02_end_to_end_machine_learning_project.ipynb

{
 "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": [
    "**Note**: You may find little differences between the code outputs in the book and in these Jupyter notebooks: these slight differences are mostly due to the random nature of many training algorithms: although I have tried to make these notebooks' outputs as constant as possible, it is impossible to guarantee that they will produce the exact same output on every platform. Also, some data structures (such as dictionaries) do not preserve the item order. Finally, I fixed a few minor bugs (I added notes next to the concerned cells) which lead to slightly different results, without changing the ideas presented in the book."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\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",
    "\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)\n",
    "\n",
    "# Ignore useless warnings (see SciPy issue #5998)\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "from six.moves import urllib\n",
    "\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml/master/\"\n",
    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
    "\n",
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
    "    if not os.path.isdir(housing_path):\n",
    "        os.makedirs(housing_path)\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>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",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16512 train + 4128 test\n"
     ]
    }
   ],
   "source": [
    "train_set, test_set = split_train_test(housing, 0.2)\n",
    "print(len(train_set), \"train +\", len(test_set), \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "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": 15,
   "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": 16,
   "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": 17,
   "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": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x138319048>"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divide by 1.5 to limit the number of income categories\n",
    "housing[\"income_cat\"] = np.ceil(housing[\"median_income\"] / 1.5)\n",
    "# Label those above 5 as 5\n",
    "housing[\"income_cat\"].where(housing[\"income_cat\"] < 5, 5.0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    7236\n",
       "2.0    6581\n",
       "4.0    3639\n",
       "5.0    2362\n",
       "1.0     822\n",
       "Name: income_cat, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13835a240>"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    0.350533\n",
       "2.0    0.318798\n",
       "4.0    0.176357\n",
       "5.0    0.114583\n",
       "1.0    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    0.350581\n",
       "2.0    0.318847\n",
       "4.0    0.176308\n",
       "5.0    0.114438\n",
       "1.0    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts() / len(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall</th>\n",
       "      <th>Stratified</th>\n",
       "      <th>Random</th>\n",
       "      <th>Rand. %error</th>\n",
       "      <th>Strat. %error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.039826</td>\n",
       "      <td>0.039729</td>\n",
       "      <td>0.040213</td>\n",
       "      <td>0.973236</td>\n",
       "      <td>-0.243309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.318847</td>\n",
       "      <td>0.318798</td>\n",
       "      <td>0.324370</td>\n",
       "      <td>1.732260</td>\n",
       "      <td>-0.015195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.350581</td>\n",
       "      <td>0.350533</td>\n",
       "      <td>0.358527</td>\n",
       "      <td>2.266446</td>\n",
       "      <td>-0.013820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.176308</td>\n",
       "      <td>0.176357</td>\n",
       "      <td>0.167393</td>\n",
