{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 2 – End-to-end Machine Learning project**\n",
    "\n",
    "*Welcome to Machine Learning Housing Corp.! Your task is to predict median house values in Californian districts, given a number of features from these districts.*\n",
    "\n",
    "*This notebook contains all the sample code and solutions to the exercices in chapter 2.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/02_end_to_end_machine_learning_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/02_end_to_end_machine_learning_project.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"end_to_end_project\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "import urllib.request\n",
    "\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
    "\n",
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
    "    if not os.path.isdir(housing_path):\n",
    "        os.makedirs(housing_path)\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fetch_housing_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Take a Quick Look at the Data Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure attribute_histogram_plots\n"
     ]
    },
    {
     "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": "markdown",
   "metadata": {},
   "source": [
    "## Create a Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to make this notebook's output identical at every run\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# For illustration only. Sklearn has train_test_split()\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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set, test_set = split_train_test(housing, 0.2)\n",
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "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": [
    "The implementation of `test_set_check()` above works fine in both Python 2 and Python 3. In earlier releases, the following implementation was proposed, which supported any hash function, but was much slower and did not support Python 2:"
   ]
  },
  {
   "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 hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want an implementation that supports any hash function and is compatible with both Python 2 and Python 3, here is one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "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()   # adds an `index` column\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": [
       "<AxesSubplot:>"
      ]
     },
     "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": [
    "housing[\"income_cat\"] = pd.cut(housing[\"median_income\"],\n",
    "                               bins=[0., 1.5, 3.0, 4.5, 6., np.inf],\n",
    "                               labels=[1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    7236\n",
       "2    6581\n",
       "4    3639\n",
       "5    2362\n",
       "1     822\n",
       "Name: income_cat, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"income_cat\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114583\n",
       "1    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 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    0.350581\n",
       "2    0.318847\n",
       "4    0.176308\n",
       "5    0.114438\n",
       "1    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 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</th>\n",
       "      <td>0.039826</td>\n",
       "      <td>0.039729</td>\n",
       "      <td>0.040213</td>\n",
       "      <td>0.973236</td>\n",
       "      <td>-0.243309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.318847</td>\n",
       "      <td>0.318798</td>\n",
       "      <td>0.324370</td>\n",
       "      <td>1.732260</td>\n",
       "      <td>-0.015195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.350581</td>\n",
       "      <td>0.350533</td>\n",
       "      <td>0.358527</td>\n",
       "      <td>2.266446</td>\n",
       "      <td>-0.013820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.176308</td>\n",
       "      <td>0.176357</td>\n",
       "      <td>0.167393</td>\n",
       "      <td>-5.056334</td>\n",
       "      <td>0.027480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.114438</td>\n",
       "      <td>0.114583</td>\n",
       "      <td>0.109496</td>\n",
       "      <td>-4.318374</td>\n",
       "      <td>0.127011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Overall  Stratified    Random  Rand. %error  Strat. %error\n",
       "1  0.039826    0.039729  0.040213      0.973236      -0.243309\n",
       "2  0.318847    0.318798  0.324370      1.732260      -0.015195\n",
       "3  0.350581    0.350533  0.358527      2.266446      -0.013820\n",
       "4  0.176308    0.176357  0.167393     -5.056334       0.027480\n",
       "5  0.114438    0.114583  0.109496     -4.318374       0.127011"
      ]
     },
     "execution_count": 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": [
    "# Discover and Visualize the Data to Gain Insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Geographical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure bad_visualization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\")\n",
    "save_fig(\"bad_visualization_plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure better_visualization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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L5xqUHPOOZeOTu/T1jk8QZ1Rck5mKsz96XYvciN080tuzOx4JfzD8BHCjNeLd7QHdIMWUgmGU4toGaaY5N+Xh2eahvivgUI4qHhsrKaEzSvDThPd2+ggpqDkWtm3imMapRlEopdkZRHi2gRS5MdrsBpybKrFU91BKc601IssUNc/mxZXGfmXgWYzOWUJx96owUeScCgoeTz7yBipMMtY6Plv9iO4oOvF9ddfkc88v8MkLDUxpUrbNQ+MrTsoZuZbBhekyaHAsiW0axGkeHhQaMo7brBUzVYfWMN6vVmuWbb7RGiGkwLUkliHphwlTJYtUaewD02snmz1w6Ny2ZVC2Db55o8soSnh9vcOb2wMMJIs1j2dWqvhRimtJLjQr+0UcxzExMqmC3UH+vaMkX/f5mTKGIZit2kghuNCsUPGs/WPPYnRO27B8PyXjRS9UQcHjyUfaQE1yPZ5tcKFZolkrcWPg3/a+i1OCH35+kY+fa1CybKqeeag0+m4ly6YpWZkusdULaQ8jWqM4l0TqBreVqE8267Jt4jWM/b6kbLzxmkLkDcNKkWSaTCkMmasyYNwugnrw3HGSsTeMGEURv/vWLl+/3mWUQsmA/shnGMU8tVhjvuHQHiW8vDp1opEyRB7O2+wGY0Mg0FqzO4hYqLkYQuBaJpZxe0/VWVUy7haKu5+S8aIXqqDg8eUjbaCSTBGnGZYhKVkWTy82aA8jrvduJezrBsxUKsxVXIZRhiUzUpXncial3KcJWU3UxK+1R5yfLmFbxrEl6pCXU4dJxlY/3DcuCzWXhZrLjoAwVWx1A5TOizfmqzZ+nJGoW5vsZF0Hz51mCj9K+fL7Hb58pUsw/o79DPoDzc6gj5SC5UaJCzMVNroBl2Yr++c6ahzqJYv1lp/r/EnBQt3LiyR6AZ5lUHLME43FWfM/dwrF3WuequiFKih4vPnIGqgwydjoBmz2QjqjmHrJ5FKzQhCGBGGbDDAlGIZkbxjS8WNmqx6DOOWZ6So7g4jVcVhtErKK02y/v+e4kNVBNXG4tZFOStRHccpOL+Rmx+dmJ8Q2BI5toMfFDeemS1impFmxmS5ZLNRdGiUnH/WRKpYaHpYh93UBDzYSW6ak78e8u9PnrfXWvnE6dE2AgR9yszNCCTjfzNfUGsaHPAxgX89Pyzz8WSvZebVgmgvrHhyXcZLU01nzPyeF4k4KGQqd34ScZACLXqiCgsebj6SBmtw5O6ZkZcqjNQh5b2eENCUXZhu8sRmQZhmJ1mRKEySKwShhoeGCFpRskyhVh0RYGyWL1272yJTe17I7zSgJMV6PELDRDeiMYjKluLo7YK7mcrFc2S9IuDBT5kKzTJhmOIaxn9fJ805q//OOegU7g4iVhkfLjzFNSRCdXKLdGsV0g5RamJAoxd4gOqR4sdkN0IBjSjzbYqUBG70wr9wbjyTZGUSHvuNxUk9nDaPdKRR3XMiwUbJY7wa3GemDxuq0NxYFBQUfDB9JA3XwzrkkBQtTHonWnGt4fGu9g2MKun5C2bFRIi8pf3dvwFpryPJ05baNTClN1084P11CSIEeP6651iEjdXQjTTIFGm52A9JMsdbxmSk7KJWXX3f9mCTN9ef0uOhBSoFrGhjG7Y2+QkOYZaRK3SaoGiuF0FC1TGxLwAlGqj3SDEY+QcVhpuzgJ+qQNzSK8jBkeZybKrsWC+S9Xq6Ze5QLUhwal4HmVCXlJ3GaUNzBkKHQsN4N0Ci00oRK8Y0bPou1PM81U3Uo2+P1OwZvbvbRmhNvLAoKCj4YPpIG6qgnM1d22OyGIAQvLE/R8SP+zTc3UGhsKZhreFQdiyiBIMpIMn1reCG3DJ433vQwYBQdL11kSMFKwyPTmpvdYH/j9qOE7ihh2rMxTcl02Wa94/N+a4QhJA3PIlO3jNRJHkOWKbb6IRL2pZgmwxQ14Nomn7www2+9sctQcRsKuNaJefG8gWMZhKk+ZAgtQ94m2GpKuW+c4LCxUCr/nkdDfmcJo93SSJT7ITul1W3nmIQMk0yx3fd5Y7NPnCiGccrluSqzVYdWL2S9EzBTsQHYG8ZYUjBXc7ENeeyNRUFBwQfDR9JAHd3gTdPgh5+Zoz2MQcCPP79Ef5SQZgIlMhqeg2FIPn2piQaWGx7OgRDVacqmj4aomuMNcrJxu7bJVCkXUjWVomqbNDybuYqD55g0S/ahvNdxHoNlCDzbYlHkYbcF2G8kNk3JfM3FNg2eW2wwijPe32xzbTD+DoBngGlCo2SQKdgdRqw0SofEWRcbHsCpFdOV0GeSFEpTtV+5KGWuFK+zXCVjuxdgjCWjpsr2iefIEsW31rqULIta1WawO+D3r7YIkgzPyhUp9kYKQS5B5VgG3SBhqeGRxFmRgyooeEz4SBoouL2KDNgP+1yYLpNqzTeutmn7CbYhefF8A9cyyDT71XunHXNxXIjqaDOuUpqVKY9MKbYHEVrmqhDLUyVKTi6uetQrO+gxHEz2V1yLRX047AZQdS0+tlznPUtyo+NTdecx1ju0hzGJAtcSVB2bumsjpGAQxFgzlWOr7U5bgXeWkvKuH/Pqeo84y0BDs5obofYowY/T/T4rQ4zlnU5oKI60omyb2KYkSDKiJM/xtQYRkdLEccaF2TKLNRc9vs/IlCZOskOagkVvVEHBB8tH1kDBrQ3+uAT8SyvTTJccuqMIP843xihTLNXz0N6dxlwInVfsTTbQo9ViUuYexXzNpeMn+xv3uekS2/2QizMmlpSsdXz2hhGr43LtkzyP4zw4wzgcdpt87mqzzO4g4vmlBmutAeLcFNdbQzqjCFsIFhoe81WPMEx5a2vI5bkatZJ9m0dxlgq805SUp6nim9c7jOIUKQU3WiPe2FQsN0oY4+t1YabMKE4xDYO9YYxjjliZLt1WcOEZBp5t4pkS07QYhjFrrZRhmFJxTRo1h4EfYxmSj6809oc2ZhqWGu49KbwXFBQ8eD7SBgrunIB/YrZKNlMhivOeJMg15+bG/7cMgZR5A+xGN+BCs0w2Pt/Bzc025L4BSZVmqxcQpxrLyMNuk+qyibhsaezJLTY8brR8BkGCbRoneh5n8VIsM6+0q3omG90RK40SnmngWSZvb7YJkpibPU2mXUoDm69ebfPZyzN3VJU4DXczaGGasTuMmKk4hEnKdj8kTHJx2YW6S6sfsdRw6fop81VJyTEwJPvX/eB3tW2D731yht99b484TNFK8KkL0wgBwzhDayg7FhXbIM4U02Wb+ZpL1c0LS260/aI3qqDgMeAjb6Du1AtjGRIU7I1iPNu4NXq8l5dMCyHZ64d5KXqcUXNN+mE6Lnw4vLkt1F02ugE3O3muaLVZwpQizytNNj91WPnBlIKVKW+/v+lOG+RpG18nrw3ChPm6hzM2WDv9gM1BTHcUIyUMgpSSa7M7DLjRHvHUfO2On3+/IbGJF5hkio6f5AMdjbwy8crOEMuQXG+NyJRgumwjUewOY6JEMVt19o3LZC3NisNPPL9IpPNKvtdu9tkdRkx5Fo2yhURQ86xcEUMKOn6CYxkYY0X3ojeqoOCD5yNvoO5W4DBRm3DMW2XbYpxI3xoGOKZBqhWdUcwr610sIVmdLWMahzc31zJYbnikmaJ6oErs4OZ3nCe0eKQg406cJuw2KbNea/tUXZNhmOI4ki++t0emNI2yS5KmrPcCkis7CKDqWczVXBqefazxOY1c0N0MmGMZPLVQ4d3tAYMwwbNMKo4EAUkGS3WHxbqHn2QIoSlZVt6gLDQ7/ZCybZ4YenUtg5fPT3Ftb8juMEYpwXQl/y6l8Uyvyc3ESsMr5kQVFDwmfOQN1J3CYxO1ie1+RHsUs9jwMKXAMCQzJYvtfoQmY6cfMVvLhVEznYfwVpvlPAd1YHOzjFwsVo0N0nGb36MYAVG2TVamSyzUHDphwmZrhBRQ9/JRG3vDjDhV9OOUIM149UYHgGcX6iwfyfmcpkfpNAZMSsFT8zU80+Bae4QlBEIItgchs1WX5xdrlD2LvUHE3jAiUwntYcx0xWajGzJXcynb5olrKTkmzyzWeSK7VVt/XPm7FpxJJ7CgoODh8YEMLBRCXBZChEKInzvw3OeFEG8JIXwhxG8KIc4/qvVMjMK56RKr4w34oNrEarOEEHCj5ROnioV6nq9YmfKo2rnB6Y5S9gYxs2WXONUMguS2IXinHZJ3dPDhg0ZKwVLDw7ZMmmWHC/MVlqc8LAMSnZFmKZnO7166g4hOkPLqeo/3W0Out0aHhgXeCpHe2ugPDj08aMA8ywA0m93g2IGDrmVwcabCJ89Ps9IsszjlsVD1eH6xRnUspVRxTGYrNruDCEPmlY1a54oXt6oZj1+LlALHyvu7rAN5QTgssnvcv4eCgoJHzwc1UffvAb8/eSCEmAH+FfDXgWnga8D/9CgXdNw03Mlm51oGq80y8zWHpYaHO+5Fmq+5tPwErfPhf3M1h44fs1R3Od8sH7u5PS6b32Qd55tlXlye5s9/3xPM11x6owwQLNZMFhsVtocRozhDCEEQ555QcsALORgihdvV1G+N5cgbdncGEeudgNF4+u9BwiRjvRvQ9RMMIVise3zq4jTmeH5Wkmnmay5C5EMgDUOQZnml5MTg3WktB7nbzcLDvkkoKCi4O488xCeE+BNAF/gS8OT46Z8CXtda//z4PT8D7AkhntFav/Wo1wi356aU0timsd8DBXlF3HLdY7nh0R7FaCBOFXN19455o0c9JO+k/M/Bdbx4bpr/5A8/z//362v0Bz6vbAwZRjEZBpaEKFMofUty6eA5JiGxIElAc0hl405jOSZ5o8kaj4bnWsOY1bEhn6w/0xrHMpituXRGMaYU7A4ipko2liFvTfkVd552DKcPpxY9UQUFHwyP1IMSQtSA/wz4Px956Xng25MHWusRcGX8/NFz/LQQ4mtCiK/t7u4+tLWeJhxniDwf5VoGK9Ml5qsOy1PefsPv40CYZNxo+6yNR8WHSXbs+5TShImmWfLwU4FhCpSQOJZEGhJT5FNz52vuISMNt8a/Q65kvjOI9j9HyrzqLkwywiQvLlmo52oUmb5zqDDLFGGan2fizUxuHLTSmKZEaRDkz4VJxs64ARpgrurc1UO9m6d02utXUFDw4HnUIb6/BfxjrfXakecrQO/Icz2gevQEWuuf1Vp/Smv9qdnZ2Ye0zJzThOOaFZsoVQRxhkYc8h6OopTO8yTH5F9O8/pZOeiVlB0Ty8hFXI87f6Y1gyihG4QESUbZslmsu3mjLJqyY/DsYo3zzfK+xt7kM6IkY7sf4lm5wvrBz+mOIt7Y7NAdhfhpQqNkwdgwHQy9HQ0VDsOEzX7IRjc4ZBikFMxWHRCCqZJFs2Lz8XMNbEOy0Q0wBDi2gWNIdgbRoe86ub5pqoiSjCjJSFN14jVXKs+XgcazjDtev4KCggfPI7vVF0J8HPgR4BPHvDwEakeeqwGDh7ysu3JSOO5gZZqAfYXsO92J36mS7WFMdj3LvKMoznhzq88wzHAdk5pn0RrFlG3BYr3M/+rlZWYqbr7pj9dYc03WOyO2BgHtQcQLy1M0K+7+57y52eH/9Rtvc3V3hDQkF+drvLhY5/JCnfl6rtjgyttHZoRxbpyW6u6+4O2kGg/ANiRLdRfDEDjjqsg0yYjSjG6Q7K+v7Bj739WPUjZ6AdH4OqfjCkvTyD/Xs8zbrvkoTlnvBLkXKXLDOCm6KHqiCgoePo8yFvVDwAXghsjvnCuAIYR4DviHwP928kYhRBl4Anj9Ea7v1JyULylPH38571aKfdzrG92A5VM06B79nIO5ktOI2O6vbxAyX3Xx44zuRszWIKTiGsxVPb7vqSYLVY/r7RGGIfAsk1GY8HNfvs7vvLPL1a0hSsPSlM2f+f7LvHx+mpsdn//uV9/g2xsBk6DYu1sBSaT4oWfmcW2TzW5wqAl54rGGaYYWuRo73DKsB4cnpkqTao3S4xlTVZdvrXdxTUnJNgnjlNYg5sK0IoozvrXeRaDZ7IW0hjGWAUII6p5NxTGpjEvUD/5MdgcRtpkLykKeR5sfj7MvKCh4+DxKA/WzwD8/8PivkBusvzh+/F8LIf494BeBvwG88kEVSNyNs3gmSmnC9PgZTZP3Hz1fqjQ3O/mMqInE0d28qZM8sLuJ2E7CdUJDzbO5NAObnSAXih0XHSgN7+z0eWNzgGsZTJdtdno+//M317nWTvYNUGc35r/7tbf4M5+5wLW9IW9u3TJOAIGG37u6S290EdM0WO8EJEe+42TelSkPz7sCDg9PNBVRqvaNeKY1zXKuBu/H+aytKFVcb49ymSoNNc+iFyREWZbP+tIwijPiNENIgRoPoVSpZpSkZJlioe6N+640SaaZqTpFoURBwSPikRkorbUP+JPHQoghEGqtd8eP/z3g7wI/B/we8Cce1drOymk9k4nROGlG0+T9B88npWCrl8shVd1cLeFuWnB38tBOqlQ7GqJMlabumdxojbCk4KnFOpdmSxiGwfs7I+YbDlXXxJCC3WHIV67sstVNOFoycLOf8a1rbfws47hUTZhAyw+JtcA2BVXP2l//JISXac1c1Tk05mO26rA7iI401ubXayIT5dkmFdcEDWsdn4pr5Lk3KWn7cS4ea0gylXtehhSEcbZfdCGFYBAkvLHZJ8kU7VHMswtVlhoecZKRaR6rApiCgg87H9hvm9b6Z448/jXgmQ9mNWfjNOKshxpUT5jRdLSBd6sXEkcpcapZbZb2819304K7m0d3NI92nEGLs3xycMU1mao4rDQ8yq5FP4iJVa6OPlezaY9i4liRKI1lAdHt6+knCY408WyIjrxe9ySDSOFYivMzZaQQSEPcFsI7OqYdoCXiE28KDl3DNCNTsDzl5X1stkE1M8kA15K4Zl55qTWU3VyTL9MwU7b3w4SNko1jCt7cGvDissQyDZYahaJEQcGjpLgdvEfu1kNz1GicNKPp6PmS8dRac/z6abTgTuvRnbQ205BYhmSx5qLQoBSjJCMNFJnKG5Il+fmaZRvbEDy7VOO19Tbd6HDDrQ1YUrLSrNAPE15dH+3bsHkPfuKlZVany7i2gYRcTy/J9nujJkK7cZKx0Qu4MF1GGvn3OHhTMClMOekaTsR2pRBMeRZBnDFbcag6JgqNJXNvaqnu4dgGhhCEaUY27ndLM0XFsZkua+bqLlWnmLJbUPCoKQzUfbA/NXZcvnzQUJ12RtPR8znSYLHhnUkL7rQe3cSYnmTQNNAPEl7f6NMe5Tp33/vkLE8t1Njuh+z0c1MzV3UpOU3iVPPzX73O9V5upDwBn7o0xZPzNSquwcXZKjNVmzc3OsQRuJ7JW1s9Mq15YWUa25R4toFtGDTLFkLk5fB+nLLdy8vdJ42/limxDcnqdIlRnLI7iNgdRLREfChHN7mGSwevoZS8vDpFNjaCSuv98x4cI2LLXBrpemuEZUqSVFFxTcrWydWZBQUFD4/CQN0HSmlGccq11oDBKKZatrnQrO7nKZoVm71BlOdKjilQmEgGHa3UuxfB2Dsdc1wBxVGDNld1uLY35CtXW3SCBIRgFKbsDiNetk1qc1UuNMv7a1/r+PzkJ87x2UtNfuetTdbaIZYtOT9b389VJRlEqSJKJVuhgjDl/Xaf1zf6DIOEZtXlY+emeGquSpop1sZ9TG9uD8gyjWnmob9v3OiwWHPzMGPVoTWMx56WvK0i8qTrAfmcp4PH7QwiVi3jUKi1WbbpBwlhkiGAZvl4BfeCgoKHT2Gg7pEwydjsBvz2u9t84a1dhMj7T3/w6Vm+++IMUggsM0/ozx7pkQqTjOutETc6I0I/ZXba4YWFKSrerZlGd5NDOk5+57hj7lRAcXADTzLFjbbPdi+kWXEwpGAQpFzZGeI/mVIr2TjSyL93P2S7nw9sXKh5vHRxnpXZCC3AQFBycxUIywA/ien66tCa2iF8e73D916e48rukKpj4dgGUZLx1taArX5A2baoCIO3t4fMVR0c20CQDygUgGfnBSQAqVLH5ugOXo9bQrInV15mWlNxLT51cZo0VZimJEryRl6pRSF1VFDwiCkM1D0w2fT9KOYLb+3gWSYKkGi+8PYuCw2Hhufuj9w42CM1USd4/WaXr1xpsdMPERI+fanJv/9d55kuO4c+57Qe0Ull6HcbyLi/qY/L8cSBvFU+3/fWWpJMsdkN9hXet3oB77dGmAhevjBNEKd8a63L199uEYQZMRmDQUo6Po0EJqZqbxCz0fHpBhkrjRJLUx6uLWkNIhZqHhXHJNOaa7tDFsa9R1IKEBkaGIUJnSAhTnLjtFT3bpNgOsid8nST6yx0nhPr++Nm5FiQKrjeUvuyVsX494KCR0dhoO6ByabfCxP6QYIpJR0/Zrri0A0SeqOMupdv6sf1PO0NQ37zzR2GUUrZNUEIXlvvsTqzyw9fXsCxck9lveOjtMYx87zUwTEgpx1JfrSEPR5LBh0toLAMybnpEvMVh71RDGgsw+CJubzS7kbbJ04ztvsRq83SvsJ7z49zT0VDx4+5tj3k7e0BIz+h7UdopfbNnDq0LmhWXBQCyL/T03NVNrsRM1WbUaSI0ly7r+oYKDTxuCR8ruLwjbUOCoUtDRbGJekHw3VHOSlPF4+vX5Yp+mHMm1t9vnKlTWsUU3VMVhoOn7o4w0zVZapkFePfCwoeIYWBugcmm3t/lGAaBluDEK1hdxBiCcnWwMcyJfM1F+BQRZ3QsNMPibMMy5JIkYeohIbNTsiV3SFKK663AuJxRV+jZBJnistz1TM1CcOtjfn63oidwbjIoeYckhmavO+JuSpJpvj2WoeOnzBTdlhplFjr+JRsg7JrYvsxm92A880ySmtcy6Tmmry23uOtnS5fvrqLbUocx6SBph/ELJY1myNNRu5FTXmCF1YaXJqrYpoSIfJG291RSM3Nja4tIRFwbrrMdj/iza1hPguqaoNS+FFKkGRIUpSGumeSZC6OPN67UUpjSMFKw8tDkQfyUplS7I4ivnplj2/e6OQSSBI2+wHtUYRlSL73qTm2Bxl128SPbUp3kLUqKCh4MBQG6h6YJNMHieJ7Lzf5zTf30DLDj1L+4ItLLNdL1D2T9XbA8pR3SEBWC1hplKh4Ftf3RvlYDg0V28A2DXZ6Ad/e6LLZDnhmucZSvcwwzECHXGiW9wftxUmufjBpML1TGbptSGxTcm7a29euO6mw4IWlRl5pKKDsWAyjhFdv9lmactEKyo6k56cMwgTbzFXMt/ohy9Mu7chFGoJ+lGFJQYbAtmwuTru8aEKYpoQprEyVeX5lilrJIk40QoApBVpBksLNdp/3d0coAZfna0y5FuvdAe9tD1GJQpoGzYrDiyt1yq7NO1t9yq5JyTJvm/gLx4dELUuSZIosU3SCBK0UozgDmWvwWVIQpxpLKN7bG4KUGFJgCXiyXeXCTIUn5qpFuK+g4CFSGKh7xLUMFiou56dLrDQ8hlFGdxjxyfPT46ZOj1GU7kvxTMrQDSGolWy+78kZpBZsDwK0gHPNEjXP5MvX2my0R7RGMaYhsAyDRsneLwiQUtAoWbyy3t3vmXpxpXHHu/lM59mk0ri68E7Nv1qAbeUKDEpp+mFKphRreyPWugFpqlhplnhmscZs1WUUp9wcC6qWbRPXNOiMIiIkYhzcG0YRT8xO8/yFBv1AMQwTzs2U6YwSUq1YaLhMl2zafky7PeLd3RH9MKXjx+z1I97a6rPTTwjHU36nvFws1o9SFuseVc9itmJjjNXGDxreO4VEDSHQAuI0wzHzXigJDMIU0MSZwpMGV7cHXN8bUrFNLsxV8JM8j5hkihdXpgpPqqDgIVEYqHvEMiRzdZduEDNbKyEGIaZhjCfr5qE92zTIlGaz5x+6e5+pOsxUPH7yk8sEQcJMw2G3G/HN9S5vbXTo+ilJnPKeUjim5KnFGk/M5N6TUprtXoghQSlIs4yNjk/NtQ5tyvciGgu3BgwGcZ6nCeMUTa4N6NkmqZFhSnh9s8enbZOdfr7524ZkynX4vqdm+cKb29zsBgRJhsrgZjfh9fWbLL6xyZPLDZ5bnmKp6vLEbIV3tgdMlyxsQzIKE9baPnGqsKVAZZpXd9psD/V+/ioFdgNFnPrYlsQQeSn7fM0FDYrxiHfFvs7gnYpEluqTfinF6kyZXhjRHSXs+glSKa4PQ1C50UYJdgYx0xUXKSVvbQ14er6G5xS/RgUFD4PiN+sekVJwfqaM1c03/MW6S82zGIQpmdJozb6e3NG795WGx/KUhyHAnJGkqWIYpHz1yh5vbw3RgAG0/AjHFJxrlkhVfkcvgGutETs9n5vdAKXAsSWuI5mreKBhbxTfVuE3V3XY6AWIONuvRjvOoMWZIk7Vvg7e9mT+0Xh8xSjSDIKM3UGfqmMSJprZmk13lKDQzNU8fvC5Of75l67TPSBzlAJX+oqNYRvPNNhcaeCGBkmm+fZan5prsDuMMAT4cUo/SFFoguRgLeEtgiSXjpou2RiGQaoUNzojzk2VSVLFzUlDLnmJ+UnGueSYvLw6xUY3oOzkRuzFlQa/8foG7+36xCm4NoBmFCfs9OBco4Q1lVcMHhy6WFBQ8GApDNR94FoGF8YD/CbeSrN8a7M/qaBBi1zF4PreiJ12MM4JjdjsBlgSDEMwijRJBDXb5NJslSDJ2OgGOFLw2+9s8+ZmjzTT2DLPF11vjfjEuSks0+C5xSo1z0aT9w0t1Nz9AgnN4UmzB/MzkG/mFTcXXX1/b8jSlEcvyOczXdsbcnG2QneUULbzSsM007y1MWC+5qI0zFZsfvHb6+wMj588Gyj46tVd5usen7zYpGwZ1F0DKSXLdY8r20NKpsFuGhOnCWPRh9twDPAMwep0Gce1EMB6J+D5xfptNwVJmhvd4xqmITdSl2YrJJnCs01utkYsNcp0gowgUSgEOsuIBegwJUxSXFsyU3FxzSIHVVDwsCgM1H1ytDn20GPFiaE1IUAIWG7kA/42e/lQv5IjSLTAsRK0gmrJYRRnuLZJmKS8vjvk2t6Q9XZIkqYoBSVLYjuSm92I9ijma9f2mCrZNCsuF2cqBHFKzbOwjLxAYlKSDRzKz4Rxys1+RG3OItO5Jp1tGqxMlXBNg6++32LgJyByHby9YUyWaTINoDEEdP2ENFUYQHLCNesFsDsMuNn2iZSmbFt4hqS6XOPj5xv0/QQlWrSHJlJIrm6NGByoUTcA0wRpSrphwkrJoh2kxEnGjfYI1zbx7LyfzDQklin3C1VOarbdl5mqe1zfG6KFwDYlMxWHnX6EFIKSk6tTLNVdmmWHT6xOYZqPeih1QcFHh8JAPUQmJd6b3YBRlOaCrA2POFOst302eyFlx2SmbDNTdam5FnGaYiuJIMNAMj/lkmaaTKlxk69PP8zzQMK0aI8S/ERhbA9oliz2/JROP2Cu6vHmzTbv3izx8uUpnlmYRiAwpKBkGfuhqYMenj02WtG4aECpvMKu4lpcmi3T8RMaJYONTogQYBsGaW6FWWx4ALSGIY2yhWVAeLwThWmAH2a8udlncaqMJTP2+iFKaIQQvLTaoOyafPndHUxDselC5t+a1aIB1wTblPT9mC1Dctk1WZ4q48cZwzDDMSSebe6HOk87+LFsm6w2yrzqdpmquKQ6xPTz2VgfW53ik6tTKJ2hdcKVrTYXZutMlQuV84KCh0FhoB4wx6k/TEJUevz6ziDCsSQV10Qrzd4oZrVR4oefnedL7+7QC1LKlsX5mRKDIPcKzjdLLE+VMMg327pnsd4JmNy/G4bgzY0+aaYJU832oEMnAIHPb763xx/7+BI/+cnzaKVpjWIu67G3d8DDUyqfw6R1XmpddU2kEIyilJ1BzEsrdQZRSmeUP35itsxIaapuXvG33Q8JEs2L55rs9mNeWRvcNo2jbkLVM3FdiyjTuAastUfUPQs/Tnl+scZWJ+J6a0SS5c2/wwPGyQBsCYZlMFt2WWyUME0DjWC+ZpOmGj/JuNH2MaRkruZwvlk+tQGRUrAw5dGsOoQZVCxJ2TIo2SYvLtX5jTc2+f0rPQKdK7dfmivxH/7QZX70Y4tFyXlBwQOmMFAPkKP9NpMiCceUlB1zf5T7REtupuKwN4wYhimNksVf+L4n+Pyzc7yy3qXjx5ybqmCbkrJtUHUsqo7Fi+cb/N77LTbiDMsU2HJc4SYEfqzw47xIox/fMox+DP/mlQ2Wp10+cX6OZsVGi+PVFc7P5PJMG71gX3m9XrIQGiqehWsnbPVCTFNgmwbzVYeWn7DeCbBNyUurDTa6PqbQPD3r0fETrrf6dMJcmdy1bJanSyzVSwgNQgqCKOXa3gjPMri2O6JRsZECNro+72z6t6ZckisyCXLl8amyhRxb6PYoYb3l0wsSLs9XeXKhSpoqMs3+yPbT4loGF2aqPLNQY5SkvLnRZ28Q8Uvfus7XN26Z3BB4b8fnH//2OyxPO3xidabwpAoKHiCFgXpAHNdvs9EN0FpjGeY4lCb3teTSTO1X19VdiwvTZcyxIXNNk26YUBqPeQiTjCjNGCUpz843+JOfvcAvf2udURhTcg2mah6uhN1hhBsJdvsxmly1wRTgWZBpWNuL+IHLcr/nB25X/VZKc63r41hyfy5Sz08QUhCnGR0/YbZq0xomCDSdIOVjy3V2BxFV1yJKMtAC2zS5vNxkZcojSjVdP6I9jAiSjJJl0qw69IKEdzZ7vLMz5MJ0mWrZ5EZrwGvrCSVbsjsYMjomTCgFuKbEjxSWoZivWySZphvEVF0b08w1Bl3bZBTdedjjcVjjKsfWMGIQZLiWhWGEvL5x+3TGGOiFKVe3R7ywPH2ikkVBQcHZKQzUA+L4ir2Y1jDPX9iWZMqzMKW8bZz5ynRpP9luGRLXNjHGgwCV0vSCmOt7CTd7Aa5p8uLSFC8uNfjd93b51rUWN/sxlhQ8v+wx8lO+PNrJjwVsM1dnME1oDQLe3h7w9EL9kNTRpLBjov93sxtQcU1mKnm1X5QqZqsO272QYZhSdkw+tpyvOYozSraJbaYMgpjXN/rs+RGmzCWa+kFKhsY2JI2KwxMlm/mqy3ovxDIlo6hMkiniLGO9k7C2O2TPT0njw9V74sAf04AnZivMVDykhEGU8fRilZmyjWebZEoRZRkiBa1zDb+RSvJCB9u8a2GDlILzzXLeD5ZkuKbk1Rt7pCceobCcwnMqKHjQFAbqAXG0GXbibSzVXbphSpRkrEcpL600TpzdNMlfLdRcknEvUqYUW52AsmuRKej