Week_1_김희숙_예습과제.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CHAPTER 2\n",
    "# 머신러닝 프로젝트 처음부터 끝까지\n",
    "\n",
    "[머신러닝의 주요 단계]\n",
    "1. 큰 그림 보기\n",
    "2. 데이터 가져오기\n",
    "3. 데이터 탐색과 시각화\n",
    "4. 머신러닝 알고리즘을 위한 데이터 준비\n",
    "5. 모델 선택과 훈련\n",
    "6. 모델 세부 튜닝\n",
    "7. 론칭, 모니터링, 시스템 유지 보수\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "import urllib.request\n",
    "\n",
    "#데이터를 추출하는 코드\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/rickiepark/handson-ml2/master/\"\n",
    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
    "\n",
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
    "    if not os.path.isdir(housing_path):\n",
    "        os.makedirs(housing_path)\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fetch_housing_data()\n",
    "#fetch_housing_data()를 호출하면 작업공간에 datasets/housing 디렉토리를 만들고 \n",
    "#ousing.tgz파일을 내려받고 같은 디렉토리에 압축을 풀어 housing.csv파일을 만든다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#판다스를 사용하여 데이터를 읽기\n",
    "import pandas as pd\n",
    "\n",
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "#load_housing_data() 함수는 모든 데이터를 담은 판다스의 데이터프레임 객체를 반환\n",
    "housing.head() #head() 메서드를 이용해 처음 다섯 행 확인\n",
    "#각 행은 하나의 구역을 나타냄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info() #데이터에 대한 간략한 설명\n",
    "#특히 전체 행 수, 각 특성의 데이터 타입과 널이 아닌 값의 개수 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Non-Null Count: 20640, 즉 20,640개의 샘플\n",
    "- 4 total_bedrooms 20433 non-null (float64): 207개의 null 존재\n",
    "- ocean_proximity(object): 데이터 타입이 object 이므로 어떤 파이썬 객체도 될 수 있지만, 데이터를 CSV 파일에서 읽었기 때문에 텍스트 특성<br>\n",
    "    head()로 데이터를 살펴보았을 때 'NEAR BAY' 값이 반복 -> 범주형 변수로 추정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ocean_proximity에 있는 카테고리의 종류와 각 카테고리의 수 확인\n",
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#나머지 데이터 열 확인\n",
    "#describe() 숫자형 특성의 요약 정보 출력, null값 제외\n",
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# 그림을 저장할 위치\\n\\nPROJECT_ROOT_DIR = \".\"\\nCHAPTER_ID = \"end_to_end_project\"\\nIMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\\nos.makedirs(IMAGES_PATH, exist_ok=True)\\n\\ndef save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\\n    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\\n    print(\"그림 저장:\", fig_id)\\n    if tight_layout:\\n        plt.tight_layout()\\n    plt.savefig(path, format=fig_extension, dpi=resolution)\\n    \\n'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "# 그림을 저장할 위치\n",
    "\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"end_to_end_project\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"그림 저장:\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)\n",
    "    \n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#주피터 노트북의 매직 명령 #맷플롯립이 주피터 자체의 백엔드를 사용하도록 설정\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50, figsize=(20,15)) #hist ()를 이용하여 숫자형 특성에 대한 히스토그램 출력\n",
    "#히스토그램은 주어진 값의 범위(수평축)에 속한 샘플 수(수직축)를 나타냄\n",
    "#save_fig(\"attribute_histogram_plots\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 확인할 수 있는 사항\n",
    "- median_income:  상한 = 15, 하한 = 0.5 [단위: 1 = 10,000 달러]\n",
    "- 특성들의 스케일이 서로 많이 다름\n",
    "- 대부분의 히스토그램의 꼬리가 두꺼움 -> 가운데에서 왼쪽보다 오른쪽으로 더 멀리 뻗어 있음 -> 이러한 형태는 일부 머신러닝 알고리즘에서 패턴을 찾기 어렵게 함 -> 종 모양의 분포가 되도록 변형 필요\n",
    "- housing_median_age 와 median_house_value: 히스토그램은 오른쪽에서 그래프가 심하게 높아지면 히스토그램이 끝나는 것으로 보아 마지막 값으로 한정되었음을 짐작\n",
    "- 가격이 한계값을 넘어가지 않도록 머신러닝 알고리즘이 학습 될 수 있음\n",
    "- 해결방법\n",
    "    - a. 한계값 밖의 구역에 대한 정확한 레이블을 구함\n",
    "    - b. 훈련 세트에서 이런 구역을 제거함"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터 스투핑 편향\n",
    "- 테스트 세트에서 겉으로 드러난 어떤 패턴에 속아 특정 머신러닝 모델을 선택\n",
    "- 테스트 세트로 일반화 오차를 추정하여 매우 낙관적인 추정이 되며 시스템을 론칭했을 때 기대한 성능이 나오지 않는 것\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#테스트 세트 만들기\n",
    "# 모듈 임포트\n",
    "import numpy as np\n",
    "\n",
    "# 노트북의 실행 결과가 동일하도록\n",
    "np.random.seed(42) #rnadom.seed() 설정을 하지 않으면 프로그램을 실행 할 때마다 다른 테스트 세트가 생성됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 예시로 만든 것\n",
    "#실전에서는 사이킷런의 train_test_split()를 사용\n",
    "def split_train_test(data, test_ratio):\n",
    "    shuffled_indices = np.random.permutation(len(data))\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "    test_indices = shuffled_indices[:test_set_size]\n",
    "    train_indices = shuffled_indices[test_set_size:]\n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set, test_set = split_train_test(housing, 0.2)\n",
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#각 샘플마다 식별자의 해시값을 계산하여 해시 최댓값의 20%보다 작거나 같은 샘플만 테스트 세트로 보낼 수 있음\n",
    "#여러번 반복 실행되면서 데이터셋이 갱신되더라도 테스트 세트가 동일하게 유지\n",
    "#새로운 테스트 세트는 새 샘플의 20%를 가지게 되지만 이전에 훈련 세트에 있던 샘플은 포함X\n",
    "from zlib import crc32\n",
    "\n",
    "def test_set_check(identifier, test_ratio):\n",
    "    return crc32(np.int64(identifier)) & 0xffffffff < test_ratio * 2**32\n",
    "\n",
    "def split_train_test_by_id(data, test_ratio, id_column):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hashlib\n",
    "\n",
    "def test_set_check(identifier, test_ratio, hash=hashlib.md5):\n",
    "    return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio\n",
    "\n",
    "#모든 해시 함수를 지원하고 파이썬 2와 파이썬 3에서 사용할 수 있는 함수\n",
    "def test_set_check(identifier, test_ratio, hash=hashlib.md5):\n",
    "    return bytearray(hash(np.int64(identifier)).digest())[-1] < 256 * test_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index()   # `index` 열이 추가된 데이터프레임을 반환\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 행의 인덱스를 고유 식별자로 사용할 때 새 데이터는 데이터셋의 끝에 추가, 어떤 행도 삭제되지 않아야 함\n",
    "- 이것이 불가능 할 땐 고유 식별자를 만드는 데 안전한 특성을 사용해야 함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#구역의 위도와 경도는 몇백 년 후까지 안정적이라고 보장\n",
    "# ->위도와 경도 두 값을 연결하여 ID 만들기 \n",
    "housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2.0804</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2202.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.2031</td>\n",
       "      <td>281500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>752.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>3.2705</td>\n",
       "      <td>241800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>3.0750</td>\n",
       "      <td>213500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>2.6736</td>\n",
       "      <td>191300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "8       8    -122.26     37.84                42.0       2555.0   \n",
       "10     10    -122.26     37.85                52.0       2202.0   \n",
       "11     11    -122.26     37.85                52.0       3503.0   \n",
       "12     12    -122.26     37.85                52.0       2491.0   \n",
       "13     13    -122.26     37.84                52.0        696.0   \n",
       "\n",
       "    total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "8            665.0      1206.0       595.0         2.0804            226700.0   \n",
       "10           434.0       910.0       402.0         3.2031            281500.0   \n",
       "11           752.0      1504.0       734.0         3.2705            241800.0   \n",
       "12           474.0      1098.0       468.0         3.0750            213500.0   \n",
       "13           191.0       345.0       174.0         2.6736            191300.0   \n",
       "\n",
       "   ocean_proximity         id  \n",
       "8         NEAR BAY -122222.16  \n",
       "10        NEAR BAY -122222.15  \n",
       "11        NEAR BAY -122222.15  \n",
       "12        NEAR BAY -122222.15  \n",
       "13        NEAR BAY -122222.16  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#train_test_split : 데이터셋을 여러 서브셋으로 나누는 간단한 함수 #random_state라는 난수 초깃값 설정 매개변수가 있음\n",
    "#행의 개수가 같은 여러 개의 데이터셋을 넘겨서 같은 인덱스를 기반으로 나눌 수 있음\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20046</th>\n",
       "      <td>-119.01</td>\n",
       "      <td>36.06</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1392.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1.6812</td>\n",
       "      <td>47700.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>-119.46</td>\n",
       "      <td>35.14</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1565.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>2.5313</td>\n",
       "      <td>45800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15663</th>\n",
       "      <td>-122.44</td>\n",
       "      <td>37.80</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3830.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>963.0</td>\n",
       "      <td>3.4801</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20484</th>\n",
       "      <td>-118.72</td>\n",
       "      <td>34.28</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3051.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1705.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>5.7376</td>\n",
       "      <td>218600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9814</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>36.62</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2351.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>3.7250</td>\n",
       "      <td>278000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "20046    -119.01     36.06                25.0       1505.0             NaN   \n",
       "3024     -119.46     35.14                30.0       2943.0             NaN   \n",
       "15663    -122.44     37.80                52.0       3830.0             NaN   \n",
       "20484    -118.72     34.28                17.0       3051.0             NaN   \n",
       "9814     -121.93     36.62                34.0       2351.0             NaN   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "20046      1392.0       359.0         1.6812             47700.0   \n",
       "3024       1565.0       584.0         2.5313             45800.0   \n",
       "15663      1310.0       963.0         3.4801            500001.0   \n",
       "20484      1705.0       495.0         5.7376            218600.0   \n",
