{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython 3.7.3\n",
      "IPython 7.5.0\n",
      "\n",
      "numpy 1.16.3\n",
      "scipy 1.2.1\n",
      "sklearn 0.21.1\n",
      "pandas 0.24.2\n",
      "matplotlib 3.0.3\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,scipy,sklearn,pandas,matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2장 – 머신러닝 프로젝트의 처음부터 끝까지**\n",
    "\n",
    "*머신러닝 주택 회사에 오신 것을 환영합니다! 여러분이 해야 할 일은 캘리포니아 인구조사 데이터를 사용해 이 지역의 주택 가격 모델을 만드는 것입니다.*\n",
    "\n",
    "*이 노트북은 2장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**노트**: 이 주피터 노트북의 결과가 책에 있는 것과 조금 다를 수 있습니다. 대부분은 훈련 알고리즘들이 가지고 있는 무작위성 때문입니다. 가능하면 노트북의 결과를 동일하게 유지하려고 하지만 모든 플랫폼에서 동일한 출력을 낸다고 보장하긴 어렵습니다. 어떤 데이터 구조(가령 딕셔너리)는 아이템의 순서가 일정하지 않습니다. 마지막으로 몇 가지 사소한 버그 수정(해당 부분에 설명을 추가했습니다) 때문에 결과가 조금 달라졌습니다. 하지만 책에서 제시한 설명은 유효합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 설정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "파이썬 2와 3을 모두 지원합니다. 공통 모듈을 임포트하고 맷플롯립 그림이 노트북 안에 포함되도록 설정하고 생성한 그림을 저장하기 위한 함수를 준비합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 파이썬 2와 파이썬 3 지원\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# 공통\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# 일관된 출력을 위해 유사난수 초기화\n",
    "np.random.seed(42)\n",
    "\n",
    "# 맷플롯립 설정\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# 한글출력\n",
    "matplotlib.rc('font', family='NanumBarunGothic')\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 그림을 저장할 폴드\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"end_to_end_project\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\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",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 데이터 다운로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "from six.moves import urllib\n",
    "\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml/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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "housing.head()"
   ]
  },
  {
   "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",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "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": [
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "    </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": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50, figsize=(20,15))\n",
    "save_fig(\"attribute_histogram_plots\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 일관된 출력을 위해 유사난수 초기화\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 예시를 위해서 만든 것입니다. 사이킷런에는 train_test_split() 함수가 있습니다.\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16512 train + 4128 test\n"
     ]
    }
   ],
   "source": [
    "train_set, test_set = split_train_test(housing, 0.2)\n",
    "print(len(train_set), \"train +\", len(test_set), \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "위의 `test_set_check()` 함수는 파이썬 2와 파이썬 3에서 모두 작동되고 다음의 hashlib를 사용한 구현보다 훨씬 빠릅니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hashlib\n",
    "\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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 이 버전의 test_set_check() 함수가 파이썬 2도 지원합니다.\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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index()   # `index` 열이 추가된 데이터프레임이 반환됩니다.\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 19,
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f474cc131d0>"
      ]
     },
     "execution_count": 22,
     "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": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 소득 카테고리 개수를 제한하기 위해 1.5로 나눕니다.\n",
    "housing[\"income_cat\"] = np.ceil(housing[\"median_income\"] / 1.5)\n",
    "# 5 이상은 5로 레이블합니다.\n",
    "housing[\"income_cat\"].where(housing[\"income_cat\"] < 5, 5.0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    7236\n",
       "2.0    6581\n",
       "4.0    3639\n",
       "5.0    2362\n",
       "1.0     822\n",
       "Name: income_cat, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": [
    "housing[\"income_cat\"].hist()\n",
    "save_fig('income_category_hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    0.350533\n",
       "2.0    0.318798\n",
       "4.0    0.176357\n",
       "5.0    0.114583\n",
       "1.0    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    0.350581\n",
       "2.0    0.318847\n",
       "4.0    0.176308\n",
       "5.0    0.114438\n",
       "1.0    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts() / len(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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),\n",
    "    \"Stratified\": income_cat_proportions(strat_test_set),\n",
    "    \"Random\": income_cat_proportions(test_set),\n",
    "}).sort_index()\n",
    "compare_props[\"Rand. %error\"] = 100 * compare_props[\"Random\"] / compare_props[\"Overall\"] - 100\n",
    "compare_props[\"Strat. %error\"] = 100 * compare_props[\"Stratified\"] / compare_props[\"Overall\"] - 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>1.0</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>2.0</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>3.0</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>4.0</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>5.0</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  0.039826    0.039729  0.040213      0.973236      -0.243309\n",
       "2.0  0.318847    0.318798  0.324370      1.732260      -0.015195\n",
       "3.0  0.350581    0.350533  0.358527      2.266446      -0.013820\n",
       "4.0  0.176308    0.176357  0.167393     -5.056334       0.027480\n",
       "5.0  0.114438    0.114583  0.109496     -4.318374       0.127011"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for set_ in (strat_train_set, strat_test_set):\n",
    "    set_.drop(\"income_cat\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 데이터 이해를 위한 탐색과 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n1P2dJwTrdh6xDNtZdY1nMBhtyxnnQcVCtQeoBxha1eookTopcCo5O88RbKw+hkXrquENiPAGjK8XEIHeZ6Vh8wNjVH30hFYPkBKC1dNHILdHJ13IavbYPBT0ysQH39bgf7dY55aU+AQaXJfSuzjZEgjWMomUwskTpDj4YK+8xev36jpyXPvCBrgdfFQhNKtQnNGDQpNPwNyVlfhrSUXwM9B6a1KoMrQSksFgJJ4zzoMyemKOBOUmZrdWx3AT9Qp4/OOdli2WTkORleFGZqpTL73mCcoOHkf14ZO6Oqr5q6uQ7uKx9KsDYd3j5AtzdNfhOYJ5KyuD5w+IAEeARVMvwKZZo3HT0D66e2zxi/AJoSXlVoSqD9PO8JJp9Apo8Yt46MMKTH1tKy59fC1e/Hx38LiN1cegjPI6ODCpOYORJJxxBiravAwAuB1EtYmFU6tzx6hcuB0c0t2nN9EWmwXCB3+Wzmdm6OasqMS1L27U5dVOKwZtXSZI13SXXsQhUDh5vfIwM9UVzBMpVXcuBwc3b7+Y2Iya+uaQRcnycMF5E/LBG4gVG70CvAERT6/ZhUsf/xyLv9yDGZquFzzHYXhut7DWxmAw4sMZZ6CMZMtTinvBxROku3iLMlOJNBePV28tVoWo7Mim5bzX4vV7AVBcX9jLtIu3GfNXV8HT6FV7CwpD1+QT4AuIuryaXxRR1LuLSkVoh//ZuFeVw0pxcpgzPk+nBNTeq3IK7Ud3jpD671m8X4lRjsnT6MXHFT/ZKkrOynCjX1ZayHoxb4DisY936j4TF8/yTwxGsnDG5aAAvYIMAMYNyQZAkJ2ZgiueXW96rEhpcFaS3S7iRkqx5d8ehLYglycApTBtV6tUB8r3sG7nEcxZUanqCJ7i5CCKVJXzye3RCTOvGozHPt5p+3MSKVCQnalrPtvJ7QgpEVeq7uxKyuUmsxwBBBG4/8pBqD/lw2sb9sFvoLb83aX9DNf9o+eU7XvUIhs9NoKDwWh7zviJukaJdwC4550ylaFwOzhQSN0Qpg7ta3icPLpCrl2SN7byg8dx82vbcFKhgEh38fjjL89VjY2YPTYPj6yqMs1HGU2ONZtSu2r6iOCYC+X773r7W6wo/8n25/OXawfj9pHn6l4PdwMP9X5PoxfD/v65aR8/I9KcvGrGlkz14ZOWDxlmuHiCpyYXggKsLorBiCFsom4EWHUD//qvV7QWx1Ls/Okknvr0Bzh5DvNXSYW981dVqY6bsbwcD4/LD6rblBubmcLsrAyXyjuRc1laA6XciI08FSMPJdekZdHvRwzA598fMZ3BpGXBJzsx8cIcSw/JDqHeb9Vk1gzljC1l3VJuj0649ZI+wa7zdnDyBG9NG4r+3TOCBp/VRTEYbcsZbaCsimwLe3fByEHd4Wn04vY3voFPoEE58ryVVXBq8ireAMXDJRUQKAw3ttlj8/DQhxWqY+avqsLV+T1VozG0hszt4PDyLRfpxsUrCafoNadrqq1uEqfXA7y2YS9mXXNeyPdGFxYLlf0zh4pU18fv7jGD8It+WdjvacT3P53Equ8OWZ6DA3Dz61+xkfIMRhJxRhsoO+IGQyPGE/j8eg9E6wAoN7aCXpnIcPOqRL924zPzhkYO6h7yXux6NFkZbswelye1MrLJS1/uBQBMu2xAzNsMyeRndzZtMhsKr0BVghOjOrA13x/ReaZOTsr3CaJ0Dgg0LiPlGQxGZJxxKj4loRqRehq9aGj263rOCSLFtMsGhDy/tu1QQLRWvwFqBdymWaPjkvsoyM4MW0H40pd7cenjxnObQtUoye+xauKaleHGszcUIcxmF0E+rjhkupb5q6vw8Hh1R437fjUI//NfFyPNqX5GM2vWy7wnBiPxtIkHRQjJB7ABwAuU0rmEkF8CeLZ1PT4A0ymlWxOxFrPwmPIpXBBFVbcEWRDx+qYfTQUNLl5dKxVOg9Rw8zvhEm6YT8YbEHHfe+W6fEyofoR2vSv5u3hx7W68sXU/XDyBX6CgIMGOGWa8sHZ3cAy80VqM1IieRq9ls16m4mMw2paEGyhCSBcAiwC8rfj5/wCMo5RuIYSMAlBCCOlPKY1cLxwGWoNgJJ5wO6RuCfnZmcH3PjxOn1cCpFqaj+4aoRMqJMvsISNjCVFEiw3dhF+gqKxrwMhBZwdfswqVGn2WM5abiw42Vh/DO18fRKqTh0+gmHddvk7WPiI3C599f9RwXfnZmaZr0X7PoR4amGFiMNqWhBooQggH4F8A/gLgytaXrwLwA6V0CwBQSr8ghPwEYAyAlYlcn4zRU7iyW4KMUV4JAO4cnWuqoou3d2SEkXjByFhe9cw6/HDEzjOBOkej3Oh5QuAXpLyP2XBCb0DEW9sO4M4xA3Xr1PZJnL+qCptmjVZ5P5V1J3QGSl6XLEiZt7ISTp6DQI3VjzJ2HxpYXRSDkXgS7UH9DcAaSulmQohsoAYA2KN5357W11UQQm4HcDsA9OnTJ26LtDtQzyiv5HYQ3DQ0fmsLF6vwmtJYehq9+PHnlpDn44kkaNAyoagXTrYEMG9VFVwODvNXV6FTigPDc7uZzo26aWifkKFCnpM6kl8++Oyg2jE7M8VwbdmZKSgpq8X81dIafALFnPF5IfN4oR4a2LwoBqNtSJhIghAyEUAfSulC7a8AaINLARisjVK6mFJaTCkt7t49tLItUsKZ4ir11iPB9y2YVBjxtNdIpsFaYUe8IFNT3wwxRF6KAHjkugLV5Fl5zdWHT2L+6ir4AmKwQevM93fg2TW7DPN0DoN+fFY9BpWjPpp8AlI0jQVTnBzqGpqD99voldo+zV9VZSjWKN3nwfLSg6g+fNLyc68+fBIzltv7DBkMRmxJpAd1DYDzCCGy+CGn9f97AVineW8fAMsTtTAjQoV+lE/VAMHtIwfoPAKz9xs9hcfjKd3OMEWZbXs9IQtlH7xmsKoQeUJhNpaX1gTPrm3Q2uIX8eY242LZJp+AiroGVQ2YKlTIETS1hk7louKZ7+9A3jmd0dDsh7EtNa6lkg3h0m0HsGhdNQSRqjxfniNIc+pHgZSU1WLGe+W6fn2sLorBSAwJM1CU0mnKnwkhc1v/+CyAPYSQ8yml3xFCfgFgMIA1iVqbGWahH6PE/6Ivqk1De6GmthoLCcrRJc2pEmWEQpsnsRuq9DR68ZRmMq6Wu8fkYsGnP8Av0OAal5XWqN4Tbg3TIyur0LtrmqoI2arHIBUprn1xI9y8sbIyOzNFN+erxS9i7feH8fL6PfAGjBcoiDTYhkr+XgCp3ZFRg11WF8VgJIY2L9SllDYQQiYDeJ0QQiGF966llB5v46WZYtczkQ1GQ7Pf8v1G5/MGKP745rcQqb7XnBFmHlioJrby+lw8gc9kWCIALFy3J6TUO1y8ARF/fOMbVT89eU1FvbvopPBSl3YaHPioVVaWHzwON0903dyfX1tte01OjsOWPcew63Cj4cgOl4PVRTEYiaLNDBSldK7iz+sAXNxWawkXu+M1ZIPhE0QIFu83m1F1ymfca06LlYdmp87LaH1aYm2cZJT99JSTev2iiCnFOVhWWgMnx8EbEMBxROUhaZWVOV1TpdEekbSjaKXJF8D0t8sMf+fiCT66U18+wGAw4sMZ3UkiUux0oFCKE7wBEYQQlZjCqIg3xckhzanv8BBqwJ/RlGDlMVkZbhT27hJyfeEONIwl2km9LX4Ry0prsGr6CGmu1F2X6Y7xCSIamv26qbquMNtR8ET6Xlw8gZEdTnNxSHFyeGpyITNODEYCafMQX3vFSkRhFLJLcfBYNPUCZKa6DEUX8vkq607gtiWlKuVbqJyH3VxTqPX9/Tfn4553t8PmgN+I4QlAAZUx8AVEuHgu2JAXADhCUNfQHCwKVoYrWwICBFHEHUu/VYU0JxT1Qt45nXHtCxt0+aN0Nw+vX4AoSqE6gVLMuPI/MPGiHNTUN2P7gXrMXVmlW++0EQPwn5f2Y2E9BiPBMA8qCmQhQmVdA9bvOhp8kjczGPnZmSpPxuh8Iwd1x4JJ9iTuyuNCyeKVUmqz9TU0+6EtWeIIcOslfeBUJGSMcjPhIFAYeiregLra4JRPwG1LSrF0636UHzyO4bndsGnWaCyaegE4AgREGEq/c3t0wlOTC1Wfx6PXF+API88FzxGkuniIlGLu+Hzc/stzgx5mgUF9FwCMHMhGbTAYbQHzoKJg6db9wREbgCRyvvdXg3BNQU/cMSoXC9dVw8UbixMq6xpwollSJXROdRoq2UIN+FP+3uoYIwGFVjwxe1weHv6wAlq7IVLg1mH9cPeYQcFhjCea/Zj5/o5gjiwW+AUKnpM+Q+UavAGKhz6sQIabR0CUxBR9s9Lh4nl4A6dVHVqRitHU5OFPrIU3QOFtLbubv7oKVxf0DB7jdPC6juo8kV5nMBiJhxmoCFm6db+uDx8F8PSaXXh6zS64eQIQ6OqjSspqcf975bqaIwcHPDOlyLDLgxYzxZ7RMVZDGbXtg8y0Bf+u/Al3jB6EkYPObh3LXm4q2Y4Gg4YTQeR2UjPf34FV00fYCmkqPw+jlktao5bTNRVOBwdBIcRwOjg2Ap7BaCNYiC8CPI1ezF1Zafker0DhDVAs+uK0xNnT6MXM5TsMC2IDInD/e2VYv+uIrkuDthOC3e4QgLWAQi2esDI4BJ5GL9bvOoqZy3fExTjZxclxaPIJmHJRjur1KcX6qb9KQuXpZAN07xWDwJPT/zCuzuuBVzdIo0Zufm2bqqMFg8GIL8yDioCa+mZwRBuMMkZb78Rz5gkcn4Bg7dOEwmx8uL0OPCeF2RZMkrykcLpDAMYbs08QdN5GfnYmHBwMBRJOnsPwJ9aCA9G1LUpxcPALYjTK7rDwCQLSXTzeLT2oev3dr2tw95hBlvk9s5owySvcAZFS3cPDh+U/Bf8s3zsbAc9gJAbmQUVATtdUUJvzlHRDC63iWJCEAbLE2ieIaPaLwTlMVgIHM8WevDErldciBTZVH9O975kpRdDaT54QPPHJTrT4xWDNkpJE+1K+AMXtb5TqvDhvQMQzn+6y7HPYNys9KFuXh0F6Gr24/71yeANiyFZPMv6AiNc37mX9+BiMOMMMVARkZbjx+xH9bb1XHjshH3fn6IEhjjBGnnckGxy3g0Oai4fbRmeD4bndwCvCfH6BGoYFJxT1wqf3jFQp9gRKDb2jNBePFCfXOn02cSICCmDvMeORIEu/OmA49bekrBbDn5BCdOMWbsR+T1Pw86qsa7BtmGQECiz6Yi8u+ttnLNzHYMQRZqAi5JJzu4V8T7qbR0GvTFUu6aahfeCOdK55azNUCoBSEYJAQWnooqWa+ma4ePNCXiVNPsHW+or7dsWmWaNxVrpL1S+vrfEG1Dm50Dm76DTz9y0rY54UgxEnWA4qQvKzO5vmbGQEkaKitgE3LN6iyntMvzwXT4dozqqFtF6z+vBJ3PtuWatXI138z++WqRrPatVmdgUCOV1TUVHboBvAaMT63cew4JOd+KCsLqz70OLiCUSRIpa6CyfHobLuBDJTnWho9lnm7Ox8j9YQ1tmcwYgTxG4uJdkoLi6mpaWlbbqGFWW1mLG8HDzhEBBF/Oq8Hvhs52G4eKnD9uyxeZi/ukrVPy7FyWHV9BG46rn1YQsLphTn4MPtdYYDAAt7ZWLUf3THKxv2Go7sWLptP+atrIKTJxDE081Z1T35BIgUYYe8ooUjxoW7keLgAJ7jgp0ptPeU4uSwadbooFGRv0dRpPCHaaicHLD1L1cwA8VgWEAI+YZSWhzuccyDigKj4lilN2KmuKtraAHHEQhhGgLteAsl5bUNKK9tAIDg9e5dVoa8czqj8qcTmL+qCk6OwB8QMWd8flAgoK2RagtiaZx4AhAiqQ1l1Z2DA9wOzrBoGlB/j8dOtuD3S76xfb2npxQx48RgxAlmoKJEWxyr/dkotAZQuB0c/EJ8czcBEfjVs+vBa0JYcgcFIwPa3hGoPrGa6nRg0dQLkZnqNC20lb+38oPHpdEjNh4elv9hGIr7Z8Vo5QwGQwsTScQRM8XdwZ+bbeV5YgGFPr9CRRr08rQGVPY20t3tt72PNkQp9UHsbNkHUSanayoIsSecmPTKVhTO+QSvrNsd8VoZDIY5zEDFGG33Byr/L5VeTnWNAAAgAElEQVT+X555pGVC4Tkhz92zkysma/QKFOku3rDJ7DNTirD5gdF4aepF+NMvB8DtIEh3hTZWTosC5LbEyZOwBgxmZbixYNIQ2w1xG7wC/v7vXRj80OooVslgMIxgIb4You2RJ4skpKJSyWOat7JSN68o3cXj9yMGYOiALMwpqUTAICmT5uIx7bIB+NtHO6NeZ4qTC0rDjZqqLt12AIvW7YaL5wEQXFeUjRXldaZen4sn0Ld5TQ44guAId7tMKOqF7MwUTHplq+1jWgTgzqXf4MWpF4W7RAaDYQLzoGKEUb3N3BUVuiobJ8/p8hsCpcjpmoqpQ/vi9f+6GCkGdUh+QcShE1707hp9Qp62Xk9G7sm3sfoYLn18LZ5ZswveAA0OM3z/21r4LTpg/H5E/yhqu+KLi+cthz2aUdw/C7de0iesY1Z+d4jVRDEYMSQ5d5V2iFFTVr8INGt0ywKlmDM+z3R2U352Z8PaUb9A8drGfThY78V5PdNRkB35ZFejVkeygdX22gMAF89h+uUDWz0lNeluHpecm2U4sj4Z8AYEHPz5FF5dvwePrq5E6T6P7WMfue58LPxtERw2w5dOgoiMIYPBMIaF+GKEkeBAi5OXWh9NHdoXV+f31BXUyhJ1OTTIE4IWv6Crl/r+UBNuHtoHFXUnI1qrX6C4771yVcNTK0WfXxRxTUFPdMtwYbYmBOkXKLIzUzGhMNtSBt9W+ASK6W9vD/786oYfcVluFt6YNszW8YN7djYMuRrBOziku3iUHzzOxnIwGDGAGagYoeyWzYEYNlZ1cBzmr65CpxRHcH6TjC5/NS4PBdmZWLWjDq9u2Kc715vbDkS1Xrm3nzxOPd3Fw2sQxnM7CKYU5+gKix0cQUCkIJTi6uc32N7Ek4EN1R6U7vPYkog3+QS4eQJvCNk5T4ApF+Vg3MKNhoXSDAYjfFiIL4ZMKOqFTbNG4+VbLjLMyTT7RcP5TUb5q/mrqpDTNRVX5fWI44ql0FVJWS3GLdwI0tpVJMXJwe3gcN+vBmH1nZdh6dYDOi9ONkhegbYr4ySzfvcxw3lbWnK6poLYCPE5eA7vlh60PaeLwWCEhhmoGJOV4cbIQd2xYJIk304z6PStbdRqNVSwf/cM3QiMWPHN/p9VxlH2EkSRYvWdI3DnmIGoa2hJ2KynREKAYIdz7RBCpeFS1rJZwXMEPLHXkJfBYNiDhfjihCzfrqw7gduWlKrEB9r5TVbNXGvqm5HucuCkNxDzNT7/eTUq607ock9uB6/oUN7xrNOw/l3xyoa9qhZP8hDCjdXHVKFWOUw3PLcb3tp2AAvX7QbPEZzyacQvIoX2szKa06VshQWAjZFnMCxgBiqOKL0po0muyveZTXutb/IZ5rNixWffH9Gp1FoCAhqa/fA0epGdaTwIsb3CAbigb1dU1p3U9UisrGvQ9SZUTs+9c8xAnJXukmrZWtshpTglr+nJiUOC7zf7npV5xmZ/AIQQpDh4lq9iMExgBioBGDWVtfOekrJa3P9eeevTefzQ5pD8AsWf3iyFQIE7RuXC7SC6CbbtFRHAS1/sVQ1lBOQeiUTnTXIgqKw7gZGDusPT6MX81VWqOjZRpPjorsuQ20OS/Zt9z8aNeSn8guQZszHyDIYeZqAShLaJbKj3eBq9mLl8R8JHX8g0tYawnvtsV4fMQfkFqutwnp/dWRdqPeUXcNuSUiyYNAR9s9JDhEPNv+dQjXmVM6oYDIYEE0kkKTX1zRCTYFZXRzROMvdfOQiLpl6AxbcUB70XSRCh9q7kKb3pLt5y8KMVoerk7J6HwTiTYAYqSUl38W3mPZ0pfPnDUdz+xje4Y+m3QSXf8NxuePCa85Dq1CvymnyCrrmu3Ua02sa8Dk5qZBvueRiMMwkW4ktSmnwCUpycahpve4IAcDk4w9ZJycKWvR4I9PSAx7veKYODAG4nr2tRJXs4hb27hMwnmmHUmJep+BgMc5iBSlLac7iH54BHJhQYjhVJJowc1AAFAoqcUrqbhyBSlYdjJ59ohtGASwaDYQwL8SUpdgtEkw0nBzx/wwX4jx4ZmHhBTtyKjBNBuovHvPH52DRrNJOAMxhtAPOgkhhlgeiza3Yl7WB2vjUs5vULoADuent70q41HARKcflgqVchawDLYCSe9vV4fgYiF4h++ueRcPHJ93XdNqI/HDwBFSkEKo2X7wjGyUGkzvNLtx3ApY9/btgSicFgxBdCk0DKHAnFxcW0tLS0rZeRUFaU1WLm+zvAcwRNJtNtGfElxclh06zRSeFJKdsmJcN6GAwzCCHfUEqLwz0uoSE+Qsh8ABMAeFuv/Sql9CVCyH8CuKf19XQAawDMpJTGvgFdO0YO+a3beQQPl1TgVDtT+HFEGtOhnSgcC1w8Ac8RnfrOzXPwCWLMOgomS0GtdjwLa5XE6IgkOgdVD6CYUuonhHQHsI8QUgXgJQD5lNJ9hJBUAF8A+B2AVxO8vqQnK8ONywefjcAH7c/zlToqxWfdNHh+NRwH8JTEbCRIMhTUGrVNYq2SGB2RhCY1KKXPUEr9rT/2A9AI4MfW/+/W+no6gFQAddrjCSG3E0JKCSGlR48ejf+Ck5SsDDfmjM9v62VExOSL+iDFyRlNtY8KDoAginDyBOnu0yNOmv1iTOdVTSnOaXMjYDWehcHoSCQ8604IGUgI2Q3g3wBuppTuBzAOQAkhpBqSwXqFUrpaeyyldDGltJhSWty9e/eErjvZmDqsLx79dQFcDg7pbl7XnidZ+fUF2dg0azSuLzonpueVBicCoBT/75fnIt2lnsOlbQ4bKctKa4JDCO0MPIwHVuNZGIyORMJl5pTS3QAGEkIuALCaEDIZwNsAplFKPyKE9ASwghDyE6X0/xK9vvbE1KF9cXV+z9aZUTyuen49DKa2JxW7jzTC6eAxbkg2Pij7KapzcZDyWspG635RmnMlaDZwniN4e9pQVNSdwN9WVSHS5uyyp2I2NyqeeBq9qKw7AYBi9rg8zF9VZTrag8HoCLRZHRSldDshZAuAywHUUko/an39ECHkfwD8FwBmoEIgdyYoP3gcKZrO2snIXz+sAB8joYQI47yTT2OlnTzBkxOHoLh/For7Z2Hv0UYs2Xogomv6RRHpLj7iHFCkyruSslrct6wMcucoJ08wd3w+CnplMhUfo8OSsBAfIeR8QsgUQghp/bkXgKEANgDII4Sc1/q6G5LS74dEra0jkNM1FUI7KBkQKOKi4gtF3jmd4Wn0Yv2uI3i39GDYx7t5ghQnh9lj81B28Dh4og4Z2skBlZTVmo6Zt0IavVIOZVtDv0DxyKqqqI1TW4UpGQw7JNKD2g/gjwBmEUL8AFwAZlNKvySETAewtPX1VACbAcxN4NraPcqpvByBbiT5mYxfoLjqufXgOAInz5kOX3Rw5mo/gVL8prAX5q+ugoMjOk9V9qzMOk5Eo7yrqW8GTzgAmmsGRDz0wXeYNqI/ivtnWZ7DCCZVZyQ7rFC3gyGHkCpqG/DIqsoOMwk3njg4gjtGnYuXvtwDngDNYXxm6S4eAqWYUpyDZaU1ppt9+cHjuPm1bTjpPV3a18ntwJvThqKwdxfLa3gavbj08c8tv8vLcrPwxrRhttftafRi+BNrVd3yk6kImdGxiLRQN6oQH5F4K5pzMGJLVoYbhb27YOqwvtj8wBjkdk9r6yUlPZRSvLx+L3wCDc84uXnMm5CPVdNHYFlpDVr8Ik56A2jxSwMOlWGzaJR3WRluLJhUCCsh4oZqD0r3eYI/hwrdMak6oz0QbQ6KA3BDLBbCiA97j51q6yUkPW4HD17Tdj3NycERohV7i0/A5YPPRpNPANWEBkVBVG322oGF4Q4pnFDUC//83cVwWqxp/e5jAOzluphUndEesDRQhJBPCSEuzWu7CCEp8V0WIxZIT8ltd33tVprm4uEwcAOuzusRszolM3gCXRGvjDcgQNAYGJ8gQhn+dnDQGTGBAp9UHEK6i4dXI/zwicAH22tUHsyEol7YNGs03pw2NKIRHvnZmSDE3MMbObCbKtdl5s0B0RtMBiMRhBJJjGl9j0/xWi5YF/R2QU7XVIBwaIv+4mkOQCTqicABkcLNcwgIp5P9aS4OV+T1wMY9x+AX4iORd3AEr9x8IZwOHiea/bjn3e0qRRzHETw8/nRdkU8QIFKoBBOESH39tOKIuSsrMG3EAMPr/u/m/Xj7qwNYMKkwaIyiHXb41OQi3P1Oma5h1GW5koS+/OBxODkuKMQAzPsHaif8MuPESDZCGaj20Z6AYYiUuxhiuKHFk0kX9sJTU4qC3delTV/Efw/vh9c3/ah6r0iBn5t8aIxjd/aASPH7Jd8AkDwpB88hoAhvpTh4FGRnYtOs0aipb0ZDsw93LN0Ov3Ba0ODieV19FQD4BWDxhn2m1/YGKO5fvgN553RG13RX1MZANipb9njw7f6f4RMori/KDqr4rEJ3RjVYssEs3efBvzb/iJEDu0WkCGQw4oGlio8QIgDoRCk9pXhNBHAXJK+KA7CIUqqPm8QZpuKzT+k+Dya9sjWh1/zszyOR26MTPI1eLN12AIvW7YaL59HsD4AQghQHD78oYvbYPMxfXaXytACgf1Ya9nkSkz9zcsDHd49EXUMzAILszBSMW7hRp3D748gBeO7z6oivQwCkOiWV37QRAzDtsgFx8VqUDwayopACppLyG17ejG0/1geP1yoC2VgPRrREquILZaBEABkGBup7nI4b5TEDlfw8XPIdlmyJrHtCpLxwYxGG53bTyZndDoJXby1GfnYmauqbdfJrALim4Gx8XHEkoeuV4QDcfEkflWxclpELgohYTTnhCfDsDUVxqT1SGhUAppLyJZt/xPNr9UZ3+R+Gobh/FquVYsSERM+DuphSeooQ4oA0w4mR5Dxy3fm4dVg//LvyEADgqvye+LjiEJ5esytu17zrnTIs+e9f6HIiLp5HZqor+DRuFDpbu/Mouqc7cLQp8SPBRABLthzA8j8Mg9PBI93F6zyqWCBQYMbyctNi3Wg8F2WuyywvVVl3AgvXGXuE63cfQ//uGWysB6NNCSV2oNAP8KEmf2YkObk9OuGO0QNxx+iByO3RCdcU9Iy7ym/7/p/hE/RdF+Qn+6wMN6Zfnqs7zsXzeO2/huKWYb3ju0ALbnx1G/Z7mtDkE3Q1Q4CkSnQ7ODii+QwpaW0AqybStkhGmOWltuw5BrOuU+dkpqCyroHVSjHalFD/tAgADyHklPxfIhbFiD8lZbUYt3AjuBC1PtGyeP1eBBT2ycFBJ2e+aWgf3bgQ2YjNv34IbizOsXWtWN9JQKS4771ypLt43QbvdhC8fPOF2PzAaDwzpSgo13byBOF8pF5BxG1LSlUGyK5UXH5vqF56RpLy2WPz8Pomc3HHYx/txG1LStESMH+4YDDiTagQ3+Umr7NHqHaMcgOMN02aa/Ach+G53VSvyZ0StIn9rAw3PI1efFCmm10ZhEDKpwREirnj89EpxYH73ysHKEUs2hH6BYqN1UeDYg7l+kYOOhuApKzLO6czNlYfw2MffW/YYd0Kb0DE/ct3IDszBU4Hj4M/nwJn0oxWadjDyQ9pJeU19c1w8Ty8AeMQqpwTdHCA28HBxbOxHozEY2mgKKVf2jgHk6K3M+Q2Ny1tUB/lDYh4a9sB3DlmIIDTeZbhud2CMm9lzkXaSDl4A8ZrpZCm5vIEmFNSgWduKMKWB8dg3c4jeOjDCtVxBJHFpJ/45AdQALPH5aEg+/R4C2Xfw/mrq8DB3hgRjrTOsVLcki8gYtIrW00b1mo9l0iaz2prsLReoZMncGlqvVKdDiyaeiEyU51MxcdIOKYhPkLIyFD/ARgBYJTiZ0Y7wCgnkUheXLsbnkavLs+yqfoYCnt3UW2CdtcqUGlw4V3vlAEA+mWl6YwaBXD3aH2+KxTNfhEtfhFzV1TCHxCQleEOrn3qa1vx0IcVaPGLOOUPXcvFc8Crt1wEB2/8T8/IOLkd+i4P0fbSMwr7zZ2QrxvZ4hdF5Gd31n0vDEYiMJWZE0JO2jwHRevDKaW0c6wWFgomM48OZa2MVxABUYxJSMwuf/rlAPxz84+2ummvKKvF/ct3wGfiRWm5rugcfPzdIUNvZviALMy7Lh/Lv63By1/ujWjtaQ4gQK29pTQXjxafYOijuhwcbrw4B+98XRPyntKcPF6+5SKMHNRd9XqsupFrlYIrymoxY3k5eMJBoKKqC0Y452EwlMRcZk4p7WRyoe4AhlBKPw/3YozkQZmTkGXUSKBX9cqXe5HqMvYAjFry5J3T2fZI+1XlP5mq0zbt9eCa59dLvYsi5FQAsAoWOjngmoIeWFn+k6ER8wVELCutwUs3XYA/LP0WAQtDJ4IiP1v/3Kec/xXN2Hdt2E9aCWl95LT3GbFaKUa8CHseFCFkCIB/UUoviM+S7ME8qNiibksk4KxUJ3466Qt9YAyx8gA8jV4Mfewz2HGiUp0cmhMgAIkGt4MDpRQE0DWaPf0eEtKDiaXnEolXxuZKMewQl0JdQkgnAC8CGAlgDYC7ARwBkB3JIhnJi1Hj0F89sw67jySusmBKcY7pplZT34xUp0PVccLJEfg1OZtzu6eh9nhLRNd3cohZl4hQaPNjPJG6pTs4qU/gnaMH4qahfUJu8tE0n9ViJJ4x82qjOYbBsEsomfmjAHpDMky3A5gN4BEA1iNAGe0S7Wb30tRiXPHs+oRdf8mWA7h7zCDDjc1ILMHzBB/ffRnW/XAElXUnMH7IORiT17M1j7LDVPlnxMDuafjR04xE1Z5re8ynudRqOQBBwUOiNvpIZkSxuVKMeBKqUHcCgD9QSlcC+D2A6ymlXgAcIeQRQsjDyv/ivlpGQsnt0Qm3XtInodd88pOdpr+7Y1Qu3A6iml+U26MTbht5Lp678QKMyesJQPIGNz8wGmPO6256Li27j57SeWPxRGs6lWq5jdXHYtZFIhwimRHF5kox4kmoZrGNAHpQSptafz5IKe3dqvD7Fuo5UZRSemVcV6uA5aASR/Xhkxj7/Hp4ExD+4jmCr/4yxrQg1SeImH55bsjwl3wMTwh8gog7Rp2Ll77cG5ZXlQicBCAcwZzx+Zg6rK9pTmfV9BFo8gkJUclFktdiKj6GFfHqZr4dwIOU0k8IIZcCeIJSehkh5DiA8yilP0W+5OhgBirxVB8+iX9u+hFvf3UgbiW+qU4e79w+DIW9pShyLBP3cjeIgEAN643aCr61cPf/jcpFzfFT+HB7nUqF6OAAjuPg5tUquUQZBe11mDFihEu8upk/DeAdQshnkIQSt7W+Ltc+Mc4gcnt0wqO/OR9Lv4rf2A4KqspfxDJxX9Dr9FDCVeV1eHWjeS+6RCJQ6T+jsRdAa8cJUQzWTM18fwdOtgR0rZfiIe3WSsinXJSDZd/UMEk5IyFY5qAopW8CmAygAsAUSmlJQlbFSFry/ro66nOkOXn8/dcFuGPUAJyf3QkckTwno/xFvBL3fxx1Lv5yzWDLdaa7eLgSPuksNDxHMG9lpa1msoC9hrJmx2mb1i7ZesD2dRmMaAk5D4pSugaSxFzJ3wA0xGVFjKSldJ+ntUg1OrwBAXNWVMHBEwgixfzrClDQK9MwZBRJQarZMRurj+kKSh+9vgAPl1ToCnvTXBx+P6I/BvXIwP3LE9NY1y5evwi3g1ONMTHzKqMporXTs5FJyhnxJOxC3WSB5aASz18/+A5vbot9eM/JE2x9cIzlJhdJHsTuVNn6Jh+ufWGDrutDhpuHX6AQRNFWgXCi4FuD69o81ba/XKH6LKItojU6XgsrymXYIdIcVJzH1TE6Ep1TIx3AbI1foHjnq/1Yv+uIabgoK8MdbFhqd5if8hir5qq5PTrhqcmFSHFySFfE9Bq9ArwBEYSQoLzd5eDg5ts2/coTgBi0aqqsa1B9fvFoKHvrJX2SRlIeaeiS0X6Iz47D6JD85oIc/OOLyBqshmLBp7sBSN7U05PN2/tEMmYCCJ2XkjtprNt5BHNXVqLRezp8luLgsWjqBchMdZ3uW2hjrEa8kJr6qq8fEIE/vvktREqDYbxYFNEadRi5e8ygNlfxsf5/ZwbMg2LYJrdHJ0yxOd1WS5pNtYFfoPjzsnK8un6P4ZNxpF6BnYLSrAw3Lh98tk6C7hdFZGdKm3rXdFfwPOkuHiZTM9qEUz5BJVyIVRGt0hM1+jnRhDNxmNG+YR4UIyymDu2LVeV1OKXJSzg4ApFS1TRZB0fg4Almj81DQa/M4GA/UbAe7SGIFI9+tBOPfrQTL9xYpHoyjsYrMPIGjLhjVC4WrqsOTpGdUpyDcQs3qp7WZ4/Nw7xVVSARj0GMH0rhgt17bk+w/n9nDsxAMcIip2uqTtPl5ICHxg7GiFyptVDZwePol5UGp4NXbYqFvbvg6oKeWLfzCOasqFRNbjXjnnfK8F3tcVyV1xPF/bOiHjNh1VxVGTYCKG4fOQDXFPTEuIUbVSHFGcvLARDb86niBc9JKkgtflFEuotH+cHjwc+/I23crP/fmQNT8THCRjmao9kfACEEKQ7edi7AjjrMiMtys/DGtGHBc0TrFcjnSHfxqGtoxm1LSuENnP73kOLksPiWi3Dbv0pVIzGcHIHTweGUDQMbTzLcvCpXBgAunuDGX/TGstKOXUyr/DvYUe+xIxGvThIMhg45bFRZd6J1UxfhF6QCKa1gwciQaL0gnyDimoIe+LDMunPWhmoPSvd5gp5UNF6B7C1RkcIrUHAE0DojTo7DieaAbl6TX6QgdiYnxpkWjYF0OTi8dNMF+NPSb+ETqKmIpCO0KuqIoUuGHmagGBGRleFGZqoTLp5TNWBV5gKslFZGG4yncSs2VHssr7t+9zEU98+Kau3KJLuMUWs+vyjiZIvf8By/HNQda74/EtU6oiVAJZVTiouHIFLccHEO/vTWdl09l93vpL3R0UKXDD1JpEFitDescgF2lFZaNdgb04bhf269CDxnXmc0cmA309/ZrYsxUgJqcTsInpw4xLT7+bqdbWucZERI6j1BFPH2VwcN82LhfCcMRjKRUANFCJlPCCknhHxFCPmWEPInxe9uIYSUEUJKCSHbCSH9E7k2RvhYyZgjlYN365SCNKexJP2y3CxT78lu8S5gbFiVcABevbUYE4p6oSC7s+F7AkmWug2IkkRfi4snUX8nZzqsILjtSHSIrx5AMaXUTwjpDmAfIeQTAKNa/7uUUnqKEJKGZNPuMgwxywWEq7RSCha0xzk4glduvjA4kNDo2HCKd2XDeu+7ZYaGhucI8rMzUVJWi3uXlYf8DNJcPHwBAYQQuBwcmrxtK56QcTk4fHTnCOT26ASAqd8ioSOFRNsjCTVQlNJnFD/2A9AI4GcAdwL4J4A1rS1cXqGULknk2hiRY5QLCEcOrhvpUJyjU6GZGScgvLoY2RAOz+2GbQ9dgV8v2oQDGg/i6SmFAICZy3cYyriVuB0EL998IfKzM4Nrkeu95PVfmNMFm/f9bHmeWJLm4oMdJWTjBETWePdMJtKuJYzYkXCRBCFkIICPAGQBmAKgG4CBALoAuAxAXwAbCSH7KKUbNMfeDuB2AOjTJ7GjyBnhY0dpZbQJLCutCWuCrF3PwOhpeP2s0fi86hBWlP+E83t1xq8vzEFWhhvlB4+bDjxz8mpZ/chBZwd/J+fVri7oqbrvAQ+sjtuQRy3/eWlfTBsxwPBzY+o3+7CC4LYn4QaKUrobwEBCyAUAVkOaNyUAeIxSKkIK+70NYAKADZpjFwNYDEh1UAldOCMiQimtzDaBJp8QnKpr5xqhPAOrp+ExeT11HlpO11TTGPPb04bqipBD3ffex8fi0ZUVWFVxCOMKeqJTmhvPrNll6/7C5Z+bfsS0EQMAmMv82QYbGhYSbXvaTGZOKd1OCNkCoBhAE6VUGbgXW/9jdHAi3QS0G6/WMwCg6qQQ7tNwVoYbD4/Pw0MfVKhe5zmC/t0zItrgHxpfgIfGF8DT6MWlj68N+3i7yPdlNP9Kmz/pCDVR8YKFRNuehBkoQsj5AM4D8B6llBJCegEYCmABgKOEkBsppe8QQnoCuAHATYlaG6PtiGQTMEtcy56B0e+H53bTGUKvIKrGa2gpyM5EuotXtWRKc/JRh3hq6pt19WOxRG51pPUYZywvR5c0ZzBftnTbASxS9ByUP8dIjZbRce3dALKQaNuSsFZHhJDOAP4OYBgAPwAXgBcppf8khPQD8AaATpBUvs9SSv9pdT7W6qhjYXcjM2qT5HZw2PzA6OAgQ7MhfZuqj2Hm+zsAAC1+EW6egHDEVJkV7cC/cO7BxUsdNaIh3S0V7D45cQj6ZqXj5te24aRXPQI5zcXDL4gQRaqbGJLi5DB7bJ5K4GFXtWb0UEABpoBjAGgHrY4opScA3GHyux8hCSQYZyh28yJGoTpvQMRb2w7gzjEDUVPfDF4zzI9rPW5CUS/kndMZ174gpTa9AgUEaqrMijTEo5TMGwk9jM57x6hcLPqi2nZ/QjdPQAmBu9X7kTvGKycOG9V6WfUP5ADMXVkJv0WbJLP7NfLWAAJv4PRrd71Thr1HG3HLJf2YJ8KwBWt1xEh6lN5VTtdUQ09j4brduGloH1TUNui6pJ/yi3h90148f+OFaPIJcDt4+ITTnoVVLircEI+2x1+KUyqM1XoP2vPWN/nwwtrdtj8TwhGstlA6Ko0gB4JT/tC1WdoRKoA91ZrRQ4MoUvgNJPrPfV6N5z6v1o1RYTCMYAaKkdQs3bofc1dUgOc4UFAsmFSI6Zfn4mmNAs7F86isO4G5KyoMz1NS9hPys/dg4oU5lqKMaFRvRj3+5D8beSLKnNk975TZqkx3cQDHc7oaJyOMmvqGi08QQgpWjIUu1ue9992yDldP1N7zbckI68XHSFqWbt2Phz6sgF8EWgIivAGKe5eV4ZqCnnA71GE8vyhiy55jlhvjk5/sRGXdCcwel2fYnimcdklGWEkAPK0AACAASURBVPX4M2sp5Gn04t537RmnVCePx34zBJtmjbbtfWRluDFyUHcsmHS6JZXDotehlkkX9rZ1DWXLKxdPgp6jGZSgQ7VYivbvDsMY5kExkhJPoxdzV1bqXg+IQF1DCxZMKlTlcGaPzcMjq/Tv1x77xze+gQhqmLOJtmuAVY8/M+n8qxv26sQKZohURIqTx6eVh+ANiBiR2w1d012orGsAQJCf3dl0rXL+TR4medNr23Rdz434sKwW72+vCSlwUIYs0108xi3caHleQtFh6olYx4n4wQwUIympqW+GgyOGDVABiuG53fDUpELs9zTCL1B8/aPHVjhAzsXMX12lUuPFomuAMu9jlIPSFg5X1jXg1fV7Q56Xg1QU6A1QTH97u+n7eAI8e4Nxbkersrv+gl5YVlqje5+TV/cTlPN5djZcZSj0yYlDcN975SbfH/DMDUUdZvNmHSfiBzNQjKQkp2sqRIMSCCdPcPDnZvz+X6Wmm58dOBBU1p3AyEHdg9eLRdcArSdhJGKQjYUowpb3ZDdzJFDgnnf0uR2jJ/wV5XX4yzWD8dSnP8DBEQREijnj83F1QU+s23kEc1ZUqsQmdjdcZa/Dj++6DL96dr0ufPnZn0eGzJ+1J1jHifjBclCMpCQrw40FkwrhUPwNdfIEc8fn45FVVVEZJ0DypG5bUhrMFViNDolk7YW9uyC3RyfVvCtAbSyirXsyQgTw2ga1V2Y2ZmPogCxseXAM3r79Emx5cAymDuuLrAw3Lh98NgSqnSIcesPV5mGqfjqB528sgosncDs4uHiCF24s6lDGCYjt3x2GmoQV6sYaVqh7ZiCFwk4AoMjPzkRNfTN+++pWy3qecNAW3sZbiVV+8DimvrYVjSYjOeRwXjQ4OIJtfxmjuqdwC45XlNXq6r+sclBW1wBwRqjbmIrPnKQv1GUwIkFWoSkJROF5uHiiEgdoQ1fhNFKNZEOqqG0wNU5uB8HmB8bghc92YcnWAxEPRAuIFC+u3Y25EwqCr90xKhcLNW2NrNYcbv2XUR6GJwTrdh7B5YPPtt34tz1j9neHGa7IYQaK0a7IynDjztEDdXVQdtEq10L14zMjkkF2nkYv5q+u0r3OA3A4CBZMKkRWhht3XTEI735TY7urhBH/u3k/TvkEDM/tFlwnQHH7yAG4aag0qkbZTNeIUMZaW0CtzcM0+QTMXVmJv5ZUnLFtjtjAw+hgIT5Gu0PuBh6LZqsunoCz6Mdndv1IevSVHzxu2B8PADgCPKdQ4MkhNgKKZn/k/0a1HqOy356DI2jxCRg24CyMHZKNK/N72n7CX7p1P+atrATPcRApxYJJQwBIaj+eEF03j3B7GHYEryNevRzbI5GG+JhIgtHukAQUUlKat19zaohPoGjxi5j5/g5UHz6J8oPH4Wn0Wh5jJjoIVXgqtWkyDu+JFLhvWXnw2hOKemHTrNGYf935cEdxk0TTl5BQYN7KKrT4RTR6BQQosHHPz3jwgwpc9LfPbBWYygXUPoGi2S/AGxBx33vlGJ7bDZtmjca8CfnIcKu9Ujufj0xHKXqN9O8J4zTMQDHaJfIG/sTEIUhzRmmlAFCR4toXN9raFCOVFWdluPHbi80nQftFire2HVC9//LBZ4OE0flBizZC0hywVg/et6xMZaA9jV6s33UEq8prsX7XUVQfPmlYQO0XKF7bsBdZGW4U9e4Cn8a7tRtKVaocT3oDwYeHUA8NyQiTn0cPM1CMdou8gYumw9nt4xUofAF7m2I0suJ+3dItf79wXbXqutpruR0cxhb0DEq3OUiqPSMbduslffDU5EJdWyhrSPAJv6SsFsP+/jluff1rTH+7DLe+/hWuem49YJIWeHX9Xizduh/jFm4E17oguUyAUoqxL24M6Q1F63V4Gr22vOBEwOTn0cNEEox2jXZ0hU8QEBABwaCTdjiEKkyNdJDdiNxulr938frrDs/thsW3FEOW2sutmZTTg2vqm+EPCNi2zwOA4Kr8nsF6oy5pzjAKmylyuqai+vBJzHhvh+6Y1gklhnAEmLeqSuU9yX+UzkODoUCzz8vI6/AJIhqa/fA0eiMaZNmWsIGH0cEMFKPdo90EPqk8pBvVHi52Q3bhbji5PTrh1kv6YMmWA4a/11431PRg5VoAoLh/lu6c2Zmptgubn55ShI3VxzDjvXLTXn08MTZSAoBUnsCn14Ccvj+BorKuASMHnW34e+0DR0tAgCCK+NOb38AnCJg2YgCmXTYA+442YuHaauw91ogB3dJxyyX9TPvhAW1bh2X196QtxCDtSYDCDBSjQ6DcBKYO7Yuan0/hpS9D97kzwu0gcQ3FPHLd+bh1WD98sL0G5Qcb8NWPP8Pt4HX1SXaakNrZbJp8AlKcnE5NtviWi1Bb34zKuhPIz+6MK/N7or7Jh2tf3GjZSJbnJLGFVkQ566rBeOYzO/J/65Dj6TEhDdKYEAEItKoCX/pyr+57PVDfgi92e3SCGSfHYem2A/jHF9WmXlVbbtZt4fGVlNVi5vJy8IRDQBRw5+hBuGlon6Q1VMxAMTok0y4bgNc3/RiRFP3pyYUYVxjfjSK3RyfMuPo8AOabZE19M6gmVElFGgwBSpvNDvAcgSBKUm+jDc7ME8zPzlR5MiVltZaeEyCZlqcmFwEAZizfAY4AggjMmZCHqUP7omdmiiLcKsIXEFUFxw4OyM/uHOrjQVaGG5mpLjg4Dl7Y6xqiXbZPELBoXbVqqq/SwLdlSLAtOqB7Gr24b1lZ64OF9Jk+vWYXFq6rNv2709YwA8XokMhS9BnLd4RtpDqnOqO+fjhP5mYhoHQXL42lV+AVKNJdPDyNXtyv6RZult+xM7pe3jBDjeCgkHJNl5zbDZsfGK27R224dVP1McxofWIXqBgsRrZDTtdU+CPoGpLu5iGIFHeMysXi9XtV379ScNGWIzLaogN6Zd0JndcLAN6AmLTjQZiBYnRY5M1ydsl3+Oi7w7aOIZA8CzPsGJ5onsyV5zcLzTX5BNQ1NOjySlb5HeU8qKLWRrZKKutOgLOphpz+dhmcHDB3QgGmDuur+71scD2NXvTNSsfqOy8zHU1vRVaGG3PG5+OhD+3nE108MG98Pi4fLH0Gi76oVv1ezvG19YiMtpGgmz98JOt4EGagGB2arAw3rso/B59VHQEhJKQ3dVV+D90/UtloVNQ2YP7qKkvDE03oRmvYZo/LM3xfTtdUNDT7Tc5ibGSsjKYcKgzH0/SLkAwHkXJ+dq4XST++qcP6oskbwGMf77T1fkI4XD74bNVcKjPPsS1rlOx4tbEmPzsTTt54xlqy1mcxA8Xo0KhDV6GVbF/sOhqUM3savXjh8914Y8t+VYdxK8MT6ZO5kWGbv6oq2JZIOTm4pr4Z2ZkpcHBqoYJZfsfKaAJSqCvStlFzSiowtN9ZKg8p1vmVoQOygrO1rHDyBAsmqTd5M5l3WxgILYmWoGdluPH05ELMWL4DlEryfTdPQLj4ioKigRkoRofGyGBYIRuTjdXHcM87ZZYmzcjwRBq6MTNsBb0ysWnWaEMP7qahffDu1wdD5nesjKb8Z7ufj5aACFz53HqkOPjgGvpmpYPXtFgKZaQ/rzqElTt+Qn52Z/zmwpzgutNdPBqa/QhoxCJuB8HTkwshe4ydUx3BGjEtZjm+ZKhRiqRUIRrsDNRMJpiBYnRojAyGFX5Raskz4z1r4yS/V2t4In0ytzJs8rE3LN6i8kqWldaEzO94Gr1oaPajJaD2PloCQnDtzX6LwiUbiFQaAAkAf363DDOvHqzzdrSflTLX9ttXt2DX4SYAwIdldXj0I3U4z8FJSkGeQ6shpHhy4hCd0lLuIqH8LELlDBNtIOKJXWFOe7pnZqAYHRqlweAIsRx0yLeGOqTNlcAqJEgAXc3Slj3HcKzRh4LsznhqUiH2exqRle5GlzSXqguC0UaiXCdPCHyCiN9d2g/1TT7U1Dejodln6AU1+QTT3I6cB3Jw+ryDIFC889UBuBwcaIh7DQeBAk/++wfd67PH5gXvVVmL4/ULIUXkcvRREIFmn4A/jBoQDE/KGOW8KJB0nSXiRTJ20YgFbNwG44xANgr3vPMt9nmM+7q5eIItD44BAPzi0c9MW/oAwDX5PfDSLcXwNHpxz9vbsWGPJ+QaRg7MwoBuGXj76wNw8bzhRvLKl3vwxCc7oYxouXgiNX0lakOjHN2gNXrVh09KBbcxGEkSLeluHm9NG4acrqmorGvA7/75teVnawe3gwvW7hiNtZD6D6pFMUajLsIt1E3GLgztYawHm6jLYFgghzXWzRiNfg+sNnyP28Gjpr4Zhb27YNbVgy2VY2t/OBIcO2GX9bs9WL9bMmTegBRWm7H8dO3S4i/34O8G1zxdm0TBEcnTIwBmj5MEE8+t+QFvfXUQLp6DCGDM4LOxpuoQoph3GFN8AREVtQ2Y8spmBAQatXECoBrxUVPfrMt58YTTCRq1ebBwvY5k9VJiJZlPRuPLDBTjjOPHx8fi/ne/xfLtP6leV+ZJbv/luag+ehLLSo27bzs4DrNLouv3BwDegDRi46x0ly0ptUgBsXWH1/YbbG71Fj6qOBT1umKJKFI8XFIRE8OkRK77Ovhzsy7nJVARoGoLpfx+w1UatkXnB7vEoqYqWY0vG7fBOCN56oYL8cKNRZajEJ6cVITP/jwSc8fnwan5l+IXRLOpE2Hz/Oe7w/LE2oLnpgzBJQPOiuhYl4OLuXGSqa1vxjyD+VT3/eo/8PC4PLh4gnQXr/t+wx3rkczDB6Md65HMM7iYB8U4Y7EjM87t0Qm5PTrhrHSXSpk3e2weZpdUxMRIaSXUycaIc8/C9Rf2xvUX9laFgT6pPIS5KyTj4BconDwBRwgEUVTVZ8Xz/uasqDRsz/TYxzvBEyDVxcMnUMwZn6fyCML1OpJ9+GA0kvm27qphBRNJMBg20cbol27bH/VYj/YEB+DsTi48+uvzMSavJ4DTn4mypmZT9TGdMZ+7stL2yI94IIsGgNOjN7TrfHLiEMNNXtlJ5JFVVcHmvNMvzzXsBB5OLicZ8j5WIgsgNqNKIhVJMAPFYERBuEKJjkiGiyD/nM442uRHs09A/25pGD24Bwb17IzszBTUNTTj/76twYrynwCKCEuCo6OT24HbRg7Qjd5QFq1+VHEIi9ZVg///7d19dJT1lcDx751JZngHG17kRThgwEJSSQstKshapNWCsO0CrlvbLmuRtktxWxU467vF9uhq2z0q1rLYai21Cl2FirXVomtBREADFUolAr4gRYkWicIkM3P3j2cmzExmJvM+T8j9nMPR5Hky88svyXPn+c393esRgiFlxtiB9PRX8NAWJwHleDBEOKyIOKnvvkh/j4VTRrYGqmzey3HT+z5r6w+0CdaFTNO3AGVMmTQ2Bdj59geAMqh3V2793V94eve7cedcPH4w8889nZWbX2fr/vcIh5VzR/bj55v2Ewh2zL/BjqTCA16PJ2naudOgsZ52Kiml5a/wcMNFTlmqTNK93ZgantiluZDjszRzY8qkqoefyaP6tX68Yu5naDh0lN/vdLLpYtuv3zizNu5rxwzuzeLf7ACIuxiYwlJ1avUFYopmeETY9FpjTI+k3AWCYW5Y+wpdK71xn0/1Xo4b3/eJrTCx/c2/u2J8FqCMKYJockV7YrvHfu1nW0owss6pS6W3TTLFR80hvvtw/sEpKhSmzcboVIkUbk+6cMv4SppmLiJLRWS7iLwoIi+JyLcSjg8Ukf0icn8px2VMOTl3YP35/hdr2z/Z5CSkTiafvyL+kteSRYZhhaf9fllfnzQ8o3TvfFPDCy1axzCaWu6W8ZX6Dup9YLyqtohIP2CfiDypqvtExAf8HHgIGFjicRlTdpeeNQwErnv0lQJVxjMAXoEF51VzYc2p9O5SwXce3p516vvcc4axcMpIntz5N254LPmm4woPzDt3BPPOHZFR5ls5qqknyxpMlazhhmrvZUuSEJFPA78FzlDVIyJyL/BHoDtwnqrOTff1liRhTlaNTQHuWr+HBze9QSjy9+kDLps8nNrBfbhq1fac+zd1Vj6Pkz2oStabhi8eP4T/mj229eNoUsym1w5z34Z9VHhOtDvJpXNyqS78yQLRxOq+JUnW6DBJEiIyEngCqAIujgSn+UCTqq4SkblpvnY+MB9g6NChpRiuMSVX1cPPTTNrWThlZNKLWFi1NSW4ORQmGAonveh6xbkgewQ6e6Jgc5bxvNIDiLDo82cw/x9OjztW1cNPzaBe9O5ayaxPDcm6p1Ix0svbC3ipSjUt/+p4VyRDpFLyAKWqe4CRIvJJYJ2IzAFmA9My+NrlwHJw7qCKOlBjyizTRnsbGw5zZUwmWoUH/uP8UXx5gvMi7q33j7F5byO3/f6vhFxetcItnBsK5YdPvcrHB/aiZlCvtEtimbazL0ZNv0wCXqqsQVBXJEOkUrYsPlV9WUQ2AWuAA8AGcSoS9wN6icgGVZ1UrvEZ42axwetEJqCzFyuxs2xVDz9jT+vDrHFD2Pn2EW5Y8wr7U7QcMfECwTCXP7CFlpAys24g3/7syLwCTKHTyzMNeKmy8moG9c6pwWaplCxAicgngNHAKlVVERkMTAAuUtUXYs6bSwbvQRljTkjci5X6nP48u2gKv3x+H9et3VWi0XVsgcj66WP1B3ms/iC+hNznbAJModO3Mw146To9uyEZIpVS3kG9DnwTWCIiLTjv+14fG5yMMaXxuguqcHdUie9nZRNg0gWKXGQT8BIDETgbcqNByU2BKapkAUpVPwAWZHDe/cD9xR6PMZ3ZiL7dyz2EDq+7z0tINesAU8g7lmwDXjQQrXzhdW6KtCkRhDvmuKP/UyKrxWdMJ9TYFGDcLU+Xexgd2oLzRnDZpBGuuPPIJm09WYFjj8CWa6cW7XvJNc3cGhYa0wlV9fBz5yV15R5GhxYtnRRbgaFYEis9JIomwmTS3uOGJJ2gwwpP7XRXJ2awWnzGdFrRpabbn9zNw1vf6jTVKwQK8r12q/Qy8bb1SdO7C7kRt5D7pna+/UHKJpsN736YxyiLw5b4jOmkEtsrvPX+MVqCIe599jVeOfgBk6v7smTaaMC5sG3ae5ifPLu3nEN2FX9F6vYdhQoohWrL0dgUYOXmN7h7/Z6kHYgB7vvauNZGlIXWYSpJGGPKL92r8hXDq9qcP3lUPyaP6se8SSNY8ae9/OT/To5A5RXweKRNt19/hYcpH+/HuKGnsOvgB/zvy2/HHe9W6W1Tzy8cVja91thmX9Ki1Tvo080Xt9k3U4XYN7Wm/gCLV+9IWx7LK9C3Z5esxlYK9h6UMZ1M7ObOo4Egx1vCLP7NDhqbAhm917HkC6PZdt1UfnHZp5lyRvq9V5nye9uvFF4M3XwV/PjiOnwJVc4DwTDrd79D/15duHb6GPwV8eMLhsM0h+Iv+M0hZeFDLxNOCFzOZt+tnHPrH1lbfyCr8eW7byr6s26vdmNlhcc11SNiWYAyppOJviqPVenxsHLzG0y8bT1fWbGZibetT3sxjW76/dm/fYZt101lzYKJPP3dyUz7xICsx3Px+CElaQNfmeRq1xIOc/bpVdwx+8w2rTgCQWXRaqeZ5O2zx8a1npg6Ovn3qZB0CS0QChMIKlc+Up9VQkW+bS+S/axjdfd7y97qIx1b4jOmk0n2qrw5FGLZMw0EgtmX8Ind5HnPpeNpOHSUqx55me0HjrY7lgtG92ft9rfbLLHlyucVRODC2lNZU38w7ph4PFxz4Sh+9NSrSasp9Onm45sPbuOjlhO93wPBML/a/AYLzx/Zunepu8/L9Ls25DS+YNh5Py9a9SOTZIp89k0l+1kD+CuEGy6qoXZwb9dVj4hlAcqYTibZ5s4F51Wz/Lm9cUtBudaIqx7QkzULJ7Ny8+tc/+grae+OLj17GM/vfS/uPZZ8NIcUf4WH6n496e57hw+bTwQbv9fDhOFVbFwyJenFvmZQL4JJLuZ3P7OHL08Y2hqIt7/5d3xeTx4tT7Q1aWHZM3vweb00h8J8+7PVrc+TKNdKD4k/6/aex20sQBnTCSUre7Ps2Ya4c/Ktan3phGFcWHOqU019XyM/eGJ33PE7L6mjZlDvNu/l5CsQDHP3M3twEspjPx+iu8+b8mK/oeEwyYq9+7zeuECd6q4kE5Ve4c33jnH5L7a1BrhAMAjAD596lbuf2ZNVX6lMuLnWXnssQBnTSSVeqItR1Tr6HGNP68OsTznV1EHiMtoG9fazr8DV1X1eL/Mnj2DZsw1oWAmEFBFh2l0buPpzo5gwoiruYh1NJkjWaTcxUFf18HP9RWO49tG2G17TqfQKN82oYem6XSnvvgJBzbv9RjJurbXXHgtQxhig+K+0o4kViQ4eOV7Q5wE41hLk7BEfY3jfsVz5SD1Aa1D4we92OxXJPR5unDGGSycMS5rODVDpddrFJ6od1JvuPm/cEmLU1NH9+dOedwnEdIn0VXh4YuEkPmwO4ZX0GYu5LK2Wo0NvKViAMsa0Kscr7cnVVfxh9+GCPmYwDLN/+gJeSd7ivTkMhMPOXZA6SRWJy3YVHkGA5c/tZdmzDXF7xYac0pVQQpGDCq/w63kTGD+8irX1B9rcjVYP6MniVfVJg1qsbJdWi9Gh1y0szdwYU1bL504o2mNnkhx4c6Sqd2w6t5NyrjSHtM1eMUie/v2jOWMZH9nkPLNuMBuXTOGX8yawcckUZtYNpuHQUR7Zln4fVIUn+R1bKun2tJ0M7A7KGFN2axZM5B+XbSzLc1d6nSW12CXOI8daWLDyJY4GgifOS1h6a29JNPFutP7Nv7c7Fo9I0ju2VArdoddtLEAZY8puyCldUy7HFVtItXVJLRpUGpsCGVVwyGZJtO60Pu2e0xxSmkNOUMwkWaLQHXrdxpb4jDFlV9XDz7c/m/nSViH4vJKyikJVDz/XTx+Dzyt09xWm2kL1gJ5ZVdpoaQnzzO530i7X5Vtpwu2smrkxxjXGL/0Dhz9sKclzRTPrqgf0bHMsmnhQ4RGaQ9qa7ZevhkNHmfrj5zI+XwB/JOikW+7LNYuvVNl/Vs3cGNOhNRw6WrLgBE5liWQZdQ2HjrJo9Q6aY/YqLX18FxfWnNrmIp7pBT563ov7GrMao0Jr4kO65b5MlxobmwKte9HefO8jlq7b5ersPwtQxhhXyCSJoJCaQ23fq1lTf4BFq7a3KfiaLPEgWXp3sqSJ2POaYpIuslGIxIc19Qe4etX2NnUPs629WEoWoIwxrpBJEkEhBUNhNjYcZmbd4NY7i8Wr2wYncILZkWMtNDYFWpMoEvs+XflIPV6PB583PmAlnpeLfBMfGpsCLF69I21RXjdm/1mAMsa4QvWAnowZ2INdB5tK8nwhde4ajh4PsnTdLjwicdUfoio8QigcZsHKl1oDz7Cq7m3Su4Nhp09UtGLF4t/sYPlXxyetUJGp2Peg8gkcb71/DK8nfQULN2b/WRafMcY1vjeztiiP6wF83raXO68INz++i+MtYT5K8n5UpRc84gSf2I2w3X3edgvGOn2YNOfCsgA3zhjdutE3H0NO6UooWSVc3N0TygKUMcY1xg+v4tzqti3n87X0i7U8ccUkfAmde1tC4TafA6ele5dKD1dMGYW/wht3rNLjJFfEV54QKhMfOxymZlDvds9LpdIrzBg7uCBBo6qHn9tnnxn33BUe+P4Xa/nVvLMKEgSLwZb4jDGu8uC8s9i6r5GFv9rGwaP5Z/V193upHdyb6gE9uWPO2LgaeddPH8PSdbvizvdXeLj3q+OoGdQLSN2GZOxpfeKSIjY2HE5aDT6x4kT0vGBIk1ZP7+bzEgort88u7B1NdBzJKsq7le2DMsa4VsOho3zux8+R6VXq7n+p46pVO+LaWXSp9LBxyZS41hqxmXbJCrvG3k20dzxWtmnnLcEQ6/58kINHjjNn3BDqhp5yUlYlz3UflAUoY4yrra0/wBW/rm/3vDsvqWNm3eCsAkpUe4HlZG1nUSoWoIwxJ63GpgCTbn2aYym2EW27bmpc4LCA4i65BihLkjDGuF5VDz9/uWU6t8wcw6k9/VR6wAvMqO3P/lunJ62lN/a0PhacOjhLkjDGdBhfOWc4XzlneLmHYUrE7qCMMca4kgUoY4wxrmQByhhjjCtZgDLGGONKFqCMMca4kgUoY4wxrtRhN+qKyLvA6yV+2r7A4RI/p9vYHDhsHhw2DzYHUenmYZiq9sv2ATtsgCoHEdmay27ok4nNgcPmwWHzYHMQVYx5sCU+Y4wxrmQByhhjjCtZgMrO8nIPwAVsDhw2Dw6bB5uDqILPg70HZYwxxpXsDsoYY4wrWYAyxhjjShagjDHGuJIFqCREpFJErhaRFhG5JOHYN0Rku4hsFZEdIvLvSb5+oIjsF5H7SzboAst1DkRkloi8KCIvi8grIvK90o++cPKYh1oR2Rj5/J9F5KLSj75w2pmHdMcWReZgc+S/15R25IWT6xxEjl8V+XvYEvl96Va6kRdWPvMQOadGRN4TkZvaey5rWJjc5YACL8R+UkS8wEhgoqo2ichgoEFE1qjqgcg5PuDnwEPAwNIOu6BynYNxwHRVfVdEegK7ReRJVX2+1N9AgWQ9D8BB4DHgWlV9WERGAxtF5ExVfavE4y+UpPOQ7piInAt8BzhTVRtF5GPASyKyRVWfKvaAiyDrOQAQkZsBP1CnqkER6QMcL+ZAiyyneQCIfO/LcK6P7bIAlYSq3gMgIjMSPh8Cro75VCPQjNN9OupO4D6gOx04QOU6B6oa+wq5KfKvw/6e5TgPnwa6Ao9Ezv2LiDwHfAm4qwTDLrhU89DOsXcj/z0FZ3564azaHCreSIsnlzmIXJAvAH6L8yLlOHCrqv6u+CMujhx/FxARD/AAcA3w+Uyeq8NeOPIVudN5LsmhyaranOHD/DfwsKq+EXnM+UCTqq4SkbmFGWnxFGMOEiwB9qtqsudwjULPXP8DTQAABBNJREFUg4hMBPZq/B6O14AReQ61qAo0D61UdXfkb2JLpHZmf+AyVd2R51CLptBzgLOiUAP8RFUniMgngfUiMl5VX8tnrMVUhHkAuAV4SlWfFxELUOlEJvmsXL9eRG4BBgOzIh+fDcwGphVkgCVQ6DlIOHY5zh1DRr+I5VSEeRAglHBaEOiS63OUQr7zkEhERgH3Ap9T1a0iUg08LiJvq2rSJaByK/Qc4ATl/ar6QOTxXxaRPwIX4ix1uVIRfhdmAUMTVlja1WkDVD5E5A7gdGBWzKuJC4ABwAYRAegH9BKRDao6qTwjLZ4UcxA9dgXwz8DnVfVIOcZXKinm4S1gaMKpQ4GXSjk2F5gJbFDVrQCq2iAijwJfJsV7FCehd4APEj4Xpu0LmJPdF4DRIhL9uQ8BJ2FCVeek+iLL4suCiHhE5F7gNGBO7IVZVW9S1bGqepaqngUsBdadbMEp3RxEjl8D/BNwwckcnNqZh42AisgXIueOwHkB81jpR1pWrwKTRGQggIj0wpmHv5Z1VKX1J2CEiEwCEJEzgCnAH8o6qhJT1XmqOi7m+rgCWJEuOIHdQWVrGvANYCsn7pQArlPVp8s2qtJKOQfA34DvAzuAp2OOLVPVB0s8zmJL+7sgIl8ClsmJNPt/VdU9ZRhn2ajq2siy3pMicgzoBqzDWfbrFFS1WUSmASsiSQJhnN+FvWUeWodgtfiMMca4ki3xGWOMcSULUMYYY1zJApQxxhhXsgBljDHGlSxAGWOMcSULUMaUiURy00VkvIhYOq0xCSxAGVNkIvJDEQkm/FNSlIESkfNERNP86yx77kwnZxt1jSm+/wRujPx/GKgFNuCUwkp15xQA+qY4Fizs8IxxJwtQxhRZpAxSaykkEfkWsBJYDVQCY3EqUiR+XVOpxmiMG1mAMqaERGQecD5OGwZp53RjOjULUMaUgIj0A34ETATOj3QcXgF8vbwjM8a9LEnCmCISkT4i8mtgD05X2bpo0dhIhWfB6cCbyJ8mSaKhhN+CMWVjxWKNKTIRmQpsU9X3UxwfBdyjqlMjH3txWsZHvQj8FLgv8nFYVT8q4pCNcQVb4jOmyKKtWESkBrgapx/QqTjvQe0F1uJ0Y46eHwJaEyREJAwELGnCdDa2xGdMCYjIaThdZD/C6S7aB6cd+OXA2cDvxVGR+C/yEJ4kx+zv15zUbInPmBIQkUuA5araK8mxs4Hnce6iVmfxsA+r6iUFGqIxrmMBypgSiNxB7QQeAO4B9gF+4BM4XYi7qWqyZAljOi1bIjCmBFT1TeAzOG3PHwfeBw4B/wNsIUXZI2M6M7uDMsYY40p2B2WMMcaVLEAZY4xxJQtQxhhjXMkClDHGGFeyAGWMMcaVLEAZY4xxJQtQxhhjXOn/AY1m7iaSAechAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\")\n",
    "ax.set(xlabel='경도', ylabel='위도')\n",
    "save_fig(\"bad_visualization_plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "ax = housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.1)\n",
    "ax.set(xlabel='경도', ylabel='위도')\n",
    "save_fig(\"better_visualization_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sharex=False` 매개변수는 x-축의 값과 범례를 표시하지 못하는 버그를 수정합니다. 이는 임시 방편입니다(https://github.com/pandas-dev/pandas/issues/10611 참조). 수정 사항을 알려준 Wilmer Arellano에게 감사합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ax = housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "    s=housing[\"population\"]/100, label=\"인구\", figsize=(10,7),\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    "    sharex=False)\n",
    "ax.set(xlabel='경도', ylabel='위도')\n",
    "plt.legend()\n",
    "save_fig(\"housing_prices_scatterplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.image as mpimg\n",
    "california_img=mpimg.imread(PROJECT_ROOT_DIR + '/images/end_to_end_project/california.png')\n",
    "ax = housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", figsize=(10,7),\n",
    "                       s=housing['population']/100, label=\"인구\",\n",
    "                       c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"),\n",
    "                       colorbar=False, alpha=0.4,\n",
    "                      )\n",
    "plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5)\n",
    "plt.ylabel(\"위도\", fontsize=14)\n",
    "plt.xlabel(\"경도\", fontsize=14)\n",
    "\n",
    "prices = housing[\"median_house_value\"]\n",
    "tick_values = np.linspace(prices.min(), prices.max(), 11)\n",
    "cbar = plt.colorbar()\n",
    "cbar.ax.set_yticklabels([\"$%dk\"%(round(v/1000)) for v in tick_values], fontsize=14)\n",
    "cbar.set_label('중간 주택 가격', fontsize=16)\n",
    "\n",
    "plt.legend(fontsize=16)\n",
    "save_fig(\"california_housing_prices_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n",
    "              \"housing_median_age\"]\n",
    "scatter_matrix(housing[attributes], figsize=(12, 8))\n",
    "save_fig(\"scatter_matrix_plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E8OH/68oghMA4F+RRe7OflUFb/OG7/Reun5e9/zrwWmNQ3vuifXXA/yKE+OftIfuhjxqers3XeexTQ2vJu9dH/ODhnHVdoaTgj794QNk4itoyzmIQUDR25/+VMlhV794Y8d17U1aV2cWgDvspV8fZbpckZfjOk3lJL1FcrCxvHfTItOZyXXF1lPJkUZHFwWf9J+8cMs5jOF+zqSzXJxkPpxvyRBBHkt9/68ouBqWEIIsi3j5MqRrL1WFKP4340rUhP3w4w96f8aC0CKm4NsmJlGBVGYraUNWOw2HGOO2zXxieLEvWVYNzDre94SpYTZHWfPko3sWg5AtiUAlwMNCAoDANUoTdvpQCicB6yBPduphCDGpPpUzaGJR2nvgFMahEQRIL3roy4s5hj/NlhVIFdy/XjDLYlMGtV7crI1IQxxBJhXEu7EJFUEqRCoIqUlCYsAvNZVBklYOy9VdJEdyRzgc3leSpe1J4iFtL0NpgLWgtSCONVrCXR6wrjxSebBDhHRTGUhrDgfJMN/65RRy39zBtBWnjni7qiOA6TGJNUduXxqD2tcb3QwxKWkv2khjUXu+jMajRh2JQeZLuYlDWfjQG1ZQewUdjUEUZlFesYNyP6McaYz3zomRefDQGhYRMCiIhGfd6VMayKBoiIVFKcjhIEELxR28/H4N6az97Jgal+dq1IZWFL10dYFzvuRjU79za28Wgxj3BddunNpb5pmZ/kOxiUEXtOBokuxgUUvDwsmSSJTTWcXs/Q0nNP/vmDX7waMGqrvnq9VGIQdUW5wW/d3tEpORzMah1bdnrxdzcG3wkBrXXT14qJ27t5yEGpSTGsZNH68o8J4O2+LOvvsM3rz3cxaAAvnmt95kSJT5rFp8CPgD+4YfevwX870AG/OPXeOwXwjiP+aO39l+ZxTfOY/7ki4efyOJLI8WtvRzrPe8chh3bu1c/nsX3zpUBx6OU65OMprF4CbfGPXpZhPDwT75kmFU1i6LBt0Gl41HGMIkQSvA7N8b84ReXrIsaIQWH/ZSqcSEuYi1XhilprImF5Mmi4IPLNc56LooKHAzziGujlE1luXmUM9LJR1h8m9pwsSo4X1nG/QhvJUJ4Kusp64Y4Ulwbpe29hOujfMdo+jgW37Qsg2tGes7XhlVlmGQpt/f7COs4XVV46VgUlvm6pDTQNBWNVTTCkwjQWmGMo9+XXO312B9osPDTyw2b0mI8HOYaI8Aaz9EwxhpP7SzOwwfnG4TwLFaGvaHGo/nycc6ycdwZZ5yuau5dFsxWNdKDUJIb+ynHgwyLI5GSPIswNhA19gcxZ4saLxx/92jJclWzN9T005TKWo77KXEkWawbLsqa/SwhjSW3Jz2GvfjXgsUX3N4QKUWsBMf9DKf8C1l8TWO5Ny1YlIZBojAtS6+XKO4c9JHIF7D4rnzmLL7bkx4rY+jrIIJ/mSy+p3Ii4ytXhq/E4vuX/+JP3ygW32sjSQghfh9w3vvvtH//GfA/AP8A+A7wH3nvvy+E+APg/yIQGjzB/fZajnnvZy+b/yeRJN5UbIOmkQrB0g8HRz/uOPDcsdpY7l5suL2XE0eKuglMpu2DZJ2ncY7puuFsWXE0SDgepUw3NbXx3JhkXB1nL2UmPp6VNNYxLxpuTDKuT3Kc93xwvubqKCWN9EeYRy974LaMSmMd56uaL13p0c9iThcltfFcG6UY58ljhZCCpra8f7kG4WkMWOs4W1WUjWXci/nGjTF5rLEebowz3jtf8WReMEgjABrnSbQgizS6ZWpO8oi/vT8jjSSXm8ACLK3lt6+NEEJydZRy72LNybLiclXRTyPWteGgF9PPgtIf95PdNa0r84wVrVmVDX/3aIHznqujlKNBSmUs10YZSStUveClwujnoRy/yjp71bE+7jvb9AjgpVTq7efOliX/9ifnSCCOJV/Y75El0XMEgDcJ2zX/cWy6zyM+DUnidVpQK+C/FkIcE4hWl8A/8d7fFUL8Z8D/KITwBM/En22Vxes+9nnDNmiqVXgAtJJUxgSLC/Gxx4HnjkkRcpuEFJSN5WRR8tOTBXGkOB6kaCUZZRF/cGeP2jrOVxWPZiWREtzaz9FSBNfDM8rxybxECVhXlmGqsXh6z+RwnS7LnV/9aJC8ULl9+EE3xvGDh3NSLbFKMC8a/vL9S75ybcjxKMNaz5VRyv3phpNltfv+JI+ZFjWzTUXZOHqRYlHUSGBaNCwrSy8Ju+WDQcL3Hsx5PC8QXjAexGRa00si9nsxaasgroxSLlcVqZZMNw3DVOMRXB2lWOc5WVbU1nK+Cq9SSirjaFZNIFJoST+NdjGDWAblZ6wjjzUH/Rjvw3zuT9ecLmpOFhXHw5TbBz1S/WLB98sUkp9mrE/6Tm3dJ45ZNpZHs4KH04KDQdTmfAkuNg2/e9B/I5XTds2HTV+IPT/7THR4Hq9NQXnvfwT8py859q+B338Tjn3esBXuW3fEh4Ojn3T82WPOh/wuaxxn6xprLZvGsddLKIzjINGcr2rePuwzSuKWARUE6XYX/GLlGMbOY82mNlwbZ9yfFty9WJNGipv7ORJ4sii5Ncl3Ccove9BLY6mMpR9rLpYlWgrOlg2ns5LzZc1vXRkQS8nFqibVkjzWlLVh3Th+9+aEf/fBJavSICVopQI1XyvK2nBWG76w3+dsWTFMFe9dlFwuG/aKmj/90hES+P7DOVeHKapV2KeLCus9B72YL18bst8LVtG9+YZro5Rp0fDWAZwua4apJotVq0gdj+YlVz0oJTkepWgtd1aU8479foJ3noeXBZfrhpt7GYlWzIqaaCa509KPn8WLhOSjWcH1cfax1sqL8GkE7id95+cZ89nNTRJJYqVprOPKMKUxjki/mUVyPmnD2OF5fNYxqA6/YjwbNK3MR4Ojn3T8w8fevT7ifFWxrgyRDlZHpCWN9Vjn8T64ZiIlsc5zvqqRoibWikkeklu31S+E5zlmYlkblBREOri/nPckWnG5rrHOsywbitqQxTq4gJwji4Ow3z7o69pwsii5WNXMNjVSgPMOKSGOFbUJc/QC9nsxm8ayac+734uJI8Vb+722YoVnr5dwuqj44HyNVNCLNIuq4WReYhzc2e+RRzXGWS7WFbFWSAFJrPDO89PTFbf2M7QMSnhdWfZ7TwVVL43IEs1RP2G/XyGFYH+QIoWASHEMwS36DK14G2PYxiIaGwgMcSTpJcHlWNsQI3mR4PuwkDTO83BaYGxIM3gVa+rTCFzrA7NOCol1ge3qfKjuIH1QUJ805naMKHp6Fk+w+NUzrLY3DZ+0IezwPDoF9RuAZ8kV2wdhqyC8gFjJ544/u0tVUnBjnD0Xy+jHOuSBKIH3cLmq8cKzqQzGw8m8RLZuwINexLRoqI3l8dzy1atDHsyKnetmnEfMNk3LTKzZ78VYBzcmOU8WJafLss35gGVhyOOGcS+mbiyP5zX9WBNHYZctgLNlhQSOBgk/frLgZFGy14v5wzv7QWn4p6SKLNb0Ux0EhguVFIxxnCyrlj0o8c6jJFwfp62gDK5L49yOoBG1JXWM9ThnybYBeRniclrKlsYc4ki7+9wKKuM8j2dFSPAUgiw2O7eelnLnptv+Zo11rGvD6aLkYt3sqNyxfvpZ31qm29/72XjPs+eWUvBkXhApwSCNcN6/ksvp0wjcxjjuXW6YFQ1KCsa5ZpLHPGyd7KL9jNEvH7MxjseLEtnmVpV1oEJ6D1fH6RvrLvukDWGH59EpqN8QbGnaHy6XtN+LyeKPkg9eFCOIoiBktZbc2Mt5Mg/Cv7GOQaxY1pbbezn9NGK2rvjuvRl5okB4JnnMXh5zuanJIrVz3UzXNcfDFClT3jl8Pqh/OEh4OC0QIpRnOegH5fXgchNK8tSGeVGTuwgpBAeDhJNFybxoGOcxv3dnjweXG1ZVE2r5IdjrBSsuUk9dZau64WJdM8kjvvdwzn4esTGOunFU1tGPQ208RIhd1bXloB/z3tmGjQ9EC9UKR4tnkAT+uLdBwbs2nvesoN0KqsezggfTglgL7hz0XujW28Zjitpw73LDxbJkWjQIBF86HlBbx6JssM5TNBatBNdGGdda+nHZ2FYBWpQQXJ/ku2uvK0NtPLf2890aeRWX06sKXOc8TxYlWgnSKLDKLrblkI5jYh3WRW0dlXE7pf3smCE2We3co3XjsMLzOzdGDNLojRf2H94wvunz/SzRKajfIHyYlJBqyaax9FP9yjGA7UO2rg2xlJTWsqnaWJXzzIqGVdkw7kWkWrEsDM7BjZFCpy1d1jg+uFhTGfuUodfSbBvryLTixiRDCIik5O7lOrADhwmzTUPZuDbu0tux6awJ9c0iKfnp2YrGetJIEivJJAvKaSvsUqm4Mc744HLN7b0cIQOhYmMcx6MUPJyvS87mgcBgPTy42IAQHPRj7hzmTNcNUgiuDBL2Bwnny4rTZc3psuZokPDu9RGzTfPC3JO4zdOpjGWUxy9060FgUioJi7Lh4XQT5h5LpsuKv3kw42vXRoyyGOsc01XF4TBhum4om0CTvnux5vGsYLYJrtIHs4I/fGt/5yaMlES3c/o0LqdXEbjWB+p1EilGeYxznnXVULaKCILlGinJ9VbBfnjMD7tHXauY80T/2gj77Wagw8ejU1C/QXgZKUEKQePdz8Xsk4jnKqpfrGrSWJELzbIwPJkXXB1n1MayP0iQQlBbh/Ow14sRSlC3OSbffzhHIuinmlhJnsxLjgYJp88y63oxs01DZR3jLGJdWc6WNVoGZmDdOM6WFYM0YlE23J8V/OjxnEezkjv7OdcnGc5BUTuuXQv062eFmBchDhZHQTHGWlE3Dnyo9P7TkzX7vbgtZBpiTN+4OSbSkk0tuDnOSbUiiRQPZgXDLGKcx1TG4j0M04hhGu3iRaGSgt9ZRbalwcdK0vuQW0/KNvemLcNTVAZjHVXtqY3l4bxECRmK+yaK6abhsJ9w0E+pjeX7D+b87s3xTkHJtiTU6bzgwXTDF48GJJHi6jj7hV1OP6/AVSKkLGwrq0AoDZQo9RFL82WEjQ+7FR3BjfrrEsf5ZdL7P+/oFNRvED5cLmlLStgqg09i9gkPy7LhfFntAtKNcWRtrs7Vcca9iw3rosE4OOjHDJIIR/jcMI0YZRE/fLTg3nTDuqz52o0xkZRMi4ZBqnk0L55zAc42zS4GJnxQlieLglGW4LwnjmQo/dJYfvBwzjiL+PrNMZerUx5OCxIlmfQTqsa0pWjEC+/J9lonecTjecm6NjyZl0xyzTiPSSPJE1tyZZSgtWRZNPztgxn7/Zg00nzpePCcUs9ivYs3Wee5e77mdBmqvB70g7XUTzVZHHFVwKN5yYELJYFuTJ5aqttYS20NPztd8bOzDb1YMkw1/Ugxayt6pK3LTEoREpA9LIqGeWvlGedx3lIZR1EbvnT16YbjF3E5vaqwlVJwbZzRGLe7H0fDhCvD9KWW5ovG+HWN4/ym5kB9WnQK6jcIHy2X9JSU8CJm36NZyNzXSrLXi7k33fBwGgLqV8cZESGvqZ9qYq3QUnDYj5Ey5CydLiqmmwatwvuH/YTzdc21SYoQnmkRMd809CcRRWlxkUe1ScG73bQLBS63WfQ3Jjmny8AijKPgtpNS7hSBkIJVabDOUTUO42G6bnhiDX9kXKgJ1GIrXLdWW2UMSkp+99YEBNS15f3LhqLeoJSkNuEcZW34f39yhpCSUQ5CeP7+8ZLjcfpCpf5oVjAragZpeNwuNhXOwjAfANBPI7JNxb3zNVpJztc1714fMUwjThYl+3nET85qBllMPylRUnCyqvjiUZ+jQcY4i6iM5WJd72j9xjliJZltGkaZ5sFsQ6ICoUMnmtmm2dVl2/7muGBl4z6qyJ+9X1tl9GmFbRop3rky4M5B77mKCP1YP1eF5ZPG+HWL43Q5UK+OTkH9huFF5ZJe9oDv6LvOczIvSSPZ5pxIThclV4YpkzyiakL1Cb+NDUSaUR4zTCPmRR2KWSrJk0VJ44IlhYB+LLjcGGabCikkNyY55+uaddk8DX57z7VRyM9xzhNpyTdujHmyKEM17jampFvr78HFmiTWHI8yfnyy5HJZcTzJeGvc57St1vAi4Xo0SIjaMjdShlYHs9Kw14tYV45+pLypAAAgAElEQVSyNjg8kyzi/Ys1zntut/GxVWlJdFDiWytAK8m11vIzNlST3zL5IiHZeEvdWNJYM99UfPvulKuDjCyVSOH5wcM5X7s25MG0QMtgrd7ez4m15LAf8WRecXWckkYRh/2Yxnq+dl3x//30gkUZ2HFfvTZECcG1UcrlpqaoLV7AXp5w0I+pnUO6n0/ZvOh+nS6rTy1spRR4yy5R+uP6pX3cGL9OcZwuB+rV0SmozyE+ye3ySQ/2dqcXa0meaNZlw8PZhrcOewgRKimfLCrKxiCE5OvX8xBTWRScL2s2jeNwkBAryXRjuLWXkcaa2ljunWx4YNe8d75mum5IY8m1ccq7V8f0s6A8/vreFClCW5CjPOJ0WXEEPJxu2DSGPNJcHWWh/YMQxCrUhRukih8+ChaGQPCHb+/RiyOOhglahbyk0lhiKXdkkS2V/HRZPUcSqZ1j0osoaocWjmGmGSSam5Og3OfrZieg15Xl9l4WuhC393D7qoRASUFtHNKb0KbCe46HKbZ1mT64DLGrUS/GOs+6dgg89y/WbfVrRaIl58uao0GKkrDXS8HLUNkCwY29jFhJvnlrjPeerG370ljPtXHOj0/XqATiSHNrkjIrDI9mBVrKT1Q2L0zsnRcAZHEgp7yqsH12TCklZ8vyuX5pn0fLosuBenV0Cupzhl+Gj/vZnV7ZWB7NS/79kyWX64q9POVsVaIkxFHCMNb81QeXXK5rpAwB70gLzpahIjdAHD3dMRpref9sQ55EpJFmXTd8cL7hoJ9yTQoQcGWQkCV6p2BXRcP3Hkz5wYM5rp3ffi9mv5/sBKwUgsNByjdujakbg0Mw3xgaFwgaaSTa/KbQ/n1dm9Dnp71PvUSF/lzNU/LCdN1wPEhIE413nlVleDQPFRsq46gbQ2MEeM9eP+ZsVe2U+lbIHw0SjPUsi4bvX64ZphHXRqEm4TCNKI3FGse6CdUvEq2YLmtqaxEixIdKYxmkEU/mJaPsaTmluCURbN2f1nuujrLWXel2ls6TRcm7VwdcFiFn6oOLYkfJ3laRELxc2Vjv23YNakeyEbXFw6cWto111MaSRNGuX5oQ4Rqiz6ll8escO/us0CmozxF+WT7u7U6vbuvtTdc118cZWgoezzdcrGu+en0IwMWm4XJTk0SyLWBqOFvW9GNLP1YcDZLA1pKwKRuEgP1RzCQNAl2rhEhJ6sbw1/emXOknnKwqrklBFocW5UVj+M69KXtZTBIrPjhb8W8fzvnTLx2SJxGPZhaJ4NZhDw/My9DH5/o4o6htmwdU8s6VPnkSLLmfnq64NcnIk4iyDjlhX9gPOTqREjvywuN5yRWC1fhkUdFrk3AjJUmTmIN+zNEgxVpPZQLVGYKQL+uGR7OCPFEcDBNGmQYhuD7OmG0ahmmg4Cex5gsHPX56tuZiXTHbNPwHX9hve0+FqhcHw5jjUcrNSU5j3M7FqWUgTFxu6lBZXMmdu1J4mBU1H5yvduWm9nsxxrGzsLSSID5e2TTGtRan2MX9tufZxu5eRdhu87JOFhXTTc3RMA3VPdoeRp9ny+LXMXb2WaJTUK8Zr8p6cs5TNRbrPalWu+Dxi8bZ7Ur1053wpqpZVg2ZUggldtUjPnz+be7RNmh9NEh4NC9Ylg1eeG5Mcoxx3J9tQHhWhSFViieLAiUliVYIWuukqklUghAw7kWcLkqm6wbXUott41mLmrIxbV+imHll8N6RxIqro5S7lxtkqyjTCEzj0IPgKlvVFpwPO3rgg/M1cayYFg3XJxk3JhmrKiSuvnOlT9lYpuuaDy42lMax3wvxMQfPlTkytBaVDEI6TzSTPFgZjxcl803NKI9IIk1cSGIlcA7+/vGSyjgmeYQWgkEehwaU7e2VbRLvME/C+ZQMnV1ba+F4lHL33DJKNJEW7OcxwzwwI8+WFZvKYDO4Ns4om0AfVzIoi1QJvnP3kuNhQhKF+Z62leTvXq75/oM5s03Dfl9zOEipKvuR5OGtFXqyKFmWIa9ryyQ0JrjzjgcJs9JQNZZHteV3b03IE82tSL3yet66j2/t5yFR+bIIzEYpPtIv7fOIX7fY2WeJTkG9Rryq+61sLD9+suDHJyuc9xwOEr55a0IaqY+MA4EtdrKouFzXXB1nrMuGHz1Z0j9fs6oMNyYZjfUfqR5RNpa7F2vuTzfM1g2TPA7tLsZZyPRfVwjgdFWBE/STmMtNw/mqItGKG5OYXhLxcFbwdw/nbQxI8lcfXIQW81Iy6cf0Yk0tBPcv10yLhstVxZVhyjfzmPsXG9JIk0Ylh/3gsrsxTsnTYOF4Ibh3EZTWk3mJEIENtyptyIkaJEw3hoezgqNBwmE/4ckiuPTmpSFPQrt7JeBkUZJFoebctkaedWGejXE8nhY4PMJB7T1XRwmbyrKqHD98OOcLVwaUleHeskIJ2OslZFHIQ/rugxlfvTqklwRX3umy2jU83FQNUsodzX9rIcQqtEW/fdgjUpIH0w2PZwW393tcGSRUecTxIOXJouTexZqLdc2NSYb3np+eb3De7WrwXa5rerHifm24WFf0YoUUnu/fn5NEKyZ5zD/+6hWs4zlKN0BpwvrcKtQro5TzZcXDWUEv0Rz0YrSWVLXdFWN9VWH7rPtYqxBzWpYNt/fDtXeWRYdn0Smo14RXdb8553kw3fD++ZphptFSsioN37s/4/o4I42f5go9nhVtXbiwK30yL3j/bMX5quZ4FLOuHJEMVbZ/+/rwueoRN8YZj2cFl6uKqg7tJEoTutNGOjDrvIdH8yJU+BZwZRCTJ6GcUS+JuDLMOF/VbGrDKI+4c9DjYlXz/vmaLFLs5TGr2nI8SlBKMswj+lnE128MmW+CG0wrwc0boRHhg/kmdFttq0NoKbk1yXi4KBDe008kR/3QaG9dWfqparu7eh4tNtSN5eGsYF02JBF4J9jrBZdi44Iy+vLVAevKfqSUzrJq+OGTxc7ldGWYkCeKWEuuDBMezwp+8GAWEnDziJNpwcmi5M5Bzn4/Y5xHKBHqF0oZYls/fLSgqBqWteWdK32sD7UCG+t2vZ09kLdut+NRyCdblg2xVjtFp9o8rnVl+NGjBUfDlHVpyNviuV7Ag2nBIAmNBhUCIeHxvGJ/kDBIY0aZ4tG85A9u7eFkUMpSCt4/W3GxqhhnEUIILtcVJ8uS2/s5/TTE4M7XNVcGyS9UjPUjSbY+JEdvk3I7y6LDs+gU1GvCq1JMQ8DePtcxNtIytP22lr566sZbVwaAXhLahd/a73E2L3aB7bNlgUBwsaxw1mOF31WPqF2oIl0Zx+WmIVLBjZbq0KAw0pJ3rgy4tZfz/vmKJ4uCxsKyaFBKcX0v4wuHA25MLHkseP9yg5KCeVGjpCCLJZGG81XNwSCmsaF6dUgclQgpMW28yFhPicUb2B/EFJVhWjRMVyWPFxVfvjJAKMHbh33WhWVvELfVJQzLyrIsai7mFd7BvKh5MN3wF++dczxI+aMvHPK16yMkoaX4fi/ZVRXfuj2LyvD++Zqbk4xYK4q64b2zNf1MczRMOV1WjLKYojFcGSbcn26IIsVmVXN/VjEtDL//1h66/Z3OlxU/O1+xKoJVOsgiYik56MXcn254NAu/0fEwWHJShN9aS8H11oLdWhXOeyItmK5rxnnEvDAUTcOyavjt60NK43lwEcogXZtk/PjJkpNlxSQLccEslngCo29RGN6frkMR3pYgcm+6YV40FIljrxcDgsZatAyVKs5XFavSUOXRc4nEr4pfBVGgq8zw+UWnoF4TXpViqtryO4IQoNet+2mURaSt5bQdJ1LyuSC3c6FOWVyE1hNKCrz3IX9pVXGt7VS7bYCnhAiJm4C1nummpmpjJbf2ekSJJks0dw76nCwrVmUVqlBnEbq1XB7OCt47LzhfVlyIinXZYKxnmGoO+xmni4rSWLJI00s0D6YlkZQsyoaTWcnjecnXbgw4HGZcH2Xc3uvxNw9mNMZw9zKwBh/NSo5HKT94sKCfanQUKOrfvT9nkDniSHPzIOP90w1F1XDQS8kihVKen50vQ6+kSHNtlO0Emm0tW+dDAm5jLCoPia/zImwgnswLIqE46Mf0JimP5iVCwbiXMF3VeDzeevJIUdWhsO35smrdeoE4UVnPIIs4W9VIIXgwLXg422Cd52cnK45HGft5DFJwNEy4OQkdjavG7l4fz6tdJYg8Vlwd5bx90CdLIiLtGPdibu3nzDYNV8fhGhvraIxhkKZMehEn85JNY/mtoz5xrNgUDd8/WxIpQazVjnI/THTbBsOTRoHsMkoj7uz1PjGJ9pPwyyQKdJUZPt/oFNRrwqvuHKUMgepNZZ6LQX395ngXg9qOc3WcATz33vVJzjCL+Nf//hSBZ9NYvnJ9wKoM1tG2eoTWkqNRyuBiTWMd75+vGeeacS/hams13GofeAHs9SK0gkipXV25x7OCedFwez8nVZIPLtdUjWPcS6iMZ900fP3mZOf2ejQrWFUNJ4uCxjomeYzHc+8yVCIXPlTj3ssjzteOo2EoJ3Tvcs13780YZRHX2mt+/2LD23s99voxgpKfnJScrYpA4ogb+knEINUI33D3ckOqNQ9nxa4wbW1cUHZKoWSwru6erZiXgcBxbZjylatDZpuGVEdcbgyTPObHp0uc9Yz6MTcPcs4WFVoGKvtBP3T+7W0ZfVKGnk3WUVvLqm54/3zJIIvQSvLB+ZpH8zVfvzVCIdjUISb4aFYy3dSMUkXtPFmkSKOQEzXpRVwfZyEHqlVGsVYoEVp8JFpxc6/HYS8mjzW1ceAFtQs1DZ2A985WnC5KHs4KvnQ8IIvChqFqHLcmGdcn+XPlh27s5S9VTp+m5NEv6s7rKjN8/tEpqNeIV905ppHi3etj3jkafITF96JxPvyec9uE0FAtfC+P0SrElZ4txNmLNW8d9rm5F+JEiVYIIRjmMUVtWdchqTOQMEI9usOeZpDHrEtDaS1SCvJYc+tA0TjHO1f6uzwb4zx/cHs/ULyd4wv7ff7uScTf3J9yOqtYVg3ee/bymFhpLlYlf/neOVKGmnfbfk8HvZiyari13+N6q6Au1hX7/TDP2jiiGPCCWdGQNBZv4WxZcmsvY9KLmK4Nrvb0E03Sk8Fa2OZr6WAlXSw9J8sa5x0/PV+DCom//UTt2GyH/ZiH0xKNQHjBJE+Ciy5SrBvDrGi4s5czzjSzoqE2nqI2RErw40cVj+clVVs6aV1ZrPXUlWXUT7h7sQHpKU34/N3pmqJ23JzkvH3YY11ZSuNonOfGJENriSaw/B7NCqomsDGvjjPwnkmecGcvx+JRCN67WPPgcs2ycuSRIo81s3XF0Sjjq8cDvBC8vd9Ha8kwjT6ypj687j4rK6arzPD5R6egXjNedecopSBLPvozvWicZ9/b9sy5Mcl2ZYPOVjW/e2tC8iHhsS3g+XhWAMFdeGWY7qpNn85DLlQWSdZFw3cu14zzmHeuDDgapKRKsXSh0vbWdTnKY476CeermqKx3J9t0EqihWBjDI9nFY+mFSeLDavaUlSGYpQx6kcsS8esMLxz1ON0XVE1ln6iyRONR9Jv23XUbWLrFw8GvH+xwjjP1UFOcWSxznGyKFlUDVIo+knCxTKUUJJCMKgMkzyitpayMuRpRN1YenFE/1CTRIL3zzcsC8PDacEgkfzoieVLx+399TLMKVYc9mN+er5hkEUcDVMk8GC14YOLNda3rSAixaQfh+rtmebedMXjeUGsQsv5URYxrw1JHWjbrnacLArmm9DzKtaKh9OCxjuuDjMUguPBU0WwbS55a5Jz2Lb9sO3vdzCIcXiStmL7YT8OBWiNxbW1BX9ysiHVS37vzh7/4IsHeMEu5WC7praKyLYU+mujbGfNfxZWzJtUmaGLg/1q0Cmozyle1jMneomLJo0Ut/d7HAwSzlrhZp1nnIc8JgTM14ZIqxCrQHC+qjgaplwdZ5i2PE1RN4HIURt+9Lhmrx/RTzWLsqExoQ/QvcvQ/sF6S9F4ytoy3VRoJTmdh8aBvTRmnMUcD4Nb9J0rA1TL5jtZ1DyeVURK8sdfPCBPNFmsubWXoyRcLEuUlPzWlSG9WFFbw6Y2eDyRFCyKmr9/VHO+CPGYprFcnWREUpLFggfTinXl+Ot7M/qR5nRR8lvHA54sKvb6wcoDzzCPyWJJFmuOBgnjPMYDjxcl96cFX7025K1Jj7f2PfemBTdHOSerimEW841b+7x/tsQDB/2Ew2FKWYeahuNc81fvX7IoGk4WFaNME2+JF4uaG6Ocq3sp5+s6WKWtUnjWgrk5yamdw1nPw3nBvctQmuhokHDnoE+iFQ9ma947XWHaCupZpJhvKr53f8bNSb5rmJi2iu3JvAw9p9oOyU/mJb99ffSZWTFvSmWGLg72q0OnoD6n+DQ9c6QMbb97sWZdG86XIafqbFnTuODKU0qylycMcs1hLwlVFbQMQX3nuTezHA9C8dbGGC7XDV++knKxqZkVhiMtySPNurZMlw1Hg5SpFoAnUQLnQ3+nK+MU39bKOxolXBunxFpx93zDN26MUG2lhLIJLMRtjOTJosAiQAjWdXCFCTzXRqE542xTc7mqaKyltp4kEvzoccW/P1nxpSt9ZpsG01pf/VThjMdaz4+frOgngoeXoSzTMI8ZpRopJbFSXBmkLKqGs1WFs4EJ14+Ce2+bb2X90zYn+4OYfjrGWsfbB4PQMsQ6bo1zfnK6ZJyFsusPLgvKxrHXU4yziDgKTQ7zWFM0lqZVTs9aMHfP18Q6kGUeL0qujVK+eNTf9ahKI8X1vZx11bCsLNZ5BlmMliEHSyvFzb0cJeDBdMOdvR5eEMo/FQ1aCtIsZlk0nCzKwMp8RSvml2VxfNaVGbo42K8WnYJ6w/FpH+RfZHfp2urlkRbEUnF1lHD3wlLWltmmIlaKvtOcLism/RjhQ/JrbR3LTUPUVhfYG0ZoIYkiSVMHYZpGGudrxlnEPK0pGsOmCkotiyNGWczlqmKxqfn7esmsCLTqHz1Z0YsUl5uaW3v5rlTPlmIvhUDLQK744kGPexdrUi1JIo3Ak8aacaZJtcQ4x49PSqwXHI9TLtcNvVRTGEMWK4o2RvX2QZ9vf3DJ8TDDec/N/QGRlgwSTRZLpAz9o4QQ3BilPJ4XzDcNhfNoFRKbh1mEMY6jQYJoKd3bNifDJFR1b1pr48YkdPVNYsVbR32ccwyziJ88XjJd11yuaoZ5oLxHsmKUhTp21jq0UjuX3OmyCozFWCEFTIuGrLUytz2q0kjx5eMRF+uas3VJ1bTxHC3JY8XjWUEcKYragmdXmb02ljQLlTLitv/UwSDhYlXv1tnRIPnYth0vchXmrZX/adf6ZxVz6uJgv1q8koISQmjgvwK+5b3/p+17/yHwwHv/3q9gfr/R+EVdB59md1k2dpcgXFnLOIvJ4lB14WtXh/z1vSmbxiClYJJHSATr2vCz0yUfXGyYFQ3/P3vvGitZlqZnPWvt+95xPfeT18rq6uqu7ma6ZrqZASybQVgGLARCI/PTEgJsIYHEn5GQhWwzwj/Aki2MjJGRLBkhkGCEZPkPyLIGkDw3T3dPV3dPX+qWl5PnHiduO/Zl7XXhx4pzKis7qyqzO6s6eyZfKZV5Ik5ErIzYsb71fd/7ve9uP6GfhhjtaKzh3ukKtZZSmlWKcRGzajuUsQhAAhYvi2StQzt4MKkpVclWL2aYxVhjKZ1jp+clmG6Mc//YtS7eZTDW1hJGkq9cHxDIEG2170HFAdOVoZdKOusYpTHKOLrOMasVYSiYlIo4DAkF7PRT+lnE0bQiDQNmTUvbaQ6nmn/5ziZBEKx14xwIx2SlENa/h9cGKWHoN/njecP+MOX2VkG8nmm6tDkRbu2/xAd+V5fZ4GYeM1kpnPOSRjc3c8o1s/B41oBwLKoYg+N84bUQ4zCgF/kAHElvW3HpEGytw/KYgkUc8Ma1IYd/VHP3fEUSeouQ7V7CaanY7XtTxSTyZJK9vn+PPXHlAy+uIg4pNkJPa18bEX7UNftRpcIv7Q9YNPrnrkz2IvXB/jjiWTOovwF8HfjqI7dtAf8p8O8/r0W9xPMpHfwkun/H84ZIChptCITXRksCwWnlPYayJGS8Nh8s0oiq0RzNa2aNuvIeOl36OaiqiNnMI4Iw5LVRxh2bczCrGWYhbT+ll8TMm5ZWWYo0ZKsf8833Z9TGcGcrxzgv3dNLQsZFj34SsdGL+O7DBZXSpFHIV64PkVKQyg+C8VYR838vFVI4RnnKMA3RFnrWkoWSO1sFAsF7pyWTsqZsOp8VRRG9xFu3Xx+nnE8VnXEczlbs9FOWrSYJAr55f87Xb4/ZGqVcrBRx6AePD+uWSalQ1rKRJ+wOU9K1GscVMcWC4cc38lEeMas6jLG02iuZ99OQYRZya6ugiCM6DVkiOJ415LFguuo4WzXsDjLyIGBWtby7UmzkfhB4f5RdOQTXnZ+lezSLttbRdJZ/5XNb3NrMEQJwEmUdTWeQgR/SjcOAVatJ4oBfujXmcFZ/yIvr6tqy8PATPKKMcz9WKpxXiu8ezLmzXZDF4c9VmexF6YP9ccWzBqhfA74G/OCR2/4x8N89txW9BPDTlw6eJvt6PIBdvmYSSsZ5TKUMq1bTiwOU1iilSSJJJCXzRpNEAW7tD+WcYFYrglaQx5I49AKsRRSQxwHnZcvuMGW3l2Cs47XdHnEYUNaKd4uSQe6zKiscwzQii0K0VTTaEoVev+6sbL3Vh/CyTjv9+EoVXEr/f1Cd5Whdnrx7VrPZs+RrS/YfHi85XXqX3/2Bp4VHQhIFIWkSULaKXpKxO0gYZBE3tjLKRnG2UnTW8M5pyxf3B5Rtx9G8ol0PHqdRwMmiJpGSLAkYZSGB9IQE5z6ww6jWdh3OOk6WLfvDlF4aobS3q7+ksHt2Hbyx3+d40WAspLFktnK8e1ITSsG4yJiWDfNas2w0r+/2WdaaSEpujDIWrebu+Yr9YcovrIO4cFzNrj36eRdpxK3NHmdrp+K9QbIWrI2IQx9oLq+XNAp4dbv3xIPP01yzgRA/VioMA+Gz6fVz/byVyX7WfbA/znjWABU75+bikfTVOdcJIZLnu6yX+GlKB9Y6Dmc1gYAs/oCB9eiJ9EkBLF67mXqJJMEwDRhkXoftG/dnbBQxAsfNjR5ZHDBMI/YGKd+aefHV1/f6nC8Vs6qlVoI86jhoawZJiAWmdct8pRlmIZvWsdVL6GUxRRKzWySoImZ3kPJwVnM0b2g6QxIKekmEdr7fFErJIA9Z1Jp5U3Jns7jayKx1vH9W8of3pxwuGqy1zKqOL12TtNpyc5yRRpJeFjEtFV/c69NPYrS15HHApFJoZzk4ba4cfJWDQRrQS0JabXg4bdguEqZNx9GiZX+YsaENh7MapQ0ny4ZKRQgEeRzw6ra3da9afWXEePm5Xqy827DAl/bOK0UaBlcEhLOyZbuXEK/NCuNQEkqIQ38Y2OilnC5r5pVi3nREgSQMA8rOsNVPuD+prqxKGqVZKT8UfWMj4/ZmcfV5q84rnG8XMaM84tYop7WerNIZfeV2+3BWf2z57fFrVnUGu5aSuoRc9wkfLRVuFjGdaXHWQcALXSb7qKrESx3BTwfPGqAmQojBozcIIf40cP78lvQS8PGlg08q3a2Un91JIkkgBVu9xCt2P7KRf1T5cJRHfPfhnEZp5o13w/39B1M28pgiCZmWiu88mPG1V8bsDVKSOGCr7wPXWako0gBHSBKEjPKYNAz43bsT8kCyNUp5dbOg1Q7VaR5cGK4PU4ZZyMN1QBLCU56NcXTasDNI+frtMVIK7k5WlK0hDry9x7zqmCwVYtdvHJXSPJiuOJjX5EFAvNah+8HRki/uC8BvoIPUq4PHgaRIvcr5j44WfOvelH7ivZWiQHol7/MVS2XpVEOlLdZJLsqWXhajnWGYBXz34YKLsuZw3mKN4VAKNnoJ2hi0tRRxtM4ynX+csRzNayYr5W058JuyMYIwia4ICAK/6e8PUzayiJNFgwGcNsxbw0XVUiuLQNB1FhELdvNoLd9Uk4SSRlmWdces7tgZpChjmJQtcSC5vVlcfd5mPUf12k7PB3fn1sohMRcrRSgFQgrcEw47T7pmF017RQY5uFTuWAe1PAk/VCoMpeQr14cfUq14EctkL+nknz2eNUD9D8DfBKQQ4mvALwN/Ffhvn/fCXuLJpYNP+pJY6zhdNAjhCKVY21PUbPdTAuGDU6P9xnnpoCqlQLXe62dWddze8Gwyoy3Hy5pW+Z7Ie+cr+kmIELDVTzhfKW5EGRJva77TT+i0oReHFHFA1VlOlt4ao0hDZquOe1QEgePuucM6w/u99MquPHHelygMAnaGMcJdkhAEgZTsDzNOlg2NsrSdQgjBZj9mpTRnyxZlDHfPS+6flfSzGCm8QvjEtfzg2FO390Yp1vjM6svXBpyV3g7jvGx5dbtgq5/yrftTDmY10hiWjUY7S1akmFYjcJRaY5xlq0gplaGzhnGRcFYqWidpO8tk2XIybTictuyPUuaN5tooo2w1W70EpR1R4Dd9CWz2Yha1ZlEpkjggDyVnpR+O/vZBidaWIo34hRtDzpYt996ZsNmL+fKNIVoZjIAv7w85L71/lNdR9BqIgfQ+XXHgJZfAl2UrpTkvW66P0itl8bdPSq6PUrI4xDrH2bKl1Ya6sz/mPvxoxnB5aIoDX2K8O1lxezO/KhE+HtTyJLwqFV6K9fbi8IleZS8CXtLJfzZ4pgDlnPufhBAjIAP+OdAAfwv4O5/C2l6CH1eH+KQviZclahBC8HBWe507B9v95Gqg0xjvyCrxDqzH8xql/YlZO8coizHOW3sva02jHb04YLMX03aGOAwYp/GVGKm2PguIQrm27fbPOwwlEsFmkWKdQWvLu6clq1YxKhLSMMBS852DOV+7PSKLI7QVLNuWL+/3GeQxxsL+4AMrjB8cL3HOG/29tmQhDNMAACAASURBVF0ghbgy8MM5Vo0BIbDGooVgNq/4lTubXB9lTCvFt+5eMMoT6k4zbxS1sqRr5ttG6t1/N4qE82VN2zle3SmYVQptLWkgGWUhvTgCHJv9mKNpw7T0RI5+FhOpjmlnOCsVYSDIVcDpssWt7RwdcG+yQgj46q2R1wM0loOL2ksxVR39MGRSdYyziPfOV4QBzGvNne0CpR2vbBasmo5BFiGE5My0aGW4e1YSBJLtQcKy9hlbGHhBXAEo432plLacLxWtNrx9smJvmJLFAVkoOZrXhBKiMGC7n+CcY7JU5ImXRLp0H/789gfX6OOHps21+WAcfnz/9LJn+PiBK4p+OjHaTwMv6eQ/GzzzHJRz7m8KIf42sA1MnHPq+S/rJZ6ET/qSWOtVtKNAkEYBeRRQdYZro5Qs9Oy0R+3MD2Y1OK7cTaWAd05LpivlewjaoKzljWt93jstKVsNFr52e4iT3gr83sWK40VDLw7ZLGLSKOBi1QJ4O3BruTaMOZg25EnAw3lFpzXTM81mL2JsU5y1PLio2B/lDDLJ4VTxjfszXt3q8dpuj4ezGuc8seCLuz2WymCNY1IpRmmIw1IkCZXqSKKA13f7nnBhIQ4Er24XPJy13L9Y0WkfUFlnZ3vDFL2WI6q1BhlytmjpxxG7o4B5qamUIcP7U43y2L+fccDxzCvFf/n6kPfOVnRaU3eW1Xp2SOCb/0fzhs9tFxgHgzggDSXbPR+gpRScTv2hY6efsVEklHXHKItYrN1tN4qUZeODXj+1lI2i0Y6xkFgHN8cfzCgFQrLVS0nDjsN544kq66x3sbb9cMDuMGFWdSShZNl0pJHkRydLQim8DYcUHM1qNoqYzX5Mo+2H3Icv3YIfPTRJ6ftOJ4vmyrr94/qnP09ZyUs6+c8GzzoH9RefcBsAzrn/+Tmt6SU+Ap/0JTHOn9D3Rxmniwa9tnDfGXhVhkeDWy+N2O1ZNI6NPLnqbVkL69EcaqV597QkWltrfH6nh9KOXhphjKPVhrLRrFpD1RpPSY8knXbs9BPevD5ip59wsWpZNGvFglhypgTWGj84qjoCKdEOFk3Hg4t2XcrULFvF//ejM26Nc2Tg/9+NcdzcyMHB+bLhrFKsGsOyMQzSkGWtyRPJRs+rXBzPG94/X1G1PmiEgeRg2pJF0GpL5wyhCNgb5tRKY6wjDgU7w5zDqZd4ss563ypj2BkmzGuNBfb6Ca9s9pjUvteyqBW92LKMvCNtEgXkSUhVKmYrxe4wpdaGzSLm9mbBRdWhWo3Szh8QpMAaOC0VnTaeKRlK6s6QRZKD6Qrn/Of72k7BstUcz1tub+WM04iLGrRx3LtYIZzvF42LmCQKMNoPxW71EuZVRxp7B+Br44yH05q61Sjj+Mq1AbW2KONll3YHKdOqY5B5csel+/Cj15x1Dm3hfNFgrKPt7COGkB/dU/p5ykpe0sl/NnjWDOq/euTfAri2/vsceBmgPmV80pfk0QDmAG0tQnh5oycFtygKiPGBSyJotSEOJa9sF/40u6jZX7P7zkp/Iv9Tr23xylaPUAj+4N6UfhpyfRxwPK/5/tGCz+/2yCNPNDhfKb50bUAehdTK8L3DOcvWgoNWu7WhXsSXbvTXsj3az1KNChxwMm9RxvL6bh8cnLVefw/n1zxvNKkMSHuSi1Lx/aMF24OEk0VN2frA+Yu3x1yUytOsA+jHAUI6TmYN06rjtFTkkeTVtsef+cI21vgeXYhgkXYcThX9NKafRFxUih8dLRnlEXe2c6SUXFRqreKeo51hs5dijeOdkyVLpbHWMS5CelnEqta8f1qSxiGzSvPmrTFFkiIcdNogHRzNa9JIcnOc8dbBnPNly8mypm4MQSj40rUhWRTy4KJhuxeTr40lL2o4mvlAMyoSnPPMwO8fLnh9r0+8VpZfNH7I2jpPinDWXZEwhJD0s4jxmoFnHPTTiGQtCNs5+8RrDliTMgKCwB90ykZzc5z/2CDyo/h5y0pe0sk/ezxTsdc5d+eRP68AG8D/Dvz6szyPEOLLQogLIcRfX//8rwohvimEeEsI8QdCiH/pkd/9TO/7WcGulRYuFcQ/CpdfkpsbObc28g8RJOSaqHA0b9DaEoUB+4OE06Uvue0NUzrjWLWazjiujTL2R9nVbW5tRY7Db9TK0E9jbm30+MVbI17b6XF7w9OT2854PTttcNaxkUcME18aigJBL/XW55Nly7VRxpu3xiRhyM3Ngt1Rxpf3ByTS95fSOOAXbo64Oe7Rz2KE9H0Mv7l5Nl8YSLLIN/nrzssu5bGfwzpbKmplkBKuj1LeuDbkqzfG3NrMyaKQURHRTyPSUDItO8qyY14q8iggFNBLY+ZVR9n4gJUEAbOm43jmqeq/eGeDKPDaglu9mDeuDZHCD+dWnaFsfd8vkAG1slwf5+wMM3pxwP4449Zmzs4gwQq4uVkwzmPmdcf3Dhcsm44Hs5pv3p/x2++dM61a9oYZRRrxxvU+k5UijwN6WcTeMOFHRwuU1gQCwLFo9ZVqeT8NmVQKa/3s2E7fq1FcIlwHiY0iplGGUHotw14aEoQBX7kxxFiolQ9O2/3kqa657b4nfai1tcr+KMMBtTYczb3f1P2LimZtvvjoYx+/Jl/0rMRf3/KFXuNPi6fdiz4L/FRafM65Ugjxl4FvAP/L0zxmTbL4u8D/9sjP/yfwbzvnfkcI8avAPxJC3AHiz/I+51z107wfPymelb76cTMX0drMbll7jbpZoykSd6W/9kk+UsrYtVlefUUvNv0EZ32b//1JyenCD7telDUXZceoF+Ocwzm4OJhRJBHjImGURxzOGxy+P9JPA25tZsyqjuO5ouo0N7Zy0jCgrDuUthSRJF9rwF2sFNeGGQ9mNcbUSAGf3y3IIknZau6eV1hrfbPdGI5nDfGll1UiyOOIsvHvw+mi4az0g6h5IBDCsTtMGWcxSSR592zF9w/nHC87Xt/tE0tJkQQ8vFAcTlcA5HHMqEjopRGLWpFEAbv9hOmqY2+QcLHyyhPzWtFPAo4RVI1mXmnisCNLAzrtvN09EIUtbz0wDLKIrX7CqlEczBrAKzkcTmuqtmOrVyBTX/a8f1GhjVvLOQ1xTqw/N8NeP+F0mbDVT8jj6CojUdpclec6bTmc1Z644bwJ5o1xThGHSOkp+JdCwWfLlolQV9fjR11zRRxyY5whhLes92VA3w+NQ/mx/aWXWcmLhReNSv88xGIrfCb1iRBCSOAfAn8F+HPrm/8N4IfOud8BcM79P0KII+BfB/LP+L5//NO9Fc+O590oFg6mq440lKRPYF19ko9UjCQKJK9uFewPU354vOTd05KNPEIZxzsn3m12IwuYN35eJw4DWuXt5Y11CKnoLxpubGZ8btsLrJ6VDYtGYyyM8ohe3LGxP0AIeDAtcdrx6l4PCGk6jXOCX7o5IksiTuY1OMuol/Du6YrffveCV7ZynDN853CONpCE3kW4VYZhETGpWnb7KQ+mFXe2ezjnWNYdWRGThJ4ReDhr2OknPJjWDLKI7UGOsiveOpitTRA1HY5FpUnCgDwJWDUd33848zNimwX9NODuZEUWB15Z3VpOljXLRpMFIUUmiULJg4sVJwvHzc18ba3h55HEAEbSK5f3s4SxMqwazWTVIQTsDFKE8MaMb58sOSu9eO4gjPmDuxNujXOOF4JlrWg6691zO4uUnsX3ylbGw1mD82x9hllIrSzDzI8YlK2n6BebH2wFXpNQXpXdPul6lNK7Oh+vJZWk8FnV2bK9yto+rr/0csj1xcCLSFp5VpLEn3vspgKvwffdp3yK/xr4J865337kuV4F3n3s995d355/xvd9CEKIvwT8JYBbt2593P/rJ8bzbhQ7AZtFTNWZJ7KunmY9AGkcksYhX78TMS1bnPCK5SdLw7xWzCuHNZbrmzlZLJlYh9LeDG/VWaarBiEFX7u1wcVKUcQRX9ob8HBR895pSa00cRjwjbsXPJjWVMqy+XbIm6+M+dOvbTPOYoZ9T/tGOE5nDavW6wLOq4637vnHbeQJewMvn1S1hq9cH5BEAa227PYSbm0U9JOQf75SyECitKWpDXESk6418bQxDHrJelYopFErsjBHCEEe+ucapTHWOb73cE4SC64NckZpwFv358wrRZaE3J9UVEpTJCGdsWz3UpyIuag8nX2QhkghmK4UOEsgBd+6O+d+UbM3StnIQ+Ig8Hb2Aoo4o9OWP3wwo+0MWMedrZwiCZEC3j1doZSBYMUoSwikoJcKWuPYScO1DYZjtx8gpKBVmvcmK/IwgChACoFY94Au1ccfn5F72usxjQJujDKUtcTSl8AmQr2Q/aWX5oJPxotIWnnWDOr/euznBfA7wH/0SQ8UQvwacMs591cevwswj92m8f2xz/q+D8E59/eBvw/w9a9//VMpyD7vRnEgBFkc0ltvho+zrp51PThIoxBjLMtaY7SmVoa2NUwbxWY/YpTnrFTFpGzpJQGr1lA2Bm1q7k1W5ElEKGCpDMn6ZH57p+C792e8c7Kk1o4iDZi3mm8fzIgjyVevj1l2hiiQlI3mpGxIwoBlo2k7TT/zfaXaWC5WHdv9BCksQSC5PsxojWWv732onIAiCTmcrahby84wZSuPSKOA7aFX7vYB3VAkAVJKrLOMi4TNfsLxXNFaP0TbSwJvXZF4U8E4CtntJ7w/qThZNgyzmBvjjHfPSt6bLOhMjzT2M0X9POTzOwWxCJjWinlteG0n5rsHM94/X1HEki9fH/LDE8GD85oilSwbP3cWCkEaB16j0MHptCa+JCog6LTh+lbBIAmvSneRlJwtW+IooOkMF3XHyayh6jTbvZQkDunHAWEvodOWg0VDqw3nS4UcQZFGT309Pqk09CKy3l60EtaLhBeRtPKsg7o/zQTdvwW8IYT43fXPN9Z/Xwd+67HfvQX8Jn4g+M9+hvd95nje9NVHn+9JrKtH8aST5JPWc22U8f55SdNpprWXoxHAjY2MVjkCAQGSa8OERa39XE0iublZcLZULE9XyECwkflBVG0d06Xi4axGrV/TOnAI0kDijEDgmV+dtlysy1qzUrGslVdgwJ/wRknIKI/I45DDec1Kaf7oaHGlHWetIw0lb58sGWYxQniCQdUZtocpSjte3+0zWbU8nNaMs5CvXB8wqzocUDWasmnBhqzajlEvoWm9skatOmpl2B0k3pYi8EK5vThkkEZYY9fvIfSSgFvDHG0F2nox2FER0ijL1iAlDQXnS8U7pyt+dFxSZCHVmfazZQEM+xlBKDibN5zMOlql+cJ+n0pZFk2Hc37zXSnvr6Wt97TS1nFzI2PRaLCObG1TP1m1DKwlDVJ2egkPphXTlVp/FpZ7FxU3N/IfU0B/Ej6uNPST9Jc+rQznRSxhvUh4Ean0nxighBDx0zzRJw3sOuc+lGVdMviAvw28K4T4F5xz3xFC/DLwReCfAA74W5/hfT8TPO9G8dM83+VJ0g+tekO6PAmf+HjwMzBJELJRJGz3U6IQ9ocZu72U3VHGtXHOdx7M+M7BjDAQ6A6ccywaxVbfl89KZVh1ho1ezNG0wjlDYMEIRygdWeQlknqZF3Td7idrarSiUpZ3T6ecLL2X0C/e3uD13T7H85osDkmigD/7xg6tdnzz/hTVWW5t5JwuG06WLaMs9HTxOCKKJFtFxLTufPDIYq6PU6YrTRDApOzAWSIhWRlfsgqlpJdEzCqNdZYfHC2YrBTb/YRJWXP/vOa8bGg6SxIEpJFnVO4Mc5JIYCy8d75iox8zTCMsDqdh0SiMdqysQwODRHJeKgoXEgaS6+OM5Zph6eu0gs76AWrr4Cs3R/zg4Yxlq6k7zarxrrkni5rx/gDXOd6frAiFII9DdgeJV6E/h81+jMBbq5wuWvqpf804kMzrjr1BSr4mT3wcPq40FAXyx8pDHxeAPs0M50UsYb1oeNFIK0+TQXla0UdDrO//ia6itTr6XwD+gRDC4cttf945NwP4rO/7WeF5N4o/7vkuT5LWWuZ15+VmFg2/dGt8FaQefXxnLEkU8OatEX9w74JppThZdAgnEU5yc1yQRSHbw4T9ZcYPT5eozvD2Sen7O2nI/kZOHkXc3MhQ2nAwbZAiZLsnmCvjCQxhyO2dHqPUqykcreeD3j5ZcrZoGeYRvSzk7tmKo1nNne0eX9/cYJhGJHHAstJ01tBphxCC7x3PwcC0UtzYyIiE78koazhetNRKEwaCSlmOpo6NXkyjLMI5KuUom5pVp2mUZbNICKOIBw/npCHsDXPe2O9TtprJUpFGgi/uDdeuw4o3b474wrUBVWv45r0LVq2h0QYQCBegreZ45pmFzkE/kURhiDGOUPhYFODLs5GUdNaQRAGf3+2z0Y+pa0MUBpwvW65t5HTG0HSOzjp2BjFREHC6aHFAEUuc8BnsvPWvmychgzT2KuplC49Rii+NIJ9mg3qW0tBHBaBLavPhrF6XgT8+w/lJsqwXsYT1IuJFIq08TYD61z6NF3bO/fVH/v1bwL/4Eb/3md7384yn/dIa59Dr4BQIQT/ztOnDec2rW70PPdarS7gr99rtXsK8Uuz0UoZZhHGWbz+cI4TjD96fUXe+BNjPYtpWU7YaZR1h2IJt0TZnux+zPUgY5yGBFFRth3OOP/WFba6NeixbzQ8OF2wNfAY1Wyn+6GjJ3jBhd5RxZ7uHFI4v7PbZ6qVcrFruTyrunZcgHQ/WigtCQiQEQeB3fCEF/Sygn2UcXpQM84hlq7koFaum4439Pg+nNfNGU7Ydu8OU+VnHIAvJs4i67chDSR6HFFHIRi9lbyg4iirSJOL2Zo6xjtNFzWYvQWk/GDssQvYGKe9fVHTOcjiv6McBSht+9Y1tFquOb96fUS4rRnlIFoaUlWKQJ34ANgropxGDTKC0Y6uXUsWaXhwyzmM2+hHvn1acLUomy5rjec2drYJKRewOUoZ5wkYWcbz0WVKrLeMixQF7w4zOWDb6sf+s1np9O/3kicO1T8LTloYuD0aB8NnLpTL6Tt/P6iltOFm03NrMCYOPznB+0izrRSxhvcTH4xMDlHPu//0sFvISn4znVRoJhAAHqrMUiad1R4G8siC/3Awefc6y9Q3207LFOcEr2wWjNOLBrEaiuagUO4OYWSV4ZUOwaDuCWNJowEGnHf0sJA0lsQyolEFpjbEC5xyjPOHLexvkach7ZyVRKEnDgB+er1i2HYEE7Sxl1REHgjAMyKQ/BV+UCuMc81pT1oqzZYsQjnGRIIRjEEecziuKNGJnkHJ7nPKH96bkSUjTeuLGCkBAFodkoeFwumK+6miN8eSMqqVIYj632ycIJBJvkdFLvP5gILmSaDqct+RJxCCTrFrFw4uGIg5Z1oqy1mSRwGQJjXWczL1M0p2tnhekDQMabXE4vnitTxp6dfTTpTdhTELJqtEs6w5rHONexKLyPbGLqqXSjmXj369RnvD67oCdXkIYSnYd7I8ziiQkkoJ4nbk4J7m2lXG8lscKA2/9/iwb99OUhoxzvgzZmqvrNIskh8aSxQFJGHGxUhzPa25tFt5Y8bEM56ftI71oJayX+Hg8K81cAn8BeI3HSnrOud94jut6iTUug9LjFuGPBiCtLQcXFUkkycJPtsyWUvgT/aTkcF4TBZJB6p1vhfPP12jDybwhjQOkkJzMa4IAxpknC9ybVMzzgHllaDvJZKXoJRFT11FkMedrRQMpA3b6KYM8JA5Cbm14M7xeIqnwjLnOGKJA8GC+4rWoz8VKkcUhoYRZ1XFatkgJJ7OGA9ey3Q/ZHxT87r0pn9spvIL7tCYMJcMiYsfEVK1hv5+wajVvn5UILLc3+/TTkG/cn2HXskLHixqlvRX8wbRmkETcn628MC7OK64bn/VcG/k+zs1xxnTVMasV2/2Ur74yZlZqTuYN985X5ElInvh+TqMvg47BWFg0DdeGGZXSbBYJAsHhtKLqLDv9hHEREQrfa3p1s+B43qC0BeGtVs5mis46tnoRW/2MJAg4nHvb+mESkscheeJdj4tY0ijNwaxaMzrh+kbOjXHuZ5aU+dC19Mra/PEn3bg/qTQknJ+xStdZ6OWM3t7a7gO8zuD9SeXNDMPgxzKc59FHepFKWC/x8XhWmvn/iB9o7YCHgFr//I+e87pegg8yGGMsR4uGa8P0ivp7GYCUsRxMKx7OanppyFYvIY2Cj/3SNp3hbN13sMaSxCEXqw5lHNpYJqsOh+NipfjqzRGBENw9r3gwWVF3mlAK0iRipWCy7PjCbp8kkDgHW72ULJaE0jFZtgg6GmOIu4A8FF48dakoa8Oi6ciTkCjwdPjvHiyoGstKae5s5pwsW949XVKpjt1+iu4Mxlp++c4mtzf6tNpwf1Kx0/f2Dp0y3LuoaDuv6TetFK0ytJ1lf5RwUSk4E+wPEvYHCd986xwhvUttlsccT2tuvppxbZAiBcyrjiLzA8pZHFF1hv1h5q0+dnpejHKckUUhX9iOeOd8yXYvxiCuZJ66zq29nhQ6DmmVF4DdHaWM85hlrTlfKbaLhH4Wcr5UnCwaRmlEGHqNwaYzzOqOIg5wwvHm7RFbvQSJYNl0aGM5mSumjWaUhozSmCwUJFHA2+de7PfLeyOub2ScLtuPZNd92hv3k2b0tnoxkg/6QqEU3BhnXBtlT+yBvewj/cnCswaoPw98CfiLQOWc+wdCiP8MTxV/ieeIx0sZUsC07sjWJ/NWa7p1oEoCSZF4a/bzsmWnn3zkl/byeZU21NohpODbD6a8ulNQtZrvl4oiCbm5mVO1hu89XLBZRNyfrpBSEEpJayzzecWvvrHDOOvI4hAHrJQhCQL2hxl/5vVtvnV/ymSlKBvFdNkxaxRvnyyplOHV3YLThWJattTastlLiEPBg2lFkUhOly3LpkUKQRpFlMpQdppIer+jIvGvWSnDTj/jcN7w9mkJWAZZxKxS3J2syAMBwnKxtpZYVC1tZxjnIdOVZrMfM0hj9gYpF1WHcbDVT0njEDt2WGu5OykZZxltZzhbKk6XLW/eHvOlvQFFGlE2Hb9374KzReOtOWI/d1S2mmnZ8vpen2XTkUSC6xs54Vq1/PZmQS8Jee+0ZNVp3j5aUnaG/UHCF/eHnJQVPzhe0k8j9oapL+OVLeerBtVZOmOYrBS7g4xZpTicdywrxSCLqKOQ62mE0pYgCjmrWm5s5VfOyk9i133a+KgZvcse1GVfaH+UkXxEefplH+lPFp41QAnn3EIIcQ/4xfVtfw94B/gvnuvK/oTj0VKGtY44DFCdF3C0+DIfeFXvLA7Z7iecLBqWlaKfhNzaLD6yD2CMZd5oAokXDQ3E2rgPZo1imEeeej7O+N7BlMmqpVEaa/3r1Z1XG/jOgzmDLGKwdqq9NsywDm4Nc87qlvnKi7jOVh1pEjAMJCvliRPjImFvkFK2HbKz61IfgCWUEUkgOJ633p8oChhmIUkUUjWaybJjlFqMtQjgom4ZpiFxKGg6L+kzyCOqmWbWaHCC2mnK2lEGkusbKReVJYpArEVoV8oyTCPubOZsZDHHy4a3DmacLHwwUEYTJxHjXkAShUhriaTEWsfRvOHe2RIHLGvDuydLQgkbgxQcvH20pNIGbSGPA7YHCYH0DrKBEKw6L3Q7zHxpMI1D8iRAlj6IXCxqhPQHju1BwmLVce+sRghoO03VWrIo4vO7Q6pWMa8MvTRgXCTMVh3GOkIJD6cV10f5Rx5cnlTee54zSR81o5dGAbei4Klf52Uf6U8OnjVAnQkhXgW+B/zn69sCYPBcV/USP1bKGOcRR2uts8vhySiQV78DYD6m3HG50Vzaa2tj2er5k6sFThYtd7ZzZquOzVwjx75kJqWkCL300aJWLBuNc45GWUzfs7+UthzNO/b7GYM84vcfXPDdh3Oa1nB9I+dirSOXxZJBErFUhl4U8ebtIUXijfKGaUCrHcbBe2clm70ApS0bRcSq1cwqRRwIhsNoLTZbs9NLGG9mXtR02XC+bIjWEj6zSmERBEKicVjjGOQx9Xqmqkgk14YFteqwAsDx5itjPr8z5MG04v2LkgBJiCMKJSdlxyAxHE0tRRIwGaY0Bl7b7fFgWiIDPytVd47aGK4XGUI4DHCx8u/btXHKTt/3x1aN4WBWMUh9z2lSKX5wsqTTjuvDlFB6kddACkQQoJQhigIWdceyNXx5v0+eRHzvcM7xouaNa0OGecR8Jdkde6fapjOMi5hKaTrD+v2Mn5pg82nMJH1UcHnW8uLLPtKfDDxrgPo/gF93zv0nQoi+EOI38cHpO89/aX+y8XgpI5CSX7o1Jgrlh77Ye8OUw1nNw6l3y311u0coxYdIEo9vNBt5zPG8wRnHMIuQ1qGFIw4Cbm1kdNZ6IVNgo5ewWcRIJL/1wyWn85rXdvqMsxghJLOyY5gLTGdZth3fPJgyyiLSSJLHkm/fvwAHR9OWz+32SELv7np/uuLVnQLrBAcXNfcnNXZNd05Dn0nsDFLKVnM8rxFCsplnjHsxozxio4gZZCFvPZzRKMPRrGGUJ5wuGx50K2rttfA6Z4kECCnpJ4ITHXBrXCDWvkWzWiGd49ow41fubJInIdtFDFbw5u0Bd89DZqXircMFwSBmWinyKGNaKha9hn/6RyXWeR3CQHon4ywIGRcx708qYimIA5BYfnAw5d55xE4vZW+cs6wVPzxecmMjo1GW64Och/OaadNx8M45v/LaJnd2+6waxTunFZ/recagcw3L1pLGjlEWMykVy7qjUpqLVYu2lv1BzkYmGSQhWRwwykMCKblYeRX2y0BjreNwVhMIyOLgqgR8Yy3++mmoLrwMLi/xtHhWqaO/8ciPvwb8Bp4o8Vef56JewuNpShlpFHB9lKGNpZ96gc/LMt6lAOjjG82i0bx5Y8SPThYs6o57FxVbg5gslnzlxjZV7Wd3sijgrYcLrHNEkSAUgs4JFkqjvGs6n9/tEQcSemml8AAAIABJREFUJeFHJyXvHC+8hXqraa3jbNFSZAFB6MtR55Xji3sDYokPMAczNoqYo4U325vVHV+9OSSUjlpZXtvuoTqN0hZt19p8rWZet3zvYMrv352RJwFJAIGUDFMvfXS8aIhDuDMueDj1wXlvkKOdpO40y1IzWSk2ipiv3dnk9maPedVxumj49oMLvv1gxvWNHOtAygCJ5cFsxemipWwMUsCi1WwXMVkS+H7goiMLPFvuYtUwLVvSyMs/BoGkNaDrjkhKro1z4iDk4GJKGge8f1YSrnt8v3R7zPcOZxxOa5IwROLY7Ufc3sg5LxWLRvPe+YRroxSB5HPbOUkUcjitCZAMi8QTYVaKYRbRi0OKOGJ/lCEFHFxUvLJZEIa+5Hp5uEHAbt/PR3mlipeqCy/xs8Wz0sx/A/h7zrkj59wD4D/4dJb1Epd4mtNmFHjLi0ppplWH0ga7tgYnlE/caAIpmFWaYRZye00vnq46JouGbzyYkq49mca9mHmpqDtNoy2f2/aqETjH6byl3jZcmI4gEHSdxgDSCea14mhRU1aGfZmx3Y8YFzHLRmOsxYUB96cVk1IxrTo/jyMksfSMtX4WMakVeSiplGWce0fb+arl7FzTtDkny4Yg9OW7lTZYJHkoicOQfhYzKlKWjWVea3pJwHSlEE6wbDVtZ1nWLV0Ysmo8OeJwWvFwVnFReh2+H54sGSQBkfS9t0VlSKSfGzPW8c7xgvzWiEXbUSQhaSA4XbTMG8XRLCBPI1adJsRRKcMoC2itQwj/Wmkc4HCczWtwjs7Aa3sZTWewBkIp2ez5bPdwVtG0miAICKRklIU4BL04YKufEYeC3WGKsY40krTaS1gJ4f9s9xOs89lRpQwIuDbMOF+2OCwXK4MDjmcNX9j3flg/KVvupVr4SzwvPGuJ75eBXxdC/CPg7zjnfvtTWNNLPCMunXS/eX9KIDypYJxFnC5bboyyHzOtk0J4iZtVy2YvppfGnC9bHkxX3DsvubGRgxBIJMcXNVY4Jku1brYHOAeDNCIIBfubGa2ynCwa5o0hjQR3JxU/OqsYxJKNfsJOP6XVDme8OZ/WmgbBW/cuOK/8Zp+EIaflin4U0HaOSdnSabDO99eOlw1N65iUS+rO0qmONAqxBi6ahiwOUVqR5DG9SNBLMjSO40XjpZpkyLzRzNZ9tCKV5EnMIIt4+6xCW3h1p8fRvGWjiHl9b8B7JwvOS8V2L2F/kDJbaUQgWDSKLIpoDayUpW793NjpvKWzFhBs91OQgrJSzDuDcDAsEtrOoZ3jaF5zvmr53FaPKAjYHaQcXFQIB7WyvLLls6JhGmJdTCQcaRqjOkPTaG5tFRjr6GcRoYRxHpFEkqbzAeVs2RIHkloZqs7ye++dEwYBSbimblvHw2lFZywSgRBeKaRb9+t+UrbcS7Xwl3ieeNYS378phHgFb6/xm0KIY+C/B/5X51z7/Jf3Ek+LKPTEiSzytutSCG/jLrxB4HcfzjHW90m+cn3olSMEGONIooAoEJwtW8q2Y9FoemnA7iDncF5xY5xhnGO3n2CFIArAYnl1s+/FV4XhwFrKSmFwSOeIhSOOArI44GLVsZmHvHZ9SNda7s9qjFbcm9Y455hWmixyhCKgl8W0xhDLGBv4U7tde00tlVoz6iwPtS/7jbMY7XzATUJvhTFpNDhLkcU0uiOQgoNZzSCNqBpNGji0EWwXklYbFgaOFzVfuD6g6TQXlVdLT0I/l7NsO0plkIEnogggCh1ZDHWrqJSj06CNI428gvisVqyUoQglm72IQISUbQc4BnHA3iCi7iyDLCEMBMYKNnte2imPA1atpYjXQUNDkcVc38iYLRXaOA7Oa6JI8uCiZrefMqs6eknA/UmF0pq2M2z2Ei7qjs0i5r2zFSD46s0hO/2Ek7JFaettVFrDjY3ME2h6MUkcfKwD80fhpVr4SzxvPLOjrnPuLvBfCiH+GvDvAn8N+G+Anee7tJ9fPFriAD70Bb8UxQR+bBDxcabd5eMvfz8QwuvoaXs1y5KsA1IgfP9Ca4sTfmpf4FUhzuYNW0WEFJJAwHSluDnOeW2n4PtHSzpreTCp2Cxi6kazqFruniveT0qkEAwyTw5QwMmyIo9ichvQSwPiQFAbr2zw/UNDIH3WYoVguuroZzFaW8Z5TD8N+c7JnEgISqMRDprO+gBoNVVj2cgjojCk0paDi5phKpksG/pZhHT4LEFrOhewaA2SljiKubnVR1vL6/sD3jlakEbSi7EuFfNKY5ylVAnK6LW9uWGlApJIkkcC6wRl3WEMXJQtwlnePVshJYySkEppBklA2Ro64/Xstnox2kkGmWSQCJpJjXNerPbepMZZge4l5GnE7Z2Mo7lgWiqGRcy1cc6sUjjpSQpt58Ve41AwzGOsdbx3tmKzH6E6y7iXsJHHaO2ozisWrULXnuRicDhhOZ37oemjec1ypWgNhFKwaDR74xTTGZTx9PjD6YqdYcL+Ro9vH8y4d15yc7NgO0+QUn6gYv8MhIbOWJQ2JNHTmx2+LAe+eHiRPpOfyPJdCBEA/w7wl/G+Tv/weS7q5xmPljg64/sAUejr+aM84mTecLr0yebOIOH2ZvEhSm/defmXzTUduO0M81rTGoPRlqZz3J+usM6y3U/5yvUhr+8OSKOANJL8s3fOr173c7sFk7Lhd96d4AS0neXGKGN7mBIGgoNpw3snJbXWtNqRx5I4kvzguKTVHSwEO8OEf/q9E7T1unlpKEhjh9GWb9yb8s/eOeOVrT7TsqXpDJOyplxT0fMkZFk3DNKEIJAcTmrePpkThhJjvZ5etWqpLiw4jUUSXKyoO4d2+PJUHjJZKSarllo74vVjEwlxIIjDkEEaEkVwNlfcO6tYtJpFY2k7xyCLmNaKVjmqQDNMIzZ7Kcpolo2iVAHxKOD6OEMZx5duDHj7eM7hwvfddvspZeezvF4UMM5jXt1NKKuWMIgR1hDKgHfOKpQynK8aOguhhBsbBf00RAqJ0o7ro4xeFLIziGmN37Qn88ZrERYRs7rDiYCjaU0aBwgBJ7MW47y6hzGW6+Ocr90ZMSlbDiY1cRwwXSrOFo1XLk8CNoqYzV7CwdSTOqT0hoZCSGZ1Sdn4HuTeYISQglERcThtWDWaURpze/zsg69NZzia1ZwsWqaVYm+YEa4z+Y/qW70sB754eNE+k2clSdwC/mPgPwQmwN8F/j3nXPUprO3nDo+WOKSQnC4ahIBbmwXaWN46mBFJQT/1b/t0pYgDyc21NlogYNUa0tAz1VptmJSK66OMRe1FNKe1IgokoQzQ2vHeaUkRh9wc5bxzWnJznBEGkoNpxf1JhQCUcdTKsFnEnCwbf9qe1lTG8tpuH2Usv/feKZOlYW+Qc2sjY1qFtG1LPw6Y1R3L1rKsO6R0RKXixjBFO4E2xg+2LiruT1sSCR2OZa3RDm5uFPTSCIQ3CZSB4N7ZiiCUBAgC6YVXDZIsCDgta9IwAelnlWa18bptreHaQHK6tCShoLOwP0io12WqHx0tGfS8qV8cBlwsO9T/z96bhVqWZ+l9v/+0xzPdMcaMzIzOGrqqq7pbLqyWhDDGeNCDLQthPxiDwWA9eACDjcHYki0P2HqRjURjLBkMBtvYAsug9oBtJBCYpkV1W901d1ZVZmRGxI174w5n3NN/8sP/xM2IqMisjOysVGT1/eDCuWcPZ5+zz9lrr7W+9X3OkRtFpTPGuSBTAqUlRgu+dmuX0/WA9YFMCx7OO94/35BrjSJiYgqgq96ilMR7T1Zl3N0vOW8crYtMdKTpPPcv5vgYyIxBK0kgMCsMpdaAYGMH2l4zqyvu7tWsrGMYAlqCVnCy6pjVBZlUBDwny6RP985pymBbF7g5Llg0ll+5nVOXhkIpfni84Xzd0zjPSEtQiv1Rxr3zllmZ7OOrrWp6bhQCQZ0rNr1jUhjunW2YP1G0Lww3JgWZTn5Qn+R7n2nJnb2Ko3nLe2cNt7aSRR9mlnlVDny18Cqek5fNoH4I/AbwL8YY//bP4Hg+13ha/cH6cHlSn6gyWx8wUl8KY0ofUlnkktKbGHdVppNadYzJiGv73QgRvIc6UygpUNuSYW89rff4EKmqDOcDmVZ0g8fFpAX33tkGGwLnmySFc7HpGOcZRaYwQXI4qXj/bM2qTwy2MpP0VvF4bVm2SXWiyCSDjbSd5VhEbEzsLgE0Q0AR2Wwt2Z0PhBhoBosUIl0oQ2CSG4SUDINFKkUmBSsbGJeaOs9wMWKkoLGRcWHwIdlenCxa6jLDdA1aSdrBMYQMiWJaG7rOMsolZ8seTxIlRYJFsjsySbTVKDIJlRFY5wgh0PQD500EJNNcc7rakGvJqrc47xmGiBCSKtcIAovB8u55S/CpzxQC9D5QKMEwOIQUyADzLjCEhmmZcWevSqXO3vG7ywXXRhmTKqfOFVEWrLt07jrnOF2nPpte96w6S2Y0gwusB8ePT1tm1Zx//KvXEUCVCS7Wgb73BB/YGSkeb3qqTGJDZFxp9sc5wQtsCLgQGGVpLmpaGk7XPWdry5v7NbPK0LiAMfGlqeRPf++1ShJOq85y6yMki67MA189vIrn5GUD1BdijPc+bKEQ4s/EGP/mH/CYPrd4Wv1BbZv7QqQ6vvNh668TL5UfQkh9pCeU3rgNZN3WSM/H1Kx/YhcpBSgFg/foqPAhsa1yo8hFsstoe0ueJUVzpQSZSHYNk8LgfGSvTnRvawMXrWVaGqKIVJnia7emGKMhBH7r3kV6PSmRItA7KLVg5S3LxqNURAhFtlXs7p3lbGOplMDHVH7LlaLUMN/0+OgAiXMeLQTKpEC9aHqEkuRK4okM1jOqFZlWuOggJtJFYSSFUtw9GGOd43iVeiOTkSFTilZFFpuOGCVFppiNMrrBEj1En7KnGAIByUVj+eFJy7XJ1lHWB5rWcmtW0A6Wzga8jygka9uDgEJK5pukrdfFSI5k0Ser9RDAVJK+DwSRssIQPQHYrQ2lkXz/aImRyb1XC5i3lreujzmoc36k12xaS++SUK51kZHX5FpytmoZlxlaCpSCh4uWbz2YpxKoDUzKjL1Rxu4op+kdbecpMs3+KCMEwd7IUBjFvdMNZ8uBC3qMlnz1xpSbswotO2aFJteKpndQmJcWXn1e9STEJM31UX5SV6Kvrx5exXPysiy+Dw1OW/w68Ic2QD1NzQ0xsFNnsO2lSCH4+u3ZT/SgbsxK9JaB92jRJRmdbQ9qUhomhWHROvJMcXunYn9U8t7FhsF7kJo3Dmr2RjmP1j2TWvOjkw3TQlNnmjt7FSFEuqMVvkz9r7v7Iw7HBW8djPide+c8XnVIJfnytSnXd5J228N5x/VRga8zNr1j1QgGF8iNYKfOiXFAC4kTSWw2CbTmrBpLGxPDI99ad1xskoZf30s6NzBvLYJEi1cSmsEiRFJC73qX1M2lJsrAemXJTeTxakAKaLynzgxnXWBWpvLZJNd0LlIZydFFT+8CnjRAXOxUCCGYZJLjZUcboFTpx5Zpz7J1BCL94DFS8IOTJevGcbruOJiUHNQGITMWzcDGJgKAC4EQYO2TfUYQkGvouoAjMflGuQIJszyjNoLN4DlZtezWGUUIVEZxvOp5/7zldFvO++HxinGuqfbGaBU5mqeSWYhcKpa/uTdmr85pbWRvlLKj3gW0SIzMukjzaV+6MWJ/VLLqBh4teu6OM86bgSJXWBeZ5Ipl76gLw6jQnKwHBt8B8OWbk5cu53wSSvqV6Ourh1fxnHwiksRH4A/9t+t5ai48y+KbFIY39mvgWRbf09t94eCjWXzrznI0b5HbLOV40TEqNDemFTtlRms9d/dGiYwQI1+5PqVzSfut1Ioi14QQ+RNfOGS/SmSMKtOpme4Di7bn8apFCIERgnFuOFo2vDYrOVr2zMoMHwNNH2i6gVgYIKKMZqQigwGiQBmB94Eqz/nyjTGLzvH28ZLWRowWCCEYFzm5kdzerZivE4X6rRtjFJHfcwukEOzUmsFGlr1jbKA2giA0s1rTdJbzZqDSmlltsD7SDo55Y9nXip1S0/rApMoYhYjzcLzs8CHQBDisMubNwOPOkTWC/bpkXBiMlkzLDOs7JBIlPKVWzNtAm9jilBkMDmSEwoBUaTh3lGlWg6N1gQfLHhWTEO7xomfROTadZVIk4dvpTNMOKds92/QMXiBI1u3TKqfUhmZwDNGnF5WR3nmaIfl0iRBQJhktFloSY0QKybqzaCn50rURO3XG7qhgnKvLUYPN4KmMZHCSvVFyNt6rM5atY1b+pF7fy37vr8wDP5941c7Jpx2g4qe8v88lnqfmPvNYCnL54VYCL6r1Pr2+DJFV79kZ5Wgl2fSWhxctXyzHQBJ19RGEEh/sT0GWKe4a9YxR3e2d6icYOtEnaaRFa1n3nmlpGJeazhfM6oLGBVad52ztyRRchDSAa4Xii9dGnK0HxiJyNG/xUeADIBwP5h2lVowyjRaOGAQogVSpROZdoMwk89Zy/6xh3lnmzcC01Fy0UBrJ6VbRorGBXHnmm6SK4GLgNPRcmxSsO0euFdalvs6qHyiN5nBc8s17Z8w3lmUzoDUIJAsFO6XBCIWLligCr+1W2ACOgIsQJAx9+j8AkqTv5QPUGZRZxrTU1IWmKhTdEKkBXUiU2DLwgqcwhn5wvNs4vny9Rgo4bwaWTWLhrXvPjWmVlB3OBw7GGX/s7gG/f7Li6KJFSIn3IvURC8Ob+yPW3cDbx2uuTwr2RwWnW0faXCumlcG61MesM0VuFFqm4HV9UvDWwZgHi5Z6a+HyZHbuk/YcPonG3pUu36uHV+mcfNoB6go/YzzdyEy07uFSNeD21mvoRTbZPkYyJS/vjp7MWoVtH+vJekfzlnlreW2nonUeO3i8EPxDr9dIITld9XTDwKRQiaLsA9MyR4lIoQ25TgZ7UcAoNyAEmz4Fk43RlLlBqURCKIxhkmcoKbloBgqj2fSeR/OWSGTVBZatJTOJ6bdsAy4kN9ZSR84bDwTqTDIMgfNNjwKiEoDCO89p61HCMd90XGwsnUsEktY6sixdvG/NCh4uB8osx2yzyUerjuAlhVF86SDnWw8W4AJaSwoT6AewAQIghCfPMkqjqY2mGYatdFGg9YF5a2mGQGU8B+MCVUCeG4ySrFpPphRv7I94+2TFou0ZbW0/ZlXO6Wqg6Ty3d2te3y0pcsX7Z8kF+HCcJ4uOTPGl6xPmraPKFd85WpBrxabz3DgsOF1bvnR9zA8fr1NA95FfOBxxuhl4vBq42AwcTgqybZC66gNd4VXBVYnvc4YnjczBeU7XPSEEDkaGQHiG2gupNPgiq3jgJ2YdEtnB0ztPiKkMFCJ4xJY+PMKHwPsXOb97f45UQIAyM7TObaWVWqyLHC2Tk23EcXOaJ+UFoxnnmt1RzqNFywjDONecrFq0kMQ0Vsy6t8xbj5BgpEjSQCGwUyUfpU3n6B24EGgHx6TMybRkp1b8+HjNTmWwCK6PchqXtASlULz/eEnTOaQSFBkYnWO0oFSa443nYFyy7gbmjWXZWG7ulPzK67s0nWXRDtw/b1hagUFS5IqTeUOAxILUBh8Fo1LR2zRfJYnEKFh2Lc7FLaVcgQh4L7lY98zqnMVmYFKZJAg7ypMvlxLILaGm3Z6TCYbOw0go9uqMr92cIJXk0aKltZEH8wYlUvDeGxW8vlPR+0CdG07XltIofvXODl2f9BKdTzNl16Y5332w5P2LluvTgq/fnv0DL+tc4QpP8GkHqP/+U97fzxU+7oT2i9Z7+rnr06TbdnTRcLzoqEpDqSU3ZgWH45wQIu8tGlxIcww3pgWjrVX8w3mLADItL2cd3jldp7tmAffPW95+vCLfOq5mOunhTTPF75+1PDzfsFvnxBg4WnWEwGXf5mRlORil/sV7Zxva3vJoATtVhpaRGAJn6+0gqxYczXtcAC8DgcjZxtNbt1UmVxgN3nuMhlmVkxtFiJ5cw7Q0dNYjRJJV33QDUgl2a0PrBeshMFjHtVHGxZayr7SC6EAaaiMY5RqEQGnBzVnGySIyqxSz3JBlOtmtO8/3j9YMATIpybVkUhiGUUGuBZMiSTNZGyiMJlOSQwcues7XA8FLxmUaom6tR0jJ1GTMakPbWso8ndsql5z2jovWsSsyhPWMC8Wo0JdqELujNI5wMCmJMs1uxZgcac/XPcfrni9fH3Mwyre6ialQczjJ8RG8DSilOBxlPF4lt+Jl63h9r6KzgevTnHljmRTmKkhd4ZXAyw7qToG/AvxqjPHr2+f+NPB+jPF3Yoz/9s/gGH8u8HEntF+0HvxkxrM/ynjnfING0K57ZITvP1qSGcmmC9zY6vIpIbhoLFWmtz0rB0CVp1M/uMB3Hiw5HGcYLXEhKWl7EZlvkipAmUn+7++f0HuXlLClRAG3pjVSRG7uVmRKcn3iUUogO4tWaZh28I55G1j3GmMk41wTo6AdLDEGxrnGmMiyAUJAK4PSsB4sRhjGlaRQkBlF55KliPPQ9JEiMww+IInYCLtVRpEbxlJwuulpe8+DectgAwhBmcHFBhrXU2nNjVmFD4Lr04x161kPHgGUBtrO40LEukDvPG8djlgNjn7waC25OSnoXKQLKYi2NvLe2RoPtF1gVuvte4wYlew6JIJsK0l1vhqQyicVdBNQQ2RaZ9y9NkYIwcm8ZdV5bu1obu1WPF72WO+5Ni2YlVlyLw6RVecwSrI7ypksWwSCWWm4aCx7owwf01xSpuQzxJ0zMVxmy1omBZEi07SDv5pFusIrg5fNoP4SqUd8/ann1sB/CvypT+ugft7wcSe0X7Te0bwlshUufeq5edMTY+SstYSYZnz2RznLxqFlmvW5MVVkRtLbdNEJPg0DC5KjrpSC++cNvXXMW4H1kbNNzzhXnLcDZ81A2zsmRUl8sr0EEwIxgpAkynKTBFlXnaWznvvzlt4GlJQYqckzSa4UvfXMNwM7lcFH6FzgdNMkKryFzMBBneFioAiSUkFd5uxUmnXv2R8VjHIJ22wyILh/vqFxkZ1MYWRy7L0/b4leMK4yrHOsuoCSsDcpUCK9zzf2U9Z37yzJRpW55HSV5IIUoLRAiIxxJrkxK7k2KfjyyPD28ZraSBobiBtLtB4bINNQGMO8TeoUq37AeghS0gw9Qkhyrbm5U9PagBCwXxcURmzV1QvOV462T32uUa6Tk673iWQRk3L5aEt2KHQiPDgfKbM0szIpM8ZG8dpezZeuqUsm6KVr7VNB5/q04MF5k0gROvlTPRkov+pBXeFVwcsGqH8C+Crw46ee+ztcafF9JD7uhPaL1nuS8dTbjEcryaqznK8HRplBREmmBQ/OG6oiZUlP7MIjiaH2cPD0g0cpedmferToWG167p1veOdkTe8DO6MsDYxue1dd78l0GuJc946NdWx6x+O1RcnIpDJcdIqZVLy1lyw6vvX+OcEHKq0R0rNsBxCatbdIlaziITK4sJ3hEXQxEgK0HYTaMyk1lVFoFfnqzQn7dcbDRccbezVvHCSB23dP15wuOoSU1CaSSYXUgrX17JSGUWUAgQyeVe8RQtBaz26V03nH4ahgPXgIgUfLnsF5eheoMsl5N5ArRaEUx73lZN0zby17m5xMqtTjKRSni57Hm47355Fxrnlzr+L9hWClLFoGyp2MENJ5kER0ZnhtWvBw2dO6FMBzozjbWHIhmNWadWMpheb2Xs1FM3C26ii0JJew2Ax85+GKX7o1oXWe65OCR8uOZTsQgeAjvQ0Uq54bs5JCf7SGmpCC/TqVQJvBURr9D3zu5QpXeBovJ7oFMsbYPvdcBLJP6Xh+LvH0hDbwoRPaL1rPKImSgnZwhLBVoYiAFNzdrwkxprtg4NYspzCpie5CZNNaEII/cmeHO/s1d3arrais4vasvFS2aF1I5IZ5x6pLA3q7o4xJZSgygSNysm55cLZhZT1FLggIMplUI3aqNExcGM2kzrk2rZmONK2N9BbawdE5z7oNDA466whCkGuFUkmNfVIJqhwigrZ3SAGLxvHdByu+f7ym95HHm34bkCR392pGhWaSG7RWKJMytKZ3KC04nrdJnRxJEJIILBqLAypjmFQZLsKkytmrNaNKI7e/htamjGtlXSI/ZIZusJwuO65NM/7kl/aoMsPuJOfapGRW6ERGWXY0raUZPNMyeUhFoVBa07hU2jtr0qDy3f0xt3YKYoTBOq7vVnzxcMwbhzWv7ZT84o0x40zS9gEjoSoMxqQS7LcfzPmtH51xsu75yo1JkjNyEbPVwsu0TCXh8OKpD+cC988bjBIcTkve3K8xUnJ7Vl6JtV7hlcLLZlDHQogDnp13+qeBh5/eIf384eNOaL9ovZ0643jR8XCeJv1nlcbIdMGdt5YvXh+zbC1fuD6mMIbKKKyP7JQm2W5s9/u87ExSN/dcNNu75yzJJRmTtOt2qpxRYfj+wxX3VmseLTZsbCBEOBgXmAx6HylNIjAcL1vW/YDznlIJln0KwrmODB7M9q1OSo1SKevrBo+SgfyJBJSI9MMAJuPWuEBriRSRdWuZ5pofH6/JhQQpWffJhXc9eGalwRiB9R7vPEfnA9ooLi4aJqXhF/YKHq4GxpVhXKSh2bcfrxhnhvE4592zFr/1mZqUhtJo8iwFytnEkGsBRDa95WzV8/ajNcvW8Tv3zrE+suot3eDZ9I69qSHKpMs3+MBeoWl8IKsMhVaECDt1RlWkmS8fArd3K0QUaC05nKQbBxciLgrqQuMJNI2ls5IH5x27lWHeOm7vFCyF4LVZBRHGT5EbPkxDrbOe+xcND+Yto0KzP0o3NdbHy7GDV2VI8+Pg83a8V3g5vGyA+s+B/wbIhBB/luSw+69t/67wEfi4E9pPspshBDSCB4uW3EjuHtQM1vP+vOXN/Zo7quI7D5ZcNAM3pyVfvTVlp8zYDI5v3V+gpCALafbpRf0uEeHRsqcbHDt1xqbNEi21AAAgAElEQVRzRBkRDiqticC6sbx5UNO0aUDWbTqcF2y6HpNl7OaGr96c0VvPsrXbzEfwcNnT9BatNZNKsuocQoJGcH1W0nYWkxnKzHOy6Nj0nkwLDqqcJniUTOVOrZJShJOejQ1Mq4ws1ywby8WmR0rIZGQ1OOqoKJTkvLWsh8BEKupMEWLkaNlxf96zV+eUOufNvZLzpqfQikWXyBqDdeRKsVtmdC7gbaQwhsY6LjaW1nkIkffOG7yH3ZFKyvO55PZOzaN1w2JluU7GdJzz3nmDwVMXGUYLbu/UeBe4tVvhQuD13QofIIrIt96b8/bxkhs7KdDsj3LunTfUhWFSZjxatnz7/jm3Zzk3dkaUmWbRWs6agWs6ZaCZTu9VIj40Q3/S48yVpM41MURO1z2H43xrjBh48NxIwqucUb1q1hBX+PTxslp8f1MIoYEvAv8D8AD4N2OMVz2oj4GPM6H99I+u7R1Hqy7RfoVgpzLECERYto43nqIHb3rPTplkfCKBukh08yeCsC/qdwkRmZQZIQ6cucDGOg5Kw7yz7CvJqMo4qDN+e95Ql4bzxqF0pLWeg4lmp844mGbJNFHA28crtI9Y5wkRrHP4AFJCbRRFnuFDYFTkfOH6iE03oLbySiIIgpDUQbJTJoFV65J2Q5FppIBpqTledKzbgcZFEpfQkynJ3ijj8aLjwbyjMJJukBxOMo7XA7t1zrVtRrZoLVJBoTXXd0p+8P01g40Yo9nJUnb3C7vlpcL442XL+cbhomNUGLrBc+9iw9rmFErSdh7reubLgVVvGVxBYwdiDMwHx93DmvONQxHZnxbc3q3ZdAP3L1ru7NZYn1h+m8HjQ+BgXG5V6mG/zjhdDwSfRIXLrRHgYD2TIqmcE5Nk1pPMu7UWIi+0uXjS4ywzzcE4Tw7KXfLIujkrOVn1r5TVwkfhVbSGuMKnj0/iqPs3gL/xMziWP/R4xk9KylQ2ax27W22042WXWGhP7Dmeowcve8ujRce8dax7z+GkoO0duRJEHxM9bYvOeuaNY1ZlzNuew1GOEBllpogk+nllFPcvmkSVRnJtktEOnrWEUS5563CE9ZG+71i0A3F78dNa0dlAb2FcQKY0h5OCIk/lydu7JZlWKHKmhWdvlPTwMq0pMol3yc3We48xCiUVF+2Acw6lNEOIzEpDlSvO14JHi5ajReB02aeBWC1wMXLeDHgfuTGu6GvJw3nDRWuRQDaR/P17F3jvmNQZoyzj0bJnMU+2JDtlxtduz/jO/UDTO/KiYFoYztYDwTmCV5w3A6NSIWRSW187wem642yTJKJA0gWByeBsY5nVBYMLfO21Gd+6v2BaaeaNoyo0o1Jzc1qCEBRGcaBEUlXfmhn+8mszCi3oQ6B1kYjmfG355dvpu1FIxeE45+EizbmdrHquS/FMRvF0jzNTkt3KMCk0b+zVqbz3ilktfBReRWuIK3z6eNk5qD8NHMYY/7oQ4jrwvwNT4J+LMf7Oz+IA/zDh6R+d84nltT/OGUJAbnXtvnxjzLpz9DYQY+T6dh4GkgFibiTXTM7ZeuB7D5esessv7NfMW8cv3Zoy29qJH81bnPM8nLecryw2BN7YLTnZJJO/3UqSa8Hvvt/iCVz0PeM8Y5Qr3sxGTGqNC3B2tuH9i5ZF2+OCJBOCGNMMlJJJxLTODZ117I4NtVbEAOs2KZu7EJhvLMYoRIyA4OZOwXpwFFqy7ByDCyipsT5y3nQoBUJElErvs8oN16Y55xuLMslqpMwFQkiuzwz7k4rcSCKOVSc4GBf0vccDdZ6xbh1H5wMgqHJJqSWjInly7U9KvIflYLnYWJadJVeKdR+JIrLpAyEEduucyqTjDT6wansypfj+0ZK7BzVvHlRcm+ZolWxVQKSAuBkolIStPcsQIm/u1Vyflvze/Tmr3jHKcm7v1Mybnh8crXhtv+buXp2MLLt0kwEpKJVGXVolPJ9RPOlx3jtLTruQhnxdjJeWL6+S1cJH4VW0hrjCp4+XzaD+feBf3j7+88DfA+4D/wXwj3yKx/WHEk//6KQUxBjRUvDaToXzAR9hr87Zq3P2tyUaHyIxclmyuT4tOV33zErDj0/XfO3mhL1xSTc4vv1gwa+9uYePkcfLpPm2OwosOsfJWcdenTHOU+noZNEhIhgteGtnxLTMsTa5/QoJPzrZIOKaPkQEkdZFgh847QMiBoxOZb2ASv5IIVLnGTtVxrK1HC87BJKuD8xdT+cDr80qpqMM5wTewxAj75+1lEZitCPkkkfLdBFeNpZVmwZhb+1UOB8pMsnYKcrCoKMAGXlzt+R03dJ0jmVn2RvlxABnjWUzWJzzPFr0BBcY14bSGDa95+ZEcDApcD7QdI46VzzwDRuRynAHdbKw0DpwfVpwOCk523T0DpR0RARF/mTmLA0VT4vUI/zeozVfvzVlNTjesZHjdYeW8O55w06VcX1asjvK+eN39/n9kyVnTbo5qDPJ+WZgnCvyTGFMUif3Md2gPJ9RdIOlc55i26cCyJTEKMmd3ZLMqMus/c5u9cpZLXwUXkVriCt8+njZAPVajPFbQoga+GeBt2KMrRDiX/04Gwsh/mPgnwH67Wv/9RjjfyWE+CXgvwbGJOLZvxtj/I3tNp/pss8SzzOQnvGTcoFZmYGA3qW7w5uzpCrhY6TONPWexsdI9PHSlTeXkuuTgrNly7Q0TKscSCrnm6FnCMlM0ZHMFOtM88ZBleamYkyZTp0zuIAxgrfqEaM8I8SGk3VEi8iPTlta60EIThYtUkoqs5UnUp5pkTNynlxndMOAkIJpJulsYN44EIJpqTlaWJpuwBjFuNBsBocgYHSyJdnYAaUEWifGoO0C7eCZVMl8sRk8BNgf5yw2joM6p+0D08Kw7iOVEcy7wPlqg9xKN/WDY9CKRTNw0Q40vWXepHkLqQNCWJRI6hlKJ+r19VnJ+xctSEGm03zVRe+YlBnNYJn3gdVZw15tuDkrUplJRJreM64MArbnQpNvy4/TOmNWZXTWc9705FoiZAoeJ8suZboCpBAcLwbmKrECb80qlJIYKTiat1zbDh8Dz2QU685ytOyIArSUlwSCJ8GsyNJPXypxWRp71awWfho+b8d7hZfHywaoQQgxIWVRf2sbnNIv9uPhAvhGjNFu6ervCCH+L+B/Bf69GOP/JIT4ReD/FUJ8nURf/8yWxRjvv+Tn8YnxYQykj/KTGnzgvfPmmW066/n2gwU+pPLgqFAsWsfpqmfeWH78eM3r+zVha82uEWiVgth3NmkAtektUkjcVp07DeP2vLZTEQScrVu+d7TE+vQa800HQiHxrLsBJST79Rij04zTwShDZZrl2mKDJFcCi+C8sYxMctbtvWPVDwQR2ckzdkcZg3OUStMOjs1WqT1T0PSBXAu6IZBpSfTJdK93AW3geJEo+K113N0vyIyiHzoO6pyTZuDhoicSGOWaxxvJYC1KwjAEhsQpQAkYnOd44cmN4AvXBNcnBTZEgkiK56M8BY95Y9kfFRA9o0JyWBdUhaK3kUU3cLDV/xvXmmujHCUF75+taQZHlSlWvePWtGJaZwgiuVHc3q3JtaQZPI+WHa/tVBytOupM8+Z+Te8cp2vLwSTnbDNgQ8T6yG6dPaPR+GjR0Q0pON2cFtRbDcYnWdJPK429SlYLHweft+O9wsvhZQPU/wIcARb4le1z/xjwrY+zcYzxLz/17xskmaTbQAn8z9t1vieE+LvAnyGVED/LZX/1pT6NT4ifxkB6kZ/Ui7Z5cNHwcN5RPiFK9JZ7jxv2JxlfvDbm1qzk9x8t2fSOKtPcPah4b95wfVxwc1bSWsfDiwYbIm8eVLx/1gIRbSRfPBxjY+B01fLthysG6zmcFmgirQf8wOCTJ5ILASE9Ac1OlXHRet6sc6qJStmYdwwWNtZx1jm6GFEkrT8FPI4wqdLFedENCBS7IwMxsOk955uOMlMgIj44jlcdea/JiBxOUg8uIHA+shwiruk53fRshp5F4+lcQErJtBJ4C93g0Vol64wQQIEBbEzBqso1IQhOVmnY9Xjds2o9g/WUmcIoAQhyLbEhcO+i47VpRlUYamM4vFbw7YdLBJHOer50ME5BZy+ZB85Kw9+/f8HXb0y3UkUGuXUU7p3DiMi98w2P1wNlphjlmhAjg0sU8K/enCKBZvCcbwbONsPlDcud3YrOJbuTukisv6cJBE8z/q5KY1d41fGyAerfAv4f4Lsxxne3zx0C/8nH3YEQ4gskcsUe8M8DN4EfxxifHv79EXAXOP2Mlz1/rH8O+HMAd+7c+bhv8afikzCQXrhNMzB4n6zlAaMV/faOONOKTCu+fHPKsrPcnhVs+sDF2Ya/9+Mzdus01HljWjEET200q94zzhRDCBxOM779YEUIiVSghKAPjnXjCD7S2UCZa6qQnGFP5h1KKa6NS3bHmt1Rybuna8pMcrpxjHONcJEoBc3GIYgIIehdwLWWbz9YMi40vfccjHK0kExzidSCKquYFYa3T1YMLrDuIs1g8Q5CTJb1YlsKHWvJenCsWsfJIl4GHU1gkrvt55NUF4J3DAG8B6EgRsgVFFphI/z2u2cIqVi1A7M6I9eKzll66xiZgsEGln3q8Ryve/p5h/ORr92e8JUbE05XPVHAbMv+64aBB/3AReM4WXZAZFwYlm3P9x7O6W1gXBompeH6rERKLrUY9yrDG/sVsyojRvCwnX+SP0GK+ECn78VZ0lVp7AqfF7zsHJQHfuO55/67l9zH28AXhBC/CvxvwK+Tfm9PwwEFqXT4WS57/lj/GvDXAL7xjW98am7Bn4SBpIRAkGSD8u1QZm4UmVJ0g6PINPZJQ/wpuSQiKATvnLYIETledhxftMzHGaVOfR+jJTcmBSIEvvNwhUPwg0crqkxRaMGoSOaIDx63DN4zKhR5Blpq1JbIsRk8hYk83rR0TvPgvMUYBcEwXyfh2VJJnPcsNgPjSlIbQ9CRZjskW2hJa+HBRYuWgp06585hTa0VD842SCVwgJEQRBJoPWssijV1lXOxHjhZ9vTOESK4bXDy278Hc8e0llyf1igROW8CNgRG+dZ80INRMC4Up8uO3nkOxgYpDCeLHqOgs5HcwLeP1hzOSmaVxobI+cYRYyJGvHvWMDKSk5VlpzZ892jJorUczzvu7JbkmSE3ku88XHEwyjhadCzWPXmmeWNUQRR892iZ3G/7pH+46TXXR9WWJJOhpOB40aG3CiEvmyVdlcau8HnAy9LM3+FDbN1jjD+RgXwUYoz/nxDiN0nTOc+nJ3eA3yExBD/LZZ8JPgkDafAhsetWiR68P8q4vVuxU2V892jJZuhRUvAnvrDPorWXNOLd2tAODmJyzn33ZMNqcAwh8vqexmgJUfDNH59y76KFEBlVGfNNz8OLllmpWDceVBq4LYVit8oYfCJZ2FyhmyGxDr0nBkHnAuveY2R6b0oL+tYRK8GqtSgN1otUsnMD48JQqkSjVzJCjGyGVJqb1ZpzB987WuIDNG2STnJAIUAqWPQeJwaG4BlcWi5JdyKFgj5xKZL7bYh0g2V/XDANgYO6YFYpHi46Hi97jEzqEt3gOagyHi06pIhsugHn006FUCzp6U4Dt3ZyvJcoKcmUQElF8AGnBJMiqTVkheZQK94+XvFgITDKcjgpWHWWpnMYnZTX60xxsuypjOR03fHlaxN2Ks3xsqfQgqW1bHrL0aLjcJJzsuy5IWBUGAabrDPE9tf5ol6m9eEqY7rC5wovW+L7D596LIAvA/8K8F/+tA2FEF8DfhH4GzHGKIS4BfxR4C8D/5IQ4k/FGP8PIcRd4J8E/gJJNT1+hss+M7xMmeVJ/2lUaCaVYdUMPNravCsl+ZXbM6QSZFKitWS3zDgc55f/A/zw8Zr3Tje0zuN95GjRMbjAqh1ACJqtGO3tWYXJJCFm+ABFrjheDuAiv3Jnl51K88OTDa2znCw7REx9lio3rAdL6wbGQuO8hyh4vOoopCSqrbgsEikjIUL0jsZCO1iuTSXaR7yXdEOP0gYt4WQ5cLoeWA+B6Ac6n7IhAfgIMqY+WNs7kCn4qxjpBxiAuA1WRkGVJRuNwUUer3oigkylYGl9YrQFAsuNJwZ4z7ZURjKEyLpPr1vodLOQXm6gHRyzOqfrA3mueH1mOFomtfeDacW6caw7R4yRw2nBTmXobKCzPlmQOMswCKzzrHpJISV6nLFqLW8/XmGUSoLBWqOF4J3ThoNxTp1pbk4FDxcdO1tNxb064/68vSTcPMmSriSBrvB5hXi2FfMJdiDEPwr86zHGP/tT1psA/xnwaySSRQb81Rjjf7tl1/06H5TZ/qMY49/abveZLvswfOMb34jf/OY3P/Kz+FnB+sD75w31tln+cN7SW8+dvRoBWB8vCRbzZrhk9Skp+MrNCcvWsW4H/u7bpyybgUerjhA9pyvLTmUYfKTOFffPW3bHGXaIDN7hguDWLGfTO1rnORzndC6ybh2zUrPpLT84XuFCMmxf9R4tYFJmDMGzaDy5gWmhUEITCLRDYFZqztc9fYDKKCa5pvWei8bhfWLU7Y8NRabSDJFWbAbHRTMw71ImBInYMCkSVd6GRMSAtHzw4Dz0EXLS0PC1qaEbIkUGhdHMNx3Wp57Txnq0THNCQwAtAp2DaZXR9o7OBlq7zcJIQW+nglFumI0KeusRCDzQ9D0CxSiTaK0RIqb+WG5QWhJ8YFQY7h7UHC16ci0IEe6dbVBS8cu3J9R5EvstMsG6SxYlv3Z3j0eLnts7Jbd3k27fqk80xDrXZDqRZ57+PoStfmAi18ifWH6FK3xWEEL8dozxGy+zzR/Y8j3G+HeEEP/jx1hvyYeIysYYfw/4k6/CslcRz9twDDYkqaBt5vWk9xBc5NsPFhQ6sfqa3vJ79+d85dqEdzcDMQSmI8O0MjgCTXfB4TgJk1qnmFSGdeewNilV5Fpy0Uga6wgxBb9uiOSZRhuJa9PszZ29gtOlpe896x56O5CZ7ZcrpkFVncGmDYwLzaJz+JgCyHiksCHS+cj+yHC+sYQI68FTZZpm8IjocT7QdVunEVJdWABawt5Ic9FJ1t2Aj1AZ2Kk1tRE8XiVKeT/AorWEAF2E0gWGkPY1bF2EuwidT2aMo0wwqzIOJzmPly1RAAQGTwpgKhk2GiNYtQPjUtMPgVU3MCoyduqMi42j7y1aRIYIUkR+7c4Bg40EIrd2S6RMhpKjXKG14vas4PX9mqNFh3WB4KEyEh8Dmy55g41yxaNFx2ADnfPs1RnT8id7URJxJQl0hc81/sABSgjxy6SM6Ao/Izzds3Ih6bMdVomF9zTBovNJ4LTINL3zzFvHyapjcJ47OxXytRnnm4EH85ZgA+PKsGwt687TOTBSMMsVPlOE6Hn3rGNjA3WmKTKY5IZMeWaV4WTVEwMUWdLna60nSCgy2Axg+3TxLwSsugDREYJn00dCCKz7xJpbdhajBU2bWAoC0AIISeop4lj1gsYGAh8Ep2H7eN3AY92TacW0VFw0SahWxpgCRABsCibOQufBDdAQErtPpdfTMmVHmQSQaAm5TkSMcWHQUrCIA7mG0zax/TY9rLsB5+D1fYHJJIJEUim0osoi7RCpMoXygcFFjhct+5OKaabRUlHnmsNJxs1JzeuDZd17JJIYIjtVxsEk493TBjxcNJY/9tYe723HAUKIRAK/f7xm3Ttu7VRoKZ4h3DxPyBmcJ4QPelVXuMKrjJclSfwmz5IkalJf6c9/mgf18wjnAkMIz/SFnseLvG1CiEntG7g+yulj4HCUc95YNv2zBItMJrr1su1Zdp7BOprecSoirQ1ptikEFg965puBUilONh2ZEfTWIRXEPMe5gJGSL14bs277VHoLglmZseo8DxdrTteJ1aYFND7J7MQA1j17t+Js+pJlyiNEMgMc+rSOBBZNxMVIYBssJFgPrYV131FnWWLPLfrL7OlJfbjSiSxxuozkxjEuU4C0Fs4ajwTqXDFYT29BZaBDYve57b4k0LgU9KSEw4lBGUW5LSsuG08AikxxvgGUpFKB9ZDeV52n8uFZO1A5xXpwRJEcfhvXc74Z2KsLrE8/m3cfN2w6y5du7VAYyfVZwfG848Gy4fXdisNxQSTypqrprOfe4w1SSG7OKiaFZt066lzSdIHzZiDXklllsD7w7umGO7sVN55SMn/65mbZ9pxtBvZGz/aqrnCFVxUvm0H9n8/9vwB+M8b4W5/S8fxc4vm+0BPR1qfxokY2cCnsue4ci3Zgf5xRGM1XbkwYl+aZYOZiZKcyfPvhMs3gENkfZcktt2lZtpb5pudo1eNCBBu4WPdUuebWrEAoQW/TAO2qSx5NvYvsVgopJe88XjO4QG4ktflAnmnTBaQErSHEbRDYvi9HIhe0LtHDrYNmu8zzARUc4KJPgacwKXOJSKSAcZ5zIiyTKjJvYlJbB8YqUcM7wNtUejMy7VMKGJWaXIMSKkk5+fRa4anXt9uBA0NasOgte5miKhT744xMBn70uKPtLa2D0IXL96VIxo9EaLvAyAhKk9Q0TtYtIQasg3UfcCEwzjNsSBnmeydrDkY5McK6dygBLsDhOKPIUl/ub//gBBsik1IjRDKodETOV5bcpCzJh8ij5YYvXZswuMBunf1E0HniMfbu+YbXdysyc2VPcYXPB152DuovPnkshNiLMZ59+of08wXnwjN9oadFW59kUi9SiTiat4QYudgkXbgfnjRJJdxHpkWak/mHX9/FKy6vuEfzlnFh+ONv7PGd4wW/d2+Or7Lka7RqebSA3VEGIbJoBlaN5aztsWcNdaHZLQz700SKCCEFTecD9xeO29OSgMALwaILiBgJJFKEczCrFQ8uPJ39IDg9QQR6mwQYn79ff77S5IEQIDOwaixIwaa3rPtACM/uu7GpTPikHxV9opRnOmU100zReY8LgUwl8kXz/MFtIQ2omALesrHICAst2HSOxcbSD+ljjiKV/qRN+1MClkM6rmHumZZglGBvbLAWJnkAkcgSgw/8wsGEW7OSd84a7p+3eJGC8nsXLa/tVZwsO/7InR1ONwO7leZk1XK8aHm8Sv5Qu3XGjVnBqrU8XLRoJdgpc3Kd1MjPN8MzzrqXn/NW1y8zV72oK3x+8LIlPgn8ReDfAMZCiDXwV4C/EP+gdMCfUwwhXPaF4FnRVk0KUC9qZG96d6lq3jvPvLEoJXgwD8yqZF/x49M1mUk9jzpX3L9I7rup3BZwIbJqB+6ft5xtWiKSSCoZXmx65s2QPKVyTdhmD9mgcDFRqXMDWmsqLdgfZxwvWpqtIV5vwRiYZBIHLJvkhPvi4mXKeApStvNR8MDag+rAS5BEmj4ybHUCNR+UEF34oNwHabkDdExB7njVp4xOQpkJeh/RgkslhqfRXJYiIXeex+sU1FZdxLoPjlvHVIrMbKK5n7fpdQuRguJ6gFxFms7RBclrs5LGOsS2tHhjVrE7zhhc6hdaH2i2LMiH85ZplfPNe+fsjAydgzf2Rmx6z6LpWXSOP7pTsmwdo1yTm6RuLqXAR7gxKy/VzZ8POlf2FFf4POJlS3z/DvBPAf8CcA94E/gPSJp6f+nTPbSfD2RSoqS4VHvoBpfs2OUHl/IXXTyMStstWptM8mJk1aRZpd99/4JJbvjCtTGF0QzW872jJUYli46HFw3fP1oxeM+37m8wWhECGCOxDpSSKYD5QKkkJtOsW4u1keOzFqU101Jza1qxtgP/P3tvGmP5tp53/dbwH/dUU1fPfcY7+uZ6jDE2RgkWQY5AFphIfEGgALYDSFiCICBWpKAQkYghEYlBifMhHwhKHEQiIhnEEBxxA7ZvfH1n7vW595zTp7uru6Y9/8c18GHtXV3dXd1ddW533+ru/UjV6trj2rX3Xs963/d5n7dqHDcPCmaNw7ogDmgADIxKR6pCWq6uH+8anBCIonKPucFDGDsQbpFG48HIa5FVI9HgTCCb46m7+YJ9CqAjoZ8LMq0x85ZIBLXew/AEsmtt6K0qnKcfQ2UeJF1LqKvFEXTiQEi5Dve1DqoGvALnBNZaatPwxkaXREkOC0MaS5yHi2sZ792bs9aJiJ1DSMWksmx1BcOyYXfaIIWnk0RksSTVKZ0kIpaSrW7C3XFJpBSXexnbg2QxGTmMXjmJdFbjKVZ4GXFWgvol4Ce993cWv39dCPF7wBdYEdSJ0FryuasDvnZ7fOT28LmrgweEEidtHpfXMgDq1nLzsFgIGUKT6ays0WuS1jiUDGo072GzE/P1nQnDoibWgk6SsDstmVUNqVYoAR7H9fWUg1mNsWGUhfeOWW3II4URkkh5auOJYkE593jv2ZsWFE1QuS37gJYRS2XDz5O4RxHqU2eB535Kb9mcu7wcQl3L8SiBHb9/42Bv4hnkLbUlqPqegunSJcrf98VaPveSHGMNsdZQG5pFbcv7IBpZyzStdwxSxXBuwJes5zF/+LPbDLKIPI7Ym9b00jpMK5YyFM2EoLGWPI7Y7Cp2xxW3RwUXuynr3ZjNboJdPNGFXsqFXoISgt1pTdnap5LOyoNvhZcNZyWozjFyAsB7f0sIkT/DNb1yWMtjfuKtzSeq+B63eXzyYh+Ae9OEeWOpW8ekapiWLf/nN+/x6SsDtAxuAbuTOvRKecH1jQ67k5K9acusatnqJigt6aWKH7y2zmY34eZ+we1RwQcHc2ItubqeUVlP1VjWO4qtPMYZx7d2Z0QqQor2xBrTY8o6D6AgpMeWxPZxsPTVW8IS0nuWR1N2SzSLO2ZtiPLKRX5wSTQPYyljdwTSbY5dpxfXdRNBN9Wsd1I8JdY5Gutpak+SCDY6MY4wH2urp7g8yImV4MsfjvnUlS7vXoh5Z7uLkoLv7M5JdFA39hLJpDRESjOrLZu9BKUEG/0Y6RY9Z+s5XvDAZ+TGYs7TaUhn5cG3wsuEsxLUSAjxSe/9t5cXCCE+TVDzrfAEaC2Pak5LnPhYP28AACAASURBVDSwcLl5HF0nBW9uddmd1ZS1oTaerTzBO4H1jt1JxcVBAt6HxlGlEMLinOXOuAhzkLKIojWYKky7nTaW2njeudTl0nrCqAwS7kltGKRxEBgkmnujOXemLZEEH0kiEXK5R6+J++m1pxGPPOXtHocloWgeJMTHEc3DmDUQLSKupbx8ufbjWK5t6eWX8CBJeQARhiVK0RJJRZ6o4MMnBHEUoyUM8oi69QyyCCFASkXtaj7aq0iU5DNX1tBKsN1P6CWafqbYm7bEWpBGkn4asTetWM8TDmctHs+9WcN2P+VCLyg8j39+IvW46t8KK7y8OCtB/QXgN4QQ/xXwEfAG8MvAn3vWC3vVcZKsPFYS6z3twhT2+HU/dmOD3/7ggMN5jdKSbqKo7f2zsJKC6+sdLvUz7gwLfvfmEGNAS4UU0LiFV58Q7E9KrPN86cMJt4cl49KRSIVxjr15MJmdFoZJVR8ZpEJQyMH9mszxDX4pYHhcNLUkBS2CWOAsUIuf9oTHj3VQEZ4GxkGmFuIKfz9VqVn49i1ut3wtiQo1s3UNE3P/uQ/mEGtHayt6SYJCstVLuT0sECJM0X1zs8PutEUJT9FalFCkWrPeVXywXyKkpGwdn7jYQaPY6EdIWXJ9PWdcGZwP95vXhs1eQqwU89rwrZ0p61mM8X7lr7fCK4+zysz/2mKC7r9LIKebwF/w3v/157G4VxUnyco/3A9pNrvYeC4PUroPTUP98Tc3+eKHhzjnuFMbtrsxaRwRSclu1WCNI4kVVwYZw+2GvUmFdZbDog21LalAwf/2zbtkkUaKMNJiLdXsTGr2xjV4yJNgXR5Gh7sHRAWa0DMUphkFxITa1OwJoZEimLW6RT5uGaEsG28fxlKZ13KfCI/z2vL+pQnredzjLNebJkHA0E0FRevxi7AoFhCnUFbBt88uHitWYEV4zMOHCNAB3i2eWxvqEjZ7CRudlCzSXF7PuLre4fomfPXWiMNxQ9UEN48oini7r7m6nnMwrbl9WKKU5KORZ1i0XOqnXF/PmZQN62nEd/ZnWA8bnYQr6xmzylAZy/6seezAyxVWeFVwZqsj7/2vAr/6HNby2uBhWbmUgruTiqtrKUkUPPaGRUse66N+ldY6vIDtXsLtcUmiQhNtPw/TXa+up3w4LI5SPkVtmJYteaTYaYKvW6SgMWGmVGs9dWMwNoyfEELQiSRSCVoH03lLFAk8D6bRDA9GMZr7aTHF42tBlhDtlISIpG4CISxJRTz8WCKIKo5HNcehCA7lWLjYlxwUoSn2OJYktiQUraFp/X2iE2BEsGVyHjoKlIIkkgjhOJg9+nqWj2dciAY9jiyOcD7UuHpZxHY/QSlJL9X8+Nub9LPZYsy8YDgt+fTFbd7d7nG5l/K/fGOHVEsirVjPNV+7M+GHr0v2Zg3vbHcX0aYnkgJrHUqGVOLKX2+F1wFn7YNSwL8C/EGgf/w67/0ff4breqXxsKx8WjTsTWsSLVFK4IUPfTLe42zolfloWLAzClHXtbWcSRHGgx/OarJIcjBtuTCI6SQRo1nNb713wHcO57g26LWt9cTaUzaGWEkmlWFaNJRN2JyV8CSRp58KrBUhDdZ6Fg49CAIpPJxNW85Zqt190ULDozAEFwnlg+uDVkEirnlQnRcLKBbh0pJgTsoIasIk3EEeerVq0zyyxiWpJRrKhXzc+GBPpNOQGqxNIMNEh74ua6Fqgu9fpMJ6S3effJdraYFIQN14nGuoMk2rIrZ6gvVOzKRsUBKurOU0reeDg2Lh4h7mVkkhjgpyWRKhZDhoGGNZ78ZoKelmEZ+6LPjWzpRh2ZDEkh+6vk4SqVVP0wqvBc4aQf1l4F8CfhOYP/vlvB44Lisv25Z704bt/n2Lmqq2eAF1Y0PKxgcj1ySSRFLw3u6MK4OEL9+uGM5bDuc1o7Jle5zy1lbO79+bcmtYkEhBqQRVFeYXCeFI49BQKn148yWBLIwLMul56zHW41wYWfGwQevDOB496cex2ALjBpJFDSpxIU0Yc982aFnuWj6E4fECiHKxrsiDccHLL5NBVv7w+mbmfh9W44OlUsR99V8cQT9VlJXF+1Br6yYg/MJ5wj0aSUXcl6BfzBM6aYLxnpsHc9byGAG8sZnjvGPeWi6vZWx2Irb7GZPSMi4bvPUIJVBCkseKorbMGkumFIUKfWqDLObzVwcUxvLOZpc4Dp+R16Wn6SR/yhVeH5yVoH4O+EHv/d3nsZjXCUtZeWUswgeV395CGIEQ/OC1AXmicc5ze1QSRwolxZFTQLCugSzVwUncCxrn+O7+nFHR0I0VTsLOvRlJrOikgs1uRGGCFU8aC2KdoCuzcEAIzTyNCeMqchVScGW1sPF5wmuJASEgiUJE8jhYwppNsAGEY4+7JIDY3xdEPE1L4QARCZwLrhfNCRL4JWrCcy5J5TjhtA202iJUILnESyIJk8oxP+EBBWEOVazlwl9R8fZ2lw/25oyrhpv7c9YyzbfvzvinP7lFaxxCgZKSK2sRZeu4tp6jhODd4ZzdScO4dOADqcWR4tJAPUBAb211j2yKIMyturzwa4yUfCU379WgxRXOSlBuRU7PDlKGsQxKSSShvmSsQwhx5KfmRPhyOufZ6ibcGRW01mGNp5+FURdJJIkjyZ2DOaO6ZVoGp3PjHNZCEkuuDUIBXTWGQabJIsfepFr0VoXoKYtgkKngxN02zMvH15QeeB0svN7kfeJ5HJ4kpIBAMMuhgI9LFx7HZO6JtcW1T7/t457aAOMi1MgGXclaJ2VUVDSzRWRIIMzjgxKNg41Y0Vn44OWxREnod2I6qebiepdJ2bIzqbi2kaEQ9DON84Qa1SId9+Zml37aHG3CW93kiHAe11R74sYtX62N+yQh0UoI8vrhrAT1d4UQ/6b3/teey2peQ0gpWMujR9zOj49L2Mgjbh7OUVKw2Ul4e6vLrDbU1vFeMUUIgXeWcW1RXtBLNM4JJlVNEgkyKdmfNqzlim4Wc2mQhsbecRnECSpIqhsL47kl0pbaBOueSED5hFAmJqQMhYd5dbqm3SfhuBLvNNvQ5ElduqfEMqqaGyhGjv15gfD3pedLYcTytoIQaVYOfvhSh9HcIqQkVpLtXkoWay4NEkZzqIwnk5JxbZACtnoJZWP5nQ8OEUIwyDUbnRglw8TbKw+NynhY9PAiN+7vZ3ptNWhxBTgFQQkhjvc4tcBfFEL8HPA1jmVhvPf/ybNf3qsP5zyjouWNzTxESj783l9EUHfHJV94b5+iDoMFf+iNNWKtuDzISLTk3rTm1uGM7+wVRDKcznfnFVXTkkWa6xspw7lhXLZcWst592KXW6OKu5MSZw3dKIx/yGJwzWJ8xTHb8fopebaGUPfpSEg1+FNEMqfF90p2H+d5JDBvw8tPWDixH7s+FkEur5Ug1oLGwk99aot7wxprHd/Zn7HRSdjsJCDAOceF9ZwLwOG84tv3ZkRKkkaKzW6IgDtdzbX1/ChyehIxnGXj/l4I5vudXluZ264Ap4ug/smHfv8doAv8xLHLVk7mHxPLDSfT99+KeR02HNM4vvDePpmWOB8kDd/cmXCpn7IzLrkzqnj3QoduqkmU5Jt3J+xOKu5OKjyeNFJ04xRFQy+N6cURe5OaL353n91RE2oxC0fw0WJTXvYfGftg5PA0FAtz15f5g7AUPhiCsi+NoVyEUUtFYS+BTqbRKLSQOOfZmzRM65ZLg5x2VDIrW761O+an3tlmu5cRaxXMfqswDbmrFWmkGJeGfqqxzh85iTyNGE67cX8vBHMe0msrc9sV4BQE5b3/w6d9MCHERe/9ve9tSa8XnrThTG3of1rPYya1oZdpdicG6zyNcTTW0ksTmqbg9rhiXlta66lMOE3PKseeKFBCcnUzZzhvOJg11NZRuUeVecezZSc1vT4JDzfSvoxoCSS1dFBvbKjNLRuBDTCroXWGLPas6SREwNOKwlje2OpybbPDvDF46+hkCiXBWId1YWhhqiV+MW+9tWEkilYy9DYtiEFJiEQYm/IwMZxm4/5eCea8pNdW5rYrnLlR9yn4EnDlGT/mK40nbTiZUkRKHo3omJZBcee9J9KSWCnKpuWjcRXGj2cRtm6ojeD6IKbykoNZRaoF792boYTH4sGHQYMnpe+OE9bSP+9lJ56zoCXMrVrvSCrjqBa9WkkMuQ/iCADrHIkW1M5yd2pJI8WwaJEdyay2RIu+pkRLppWhbsPBYi1PmDUt+1UFQnBjI+NSP7zfrXWUjaFo7VE9Ml8YwUrup/5iJZ+4cX8cgjmeDjxP6bWVue3rjWdNUKtP0ilxfEOIleRiLwmbjwxjNIaNIVOKf+KtDf6f7x4wqxsOJw1vXuhwc1RysZvwznaH/29nQlW17M+DEmxUWyIl+GhY0jiBFI43N3JGZcP+rEUuFAHHR60/do0v4g9xDhFrGM7Dq/fAWkcgF4MOY+fJIoWONN1EM6sdW3nMtY0Oo7Ll23cnXN/o8OZWh/UsZpDGfGo75c645I2tDuPSkLWSeW25NEjIYs3utOaSFGghOJg3pFqSL2aHHcwbPuHvp+yMC3nUK2sZeXLy1/esBHNSOvBFpddWfU4rPAnPmqBep8P2x8bxDaFdpOpGhaExjmFRszOusM6hpOQPvrXOpV7Ct4sGJ+AbOxNubHSYFIa1WcPnLvX56u0R727n/M4HY27uz6mt51IvAi/xCv7xzQnOWoy3Ieo6q1vra4RMBI89TxhEiIHhPNgjRXo5aiMmzyMiHYZKdrKYP3B1gFSCr98ac2Mz59Ig42I/BU9oxlWSTqLpJhGtdXywP2cti0ljfZSCuzxI2ezGzGtL0QRi2OzGR/6MzjnGZUvTOu4uRsMve+UedsV/EsEcvz3w2HTg806vfb+FGCucfzxrglrhKTheH5BScm9ccm9Sc30j47Co+fLNIRvdmEgrytrym9/YpfWOaDFw0HnBBwdzfvyNhP1ZxQdRcJj40vszRkVNvxNRN45Z1TIqgz/coBNhhMfUjlY61rsR1rSMv0d59qsIv+Buufgn0YGwunFoRM5iycwYmhnITpj9tJVHdDLN1UFGrBRb3ZhZZdiZlOAFF/vpAxGNMx4hxFHj7TIFB5BFmm6sEVLg3X2rKbMgJyUEvSxiUjbcGZdcGWSPON+nkXps/eZhUtjsxo9NB0ZKPrf02nkQYqxw/rEiqBeM4/UBYxctqYv0kXMe4z3T2uIrs/DNU8RaoqXgo8M5qQ7OEe8fzkAKxkXDwaSmWhjKOsImI3VMl5am9YzKFokgTzXzyjAvW5Cg7P2JtCsE1Cw8AQlefW6R58wTxWYvpW4d1jsudGOubuSUdcutw4IL/ZTNPOFTF3v8o+8c4L0jiTWf2OqwP2/Y7iXsTmtqYxCEpmznPFKJoxRcpORR5OOMOyKcSMng8tE6ellwuI+1wjvPnXEZUo4nbPIP129OIoX9aRDRv+h603kRYqxwvvGsCeqDZ/x4rxyO1wfCSTEUg4QIo9dHs5omsURacli0rKUeYwSH8xoJzOqWw7Il3wuD8mrnmJcN88biHNTW0Y0VZWNIIo1wLUqFaaxta3CEvifv7o+WWBFUQMp9J4skCgXV2kEkIY+C4ME6T9tAFgtuHpQ47+il4e99e1hQtQYhIIvDAxyWLRcjRaQfFDY0CzJZpuC2lzXIxwggrqxl3J1UTMqGWCvW8yiMpPdhc4enb/Ink4LjQi8J6s4XKOc+T0KMFc4vPhZBCSHWeNTN/Kb3/iefyapeYRyvDzjj2OgkdFPNR4cl7+/PsQiGRcNaHpNogdaSd7Y6fHtvjreeom25Pkgp2pY8VRzMGhSC65sdIlWyMy6pjWMtS9jsJ+yMKvanobiuZHCMEATTVHh9hRAPIyaIIzIdLJsu5jGtl8zqBmthb2YY1TO6scKjmJSWSAdXca0kRWP5/XtT7ow1W92UbJG+25tUXOgm9+tDC+JI5f0U3EkDKh+uxeSJ5kdurHNnXCJ88PVbRmWn3eQfRwqdWNPZ0A+Q4vMWL6z6nFY4Dc46buOngL8BvHX8YsIhfFXdPCUerg8Y49gZllzfyPEIBKEgf3095csfTokjyVvrOZfXU0ZlSCmpOpyibw/nlNbTTzVvbnW50A0OFPuzhr1JSZ4I3ogTnIe7o4bGedZjxVx5qEI/1OteihIE4h7kCi9hs5Pw7oUOX7k9RUtNpB1dqUEKepHAe0XrHFXj0ULwie2cSGm0hEnZ8s52l7Lxiz4nQg3qhI1XSgEObk/rU9Vi8kTz9lb3AeK4JMWpN/mnkcKSPF+UeGHV57TC03DWCOqvAH8T+HWgePbLeX1w/DTdOMeoMqzlCWXjOJw33Dwo2Rs3zOuWvVmEFoL39wumlaGfxnRTmFYt49LSSzRFY7HOUBnHlUHOdi/jy7dGHBYhwpLCIYQnEuCVxLUh9Wdf4xBqaWXUAFJBHmsirbi+0UEikUIw6Gi0VBzMKprK4hLJpy526GYRRWUoTUjpeRwX+hlVE+i+n2pa47ixkdGJH/81O2st5uG60lk3+afd/kWLF1Z9Tis8CWclqCve+z/9XFbyGkN4qNuwEQghGM4rqtaihWejl/DNnSmbnZhBovn0xR7TpiXRkq+PKy6vpXRjjVRwZ1iy1omJteDWuMRaR9MaFBIvxVGoWzQtSaSoW0vCg15zrzqW86cyQEVQtMEpopcIkliwnkV89lKPNNbUNghMppVhuxdTNEHNVznH9TxCIFjTKeudmEv9lAvdlEEnIo81ZWMYWoeWkluj8rFRyLOoxZx1k3/S7VfihRXOE85KUO8LIa56728/l9W8hqhaG7zzvGdvVtFNI9JIs5FLOrFkWLRoAa1x9AYRSgu244T9oqExllgruplmb1LhCenC2+Oa0bRmWjmk0LTWBZ85KUmEwwkJzmJ9mBrr7NmtjY5jSXwvAxyBkNI0pPVcCyKCREqED4rJnXFNLFs2ugmzukV4QCmurmkiLUkjSSeN+Pz1DX70jXWmlcE7j1KSa+s5sZJ8cDCnvxmh5cmWRUucthbzohpaV+KFFc4TzkpQfxr4H4QQv+S9/8bzWNDrhCPvNSV4e7vDejembFrW8h63hiUHk4px2dAsxrUrIShqy+2iReEZ5BF5pNif1Xx4WHJpkHB1kPPteyNGZYOxjk6iaawk0VA00IsiKutQQnNYNjj/oFDi45DNy0JOMYGQGxvk48MmGMLmmUZEEZPaEs1aBBVChSGB1zc7xKphvSPJowglBF4IPnupzxubXS4PctZzeyRe2J3WbHZjWusYVydbFj2Mp6XdXmRD60q8sMJ5gjzj7X8D+KeArwoh7PGf09xZCPGLQogvCyG+KIT4ihDi315c/jkhxBcWl31VCPHPH7vPC73uWcO5UCx37tFtfJlOSbQijTTX1jKur3f5gYs9tBQkWpBGio08Io0khTHEUvDZy32ubGRs91KQknFpSCJJN4mIIkkcKT55ZcBnr64hkRjjaK0DH9y0My2hbajcowMGo+f1h3jOiHn62lugWri0N0147c6BMQ6wSMApKI1FAEVjuNLP+JnPXGCzm9PNIvIs4u3tLpUNvoatDQq8REmSODRT744r9mY13nnyWOOd52DeIJ7A5FKKEyfjHq8JdRJNpMTCVeL5HQuWhHl94SbxLMnwSd+HFVZ4GGeNoE7tbP4whBAK+ATwU977mRDiKvCeEOLvAX8X+FPe+78lhPgM8AUhxOeBOy/yOu/9rY/7+k7CaUcnOO+50EvYGZUY54kTzeevD/jt9wx9DwdFS45nVlnWujFJpChaDTR0Y4HAk2rJzrikdZ6ydkgRhg02tkF4x6yAsvXs2uao1wceTe09q1lOLwopQYV4mnXLxY9ejJ1XhGbcNnIUVcNaNyVXilgpNvMEbz23DgvmraOfK6rW8cZmzlqWMioa9qY1b292KVvDvLY47xcDIAWDTNNajiyL1jsRjXNId7YU3ferJvQ8xAsra6MVzoozEZT3/jc/7hN57y3wHxy76ICwr7xLqFn/7cXtvimE+IfAvwj89gu+7r/5uK/vYZxGDfVAT5T3XOynbPUSIgRf/mjEmxc63B6VOB8czD99qUdRO1LliJVnf1rhPfTSiJ1JSdM6cGFDrFrLrVnDuLahb0YsIoenrDsiiAhelvPtcuotBMJ5UihvF7eNjjm51wBtUPC9udFh1ji2eglxLJmVLbVxbA4irJPMSsfetEWiUEpwoZsAcDALBq9KCu4MS+rGsjVIuDbISBNNVRvuTmtSVaIWbhGn3Zgfrgk1xmKswxj31HrUeTJiXVkbrfBxcNY+KAn8MQKpPPAN897/p2d87r8I/C3CeI7veu+P74nfAd4G9l/wdQ9ACPELwC8A3Lhx40wv7rQn35PqD4fzmnndcjBvOZzUKC3Z6iZoLdgZl3w0nPPmZoc3t3K+szsLm2llySLJ4aylMYadSYNwDuFgWt0fVf40eEKU8bL0Rh3PUD5tzQnhtRXH7hQT/PYSJbk9rrnYizDec3dUUbWGNJJMSk8ntigJdWtYyyMuD1K0Ukgp2OzEzOrgJBEryfp6xoVuzN1pzbYIdanLg5RuGp15Yz5+iJmUwUjY47k3qdnuJ7yx2TmR7M5btLJSB67wcXDWFN9/B/wMITN0m3CA/Rng753lQYQQfxa4Cvw88C/z6N5iCNkb8YKvewDe+78K/FWAH/uxHztTUHEWNdTxdIpznoNpjTGerU5MJGFvUrM/bXhnq0MWa5LIUxjLsGgpWof0nljBrGkpgOG8RggNSmDsg4MIn4YXNWb9+4Gl7+BSCCIJ5JTHEZcGGdY5LnZyrl3I+GCv4G5riHREJ9aUjWWzo8jTiI08Qit15JMX3pMQ3URKIoSgn8doKdnqJygE3TRUyD7OxpxGimtrGd89mJFF4fkAhvOGWEne2Ow804GFzwMrdeAKHwdnFUn8UeCHgb8M/Pfe+58F/n1CBHIqCCH+C+AHgJ/33jfALeDh8OTG4vIXfd0zw/LkWxvHcF4zrw1bnTA6oaoMw6JmXraPFIutD2mZS2sph0VL0TjK1qAlHBQNRdNwOGs4nFZoKdAS3tufMyxb9icN+9OGqjXM64a7Bw3j9uWJhp43loGTIkRTiiCSCA3LwfXBS8estngpFpJ/Gd6ruqV1gku9hEgrtnsJaaTuv8+t48644vaopLGOojZhxEakUYsNGXjqxvw4EYFfsKpSEr34WQ45tP7Rz1CIVsLXW0pBY2wQynyfsPw7tdYzrw2t9St14ApPxVkjKOG9nwghPiQQFcB/C7wH/EdPvGNID/4qsA78Me/98rD+BcALIX7We/8bQoi3gX+OIGn/7gu+7pmjWfQ5Geu4PSzRCr58a4S1kESSH3tzg89dXSNWEus9dWPZndTsjErWsgjdicA5dqcVv/nNOQezGu8FV9ZTLvZSitowLmqEEGzkEa137E5ajHErq4+HYAnRkwa8DORUOpiWLf005p3NnFFpkSrU8WIlmVUN/UjT4FnLIz51aUCkJbvTmhsLgoqVJIkUP3h1wLg2GOO5M6r4/LXBmWTbT0rLqYXnn/f+iOycCyMxHia749GKcZ6745LGhNteXsu+b6m+lbXRCmfFWQlqb7Ghfx345cVlioeMYx+DPwr8IvBF4P8W979Uv0IQKPwVIcSyjvWvee9/H0AI8UKve1ZwznNnVDKuWvppxN1JRVnXvHd3glaaTqLpJ5ov3RyRaUmWRHjn2RlX5LEgiTU7w4Jbi+m509pQGkcUa4qq4f39OZWxrGWaPBJMa4+3HhkJtAzjyld4FArQCurFqJGOgutrCVmiSJMI8HRiTVE3eKUYZJqLaznGOBKtUFI8kqZbRiydNKKTRVSN5daw5N6kYli0XBqkRxuz8CEacs6fKS0npeDKWnZkLAuw3U+4vJY9tvn3zqjk9rAkUoIbmzl64dv3vFN9TxJnrKyNVjgLzkpQfxv4k977PyGE6Akh/g6BnL76tDt67/8+T67T//Rj7veVF3nds4JdnHTF4ktqnedw2rA3aVjvhv6mJFZMasOtYcGnLw8wAm6NCkZFzXje0s80F7oJWklGZUsn1tSVQSpFbSxFbdnsxCRRjJctjQHnHMY6Wh/e3Fe5pnRW9CVYcT/Vl2nIYoh08ODb7KY452itZVobvBesd1JubHTYnVTszSo+2J9zeZCCFEd9Ta0JZKKEQGtB1VgSLeml0QMuEnZBQidFSKcREaSR4hMXe7y51QE4sW9qiTRSXF3LMNbRS6Oj2z1vYcJ5E2es8HLjTDUo7/2f897/icWvPw/MCKq4f+NZL+xlx8MpmcN5g5SCzsJEdFy0FHWLWxSNtZLcHhXcGVVMS4uQkuG8YXdWh76aJGJcNTTW0rQtxhqG85pZ1bLRjbAWispQ1KEhtGFFTksIoK+hl0s6OlgcSQAfzHJLYxnOa26NZuzPSm4PC7Z7KWudGICvfjSkqA1F47h1OOe33j+gbAy3RiVFbY5UepGWVI3h3qTm4iKNp1WwOmoXEdHDDbfLJmrhOUrLweNrVVIKkkiRLNKLT0KkJLFWuEWN6nkLE74fTcUrvNr42AMLvfcfAf/6s1vKq4XjKZmdcYlSgm4SsX59nW/dnTCvWz488Hzuah+P4HBasTdtGGSaWdUyLFvqxnClm5DFmrcudJneMsyrBmM83VQjveNwXpFGMZsdTdsa5rVg/rJ1274ASAWlcSBCo+6gIzDGEwlB1Xoi6dib1CRa0Ukkn7u+Tt1afu/DITvjik9c7PP5a31ujyvq1jCpQkR7Z1QiILhMxJrWJng/Pzr5LUkBeCRCmpQ1HxzOkYua0VoeMSraZ2Yx9KJti1ZS8hWeNZ5KUEKI/xf4qvf+3xJC/AMe08Ppvf9nnvXiXnYsUzI3NnK2dqeMqxYpJBupZli3fHq7R5pG3Doo+NrOlP1ZRRJJBnlCJ1V8sDun9o5J0fCZKwPyGL52YeFOIAAAIABJREFUe8J7u7Og8KsMaaqIaLDWMK6CbHpVfXoU1oCKYbujcUhK46jxdONgFLs1CKq8SArGpaWsW4wTXNvMSLTkk5dybg1Lpk2LQHIwaxBCsNVNHpBPKy+4OEixHub1fVKIlHyk4fZg3vDGRr7YyC3DecP19fxIlRepRxMcZ22+fZHChJWUfIVnjdNEUP8QeH/x/y/w8pgMnAsc91iTQtBay+68oTaOcWM5rAy9LOINAYNUsTevqeqGWeO5sdWhn0UMZw23xwUCya1RRW0tsdQkWlCUlkgGA9TKQvs9OpO/ihAE371cwLB0bHY0FwcZZdPStC2Nc4AgUoqLvYRIt3y4V5Akigu9lE4ScTBt+O7enPVOwqVBTBJJdoYVG3nMtY2c3Wl9FKW8sdk5UmUeJ4Xj0Yxzns1ujIOFW4inbh3dVB/ZJj1cw/m49Z0XJUxYGc2u8KzxVILy3v+Hx/7/K893Oa8eqtZy67Bgb9Yg8NydVKSxYtoYiqZlWllUF+5Oa1rnOJy3dGLJpUHCtfWM7+7PSSLF7nBOL43IYomxmrJ1aCWg9RhHEEiwip5OggSECCQuhaP1lktdzbgWtLWm9kFyjhAMy4ZPbnf5iXe3UAimjUUK2J1U3J3UrGeKPNbcGZYUjeXtCx2c80E4wYPChYdJ4Xg0Izx8NCzYGZULkhFY6/jmzoS3tjpkWj+g5APOXfPtSVhJyVd4ljhNiu8XTvNAC9eFFY5hWTROIkkeK26PCorGst6J2eokTCpD0VhujRx4aK1nLY3QahFpTSsEgm4qaSzUdYtCUrWWsrZ4F4r81q7C2odx3JfPAMJBUUOsIZGKWe2ItGJctGwPYiaF5d6oZKsT8weur7PRychiybe/tYeQgPd89lqPpvVIoJso+mnEsGz4X79xl09f6tFJohDVyJOjmgfSc0qw1Uu4NSyR0qGkYLufht8XKbHjNRx4tIZ1Xus7Kyn5Cs8Kp0nx/cfH/i+Ba8CcYPZ6kWARtM/CEuh1hzGOytij06PznkQq+qnmtoPGOuaV4UI/obWOy92Ucdnw4agkU4pBHnE4b7De07QNeRKxM6rYnxS8V7SMq4ppEWZGaAnRSrF3Iizhw7qMKA2QihBJ7Zc1jQuNt8Y69kcN/TymaB3r3Zi6DTZTH40KtBLUzWLekxTEUnJ5PSWREdu9mElliaRkWpnQ7/aYqOak9Fwn1lxdz1AC4kgFA9jFZwYeVd2t6jsrvG44TYrvreX/hRB/Ctgg9EI5IUQM/JcEgnrtMSoavnRzyN60RgrBu9sdlBTsToJbqwM285i9acWH+3MmleEPffoCUkuE8+xXNQd3m0VzryJRijSuGZeGm4cFh7MK63wYMmggz2FqVuT0OBwnKA90E3BCIFHUraeyjlnVEmvN9Qsx73YTpJQ473Desz9t2O6nfPH9Q7QSGC9452LO/qThylpErBWtacgSBQKEFDjjjqKaZcQk/OPTc1fWMu6OK8rGIoXgc1cHjIr2AYHFSTWsVX1nhdcBZ5WZ/xLwtvfeAXjvGyHEnwS+BvyZZ724lwnGOL52a8ysMmx1E4xzfLA/J080vVSjhGQjj/jazoRBorl9WBJHkt96/5CffGeTu5MKJQXjuqZqHN47tjuCr3w0pWzd0Syjqr0vhKiL0zmUv65YpviWRNVaiCNPLAWVcWRSUUUaoSBSQX03Lu0Df1MF9DKNlpL9aUVRO/bmDcZ7RmULi1lPWkq880dRzfGIyTlP6xxZHMZzHE/PnVSz6afRiTWcVX1nhdcNZyWoHo8299rF5a81GueorSVazAVSUjErDR64tp4jhaAxMbdGJWVj2Oyn9LOIg2nNBwdztnspzjtuDcujzWd31jCvLZ1UUxlDZT2NC+S0EkM8HUvH8uWHvGohjQTGQ2Usk8qQRIr1bsywaDiY1fTTmMtrGZGSbHVj7k5K9iYVo9KwnmuG84ZBGvPWZgchoW491gk6icL6EOXAgxFTYyx3Dyq6sQ6pvIdTd4uazdIoVglxosT8+G1XWOF1wFkJ6v8Afk0I8e957w+FEDkhxfdUq6NXHbGUJEpR1GEEgnEOKSHR8qjugAAlBRbIYkXZGNJYYfE01nN1PWVSWd7fmzKrDZmWNNYzG5cMZzWj6kExhCJswCtZ+ePhgEjdl5orHcxd1zKNwHOpnzLINKOyJYkU1zcyPHB7XNIYy7hs6CaKm4cF1loO5y1/5AcukSWay/2UsrVcX8/RWh4dLFrrHhA0xFqx2YmpraN1/sT03MoiaIUVHsVZCeoXgb9PMI0dEyKnHeBnn/XCXjZoLfnctcEDNahPXuyy3U95b3eGdR4h4Eeur/G/f2sXY1oaJ9juBJuiT13sMq5aUi3pJJrceRrjubKe8f7+jLKFRECzYKjjtZUVngznQ/1pIw5ilYuDDrdH1WKmVsPb2x1++lPbfOpin91ZGOUeyZYPD+f83geH9DLNjfWMzV7MqDCMipZeGoEI5POw7dBJDatZrLm2luEFj6TnzuP8phVWOA8468j3feAnhBA/SphAewD8I+999TwW97JhLY/56XcvHKn4IiW5NSp5YyOnsY7dScVHo4o3NnLuDAvKxvDRYYtUgg/2CnqZop9ormzkDOctXQFtG5wk2sZQtAbvYFYF5V4swhs4XbHUE+EcdJKIbhpTNIZJ2ZBqQaIVQvogoEhjdiY17+1NaFpHEmmsdZStZdyEcRxz40MkZBxpJLGOE4UKj2tY1frktN3KImiFFU7GWUe+D4C/BPyI9/7zi8t+Tgjxkff+d5/HAl82aC3pLjaiZaoniRS7s5okUuzNGvqJQghBFknuli3rnZi1juBw3tAYz3Y/wVnPzrAkiiQbnRiE4KODOdPWHKn2jIfy+/dSzz0U0I3CWIrrGxmtE1hn2Z3WdJKIjU7MpX5Ka2A8b0hTjUQxawxp7LgzKkkSjW8sAoExjo1c84M3BtxY75Bq9VjSOYugYWURtMIKJ+OsKb4/T/jeXzp22Qz4z1il+R7BcuNpWot1HiVCs+W8CdY6WRzjfU3VWtaimF6iqY3j9rBgkGqc8JR1y7Q2aC3x3gZPOQHKwypsPRk5YeaT0nCxG9HrJlzf6DKtDd1EsD9tiLRCK4EQ0M8ijA+TXrd6MY2xNG0Yo7HdTSmalkllUA5q46maQHIC2OoldGJ9IgGdVtCwsghaYYWTcVaC+iOEce3fPXbZPwD+xjNb0SuE44Pj6tahJGzkEaN5i5SCUVlTtoayDfODJlWLs7DZS5BSsJ5qDuYN/VhROc+1rRxNybx1zOvv96s7n4iBOA7u5RtZwlY/4caFLpGS9DKNAHopTCpD2QZPvEtrOZudmCzRaCHY6CSUTUvZmiBWiRTXN3LWOjFKCmrjEALujStuDUuurmdc+R4n1a4k5Cus8CjOSlDSe1+KB1MPnrAvrHACYiW5upaxnkXszUK0dHtUIgV0Is3VtYwPD+bcG5f0Ek2aa9JIcG9c8Y3bU6Z1Sx5p1jqaSAiMcLRNqEGt8Ch6STgYdBLN2xe6eDy7oxLrPFc3c3qpJtYSZEXdWi72UzY7EcZ5eknEqGzZ7MTQjVnPY+6Mw5DCtTzmUi9DSIEgvD9ppIJNkeCZiBpeRwn5Wd3ZV3i9cFaCuieEuMCD4rF/Abjz7Jb06mApHS5bw8GsIY/DmAYpBR8NS2Ip2BlXOOu4M28Q3rPZTRikmm/sTNibVnghmVUt3923SOGZVbAKnk6GAmKtSWO4NMjweAZ5TFk7PJbDWYuSgsY4hJdc7Md85soaeaoX1lGCH7q2RqQlt0cl/bWIy+s563mMx/PmZofboxLjfDDqJbQNxJGibOxK1HBGrKT1KzwNZ5qoC/znwK8BsRDi54UQfx74m8B//cxX9pJjKR0W3jMuWiIFt0YV88Ywrw1X+wk7o0BeDqiNw0uBE3B3UnFrVOKEZy3TKCkpG09tIV59fx8LC0jpWMsjlAzTXG8eFuyXDVmsqRqLVgRCwnEwr/nihwfcGxZUJpDO/rFpj1pJtJTc2OwgkJTt0ug3pjWe2li2ugnumIPECqfDavruCqfBWWXm/5MQQgOfIhDTbeCXvferGtRDsN5TtoZx2XJ3UpFoybwydJJAON00orUWaz1OgBQS56CsWy4MUpK7EkWw5DHW4j3EEpwAbV9v/73jTuXHEQPDwuFcTS+zKCmpjGM9VQznhnHVhshHhr4kCwgEs8azkUWMK8Mge9SYVUvB1fWMqwuHCYCrjWFvWmOdx/uT5eYrPB4raf35xXlKu5555Lv3/teBX38Oa3mlIDwczBoSJcIocGMpWkMSCZxzGCcQgLFhOJ2xjqKxVC1Ma0s3jzAmzHqqrSOLwFkoX3Nz2JhA1JEOzhDOgXHQScA7uNCP2egkbHYSJnXL7qTiXmvRUqK1wDpPqhUX+gm704ZBpsFbDoqaNecZpBGRko+o6q6sZSTH0k+9NKIT63PzRX7ZsJLWn0+ct7TrWfugEuBfBT4PdI9f573/489wXS89vIDNTkzRWvJYMTSWS70MhKd1Nb/z/phOpvFCMK9bGhPUfC5SGGv5xIU+FsO9Uc1altFaz91xyeghdjru2P2qIwYiCUkEa3lEIiUGkErQTWPyOLgw9LOINNbcGgcxihaefibpZQl5pKmMw3sQeKz1VAbujWvKxvGjb2wgpSCVT1fVnVbUcNoT6Xk6uT5vrKT15w/n0dHkrBHUrwF/iDD6fdUj+gQoIchiTTfVSCG4bjNq66hrw7Q0vLnZofWeVEBhPbujgvVeigS+cWfMt+5NiLUAT0jvaU0/iRkXzQNmsWrx8yr68XVUiI5av3AjB7SHbqbY6KVkkcY6TzfVDDLNYdHSGkvTWsrGEUlAa/CgpGI9j+lnMf1UYS3Ma4NWks9c6dNNNIlWRI9pvP24OO2J9LydXF8EVtL684XzmHY9K0H9s8Cnvfej57GYVwnHT4itD+mLi72U3x0OyWIZht8hMc6ylkXcOvQoCbMqmMw2jaVuHNYLvHcoqTicNY8o+FogI6T9XqXycgbgQHpIAKHC6xMiTBCeloa3tnts5TFvXeiyMy7Jk5ZZ3ZAoxUcHMzppQjcRFJVj3hrGRcO1QUYWaXo9zXo3Zi2LubYYqT6v71e2ngVhnPZEeh5Pri8Kr6O0/rziPKZdz0pQY1YGBqfGwyfE1jo0AuMElfFMy4qDecMbGzmdWFNXlu/sThnOayrnWEs0tXNY4ymrGuGCK/fDRPQqhrIlMNDQywAvaa2ndR68oDSOi5nmkxe6fPbqgFllmNaGS2spn9ruobXgf/7SDmVr8F4yixrMBLYHKf1uzNW1DOs81kKeKIzzeO/Z7iVESp5IGDujkisLkcRpSeO0J9LzeHJd4fXDeUy7npWg/gzw14UQv+C9nz+PBb1qOH5CjJBs9RN2JiXN4kTf0ZrSODY6CXdGM6z3RHI5VM8yqQ3eB+cI416tKOlJSID1XLA9yNmbGYRweGvwXuA9dBONUoLv7s1wHoomiE3uTRryWLHZS/hgr8U6y3oeo6UgFpJ5Y6gaS1E73t7uUDSOLJJ0kogra9mJ4zKM89waljStRWnJlUFGnjz9q3PaE+l5PLmu8HrivKVdz0pQ/xfw7wCHQog7HNsvvfdvP8N1vZKQUnBtI+fupMIYj1SCjU7M+/fmbGzEbPRyHJKbrUVJw6SsqZqQ+tPi1awznQQJIKBuBbmWKAEHZYsDeomkk0UYa/nW3Skf7M3pxIobF7pkWnLroOCnPnmBz1zq88ZGzj/+8BDvYKubkKcR0gvujGs+c6kLQnBjI6N1nhuLmU7wIGFIIdgZleA949pgipB++5Eb608lqdOeSM/jyXWF1xfnKe16VoL6O8BXgD8LFM9+Oa8GlmosETJSCB/SOACZVlzsJ1TGEEUdDosa5x0f7M/oJAolPELBRkfT1DVWhd6nRApa6195ibkEIiBLwHjH7qzhExc7KBEGPTbGsdWJGc4dWx1DYz2ZEHx4MONKL2VcG5rGoATkieYHrq1xOG250A+2ReuZ5nDeEmmFdR6tJNY7/LHv43HCaIyhbR1yMegwjzXTsuXOqOTtC92nkshpT6Tn7eT6ovAqKBdfhddwXnFWgvqE9/4nn8tKXhEc2Rs1hoN5Qy/VHM4bnINYS9JIsDOq+MqtEQezGuPC0Ly7o4Y0CjY8Sgqs93gRRrx7D6Xxr3TxTxLUiAKIdRBHeIKpa23gcj8jiSXTqsEL0BK6qSZPJMYFqeO9ac20sXzp1pA00mx2Ei73M65upoyLhgudmDTWzGvHvXHFxX5KbcIoDSXEAwcLJQXX1jJa62idY1g0R+m3OJIgOHWN6Cyu5ufl5Poi8CooF1+F13CecVaC+o4Q4i3v/fvPZTUvOZbFdSWhaC2JEtw8KPDeE2lJL1N8+eYQIRXvXuhyZ1iwN6ko24ZICsrG0NqwQVsPjYW6DQq9V7nXSRCipliFZtuNjqZuDEhB00JjDI2Bw6pmM4/Z6CbMMovwgs1Ows64wDpBUTX08yQ4crSOb94Zc209482NDl8rLcZ5GuO4tpFxb1yRlC2NDeKISdUyKtoj38TNThxOwz5EwDujirqx9POYzTxGSrmqEX0PeBWUi6/CazjvOCtB/SXgfxRC/ArwdR6sQd18lgt7GbFUY0VCYp0n1oramOCeDTjrMc7Ttg0fHTZUxjJtLForskhSl47/v71zj5E0qw7779zv/dWru/o1M93TM7MvdmF3DcvahoDFJhCBiYniEGLkJEr+iLEV25Kl4ERySGRHyH9EQYkiB1kb4hiJJDKxHWEnUhQcGRRsIMAGCF6zC+x7nj39rK7X9zr546vu6enp7u3p6emq7rk/qdRd96u6db6v6t7znXPPPaedpHSTMrqsM1h0OmnKaXuqIqVUUqfrDo7nkiQFfYFKGBI6ZSqiLM/wXZdmJSD0XWbGYtZ7Ke12RsXzOD8Z8fxCB8cYAr/M/N42gmcMoeswNx7RS3PmmzGFKo4Rzo3HhIFLlhV85+IqZ8cj2v2c0DWs9zPSvLRmp2oBzdjjaquPiBC7DhemIjsJ3QEnIXLxJJzDqHO7Cuo/Dv7+t23tSjnv3NNsLK5vTIDrvYS1bk6uGb4jVEMHDLx2vUs19GhEHi9cb9NpJWSF0s9Kq8kTBmsfJ6+shuGGQnIHzzPAcQDHo1kJWXdTKrGLbzxqsUM/zXH8gEbsc26qRprn1EKPmarP42cb1H2f6+0+3bSgnShLaz3SQaaIJM9x3bIq8eXV3iBUHWbqIXHoASCmTIGklAUlN9aZoEzi++1XVzAiuFK+brGdEAc9Tt9hDah7mZMQuXgSzmHUua1t86pqdnnsa5SKiCciHxWRVEQ+vKX9XSLyjIh8W0S+LiJvG9axO8EYYboW0E8LAke42kqYHw+ZqPqEnsPV1YSHZ+pUA4crqx2W1xPGQw+lVE5ZXk7emUJvYD2dpEJbLlB1b6w1KeA5pYVoBDwjzI5FBJ7LubGIeuxhCmh1Mhqxx2QtIMsLkqSgGrpM1SOalZD5iZjzE1XePD9BPTAYp5w0ZmoxS+2MpXYfxxiemB/n3ESF8xMVIs8ly0vbVIvyhkIok8T2kgzXKW80Li13WeokrHZTXlvp0elnBK5BBjWgbPbtg7ERiJLmZSXjNNdjF7l4Es5h1LntZLF3yM9Qzktf2WgQkTHg94GfUNUvi8hTwOdE5ALl/Hxkx1T1jiITe+mNUuCOY3hopkqzEoCU7r2lTp9OktPqZfjGwKAO0fK6Q57mdLh5n5OhnMhPQr69jei8JIPAgfW8PKf1ga8vcCD2HdKi4JEzVTxxWel0SBLloVNVmrVwUHvJcGY84sJEBdd1qIcul1d7oDBd93ns7Dgr3RRX4I2zDbppDoUyNxZthpEDt4R1PzrbYKWTUgmczTWo0DO8VLQREUSEZsVjuZsxWStwROjnhXXn3AEnIXLxJJzDKHOkCkpVPwkgIh/Y0vxe4DlV/fLgNV8QkcvAu4H4iI/94UHPbeuCaeR7JGnOy0sdxmIf1xiSImelnbKw3uO+yRoLnR5Xl7sst3vkKJ3s1k24x10pbaWgzA7hwS21Mgyli08VXri2RqMS4jvCwlof34XT4z4V38NzHPIsox66uK7DZDUoq9oijFd9ltcTFMF1hMk44HtX26R5zuJ6ynQjYqoWbn7mThNLPfTIVXlwqtwekGUFy+0UxxHWOilX1rp0kpwsLzfu5qqcadwowWG5fU5C5OJJOIdR5agtqJ24D/jBtrYfDNrjIz52EyLyEeAjAPPz83uexNYF06JQxAjjscdaN2WpnZIVBb0ko5vkJEVOxXepRy7X1xNa3eJEh5BvJeXGhmOPG2tSSx0oinWSAk6nBau9gmpoyI0HRcFzV1Z5fK5BrRYyUwtpxh6uU7rjxAhjUZlXb7IW8M1XlnnuSos4cJipV8iKgu9ebjEe+TdZUdsnlu3PHRFmGiHL7YTxqo/nCtdafVxH8FzDdOxxrdVn3nPsnbPFchcYBQUl3Fp/LuOGh+soj92Eqj4NPA3w5JNP7rnYsLFg2uokXG8nZFpQFDARe5xuBASuw3NXVvnGK8tkSUY7y7m0tM5yOyfwwL0H6zyllCmNMsofolKG1y93UkRAcej2Uq60ughCP4cHagEr3YzlTsZKN2UscjnVCGn1UmqhRyP2edNcg0urPRqxh+MIU/WI9V5GUhS4t7HsaoxwbqKC7xjSvGAi9jndiGjE/qbV1e7bqC2L5W4xCr6J14Dt5sn8oP2ojx2Y0kXk8q2Lq7y61Obaah/fUZ690mJxPeGl6+u8cL3NpaUOz19v8/3LLV5ZTOkXZXbue005bZBSBk3EfpnOSfMyo7uI0utnpfusgMBzacYeQeASeIY4cJiueax2M753ZZ3PP3uV566u0UtzxkKfufGYidhjph5uBkH45vZ/7qHncG6iwoWpKhcmq1QCr1wXNGKjtiyWu8woKKjPAY+LyGMAIvIjwMPA54dw7MAUhbLUSaj4hk5acL3d50vfX6SbpCRFzvNXWvzZpRU6/ZRqYIgCwR1EtK3fq9qJcm1KgVroEIUehTKwc4VeXtBNMozAw6driGNYWO2V2R4QupnS6qdUQpfIMyyu97m80sUY4bG5BmkBy+2EXlbw6GzjJvfe7WCM4DkG1zU2astiOUKG7uJT1VUR+RDwWyKilMbE+zdqTh31sYOSq5JnBReXu/iuy3jssdJOafVyXlvq8PJym0tLXXpZzmo/KxfzpdzndJKCIW4Hhxt7otr9HM8oY7GhEQWM133WO32accD56Rq+43Bpucf5ibi8YI7S6WcYI4gBTxzcgSsuV2Us9nnbhQmSosA35sDKaTs2astiOTqGoqBU9altz/8Y+OFdXnukxw6KI2V5jG6W0cuUpU6OMWVZiE6SsdLJqEUu7X7K4nqPJAHXA+ckxJAfgI0fngweFQ+C0MUzhrHY54GJGstBQBg6hK7D6bGIwDVMVQMQKcu7F0roGLJcaVY8VMFzbqQgcl1zW2tO+8VGbVksR8PQLaiThOsYQsfFdx2M8SiKgpVuykKrjxYFtdBnNcyJuxlJfsOv53Ly16A8ysiU2Lmx5lZQJn2NXUBcmnFIJXRphB5raUat6nKqHjIWl6mGHMfBcwxTtYD5iQr3T9Z4ebHN9XZCkinT9YDTYzYFkcVyUrAK6pDIVYl8lx97cIo/v9KiKAqurqe8/b5xvrfQ5tmLBddW+8zUfAIX/OUOvuPQTnLWuhmdtJywC8ovxaGcxLeHGx43NhLBRkG5tHRmLKTbz7i0luFLaUFGvksQOpyph+SiTNR8VtsZj8+Nlxtkqz79pCAKyuszOx4TeA6B5/DImQbpICPE7VS7vV1sSQWL5eixCuqQ2AgzjwOXN8+P0emlNNcDzjSrpFpmxV5oLdDpZWSp0IhCVAt6WU6aloqo5kGelV9Kwo2SE8fJA+hSpuowBlwXfANx6OO7wlgloOI7NEKPeKGFa4Rq5NPrlcUIK7HPE/PjqEI9cnnH/VMocKXVQ3NFHLmlmq0xQmD2lw/voErGllSwWIaDVVCHhDHCWOzxnYur5EU5CTYrPiicbkQkac6Dp2qgBVfWulxdTbje6hJ7HkGQ4RSQ5mU2BfGg7sJK93gpJwBfwPdLy2i6FmEcIXaF2WbMfLOCYxxmxgLuP1Wj1clYWO/S8QxnJypMVHwKQET4oflxfN/BEeG+oHrH1stBlYwtqWCxDA+roA6JolBWOinnJuLNjOadJGetm3J9rU8/Vx6YqhAMsg70koKVttJJM/IMigIKLS2pNKWsgzTsk7pNPEqLrxEa8rwMHT/fjDDigCMghkbkM1OLmanFXF3r0s9yZsdjLkzWmG6EFFowFvusdlLWuxmOY5iuBXh3EIW3Xckkac5ryx3ONyuvG903CiUVrHvRcq9iFdQhsTGRRe6NS5qkBZ08RxwIxPDKQp/lXkaWFCjQTiDLSqsp05sV0nFce0qBVGGtW5ZQf+l6j8BzeGCqhuu4vHG2QcUrw8HnxmMEJS+UWuBxvZ2w1F5luh6Rpkoc5PiuQ+wbnnmlw6lGiGvMgdxrW5VML81ZaPVp98vkh3PNeM/+hl1SwboXLfcyo7BR90SwdSIDyAb7cZY6KbHvkmRKsxbw6mIbcQp6Sc5Y7JEXOWlx/KylrWyfqtN8kE8qz8gK5dJyh8AV1roppxoRWQ6tbooixL6LY4SK75AV8MpiBxGoRz6OwA+utTECkefgOXKgEhcb300yUE6oUg3LjBSv198wSypstfwqgXvg87dYjivWgjokNiayrSUcZuohV9f6pYumKO/AQ1cw4pAXYIwh043KSMeLCBCBIIB2HxhYgEEZD0LgeRQUKIYrrT6nx3LWOilZVjA3HnGqHpZpjFS5uNJDB4UCjYFWL2c8zjFGSAZVbY0plcxB3Gsb381ryx3a/Yxq6DJZDfBdZ1+59Ia1OXcU3IsY0Pr0AAASTklEQVQWyzCxCuoQ2T6RAUzXAq6sdbm00qWXZmR5wYsLLVY6Cd1+jsjxM2MbPjRDIRVDr1/QCJR66FII5LniOYZq6BJ6LkagGZdRfLXAZbGd8sRklShwmR2PubzaI/QMceAyFrmlxek5JEVBkZaKe6Lib1qnB3WvhZ7D+WYFFALP4LvObfU3jM25w3YvWizDxiqou4gxwtlmzLVWn7lmxIsLbeYmqrx0fZ2xikdaZDipIfQL0mS0N+sGlJaR78CZRkQUuLxptkarm5NpQadf4LmGqufiusL1Vp/pWkBWFDw+P04l8HjTmQauyGbAQxy4PDE/zjMvL+O6QuA53D/hs9hJmYh9RISHz9RZ62a0+9nmGsxBLRjXNcw1Y66s9g6lv7vNTlb5KMtrsRw2VkEdIjstaHuuoVnxMB1lMvIJvLLQ3rdeXWGtm+CQ0k9Hz8lX4eY8gb4LhcCpms8js2O89dwYpxsRzWpZlD7JcvpZznI3J8sLXrq+zrnxGBzDdMUn8D1izyHX0jLYiEyLA5cnLzS5tNIFAdcYnpis4rlm0502FvmH5l47brn0jpu8FsthYhXUIbHbfpkz9ZDlTkrgGGpVn/XlLldaPcZCQz3yWV7vEfiQ94cfuRdQuhtDBxpVh3Y/Z71XpiOabvj4jstkzef8ZMTseGXgbjLUIw8jwjdeXkYEKr7HA1M1rrdTzjUDrq2nPHwqJNey1HoyuDZbFfl9U7vvdTps99pxy6V33OS1WA4Lq6AOid0WtDOUiYpPJ82JPAcjsNZJWGz1CXyHyVpMK2kN1YJygVMNh15SUItc8qygwBCFDvWKoRo4zI4FTFQixqs+P3R2nLPjMZ0kY7Gd0s0KkqwgyQuqgUuWFzTigExhfrLCfAGzzZjQLa/NK0udHTe+3s3S6XYvkcVy/LAK6pDYbUHbN4bIL0OEe0mG65a55RxRXlvustpJEBWKIagooUzeOlXzKBBqoUsjcjjdiGj3c5oVHyOG+cmI2POohC7r/YxmJUBEcI0hcA2+YzBaKl7PKUPH2/0Mzxh8MagrhG65QTnNiyOPTLN7iSyW44lVUIfE1gXtXpKiUgYTuG6ZCeGrLy5ybb3HaidFjNBNClzXJVEly/XIIvkEmAygXvUQMYzHDkkq+K4wVYuoRx7VwKGbFASuIVdlvVfQiITZsZhHzzRoVHyKQrm40uV04LLQ6lMIzDUjjDEIwnqa8cB0Fd22sH/UkWk2VZHFcnyxCuoQCT2H6VpQLvgD11p9ThnBMUKeF0SD6LVXF7s4Aq4UnK4HeEYIOn2udu5OsERAGX0XBjBRCZmfqNDLlNM1j34BV1a7gHC2GTFRi7i21sVzhChwGI9DJmseoefytvMThGH5kymktEZcI8yORfSznKlqwGwjIkNxEXQw/2913R11ZJrdS2SxHF+sgjpEikK51uoT+c6mdXBltcdUxWelW24QjX2PAqXVyWhELu1+wWzDcM1xaPU7tO8gUsKhDLQIgTN1Q2IgSwqmagGuMfiex7vfOEMt9Lm03CYrFN9ziDyX6WrAeC3g8nIXFCZqIc1qQDV0OTdRYbWTkqGkebG5jrOhaAotraDTYyG+5+Czt1vtKCPT7F4ii+X4YhXUIbLb3boKjMc+3TQj04J64OKIEHsOoZfRSZQHp6ukhbK61qWbQ2ufm6IcyiStzqC0hXGEyUrA7ETI5eWEM9MhD8zUUIVCC842K/jGkKOcaYT0UuXsRMpL1ztEnqEaubzzgQleWuoSuQ4iQq+fkRfK1dUeMsjosKFwdlI0+3GrHVVkmt1LZLEcX6yCOkR2u1sPXYe5ZsRCq8daNyV0HdK8ICuUauThOgIiPH62xv97VfH6CW4Kjha0UyhyCD3IizKxbFGA54PrwKl6SKub0Ix9To3HTNQipmo+c42Ib766ylwzphq69NOCdpKXVo7j0KwHpLmy3u8R+z5PnguZqgUsrSdM1iIqoc+zF9dI8oLAM0zVfMJtluGGwtmuaEbNrWb3ElksxxOroA6R3e7WXddwbqKCa4R2L2O1E5SKy3foZ2WOOtcR5psxNcfhucUuBqWfK6FjWO/nVAPD8npCL1c8o7T6OZFvcB2XZs1wul7hR++fYKIastzpEwc+8xMx7aRAJKeT5MzUAyaqIQBLix3OjkVMVHyurfXICoh9l9mzMSudFNcYHpttMF71iVyHy2s93MFa0uspnFF0q9m9RBbL8cMqqENmt7v10HOYG4/JC2V+ssIXn79Gp5/Rz5X7Z6pcb6X00pxmo8JPXZgk8BycQsmNMFX18UR4YbnDiwttnru8Sq4FSaacaUQstRPmJ8uNs82KSzfNeGiqSpIXrHRSVBUTCJHvYkwZHj5R8UkH60MzjYipWkDkOqhA1XdRYVP+otDbUjjWrWaxWA4Dq6DuArvdrXtOmaTUEXhkps6fX14FpIz+qxvGY49a4OK4BhRmxyJmx0s32mtLHZr9gunzIQ+dqtHuZnzj5WVOjcWcacbEnstaL2U9yXn4VJ0o8pisBPiuodPLMI7QCD2KQsm0IPJd5saiTUWU5AWvrXRvTtPkmc3zuV2FY91qFovlTrEK6gjZOtFP1gPGWwH1vKASeBRBgSC004LJwZrRdD0kDsqv6PxEBQQ8EYK2S1rJaPVSJmsBke9RDxw6WcHbz01wrZPQ62esdFPyokBFqAYOS52EFxfazNRDzk3eqCa7n6CGgygc61azWCx3wnGr9HBsKYoyRNt3DPPNmIdm6vyVR8/w2NwYFd8hTZUkK5ipB8SeS+w7LLaTzeJ0rltWoVURKoFDXgiPnR0jV8ExkCO89VyTOPbKEh+tPvXQxThlgaZuorx1fpz5iRh/kP1hg1yVrCjTwpbBDYZCy9RAWzFG8BxjrSGLxXIkWAvqCNhtT1DuKpXAY65pmKz6LKyniAi5KqcaEfkgf9yGFbLVinlwClTgLXNKhuIbs2kReW5ZGj3yHLKi4NJyDzHgew6eY24p0pdmpcXkiOB7hvHIwxhj9wpZLJahYhXUXWY399ncWMSV1R6Ba6gELonv0uoXTFV9Qt+lKBQdlKbYyi1uMwf8bZ/pDPLkAfiugykL1+LsUPRvY3Px6UaZdb2f5lxKcp6YH7eWksViGSpWQd0FtmbO3m1PUFLcnDTV9xwmaz55Ad0k3zUQYb9ZuSeqPgutPgBjkQ8C3fTWfjfkq4Yese+Sq9JP8s2ighaLxTIsrII6ZLa786Zrwa5Zzre3R97NkXXbFdB+snJvfY0Ak7WAil9+zTsptu17lopccRzr3rNYLMPH3iYfIlvdeZWgLLFxbVD6PM2Vdj8jzXVz8+6pRrhj+06BCDv1fWW1txlEsdNrfNewuJ4Auwc4bEQWbpfDuvcsFsuwsRbUIbKbO89zza6bd/cbur2f9EEHTTFk9yxZLJZRxCqoQ2SvFD+77Qna716h/aQPupMUQ3bPksViGTWsi+8QuZvusv30bd11FovlJHHPWlAi8i7gX1FegwT4BVX9yp32ezfdZfvp27rrLBbLSeGeVFAiMgb8PvATqvplEXkK+JyIXFDVzp32fzfdZfvp27rrLBbLSeBedfG9F3hOVb8MoKpfAC4D7x6mUBaLxWK5wT1pQQH3AT/Y1vaDQfsmIvIR4CODp30R+c4RyHanTALXhy3EPrByHi5WzsPnuMh6XOR8w+2+4V5VUALk29oytlmUqvo08DSAiHxdVZ88GvEOjpXzcLFyHi7HRU44PrIeJzlv9z33qovvNWB+W9v8oN1isVgsI8C9qqA+BzwuIo8BiMiPAA8Dnx+qVBaLxWLZ5J508anqqoh8CPgtEVFK9977VXVlj7c9fTTS3TFWzsPFynm4HBc54fjIemLlFN1WlM5isVgsllHgXnXxWSwWi2XEsQrKYrFYLCOJVVAWi8ViGUmsgnodRORdIvKMiHxbRL4uIm8btkw7ISI/KyLfGsj4bRH5B8OWaS9E5E0isiQivzpsWfZCRP6hiHxHRL42uLbxsGXaioj88uD7/urg768MW6YNRMQTkY+KSCoiH97SPlJjag85R2pM7SbnluMjMab2kvN2x9M9GcW3X+52zr7DQkQc4EHgHaq6LiKzwPdF5HOqenHI4t3C4Lr+W+A/D1uWvRCRXwMC4M2qmg3k7g1ZrE1E5MeAXwIeV9VFEWkCz4jI11R1FLZM/AygwGYS5hEdUzvJOYpj6hY5NxixMbWjnAcZT9aC2ptjkbNPVXNV/aiqrg+aFikztDt7vG0oiIgBPg38CrAwZHF2ZTB43gu0gD8RkS8Cb1fVYriS3cTG9Rsf/K1TjumrwxHnZlT1k6r6CW7O2jJyY2onOUdxTO1yPUduTO0k50HHk1VQe7OvnH0jyL8GfkdVXxm2IDvwceDzqvqnwxbkdXgr8CbgNVX9UUpL5T+JyP3DFesGqvpdylyRXxOR54FvAr+kqt8ermR7YsfU4XMcxtSBxpNVUHuzr5x9o4SIfByYBX5h2LJsR0Q+CMyr6m8MW5Z9MA28pKqfBlDV/wv8L+B9Q5VqCyLyEPCbwF9W1YeAJ4FfH/aazutgx9QhcozG1IHGk12D2pvXgPdsa5sHfncIsrwuIvIvgfuBD6pqMmx5duDHgUdEZMM3PQfl4q6qfmh4Yu3INWBtW1vBrZPrMPmrwJdU9esAqvp9EfmvwE+zwzrFiGDH1OFyXMbUgcbTyN61jAjHImefiBgR+U3gLPChER1IqOrfV9W3qurbVPVtwKeAT43YQNrgfwP3icg7AUTkDcBfAv7nUKW6meeBd4rIaQARqTNY4xmqVHtjx9QhcozG1IHGk7Wg9uCAOfuGwfuBnwW+DnxJZLOa7sdU9Y+GJtUxRlUTEXk/8KnBInQB/F1VfWHIom2iqn8gIg8A/0NEukAM/HdKt99IYsfUvclBx5PNxWexWCyWkcS6+CwWi8UyklgFZbFYLJaRxCooi8VisYwkVkFZLBaLZSSxCspisVgsI4lVUBaLxWIZSayCslgsFstIYhWUxXIEiMhLIvJzg///hYh89Qg+87Mi8tm7/TkWy93CZpKwWI6el4DJI/ic5ynr8lgsxxKroCyWI0ZVP3lEn/Oxo/gci+VuYV18lnseEVER+elB2fTrIvIVEZkdtD0vItdE5DMiEm15z18Xkf8jIq8M3HdPD4qyISJTIvJ7IrIqIhdF5GNsGWsi8nER+cKW5/9kUFr8sohcEpFPiYg7OPbUQL73DT5vYVB+/J37OK/PiMhv77cfEZkUkU+KyPdF5IqIfFNE3jc49qiI/JGUJcUXB+d39m5dQ4sFrIKyWDb4R8DfUtVJ4FvAfwE+ALwFeAPwFPB3AAbJTv8e8AFVnQfeSJmo9d8M+vpDylLWpykL8TUps2LvxjzwN1T1NPAIZZbn7dmoPzZ4zRRlRvBPH/A8d+xHRDzKjOLTwF9Q1VPATwKOiJwC/gT4LKVr8hTwLPDHIhJu6fswr6HFAqpqH/ZxTz8o12k+sOX5+4EUqG9p+x3gNwb/fxdYAq5seVwH/oyyaKACU1ve61CWuv65wfOPA1/YJsP9lPWdfpGyXMY/HbQ/NejvsS2vfXzQ1nyd8/oM8Nv76Qf4MGW9nuoO/fxj4JltbRul5T942Ndw2L8H+xidh12DslhKvrPl/w5wSVW3FljrAtXB//cBf1tVb4mQE5G/Cayq6sJGm6rmIrK404eKyDzwe8A68EXKgn4XKZXaXvIB1Ckn+dtht37uA15U1fUd3nMOuKnUuaoWIvIqN5dqP5RraLFsYF18FkvJ9mi3vaLfLgI/vMuxRaAmIhsTMSLiAzO7vP6fA6+q6l9U1V9V1U8BtxTHU9VDicbbo5+LwPw2l90Gr1AqqU0GNX3OAi9u7X77x+0hyl7X0GIBrIKyWA7CrwM/LyJ/bVB51RGR94jITwJ/ClwGPiEi/mDC/wSw08QPEAB1EQlFxBWRXwTefSRncTO/S1mW+9+LyCSAiJwdWIT/AbggIh8ZnK8H/BrQplxvOwh7XUOLBbAKymK5bVT13wE/TzlJXwNeBX6Z0hLqAj8OPEjprnuW0g23o4sP+GeUSuoy8AKlVfIHd1P+nVDVNvAeoA98Q0SuUlbnXVfVq8A7gJ+iXCe6AjwKvEtV+wf8vF2v4R2eiuUEYSvqWiwWi2UksUESFssxRkS+DFzY4dBbVPXyUctjsRwm1oKyWCwWy0hi16AsFovFMpJYBWWxWCyWkcQqKIvFYrGMJFZBWSwWi2UksQrKYrFYLCOJVVAWi8ViGUn+Pwhj+8o2CMY7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"rooms_per_household\"] = housing[\"total_rooms\"]/housing[\"households\"]\n",
    "housing[\"bedrooms_per_room\"] = housing[\"total_bedrooms\"]/housing[\"total_rooms\"]\n",
    "housing[\"population_per_household\"]=housing[\"population\"]/housing[\"households\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": [
    "housing.plot(kind=\"scatter\", x=\"rooms_per_household\", y=\"median_house_value\",\n",
    "             alpha=0.2)\n",
    "plt.axis([0, 5, 0, 520000])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 머신러닝 알고리즘을 위한 데이터 준비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"median_house_value\", axis=1) # 훈련 세트를 위해 레이블 삭제\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <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",
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       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
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       "  </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",
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       "    <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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows = housing[housing.isnull().any(axis=1)].head()\n",
    "sample_incomplete_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows.dropna(subset=[\"total_bedrooms\"])    # 옵션 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <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",
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       "      <th>median_income</th>\n",
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       "      <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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows.drop(\"total_bedrooms\", axis=1)       # 옵션 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
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       "  <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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = housing[\"total_bedrooms\"].median()\n",
    "sample_incomplete_rows[\"total_bedrooms\"].fillna(median, inplace=True) # 옵션 3\n",
    "sample_incomplete_rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sklearn.preprocessing.Imputer` 클래스는 사이킷런 0.20 버전에서 사용 중지 경고가 발생하고 0.22 버전에서 삭제될 예정입니다. 대신 추가된 `sklearn.impute.SimpleImputer` 클래스를 사용합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import Imputer\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy=\"median\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "중간값이 수치형 특성에서만 계산될 수 있기 때문에 텍스트 특성을 삭제합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num = housing.drop('ocean_proximity', axis=1)\n",
    "# 다른 방법: housing_num = housing.select_dtypes(include=[np.number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(add_indicator=False, copy=True, fill_value=None,\n",
       "              missing_values=nan, strategy='median', verbose=0)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "각 특성의 중간 값이 수동으로 계산한 것과 같은지 확인해 보세요:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "훈련 세트 변환:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
    "                          index = list(housing.index.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.loc[sample_incomplete_rows.index.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'median'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0</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>1</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>2</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>3</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>4</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",
       "0    -121.89     37.29                38.0       1568.0           351.0   \n",
       "1    -121.93     37.05                14.0        679.0           108.0   \n",
       "2    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3    -119.61     36.31                25.0       1847.0           371.0   \n",
       "4    -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "   population  households  median_income  \n",
       "0       710.0       339.0         2.7042  \n",
       "1       306.0       113.0         6.4214  \n",
       "2       936.0       462.0         2.8621  \n",
       "3      1460.0       353.0         1.8839  \n",
       "4      4459.0      1463.0         3.0347  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 범주형 입력 특성인 `ocean_proximity`을 전처리합니다:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 책에 실린 방법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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\n",
       "Name: ocean_proximity, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "판다스의 `factorize()` 메소드는 문자열 범주형 특성을 머신러닝 알고리즘이 다루기 쉬운 숫자 범주형 특성으로 변환시켜 줍니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 2, 0, 2, 0, 2, 0, 0])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoded, housing_categories = housing_cat.factorize()\n",
    "housing_cat_encoded[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['<1H OCEAN', 'NEAR OCEAN', 'INLAND', 'NEAR BAY', 'ISLAND'], dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`OneHotEncoder`를 사용하여 범주형 값을 원-핫 벡터로 변경합니다:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "사이킷런 0.20 버전에서 OneHotEncoder의 동작 방식이 변경되었습니다. 종전에는 0~최댓값 사이의 정수를 카테고리로 인식했지만 앞으로는 정수나 문자열에 상관없이 고유한 값만을 카테고리로 인식합니다. 경고 메세지를 피하기 위해 `categories` 매개변수를 `auto`로 설정합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "encoder = OneHotEncoder(categories='auto')\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1,1))\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`OneHotEncoder`는 기본적으로 희소 행렬을 반환합니다. 필요하면 밀집 배열로 변환할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [PR #9151](https://github.com/scikit-learn/scikit-learn/pull/9151)에서 가져온 CategoricalEncoder 클래스의 정의.\n",
    "# 이 클래스는 사이킷런 0.20에 포함될 예정입니다.\n",
    "\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.utils import check_array\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from scipy import sparse\n",
    "\n",
    "class CategoricalEncoder(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"Encode categorical features as a numeric array.\n",
    "    The input to this transformer should be a matrix of integers or strings,\n",
    "    denoting the values taken on by categorical (discrete) features.\n",
    "    The features can be encoded using a one-hot aka one-of-K scheme\n",
    "    (``encoding='onehot'``, the default) or converted to ordinal integers\n",
    "    (``encoding='ordinal'``).\n",
    "    This encoding is needed for feeding categorical data to many scikit-learn\n",
    "    estimators, notably linear models and SVMs with the standard kernels.\n",
    "    Read more in the :ref:`User Guide <preprocessing_categorical_features>`.\n",
    "    Parameters\n",
    "    ----------\n",
    "    encoding : str, 'onehot', 'onehot-dense' or 'ordinal'\n",
    "        The type of encoding to use (default is 'onehot'):\n",
    "        - 'onehot': encode the features using a one-hot aka one-of-K scheme\n",
    "          (or also called 'dummy' encoding). This creates a binary column for\n",
    "          each category and returns a sparse matrix.\n",
    "        - 'onehot-dense': the same as 'onehot' but returns a dense array\n",
    "          instead of a sparse matrix.\n",
    "        - 'ordinal': encode the features as ordinal integers. This results in\n",
    "          a single column of integers (0 to n_categories - 1) per feature.\n",
    "    categories : 'auto' or a list of lists/arrays of values.\n",
    "        Categories (unique values) per feature:\n",
    "        - 'auto' : Determine categories automatically from the training data.\n",
    "        - list : ``categories[i]`` holds the categories expected in the ith\n",
    "          column. The passed categories are sorted before encoding the data\n",
    "          (used categories can be found in the ``categories_`` attribute).\n",
    "    dtype : number type, default np.float64\n",
    "        Desired dtype of output.\n",
    "    handle_unknown : 'error' (default) or 'ignore'\n",
    "        Whether to raise an error or ignore if a unknown categorical feature is\n",
    "        present during transform (default is to raise). When this is parameter\n",
    "        is set to 'ignore' and an unknown category is encountered during\n",
    "        transform, the resulting one-hot encoded columns for this feature\n",
    "        will be all zeros.\n",
    "        Ignoring unknown categories is not supported for\n",
    "        ``encoding='ordinal'``.\n",
    "    Attributes\n",
    "    ----------\n",
    "    categories_ : list of arrays\n",
    "        The categories of each feature determined during fitting. When\n",
    "        categories were specified manually, this holds the sorted categories\n",
    "        (in order corresponding with output of `transform`).\n",
    "    Examples\n",
    "    --------\n",
    "    Given a dataset with three features and two samples, we let the encoder\n",
    "    find the maximum value per feature and transform the data to a binary\n",
    "    one-hot encoding.\n",
    "    >>> from sklearn.preprocessing import CategoricalEncoder\n",
    "    >>> enc = CategoricalEncoder(handle_unknown='ignore')\n",
    "    >>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])\n",
    "    ... # doctest: +ELLIPSIS\n",
    "    CategoricalEncoder(categories='auto', dtype=<... 'numpy.float64'>,\n",
    "              encoding='onehot', handle_unknown='ignore')\n",
    "    >>> enc.transform([[0, 1, 1], [1, 0, 4]]).toarray()\n",
    "    array([[ 1.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.],\n",
    "           [ 0.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.]])\n",
    "    See also\n",
    "    --------\n",
    "    sklearn.preprocessing.OneHotEncoder : performs a one-hot encoding of\n",
    "      integer ordinal features. The ``OneHotEncoder assumes`` that input\n",
    "      features take on values in the range ``[0, max(feature)]`` instead of\n",
    "      using the unique values.\n",
    "    sklearn.feature_extraction.DictVectorizer : performs a one-hot encoding of\n",
    "      dictionary items (also handles string-valued features).\n",
    "    sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot\n",
    "      encoding of dictionary items or strings.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, encoding='onehot', categories='auto', dtype=np.float64,\n",
    "                 handle_unknown='error'):\n",
    "        self.encoding = encoding\n",
    "        self.categories = categories\n",
    "        self.dtype = dtype\n",
    "        self.handle_unknown = handle_unknown\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        \"\"\"Fit the CategoricalEncoder to X.\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like, shape [n_samples, n_feature]\n",
    "            The data to determine the categories of each feature.\n",
    "        Returns\n",
    "        -------\n",
    "        self\n",
    "        \"\"\"\n",
    "\n",
    "        if self.encoding not in ['onehot', 'onehot-dense', 'ordinal']:\n",
    "            template = (\"encoding should be either 'onehot', 'onehot-dense' \"\n",
    "                        \"or 'ordinal', got %s\")\n",
    "            raise ValueError(template % self.handle_unknown)\n",
    "\n",
    "        if self.handle_unknown not in ['error', 'ignore']:\n",
    "            template = (\"handle_unknown should be either 'error' or \"\n",
    "                        \"'ignore', got %s\")\n",
    "            raise ValueError(template % self.handle_unknown)\n",
    "\n",
    "        if self.encoding == 'ordinal' and self.handle_unknown == 'ignore':\n",
    "            raise ValueError(\"handle_unknown='ignore' is not supported for\"\n",
    "                             \" encoding='ordinal'\")\n",
    "\n",
    "        X = check_array(X, dtype=np.object, accept_sparse='csc', copy=True)\n",
    "        n_samples, n_features = X.shape\n",
    "\n",
    "        self._label_encoders_ = [LabelEncoder() for _ in range(n_features)]\n",
    "\n",
    "        for i in range(n_features):\n",
    "            le = self._label_encoders_[i]\n",
    "            Xi = X[:, i]\n",
    "            if self.categories == 'auto':\n",
    "                le.fit(Xi)\n",
    "            else:\n",
    "                valid_mask = np.in1d(Xi, self.categories[i])\n",
    "                if not np.all(valid_mask):\n",
    "                    if self.handle_unknown == 'error':\n",
    "                        diff = np.unique(Xi[~valid_mask])\n",
    "                        msg = (\"Found unknown categories {0} in column {1}\"\n",
    "                               \" during fit\".format(diff, i))\n",
    "                        raise ValueError(msg)\n",
    "                le.classes_ = np.array(np.sort(self.categories[i]))\n",
    "\n",
    "        self.categories_ = [le.classes_ for le in self._label_encoders_]\n",
    "\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        \"\"\"Transform X using one-hot encoding.\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like, shape [n_samples, n_features]\n",
    "            The data to encode.\n",
    "        Returns\n",
    "        -------\n",
    "        X_out : sparse matrix or a 2-d array\n",
    "            Transformed input.\n",
    "        \"\"\"\n",
    "        X = check_array(X, accept_sparse='csc', dtype=np.object, copy=True)\n",
    "        n_samples, n_features = X.shape\n",
    "        X_int = np.zeros_like(X, dtype=np.int)\n",
    "        X_mask = np.ones_like(X, dtype=np.bool)\n",
    "\n",
    "        for i in range(n_features):\n",
    "            valid_mask = np.in1d(X[:, i], self.categories_[i])\n",
    "\n",
    "            if not np.all(valid_mask):\n",
    "                if self.handle_unknown == 'error':\n",
    "                    diff = np.unique(X[~valid_mask, i])\n",
    "                    msg = (\"Found unknown categories {0} in column {1}\"\n",
    "                           \" during transform\".format(diff, i))\n",
    "                    raise ValueError(msg)\n",
    "                else:\n",
    "                    # Set the problematic rows to an acceptable value and\n",
    "                    # continue `The rows are marked `X_mask` and will be\n",
    "                    # removed later.\n",
    "                    X_mask[:, i] = valid_mask\n",
    "                    X[:, i][~valid_mask] = self.categories_[i][0]\n",
    "            X_int[:, i] = self._label_encoders_[i].transform(X[:, i])\n",
    "\n",
    "        if self.encoding == 'ordinal':\n",
    "            return X_int.astype(self.dtype, copy=False)\n",
    "\n",
    "        mask = X_mask.ravel()\n",
    "        n_values = [cats.shape[0] for cats in self.categories_]\n",
    "        n_values = np.array([0] + n_values)\n",
    "        indices = np.cumsum(n_values)\n",
    "\n",
    "        column_indices = (X_int + indices[:-1]).ravel()[mask]\n",
    "        row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),\n",
    "                                n_features)[mask]\n",
    "        data = np.ones(n_samples * n_features)[mask]\n",
    "\n",
    "        out = sparse.csc_matrix((data, (row_indices, column_indices)),\n",
    "                                shape=(n_samples, indices[-1]),\n",
    "                                dtype=self.dtype).tocsr()\n",
    "        if self.encoding == 'onehot-dense':\n",
    "            return out.toarray()\n",
    "        else:\n",
    "            return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`CategoricalEncoder`는 하나 이상의 특성을 가진 2D 배열을 기대합니다. 따라서 `housing_cat`을 2D 배열로 바꾸어 주어야 합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from sklearn.preprocessing import CategoricalEncoder # Scikit-Learn 0.20에서 추가 예정\n",
    "\n",
    "cat_encoder = CategoricalEncoder()\n",
    "housing_cat_reshaped = housing_cat.values.reshape(-1, 1)\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat_reshaped)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "사이킷런 0.20 개발 브랜치에 있던 `CategoricalEncoder`는 새로운 `OneHotEncoder`와 `OrdinalEncoder`로 나뉘었습니다. `OneHotEncoder`로 문자열로 된 범주형 변수도 변환할 수 있습니다:"
   ]
  },
  {
   "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\n",
    "\n",
    "cat_encoder = OneHotEncoder(categories='auto')\n",
    "housing_cat_reshaped = housing_cat.values.reshape(-1, 1)\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat_reshaped)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "기본 인코딩은 원-핫 벡터이고 희소 행렬로 반환됩니다. `toarray()` 메소드를 사용하여 밀집 배열로 바꿀 수 있습니다:"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "또는 encoding 매개변수를 `\"onehot-dense\"`로 지정하여 희소 행렬대신 밀집 행렬을 얻을 수 있습니다. 0.20 버전의 `OneHotEncoder`는 `sparse=Fasle` 옵션을 주어 밀집 행렬을 얻을 수 있습니다:"
   ]
  },
  {
   "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": [
    "# cat_encoder = CategoricalEncoder(encoding=\"onehot-dense\")\n",
    "cat_encoder = OneHotEncoder(categories='auto', sparse=False)\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat_reshaped)\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_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### future_encoders.py를 사용한 새로운 방법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing[['ocean_proximity']]\n",
    "housing_cat.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "주의: 번역서는 판다스의 `Series.factorize()` 메서드를 사용하여 문자열 범주형 특성을 정수로 인코딩합니다. 사이킷런 0.20에 추가될 `OrdinalEncoder` 클래스(PR #10521)는 입력 특성(레이블 `y`가 아니라 `X`)을 위해 설계되었고 파이프라인(나중에 이 노트북에서 나옵니다)과 잘 작동되기 때문에 더 좋은 방법입니다. 지금은 `future_encoders.py` 파일에서 임포트하지만 사이킷런 0.20 버전이 릴리스되면 `sklearn.preprocessing`에서 바로 임포팅할 수 있습니다.\n",
    "\n",
    "0.20 버전 릴리스에 맞추어 `sklearn.preprocessing`에서 임포트합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from future_encoders import OrdinalEncoder\n",
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_encoder = OrdinalEncoder()\n",
    "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoded[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_encoder.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "주의: 번역서는 `CategoricalEncoder`를 사용하여 각 범주형 값을 원-핫 벡터로 변경합니다. 이 클래스는 `OrdinalEncoder`와 새로운 `OneHotEncoder`로 리팩토링되었습니다. 지금은 `OneHotEncoder`가 정수형 범주 입력만 다룰 수 있지만 사이킷런 0.20에서는 문자열 범주 입력도 다룰 수 있을 것입니다(PR #10521). 지금은 `future_encoders.py` 파일에서 임포트하지만 사이킷런 0.20 버전이 릴리스되면 `sklearn.preprocessing`에서 바로 임포팅할 수 있습니다.\n",
    "\n",
    "0.20 버전 릴리스에 맞추어 `sklearn.preprocessing`에서 임포트합니다(사실 우리는 이미 위에서 0.20 버전의 `OneHotEncoder`를 사용했습니다)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from future_encoders import OneHotEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "cat_encoder = OneHotEncoder(categories='auto')\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "기본적으로 `OneHotEncoder` 클래스는 희소 행렬을 반환하지만 필요하면 `toarray()` 메서드를 호출하여 밀집 배열로 바꿀 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "또는 `OneHotEncoder` 객체를 만들 때 `sparse=False`로 지정하면 됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder = OneHotEncoder(categories='auto', sparse=False)\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 다시 책의 내용이 이어집니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "추가 특성을 위해 나만의 변환기를 만들겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# 컬럼 인덱스\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6\n",
    "\n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y=None):\n",
    "        return self  # nothing else to do\n",
    "    def transform(self, X, y=None):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_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.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>0</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>1</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>2</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>3</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>4</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 population  \\\n",
       "0   -121.89    37.29                 38        1568            351        710   \n",
       "1   -121.93    37.05                 14         679            108        306   \n",
       "2    -117.2    32.77                 31        1952            471        936   \n",
       "3   -119.61    36.31                 25        1847            371       1460   \n",
       "4   -118.59    34.23                 17        6592           1525       4459   \n",
       "\n",
       "  households median_income ocean_proximity rooms_per_household  \\\n",
       "0        339        2.7042       <1H OCEAN             4.62537   \n",
       "1        113        6.4214       <1H OCEAN             6.00885   \n",
       "2        462        2.8621      NEAR OCEAN             4.22511   \n",
       "3        353        1.8839          INLAND             5.23229   \n",
       "4       1463        3.0347       <1H OCEAN             4.50581   \n",
       "\n",
       "  population_per_household  \n",
       "0                   2.0944  \n",
       "1                  2.70796  \n",
       "2                  2.02597  \n",
       "3                  4.13598  \n",
       "4                  3.04785  "
      ]
     },
     "execution_count": 80,
     "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",
    "housing_extra_attribs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "수치 특성을 전처리하기 위한 파이프라인을 만듭니다(0.20 버전에 새로 추가된 `SimpleImputer` 클래스로 변경합니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "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": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### future_encoders.py를 사용한 방법 ==========================\n",
    "사이킷런의 0.20 버전에 포함될 `ColumnTransformer`를 사용하면 책의 예제에서처럼 `DataFrameSelector`와 `FeatureUnion`을 사용하지 않고 간단히 전체 파이프라인을 만들 수 있습니다. 아직 사이킷런 0.20 버전이 릴리스되기 전이므로 여기서는 future_encoders.py에 `ColumnTransformer`를 넣어 놓고 사용합니다.\n",
    "\n",
    "사이킷런 0.20 버전에 추가된 `sklearn.compose.ColumnTransformer`로 코드를 변경합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from future_encoders import ColumnTransformer\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(categories='auto'), cat_attribs),\n",
    "    ])\n",
    "\n",
    "housing_prepared = full_pipeline.fit_transform(housing)\n",
    "housing_prepared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ===================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "판단스 DataFrame 컬럼의 일부를 선택하는 변환기를 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# 사이킷런이 DataFrame을 바로 사용하지 못하므로\n",
    "# 수치형이나 범주형 컬럼을 선택하는 클래스를 만듭니다.\n",
    "class DataFrameSelector(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": "markdown",
   "metadata": {},
   "source": [
    "하나의 큰 파이프라인에 이들을 모두 결합하여 수치형과 범주형 특성을 전처리합니다:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0.20 버전에 추가된 `SimpleImputer`를 사용합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(num_attribs)),\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(cat_attribs)),\n",
