAACgoBShTSlhjuDFDa+iHFS5ISU4MBSunGt3Ty2FAdSgMBCgAAAApbjwMUM1tqZmXdn4k0aSle2WtQKo4yQIlPmx1ol2x6W6eMDd4uCuUBAABQ6Hozg/ItSfVm9i9mNj9XAypKXc2ghOpCKo9yTisxeVogdaxk3y7NLW8KnPMGhfIAAAAocD3+Ouyce6+kiyQdkbTCzN42s6+Z2ZQcja1oWNoyw6kzKMFzjzbFS+UVSpwwI9A1t2ljoL2OGRQAAAAUuF79vd45t9Y59xVJkyV9TtJVkt41s6fN7ONmRk1LJlk2aZT6Z5nhDuFC+Sm7Nyj16bY1x3UgXJUPAAAAFJBeBxRmNl3S1yX9q6Qq//F/SPq8pOX9OrpiEe/NMsNHH6AkQgFKxea3NHNUsA7lTWZRAAAAUMB6UyT/OTN7QdKLko6V9Ann3EnOub93zv1c0kJJH8zROAe2tBqUlBSvUA1KRR/moMKF8qUb39bJY0KF8tShAAAAoID15uvwpZK+K+l459yNzrkXUg865w5LuqI/B1cs0vZBCSwzHDy3LzMobsJkuaqhna976KDOK9kXOIc6FAAAABSy3gQoTzrn/ss515baaWZf7njsnPuffhtZMQkvMxzYqLF/lhmWJJWUKD71pEDXvKZ3A21mUAAAAFDIehOgfD1L/9f6YyBFrTcbNR7lTvIdwhs2zthTG2ivPxBTPBQUAQAAAIWi240XzezCjnPN7AJJqd+gp0lqSr8KqSw0gxJcZjg8g9K314pPD9ahjNz4usbOukoN/updh2NOm5pimjGqPNPlAAAAQF71ZGf4H/v/rJT0k5R+J2mnpC/096CKTpcbNQYPVfUlxUtS/KTTAu2SrRt0zplHtKq1Mtm3bj8BCgAAAApTtylezrmpzrmpku7teOz/THPOneuce6gnL2Rml/ibO24ws5syHK80s/v94y92bABpZuVm9jMze93M1pvZzb18j/kX2qgxdRWvln5cZliSNGK04pOnJ5vmnD585J3AKa9ThwIAAIAC1Zud5D95tC9iZqWSfiBvJbA5kpaZ2ZzQaZ+StN85N0PSnZLu8PuvklTpnDtV0lmSbhhwu9fHs2/UGK5BqehjDYokxWefHmifs29doL2OAAUAAAAFqssAxczWpzzeZmZbM/304HXmSdrgnNvorwJ2n6TLQudcJuln/uPlkhaamclLJRtmZmWShkhqk3SwR++uQKTVoHS5UWPfXy8++8xAe9q21wNtlhoGAABAoequBuUzKY+v7cPrTJS0LaVdJ2l+tnOcczEza5Q0Tl6wcpmkeklDJX3JOdfQh7FEr8uNGoOH+mUG5aTT5KxE5rwnH7ZzsyZMb9TO8lGSpK3NcTW2JTSqL7tCAgAAADnQZYDinHs25fFTfXidTN+6w2vdZjtnnqS4pOMljZH0jJk97pzbmOmFamtrM3Xn1bG7dun4lHbDwSbV++M80HRIUue0yd5dO1Qbrpw/CidNmKyh9VuS7SsOv6kfjlqQbP/Pa5t0+qi+vw46FeJnD0eHe1kcuI/Fg3tZPLiXxaOv97K6ujrrsZ6s4iUpuSHj75xza8zsHEkPSIpJ+rhz7vluLq+TNDmlPUnSjizn1PnpXKMkNUj6mKRHnXPtknab2R8kzZWUMUDp6s3mS/m65wLtsePHa3h1tWpra1Va2ZG15pk2eZKqj6tUX5WdsUBKCVCWHK4NBCgHhh2r6urhfX4deGprawvys4fe414WB+5j8eBeFg/uZfHI9b3sTY7PlyRt8h/fJul7kv5e0l09uPYlSdVmNtXMKiRdIym8+tdDkq7zH39UXjDkJG2VdKF5hkk6R9JbvRh33lkoxculFskn+r8GRZLis84ItM/c/UagTaE8AAAAClFvApRRzrlGMxsh6b2Svu+c+7Gkk7q70DkXk/R5SY9JWi/pAefcOjO71cyW+qf9WNI4M9sg6cuSOpYi/oGk4ZLekBfo3OOce60X486/eLBIPrjMcPBQf9SgSFJ85qlyJZ239z37t+u41v3J9hsUygMAAKAA9TjFS9I2MztX0smSnnbOxc1spLz6kG4551ZJWhXq+3rK4xZ5SwqHr2vO1D+gdLVRY3/vg9JhyFAlps1W6YbOJYYv2v+6fj7hfEnSm/tjSjinEuun1wMAAAD6QW9mUL4ib0Wtv5P0Lb9viaQ/9vegik5oo0aXuopXrgIUSfGT5wbaHz7QOfF0OOa06WCPYksAAAAgMr3ZqHGVc+5459wU59xqv/u/JC3t6jqk16CoixqU/lz5N3bavEB7YcPrKnGdK3eR5gUAAIBC06uvw2Y2yszmmdmFZnahpPP8H3QlFq5BSd2oMXioP2dQEtNmyQ0bmWyPamvWvIPvJttvUCgPAACAAtObZYavl1ew3izpcMohJ2la/w6ryKTNoHSmeOWsBkWSSkoVO2Wuyl/8XbLrkoa1emGUtyzcmr1t2a4EAAAA8qI3Myh/L+mjzrljnXNTU34ITrphoVW8XOoMStoyw/1btB4/bX6g/aGGtcnHL+xuUywR3i8TAAAAyJ/eBChlkv4nVwMpauEUL78GJeGk9tBm7v1ZgyJJ8VPPDrTPbtqo8W2NkqSmdqe1+0jzAgAAQOHozdfhOyR9zcz6+Sv0IBDPvMxwW4bgxPp52V83aqziJ84M9H2woXM1r2fqW/v19QAAAIC+6O1O8l+T1GRmW1N/cjS24pFWJO/VoLSHsqv6O72rQzy0mtclKWlez+wkQAEAAEDh6M1GjdfmbBRFLrzMsMs6g5KbACV22nxVPPyLZPvihtdlLiFnJXphV5vaE07lOXptAAAAoDd6HKA4557K5UCKWmijxo5VvNoTwaCgsjQ3L5+YPltuyDDZkUOSpPfEmjX78A69OWySDsWcXt3bpnnHVObmxQEAAIBe6HGKl5lVmtnfm9lGM2v0+z5oZp/P3fCKRHiZ4Y4ZlIhSvFRapvjMUwNd5x9Yn3z87E6WGwYAAEBh6E0Nyp2STpH0cXl7n0jSOkmf7e9BFZ0sq3iFU7xyFqBIip/03kA7NUChUB4AAACFojc1KB+RNMM5d8jMEpLknNtuZhNzM7TiYfFwDYqX4tUWSvHKVQ2KJMVnhQOUtyTnJDO9uLtNbXGnihwGSAAAAEBP9GYGpU2hgMbMxkva168jKkZpq3h5v8b0VbxyN4TEiTPlKquS7Qntjao+slOSdDjm9Aq7ygMAAKAA9CZA+S9JPzOzqZJkZsdJ+hdJ9+ViYEUl3rMUr5zOYJSVKT7jlEAXaV4AAAAoNL0JUP5W0iZJr0saLalWUr2kb+ZgXEWlp8sMV+Z4qd+MaV4+CuUBAABQCHpTgzJD0luS/kFSqaT/ds69npNRFZusGzWGalByXAOSFqA0rk+pQ2lVa9zltFAfAAAA6E63Myjm+Ym8mZO/lfRhSZ+R9KqZ3WNmfKPtTqhIPluKV1WOg4PE1Fly5RXJ9uTWBk1p2SNJaolLq/cwiwIAAID86kmK159L+oCkc5xzJzrnFjjnTpC0QNJ5km7I4fiKQyy0UWNHkXxaDUqOx1Feofj0OYGuQB3KTupQAAAAkF89CVA+Ien/OOdeSu3023/pH0c2ibjMdUYizkqkEi8SaQ2v4pXjGhRJSqSleaXUoVAoDwAAgDzrSYAyR9JTWY495R9HNlmWGJak9tA+KFHUf8RnnR5op86g/HFPm1piLnwJAAAAEJmeBCilzrmmTAf8/t6sBDb4hFbw6iiQlzItM5z74cSnz5Er7QySprXs0aQWbyub1rj0EnUoAAAAyKOerOJVbmYXSMr25/3erAQ2+HQ1g5KHFC9VVCoxbZZKa99Idp3X+JZ+VfU+SdKzO1t13nGVuR8HAAAAkEFPgovdkn7SzXFkYaFNGlNnL9oS0S4z3CE+6/RAgHL+gfX61bFegPJMfatuPiOSYQAAAABpug1QnHNTIhhH8UpL8UqtQQkeyvUywx3iJ71XevgXyXbqho0v72ljPxQAAADkDfUjuRaaQVFpSg1KKMUrshmU6pPlSjpv/UlH6jWhdb83poT0RkN7tksBAACAnCJAyTELzaC4stQUr+C5lVHdjaqhSkw9KdBV0/h28jEbNgIAACBfCFByLR7epDF1Fa/81KBIfppXitTlhlfvJUABAABAfhCg5Fq4BqW0i1W88hqgdNahvLKXFC8AAADkBwFKrnWxzHB6ileEAcrMU71d7X2nHK7TuDZvu5vaxpgOtCayXQoAAADkDAFKjqXXoHSmeIV3ko9io8akocOVOGFGoGvBwdrk41dJ8wIAAEAeEKDkWngGJXUflFCKV1TLDHeIz5gTaJ/VtDH5eDVpXgAAAMgDApRcS1tmOPs+KFEWyUtKW8nrrKZNyces5AUAAIB8iCxAMbNLzOxtM9tgZjdlOF5pZvf7x180sykpx04zs+fNbJ2ZvW5mVVGNu8/i2TdqDJd5RFmDImUKUDZKzpvWWb23Tc65TJcBAAAAORNJgGJmpZJ+IOlSSXMkLTOzOaHTPiVpv3NuhqQ7Jd3hX1sm6ReS/sI5d7KkD0gaMPlHFkrxKpgaFEmJ406Qq+iM9Y5tP6hJrQ2SpN1HEtp+KJ7tUgAAACAnoppBmSdpg3Nuo3OuTdJ9ki4LnXOZpJ/5j5dLWmhmJumDkl5zzq2VJOfcPufcwPnmHF5muCx7DUqUywxLkkrLlDgxWChPHQoAAADyKaoAZaKkbSntOr8v4znOuZikRknjJM2U5MzsMTN7xcz+JoLx9p/wRo2lqTMowUNRp3hJUnzqrED77JQA5RXqUAAAABCxsu5P6ReZvnmHCxyynVMmqUbS2ZIOS3rCzFY7557I9EK1tbWZuvPmPfU7NDmlfaC5WXX+GNtcsJRm+9bNilVGW/cxZsgoTUlpp86gPLutUbWjd0c6noGs0D57OHrcy+LAfSwe3Mviwb0sHn29l9XV1VmPRRWg1EmB7+mTJO3Ick6dX3cySlKD3/+Uc26vJJnZKklnSsoYoHT1ZvOh/N01gfaoce/REH+M7S/UBY7NnjFV46qiLUSx4ZXSyh8n22c1bfIK5c309qEyTZs+WaV5mNkZaGprawvus4ejw70sDtzH4sG9LB7cy+KR63sZVYrXS5KqzWyqmVVIukbSQ6FzHpJ0nf/4o5J+57xlpB6TdJqZDfUDl/dLejOicfdd2ipenSle4Z3ko15mWJLcsZPkqoYm22NjhzS1ZY8kqTnm9E5jLNulAAAAQL+LJEDxa0o+Ly/YWC/pAefcOjO71cyW+qf9WNI4M9sg6cuSbvKv3S/pe/KCnDWSXnHOPRLFuPtFeKPG1CL5AqhBUUmJ4lNmBrqChfLUoQAAACA6UaV4yTm3StKqUN/XUx63SLoqy7W/kLfU8IBjoY0anb9RYzzhFA+V3ZTnadvMxLRZ0ludqWhzmzZq+THnSJJe2dOua5mNBQAAQETYST7Xsiwz3JoIFsNXlkreqsrRS0wJbtg4N2UG5WVW8gIAAECECFByLS3Fy6tBaQutPpyX9C5fPG1H+U0qTXgDXLe/XUdi7CgPAACAaBCg5Fp4BsVP8WqNB7/056NAvoMbf5wSI8ck2yPiLTrt0FZJUtxJr+1jFgUAAADRIEDJMQtt1Oj8GZRwildVHgMUmSkx89RAV03j28nH7CgPAACAqBCg5FqWGZS28AxKnu9EvDoYoLwvJUB5hZW8AAAAEBEClFyLZ15muDVcg5LPGRRJ8ZnhAOUdb8NGSasplAcAAEBECFByzEIzKMkUrwKqQZGkxAkz5Cqqku3j2g5oWstuSdKmprgaWuLZLgUAAAD6DQFKrmXZqDFtmeE8ruIlSSorU3zGnEBXzYG3ko9f2kMdCgAAAHKPACXXwile2WpQSqMaUHbhQvn3Nb6TfPzbrUeiHg4AAAAGIQKUXMu2UWOB1aBImepQOgvlH97SoliC/VAAAACQWwQoOWahFC9XmnmZ4YIIUKbNkSvp/EjMOlKv8W2NkqR9rQk9u7M1X0MDAADAIEGAkmvxzDMo4RSvvNegSNKQoUqcMCPQdW5Kmtd/byLNCwAAALlFgJJrsVAuV9ZVvKIaUNfiM08LtD9w4M3kY9K8AAAAkGsEKLkWnkEpLdwaFEmKz3pvoP3hhleT+6GQ5gUAAIBcI0DJsbQalEJdZtgXP/ms5F4tkjTlyB7NPrw92SbNCwAAALlEgJJraat4eV/+05cZLowARVVDFZ99RqBryb5Xk49J8wIAAEAuEaDkWnijxmSKV3gVr6gG1L3YGecG2pc1vJJ8TJoXAAAAcokAJdfCGzUmU7yC3YVSgyJJ8feeE2jPa9ygcW1NyTZpXgAAAMgVApQcs1CRvMuyk3yh1KBIknvPBMUnT0+2S1xClzSsSbZJ8wIAAECuEKDkWjjFK+syw4UToEhS/PQFgfZH9nfWoZDmBQAAgFwhQMm1tCL5wq9BkdLrUD7U8JrKE53BFmleAAAAyAUClFxKJGSJzmITZyaVeJFIuAalooBSvCQpMXWWEiPHJNtD2o/oov1vJNukeQEAACAXCFByKVwgX1ommReIpNWgFFiKl0pKFD/jfYGuT+75Q/IxaV4AAADIBQKUXEqrPylLPkxP8SqwAEVS+7kXBdpL967WsFhLsr1yM2leAAAA6F8EKLkUWsFLpZ07tLeFd5IvsBoUSUrMPE2Jscck25WxVl229+Vk+5n6tnwMCwAAAEWMACWHLDSD4gIzKMFzC2mZ4aSSEsUWLAx0Xbu7M81rw8GY9hyJh68CAAAAjhoBSi5lqkHxhWdQCm2Z4Q6xBRcH2gsbXtcxbY3J9vO7mEUBAABA/yFAyaUsSwxLUssAqEGRpMTkaYFNG0vl9Ce7n0+2X9hNoTwAAAD6DwFKLmXZpFHKsFFjAd+J2IJgsfzHdnWmeb3ADAoAAAD6UQF/LR74LDSDklqD0hauQSnQGRRJip2z0NvDxXd200ZVH66XJK3d165D7YlslwIAAAC9QoCSS/FQFJKyildr2ipehRuguHHHKH7SewN9HbMocSe9vKc902UAAABArxGg5FK4BiW1SH6A1KB0yJjm5bz3QB0KAAAA+gsBSg5ZvKtlhkMBSoHfidjZ75dLqaGZ3rJb8w9ukEQdCgAAAPpPZF+LzewSM3vbzDaY2U0Zjlea2f3+8RfNbEro+Alm1mxmfx3VmPssbRWv1I0ag4cKdZnhpGEjFD99QaCrI83rpd1tioVS1gAAAICjEUmAYmalkn4g6VJJcyQtM7M5odM+JWm/c26GpDsl3RE6fqek3+Z6rP0qbRWvgbfMcKr20J4oV+95QWWJmJpjTm80UIcCAACAvotqBmWepA3OuY3OuTZJ90m6LHTOZZJ+5j9eLmmhmbd0lJldLmmjpHURjbd/ZNmoMZZwSp1wMEllhR+fKP7e+XJDhyfb49ubdPH+NySxYSMAAAD6R1QBykRJ21LadX5fxnOcczFJjZLGmdkwSV+V9M0Ixtmvsi0znFZ/UiqZDYAIpbxCsbM/EOj62K5nJVEoDwAAgP5R1v0p/SLTt+9w0UK2c74p6U7nXHNPvsTX1tb2fnQ5MnZ7nU5MaTcdPqIttbVqbJekocn+crmCGndXhp8wS9X6TbJ92d7VGh47ome3O73zTq0GQpyVKwPlHqJ73MviwH0sHtzL4sG9LB59vZfV1dVZj0UVoNRJmpzSniRpR5Zz6sysTNIoSQ2S5kv6qJn9o6TRkhJm1uKc+5dML9TVm41a2fa3A+0RY8aqurpaOw/HpRd3JvuHVJQW1Li7NH26Eo/8p0oadkuShibadNnel3XvhPNUPmGqpo6M6iNVWGprawfOPUSXuJfFgftYPLiXxYN7WTxyfS+jSvF6SVK1mU01swpJ10h6KHTOQ5Ku8x9/VNLvnOc859wU59wUSXdJ+odswUnBCdeg+Kt4hVO8KkoG0LRDSYliCxYGuj626zlJ0vO7SPMCAABA30QSoPg1JZ+X9Jik9ZIecM6tM7NbzWypf9qP5dWcbJD0ZUlpSxEPNGn7oPhF8m1pu8hHNqR+EQut5nXR/td1bOsBCuUBAADQZ5Hl4zjnVklaFer7esrjFklXdfMc38jJ4HIlyzLDLfFgd+VAmkGRlJg8TfHJ01W67V1JUqmcrt7zgh7dvSTPIwMAAMBAV+D7lw9wWTZqbAuneA2APVDCYgsuCrQ/tusPqm2MaW84+gIAAAB6gQAlhyyWOcUrfZnhARignLNQLmXJrrObNmrWoe16gTQvAAAA9AEBSi6lFclnq0EZeAGKG3eM4rNOD/T9Wf2TBCgAAADoEwKUXAqneCVnUILdlQP0LsTOuzTQvnbXs3p556E8jQYAAADFYIB+NR4g0mpQMqd4DcQaFEmKnf1+xYcMS7aPaT+oSW+9qG3NsS6uAgAAALIjQMkhawvuC+IqqiQVR4qXJKmiUvFzg0sOX1f/pG5f05SnAQEAAGCgI0DJpZYjwXbVEK87baPGqAbU/2LnLwq0P9jwmp55Y5vePtCe5QoAAAAguwH81bjwWWswQHF+gNIWrkEZqDMokhJTZip2QnWyXSqnT9Q/rW+/cjCPowIAAMBARYCSQ+EARZVegFIsNSgd4u8PzqL8Wf3v9cjmw1q9hxW9AAAA0DsEKLkUSvFyfoASrkGpGuABSvuCi+QqKpPtE1v3adG+V3XramZRAAAA0DsEKDmULcUrbaPGkoEdoGjYCMXOWRjo+uz2x/VUfaue3NGSp0EBAABgICJAyaXW0Jfzysw1KBWlEY0nh9ovvCzQ/tD+1zT98E59c/VBOeeyXAUAAAAEEaDkkKWleHnLDLcWyzLDKRJTT1J8+uxA3w07ntCre9v10BZmUQAAANAzBCi54pwULpLPuszwwA9QJKn9wssD7et3PqUh8VZ9+5WDiiWYRQEAAED3CFByJdYui3fmcrnSMqmsXJLUFq5BKYIZFEmKzfuA3PCRyfbY2CF9bNcfVNsY08rNR7q4EgAAAPAQoORKuP7Enz2RMi0zHMWAIlBRqfbzFwe6/mrbIypxCf1yw+E8DQoAAAADCfl/cQcAAB/aSURBVAFKjqSt4OXXn0hSWyJ47kBfZjhV+0Uf8WaLfDOP7NQVe/6o3+9oVf3heBdXAgAAAAQoudOSeZNGKcMMSpHUoEiSG3eMYudeHOi7actKJRJOD7zLLAoAAAC6RoCSI2kreKWkeBVrDUqHtsXL5KzzPZ1+aKsubVirX204zJLDAAAA6BIBSo5YW3gPlM4Ur9ZQildlsdSg+NxxJyg29/2Bvpu2rNRbB2Jas689T6MCAADAQECAkitpe6AMjhSvDu0f/nig/b6D76jmwFsUywMAAKBLBCg50lWKVzhAKbYUL0lKnFit2KnzAn03bV2pFRuPpKW4AQAAAB0IUHIlvEljZfYalIoiDFAkqe3D1wbalzS8phP3vqvH6thZHgAAAJkRoORI+jLDKTMooV3Vq4qsBqVD4qTTFJ95aqDvq1sf0q9I8wIAAEAWBCi5El5mOJDiFTxUjDUoHdqWBGtRrtjzkja9vVF7W9gTBQAAAOkIUHKkqxmUtkTx16B0iJ82X/HJ05PtEjn91ZaHtXzjkS6uAgAAwGBFgJIrXc6gDJ4ARWZqD9WifHzXH/T7tVvyNCAAAAAUMgKUHLHWYCG4S9kHpS0txSuKEeVP7Ozz1XbMxGS73MW16LX/1pv72RMFAAAAQUX+1Th/wileHat4OefUMphmUCSppFSJ0L4on6p/UivX1OVpQAAAAChUBCi5kmUflJiTUsOTEpPKirhIvkPs3IvVPHJ8sl3l2jX+9w9SLA8AAIAAApQcSU/x8gKUcP1JVbHPnnQoK5dbsizQ9am6/9W//3FnngYEAACAQkSAkitpKV5eDUraJo2D6A7YBYt1aOjoZHtEvEUVT67UzsPMogAAAMAziL4eR8uypHi1JoLnFX39SaqKSrlLrw50fbrucd316v48DQgAAACFJrIAxcwuMbO3zWyDmd2U4Xilmd3vH3/RzKb4/Reb2Woze93/54VRjblPshTJh1O8KgZTgCJJCz+s9vLKZHNya4P2P/e06ppjeRwUAAAACkUkAYqZlUr6gaRLJc2RtMzM5oRO+5Sk/c65GZLulHSH379X0oedc6dKuk7Sz6MYc19lW2Y4nOJVOQgK5AOGjVD8fR8MdH1222P67mtNeRoQAAAACklUMyjzJG1wzm10zrVJuk/SZaFzLpP0M//xckkLzcycc68653b4/eskVZlZpQqZc+kbNfozKOElhitKoxpU4UhcfEWgXdP4tl5fvU6bm5hFAQAAGOyiClAmStqW0q7z+zKe45yLSWqUNC50zpWSXnXOteZonP2jvU3mOotNXHm5VFYmSWobzDUovsSkqWqfc2ag7y/q/kf/tJZZFAAAgMGuLKLXyfQt3PXmHDM7WV7a1wcznJdUW1vb68H9//buPE6Ous7/+OtT3dM9RzIzOSEhgRBIgsohyI0HHixJREAFARHQFYQVvFkx6C54oOLPn8qKioKiuJyiYkQBBVTYJSCnQghHLkhCLpJMJnP2UZ/9o6pnuntmkhkyd97Px6Mf01X1rfp+u2pqpj79vfpbsnkbBxQt55PpjnIt2xoAnbPKe6ZtWJR5sNXufxT7PPtEx/IZ6x/ismdO5b7aTexZVf6rMTLsitdxtNK1HB10HUcPXcvRQ9dy9NjZazlr1qwetw1WgLIamF60PA14pYc0q80sCdQBmwHMbBrwW+Bsd1+2vYy292EHi21cW7IcVNd0lGv1mjZ4elPHtrqaambN2nNQyzcs7DOT/F9+QyI+V2nP8cnVd3Prlo/ykwPHD3Hh+u7FF18cFr97svN0LUcHXcfRQ9dy9NC1HD0G+loOVhOvR4FZZra3maWA04GFZWkWEnWCBzgFuN/d3czqgT8AC9z9fwepvDvFykbwKkzSCNAelnWS3wX7oAAQJMjNLR1y+Pw193Lf8xt4fGNmiAolIiIiIkNtUAKUuE/JRcA9wBLgNndfbGZfMbMT42Q/BSaY2VLgs0BhKOKLgH2B/zCzp+LX5MEo92tW3kG+sihAKZuTMLWrjeJVJPvW+YS14zqWx4TtXLj6T3xuUQP5cGQ28xIRERGRnTNo86C4+x/dfba77+PuV8Tr/tPdF8bv29z9VHff190Pd/fl8fqvuXuNu7+x6LVhsMr9WnSpQSkKULoMM7wLdpLvkEqTnXdayaqL1tzD0vWN/PyF5iEqlIiIiIgMJc0kPxDK5kAh1dkpvusww7twgAJk334iXjO2Y3l8rpl/e+VevvJ4Ixtb89vZU0RERERGIwUoA8DatlODUt4HZVe/AlXVZMvmRfn3l3+PNTdx2WONQ1QoERERERkqu/rj8cBo736SRujaB2WXbuIVyxz3fryqpmN5fK6ZS15eyE1LW3h4/fCe8kZERERE+pcClAFgZU281AdlB8bUkpl/esmqT6y+h2ltm/jcogZy6jAvIiIisstQgDIQykfxSnf2QSkfZnhX74NSkD3+VML6iR3LlZ7l8pW3s3hLjmuXqMO8iIiIyK5CAcoA6NMoXroCkXQlmfd+uGTV2ese5LDGZXz9yUaea8gOTblEREREZFDp8XgAlHeSVx+U3sm9ZS7h1L06lgOcm579PomWJub9cSN/36D+KCIiIiKjnQKUgVDeB6W4iZeGGe5ZIkn76R8vWbV320aue/4nbGkLOenuTfx5dVsPO4uIiIjIaKAAZQBsr4lXeR+U9C48k3x38gcdQea495esO/nVx/jsqj/SmnfOuHcTty5rGaLSiYiIiMhAU4AyELbbxKt8FK/BKNDIkjntfPJ771ey7lvLb2LBS3eQC53zH9jC1c9sG6LSiYiIiMhAUoAyALY7UaP6oOxYRYq2Cy/Dq2tKVn91xa/43tIbCDzkS4828n0FKSIiIiKjjgKUgZDZTg2KhhnuFZ80hbaPX4ZXpErWX7TmTzz4xOXMaX6Fyx9r5H/XqeO8iIiIyGiiAGUAdKlBSW9vmGEFKD3JH3A4rf/+7S41KUdsW8bjj13Kp1+6k/P+8iobWvM9HEFERERERhoFKAOhrJM8leqD8lqFcw6k9dLvE9ZPKFlf6VmuXH4ztz10GV/+/TPkNdu8iIiIyKigAGUAbK8GpXweFDXx2rFw+kxav/wTcocc02XbkY1L+fEfL+aha6+HXG4ISiciIiIi/UkBSn8LQ8iU9YtIpzveapjh18brJ9D2ya/Rdv4XCWvGlmyr9CzzHrqB/BfPI1j27BCVUERERET6gwKU/pZtx7wzCPFUGoLOdlxd+qCoBqX3zMgdfRytX/85zQcd3WVz3boVVH31QtLXXYk1bBqCAoqIiIjIzlKA0s+217wL1AelP3j9BPwzV/CP0y7h1YoxJdvMnYoH76L6kg9R8fsbu9ZmiYiIiMiwpgClv5VP0lhZGqBkwtLN6oPyGpmxz/x5/Pzca/jlbm/uurmtlfTt11K94GwSf/8ruDrRi4iIiIwEClD6mbW3lSx7urJkuUsNivqg7JSPHbEHtxz3aY4/cAGLq/fosj14dT1VP7icqq9/kmDFc0NQQhERERHpCwUo/cyaG0tXlDfxKu8krxqUnRKYcf3bxzPliMM55NBv8IlZ57ApOaZLusQLT1N9+QWkr/0mNG0dgpKKiIiISG8oQOlnweoVJcvh5Kkly+U1KCn1Qdlp1cmAq988jtvnTubOOfOYc8R3uGraXLLW9eRW/M/dVF/6ERJPPjQEJRURERGRHVGA0s+Cl5eWLIfT9+l47+5d5kFRE6/+8/Y9Kll08mQ+cOBkLt73LA467JvcOeHgLumCrZup+t6lpK/5GrZpwxCUVERERER6ogClnwWrlpcsh9NndrzPlnWQT5iTUIDSr8ZUBHzryHr+OH8iPmVPTj7gYuYdeAnPVE/rkrZi0b1Uf+EsUr/5mZp9iYiIiAwTClD6U5gnWFPWxGvPfTvel/c/SSk2GTBH7ZbmwRMnc8kbx/K3iQdy2KFX8NW93kuu7FfeMu2kfncDVZ86hYprriB4/p/RZJsiIiIiMiSSQ12A0cTWrcaK5t0Ix9bjdeM7lssnaUwpPBxQlUljwcG1vH/vKj6zqIEvB6fw+4lv4trnfsJBzS+XpE3ksiQW/RkW/ZncuEmER7yd7NvejU/da4hKLyIiIrJr0iNyP0p017zLOqtJyvufVASam2MwzK6v4M65E/nBm+tZOXEfDjv0Cs6f/VHWV9R2mz65ZSOpu2+jZsE5VH73UoIX/ql5VEREREQGiWpQ+lGwalnJcnHzLoCMmngNGTPjzFk1zJteyS9fbOGeVXN5w25Hct6a+zjvlfuY2bax2/2STz1E8qmHyO/zejLzTyd/yDEQaOg1ERERkYGiAKUfdTeCV2Mm5MuPN/KROTUky+qrKlR/NejGVyb41AFj+dQBY2lon8Df1k7jq6tOpf3Jv3Piqgd4z6tPUBO2d9kvsexZqr7/nzTXTSJ/6FtJHv0Owr3nQEK3kIiIiEh/0tNVPyofweuR9DTOuWMDq5vzPLYxw3ePqi/ZrhqUoVWfDjhpRhUnzaii7ai5/Oz5t7D/4xs55pVH+dTquzls2/Iu+9Rs3Qj3/Rru+zXtFZW07TWHqn3nMMEqSLQ1EE6eik/YDZK6tUREREReCz1F9ZemRoLNnXNqhEGCef+sIRNEHU/+sSnLlf/YVrJLSn1Qho3KpPHxN4zhrNnV/HDxBP7l6WM4ZOOzfG7VH5i/+alu90ln20gv/Qcs/Qd7Atx1IwAeBPiE3Qh3n044ZTrh7nviU6YT7j4dHzexpF+SiIiIiJRSgNJPEmX9T3zqXpywTy2/WdHase6eVW0laTSK1/AztiLgkjfWct5+NfzXM2P4+PIDqFu/ks+u+gOnbnyE6jCzw2NYGGIb1xJsXAtP/71km9fUkp8xi3DGbMK9ZpOfMQufNBUC/TKIiIhI/8iHzgNr29nSHvIv0ysZM8L6FShA6SfdTdD4rSPreGBtO6+2dT+vxgj7XdmljK9McPmhdVx+aB1rmifxyPoDuXTNVtL/fITDViziLQ1L2C3b2OfjWnMjycWPw+LHO9Z5VQ35vQpByyzyM2bju09TZ3wRERHps5XbclzwwBYe3hB9qTqtJsGP3zqOY3ZPD3HJem/QAhQzmwtcBSSA69z9m2Xb08ANwJuATcBp7r4y3rYA+CiQBz7p7vcMVrl7q7sRvCZWJvj/R9Vzzl82d7uP+qCMDHvUJHjfzGreN7Ma3nIyDe0n8ruXWnhk8UvkXniWmS1rmdm6IXq1bWBKpqFPx7fWZpLPPQXPdTYlyyRSNNROoqluMtmx4wiqa0jW1FCZTlGVSpJOJQmSSQgSeCIRBTMdP5N4Oo3XjY9eNbWQrlQtjYiIyDDVnneSBomg+4dDd+eh9RlueKGZZ7fkOHK3FB+eXcMbxleUpLllWSuff7iBbdnObgSrm/OccNerfPqAMSw4uJZUYvg/gA5KgGJmCeAHwHHAauBRM1vo7s8WJfsosMXd9zWz04ErgdPM7PXA6cAbgKnAvWY2293LZhUZWra1NAgJp+8DwEkzqjh5RhV3rGztso/mQRmZ6tMBH5w9hg/OfgONmddxz6o2bli8kRda06xpyVOdb2Pf1vXMblnLnJa1zG5Zy+zWtbyueQ1juhkhrDupfIbJW9YwecuafilziJFNVZJNV5NPV5OvrCabqiKTriaTqiKTqiabriYxtpaK2lqq6+sYM66eiro6qEjhFpDxgDYCWjzAgSoLqQyg0vKYO4Rh9PKQMJcnk8uTy4dUpNOkq6vwdCWkqzSAgIiIvGbZ0MnkneqkYdvp09mWcza1hwQGE9JBjw/lTdmQtS15NraG1KYCplQHjE8HXY7d0B6ycluOl5ryNLSHTK1JsPfYBHuOSZYce0t7yDObszyzOcuShiyhw371SQ4YX8H+4yuYUBm1jtjclufBdRkeWNvO315pZ2ljjrEVxtG7pXjr1EreNiXN68cl2Zpxbl7awi+eb+b5rbmOfJ7enOXaJc0cPinFR/ar4W1T0lz6963dPm8COPDdp5u4b007175tHHPqK7pNN1yYD8IEdGZ2FHC5ux8fLy8AcPdvFKW5J06zyMySwDpgEvCF4rTF6Qr7bt26dXg86TdtJbFqOcGqZWSPPg7G1AGwsTXPkb/dwKb20qZex0/KcesJmql8NHjxxReZNWsWWzMhLzTkWNKQ5fmGHM81ZHluS441LXnMQ2a1ruPgbSs5pGklh2xbwcHbVlKfbxnq4g+qrCVoS1bSmkzTGqTJWkAeI2cBWBANIhDE/xyCgCAOeiwMMY+CoMBDkjgBIWaGm0F8nDxGjoAcRkj0T8PMCMyiQ5uBRUGbO4TE76OUuIckg4A4GZgROjiOe/RHPsQwIIjTFNJ6fJwQOtJGRy2ksY4xErwoTfExC8cqvPe4nIX8wzif4rRBYQHispYeuzj/ID64l6UrnKvCl3dBcf4d6aJP1F1ZC/l3lrHz2B3HNIvTlpYPd6qybYzNNvHtwy7g+Ulzom8SLTpsLoS8Q86dfAh5dxJmJAJImJE0SAbRMaN00NTcQrqqitCj/AvfTBaOmy+kDZ28R+et8PmTgZGI97Ee0gbxcRIWpy3Kv1DWsChtIf9EvF9Ydsx8nH9he6EMQTf558M4/6LPX3yucnHaKI/Oc5U0IxlEad070+XicroXjhnlnyzKPzpudMxcGP0GRsfsTFuef744/6J0hXOVLUqXDaPf78Lnrgg6y9rY1EKqqjoqa1wOI0pT+FwVQVTWbBgdKxf/zHvR5wmifSos+r0rpI1e0TmoCDrzrgii4+Y60sUPx/G/8lRRmlTCCIg+UybvHemyeScRGKk4TUX8HqJvzDNhNEdaez763a5IGOk4bSpOm/MobXveaQ+j94X80wnreFUYtIfRg3lbvvOVNKhMGOmkUZmIXga0FtLknNa8k81DKgFVCaMyTluVNHIhtORCWnJOa85pyUXntSpp1CSjNNXJqAytOac55zRnneZcSHPWCQzGVASMqTASuXYmjK0mYVFg0JhxtmVDtmWdpmwUdNSmjLEVAWMrjNpUQCZ0GtpDtmain0256PMnDepSAePSAfXpaJ/GTMirbSGb2jrTFdSmjInpgImVCdIJWN8aBSbFNQ0F6QTsXpVgSnWC9tBZuS3HlvbuHzWNqKXFtJoEq5ryrGnZ/vfnU6qjMi/ZkqP7I3aaWBnQlA1p6+ev5CsTcP2x45m3Z9VrPkbhuae/1NXVlUSEgxWgnALMdfdz4+WzgCPc/aKiNM/EaVbHy8uAI4DLgYfd/b/j9T8F7nL32wv7DpsARURERERE+qQ8QBmsRund1auVBxU9penNviIiIiIiMgoMVoCyGphetDwNeKWnNHETrzpgcy/3FRERERGRUWCweqs+Cswys72BNUSd3j9YlmYhcA6wCDgFuN/d3cwWAjeZ2XeIOsnPAkomlyivFhIRERERkZFpUAIUd8+Z2UXAPUTDDP/M3Reb2VeAx9x9IfBT4JdmtpSo5uT0eN/FZnYb8CyQAy4cbiN4iYiIiIhI/xiUTvJSakdzwsjwZWbTiebr2Z1osKKfuPtVZjYeuBWYAawEPuDuW4aqnNJ78TDojwFr3P2EuKb3FmA88ARwlrtnhrKMsmNmVg9cB+xP1E/xX4Hn0X054pjZZ4Bzia7j08BHgCnovhz2zOxnwAnABnffP17X7f9Hi8bxvQqYD7QAH3b3J4ai3FKqh+v4/4D3ABlgGfARd2+It/X7fIWauW2QFc0JMw94PXBGPNeLjAw54HPu/jrgSODC+Pp9AbjP3WcB98XLMjJ8ClhStHwl8N34Wm4h+qMrw99VwN3uvh9wENE11X05wpjZHsAngUPjB6MEUYsK3Zcjw8+BuWXreroP5xE1258FfAz40SCVUXbs53S9jn8G9nf3A4EXgAUAZfMVzgV+GD/r7hQFKIPvcGCpuy+Pv/25BThpiMskveTuawvf8Lj7NqKHoD2IruEv4mS/AE4emhJKX5jZNODdRN+8E3+j9w6gMIy5ruUIYGa1wFuJmgrj7pn4mz3dlyNTEqiKB8ypBtai+3JEcPcHiJrpF+vpPjwJuMEjDwP1ZjZlcEoq29PddXT3P7l7YabIh4kGrYLoOt7i7u3uvgJYSvSsu1MUoAy+PYBVRcur43UywpjZDOBg4BFgN3dfC1EQA0weupJJH3wP+DxRcz2ACUBD0R9h3Z8jw0xgI3C9mT1pZteZWQ26L0ccd18DfBt4mSgw2Qo8ju7Lkayn+1DPQyPXvwJ3xe8H5DoqQBl8mtdlFDCzMcCvgU+7e+NQl0f6zswK7WsfL17dTVLdn8NfEjgE+JG7Hww0o+ZcI5KZjSP6RnZvopE7a4iaApXTfTny6e/tCGRmXyRq7n5jYVU3yXb6OipAGXya12WEM7MKouDkRnf/Tbx6faFqOv65YajKJ712DHCima0kamr5DqIalfq4aQno/hwpVgOr3f2RePl2ooBF9+XI8y5ghbtvdPcs8BvgaHRfjmQ93Yd6HhphzOwcos7zZ3rnKFsDch0VoAy+jjlhzCxF1LFo4RCXSXop7qPwU2CJu3+naFNhHh/in78b7LJJ37j7Anef5u4ziO7D+939TOAvRHMxga7liODu64BVZjYnXvVOoqHpdV+OPC8DR5pZdfz3tnAtdV+OXD3dhwuBsy1yJLC10BRMhp94BNpLgBPdvaVo00LgdDNLx6Ngdpmv8DXlp2GGB5+ZzSf6prYwJ8wVQ1wk6SUzezPwINHQl4V+C5cS9UO5DdiT6B/sqe5e3lFQhikzOxa4OB5meCadw5k+CXzI3duHsnyyY2b2RqLBDlLAcqKhaQN0X444ZvZl4DSiZiRPEg05vAe6L4c9M7sZOBaYCKwHLgPuoJv7MA5AryYa+amFaNjax4ai3FKqh+u4AEgDm+JkD7v7BXH6LxL1S8kRNX2/q/yYfS6DAhQRERERERku1MRLRERERESGDQUoIiIiIiIybChAERERERGRYUMBioiIiIiIDBsKUEREREREZNhQgCIiIgCY2V3xRFyDne8MM/OiifhERGQXpgBFREQAcPd57v6LoS7HrsDMPmxm/zPU5RARGY4UoIiIjBKqgRARkdFAAYqIyDBnZivNbIGZPWtmW8zsejOrNLNjzWy1mV1iZuuA6+P0J5jZU2bWYGYPmdmB8fovmNntZce+ysz+K37/VzM7N34fmNmXzOwlM9tgZjeYWV287VgzW91NGd8Vvz/czB4zs0YzW29m3+nj551qZgvNbLOZLTWz84q2HW5mi+LPttbMrjazVNF2N7MLzOzF+Fz9IJ6xekd5nmdmS8xsW3yeD4nXvy4+Lw1mttjMTizap+N8xcsltSI9lcXMXgdcAxxlZk1m1hCnnx/nvc3M1pjZxX05byIio4UCFBGRkeFM4HhgH2A28KV4/e7AeGAv4GPxg/XPgPOBCcCPgYVmlgZuBuabWS2AmSWADwA3dZPfh+PX24GZwBjg6l6W9SrgKnevjct7Wx8+J3E5VwNTgVOAr5vZO+NteeAzwETgKOCdwMfL9j8BOAw4iOjzHb+9zMzsVOBy4GygFjgR2GRmFcDvgT8Bk4FPADea2Zw+fJYuZXH3JcAFwCJ3H+Pu9XHanwLnu/tYYH/g/j7kIyIyaihAEREZGa5291Xuvhm4AjgjXh8Cl7l7u7u3AucBP3b3R9w9H/cpaQeOdPeXgCeAk+N93wG0uPvD3eR3JvAdd1/u7k3AAuD0XjYjywL7mtlEd2/q4fjdMrPpwJuBS9y9zd2fAq4DzgJw98fd/WF3z7n7SqIA7G1lh/mmuze4+8vAX4A37iDbc4FvufujHlkan6sjiQKzb7p7xt3vB+6k89z3Rl/KkgVeb2a17r7F3Z/oQz4iIqOGAhQRkZFhVdH7l4hqFwA2untb0ba9gM/FTZIa4uZD04vS30TnA/YH6b72hDj9S2V5JoHdelHWjxLV8jxnZo+a2Qm92Kc4383uvq0s7z0AzGy2md1pZuvMrBH4OlFtSrF1Re9biIKM7ZkOLOuhLKvcPeyuLL3Ul7K8H5gPvGRmfzOzo/qQj4jIqKEARURkZJhe9H5P4JX4vZelWwVc4e71Ra9qd7853v4r4Fgzmwa8l54DlFeIgp3iPHPAeqAZqC5siJuKTSosu/uL7n4GUbOoK4Hbzayml5/zFWC8mY0ty3tN/P5HwHPArLgJ2aXADvuY7MAqoqZo3ZVlupkV/68sLkvJeSBqbtdb5deNuAbnJKLzdgd9bxonIjIqKEARERkZLjSzaWY2nuih/NYe0l0LXGBmR8QdsmvM7N2FB3533wj8lahD/Yq4P0R3bgY+Y2Z7m9kYopqKW909B7wAVMbHrSDqD5Mu7GhmHzKzSXHNQ0O8Ot+bD+nuq4CHgG/EAwEcSFQjc2OcZCzQCDSZ2X7Av/XmuDtwHXCxmb0pPmf7mtlewCNEQcjnzazCzI4F3gPcEu/3FPA+M6s2s33jcvbWemBaoYO/maXM7Ewzq3P3bPwZe3XORERGGwUoIiIjw01EnbWXx6+vdZfI3R8j6odyNbAFWErU2b38WO+i59oTiDra/xJ4AFgBtBF1EsfdtxJ1TL+OqDahmahTe8FcYLGZNRF1mD+9rBnajpwBzCCqwfgtUR+bP8fbLiZqmraNKBjrKVDrNXf/FVG/npvi494BjHf3DFGH+XnAq8APgbPd/bl41+8CGaJg4xd0BlG9cT+wGFhnZq/G684CVsZN1y4APrQzn0tEZKQy9y61zCIiMoyY2UrgXHe/d6jLIiIiMtBUgyIiIiIiIsOGAhQRERlwcf+Kpm5eiwcp/2t6yP+awchfRER6T028RERERERk2FANioiIiIiIDBsKUEREREREZNhQgCIiIiIiIsOGAhQRERERERk2FKCIiIiIiMiwoQBFRERERESGjf8DAEUGMXu+VNQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kde_target('previous_loan_counts', train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이를 통해, 새롭게 생성된 변수(previous_loan_counts Distribution)가 중요하지 않음을 알 수 있습니다. 상관계수가 너무 작으며, target값에 따른 분포의 차이도 거의 없습니다.\n",
    "\n",
    "이제 bureau 데이터프레임으로부터 몇개의 변수들을 새롭게 생성해보도록 하겠습니다. bureau 데이터프레임의 모든 수치 데이터 Column(numeric column)들로부터 평균, 최소, 최대값을 가져올 예정입니다. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 수치 데이터의 대표값들을 계산 (Aggregating Numeric Columns)\n",
    "\n",
    "(*agg를 활용하여 데이터프레임의 평균, 최대값, 최소값, 합계 등을 구하는 것을 대표값들을 계산한다고 번역하였습니다.)\n",
    "\n",
    "`bureau` 데이터 프레임 안의 수치 데이터를 활용하기 위해, 모든 수치 데이터 Column의 대표값들을 계산할 것입니다. 이를 위해 고객 ID별로 `그룹화(groupby)`를 수행하고, 그룹화된 데이터프레임의 대표값들을 `agg`를 통해 구한 뒤, 결과를 훈련 데이터셋과 병합합니다. `agg` 함수는 오직 수치 데이터행들의 유효한 대표값들을 계산하는 역할을 수행합니다. 계속해서 `mean`, `max`, `min`, `sum` 함수를 기본으로 사용할 것이지만, 상황에 따라 어떠한 함수들은 생략될 수 있습니다. 또한, 별도의 함수를 작성한 후 이를 `agg` 함수를 통해 불러올 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th colspan=\"5\" halign=\"left\">DAYS_CREDIT</th>\n",
       "      <th colspan=\"4\" halign=\"left\">CREDIT_DAY_OVERDUE</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"5\" halign=\"left\">DAYS_CREDIT_UPDATE</th>\n",
       "      <th colspan=\"5\" halign=\"left\">AMT_ANNUITY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>...</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>7</td>\n",
       "      <td>-735.000000</td>\n",
       "      <td>-49</td>\n",
       "      <td>-1572</td>\n",
       "      <td>-5145</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>-93.142857</td>\n",
       "      <td>-6</td>\n",
       "      <td>-155</td>\n",
       "      <td>-652</td>\n",
       "      <td>7</td>\n",
       "      <td>3545.357143</td>\n",
       "      <td>10822.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24817.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002</td>\n",
       "      <td>8</td>\n",
       "      <td>-874.000000</td>\n",
       "      <td>-103</td>\n",
       "      <td>-1437</td>\n",
       "      <td>-6992</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>-499.875000</td>\n",
       "      <td>-7</td>\n",
       "      <td>-1185</td>\n",
       "      <td>-3999</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003</td>\n",
       "      <td>4</td>\n",
       "      <td>-1400.750000</td>\n",
       "      <td>-606</td>\n",
       "      <td>-2586</td>\n",
       "      <td>-5603</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>-816.000000</td>\n",
       "      <td>-43</td>\n",
       "      <td>-2131</td>\n",
       "      <td>-3264</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004</td>\n",
       "      <td>2</td>\n",
       "      <td>-867.000000</td>\n",
       "      <td>-408</td>\n",
       "      <td>-1326</td>\n",
       "      <td>-1734</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>-532.000000</td>\n",
       "      <td>-382</td>\n",
       "      <td>-682</td>\n",
       "      <td>-1064</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005</td>\n",
       "      <td>3</td>\n",
       "      <td>-190.666667</td>\n",
       "      <td>-62</td>\n",
       "      <td>-373</td>\n",
       "      <td>-572</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>-54.333333</td>\n",
       "      <td>-11</td>\n",
       "      <td>-121</td>\n",
       "      <td>-163</td>\n",
       "      <td>3</td>\n",
       "      <td>1420.500000</td>\n",
       "      <td>4261.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4261.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  SK_ID_CURR DAYS_CREDIT                               CREDIT_DAY_OVERDUE  \\\n",
       "                   count         mean  max   min   sum              count   \n",
       "0     100001           7  -735.000000  -49 -1572 -5145                  7   \n",
       "1     100002           8  -874.000000 -103 -1437 -6992                  8   \n",
       "2     100003           4 -1400.750000 -606 -2586 -5603                  4   \n",
       "3     100004           2  -867.000000 -408 -1326 -1734                  2   \n",
       "4     100005           3  -190.666667  -62  -373  -572                  3   \n",
       "\n",
       "                ... DAYS_CREDIT_UPDATE                               \\\n",
       "  mean max min  ...              count        mean  max   min   sum   \n",
       "0  0.0   0   0  ...                  7  -93.142857   -6  -155  -652   \n",
       "1  0.0   0   0  ...                  8 -499.875000   -7 -1185 -3999   \n",
       "2  0.0   0   0  ...                  4 -816.000000  -43 -2131 -3264   \n",
       "3  0.0   0   0  ...                  2 -532.000000 -382  -682 -1064   \n",
       "4  0.0   0   0  ...                  3  -54.333333  -11  -121  -163   \n",
       "\n",
       "  AMT_ANNUITY                                      \n",
       "        count         mean      max  min      sum  \n",
       "0           7  3545.357143  10822.5  0.0  24817.5  \n",
       "1           7     0.000000      0.0  0.0      0.0  \n",
       "2           0          NaN      NaN  NaN      0.0  \n",
       "3           0          NaN      NaN  NaN      0.0  \n",
       "4           3  1420.500000   4261.5  0.0   4261.5  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 고객 id에 따라 데이터프레임을 그룹화하고, 대표값들을 계산\n",
    "# Group by the client id, calculate aggregation statistics\n",
    "bureau_agg = bureau.drop(columns=['SK_ID_BUREAU']).groupby('SK_ID_CURR',as_index=False).agg(['count','mean','max','min','sum']).reset_index()\n",
    "bureau_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "생성된 Column들에 대해 새롭게 이름을 적어주는 것이 좋을 것 같습니다. 밑의 코드들은 원본데이터의 Column에 대표값들의 종류들을 추가적으로 기입해주는 역할을 수행합니다. 여기서는 Multi-Level index 데이터프레임을 작업의 대상으로 합니다. 이러한 부분은 혼동을 줄 수 있고, 작업하기도 어렵기 때문에, single-level index로 최대한 빠르게 변환하고자 하였습니다.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Column 리스트를 생성 \n",
    "# List of column names\n",
    "columns = ['SK_ID_CURR']\n",
    "\n",
    "# 변수(원본 데이터프레임의 column name)에 따라 반복문을 실행\n",
    "# Iterate through the variables names\n",
    "for var in bureau_agg.columns.levels[0]:\n",
    "    \n",
    "    # id column은 생략\n",
    "    # skip the id name\n",
    "    if var != 'SK_ID_CURR':\n",
    "        \n",
    "        # 대표값의 종류에 따라 반복문을 생성\n",
    "        for stat in bureau_agg.columns.levels[1][:-1]:\n",
    "        \n",
    "            # 변수 및 대표값의 종류에 따라 새로운 column name을 생성\n",
    "            # Make a new column name for the variable and stat\n",
    "                columns.append('bureau_%s_%s' % (var, stat))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>bureau_DAYS_CREDIT_count</th>\n",