       "9814       1063.0       428.0         3.7250            278000.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "20046          INLAND  \n",
       "3024           INLAND  \n",
       "15663        NEAR BAY  \n",
       "20484       <1H OCEAN  \n",
       "9814       NEAR OCEAN  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "계층적 샘플링\n",
    "- 계층이라는 동질의 그룹으로 나누고, 테스트 세트가 전체 데이터 세트를 대표하도록 각 계층에서 올바른 수의 샘플을 추출하는 것"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"median_income\"].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 중간 소득 대부분 1.5~6 사이에 모여있음 [단위 1 = $10,000]\n",
    "- 계층별로 테이터셋에 충분한 샘플 수가 있어야 함\n",
    "- 즉, 너무 많은 계층으로 나누면 안되고 각 계층이 충분히 커야 함을 의미"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.cut() 를 이용하여 카테고리 5개를 가진 소득 카테고리 특성을 만들기\n",
    "housing[\"income_cat\"] = pd.cut(housing[\"median_income\"],\n",
    "                               bins=[0., 1.5, 3.0, 4.5, 6., np.inf], #카테고리 1:0~1.5/ 2:1.5~3.0...\n",
    "                               labels=[1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    7236\n",
       "2    6581\n",
       "4    3639\n",
       "5    2362\n",
       "1     822\n",
       "Name: income_cat, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"income_cat\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 소득 카테고리 기반으로 계층 샘플링 \n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114583\n",
       "1    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 테스트 세트 소득 카테고리의 비율\n",
    "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350581\n",
       "2    0.318847\n",
       "4    0.176308\n",
       "5    0.114438\n",
       "1    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 전체 데이터 셋에 있는 소득 카테고리의 비율\n",
    "housing[\"income_cat\"].value_counts() / len(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def income_cat_proportions(data):\n",
    "    return data[\"income_cat\"].value_counts() / len(data)\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "\n",
    "compare_props = pd.DataFrame({\n",
    "    \"Overall\": income_cat_proportions(housing), #전체 샘플링\n",
    "    \"Stratified\": income_cat_proportions(strat_test_set),#계층 샘플링\n",
    "    \"Random\": income_cat_proportions(test_set),#무작위 샘플링\n",
    "}).sort_index()\n",
    "compare_props[\"Rand. %error\"] = 100 * compare_props[\"Random\"] / compare_props[\"Overall\"] - 100 #무작위 샘플링 오류율\n",
    "compare_props[\"Strat. %error\"] = 100 * compare_props[\"Stratified\"] / compare_props[\"Overall\"] - 100 #계층 샘플링 오류율"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Overall</th>\n",
       "      <th>Stratified</th>\n",
       "      <th>Random</th>\n",
       "      <th>Rand. %error</th>\n",
       "      <th>Strat. %error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.039826</td>\n",
       "      <td>0.039729</td>\n",
       "      <td>0.040213</td>\n",
       "      <td>0.973236</td>\n",
       "      <td>-0.243309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.318847</td>\n",
       "      <td>0.318798</td>\n",
       "      <td>0.324370</td>\n",
       "      <td>1.732260</td>\n",
       "      <td>-0.015195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.350581</td>\n",
       "      <td>0.350533</td>\n",
       "      <td>0.358527</td>\n",
       "      <td>2.266446</td>\n",
       "      <td>-0.013820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.176308</td>\n",
       "      <td>0.176357</td>\n",
       "      <td>0.167393</td>\n",
       "      <td>-5.056334</td>\n",
       "      <td>0.027480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.114438</td>\n",
       "      <td>0.114583</td>\n",
       "      <td>0.109496</td>\n",
       "      <td>-4.318374</td>\n",
       "      <td>0.127011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Overall  Stratified    Random  Rand. %error  Strat. %error\n",
       "1  0.039826    0.039729  0.040213      0.973236      -0.243309\n",
       "2  0.318847    0.318798  0.324370      1.732260      -0.015195\n",
       "3  0.350581    0.350533  0.358527      2.266446      -0.013820\n",
       "4  0.176308    0.176357  0.167393     -5.056334       0.027480\n",
       "5  0.114438    0.114583  0.109496     -4.318374       0.127011"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_props"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 계층 샘플링을 사용해 만든 테스트 세트가 전체 데이터 셋에 있는 소득 카테고리의 비율과 거의 같음\n",
    "- 반면, 일반 무작위 샘플링으로 만든 테스트 세트는 비율이 많이 다름"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for set_ in (strat_train_set, strat_test_set):\n",
    "    set_.drop(\"income_cat\", axis=1, inplace=True) #incom_cat 특성 삭제하여 데이터를 원상태로\n",
    "    \n",
    "#drop(): 행 또는 열을 삭제, axis=0 (행삭제) / =1 (열삭제)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 이해를 위한 탐색과 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy() #훈련 세트 복사본 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 지리정보(위도, 경도)를 이용하여 모든 구역을 산점도로 만들어 데이터 시각화 하기 \n",
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\")\n",
    "#save_fig(\"bad_visualization_plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.1) #alpha 값을 0.1로 주어 밀집된 영역 확인\n",
    "#save_fig(\"better_visualization_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 베이 에어리어와 로스앤젤리스  근처, 샌디에고  같이 밀집된 지역이 눈에 잘 띔\n",
    "- 새크라멘토와 프레즈노 근처를 따라 밀집된 지역이 긴 띠를 이루고 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20a7e6f35e0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 주택 가격 나타내기\n",
    "#매개변수 s(원의 반지름 - 구역의 인구)\n",
    "#매개변수 c(색상 - 가격) #파란색(낮은 가격)~빨간색(높은 가격) // jet 컬러맵 이용\n",
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "             s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "             c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    "             sharex=False)\n",
    "plt.legend()\n",
    "#save_fig(\"housing_prices_scatterplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 주택 가격은 지역과 인구 밀도에  관련이 매우 큼\n",
    "- 군집 알고리즘을 이용하여 주요 군집을 찾고 군집의 중심까지의 거리를 재는 특성을 추가할 수 있음\n",
    "- 해안 근접성 특성이 유용할 수 있지만, 북부 캘리포니아 지역의 해안가는 주택 가격이 그리 높지 않아 간단한 규칙 적용이 어려움"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# Download the California image\\nimages_path = os.path.join(PROJECT_ROOT_DIR, \"images\", \"end_to_end_project\")\\nos.makedirs(images_path, exist_ok=True)\\nDOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\\nfilename = \"california.png\"\\nprint(\"Downloading\", filename)\\nurl = DOWNLOAD_ROOT + \"images/end_to_end_project/\" + filename\\nurllib.request.urlretrieve(url, os.path.join(images_path, filename))\\n'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "# Download the California image\n",
    "images_path = os.path.join(PROJECT_ROOT_DIR, \"images\", \"end_to_end_project\")\n",
    "os.makedirs(images_path, exist_ok=True)\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
    "filename = \"california.png\"\n",
    "print(\"Downloading\", filename)\n",
    "url = DOWNLOAD_ROOT + \"images/end_to_end_project/\" + filename\n",
    "urllib.request.urlretrieve(url, os.path.join(images_path, filename))\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# 다운 받은 그림을 이용하여 그림을 지도 위에 그리기\\nimport matplotlib.image as mpimg\\ncalifornia_img=mpimg.imread(os.path.join(images_path, filename))\\nax = housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", figsize=(10,7),\\n                  s=housing[\\'population\\']/100, label=\"Population\",\\n                  c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"),\\n                  colorbar=False, alpha=0.4)\\nplt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5,\\n           cmap=plt.get_cmap(\"jet\"))\\nplt.ylabel(\"Latitude\", fontsize=14)\\nplt.xlabel(\"Longitude\", fontsize=14)\\n\\nprices = housing[\"median_house_value\"]\\ntick_values = np.linspace(prices.min(), prices.max(), 11)\\ncbar = plt.colorbar(ticks=tick_values/prices.max())\\ncbar.ax.set_yticklabels([\"$%dk\"%(round(v/1000)) for v in tick_values], fontsize=14)\\ncbar.set_label(\\'Median House Value\\', fontsize=16)\\n\\nplt.legend(fontsize=16)\\n#save_fig(\"california_housing_prices_plot\")\\nplt.show()\\n'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "# 다운 받은 그림을 이용하여 그림을 지도 위에 그리기\n",
    "import matplotlib.image as mpimg\n",
    "california_img=mpimg.imread(os.path.join(images_path, filename))\n",
    "ax = housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", figsize=(10,7),\n",
    "                  s=housing['population']/100, label=\"Population\",\n",
    "                  c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"),\n",
    "                  colorbar=False, alpha=0.4)\n",
    "plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5,\n",
    "           cmap=plt.get_cmap(\"jet\"))\n",
    "plt.ylabel(\"Latitude\", fontsize=14)\n",
    "plt.xlabel(\"Longitude\", fontsize=14)\n",
    "\n",
    "prices = housing[\"median_house_value\"]\n",
    "tick_values = np.linspace(prices.min(), prices.max(), 11)\n",
    "cbar = plt.colorbar(ticks=tick_values/prices.max())\n",
    "cbar.ax.set_yticklabels([\"$%dk\"%(round(v/1000)) for v in tick_values], fontsize=14)\n",
    "cbar.set_label('Median House Value', fontsize=16)\n",
    "\n",
    "plt.legend(fontsize=16)\n",
    "#save_fig(\"california_housing_prices_plot\")\n",
    "plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 상관관계 조사: corr()\n",
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.687160\n",
       "total_rooms           0.135097\n",
       "housing_median_age    0.114110\n",
       "households            0.064506\n",
       "total_bedrooms        0.047689\n",
       "population           -0.026920\n",
       "longitude            -0.047432\n",
       "latitude             -0.142724\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 중간 주택 가격과 다른 특성 사이의 상관관계 크기 확인\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- median_house_value가 증가함에 따라 median_income는 증가하는 경향을 보임\n",
    "- 상관계수가 0.5 이상이면 강한 상관관계를 가진다고 판단"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "피어슨 상관 계수:\n",