    "        ('cat_encoder', CategoricalEncoder(encoding=\"onehot-dense\")),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### future_encoders.py를 사용한 방법 =========================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(cat_attribs)),\n",
    "        ('cat_encoder', OneHotEncoder(categories='auto', sparse=False)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ===================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "사이킷런 0.20 버전에 추가된 `ColumnTransformer`로 만든 `full_pipline`을 사용합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "# full_pipeline = FeatureUnion(transformer_list=[\n",
    "#         (\"num_pipeline\", num_pipeline),\n",
    "#         (\"cat_pipeline\", cat_pipeline),\n",
    "#     ])\n",
    "full_pipeline = ColumnTransformer([\n",
    "        (\"num_pipeline\", num_pipeline, num_attribs),\n",
    "        (\"cat_encoder\", OneHotEncoder(categories='auto'), cat_attribs),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = full_pipeline.fit_transform(housing)\n",
    "housing_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 16)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 모델 선택과 훈련"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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 적용해 보겠습니다.\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": "markdown",
   "metadata": {},
   "source": [
    "실제 값과 비교합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "레이블: [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]\n"
     ]
    }
   ],
   "source": [
    "print(\"레이블:\", list(some_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_data_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68628.19819848923"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49439.89599001897"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 97,
     "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": [
    "# 모델 세부 튜닝"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)\n",
    "tree_rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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",
      "표준편차: 2439.4345041191004\n"
     ]
    }
   ],
   "source": [
    "def display_scores(scores):\n",
    "    print(\"점수:\", scores)\n",
    "    print(\"평균:\", scores.mean())\n",
    "    print(\"표준편차:\", scores.std())\n",
    "\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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",
      "표준편차: 2731.674001798349\n"
     ]
    }
   ],
   "source": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "사이킷런 0.22 버전에서 랜덤 포레스트의 `n_estimator` 기본값이 10에서 100으로 변경됩니다. 0.20 버전에서 `n_estimator` 값을 지정하지 않을 경우 이에 대한 경고 메세지가 나옵니다. 경고 메세지를 피하기 위해 명시적으로 `n_estimator`를 10으로 설정합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                      n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
       "                      warm_start=False)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=10, random_state=42)\n",
    "forest_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21933.31414779769"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "점수: [51646.44545909 48940.60114882 53050.86323649 54408.98730149\n",
      " 50922.14870785 56482.50703987 51864.52025526 49760.85037653\n",
      " 55434.21627933 53326.10093303]\n",
      "평균: 52583.72407377466\n",
      "표준편차: 2298.353351147122\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": 104,
   "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": 104,
     "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": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111094.6308539982"
      ]
     },
     "execution_count": 105,
     "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": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=RandomForestRegressor(bootstrap=True, criterion='mse',\n",
       "                                             max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators='warn', n_jobs=None,\n",
       "                                             oob_score=False, random_state=42,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='warn', n_jobs=-1,\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",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    # 하이퍼파라미터 12(=3×4)개의 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 폴드에서 훈련하면 총 (12+6)*5=90번의 훈련이 일어납니다.\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5, scoring='neg_mean_squared_error', \n",
    "                           return_train_score=True, n_jobs=-1)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최상의 파라미터 조합:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 30}"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features=8, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=30,\n",
       "                      n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
       "                      warm_start=False)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그리드서치에서 테스트한 하이퍼파라미터 조합의 점수를 확인합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63669.05791727153 {'max_features': 2, 'n_estimators': 3}\n",
      "55627.16171305252 {'max_features': 2, 'n_estimators': 10}\n",
      "53384.57867637289 {'max_features': 2, 'n_estimators': 30}\n",
      "60965.99185930139 {'max_features': 4, 'n_estimators': 3}\n",
      "52740.98248528835 {'max_features': 4, 'n_estimators': 10}\n",
      "50377.344409590376 {'max_features': 4, 'n_estimators': 30}\n",
      "58663.84733372485 {'max_features': 6, 'n_estimators': 3}\n",
      "52006.15355973719 {'max_features': 6, 'n_estimators': 10}\n",
      "50146.465964159885 {'max_features': 6, 'n_estimators': 30}\n",
      "57869.25504027614 {'max_features': 8, 'n_estimators': 3}\n",
      "51711.09443660957 {'max_features': 8, 'n_estimators': 10}\n",
      "49682.25345942335 {'max_features': 8, 'n_estimators': 30}\n",
      "62895.088889905004 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54658.14484390074 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "59470.399594730654 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "52725.01091081235 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "57490.612956065226 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "51009.51445842374 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "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": 110,
   "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>0.050272</td>\n",
       "      <td>0.000983</td>\n",
       "      <td>0.002671</td>\n",
       "      <td>0.000031</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>-4.053749e+09</td>\n",
       "      <td>1.519609e+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>0.155895</td>\n",
       "      <td>0.002054</td>\n",
       "      <td>0.007374</td>\n",
       "      <td>0.000096</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>-3.094381e+09</td>\n",
       "      <td>1.327046e+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>0.466790</td>\n",
       "      <td>0.003222</td>\n",
       "      <td>0.020763</td>\n",
       "      <td>0.000376</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>1.626879e+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>0.080077</td>\n",
       "      <td>0.000765</td>\n",
       "      <td>0.002662</td>\n",
       "      <td>0.000017</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>-3.716852e+09</td>\n",
       "      <td>1.631421e+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>0.260610</td>\n",
       "      <td>0.002850</td>\n",
       "      <td>0.007349</td>\n",
       "      <td>0.000147</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>-2.781611e+09</td>\n",
       "      <td>1.268562e+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>0.771269</td>\n",
       "      <td>0.001967</td>\n",
       "      <td>0.020760</td>\n",
       "      <td>0.000226</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>-2.537877e+09</td>\n",
       "      <td>1.214603e+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>0.109181</td>\n",
       "      <td>0.003021</td>\n",
       "      <td>0.002627</td>\n",
       "      <td>0.000022</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>-3.441447e+09</td>\n",
       "      <td>1.893141e+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>0.359064</td>\n",
       "      <td>0.002993</td>\n",
       "      <td>0.007470</td>\n",
       "      <td>0.000265</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>-2.704640e+09</td>\n",
       "      <td>1.471542e+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>1.085904</td>\n",
       "      <td>0.007037</td>\n",
       "      <td>0.020348</td>\n",
       "      <td>0.000221</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>-2.514668e+09</td>\n",
       "      <td>1.285063e+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>0.140850</td>\n",
       "      <td>0.003441</td>\n",
       "      <td>0.002625</td>\n",
       "      <td>0.000012</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>-3.348851e+09</td>\n",
       "      <td>1.241864e+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>0.464359</td>\n",
       "      <td>0.003905</td>\n",
       "      <td>0.007256</td>\n",
       "      <td>0.000095</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>-2.674037e+09</td>\n",
       "      <td>1.392720e+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>1.401487</td>\n",
       "      <td>0.007872</td>\n",
       "      <td>0.020617</td>\n",
       "      <td>0.000496</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>-2.468326e+09</td>\n",
       "      <td>1.091647e+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>0.074540</td>\n",
       "      <td>0.001222</td>\n",
       "      <td>0.003115</td>\n",
       "      <td>0.000243</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>-3.955792e+09</td>\n",
       "      <td>1.900966e+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>0.246445</td>\n",
       "      <td>0.001551</td>\n",
       "      <td>0.008540</td>\n",
       "      <td>0.000139</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>-2.987513e+09</td>\n",
       "      <td>1.539231e+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>0.100478</td>\n",
       "      <td>0.002083</td>\n",
       "      <td>0.003043</td>\n",
       "      <td>0.000110</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>-3.536728e+09</td>\n",
       "      <td>7.795196e+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>0.329762</td>\n",
       "      <td>0.002839</td>\n",
       "      <td>0.008933</td>\n",
       "      <td>0.000553</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>-2.779927e+09</td>\n",
       "      <td>6.286611e+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>0.125876</td>\n",
       "      <td>0.002710</td>\n",
       "      <td>0.003012</td>\n",
       "      <td>0.000109</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>-3.305171e+09</td>\n",
       "      <td>1.879203e+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>0.410446</td>\n",
       "      <td>0.007215</td>\n",
       "      <td>0.008174</td>\n",
       "      <td>0.000232</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>-2.601971e+09</td>\n",
       "      <td>1.088031e+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        0.050272      0.000983         0.002671        0.000031   \n",
       "1        0.155895      0.002054         0.007374        0.000096   \n",
       "2        0.466790      0.003222         0.020763        0.000376   \n",
       "3        0.080077      0.000765         0.002662        0.000017   \n",
       "4        0.260610      0.002850         0.007349        0.000147   \n",
       "5        0.771269      0.001967         0.020760        0.000226   \n",
       "6        0.109181      0.003021         0.002627        0.000022   \n",
       "7        0.359064      0.002993         0.007470        0.000265   \n",
       "8        1.085904      0.007037         0.020348        0.000221   \n",
       "9        0.140850      0.003441         0.002625        0.000012   \n",
       "10       0.464359      0.003905         0.007256        0.000095   \n",
       "11       1.401487      0.007872         0.020617        0.000496   \n",
       "12       0.074540      0.001222         0.003115        0.000243   \n",
       "13       0.246445      0.001551         0.008540        0.000139   \n",
       "14       0.100478      0.002083         0.003043        0.000110   \n",
       "15       0.329762      0.002839         0.008933        0.000553   \n",
       "16       0.125876      0.002710         0.003012        0.000109   \n",
       "17       0.410446      0.007215         0.008174        0.000232   \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  ...    -4.053749e+09    1.519609e+08               18   \n",
       "1       -3.254861e+09  ...    -3.094381e+09    1.327046e+08               11   \n",
       "2       -3.021086e+09  ...    -2.849913e+09    1.626879e+08                9   \n",
       "3       -3.786886e+09  ...    -3.716852e+09    1.631421e+08               16   \n",
       "4       -2.784511e+09  ...    -2.781611e+09    1.268562e+08                8   \n",
       "5       -2.588448e+09  ...    -2.537877e+09    1.214603e+08                3   \n",
       "6       -3.586319e+09  ...    -3.441447e+09    1.893141e+08               14   \n",
       "7       -2.782039e+09  ...    -2.704640e+09    1.471542e+08                6   \n",
       "8       -2.583638e+09  ...    -2.514668e+09    1.285063e+08                2   \n",
       "9       -3.348552e+09  ...    -3.348851e+09    1.241864e+08               13   \n",
       "10      -2.718994e+09  ...    -2.674037e+09    1.392720e+08                5   \n",
       "11      -2.546640e+09  ...    -2.468326e+09    1.091647e+08                1   \n",
       "12      -4.166012e+09  ...    -3.955792e+09    1.900966e+08               17   \n",
       "13      -3.107789e+09  ...    -2.987513e+09    1.539231e+08               10   \n",
       "14      -3.441527e+09  ...    -3.536728e+09    7.795196e+07               15   \n",
       "15      -2.851737e+09  ...    -2.779927e+09    6.286611e+07                7   \n",
       "16      -3.559375e+09  ...    -3.305171e+09    1.879203e+08               12   \n",
       "17      -2.710011e+09  ...    -2.601971e+09    1.088031e+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": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(grid_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators='warn',\n",
       "                                                   n_jobs=None, oob_score=False,\n",
       "                                                   random_sta...\n",
       "                                                   warm_start=False),\n",
       "                   iid='warn', n_iter=10, n_jobs=-1,\n",
       "                   param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f474dc82320>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f474dc82dd8>},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=0)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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', \n",
    "                                random_state=42, n_jobs=-1)\n",
    "rnd_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49150.657232934034 {'max_features': 7, 'n_estimators': 180}\n",
      "51389.85295710133 {'max_features': 5, 'n_estimators': 15}\n",
      "50796.12045980556 {'max_features': 3, 'n_estimators': 72}\n",
      "50835.09932039744 {'max_features': 5, 'n_estimators': 21}\n",
      "49280.90117886215 {'max_features': 7, 'n_estimators': 122}\n",
      "50774.86679035961 {'max_features': 3, 'n_estimators': 75}\n",
      "50682.75001237282 {'max_features': 3, 'n_estimators': 88}\n",
      "49608.94061293652 {'max_features': 5, 'n_estimators': 100}\n",
      "50473.57642831875 {'max_features': 3, 'n_estimators': 150}\n",
      "64429.763804893395 {'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": 113,
   "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": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "사이킷런 0.20 버전의 `ColumnTransformer`를 사용했기 때문에 `full_pipeline`에서 `cat_encoder`를 가져옵니다. 즉 `cat_pipeline`을 사용하지 않았습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "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": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_attribs = [\"rooms_per_hhold\", \"pop_per_hhold\", \"bedrooms_per_room\"]\n",
    "# cat_encoder = cat_pipeline.named_steps[\"cat_encoder\"]\n",
    "cat_encoder = full_pipeline.named_transformers_[\"cat_encoder\"]\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": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47730.22690385927"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "테스트 RMSE에 대한 95% 신뢰 구간을 계산할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([45685.10470776, 49691.25001878])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confidence = 0.95\n",
    "squared_errors = (final_predictions - y_test) ** 2\n",
    "mean = squared_errors.mean()\n",
    "m = len(squared_errors)\n",
    "\n",
    "np.sqrt(stats.t.interval(confidence, m - 1,\n",
    "                         loc=np.mean(squared_errors),\n",
    "                         scale=stats.sem(squared_errors)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음과 같이 수동으로 계산할 수도 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45685.10470776014, 49691.25001877871)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "또는 t 점수 대신 z 점수를 사용할 수도 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45685.717918136594, 49690.68623889426)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "# 추가 내용"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 전처리와 예측을 포함한 파이프라인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([210644.60459286, 317768.80697211, 210956.43331178,  59218.98886849,\n",
       "       189747.55849879])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "## joblib을 사용한 모델 저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model = full_pipeline_with_predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "joblib.dump(my_model, \"my_model.pkl\") # DIFF\n",
    "#...\n",
    "my_model_loaded = joblib.load(\"my_model.pkl\") # DIFF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `RandomizedSearchCV`을 위한 Scipy 분포 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "# 연습문제 해답"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "질문: 서포트 벡터 머신 회귀(sklearn.svm.SVR)를 kernel=“linear”(하이퍼파라미터 C를 바꿔가며)나 kernel=“rbf”(하이퍼파라미터 C와 gamma를 바꿔가며) 등의 다양한 하이퍼파라미터 설정으로 시도해보세요. 지금은 이 하이퍼파라미터가 무엇을 의미하는지 너무 신경 쓰지 마세요. 최상의 SVR 모델은 무엇인가요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "[CV] C=10.0, kernel=linear ...........................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ............................ C=10.0, kernel=linear, total=   4.3s\n",
      "[CV] C=10.0, kernel=linear ...........................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ............................ C=10.0, kernel=linear, total=   4.4s\n",
      "[CV] C=10.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=10.0, kernel=linear, total=   4.3s\n",
      "[CV] C=10.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=10.0, kernel=linear, total=   4.3s\n",
      "[CV] C=10.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=10.0, kernel=linear, total=   4.3s\n",
      "[CV] C=30.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=30.0, kernel=linear, total=   4.3s\n",
      "[CV] C=30.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=30.0, kernel=linear, total=   4.3s\n",
      "[CV] C=30.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=30.0, kernel=linear, total=   4.4s\n",
      "[CV] C=30.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=30.0, kernel=linear, total=   4.4s\n",
      "[CV] C=30.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=30.0, kernel=linear, total=   4.3s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   4.3s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   4.3s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   4.4s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   4.3s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   4.2s\n",
      "[CV] C=300.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=300.0, kernel=linear, total=   4.3s\n",
      "[CV] C=300.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=300.0, kernel=linear, total=   4.3s\n",
      "[CV] C=300.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=300.0, kernel=linear, total=   4.4s\n",
      "[CV] C=300.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=300.0, kernel=linear, total=   4.4s\n",
      "[CV] C=300.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=300.0, kernel=linear, total=   4.3s\n",
      "[CV] C=1000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=1000.0, kernel=linear, total=   4.5s\n",
      "[CV] C=1000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=1000.0, kernel=linear, total=   4.5s\n",
      "[CV] C=1000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=1000.0, kernel=linear, total=   4.5s\n",
      "[CV] C=1000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=1000.0, kernel=linear, total=   4.5s\n",
      "[CV] C=1000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=1000.0, kernel=linear, total=   4.4s\n",
      "[CV] C=3000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=3000.0, kernel=linear, total=   4.8s\n",
      "[CV] C=3000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=3000.0, kernel=linear, total=   4.8s\n",
      "[CV] C=3000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=3000.0, kernel=linear, total=   4.9s\n",
      "[CV] C=3000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=3000.0, kernel=linear, total=   4.9s\n",
      "[CV] C=3000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=3000.0, kernel=linear, total=   4.7s\n",
      "[CV] C=10000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=10000.0, kernel=linear, total=   6.3s\n",
      "[CV] C=10000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=10000.0, kernel=linear, total=   6.4s\n",
      "[CV] C=10000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=10000.0, kernel=linear, total=   6.5s\n",
      "[CV] C=10000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=10000.0, kernel=linear, total=   6.0s\n",
      "[CV] C=10000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=10000.0, kernel=linear, total=   5.8s\n",
      "[CV] C=30000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=30000.0, kernel=linear, total=   9.9s\n",
      "[CV] C=30000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=30000.0, kernel=linear, total=  10.1s\n",
      "[CV] C=30000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=30000.0, kernel=linear, total=  10.5s\n",
      "[CV] C=30000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=30000.0, kernel=linear, total=  10.1s\n",
      "[CV] C=30000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=30000.0, kernel=linear, total=   9.1s\n",
      "[CV] C=1.0, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=1.0, gamma=0.01, kernel=rbf, total=   8.9s\n",
      "[CV] C=1.0, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=1.0, gamma=0.01, kernel=rbf, total=   8.9s\n",
      "[CV] C=1.0, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=1.0, gamma=0.01, kernel=rbf, total=   8.9s\n",
      "[CV] C=1.0, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=1.0, gamma=0.01, kernel=rbf, total=   8.9s\n",
      "[CV] C=1.0, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=1.0, gamma=0.01, kernel=rbf, total=   8.9s\n",
      "[CV] C=1.0, gamma=0.03, kernel=rbf ...................................\n",
      "[CV] .................... C=1.0, gamma=0.03, kernel=rbf, total=   8.8s\n",
      "[CV] C=1.0, gamma=0.03, kernel=rbf ...................................\n",
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      "[CV] C=1.0, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=1.0, gamma=0.1, kernel=rbf, total=   8.8s\n",
      "[CV] C=1.0, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=1.0, gamma=0.1, kernel=rbf, total=   8.8s\n",
      "[CV] C=1.0, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=1.0, gamma=0.1, kernel=rbf, total=   8.8s\n",
      "[CV] C=1.0, gamma=0.1, kernel=rbf ....................................\n",
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      "[CV] C=1.0, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=1.0, gamma=0.1, kernel=rbf, total=   8.8s\n",
      "[CV] C=1.0, gamma=0.3, kernel=rbf ....................................\n",
      "[CV] ..................... C=1.0, gamma=0.3, kernel=rbf, total=   8.6s\n",
      "[CV] C=1.0, gamma=0.3, kernel=rbf ....................................\n",
      "[CV] ..................... C=1.0, gamma=0.3, kernel=rbf, total=   8.6s\n",
      "[CV] C=1.0, gamma=0.3, kernel=rbf ....................................\n",
      "[CV] ..................... C=1.0, gamma=0.3, kernel=rbf, total=   8.6s\n",
      "[CV] C=1.0, gamma=0.3, kernel=rbf ....................................\n",
      "[CV] ..................... C=1.0, gamma=0.3, kernel=rbf, total=   8.6s\n",
      "[CV] C=1.0, gamma=0.3, kernel=rbf ....................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..................... C=1.0, gamma=0.3, kernel=rbf, total=   8.6s\n",
      "[CV] C=1.0, gamma=1.0, kernel=rbf ....................................\n",
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      "[CV] C=1.0, gamma=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... C=1.0, gamma=3.0, kernel=rbf, total=   9.1s\n",
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      "[CV] C=3.0, gamma=0.01, kernel=rbf ...................................\n",
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      "[CV] C=3.0, gamma=0.03, kernel=rbf ...................................\n",
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      "[CV] C=3.0, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=3.0, gamma=0.1, kernel=rbf, total=   8.7s\n",
      "[CV] C=3.0, gamma=0.1, kernel=rbf ....................................\n",
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      "[CV] C=3.0, gamma=0.1, kernel=rbf ....................................\n",
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      "[CV] C=3.0, gamma=0.1, kernel=rbf ....................................\n",
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      "[CV] C=3.0, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=3.0, gamma=0.1, kernel=rbf, total=   8.8s\n",
      "[CV] C=3.0, gamma=0.3, kernel=rbf ....................................\n",
      "[CV] ..................... C=3.0, gamma=0.3, kernel=rbf, total=   8.6s\n",
      "[CV] C=3.0, gamma=0.3, kernel=rbf ....................................\n",
      "[CV] ..................... C=3.0, gamma=0.3, kernel=rbf, total=   8.6s\n",
      "[CV] C=3.0, gamma=0.3, kernel=rbf ....................................\n",
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      "[CV] C=3.0, gamma=1.0, kernel=rbf ....................................\n",
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      "[CV] C=3.0, gamma=1.0, kernel=rbf ....................................\n",
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      "[CV] C=3.0, gamma=3.0, kernel=rbf ....................................\n",
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      "[CV] C=3.0, gamma=3.0, kernel=rbf ....................................\n",