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       "      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_max</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_min</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_sum</th>\n",
       "      <th>bureau_AMT_ANNUITY_count</th>\n",
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       "      <th>bureau_AMT_ANNUITY_max</th>\n",
       "      <th>bureau_AMT_ANNUITY_min</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  \\\n",
       "0      100001                         7              -735.000000   \n",
       "1      100002                         8              -874.000000   \n",
       "2      100003                         4             -1400.750000   \n",
       "3      100004                         2              -867.000000   \n",
       "4      100005                         3              -190.666667   \n",
       "\n",
       "   bureau_DAYS_CREDIT_max  bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\n",
       "0                     -49                   -1572                   -5145   \n",
       "1                    -103                   -1437                   -6992   \n",
       "2                    -606                   -2586                   -5603   \n",
       "3                    -408                   -1326                   -1734   \n",
       "4                     -62                    -373                    -572   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\n",
       "0                                7                             0.0   \n",
       "1                                8                             0.0   \n",
       "2                                4                             0.0   \n",
       "3                                2                             0.0   \n",
       "4                                3                             0.0   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  ...  \\\n",
       "0                              0                              0  ...   \n",
       "1                              0                              0  ...   \n",
       "2                              0                              0  ...   \n",
       "3                              0                              0  ...   \n",
       "4                              0                              0  ...   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_count  bureau_DAYS_CREDIT_UPDATE_mean  \\\n",
       "0                                7                      -93.142857   \n",
       "1                                8                     -499.875000   \n",
       "2                                4                     -816.000000   \n",
       "3                                2                     -532.000000   \n",
       "4                                3                      -54.333333   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_max  bureau_DAYS_CREDIT_UPDATE_min  \\\n",
       "0                             -6                           -155   \n",
       "1                             -7                          -1185   \n",
       "2                            -43                          -2131   \n",
       "3                           -382                           -682   \n",
       "4                            -11                           -121   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_sum  bureau_AMT_ANNUITY_count  \\\n",
       "0                           -652                         7   \n",
       "1                          -3999                         7   \n",
       "2                          -3264                         0   \n",
       "3                          -1064                         0   \n",
       "4                           -163                         3   \n",
       "\n",
       "   bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  \\\n",
       "0              3545.357143                 10822.5                     0.0   \n",
       "1                 0.000000                     0.0                     0.0   \n",
       "2                      NaN                     NaN                     NaN   \n",
       "3                      NaN                     NaN                     NaN   \n",
       "4              1420.500000                  4261.5                     0.0   \n",
       "\n",
       "   bureau_AMT_ANNUITY_sum  \n",
       "0                 24817.5  \n",
       "1                     0.0  \n",
       "2                     0.0  \n",
       "3                     0.0  \n",
       "4                  4261.5  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 생성된 list를 데이터프레임의 column name으로 지정\n",
    "# Assign the list of columns names as the dataframe column names\n",
    "bureau_agg.columns = columns\n",
    "bureau_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제, 이전에 했던대로 간단히 훈련데이터와 결과를 병합합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
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       "      <td>Cash loans</td>\n",
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       "      <td>N</td>\n",
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       "      <td>202500.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-816.000</td>\n",
       "      <td>-43.0</td>\n",
       "      <td>-2131.0</td>\n",
       "      <td>-3264.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-532.000</td>\n",
       "      <td>-382.0</td>\n",
       "      <td>-682.0</td>\n",
       "      <td>-1064.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</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>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-783.000</td>\n",
       "      <td>-783.0</td>\n",
       "      <td>-783.0</td>\n",
       "      <td>-783.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 183 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "   ...  bureau_DAYS_CREDIT_UPDATE_count bureau_DAYS_CREDIT_UPDATE_mean  \\\n",
       "0  ...                              8.0                       -499.875   \n",
       "1  ...                              4.0                       -816.000   \n",
       "2  ...                              2.0                       -532.000   \n",
       "3  ...                              NaN                            NaN   \n",
       "4  ...                              1.0                       -783.000   \n",
       "\n",
       "  bureau_DAYS_CREDIT_UPDATE_max bureau_DAYS_CREDIT_UPDATE_min  \\\n",
       "0                          -7.0                       -1185.0   \n",
       "1                         -43.0                       -2131.0   \n",
       "2                        -382.0                        -682.0   \n",
       "3                           NaN                           NaN   \n",
       "4                        -783.0                        -783.0   \n",
       "\n",
       "  bureau_DAYS_CREDIT_UPDATE_sum bureau_AMT_ANNUITY_count  \\\n",
       "0                       -3999.0                      7.0   \n",
       "1                       -3264.0                      0.0   \n",
       "2                       -1064.0                      0.0   \n",
       "3                           NaN                      NaN   \n",
       "4                        -783.0                      0.0   \n",
       "\n",
       "   bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  \\\n",
       "0                      0.0                     0.0                     0.0   \n",
       "1                      NaN                     NaN                     NaN   \n",
       "2                      NaN                     NaN                     NaN   \n",
       "3                      NaN                     NaN                     NaN   \n",
       "4                      NaN                     NaN                     NaN   \n",
       "\n",
       "   bureau_AMT_ANNUITY_sum  \n",
       "0                     0.0  \n",
       "1                     0.0  \n",
       "2                     0.0  \n",
       "3                     NaN  \n",
       "4                     0.0  \n",
       "\n",
       "[5 rows x 183 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 훈련데이터와 병합\n",
    "# Merge with the training data\n",
    "train = train.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 목표값과 대표값들의 상관계수 분석 (Correlations of Aggregated Values with Target)\n",
    "\n",
    "새롭게 생성된 값들과 목표값(target)과의 상관계수를 분석합니다. 다시 말하자면, 이러한 상관계수들은 모델링에 중요한 변수들을 대략적으로 알려줍니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 새로운 상관계수들에 대한 리스트를 생성\n",
    "# List of new correlations\n",
    "new_corrs = []\n",
    "\n",
    "# column별로 반복문을 실행\n",
    "for col in columns:\n",
    "    \n",
    "    # 목표값(target)과의 상관계수를 계산\n",
    "    # Calculate correlation with the target\n",
    "    corr = train['TARGET'].corr(train[col])\n",
    "    \n",
    "    # 튜플(tuple)로 리스트에 추가\n",
    "    # Append the list as a tuple\n",
    "    new_corrs.append((col, corr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "밑의 코드를 통해 `sorted` 함수를 통해 상관계수들을 절대값에 따라서 정렬하고자 합니다. 또한, 익명의(anonymous) `lambda` (알아 놓으면 좋은 파이썬의 중요한 기능) 함수도 활용할 것입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('bureau_DAYS_CREDIT_mean', 0.08972896721998122),\n",
       " ('bureau_DAYS_CREDIT_min', 0.0752482510301036),\n",
       " ('bureau_DAYS_CREDIT_UPDATE_mean', 0.06892735266968668),\n",
       " ('bureau_DAYS_ENDDATE_FACT_min', 0.05588737984392113),\n",
       " ('bureau_DAYS_CREDIT_ENDDATE_sum', 0.05373489560102051),\n",
       " ('bureau_DAYS_ENDDATE_FACT_mean', 0.05319962585758661),\n",
       " ('bureau_DAYS_CREDIT_max', 0.049782054639973095),\n",
       " ('bureau_DAYS_ENDDATE_FACT_sum', 0.04885350261111589),\n",
       " ('bureau_DAYS_CREDIT_ENDDATE_mean', 0.04698275433483543),\n",
       " ('bureau_DAYS_CREDIT_UPDATE_min', 0.042863922470730176),\n",
       " ('bureau_DAYS_CREDIT_sum', 0.04199982481484684),\n",
       " ('bureau_DAYS_CREDIT_UPDATE_sum', 0.04140363535306004),\n",
       " ('bureau_DAYS_CREDIT_ENDDATE_max', 0.036589634696329046),\n",
       " ('bureau_DAYS_CREDIT_ENDDATE_min', 0.03428110992161599),\n",
       " ('bureau_DAYS_ENDDATE_FACT_count', -0.030492306653325547)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 상관계수들을 절대값에 따라 정렬\n",
    "# Sort the correlations by the absolute value\n",
    "\n",
    "# 큰 값부터 앞에 나오게하기 위해서는 'reverse'를 True로 설정해야 합니다.\n",
    "# Make sure to reverse to put the largest values at the front of list\n",
    "new_corrs = sorted(new_corrs, key = lambda x: abs(x[1]), reverse = True)\n",
    "new_corrs[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between bureau_DAYS_CREDIT_mean and the TARGET is 0.0897\n",
      "Median value for loan that was not repaid = -835.3333\n",
      "Median value for loan that was repaid =     -1067.0000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kde_target('bureau_DAYS_CREDIT_mean', train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 column은 '고객이 신용관리국 신용등급을 신청한 날로부터 현 대출신청까지 걸린 기간은 몇 일인가? (How many days before current application did client apply for Credit Bureau credit)'에 관한 데이터입니다. 이것을 이전 대출을 받고나서 'Home Credit'에서 대출을 받기전까지 걸린 일수(the number of days)로 해석하려 합니다. 그러므로, 마이너스 수치가 크다는 것은 이전 대출이 이루어진 시점이 더 오래됐음을 의미합니다. 여기서, 이 변수의 평균들과 목표값(target)이 매우 약한 양의 상관관계를 갖는 다는 것은 더 오래된 과거에 대출을 신청했던 고객들은 'Home Credit'에서 대출을 상환할 가능성이 높다는 것을 의미합니다. 그러나 이렇게 약한 상관관계는 그저 노이즈일 가능성도 있습니다.(With a correlation this weak though, it is just as likely to be noise as a signal.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**다중비교문제 (The Multiple Comparisons Problem)**\n",
    "\n",
    "변수가 매우 많을 때는, [다중비교문제](https://towardsdatascience.com/the-multiple-comparisons-problem-e5573e8b9578)로 알려진 우연에 의해 변수들이 목표값(target)에 대해 연관성을 가질 것으로 기대할 수 있습니다. 수백개의 특성(Feature)들을 만들 수 있지만, 몇개의 경우, 그저 데이터안의 랜덤하게 있는 노이즈값들에 의해 목표값(target)과 연관성을 가지는 것처럼 보일 수 있습니다. 이러한 변수들은 훈련 데이터상에서는 목표값과 관련성이 있는 것처럼 보이지만, 관련성들이 테스트 데이터까지 일반화될 수 없기 때문에 모델에 있어 오버피팅(과적합, overfit)을 야기할 수 있습니다. 그렇기에 Feature들을 만드는 것은 많은 고려가 필요합니다. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*(역자의 주) 여기서 다중비교문제란 한 가지 통계적 사실에 대해, 가설을 많이 세울수록 우연에 의해 가설을 충족시킬 결과를 얻을 확률이 높아지는 문제를 말합니다. 마치 원숭이한테 타자를 치라고하고, 유의미한 단어, 문장 등이 나올 때까지 프랑스어, 영어, 스페인어, 독일어 등등일 가설을 세운다면 언젠가는 원하는 결과가 나올 수 있는 것과 비슷한 거 같습니다. \n",
    "\n",
    "- 예) 한 가지 약물의 효능을 질병의 여러 증상들 중 하나라도 개선시킬 수 있는 것으로 가정할 경우 : 질병의 증상을 더 많이 고려함에 따라 적어도 하나의 증상에 대해 효능을 가지는 것처럼 보일 가능성이 점점 높아짐. (출처 : [Wikipedia, Multiple Comparisons Problem, 접속일 : 2019-05-30](https://en.wikipedia.org/wiki/Multiple_comparisons_problem))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 수치데이터의 대표값 연산을 위한 함수 생성 (Function for Numeric Aggregations)\n",
    "\n",
    "이전의 작업들을 요약하여, 수치데이터의 대표값 계산을 위한 함수를 생성하도록 하겠습니다. 이 함수는 데이터프레임 전체에 걸쳐 수치 데이터 column들의 대표값들을 계산하는 역할을 수행합니다. 추후 다른 데이터프레임에 같은 연산을 적용할 경우, 이 함수를 활용하고자 합니다.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agg_numeric(df, group_var, df_name):\n",
    "    '''\n",
    "    데이터프레임안의 수치데이터에 한하여 대표값들을 계산합니다. 이것은 새로운 feature들을 만드는 데 활용될 수 있습니다.\n",
    "    (Aggregates the numeric values in a dataframe. This can be used to create features for each instance\n",
    "     of the grouping variable.)\n",
    "    \n",
    "    \n",
    "    파라미터(Parameters)\n",
    "    -------------------\n",
    "        df (dataframe): 연산의 대상이되는 데이터프레임 (the dataframe to calculate the statistics on)\n",
    "        group_var (string): 그룹화(groupby)의 기준이되는 column (the variable by which to group df)\n",
    "        df_name (string): column명을 재정의하는데 쓰이는 변수 (the variable used to rename the columns)\n",
    "        \n",
    "        \n",
    "    출력값(Returns)\n",
    "    -------------------\n",
    "        agg (dataframe): \n",
    "            모든 수치데이터 column들의 대표값들이 연산된 데이터프레임. 각각의 그룹화된 인스턴스들은 대표값(평균, 최소값, 최대값, 합계)\n",
    "            들을 가짐. 또한, 새롭게 생성된 feature들을 구분하기위해 column들의 이름을 재정의    \n",
    "            \n",
    "        agg (dataframe):    \n",
    "            a dataframe with the statistics aggregated for \n",
    "            all numeric columns. Each instance of the grouping variable will have \n",
    "            the statistics (mean, min, max, sum; currently supported) calculated. \n",
    "            The columns are also renamed to keep track of features created.\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    # 그룹화 대상이 아닌 id들을 제거\n",
    "    # Remove id variables other than qrouping variable\n",
    "    for col in df:\n",
    "        if col != group_var and 'SK_ID' in col:\n",
    "            df = df.drop(columns =col)\n",
    "        \n",
    "    group_ids = df[group_var]\n",
    "    numeric_df = df.select_dtypes('number')\n",
    "    numeric_df[group_var] = group_ids\n",
    "    \n",
    "    # 특정 변수들을 그룹화하고 대표값들을 계산\n",
    "    # Group by the specified variable and calculate the statistics\n",
    "    agg = numeric_df.groupby(group_var).agg(['count','mean','max','min','sum']).reset_index()\n",
    "    \n",
    "    # 새로운 column 이름들을 생성\n",
    "    # Need to create new column names\n",
    "    columns = [group_var]\n",
    "    \n",
    "    # 변수(원본 데이터프레임의 column name)에 따라 반복문을 실행\n",
    "    # Iterate through the variables names\n",
    "    for var in agg.columns.levels[0]:\n",
    "        \n",
    "        # id column은 생략\n",
    "        # skip the grouping variable\n",
    "        if var != group_var:\n",
    "            \n",
    "            # 대표값의 종류에 따라 반복문을 생성\n",
    "            # Iterate through the stat names\n",
    "            for stat in agg.columns.levels[1][:-1]:\n",
    "        \n",
    "                # 변수 및 대표값의 종류에 따라 새로운 column name을 생성\n",
    "                # Make a new column name for the variable and stat\n",
    "                columns.append('%s_%s_%s' % (df_name, var, stat))\n",
    "                \n",
    "    agg.columns = columns\n",
    "    return agg\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
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       "      <td>4</td>\n",
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       "      <td>-2131</td>\n",
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       "    <tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  \\\n",
       "0      100001                         7              -735.000000   \n",
       "1      100002                         8              -874.000000   \n",
       "2      100003                         4             -1400.750000   \n",
       "3      100004                         2              -867.000000   \n",
       "4      100005                         3              -190.666667   \n",
       "\n",
       "   bureau_DAYS_CREDIT_max  bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\n",
       "0                     -49                   -1572                   -5145   \n",
       "1                    -103                   -1437                   -6992   \n",
       "2                    -606                   -2586                   -5603   \n",
       "3                    -408                   -1326                   -1734   \n",
       "4                     -62                    -373                    -572   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\n",
       "0                                7                             0.0   \n",
       "1                                8                             0.0   \n",
       "2                                4                             0.0   \n",
       "3                                2                             0.0   \n",
       "4                                3                             0.0   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  ...  \\\n",
       "0                              0                              0  ...   \n",
       "1                              0                              0  ...   \n",
       "2                              0                              0  ...   \n",
       "3                              0                              0  ...   \n",
       "4                              0                              0  ...   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_count  bureau_DAYS_CREDIT_UPDATE_mean  \\\n",
       "0                                7                      -93.142857   \n",
       "1                                8                     -499.875000   \n",
       "2                                4                     -816.000000   \n",
       "3                                2                     -532.000000   \n",
       "4                                3                      -54.333333   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_max  bureau_DAYS_CREDIT_UPDATE_min  \\\n",
       "0                             -6                           -155   \n",
       "1                             -7                          -1185   \n",
       "2                            -43                          -2131   \n",
       "3                           -382                           -682   \n",
       "4                            -11                           -121   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_sum  bureau_AMT_ANNUITY_count  \\\n",
       "0                           -652                         7   \n",
       "1                          -3999                         7   \n",
       "2                          -3264                         0   \n",
       "3                          -1064                         0   \n",
       "4                           -163                         3   \n",
       "\n",
       "   bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  \\\n",
       "0              3545.357143                 10822.5                     0.0   \n",
       "1                 0.000000                     0.0                     0.0   \n",
       "2                      NaN                     NaN                     NaN   \n",
       "3                      NaN                     NaN                     NaN   \n",
       "4              1420.500000                  4261.5                     0.0   \n",
       "\n",
       "   bureau_AMT_ANNUITY_sum  \n",
       "0                 24817.5  \n",
       "1                     0.0  \n",
       "2                     0.0  \n",
       "3                     0.0  \n",
       "4                  4261.5  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_agg_new = agg_numeric(bureau.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
    "bureau_agg_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "함수가 제대로 작동하는 지 살펴보기 위해, 수작업으로 만든 데이터프레임과 비교해보도록 하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  \\\n",
       "0      100001                         7              -735.000000   \n",
       "1      100002                         8              -874.000000   \n",
       "2      100003                         4             -1400.750000   \n",
       "3      100004                         2              -867.000000   \n",
       "4      100005                         3              -190.666667   \n",
       "\n",
       "   bureau_DAYS_CREDIT_max  bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\n",
       "0                     -49                   -1572                   -5145   \n",
       "1                    -103                   -1437                   -6992   \n",
       "2                    -606                   -2586                   -5603   \n",
       "3                    -408                   -1326                   -1734   \n",
       "4                     -62                    -373                    -572   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\n",
       "0                                7                             0.0   \n",
       "1                                8                             0.0   \n",
       "2                                4                             0.0   \n",
       "3                                2                             0.0   \n",
       "4                                3                             0.0   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  ...  \\\n",
       "0                              0                              0  ...   \n",
       "1                              0                              0  ...   \n",
       "2                              0                              0  ...   \n",
       "3                              0                              0  ...   \n",
       "4                              0                              0  ...   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_count  bureau_DAYS_CREDIT_UPDATE_mean  \\\n",
       "0                                7                      -93.142857   \n",
       "1                                8                     -499.875000   \n",
       "2                                4                     -816.000000   \n",
       "3                                2                     -532.000000   \n",
       "4                                3                      -54.333333   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_max  bureau_DAYS_CREDIT_UPDATE_min  \\\n",
       "0                             -6                           -155   \n",
       "1                             -7                          -1185   \n",
       "2                            -43                          -2131   \n",
       "3                           -382                           -682   \n",
       "4                            -11                           -121   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_sum  bureau_AMT_ANNUITY_count  \\\n",
       "0                           -652                         7   \n",
       "1                          -3999                         7   \n",
       "2                          -3264                         0   \n",
       "3                          -1064                         0   \n",
       "4                           -163                         3   \n",
       "\n",
       "   bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  \\\n",
       "0              3545.357143                 10822.5                     0.0   \n",
       "1                 0.000000                     0.0                     0.0   \n",
       "2                      NaN                     NaN                     NaN   \n",
       "3                      NaN                     NaN                     NaN   \n",
       "4              1420.500000                  4261.5                     0.0   \n",
       "\n",
       "   bureau_AMT_ANNUITY_sum  \n",
       "0                 24817.5  \n",
       "1                     0.0  \n",
       "2                     0.0  \n",
       "3                     0.0  \n",
       "4                  4261.5  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "값들을 비교해보면, 두 데이터프레임이 동등하게 생성되었다는 것을 알 수 있습니다. 추후, 이 함수를 데이터프레임의 대표값들을 계산하는데 재사용할 수 있을 것입니다. 함수를 사용하면 일관된 결과를 얻을 수 있을 뿐아니라, 해야 할 일의 양을 줄일수 있다는 이점이 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**상관계수 함수 생성**\n",
    "\n",
    "계속해서 진행하기 전, 목표값(target)과 변수간 상관계수를 계산하기 위한 코드도 작성하도록 하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터프레임상의 목표값(target)과의 상관계수를 계산하기 위한 함수\n",
    "# Function to calculate correlations with the target for a dataframe\n",
    "\n",
    "def target_corrs(df):\n",
    "    \n",
    "    # 상관관계를 저장하기 위한 리스트 생성\n",
    "    # List of correlations\n",
    "    corrs = []\n",
    "\n",
    "    # column별로 반복문을 실행 \n",
    "    for col in df.columns:\n",
    "        print(col)\n",
    "        \n",
    "        # target column은 생략\n",
    "        # Skip the target column\n",
    "        if col != 'TARGET':\n",
    "            \n",
    "            # 목표값(target)과의 상관계수를 계산\n",
    "            # Calculate correlation with the target\n",
    "            corr = df['TARGET'].corr(df[col])\n",
    "            \n",
    "            # 튜플(tuple)로 리스트에 추가\n",
    "            # Append the list as a tuple\n",
    "            corrs.append((col, corr))\n",
    "            \n",
    "    # 상관계수들을 절대값 크기에 따라 정렬\n",
    "    # Sort by absolute magnitude of correlations\n",
    "    corrs = sorted(corrs, key = lambda x: abs(x[1]), reverse = True)\n",
    "    \n",
    "    return corrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 범주형 데이터 (Categorical Values)\n",
    "\n",