4EV+/3ub7n5jlqYX6fjn4ZjfCNASLVY9elGDc7LHtK/wEPAOenKsy2/BIlabrx1xvjbg8V71NccExci9OK83eMGKu6iCFyIsHpkokSlEyDVzHzCfhjnXtaq7Jv3t/j1Y/IlGaizMuQsD2KKLqmcxXHbTWqEyw50dESYxjSBplydaVgHc3h/STfVH129CAK6BagtlyidmKyzBO0VojpOC9jQHXbFhtePgJrLd8GmWbJNX8Wn+LnUFEzbN4dqHKd1+aoVGy7/jzdC2DSzOVfMRHz2dltkTVhW542HCWgO+5NMuTzdod1dQLCgrOTmGgHhBH8zlKaZoVm2rJpuzmBQAb7YDtfkjHT24b23A0f3VuusSFmTLDMGGrH1Lz8jDWIExZa/ss1V2S8XjyCzNV0gw8yyRKM37o2XkWah7Xd9u0RorzDRvDdinZJqMoH6++04+40Czvh6QyrckyhWkbzJRt9kYxwzCl7lqsTJfwo5S1rk+WKtaGMVXXZBCmNMs2ax2f6+0+rb5PlKaUbBs/yXAMKNsGS9Vco8+1DDZ7Id++0eL3rrbYbvm0Yu7gmdzCABplwXTJZmnao+UnBElGlGhGUUKUZuwMI2quxUzZ4YWVKbxhiO9H7Pgpc5VcaWK9E+Ld6PKZJ2fu6kmZpmSh4bHZC1mpV7g8X+e9rR79KF/zrAd/8GMLPLPcZGkql1c6aURKQUHB2SkM1APkoGckNKx3g/0xF3vDCMuSVF3rNrHW4/JXO4OI1ekSJdtE67xgYBCmKK0xpCRIMt7ZGjBbdzAQXJopo8YDl9baPlNli5naInGiidKUXpgQZ5pekORyPeXDHkSSKjb7IVLkEk0N16RRynNj/SjhN9/Kw4b52HWP1jDiYrNMybX4hW+u8Q9+8x16YZ73WqjbfPZyk+WpElXXYr0fgNIMo4SvXdnjlRsdNsOzXVsbmKrYVB2Ha60ROtOgNWXPRKDpBwmWNBBCECnNG1sdXMMgUfm4ktjTDKKEsmsQZClhmuGMvb87GZKybbIyXaJZtsiU5t3tCu1hhGsbXJqr8fxincW6Rz9McS2DnbGCxd3mdBUUFNydwkA9YA426k48qjhKiVPNarO0//rRGVF30oubLlvcaKWEiUJrjW3CN290+NZalynPoh8lLNZcaiUb1zLIFCxOlRmFKUkaMQry/IvSmoptsN4ZsVR3EONY1aT0fanu7g8B3BpEvLw6BcAr611MKWiUbIZhzFevdfBMiWNKRG/EP/7t9xiEUDIBAVu9mK9f2eGP/NSLaExag4ivX2/x5kaLr62d0TKNqblQtS02Oj6WKSg7JsMo43rLZ6pkjw0ReeNwluJHkkSCkIow1jRLKUEMMxUHiWCrHyLHYdk7GRIpBUsNj80urEyXWWl6THs2nSDBNgzOTeW5uGGQsNEN8GzjVHO6CgoK7k5hoB4iE49qojpujjeqo3pwdxJzzdCsNstUHZPrLZ+enxCkKd+40ccUmuvtfGTH21sDfvSFOQaRYtpzGMYZaaq4sj1ibxRSdUxmKzYtP2anG1JyTEax4hPnp3Llcq3xxgUEQkOUKSxTEmcKrfPvEiQZ3TBlEESElsTtS1690SeIFca4gkGpPEcTZpqtdszclEmiFL/+2gYbo3vXrWtUHRzLQhoRlmkxijIcU+KaBoYUeROyAX6UkGUSITWL9RIzFY8402wNQhqexfKUx1zNxbOM/Wt91Js9GqJzLYPzzTIzVYedfpiPgCdjtupgmvk59NgGmeM81N3mdMHJE5MLCgpyCgP1kNmX0Gl4t01znWxKJ017lVLkpXg6H2roWIK3tvukWrPbC6iWbPp+imUqdvsh/9NX16k6Ft99cYqyZ/LVq7uMopiqY2NKyWsbA9BZHoZUGa1RxKvrPT59YZokU+z0Q6TMDWXDy3ufbCnRGoZhylY/4LX1LqMo48nZEgYGfhyRKQg1hAe0jXb7iq+tt1gehnzl7a37M042OIYkUwopoGRLTM9EkOe18oKJhPVu7p05jsUTMx61kkPJNvIBiobgibkan31ihl6QHmtI4kTdNjdq4llJmTdIi/HNRLNikyrNKMp/Xkt1j51BdEfF+KOivCd9VkFBQU5hoB4RJ1Xu3e11P0q5ujfk3e0B13aHrLVGeLZBECestwdEWS6cmilBP4gZxPDF97v7551xYHXWpCQ0rWHCME7o+Bnv7QQ8vVjlM08axGO3RwiIU0VrGBHECs/2ma+5zFZt2qOIvX6IH6c0PAvLshgEISVLsjpt8cbuLeskgYVpi3c3RzhScqU1uufrVjfh6fkqUxWbMNW8eG6KrX5MmirKtsHHlupUSxZaaV7MMkyZl+1f3wtIs5R+mDFdcmhWbc5NeewNo1yi6YghEZoT50YdzBMebLqOUrU/70tKwYIUJ96EHCvK65hFOLCg4A4UBuoRclRY9m6v+1HK1663ubo7pD2MSNIMJWAYpYzihE4A8b7a9/Eeyl4E0daAxYbJXi9lumpTLVlYAl5b73JxzmMUJiQqY67msNENWKy7KHKDtdHNFSWenC7x5kaX89Ml9kYJQZwSpRm7/ZDdYUbNhH4KFQMaFZO5qpcPSfRMopNUY++AAfyBZ6d48dwUjZKLaUEaw1Ld5UY7IMpSglgjZR4yLbsmjjIwpSRTgoprgpbUPfAcyZWdIe1RjNLwY88t5MUUBwyJFod1CaUUxFFKkikcaZyQJ8wLYA6GAk9qHzhJlPfWuc7eUFxQ8GGnMFCPKRO5oUznIx/yUJGkZJncaA/wo1w66DSBs0EKdj8lk1ByDPp+hGVI/CTj6taQfxWvsd2PmKvZaARzVRfLNJitOLkRGkRs9kN2+z43OgOG/Zj3XUnJdeiNUholkzDOiAYZYQZpqhmFMQ3PZanu0azYdNrxqb/7chl+/MVzzNY9pksWnmNhGYIRGVoKBnHKXM2h5gkWGjY6g81BiCVz9YuN9pCNTsgTsx6DMONb17t5w26q0UrxK29s8ae/5wJl19o3JErp/TxgqjRbvSAf+T4W+LUNeaqhj8fdhBw1bgdFeT3bvOMAyYKCjzKFgXpMyXMiGYMgI0pStgchfpTiRylBlBGkoO5+mn1sCywEnVGcu0ZaA4Kb3ZB3t3r4UcheX7EwZXF5ocEnLs7x6nqXqmuzUHP41Vdv8ouvtW410g4UgoCmJ3h6scF2L6TihfQCjWWAbZr84LOzNEsuT8xXWGu3bxOOPY6XF10+98IyTy/UUArWOj5tP8Y2DFamS0gJi3WPpUaes7u261OyDAQTDwgiremHCe/sKOYrLp0gJUpSNIKlKY/OKOFGa8RzSw2yfAzvfh5woxtwsxNgGYLVZglT3hojf2Ke8C4cLYI5JMobne1cBQUfJQoD9ZgiNLT9hGbZomSbVB2LIM6oeHm1misgPk2H6xhHGiRSs93LiMlDaMt1k/d2u6z3bvlhW1sJV9u7gOT5c9NIIXh1a8i//PrmbSoPGtgLNM1+wHzNoVYyMbXipz/3JDXPoWJblB2TDIEfp/zulf6JRtUC5stQK7loDZZp0BnFTJVshAF7/Yi3N/o8tVDlheU6LT/iZjdgEGZcbHqsdQIcQ2BbEscwWG2WCaKUnWFAFCdYpolpSPpBCgKCLOXd3QHm2GuZqTqUbJP5ikOQpDTGmn7AfvjtbnnEkzhJlNc2ZFHFV1BwBwoD9ZiiBTQrNr0gYalRQgJzNQc/ztAYbLR9wiQmPaWRaocZWQaWCTrNlRBu9tJjpYX6Mfzmm9vYpsQwDL7w5vqJ3o8FJCpjFGdMlW3+wPNLXJ5rkGQaAbi2yYWZEt//1AJL9RJr7SFpHPCtjdxQSnIppiSDbgRb/Yhru0POTZcpWZJBkIHQ+RBCCzpBwkqa5RVxmcIyYBBnzFQsekGKH2coBXXPJEwzdCKouNZYcDfFNgXNks37OwHzNUXbj+kHCYMg4cJMmZKTT+cdOCmLDQ9zPJJkEn67Wx7xJE4ybkXOqaDgZO7JQAkhPgU8AfwbrfVICFEGIq31Ge7pC+6EIQSeZVIyDbI0LzHv+QkNNEGSYUmJZ/mst0OCNN/o79QC20/AgbFSBIj0zhJDrQj2BgGb3Yjr/ZPf5xjwU59Y5qXz0yQpWKZJlCmmS7lxDaOUq7sj5qsuS40SUZqHKb9/lPDVazu8tzVgb5QPIFxtejiWyVrbZ6vns1B3CVNFHCtmyw6ObSKl5vXNAbNVi5KTq130w4zZmkuSjgiSvOQ+zTRJosiUZqpkUS+VmSm7mJYgiDMaZYthmKtxXGv5OAbc7IV8/Fydc80KGs2Nls/KlMfieBrv/XKvxq2g4KPKmQyUEGIe+AXgu8gjPJeBq8D/nXx//MsPeoEfVQ6GhRplm/VegGcZuLZFNJX3KZ2bLvPkfMh6y6dZt3h3rcv7w5PPmQHGOP1kSDAUBHdYQz9MGcR3LsH7gWdn+MxT81Qde+wZwJWdIevSRyCwTMF626fmWkyVHRZqJb691uHSfIX5msvXG3v83pU9TAQ73QCloWzDWxsOWx0HYQiiTJBMK55fqmMYAlkRLFRd2qOUfhCzOwiJ4oTFRpkgSRkECbuDENeWLDZcZiouhhhP/dU6r8yzJTf2+nRGMY4hqHk2QZxysxOwPF3i/HSFIM5YqLkYkyKKIgxXUPBIOasH9beBLaAJ3Djw/M8Df+dBLaog52BY6NxUiW+tdRFCI0SJmmcgEfTChGvbQ2YbLs8uNPkXX73CzRPajhxAZYCEkg1PLdTYbPe5doyHpIEwianZBif5Wn/w2QZ/5UefBZFXtXWCiK9cbWMbAscyWW647HRi5qouVdfEMiUb3REaqLkWlilBGwSxYtu/lQdrxeC/t8cPPbvAfMnDiDOu7o6YrtrMVVyUhm6QsNRwsaRACkk/inEtSWuU8cxSjbc2h1yer1D1LBwpeGO7T8XJ1SM0gizK2BlE+HGKISUVxyBOM4Iol5NSWpOkio1esN+cWzTTFhQ8Ws5qoD4PfF5r3RGHS2KvAKsPbFUF+0zCQlbJ5lMXptnoBTTLGduDiH6YsN4JadbyXMmzyzW+7/Is//pbu8fmjFLgyRmHRGvqJZdLsxUuzdWQb61z9Rgj9c2NmPNVWCrDxgGjZwPf90Sdzzw1x9vbXaZLNtWSw7vbA3qjhLmawyBI+FYvoOIZfGq1yWY/IkgS9oYJT85VKTsWm52Adzdbh4zThL0A3l5vU740h23kU4WnSzaOaVBzBXvDXM1cAReaJYLEwTUlC3UPiWa+7uLHKZYp6aeKNFOsdwPaw4SyY7DQ8PCcvAii4Rn0g5g4TRmhuNkZMQwzDENSd02WGoer+R4HT6qQSSr4KHBWA+WRDxE9yix3ToEAIIT4OXIjVyb3xP4rrfV/P37t3wf+JrACrAF/TWv9r8+4vg81Jcfk0kyFJFOYpmS7f0uCJx4rK/zhl1fZGSR84Ur3toq5COgHEednSpxrlnluuc5mP6RkCU7qqLo+gBpwuZY3ysYpZBK+caPHb1/pAblnNl+H2bLFTL2GZwlSLXjtZgfHMNBa8JknZnAsyVTJ5vxMma1ewBff2+UbmycXn7+7EzPX8Mk0fGyljmMazNfcfBjhMGamYlFxbOI0I1YKLQR1z6Q9Spgq2Ug0T8yU+MqVFgLJdNmh7Se0hzGWKekNE1qDkN3+CI1Ea4VnW9z89ib1ks1Lq1PoqTIan4szFVSqHotm2qOjWQrPruDDylkN1G8Dfxb4a+PHWghhAP8x8OunOP6/BP6C1joSQjwD/JYQ4pvkxurngJ8E/i3wh4CfF0Jc0FrvnHGNH2qkFEgtQINjmizULVrDmFRpkkxTcQ0+fXmGbhjy6s3wtuDc2hD2hj6jWONHMV98o0XrLg1VfaB/h0KJFLjag6u9BDZarNzskWlF1bb47ovTSAlfudrihy/PsVjPm15tqfnCm3f+0YbA9iBkquxiGQZoWGuNSNFonUs3KQ2uZbLU8MaNupKLTckwStjpR2RKE2eaRsnKJYmEYBAlDDZjtgYRG12fME5JNZiGwDZErkThWAzDlB9/0SJKFPMVB8MwPvBm2uNGszxOnl1BwYPkrAbqrwJfEEJ8F/mN838LPA/Uge+928Fa69cPPhz/eWK8jq7W+pfHr/2iEGI0fq0wUEcwhMhzKVpjCMlMxcaPJc2yzUYnYKsX5UbsBALg2xsB39y4U4nEvbPeTzGBtBTz9s6IhYZH1XWYm3LxLJOtXshaL6R3iprPURDxuafniRLFqxs90JrPXGqSIVCZItPQ9CwyAQtVl71RTJQpEILZqoPSmmbZZhinBFIitGIUJWx0AgZRhB8LsgxGMQg0lqFz2SOl6TqCr1/tsDrjUSuZvLw6/YEbgTuNZvmgPbuCggfNmWZUa63fAD4GfAn4VcAlL5D4hNb6ymnOIYT4+0IIH3gL2AR+Cfga8KYQ4o8KIQwhxB8jj0i9cszxPy2E+JoQ4mu7u7tnWf6HhsmMooZnMwhTRlHG7LiMexgnbPUiYg3mHfars6hQ3AumBK00a60RX7q6x7XWgCRT+4UfTza9U51nb5jRi2IWai5TFYvpkk2iYbpsIw3JMExY6wYkqWJvFDNXdfYFXKueRbPq8vHzU1QcG4GibFtcnC1jSUmmDQyZK6Qn5LHrKMsrHBOlCGKN0hlPLdR4YrZC109Q6lYoVKm8IvDgcw+bg6oUcPvoloKCDxNn7oPSWm8B/+m9fqDW+j8SQvwl4DPAD5H3T2VCiH8K/DNyoxcDf1xrfVs9mtb6Z4GfBfjUpz716HaGxwzXMrg8X+XCTBkg19aLUtrDGD/OPRhbQnhcJ+4jYKZqMgxTskQRRBkrjTKvrff5gcv5DKWG57Hkwd2cOAkMA0U/inFSSdWzaQ0j5ioO0yWLnh9zbsqj5FrEacZGN2C5kRu/yUiNZsXlu85L6hWLVj8iRbHe9tkbhSSpJkxu3akZIlfZkAjKbq4+sdhwcS2TUXTLU/kg8kCTwoi5qsPOIDqz5FJBwXcadzVQQogfOO3JtNa/fcr3ZcAXhRB/CviLQog3gP+K3GB9A/gk8AtCiD+otf7WaT//o8Zk1hSMcxP9kPPNCk/PB7x6M2OmltDvPPre6boDriFRtqTsWHzi4hTLM2V2BiG9KMZNDDa7Pt/71Cw//+07e8FpCkEcI0SZkm2y0w95d7PPZmfEVNlFCM3XbvhIJFXHwjCNfe9CCnBMA6U1lmUw7Tn4kUKg+a4LM3T8mJ1+TBDFBKnGtSWuNMk0VNxcLHd5ukQ/yJgqZfueyr3mge6n8s6P0rzkXYNhSOaqDpYpiyq+gg81p/Ggfos8VzT5LZh4LUcfQ37zedbPf4K8cvm3tdZfGz//+0KI3wN+BPjWGc/5kSTTGgTUyzbf8+QMhhBslk12Oi3u0Lt735SAH3i6hGGYzJctEm2z2Q8YxArDT1id9liqVdjsBHT9mK++1+LVzR6/9cYmYZZSM6B/By9vBPhpTByEfGN7hNIZmYLdYUA3iNnp+1xrJQgBFUfyEy8u8b2X50gyRcm2kEIwV3M43yxjmpKFusuVnQGOZfKpCzPcaPlI8lxWvWSPm3wjLs9XeW6ljmsabHQD5qoOq83cWw3TjFTlFX9wujzQ/XhcfpTyjRudsQqIwVTJYmcQFYURBR96TmOgZg/8/dPAfwP858CXx899hryq76/e6SRCiDngc8C/Ic/T/wjwvwb+JNAF/i9CiI9rrb8lhPgE8P3A3z/1N/mIYwiBKSVTnoVSmifnq1yYqXBhscr/89euPbTP9YFhBD/5qWW6/ZBtP+MJx2SpWaLvj70OQ7Dbj3BNQTuI+P99fY3NwS3PTnLnnNg3bwx4bysgS1M6/u39DBZgmhAlin/76gZVx6Ts2PzwM3NYhiTRGnPs+QjANiTnZ0o8tVhhox3gRynSkAgBwyhBK3hqsUrddYjTjK4fs1zPw4Y32j5Zptjqh0ig7FqHhh4mmbrNq7mfyjulNBvdAEMIql7+WR0/Ycqz7rswouilKnjcuauB0lq3Jn8XQvwt4C9rrf/dgbdcFULskIfofvFOpwL+IvAPyfek68D/QWv9P4/P/TPAvxjLKe0C/4XW+lfP9nU+uhyURpoq25TsOkprprsOTXGN1kPM1n3xms8Xr72JBbgWzFYtPv/sIi9faNJwLYIso2obxJnmnc0Wm4MUQe5up+TGadaE3ROikZbIKwNPIiHXFtQ2RJlioxuz2BBcb42olx2COCNJ81EXAtjuhjSrNsM4o1l1SVTIk7NlbFMyX3XZGeZN0P0woTWMqDgWW/2QVGkqrolnWywK2OiFLACmlDRKFuvd4FgP6X4q7yaesW2NZ1BJQd8PQWfURiZ118a2z577KnqpCr4TOGuRxHPA+jHP3wSeudOBWutd4Afv8PrfBf7uGddTcICjitn5HbKiZuXyQQ+bhHyibmeU8OtvbvPcYp3Z+QoGAlMKdgYhwyg3NHkhQv7/DFiakWRtxdG5hiUJd5EDBPKqmjjOex+6I59G2eCdnQEfP2dQckz6YYJAMFd1aPkxe37EUsNjtmIzNemRkoJBnLHY8LCGkrW2z2zVYXmqhNaajV5IxcsHDJYck8Wamw8zlJIbHR9DgGcb+x7TxEM6Og/qLJV3Bz3jrUHI+zsjXt/sEcYpddfm4myFH31hgYX6rarIu3lGRS9VwXcKZyozB14H/lMhxP5vw/jvf2P8WsEHzERZQkqBaUqmHRuvftYf871jSIGJIEgV2tBsdSNudAIyBeemSsw3SghyzyklN04m8PkXVvnff/5JXl4u0ZBQFjDtwmLDwj9Dncd8wyRG8O7WkDc3+mz3Y8qWgSElCNgZRiw1XAxDEKeKnUGMIQRlx6Tq5pN7u37CYtXNc1fTZVzLwDEN4lRxbXfEzW7AjdaIVGlc0yBIM252ArYHERvdgFTlHlOmc7d14t0mmWYUpSSZPnXl3eRYhCCMM7pBiADmqiU8x2R3EPKFd3aI4zyRFyYZN9o+a22fG22fMLk9wXfLo8v/XZiGPLTegoLHhbN6UH+RPId0Uwgx6VH6GPk+8xMPcmEF949Smk6U8MRcgzd224/kM+NYg4SGLVgqeyw3Swgp0EoTp4ofeW6JtXbAv3tll5Tc4/kjn1zi+y4vMPBT7E9Ihs9llG2Dmx2fL7y9e6qx9hOGQcp2L2KxZo9DcpJRkpGpfPSGJSWmKZmtOCw1PII4z5Md3Kz7QcTNLKM9TBgE+VwoKUDK8TBipUm0QmmNUpq9QYQhAa3JMs1G12e+5h3ykO512OHk2OWGxzCIaXgue6OUsmcQJwpT5rm3IMswlTyVZ3Q/Hl1BwaPkTAZKa/37QoiLwJ8iD+kJ4H8E/tlxPUsFHyyZ1riGwepMnabVpnWKUNm9IsiNjWnAXMPjz372ErN1D3uS1zAgSDLKtsX/8Uef4/PPtNjsh0yVTH70uRXao5iSZWCZDXphQsePMQ2TS80SURSxdspSxHYEYhBwruGyWHPpBxmWkTJXdZBCcLMbMOqnNCsOW71wX1V9slnHaUZrFHN+usRqs8RWL+BGy2eh5rBQd/FMg61+iCkle8OY+XpCmGTEqeKN3SFoTdmxuDRTyT2S8Th5uL95UJYhKbkWjiXRSjMKMqQkl7yyJJ5hnDrXddyE36KXquBx5F4adX3GjbIFjzeGEDi2yWeenObX39iitxvccUjh/fDyisGPPr/KynSF1UaZJxbq7AyiQ3fpliHR5FV03/3EHHGSkel8w9zohVimoOXHVG3JV7cHbPV8lJA8vdigc6XL8JTyF/1AszMKeHalwXTJpFm2WJ0q7XsNAzsfuzGZ8TRfc/cbX5XSNCv2vmFdbZYZBAkrUyW2+iFb/RDHlGRKow3N3jBipx/SC2JWp0qkWjMMYt7eGrBYTxBSsFT3cC3jvirmpBSsTJXwo5SuH/HO9oiyZXBxtsIPPjWHPc59neQZHc1L3Y9HV1DwqDjrwMKfutPrWut/dX/LKXiQTO6Uwzjl88/N477XIk5SWoOA7Qcsw/f19YwbG+/zd/7CJ3l2eSrvORqPqIjSvGpvpupgCMHOIEKl+ea5UHPY6ofYpsAxDeaqDld2+3RGEY5hMF026QwT5hsOi6S82767NIYG/EBxZbtPnKRUbcG/TTTLzQrNks0Tc1Uc08AyZD6d2JT7m7XQsN4N9jd5pTS2aeBYBjNVh6u7Q3aGKWjBTNWm78eM4oSdQYxrZTQ8C0NKojRjcxCgleZ6a8RS3cOxjPuqmHMtgxeWG1yeqxLEKRqoOtZ+Fd9JnlE8DvUdrdiTUqBSzTDKXeuSbWKah/OVRSl6wQfJWT2of3HC85M0QVGn+pjhWgaXZip88nwzV1owBIYh2W73+Y3XNnj3DirlZ2VXwZ/4R1/nb//xF/ixF5YpOSar0yVGccruIGJ3EOWNswdUECaJ+bmqy/YgBA2NksN8zaU1jDClQapi6iWHqVKJRjmh3fcJU8XG6PghIa4AYWg2Oj7fur5HewgIqHmSp2ZLXJqtsDjlcmG+xnK9jM48MgOEBi04VkoIwBznquZsh5JrEcUp77d8FqsuNPJ4W5ppojTlyk6IbeZJq66fEGcpL65Mk2aKa7tDLs1U7qk8XEqB55h4zvG/ukc9I8h7t47LS/XDhN+72uLK7hABXJop8+knZmiUbKAoRS/44DlrDurQ7ZUQwgQ+AfzXwP/1Aa6r4AFi2wYvX5gmVYr3dockSvHxCwv84HMr/Obr6/zTL28cO+DwXvk//fxr/DU/5H/z3U/gWgatYYxjyv2w0yEVBJU3t3ZyOXFSpTg3VSJVmi++vccwSgiSlOmySTdMQIMwLV5cKuFsj1hvx4cGlFnA+fkyUsD1vRGDeJwfM2HHV+xcH/LF63lCywE+fr7GH/vkOS5NV2gHEYs1l0bZPWRE40xxo+0TpxmJUljSIEwylIapksXSlIfoh+wNI/woJUkBBIY0eG+7z1rXZ6sfMPIztAFJCjuDiBfPNfaNwVHux3M5mOtKMnVsXipKMr59o8Naa0izbCPGfV2vrHX57BMzyLH3W5SiF3yQnDkHdRCtdUouS/TXgH8AvPRAVlXwwGmUbD73zALfcynPQpVskzhTJJmmWfP4Z1++wo0H5E1p4H/8nfd4aWWKl1aad0/c67w6Toi8RN42DD57aYahn7Aziqg6Bhu9kJplsZvkBQpXdgOenq/w9Dz4fsDuKCZRCgyLkiXoRxki73HFkBxbmhABb6z3MbhOo+xScW0sU/IHnp8jTstcmqmAYH+jdiyLqm+ilGah5uZq7V2NZUouzVZYqrn4aUYYp3zl/RbfXuvgxylZBn0/4Rdf3+TybIVzzQoCxSs3unzPpeZtntSD9FxOqtgbxSlrnYBelKFFSqNkIaUgSlJipbCELMZ6FHzg3JeBOkCXXFOv4DHGNCU189Ydu5SCj59r0CiZhKnmX/7+Ndb6D2YQx3of3tns87GlaQDCOMW2biXyJ+GnTOcb/GqzvF+0EMQZ9ZLNH/34CjfaI1qjiH/72gZX9nxmyw6mabLWGrLTT/nsU00yXac1iNgbBHh2Lva6ZEgGo5B+kgcBwxMqGOMMru4O+UTZY67ucLMd8i9+/yafvTRNkql8XQc26oW6x41W3l9kmwYvLNfp+glJliGl5PyUy3rXp2qbWIZASknVhSTTkCnCVONZguudECkEV1tDLsxU9g3Qg26iPS4vNVd12OwFlOxxmFWpXIrKktimkRt2TVGKXvCBc9YiiZePPgUskk/U/eaDWlTBw+fgXXrFsfj+y7OsNEp84/093t/p8X47YMu/9/OnwNXtAWutIWGm6Pi3NsfzM+X9zXZyh6+Uvm0jrHgWzyzW6QwjvnKlhUmI65gIBLN1j+WqwwvLdWxLkiSaZtUmSzXXuz7bvQDPNvjSe3vsDuITe6kUkCnwLEFnlKCFJlMKMW7YNaWPYdwqQzelYHnK2585JaWg5ua6eEmahy+TTJGoPFwZRBmzVTsf0ZGlVB2DKM3fO1Wy8SyDrV7ISsPbH5R4Ws/ltGHAo3mpTGuEELyw3CDJNNdaPkmqeGG5xlTZZrOfG89GyaLrJ0UpesEHxlk9qK9xWNl8wleAP/9AVlTw0Dl6lx7GKX6s+fSlGZ6aq/Kt9Q6v3exxfXfAN9aG3Ev7lAS+dGWHqy2fF5YavHh+iuVGCVMKbONWKvM0PTmDOOP7L8/w1mafMEpxbJOPLdVJlGalUcIwJTNlB9c02BvFTJUdhjMVducivnt1mte3u0gEX3x3h2s7EQcLGFenLBoVFykM+n7MzjACLdjrR4AkTDMWay5Zlnt6UuTDIp0jITelNNv9vDBiuuLQLNu8tNpgvRXQDxJGScpLy3VipekFMa5l8MxiFds02BuGXN3L9qckR0mGJQW2ZZzouZw1DHioB0vl3lHZMfm+y7O8tBITpwrXMSnb5v6NQtdP9g1nUcVX8EFwVgN18chjBexqrY8KTBc8xhxt6LQtgzjLuNYasjdMCBOFIQX9KMUyGSf9z0bVhig1uN6JGAZ7bA5CPn1xhoW6x1zdpermoyqU0hhSnLgRTta6OFXmD7+0zO+8u0s/iBnFKT/y3DwVz+KdzT5XtgdUHIuPrTSoeh5+lDIIM6rTFvMNj0bZ5jNPzvKVd3e4sjOkMwr45IUZPvXELFLAL317g5u9AD9MeHqxxvvtET0/5fxMiaprkSrN0gGvaeK9TLymOM3Y7kesNnMjPFN1yTRcmDHo+TGGAavNCgt1l71hxErDw7FN3t8bstENWWq4NCsOu4OQMFY4VsJM1cazzNsM9v2GAQ/eFCitcW2L5Wmb3UF0SFEjSlO0yJuECwo+CM5qoM4DXxoXR+wzrub77GkHFhZ8sNyWOE8VYvyfRqMFtIchWoAj8tkoZ5EbmrKgXjLRQqGz3LPohgllQzD93AI7/ZDyuEjjqBdgWYc3Q0MIBLDZDbgwU0YI8JOEc1NlZioOP/+1NTqDCGlISrZJL0r48WcX2RtGCCBMUvaGMRvdgIVGif/wc08TK8VOO8B1TKQUvHazz0vnpolVC9koEStNFmdAQt3NZ0oNwph+YFBxLDR5FV6WKTb7IUt1l6pn0fFjNrsB56ZL+4UZT85WMAzBKEmZ9RwuzFZ4cq7Kdj/MZ1HJsS6fgN99b4/5ikMmNCtTLqaUrDS823qT7kcdfcJx5egtERc5p4LHirMaqN8kzzntHHm+Pn6taJL4DuBoWE1pzWLdpVbKZX9qrsHX3tsjSwVS3tk4CcAF6jaUPUG5ZJEkuSq4BpIsZXekEUTcbG/Sj1N+7IVzzFQcOn5yVy9ASsFM1WG9E5CpvEn38lydTGvWOj5Xd0dcmC5h25IgUrxyo8fLyw3eb424tjvgy++1sC2DqmfxEzWHXpAyX3PpOSnbvTCfUisEF+bKfGOtQ9WxqIzHx4+ilGEU8/vvt/j2jT38MGV5usTzy1O8tDqNa5sgNLvDiBXb2C+gGEUpFduk6hhoIEgUQgm2hhGWbbAyVWKp4ZFkirJrstEN2O7lQQjLkkilGcQZUyUDfYx9eFBaekell+aqDhvdAESGKeWpc05FM2/Bw+KsBkpw/H7VJB9+WvAdwsE76Il6AjqvUkuV4tJcmY1+eNf7cc14ppMJnVAzjGIyDVVHMIx0ro0HOBJsA752vcO5mTLLUw6eZeJY1v6cI5WqY72Asm0yU7XZHYQg8v6chmcRZwopyJuPpQStSLKMrWFI149YawfMVh2kzsunv3K1TaI0QZxSL9l4TplMKvpBim1IFhslWoMIK81wTEmiJL/66iZfeGeX661onIvrMl/e5E98+iI/+vwC7WFCEKfEWcZ81WW2atMoWfT8fPptN4gYBHnFnxDAODy3VMs3f5VpmiWb9baPUpo0U8zXPeJEgeZYo/MwtPTCJGNnEO3/gs9VnVOVthfNvAUPk1MZKCHEL4z/qoGfE0Ic7Os0gBeALz3gtRU8ZA7eQR/MSSzWS/zJz17Cdkz+P7/7/l3PkwA7Ryr+YqWpewYqyiibeZWcY9uEcUpnmPD+3pBMKWzTpFG2saSk4dknegGSXApppiJpDSPCVFEyTC7OlBmFGUGsSbKU880yrmlStfO8kWka9IKEuYqLAtJxWK5sm2Ra45kmwhEIIVipO2SZYmXaI0w0zyxU+JVXN1hr58ZpMrtqe6T5xW+toTVMjUVnt3oBcaaYq9sM/JRmJc8frbVH9ALFx1ZqzNc8umGKZyludBTJ2GucKls0Kw5PzJYJ07xIQmlYangnGp0HqaV3MKfl2datZuqxHNJpjiuaeQseBqf1oCZTdQXQgUOFUDHwReAfPcB1FTxijtvw/sx3X+SNay1+/erZO3j7CTRKGSZ5GbdAECcxcaa5sTckCBPW+yFZqrg8X+WzT86eqKpwtFfq4myZUZiH6iolk69ebROnitlqlc89O0+UKEquwUzZJtUQp4pMKSwtcE2D622ft7f7TJXy8u9GyWK6arPQKDFfc9FCILWgXjLpRwlq3PArYX+WVT9KeP1ml2bJoVk1+dr1Nm9dHTFUucFOgaYF52dsnlqcZrdvM1dziaMEPxRcnCtT9SwqrkmUKF4+N8XeKKacKTI089W7eyL3o45+9PreS07rQeTCCgruxKkMlNb6zwEIIa4B/00xWuPDydENr1lz+anPrPLrV1+7p/Pt9mGxbjIMUwZxHo5bqnukWvCtmz3iTBMlit1hQDdMma07ZLpy2+Z2XK+UbRpUXYtPrjZ5br5OrBRly8S2cxmiOFNcnKvw+s0+niVpVh0uz1fwLINhlBLGGVESUTbzkuvluuaZhRqmIQnilLW2zyBOuDhb4ZvXByRjwwO5oSoZkkEY8952j+u94wVsWwm0NmNe29zih59NGMWKuZrDE3Pl/Wo52zRIMo1jG6w6uW7h3iCiNYrp+MkjCZnda06rmCtV8LA5qxbf33xYCyl4/JBS8PlnVvhzn+nyT768ftvrSxVByXXpDANaxzQaKA1Vz+AHn2qy1UnwHJgue9zo+PTDGEPI8eYreG9rwBff2eO5+QZW+bAnJaW4YwK/7FmUD7zftQwuz1VZnSrxA0/OEqYZPT9hZxgTKYVEsDpTYncYs9YNeH1zwM2uz+efm2eu6mEYuRp5a5TwzEKD73ki5Hff7exr/jVsuDjfoDMITjROB4mBr77dYqZqs1CzeXOzT5BkrE5XsA15aFNvDWNMKfaHPD6KkNm95rSKuVIFD5u7Gqjx5Nwf1Fp3hBCvcoeiLq31iw9ycQUfPK5l8Nf/yIv84Rfn+dVXNumHPtJweHq+wSBO2enF+EnMb761zd6RER4KWNuJWCz7eK7NVNVlvl7iaitPWEkjz/0IIbBMmU/R7QdUPOvQJncvCfw4U2wPov3k/bnpMpZloDPFO9YQP8p4b2uAKQWmlCSJ4ovv7PGHXlhksx9S9SzqZZssU0TpDBLN9fYIFNi2RGvFVu/0M0s6Cn7llU2+da3HdM3l+cUaa1MBL51vcKFZAfKQWZCkjKJsf91lxyDJFFKLh1old685rWKuVMHD5DQe1L+EfbHrf8nZWmIKPgRIKfjkxQVeOjdHP0rY7AZ8e32P0SjjpdUyqarwxs0+OgvoxnkILJn8UfBrVwY0HHhuucF0yeHctMve0KU9jMCWVByTc3WPubqHiTiUw7iXBP5xyfu9UcxS3WO7H7LScHl7e8AoSpmuOKw0XaZLDld2h2z3A5IMVpslAG60h7y+3gdp8OlL8/SDhOvtAUGqzpxlaYeQKJ+dYYiNwpAadT2v3Ku6NmVbcmVnwFTJol7K53htdkNsKRFSHKqSexil3cfltI5+znGf+6ByYQUFR7mrgToY1tNa/8xDXU3BY41pSqZNh3/yO+/wD3/rxn5OZsaCes1gqmxi2Rlbw9vvYXoR7PUCVmc8nl6o89xyna+932ajG1L3LL7ryRmeXaxhWcahHMa9JOJPOsYyJeebZWaqDos1l84ooVGyaZYc2qMYQ4BjSqqemXtWhqRsSTLAkhCNpwLPVT1MAd2yzWYQH7uG48irCCGKFa/c7NIejpifLrPZGVJxbN7dHZJmmpJl8NmnZ1hulNFaYxgCzzb3q+Qm86pOU9p9P4bsaAn5RJuvKCkveFScVSz2N4Cf0lp3jzxfA/611vpzD3BtBY8hv/XWOn/nt24cem4vgb1WhgWUjy/EQwBxljFbdqiXXS7MlnlxaYpuGOOYBlOeg2UZt+Uw7iURf7djXMvg2cUGQar49nqXtY6PKQXfdbHJdNXFjzOiVBEkGf1QcXGmzI12SBhldMMIS0ieWapTdQ0GwR4bo9MrwI/GKaswgN0gga0uDl0Wa4LzM3U8x2Q7jPntN7b5U5+9gG0aWFKSZCoXes0yNrq5EO7dSrvvp0fpqBcapxmv3exxfrp0yFgWJeUFD5OzNur+EHDcFuQC33/fqyl4rAnDlH/2xZP7ohKge4JDoQDbNLEtg84o4VzDo1K1aJQdEqUPqYMf5F4S8acdff7MYp2aZ3Gt5eeDBxslbNMgShVzVQfIx040Kw6/9fY2N1o+QWLw9GKVj5+fpjUss9qs8iuvXee17XsQLBwTAdf6mmHSZXGqTJQo1lo+59/Z4TOX51nr+PvhtZqbX0MpxR0bnO+3R+moFyqFIFMaMT62KCkveBSctlH34JiNF4UQ7QOPDeDHgJsPcmEFjx/9JCHO7q5tPuNCN8x7gSZMe4Kf+Ngy77cCSpakFyRMlS1cy6Ts5BvuaZtSgX2P4izHHB193vUTnpypYhsGzngW0ihM2OqF+TwkKUiVZrps88deWmHPD9nqhDy3UseQkiRVXN0ZEGbnaJZavLHeZ3d8eQzy6b4h+S/ZacxXO4A0G1H1LARwoxtweRAxVbYBgRC5oUhSxY3hCCEEWutjG5zvt0fpqBeqdC7qq5UGg6KkvOCRcFoPajJmQwO/eszrAfCXHtSiCh5PPGkwU61yuE/7yHuAF1fr9EYpe30fKeG55Sn++KfP45o2cZblGnxS4McKx1DsDlJWGyWUqY/1ouBWIv4sYavTjD4XhmBlusRWL2QwNk6LdZeKmxdkxJkiSvMQXsNzKFkWpszH1wtgumpTc03mpiqUbJMb3RChNa5jkqYpb2wGp64qUuTK8UIIpss27+8EdFYjnl+uEycZQZySZBmGlOjxFGI9Hn5zNNd0vz1Kx3mhk+GMo+iDLSkvtP8+OpzWQF0kTyNcBb4b2D3wWgzsaK3v3hBS8B1N2bP4gx9f5qs3Otzo3u5JVU14YaXG+WadUS3lwkyF6YrLH3lpiemqy43WKM83lQRhohhGCaYBQZTx+9fbGFIyV3NyuaJjjM79hK3utGFblmR1ukSY5mPiK+NRIKYhsQzJ8lhyyBBiP0wYpelYLV3RHiWEcQKGZLXpgYZEaTJl8twCXNkN0Fl+d+eOv9bowG+LAcyXBa3RWLJC5dcmU4JX17tkSvP6xoBsvOYffWGB55ca+5t014+51h4hxeFKv/vtUTquhHwynPGDMg6F9t9Hi9MqSVwf/7UYDPMRRkrBZ56Y4899/yV+8dvrDEYhYZIx3/B4cqaGloKS7VD1TBYtl1RrvufiNFXPJopzfbm5sk0vTJGkjCLYG0T0/YTnl+u4lsFGd0QQpTwzX8N1D//zvJ+w1d1yWVLmMkgHp+dOjNhkHlKmNbYh9zftMEr56vttnpwt40cpN/Z8emFExZHUSjY7vZidQV627kholAVTFY9hkHDRNlmZtnj1Zp8kFYwiTQZEGYz6KQ4p8w2TIAz55W/7lByDxbqHFIJfeW2T89MlKl6ubbg7jLjYLO8POJwY7QfRo3S0hPyDLCkvtP8+epy1SGIy++m7gVWOFExorf/pA1pXwWOKZUq+//I833VhmpvtAGEI9gYxTy9UkQg2OyHdOMbQgmcWqzy9WMc2JJnWLDY8dgYRrqm4thdR90z6YcpM1aXlxwyDhFfWe9im5PJcn88/N89C3dv/7PsNW91tw95XrOgFiDjDMOSxxRULdReAtY7PZs9HIRBCszXw2RuGWEIQxJrewcSTAjnSNMuaJ+ZqvLRaR0qJZztc3e3z7c3o0FoiYLObEsQddAZPLdbIxuK0g/YobxLux6QqD11O6giPGu0PU49Sof330eOsZebPAP8Lt0J+2fgcCfnvVGGgPuQYIldemCq5NCsecZJxoalYqnu4psHHliFMMwwhcA4000pyT2TVMgjTDFtKHNtgsxewN4jY6AastXxK49Eacab44rt7/Piz82QSPMPAtu8/bHWnDXuiWAG3FCtsQ3KtNcIQ4Nl5g+xGN0AAnmngmgYbHZ+b3QA/VljSIMmyw8ZpTKTg3LTDj7+4gmMahHFCFof8L69Et7+Z/Jdq4Ocx9Le2+vhxxlLDo+ra1F2b2lgFfq3js9UL9sV08yihRgn9ofIsCu2/jx5n9aD+H8DXgY8DW+P/14F/APwnD3BdBY8ph8aFp4okVSDyCbMTgzHJ4Zx0vGsaeUOuFCzUPUZRQj9MidKMxSmP6bKD0pqdvs+/fuXmfi7oe5/MR8Y/CGmd4xQSjlOsmCpZrLd9LENimoK5qkua5f5KyTG5MFOi40eMogTHyLUGO35IK7xloSYSTVLCQq2Ma8n8c03JV9d7d6zwi8nj6u0Qhhsjru6N+OkfvETZtfDs/Nd3sZEPShwESV6QoeFmN/jQ5WgK7b+PHmfNKX0X8H8bq5krwNRafwP4q8B/+6AXV/B4MgmVLTc8bDOXKio7JpaRDxNU6lbdmlI6r6A78Nxko0kyTaY056cr/PDTM1ycKVNzTYSAfpBwreVTc00W6x6eZfC77+0RxxlSihOr/U5DmGTcaPustX2u7A5Y7wzZ6vlESbqvMm4aklQp1toj9oYRu4OInUHEjb3R/ueHUUqcgWsb2IZB2bFIsgzblIdGS0++ec0VLM+W6fopC3UHC8HNzcEd16rGf8pAyckboV9b7xFEyb6hNKVgZcpjue4hgJJjnOnn8Z3E5N/euenSfp6t4MPLvUzUnYym2wWWgbeBdeDJB7iugsccKfOZSRoObeoHcwJ3qrg6mg+KsxKZhq9cadHzE1xL0izbeHbujZUdk36YEGQZNve+KR30lNqjmF99fZt3dwaUbYOlusvnnp1naapMOt7Et3oRhhT0goRUZfQsgxeW60gpeHW9R8UxKFkmF2er3OwFtAYhaaY5N2Wx00/ws/wucMqGH3x6novNMnGi6QQJNzo+mTxpSHX+y2aQH++4EKcQanhzo8fr2z1eGHuWliGZKttsDkI2eyEV12Smkgvqnvbn8Z3EhymvVnBnzmqgXgNeIi83/yrwHwshMuB/B7z3gNdW8Jhzp5zAaSquDm40rjT4xMo0ZctEA54h+fV3drnZ9TnfLBPEGZYh8YyTN9TT9MdMEu1Kw5eu7LHZ85n2bOqexd4w4SvvtfjccyaOZTJbdXhlvUfNM6l5FkGc0RlFbA/CXFlB5woYnmXy4kqDtZZPkCZkmeb5lRob3ZDuKKQ1ividd1p8/XqX1zf6PLdY5+ULjfx6cbyBcoDnV0oMRhGbw4xRmCd8h0AnTvnvf/0Nfuzj5/nc0wuUPIvNbrDvyWql2RtGzFWdM/08CgoeN84a4vvPYf/W5a8D54DfBH4U+Mt3O1gI8XNCiE0hRF8I8Y4Q4j848FpJCPH3hRB7QoieEOK3z7i2gkfMwVDdKEpJMr2fE7hVcXXLu1I6NyAnoQW4tslM1aVcsvmeS03CJNeeC5KM731yBts+3kAdDNvdaPuEyfFteROjOgoTokQhEHiugecauI6B4+TDDVenS5Rsk6ot6YcxfpiitQYhx4KyFq4laY1iTCMP+Z2bKXFhpsLHVqdZma6y2qyyNF3mjY0RtmVyfqbMfNXhlZs9ru2OqJdsVmcrOEfW6AAvr9b4gWcW+fSTs8xXLSIOq1G824X/4UvX+YVXbnKjNeK1jR6Z0sxWHRCCYZgSJeq+fh4fdb7Tw6EfBs46sPBXDvz9CvCcEGIa6Gh9qn/p/yXwF7TW0bgi8LeEEN/UWn8d+Nnxep4F2uQFGAWPOSeVbp+l4mri+QjNoWOmyzaff3qembpD2TRPNE5n8Q4mRnWtnSEEpEphCkkUK7JUUbEsanY+j6rVj3h3b0R3FGMZkmcXqyzUHFzbHIfI8uKEqivpBSlTJYuOD82SnVc6ehbX9jSjOMOzci8M4GY/LyhxbYM/9OIKnr3Nle0unSDDlDBTKXF+rsJC1WF1ymN7ELLe7nK01m8YwzeutnnhXANTSta7Pk/MVpmvOkQliwvTZUxTnvnnUVA0BD8unGZg4S+c5kRjXbA/eqf3aK1fP/hw/OcJIcQQ+KPAita6P37966f53IIPnuNyAqetuDpppMPkmHMzx6tKHOQs/TFK5ZpyF5sV/tgnVvjVV7d4a7uPAJ5drPIDz8xh2wZxnPHlKy1WGiVmKg7DMGWtG/CDT1ZQSiMNgSkFy1Meyw0PQwi0gCTNq/9GUYqUku99cppfe22DUZJLSYyihJJlYErY60fYpsFnL8/x5HyFwShhrmZhmTZBkvJ+Kw8/erZDvQQ7/uHvLYBYK4IwY7XpEqeKXhDjWiYrU6V943SWn0dB0RD8OHEaD6r1ID9QCPH3gT9LLtv2TeCXgJ8CrgN/Uwjxp4FN4Ge01v/ymON/GvhpgNXV1Qe5tIIHzN0aY4/bCLp+wkrDQwtOXUZ+Wu/guLviP/2ZC/TCXIK97tr7XlqQZSSZYrrmUlEmzYrN7iBitu4SxGp/k19qeDgHDOik1+vgd/7zP3CJf/SFq7lYrRT82PPznGuWGEUZZcei4Zn4UUrFsXBtAz/O2BpEzFddZqsOLztT3GwN2fNHHBzsYQlwTLEfqDdkXr5+0sThYvrt6Sgagh8fTjOw8M89yA/UWv9HQoi/BHyGfHxHBKzw/2/vzYMkyfL6zs/zO+6MiLyzKrPOrr6nrzmaOZmLY9CIMROSQCwCgZBAWkmGhEliVwtCsmXBTKaVWCEk0y4ILSYNQrAI0CA0MD3DMEcfM33UTHVVV9eZ9xV3ePj59g+PyI7KzMrKrMrKjKp+HzOvrAj3cH/+MuN9/fd7v/f7waMkFXsnu/t+XwjxTSnluU2f/7ck7kCeeeYZ5RwecHaKuLrZQCAFG+mFdnuNW1kHOz0Vj+RTW86Z0nWkhGtrbRxToxPEWIZG0bEYyWo7DvKb7/mdx0c4M5JnueGSTZvYusHZ2RpZW1LvhMRS4JiC84st1poeMRqWkJwcSVPMWKy2fE6O5vCimOvrLi0/KaA4kjN5ZLLEUMrENnQmhlIYmtioONzrY1X9dm8od+jgsOdUR/tBN7HsF4UQ3w/8GEl67IBkjVUIfF4I0Qu+OHfzMynuZe5kINgcsXerkhx7fSo2DI3TYxlevlrFCyM04JGpHIah3dYgn8/a5LM2cSy5utbCNDVypk4+ZdLupns6N99gKGNBJGn6EbNrLg+M5DkxkkHKmELa4KmjMV4UI4TG0WKK9z8wwlo75GifS88LQ1p+yFrTV3Mot4Fyhw4OhyJQm65/EtjVPJfi/uJ2B4KbTWDvVJLD0rUNMdSEwAujZJ3RTcQwkpJSxuHbHp3ACyNsQ08ylN+hmyeSEkmS/WGlW7Y9kJJyxiSftmh5AZfWWrT9iNlqm0LK4NhonnzKxjZ0rq61Gc0aTJczPDZVoJC2qHWaBHGMQSLyAlhpeNiGtmEtzlfdLUUht8umcbvuv/081yCg3KGDwYEJlBBiFPgw8HskFtNHge8Fvg/4AnAN+IdCiJ8D3k3i/vvJg2qf4nDY60DQ76rThIYXRixUXWbKmS0piza78sYLDlfXWizXk3i40ZyNH8U42lbLomfdaZogl7ISYZM3F7Td0juvoQmmhlJ4YUSUtvDiCBlFvL7YIAgjgkhCFPPmapvjo3nKGYtQmsyU03ix5MxojrSdpGQazdlIyUadpuGczUrD2wgpD2PJXMUljGIsQ2c0ZxNJyWrDS1Iw9QWn3I7FdbNAl3vdelPu0MPnIC0oSeLO+2WSad2rwN+RUv4OgBDizwL/DvgH3X0/IKV8/QDbpzgk9jIQ9Fx1YQwrjWRQ9IKY4ZxNrlurKIyTEujtZoBl6gihbZTKMHWN6VIKy9Q3xGyncPS9Wnf9IfPbBXrckMtQJtbcVClFEMUcG8ny3IUVJAIBOKbGUsNjrdUhY5uEkWRmLIcXxkR9gjQznNnIGN8T0DXhb5SEX6y5mLog55i0/ZCXrlaIpcQ2NMYKDlLGvDZbY7qUwjaS9VG7jVrb/EDghxFn52ocHUqhCViqNfnmQoUnjw4xls8oS0SxJw5MoKSUK8AHd9j/DZLgCIXiBvrdRbpIBu+Fqtt9Kk/Knq80PDKWQRDGvHytwpcurlFpJW6ux48O8eefmaaQSarDON0kq5oudpyH2qt117MkXD9kreVTzlqkTGOLBbHdeeMgZmLIIZdKslikDY3VhkejFeC6AbYhKGUdTEND07RtIx3776Engr4X4oeS6XIagEo7QCDRu2XbX75WYThrc229DVJiW0kS33Q3EvFWDw6b5/Y0Iej4IReXG/z3b8zxhQvrBFFMOWfxQ99ynE89PX1PWlOKw+Gw56AUipsSx5KWH7LSLYHRs2KGczazFRdNizcyokfdVf/X11qcm6/h+j5Zx6DlhXxjrspn0xbf+fjkjkEZ282b7Na661kSugbtIMIxNFpeRNYytrVG+s97aaXBH5xdoNr0iGJouiGekZQjmRiyyWUcVuo+IzmHKE7Ep3+N03b0RDCI4iQTezdQxA8jEIL5apvrlQ4yivHjiHrHp9I2OJbO4ocRay2f07uIkd0S6BLHVNoBV9YbfOHCGp4fIjSdatPl333hEsdH07zr2Oh9ZUnd6/Ntg4wSKMVA0gmSuaVr620MHSaH0hvZuY8MpZgqptAFG666Xh6Thh8QxJKsY+D7ERlLRwhw44j5mst0KcNyw9vitrvTzAE9S8IUGlEsSVsGbT9EaII4jG9qjVQbHv/p+WvYhk4xZ3OsaPHGUotj5TSj+RxjeZvTYzlGMnayaNnQdz0IaprA1pLw88VahyiKiWKIZYypaWhSopsaLTcib1sbC4lNQ6ectZC7uMxmV2gcS44UHb5ycYV2GGLqBoYBxCaNTsDFhRZPHo2xt5n3uxdRGSfuLkqgFANHzxrxw4iaG6ALqLRrPDJZQHbndia7g67rRxsDQxRLVuse85UWV1fbNDsBQhcMOQaPThWIZZJFYot7bR8yB/Qsid41On4igDKWO6Z4Or9Sp9ryGSukODff4NX5NvWOT2M25n2nUzw4OcZEIYWuaViahhdERFJuCFXQLbmxU/kRx9Q5MpTCj2NKWYuXr1eRQDlrU8pYIKDjhxRzJlPFNIIksGK3RQ/7XZZCJumjRrMGmoQ4DhGGSRAH6JqGae14qnsKlXHi7qMESjFw9AId6p2kPpNjaLhBxHzFZaKbVkgIGM5ayFgiNIEmYb7ewTZ1hrM2r81WWG8l1WU7fshz51eZyKdxTIPJodQNT7n7kTmg35JIm/rGHFQk2Ta4Io4lzU7AetPDMQ3qbZdXrlUQMuJoKc1IxubSUpPM04I4kuhazBcuLPLSlVVqTZ+xos14IYMUMOTYHC1nOT6S3fbpvf8pXwBjORvTEEwWHCrtAC+IKKYdhjMOYddVuteih/0uy+lShoePlDh5pcaFlQZBJ8AyNJ6aKfPQ2NCeFmEPMirjxN1HCZRi4NCFAAlhJBnL2yzVOkSxJOxm6653As7O1ai0O1xdcxnOGNi6xkg+lSzatQ1ShkEpG2OIJAv4Uq3FastFiAKz622Olfc/kWq/JXH6JlF8kAjG1bUWs5U2l9dcpsspXr62TisIKTgmM6UsWcdkrV3lesXlq5dW+eqlVV650qARb73uUxM2H3t8mlhKHpoo3DKDhh9plDM2qw2ftGUwXUxviFsQxcxV3e4aKm1PVkFvLsYxdb71wXEQ8OqVNdbbAaM5i3ccG+bUWP6+sS5Uxom7jxIoxcChaUmOu8V6By+IGMnZ5B0Dy9CxNY3nr68j45jzi02uLDW5uNogbQks3eDjj4wjo8QCMzWNtKXjhXE3BDpZsBpGgIAjxfTGAt/9yhxwq6CKOJYsVF0qLZ9S2uJIMcVqw+PJYwXeXGlRypikHZNLq02Qgq9fWuELb6xyrb59+RCAry14LDUuomuCI8U0hfRbfrTtnvJNXWNqKMWJ4eSYfveg1p14ulkRypux3VzMRx6a4IHRPF4UY+ka0+UMafv+GXJUxom7z/3z16K4r0jbBk9NF5mvuQgJuq4xXnAIZeKCWm95XF1vcm6pwXoreeKPY8HvvDzLE5N52kFEtROhETBkw3ghS4zAEALb1rF17QbL4KAyB0RS4oURUkp0XWOqmCYGUmaav/wtNr//yjznF2oIBN/x6CifObvI8g7i1GOuKXl9dp25k8PkHPOWZU9uNmd1O1bBTnMxp8fz93WEm8o4cXdRAqUYWNK2wYnh7A1f/jCMkUhWGi5hJKm0XHQ0DE3DMHRqHZ9vLjVwOxCTbKseOBWXfMpAIhjOWFimTsu70TI4iMwBQRiz2vBZaXo4bkApYzGWsylnbD5yZoyPnBnllfkqmgZ+EON6c3R2ee6lps+VtRZTpcyGFbXXp/zbsQq2rIXSBL4XEkQxtqnf9/MxN/u7UeHnd44SKMVAs/nLbxgaj0wWeH2hQc4yiCToBqBppCyNhhtyaamFu+k8s/WI8/MVpgopVlsaQ1EMmkDcQT78vQ5AcSxZbniMF2yiOGa+7lJtBzx+pMB0d06snE9xKpREccz1ShPHNmBLqcLtWay0eX25gWnovPfUCGnb2Kh/tZcSJnu1CvqtrjCWLNZc/FBi6hoTmwJS3i6o8PP94f4Ip1G8rRjJOnzogRE+eHqURydzSWhzFOEHEcWcSXgT0Zlfb7HS9Km0OrwyV8MLImar7k3Lw+/EbkvM9xNJieuHLDU8Lq01ubLUoNpq01+MumfB6JrGRD7N+04NU95cE34bRlIgdJOmG3B+scmllQZtL9xo42zVJYrlRli9F0Q02j5rjQ4tN9hS1lzTbu4GjGOJ64U0OwFhGG+02Qtjrq21kRKmy2ksI3Gj7rVk+r1ear3f5ZmxjY31e/fq/RwmyoJS3HNomuDEaA7HMvgh+zT/5cUrNDoROctgvJjC68xRW986GDx/ucF4cY2HpkrMlNMMdd1gvfkS2Fo/aTtud/2LkLDa9Hnh8gp/fG6ZMAZNSGzDImObGwlvexZMJ4z4zsemyDgmv/XCVS5XAjYH8aWAhycdhG5gGAbFrEMQRCzUXGxDJ20bGznyZtfbjOcd5mou5xbqvHq9QiyhnLF5z8kSjx0p3vIpvxNEXFiq8/pCgzCMGc5bPDNTppS1mRpKEUbxDXNgew27vh8sDxV+vn8ogVLckzimzrFyhvGCw0TeQSIxNY1Yg1rTY7ayQnuTRvkRPHd+GS+UjOUsYiSWru+5ftLtDkBSgK5JPn9hBVMXlLIWXhDxmW/M8/iRPEdK6RvmwxxDJ5uy+PgjExwfzvLVi4t87eo6rY7PSC7FcD4Fmo7rh1TcmJPFNKYQ2CkDKZN2GrpGJ4hYbXrU3YDZSpswjnhzuYEmNGxDECN55VqFQtri1EjuhnIcPUtG05JAiblKm/MLdZqdkFon4Hq1zWrD55PvmEoiIoUgjGMsTd8o/bHbBb/3y8JXFX6+fyiBUtyz9AbxjGN1BzWNpuvz4JES9Y7Pa9dqLHWnbzQgApbqES9dXaeUtql2Qh4Zz2NbBqsND6uvftJOA+PtDkB6d/AWSEpZJxERXWO95XFlvc3DU8UbFrH2ByycGctzYjjLD71fo9L2kHGS5imKJZeWm1yttHAMA8vSyVsGU8U0tpFYTqtNjyiKMfUkyGS1GRBFklS3vL2t64RxTKcvaKS3Vut6pU21FVBMW4znbbwwYrXuUemEGFqSe2+14fHGcp2htE0QxyyudShnLDQtyaRxda21q/mo+8XyUOHn+4cSKMU9zebBQArBdCnFXCnHYs1jeblDz5AKAQEUHJ0rqzW+dmWJo0WbH/7wg9iGveu1P7c7AGma4MGRPPmURSeI8COI4pi8Y3C6nN0o1d5/HkvXmCg4wFvrlabjtyIbAU6M5ri21mKx3kEAE4UUx0eyAMyut1lv+nTCiELKpO5HRFGM0CSuH2HqAi+KyJgajm2gC7GxVmu96eEHMfmUgR9FVDs+LS9kre0jENi2IPQ03DBgpelRzNiUMjZZy8ANIoI4puGGG/NefhRzejR3037aTvj3aoENStScCj/fH5RAKe55Ng8GpbTFUq3DfKXN5bUOrYiNuRsLuLTq8vX5JM7vhbkOnzn7VX7wgzN8z9MzZBxzVxbR7Q5Ao8U0f+PDp/mVP3mTStsn55j8+XdNM17ObAl733Y+RnurcnCPnGPy0ESBU6M54MaFt9PFNPNVl6GUQco2MTRod0JODuc4O1/DDSBtGbxjush0KZkD84KIdhAm19UEtqHTCSIEgnLGYrJgc3nNJWhKChmTcjoJ6rCNxPKxTJ2WH7Jc9yimrQ2xWa57HCtnbpoodrPw91IuXa+0QbKRosoLoo0s7XZX0Adx7mqnZQuDJKaDjBIoxX1B/2BQytp86qmjlDIWw3mbL51foh3GGJqg40sq/o2fdYFPf+UqJ4YzPDkzjKFpu7aIbsf19O4TI5wZyXF2scZwyqKYT21b+mMv8zG9zOWbEbpgJG/T8iLafohlGDx2pMBMOcPHHh5PKgobOrapIwW0vZDFeoe17lqtOI7wAh0NDZGCQtrm3SdHOFJuU2kH6EJQTFtMFlKJOCA27kW7jTmX/jIhc1UXpKTiJvkCL680Sdkar8/VWKi7FFMm7zo5wqNTRZYb3pa+2im0/jAF4rDE9F4URSVQivuSlG3w8OQQR4czGIZBJ0jCol+6vArB1uNrHtTaIeN5h7Rl3PUv8FDO4RnHZLHW2aiM2y+KvfkYTdM2KuNuLtsRhjF+HGNp2k3rQ+lCkDINspaB6M4JRZIb7rHTDbcP42Rgnyg4jBccLq02eG22lrgNiykmiiMcKSbRjmnLwAuTTPJHiulu5d63XJ5HimmkhKrr40cxUkpGc/auEsVqmkCTSWb4mhsQRjENL+TsXJXXrleYr7isdwIMTfLcG6v88PtO8sBYnpRlAomLtu56XFlvbQhlvwgcprV1WIEgnSDqpvlK/pbG8g6ZA/g7v1OUQCnuS3Qh0HWNomPx2NQQ19daRFmb64urLLe3Hh8Btil3LFuxF3bztLqTm1AXgiCMWWl0ECKpGjyUsjYsrGrb5+xcjai7EPfRqcJG2Hw/N5SYD+MtQtg/YJq6ji4Eay2fKIyRUvDgeI6xnEMkJU0vwhACw9CYKWe2tHvzvcwMZzCr2sac0uRQak/lS5DgBREtPyIIQpbqbiJOrk/WNvGCmKVah998/go//pEzOJaOoWsbBRdnSmks80YRAA41UvAwAkHiWHJ1tUXV9QkiyVrT4+pqmxMjmYFfSK0ESnFfckME3HiOMI7xwogTk8OcXVvdcnzZgiPl3I5P+Lt1kezlCX1HN6EAKUF0f/YOC8OYs3M1HEPDsQw6fsjZuRrvOV7e1pLqrwe12drqHzBjKbFMjZYX4PoRq02flKXRDGLSpoYXxrSCkJxmbtvuze/1lgLcjluplzB4vuJSdwN0TWIKgRQSEQskoOnJvYRC4AYhfhjjhUlYfDmbpLOCG0UAONRIwcMIQQ+imOWGR8bWqXd8UlYyRyiRAx/GrwRKcd/y1nyGw6luTr9njuZ57vVV6pvcfA8dyfPYxNBNv6i7FZ07ceH0C2AkE2uu31Jxu8UKgzgmiiWOlXx9Hcug5Xv4cYyxTXKYndouZHJdP4ywDJ1iKslGsVx3CYIAU+g8/8YScxWfI0MmtiGZLGQ5UkzfNDP5ZiG/3YE/bRs8NVPky5dWqbR8bDNZeFxp+ridAN3QsHWN0axDMW1vWGhCkrgsbyICh7lG6TBD0KWURLHE0jUkEaamEXR/V4Maxq8ESnFf43cFIu4+Pa+2Ik6UbV5efCu/3QPDDt//7Amyqa0usl5aoKvrLWxdI9XNb3cz0bldF85mERnN2RsVes1NA6nQtI2qvT0LStcElrZVnHYSzF7f3LB2SQiyjk7FDXhltsrzV+sbYfovzHX44wtf5/veM8M7T4zwnhNlHFO/QYz2e34nbRscKaVJWTqlrIVt6LykV5irtMk4BmfG8vyZJ6a6a+Hecs/uJAKHvUbpoEPQTV1jNG+z1vTww5ggjBnO2iAY+AXESqAU9y2bB+dqu8O/+qNzN4gTwFje5vRobovoNN2Ab85XOL/SZHa1xWQxw/HhLDPDGWK5/ZPnfpWrWG54jOZslhvexkA6mrOTQa0753R2rkbL9zZeb+fe2wi2EBpBFCfrnGRM0BUnUxfYpoWja7hRRBxLrq+3+crFFV7sE6cetQD+vxeuUkjrFDMGecciipOMFeN5h+WGh66BKTRieWsXUi+vXyeMcAydlG1srJuKZFJ23jZ0To7kiKTkzFiejz0yhqlBrRNSTNtkHGuLyOwkAoOwRukgMuf3X2umnMHSNXKOSa0dUEpbRPH21Z4HCSVQivuWzdbM4nqTr81ujZB46WqNxVqLiWJuQ3TWmx6/8eIVPndumflqhyiOGCukeOJokfeeHOHEWG5b0dmPchU9q8s0tI2BNAgT0eq3TN5zvLyrKL4gilmudzYG/mImsRRjKQljWK13cP2IuUob24AvXazQ7ITcLP3tQgc+e26FWjviiekSxayN7CbCFYAbxhvBG+muhdUbjPvdf34U89psha+8uUbF9ck7Fs8cG+LoUJpWGGEKDdntH9tICi2GxJSyqV3lTtxJBA5SIO42uw3I6bmLxQ7VngcNJVCK+5bN1kw1iLckWwWIIlhtuEyV8kk6ojDmxStrvHK1QjsIkwg3zaDW9rm00qSYtnj6eOmmX+7tsj/spZ39VpemCYhhbpt1PtOlNGnj5l/h/lx6PS0VApB0LSnJwrqLbWrUXR/LELS9CD8OiYLteuot1uptLi2bZG2Td2YtZLfMhq5pZG2DdNf1uNbyOd01w/rdfwJoegGvXKsQyiTd0rX1Bn96YYmsISnm08yUE2tVEzBZSJHP2Fi6foPga4iN+9w84N6L6372yr4F5AwoSqAU9y2brZnJnMN4ChY3FYvK2KBpBkPpJDqtE0Z0gohY05Lqs5oGmgAR4xgGGcfA1LUN91Q7CAmiiFrbo+b6rLQ7tP0ITWg8NJbnaDHJvN6LENw8aPa3s9pyafkBM6XsxsAbRDFRFN+wzudW81q9gSvJxeczOeSga4IgiliotojCgJoXcmXNxRBQcYMkGzkhRIIASGvQvolOtf2IuhfwtWsV6h2PUjZFEEsenyoQxpKmF2BoGuWshRRJ5OHsehvTENi6hhd2S51EEW0/oun6fPmNFdaaMT5g0cTSlxnJmxQyDmdGM3zLqVHee3rkhgH4ZgP0rQbu26nlNWhid78k190JJVCK+5r++YZj5Qw/9cnH+dn/+iqrXZEqmPC3PvYgT8yUqbYD8o6JpWnkbJO8ZbAidEIZ4ncklilASGw9GQBfX6zxR99Y5Ox8jTeXa1RbctsB/V1Hsnzy6aM8NFEATRL5MdmMxYnh3Mag6Zg6bc/n089fo9r20Q3BUzMlJgsZDD2xdo7KFI5tIGO5kaOu7QU0o5CUrpOyDKQAGUlmq21sXSNjGwRxi1euV/GjiM+8Os98rY0XRIxmHR6dzJNNW9hGkocvCCRTpRQ1t4MXBlRbEbW+iEcDMARomoZp6Ky3fRZrBrZlM5az+OZCnYfHc1iWwZBjYBg6QRgzX3O5vNrCDSOytp70oR9RbSUZ0c/OVlhuxhtuRZ8k+3yzElBoBbS8AKHpeGHMdz42STZlEseS+aqLLiBl6RsD9pGh1I4D914DOQYxjRLsz5qqQRTefpRAKe57+l0bn3zyKO85XuLFa8tUmiHvOFpipJAB2MiFZxoaTx4rse4GtMOIIIrQNTgx7PAtp0d4YmaIb8zVeO78Il++uMqVlc6OZdmfn22yUL/AQxN5Gl5MMW2Rd3Q++ugkH35wHE0TrFZdfulzF1iqeWRskyAK+cPXlvjU01NMlzMs1lz++FwV2zYYSpkcH8nx1UstPvf6MpFMROPhIwVOjWapt0K8KCZt68gYKs2Aq2sNvvrGGpfXGrR96ESwUG3yxnKTqSGHci5F2w+wTYNS2uQ7Hp+i3vH50sUVlhsdrq520JO1s+gC0pbJaDZFIR1TylhEcUTaNtA1gaYn111seDxxZCgJnBBJVolK06cdhmRMnZYfcWosy9nrVert6KZzXjKGthdwcanOaNbmzdUGj00Vafkhc5XERalrguGsTSwlfhzfdOAm3ttC3UG2Uu50TdWgCm8/SqAUbztGhzJ8e/4Y19bbmHo3bdCmL/dQ2uLPvmOKb31ghGrHY7HmMpxNkbETN9uFxQavXq8RRnJLpNt2XK9HVNoVHFMwlHHIpXSWv3KFRybypG2T//raLC9fr6EJDccNsAyNKApYbXTohDGfeXWW8/N1HNskY+l8+6PjLDWTHHWWoYEQfO1qBQ2YGkpR9ULaXoAEYiTrDZ/LlQbLm9ybdR/sWoejRYdWJ8T3I/JOlpRlYpkmHzijYxmCc/M1ZtddbF1HE5Jc2uahqQKVpo8QkHMM4hgyjslMOY3WXbel6QLXD6m6PssNl9VGQNbWsVI6XhQzlLJ4+Eie2fUmzSDeti/dELIkmTXaYchyrYM3GrHanZezuq7T+WqbcsbG4OYD916tjkEuAXIna6oGWXj7UQKleFuymy+3YWiUcg5DGRtNGBs1p/wwAmIiGROL3QkUgB9CxknCr02hsVhr86dvLpNJ2bx2rYqU0PI8qj7UYzCB//KVN3Bsi4urPilbEBFT70T85ovXePxoiVLGYb3tEYQxCLiy0qDadrkyX2Wh1SKj2wjToOn6VFpb2ySBZQ++cL7Kg5MeI1kTxwTHFDimydMzJc6M5mk9EnJ1rUksQdNgrtKhE0ZYuk7N9bEMnRjJyeEMlpEMeIamYZCkTtKFJGOZBKnkab2UsQjCiErbZzhvMzmUZrnZxN/axMRq06CYsXhgNEetE+GFSUbzsYLDWtPH9UKuVlpogK4JihmL9aYPIrox+W+8t4W6g1588HZD5gdZePtRAqV427LbL/d2Yvae4yP80bkVWgsNco5grXNrmXIMMIRAE+DFMULTMYSGJqETRqw1Azp9c1gBcL4KdIdtK5bUmiHEEMew3HBZbHRotANqrQ4tH/7o3OomV1liMllsmyP3hqOSEiQuf3ihjg08PW3w1771YYJyNinpMTkEJJGJD4xFzFbbyCgpyVHO2ViaxmrLvyH5rdAE5axFoxOSsQyW6x3iGJbqHWzTIJYwnkvhWAb5tKDRVwa51xW2DjlTZ6qUJooltiWYr7qstQIMHbKWzteXqlxdaTNfaSXZ1YsZHhjJIfRk/Zilv7UObC9WR+93v1B1aXnhRuHFO7Uy9nPu53ai8wZdeHsogVK8rdntl3s7Mfvxbz3Fv/38m6w0TNZbHp4XsN6KqYVbP//4qIOu66y5ATIGPYYz4zlGsmk6MuTaavUGcdqOyqaJrovXE4ujtQsTbjvLZCc84EvXQr70719lwniV4+NwfDjPkw+M89TUKOWsw9GhxB3UH0qfto0b+iiO5UY29WI6CWyouj6lnMVwymat7XN1vUUpa5I2oS2gk0wVYQIjWUE562CbBos1l3bH58hwjrytM5xL8eZykz+erfD85TWCKMYPJbalc2I0w9H3n6TsOFxfb2MZyZqqniBt/l32CwZww3qhOH7LSpa8lV0Eti4j2I3wDMLcz608CIMSPKEESqHYJZvF7JGpIj/9iUe5uNbA0ARCCLwg5MsX1/j8q5dZceGhYfgzzz6KFIKFisu5+TqGrjEx5PDg5BATRYd6J8AyDLhpmMD2VHbrW7xDFkJYmIUvzdb59ZfrwAUmLHhyyuBd7zjOg8NZltsehhQ8MllkNJ8h1gR6nBQ/9EOfaystNFMjZWmcHBkibRvYhs65hSrXVpqs1D2WapL+HB8B0GpKpgo6lXaAF8TUTI2JYpqr6y6rLZ+RrM1Co0Oj44MwSFmCjh+xVPN4dbbK+x8YZbnhcbSUIm0ZN8y19JYKNDoBy7VOd0F0hBfHaDE0/JhSxqTSDpgsOBTSFg3X54tvrKAJsAydsYLDTDmzq9B2GKy5n5t5EAZBQHsogVIo7oBSweGZXgqi7pf8qelhfuC9JxACUqaBbSYh0J0wKbceSomj6whNsNzwsDWN4+U0X1/wbn3BAWHBh4XLIf/t8htb9n3gWIpjo1mqLY9LS3W+ufqWu27ChumxLJMFByEkF+bWWFiHtZuIbQ14Ya7JkAVHSg5DKYuWH+P6AYvVDh0vYr2ZzIF5sURoBl7sEUURKw2fN5YbgNio9ts/1+IHMfNVlzeXG6y3PIQG5+cahDImbZkcG04jZQpNF1TcACngldkqFxabjORtylmTME5KkMyUM7sSnkGb+9n80DVIAgpKoBSKO2bzl9yydMqWvuWY7DbpiKa7qYD+6ocf4tX5F7i4vtNM0b3BF664fOGKu+2+BQ8WrjWB5p7OWfWhvtjB1gX5jI2IQTcECBgr2LQ6AaEfEYURtm5QylpkbB3b0AjDJErT0nT8ICKWEhlJFusd/CAphHhlrcV8pU0QxUwW04xmNearHUwtmXPygoj5asBqwydt62RMg+vrLquGj0RQzFq7Ep5Bn/sZOAE9yIsJIf5fIcSCEKIuhLgghPiRbY75aSGEFEJ89CDbplAcBr05nDPjQ/zMd7+DZyfNw27SwBIDL825/On5RV6fW2G96bLe9Mg5JqNDDhNDDrZlMDVk8+ypEd57aphTo3nGCw5eFLPe8ri61sb1Q66st2h5AUvNDlfW2qQNA03TAI21uo+pQaUVEMWSobRJGErafoSuSfIpk1onQBPJ/JOlaay3fASJEMLWZQs9enM/QSRpeSFBJAcqYWu/gMLN7+OgOGgL6ueAH5ZSekKIB4HnhBBfl1K+BCCEOAn8OWDhgNulUBwqmiZ4aqbM97z3QfwvX+Sl2a0WyNOTaWZKDg0/5sJileUmuNsEVujAyZJBhODN+8Ai28xSG5baHUprHdzpiBOjOabyKR47midnm3RCSSlr0wmTVFQpy2Ay7/DGSgNNg7YXsVxzqbR8hAFpSweZ5PVzzGSdl6YJgiDJeuGYBk/NZJivuzi6zlrL51KjiRCCybzNkVIaCQznbNaa/i2jAwchm3qPLbW7DrFe1XYcqEBJKb/R/7K7nQRe6r73fwF/H/ilg2yXQjEIpG2Djz88gaELZl5f4nOvLdGOIGfB971/hu95+hhtLyKMY66utvja5WUuLjVouB2ur4c0I7A0eGAyz6mRLOttn3K6zauz7R0zXdyr1Fx47vw6c9Ump8eGmKt55KyQ6XIW2U0D1XZDHp8eIpKStaaPJiTnlxu8eGWd1ZaPo2vYhs5Y3mamnGG56WLrGvmUwePTQ3zLqVFyTpKjcdrQkHFiyU2GKUbzNidHct0QdknGMsiUjF0Jz0Enbt0uKu9mwRCDJKBCygMKBepdUIhfAn4QSAFfBz4gpWwKIb4H+H4p5Z8VQlwBfkRK+dltPv+jwI8CTE9PP3316tUDa7tCcRC0vZBray0qrQ6tKOJ0OctUMYthaBuDShTFuEFIyw8IY/CikKurLTphTDntUEiZLNQ6dPyQi4t1Xrm+xsX1cNeLiu8lbCDnwFQ5jakbfPihUSzD4GgxRdUNeHg8TywlL15dY7nucXa+jhCgyWQBcCuQjOUspgopTMvgyaMFpobSHC1ltlQNDsOYThgRhDFVN7ghdH03kW6HEb69nRBZuraRSaU3FxZEcl+DIYQQL0kpn7mjcxy0QAEIIXTgWeBDwM+T/I19Hfi4lPLyTgLVzzPPPCNffPHFu9xaheLg2Wkg6+3rlTaP45iKG9B0Q1ZbHmfGcjiWznjOIYhjvnZ1nStrLb5wbo7PX9pbcMK9QsaAibyJZRqM5R2ePTWMQGc0Z1HMWSzXOnxzvk697fPmSh3X83Esk4lCGoTg0Yk8MyM5jpcz2LbBeM7BtvQdLY7RnI1paLsWm7sRvn0rwYtjua0QTRQc5qoumT4BbnkhR7vh9/vBfgjUoUTxSSkj4ItCiO8HfgyYAf6DlPLyYbRHoRg0dltsrzdfUEiZFByTp2eKWwbWZ0+OMFFIUc5atIPLvHD9/hEpnWQQiyW0/Zh8Smex1uHaWpuMZTCcs1ioeJhdt90bi3XOL7S7c3chFxddilmdrKUzM5ojl7YIopiXZ6uM5m00BJNDKRxT37bq8W4tjrsRvr0bwbtZVB7sLeXTYXHYYeYGyRzUB4EjQogf774/AvyGEOLnpZQ/f2itUygGnN3MF6RtgwcnCpwczfHOY2U+f2GRn/rt1w+sjTmgsQ/neXjYpBNJNBkxW5V0SJY2RyT/pIOItu8TRgJdk9iWQRhGVN2AcsZifr3NfKVNHLPh6mzG0K5HPPf6Eq0gIowj8o5FGMZc7vjUvYDX53SePTVyR+HX+x2+vVvBu1lYu6lrAxUMcTMOTKCEEKPAh4HfI0n99VHge4HvA36WJLNJjxeAnwA+c1DtUyjuVXYz4a5pAlvTmSpm+MSjR/iT1y7xmYt7TYB0e4wOQbu61zwZN2IDXihBGERSIETA5gm1qgdOJ2RmOEvecShnbC6ttTGE2MjF5wYRKRsi763chDGw2oGvXFxloeaRswRBDEtVlzCMMQ148coI3/bYUY6U30qGuxeLY7/XP+1W8HaKynO0wQmGuBkHaUFJEnfeL5Osv7oK/B0p5e9sPlAIEQEVKeX944tQKAaEGJgcHYWLswdyvbUqDNmwtk2iDBs4VYLlNrQ70GaL7gDJgOFGICOPqitxtzkoAkbSJvmUxdFiCtPU8EKLnG0ghMTUBSlTI4q3SqUE2h5cXGqgA9VN0fnXn1/kwlKLP/fuYzw0XiBlGXuyOPY7fHsvgrfZyo5jSdsPsTQNw9AGKnv5Zg5MoKSUKySuvN0ce+zutkahePuSMQ3Gh9IcH7K4XL37VlQVYJM4FXV44kSBR6aK1N2Ab16v0vADWp2Q1Wa8JSw+BKrNkFJOkLLA97a3yKQwcCyDTMrk+moby9Q4MZpF1wTrLZ9qO+Ls3DqSGxeQmSLJmu4FbBuS7wMXlhu8+OYqJ8pZTo/mMLbJDLIT+xm+vVfB61nZ602Pl69VCKKYtGnw2PQQQ2nrtttxtznsOSiFQnHAWJbOdzw2yfmFOp0LiyxsUyfqbmID2azBX//QKTRN5wvnl8EwKNoGET6ZyCVyEwHSupsAdDMpVzJecGgvu1sEygLSluCxiRxjOZvLqy1GUiY1NyTn6Aylbb7z8Qkmig6LlRbnFqpU2xI/BMtKRCqIk4rBwaaT64CUgrofsFBv89Bk4bYGz72uf9opSm+vgtd0A37/lTkW6h6GLsjYBm4Y8uEHx/cstgeFEiiF4m3IkWKGv/fxhzgzlmO20uRLF5Z5o7LVJimQJGzdT3RAExqVToStC+i6q1KGwDYFILD1xIenCYgFEEHWgHzGophOEQmYXXFpdQ0hR8DTx4d44miRjz08QTOIeHQiTyuM6fgRtbbPcNbi9EiOYtai0vT5wIPjXFxq8OKVVYJIQixZ0wOCQJIV0Owrm+LoScYJW9MxRJL8t1dfaq/lNnbLbqL0dit4YRhzcbnO9YrLcM5CCA0vjLi43OQ9J0LyxmBaUUqgFIq3KePFNN/77uNcX2/xyaemeeHNZX7v+cvUfXhiOs1PfNc7KaUtLq81ubLc4L+9cp0/vVDbY5rXrUSARFB1A56ayhIdLVJvh7w6V6Ha9ElZBsMZg0gmodwpHXRTY2o4Q9YyGc3bnB5JMfSoxVy1jalrTJXTzJTz6EA7jFmqe4RS0vZCdAReGBHGOosND4TAiyS5lM17T6eYKqW4uNRGaJIolizUPNyOx5urLTp+UhzSsaCYdTgzmWckb7NY72xZpLuf65z2Myy9E0TMVtos1Fxqro8ksb5cPyTtDLYEDHbrFArFXaH3pJ+2Dc5MFIik5B1TJf7Su0/hxhF508TpDl6POUUenhzi/WfG+Ow3FvnN5y/y/NztlwYRwLc+UGY065BJmRR9mw88OIzQJPXRkLYXohFzfb3D8bJN04tJWQY52+TkaJZsyoQ4ZqKYYbKUZ77WYrURUnB8htI2URzT8EKWax10XXBqOMNqK7GAdF2gS4GhaZwcSdP2Y96RKvHQxBCllEkgJVEYc73qUvd83lyoE0YgNTheznJ0OItt6BiaQJIkU12ouhwtpvd1ndN+haWHYcxsd6FuIWOTs02qLZ982iIII6bTGRzjcGo97QYlUArF24ydnvRzhkVu0/E9N1Ip6/BdTxzhwck8L1xe4T9++SIXK7dxfaBccNCFRhjGlDIWV1dbjOYdjlsmji44O1el2QlpuDFZ28QwDTK2jmUYyFgynE2TdSz8MMYxNE7OFDk9lmWx1uHyWpti2qDWTnLkLbc8RrLJPY7nk0CCUtpC0wRtPyBjGxwpJItxm27A2EhSTDKSkrmZNqaRSIKhaxsZyK9X2qw2kwCTtKmRtnWiKCZlJatlDF2j4wd0wgjH0PcsUvsRlt6znOaqLlnHYMgxOTGW49pqk4xtMjaU5cxYHqGrKD6FQjEA7OQ6Am45f5K2DR6dKnJmvMB3PznD85dXeOnKCl4YsFTx+cqlOvVdLHh67ptLPHtimCCW+EFEIW0yki2i6fDZbyzyuQvLNDshcQyjOZuZvI2mabhBSNuHhyZNhrMWUSxp+QFDKZOUaWBoGr4fky2YFNMmQRwzknEQWnLvpq4RS0naNpnMOxi6hq1rWKZOsxOwUO8gBRiaxmjOxjaNG9IEmbrGUq1Npe2Ttg1cL+LKahMpwNQ0pkiRT1k0OwHztQ5BHGMZ+kY2it1yp2Hpvd+zrWtk7ETUW0HETCnFVMFmqpjG1DTkAGaP6EcJlELxNuJmrqOWH7LW9Hc1f9Jb9GubOt/26BE+/NAkAI1OwK996U3++6tznF/bucxHJ4xImW/lsbN1nVLGZKHa5itvrhIEEY6h0fZjFmoeyJiHpobIpXSG00lZi3o7JJSSuhswno8JY0k5azFXdam0PfxQEsSSxbrHqdEMKcvADaK3EqaaOke6rrmmm4jTeM7GMXVkLFlueIzmbJYb3oZI5FMmXhizUO/g+hFBGOGYBiv1DqN5h1dna5wZzbLS9DF0Qb0TImVAEMacHsvtyZK6k7D03u85ZRmM5GxWGh5tL2Q4YyVrn7riNIjZI/pRAqVQvI3YznUEsNrwsAxtz/MnPbECsE2dH/3AA3zgzCifPbfAH3z9OlfqWz/jANPlFEJoWIZG2jYwhODCcp2ryzWWqi4a0A4kmoBQJgNuEEXEcYxEMLvuYhngmAbPHC/ih5Irqy2mS2k+eHqEby7UGc8bmIZG3jGwDJ2jxTRScMNg3xOBThjRiSKqnZDYDdCEIGPrmEZqQySEhOuVNhlHT+ZtJFxbbYKA1abFdDGNU7AJY0koYwq2vZF1YrnhcWw4s9FXsLuIv9sty9H/e3ZMnbGcjZc2OVbKoGlioLNH9KMESqF4G7Gd66j3hG10s1jfSZ64bMrkqelhzowP8YlHjnBxrcFnvn6V5y81aIdJeYuHJ3P8yPtPk7LMZK4miPidVy/z6S/Psry5TmM3Y0TbC1lpBBiixfW1DuWUiYwNHFPy6vUaJ0dzIKGUsUjbBpPFFCkzmfvRhKDlhYkbbptM3b2quGtNH0sTpB0TP4hYa/qcHgFNT0QiiGIkMJ5PMbfe5tJKgwhBOWVSzFhcWGlyaiTLqu+x2vAJI8lo3rnBhdYTpSBMRCuKYqSAyUJqS2mPO2G73/ORYnpjvdMgZ4/oRwmUQvE2Y7PrCGBN+PuWJ07TBDnH5JEjRR6cGuJjD05ybqHC9WoLxzR417FhShmHa+tt/CDiX/zhWX7rldUdz7nkgrfQoNZsABrTIznStoUmMowUDBpuSDlrsd7yyVjJXFSPjp8saNrpftxejacghKbPcM6inLGQfR/pWSW6JihlE1fgTMqk6YVd0ZF0wpicbeCUdCqtgLmKy3DWYjRvd0PY20RRzHytQyGl0wnlhsX61HRxX0VqkAoP3i5KoBSKtyGbXUd3I7N17xpmSuPpYyM8IYdvGCjHCw5fvDh3S3HqUY2gWgWIuVitcbxooWkahmWQsSPG8k5Splsk57662mK54RFLyUjWohNEOKa+ZcCOY8lqwyNjG5QySeBFEMfYpn6DqPWskoWqmwRcaBrFtMNwXuB6AVnHwNY1JotJwIltdKh3AkaySVDCcsPD1AVRDMs1l3MLHhNDKSaH0vhBzHzV5cRIdl+F5KAr9+43SqAUCsVdf9rebqB0TB3v9pdTcbniM1NyGTlewtIFQRwhI5CRxNAEQsBw1qTpJSU3vnp5jbGcjaFrmLrGRDeyLpISCUwMpVhpeMSAH0qG0uaWazpmEpEXRDFHi2nOLzWRSNKmzntPlvG61zZ0jfG8QzFjcayUQQoIgogwgqurLaodj1o7IGUZxHGbyWIKBHt2qx5Ghd6DRAmUQqEADudp+6HJHDq3X4pjtuoyv94migWvz9coZS3eWG0ynrVpBBHtTsRo3iZrm1yv1Fhv+kyVUiDBj2JOj+Y2XHeGJpgaSlF3ffwgZr3lU3fDLRGNpq5hGTqmbfDO4yVcP0QIwcRQGr/rrts877Pe8njxWoX1psdczWUkl0QDul6IF4acHs2ABLGHAud3o0LvoKEESqFQHBonRov8yPsm+TdfnL+tz8s4ptb2WGz4uH7IUMbCvbjGI0fynBzNIWOotANCK6LhBozlnY3MCct1j2PlDLapb7g4oyhipekzOeSQccxtIxr7AxB6Ifs71VgKw5izczUsXSPj6LQWfb45VyFtaczaNkdKaa5XOkyX08xW3V0Jzd2o0DuIKIFSKBSHyk982+OsVF1+6+ze01Is1CIuLDcJY0kxYxFESSGN62suT0wPcXm1zXKzQzlt44WSWCYCE8c3mir94eZSQMZ5KyPEdhGNO7lEN1uifhwTRDGWodGq+nz92joLtZCUDRmzjRvFvO/0CENpa0N4biU0+12hd1BRAqVQKA6dDz86zVffrDC3Ocz8FrRjWG26aFLDC2MytokuBL6lkTJ08raFJmCy4BDHEi+MaPshgiRDRX/YuaYJHEPH0LRdRTTu1iVqaRq6JliotnhtrspaKyRtgq3pCAFvLtZptH38IMIydeIwvqXQ7HeF3kFlMIuAKBSKtw2mrnG0nOapUyO39fkrlYhL1YA3l1zmKm28IETImKWGRyRjHjsyhGHoTJZSJKU8BCM5h5nhzBYrpee+88KYWtvHC+M7jmg0DI1HJgvEUmOx2iGOQQjQLQFCx49jLq03ubrW4rXrFequRxzLLVbedu0MIrmRH3DQs0LcDsqCUigUh4qmCU6P5nn3iWG+cmGFlduM7POBhUYIcUjDi5BnFxjJp0hZBtOlNJZucrSokXFMpvsWrW6H2PTzTimmLMo5gwiJG4PrQ9UPKVgwmrMIY/iDswt0gsQS+uQTIdPl7I7zUXcSeXmvRP8pC0qhUBw6hhC0PR935xR+u6Llg6lJpBBoJMlnLy03mKu5lLM2hqbdsAC3RxxLvCBioepiGRqFtIVlaEkwxDbWTK9o4U6WTu8Y1w85v1Dj8sKNPsyaD88cL3J5tU3ONpgspck5Bl++tE7Ui9K7hSVl6totRaZ3b14Q0fZCrq23ub7e5tp6m87m8sEDhLKgFArFoeNGEZV2SDO+83NFATiWhRdELDY8HEOjlLMopR3Wmh7lrL0lnLsXsu2HEUt1j+lyGkO/efDB5hDv0ZyNaWg3WCT9x1SbHc5eW9m22OMb8xVMK0XW1BGdkGLGJogjfCS2lHcc+NAJIq6utViue0gpCWPJTDlN9iZRioOEsqAUCsWhk9J1htL7s4ZH18AywNINwjjG1pNS7W0/Yq7awQsjZqsunSDasCzmqy6mLsilTCxDbGSL8IOIuJsotkd/iHfGNojjmK9dq3BttbVhkWw+xrF0Wv72lsqFJZ/1lsdy06XhBVRbPqauYyHuOPAhjiULVZdKyyfnGKQtnbobsNbyiWOJ0S0/Esk9LMA6QJRAKRSKQ8eydD70wATFfTjXAxNphNCQMiZt6rzrZJmj5QwgmSo6DOccTF1wdbXF1bVkm6u4hHFvwWuKIJKsNj2urrcJwnhD0KA/xDsZ3CtugCbAtnRMXbBY6ySuv+4xALmUxZnx/LbtNQwYydpIKbB1jWrH5+mZIXRTv+PAhyQLfIzWzW5hmTqaDp0gsQoHPfpPufgUCsVAcHw4x194/yS//Ce3t2g3C3z08RGOljIUszaOYXBiNEUYJRnTpRRMDqXRhAANlhseR0spco7JestnseYyXc5gaIKJIQcZS4bK6Y2SGT1XWH+IN4AfJEUJe+49L0yS0/aHgcex5FsfOcqVSsCL1xobbc4ZUMxYRAg+8uAIR4sZYil59/Fh0pZxx243XSRzVHEsN9pbdCwk4PkRuq4NdPSfEiiFQjEQWJbOD7z3DK9cb/Ll7QpJ7UAG+J/eN82Tx4cxNLB1HalBGAmeODKEaWhYhovRHYj9rjVkd8uxTwyluLbWpuEGWIbOWN5hpeFhGVsXwprdQX2x1iGMkzVLo2kzyRrRtUj6j+kJ1qnRPP/4k4/zGy+9yXNnl9F0OFouoAuBbWqkTIOMZVDK2vsiTsDGvflRzHI9CY8czTscLaW3zJkNIkIOqO9xNzzzzDPyxRdfPOxmKBSKfaTe9vn8hXn++ytX+L1zrVsebwJ//xMP8IEHRllrBJiGxkTeQYrESpgezmB26071ghYAgigmaxsbC139MGZyKLWxePfaevuGcu9BJG8IJthc22m7nHj94dx+FLNQdbm02uTlaxWaHQ8/SlIhjRYcPvLQGIWUtZHEdj/pRRMCu4r62w+EEC9JKZ+5k3MoC0qhUAwU+bTFJx6f4SMPH+GZFy7xM7/7xo7H/8PvOsUDY0MIKTasGcNIREXXtY35lc3rhjYndp0YSmH3CcOtSpBslBPRNaa3KePRfwyAo+nMlDMM52zKGYsLCw06UcxYzuLpmTKFjHXXLJr+ysf3EkqgFArFwNFLO/To0SLjDix2tj/ukw/n+MADk0wWUpiGtlF3qeXtLCrAtold+9nLQtjdpj3qFXN8/EiRM2N5IimT9Eo7LBp+O6MESqFQDCSaJnh0osSPf+wh/vkfnqOyKcPEx88U+Uefeopyxt4Qj52smZtdYydhuVslSDRNkNrH6rn3K6qHFArFwOKYOt//7HE+9sgoL19b4c21KiYmz54c4eHJkW0tj3u9iqziLZRAKRSKgSaJRMsyMZQ97KYoDhjl+FQoFArFQKIESqFQKBQDiRIohUKhUAwkSqAUCoVCMZAogVIoFArFQKIESqFQKBQDyT2di08IsQJcPcBLDgOrB3i9QUT1geqDHqofVB/AzftgRko5cicnvqcF6qARQrx4p8kP73VUH6g+6KH6QfUB3N0+UC4+hUKhUAwkSqAUCoVCMZAogdob//awGzAAqD5QfdBD9YPqA7iLfaDmoBQKhUIxkCgLSqFQKBQDiRIohUKhUAwkSqAUCoVCMZAogdoGIcTfFEK8KITwhBC/umnfe4QQ/0MIsS6EWBFC/GchxMQ257CEEK8LIWYPrOH7yJ30gRDiJ4UQZ4UQDSHEZSHETx74DewDd9gHQgjx80KIte72C0KIe66K3i36wBJC/KYQ4ooQQgohPrRpvy2E+GUhxFK3n35XCDF1gM3fF+6kD7rHPCWE+IIQotnti799QE3fV+60H/qO2/W4qARqe+aBfwr8P9vsK5JErRwDZoAG8CvbHPeTwPJdat9BcCd9IIAf6B737cDfFEL8xbvZ2LvEnfTBjwLfDbwDeBz4LuCv3b2m3jV26gOALwLfDyxus+9vA8+S3P8kUAV+cf+beNe57T4QQgwDfwD8G6AMnAL+8O40865zJ38LPfY2Lkop1XaTrfvL+NVbHPMU0Nj03nHgHPAdwOxh38dh9MGm/f8S+MXDvpeD7APgS8CP9r3+YeArh30vd6sPgFngQ5ve+9fAL/S9/gRw/rDv5YD74H8H/sNht/2w+6H7/p7HRWVB3TkfAL6x6b1fBH4KcA++OYfCdn0AJK4u4P03238fsbkPHgFe6Xv9Sve9txP/N/BeIcSkECIN/CXgM4fcpoPmPcC6EOJLQojlrptz+rAbdUjseVxUAnUHCCEeB/43ErO1996nAENK+duH1rADZLs+2MTPkPydbecGvS+4SR9kgVrf6xqQvRfnoe6AC8A1YA6oAw8BP3uoLTp4jgB/mcTdOQ1cBv7jobboELjdcfFtJ1BCiOe6k3jbbV/cw3lOkTwN/m0p5Z9038sAvwD8z3en9fvD3eyDTfv/Jslc1CeklN7+3cGdcwB90ATyfa/zQFN2fR2DwH71wQ78a8AhmXvJAL/FgFlQB9AHLvDbUsoXpJQd4B8D3yKEKOzDufeNu9kPdzIuGndy4XsRKeWH7vQcQogZ4LPAP5FS/oe+XadJJs3/pPugbAEFIcQi8B4p5ZU7vfZ+cJf7oLf/rwD/APiAlHLgIhkPoA++QRIg8Xz39TsYMDfnfvTBLXgH8L9IKdcBhBC/CPysEGJYSjkQJSoOoA9eBfofSnr/HyhL+i73w22Pi287C2o3CCEMIYQD6IAuhHCEEEZ33xTwx8C/klL+8qaPngWOAk90tx8Blrr/v34gjd8n7qAPEEL8JZLJ4Y9JKS8dZLv3kzvpA+DXgJ8QQkwJISaBvwv86gE1fd/YqQ+6++3ufgCru783+L4A/IAQoiCEMIEfB+YHRZx2yx32wa8AnxJCPNHtg38EfFFKWT3Ie9gP7qAfbn9cPOyIkEHcSOZN5KbtZ7r7frr7utm/3eQ8H+IejeK7kz4g8bMHm/b/8mHf0wH3gSBxa6x3t1+gm/vyXtp26oPu/ivb7D/W3VcGfp0krLhKEob8rsO+p4Psg+7+HyOZh6sAvwscPex7Oox+6Dtu1+OiSharUCgUioFEufgUCoVCMZAogVIoFArFQKIESqFQKBQDiRIohUKhUAwkSqAUCoVCMZAogVIoFArFQKIESqHoIoT4VSHE7x3wNX9QCNG8i+dvCiF+8G6dX6G4myiBUigOl08DJ3ovhBA/I4Q4e4jtUSgGhrddLj6FYpCQUrq8fcqyKBR7QllQCsU2dPOK/Z8iKdHdEUJ8RQjxvr79H+pmev6IEOKrQoi2SMphP7XpPH9FCHGtu/93hRA/LoSQffs3XHxdV9xPA4/0ZZL+we4+KYT4c5vOfUUI8ff6Xp/qZqXuCCHOCyG+a5v7mhJC/CchRKW7/b4Q4vT+9JpCsb8ogVIotucXgL8A/BXgSeA14A+EEBObjvs5kqztTwFrwK/3EoUKIZ4F/h3wr0gSY/5XknILN+PTwD8DzgMT3e3Tu2msEEIDfpvkO/1st90/A9h9x6SBzwEd4IPd4xaAz3b3KRQDhXLxKRSb6Nav+THgR6SUv999768DHwb+BvC/9h3+j6SUn+se87MkCVGnSMpe/y3gD6WUP9899oIQ4p3AX93uulJKt2tNhVLKxT02+6PAw8BxKeW1bnv+DtBfo+ovkiSx/SHZTcIphPhrJMlcvwv4jT1eU6G4qygLSqHYyknABP6094aUMgK+TCIC/bza9//57s/R7s8HeaseVI+v7l8zb+AhYK4nTn3XivtePw0cBxrd6L4mSaXfIsk9KxQDhbKgFIqt9Gr5bJfqf/N7wTb7eg9+4ibnuB1kX7t6mH3/300BPA14mcSS2sz67TVLobh7KAtKodjKRcAH+oMidJI5m2/u4TzngHdtem/z6834JAXhNrNCMifVa89Y/+tuu6aEEEc3Xav/O/414BSwKqW8uGlTAqUYOJRAKRSbkFK2gH8N/B9CiO8UQjzUfT0G/NIeTvUvgY8LIX5SCHFaCPHDwKdu8ZkrwIwQ4ikhxLAQohfk8MfA3xBCPCOEeJKkOm+n73OfBV4Hfq1bvfVZ4J8DYd8xv05SyfR3hBAfFEIcF0J8QAjxz1Qkn2IQUQKlUGzP3ycJGvgVErfY48C3SykXdnsCKeWXSQIi/hbJXNV3Az/PjcKymf8C/Dfgj0ispu/tvv93gUvAc8BvkkQHLvddKyYRP41k7unXgH8KeH3HtIEPdM/zn0kE7d+TzEFVdntfCsVBoSrqKhQHiBDinwMflVI+dthtUSgGHRUkoVDcRYQQPwn8D6BJEgr+14GfOtRGKRT3CMqCUijuIkKITwMfAgrAZeDfAP9Cqi+eQnFLlEApFAqFYiBRQRIKhUKhGEiUQCkUCoViIFECpVAoFIqBRAmUQqFQKAYSJVAKhUKhGEj+f6/AwS+OKtjoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.1)\n",
    "save_fig(\"better_visualization_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The argument `sharex=False` fixes a display bug (the x-axis values and legend were not displayed). This is a temporary fix (see: https://github.com/pandas-dev/pandas/issues/10611 ). Thanks to Wilmer Arellano for pointing it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure housing_prices_scatterplot\n"
     ]
    },
    {
     "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": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "             s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "             c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    "             sharex=False)\n",
    "plt.legend()\n",
    "save_fig(\"housing_prices_scatterplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading california.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('./images/end_to_end_project/california.png',\n",
       " <http.client.HTTPMessage at 0x7fd1784e5050>)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download the California image\n",
    "images_path = os.path.join(PROJECT_ROOT_DIR, \"images\", \"end_to_end_project\")\n",
    "os.makedirs(images_path, exist_ok=True)\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
    "filename = \"california.png\"\n",
    "print(\"Downloading\", filename)\n",
    "url = DOWNLOAD_ROOT + \"images/end_to_end_project/\" + filename\n",
    "urllib.request.urlretrieve(url, os.path.join(images_path, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure california_housing_prices_plot\n"
     ]
    },
    {
     "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(os.path.join(images_path, filename))\n",
    "ax = housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", figsize=(10,7),\n",
    "                  s=housing['population']/100, label=\"Population\",\n",
    "                  c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"),\n",
    "                  colorbar=False, alpha=0.4)\n",
    "plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5,\n",
    "           cmap=plt.get_cmap(\"jet\"))\n",
    "plt.ylabel(\"Latitude\", fontsize=14)\n",
    "plt.xlabel(\"Longitude\", fontsize=14)\n",
    "\n",
    "prices = housing[\"median_house_value\"]\n",
    "tick_values = np.linspace(prices.min(), prices.max(), 11)\n",
    "cbar = plt.colorbar(ticks=tick_values/prices.max())\n",
    "cbar.ax.set_yticklabels([\"$%dk\"%(round(v/1000)) for v in tick_values], fontsize=14)\n",
    "cbar.set_label('Median House Value', fontsize=16)\n",
    "\n",
    "plt.legend(fontsize=16)\n",
    "save_fig(\"california_housing_prices_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking for Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.687160\n",
       "total_rooms           0.135097\n",
       "housing_median_age    0.114110\n",
       "households            0.064506\n",
       "total_bedrooms        0.047689\n",
       "population           -0.026920\n",
       "longitude            -0.047432\n",
       "latitude             -0.142724\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure scatter_matrix_plot\n"
     ]
    },
    {
     "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.tools.plotting import scatter_matrix # For older versions of Pandas\n",
    "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": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure income_vs_house_value_scatterplot\n"
     ]
    },
    {
     "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=\"median_income\", y=\"median_house_value\",\n",
    "             alpha=0.1)\n",
    "plt.axis([0, 16, 0, 550000])\n",
    "save_fig(\"income_vs_house_value_scatterplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experimenting with Attribute Combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 43,
   "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": 43,
     "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": 44,
   "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": 45,
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare the Data for Machine Learning Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"median_house_value\", axis=1) # drop labels for training set\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the book 3 options are listed:\n",
    "\n",
    "```python\n",
    "housing.dropna(subset=[\"total_bedrooms\"])    # option 1\n",
    "housing.drop(\"total_bedrooms\", axis=1)       # option 2\n",
    "median = housing[\"total_bedrooms\"].median()  # option 3\n",
    "housing[\"total_bedrooms\"].fillna(median, inplace=True)\n",
    "```\n",
    "\n",
    "To demonstrate each of them, let's create a copy of the housing dataset, but keeping only the rows that contain at least one null. Then it will be easier to visualize exactly what each option does:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0             NaN   \n",
       "6068     -117.86     34.01                16.0       4632.0             NaN   \n",
       "17923    -121.97     37.35                30.0       1955.0             NaN   \n",
       "13656    -117.30     34.05                 6.0       2155.0             NaN   \n",
       "19252    -122.79     38.48                 7.0       6837.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \n",
       "4629       3296.0      1462.0         2.2708       <1H OCEAN  \n",
       "6068       3038.0       727.0         5.1762       <1H OCEAN  \n",
       "17923       999.0       386.0         4.6328       <1H OCEAN  \n",
       "13656      1039.0       391.0         1.6675          INLAND  \n",
       "19252      3468.0      1405.0         3.1662       <1H OCEAN  "
      ]
     },
     "execution_count": 47,
     "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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, ocean_proximity]\n",
       "Index: []"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows.dropna(subset=[\"total_bedrooms\"])    # option 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  population  \\\n",
       "4629     -118.30     34.07                18.0       3759.0      3296.0   \n",
       "6068     -117.86     34.01                16.0       4632.0      3038.0   \n",
       "17923    -121.97     37.35                30.0       1955.0       999.0   \n",
       "13656    -117.30     34.05                 6.0       2155.0      1039.0   \n",
       "19252    -122.79     38.48                 7.0       6837.0      3468.0   \n",
       "\n",
       "       households  median_income ocean_proximity  \n",
       "4629       1462.0         2.2708       <1H OCEAN  \n",
       "6068        727.0         5.1762       <1H OCEAN  \n",
       "17923       386.0         4.6328       <1H OCEAN  \n",
       "13656       391.0         1.6675          INLAND  \n",
       "19252      1405.0         3.1662       <1H OCEAN  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows.drop(\"total_bedrooms\", axis=1)       # option 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "median = housing[\"total_bedrooms\"].median()\n",
    "sample_incomplete_rows[\"total_bedrooms\"].fillna(median, inplace=True) # option 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy=\"median\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the text attribute because median can only be calculated on numerical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)\n",
    "# alternatively: housing_num = housing.select_dtypes(include=[np.number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that this is the same as manually computing the median of each attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
    "                          index=housing.index)"
   ]
  },
  {
   "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>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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.loc[sample_incomplete_rows.index.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'median'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
    "                          index=housing_num.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  \n",
       "17606       710.0       339.0         2.7042  \n",
       "18632       306.0       113.0         6.4214  \n",
       "14650       936.0       462.0         2.8621  \n",
       "3230       1460.0       353.0         1.8839  \n",
       "3555       4459.0      1463.0         3.0347  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Text and Categorical Attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's preprocess the categorical input feature, `ocean_proximity`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19480</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8879</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13685</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "17606       <1H OCEAN\n",
       "18632       <1H OCEAN\n",
       "14650      NEAR OCEAN\n",
       "3230           INLAND\n",
       "3555        <1H OCEAN\n",
       "19480          INLAND\n",
       "8879        <1H OCEAN\n",
       "13685          INLAND\n",
       "4937        <1H OCEAN\n",
       "4861        <1H OCEAN"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing[[\"ocean_proximity\"]]\n",
    "housing_cat.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "ordinal_encoder = OrdinalEncoder()\n",
    "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoded[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_encoder.categories_"
   ]
  },
  {
   "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 OneHotEncoder\n",
    "\n",
    "cat_encoder = OneHotEncoder()\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the `OneHotEncoder` class returns a sparse array, but we can convert it to a dense array if needed by calling the `toarray()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can set `sparse=False` when creating the `OneHotEncoder`:"
   ]
  },
  {
   "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": [
    "cat_encoder = OneHotEncoder(sparse=False)\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a custom transformer to add extra attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# column index\n",
    "rooms_ix, bedrooms_ix, population_ix, households_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):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, households_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, households_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household, population_per_household,\n",
    "                         bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]\n",
    "\n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that I hard coded the indices (3, 4, 5, 6) for concision and clarity in the book, but it would be much cleaner to get them dynamically, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_names = \"total_rooms\", \"total_bedrooms\", \"population\", \"households\"\n",
    "rooms_ix, bedrooms_ix, population_ix, households_ix = [\n",
    "    housing.columns.get_loc(c) for c in col_names] # get the column indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, `housing_extra_attribs` is a NumPy array, we've lost the column names (unfortunately, that's a problem with Scikit-Learn). To recover a `DataFrame`, you could run this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</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",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4.625369</td>\n",