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      "[CV] C=3.0, gamma=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... C=3.0, gamma=3.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=10.0, gamma=0.01, kernel=rbf ..................................\n",
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      "[CV] C=10.0, gamma=0.03, kernel=rbf ..................................\n",
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      "[CV] C=10.0, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] .................... C=10.0, gamma=0.1, kernel=rbf, total=   8.7s\n",
      "[CV] C=10.0, gamma=0.1, kernel=rbf ...................................\n",
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      "[CV] C=10.0, gamma=0.1, kernel=rbf ...................................\n",
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      "[CV] C=10.0, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] .................... C=10.0, gamma=0.1, kernel=rbf, total=   8.7s\n",
      "[CV] C=10.0, gamma=0.3, kernel=rbf ...................................\n",
      "[CV] .................... C=10.0, gamma=0.3, kernel=rbf, total=   8.6s\n",
      "[CV] C=10.0, gamma=0.3, kernel=rbf ...................................\n",
      "[CV] .................... C=10.0, gamma=0.3, kernel=rbf, total=   8.6s\n",
      "[CV] C=10.0, gamma=0.3, kernel=rbf ...................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .................... C=10.0, gamma=0.3, kernel=rbf, total=   8.6s\n",
      "[CV] C=10.0, gamma=0.3, kernel=rbf ...................................\n",
      "[CV] .................... C=10.0, gamma=0.3, kernel=rbf, total=   8.6s\n",
      "[CV] C=10.0, gamma=0.3, kernel=rbf ...................................\n",
      "[CV] .................... C=10.0, gamma=0.3, kernel=rbf, total=   8.6s\n",
      "[CV] C=10.0, gamma=1.0, kernel=rbf ...................................\n",
      "[CV] .................... C=10.0, gamma=1.0, kernel=rbf, total=   8.4s\n",
      "[CV] C=10.0, gamma=1.0, kernel=rbf ...................................\n",
      "[CV] .................... C=10.0, gamma=1.0, kernel=rbf, total=   8.3s\n",
      "[CV] C=10.0, gamma=1.0, kernel=rbf ...................................\n",
      "[CV] .................... C=10.0, gamma=1.0, kernel=rbf, total=   8.4s\n",
      "[CV] C=10.0, gamma=1.0, kernel=rbf ...................................\n",
      "[CV] .................... C=10.0, gamma=1.0, kernel=rbf, total=   8.3s\n",
      "[CV] C=10.0, gamma=1.0, kernel=rbf ...................................\n",
      "[CV] .................... C=10.0, gamma=1.0, kernel=rbf, total=   8.4s\n",
      "[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n",
      "[CV] .................... C=10.0, gamma=3.0, kernel=rbf, total=   9.0s\n",
      "[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n",
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      "[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n",
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      "[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n",
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      "[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n",
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      "[CV] C=30.0, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ................... C=30.0, gamma=0.01, kernel=rbf, total=   8.8s\n",
      "[CV] C=30.0, gamma=0.01, kernel=rbf ..................................\n",
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      "[CV] C=30.0, gamma=0.01, kernel=rbf ..................................\n",
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      "[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
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      "[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
      "[CV] ................... C=30.0, gamma=0.03, kernel=rbf, total=   8.8s\n",
      "[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
      "[CV] ................... C=30.0, gamma=0.03, kernel=rbf, total=   8.8s\n",
      "[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
      "[CV] ................... C=30.0, gamma=0.03, kernel=rbf, total=   8.8s\n",
      "[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
      "[CV] ................... C=30.0, gamma=0.03, kernel=rbf, total=   8.8s\n",
      "[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] .................... C=30.0, gamma=0.1, kernel=rbf, total=   8.7s\n",
      "[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] .................... C=30.0, gamma=0.1, kernel=rbf, total=   8.7s\n",
      "[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
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      "[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
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      "[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] .................... C=30.0, gamma=0.1, kernel=rbf, total=   8.7s\n",
      "[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
      "[CV] .................... C=30.0, gamma=0.3, kernel=rbf, total=   8.5s\n",
      "[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
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      "[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
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      "[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
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      "[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
      "[CV] .................... C=30.0, gamma=0.3, kernel=rbf, total=   8.5s\n",
      "[CV] C=30.0, gamma=1.0, kernel=rbf ...................................\n",
      "[CV] .................... C=30.0, gamma=1.0, kernel=rbf, total=   8.3s\n",
      "[CV] C=30.0, gamma=1.0, kernel=rbf ...................................\n",
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      "[CV] C=30.0, gamma=1.0, kernel=rbf ...................................\n",
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      "[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n",
      "[CV] .................... C=30.0, gamma=3.0, kernel=rbf, total=   9.0s\n",
      "[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n",
      "[CV] .................... C=30.0, gamma=3.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n",
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      "[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n",
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      "[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n",
      "[CV] .................... C=30.0, gamma=3.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
      "[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total=   8.8s\n",
      "[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
      "[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total=   8.8s\n",
      "[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
      "[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total=   8.8s\n",
      "[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
      "[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total=   8.8s\n",
      "[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
      "[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total=   8.8s\n",
      "[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
      "[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total=   8.7s\n",
      "[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
      "[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total=   8.7s\n",
      "[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
      "[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total=   8.7s\n",
      "[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
      "[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total=   8.7s\n",
      "[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
      "[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total=   8.7s\n",
      "[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=0.1, kernel=rbf, total=   8.5s\n",
      "[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=0.1, kernel=rbf, total=   8.6s\n",
      "[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=0.1, kernel=rbf, total=   8.6s\n",
      "[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=0.1, kernel=rbf, total=   8.6s\n",
      "[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=0.1, kernel=rbf, total=   8.6s\n",
      "[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................... C=100.0, gamma=0.3, kernel=rbf, total=   8.5s\n",
      "[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=0.3, kernel=rbf, total=   8.5s\n",
      "[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=0.3, kernel=rbf, total=   8.5s\n",
      "[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=0.3, kernel=rbf, total=   8.5s\n",
      "[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=0.3, kernel=rbf, total=   8.5s\n",
      "[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=1.0, kernel=rbf, total=   8.3s\n",
      "[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=1.0, kernel=rbf, total=   8.3s\n",
      "[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=1.0, kernel=rbf, total=   8.3s\n",
      "[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=1.0, kernel=rbf, total=   8.3s\n",
      "[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=1.0, kernel=rbf, total=   8.3s\n",
      "[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total=   9.0s\n",
      "[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total=   9.0s\n",
      "[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n",
      "[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
      "[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total=   8.7s\n",
      "[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
      "[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total=   8.7s\n",
      "[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
      "[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total=   8.7s\n",
      "[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
      "[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total=   8.7s\n",
      "[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
      "[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total=   8.7s\n",
      "[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
      "[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total=   8.5s\n",
      "[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
      "[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total=   8.5s\n",
      "[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
      "[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total=   8.5s\n",
      "[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
      "[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total=   8.5s\n",
      "[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
      "[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total=   8.5s\n",
      "[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total=   8.4s\n",
      "[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total=   8.4s\n",
      "[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total=   8.4s\n",
      "[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total=   8.4s\n",
      "[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total=   8.4s\n",
      "[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total=   8.4s\n",
      "[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total=   8.4s\n",
      "[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total=   8.4s\n",
      "[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total=   8.3s\n",
      "[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total=   8.4s\n",
      "[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total=   8.3s\n",
      "[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total=   8.3s\n",
      "[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total=   8.3s\n",
      "[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total=   8.3s\n",
      "[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total=   8.3s\n",
      "[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total=   9.0s\n",
      "[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
      "[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
      "[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total=   8.4s\n",
      "[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
      "[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total=   8.5s\n",
      "[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
      "[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total=   8.4s\n",
      "[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
      "[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total=   8.4s\n",
      "[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
      "[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total=   8.6s\n",
      "[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
      "[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total=   8.6s\n",
      "[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
      "[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total=   8.8s\n",
      "[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
      "[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total=   8.9s\n",
      "[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
      "[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total=   8.8s\n",
      "[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
      "[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total=   8.8s\n",
      "[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total=   8.7s\n",
      "[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total=   8.6s\n",
      "[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total=   8.9s\n",
      "[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total=   8.9s\n",
      "[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total=   9.6s\n",
      "[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total=   9.5s\n",
      "[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total=   9.1s\n",
      "[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total=   9.1s\n",
      "[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total=   9.1s\n",
      "[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total=   9.1s\n",
      "[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total=   9.1s\n",
      "[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total=   9.9s\n",
      "[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total=   9.9s\n",
      "[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total=   9.9s\n",
      "[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total=   9.9s\n",
      "[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total=   9.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 250 out of 250 | elapsed: 34.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,\n",
       "                           epsilon=0.1, gamma='auto_deprecated', kernel='rbf',\n",
       "                           max_iter=-1, shrinking=True, tol=0.001,\n",
       "                           verbose=False),\n",
       "             iid='warn', n_jobs=1,\n",
       "             param_grid=[{'C': [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0,\n",
       "                                10000.0, 30000.0],\n",
       "                          'kernel': ['linear']},\n",
       "                         {'C': [1.0, 3.0, 10.0, 30.0, 100.0, 300.0, 1000.0],\n",
       "                          'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0],\n",
       "                          'kernel': ['rbf']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='neg_mean_squared_error', verbose=2)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "        {'kernel': ['linear'], 'C': [10., 30., 100., 300., 1000., 3000., 10000., 30000.0]},\n",
    "        {'kernel': ['rbf'], 'C': [1.0, 3.0, 10., 30., 100., 300., 1000.0],\n",
    "         'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0]},\n",
    "    ]\n",
    "\n",
    "svm_reg = SVR()\n",
    "grid_search = GridSearchCV(svm_reg, param_grid, cv=5, scoring='neg_mean_squared_error', \n",
    "                           verbose=2, n_jobs=1)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최상 모델의 (5-폴드 교차 검증으로 평가한) 점수는 다음과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70363.90313964167"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_mse = grid_search.best_score_\n",
    "rmse = np.sqrt(-negative_mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이는 `RandomForestRegressor`보다 훨씬 좋지 않네요. 최상의 하이퍼파라미터를 확인해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 30000.0, 'kernel': 'linear'}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "선형 커널이 RBF 커널보다 성능이 나은 것 같습니다. `C`는 테스트한 것 중에 최대값이 선택되었습니다. 따라서 (작은 값들은 지우고) 더 큰 값의 `C`로 그리드서치를 다시 실행해 보아야 합니다. 아마도 더 큰 값의 `C`에서 성능이 높아질 것입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "질문: GridSearchCV를 RandomizedSearchCV로 바꿔보세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "[CV] C=629.782329591372, gamma=3.010121430917521, kernel=linear ......\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=629.782329591372, gamma=3.010121430917521, kernel=linear, total=   5.0s\n",
      "[CV] C=629.782329591372, gamma=3.010121430917521, kernel=linear ......\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=629.782329591372, gamma=3.010121430917521, kernel=linear, total=   5.0s\n",
      "[CV] C=629.782329591372, gamma=3.010121430917521, kernel=linear ......\n",
      "[CV]  C=629.782329591372, gamma=3.010121430917521, kernel=linear, total=   5.0s\n",
      "[CV] C=629.782329591372, gamma=3.010121430917521, kernel=linear ......\n",
      "[CV]  C=629.782329591372, gamma=3.010121430917521, kernel=linear, total=   5.0s\n",
      "[CV] C=629.782329591372, gamma=3.010121430917521, kernel=linear ......\n",
      "[CV]  C=629.782329591372, gamma=3.010121430917521, kernel=linear, total=   5.0s\n",
      "[CV] C=26290.206464300216, gamma=0.9084469696321253, kernel=rbf ......\n",
      "[CV]  C=26290.206464300216, gamma=0.9084469696321253, kernel=rbf, total=  10.8s\n",
      "[CV] C=26290.206464300216, gamma=0.9084469696321253, kernel=rbf ......\n",
      "[CV]  C=24.17508294611391, gamma=3.503557475158312, kernel=rbf, total=  10.3s\n",
      "[CV] C=24.17508294611391, gamma=3.503557475158312, kernel=rbf ........\n",
      "[CV]  C=24.17508294611391, gamma=3.503557475158312, kernel=rbf, total=  10.3s\n",
      "[CV] C=24.17508294611391, gamma=3.503557475158312, kernel=rbf ........\n",
      "[CV]  C=24.17508294611391, gamma=3.503557475158312, kernel=rbf, total=  10.3s\n",
      "[CV] C=24.17508294611391, gamma=3.503557475158312, kernel=rbf ........\n",
      "[CV]  C=24.17508294611391, gamma=3.503557475158312, kernel=rbf, total=  10.3s\n",
      "[CV] C=113564.03940586245, gamma=0.0007790692366582295, kernel=rbf ...\n",
      "[CV]  C=113564.03940586245, gamma=0.0007790692366582295, kernel=rbf, total=   9.2s\n",
      "[CV] C=113564.03940586245, gamma=0.0007790692366582295, kernel=rbf ...\n",
      "[CV]  C=113564.03940586245, gamma=0.0007790692366582295, kernel=rbf, total=   9.2s\n",
      "[CV] C=113564.03940586245, gamma=0.0007790692366582295, kernel=rbf ...\n",
      "[CV]  C=113564.03940586245, gamma=0.0007790692366582295, kernel=rbf, total=   9.2s\n",
      "[CV] C=113564.03940586245, gamma=0.0007790692366582295, kernel=rbf ...\n",
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      "[CV] C=34246.75194632794, gamma=0.3632878599687583, kernel=linear ....\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=34246.75194632794, gamma=0.3632878599687583, kernel=linear, total=  12.1s\n",
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      "[CV] C=717.3632997255095, gamma=0.3165604432088257, kernel=linear ....\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=717.3632997255095, gamma=0.3165604432088257, kernel=linear, total=   5.0s\n",
      "[CV] C=4446.667521184072, gamma=3.3597284456608496, kernel=rbf .......\n",
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      "[CV] C=2963.564121207815, gamma=0.15189814782062885, kernel=linear ...\n",
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      "[CV] C=91.64267381686706, gamma=0.01575994483585621, kernel=linear ...\n",
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      "[CV] C=22.76927941060928, gamma=0.22169760231351215, kernel=rbf ......\n",
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      "[CV] C=16483.850529752886, gamma=1.4752145260435134, kernel=linear ...\n",
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      "[CV] C=101445.66881340064, gamma=1.052904084582266, kernel=rbf .......\n",
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      "[CV] C=56681.80859029545, gamma=0.9763011917123741, kernel=rbf .......\n",
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      "[CV] C=48.15822390928914, gamma=0.4633351167983427, kernel=rbf .......\n",
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      "[CV] C=399.7268155705774, gamma=1.3078757839577408, kernel=rbf .......\n",
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      "[CV] C=399.7268155705774, gamma=1.3078757839577408, kernel=rbf .......\n",
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      "[CV] C=399.7268155705774, gamma=1.3078757839577408, kernel=rbf .......\n",
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      "[CV] C=399.7268155705774, gamma=1.3078757839577408, kernel=rbf .......\n",
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      "[CV] C=399.7268155705774, gamma=1.3078757839577408, kernel=rbf .......\n",
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      "[CV] C=251.14073886281363, gamma=0.8238105204914145, kernel=linear ...\n",
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      "[CV] C=251.14073886281363, gamma=0.8238105204914145, kernel=linear ...\n",
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      "[CV] C=251.14073886281363, gamma=0.8238105204914145, kernel=linear ...\n",
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      "[CV] C=251.14073886281363, gamma=0.8238105204914145, kernel=linear ...\n",
      "[CV]  C=251.14073886281363, gamma=0.8238105204914145, kernel=linear, total=   5.0s\n",
      "[CV] C=251.14073886281363, gamma=0.8238105204914145, kernel=linear ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=251.14073886281363, gamma=0.8238105204914145, kernel=linear, total=   4.9s\n",
      "[CV] C=60.17373642891687, gamma=1.2491263443165994, kernel=linear ....\n",
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      "[CV] C=60.17373642891687, gamma=1.2491263443165994, kernel=linear ....\n",
      "[CV]  C=60.17373642891687, gamma=1.2491263443165994, kernel=linear, total=   5.2s\n",
      "[CV] C=60.17373642891687, gamma=1.2491263443165994, kernel=linear ....\n",
      "[CV]  C=60.17373642891687, gamma=1.2491263443165994, kernel=linear, total=   5.9s\n",
      "[CV] C=60.17373642891687, gamma=1.2491263443165994, kernel=linear ....\n",
      "[CV]  C=60.17373642891687, gamma=1.2491263443165994, kernel=linear, total=   5.7s\n",
      "[CV] C=15415.161544891856, gamma=0.2691677514619319, kernel=rbf ......\n",
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      "[CV] C=15415.161544891856, gamma=0.2691677514619319, kernel=rbf ......\n",
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      "[CV] C=15415.161544891856, gamma=0.2691677514619319, kernel=rbf ......\n",
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      "[CV] C=1888.9148509967113, gamma=0.739678838777267, kernel=linear ....\n",
      "[CV]  C=1888.9148509967113, gamma=0.739678838777267, kernel=linear, total=   5.3s\n",
      "[CV] C=1888.9148509967113, gamma=0.739678838777267, kernel=linear ....\n",
      "[CV]  C=1888.9148509967113, gamma=0.739678838777267, kernel=linear, total=   5.3s\n",
      "[CV] C=1888.9148509967113, gamma=0.739678838777267, kernel=linear ....\n",
      "[CV]  C=1888.9148509967113, gamma=0.739678838777267, kernel=linear, total=   5.3s\n",
      "[CV] C=1888.9148509967113, gamma=0.739678838777267, kernel=linear ....\n",
      "[CV]  C=1888.9148509967113, gamma=0.739678838777267, kernel=linear, total=   5.3s\n",
      "[CV] C=1888.9148509967113, gamma=0.739678838777267, kernel=linear ....\n",
      "[CV]  C=1888.9148509967113, gamma=0.739678838777267, kernel=linear, total=   5.2s\n",
      "[CV] C=55.53838911232773, gamma=0.578634378499143, kernel=linear .....\n",
      "[CV]  C=55.53838911232773, gamma=0.578634378499143, kernel=linear, total=   4.9s\n",
      "[CV] C=55.53838911232773, gamma=0.578634378499143, kernel=linear .....\n",
      "[CV]  C=55.53838911232773, gamma=0.578634378499143, kernel=linear, total=   4.9s\n",
      "[CV] C=55.53838911232773, gamma=0.578634378499143, kernel=linear .....\n",
      "[CV]  C=55.53838911232773, gamma=0.578634378499143, kernel=linear, total=   4.9s\n",
      "[CV] C=55.53838911232773, gamma=0.578634378499143, kernel=linear .....\n",
      "[CV]  C=55.53838911232773, gamma=0.578634378499143, kernel=linear, total=   4.9s\n",
      "[CV] C=55.53838911232773, gamma=0.578634378499143, kernel=linear .....\n",
      "[CV]  C=55.53838911232773, gamma=0.578634378499143, kernel=linear, total=   4.8s\n",
      "[CV] C=26.714480823948186, gamma=1.0117295509275495, kernel=rbf ......\n",
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      "[CV] C=26.714480823948186, gamma=1.0117295509275495, kernel=rbf ......\n",
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      "[CV] C=26.714480823948186, gamma=1.0117295509275495, kernel=rbf ......\n",
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      "[CV] C=26.714480823948186, gamma=1.0117295509275495, kernel=rbf ......\n",
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      "[CV] C=26.714480823948186, gamma=1.0117295509275495, kernel=rbf ......\n",
      "[CV]  C=26.714480823948186, gamma=1.0117295509275495, kernel=rbf, total=   9.2s\n",
      "[CV] C=3582.0552780489566, gamma=1.1891370222133257, kernel=linear ...\n",
      "[CV]  C=3582.0552780489566, gamma=1.1891370222133257, kernel=linear, total=   5.9s\n",
      "[CV] C=3582.0552780489566, gamma=1.1891370222133257, kernel=linear ...\n",
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      "[CV] C=3582.0552780489566, gamma=1.1891370222133257, kernel=linear ...\n",
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      "[CV] C=3582.0552780489566, gamma=1.1891370222133257, kernel=linear ...\n",
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      "[CV] C=3582.0552780489566, gamma=1.1891370222133257, kernel=linear ...\n",
      "[CV]  C=3582.0552780489566, gamma=1.1891370222133257, kernel=linear, total=   5.5s\n",
      "[CV] C=198.7004781812736, gamma=0.5282819748826726, kernel=linear ....\n",
      "[CV]  C=198.7004781812736, gamma=0.5282819748826726, kernel=linear, total=   4.8s\n",
      "[CV] C=198.7004781812736, gamma=0.5282819748826726, kernel=linear ....\n",
      "[CV]  C=198.7004781812736, gamma=0.5282819748826726, kernel=linear, total=   4.8s\n",
      "[CV] C=198.7004781812736, gamma=0.5282819748826726, kernel=linear ....\n",
      "[CV]  C=198.7004781812736, gamma=0.5282819748826726, kernel=linear, total=   4.9s\n",