    "지금부터는 범주형(categorical) column들을 다뤄보도록 하겠습니다. 주로 문자열 데이터들로써, 이러한 데이터들에 대해서는 평균이나 최대치 등 통계를 활용하기가 어렵습니다. 그 대신, 각 범주별로 값들의 갯수를 셀 것입니다. 예를 들어, 우리가 아래와 같은 데이터프레임을 가지고 있다면\n",
    "\n",
    "| SK_ID_CURR | Loan type |\n",
    "|------------|-----------|\n",
    "| 1          | home      |\n",
    "| 1          | home      |\n",
    "| 1          | home      |\n",
    "| 1          | credit    |\n",
    "| 2          | credit    |\n",
    "| 3          | credit    |\n",
    "| 3          | cash      |\n",
    "| 3          | cash      |\n",
    "| 4          | credit    |\n",
    "| 4          | home      |\n",
    "| 4          | home      |\n",
    "\n",
    "\n",
    "각 고객의 범주별 대출 갯수를 활용하여, 이런 정보를 만들어낼 것입니다. \n",
    "\n",
    "| SK_ID_CURR | credit count | cash count | home count | total count |\n",
    "|------------|--------------|------------|------------|-------------|\n",
    "| 1          | 1            | 0          | 3          | 4           |\n",
    "| 2          | 1            | 0          | 0          | 1           |\n",
    "| 3          | 1            | 2          | 0          | 3           |\n",
    "| 4          | 1            | 0          | 2          | 3           |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그 다음, 고객별 대출 횟수를 활용하여 값들을 정규화(normalize)할 것입니다. (고객별 대출 횟수의 합계를 1로하여 계산)\n",
    "(이 부분은 아래 표를 보고 해석하였습니다.)\n",
    "\n",
    "원문 : Then we can normalize these value counts by the total number of occurences of that categorical variable for that observation (meaning that the normalized counts must sum to 1.0 for each observation).\n",
    "\n",
    "| SK_ID_CURR | credit count | cash count | home count | total count | credit count norm | cash count norm | home count norm |\n",
    "|------------|--------------|------------|------------|-------------|-------------------|-----------------|-----------------|\n",
    "| 1          | 1            | 0          | 3          | 4           | 0.25              | 0               | 0.75            |\n",
    "| 2          | 1            | 0          | 0          | 1           | 1.00              | 0               | 0               |\n",
    "| 3          | 1            | 2          | 0          | 3           | 0.33              | 0.66            | 0               |\n",
    "| 4          | 1            | 0          | 2          | 3           | 0.33              | 0               | 0.66            |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다행히도, 이러한 방식으로 범주형 변수들을 인코딩(encoding)함으로써 데이터들이 담고 있는 정보를 확인할 수 있습니다 (이 과정에 대해 더 좋은 아이디어가 있다면 댓글을 남겨주시기 바랍니다.) 이제, 과정을 단계별로 밟아나가도록 하겠습니다. 마지막에는 데이터프레임에 재활용할 수 있도록 모든 코드들을 함수로 정리할 것입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저, 데이터프레임의 범주형 column들에 한하여 one-hot 인코딩을 적용하도록 하겠습니다. (`dtype`==`object`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CREDIT_ACTIVE_Active</th>\n",
       "      <th>CREDIT_ACTIVE_Bad debt</th>\n",
       "      <th>CREDIT_ACTIVE_Closed</th>\n",
       "      <th>CREDIT_ACTIVE_Sold</th>\n",
       "      <th>CREDIT_CURRENCY_currency 1</th>\n",
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       "      <th>CREDIT_CURRENCY_currency 3</th>\n",
       "      <th>CREDIT_CURRENCY_currency 4</th>\n",
       "      <th>CREDIT_TYPE_Another type of loan</th>\n",
       "      <th>CREDIT_TYPE_Car loan</th>\n",
       "      <th>...</th>\n",
       "      <th>CREDIT_TYPE_Loan for business development</th>\n",
       "      <th>CREDIT_TYPE_Loan for purchase of shares (margin lending)</th>\n",
       "      <th>CREDIT_TYPE_Loan for the purchase of equipment</th>\n",
       "      <th>CREDIT_TYPE_Loan for working capital replenishment</th>\n",
       "      <th>CREDIT_TYPE_Microloan</th>\n",
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       "      <th>CREDIT_TYPE_Mortgage</th>\n",
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       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CREDIT_ACTIVE_Active  CREDIT_ACTIVE_Bad debt  CREDIT_ACTIVE_Closed  \\\n",
       "0                     0                       0                     1   \n",
       "1                     1                       0                     0   \n",
       "2                     1                       0                     0   \n",
       "3                     1                       0                     0   \n",
       "4                     1                       0                     0   \n",
       "\n",
       "   CREDIT_ACTIVE_Sold  CREDIT_CURRENCY_currency 1  CREDIT_CURRENCY_currency 2  \\\n",
       "0                   0                           1                           0   \n",
       "1                   0                           1                           0   \n",
       "2                   0                           1                           0   \n",
       "3                   0                           1                           0   \n",
       "4                   0                           1                           0   \n",
       "\n",
       "   CREDIT_CURRENCY_currency 3  CREDIT_CURRENCY_currency 4  \\\n",
       "0                           0                           0   \n",
       "1                           0                           0   \n",
       "2                           0                           0   \n",
       "3                           0                           0   \n",
       "4                           0                           0   \n",
       "\n",
       "   CREDIT_TYPE_Another type of loan  CREDIT_TYPE_Car loan  ...  \\\n",
       "0                                 0                     0  ...   \n",
       "1                                 0                     0  ...   \n",
       "2                                 0                     0  ...   \n",
       "3                                 0                     0  ...   \n",
       "4                                 0                     0  ...   \n",
       "\n",
       "   CREDIT_TYPE_Loan for business development  \\\n",
       "0                                          0   \n",
       "1                                          0   \n",
       "2                                          0   \n",
       "3                                          0   \n",
       "4                                          0   \n",
       "\n",
       "   CREDIT_TYPE_Loan for purchase of shares (margin lending)  \\\n",
       "0                                                  0          \n",
       "1                                                  0          \n",
       "2                                                  0          \n",
       "3                                                  0          \n",
       "4                                                  0          \n",
       "\n",
       "   CREDIT_TYPE_Loan for the purchase of equipment  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "\n",
       "   CREDIT_TYPE_Loan for working capital replenishment  CREDIT_TYPE_Microloan  \\\n",
       "0                                                  0                       0   \n",
       "1                                                  0                       0   \n",
       "2                                                  0                       0   \n",
       "3                                                  0                       0   \n",
       "4                                                  0                       0   \n",
       "\n",
       "   CREDIT_TYPE_Mobile operator loan  CREDIT_TYPE_Mortgage  \\\n",
       "0                                 0                     0   \n",
       "1                                 0                     0   \n",
       "2                                 0                     0   \n",
       "3                                 0                     0   \n",
       "4                                 0                     0   \n",
       "\n",
       "   CREDIT_TYPE_Real estate loan  CREDIT_TYPE_Unknown type of loan  SK_ID_CURR  \n",
       "0                             0                                 0      215354  \n",
       "1                             0                                 0      215354  \n",
       "2                             0                                 0      215354  \n",
       "3                             0                                 0      215354  \n",
       "4                             0                                 0      215354  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical = pd.get_dummies(bureau.select_dtypes('object'))\n",
    "categorical['SK_ID_CURR'] = bureau['SK_ID_CURR']\n",
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>2</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           CREDIT_ACTIVE_Active           CREDIT_ACTIVE_Bad debt       \\\n",
       "                            sum      mean                    sum mean   \n",
       "SK_ID_CURR                                                              \n",
       "100001                        3  0.428571                      0  0.0   \n",
       "100002                        2  0.250000                      0  0.0   \n",
       "100003                        1  0.250000                      0  0.0   \n",
       "100004                        0  0.000000                      0  0.0   \n",
       "100005                        2  0.666667                      0  0.0   \n",
       "\n",
       "           CREDIT_ACTIVE_Closed           CREDIT_ACTIVE_Sold       \\\n",
       "                            sum      mean                sum mean   \n",
       "SK_ID_CURR                                                          \n",
       "100001                        4  0.571429                  0  0.0   \n",
       "100002                        6  0.750000                  0  0.0   \n",
       "100003                        3  0.750000                  0  0.0   \n",
       "100004                        2  1.000000                  0  0.0   \n",
       "100005                        1  0.333333                  0  0.0   \n",
       "\n",
       "           CREDIT_CURRENCY_currency 1       ... CREDIT_TYPE_Microloan       \\\n",
       "                                  sum mean  ...                   sum mean   \n",
       "SK_ID_CURR                                  ...                              \n",
       "100001                              7  1.0  ...                     0  0.0   \n",
       "100002                              8  1.0  ...                     0  0.0   \n",
       "100003                              4  1.0  ...                     0  0.0   \n",
       "100004                              2  1.0  ...                     0  0.0   \n",
       "100005                              3  1.0  ...                     0  0.0   \n",
       "\n",
       "           CREDIT_TYPE_Mobile operator loan      CREDIT_TYPE_Mortgage       \\\n",
       "                                        sum mean                  sum mean   \n",
       "SK_ID_CURR                                                                   \n",
       "100001                                    0  0.0                    0  0.0   \n",
       "100002                                    0  0.0                    0  0.0   \n",
       "100003                                    0  0.0                    0  0.0   \n",
       "100004                                    0  0.0                    0  0.0   \n",
       "100005                                    0  0.0                    0  0.0   \n",
       "\n",
       "           CREDIT_TYPE_Real estate loan      CREDIT_TYPE_Unknown type of loan  \\\n",
       "                                    sum mean                              sum   \n",
       "SK_ID_CURR                                                                      \n",
       "100001                                0  0.0                                0   \n",
       "100002                                0  0.0                                0   \n",
       "100003                                0  0.0                                0   \n",
       "100004                                0  0.0                                0   \n",
       "100005                                0  0.0                                0   \n",
       "\n",
       "                 \n",
       "           mean  \n",
       "SK_ID_CURR       \n",
       "100001      0.0  \n",
       "100002      0.0  \n",
       "100003      0.0  \n",
       "100004      0.0  \n",
       "100005      0.0  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_grouped = categorical.groupby('SK_ID_CURR').agg(['sum', 'mean'])\n",
    "categorical_grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sum`는 고객별 해당 범주에 속한 대출의 총 횟수를 말합니다. 그리고 `mean`은 횟수를 정규화시킨 것입니다. one-hot 인코딩을 통해 이러한 수치들을 계산하는 과정을 매우 편리하게 만들어줍니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여기서, 지난번 column 제목들을 재정의할 때 사용하였던 것과 비슷한 함수를 활용하겠습니다. 다시 말하면, multi-level index로 작성되어있는 column들을 다룰 것입니다. 범주형 데이터가 속한 column의 이름을 차용한 First level(level 0)을 따라 반복문을 먼저 실행하고, 그 다음 계산된 통계치들을 따라 반복문을 한번 더 실행할 것입니다. level 0의 이름에 통계치의 종류를 합쳐 column 제목들을 재정의할 것입니다. 예를 들면, `CREDIT_ACTIVE_Active`가 level 0이고, `sum`이 level 1인 column은 `CREDIT_ACTIVE_Active_count`로 정의됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CREDIT_ACTIVE_Active', 'CREDIT_ACTIVE_Bad debt',\n",
       "       'CREDIT_ACTIVE_Closed', 'CREDIT_ACTIVE_Sold',\n",
       "       'CREDIT_CURRENCY_currency 1', 'CREDIT_CURRENCY_currency 2',\n",
       "       'CREDIT_CURRENCY_currency 3', 'CREDIT_CURRENCY_currency 4',\n",
       "       'CREDIT_TYPE_Another type of loan', 'CREDIT_TYPE_Car loan'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_grouped.columns.levels[0][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sum', 'mean'], dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_grouped.columns.levels[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>CREDIT_ACTIVE_Active_count</th>\n",
       "      <th>CREDIT_ACTIVE_Active_count_norm</th>\n",
       "      <th>CREDIT_ACTIVE_Bad debt_count</th>\n",
       "      <th>CREDIT_ACTIVE_Bad debt_count_norm</th>\n",
       "      <th>CREDIT_ACTIVE_Closed_count</th>\n",
       "      <th>CREDIT_ACTIVE_Closed_count_norm</th>\n",
       "      <th>CREDIT_ACTIVE_Sold_count</th>\n",
       "      <th>CREDIT_ACTIVE_Sold_count_norm</th>\n",
       "      <th>CREDIT_CURRENCY_currency 1_count</th>\n",
       "      <th>CREDIT_CURRENCY_currency 1_count_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>CREDIT_TYPE_Microloan_count</th>\n",
       "      <th>CREDIT_TYPE_Microloan_count_norm</th>\n",
       "      <th>CREDIT_TYPE_Mobile operator loan_count</th>\n",
       "      <th>CREDIT_TYPE_Mobile operator loan_count_norm</th>\n",
       "      <th>CREDIT_TYPE_Mortgage_count</th>\n",
       "      <th>CREDIT_TYPE_Mortgage_count_norm</th>\n",
       "      <th>CREDIT_TYPE_Real estate loan_count</th>\n",
       "      <th>CREDIT_TYPE_Real estate loan_count_norm</th>\n",
       "      <th>CREDIT_TYPE_Unknown type of loan_count</th>\n",
       "      <th>CREDIT_TYPE_Unknown type of loan_count_norm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100001</th>\n",
       "      <td>3</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>2</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>1</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>2</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            CREDIT_ACTIVE_Active_count  CREDIT_ACTIVE_Active_count_norm  \\\n",
       "SK_ID_CURR                                                                \n",
       "100001                               3                         0.428571   \n",
       "100002                               2                         0.250000   \n",
       "100003                               1                         0.250000   \n",
       "100004                               0                         0.000000   \n",
       "100005                               2                         0.666667   \n",
       "\n",
       "            CREDIT_ACTIVE_Bad debt_count  CREDIT_ACTIVE_Bad debt_count_norm  \\\n",
       "SK_ID_CURR                                                                    \n",
       "100001                                 0                                0.0   \n",
       "100002                                 0                                0.0   \n",
       "100003                                 0                                0.0   \n",
       "100004                                 0                                0.0   \n",
       "100005                                 0                                0.0   \n",
       "\n",
       "            CREDIT_ACTIVE_Closed_count  CREDIT_ACTIVE_Closed_count_norm  \\\n",
       "SK_ID_CURR                                                                \n",
       "100001                               4                         0.571429   \n",
       "100002                               6                         0.750000   \n",
       "100003                               3                         0.750000   \n",
       "100004                               2                         1.000000   \n",
       "100005                               1                         0.333333   \n",
       "\n",
       "            CREDIT_ACTIVE_Sold_count  CREDIT_ACTIVE_Sold_count_norm  \\\n",
       "SK_ID_CURR                                                            \n",
       "100001                             0                            0.0   \n",
       "100002                             0                            0.0   \n",
       "100003                             0                            0.0   \n",
       "100004                             0                            0.0   \n",
       "100005                             0                            0.0   \n",
       "\n",
       "            CREDIT_CURRENCY_currency 1_count  \\\n",
       "SK_ID_CURR                                     \n",
       "100001                                     7   \n",
       "100002                                     8   \n",
       "100003                                     4   \n",
       "100004                                     2   \n",
       "100005                                     3   \n",
       "\n",
       "            CREDIT_CURRENCY_currency 1_count_norm  ...  \\\n",
       "SK_ID_CURR                                         ...   \n",
       "100001                                        1.0  ...   \n",
       "100002                                        1.0  ...   \n",
       "100003                                        1.0  ...   \n",
       "100004                                        1.0  ...   \n",
       "100005                                        1.0  ...   \n",
       "\n",
       "            CREDIT_TYPE_Microloan_count  CREDIT_TYPE_Microloan_count_norm  \\\n",
       "SK_ID_CURR                                                                  \n",
       "100001                                0                               0.0   \n",
       "100002                                0                               0.0   \n",
       "100003                                0                               0.0   \n",
       "100004                                0                               0.0   \n",
       "100005                                0                               0.0   \n",
       "\n",
       "            CREDIT_TYPE_Mobile operator loan_count  \\\n",
       "SK_ID_CURR                                           \n",
       "100001                                           0   \n",
       "100002                                           0   \n",
       "100003                                           0   \n",
       "100004                                           0   \n",
       "100005                                           0   \n",
       "\n",
       "            CREDIT_TYPE_Mobile operator loan_count_norm  \\\n",
       "SK_ID_CURR                                                \n",
       "100001                                              0.0   \n",
       "100002                                              0.0   \n",
       "100003                                              0.0   \n",
       "100004                                              0.0   \n",
       "100005                                              0.0   \n",
       "\n",
       "            CREDIT_TYPE_Mortgage_count  CREDIT_TYPE_Mortgage_count_norm  \\\n",
       "SK_ID_CURR                                                                \n",
       "100001                               0                              0.0   \n",
       "100002                               0                              0.0   \n",
       "100003                               0                              0.0   \n",
       "100004                               0                              0.0   \n",
       "100005                               0                              0.0   \n",
       "\n",
       "            CREDIT_TYPE_Real estate loan_count  \\\n",
       "SK_ID_CURR                                       \n",
       "100001                                       0   \n",
       "100002                                       0   \n",
       "100003                                       0   \n",
       "100004                                       0   \n",
       "100005                                       0   \n",
       "\n",
       "            CREDIT_TYPE_Real estate loan_count_norm  \\\n",
       "SK_ID_CURR                                            \n",
       "100001                                          0.0   \n",
       "100002                                          0.0   \n",
       "100003                                          0.0   \n",
       "100004                                          0.0   \n",
       "100005                                          0.0   \n",
       "\n",
       "            CREDIT_TYPE_Unknown type of loan_count  \\\n",
       "SK_ID_CURR                                           \n",
       "100001                                           0   \n",
       "100002                                           0   \n",
       "100003                                           0   \n",
       "100004                                           0   \n",
       "100005                                           0   \n",
       "\n",
       "            CREDIT_TYPE_Unknown type of loan_count_norm  \n",
       "SK_ID_CURR                                               \n",
       "100001                                              0.0  \n",
       "100002                                              0.0  \n",
       "100003                                              0.0  \n",
       "100004                                              0.0  \n",
       "100005                                              0.0  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_var = 'SK_ID_CURR'\n",
    "\n",
    "# 새로운 column 제목들을 저장하기 위한 리스트를 생성\n",
    "# Need to create new column names\n",
    "columns = []\n",
    "\n",
    "# 변수(원본 데이터프레임의 column name)에 따라 반복문을 실행\n",
    "# Iterate through the variables names\n",
    "for var in categorical_grouped.columns.levels[0]:\n",
    "    \n",
    "    # 고객 id column은 생략\n",
    "    # Skip the grouping variable\n",
    "    if var != group_var:\n",
    "        \n",
    "        # 통계치의 종류에 따라 반복문을 실행\n",
    "        # Iterate through the stat names\n",
    "        for stat in ['count','count_norm']:\n",
    "            \n",
    "            # 새로운 column 제목을 정의\n",
    "            # Make a new column name for the variable and stat\n",
    "             columns.append('%s_%s' % (var, stat))\n",
    "                \n",
    "# column들을 재정의\n",
    "categorical_grouped.columns = columns\n",
    "\n",
    "categorical_grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sum` column은 총 횟수를 나타내며, `count_norm` column은 횟수를 정규화시킨 것입니다. 이제 이 데이터프레임을 훈련 데이터셋과 합치겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>CREDIT_TYPE_Microloan_count</th>\n",
       "      <th>CREDIT_TYPE_Microloan_count_norm</th>\n",
       "      <th>CREDIT_TYPE_Mobile operator loan_count</th>\n",
       "      <th>CREDIT_TYPE_Mobile operator loan_count_norm</th>\n",
       "      <th>CREDIT_TYPE_Mortgage_count</th>\n",
       "      <th>CREDIT_TYPE_Mortgage_count_norm</th>\n",
       "      <th>CREDIT_TYPE_Real estate loan_count</th>\n",
       "      <th>CREDIT_TYPE_Real estate loan_count_norm</th>\n",
       "      <th>CREDIT_TYPE_Unknown type of loan_count</th>\n",
       "      <th>CREDIT_TYPE_Unknown type of loan_count_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</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>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 229 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "   ...  CREDIT_TYPE_Microloan_count CREDIT_TYPE_Microloan_count_norm  \\\n",
       "0  ...                          0.0                              0.0   \n",
       "1  ...                          0.0                              0.0   \n",
       "2  ...                          0.0                              0.0   \n",
       "3  ...                          NaN                              NaN   \n",
       "4  ...                          0.0                              0.0   \n",
       "\n",
       "  CREDIT_TYPE_Mobile operator loan_count  \\\n",
       "0                                    0.0   \n",
       "1                                    0.0   \n",
       "2                                    0.0   \n",
       "3                                    NaN   \n",
       "4                                    0.0   \n",
       "\n",
       "  CREDIT_TYPE_Mobile operator loan_count_norm CREDIT_TYPE_Mortgage_count  \\\n",
       "0                                         0.0                        0.0   \n",
       "1                                         0.0                        0.0   \n",
       "2                                         0.0                        0.0   \n",
       "3                                         NaN                        NaN   \n",
       "4                                         0.0                        0.0   \n",
       "\n",
       "  CREDIT_TYPE_Mortgage_count_norm  CREDIT_TYPE_Real estate loan_count  \\\n",
       "0                             0.0                                 0.0   \n",