    " ![nn](https://camo.githubusercontent.com/f539b33f587d31ba0c29fc9224f08421498d939d/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f642f64342f436f7272656c6174696f6e5f6578616d706c6573322e737667)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='median_house_value', ylabel='median_house_value'>,\n",
       "        <AxesSubplot:xlabel='median_income', ylabel='median_house_value'>,\n",
       "        <AxesSubplot:xlabel='total_rooms', ylabel='median_house_value'>,\n",
       "        <AxesSubplot:xlabel='housing_median_age', ylabel='median_house_value'>],\n",
       "       [<AxesSubplot:xlabel='median_house_value', ylabel='median_income'>,\n",
       "        <AxesSubplot:xlabel='median_income', ylabel='median_income'>,\n",
       "        <AxesSubplot:xlabel='total_rooms', ylabel='median_income'>,\n",
       "        <AxesSubplot:xlabel='housing_median_age', ylabel='median_income'>],\n",
       "       [<AxesSubplot:xlabel='median_house_value', ylabel='total_rooms'>,\n",
       "        <AxesSubplot:xlabel='median_income', ylabel='total_rooms'>,\n",
       "        <AxesSubplot:xlabel='total_rooms', ylabel='total_rooms'>,\n",
       "        <AxesSubplot:xlabel='housing_median_age', ylabel='total_rooms'>],\n",
       "       [<AxesSubplot:xlabel='median_house_value', ylabel='housing_median_age'>,\n",
       "        <AxesSubplot:xlabel='median_income', ylabel='housing_median_age'>,\n",
       "        <AxesSubplot:xlabel='total_rooms', ylabel='housing_median_age'>,\n",
       "        <AxesSubplot:xlabel='housing_median_age', ylabel='housing_median_age'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 특정 사이의 상관관계 확인 -> 숫자형 특성 사이에 산점도를 그려주는 판다스 scatter_matrix\n",
    "# from pandas.tools.plotting import scatter_matrix # 옛날 버전의 판다스에서는\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n",
    "              \"housing_median_age\"] # 중간 주택 가격과 상관관계가 높아보이는 특성만 살펴보기 \n",
    "scatter_matrix(housing[attributes], figsize=(12, 8))\n",
    "#save_fig(\"scatter_matrix_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 이 산점도 행렬은 수치형 특성에 대한 각 수치형 특성의 산점도와 각 수치형 특성의 히스토그램을 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 16.0, 0.0, 550000.0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 중간 주택 가격을 예측하는 데 가장 유용할 것 같은 특성 = 중간 소득 \n",
    "# 상관관계 산점도 확대\n",
    "\n",
    "housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\",\n",
    "             alpha=0.1)\n",
    "plt.axis([0, 16, 0, 550000])\n",
    "#save_fig(\"income_vs_house_value_scatterplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 상관관계가 매우 강함\n",
    "- 가격 제한값 $500,000이 수평선으로 잘보임\n",
    "- $450,000, $350,000, $280,000 에서 수평선이 보임\n",
    "- 이러한 이상한 형태를 알고리즘이 학습하지 않도록 해당 구역을 제거하는 것이 좋음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 여러 특성의 조합 시도\n",
    "# 유의미한 새로운 변수 만들기\n",
    "housing[\"rooms_per_household\"] = housing[\"total_rooms\"]/housing[\"households\"] #가구당 방 개수 \n",
    "housing[\"bedrooms_per_room\"] = housing[\"total_bedrooms\"]/housing[\"total_rooms\"] #방 개수당 침실의 수\n",
    "housing[\"population_per_household\"]=housing[\"population\"]/housing[\"households\"] #가구당 인원수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.687160\n",
       "rooms_per_household         0.146285\n",
       "total_rooms                 0.135097\n",
       "housing_median_age          0.114110\n",
       "households                  0.064506\n",
       "total_bedrooms              0.047689\n",
       "population_per_household   -0.021985\n",
       "population                 -0.026920\n",
       "longitude                  -0.047432\n",
       "latitude                   -0.142724\n",
       "bedrooms_per_room          -0.259984\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#상관관계 행렬\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "- 침실/방의 비율이 낮은 집은 더 비싼 경향\n",
    "- 가구/방의 비율이 큰 집이 더 비싼 경향"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 상관관계 산점도 확대\n",
    "# 방/가구의 비율과 중간 주택 가격 \n",
    "housing.plot(kind=\"scatter\", x=\"rooms_per_household\", y=\"median_house_value\",\n",
    "             alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_room</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16354.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16354.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.575834</td>\n",
       "      <td>35.639577</td>\n",
       "      <td>28.653101</td>\n",
       "      <td>2622.728319</td>\n",
       "      <td>534.973890</td>\n",
       "      <td>1419.790819</td>\n",
       "      <td>497.060380</td>\n",
       "      <td>3.875589</td>\n",
       "      <td>206990.920724</td>\n",
       "      <td>5.440341</td>\n",
       "      <td>0.212878</td>\n",
       "      <td>3.096437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.001860</td>\n",
       "      <td>2.138058</td>\n",
       "      <td>12.574726</td>\n",
       "      <td>2138.458419</td>\n",
       "      <td>412.699041</td>\n",
       "      <td>1115.686241</td>\n",
       "      <td>375.720845</td>\n",
       "      <td>1.904950</td>\n",
       "      <td>115703.014830</td>\n",
       "      <td>2.611712</td>\n",
       "      <td>0.057379</td>\n",
       "      <td>11.584826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>1.130435</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.940000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1443.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>784.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>2.566775</td>\n",
       "      <td>119800.000000</td>\n",
       "      <td>4.442040</td>\n",
       "      <td>0.175304</td>\n",
       "      <td>2.431287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.510000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2119.500000</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>1164.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>3.540900</td>\n",
       "      <td>179500.000000</td>\n",
       "      <td>5.232284</td>\n",
       "      <td>0.203031</td>\n",
       "      <td>2.817653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.720000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3141.000000</td>\n",
       "      <td>644.000000</td>\n",
       "      <td>1719.250000</td>\n",
       "      <td>602.000000</td>\n",
       "      <td>4.744475</td>\n",
       "      <td>263900.000000</td>\n",
       "      <td>6.056361</td>\n",
       "      <td>0.239831</td>\n",
       "      <td>3.281420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6210.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>5358.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  16512.000000  16512.000000        16512.000000  16512.000000   \n",
       "mean    -119.575834     35.639577           28.653101   2622.728319   \n",
       "std        2.001860      2.138058           12.574726   2138.458419   \n",
       "min     -124.350000     32.540000            1.000000      6.000000   \n",
       "25%     -121.800000     33.940000           18.000000   1443.000000   \n",
       "50%     -118.510000     34.260000           29.000000   2119.500000   \n",
       "75%     -118.010000     37.720000           37.000000   3141.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    16354.000000  16512.000000  16512.000000   16512.000000   \n",
       "mean       534.973890   1419.790819    497.060380       3.875589   \n",
       "std        412.699041   1115.686241    375.720845       1.904950   \n",
       "min          2.000000      3.000000      2.000000       0.499900   \n",
       "25%        295.000000    784.000000    279.000000       2.566775   \n",
       "50%        433.000000   1164.000000    408.000000       3.540900   \n",
       "75%        644.000000   1719.250000    602.000000       4.744475   \n",
       "max       6210.000000  35682.000000   5358.000000      15.000100   \n",
       "\n",
       "       median_house_value  rooms_per_household  bedrooms_per_room  \\\n",
       "count        16512.000000         16512.000000       16354.000000   \n",
       "mean        206990.920724             5.440341           0.212878   \n",
       "std         115703.014830             2.611712           0.057379   \n",
       "min          14999.000000             1.130435           0.100000   \n",
       "25%         119800.000000             4.442040           0.175304   \n",
       "50%         179500.000000             5.232284           0.203031   \n",
       "75%         263900.000000             6.056361           0.239831   \n",
       "max         500001.000000           141.909091           1.000000   \n",
       "\n",
       "       population_per_household  \n",
       "count              16512.000000  \n",
       "mean                   3.096437  \n",
       "std                   11.584826  \n",
       "min                    0.692308  \n",
       "25%                    2.431287  \n",
       "50%                    2.817653  \n",
       "75%                    3.281420  \n",
       "max                 1243.333333  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 머신러닝 알고리즘을 위한 데이터 준비"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "머신러닝 알고리즘을 위해 데이터를 준비할 때 함수를 만들어 자동화해야 하는 이유\n",
    "- 1. 어떤 데이터셋에 대해서도 데이터 변환을 손쉽게 반복할 수 있음\n",
    "- 2. 향후 프로젝트에 사용할 수 있는 변환 라이브러리를 점진적으로 구축함\n",
    "- 3. 실제 시스템에서 알고리즘에 새 데이털르 주입하기 전에 변환시키는 데 이 함수를 사용할 수 있음\n",
    "- 4. 여러 가지 데이터 변환을 쉽게 시도해볼 수 있고 어떤 조합이 가장 좋은지 확인하는 데 편리함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"median_house_value\", axis=1) # 훈련 세트를 위해 레이블 삭제\n",
    "#예측 변수와 타깃값에 같은 변형을 적용하지 않기 위해 예측 변수와 레이블을 분리\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy() #원래 훈련세트로 복원(복사)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0             NaN   \n",
       "6068     -117.86     34.01                16.0       4632.0             NaN   \n",
       "17923    -121.97     37.35                30.0       1955.0             NaN   \n",
       "13656    -117.30     34.05                 6.0       2155.0             NaN   \n",