      "[CV] C=198.7004781812736, gamma=0.5282819748826726, kernel=linear ....\n",
      "[CV]  C=198.7004781812736, gamma=0.5282819748826726, kernel=linear, total=   4.9s\n",
      "[CV] C=198.7004781812736, gamma=0.5282819748826726, kernel=linear ....\n",
      "[CV]  C=198.7004781812736, gamma=0.5282819748826726, kernel=linear, total=   4.8s\n",
      "[CV] C=129.8000604143307, gamma=2.8621383676481322, kernel=linear ....\n",
      "[CV]  C=129.8000604143307, gamma=2.8621383676481322, kernel=linear, total=   4.9s\n",
      "[CV] C=129.8000604143307, gamma=2.8621383676481322, kernel=linear ....\n",
      "[CV]  C=129.8000604143307, gamma=2.8621383676481322, kernel=linear, total=   4.8s\n",
      "[CV] C=129.8000604143307, gamma=2.8621383676481322, kernel=linear ....\n",
      "[CV]  C=129.8000604143307, gamma=2.8621383676481322, kernel=linear, total=   4.9s\n",
      "[CV] C=129.8000604143307, gamma=2.8621383676481322, kernel=linear ....\n",
      "[CV]  C=129.8000604143307, gamma=2.8621383676481322, kernel=linear, total=   4.9s\n",
      "[CV] C=129.8000604143307, gamma=2.8621383676481322, kernel=linear ....\n",
      "[CV]  C=129.8000604143307, gamma=2.8621383676481322, kernel=linear, total=   4.8s\n",
      "[CV] C=288.4269299593897, gamma=0.17580835850006285, kernel=rbf ......\n",
      "[CV]  C=288.4269299593897, gamma=0.17580835850006285, kernel=rbf, total=   9.2s\n",
      "[CV] C=288.4269299593897, gamma=0.17580835850006285, kernel=rbf ......\n",
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      "[CV] C=288.4269299593897, gamma=0.17580835850006285, kernel=rbf ......\n",
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      "[CV] C=288.4269299593897, gamma=0.17580835850006285, kernel=rbf ......\n",
      "[CV]  C=288.4269299593897, gamma=0.17580835850006285, kernel=rbf, total=   9.2s\n",
      "[CV] C=288.4269299593897, gamma=0.17580835850006285, kernel=rbf ......\n",
      "[CV]  C=288.4269299593897, gamma=0.17580835850006285, kernel=rbf, total=   9.2s\n",
      "[CV] C=6287.039489427172, gamma=0.3504567255332862, kernel=linear ....\n",
      "[CV]  C=6287.039489427172, gamma=0.3504567255332862, kernel=linear, total=   6.2s\n",
      "[CV] C=6287.039489427172, gamma=0.3504567255332862, kernel=linear ....\n",
      "[CV]  C=6287.039489427172, gamma=0.3504567255332862, kernel=linear, total=   6.2s\n",
      "[CV] C=6287.039489427172, gamma=0.3504567255332862, kernel=linear ....\n",
      "[CV]  C=6287.039489427172, gamma=0.3504567255332862, kernel=linear, total=   6.4s\n",
      "[CV] C=6287.039489427172, gamma=0.3504567255332862, kernel=linear ....\n",
      "[CV]  C=6287.039489427172, gamma=0.3504567255332862, kernel=linear, total=   6.3s\n",
      "[CV] C=6287.039489427172, gamma=0.3504567255332862, kernel=linear ....\n",
      "[CV]  C=6287.039489427172, gamma=0.3504567255332862, kernel=linear, total=   6.0s\n",
      "[CV] C=61217.04421344494, gamma=1.6279689407405564, kernel=rbf .......\n",
      "[CV]  C=61217.04421344494, gamma=1.6279689407405564, kernel=rbf, total=  31.8s\n",
      "[CV] C=61217.04421344494, gamma=1.6279689407405564, kernel=rbf .......\n",
      "[CV]  C=61217.04421344494, gamma=1.6279689407405564, kernel=rbf, total=  36.3s\n",
      "[CV] C=61217.04421344494, gamma=1.6279689407405564, kernel=rbf .......\n",
      "[CV]  C=61217.04421344494, gamma=1.6279689407405564, kernel=rbf, total=  34.2s\n",
      "[CV] C=61217.04421344494, gamma=1.6279689407405564, kernel=rbf .......\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=61217.04421344494, gamma=1.6279689407405564, kernel=rbf, total=  34.8s\n",
      "[CV] C=61217.04421344494, gamma=1.6279689407405564, kernel=rbf .......\n",
      "[CV]  C=61217.04421344494, gamma=1.6279689407405564, kernel=rbf, total=  32.6s\n",
      "[CV] C=926.9787684096649, gamma=2.147979593060577, kernel=rbf ........\n",
      "[CV]  C=926.9787684096649, gamma=2.147979593060577, kernel=rbf, total=   9.4s\n",
      "[CV] C=926.9787684096649, gamma=2.147979593060577, kernel=rbf ........\n",
      "[CV]  C=926.9787684096649, gamma=2.147979593060577, kernel=rbf, total=   9.4s\n",
      "[CV] C=926.9787684096649, gamma=2.147979593060577, kernel=rbf ........\n",
      "[CV]  C=926.9787684096649, gamma=2.147979593060577, kernel=rbf, total=   9.4s\n",
      "[CV] C=926.9787684096649, gamma=2.147979593060577, kernel=rbf ........\n",
      "[CV]  C=926.9787684096649, gamma=2.147979593060577, kernel=rbf, total=   9.4s\n",
      "[CV] C=926.9787684096649, gamma=2.147979593060577, kernel=rbf ........\n",
      "[CV]  C=926.9787684096649, gamma=2.147979593060577, kernel=rbf, total=   9.4s\n",
      "[CV] C=33946.157064934, gamma=2.2642426492862313, kernel=linear ......\n",
      "[CV]  C=33946.157064934, gamma=2.2642426492862313, kernel=linear, total=  12.1s\n",
      "[CV] C=33946.157064934, gamma=2.2642426492862313, kernel=linear ......\n",
      "[CV]  C=33946.157064934, gamma=2.2642426492862313, kernel=linear, total=  11.8s\n",
      "[CV] C=33946.157064934, gamma=2.2642426492862313, kernel=linear ......\n",
      "[CV]  C=33946.157064934, gamma=2.2642426492862313, kernel=linear, total=  10.9s\n",
      "[CV] C=33946.157064934, gamma=2.2642426492862313, kernel=linear ......\n",
      "[CV]  C=33946.157064934, gamma=2.2642426492862313, kernel=linear, total=  12.4s\n",
      "[CV] C=33946.157064934, gamma=2.2642426492862313, kernel=linear ......\n",
      "[CV]  C=33946.157064934, gamma=2.2642426492862313, kernel=linear, total=  11.2s\n",
      "[CV] C=84789.82947739525, gamma=0.3176359085304841, kernel=linear ....\n",
      "[CV]  C=84789.82947739525, gamma=0.3176359085304841, kernel=linear, total=  29.3s\n",
      "[CV] C=84789.82947739525, gamma=0.3176359085304841, kernel=linear ....\n",
      "[CV]  C=84789.82947739525, gamma=0.3176359085304841, kernel=linear, total=  21.7s\n",
      "[CV] C=84789.82947739525, gamma=0.3176359085304841, kernel=linear ....\n",
      "[CV]  C=84789.82947739525, gamma=0.3176359085304841, kernel=linear, total=  32.3s\n",
      "[CV] C=84789.82947739525, gamma=0.3176359085304841, kernel=linear ....\n",
      "[CV]  C=84789.82947739525, gamma=0.3176359085304841, kernel=linear, total=  24.0s\n",
      "[CV] C=84789.82947739525, gamma=0.3176359085304841, kernel=linear ....\n",
      "[CV]  C=84789.82947739525, gamma=0.3176359085304841, kernel=linear, total=  18.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 250 out of 250 | elapsed: 53.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "                   estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,\n",
       "                                 epsilon=0.1, gamma='auto_deprecated',\n",
       "                                 kernel='rbf', max_iter=-1, shrinking=True,\n",
       "                                 tol=0.001, verbose=False),\n",
       "                   iid='warn', n_iter=50, n_jobs=1,\n",
       "                   param_distributions={'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f474838d278>,\n",
       "                                        'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f474838d128>,\n",
       "                                        'kernel': ['linear', 'rbf']},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import expon, reciprocal\n",
    "\n",
    "# expon(), reciprocal()와 다른 확률 분포 함수에 대해서는\n",
    "# https://docs.scipy.org/doc/scipy/reference/stats.html를 참고하세요.\n",
    "\n",
    "# 노트: kernel 매개변수가 \"linear\"일 때는 gamma가 무시됩니다.\n",
    "param_distribs = {\n",
    "        'kernel': ['linear', 'rbf'],\n",
    "        'C': reciprocal(20, 200000),\n",
    "        'gamma': expon(scale=1.0),\n",
    "    }\n",
    "\n",
    "svm_reg = SVR()\n",
    "rnd_search = RandomizedSearchCV(svm_reg, param_distributions=param_distribs,\n",
    "                                n_iter=50, cv=5, scoring='neg_mean_squared_error',\n",
    "                                verbose=2, n_jobs=1, random_state=42)\n",
    "rnd_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최상 모델의 (5-폴드 교차 검증으로 평가한) 점수는 다음과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54767.99053704409"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_mse = rnd_search.best_score_\n",
    "rmse = np.sqrt(-negative_mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 `RandomForestRegressor`의 성능에 훨씬 가까워졌습니다(하지만 아직 차이가 납니다). 최상의 하이퍼파라미터를 확인해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 157055.10989448498, 'gamma': 0.26497040005002437, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이번에는 RBF 커널에 대해 최적의 하이퍼파라미터 조합을 찾았습니다. 보통 랜덤서치가 같은 시간안에 그리드서치보다 더 좋은 하이퍼파라미터를 찾습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여기서 사용된 `scale=1.0`인 지수 분포를 살펴보겠습니다. 일부 샘플은 1.0보다 아주 크거나 작습니다. 하지만 로그 분포를 보면 대부분의 값이 exp(-2)와 exp(+2), 즉 0.1과 7.4 사이에 집중되어 있음을 알 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "expon_distrib = expon(scale=1.)\n",
    "samples = expon_distrib.rvs(10000, random_state=42)\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(121)\n",
    "plt.title(\"Exponential distribution (scale=1.0)\")\n",
    "plt.hist(samples, bins=50)\n",
    "plt.subplot(122)\n",
    "plt.title(\"Log of this distribution\")\n",
    "plt.hist(np.log(samples), bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`C`에 사용된 분포는 매우 다릅니다. 주어진 범위안에서 균등 분포로 샘플링됩니다. 그래서 오른쪽 로그 분포가 거의 일정하게 나타납니다. 이런 분포는 원하는 스케일이 정확이 무엇인지 모를 때 사용하면 좋습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reciprocal_distrib = reciprocal(20, 200000)\n",
    "samples = reciprocal_distrib.rvs(10000, random_state=42)\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(121)\n",
    "plt.title(\"Reciprocal distribution (scale=1.0)\")\n",
    "plt.hist(samples, bins=50)\n",
    "plt.subplot(122)\n",
    "plt.title(\"Log of this distribution\")\n",
    "plt.hist(np.log(samples), bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "reciprocal() 함수는 하이퍼파라미터의 스케일에 대해 전혀 감을 잡을 수 없을 때 사용합니다(오른쪽 그래프에서 볼 수 있듯이 주어진 범위안에서 모든 값이 균등합니다). 반면 지수 분포는 하이퍼파라미터의 스케일을 (어느정도) 알고 있을 때 사용하는 것이 좋습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "질문: 가장 중요한 특성을 선택하는 변환기를 준비 파이프라인에 추가해보세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "def indices_of_top_k(arr, k):\n",
    "    return np.sort(np.argpartition(np.array(arr), -k)[-k:])\n",
    "\n",
    "class TopFeatureSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, feature_importances, k):\n",
    "        self.feature_importances = feature_importances\n",
    "        self.k = k\n",
    "    def fit(self, X, y=None):\n",
    "        self.feature_indices_ = indices_of_top_k(self.feature_importances, self.k)\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[:, self.feature_indices_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "노트: 이 특성 선택 클래스는 이미 어떤 식으로든 특성 중요도를 계산했다고 가정합니다(가령 `RandomForestRegressor`을 사용하여). `TopFeatureSelector`의 `fit()` 메서드에서 직접 계산할 수도 있지만 (캐싱을 사용하지 않을 경우) 이렇게 하면 그리드서치나 랜덤서치의 모든 하이퍼파라미터 조합에 대해 계산이 일어나기 때문에 매우 느려집니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "선택할 특성의 개수를 지정합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최상의 k개 특성의 인덱스를 확인해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  7,  9, 12])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_k_feature_indices = indices_of_top_k(feature_importances, k)\n",
    "top_k_feature_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['longitude', 'latitude', 'median_income', 'pop_per_hhold',\n",
       "       'INLAND'], dtype='<U18')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(attributes)[top_k_feature_indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최상의 k개 특성이 맞는지 다시 확인합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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')]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(zip(feature_importances, attributes), reverse=True)[:k]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "좋습니다. 이제 이전에 정의한 준비 파이프라인과 특성 선택기를 추가한 새로운 파이프라인을 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "preparation_and_feature_selection_pipeline = Pipeline([\n",
    "    ('preparation', full_pipeline),\n",
    "    ('feature_selection', TopFeatureSelector(feature_importances, k))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_prepared_top_k_features = preparation_and_feature_selection_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "처음 3개 샘플의 특성을 확인해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962, -0.61493744, -0.08649871,  0.        ],\n",
       "       [-1.17602483,  0.6596948 ,  1.33645936, -0.03353391,  0.        ],\n",
       "       [ 1.18684903, -1.34218285, -0.5320456 , -0.09240499,  0.        ]])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared_top_k_features[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최상의 k개 특성이 맞는지 다시 확인합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962, -0.61493744, -0.08649871,  0.        ],\n",
       "       [-1.17602483,  0.6596948 ,  1.33645936, -0.03353391,  0.        ],\n",
       "       [ 1.18684903, -1.34218285, -0.5320456 , -0.09240499,  0.        ]])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared[0:3, top_k_feature_indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "성공입니다!  :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "질문: 전체 데이터 준비 과정과 최종 예측을 하나의 파이프라인으로 만들어보세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "prepare_select_and_predict_pipeline = Pipeline([\n",
    "    ('preparation', full_pipeline),\n",
    "    ('feature_selection', TopFeatureSelector(feature_importances, k)),\n",
    "    ('svm_reg', SVR(**rnd_search.best_params_))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('preparation',\n",
       "                 ColumnTransformer(n_jobs=None, remainder='drop',\n",
       "                                   sparse_threshold=0.3,\n",
       "                                   transformer_weights=None,\n",
       "                                   transformers=[('num_pipeline',\n",
       "                                                  Pipeline(memory=None,\n",
       "                                                           steps=[('selector',\n",
       "                                                                   DataFrameSelector(attribute_names=['longitude',\n",
       "                                                                                                      'latitude',\n",
       "                                                                                                      'housing_median_age',\n",
       "                                                                                                      'total_rooms',\n",
       "                                                                                                      'total_bedrooms',\n",
       "                                                                                                      'population',\n",
       "                                                                                                      'households',\n",
       "                                                                                                      'median_...\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]),\n",
       "                                    k=5)),\n",
       "                ('svm_reg',\n",
       "                 SVR(C=157055.10989448498, cache_size=200, coef0=0.0, degree=3,\n",
       "                     epsilon=0.1, gamma=0.26497040005002437, kernel='rbf',\n",
       "                     max_iter=-1, shrinking=True, tol=0.001, verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepare_select_and_predict_pipeline.fit(housing, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "몇 개의 샘플에 전체 파이프라인을 적용해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예측:\t [203214.28978849 371846.88152572 173295.65441612  47328.3970888 ]\n",
      "레이블:\t\t [286600.0, 340600.0, 196900.0, 46300.0]\n"
     ]
    }
   ],
   "source": [
    "some_data = housing.iloc[:4]\n",
    "some_labels = housing_labels.iloc[:4]\n",
    "\n",
    "print(\"예측:\\t\", prepare_select_and_predict_pipeline.predict(some_data))\n",
    "print(\"레이블:\\t\\t\", list(some_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "전체 파이프라인이 잘 작동하는 것 같습니다. 물론 예측 성능이 아주 좋지는 않습니다. `SVR`보다 `RandomForestRegressor`가 더 나은 것 같습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "질문: `GridSearchCV`를 사용해 준비 단계의 옵션을 자동으로 탐색해보세요.\n",
    "\n",
    "사이킷런 0.20 버전에서 `GridSearchCV`의 `n_jobs` 매개변수를 -1로 했을 때 에러가 발생하는 경우가 있습니다(https://github.com/scikit-learn/scikit-learn/issues/12250). 에러가 해결되기 전까지 매개변수를 1로 설정합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n",
      "[CV] feature_selection__k=1, preparation__num_pipeline__imputer__strategy=mean \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  feature_selection__k=1, preparation__num_pipeline__imputer__strategy=mean, total=   6.3s\n",
      "[CV] feature_selection__k=1, preparation__num_pipeline__imputer__strategy=mean \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    6.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  feature_selection__k=1, preparation__num_pipeline__imputer__strategy=mean, total=   6.3s\n",
      "[CV] feature_selection__k=1, preparation__num_pipeline__imputer__strategy=mean \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  feature_selection__k=4, preparation__num_pipeline__imputer__strategy=mean, total=   7.1s\n",
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      "[CV] feature_selection__k=7, preparation__num_pipeline__imputer__strategy=mean \n",
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     ]
    },
    {
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      "[CV]  feature_selection__k=7, preparation__num_pipeline__imputer__strategy=mean, total=   8.2s\n",
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     ]
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      "[CV]  feature_selection__k=10, preparation__num_pipeline__imputer__strategy=mean, total=  15.3s\n",
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      "[CV] feature_selection__k=13, preparation__num_pipeline__imputer__strategy=mean \n",
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     ]
    },
    {
     "name": "stdout",
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      "[CV]  feature_selection__k=13, preparation__num_pipeline__imputer__strategy=mean, total=  21.0s\n",
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      "[CV]  feature_selection__k=13, preparation__num_pipeline__imputer__strategy=mean, total=  22.5s\n",
      "[CV] feature_selection__k=13, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=13, preparation__num_pipeline__imputer__strategy=mean, total=  20.6s\n",
      "[CV] feature_selection__k=13, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=13, preparation__num_pipeline__imputer__strategy=mean, total=  17.4s\n",
      "[CV] feature_selection__k=13, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=13, preparation__num_pipeline__imputer__strategy=median, total=  18.8s\n",
      "[CV] feature_selection__k=13, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=13, preparation__num_pipeline__imputer__strategy=median, total=  22.2s\n",
      "[CV] feature_selection__k=13, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=13, preparation__num_pipeline__imputer__strategy=median, total=  23.2s\n",
      "[CV] feature_selection__k=13, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=13, preparation__num_pipeline__imputer__strategy=median, total=  22.6s\n",
      "[CV] feature_selection__k=13, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=13, preparation__num_pipeline__imputer__strategy=median, total=  20.7s\n",
      "[CV] feature_selection__k=13, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=13, preparation__num_pipeline__imputer__strategy=most_frequent, total=  19.6s\n",
      "[CV] feature_selection__k=13, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=13, preparation__num_pipeline__imputer__strategy=most_frequent, total=  21.3s\n",
      "[CV] feature_selection__k=13, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=13, preparation__num_pipeline__imputer__strategy=most_frequent, total=  21.8s\n",
      "[CV] feature_selection__k=13, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=13, preparation__num_pipeline__imputer__strategy=most_frequent, total=  21.5s\n",
      "[CV] feature_selection__k=13, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=13, preparation__num_pipeline__imputer__strategy=most_frequent, total=  19.2s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=mean, total=  18.0s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=mean, total=  20.8s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=mean, total=  21.1s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=mean, total=  21.2s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=mean, total=  19.4s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=median, total=  20.6s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=median, total=  21.5s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=median, total=  21.0s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=median, total=  21.0s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=median, total=  19.1s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=most_frequent, total=  20.8s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=most_frequent, total=  18.5s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=most_frequent, total=  19.4s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=most_frequent, total=  21.6s\n",
      "[CV] feature_selection__k=14, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=14, preparation__num_pipeline__imputer__strategy=most_frequent, total=  25.0s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=mean, total=  21.6s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=mean, total=  20.6s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=mean, total=  22.1s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=mean, total=  17.5s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=mean, total=  20.3s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=median, total=  18.6s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=median, total=  20.8s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=median, total=  22.7s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=median, total=  22.7s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=median, total=  21.0s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=most_frequent, total=  22.3s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=most_frequent, total=  22.3s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=most_frequent, total=  22.9s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=most_frequent, total=  18.8s\n",
      "[CV] feature_selection__k=15, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=15, preparation__num_pipeline__imputer__strategy=most_frequent, total=  22.3s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=mean, total=  20.9s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=mean \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=mean, total=  21.3s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=mean, total=  20.1s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=mean, total=  21.4s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=mean, total=  18.4s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=median, total=  19.2s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=median, total=  21.2s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=median, total=  20.2s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=median, total=  17.5s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=median \n",
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=median, total=  20.6s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=most_frequent, total=  18.7s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=most_frequent, total=  21.0s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=most_frequent, total=  19.9s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=most_frequent, total=  20.0s\n",
      "[CV] feature_selection__k=16, preparation__num_pipeline__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=16, preparation__num_pipeline__imputer__strategy=most_frequent, total=  21.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 240 out of 240 | elapsed: 52.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('preparation',\n",
       "                                        ColumnTransformer(n_jobs=None,\n",
       "                                                          remainder='drop',\n",
       "                                                          sparse_threshold=0.3,\n",
       "                                                          transformer_weights=None,\n",
       "                                                          transformers=[('num_pipeline',\n",
       "                                                                         Pipeline(memory=None,\n",
       "                                                                                  steps=[('selector',\n",
       "                                                                                          DataFrameSelector(attribute_names=['longitude',\n",
       "                                                                                                                             'latitude',\n",
       "                                                                                                                             'housing_median_age',\n",
       "                                                                                                                             'tota...\n",
       "                                            kernel='rbf', max_iter=-1,\n",
       "                                            shrinking=True, tol=0.001,\n",
       "                                            verbose=False))],\n",
       "                                verbose=False),\n",
       "             iid='warn', n_jobs=1,\n",
       "             param_grid=[{'feature_selection__k': [1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                   10, 11, 12, 13, 14, 15, 16],\n",
       "                          'preparation__num_pipeline__imputer__strategy': ['mean',\n",
       "                                                                           'median',\n",
       "                                                                           'most_frequent']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='neg_mean_squared_error', verbose=2)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = [\n",
    "        {'preparation__num_pipeline__imputer__strategy': ['mean', 'median', 'most_frequent'],\n",
    "         'feature_selection__k': list(range(1, len(feature_importances) + 1))}\n",
    "]\n",
    "\n",
    "grid_search_prep = GridSearchCV(prepare_select_and_predict_pipeline, param_grid, cv=5,\n",
    "                                scoring='neg_mean_squared_error', verbose=2, n_jobs=1)\n",
    "grid_search_prep.fit(housing, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'feature_selection__k': 15,\n",
       " 'preparation__num_pipeline__imputer__strategy': 'most_frequent'}"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_prep.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최상의 Imputer 정책은 `most_frequent`이고 거의 모든 특성이 유용합니다(16개 중 15개). 마지막 특성(`ISLAND`)은 잡음이 추가될 뿐입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "축하합니다! 이제 머신러닝에 대해 꽤 많은 것을 알게 되었습니다. :)"
   ]
  }
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