       "1                             0.0                                 0.0   \n",
       "2                             0.0                                 0.0   \n",
       "3                             NaN                                 NaN   \n",
       "4                             0.0                                 0.0   \n",
       "\n",
       "   CREDIT_TYPE_Real estate loan_count_norm  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "3                                      NaN   \n",
       "4                                      0.0   \n",
       "\n",
       "   CREDIT_TYPE_Unknown type of loan_count  \\\n",
       "0                                     0.0   \n",
       "1                                     0.0   \n",
       "2                                     0.0   \n",
       "3                                     NaN   \n",
       "4                                     0.0   \n",
       "\n",
       "   CREDIT_TYPE_Unknown type of loan_count_norm  \n",
       "0                                          0.0  \n",
       "1                                          0.0  \n",
       "2                                          0.0  \n",
       "3                                          NaN  \n",
       "4                                          0.0  \n",
       "\n",
       "[5 rows x 229 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train.merge(categorical_grouped, left_on = 'SK_ID_CURR', right_index = True, how = 'left')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307511, 229)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bureau_DAYS_CREDIT_count</th>\n",
       "      <th>bureau_DAYS_CREDIT_mean</th>\n",
       "      <th>bureau_DAYS_CREDIT_max</th>\n",
       "      <th>bureau_DAYS_CREDIT_min</th>\n",
       "      <th>bureau_DAYS_CREDIT_sum</th>\n",
       "      <th>bureau_CREDIT_DAY_OVERDUE_count</th>\n",
       "      <th>bureau_CREDIT_DAY_OVERDUE_mean</th>\n",
       "      <th>bureau_CREDIT_DAY_OVERDUE_max</th>\n",
       "      <th>bureau_CREDIT_DAY_OVERDUE_min</th>\n",
       "      <th>bureau_CREDIT_DAY_OVERDUE_sum</th>\n",
       "      <th>...</th>\n",
       "      <th>CREDIT_TYPE_Microloan_count</th>\n",
       "      <th>CREDIT_TYPE_Microloan_count_norm</th>\n",
       "      <th>CREDIT_TYPE_Mobile operator loan_count</th>\n",
       "      <th>CREDIT_TYPE_Mobile operator loan_count_norm</th>\n",
       "      <th>CREDIT_TYPE_Mortgage_count</th>\n",
       "      <th>CREDIT_TYPE_Mortgage_count_norm</th>\n",
       "      <th>CREDIT_TYPE_Real estate loan_count</th>\n",
       "      <th>CREDIT_TYPE_Real estate loan_count_norm</th>\n",
       "      <th>CREDIT_TYPE_Unknown type of loan_count</th>\n",
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       "      <td>-103.0</td>\n",
       "      <td>-1437.0</td>\n",
       "      <td>-6992.0</td>\n",
       "      <td>8.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>-5603.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-867.000000</td>\n",
       "      <td>-408.0</td>\n",
       "      <td>-1326.0</td>\n",
       "      <td>-1734.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
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       "      <th>4</th>\n",
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       "      <td>-1149.0</td>\n",
       "      <td>-1149.0</td>\n",
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       "      <th>5</th>\n",
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       "      <td>-78.0</td>\n",
       "      <td>-1097.0</td>\n",
       "      <td>-2272.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>-239.0</td>\n",
       "      <td>-2882.0</td>\n",
       "      <td>-22887.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>-2741.0</td>\n",
       "      <td>-3879.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\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",
       "      <td>...</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  bureau_DAYS_CREDIT_max  \\\n",
       "0                       8.0              -874.000000                  -103.0   \n",
       "1                       4.0             -1400.750000                  -606.0   \n",
       "2                       2.0              -867.000000                  -408.0   \n",
       "3                       NaN                      NaN                     NaN   \n",
       "4                       1.0             -1149.000000                 -1149.0   \n",
       "5                       3.0              -757.333333                   -78.0   \n",
       "6                      18.0             -1271.500000                  -239.0   \n",
       "7                       2.0             -1939.500000                 -1138.0   \n",
       "8                       4.0             -1773.000000                 -1309.0   \n",
       "9                       NaN                      NaN                     NaN   \n",
       "\n",
       "   bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\n",
       "0                 -1437.0                 -6992.0   \n",
       "1                 -2586.0                 -5603.0   \n",
       "2                 -1326.0                 -1734.0   \n",
       "3                     NaN                     NaN   \n",
       "4                 -1149.0                 -1149.0   \n",
       "5                 -1097.0                 -2272.0   \n",
       "6                 -2882.0                -22887.0   \n",
       "7                 -2741.0                 -3879.0   \n",
       "8                 -2508.0                 -7092.0   \n",
       "9                     NaN                     NaN   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\n",
       "0                              8.0                             0.0   \n",
       "1                              4.0                             0.0   \n",
       "2                              2.0                             0.0   \n",
       "3                              NaN                             NaN   \n",
       "4                              1.0                             0.0   \n",
       "5                              3.0                             0.0   \n",
       "6                             18.0                             0.0   \n",
       "7                              2.0                             0.0   \n",
       "8                              4.0                             0.0   \n",
       "9                              NaN                             NaN   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  \\\n",
       "0                            0.0                            0.0   \n",
       "1                            0.0                            0.0   \n",
       "2                            0.0                            0.0   \n",
       "3                            NaN                            NaN   \n",
       "4                            0.0                            0.0   \n",
       "5                            0.0                            0.0   \n",
       "6                            0.0                            0.0   \n",
       "7                            0.0                            0.0   \n",
       "8                            0.0                            0.0   \n",
       "9                            NaN                            NaN   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_sum  ...  CREDIT_TYPE_Microloan_count  \\\n",
       "0                            0.0  ...                          0.0   \n",
       "1                            0.0  ...                          0.0   \n",
       "2                            0.0  ...                          0.0   \n",
       "3                            NaN  ...                          NaN   \n",
       "4                            0.0  ...                          0.0   \n",
       "5                            0.0  ...                          0.0   \n",
       "6                            0.0  ...                          0.0   \n",
       "7                            0.0  ...                          0.0   \n",
       "8                            0.0  ...                          0.0   \n",
       "9                            NaN  ...                          NaN   \n",
       "\n",
       "   CREDIT_TYPE_Microloan_count_norm  CREDIT_TYPE_Mobile operator loan_count  \\\n",
       "0                               0.0                                     0.0   \n",
       "1                               0.0                                     0.0   \n",
       "2                               0.0                                     0.0   \n",
       "3                               NaN                                     NaN   \n",
       "4                               0.0                                     0.0   \n",
       "5                               0.0                                     0.0   \n",
       "6                               0.0                                     0.0   \n",
       "7                               0.0                                     0.0   \n",
       "8                               0.0                                     0.0   \n",
       "9                               NaN                                     NaN   \n",
       "\n",
       "   CREDIT_TYPE_Mobile operator loan_count_norm  CREDIT_TYPE_Mortgage_count  \\\n",
       "0                                          0.0                         0.0   \n",
       "1                                          0.0                         0.0   \n",
       "2                                          0.0                         0.0   \n",
       "3                                          NaN                         NaN   \n",
       "4                                          0.0                         0.0   \n",
       "5                                          0.0                         0.0   \n",
       "6                                          0.0                         0.0   \n",
       "7                                          0.0                         0.0   \n",
       "8                                          0.0                         0.0   \n",
       "9                                          NaN                         NaN   \n",
       "\n",
       "   CREDIT_TYPE_Mortgage_count_norm  CREDIT_TYPE_Real estate loan_count  \\\n",
       "0                              0.0                                 0.0   \n",
       "1                              0.0                                 0.0   \n",
       "2                              0.0                                 0.0   \n",
       "3                              NaN                                 NaN   \n",
       "4                              0.0                                 0.0   \n",
       "5                              0.0                                 0.0   \n",
       "6                              0.0                                 0.0   \n",
       "7                              0.0                                 0.0   \n",
       "8                              0.0                                 0.0   \n",
       "9                              NaN                                 NaN   \n",
       "\n",
       "   CREDIT_TYPE_Real estate loan_count_norm  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "3                                      NaN   \n",
       "4                                      0.0   \n",
       "5                                      0.0   \n",
       "6                                      0.0   \n",
       "7                                      0.0   \n",
       "8                                      0.0   \n",
       "9                                      NaN   \n",
       "\n",
       "   CREDIT_TYPE_Unknown type of loan_count  \\\n",
       "0                                     0.0   \n",
       "1                                     0.0   \n",
       "2                                     0.0   \n",
       "3                                     NaN   \n",
       "4                                     0.0   \n",
       "5                                     0.0   \n",
       "6                                     0.0   \n",
       "7                                     0.0   \n",
       "8                                     0.0   \n",
       "9                                     NaN   \n",
       "\n",
       "   CREDIT_TYPE_Unknown type of loan_count_norm  \n",
       "0                                          0.0  \n",
       "1                                          0.0  \n",
       "2                                          0.0  \n",
       "3                                          NaN  \n",
       "4                                          0.0  \n",
       "5                                          0.0  \n",
       "6                                          0.0  \n",
       "7                                          0.0  \n",
       "8                                          0.0  \n",
       "9                                          NaN  \n",
       "\n",
       "[10 rows x 106 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.iloc[:10, 123:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 범주형 데이터들을 처리하기 위한 함수 생성 (Function to Handle Categorical Variables)\n",
    "\n",
    "코드를 좀 더 효율적으로 만들기 위해, 범주형 데이터들을 처리하기 위한 함수를 만들 것입니다. `agg_numeric` 함수를 만들 때 했던 것과 같은 방식으로 데이터 프레임을 그룹화할 것입니다. 그 다음 각각의 범주에 따라 `counts`와 `normalized counts`를 계산할 것입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_categorical(df, group_var, df_name):\n",
    "    \"\"\"\n",
    "    group_var에 대해 각 고유 범주들의 counts 및 normalized counts를 계산  \n",
    "    \n",
    "    Computes counts and normalized counts for each observation of `group_var` of each unique category\n",
    "    in every categorical variable\n",
    "    \n",
    "    \n",
    "    파라미터 (Parameters)\n",
    "    ---------------------\n",
    "    \n",
    "    df (dataframe): 연산의 대상이되는 데이터프레임 (the dataframe to calculate the statistics on)\n",
    "    group_var (string): 그룹화(groupby)의 기준이되는 column (the variable by which to group df)\n",
    "    df_name (string): column명을 재정의하는데 쓰이는 변수 (the variable used to rename the columns)\n",
    "    \n",
    "    \n",
    "    출력값(Returns)\n",
    "    -------------------\n",
    "    categorical : 데이터프레임\n",
    "     group_var에 대해 각 고유 범주들의 counts 및 normalized counts의 값이 포함된 데이터프레임  \n",
    "    \n",
    "    categorical : dataframe\n",
    "    A dataframe with counts and normalized counts of each unique category in every categorical variable\n",
    "    with one row for every unique value of the `group_var\n",
    "    \n",
    "    \"\"\"\n",
    "    # 범주형 column들을 선택\n",
    "    # Select the categorical columns\n",
    "    categorical = pd.get_dummies(df.select_dtypes('object'))\n",
    "    \n",
    "    # 확실히 id가 column에 있도록 지정 \n",
    "    # Make sure to put the identifying id on the column\n",
    "    categorical[group_var] = df[group_var]\n",
    "    \n",
    "    # group_var를 기준으로 그룹화하고 sum과 mean을 계산\n",
    "    # Groupby the group var and calculate the sum and mean\n",
    "    categorical = categorical.groupby(group_var).agg(['sum','mean'])\n",
    "    \n",
    "    column_names = []\n",
    "    \n",
    "    # level 0의 column들에 따라 반복문을 실행\n",
    "    # Iterate through the columns in level 0\n",
    "    for var in categorical.columns.levels[0]:\n",
    "        \n",
    "        # level 1의 통계값들에 대해 반복문을 실행\n",
    "        # Iterate through the stats in level 1\n",
    "        for stat in ['count','count_norm']:\n",
    "            column_names.append('%s_%s_%s' % (df_name, var, stat)) \n",
    "    \n",
    "    \n",
    "    categorical.columns = column_names\n",
    "    \n",
    "    return categorical\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>bureau_CREDIT_ACTIVE_Active_count</th>\n",
       "      <th>bureau_CREDIT_ACTIVE_Active_count_norm</th>\n",
       "      <th>bureau_CREDIT_ACTIVE_Bad debt_count</th>\n",
       "      <th>bureau_CREDIT_ACTIVE_Bad debt_count_norm</th>\n",
       "      <th>bureau_CREDIT_ACTIVE_Closed_count</th>\n",
       "      <th>bureau_CREDIT_ACTIVE_Closed_count_norm</th>\n",
       "      <th>bureau_CREDIT_ACTIVE_Sold_count</th>\n",
       "      <th>bureau_CREDIT_ACTIVE_Sold_count_norm</th>\n",
       "      <th>bureau_CREDIT_CURRENCY_currency 1_count</th>\n",
       "      <th>bureau_CREDIT_CURRENCY_currency 1_count_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>bureau_CREDIT_TYPE_Microloan_count</th>\n",
       "      <th>bureau_CREDIT_TYPE_Microloan_count_norm</th>\n",
       "      <th>bureau_CREDIT_TYPE_Mobile operator loan_count</th>\n",
       "      <th>bureau_CREDIT_TYPE_Mobile operator loan_count_norm</th>\n",
       "      <th>bureau_CREDIT_TYPE_Mortgage_count</th>\n",
       "      <th>bureau_CREDIT_TYPE_Mortgage_count_norm</th>\n",
       "      <th>bureau_CREDIT_TYPE_Real estate loan_count</th>\n",
       "      <th>bureau_CREDIT_TYPE_Real estate loan_count_norm</th>\n",
       "      <th>bureau_CREDIT_TYPE_Unknown type of loan_count</th>\n",
       "      <th>bureau_CREDIT_TYPE_Unknown type of loan_count_norm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100001</th>\n",
       "      <td>3</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>2</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>1</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>2</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            bureau_CREDIT_ACTIVE_Active_count  \\\n",
       "SK_ID_CURR                                      \n",
       "100001                                      3   \n",
       "100002                                      2   \n",
       "100003                                      1   \n",
       "100004                                      0   \n",
       "100005                                      2   \n",
       "\n",
       "            bureau_CREDIT_ACTIVE_Active_count_norm  \\\n",
       "SK_ID_CURR                                           \n",
       "100001                                    0.428571   \n",
       "100002                                    0.250000   \n",
       "100003                                    0.250000   \n",
       "100004                                    0.000000   \n",
       "100005                                    0.666667   \n",
       "\n",
       "            bureau_CREDIT_ACTIVE_Bad debt_count  \\\n",
       "SK_ID_CURR                                        \n",
       "100001                                        0   \n",
       "100002                                        0   \n",
       "100003                                        0   \n",
       "100004                                        0   \n",
       "100005                                        0   \n",
       "\n",
       "            bureau_CREDIT_ACTIVE_Bad debt_count_norm  \\\n",
       "SK_ID_CURR                                             \n",
       "100001                                           0.0   \n",
       "100002                                           0.0   \n",
       "100003                                           0.0   \n",
       "100004                                           0.0   \n",
       "100005                                           0.0   \n",
       "\n",
       "            bureau_CREDIT_ACTIVE_Closed_count  \\\n",
       "SK_ID_CURR                                      \n",
       "100001                                      4   \n",
       "100002                                      6   \n",
       "100003                                      3   \n",
       "100004                                      2   \n",
       "100005                                      1   \n",
       "\n",
       "            bureau_CREDIT_ACTIVE_Closed_count_norm  \\\n",
       "SK_ID_CURR                                           \n",
       "100001                                    0.571429   \n",
       "100002                                    0.750000   \n",
       "100003                                    0.750000   \n",
       "100004                                    1.000000   \n",
       "100005                                    0.333333   \n",
       "\n",
       "            bureau_CREDIT_ACTIVE_Sold_count  \\\n",
       "SK_ID_CURR                                    \n",
       "100001                                    0   \n",
       "100002                                    0   \n",
       "100003                                    0   \n",
       "100004                                    0   \n",
       "100005                                    0   \n",
       "\n",
       "            bureau_CREDIT_ACTIVE_Sold_count_norm  \\\n",
       "SK_ID_CURR                                         \n",
       "100001                                       0.0   \n",
       "100002                                       0.0   \n",
       "100003                                       0.0   \n",
       "100004                                       0.0   \n",
       "100005                                       0.0   \n",
       "\n",
       "            bureau_CREDIT_CURRENCY_currency 1_count  \\\n",
       "SK_ID_CURR                                            \n",
       "100001                                            7   \n",
       "100002                                            8   \n",
       "100003                                            4   \n",
       "100004                                            2   \n",
       "100005                                            3   \n",
       "\n",
       "            bureau_CREDIT_CURRENCY_currency 1_count_norm  ...  \\\n",
       "SK_ID_CURR                                                ...   \n",
       "100001                                               1.0  ...   \n",
       "100002                                               1.0  ...   \n",
       "100003                                               1.0  ...   \n",
       "100004                                               1.0  ...   \n",
       "100005                                               1.0  ...   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Microloan_count  \\\n",
       "SK_ID_CURR                                       \n",
       "100001                                       0   \n",
       "100002                                       0   \n",
       "100003                                       0   \n",
       "100004                                       0   \n",
       "100005                                       0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Microloan_count_norm  \\\n",
       "SK_ID_CURR                                            \n",
       "100001                                          0.0   \n",
       "100002                                          0.0   \n",
       "100003                                          0.0   \n",
       "100004                                          0.0   \n",
       "100005                                          0.0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Mobile operator loan_count  \\\n",
       "SK_ID_CURR                                                  \n",
       "100001                                                  0   \n",
       "100002                                                  0   \n",
       "100003                                                  0   \n",
       "100004                                                  0   \n",
       "100005                                                  0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Mobile operator loan_count_norm  \\\n",
       "SK_ID_CURR                                                       \n",
       "100001                                                    0.0    \n",
       "100002                                                    0.0    \n",
       "100003                                                    0.0    \n",
       "100004                                                    0.0    \n",
       "100005                                                    0.0    \n",
       "\n",
       "            bureau_CREDIT_TYPE_Mortgage_count  \\\n",
       "SK_ID_CURR                                      \n",
       "100001                                      0   \n",
       "100002                                      0   \n",
       "100003                                      0   \n",
       "100004                                      0   \n",
       "100005                                      0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Mortgage_count_norm  \\\n",
       "SK_ID_CURR                                           \n",
       "100001                                         0.0   \n",
       "100002                                         0.0   \n",
       "100003                                         0.0   \n",
       "100004                                         0.0   \n",
       "100005                                         0.0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Real estate loan_count  \\\n",
       "SK_ID_CURR                                              \n",
       "100001                                              0   \n",
       "100002                                              0   \n",
       "100003                                              0   \n",
       "100004                                              0   \n",
       "100005                                              0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Real estate loan_count_norm  \\\n",
       "SK_ID_CURR                                                   \n",
       "100001                                                 0.0   \n",
       "100002                                                 0.0   \n",
       "100003                                                 0.0   \n",
       "100004                                                 0.0   \n",
       "100005                                                 0.0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Unknown type of loan_count  \\\n",
       "SK_ID_CURR                                                  \n",
       "100001                                                  0   \n",
       "100002                                                  0   \n",
       "100003                                                  0   \n",
       "100004                                                  0   \n",
       "100005                                                  0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Unknown type of loan_count_norm  \n",
       "SK_ID_CURR                                                      \n",
       "100001                                                    0.0   \n",
       "100002                                                    0.0   \n",
       "100003                                                    0.0   \n",
       "100004                                                    0.0   \n",
       "100005                                                    0.0   \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_counts = count_categorical(bureau, group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
    "bureau_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 다른 데이터프레임에 대해 적용 (Applying Operations to another dataframe)\n",
    "\n",
    "이제 'bureau balance' 데이터프레임을 활용해보도록 하겠습니다. 이 데이터프레임은 월별 각 고객의 과거 타 금융기관 대출 데이터를 포함하고 있습니다. 고객들의 ID인 `SK_ID_CURR`에 따라 그룹화하기보다, 이전 대출의 ID인 `SK_ID_BUREAU`를 활용하여 1차 그룹화를 진행할 것입니다. 그룹화한 데이터프레임은 각각의 대출에 대한 정보를 행별로 포함할 것입니다. 그 다음, `SK_ID_CURR`을 활용하여 그룹화한 뒤, 각 고객별 대출의 대표값(aggreation)들을 계산할 것입니다. 최종 산출물은 각각의 행에 고객별로 대출에 대한 대표값들을 포함하고 있을 것입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_BUREAU</th>\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "      <th>STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>-2</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5715448</td>\n",
       "      <td>-3</td>\n",
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       "      <th>4</th>\n",
       "      <td>5715448</td>\n",
       "      <td>-4</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_BUREAU  MONTHS_BALANCE STATUS\n",
       "0       5715448               0      C\n",
       "1       5715448              -1      C\n",
       "2       5715448              -2      C\n",
       "3       5715448              -3      C\n",
       "4       5715448              -4      C"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'bureau blance' 데이터 불러오기\n",
    "# Read in bureau balance\n",
    "bureau_balance = pd.read_csv('../input/bureau_balance.csv')\n",
    "bureau_balance.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저, 각각의 대출별 상태(status)의 갯수를 세도록 하겠습니다. 운 좋게도, 이미 함수를 만들어놓은 상태입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <th>bureau_balance_STATUS_0_count</th>\n",
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       "      <th>bureau_balance_STATUS_1_count</th>\n",
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      ],
      "text/plain": [
       "              bureau_balance_STATUS_0_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                   0   \n",
       "5001710                                   5   \n",
       "5001711                                   3   \n",
       "5001712                                  10   \n",
       "5001713                                   0   \n",
       "\n",
       "              bureau_balance_STATUS_0_count_norm  \\\n",
       "SK_ID_BUREAU                                       \n",
       "5001709                                 0.000000   \n",
       "5001710                                 0.060241   \n",
       "5001711                                 0.750000   \n",
       "5001712                                 0.526316   \n",
       "5001713                                 0.000000   \n",
       "\n",
       "              bureau_balance_STATUS_1_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                   0   \n",
       "5001710                                   0   \n",
       "5001711                                   0   \n",
       "5001712                                   0   \n",
       "5001713                                   0   \n",
       "\n",
       "              bureau_balance_STATUS_1_count_norm  \\\n",
       "SK_ID_BUREAU                                       \n",
       "5001709                                      0.0   \n",
       "5001710                                      0.0   \n",
       "5001711                                      0.0   \n",
       "5001712                                      0.0   \n",
       "5001713                                      0.0   \n",
       "\n",
       "              bureau_balance_STATUS_2_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                   0   \n",
       "5001710                                   0   \n",
       "5001711                                   0   \n",
       "5001712                                   0   \n",
       "5001713                                   0   \n",
       "\n",
       "              bureau_balance_STATUS_2_count_norm  \\\n",
       "SK_ID_BUREAU                                       \n",
       "5001709                                      0.0   \n",
       "5001710                                      0.0   \n",
       "5001711                                      0.0   \n",
       "5001712                                      0.0   \n",
       "5001713                                      0.0   \n",
       "\n",
       "              bureau_balance_STATUS_3_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                   0   \n",
       "5001710                                   0   \n",
       "5001711                                   0   \n",
       "5001712                                   0   \n",
       "5001713                                   0   \n",
       "\n",
       "              bureau_balance_STATUS_3_count_norm  \\\n",
       "SK_ID_BUREAU                                       \n",
       "5001709                                      0.0   \n",
       "5001710                                      0.0   \n",
       "5001711                                      0.0   \n",
       "5001712                                      0.0   \n",
       "5001713                                      0.0   \n",
       "\n",
       "              bureau_balance_STATUS_4_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                   0   \n",
       "5001710                                   0   \n",
       "5001711                                   0   \n",
       "5001712                                   0   \n",
       "5001713                                   0   \n",
       "\n",
       "              bureau_balance_STATUS_4_count_norm  \\\n",
       "SK_ID_BUREAU                                       \n",
       "5001709                                      0.0   \n",
       "5001710                                      0.0   \n",
       "5001711                                      0.0   \n",
       "5001712                                      0.0   \n",
       "5001713                                      0.0   \n",
       "\n",
       "              bureau_balance_STATUS_5_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                   0   \n",
       "5001710                                   0   \n",
       "5001711                                   0   \n",
       "5001712                                   0   \n",
       "5001713                                   0   \n",
       "\n",
       "              bureau_balance_STATUS_5_count_norm  \\\n",
       "SK_ID_BUREAU                                       \n",
       "5001709                                      0.0   \n",
       "5001710                                      0.0   \n",
       "5001711                                      0.0   \n",
       "5001712                                      0.0   \n",
       "5001713                                      0.0   \n",
       "\n",
       "              bureau_balance_STATUS_C_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                  86   \n",
       "5001710                                  48   \n",
       "5001711                                   0   \n",
       "5001712                                   9   \n",
       "5001713                                   0   \n",
       "\n",
       "              bureau_balance_STATUS_C_count_norm  \\\n",
       "SK_ID_BUREAU                                       \n",
       "5001709                                 0.886598   \n",
       "5001710                                 0.578313   \n",
       "5001711                                 0.000000   \n",
       "5001712                                 0.473684   \n",
       "5001713                                 0.000000   \n",
       "\n",
       "              bureau_balance_STATUS_X_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                  11   \n",
       "5001710                                  30   \n",
       "5001711                                   1   \n",
       "5001712                                   0   \n",
       "5001713                                  22   \n",
       "\n",
       "              bureau_balance_STATUS_X_count_norm  \n",
       "SK_ID_BUREAU                                      \n",
       "5001709                                 0.113402  \n",
       "5001710                                 0.361446  \n",
       "5001711                                 0.250000  \n",
       "5001712                                 0.000000  \n",
       "5001713                                 1.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 각각의 이전 대출에 대해 상태의 갯수를 파악\n",
    "# Counts of each type of status for each previous loan\n",
    "bureau_balance_counts = count_categorical(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
    "bureau_balance_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제, 수치형(numeric) column을 다루도록 하겠습니다. `MONTHS_BALACE` column은 신청일을 기준으로 한 남은 개월 수를 나타냅니다. 별로 중요하지 않은 변수일 수도 있겠지만, 추후 time variable로 활용할 수 있을 수도 있습니다. 지금은 이전에 했던 것과 같이 대표값들을 계산하도록 하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_BUREAU</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_count</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_mean</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_max</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_min</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5001709</td>\n",
       "      <td>97</td>\n",
       "      <td>-48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-96</td>\n",
       "      <td>-4656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5001710</td>\n",
       "      <td>83</td>\n",
       "      <td>-41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-82</td>\n",
       "      <td>-3403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5001711</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5001712</td>\n",
       "      <td>19</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-18</td>\n",
       "      <td>-171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5001713</td>\n",
       "      <td>22</td>\n",
       "      <td>-10.5</td>\n",
       "      <td>0</td>\n",
       "      <td>-21</td>\n",
       "      <td>-231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_BUREAU  bureau_balance_MONTHS_BALANCE_count  \\\n",
       "0       5001709                                   97   \n",
       "1       5001710                                   83   \n",
       "2       5001711                                    4   \n",
       "3       5001712                                   19   \n",
       "4       5001713                                   22   \n",
       "\n",
       "   bureau_balance_MONTHS_BALANCE_mean  bureau_balance_MONTHS_BALANCE_max  \\\n",
       "0                               -48.0                                  0   \n",
       "1                               -41.0                                  0   \n",
       "2                                -1.5                                  0   \n",
       "3                                -9.0                                  0   \n",
       "4                               -10.5                                  0   \n",
       "\n",
       "   bureau_balance_MONTHS_BALANCE_min  bureau_balance_MONTHS_BALANCE_sum  \n",
       "0                                -96                              -4656  \n",
       "1                                -82                              -3403  \n",
       "2                                 -3                                 -6  \n",
       "3                                -18                               -171  \n",
       "4                                -21                               -231  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 각각의 'SK_ID_CURR'별 대표값들을 계산\n",
    "# Calculate value count statistics for each `SK_ID_CURR` \n",
    "bureau_balance_agg = agg_numeric(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
    "bureau_balance_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "위의 데이터프레임들은 각각의 대출에 대해 계산한 것입니다. 이제 이것을 가각의 고객별로 계산할 필요가 있습니다. 이를 위해 먼저 데이터 프레임을 합치고, 그 다음 모든 수치형(numeric) 데이터에 한하여 대표값들을 다시 계산하도록 하겠습니다. 이번에는 `SK_ID_CURR`을 기준으로 그룹화를 하도록 하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_BUREAU  bureau_balance_MONTHS_BALANCE_count  \\\n",
       "0       5001709                                   97   \n",
       "1       5001710                                   83   \n",
       "2       5001711                                    4   \n",
       "3       5001712                                   19   \n",
       "4       5001713                                   22   \n",
       "\n",
       "   bureau_balance_MONTHS_BALANCE_mean  bureau_balance_MONTHS_BALANCE_max  \\\n",
       "0                               -48.0                                  0   \n",
       "1                               -41.0                                  0   \n",
       "2                                -1.5                                  0   \n",
       "3                                -9.0                                  0   \n",
       "4                               -10.5                                  0   \n",
       "\n",
       "   bureau_balance_MONTHS_BALANCE_min  bureau_balance_MONTHS_BALANCE_sum  \\\n",
       "0                                -96                              -4656   \n",
       "1                                -82                              -3403   \n",
       "2                                 -3                                 -6   \n",
       "3                                -18                               -171   \n",
       "4                                -21                               -231   \n",
       "\n",
       "   bureau_balance_STATUS_0_count  bureau_balance_STATUS_0_count_norm  \\\n",
       "0                              0                            0.000000   \n",
       "1                              5                            0.060241   \n",
       "2                              3                            0.750000   \n",
       "3                             10                            0.526316   \n",
       "4                              0                            0.000000   \n",
       "\n",
       "   bureau_balance_STATUS_1_count  bureau_balance_STATUS_1_count_norm  ...  \\\n",
       "0                              0                                 0.0  ...   \n",
       "1                              0                                 0.0  ...   \n",
       "2                              0                                 0.0  ...   \n",
       "3                              0                                 0.0  ...   \n",
       "4                              0                                 0.0  ...   \n",
       "\n",
       "   bureau_balance_STATUS_3_count_norm  bureau_balance_STATUS_4_count  \\\n",
       "0                                 0.0                              0   \n",
       "1                                 0.0                              0   \n",
       "2                                 0.0                              0   \n",
       "3                                 0.0                              0   \n",
       "4                                 0.0                              0   \n",
       "\n",
       "   bureau_balance_STATUS_4_count_norm  bureau_balance_STATUS_5_count  \\\n",
       "0                                 0.0                              0   \n",
       "1                                 0.0                              0   \n",
       "2                                 0.0                              0   \n",
       "3                                 0.0                              0   \n",
       "4                                 0.0                              0   \n",
       "\n",
       "   bureau_balance_STATUS_5_count_norm  bureau_balance_STATUS_C_count  \\\n",
       "0                                 0.0                             86   \n",
       "1                                 0.0                             48   \n",
       "2                                 0.0                              0   \n",
       "3                                 0.0                              9   \n",
       "4                                 0.0                              0   \n",
       "\n",
       "   bureau_balance_STATUS_C_count_norm  bureau_balance_STATUS_X_count  \\\n",
       "0                            0.886598                             11   \n",
       "1                            0.578313                             30   \n",
       "2                            0.000000                              1   \n",
       "3                            0.473684                              0   \n",
       "4                            0.000000                             22   \n",
       "\n",
       "   bureau_balance_STATUS_X_count_norm  SK_ID_CURR  \n",
       "0                            0.113402         NaN  \n",
       "1                            0.361446    162368.0  \n",
       "2                            0.250000    162368.0  \n",
       "3                            0.000000    162368.0  \n",
       "4                            1.000000    150635.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 대출을 기준으로 데이터프레임을 그룹화\n",
    "# Dataframe grouped by the loan\n",
    "bureau_by_loan = bureau_balance_agg.merge(bureau_balance_counts, right_index = True, left_on = 'SK_ID_BUREAU', how = 'outer')\n",
    "\n",
    "# SK_ID_CURR을 포함하여 병합\n",
    "bureau_by_loan = bureau_by_loan.merge(bureau[['SK_ID_BUREAU', 'SK_ID_CURR']], on = 'SK_ID_BUREAU', how = 'left')\n",
    "bureau_by_loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100013.0</td>\n",
       "      <td>4</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>69</td>\n",
       "      <td>40</td>\n",
       "      <td>230</td>\n",
       "      <td>4</td>\n",
       "      <td>-28.250000</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>-34.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.254545</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.018182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  client_bureau_balance_MONTHS_BALANCE_count_count  \\\n",
       "0    100001.0                                                 7   \n",
       "1    100002.0                                                 8   \n",
       "2    100005.0                                                 3   \n",
       "3    100010.0                                                 2   \n",
       "4    100013.0                                                 4   \n",
       "\n",
       "   client_bureau_balance_MONTHS_BALANCE_count_mean  \\\n",
       "0                                        24.571429   \n",
       "1                                        13.750000   \n",
       "2                                         7.000000   \n",
       "3                                        36.000000   \n",
       "4                                        57.500000   \n",
       "\n",
       "   client_bureau_balance_MONTHS_BALANCE_count_max  \\\n",
       "0                                              52   \n",
       "1                                              22   \n",
       "2                                              13   \n",
       "3                                              36   \n",
       "4                                              69   \n",
       "\n",
       "   client_bureau_balance_MONTHS_BALANCE_count_min  \\\n",
       "0                                               2   \n",
       "1                                               4   \n",
       "2                                               3   \n",
       "3                                              36   \n",
       "4                                              40   \n",
       "\n",
       "   client_bureau_balance_MONTHS_BALANCE_count_sum  \\\n",
       "0                                             172   \n",
       "1                                             110   \n",
       "2                                              21   \n",
       "3                                              72   \n",
       "4                                             230   \n",
       "\n",
       "   client_bureau_balance_MONTHS_BALANCE_mean_count  \\\n",
       "0                                                7   \n",
       "1                                                8   \n",
       "2                                                3   \n",
       "3                                                2   \n",
       "4                                                4   \n",
       "\n",
       "   client_bureau_balance_MONTHS_BALANCE_mean_mean  \\\n",
       "0                                      -11.785714   \n",
       "1                                      -21.875000   \n",
       "2                                       -3.000000   \n",
       "3                                      -46.000000   \n",
       "4                                      -28.250000   \n",
       "\n",
       "   client_bureau_balance_MONTHS_BALANCE_mean_max  \\\n",
       "0                                           -0.5   \n",
       "1                                           -1.5   \n",
       "2                                           -1.0   \n",
       "3                                          -19.5   \n",
       "4                                          -19.5   \n",
       "\n",
       "   client_bureau_balance_MONTHS_BALANCE_mean_min  ...  \\\n",
       "0                                          -25.5  ...   \n",
       "1                                          -39.5  ...   \n",
       "2                                           -6.0  ...   \n",
       "3                                          -72.5  ...   \n",
       "4                                          -34.0  ...   \n",
       "\n",
       "   client_bureau_balance_STATUS_X_count_count  \\\n",
       "0                                           7   \n",
       "1                                           8   \n",
       "2                                           3   \n",
       "3                                           2   \n",
       "4                                           4   \n",
       "\n",
       "   client_bureau_balance_STATUS_X_count_mean  \\\n",
       "0                                   4.285714   \n",
       "1                                   1.875000   \n",
       "2                                   0.666667   \n",
       "3                                   0.000000   \n",
       "4                                  10.250000   \n",
       "\n",
       "   client_bureau_balance_STATUS_X_count_max  \\\n",
       "0                                         9   \n",
       "1                                         3   \n",
       "2                                         1   \n",
       "3                                         0   \n",
       "4                                        40   \n",
       "\n",
       "   client_bureau_balance_STATUS_X_count_min  \\\n",
       "0                                         0   \n",
       "1                                         0   \n",
       "2                                         0   \n",
       "3                                         0   \n",
       "4                                         0   \n",
       "\n",
       "   client_bureau_balance_STATUS_X_count_sum  \\\n",
       "0                                      30.0   \n",
       "1                                      15.0   \n",
       "2                                       2.0   \n",
       "3                                       0.0   \n",
       "4                                      41.0   \n",
       "\n",
       "   client_bureau_balance_STATUS_X_count_norm_count  \\\n",
       "0                                                7   \n",
       "1                                                8   \n",
       "2                                                3   \n",
       "3                                                2   \n",
       "4                                                4   \n",
       "\n",
       "   client_bureau_balance_STATUS_X_count_norm_mean  \\\n",
       "0                                        0.214590   \n",
       "1                                        0.161932   \n",
       "2                                        0.136752   \n",
       "3                                        0.000000   \n",
       "4                                        0.254545   \n",
       "\n",
       "   client_bureau_balance_STATUS_X_count_norm_max  \\\n",
       "0                                       0.500000   \n",
       "1                                       0.500000   \n",
       "2                                       0.333333   \n",
       "3                                       0.000000   \n",
       "4                                       1.000000   \n",
       "\n",
       "   client_bureau_balance_STATUS_X_count_norm_min  \\\n",
       "0                                            0.0   \n",
       "1                                            0.0   \n",
       "2                                            0.0   \n",
       "3                                            0.0   \n",
       "4                                            0.0   \n",
       "\n",
       "   client_bureau_balance_STATUS_X_count_norm_sum  \n",
       "0                                       1.502129  \n",
       "1                                       1.295455  \n",
       "2                                       0.410256  \n",
       "3                                       0.000000  \n",