       "19252    -122.79     38.48                 7.0       6837.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \n",
       "4629       3296.0      1462.0         2.2708       <1H OCEAN  \n",
       "6068       3038.0       727.0         5.1762       <1H OCEAN  \n",
       "17923       999.0       386.0         4.6328       <1H OCEAN  \n",
       "13656      1039.0       391.0         1.6675          INLAND  \n",
       "19252      3468.0      1405.0         3.1662       <1H OCEAN  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터세트 null 확인\n",
    "sample_incomplete_rows = housing[housing.isnull().any(axis=1)].head()\n",
    "sample_incomplete_rows\n",
    "# total_bedrooms 특성에 값이 없는 경우가 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, ocean_proximity]\n",
       "Index: []"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows.dropna(subset=[\"total_bedrooms\"])    # 옵션 1\n",
    "# 해당 구역을 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  population  \\\n",
       "4629     -118.30     34.07                18.0       3759.0      3296.0   \n",
       "6068     -117.86     34.01                16.0       4632.0      3038.0   \n",
       "17923    -121.97     37.35                30.0       1955.0       999.0   \n",
       "13656    -117.30     34.05                 6.0       2155.0      1039.0   \n",
       "19252    -122.79     38.48                 7.0       6837.0      3468.0   \n",
       "\n",
       "       households  median_income ocean_proximity  \n",
       "4629       1462.0         2.2708       <1H OCEAN  \n",
       "6068        727.0         5.1762       <1H OCEAN  \n",
       "17923       386.0         4.6328       <1H OCEAN  \n",
       "13656       391.0         1.6675          INLAND  \n",
       "19252      1405.0         3.1662       <1H OCEAN  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows.drop(\"total_bedrooms\", axis=1)       # 옵션 2\n",
    "# 전체 특성을 삭제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "median = housing[\"total_bedrooms\"].median()\n",
    "sample_incomplete_rows[\"total_bedrooms\"].fillna(median, inplace=True) # 옵션 3\n",
    "# 어떤 값으로 채움(0 or 중간값 or 평균 등) #여기서는 중간값으로 채움"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0           433.0   \n",
       "6068     -117.86     34.01                16.0       4632.0           433.0   \n",
       "17923    -121.97     37.35                30.0       1955.0           433.0   \n",
       "13656    -117.30     34.05                 6.0       2155.0           433.0   \n",
       "19252    -122.79     38.48                 7.0       6837.0           433.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \n",
       "4629       3296.0      1462.0         2.2708       <1H OCEAN  \n",
       "6068       3038.0       727.0         5.1762       <1H OCEAN  \n",
       "17923       999.0       386.0         4.6328       <1H OCEAN  \n",
       "13656      1039.0       391.0         1.6675          INLAND  \n",
       "19252      3468.0      1405.0         3.1662       <1H OCEAN  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 훈련 세트에서 중간값을 계산하고 누락된 값을 이 값으로 채워 넣어야 함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SimpleImputer: 누락된 값을 손쉽게 다루게 해줌\n",
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy=\"median\") #누락된 값을 특성의 중간값으로 대체"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 중간값이 수치형 특성에서만 계산될 수 있기 때무에 텍스트 특성인 ocean_proximity를 삭제\n",
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)\n",
    "# 다른 방법: housing_num = housing.select_dtypes(include=[np.number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# imputer 객체의 fit() 메서드를 사용하여 훈련데이터에 적용\n",
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- imputer는 각 특성의 중간값을 계산하여 그 결과를 객체의 statistics_속성에 저장함\n",
    "- total_ bedrooms 특성에만 누락된 값이 있지만, 나중에 시스템이 서비스될 때 새로운 데이터에서 어떤 값이 누락될 지 확신할 수 없음\n",
    "- 따라서 모든 수치형 특성에 imputer를 적용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  학습된 imputer 객체를 사용하여 훈련 세트에서 누락된 값을 학습한 중간값으로 바꿀 수 있음\n",
    "# 각 특성의 중간값\n",
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing_num) #변형된 특성들이 들어있는 평범한 넘파이 배열"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 변형된 특성들이 들어 있는 평범한 넘파이 배열\n",
    "\n",
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
    "                          index=housing_num.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0           433.0   \n",
       "6068     -117.86     34.01                16.0       4632.0           433.0   \n",
       "17923    -121.97     37.35                30.0       1955.0           433.0   \n",
       "13656    -117.30     34.05                 6.0       2155.0           433.0   \n",
       "19252    -122.79     38.48                 7.0       6837.0           433.0   \n",
       "\n",
       "       population  households  median_income  \n",
       "4629       3296.0      1462.0         2.2708  \n",
       "6068       3038.0       727.0         5.1762  \n",
       "17923       999.0       386.0         4.6328  \n",
       "13656      1039.0       391.0         1.6675  \n",
       "19252      3468.0      1405.0         3.1662  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.loc[sample_incomplete_rows.index.values] #판다스 데이터 프래임으로 간단히 되돌림"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'median'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  \n",
       "17606       710.0       339.0         2.7042  \n",
       "18632       306.0       113.0         6.4214  \n",
       "14650       936.0       462.0         2.8621  \n",
       "3230       1460.0       353.0         1.8839  \n",
       "3555       4459.0      1463.0         3.0347  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19480</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8879</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13685</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "17606       <1H OCEAN\n",
       "18632       <1H OCEAN\n",
       "14650      NEAR OCEAN\n",
       "3230           INLAND\n",
       "3555        <1H OCEAN\n",
       "19480          INLAND\n",
       "8879        <1H OCEAN\n",
       "13685          INLAND\n",
       "4937        <1H OCEAN\n",
       "4861        <1H OCEAN"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#범주형 입력 특성인 ocean_proximity을 전처리\n",
    "\n",
    "# 처음 10개의 샘플에서 이  특성값을 확인\n",
    "housing_cat = housing[[\"ocean_proximity\"]]\n",
    "housing_cat.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 가능한 값을 제한된 개수로 나열, 각 값은 카테고리 의미 -> 범주형 특성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [0.],\n",
       "       [4.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 카테고리를 텍스트에서 숫자로 변환\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "ordinal_encoder = OrdinalEncoder()\n",
    "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoded[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# categories_ 인스턴스 변수를 사용해ㅑ 카테고리 목록 얻기\n",
    "ordinal_encoder.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 범주형 특성마다 카테고리들의 ID 배열을 담은 리스트 반환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder #범주의 값을 원-학 벡터로 바꿈\n",
    "\n",
    "cat_encoder = OneHotEncoder()\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- OneHotEncoder는 기본적으로 희소 행렬을 반환\n",
    "- 필요하면 toarray() 메서드를 사용해 밀집 배열로 변환할 수 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# OneHotEncoder를 만들 때 sparse=False로 지정할 수 있음\n",
    "cat_encoder = OneHotEncoder(sparse=False)\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder.categories_ #인코더의 categories_인스턴스 변수를 사용해 카테고리 리스트 추출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 추가 특성을 위한 사용자 정의 변환기\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# 열 인덱스\n",
    "rooms_ix, bedrooms_ix, population_ix, households_ix = 3, 4, 5, 6\n",
    "\n",
    "# fit()(self 반환), transform(), fit_transform() 메서드를 구현한 파이썬 클래스 만들기\n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room=True): # *args 또는 **kargs 없음\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y=None):\n",
    "        return self  # 아무것도 하지 않음\n",
    "    def transform(self, X):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, households_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, households_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household, population_per_household,\n",
    "                         bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]\n",
    "\n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#책에서는 간단하게 인덱스 (3, 4, 5, 6)을 하드코딩했지만 다음처럼 동적으로 처리하는 것이 더 좋음\n",
    "col_names = \"total_rooms\", \"total_bedrooms\", \"population\", \"households\"\n",
    "rooms_ix, bedrooms_ix, population_ix, households_ix = [\n",
    "    housing.columns.get_loc(c) for c in col_names] # 열 인덱스 구하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "housing_extra_attribs는 넘파이 배열이기 때문에 열 이름이 없습니다(안타깝지만 사이킷런을 사용할 때 생기는 문제입니다). DataFrame으로 복원하려면 다음과 같이 할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38</td>\n",
       "      <td>1568</td>\n",
       "      <td>351</td>\n",
       "      <td>710</td>\n",
       "      <td>339</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4.62537</td>\n",
       "      <td>2.0944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14</td>\n",
       "      <td>679</td>\n",
       "      <td>108</td>\n",
       "      <td>306</td>\n",
       "      <td>113</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>6.00885</td>\n",
       "      <td>2.70796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.2</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31</td>\n",
       "      <td>1952</td>\n",
       "      <td>471</td>\n",
       "      <td>936</td>\n",
       "      <td>462</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4.22511</td>\n",
       "      <td>2.02597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25</td>\n",
       "      <td>1847</td>\n",
       "      <td>371</td>\n",
       "      <td>1460</td>\n",
       "      <td>353</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5.23229</td>\n",