       "      <td>2.094395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>6.00885</td>\n",
       "      <td>2.707965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.2</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",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4.225108</td>\n",
       "      <td>2.025974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5.232295</td>\n",
       "      <td>4.135977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.047847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "17606   -121.89    37.29               38.0      1568.0          351.0   \n",
       "18632   -121.93    37.05               14.0       679.0          108.0   \n",
       "14650    -117.2    32.77               31.0      1952.0          471.0   \n",
       "3230    -119.61    36.31               25.0      1847.0          371.0   \n",
       "3555    -118.59    34.23               17.0      6592.0         1525.0   \n",
       "\n",
       "      population households median_income ocean_proximity rooms_per_household  \\\n",
       "17606      710.0      339.0        2.7042       <1H OCEAN            4.625369   \n",
       "18632      306.0      113.0        6.4214       <1H OCEAN             6.00885   \n",
       "14650      936.0      462.0        2.8621      NEAR OCEAN            4.225108   \n",
       "3230      1460.0      353.0        1.8839          INLAND            5.232295   \n",
       "3555      4459.0     1463.0        3.0347       <1H OCEAN             4.50581   \n",
       "\n",
       "      population_per_household  \n",
       "17606                 2.094395  \n",
       "18632                 2.707965  \n",
       "14650                 2.025974  \n",
       "3230                  4.135977  \n",
       "3555                  3.047847  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs = pd.DataFrame(\n",
    "    housing_extra_attribs,\n",
    "    columns=list(housing.columns)+[\"rooms_per_household\", \"population_per_household\"],\n",
    "    index=housing.index)\n",
    "housing_extra_attribs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformation Pipelines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's build a pipeline for preprocessing the numerical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ..., -0.31205452,\n",
       "        -0.08649871,  0.15531753],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.21768338,\n",
       "        -0.03353391, -0.83628902],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ..., -0.46531516,\n",
       "        -0.09240499,  0.4222004 ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.3469342 ,\n",
       "        -0.03055414, -0.52177644],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.02499488,\n",
       "         0.06150916, -0.30340741],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ..., -0.22852947,\n",
       "        -0.09586294,  0.10180567]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "full_pipeline = ColumnTransformer([\n",
    "        (\"num\", num_pipeline, num_attribs),\n",
    "        (\"cat\", OneHotEncoder(), cat_attribs),\n",
    "    ])\n",
    "\n",
    "housing_prepared = full_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 16)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reference, here is the old solution based on a `DataFrameSelector` transformer (to just select a subset of the Pandas `DataFrame` columns), and a `FeatureUnion`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# Create a class to select numerical or categorical columns \n",
    "class OldDataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's join all these components into a big pipeline that will preprocess both the numerical and the categorical features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "old_num_pipeline = Pipeline([\n",
    "        ('selector', OldDataFrameSelector(num_attribs)),\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])\n",
    "\n",
    "old_cat_pipeline = Pipeline([\n",
    "        ('selector', OldDataFrameSelector(cat_attribs)),\n",
    "        ('cat_encoder', OneHotEncoder(sparse=False)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "old_full_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipeline\", old_num_pipeline),\n",
    "        (\"cat_pipeline\", old_cat_pipeline),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_housing_prepared = old_full_pipeline.fit_transform(housing)\n",
    "old_housing_prepared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is the same as with the `ColumnTransformer`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(housing_prepared, old_housing_prepared)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select and Train a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Evaluating on the Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 83,
     "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": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [210644.60459286 317768.80697211 210956.43331178  59218.98886849\n",
      " 189747.55849879]\n"
     ]
    }
   ],
   "source": [
    "# let's try the full preprocessing pipeline on a few training instances\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(\"Predictions:\", lin_reg.predict(some_data_prepared))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare against the actual values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels: [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]\n"
     ]
    }
   ],
   "source": [
    "print(\"Labels:\", list(some_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_data_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68628.19819848923"
      ]
     },
     "execution_count": 87,
     "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": "markdown",
   "metadata": {},
   "source": [
    "**Note**: since Scikit-Learn 0.22, you can get the RMSE directly by calling the `mean_squared_error()` function with `squared=False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49439.89599001897"
      ]
     },
     "execution_count": 88,
     "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": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=42)"
      ]
     },
     "execution_count": 89,
     "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": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 90,
     "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": [
    "## Better Evaluation Using Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [70194.33680785 66855.16363941 72432.58244769 70758.73896782\n",
      " 71115.88230639 75585.14172901 70262.86139133 70273.6325285\n",
      " 75366.87952553 71231.65726027]\n",
      "Mean: 71407.68766037929\n",
      "Standard deviation: 2439.4345041191004\n"
     ]
    }
   ],
   "source": [
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [66782.73843989 66960.118071   70347.95244419 74739.57052552\n",
      " 68031.13388938 71193.84183426 64969.63056405 68281.61137997\n",
      " 71552.91566558 67665.10082067]\n",
      "Mean: 69052.46136345083\n",
      "Standard deviation: 2731.674001798342\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": [
    "**Note**: we specify `n_estimators=100` to be future-proof since the default value is going to change to 100 in Scikit-Learn 0.22 (for simplicity, this is not shown in the book)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "forest_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18603.515021376355"
      ]
     },
     "execution_count": 95,
     "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": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [49519.80364233 47461.9115823  50029.02762854 52325.28068953\n",
      " 49308.39426421 53446.37892622 48634.8036574  47585.73832311\n",
      " 53490.10699751 50021.5852922 ]\n",
      "Mean: 50182.303100336096\n",
      "Standard deviation: 2097.0810550985693\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
    "                                scoring=\"neg_mean_squared_error\", cv=10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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": 97,
     "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": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111094.6308539982"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_reg = SVR(kernel=\"linear\")\n",
    "svm_reg.fit(housing_prepared, housing_labels)\n",
    "housing_predictions = svm_reg.predict(housing_prepared)\n",
    "svm_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "svm_rmse = np.sqrt(svm_mse)\n",
    "svm_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fine-Tune Your Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             return_train_score=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    # try 12 (3×4) combinations of hyperparameters\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    # then try 6 (2×3) combinations with bootstrap set as False\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "# train across 5 folds, that's a total of (12+6)*5=90 rounds of training \n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error',\n",
    "                           return_train_score=True)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best hyperparameter combination found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 30}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_features=8, n_estimators=30, random_state=42)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the score of each hyperparameter combination tested during the grid search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63669.11631261028 {'max_features': 2, 'n_estimators': 3}\n",
      "55627.099719926795 {'max_features': 2, 'n_estimators': 10}\n",
      "53384.57275149205 {'max_features': 2, 'n_estimators': 30}\n",
      "60965.950449450494 {'max_features': 4, 'n_estimators': 3}\n",
      "52741.04704299915 {'max_features': 4, 'n_estimators': 10}\n",
      "50377.40461678399 {'max_features': 4, 'n_estimators': 30}\n",
      "58663.93866579625 {'max_features': 6, 'n_estimators': 3}\n",
      "52006.19873526564 {'max_features': 6, 'n_estimators': 10}\n",
      "50146.51167415009 {'max_features': 6, 'n_estimators': 30}\n",
      "57869.25276169646 {'max_features': 8, 'n_estimators': 3}\n",
      "51711.127883959234 {'max_features': 8, 'n_estimators': 10}\n",
      "49682.273345071546 {'max_features': 8, 'n_estimators': 30}\n",
      "62895.06951262424 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54658.176157539405 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "59470.40652318466 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "52724.9822587892 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "57490.5691951261 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "51009.495668875716 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "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": 103,
   "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>0.050905</td>\n",
       "      <td>0.004097</td>\n",
       "      <td>0.002766</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 3}</td>\n",
       "      <td>-3.837622e+09</td>\n",
       "      <td>-4.147108e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.053756e+09</td>\n",
       "      <td>1.519591e+08</td>\n",
       "      <td>18</td>\n",
       "      <td>-1.064113e+09</td>\n",
       "      <td>-1.105142e+09</td>\n",
       "      <td>-1.116550e+09</td>\n",
       "      <td>-1.112342e+09</td>\n",
       "      <td>-1.129650e+09</td>\n",
       "      <td>-1.105559e+09</td>\n",
       "      <td>2.220402e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.143706</td>\n",
       "      <td>0.002170</td>\n",
       "      <td>0.007205</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 10}</td>\n",
       "      <td>-3.047771e+09</td>\n",
       "      <td>-3.254861e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.094374e+09</td>\n",
       "      <td>1.327062e+08</td>\n",
       "      <td>11</td>\n",
       "      <td>-5.927175e+08</td>\n",
       "      <td>-5.870952e+08</td>\n",
       "      <td>-5.776964e+08</td>\n",
       "      <td>-5.716332e+08</td>\n",
       "      <td>-5.802501e+08</td>\n",
       "      <td>-5.818785e+08</td>\n",
       "      <td>7.345821e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.410306</td>\n",
       "      <td>0.004403</td>\n",
       "      <td>0.019903</td>\n",
       "      <td>0.000964</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 30}</td>\n",
       "      <td>-2.689185e+09</td>\n",
       "      <td>-3.021086e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.849913e+09</td>\n",
       "      <td>1.626875e+08</td>\n",
       "      <td>9</td>\n",
       "      <td>-4.381089e+08</td>\n",
       "      <td>-4.391272e+08</td>\n",
       "      <td>-4.371702e+08</td>\n",
       "      <td>-4.376955e+08</td>\n",
       "      <td>-4.452654e+08</td>\n",
       "      <td>-4.394734e+08</td>\n",
       "      <td>2.966320e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.069762</td>\n",
       "      <td>0.000987</td>\n",
       "      <td>0.002409</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 3}</td>\n",
       "      <td>-3.730181e+09</td>\n",
       "      <td>-3.786886e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.716847e+09</td>\n",
       "      <td>1.631510e+08</td>\n",
       "      <td>16</td>\n",
       "      <td>-9.865163e+08</td>\n",
       "      <td>-1.012565e+09</td>\n",
       "      <td>-9.169425e+08</td>\n",
       "      <td>-1.037400e+09</td>\n",
       "      <td>-9.707739e+08</td>\n",
       "      <td>-9.848396e+08</td>\n",
       "      <td>4.084607e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.227188</td>\n",
       "      <td>0.001444</td>\n",
       "      <td>0.006829</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 10}</td>\n",
       "      <td>-2.666283e+09</td>\n",
       "      <td>-2.784511e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.781618e+09</td>\n",
       "      <td>1.268607e+08</td>\n",
       "      <td>8</td>\n",
       "      <td>-5.097115e+08</td>\n",
       "      <td>-5.162820e+08</td>\n",
       "      <td>-4.962893e+08</td>\n",
       "      <td>-5.436192e+08</td>\n",
       "      <td>-5.160297e+08</td>\n",
       "      <td>-5.163863e+08</td>\n",
       "      <td>1.542862e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.711381</td>\n",
       "      <td>0.038618</td>\n",
       "      <td>0.020686</td>\n",
       "      <td>0.001864</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 30}</td>\n",
       "      <td>-2.387153e+09</td>\n",
       "      <td>-2.588448e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.537883e+09</td>\n",
       "      <td>1.214614e+08</td>\n",
       "      <td>3</td>\n",
       "      <td>-3.838835e+08</td>\n",
       "      <td>-3.880268e+08</td>\n",
       "      <td>-3.790867e+08</td>\n",
       "      <td>-4.040957e+08</td>\n",
       "      <td>-3.845520e+08</td>\n",
       "      <td>-3.879289e+08</td>\n",
       "      <td>8.571233e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.094580</td>\n",
       "      <td>0.003861</td>\n",
       "      <td>0.002426</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 3}</td>\n",
       "      <td>-3.119657e+09</td>\n",
       "      <td>-3.586319e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.441458e+09</td>\n",
       "      <td>1.893056e+08</td>\n",
       "      <td>14</td>\n",
       "      <td>-9.245343e+08</td>\n",
       "      <td>-8.886939e+08</td>\n",
       "      <td>-9.353135e+08</td>\n",
       "      <td>-9.009801e+08</td>\n",
       "      <td>-8.624664e+08</td>\n",
       "      <td>-9.023976e+08</td>\n",
       "      <td>2.591445e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.311034</td>\n",
       "      <td>0.005247</td>\n",
       "      <td>0.006980</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 10}</td>\n",
       "      <td>-2.549663e+09</td>\n",
       "      <td>-2.782039e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.704645e+09</td>\n",
       "      <td>1.471569e+08</td>\n",
       "      <td>6</td>\n",
       "      <td>-4.980344e+08</td>\n",
       "      <td>-5.045869e+08</td>\n",
       "      <td>-4.994664e+08</td>\n",
       "      <td>-4.990325e+08</td>\n",
       "      <td>-5.055542e+08</td>\n",
       "      <td>-5.013349e+08</td>\n",
       "      <td>3.100456e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.979656</td>\n",
       "      <td>0.048790</td>\n",
       "      <td>0.021028</td>\n",
       "      <td>0.001812</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 30}</td>\n",
       "      <td>-2.370010e+09</td>\n",
       "      <td>-2.583638e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.514673e+09</td>\n",
       "      <td>1.285080e+08</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.838538e+08</td>\n",
       "      <td>-3.804711e+08</td>\n",
       "      <td>-3.805218e+08</td>\n",
       "      <td>-3.856095e+08</td>\n",
       "      <td>-3.901917e+08</td>\n",
       "      <td>-3.841296e+08</td>\n",
       "      <td>3.617057e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.118484</td>\n",
       "      <td>0.001009</td>\n",
       "      <td>0.002239</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 3}</td>\n",
       "      <td>-3.353504e+09</td>\n",
       "      <td>-3.348552e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.348850e+09</td>\n",
       "      <td>1.241939e+08</td>\n",
       "      <td>13</td>\n",
       "      <td>-9.228123e+08</td>\n",
       "      <td>-8.553031e+08</td>\n",
       "      <td>-8.603321e+08</td>\n",
       "      <td>-8.881964e+08</td>\n",
       "      <td>-9.151287e+08</td>\n",
       "      <td>-8.883545e+08</td>\n",
       "      <td>2.750227e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.401726</td>\n",
       "      <td>0.005465</td>\n",
       "      <td>0.007028</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 10}</td>\n",
       "      <td>-2.571970e+09</td>\n",
       "      <td>-2.718994e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.674041e+09</td>\n",
       "      <td>1.392777e+08</td>\n",
       "      <td>5</td>\n",
       "      <td>-4.932416e+08</td>\n",
       "      <td>-4.815238e+08</td>\n",
       "      <td>-4.730979e+08</td>\n",
       "      <td>-5.155367e+08</td>\n",
       "      <td>-4.985555e+08</td>\n",
       "      <td>-4.923911e+08</td>\n",