       "4                                       1.018182  \n",
       "\n",
       "[5 rows x 106 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_balance_by_client = agg_numeric(bureau_by_loan.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'client')\n",
    "bureau_balance_by_client.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "종합하겠습니다. 이 단계에서는 `bureau_balance`데이터프레임에 대해 다음과 같은 과정을 밟아나갔습니다.\n",
    "\n",
    "1. 각각의 대출에 대해 수치형(numeric) 대표값들을 계산 (Calculated numeric stats grouping by each loan)\n",
    "2. 각각의 대출에 대해 범주형(categorical) 데이터들의 갯수를 파악 (Made value counts of each categorical variable grouping by loan)\n",
    "3. 각각의 대출에 대한 대표값들과 갯수를 병합 (Merged the stats and the value counts on the loans)\n",
    "4. 각 고객별로 3의 결과에 대한 수치형 대표값들을 계산 (Calculated numeric stats for the resulting dataframe grouping by the client id)\n",
    "\n",
    "최종 데이터프레임은 각 고객에 대한 개별 행으로 구성되며, 각 행은 이전 모든 대출들의 월별 정보들의 통계치들로 구성되어 있습니다.\n",
    "\n",
    "몇몇 변수들은 약간 헷갈립니다. 예를 들어 설명하자면\n",
    "\n",
    "- `client_bureau_balance_MONTHS_BALANCE_mean_mean` : 각각의 대출에 대한 `MONTHS_BALANCE`의 평균값을 계산, 그 다음 클라이언트별 대출의 평균값을 계산\n",
    "- `client_bureau_balance_STATUS_X_count_norm_sum` : 각각의 대출에 대해 `STATUS` == x 인것의 빈도를 총 `STATUS` 수로 나눈 다음, 개별 클라이언트별로 그 수를 합산\n",
    "\n",
    "최종 데이터프레임에 모든 결과를 합치기 전까지는 상관계수(correlation)를 계산하지 않겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 생성된 함수의 병합 (Putting the Functions Together)\n",
    "\n",
    "이제, 타 기관에서의 과거 대출 이력과 대출에 관한 월별 정보들을 구하고, 이것들을 훈련용 데이터프레임과 병합시킬 준비가 다되었습니다. 모든 변수들을 초기화한 뒤, 생성된 함수들을 사용하여 처음부터 만들어나가겠습니다. 이러한 과정은 반복해야하는 작업 공정에서 함수를 활용했을 때의 이점을 보여줍니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 오래된 오브젝트들(objects)을 제거함으로써 메모리를 확보\n",
    "# Free up memory by deleting old objects\n",
    "import gc\n",
    "gc.enable()  # https://docs.python.org/2/library/gc.html\n",
    "\n",
    "del train, bureau, bureau_balance, bureau_agg, bureau_agg_new, bureau_balance_agg, bureau_balance_counts, bureau_by_loan, bureau_balance_by_client, bureau_counts\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모든 데이터프레임의 새 복사본들을 생성\n",
    "# Read in new copies of all the dataframes\n",
    "train = pd.read_csv('../input/application_train.csv')\n",
    "bureau = pd.read_csv('../input/bureau.csv')\n",
    "bureau_balance = pd.read_csv('../input/bureau_balance.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bureau 데이터프레임 내 범주형 데이터의 갯수 세기 (Counts of Bureau Dataframe)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>bureau_CREDIT_ACTIVE_Active_count</th>\n",
       "      <th>bureau_CREDIT_ACTIVE_Active_count_norm</th>\n",
       "      <th>bureau_CREDIT_ACTIVE_Bad debt_count</th>\n",
       "      <th>bureau_CREDIT_ACTIVE_Bad debt_count_norm</th>\n",
       "      <th>bureau_CREDIT_ACTIVE_Closed_count</th>\n",
       "      <th>bureau_CREDIT_ACTIVE_Closed_count_norm</th>\n",
       "      <th>bureau_CREDIT_ACTIVE_Sold_count</th>\n",
       "      <th>bureau_CREDIT_ACTIVE_Sold_count_norm</th>\n",
       "      <th>bureau_CREDIT_CURRENCY_currency 1_count</th>\n",
       "      <th>bureau_CREDIT_CURRENCY_currency 1_count_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>bureau_CREDIT_TYPE_Microloan_count</th>\n",
       "      <th>bureau_CREDIT_TYPE_Microloan_count_norm</th>\n",
       "      <th>bureau_CREDIT_TYPE_Mobile operator loan_count</th>\n",
       "      <th>bureau_CREDIT_TYPE_Mobile operator loan_count_norm</th>\n",
       "      <th>bureau_CREDIT_TYPE_Mortgage_count</th>\n",
       "      <th>bureau_CREDIT_TYPE_Mortgage_count_norm</th>\n",
       "      <th>bureau_CREDIT_TYPE_Real estate loan_count</th>\n",
       "      <th>bureau_CREDIT_TYPE_Real estate loan_count_norm</th>\n",
       "      <th>bureau_CREDIT_TYPE_Unknown type of loan_count</th>\n",
       "      <th>bureau_CREDIT_TYPE_Unknown type of loan_count_norm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100001</th>\n",
       "      <td>3</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>2</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>1</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>2</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            bureau_CREDIT_ACTIVE_Active_count  \\\n",
       "SK_ID_CURR                                      \n",
       "100001                                      3   \n",
       "100002                                      2   \n",
       "100003                                      1   \n",
       "100004                                      0   \n",
       "100005                                      2   \n",
       "\n",
       "            bureau_CREDIT_ACTIVE_Active_count_norm  \\\n",
       "SK_ID_CURR                                           \n",
       "100001                                    0.428571   \n",
       "100002                                    0.250000   \n",
       "100003                                    0.250000   \n",
       "100004                                    0.000000   \n",
       "100005                                    0.666667   \n",
       "\n",
       "            bureau_CREDIT_ACTIVE_Bad debt_count  \\\n",
       "SK_ID_CURR                                        \n",
       "100001                                        0   \n",
       "100002                                        0   \n",
       "100003                                        0   \n",
       "100004                                        0   \n",
       "100005                                        0   \n",
       "\n",
       "            bureau_CREDIT_ACTIVE_Bad debt_count_norm  \\\n",
       "SK_ID_CURR                                             \n",
       "100001                                           0.0   \n",
       "100002                                           0.0   \n",
       "100003                                           0.0   \n",
       "100004                                           0.0   \n",
       "100005                                           0.0   \n",
       "\n",
       "            bureau_CREDIT_ACTIVE_Closed_count  \\\n",
       "SK_ID_CURR                                      \n",
       "100001                                      4   \n",
       "100002                                      6   \n",
       "100003                                      3   \n",
       "100004                                      2   \n",
       "100005                                      1   \n",
       "\n",
       "            bureau_CREDIT_ACTIVE_Closed_count_norm  \\\n",
       "SK_ID_CURR                                           \n",
       "100001                                    0.571429   \n",
       "100002                                    0.750000   \n",
       "100003                                    0.750000   \n",
       "100004                                    1.000000   \n",
       "100005                                    0.333333   \n",
       "\n",
       "            bureau_CREDIT_ACTIVE_Sold_count  \\\n",
       "SK_ID_CURR                                    \n",
       "100001                                    0   \n",
       "100002                                    0   \n",
       "100003                                    0   \n",
       "100004                                    0   \n",
       "100005                                    0   \n",
       "\n",
       "            bureau_CREDIT_ACTIVE_Sold_count_norm  \\\n",
       "SK_ID_CURR                                         \n",
       "100001                                       0.0   \n",
       "100002                                       0.0   \n",
       "100003                                       0.0   \n",
       "100004                                       0.0   \n",
       "100005                                       0.0   \n",
       "\n",
       "            bureau_CREDIT_CURRENCY_currency 1_count  \\\n",
       "SK_ID_CURR                                            \n",
       "100001                                            7   \n",
       "100002                                            8   \n",
       "100003                                            4   \n",
       "100004                                            2   \n",
       "100005                                            3   \n",
       "\n",
       "            bureau_CREDIT_CURRENCY_currency 1_count_norm  ...  \\\n",
       "SK_ID_CURR                                                ...   \n",
       "100001                                               1.0  ...   \n",
       "100002                                               1.0  ...   \n",
       "100003                                               1.0  ...   \n",
       "100004                                               1.0  ...   \n",
       "100005                                               1.0  ...   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Microloan_count  \\\n",
       "SK_ID_CURR                                       \n",
       "100001                                       0   \n",
       "100002                                       0   \n",
       "100003                                       0   \n",
       "100004                                       0   \n",
       "100005                                       0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Microloan_count_norm  \\\n",
       "SK_ID_CURR                                            \n",
       "100001                                          0.0   \n",
       "100002                                          0.0   \n",
       "100003                                          0.0   \n",
       "100004                                          0.0   \n",
       "100005                                          0.0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Mobile operator loan_count  \\\n",
       "SK_ID_CURR                                                  \n",
       "100001                                                  0   \n",
       "100002                                                  0   \n",
       "100003                                                  0   \n",
       "100004                                                  0   \n",
       "100005                                                  0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Mobile operator loan_count_norm  \\\n",
       "SK_ID_CURR                                                       \n",
       "100001                                                    0.0    \n",
       "100002                                                    0.0    \n",
       "100003                                                    0.0    \n",
       "100004                                                    0.0    \n",
       "100005                                                    0.0    \n",
       "\n",
       "            bureau_CREDIT_TYPE_Mortgage_count  \\\n",
       "SK_ID_CURR                                      \n",
       "100001                                      0   \n",
       "100002                                      0   \n",
       "100003                                      0   \n",
       "100004                                      0   \n",
       "100005                                      0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Mortgage_count_norm  \\\n",
       "SK_ID_CURR                                           \n",
       "100001                                         0.0   \n",
       "100002                                         0.0   \n",
       "100003                                         0.0   \n",
       "100004                                         0.0   \n",
       "100005                                         0.0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Real estate loan_count  \\\n",
       "SK_ID_CURR                                              \n",
       "100001                                              0   \n",
       "100002                                              0   \n",
       "100003                                              0   \n",
       "100004                                              0   \n",
       "100005                                              0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Real estate loan_count_norm  \\\n",
       "SK_ID_CURR                                                   \n",
       "100001                                                 0.0   \n",
       "100002                                                 0.0   \n",
       "100003                                                 0.0   \n",
       "100004                                                 0.0   \n",
       "100005                                                 0.0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Unknown type of loan_count  \\\n",
       "SK_ID_CURR                                                  \n",
       "100001                                                  0   \n",
       "100002                                                  0   \n",
       "100003                                                  0   \n",
       "100004                                                  0   \n",
       "100005                                                  0   \n",
       "\n",
       "            bureau_CREDIT_TYPE_Unknown type of loan_count_norm  \n",
       "SK_ID_CURR                                                      \n",
       "100001                                                    0.0   \n",
       "100002                                                    0.0   \n",
       "100003                                                    0.0   \n",
       "100004                                                    0.0   \n",
       "100005                                                    0.0   \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_counts = count_categorical(bureau, group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
    "bureau_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bureau 데이터프레임의 대표값 계산 (Aggregated Stats of Bureau Dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>bureau_DAYS_CREDIT_count</th>\n",
       "      <th>bureau_DAYS_CREDIT_mean</th>\n",
       "      <th>bureau_DAYS_CREDIT_max</th>\n",
       "      <th>bureau_DAYS_CREDIT_min</th>\n",
       "      <th>bureau_DAYS_CREDIT_sum</th>\n",
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       "      <th>bureau_CREDIT_DAY_OVERDUE_max</th>\n",
       "      <th>bureau_CREDIT_DAY_OVERDUE_min</th>\n",
       "      <th>...</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_count</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_max</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_min</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_sum</th>\n",
       "      <th>bureau_AMT_ANNUITY_count</th>\n",
       "      <th>bureau_AMT_ANNUITY_mean</th>\n",
       "      <th>bureau_AMT_ANNUITY_max</th>\n",
       "      <th>bureau_AMT_ANNUITY_min</th>\n",
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       "      <td>-606</td>\n",
       "      <td>-2586</td>\n",
       "      <td>-5603</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>-816.000000</td>\n",
       "      <td>-43</td>\n",
       "      <td>-2131</td>\n",
       "      <td>-3264</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  \\\n",
       "0      100001                         7              -735.000000   \n",
       "1      100002                         8              -874.000000   \n",
       "2      100003                         4             -1400.750000   \n",
       "3      100004                         2              -867.000000   \n",
       "4      100005                         3              -190.666667   \n",
       "\n",
       "   bureau_DAYS_CREDIT_max  bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\n",
       "0                     -49                   -1572                   -5145   \n",
       "1                    -103                   -1437                   -6992   \n",
       "2                    -606                   -2586                   -5603   \n",
       "3                    -408                   -1326                   -1734   \n",
       "4                     -62                    -373                    -572   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\n",
       "0                                7                             0.0   \n",
       "1                                8                             0.0   \n",
       "2                                4                             0.0   \n",
       "3                                2                             0.0   \n",
       "4                                3                             0.0   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  ...  \\\n",
       "0                              0                              0  ...   \n",
       "1                              0                              0  ...   \n",
       "2                              0                              0  ...   \n",
       "3                              0                              0  ...   \n",
       "4                              0                              0  ...   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_count  bureau_DAYS_CREDIT_UPDATE_mean  \\\n",
       "0                                7                      -93.142857   \n",
       "1                                8                     -499.875000   \n",
       "2                                4                     -816.000000   \n",
       "3                                2                     -532.000000   \n",
       "4                                3                      -54.333333   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_max  bureau_DAYS_CREDIT_UPDATE_min  \\\n",
       "0                             -6                           -155   \n",
       "1                             -7                          -1185   \n",
       "2                            -43                          -2131   \n",
       "3                           -382                           -682   \n",
       "4                            -11                           -121   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_sum  bureau_AMT_ANNUITY_count  \\\n",
       "0                           -652                         7   \n",
       "1                          -3999                         7   \n",
       "2                          -3264                         0   \n",
       "3                          -1064                         0   \n",
       "4                           -163                         3   \n",
       "\n",
       "   bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  \\\n",
       "0              3545.357143                 10822.5                     0.0   \n",
       "1                 0.000000                     0.0                     0.0   \n",
       "2                      NaN                     NaN                     NaN   \n",
       "3                      NaN                     NaN                     NaN   \n",
       "4              1420.500000                  4261.5                     0.0   \n",
       "\n",
       "   bureau_AMT_ANNUITY_sum  \n",
       "0                 24817.5  \n",
       "1                     0.0  \n",
       "2                     0.0  \n",
       "3                     0.0  \n",
       "4                  4261.5  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_agg = agg_numeric(bureau.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
    "bureau_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bureau Balance 데이터프레임의 각 대출별 범주형 데이터 갯수 세기 (Value counts of Bureau Balance dataframe by loan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "              bureau_balance_STATUS_0_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                   0   \n",
       "5001710                                   5   \n",
       "5001711                                   3   \n",
       "5001712                                  10   \n",
       "5001713                                   0   \n",
       "\n",
       "              bureau_balance_STATUS_0_count_norm  \\\n",
       "SK_ID_BUREAU                                       \n",
       "5001709                                 0.000000   \n",
       "5001710                                 0.060241   \n",
       "5001711                                 0.750000   \n",
       "5001712                                 0.526316   \n",
       "5001713                                 0.000000   \n",
       "\n",
       "              bureau_balance_STATUS_1_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                   0   \n",
       "5001710                                   0   \n",
       "5001711                                   0   \n",
       "5001712                                   0   \n",
       "5001713                                   0   \n",
       "\n",
       "              bureau_balance_STATUS_1_count_norm  \\\n",
       "SK_ID_BUREAU                                       \n",
       "5001709                                      0.0   \n",
       "5001710                                      0.0   \n",
       "5001711                                      0.0   \n",
       "5001712                                      0.0   \n",
       "5001713                                      0.0   \n",
       "\n",
       "              bureau_balance_STATUS_2_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                   0   \n",
       "5001710                                   0   \n",
       "5001711                                   0   \n",
       "5001712                                   0   \n",
       "5001713                                   0   \n",
       "\n",
       "              bureau_balance_STATUS_2_count_norm  \\\n",
       "SK_ID_BUREAU                                       \n",
       "5001709                                      0.0   \n",
       "5001710                                      0.0   \n",
       "5001711                                      0.0   \n",
       "5001712                                      0.0   \n",
       "5001713                                      0.0   \n",
       "\n",
       "              bureau_balance_STATUS_3_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                   0   \n",
       "5001710                                   0   \n",
       "5001711                                   0   \n",
       "5001712                                   0   \n",
       "5001713                                   0   \n",
       "\n",
       "              bureau_balance_STATUS_3_count_norm  \\\n",
       "SK_ID_BUREAU                                       \n",
       "5001709                                      0.0   \n",
       "5001710                                      0.0   \n",
       "5001711                                      0.0   \n",
       "5001712                                      0.0   \n",
       "5001713                                      0.0   \n",
       "\n",
       "              bureau_balance_STATUS_4_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                   0   \n",
       "5001710                                   0   \n",
       "5001711                                   0   \n",
       "5001712                                   0   \n",
       "5001713                                   0   \n",
       "\n",
       "              bureau_balance_STATUS_4_count_norm  \\\n",
       "SK_ID_BUREAU                                       \n",
       "5001709                                      0.0   \n",
       "5001710                                      0.0   \n",
       "5001711                                      0.0   \n",
       "5001712                                      0.0   \n",
       "5001713                                      0.0   \n",
       "\n",
       "              bureau_balance_STATUS_5_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                   0   \n",
       "5001710                                   0   \n",
       "5001711                                   0   \n",
       "5001712                                   0   \n",
       "5001713                                   0   \n",
       "\n",
       "              bureau_balance_STATUS_5_count_norm  \\\n",
       "SK_ID_BUREAU                                       \n",
       "5001709                                      0.0   \n",
       "5001710                                      0.0   \n",
       "5001711                                      0.0   \n",
       "5001712                                      0.0   \n",
       "5001713                                      0.0   \n",
       "\n",
       "              bureau_balance_STATUS_C_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                  86   \n",
       "5001710                                  48   \n",
       "5001711                                   0   \n",
       "5001712                                   9   \n",
       "5001713                                   0   \n",
       "\n",
       "              bureau_balance_STATUS_C_count_norm  \\\n",
       "SK_ID_BUREAU                                       \n",
       "5001709                                 0.886598   \n",
       "5001710                                 0.578313   \n",
       "5001711                                 0.000000   \n",
       "5001712                                 0.473684   \n",
       "5001713                                 0.000000   \n",
       "\n",
       "              bureau_balance_STATUS_X_count  \\\n",
       "SK_ID_BUREAU                                  \n",
       "5001709                                  11   \n",
       "5001710                                  30   \n",
       "5001711                                   1   \n",
       "5001712                                   0   \n",
       "5001713                                  22   \n",
       "\n",
       "              bureau_balance_STATUS_X_count_norm  \n",
       "SK_ID_BUREAU                                      \n",
       "5001709                                 0.113402  \n",
       "5001710                                 0.361446  \n",
       "5001711                                 0.250000  \n",
       "5001712                                 0.000000  \n",