       "      <td>4.13598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17</td>\n",
       "      <td>6592</td>\n",
       "      <td>1525</td>\n",
       "      <td>4459</td>\n",
       "      <td>1463</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.04785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "17606   -121.89    37.29                 38        1568            351   \n",
       "18632   -121.93    37.05                 14         679            108   \n",
       "14650    -117.2    32.77                 31        1952            471   \n",
       "3230    -119.61    36.31                 25        1847            371   \n",
       "3555    -118.59    34.23                 17        6592           1525   \n",
       "\n",
       "      population households median_income ocean_proximity rooms_per_household  \\\n",
       "17606        710        339        2.7042       <1H OCEAN             4.62537   \n",
       "18632        306        113        6.4214       <1H OCEAN             6.00885   \n",
       "14650        936        462        2.8621      NEAR OCEAN             4.22511   \n",
       "3230        1460        353        1.8839          INLAND             5.23229   \n",
       "3555        4459       1463        3.0347       <1H OCEAN             4.50581   \n",
       "\n",
       "      population_per_household  \n",
       "17606                   2.0944  \n",
       "18632                  2.70796  \n",
       "14650                  2.02597  \n",
       "3230                   4.13598  \n",
       "3555                   3.04785  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs = pd.DataFrame(\n",
    "    housing_extra_attribs,\n",
    "    columns=list(housing.columns)+[\"rooms_per_household\", \"population_per_household\"],\n",
    "    index=housing.index)\n",
    "housing_extra_attribs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#수치형 특성을 전처리하는 파이프라인\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)\n",
    "# 마지막 단계에는 변환기와 추정기를 모두 사용할 수 있고, 그 외에는 모두 변환기여야 함\n",
    "#(즉, fit_transform() 메서드를 가지고 있어야 함)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ..., -0.31205452,\n",
       "        -0.08649871,  0.15531753],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.21768338,\n",
       "        -0.03353391, -0.83628902],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ..., -0.46531516,\n",
       "        -0.09240499,  0.4222004 ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.3469342 ,\n",
       "        -0.03055414, -0.52177644],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.02499488,\n",
       "         0.06150916, -0.30340741],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ..., -0.22852947,\n",
       "        -0.09586294,  0.10180567]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 주택 가격 데이터에 전체 변환 적용\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "full_pipeline = ColumnTransformer([\n",
    "        (\"num\", num_pipeline, num_attribs),\n",
    "        (\"cat\", OneHotEncoder(), cat_attribs),\n",
    "    ])\n",
    "\n",
    "housing_prepared = full_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 16)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (판다스 DataFrame 열의 일부를 선택하기 위해) DataFrameSelector 변환기와 FeatureUnion를 사용한 예전 방식:\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# 수치형 열과 범주형 열을 선택하기 위한 클래스\n",
    "class OldDataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 하나의 큰 파이프라인에 이들을 모두 결합하여 수치형과 범주형 특성을 전처리함\n",
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "old_num_pipeline = Pipeline([\n",
    "        ('selector', OldDataFrameSelector(num_attribs)),\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])\n",
    "\n",
    "old_cat_pipeline = Pipeline([\n",
    "        ('selector', OldDataFrameSelector(cat_attribs)),\n",
    "        ('cat_encoder', OneHotEncoder(sparse=False)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "old_full_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipeline\", old_num_pipeline),\n",
    "        (\"cat_pipeline\", old_cat_pipeline),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ColumnTransformer의 결과와 동일\n",
    "old_housing_prepared = old_full_pipeline.fit_transform(housing)\n",
    "old_housing_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(housing_prepared, old_housing_prepared)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 모델 선택과 훈련"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 선형 회귀 모델 훈련\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예측: [210644.60459286 317768.80697211 210956.43331178  59218.98886849\n",
      " 189747.55849879]\n"
     ]
    }
   ],
   "source": [
    "# 훈련 샘플 몇 개를 사용해 전체 파이프라인을 적용\n",
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "\n",
    "print(\"예측:\", lin_reg.predict(some_data_prepared))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예측: [210644.60459286 317768.80697211 210956.43331178  59218.98886849\n",
      " 189747.55849879]\n",
      "레이블: [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]\n"
     ]
    }
   ],
   "source": [
    "# 실제 값과 비교\n",
    "print(\"예측:\", lin_reg.predict(some_data_prepared))\n",
    "print(\"레이블:\", list(some_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, -0.49323393, -0.44543821,\n",
       "        -0.63621141, -0.42069842, -0.61493744, -0.31205452, -0.08649871,\n",
       "         0.15531753,  1.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , -0.90896655, -1.0369278 ,\n",
       "        -0.99833135, -1.02222705,  1.33645936,  0.21768338, -0.03353391,\n",
       "        -0.83628902,  1.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, -0.31365989, -0.15334458,\n",
       "        -0.43363936, -0.0933178 , -0.5320456 , -0.46531516, -0.09240499,\n",
       "         0.4222004 ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         1.        ],\n",
       "       [-0.01706767,  0.31357576, -0.29052016, -0.36276217, -0.39675594,\n",
       "         0.03604096, -0.38343559, -1.04556555, -0.07966124,  0.08973561,\n",
       "        -0.19645314,  0.        ,  1.        ,  0.        ,  0.        ,\n",
       "         0.        ],\n",
       "       [ 0.49247384, -0.65929936, -0.92673619,  1.85619316,  2.41221109,\n",
       "         2.72415407,  2.57097492, -0.44143679, -0.35783383, -0.00419445,\n",
       "         0.2699277 ,  1.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_data_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68628.19819848923"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 전체 훈련 세트에 대한 이 회귀 모델의 RMSE 측정\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(housing_labels, housing_predictions) \n",
    "#squared=False 매개변수로 mean_squared_error() 함수를 호출하면 RMSE를 바로 얻을 수 있음\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49439.895990018966"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 전체 훈련 세트에 대한 이 회귀 모델의 MAE 측정\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "lin_mae = mean_absolute_error(housing_labels, housing_predictions)\n",
    "lin_mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=42)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터에서 복잡한 비선형 관계를 찾을 수 있는 DecisionTreeRegressor 훈련\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 오차가 전혀 없다고 나옴\n",
    "- 이는 모델이 데이터에 심하게 과대적합된 것으로 보임\n",
    "- 훈련 세트의 일부분으로 훈련을 하고 다른 일부분은 모델 검증에 사용해야 함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 교차 검증을 사용한 평가\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(tree_reg, housing_prepared, housing_labels,\n",
    "                         scoring=\"neg_mean_squared_error\", cv=10) #10개의 서브셋으로 무작위로 분할, 결정 트리 모델을 10번 훈련 \n",
    "tree_rmse_scores = np.sqrt(-scores)\n",
    "# 평균 제곱 오차(MSE)의 반댓값(즉, 음수)을 계산하는 neg_mean_squared_error를 사용하였기에 -scores로 부호 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "점수: [70194.33680785 66855.16363941 72432.58244769 70758.73896782\n",
      " 71115.88230639 75585.14172901 70262.86139133 70273.6325285\n",
      " 75366.87952553 71231.65726027]\n",
      "평균: 71407.68766037929\n",
      "표준 편차: 2439.4345041191004\n"
     ]
    }
   ],
   "source": [
    "# 교차 검증 결과\n",
    "def display_scores(scores):\n",
    "    print(\"점수:\", scores)\n",
    "    print(\"평균:\", scores.mean())\n",
    "    print(\"표준 편차:\", scores.std())\n",
    "\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "점수: [66782.73843989 66960.118071   70347.95244419 74739.57052552\n",
      " 68031.13388938 71193.84183426 64969.63056405 68281.61137997\n",
      " 71552.91566558 67665.10082067]\n",
      "평균: 69052.46136345083\n",
      "표준 편차: 2731.6740017983425\n"
     ]
    }
   ],
   "source": [
    "# 선형 회귀 모델 점수\n",
    "lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n",
    "                             scoring=\"neg_mean_squared_error\", cv=10)\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 랜덤 포레스트\n",
    "# 특성을 무작위로 선택해서 ㅁ낳ㅇ느 결정트리를 만들고 그 예측을 평균 내는 방식으로 작동\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "forest_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18603.515021376355"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 랜덤포레스트 결과\n",
    "housing_predictions = forest_reg.predict(housing_prepared)\n",
    "forest_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "점수: [49519.80364233 47461.9115823  50029.02762854 52325.28068953\n",
      " 49308.39426421 53446.37892622 48634.8036574  47585.73832311\n",
      " 53490.10699751 50021.5852922 ]\n",
      "평균: 50182.303100336096\n",
      "표준 편차: 2097.0810550985693\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
    "                                scoring=\"neg_mean_squared_error\", cv=10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       10.000000\n",
       "mean     69052.461363\n",
       "std       2879.437224\n",
       "min      64969.630564\n",
       "25%      67136.363758\n",
       "50%      68156.372635\n",
       "75%      70982.369487\n",
       "max      74739.570526\n",