       "      <td>1.459294e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.236572</td>\n",
       "      <td>0.036875</td>\n",
       "      <td>0.019325</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 30}</td>\n",
       "      <td>-2.357390e+09</td>\n",
       "      <td>-2.546640e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.468328e+09</td>\n",
       "      <td>1.091662e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.841658e+08</td>\n",
       "      <td>-3.744500e+08</td>\n",
       "      <td>-3.773239e+08</td>\n",
       "      <td>-3.882250e+08</td>\n",
       "      <td>-3.810005e+08</td>\n",
       "      <td>-3.810330e+08</td>\n",
       "      <td>4.871017e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.064666</td>\n",
       "      <td>0.001042</td>\n",
       "      <td>0.002780</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 2, 'n_est...</td>\n",
       "      <td>-3.785816e+09</td>\n",
       "      <td>-4.166012e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.955790e+09</td>\n",
       "      <td>1.900964e+08</td>\n",
       "      <td>17</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.213941</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>0.007956</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 2, 'n_est...</td>\n",
       "      <td>-2.810721e+09</td>\n",
       "      <td>-3.107789e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.987516e+09</td>\n",
       "      <td>1.539234e+08</td>\n",
       "      <td>10</td>\n",
       "      <td>-6.056477e-02</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-2.967449e+00</td>\n",
       "      <td>-6.056027e-01</td>\n",
       "      <td>1.181156e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.090480</td>\n",
       "      <td>0.003167</td>\n",
       "      <td>0.002681</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 3, 'n_est...</td>\n",
       "      <td>-3.618324e+09</td>\n",
       "      <td>-3.441527e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.536729e+09</td>\n",
       "      <td>7.795057e+07</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-6.072840e+01</td>\n",
       "      <td>-1.214568e+01</td>\n",
       "      <td>2.429136e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.286396</td>\n",
       "      <td>0.004578</td>\n",
       "      <td>0.008019</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 3, 'n_est...</td>\n",
       "      <td>-2.757999e+09</td>\n",
       "      <td>-2.851737e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.779924e+09</td>\n",
       "      <td>6.286720e+07</td>\n",
       "      <td>7</td>\n",
       "      <td>-2.089484e+01</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-5.465556e+00</td>\n",
       "      <td>-5.272080e+00</td>\n",
       "      <td>8.093117e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.109239</td>\n",
       "      <td>0.002999</td>\n",
       "      <td>0.003399</td>\n",
       "      <td>0.001579</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 4, 'n_est...</td>\n",
       "      <td>-3.134040e+09</td>\n",
       "      <td>-3.559375e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.305166e+09</td>\n",
       "      <td>1.879165e+08</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.370459</td>\n",
       "      <td>0.017424</td>\n",
       "      <td>0.007863</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 4, 'n_est...</td>\n",
       "      <td>-2.525578e+09</td>\n",
       "      <td>-2.710011e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.601969e+09</td>\n",
       "      <td>1.088048e+08</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-1.514119e-02</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-3.028238e-03</td>\n",
       "      <td>6.056477e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.050905      0.004097         0.002766        0.000256   \n",
       "1        0.143706      0.002170         0.007205        0.000304   \n",
       "2        0.410306      0.004403         0.019903        0.000964   \n",
       "3        0.069762      0.000987         0.002409        0.000080   \n",
       "4        0.227188      0.001444         0.006829        0.000090   \n",
       "5        0.711381      0.038618         0.020686        0.001864   \n",
       "6        0.094580      0.003861         0.002426        0.000118   \n",
       "7        0.311034      0.005247         0.006980        0.000283   \n",
       "8        0.979656      0.048790         0.021028        0.001812   \n",
       "9        0.118484      0.001009         0.002239        0.000068   \n",
       "10       0.401726      0.005465         0.007028        0.000345   \n",
       "11       1.236572      0.036875         0.019325        0.000252   \n",
       "12       0.064666      0.001042         0.002780        0.000240   \n",
       "13       0.213941      0.000996         0.007956        0.000267   \n",
       "14       0.090480      0.003167         0.002681        0.000082   \n",
       "15       0.286396      0.004578         0.008019        0.000384   \n",
       "16       0.109239      0.002999         0.003399        0.001579   \n",
       "17       0.370459      0.017424         0.007863        0.000056   \n",
       "\n",
       "   param_max_features param_n_estimators param_bootstrap  \\\n",
       "0                   2                  3             NaN   \n",
       "1                   2                 10             NaN   \n",
       "2                   2                 30             NaN   \n",
       "3                   4                  3             NaN   \n",
       "4                   4                 10             NaN   \n",
       "5                   4                 30             NaN   \n",
       "6                   6                  3             NaN   \n",
       "7                   6                 10             NaN   \n",
       "8                   6                 30             NaN   \n",
       "9                   8                  3             NaN   \n",
       "10                  8                 10             NaN   \n",
       "11                  8                 30             NaN   \n",
       "12                  2                  3           False   \n",
       "13                  2                 10           False   \n",
       "14                  3                  3           False   \n",
       "15                  3                 10           False   \n",
       "16                  4                  3           False   \n",
       "17                  4                 10           False   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0              {'max_features': 2, 'n_estimators': 3}      -3.837622e+09   \n",
       "1             {'max_features': 2, 'n_estimators': 10}      -3.047771e+09   \n",
       "2             {'max_features': 2, 'n_estimators': 30}      -2.689185e+09   \n",
       "3              {'max_features': 4, 'n_estimators': 3}      -3.730181e+09   \n",
       "4             {'max_features': 4, 'n_estimators': 10}      -2.666283e+09   \n",
       "5             {'max_features': 4, 'n_estimators': 30}      -2.387153e+09   \n",
       "6              {'max_features': 6, 'n_estimators': 3}      -3.119657e+09   \n",
       "7             {'max_features': 6, 'n_estimators': 10}      -2.549663e+09   \n",
       "8             {'max_features': 6, 'n_estimators': 30}      -2.370010e+09   \n",
       "9              {'max_features': 8, 'n_estimators': 3}      -3.353504e+09   \n",
       "10            {'max_features': 8, 'n_estimators': 10}      -2.571970e+09   \n",
       "11            {'max_features': 8, 'n_estimators': 30}      -2.357390e+09   \n",
       "12  {'bootstrap': False, 'max_features': 2, 'n_est...      -3.785816e+09   \n",
       "13  {'bootstrap': False, 'max_features': 2, 'n_est...      -2.810721e+09   \n",
       "14  {'bootstrap': False, 'max_features': 3, 'n_est...      -3.618324e+09   \n",
       "15  {'bootstrap': False, 'max_features': 3, 'n_est...      -2.757999e+09   \n",
       "16  {'bootstrap': False, 'max_features': 4, 'n_est...      -3.134040e+09   \n",
       "17  {'bootstrap': False, 'max_features': 4, 'n_est...      -2.525578e+09   \n",
       "\n",
       "    split1_test_score  ...  mean_test_score  std_test_score  rank_test_score  \\\n",
       "0       -4.147108e+09  ...    -4.053756e+09    1.519591e+08               18   \n",
       "1       -3.254861e+09  ...    -3.094374e+09    1.327062e+08               11   \n",
       "2       -3.021086e+09  ...    -2.849913e+09    1.626875e+08                9   \n",
       "3       -3.786886e+09  ...    -3.716847e+09    1.631510e+08               16   \n",
       "4       -2.784511e+09  ...    -2.781618e+09    1.268607e+08                8   \n",
       "5       -2.588448e+09  ...    -2.537883e+09    1.214614e+08                3   \n",
       "6       -3.586319e+09  ...    -3.441458e+09    1.893056e+08               14   \n",
       "7       -2.782039e+09  ...    -2.704645e+09    1.471569e+08                6   \n",
       "8       -2.583638e+09  ...    -2.514673e+09    1.285080e+08                2   \n",
       "9       -3.348552e+09  ...    -3.348850e+09    1.241939e+08               13   \n",
       "10      -2.718994e+09  ...    -2.674041e+09    1.392777e+08                5   \n",
       "11      -2.546640e+09  ...    -2.468328e+09    1.091662e+08                1   \n",
       "12      -4.166012e+09  ...    -3.955790e+09    1.900964e+08               17   \n",
       "13      -3.107789e+09  ...    -2.987516e+09    1.539234e+08               10   \n",
       "14      -3.441527e+09  ...    -3.536729e+09    7.795057e+07               15   \n",
       "15      -2.851737e+09  ...    -2.779924e+09    6.286720e+07                7   \n",
       "16      -3.559375e+09  ...    -3.305166e+09    1.879165e+08               12   \n",
       "17      -2.710011e+09  ...    -2.601969e+09    1.088048e+08                4   \n",
       "\n",
       "    split0_train_score  split1_train_score  split2_train_score  \\\n",
       "0        -1.064113e+09       -1.105142e+09       -1.116550e+09   \n",
       "1        -5.927175e+08       -5.870952e+08       -5.776964e+08   \n",
       "2        -4.381089e+08       -4.391272e+08       -4.371702e+08   \n",
       "3        -9.865163e+08       -1.012565e+09       -9.169425e+08   \n",
       "4        -5.097115e+08       -5.162820e+08       -4.962893e+08   \n",
       "5        -3.838835e+08       -3.880268e+08       -3.790867e+08   \n",
       "6        -9.245343e+08       -8.886939e+08       -9.353135e+08   \n",
       "7        -4.980344e+08       -5.045869e+08       -4.994664e+08   \n",
       "8        -3.838538e+08       -3.804711e+08       -3.805218e+08   \n",
       "9        -9.228123e+08       -8.553031e+08       -8.603321e+08   \n",
       "10       -4.932416e+08       -4.815238e+08       -4.730979e+08   \n",
       "11       -3.841658e+08       -3.744500e+08       -3.773239e+08   \n",
       "12       -0.000000e+00       -0.000000e+00       -0.000000e+00   \n",
       "13       -6.056477e-02       -0.000000e+00       -0.000000e+00   \n",
       "14       -0.000000e+00       -0.000000e+00       -0.000000e+00   \n",
       "15       -2.089484e+01       -0.000000e+00       -0.000000e+00   \n",
       "16       -0.000000e+00       -0.000000e+00       -0.000000e+00   \n",
       "17       -0.000000e+00       -1.514119e-02       -0.000000e+00   \n",
       "\n",
       "    split3_train_score  split4_train_score  mean_train_score  std_train_score  \n",
       "0        -1.112342e+09       -1.129650e+09     -1.105559e+09     2.220402e+07  \n",
       "1        -5.716332e+08       -5.802501e+08     -5.818785e+08     7.345821e+06  \n",
       "2        -4.376955e+08       -4.452654e+08     -4.394734e+08     2.966320e+06  \n",
       "3        -1.037400e+09       -9.707739e+08     -9.848396e+08     4.084607e+07  \n",
       "4        -5.436192e+08       -5.160297e+08     -5.163863e+08     1.542862e+07  \n",
       "5        -4.040957e+08       -3.845520e+08     -3.879289e+08     8.571233e+06  \n",
       "6        -9.009801e+08       -8.624664e+08     -9.023976e+08     2.591445e+07  \n",
       "7        -4.990325e+08       -5.055542e+08     -5.013349e+08     3.100456e+06  \n",
       "8        -3.856095e+08       -3.901917e+08     -3.841296e+08     3.617057e+06  \n",
       "9        -8.881964e+08       -9.151287e+08     -8.883545e+08     2.750227e+07  \n",
       "10       -5.155367e+08       -4.985555e+08     -4.923911e+08     1.459294e+07  \n",
       "11       -3.882250e+08       -3.810005e+08     -3.810330e+08     4.871017e+06  \n",
       "12       -0.000000e+00       -0.000000e+00      0.000000e+00     0.000000e+00  \n",
       "13       -0.000000e+00       -2.967449e+00     -6.056027e-01     1.181156e+00  \n",
       "14       -0.000000e+00       -6.072840e+01     -1.214568e+01     2.429136e+01  \n",
       "15       -0.000000e+00       -5.465556e+00     -5.272080e+00     8.093117e+00  \n",
       "16       -0.000000e+00       -0.000000e+00      0.000000e+00     0.000000e+00  \n",
       "17       -0.000000e+00       -0.000000e+00     -3.028238e-03     6.056477e-03  \n",
       "\n",
       "[18 rows x 23 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(grid_search.cv_results_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Randomized Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "                   param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fd1b96682d0>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fd1b9668b10>},\n",
       "                   random_state=42, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 104,
     "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', random_state=42)\n",
    "rnd_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49150.70756927707 {'max_features': 7, 'n_estimators': 180}\n",
      "51389.889203389284 {'max_features': 5, 'n_estimators': 15}\n",
      "50796.155224308866 {'max_features': 3, 'n_estimators': 72}\n",
      "50835.13360315349 {'max_features': 5, 'n_estimators': 21}\n",
      "49280.9449827171 {'max_features': 7, 'n_estimators': 122}\n",
      "50774.90662363929 {'max_features': 3, 'n_estimators': 75}\n",
      "50682.78888164288 {'max_features': 3, 'n_estimators': 88}\n",
      "49608.99608105296 {'max_features': 5, 'n_estimators': 100}\n",
      "50473.61930350219 {'max_features': 3, 'n_estimators': 150}\n",
      "64429.84143294435 {'max_features': 5, 'n_estimators': 2}\n"
     ]
    }
   ],
   "source": [
    "cvres = rnd_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze the Best Models and Their Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.36615898061813423, 'median_income'),\n",
       " (0.16478099356159054, 'INLAND'),\n",
       " (0.10879295677551575, 'pop_per_hhold'),\n",
       " (0.07334423551601243, 'longitude'),\n",
       " (0.06290907048262032, 'latitude'),\n",
       " (0.056419179181954014, 'rooms_per_hhold'),\n",
       " (0.053351077347675815, 'bedrooms_per_room'),\n",
       " (0.04114379847872964, 'housing_median_age'),\n",
       " (0.014874280890402769, 'population'),\n",
       " (0.014672685420543239, 'total_rooms'),\n",
       " (0.014257599323407808, 'households'),\n",
       " (0.014106483453584104, 'total_bedrooms'),\n",
       " (0.010311488326303788, '<1H OCEAN'),\n",
       " (0.0028564746373201584, 'NEAR OCEAN'),\n",
       " (0.0019604155994780706, 'NEAR BAY'),\n",
       " (6.0280386727366e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 107,
     "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\"] # old solution\n",
    "cat_encoder = full_pipeline.named_transformers_[\"cat\"]\n",
    "cat_one_hot_attribs = list(cat_encoder.categories_[0])\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances, attributes), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Your System on the Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47730.22690385927"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compute a 95% confidence interval for the test RMSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([45685.10470776, 49691.25001878])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "confidence = 0.95\n",
    "squared_errors = (final_predictions - y_test) ** 2\n",
    "np.sqrt(stats.t.interval(confidence, len(squared_errors) - 1,\n",
    "                         loc=squared_errors.mean(),\n",
    "                         scale=stats.sem(squared_errors)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could compute the interval manually like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45685.10470776014, 49691.25001877871)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = len(squared_errors)\n",
    "mean = squared_errors.mean()\n",
    "tscore = stats.t.ppf((1 + confidence) / 2, df=m - 1)\n",
    "tmargin = tscore * squared_errors.std(ddof=1) / np.sqrt(m)\n",
    "np.sqrt(mean - tmargin), np.sqrt(mean + tmargin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we could use a z-scores rather than t-scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45685.717918136594, 49690.68623889426)"
      ]
     },
     "execution_count": 112,
     "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": [
    "# Extra material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A full pipeline with both preparation and prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([210644.60459286, 317768.80697211, 210956.43331178,  59218.98886849,\n",
       "       189747.55849879])"
      ]
     },
     "execution_count": 113,
     "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": [
    "## Model persistence using joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model = full_pipeline_with_predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "joblib.dump(my_model, \"my_model.pkl\") # DIFF\n",
    "#...\n",
    "my_model_loaded = joblib.load(\"my_model.pkl\") # DIFF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example SciPy distributions for `RandomizedSearchCV`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: Try a Support Vector Machine regressor (`sklearn.svm.SVR`), with various hyperparameters such as `kernel=\"linear\"` (with various values for the `C` hyperparameter) or `kernel=\"rbf\"` (with various values for the `C` and `gamma` hyperparameters). Don't worry about what these hyperparameters mean for now. How does the best `SVR` predictor perform?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: the following cell may take close to 30 minutes to run, or more depending on your hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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=   3.9s\n",