       "5001713                                 1.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_balance_counts = count_categorical(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
    "bureau_balance_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bureau Balance 데이터프레임의 각 대출별 대표값 계산 (Aggregated stats of Bureau Balance dataframe by loan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>SK_ID_BUREAU</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_count</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_mean</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_max</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_min</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5001709</td>\n",
       "      <td>97</td>\n",
       "      <td>-48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-96</td>\n",
       "      <td>-4656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5001710</td>\n",
       "      <td>83</td>\n",
       "      <td>-41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-82</td>\n",
       "      <td>-3403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5001711</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5001712</td>\n",
       "      <td>19</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-18</td>\n",
       "      <td>-171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5001713</td>\n",
       "      <td>22</td>\n",
       "      <td>-10.5</td>\n",
       "      <td>0</td>\n",
       "      <td>-21</td>\n",
       "      <td>-231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_BUREAU  bureau_balance_MONTHS_BALANCE_count  \\\n",
       "0       5001709                                   97   \n",
       "1       5001710                                   83   \n",
       "2       5001711                                    4   \n",
       "3       5001712                                   19   \n",
       "4       5001713                                   22   \n",
       "\n",
       "   bureau_balance_MONTHS_BALANCE_mean  bureau_balance_MONTHS_BALANCE_max  \\\n",
       "0                               -48.0                                  0   \n",
       "1                               -41.0                                  0   \n",
       "2                                -1.5                                  0   \n",
       "3                                -9.0                                  0   \n",
       "4                               -10.5                                  0   \n",
       "\n",
       "   bureau_balance_MONTHS_BALANCE_min  bureau_balance_MONTHS_BALANCE_sum  \n",
       "0                                -96                              -4656  \n",
       "1                                -82                              -3403  \n",
       "2                                 -3                                 -6  \n",
       "3                                -18                               -171  \n",
       "4                                -21                               -231  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_balance_agg = agg_numeric(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
    "bureau_balance_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bureau Balance 데이터프레임의 고객별 대표값 계산 (Aggregated Stats of Bureau Balance by Client)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 각 대출별 데이터프레임 병합\n",
    "# Dataframe grouped by the loan\n",
    "bureau_by_loan = bureau_balance_agg.merge(bureau_balance_counts, right_index = True, left_on = 'SK_ID_BUREAU', how = 'outer')\n",
    "\n",
    "# SK_ID_CURR을 포함하여 병합\n",
    "# Merge to include the SK_ID_CURR\n",
    "bureau_by_loan = bureau[['SK_ID_BUREAU', 'SK_ID_CURR']].merge(bureau_by_loan, on = 'SK_ID_BUREAU', how = 'left')\n",
    "\n",
    "# 고객별 대표값 계산\n",
    "# Aggregate the stats for each client\n",
    "bureau_balance_by_client = agg_numeric(bureau_by_loan.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'client')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 계산된 특성(Feature)들을 훈련용 데이터와 병합 (Insert Computed Features into Training Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Number of Features:  122\n"
     ]
    }
   ],
   "source": [
    "original_features = list(train.columns)\n",
    "print('Original Number of Features: ', len(original_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# breau 값 갯수 센 것과 병합\n",
    "# Merge with the value counts of bureau\n",
    "train = train.merge(bureau_counts, on = 'SK_ID_CURR', how = 'left')\n",
    "\n",
    "# bureau 대표값 계산한 것과 병합\n",
    "# Merge with the stats of bureau\n",
    "train = train.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')\n",
    "\n",
    "# 월별, 고객별 정보와 병합\n",
    "# Merge with the monthly information grouped by client\n",
    "train = train.merge(bureau_balance_by_client, on = 'SK_ID_CURR', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features using previous loans from other institutions data:  333\n"
     ]
    }
   ],
   "source": [
    "new_features = list(train.columns)\n",
    "print('Number of features using previous loans from other institutions data: ', len(new_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering 결과물 (Feature Engineering Outcomes)\n",
    "\n",
    "이제 모든 작업들을 마치고, 생성된 변수들(variables)을 살펴보고자 합니다. 누락된 값(missing values)의 비율, 목표값(target)과의 상관계수, 변수간 상관계수들을 살펴볼 것입니다. 각 변수간의 높은 상관관계는 변수 간 collinear 관계를 가지는 지 여부를 보여줄 수 있으며, 이는 변수들이 서로 강한 연관관계를 가짐을 의미합니다. 종종 collinear한 두 변수를 모두 갖는 것은 중복이기 때문에 하나를 제거해야할 필요가 있기도 합니다. 또한 누락된 값들의 백분율을 활용하여 대부분의 값이 존재하지 않는 feature들을 제거할 수도 있습니다. Feature Selection은 앞으로의 과정에 있어 중요한 초점이 될 것입니다. Feature의 수를 줄이는 것은 모델 학습과 더불어 일반화를 도울 수 있습니다. 'Curse of dimensionality (차원의 저주)'는 너무 많은 feature를 가질 때 생기는 문제를 말합니다.(Feature 갯수가 많은 것 = 지나치게 고차원) 변수의 수가 증가함에 따라 변수와 목표값 사이의 상관관계를 학습하는데 필요한 데이터(datapoints)의 수는 기하급수적으로 증가합니다. \n",
    "\n",
    "Feature seletion은 모델 학습 및 일반화를 위해 변수들을 제거하는 과정입니다. 필요없고, 중복인 변수들을 제거하고, 중요한 변수들을 보존하는 것이 목적입니다. 이 과정에서 쓸 수 있는 툴들은 여러가지가 있지만, 이 커널에서는 누락된 값들이 많거나, 다른 변수들에 대해 높은 상관관계들을 갖는 변수를 제거하는 방법을 사용할 것입니다. 그 다음, `Gradient Boosting Machine`과 `Random Forest` 모델로부터 반환된 Feature importance를 활용하는 방법도 알아볼 것입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 누락된 값 (Missing Values)\n",
    "\n",
    "데이터프레임 내의 누락된 값들은 중요한 고려사항입니다. 누락된 값이 많은 column은 삭제해야 할 수도 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# column별 누락된 값 갯수를 계산하기 위한 함수\n",
    "# Function to calculate missing values to by column\n",
    "\n",
    "def missing_values_table(df):\n",
    "    \n",
    "    # 전체 데이터프레임의 누락된 값 계산\n",
    "    # Total missing values\n",
    "    mis_val = df.isnull().sum()\n",
    "    \n",
    "    # 누락된 값의 백분율 계산\n",
    "    # Percentage of missing values\n",
    "    mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
    "    \n",
    "    # 표 작성\n",
    "    # Make a table with the results\n",
    "    mis_val_table = pd.concat([mis_val,mis_val_percent], axis=1)\n",
    "    \n",
    "    # column명 재정의\n",
    "    # Rename the columns\n",
    "    mis_val_table_ren_columns = mis_val_table.rename(columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
    "    \n",
    "    # 내림차순으로 표정렬\n",
    "    # Sort the table by percentage of missing descending\n",
    "    mis_val_table_ren_columns = mis_val_table_ren_columns[mis_val_table_ren_columns.iloc[:,1] != 0].sort_values('% of Total Values', ascending=False).round(1)\n",
    "    \n",
    "    # 요약 정보 출력\n",
    "    # Print some summary information\n",
    "    print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"      \n",
    "            \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
    "              \" columns that have missing values.\")\n",
    "    \n",
    "    # 데이터프레임 return\n",
    "    # Return the dataframe with missing information\n",
    "    return mis_val_table_ren_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 333 columns.\n",
      "There are 278 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bureau_AMT_ANNUITY_min</th>\n",
       "      <td>227502</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bureau_AMT_ANNUITY_max</th>\n",
       "      <td>227502</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bureau_AMT_ANNUITY_mean</th>\n",
       "      <td>227502</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_STATUS_4_count_min</th>\n",
       "      <td>215280</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_STATUS_3_count_norm_mean</th>\n",
       "      <td>215280</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_count_min</th>\n",
       "      <td>215280</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_STATUS_4_count_max</th>\n",
       "      <td>215280</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_STATUS_4_count_mean</th>\n",
       "      <td>215280</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_STATUS_3_count_norm_min</th>\n",
       "      <td>215280</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_STATUS_3_count_norm_max</th>\n",
       "      <td>215280</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Missing Values  \\\n",
       "bureau_AMT_ANNUITY_min                                  227502   \n",
       "bureau_AMT_ANNUITY_max                                  227502   \n",
       "bureau_AMT_ANNUITY_mean                                 227502   \n",
       "client_bureau_balance_STATUS_4_count_min                215280   \n",
       "client_bureau_balance_STATUS_3_count_norm_mean          215280   \n",
       "client_bureau_balance_MONTHS_BALANCE_count_min          215280   \n",
       "client_bureau_balance_STATUS_4_count_max                215280   \n",
       "client_bureau_balance_STATUS_4_count_mean               215280   \n",
       "client_bureau_balance_STATUS_3_count_norm_min           215280   \n",
       "client_bureau_balance_STATUS_3_count_norm_max           215280   \n",
       "\n",
       "                                                % of Total Values  \n",
       "bureau_AMT_ANNUITY_min                                       74.0  \n",
       "bureau_AMT_ANNUITY_max                                       74.0  \n",
       "bureau_AMT_ANNUITY_mean                                      74.0  \n",
       "client_bureau_balance_STATUS_4_count_min                     70.0  \n",
       "client_bureau_balance_STATUS_3_count_norm_mean               70.0  \n",
       "client_bureau_balance_MONTHS_BALANCE_count_min               70.0  \n",
       "client_bureau_balance_STATUS_4_count_max                     70.0  \n",
       "client_bureau_balance_STATUS_4_count_mean                    70.0  \n",
       "client_bureau_balance_STATUS_3_count_norm_min                70.0  \n",
       "client_bureau_balance_STATUS_3_count_norm_max                70.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_train = missing_values_table(train)\n",
    "missing_train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "누락된 값의 비율이 높은 column이 여러 개 있음을 알 수 있습니다. 누락된 값을 제거하는데 있어 기준이 되는 정해진 비율은 없으며, 문제에 따라 달라집니다. 여기서는 훈련데이터 및 테스트 데이터에 있어 90%의 이상의 누락값을 가진 column들을 제거하겠습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_train_vars = list(missing_train.index[missing_train['% of Total Values'] > 90])\n",
    "len(missing_train_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "누락된 값들을 제거하기 앞서, 테스트 데이터에서의 누락된 값의 비율을 살펴보겠습니다. 그 다음 훈련 또는 테스트 데이터에 있어 90% 이상 누락된 값을 가진 column들을 제거하겠습니다. 이제, 테스트데이터를 불러온 뒤 같은 연산들을 적용하고, 테스트 데이터안의 누락된 값들을 살펴보겠습니다. 이미, 갯수 및 대표값들을 계산한 뒤이므로, 테스트 데이터를 계산된 데이터들과 병합하기만 하면 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 테스트 데이터 불러오기\n",
    "# Read in the test dataframe\n",
    "test = pd.read_csv('../input/application_test.csv')\n",
    "\n",
    "# bureau 데이터의 갯수들을 계산한 데이터프레임과 병합\n",
    "# Merge with the value counts of bureau\n",
    "test = test.merge(bureau_counts, on = 'SK_ID_CURR', how = 'left')\n",
    "\n",
    "# bureau 데이터의 대표값들을 계산한 데이터프레임과 병합\n",
    "# Merge with the stats of bureau\n",
    "test = test.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')\n",
    "\n",
    "# bureau balance 데이터의 갯수들을 계산한 데이터프레이모가 병합\n",
    "# Merge with the value counts of bureau balance\n",
    "test = test.merge(bureau_balance_by_client, on = 'SK_ID_CURR', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Testing Data:  (48744, 332)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of Testing Data: ', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 훈련용 데이터와 테스트 데이터프레임이 같은 column들을 가지도록 맞춰보겠습니다. 여기서는 문제가 되지 않지만, 변수들을 one-hot 인코딩할 때에는 데이터프레임들이 동일한 column을 가지도록 맞춰야 합니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = train['TARGET']\n",
    "\n",
    "# 데이터프레임을 align. 'target' column은 제거됩니다.\n",
    "# Align the dataframes, this will remove the 'TARGET' column\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.align.html\n",
    "train, test = train.align(test, join = 'inner', axis = 1)\n",
    "\n",
    "train['TARGET'] = train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Shape:  (307511, 333)\n",
      "Testing Data Shape:  (48744, 332)\n"
     ]
    }
   ],
   "source": [
    "print('Training Data Shape: ', train.shape)\n",
    "print('Testing Data Shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 332 columns.\n",
      "There are 275 columns that have missing values.\n"
     ]
    },
    {
     "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>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MEDI</th>\n",
       "      <td>33495</td>\n",
       "      <td>68.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MODE</th>\n",
       "      <td>33495</td>\n",
       "      <td>68.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_AVG</th>\n",
       "      <td>33495</td>\n",
       "      <td>68.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>33347</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
       "      <td>33347</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <td>33347</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FONDKAPREMONT_MODE</th>\n",
       "      <td>32797</td>\n",
       "      <td>67.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>32780</td>\n",
       "      <td>67.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <td>32780</td>\n",
       "      <td>67.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <td>32780</td>\n",
       "      <td>67.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Missing Values  % of Total Values\n",
       "COMMONAREA_MEDI                    33495               68.7\n",
       "COMMONAREA_MODE                    33495               68.7\n",
       "COMMONAREA_AVG                     33495               68.7\n",
       "NONLIVINGAPARTMENTS_MEDI           33347               68.4\n",
       "NONLIVINGAPARTMENTS_AVG            33347               68.4\n",
       "NONLIVINGAPARTMENTS_MODE           33347               68.4\n",
       "FONDKAPREMONT_MODE                 32797               67.3\n",
       "LIVINGAPARTMENTS_MEDI              32780               67.2\n",
       "LIVINGAPARTMENTS_MODE              32780               67.2\n",
       "LIVINGAPARTMENTS_AVG               32780               67.2"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_test = missing_values_table(test)\n",
    "missing_test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_test_vars = list(missing_test.index[missing_test['% of Total Values'] > 90])\n",
    "len(missing_test_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 0 columns with more than 90% missing in either the training or testing data.\n"
     ]
    }
   ],
   "source": [
    "missing_columns = list(set(missing_test_vars + missing_train_vars))\n",
    "print('There are %d columns with more than 90%% missing in either the training or testing data.' % len(missing_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the missing columns\n",
    "train = train.drop(columns = missing_columns)\n",
    "test = test.drop(columns = missing_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "90% 이상 누락된 값을 가진 column들이 없기 때문에, 이번에는 어떠한 column들도 제거되지 않았습니다. Feature selection을 위해서는 아마도 다른 방법을 적용해야 할 것 같습니다.\n",
    "\n",
    "이 시점에서 훈련용 데이터와 테스트용 데이터들을 저장하도록 하겠습니다. 누락된 값들을 제거하기 위해, 다른 비율을 설정하고, 결과물을 비교해보는 것도 좋은 방법입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('train_bureau_raw.csv', index = False)\n",
    "test.to_csv('test_bureau_raw.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 상관계수 (Correlations)\n",
    "\n",
    "목표값(target)과 변수(variable)들간 상관계수를 살펴보도록 하겠습니다. 위에서 새롭게 생성된 변수들 중 기존 훈련용 데이터(`application` 데이터에 있던) 변수들보다 더 높은 상관계수를 가지는 변수를 찾을 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터프레임상에서 모든 상관계수들을 계산\n",
    "# Calculate all correlations in dataframe\n",
    "corrs = train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TARGET</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bureau_DAYS_CREDIT_mean</th>\n",
       "      <td>0.089729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_min_mean</th>\n",
       "      <td>0.089038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <td>0.078239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bureau_CREDIT_ACTIVE_Active_count_norm</th>\n",
       "      <td>0.077356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_mean_mean</th>\n",
       "      <td>0.076424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bureau_DAYS_CREDIT_min</th>\n",
       "      <td>0.075248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_min_min</th>\n",
       "      <td>0.073225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_sum_mean</th>\n",
       "      <td>0.072606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\n",
       "      <td>0.068927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  TARGET\n",
       "TARGET                                          1.000000\n",
       "bureau_DAYS_CREDIT_mean                         0.089729\n",
       "client_bureau_balance_MONTHS_BALANCE_min_mean   0.089038\n",
       "DAYS_BIRTH                                      0.078239\n",
       "bureau_CREDIT_ACTIVE_Active_count_norm          0.077356\n",
       "client_bureau_balance_MONTHS_BALANCE_mean_mean  0.076424\n",
       "bureau_DAYS_CREDIT_min                          0.075248\n",
       "client_bureau_balance_MONTHS_BALANCE_min_min    0.073225\n",
       "client_bureau_balance_MONTHS_BALANCE_sum_mean   0.072606\n",
       "bureau_DAYS_CREDIT_UPDATE_mean                  0.068927"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrs = corrs.sort_values('TARGET', ascending = False)\n",
    "\n",
    "# 상위 10개의 양의 상관계수\n",
    "# Ten most positive correlations\n",
    "pd.DataFrame(corrs['TARGET'].head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_count_min</th>\n",
       "      <td>-0.048224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_STATUS_C_count_norm_mean</th>\n",
       "      <td>-0.055936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_STATUS_C_count_max</th>\n",
       "      <td>-0.061083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_STATUS_C_count_mean</th>\n",
       "      <td>-0.062954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_count_max</th>\n",
       "      <td>-0.068792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bureau_CREDIT_ACTIVE_Closed_count_norm</th>\n",
       "      <td>-0.079369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_count_mean</th>\n",
       "      <td>-0.080193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <td>-0.155317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <td>-0.160472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <td>-0.178919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   TARGET\n",
       "client_bureau_balance_MONTHS_BALANCE_count_min  -0.048224\n",
       "client_bureau_balance_STATUS_C_count_norm_mean  -0.055936\n",
       "client_bureau_balance_STATUS_C_count_max        -0.061083\n",
       "client_bureau_balance_STATUS_C_count_mean       -0.062954\n",
       "client_bureau_balance_MONTHS_BALANCE_count_max  -0.068792\n",
       "bureau_CREDIT_ACTIVE_Closed_count_norm          -0.079369\n",
       "client_bureau_balance_MONTHS_BALANCE_count_mean -0.080193\n",
       "EXT_SOURCE_1                                    -0.155317\n",
       "EXT_SOURCE_2                                    -0.160472\n",
       "EXT_SOURCE_3                                    -0.178919"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 상위 10개의 음의 상관계수\n",
    "# Ten most negative correlations\n",
    "pd.DataFrame(corrs['TARGET'].dropna().tail(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "목표값과 가장 큰 상관계수를 가지는 변수는 새롭게 생성된 변수입니다. 그러나 상관계수가 높다는 것이 그 변수가 유용하다는 것을 의미하지는 않으며, 수백개의 변수들을 생성했을 경우에는, 그저 random noise 때문에 상관관계에 있는 것처럼 보일 수도 있다는 것을 명심해야 합니다.\n",
    "\n",
    "비판적으로 상관계수들을 들여다봤을 때, 그래도 새롭게 생성된 몇몇 변수들은 유용할 것처럼 보입니다. 변수들의 유용성(usefulness)를 평가하기 위해, 학습된 모델로부터 Feature Importance를 살펴보도록 하겠습니다. 호기심을 충족시키기위해 (그리고 이미 함수를 작성하였기 때문에) 새롭게 생성된 변수들에 대한 kde 그래프를 작성할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between client_bureau_balance_MONTHS_BALANCE_count_mean and the TARGET is -0.0802\n",
      "Median value for loan that was not repaid = 19.3333\n",
      "Median value for loan that was repaid =     25.1429\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 원문에는 client_bureau_balance_counts_mean 을 사용하고 있지만 해당 변수가 없어,\n",
    "# 'client_bureau_balance_MONTHS_BALANCE_count_mean'으로 대체\n",
    "\n",
    "kde_target(var_name='client_bureau_balance_MONTHS_BALANCE_count_mean', df=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(원문에는 client_bureau_balance_counts_mean 을 사용하고 있지만 해당 변수가 없어, client_bureau_balance_MONTHS_BALANCE_count_mean'으로 대체하였습니다.)\n",
    "\n",
    "이 변수는 각 고객의 대출별 월별 기록에 대한 평균을 의미합니다. 예를들어, 만약 고객의 과거 월별로 3, 4, 5의 기록을 갖고 있는 3개의 대출을 갖고 있다면, 이 변수에 있어 데이터값은 4가 됩니다. 분산 그래프에 기초하여, 과거 월별 평균이 높은 사람들이 Home Credit에서의 대출에서 상환을 잘하는 것으로 보입니다. 이 변수에 대해 너무 많이 분석하지는 않겠습니다. 다만, 이 변수는 더 많은 신용 기록을 가지고 있는 고객들이 일반적으로 대출금을 상환할 가능성이 더 높다는 것을 나타낼 수 있습니다.  \n",
    "\n",
    "This variable represents the average number of monthly records per loan for each client. For example, if a client had three previous loans with 3, 4, and 5 records in the monthly data, the value of this variable for them would be 4. Based on the distribution, clients with a greater number of average monthly records per loan were more likely to repay their loans with Home Credit. Let's not read too much into this value, but it could indicate that clients who have had more previous credit history are generally more likely to repay a loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between bureau_CREDIT_ACTIVE_Active_count_norm and the TARGET is 0.0774\n",
      "Median value for loan that was not repaid = 0.5000\n",
      "Median value for loan that was repaid =     0.3636\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kde_target(var_name='bureau_CREDIT_ACTIVE_Active_count_norm', df=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 변수의 경우 모든 곳에서 불규칙합니다. 이 변수는 `CREDIT_ACTIVE`의 값이 'Active'인 것의 갯수를 전체 대출 갯수로 나눈 값입니다. 상관관계가 매우 약하기 때무에, 별도의 결론은 생각하지 않도록 하겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Collinear Variables\n",
    "\n",
    "목표값(target)과 변수간의 상관계수만 계산하는 것이 아닌, 각 변수간 상관계수까지 계산할 수 있습니다. 이러한 방법은 제거해야 할 수도 있는 collinear 관계들을 가지는 변수들이 있는지 여부를 알려줍니다.\n",
    "\n",
    "0.8 이상의 상관계수를 가지는 변수들을 찾아보도록하겠습니다.\n",
    "\n",
    "* 참고자료 : ['Collinearity', Britannica.com](https://www.britannica.com/topic/collinearity-statistics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 임계치 설정\n",
    "# Set the threshold\n",
    "threshold = 0.8\n",
    "\n",
    "# 상관계수가 높은 변수들을 저장하기 위한 빈 dictionary 생성\n",
    "# Empty dictionary to hold correlated variables\n",
    "above_threshold_vars = {}\n",
    "\n",
    "# 각각의 칼럼마다 임계치 이상의 변수들을 저장\n",
    "# For each column, record the variables that are above the threshold\n",
    "for col in corrs:\n",
    "    above_threshold_vars[col] = list(corrs.index[corrs[col] > threshold])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "높은 상관관계를 가지는 변수들의 쌍에 대해서, 하나의 변수만을 제거해야 합니다. 아래의 코드는 각각의 쌍에서 한 개의 변수들만을 저장하는 set을 만드는 역할을 수행합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns to remove:  134\n"
     ]
    }
   ],
   "source": [
    "# 제거할 column들 및 이미 검사된 column들의 목록을 저장위한 list 생성\n",
    "# Track columns to remove and columns already examined\n",
    "cols_to_remove = []\n",
    "cols_seen = []\n",
    "cols_to_remove_pair = []\n",
    "\n",
    "# columns 및 높은 상관관계를 가지는 columns를 따라 반복문 실행\n",
    "# Iterate through columns and correlated columns\n",
    "for key, value in above_threshold_vars.items():\n",
    "    \n",
    "    # 이미 검사된 column 저장\n",
    "    # keep track of columns already examined\n",
    "    cols_seen.append(key)\n",
    "    for x in value:\n",
    "        if x == key:\n",
    "            next\n",
    "        else:\n",
    "           # 각 쌍중 하나의 column만을 제거\n",
    "           if x not in cols_seen:\n",
    "                cols_to_remove.append(x)\n",
    "                cols_to_remove_pair.append(key) \n",
    "            \n",
    "cols_to_remove = list(set(cols_to_remove))\n",
    "print('Number of columns to remove: ', len(cols_to_remove))   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 훈련용 데이터와 테스트 데이터에서 column들을 제거할 수 있습니다. 다만, column들을 제거한 데이터와 그렇지 않은 데이터로부터의 성능을 비교해보아야 합니다.(앞서 저장한 csv 파일을 활용할 수 있습니다.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Corrs Removed Shape:  (307511, 199)\n",
      "Testing Corrs Removed Shape:  (48744, 198)\n"
     ]
    }
   ],
   "source": [
    "train_corrs_removed = train.drop(columns = cols_to_remove)\n",
    "test_corrs_removed = test.drop(columns = cols_to_remove)\n",
    "\n",
    "print('Training Corrs Removed Shape: ', train_corrs_removed.shape)\n",
    "print('Testing Corrs Removed Shape: ', test_corrs_removed.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_corrs_removed.to_csv('train_bureau_corrs_removed.csv', index = False)\n",
    "test_corrs_removed.to_csv('test_bureau_corrs_removed.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 모델링 (Modeling)\n",
    "\n",
    "이제 새롭게 생성된 데이터셋의 성능을 평가하기 위해, 이 데이터셋을 머신러닝에 사용하도록 하겠습니다. 여기서는 다른 노트북(커널)에서 만들었던 함수를 활용해서, 다른 두 데이터셋(기존 데이터셋과, column들을 제거한 데이터셋)의 성능을 비교해보도록 하겠습니다. 이러한 종류의 어떠한 실험도 할 수 있으며, 비교대상(control, 통제집단)은 `application` 데이터만 이 함수에 사용한 뒤, 제출했을 때의 점수입니다. 이미 그 점수는 기록해놓았으므로, 두 테스트 데이터셋(기존 데이터셋과, column들을 제거한 데이터셋)으로부터의 결과와 비교해볼 수 있습니다. \n",
    "\n",
    "**모든 데이터셋에 대해, 아래와 같은 모델(정확히 같은 파라미터)를 사용할 것입니다.**\n",
    "\n",
    "- control : `application` 데이터만 사용하여 얻은 결과\n",