       "dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = cross_val_score(lin_reg, housing_prepared, housing_labels, scoring=\"neg_mean_squared_error\", cv=10)\n",
    "pd.Series(np.sqrt(-scores)).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111094.6308539982"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_reg = SVR(kernel=\"linear\")\n",
    "svm_reg.fit(housing_prepared, housing_labels)\n",
    "housing_predictions = svm_reg.predict(housing_prepared)\n",
    "svm_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "svm_rmse = np.sqrt(svm_mse)\n",
    "svm_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 모델 세부 튜닝"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             return_train_score=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 그리드 탐색\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    # 12(=3×4)개의 하이퍼파라미터 조합을 시도합니다.\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    # bootstrap은 False로 하고 6(=2×3)개의 조합을 시도합니다.\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "# 다섯 개의 폴드로 훈련하면 총 (12+6)*5=90번의 훈련이 일어납니다.\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error',\n",
    "                           return_train_score=True)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 30}"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 최상의 파라미터 조합\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_features=8, n_estimators=30, random_state=42)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 최상의 추정기\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63669.11631261028 {'max_features': 2, 'n_estimators': 3}\n",
      "55627.099719926795 {'max_features': 2, 'n_estimators': 10}\n",
      "53384.57275149205 {'max_features': 2, 'n_estimators': 30}\n",
      "60965.950449450494 {'max_features': 4, 'n_estimators': 3}\n",
      "52741.04704299915 {'max_features': 4, 'n_estimators': 10}\n",
      "50377.40461678399 {'max_features': 4, 'n_estimators': 30}\n",
      "58663.93866579625 {'max_features': 6, 'n_estimators': 3}\n",
      "52006.19873526564 {'max_features': 6, 'n_estimators': 10}\n",
      "50146.51167415009 {'max_features': 6, 'n_estimators': 30}\n",
      "57869.25276169646 {'max_features': 8, 'n_estimators': 3}\n",
      "51711.127883959234 {'max_features': 8, 'n_estimators': 10}\n",
      "49682.273345071546 {'max_features': 8, 'n_estimators': 30}\n",
      "62895.06951262424 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54658.176157539405 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "59470.40652318466 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "52724.9822587892 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "57490.5691951261 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "51009.495668875716 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "# 그리드서치에서 테스트한 하이퍼파라미터 조합의 점수 확인\n",
    "cvres = grid_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>param_bootstrap</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.061214</td>\n",
       "      <td>0.004168</td>\n",
       "      <td>0.003401</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 3}</td>\n",
       "      <td>-3.837622e+09</td>\n",
       "      <td>-4.147108e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.053756e+09</td>\n",
       "      <td>1.519591e+08</td>\n",
       "      <td>18</td>\n",
       "      <td>-1.064113e+09</td>\n",
       "      <td>-1.105142e+09</td>\n",
       "      <td>-1.116550e+09</td>\n",
       "      <td>-1.112342e+09</td>\n",
       "      <td>-1.129650e+09</td>\n",
       "      <td>-1.105559e+09</td>\n",
       "      <td>2.220402e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.183838</td>\n",
       "      <td>0.004447</td>\n",
       "      <td>0.008802</td>\n",
       "      <td>0.001171</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 10}</td>\n",
       "      <td>-3.047771e+09</td>\n",
       "      <td>-3.254861e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.094374e+09</td>\n",
       "      <td>1.327062e+08</td>\n",
       "      <td>11</td>\n",
       "      <td>-5.927175e+08</td>\n",
       "      <td>-5.870952e+08</td>\n",
       "      <td>-5.776964e+08</td>\n",
       "      <td>-5.716332e+08</td>\n",
       "      <td>-5.802501e+08</td>\n",
       "      <td>-5.818785e+08</td>\n",
       "      <td>7.345821e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.570132</td>\n",
       "      <td>0.025954</td>\n",
       "      <td>0.025808</td>\n",
       "      <td>0.004118</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 30}</td>\n",
       "      <td>-2.689185e+09</td>\n",
       "      <td>-3.021086e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.849913e+09</td>\n",
       "      <td>1.626875e+08</td>\n",
       "      <td>9</td>\n",
       "      <td>-4.381089e+08</td>\n",
       "      <td>-4.391272e+08</td>\n",
       "      <td>-4.371702e+08</td>\n",
       "      <td>-4.376955e+08</td>\n",
       "      <td>-4.452654e+08</td>\n",
       "      <td>-4.394734e+08</td>\n",
       "      <td>2.966320e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.089418</td>\n",
       "      <td>0.003927</td>\n",
       "      <td>0.003401</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 3}</td>\n",
       "      <td>-3.730181e+09</td>\n",
       "      <td>-3.786886e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.716847e+09</td>\n",
       "      <td>1.631510e+08</td>\n",
       "      <td>16</td>\n",
       "      <td>-9.865163e+08</td>\n",
       "      <td>-1.012565e+09</td>\n",
       "      <td>-9.169425e+08</td>\n",
       "      <td>-1.037400e+09</td>\n",
       "      <td>-9.707739e+08</td>\n",
       "      <td>-9.848396e+08</td>\n",
       "      <td>4.084607e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.293668</td>\n",
       "      <td>0.005165</td>\n",
       "      <td>0.008402</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 10}</td>\n",
       "      <td>-2.666283e+09</td>\n",
       "      <td>-2.784511e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.781618e+09</td>\n",
       "      <td>1.268607e+08</td>\n",
       "      <td>8</td>\n",
       "      <td>-5.097115e+08</td>\n",
       "      <td>-5.162820e+08</td>\n",
       "      <td>-4.962893e+08</td>\n",
       "      <td>-5.436192e+08</td>\n",
       "      <td>-5.160297e+08</td>\n",
       "      <td>-5.163863e+08</td>\n",
       "      <td>1.542862e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.902206</td>\n",
       "      <td>0.022040</td>\n",
       "      <td>0.024202</td>\n",
       "      <td>0.002132</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 30}</td>\n",
       "      <td>-2.387153e+09</td>\n",
       "      <td>-2.588448e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.537883e+09</td>\n",
       "      <td>1.214614e+08</td>\n",
       "      <td>3</td>\n",
       "      <td>-3.838835e+08</td>\n",
       "      <td>-3.880268e+08</td>\n",
       "      <td>-3.790867e+08</td>\n",
       "      <td>-4.040957e+08</td>\n",
       "      <td>-3.845520e+08</td>\n",
       "      <td>-3.879289e+08</td>\n",
       "      <td>8.571233e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.129431</td>\n",
       "      <td>0.008547</td>\n",
       "      <td>0.003601</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 3}</td>\n",
       "      <td>-3.119657e+09</td>\n",
       "      <td>-3.586319e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.441458e+09</td>\n",
       "      <td>1.893056e+08</td>\n",
       "      <td>14</td>\n",
       "      <td>-9.245343e+08</td>\n",
       "      <td>-8.886939e+08</td>\n",
       "      <td>-9.353135e+08</td>\n",
       "      <td>-9.009801e+08</td>\n",
       "      <td>-8.624664e+08</td>\n",
       "      <td>-9.023976e+08</td>\n",
       "      <td>2.591445e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.407753</td>\n",
       "      <td>0.010894</td>\n",
       "      <td>0.008404</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 10}</td>\n",
       "      <td>-2.549663e+09</td>\n",
       "      <td>-2.782039e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.704645e+09</td>\n",
       "      <td>1.471569e+08</td>\n",
       "      <td>6</td>\n",
       "      <td>-4.980344e+08</td>\n",
       "      <td>-5.045869e+08</td>\n",
       "      <td>-4.994664e+08</td>\n",
       "      <td>-4.990325e+08</td>\n",
       "      <td>-5.055542e+08</td>\n",
       "      <td>-5.013349e+08</td>\n",
       "      <td>3.100456e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.219877</td>\n",
       "      <td>0.024081</td>\n",
       "      <td>0.023003</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 30}</td>\n",
       "      <td>-2.370010e+09</td>\n",
       "      <td>-2.583638e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.514673e+09</td>\n",
       "      <td>1.285080e+08</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.838538e+08</td>\n",
       "      <td>-3.804711e+08</td>\n",
       "      <td>-3.805218e+08</td>\n",
       "      <td>-3.856095e+08</td>\n",
       "      <td>-3.901917e+08</td>\n",
       "      <td>-3.841296e+08</td>\n",
       "      <td>3.617057e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.151832</td>\n",
       "      <td>0.003428</td>\n",
       "      <td>0.003203</td>\n",
       "      <td>0.000404</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 3}</td>\n",
       "      <td>-3.353504e+09</td>\n",
       "      <td>-3.348552e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.348850e+09</td>\n",
       "      <td>1.241939e+08</td>\n",
       "      <td>13</td>\n",
       "      <td>-9.228123e+08</td>\n",
       "      <td>-8.553031e+08</td>\n",
       "      <td>-8.603321e+08</td>\n",
       "      <td>-8.881964e+08</td>\n",
       "      <td>-9.151287e+08</td>\n",
       "      <td>-8.883545e+08</td>\n",
       "      <td>2.750227e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.521719</td>\n",
       "      <td>0.017378</td>\n",
       "      <td>0.008140</td>\n",