      "[CV] C=10.0, kernel=linear ...........................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.9s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ............................ C=10.0, kernel=linear, total=   3.9s\n",
      "[CV] C=10.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=10.0, kernel=linear, total=   4.6s\n",
      "[CV] C=10.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=10.0, kernel=linear, total=   4.2s\n",
      "[CV] C=10.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=10.0, kernel=linear, total=   4.5s\n",
      "[CV] C=30.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=30.0, kernel=linear, total=   4.1s\n",
      "[CV] C=30.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=30.0, kernel=linear, total=   4.2s\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=   4.0s\n",
      "[CV] C=30.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=30.0, kernel=linear, total=   3.9s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   3.9s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   4.0s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   4.0s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   4.0s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   3.9s\n",
      "[CV] C=300.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=300.0, kernel=linear, total=   4.1s\n",
      "<<434 more lines>>\n",
      "[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total=   6.7s\n",
      "[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total=   6.8s\n",
      "[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total=   6.7s\n",
      "[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total=   6.7s\n",
      "[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total=   6.7s\n",
      "[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total=   6.7s\n",
      "[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total=   6.7s\n",
      "[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total=   6.7s\n",
      "[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total=   6.8s\n",
      "[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total=   6.7s\n",
      "[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total=   6.7s\n",
      "[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total=   6.7s\n",
      "[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total=   7.4s\n",
      "[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total=   7.4s\n",
      "[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total=   7.4s\n",
      "[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total=   7.4s\n",
      "[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
      "[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total=   7.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 250 out of 250 | elapsed: 26.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVR(),\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",
       "             scoring='neg_mean_squared_error', verbose=2)"
      ]
     },
     "execution_count": 117,
     "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', verbose=2)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best model achieves the following score (evaluated using 5-fold cross validation):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70363.84006944533"
      ]
     },
     "execution_count": 118,
     "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": [
    "That's much worse than the `RandomForestRegressor`. Let's check the best hyperparameters found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 30000.0, 'kernel': 'linear'}"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear kernel seems better than the RBF kernel. Notice that the value of `C` is the maximum tested value. When this happens you definitely want to launch the grid search again with higher values for `C` (removing the smallest values), because it is likely that higher values of `C` will be better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: Try replacing `GridSearchCV` with `RandomizedSearchCV`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: the following cell may take close to 45 minutes to run, or more depending on your hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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=   4.2s\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:    4.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=629.782329591372, gamma=3.010121430917521, kernel=linear, total=   4.0s\n",
      "[CV] C=629.782329591372, gamma=3.010121430917521, kernel=linear ......\n",
      "[CV]  C=629.782329591372, gamma=3.010121430917521, kernel=linear, total=   4.5s\n",
      "[CV] C=629.782329591372, gamma=3.010121430917521, kernel=linear ......\n",
      "[CV]  C=629.782329591372, gamma=3.010121430917521, kernel=linear, total=   4.5s\n",
      "[CV] C=629.782329591372, gamma=3.010121430917521, kernel=linear ......\n",
      "[CV]  C=629.782329591372, gamma=3.010121430917521, kernel=linear, total=   4.3s\n",
      "[CV] C=26290.206464300216, gamma=0.9084469696321253, kernel=rbf ......\n",
      "[CV]  C=26290.206464300216, gamma=0.9084469696321253, kernel=rbf, total=   8.6s\n",
      "[CV] C=26290.206464300216, gamma=0.9084469696321253, kernel=rbf ......\n",
      "[CV]  C=26290.206464300216, gamma=0.9084469696321253, kernel=rbf, total=   9.1s\n",
      "[CV] C=26290.206464300216, gamma=0.9084469696321253, kernel=rbf ......\n",
      "[CV]  C=26290.206464300216, gamma=0.9084469696321253, kernel=rbf, total=   8.8s\n",
      "[CV] C=26290.206464300216, gamma=0.9084469696321253, kernel=rbf ......\n",
      "[CV]  C=26290.206464300216, gamma=0.9084469696321253, kernel=rbf, total=   8.9s\n",
      "[CV] C=26290.206464300216, gamma=0.9084469696321253, kernel=rbf ......\n",
      "[CV]  C=26290.206464300216, gamma=0.9084469696321253, kernel=rbf, total=   9.0s\n",
      "[CV] C=84.14107900575871, gamma=0.059838768608680676, kernel=rbf .....\n",
      "[CV]  C=84.14107900575871, gamma=0.059838768608680676, kernel=rbf, total=   7.0s\n",
      "[CV] C=84.14107900575871, gamma=0.059838768608680676, kernel=rbf .....\n",
      "[CV]  C=84.14107900575871, gamma=0.059838768608680676, kernel=rbf, total=   7.0s\n",
      "[CV] C=84.14107900575871, gamma=0.059838768608680676, kernel=rbf .....\n",
      "[CV]  C=84.14107900575871, gamma=0.059838768608680676, kernel=rbf, total=   6.9s\n",
      "[CV] C=84.14107900575871, gamma=0.059838768608680676, kernel=rbf .....\n",
      "[CV]  C=84.14107900575871, gamma=0.059838768608680676, kernel=rbf, total=   7.0s\n",
      "[CV] C=84.14107900575871, gamma=0.059838768608680676, kernel=rbf .....\n",
      "[CV]  C=84.14107900575871, gamma=0.059838768608680676, kernel=rbf, total=   7.0s\n",
      "[CV] C=432.37884813148855, gamma=0.15416196746656105, kernel=linear ..\n",
      "[CV]  C=432.37884813148855, gamma=0.15416196746656105, kernel=linear, total=   4.6s\n",
      "<<434 more lines>>\n",
      "[CV] C=61217.04421344494, gamma=1.6279689407405564, kernel=rbf .......\n",
      "[CV]  C=61217.04421344494, gamma=1.6279689407405564, kernel=rbf, total=  25.2s\n",
      "[CV] C=61217.04421344494, gamma=1.6279689407405564, kernel=rbf .......\n",
      "[CV]  C=61217.04421344494, gamma=1.6279689407405564, kernel=rbf, total=  23.2s\n",
      "[CV] C=926.9787684096649, gamma=2.147979593060577, kernel=rbf ........\n",
      "[CV]  C=926.9787684096649, gamma=2.147979593060577, kernel=rbf, total=   5.7s\n",
      "[CV] C=926.9787684096649, gamma=2.147979593060577, kernel=rbf ........\n",
      "[CV]  C=926.9787684096649, gamma=2.147979593060577, kernel=rbf, total=   5.7s\n",
      "[CV] C=926.9787684096649, gamma=2.147979593060577, kernel=rbf ........\n",
      "[CV]  C=926.9787684096649, gamma=2.147979593060577, kernel=rbf, total=   5.7s\n",
      "[CV] C=926.9787684096649, gamma=2.147979593060577, kernel=rbf ........\n",
      "[CV]  C=926.9787684096649, gamma=2.147979593060577, kernel=rbf, total=   5.8s\n",
      "[CV] C=926.9787684096649, gamma=2.147979593060577, kernel=rbf ........\n",
      "[CV]  C=926.9787684096649, gamma=2.147979593060577, kernel=rbf, total=   5.6s\n",
      "[CV] C=33946.157064934, gamma=2.2642426492862313, kernel=linear ......\n",
      "[CV]  C=33946.157064934, gamma=2.2642426492862313, kernel=linear, total=  10.0s\n",
      "[CV] C=33946.157064934, gamma=2.2642426492862313, kernel=linear ......\n",
      "[CV]  C=33946.157064934, gamma=2.2642426492862313, kernel=linear, total=   9.7s\n",
      "[CV] C=33946.157064934, gamma=2.2642426492862313, kernel=linear ......\n",
      "[CV]  C=33946.157064934, gamma=2.2642426492862313, kernel=linear, total=   8.9s\n",
      "[CV] C=33946.157064934, gamma=2.2642426492862313, kernel=linear ......\n",
      "[CV]  C=33946.157064934, gamma=2.2642426492862313, kernel=linear, total=  10.4s\n",
      "[CV] C=33946.157064934, gamma=2.2642426492862313, kernel=linear ......\n",
      "[CV]  C=33946.157064934, gamma=2.2642426492862313, kernel=linear, total=   9.3s\n",
      "[CV] C=84789.82947739525, gamma=0.3176359085304841, kernel=linear ....\n",
      "[CV]  C=84789.82947739525, gamma=0.3176359085304841, kernel=linear, total=  25.8s\n",
      "[CV] C=84789.82947739525, gamma=0.3176359085304841, kernel=linear ....\n",
      "[CV]  C=84789.82947739525, gamma=0.3176359085304841, kernel=linear, total=  18.5s\n",
      "[CV] C=84789.82947739525, gamma=0.3176359085304841, kernel=linear ....\n",
      "[CV]  C=84789.82947739525, gamma=0.3176359085304841, kernel=linear, total=  28.3s\n",
      "[CV] C=84789.82947739525, gamma=0.3176359085304841, kernel=linear ....\n",
      "[CV]  C=84789.82947739525, gamma=0.3176359085304841, kernel=linear, total=  20.8s\n",
      "[CV] C=84789.82947739525, gamma=0.3176359085304841, kernel=linear ....\n",
      "[CV]  C=84789.82947739525, gamma=0.3176359085304841, kernel=linear, total=  15.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 250 out of 250 | elapsed: 44.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=SVR(), n_iter=50,\n",
       "                   param_distributions={'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9bd002c790>,\n",
       "                                        'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9bd002cb10>,\n",
       "                                        'kernel': ['linear', 'rbf']},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import expon, reciprocal\n",
    "\n",
    "# see https://docs.scipy.org/doc/scipy/reference/stats.html\n",
    "# for `expon()` and `reciprocal()` documentation and more probability distribution functions.\n",
    "\n",
    "# Note: gamma is ignored when kernel is \"linear\"\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, random_state=42)\n",
    "rnd_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best model achieves the following score (evaluated using 5-fold cross validation):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54767.960710084146"
      ]
     },
     "execution_count": 121,
     "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": [
    "Now this is much closer to the performance of the `RandomForestRegressor` (but not quite there yet). Let's check the best hyperparameters found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 157055.10989448498, 'gamma': 0.26497040005002437, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time the search found a good set of hyperparameters for the RBF kernel. Randomized search tends to find better hyperparameters than grid search in the same amount of time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the exponential distribution we used, with `scale=1.0`. Note that some samples are much larger or smaller than 1.0, but when you look at the log of the distribution, you can see that most values are actually concentrated roughly in the range of exp(-2) to exp(+2), which is about 0.1 to 7.4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9SJoraamkpd3dvVaomZmZmY1Igw3MXg/sS7pz+WTgRNIdiffJ6WPpOZnxOmBs7ntWnVZJr54EGoCIWBARsyJi1qRJbdHVxMzMzKyHwQZmm0mTdZ8bES9ExJ2kKTQqc/ttAMYXth8PbMid/6vTKunrMTNrEkkn5tr25yUtqko7WNJySZsk3Z7nta2kSdIFktbk5Qv5R2UlfWreZ1M+xiHDeFpm1qYGG5jd10d6F6njf8UMXpmPrwvYQ9K4OulmZs1QGbzUY67aPOHyYtJcqxNI8/1dU9ikOHhpP9KcqicU0q8mTb+1E/B50uAlV+eb2aA0FJhJGp0nJx4FjJI0Jk/y/WPSbS7+IW/zZ8BBpIlaAa4ATpI0RdJk0kTRi+DliVPvBc7Mx3svqfC7oVknZ2ZWb/AScBTQFRHXRcQW0uTQMyRNy+kevGRmw67RGrPTSM2WpwLH5senRcSLwHtIt7pYB3wD+FBELM/7XQLcTCqwHiDNe3ZJ4bhzgFnAWtL9zo7u61YZZmZN0mNwUkRsJE3/M71WOh68ZGbDoKH7mEXEPNKvyVppXcDb6qQFcEpeaqWvINWwmZkNt7GkuRiLigOQBjJ4aUqtF4qIBaR7PjJr1izPg2dmdXlKJjPrVH0NQPLgJTMbdo3e+X/Emnrqkq3WrTj/8BbkxMxKpovUjwwASTsAe9JzgNIM4O78vObgpUJz5gzg20OdabNO02nf464xM7O21svgpRuBfSXNzulnAPcV+sh68JKZDbu2rzEzs453GnBm4fmxwFkRMS/PVHIRcBVwF2lAUsUlwB6kwUsAl7L14KVFpMFLj+PBS2ZNUauGrJM4MDOzttbH4KXbgGl10jx4ycyGnZsyzczMzErCgZmZmZlZSTgwMzMzMysJB2ZmZmZmJeHAzMzMzKwkHJiZmZmZlYQDMzMzM7OScGBmZmZmVhIOzMzMzMxKwnf+NzMzs5bo9OmXanGNmZmZmVlJODAzMzMzK4mGAjNJJ0paKul5SYvqbHOmpJB0SGGdJF0gaU1eviBJhfSpkm6XtEnS8uK+ZmZmZp2m0Rqzp4BzgW/WSpS0J3A08HRV0lzgSGAGsB9wBHBCIf1q4JfATsDngeslTWowT2ZmZmZtpaHALCIWR8RNwJo6m1wEfBZ4oWr9ccD8iFgZEU8C84HjASTtBbwFODMiNkfEDcD9wOz+noSZmZlZOxh0HzNJfwW8EBG31EieDiwrPF+W11XSHomI9XXSq19nbm5OXdrd3T3YbJuZmZmVzqACM0ljgf8HfLrOJmOBdYXn64CxuZ9ZdVolfVytA0XEgoiYFRGzJk1ya6eZmZm1n8HWmJ0FXBkRj9ZJ3wCMLzwfD2yIiKiRVklfj5mZmVkHGmxgdjDwKUmrJK0CdgWulfTZnN5F6vhfMSOvq6TtIWlcnXQzMzOzjtLQnf8ljc7bjgJGSRoDvEQKzLYtbHoPcBJwa35+BXCSpFuAAE4GLgSIiAcl3QucKek04FDSyE13/jczM7OO1OiUTKcBZxaeHwucFRHzihtJ+j2wNiI25FWXAHuQRlsCXJrXVcwBFgFrgceBoyPCPfvNzMysIzUUmOUAbF4D202teh7AKXmptf0K4KBG8mBmZmbW7jwlk5mZmVlJODAzMzMzK4lG+5iZmZmZlcLUU5dstW7F+Ye3ICfN5xozMzMzs5JwYGZmZmZWEh3ZlFldBdou1Z9mZmY2srnGzMzMzKwkHJiZmZmZlURHNmWamZnZ8Ko1ktK25hozMzMzs5JwYGZmHU3SVEm3SForaZWkiySNzmkHS1ouaZOk2yXtXthPki6QtCYvX5Ck1p2JmbUDB2Zm1um+BjwDvA6YCRwIfELSRGAxcDowAVgKXFPYby5wJDAD2A84AjhhuDJtZu3JgZmZdbo/AK6NiC0RsQr4PjAdOAroiojrImILMA+YIWla3u84YH5ErIyIJ4H5wPHDnnszaysOzMys030ZmCPp1ZKmAIfySnC2rLJRRGwEHs7rqU7Pj6dTg6S5kpZKWtrd3T0Ep2Bm7cKBmZl1ujtJAdVzwEpSk+VNwFhgXdW264Bx+XF1+jpgbK1+ZhGxICJmRcSsSZMmNTf3ZtZWHJiZWceStA3wA1Jfsh2AicCOwAXABmB81S7jgfX5cXX6eGBDRMRQ5tnM2ltDgZmkE3M1/POSFhXWv1XSDyX9TlK3pOskva6Q3uuopTwa6vY84mm5pEOaenZmZr2bAOwKXBQRz0fEGuAy4DCgi9SxHwBJOwB75vVUp+fHXZhZS0w9dUmPZaRqtMbsKeBc4JtV63cEFgBTgd1JvyQvK6T3NWrpauCXwE7A54HrJbme38yGRUSsBh4FPi5ptKTXkjr1LwNuBPaVNFvSGOAM4L6IWJ53vwI4SdIUSZOBk4FFw30OZtZeGgrMImJxRNwErKlaf2sesfRcRGwCLgL+rLBJ3VFLkvYC3gKcGRGbI+IG4H5g9iDPycysP44C3gV0Aw8BLwGfiYhuUnl0HrAW2B+YU9jvEuBmUrn1ALAkrzMzG7BmT8n0dnpW5fc2amk68EhErK+TbmY25CLiXuCgOmm3AdPqpAVwSl7MzJqiaYGZpP1IVf3vKazubdRSvRFPU+ocfy6paZTddtutSbk2MzMzK4+mjMqU9AbgVuD/RMR/FJJ6G7XU14inHjzc3MzMzNrdoAOzPHfcbcA5EXFlVXJvo5a6gD0kjauTbmZmZtZRGr1dxug8KmkUMErSmLxuCvDvwFcj4uIau9YdtRQRDwL3Amfm472XNHLzhsGelJmZmdlI1Ggfs9OAMwvPjwXOAgLYgxRcvZweEWPzw0ty+v35+aX0HLU0hxSorQUeB47OI6HMzMzMOk5DgVlEzCNN4FvLWb3s1+uopYhYQZ3RUGZmZmadxlMymZmZmZWEAzMzMzOzknBgZmZmZlYSDszMzMzMSsKBmZmZmVlJODAzMzMzKwkHZmZmZmYl4cDMzMzMrCQcmJmZmZmVhAMzMzMzs5JodK7Mtjb11CVbrVtx/uEtyImZmZl1MteYmZmZmZWEAzMzMzOzknBTppmZmTVdrW5C1jfXmJmZmZmVhAMzMzMzs5JwYGZmZmZWEg0FZpJOlLRU0vOSFlWlHSxpuaRNkm6XtHshTZIukLQmL1+QpEL61LzPpnyMQ5p2ZmZmZmYjTKM1Zk8B5wLfLK6UNBFYDJwOTACWAtcUNpkLHAnMAPYDjgBOKKRfDfwS2An4PHC9pEn9PQkzMzOzdtBQYBYRiyPiJmBNVdJRQFdEXBcRW4B5wAxJ03L6ccD8iFgZEU8C84HjASTtBbwFODMiNkfEDcD9wOzBnZKZmZnZyDTYPmbTgWWVJxGxEXg4r98qPT8upj0SEevrpPcgaW5uTl3a3d09yGybmZmZlc9gA7OxwLqqdeuAcXXS1wFjcz+zvvbtISIWRMSsiJg1aZJbO83MzKz9DDYw2wCMr1o3HlhfJ308sCEiooF9zczMzDrKYAOzLlLHfgAk7QDsmddvlZ4fF9P2kDSuTrqZmZlZR2n0dhmjJY0BRgGjJI2RNBq4EdhX0uycfgZwX0Qsz7teAZwkaYqkycDJwCKAiHgQuBc4Mx/vvaSRmzc07/TMzMzMRo5Ga8xOAzYDpwLH5senRUQ3aRTlecBaYH9gTmG/S4CbSaMtHwCW5HUVc4BZed/zgaPzMc3MzMw6TqO3y5gXEapa5uW02yJiWkRsHxEHRcSKwn4REadExIS8nJL7l1XSV+R9to+IvSPitmafoJlZXyTNkfQrSRslPSzpgLx+wDfQNjMbCE/JZGYdTdI7gAuAD5NGhb8deKQJN9A2M+u30a3OQFlNPXXJVutWnH94C3JiZkPsLODsiPhZfv4kpHsnkm+gnZ/PA1ZLmpb70b58A+2cPh/4GHDxMOffzNqIa8zMrGNJGkXq5zpJ0kOSVkq6SNL2DO4G2tWv4xtkm1lDXGNmZp1sF2Bb4GjgAOBF4DukAU9jgeooqqEbaBf70kK6QTawAGDWrFk90szaQa1WJhsY15iZWSfbnP9eGBFPR8Rq4IvAYQzuBtpmZgPiwMzMOlZErAVWArWCqcHcQNvMbEAcmJlZp7sM+KSknSXtCHwa+B6DuIG2mdlAuY+ZmXW6c4CJwIPAFuBa4LyI2CJpNnARcBVwF1vfQHsP0g20AS6l5w20zcz6zYGZmXW0iHgR+EReqtNuA6bV2S+AU/JiZtYUbso0MzMzKwkHZmZmZmYl4cDMzMzMrCQcmJmZmZmVhAMzMzMzs5JwYGZmZmZWEg7MzMzMzErC9zEzMzOztlNrYvUV5x/egpz0T1MCM0lTga8BbwOeB64HPh0RL0k6GPgqsBvpztnHR8RjeT8B5wMfzYdaCHy2rJMAV7/JI+ENNjMzs5GjWU2ZXwOeAV4HzAQOBD4haSKwGDgdmAAsBa4p7DcXOJI0+e9+wBHACU3Kk5mZmdmI0qzA7A+AayNiS0SsAr4PTAeOAroi4rqI2ALMA2ZIqkxxchwwPyJWRsSTwHzg+CblyczMzGxEaVZg9mVgjqRXS5oCHMorwdmyykYRsRF4OK+nOj0/nk4NkuZKWippaXd3d5OybWZmZlYezQrM7iQFVM8BK0lNljcBY4F1VduuA8blx9Xp64Cxue9ZDxGxICJmRcSsSZMmNSnbZmZmZuUx6MBM0jbAD0h9yXYAJgI7AhcAG4DxVbuMB9bnx9Xp44ENZe38b2ZmZjaUmlFjNgHYFbgoIp6PiDXAZcBhQBepYz8AknYA9szrqU7Pj7swMzMz60CDDswiYjXwKPBxSaMlvZbUqX8ZcCOwr6TZksYAZwD3RcTyvPsVwEmSpkiaDJwMLBpsnszMzMxGomb1MTsKeBfQDTwEvAR8JiK6gdnAecBaYH9gTmG/S4CbgfuBB4AleZ2ZmZlZx2nKDWYj4l7goDpptwHT6qQFcEpezMzMzDqa58o0MzMzKwkHZmZmZmYl4cDMzMzMrCQcmJmZmZmVRFM6/5uZmVnnmHrqklZnoW25xszMzMysJByYmZmZmZWEAzMzMzOzknBgZmZmZlYSDszMzMzMSsKBmZmZmVlJ+HYZg1BruPCK8w9vQU7MzMysHbjGzMzMzKwkHJiZmZmZlYQDMzMzQNIbJW2RdFVh3cGSlkvaJOl2SbsX0iTpAklr8vIFSWpN7s2sXTgwMzNLvgrcU3kiaSKwGDgdmAAsBa4pbD8XOBKYAewHHAGcMEx5NbM25cDMzDqepDnAs8CPCquPAroi4rqI2ALMA2ZImpbTjwPmR8TKiHgSmA8cP2yZNrO21LTATNIcSb+StFHSw5IOyOvdFGBmpSVpPHA2cHJV0nRgWeVJRGwEHs7rt0rPj6dTg6S5kpZKWtrd3d2srJtZG2pKYCbpHcAFwIeBccDbgUfcFGBmI8A5wMKIeKJq/VhgXdW6daQyrlb6OmBsrR+XEbEgImZFxKxJkyY1Kdtm1o6aVWN2FnB2RPwsIv4nIp7MVftuCjCz0pI0EzgE+JcayRuA8VXrxgPr66SPBzZERDQ5m2bWQQYdmEkaBcwCJkl6SNJKSRdJ2h43BZhZuR0ETAUel7QK+HtgtqRfAF2k2nwAJO0A7JnXU52eH3dhZjYIzagx2wXYFjgaOACYCbwZOA03BZhZuS0gBVsz83IxsAR4J3AjsK+k2ZLGAGcA90XE8rzvFcBJkqZImkzqo7ZoWHNvZm2nGVMybc5/L4yIpwEkfZEUmP0YNwWYWUlFxCZgU+W5pA3Alojozs9nAxcBVwF3AXMKu18C7AHcn59fmteZtZVa0w+OVNXnUsZpFAcdmEXEWkkrgVrBVBepHxnQa1PA3fm5mwLMrGUiYl7V89uAaXW2DeCUvJiZNUWzJjG/DPikpO8DLwKfBr5Hagr4p/yrcwn1mwJuIQV2JwMXNilPLeGJzc3MzGygmhWYnQNMBB4EtgDXAudFxBY3BZiZmZk1pimBWUS8CHwiL9VpbgowMzMza4CnZDIzMzMrCQdmZmZmZiXhwMzMzMysJByYmZmZmZWEAzMzMzOzknBgZmZmZlYSDszMzMzMSqJZN5g1MzOzEa6d5sUcqVxjZmZmZlYSDszMzMzMSsKBmZmZmVlJuI/ZMKhus19x/uEtyomZmZmVmWvMzMzMzErCgZmZmZlZSTgwMzMzMysJB2ZmZmZmJeHAzMzMzKwkmhaYSXqjpC2SriqsO1jSckmbJN0uafdCmiRdIGlNXr4gSc3Kj5mZmdlI08was68C91SeSJoILAZOByYAS4FrCtvPBY4EZgD7AUcAJzQxP2ZmZmYjSlMCM0lzgGeBHxVWHwV0RcR1EbEFmAfMkDQtpx8HzI+IlRHxJDAfOL4Z+TEzMzMbiQYdmEkaD5wNnFyVNB1YVnkSERuBh/P6rdLz4+nUIWmupKWSlnZ3dw8222ZmZmal04was3OAhRHxRNX6scC6qnXrgHF10tcBY+v1M4uIBRExKyJmTZo0qQnZNjMzMyuXQU3JJGkmcAjw