    "- test one : `application` 데이터와 더불어 `bureau` 및 `bureau_balance` 파일로부터 얻은 데이터를 사용하여 얻은 결과\n",
    "- test two : `application` 데이터와 더불어 `bureau` 및 `bureau_balance` 파일로부터 얻은 데이터를 사용하되 높은 상관관계를 가지는 변수들을 제거하여 얻은 결과"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "import gc\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(features, test_features, encoding='ohe', n_folds = 5):\n",
    "    \"\"\"\n",
    "    Train and test a light gradient boosting model using cross validation\n",
    "    \n",
    "    \n",
    "    파라미터(Parameters)\n",
    "    -------------------\n",
    "        features (pd.DataFrame):\n",
    "            모델을 훈련하는데 있어 사용되는 feature들의 데이터프레임. 반드시 TARGET column을 포함하고 있어야 함.\n",
    "            dataframe of training features to use for training a model. Must include the TARGET column.\n",
    "        test_features (pd.DataFrame):\n",
    "            모델을 활용하여 예측하는데 사용되는 feature들의 데이터프레임.\n",
    "            dataframe of testing features to use for making predictions with the model. \n",
    "        encoding (str, default='ohe'):\n",
    "            범주형 데이터(categorical variables)를 인코딩하는데 사용할 방식. \n",
    "            one hot encoding을 의미하는 'ohe' 또는 interger label encoding을 뜻하는 'le' 중 사용\n",
    "            method for encoding categorical variables. Either 'ohe' for one-hot encoding or 'le' for integer label encoding\n",
    "        n_folds (int, default=5): cross validation에 활용할 folds의 갯수\n",
    "        \n",
    "    출력값(Return)\n",
    "    --------------\n",
    "        submission (pd.DataFrame):\n",
    "            `SK_ID_CURR` 및 예측된 확률값인 `TARGET` 으로 구성된 데이터프레임\n",
    "            dataframe with `SK_ID_CURR` and `TARGET` probabilities predicted by the model.\n",
    "        feature_importances (pd.DataFrame):\n",
    "            모델로부터 얻어진 feature importance가 저장된 데이터프레임\n",
    "        valid_metrics (pd.DataFrame):\n",
    "            각각의 fold 및 전체 데이터에 있어 training 및 validation 점수가 저장된 dataframe\n",
    "    \n",
    "    \"\"\"\n",
    "    # id 추출\n",
    "    # Extract the ids\n",
    "    train_ids = features['SK_ID_CURR']\n",
    "    test_ids = test_features['SK_ID_CURR']\n",
    "    \n",
    "    # 훈련단계에 사용되는 label 추출\n",
    "    # Extract the labels for training\n",
    "    labels = features['TARGET']\n",
    "    \n",
    "    # id와 target값 제거\n",
    "    # Remove the ids and target\n",
    "    features = features.drop(columns = ['SK_ID_CURR', 'TARGET'])\n",
    "    test_features = test_features.drop(columns = ['SK_ID_CURR'])\n",
    "    \n",
    "    # One Hot 인코딩\n",
    "    # One Hot Encoding\n",
    "    if encoding == 'ohe':\n",
    "        features = pd.get_dummies(features)\n",
    "        test_features = pd.get_dummies(test_features)\n",
    "        \n",
    "        # 데이터프레임들의 column들을 일치시키기\n",
    "        # Align the dataframes by the columns\n",
    "        features, test_features = features.align(test_features, join='inner', axis=1)\n",
    "        \n",
    "        # 기록할 범주형 인덱스 없음\n",
    "        # No categorical indices to record\n",
    "        cat_indices = 'auto'\n",
    "        \n",
    "    # Integer label 인코딩\n",
    "    # Integer label encoding\n",
    "    elif encoding == 'le':\n",
    "        \n",
    "        # label encoder 생성\n",
    "        # create a label encoder\n",
    "        label_encoder = LabelEncoder()\n",
    "        \n",
    "        # 범주형 인덱스들을 저장하기 위한 리스트 생성\n",
    "        # List for storing categorical indices\n",
    "        cat_indices = []\n",
    "        \n",
    "        # 각 column별로 반복문 실행\n",
    "        # Iterate through each column\n",
    "        for i, col in enumerate(features):\n",
    "            if features[col].dtype == 'object':\n",
    "                \n",
    "                # 범주형 데이터를 정수(integer)로 맵핑\n",
    "                # Map the categorical features to integers\n",
    "                features[col] = label_encoder.fit_transform(np.array(features[col].astype(str)).reshape((-1,)))\n",
    "                test_features[col] = label_encoder.transform(np.array(test_features[col].astype(str)).reshape((-1,)))\n",
    "                \n",
    "                # 범주형 인덱스들을 기록\n",
    "                # Record the categorical indices\n",
    "                cat_indices.append(i)\n",
    "    \n",
    "    # 유효하지 않은 인코딩 방식이 입력되었을 때 오류 처\n",
    "    # Catch error if label encoding scheme is not valid\n",
    "    else:\n",
    "        raise ValueError(\"Encoding must be either 'ohe' or 'le'\")\n",
    "        \n",
    "    print('Training Data Shape: ', features.shape)\n",
    "    print('Testing Data Shape: ', test_features.shape)    \n",
    "    \n",
    "    # feature 이름들을 추출\n",
    "    # Extract feature names\n",
    "    feature_names = list(features.columns)\n",
    "    \n",
    "    # np array로 변환\n",
    "    # Convert to np arrays\n",
    "    features = np.array(features)\n",
    "    test_features = np.array(test_features)\n",
    "    \n",
    "    # kfold 오브젝트 생성\n",
    "    # Create the kfold object\n",
    "    k_fold = KFold(n_splits = n_folds, shuffle = False, random_state = 50)\n",
    "    \n",
    "    # feature importance를 저장하기 위한 array 생성\n",
    "    # Empty array for feature importances\n",
    "    feature_importance_values = np.zeros(len(feature_names))\n",
    "    \n",
    "    # 예측값을 저장하기 위한 array 생성\n",
    "    # Empty array for test predictions\n",
    "    test_predictions = np.zeros(test_features.shape[0])\n",
    "    \n",
    "    # 표본 외(out of fold) validation predictions 위한 array 생성\n",
    "    # Empty array for out of fold validation predictions\n",
    "    out_of_fold = np.zeros(features.shape[0])\n",
    "    \n",
    "    # validation 및 training 점수를 저장하기 위한 리스트 생성\n",
    "    # Lists for recording validation and training scores\n",
    "    valid_scores = []\n",
    "    train_scores = []    \n",
    "    \n",
    "    # 각 fold별 반복문 실행\n",
    "    # Iterate through each fold\n",
    "    for train_indices, valid_indices in k_fold.split(features):\n",
    "        \n",
    "        # fold에서의 훈련용 데이터\n",
    "        # Training data for the fold\n",
    "        train_features, train_labels = features[train_indices], labels[train_indices]\n",
    "        \n",
    "        # fold에서의 검증용 데이터\n",
    "        # Validation data for the fold\n",
    "        valid_features, valid_labels = features[valid_indices], labels[valid_indices]\n",
    "        \n",
    "        # 모델 생성\n",
    "        # Create the model\n",
    "        model = lgb.LGBMClassifier(n_estimators=10000, objective = 'binary', \n",
    "                                   class_weight = 'balanced', learning_rate = 0.05, \n",
    "                                   reg_alpha = 0.1, reg_lambda = 0.1, \n",
    "                                   subsample = 0.8, n_jobs = -1, random_state = 50)\n",
    "        \n",
    "        # 모델 훈련\n",
    "        # Train the model\n",
    "        model.fit(train_features, train_labels, eval_metric = 'auc',\n",
    "                  eval_set = [(valid_features, valid_labels), (train_features, train_labels)],\n",
    "                  eval_names = ['valid', 'train'], categorical_feature = cat_indices,\n",
    "                  early_stopping_rounds = 100, verbose = 200)\n",
    "        \n",
    "        # 가장 좋았던 iteration 저장\n",
    "        # Record the best iteration\n",
    "        best_iteration = model.best_iteration_\n",
    "        \n",
    "        # feature importances 저장\n",
    "        # Record the feature importances\n",
    "        feature_importance_values += model.feature_importances_ / k_fold.n_splits\n",
    "        \n",
    "        # 예측\n",
    "        # make predictions\n",
    "        test_predictions += model.predict_proba(test_features, num_iteration = best_iteration)[:, 1] / k_fold.n_splits\n",
    "        \n",
    "        # 표본외 예측 저장\n",
    "        # Record the out of fold predictions\n",
    "        out_of_fold[valid_indices] = model.predict_proba(valid_features, num_iteration = best_iteration)[:, 1]\n",
    "        \n",
    "        # 가장 좋은 점수 기록\n",
    "        # Record the best score\n",
    "        valid_score = model.best_score_['valid']['auc']\n",
    "        train_score = model.best_score_['train']['auc']\n",
    "        \n",
    "        valid_scores.append(valid_score)\n",
    "        train_scores.append(train_score)\n",
    "        \n",
    "        # 메모리 초기화\n",
    "        # Clean up memory\n",
    "        gc.enable()\n",
    "        del model, train_features, valid_features\n",
    "        gc.collect()\n",
    "        \n",
    "    # submission 데이터프레임 생\n",
    "    # Make the submission dataframe\n",
    "    submission = pd.DataFrame({'SK_ID_CURR': test_ids, 'TARGET': test_predictions})\n",
    "\n",
    "    # feature importance 데이터프레임 생성\n",
    "    # Make the feature importance dataframe\n",
    "    feature_importances = pd.DataFrame({'feature': feature_names, 'importance': feature_importance_values})\n",
    "\n",
    "    # 전체 데이터의 validation score 측정\n",
    "    # Overall validation score\n",
    "    valid_auc = roc_auc_score(labels, out_of_fold)\n",
    "\n",
    "    # 전체 데이터에 데한 score를 metric에 추가\n",
    "    # Add the overall scores to the metrics\n",
    "    valid_scores.append(valid_auc)\n",
    "    train_scores.append(np.mean(train_scores))\n",
    "\n",
    "    # validation scores를 위한 데이터프레임 생성\n",
    "    # Needed for creating dataframe of validation scores\n",
    "    fold_names = list(range(n_folds))\n",
    "    fold_names.append('overall')\n",
    "\n",
    "    # training 및 validation score가 저장된 데이터프레임 생성\n",
    "    # Dataframe of validation scores\n",
    "    metrics = pd.DataFrame({'fold': fold_names,\n",
    "                            'train': train_scores,\n",
    "                            'valid': valid_scores}) \n",
    "\n",
    "    return submission, feature_importances, metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_feature_importance(df):\n",
    "    \"\"\"\n",
    "    \n",
    "    모델로부터 feature imporantance를 플롯. 높은 중요도가 더 나은 값을 나타내는 다른 측정 방들과 같이 사용할 수 있습니다. \n",
    "    Plot importances returned by a model. This can work with any measure of feature importance provided\n",
    "    that higher importance is better. \n",
    "    \n",
    "    인수 (Args):\n",
    "       \n",
    "        df (dataframe): feature importance들이 저장된 데이터프레임. `features` column에는 feature들이 저장되어있고,\n",
    "        `importance` column에는 imporance가 저장되어있음.\n",
    "        \n",
    "        df (dataframe): feature importances. Must have the features in a column\n",
    "        called `features` and the importances in a column called `importance`      \n",
    "    \n",
    "    출력값 (Returns):\n",
    "        \n",
    "        상위 15개의 중요한 feature들을 플롯\n",
    "        shows a plot of the 15 most importance features\n",
    "    \n",
    "        df (dataframe) : 내림차순으로 정렬한 정규화(normalized) feature importance들.\n",
    "        \n",
    "        df (dataframe): feature importances sorted by importance (highest to lowest) \n",
    "        with a column for normalized importance    \n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    # 중요도(importance)에 따라 정렬\n",
    "    # Sort features according to importance\n",
    "    df = df.sort_values('importance', ascending = False).reset_index()\n",
    "    \n",
    "    # feature importance 정규화(normalize) 및 새로운 column에 추가\n",
    "    # Normalize the feature importances to add up to one\n",
    "    df['importance_normalized'] = df['importance'] / df['importance'].sum()\n",
    "    \n",
    "    # feature importance 바차트 생성\n",
    "    # Make a horizontal bar chart of feature importances\n",
    "    plt.figure(figsize = (10, 6))\n",
    "    ax = plt.subplot()\n",
    "    \n",
    "    # 중요도가 큰 feature들이 위로가도록  index를 반전(reverse)\n",
    "    # Need to reverse the index to plot most important on top\n",
    "    ax.barh(list(reversed(list(df.index[:15]))), df['importance_normalized'].head(15), \n",
    "            align = 'center', edgecolor = 'k')\n",
    "    \n",
    "    # y축 눈금 및 label 설정\n",
    "    # Set the yticks and labels\n",
    "    ax.set_yticks(list(reversed(list(df.index[:15]))))\n",
    "    ax.set_yticklabels(df['feature'].head(15))\n",
    "    \n",
    "    # x축 및 그래프 제목 설정 후 플롯\n",
    "    # Plot labeling\n",
    "    plt.xlabel('Normalized Importance'); plt.title('Feature Importances')\n",
    "    plt.show()\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Control (비교군)\n",
    "\n",
    "우선, 비교군(Control)에 함수를 적용해보도록 하겠습니다. 여기서는 위에서 작성한 함수를 `application`데이터에 적용해보도록 하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_control = pd.read_csv('../input/application_train.csv')\n",
    "test_control = pd.read_csv('../input/application_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다행히도, 시간을 들여 함수를 작성했기 때문에, 간편히 사용할 수 있습니다. (함수를 활용하여 과정을 간략화하고, 생산적으로 만드는 것은 이 커널에서의 주요 주제입니다.) 함수는 우리가 컴페티션에 제출할 submission 데이터프레임을 반환함과 더불어 feature importance가 저장된 fi 데이터프레임, validation 및 test 점수가 저장된  metrics 데이터프레임을 반환합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Shape:  (307511, 241)\n",
      "Testing Data Shape:  (48744, 241)\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.561913\tvalid's auc: 0.760007\ttrain's binary_logloss: 0.553723\ttrain's auc: 0.798103\n",
      "Early stopping, best iteration is:\n",
      "[269]\tvalid's binary_logloss: 0.555207\tvalid's auc: 0.760273\ttrain's binary_logloss: 0.544131\ttrain's auc: 0.809199\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.566651\tvalid's auc: 0.76114\ttrain's binary_logloss: 0.55348\ttrain's auc: 0.798328\n",
      "Early stopping, best iteration is:\n",
      "[289]\tvalid's binary_logloss: 0.558103\tvalid's auc: 0.761398\ttrain's binary_logloss: 0.540939\ttrain's auc: 0.812654\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.564639\tvalid's auc: 0.750232\ttrain's binary_logloss: 0.552049\ttrain's auc: 0.79964\n",
      "Early stopping, best iteration is:\n",
      "[265]\tvalid's binary_logloss: 0.558629\tvalid's auc: 0.750451\ttrain's binary_logloss: 0.543186\ttrain's auc: 0.809734\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.562165\tvalid's auc: 0.759831\ttrain's binary_logloss: 0.553965\ttrain's auc: 0.797797\n",
      "Early stopping, best iteration is:\n",
      "[282]\tvalid's binary_logloss: 0.554078\tvalid's auc: 0.760245\ttrain's binary_logloss: 0.542308\ttrain's auc: 0.811121\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.562326\tvalid's auc: 0.76071\ttrain's binary_logloss: 0.55357\ttrain's auc: 0.798106\n",
      "Early stopping, best iteration is:\n",
      "[226]\tvalid's binary_logloss: 0.559764\tvalid's auc: 0.760972\ttrain's binary_logloss: 0.549961\ttrain's auc: 0.802236\n"
     ]
    }
   ],
   "source": [
    "submission, fi, metrics = model(train_control, test_control)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>train</th>\n",
       "      <th>valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.809199</td>\n",
       "      <td>0.760273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.812654</td>\n",
       "      <td>0.761398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.809734</td>\n",
       "      <td>0.750451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.811121</td>\n",
       "      <td>0.760245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.802236</td>\n",
       "      <td>0.760972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>overall</td>\n",
       "      <td>0.808989</td>\n",
       "      <td>0.758635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fold     train     valid\n",
       "0        0  0.809199  0.760273\n",
       "1        1  0.812654  0.761398\n",
       "2        2  0.809734  0.750451\n",
       "3        3  0.811121  0.760245\n",
       "4        4  0.802236  0.760972\n",
       "5  overall  0.808989  0.758635"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "비교군의 경우, training 점수가 vailidation 점수보다 높으므로, 약간 과적합(overfit)되었다고 볼 수 있습니다. 이 문제는 regualarization을 다루는 다음 커널에서 다뤄보도록 하겠습니다. (이 커널에서는 이미 `early stopping` 뿐아니라  `reg_lambda`, `reg_alpha`와 같은 파라미터들을 설정함으로써, 몇가지 regularization 방법을 사용했습니다.)\n",
    "\n",
    "이제 `plot_feature_importances`를 활용하여 feature importance를 시각화해보도록 하겠습니다. 이 feature_importance들은 feature selection에 유용하게 사용할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi_sorted = plot_feature_importance(fi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('control.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**제출결과, 비교군(control)의 점수는 0.745를 기록했습니다.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test One\n",
    "\n",
    "이제 첫번째 테스트를 해보겠습니다. 함수에 데이터를 입력할 것이고, 이로써 대부분의 작업들이 수행될 것입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Shape:  (307511, 452)\n",
      "Testing Data Shape:  (48744, 452)\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.552795\tvalid's auc: 0.766471\ttrain's binary_logloss: 0.54244\ttrain's auc: 0.810385\n",
      "Early stopping, best iteration is:\n",
      "[293]\tvalid's binary_logloss: 0.541991\tvalid's auc: 0.767203\ttrain's binary_logloss: 0.526837\ttrain's auc: 0.827287\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.557044\tvalid's auc: 0.767523\ttrain's binary_logloss: 0.542012\ttrain's auc: 0.810288\n",
      "Early stopping, best iteration is:\n",
      "[265]\tvalid's binary_logloss: 0.549333\tvalid's auc: 0.768109\ttrain's binary_logloss: 0.531106\ttrain's auc: 0.822264\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.553525\tvalid's auc: 0.761029\ttrain's binary_logloss: 0.540763\ttrain's auc: 0.811653\n",
      "Early stopping, best iteration is:\n",
      "[267]\tvalid's binary_logloss: 0.545557\tvalid's auc: 0.761625\ttrain's binary_logloss: 0.529336\ttrain's auc: 0.824147\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.552206\tvalid's auc: 0.767837\ttrain's binary_logloss: 0.542787\ttrain's auc: 0.809671\n",
      "Early stopping, best iteration is:\n",
      "[236]\tvalid's binary_logloss: 0.548001\tvalid's auc: 0.76815\ttrain's binary_logloss: 0.536744\ttrain's auc: 0.816322\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.553059\tvalid's auc: 0.767516\ttrain's binary_logloss: 0.542515\ttrain's auc: 0.809788\n",
      "[400]\tvalid's binary_logloss: 0.532101\tvalid's auc: 0.767583\ttrain's binary_logloss: 0.5116\ttrain's auc: 0.842953\n",
      "Early stopping, best iteration is:\n",
      "[306]\tvalid's binary_logloss: 0.541445\tvalid's auc: 0.767902\ttrain's binary_logloss: 0.525454\ttrain's auc: 0.828518\n"
     ]
    }
   ],
   "source": [
    "submission_raw, fi_raw, metrics_raw = model(train, test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>fold</th>\n",
       "      <th>train</th>\n",
       "      <th>valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.827287</td>\n",
       "      <td>0.767203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.822264</td>\n",
       "      <td>0.768109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.824147</td>\n",
       "      <td>0.761625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.816322</td>\n",
       "      <td>0.768150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.828518</td>\n",
       "      <td>0.767902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>overall</td>\n",
       "      <td>0.823708</td>\n",
       "      <td>0.766597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fold     train     valid\n",
       "0        0  0.827287  0.767203\n",
       "1        1  0.822264  0.768109\n",
       "2        2  0.824147  0.761625\n",
       "3        3  0.816322  0.768150\n",
       "4        4  0.828518  0.767902\n",
       "5  overall  0.823708  0.766597"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이러한 수치들에 기반했을 때, 만들어진(engineered) feature들은 비교군보다 더 나은 성능을 보입니다. 그러나 높은 validation 점수가 test로도 이어진다고 단정하기 위해서는 결과를 제출해봐야 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi_raw_sorted = plot_feature_importance(fi_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "feature importance를 살펴보면, 위에서 새롭게 생성된 몇몇 feature들이 중요한 것으로 나타났습니다. 상위 100개의 feature 중 커널에서 새롭게 생성된 것들의 비율을 살펴보도록 하겠습니다. 단순히 기존 feature들과 비교하기 보다는, one hot 인코딩된 feature들과 비교해보야 합니다. 이것은 이미 fi 변수에 저장되어 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Top 100 Features created from the bureau data = 53.00\n"
     ]
    }
   ],
   "source": [
    "top_100 = list(fi_raw_sorted['feature'])[:100]\n",
    "new_features = [x for x in top_100 if x not in list(fi['feature'])]\n",
    "\n",
    "print('%% of Top 100 Features created from the bureau data = %d.00' % len(new_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "과반수의 feature들이 새롭게 생성된 것입니다. 이는 위에서의 고단한 작업들이 의미가 있었음을 의미합니다. :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_raw.to_csv('test_one.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**제출결과, test one의 점수는 0.759를 기록했습니다.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test two\n",
    "\n",
    "이제 다른 테스트를 해보도록 하겠습니다. 전과 비슷하지만, 강한 collinear를 가지는 변수들이 제거된 데이터를 사용할 것입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Shape:  (307511, 318)\n",
      "Testing Data Shape:  (48744, 318)\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.555617\tvalid's auc: 0.764122\ttrain's binary_logloss: 0.546022\ttrain's auc: 0.8063\n",
      "Early stopping, best iteration is:\n",
      "[264]\tvalid's binary_logloss: 0.548248\tvalid's auc: 0.764565\ttrain's binary_logloss: 0.535569\ttrain's auc: 0.817897\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.559872\tvalid's auc: 0.7652\ttrain's binary_logloss: 0.545406\ttrain's auc: 0.806611\n",
      "[400]\tvalid's binary_logloss: 0.539423\tvalid's auc: 0.765625\ttrain's binary_logloss: 0.515253\ttrain's auc: 0.839251\n",
      "Early stopping, best iteration is:\n",
      "[379]\tvalid's binary_logloss: 0.5413\tvalid's auc: 0.765829\ttrain's binary_logloss: 0.518046\ttrain's auc: 0.836334\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.557009\tvalid's auc: 0.757092\ttrain's binary_logloss: 0.543861\ttrain's auc: 0.808063\n",
      "Early stopping, best iteration is:\n",
      "[198]\tvalid's binary_logloss: 0.557242\tvalid's auc: 0.757149\ttrain's binary_logloss: 0.54424\ttrain's auc: 0.807627\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.555451\tvalid's auc: 0.764131\ttrain's binary_logloss: 0.546064\ttrain's auc: 0.806097\n",
      "Early stopping, best iteration is:\n",
      "[287]\tvalid's binary_logloss: 0.545753\tvalid's auc: 0.76481\ttrain's binary_logloss: 0.532114\ttrain's auc: 0.821551\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's binary_logloss: 0.556609\tvalid's auc: 0.763964\ttrain's binary_logloss: 0.54574\ttrain's auc: 0.806256\n",
      "[400]\tvalid's binary_logloss: 0.536359\tvalid's auc: 0.763928\ttrain's binary_logloss: 0.515821\ttrain's auc: 0.838649\n",
      "Early stopping, best iteration is:\n",
      "[312]\tvalid's binary_logloss: 0.544814\tvalid's auc: 0.764449\ttrain's binary_logloss: 0.528387\ttrain's auc: 0.825298\n"
     ]
    }
   ],
   "source": [
    "submission_corrs, fi_corrs, metrics_corr = model(train_corrs_removed, test_corrs_removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>train</th>\n",
       "      <th>valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.817897</td>\n",
       "      <td>0.764565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.836334</td>\n",
       "      <td>0.765829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.807627</td>\n",
       "      <td>0.757149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.821551</td>\n",
       "      <td>0.764810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.825298</td>\n",
       "      <td>0.764449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>overall</td>\n",
       "      <td>0.821741</td>\n",
       "      <td>0.763352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fold     train     valid\n",
       "0        0  0.817897  0.764565\n",
       "1        1  0.836334  0.765829\n",
       "2        2  0.807627  0.757149\n",
       "3        3  0.821551  0.764810\n",
       "4        4  0.825298  0.764449\n",
       "5  overall  0.821741  0.763352"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "결과를 살펴보면, 비교군보다는 좋은 성능을 보여주지만, test one보다는 약간 낮은 성능을 보여줍니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plot_feature_importances' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-91-91edcfb16b52>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfi_corrs_sorted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_feature_importances\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfi_corrs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'plot_feature_importances' is not defined"
     ]
    }
   ],
   "source": [
    "fi_corrs_sorted = plot_feature_importance(fi_corrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_corrs.to_csv('test_two.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**제출결과, test two의 점수는 0.753을 기록했습니다.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결론 (Results)\n",
    "\n",
    "결론적으로, 추가된 정보들이 성능을 향상시켰다고 말할 수 있습니다! 모델은 분명히 최적화되지 않았지만, 기존 데이터에 연산된 feature들을 추가하였을 때, 눈에 띄는 성능의 향상이 있었습니다. 결과를 요약해보도록 하겠습니다.\n",
    "\n",
    "| __Experiment__ | __Train AUC__ | __Validation AUC__ | __Test AUC__  |\n",
    "|------------|-------|------------|-------|\n",
    "| __Control__    | 0.815 | 0.760      | 0.745 |\n",
    "| __Test One__   | 0.837 | 0.767      | 0.759 |\n",
    "| __Test Two__   | 0.826 | 0.765      | 0.753 |\n",
    "\n",
    "(이러한 점수들은 커널을 실행할때마다 바뀔 수 있음을 유의하십시오)\n",
    "\n",
    "위에서 했던 모든 노력들은 기존 test 데이터에 비해 0.014의 ROC AUC의 개선으로 이어졌습니다. 강한 collinear 변수들을 제거한 것이 성능 저하로 이어졌기 때문에, feature selection에 있어 다른 방법들을 고려하려합니다. 또한, 모델이 중요하다고 판단한 몇몇 feature들은 새롭게 생성된 feature들이었습니다.\n",
    "\n",
    "\n",
    "이러한 컴페티션에서는 위와 같은 개선으로서 리더보드에서 100위 정도를 향상시킬 수 있습니다. 이 커널에서와 같은 수많은 작은 개선점들을 활용하여 점차적으로 성능을 개선시킬 수 있습니다. 이러한 결과들을 활용하여, 다른 분들이 각자 성능을 개선시키는 것을 권장하며, 저는 계속해서 다른 사람들을 돕기 위해 과정들을 문서화할 예정입니다. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 다음 단계 (Next Steps)\n",
    "\n",
    "앞으로, 이 커널에서 만들어진 함수들은 다른 데이터셋에서 활용할 수 있습니다. 아직 4개의 다른 데이터셋을 모델에 활용할 수 있습니다. 다음 커널에서는 이러한 다른 데이터(Home Credit의 이전 대출들을 담고 있는)의 정보를 훈련용 데이터셋에 포함시킬 것입니다. 그 다음, 동일한 모델을 만들고 계속해서 실험함으로써 feature engineering의 효과들을 측정할 수 있습니다. 이 컴퍼티션에서는 아직 수행해야할 작업들이 많이 남아있고, 이로써 개선의 여지 역시 많이 남아있습니다. 다음 커널에서 뵙겠습니다."
   ]
  }
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