       "      <td>0.000690</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 10}</td>\n",
       "      <td>-2.571970e+09</td>\n",
       "      <td>-2.718994e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.674041e+09</td>\n",
       "      <td>1.392777e+08</td>\n",
       "      <td>5</td>\n",
       "      <td>-4.932416e+08</td>\n",
       "      <td>-4.815238e+08</td>\n",
       "      <td>-4.730979e+08</td>\n",
       "      <td>-5.155367e+08</td>\n",
       "      <td>-4.985555e+08</td>\n",
       "      <td>-4.923911e+08</td>\n",
       "      <td>1.459294e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.591820</td>\n",
       "      <td>0.047774</td>\n",
       "      <td>0.023545</td>\n",
       "      <td>0.001769</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 30}</td>\n",
       "      <td>-2.357390e+09</td>\n",
       "      <td>-2.546640e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.468328e+09</td>\n",
       "      <td>1.091662e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.841658e+08</td>\n",
       "      <td>-3.744500e+08</td>\n",
       "      <td>-3.773239e+08</td>\n",
       "      <td>-3.882250e+08</td>\n",
       "      <td>-3.810005e+08</td>\n",
       "      <td>-3.810330e+08</td>\n",
       "      <td>4.871017e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.086020</td>\n",
       "      <td>0.003347</td>\n",
       "      <td>0.003200</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 2, 'n_est...</td>\n",
       "      <td>-3.785816e+09</td>\n",
       "      <td>-4.166012e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.955790e+09</td>\n",
       "      <td>1.900964e+08</td>\n",
       "      <td>17</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.305669</td>\n",
       "      <td>0.015313</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 2, 'n_est...</td>\n",
       "      <td>-2.810721e+09</td>\n",
       "      <td>-3.107789e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.987516e+09</td>\n",
       "      <td>1.539234e+08</td>\n",
       "      <td>10</td>\n",
       "      <td>-6.056477e-02</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-2.967449e+00</td>\n",
       "      <td>-6.056027e-01</td>\n",
       "      <td>1.181156e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.116224</td>\n",
       "      <td>0.005771</td>\n",
       "      <td>0.003599</td>\n",
       "      <td>0.000493</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 3, 'n_est...</td>\n",
       "      <td>-3.618324e+09</td>\n",
       "      <td>-3.441527e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.536729e+09</td>\n",
       "      <td>7.795057e+07</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-6.072840e+01</td>\n",
       "      <td>-1.214568e+01</td>\n",
       "      <td>2.429136e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.384086</td>\n",
       "      <td>0.014331</td>\n",
       "      <td>0.009603</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 3, 'n_est...</td>\n",
       "      <td>-2.757999e+09</td>\n",
       "      <td>-2.851737e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.779924e+09</td>\n",
       "      <td>6.286720e+07</td>\n",
       "      <td>7</td>\n",
       "      <td>-2.089484e+01</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-5.465556e+00</td>\n",
       "      <td>-5.272080e+00</td>\n",
       "      <td>8.093117e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.150833</td>\n",
       "      <td>0.009931</td>\n",
       "      <td>0.003801</td>\n",
       "      <td>0.000748</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 4, 'n_est...</td>\n",
       "      <td>-3.134040e+09</td>\n",
       "      <td>-3.559375e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.305166e+09</td>\n",
       "      <td>1.879165e+08</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.480972</td>\n",
       "      <td>0.026195</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 4, 'n_est...</td>\n",
       "      <td>-2.525578e+09</td>\n",
       "      <td>-2.710011e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.601969e+09</td>\n",
       "      <td>1.088048e+08</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-1.514119e-02</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-3.028238e-03</td>\n",
       "      <td>6.056477e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.061214      0.004168         0.003401        0.000800   \n",
       "1        0.183838      0.004447         0.008802        0.001171   \n",
       "2        0.570132      0.025954         0.025808        0.004118   \n",
       "3        0.089418      0.003927         0.003401        0.000490   \n",
       "4        0.293668      0.005165         0.008402        0.000490   \n",
       "5        0.902206      0.022040         0.024202        0.002132   \n",
       "6        0.129431      0.008547         0.003601        0.000490   \n",
       "7        0.407753      0.010894         0.008404        0.000488   \n",
       "8        1.219877      0.024081         0.023003        0.001267   \n",
       "9        0.151832      0.003428         0.003203        0.000404   \n",
       "10       0.521719      0.017378         0.008140        0.000690   \n",
       "11       1.591820      0.047774         0.023545        0.001769   \n",
       "12       0.086020      0.003347         0.003200        0.000400   \n",
       "13       0.305669      0.015313         0.010002        0.000895   \n",
       "14       0.116224      0.005771         0.003599        0.000493   \n",
       "15       0.384086      0.014331         0.009603        0.000490   \n",
       "16       0.150833      0.009931         0.003801        0.000748   \n",
       "17       0.480972      0.026195         0.009339        0.000553   \n",
       "\n",
       "   param_max_features param_n_estimators param_bootstrap  \\\n",
       "0                   2                  3             NaN   \n",
       "1                   2                 10             NaN   \n",
       "2                   2                 30             NaN   \n",
       "3                   4                  3             NaN   \n",
       "4                   4                 10             NaN   \n",
       "5                   4                 30             NaN   \n",
       "6                   6                  3             NaN   \n",
       "7                   6                 10             NaN   \n",
       "8                   6                 30             NaN   \n",
       "9                   8                  3             NaN   \n",
       "10                  8                 10             NaN   \n",
       "11                  8                 30             NaN   \n",
       "12                  2                  3           False   \n",
       "13                  2                 10           False   \n",
       "14                  3                  3           False   \n",
       "15                  3                 10           False   \n",
       "16                  4                  3           False   \n",
       "17                  4                 10           False   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0              {'max_features': 2, 'n_estimators': 3}      -3.837622e+09   \n",
       "1             {'max_features': 2, 'n_estimators': 10}      -3.047771e+09   \n",
       "2             {'max_features': 2, 'n_estimators': 30}      -2.689185e+09   \n",
       "3              {'max_features': 4, 'n_estimators': 3}      -3.730181e+09   \n",
       "4             {'max_features': 4, 'n_estimators': 10}      -2.666283e+09   \n",
       "5             {'max_features': 4, 'n_estimators': 30}      -2.387153e+09   \n",
       "6              {'max_features': 6, 'n_estimators': 3}      -3.119657e+09   \n",
       "7             {'max_features': 6, 'n_estimators': 10}      -2.549663e+09   \n",
       "8             {'max_features': 6, 'n_estimators': 30}      -2.370010e+09   \n",
       "9              {'max_features': 8, 'n_estimators': 3}      -3.353504e+09   \n",
       "10            {'max_features': 8, 'n_estimators': 10}      -2.571970e+09   \n",
       "11            {'max_features': 8, 'n_estimators': 30}      -2.357390e+09   \n",
       "12  {'bootstrap': False, 'max_features': 2, 'n_est...      -3.785816e+09   \n",
       "13  {'bootstrap': False, 'max_features': 2, 'n_est...      -2.810721e+09   \n",
       "14  {'bootstrap': False, 'max_features': 3, 'n_est...      -3.618324e+09   \n",
       "15  {'bootstrap': False, 'max_features': 3, 'n_est...      -2.757999e+09   \n",
       "16  {'bootstrap': False, 'max_features': 4, 'n_est...      -3.134040e+09   \n",
       "17  {'bootstrap': False, 'max_features': 4, 'n_est...      -2.525578e+09   \n",
       "\n",
       "    split1_test_score  ...  mean_test_score  std_test_score  rank_test_score  \\\n",
       "0       -4.147108e+09  ...    -4.053756e+09    1.519591e+08               18   \n",
       "1       -3.254861e+09  ...    -3.094374e+09    1.327062e+08               11   \n",
       "2       -3.021086e+09  ...    -2.849913e+09    1.626875e+08                9   \n",
       "3       -3.786886e+09  ...    -3.716847e+09    1.631510e+08               16   \n",
       "4       -2.784511e+09  ...    -2.781618e+09    1.268607e+08                8   \n",
       "5       -2.588448e+09  ...    -2.537883e+09    1.214614e+08                3   \n",
       "6       -3.586319e+09  ...    -3.441458e+09    1.893056e+08               14   \n",
       "7       -2.782039e+09  ...    -2.704645e+09    1.471569e+08                6   \n",
       "8       -2.583638e+09  ...    -2.514673e+09    1.285080e+08                2   \n",
       "9       -3.348552e+09  ...    -3.348850e+09    1.241939e+08               13   \n",
       "10      -2.718994e+09  ...    -2.674041e+09    1.392777e+08                5   \n",
       "11      -2.546640e+09  ...    -2.468328e+09    1.091662e+08                1   \n",