5hrJG4DxVevGA+vrpI8HNkREDCZPI0H1FE3gaZrMzMxs8HNlHgRMBR7PFV1jgVGS3gRcTOpHBoCkHYA9ga68qovU8f/u/HxGIc3MzMys4wy2KXMBKdiamZeLgSXAO4EbgX0lzZY0BjgDuC8ilud9rwBOkjRF0mRSH7VFg8yPmZmZ2Yg1qBqziNgEbKo8l7QB2BIR3fn5bOAi4CrgLmBOYfdLgD2A+/PzS/M6MzMzGwa1utZYaw22KbOHiJhX9fw2YFqdbQM4JS9mZmZmHc9TMpmZmZmVhAMzMzMzs5JoalOmmdlIImk74Guk2/5MAB4CPhcRt+b0g0nTze1G6id7fEQ8ltMEnA98NB9uIfDZTrjlj41M7k82MjgwK4nqD4zva2Y2LEYDTwAHAo8DhwHXSvpD0r0WF5MCr5tJN9O+Bnhr3rc4328APwQeIY1ONzMbEAdmZtax8lRx8wqrvifpUeCPgJ3I8/0CSJoHrJY0Ld/25+X5fnP6fOBjODAzs0FwYFZSnh3AbPhJ2gXYi3Sz649TNd+vpMp8v8vpx3y/kuaSatjYbbfdhiTvZtYe3PnfzAyQtC3wLeDyXCPWtPl+PdevmTXKgZmZdTxJ2wBXAi8AJ+bVnu/XzIadAzMz62i5hmshsAswOyJezEmV+Xwr29Wb77fC8/2a2aA5MDOzTvd1YB/g3RGxubDe8/2a2bBzYGZmHUvS7sAJwExglaQNeTkmz/k7GzgPWAvsz9bz/d5Mmu/3AWAJnu/XzAbJozLNrGPlm8Vu1Vm/kO75fs1sWLnGzMzMzKwkHJiZmZmZlYSbMs3MzNqQ58bsWxlv5u4aMzMzM7OScI3ZCFLGyN7MzMyaZ9A1ZpK2k7RQ0mOS1kv6paRDC+kHS1ouaZOk2/Pw9EqaJF0gaU1evlBrOhOrb+qpS3osZmZmNnI1oylzNPAEcCDwGuB04FpJUyVNBBbndROApcA1hX3nAkeS7pi9H3AE6Z5CZmZmZh1n0E2ZEbERmFdY9T1JjwJ/BOwEdEXEdQCS5gGrJU3Ld88+DpgfEStz+nzgY8DFg82XmZmZ2UjT9M7/knYB9iLNGTcdWFZJy0Hcw3k91en58XRqkDRX0lJJS7u7u5udbTMzM7OWa2pgJmlb4FvA5blGbCywrmqzdcC4/Lg6fR0wtlY/s4hYEBGzImLWpEmTmpltMzMzs1JoWmAmaRvgSuAF4MS8egMwvmrT8cD6OunjgQ15qhMzMzOzjtKU22XkGq6FwC7AYRHxYk7qIvUjq2y3A7BnXl9JnwHcnZ/PKKSZmZlZAzwqv3006z5mXwf2AQ6JiM2F9TcC/yRpNrAEOAO4LzdzAlwBnCTpFiCAk4ELm5SnjuR7nZmZmY1czbiP2e6kW1zMBFZJ2pCXYyKiG5gNnAesBfYH5hR2vwS4GbgfeIAUvF0y2DyZmZmZjUTNuF3GY0Ddm8JGxG3AtDppAZySFzMzM7OO5rkyzczMzErCc2V2gOp+Z+5zZmZmVk6uMTMzMzMrCdeYGeDRnGZmI4VvjdHeXGNmZmZmVhKuMbO63DfNzMxseDkw60CuBjczGzlcZncWB2ZmZmZmWav7XLuPmZmZmVlJuMbMGtbqXxFmZu3OzZbmGjMzMzOzknCNmQ1KI7/uXKtmZmbWGAdmZmZmw8DNlNYIB2Y25Nw3zczMrDEOzKwlfPNaM2tnrh2zgXLnfzMzM7OScI2ZlUKjvy6ra9bcTGpmZeAaMmuWlgdmkiYAC4G/BFYD/xAR325trqysXPhZmbj86kwuhzrPcHa/aXlgBnwVeAHYBZgJLJG0LCK6WporG7EGWmi6ps0GwOWXmTVVSwMzSTsAs4F9I2ID8BNJ3wU+CJzayrxZ5ynbr+CBBopu3h0eLr/6p9H/y6H8HDbSFcKs1RQRrXtx6c3Af0bE9oV1fw8cGBHvrtp2LjA3P90b+HWDLzOR1MTQztr9HNv9/KD9z3Gw57d7RExqVmaaYZjKr6E2kv7vnNfmGyn5hJGf14bLsFY3ZY4F1lWtWweMq94wIhYAC/r7ApKWRsSsgWVvZGj3c2z384P2P8c2Pb8hL7+G2kh6X5zX5hsp+YTOymurb5exARhftW48sL4FeTEz6w+XX2bWdK0OzB4ERkt6Y2HdDMAdZ82s7Fx+mVnTtTQwi4iNwGLgbEk7SPoz4D3AlU18mdI1HwyBdj/Hdj8/aP9zbLvzG6bya6iNpPfFeW2+kZJP6KC8trTzP7x8H6BvAu8A1gCn+j5AZjYSuPwys2ZreWBmZmZmZkmr+5iZmZmZWebAzMzMzKwk2jYwkzRB0o2SNkp6TNIHWp2nZpK0naSF+dzWS/qlpENbna+hIOmNkrZIuqrVeRkKkuZI+lX+X31Y0gGtzlOzSJoq6RZJayWtknSRpFbfP7FjSDpR0lJJz0taVCP9YEnLJW2SdLuk3Xs51rCVqZI2VC2/l3RhnW2Pz+nF7Q8aqrzVeP07cvlUee1ebx4s6TP5s7BO0jclbTdM+ezXd8ZwX9f+/H+16hrm1274Og70GrZtYEbPOeyOAb4uaXprs9RUo4EngAOB1wCnA9dKmtrKTA2RrwL3tDoTQ0HSO4ALgA+Tbkz6duCRlmaqub4GPAO8jjSX5IHAJ1qZoQ7zFHAuaYBCD5ImkkaVng5MAJYC1/RyrGErUyNibGXJr7cZuK6XXf6ruE9E3DEU+erFiYXX3rveRpLeSZqu62BgKrAHcNbwZHFA3xnDeV0b+v9q8TWE/l/Hfl/DtgzM9MocdqdHxIaI+AlQmcOuLUTExoiYFxErIuJ/IuJ7wKPAH7U6b80kaQ7wLPCjFmdlqJwFnB0RP8vv45MR8WSrM9VEfwBcGxFbImIV8H2gnX4glVpELI6Im0gjRqsdBXRFxHURsQWYB8yQNK16wxaXqUeTgvv/GIbXGmrHAQsjoisi1gLnAMcPxwuX+Tujn/9fLbuGMDzXsS0DM2Av4PcR8WBh3TLa+AtB0i6k826bm1tKGg+cDZzc6rwMBUmjgFnAJEkPSVqZm/q272vfEeTLwBxJr5Y0BTiUFJxZ600nlYvAy/dle5ja5WQry9TjgCui91sIvFnSakkPSjq9Bc3l/5hf/6d9NFX1uOb58S6SdhrKzNXS4HfGcF3X/vx/leYaQkPXsd/XsF0Ds4bnsGsHkrYFvgVcHhHLW52fJjqH9MvoiVZnZIjsAmxLqhE4gNTU92bgtBbmqdnuJBWkzwErSc1lN7UyQ/ay/pSTLSlTJe1GajK6vJfNfgzsC+xMqnX5a+D/DmW+qnyW1Jw2hXRj0Zsl7Vln2+rrWHk8rN9NDX5nDOd1Hcz/YkuuITR0HQd0Dds1MOuYOewkbUO60/gLwIktzk7TSJoJHAL8S4uzMpQ2578XRsTTEbEa+CJwWAvz1DT5f/MHpH5MOwATgR1JfepskHKn86iz/KSBQ/SnnGxamdrPfH8I+ElEPFrveBHxSEQ8mpuV7ifVsh/d33wNNK8RcVdErI+I5yPicuCn1P8MV1/HyuNBfzc1el0b/c4Yyutaw2D+F5t2Dfujkes40GvYrqOjXp7DLiJ+k9e13Rx2kgQsJNW8HBYRL7Y4S810EKlj5+PpNBkLjJL0poh4Swvz1TQRsVbSSqBd7/I8AdgVuCgingeel3QZqTP6KS3NWRuIiIMGeYguUjMh8HI/nz2pXU42rUztZ74/BJzf35cA1M99ah9oYNe4t9fvIl23a/PzGcBvI6JWH8D+vWgDeR3kd0bTrmsN/fn/GrJr2KhBXMeGrmFb1pi1yRx2jfg6sA/w7ojY3NfGI8wC0pfEzLxcDCwB3tm6LA2Jy4BPStpZ0o7Ap4HvtTZLzZFrAB8FPi5ptKTXkgKBZb3uaE2Tr/sYYBTph82YQh+XG4F9Jc3O25wB3FerSaYVZaqkPyU1D/Y2GhNJh+Z+PuSBC6cD3xmqfFW99mslvbNyXSUdQxpZ/YM6u1wBfETSm/Ln/TRg0XDkNWv4O2M4r2s//79afQ2hwes44GsYEW25kH6t3wRsBB4HPtDqPDX5/HYnRd9bSFW7leWYVudtiM53HnBVq/MxBOe1LemWEs8Cq4CvAGNana8mnt9M4A5gLbCa9CW7c6vz1SlL/txE1TKvkH4IsJzUrH4HMLWQ9jng1sLzYS1TgUuAK2us3y2Xdbvl5/8M/Dbn6xFSc9G2w3R9J5Fu5bM+f4Z/BryjXl7zupNyfp8j/TDbbpjy2ut3Rquva73/rzJdw76uY7OuoefKNDMzMyuJtmzKNDMzMxuJHJiZmZmZlYQDMzMzM7OScGBmZmZmVhIOzMzMzMxKwoGZmZmZWUk4MDMzMzMrCQdmZmZmZiXx/wHAUmOcvfqYHAAAAABJRU5ErkJggg==\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": [
    "The distribution we used for `C` looks quite different: the scale of the samples is picked from a uniform distribution within a given range, which is why the right graph, which represents the log of the samples, looks roughly constant. This distribution is useful when you don't have a clue of what the target scale is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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": [
    "The reciprocal distribution is useful when you have no idea what the scale of the hyperparameter should be (indeed, as you can see on the figure on the right, all scales are equally likely, within the given range), whereas the exponential distribution is best when you know (more or less) what the scale of the hyperparameter should be."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: Try adding a transformer in the preparation pipeline to select only the most important attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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": [
    "Note: this feature selector assumes that you have already computed the feature importances somehow (for example using a `RandomForestRegressor`). You may be tempted to compute them directly in the `TopFeatureSelector`'s `fit()` method, however this would likely slow down grid/randomized search since the feature importances would have to be computed for every hyperparameter combination (unless you implement some sort of cache)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define the number of top features we want to keep:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look for the indices of the top k features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  7,  9, 12])"
      ]
     },
     "execution_count": 127,
     "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": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['longitude', 'latitude', 'median_income', 'pop_per_hhold',\n",
       "       'INLAND'], dtype='<U18')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(attributes)[top_k_feature_indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's double check that these are indeed the top k features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(zip(feature_importances, attributes), reverse=True)[:k]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking good... Now let's create a new pipeline that runs the previously defined preparation pipeline, and adds top k feature selection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_prepared_top_k_features = preparation_and_feature_selection_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the features of the first 3 instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared_top_k_features[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's double check that these are indeed the top k features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared[0:3, top_k_feature_indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Works great!  :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: Try creating a single pipeline that does the full data preparation plus the final prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preparation',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('attribs_adder',\n",
       "                                                                   CombinedAttributesAdder()),\n",
       "                                                                  ('std_scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['longitude', 'latitude',\n",
       "                                                   'housing_median_age',\n",
       "                                                   'total_rooms',\n",
       "                                                   'total_bedrooms',\n",
       "                                                   'population', 'households',\n",
       "                                                   'median_income']),\n",
       "                                                 ('cat', OneHotEncoder(...\n",
       "                 TopFeatureSelector(feature_importances=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]),\n",
       "                                    k=5)),\n",
       "                ('svm_reg',\n",
       "                 SVR(C=157055.10989448498, gamma=0.26497040005002437))])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepare_select_and_predict_pipeline.fit(housing, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try the full pipeline on a few instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\t [203214.28978849 371846.88152572 173295.65441612  47328.3970888 ]\n",
      "Labels:\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(\"Predictions:\\t\", prepare_select_and_predict_pipeline.predict(some_data))\n",
    "print(\"Labels:\\t\\t\", list(some_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, the full pipeline seems to work fine. Of course, the predictions are not fantastic: they would be better if we used the best `RandomForestRegressor` that we found earlier, rather than the best `SVR`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: Automatically explore some preparation options using `GridSearchCV`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: the following cell may take close to 45 minutes to run, or more depending on your hardware."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** In the code below, I've set the `OneHotEncoder`'s `handle_unknown` hyperparameter to `'ignore'`, to avoid warnings during training. Without this, the `OneHotEncoder` would default to `handle_unknown='error'`, meaning that it would raise an error when transforming any data containing a category it didn't see during training. If we kept the default, then the `GridSearchCV` would run into errors during training when evaluating the folds in which not all the categories are in the training set. This is likely to happen since there's only one sample in the `'ISLAND'` category, and it may end up in the test set in some of the folds. So some folds would just be dropped by the `GridSearchCV`, and it's best to avoid that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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__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__imputer__strategy=mean, total=   4.2s\n",
      "[CV] feature_selection__k=1, preparation__num__imputer__strategy=mean \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  feature_selection__k=1, preparation__num__imputer__strategy=mean, total=   5.2s\n",
      "[CV] feature_selection__k=1, preparation__num__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=1, preparation__num__imputer__strategy=mean, total=   4.7s\n",
      "[CV] feature_selection__k=1, preparation__num__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=1, preparation__num__imputer__strategy=mean, total=   4.7s\n",
      "[CV] feature_selection__k=1, preparation__num__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=1, preparation__num__imputer__strategy=mean, total=   4.8s\n",
      "[CV] feature_selection__k=1, preparation__num__imputer__strategy=median \n",
      "[CV]  feature_selection__k=1, preparation__num__imputer__strategy=median, total=   5.1s\n",
      "[CV] feature_selection__k=1, preparation__num__imputer__strategy=median \n",
      "[CV]  feature_selection__k=1, preparation__num__imputer__strategy=median, total=   4.9s\n",
      "[CV] feature_selection__k=1, preparation__num__imputer__strategy=median \n",
      "[CV]  feature_selection__k=1, preparation__num__imputer__strategy=median, total=   4.7s\n",
      "[CV] feature_selection__k=1, preparation__num__imputer__strategy=median \n",
      "[CV]  feature_selection__k=1, preparation__num__imputer__strategy=median, total=   4.3s\n",
      "[CV] feature_selection__k=1, preparation__num__imputer__strategy=median \n",
      "[CV]  feature_selection__k=1, preparation__num__imputer__strategy=median, total=   4.2s\n",
      "[CV] feature_selection__k=1, preparation__num__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=1, preparation__num__imputer__strategy=most_frequent, total=   4.6s\n",
      "[CV] feature_selection__k=1, preparation__num__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=1, preparation__num__imputer__strategy=most_frequent, total=   4.3s\n",
      "[CV] feature_selection__k=1, preparation__num__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=1, preparation__num__imputer__strategy=most_frequent, total=   4.4s\n",
      "[CV] feature_selection__k=1, preparation__num__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=1, preparation__num__imputer__strategy=most_frequent, total=   4.7s\n",
      "[CV] feature_selection__k=1, preparation__num__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=1, preparation__num__imputer__strategy=most_frequent, total=   4.8s\n",
      "[CV] feature_selection__k=2, preparation__num__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=2, preparation__num__imputer__strategy=mean, total=   4.8s\n",
      "<<414 more lines>>\n",
      "[CV] feature_selection__k=15, preparation__num__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=15, preparation__num__imputer__strategy=most_frequent, total=  15.8s\n",
      "[CV] feature_selection__k=15, preparation__num__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=15, preparation__num__imputer__strategy=most_frequent, total=  19.8s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=mean, total=  17.9s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=mean, total=  19.2s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=mean, total=  18.2s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=mean, total=  19.1s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=mean \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=mean, total=  16.4s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=median \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=median, total=  17.9s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=median \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=median, total=  19.2s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=median \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=median, total=  20.5s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=median \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=median, total=  17.1s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=median \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=median, total=  20.3s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=most_frequent, total=  16.7s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=most_frequent, total=  19.4s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=most_frequent, total=  17.2s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=most_frequent, total=  17.5s\n",
      "[CV] feature_selection__k=16, preparation__num__imputer__strategy=most_frequent \n",
      "[CV]  feature_selection__k=16, preparation__num__imputer__strategy=most_frequent, total=  19.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 240 out of 240 | elapsed: 42.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('preparation',\n",
       "                                        ColumnTransformer(transformers=[('num',\n",
       "                                                                         Pipeline(steps=[('imputer',\n",
       "                                                                                          SimpleImputer(strategy='median')),\n",
       "                                                                                         ('attribs_adder',\n",
       "                                                                                          CombinedAttributesAdder()),\n",
       "                                                                                         ('std_scaler',\n",
       "                                                                                          StandardScaler())]),\n",
       "                                                                         ['longitude',\n",
       "                                                                          'latitude',\n",
       "                                                                          'housing_median_age',\n",
       "                                                                          'total_rooms',\n",
       "                                                                          'total_bedrooms',\n",
       "                                                                          'population',\n",
       "                                                                          'households',\n",
       "                                                                          'median_inc...\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,\n",
       "                                            gamma=0.26497040005002437))]),\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__imputer__strategy': ['mean',\n",
       "                                                                  'median',\n",
       "                                                                  'most_frequent']}],\n",
       "             scoring='neg_mean_squared_error', verbose=2)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_pipeline.named_transformers_[\"cat\"].handle_unknown = 'ignore'\n",
    "\n",
    "param_grid = [{\n",
    "    'preparation__num__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",
    "grid_search_prep.fit(housing, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'feature_selection__k': 15,\n",
       " 'preparation__num__imputer__strategy': 'most_frequent'}"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_prep.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best imputer strategy is `most_frequent` and apparently almost all features are useful (15 out of 16). The last one (`ISLAND`) seems to just add some noise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations! You already know quite a lot about Machine Learning. :)"
   ]
  }
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