       "12      -4.166012e+09  ...    -3.955790e+09    1.900964e+08               17   \n",
       "13      -3.107789e+09  ...    -2.987516e+09    1.539234e+08               10   \n",
       "14      -3.441527e+09  ...    -3.536729e+09    7.795057e+07               15   \n",
       "15      -2.851737e+09  ...    -2.779924e+09    6.286720e+07                7   \n",
       "16      -3.559375e+09  ...    -3.305166e+09    1.879165e+08               12   \n",
       "17      -2.710011e+09  ...    -2.601969e+09    1.088048e+08                4   \n",
       "\n",
       "    split0_train_score  split1_train_score  split2_train_score  \\\n",
       "0        -1.064113e+09       -1.105142e+09       -1.116550e+09   \n",
       "1        -5.927175e+08       -5.870952e+08       -5.776964e+08   \n",
       "2        -4.381089e+08       -4.391272e+08       -4.371702e+08   \n",
       "3        -9.865163e+08       -1.012565e+09       -9.169425e+08   \n",
       "4        -5.097115e+08       -5.162820e+08       -4.962893e+08   \n",
       "5        -3.838835e+08       -3.880268e+08       -3.790867e+08   \n",
       "6        -9.245343e+08       -8.886939e+08       -9.353135e+08   \n",
       "7        -4.980344e+08       -5.045869e+08       -4.994664e+08   \n",
       "8        -3.838538e+08       -3.804711e+08       -3.805218e+08   \n",
       "9        -9.228123e+08       -8.553031e+08       -8.603321e+08   \n",
       "10       -4.932416e+08       -4.815238e+08       -4.730979e+08   \n",
       "11       -3.841658e+08       -3.744500e+08       -3.773239e+08   \n",
       "12       -0.000000e+00       -0.000000e+00       -0.000000e+00   \n",
       "13       -6.056477e-02       -0.000000e+00       -0.000000e+00   \n",
       "14       -0.000000e+00       -0.000000e+00       -0.000000e+00   \n",
       "15       -2.089484e+01       -0.000000e+00       -0.000000e+00   \n",
       "16       -0.000000e+00       -0.000000e+00       -0.000000e+00   \n",
       "17       -0.000000e+00       -1.514119e-02       -0.000000e+00   \n",
       "\n",
       "    split3_train_score  split4_train_score  mean_train_score  std_train_score  \n",
       "0        -1.112342e+09       -1.129650e+09     -1.105559e+09     2.220402e+07  \n",
       "1        -5.716332e+08       -5.802501e+08     -5.818785e+08     7.345821e+06  \n",
       "2        -4.376955e+08       -4.452654e+08     -4.394734e+08     2.966320e+06  \n",
       "3        -1.037400e+09       -9.707739e+08     -9.848396e+08     4.084607e+07  \n",
       "4        -5.436192e+08       -5.160297e+08     -5.163863e+08     1.542862e+07  \n",
       "5        -4.040957e+08       -3.845520e+08     -3.879289e+08     8.571233e+06  \n",
       "6        -9.009801e+08       -8.624664e+08     -9.023976e+08     2.591445e+07  \n",
       "7        -4.990325e+08       -5.055542e+08     -5.013349e+08     3.100456e+06  \n",
       "8        -3.856095e+08       -3.901917e+08     -3.841296e+08     3.617057e+06  \n",
       "9        -8.881964e+08       -9.151287e+08     -8.883545e+08     2.750227e+07  \n",
       "10       -5.155367e+08       -4.985555e+08     -4.923911e+08     1.459294e+07  \n",
       "11       -3.882250e+08       -3.810005e+08     -3.810330e+08     4.871017e+06  \n",
       "12       -0.000000e+00       -0.000000e+00      0.000000e+00     0.000000e+00  \n",
       "13       -0.000000e+00       -2.967449e+00     -6.056027e-01     1.181156e+00  \n",
       "14       -0.000000e+00       -6.072840e+01     -1.214568e+01     2.429136e+01  \n",
       "15       -0.000000e+00       -5.465556e+00     -5.272080e+00     8.093117e+00  \n",
       "16       -0.000000e+00       -0.000000e+00      0.000000e+00     0.000000e+00  \n",
       "17       -0.000000e+00       -0.000000e+00     -3.028238e-03     6.056477e-03  \n",
       "\n",
       "[18 rows x 23 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(grid_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "                   param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000020A840F1310>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000020A840F17C0>},\n",
       "                   random_state=42, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 랜덤 탐색\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "        'n_estimators': randint(low=1, high=200),\n",
    "        'max_features': randint(low=1, high=8),\n",
    "    }\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
    "                                n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
    "rnd_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49150.70756927707 {'max_features': 7, 'n_estimators': 180}\n",
      "51389.889203389284 {'max_features': 5, 'n_estimators': 15}\n",
      "50796.155224308866 {'max_features': 3, 'n_estimators': 72}\n",
      "50835.13360315349 {'max_features': 5, 'n_estimators': 21}\n",
      "49280.9449827171 {'max_features': 7, 'n_estimators': 122}\n",
      "50774.90662363929 {'max_features': 3, 'n_estimators': 75}\n",
      "50682.78888164288 {'max_features': 3, 'n_estimators': 88}\n",
      "49608.99608105296 {'max_features': 5, 'n_estimators': 100}\n",
      "50473.61930350219 {'max_features': 3, 'n_estimators': 150}\n",
      "64429.84143294435 {'max_features': 5, 'n_estimators': 2}\n"
     ]
    }
   ],
   "source": [
    "cvres = rnd_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.33442355e-02, 6.29090705e-02, 4.11437985e-02, 1.46726854e-02,\n",
       "       1.41064835e-02, 1.48742809e-02, 1.42575993e-02, 3.66158981e-01,\n",
       "       5.64191792e-02, 1.08792957e-01, 5.33510773e-02, 1.03114883e-02,\n",
       "       1.64780994e-01, 6.02803867e-05, 1.96041560e-03, 2.85647464e-03])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 최상의 모델과 오차분석\n",
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.36615898061813423, 'median_income'),\n",
       " (0.16478099356159054, 'INLAND'),\n",
       " (0.10879295677551575, 'pop_per_hhold'),\n",
       " (0.07334423551601243, 'longitude'),\n",
       " (0.06290907048262032, 'latitude'),\n",
       " (0.056419179181954014, 'rooms_per_hhold'),\n",
       " (0.053351077347675815, 'bedrooms_per_room'),\n",
       " (0.04114379847872964, 'housing_median_age'),\n",
       " (0.014874280890402769, 'population'),\n",
       " (0.014672685420543239, 'total_rooms'),\n",
       " (0.014257599323407808, 'households'),\n",
       " (0.014106483453584104, 'total_bedrooms'),\n",
       " (0.010311488326303788, '<1H OCEAN'),\n",
       " (0.0028564746373201584, 'NEAR OCEAN'),\n",
       " (0.0019604155994780706, 'NEAR BAY'),\n",
       " (6.0280386727366e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 중요도 다음에 그에 대응하는 특성 이름 표시\n",
    "extra_attribs = [\"rooms_per_hhold\", \"pop_per_hhold\", \"bedrooms_per_room\"]\n",
    "#cat_encoder = cat_pipeline.named_steps[\"cat_encoder\"] # 예전 방식\n",
    "cat_encoder = full_pipeline.named_transformers_[\"cat\"]\n",
    "cat_one_hot_attribs = list(cat_encoder.categories_[0])\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances, attributes), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 이 정보를 바탕으로 덜 중요한 특성들을 제외 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 테스트 세트 최종 모델 평가\n",
    "final_model = grid_search.best_estimator_\n",
    "\n",
    "X_test = strat_test_set.drop(\"median_house_value\", axis=1)\n",
    "y_test = strat_test_set[\"median_house_value\"].copy()\n",
    "\n",
    "X_test_prepared = full_pipeline.transform(X_test)\n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47730.22690385927"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([45685.10470776, 49691.25001878])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#테스트 RMSE에 대한 95% 신뢰 구간을 계산\n",
    "from scipy import stats\n",
    "\n",
    "confidence = 0.95\n",
    "squared_errors = (final_predictions - y_test) ** 2\n",
    "np.sqrt(stats.t.interval(confidence, len(squared_errors) - 1,\n",
    "                         loc=squared_errors.mean(),\n",
    "                         scale=stats.sem(squared_errors)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45685.10470776014, 49691.25001877871)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#수동으로 계산\n",
    "m = len(squared_errors)\n",
    "mean = squared_errors.mean()\n",
    "tscore = stats.t.ppf((1 + confidence) / 2, df=m - 1)\n",
    "tmargin = tscore * squared_errors.std(ddof=1) / np.sqrt(m)\n",
    "np.sqrt(mean - tmargin), np.sqrt(mean + tmargin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45685.717918136594, 49690.68623889426)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# t-점수 대신 z-점수를 사용\n",
    "zscore = stats.norm.ppf((1 + confidence) / 2)\n",
    "zmargin = zscore * squared_errors.std(ddof=1) / np.sqrt(m)\n",
    "np.sqrt(mean - zmargin), np.sqrt(mean + zmargin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([210644.60459286, 317768.80697211, 210956.43331178,  59218.98886849,\n",
       "       189747.55849879])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 전처리와 예측을 포함한 전체 파이프라인\n",
    "full_pipeline_with_predictor = Pipeline([\n",
    "        (\"preparation\", full_pipeline),\n",
    "        (\"linear\", LinearRegression())\n",
    "    ])\n",
    "\n",
    "full_pipeline_with_predictor.fit(housing, housing_labels)\n",
    "full_pipeline_with_predictor.predict(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# joblib를 사용한 모델 저장\n",
    "my_model = full_pipeline_with_predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "joblib.dump(my_model, \"my_model.pkl\") # DIFF\n",
    "#...\n",
    "my_model_loaded = joblib.load(\"my_model.pkl\") # DIFF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# RandomizedSearchCV를 위한 Scipy 분포 함수\n",
    "from scipy.stats import geom, expon\n",
    "geom_distrib=geom(0.5).rvs(10000, random_state=42)\n",
    "expon_distrib=expon(scale=1).rvs(10000, random_state=42)\n",
    "plt.hist(geom_distrib, bins=50)\n",
    "plt.show()\n",
    "plt.hist(expon_distrib, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}