       "      <td>-5.056334</td>\n",
       "      <td>0.027480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.114438</td>\n",
       "      <td>0.114583</td>\n",
       "      <td>0.109496</td>\n",
       "      <td>-4.318374</td>\n",
       "      <td>0.127011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Overall  Stratified    Random  Rand. %error  Strat. %error\n",
       "1.0  0.039826    0.039729  0.040213      0.973236      -0.243309\n",
       "2.0  0.318847    0.318798  0.324370      1.732260      -0.015195\n",
       "3.0  0.350581    0.350533  0.358527      2.266446      -0.013820\n",
       "4.0  0.176308    0.176357  0.167393     -5.056334       0.027480\n",
       "5.0  0.114438    0.114583  0.109496     -4.318374       0.127011"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure bad_visualization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pnqlhw08vBgl3Lq1DEyJp3VIIXpx9IfUffsqEe1YcFludWs6Y4UXs/OQAs/+3xvM96loslJfYK2U1A+siOVgYMUwCPi0+FBBiIcf65jaK8gMOY5Rt/iedQXMrptCFYOXmj5l05omu11G9UApF/+aYzEHZQ3IHs6zcs2PfzGaeW+p4b2ZVqefx6GHD5NOOKA+v2EooatqM02GiJtQ3f8qs80ax/qcXc/fXzqIw6MwBBXWN5raOpBL1BS82dBpLbzksi1BEUhDQk873xLodHIqYDgWIgqBOnl9j3vSKJMX0Q1GD7zxRnZSfy4ZUOT4Lt+/1QNjgzmX1TLhnBYvWb0/KM6leKIWif3NMGii34oGuYNfle2Zjk+O9Z6qbXJPuiUl/vy4wTDOpudUdGT/HpDNPTDIEYcNk254DSZ6NZUgH5WfnMEuguS3zd1UQ0Jk/vYK1cyYz67xRSf1dUkpCUZnU0+WVdH1hFvbv1a7tZ/VO3fFCHbMeX88F/7mCh1/fGj928hnOuVqpHiwUCkXvc0yG+LIZf56KoE/EE/tey6atENWEsiGsnTOZ+ua2WD7KiFX1ZWLnJ4cbaBMLDQ5FDQzT5P5X3kvStrMMaSpR1lQEfAIQGb8rQ0pHGM1e1NDWEeHGRW878nPZhtG8huKs667c/DG3Lt6UlGuzlOUfeHULv35tC5omkh4Knqlu4gdTRisjpVD0A45JD8qtdHlm1XACuqAgoGf8UgYEdB67rsoxCDBTqCgxRLW2cS9F+YGkUu10LHixweE1zKgczto5k3lk1tloIhYGtE/LLQjojrLsA2GDoM97mC8UlZQU5SV9V9edPzJj2belqFFRMiirMFpMKeNjVm3ZE1fO2PnJwbjQrsWhaLJihnXdwQP8mSsVU8zB8qKkoVAoeodj0oOC5NJlgGljSwBBRckgbnjiLTbuaHM91pQyPivJ8ormTit3VMvZNeZSlaEvn32hq3fi00hqyAV3ryFWeBEzdFajL8RyQvOnVzg8m9LB+QghwHMdXyyP41bm/YMpoz1V4GVTUr6kZpcj1GmZUrfVmqZM2VBc2+T+e/NCqnEqCoWi9zlmDRQcriJz64V57t8v5FevvsfClY1oEFfllsRUud2Omzu1nJYDYYds0b2Xj2VUcUFSiEpDuI5U/9HFo7n/1S3gYrhSeR5uHpxhyqTqteLCIHOnlnPHC6lL1RNZsXm3Q1sw8bvzgpc+ppb2UFIeLp0ZNSRc9tBq7r9yXFLf0sTTh/DQikZPa7NjhW27M1ZeoVDkjmO6Dwoy98JYT9IbtrVw/yvv4dc1DCnjHpP9uGBnziaU0AO0fPaFTFu4Jknrz+qlsm/eTa0dST1VEOtJctuMLbyOh6/duY9Zj693hAIzcffXDqtA9BSrtnzMdX94K+vjgj7BGz+ZkmT0vvn4elY3tng6h18XXHluKf864RQGFwRUb5RCkQNUH1QOyJSAj48ef20LYUMS7hR9nb+sARKMu2FIdD25gu5A2ODey8cmjVa3eqnWzpnsaOhN9IYCPo2XbrqQsqEDU96HV7WF0sH5HqoFnfz8hTrGn3x82utDdxtvsyuBtwhFJU9t2MFNU053vP7rq8/mlfqP2La3HaTgsTUfuB6vi9iVl9V+yP+9syulUK/qjVIoep9jskjCTqYCB0ttwpegg+fTSBKBjUqSpvFa55pROZzHrqtiQEIlXWJS3q2A4/4rxmY0DtaxVjgu3WdmT0o/gykRQ8Klv1nFovWp9fky9SlBes27ipJB+Lr4t/HhFVsd57TW8ouXNvOn9Ts4q7SIu782hoBPI9h5kaAuCPoEmiYIG4dL4Beu3Jok4aR6oxSKvqFPPCghxOnAu8BiKeW1na9dA/wSGAK8CvyrlPKTnl5LuiS+lWPSRfJY8qghCfq0tOPh7aXoABUlRZgJXpfb5tfTYyCuGT+ShSu3Ory5TERNuOOFOg6Eotzw+dMc73kZn55J8664MMiDMyu55dlaV/V3i4CuJRmQsHHYi0q1lrVzJnNpxTDHGPq2jjA3LnqHiBG1nV/nhomn8sjfGj3pBCoUip6jr0J8jwDxhIMQogL4PTAVeBt4FPgtcHVvLMbNIKSaEVUQ1DFMGZ8Mm4oBAZ3/uvYcJo4+Mf5aNhVtPTkGorgwyH1XjIuPUj8UMVzHY7jxi79spiDoc4i/ZgqTuhmNWxcnz3+ypufOW1qf1IRsIURMrDdRbOOhFVu5ZvzItGtJ9C5b2kOu3vM140fGz6Wq+BSKvqPXDZQQ4mpgH/AGYMWaZgHLpJSrOj8zF/iHEGKglHJ/b6wr0SC4bXSWYoJVHTcwz5eUV7IwzMOl6Hb6y5C8xHXc/PQ7nosK5i+r59Ixwxzl6+nCpG7fZShqJuWOLGV5u3GyC+laBv0fH37K7/6+zXk9Q1Lf3EZFSVHWozxSPTC4NVkrg6VQ9B69aqCEEIOAu4DJwPW2tyqIGSwApJTvCyHCwGhgY2+u0cK1dNtFMeG4AX6+++TbHEwIAc6eVJZyI+vtIXmpNlf7Ov50/XncvbwhZTGBHWvYon0jtzZ6XQgiRqzk3jm00X00xzXjR8Y/52bILCFdu9DscQMCSQYqhoiX0lujTQwpuz3KQ43kUCj6ht4uklgA/LeUsinh9UIgsbuyDXCtDBBC3CCEqBZCVO/Zs6cHlul9UF5JUT5RI7F8XHDN+JE9sq5s8VK8ADEj9kSaIgg7hpSuebO5U8uJmJKAT2PBiw3xa32wp52zbVWKFgFddxSIpBLSBeEwHiVFea7rKinKY0nNLha82EDApxExY+0AbsbEXrCRrrjEiw6gQqHoGXrNgxJCVAIXA2e7vN0ODEp4bRDgGt6TUj5KLE9FVVVVjzVyZXq6jo/s0GIzzu3zmNLNIEp1vlyHkbwUL1g0tXaQqSfOr4Gua8ydWh43LK0HwtTs3MfJxQNY8GID4agZH7Vx23ObeHz1+2za5R6l7YhEHYYulb7gjYvednguB8IGeX4tqVfJruRusWB5A5dWDANiavAg2fzhfu57ZTO+zllbN00e7fDk7NQ3t6GlEN9VoT6FomfpzRDfF4CTgR0xuR0KAV0IUQ68DIyzPiiEOBUIAlt6cX2upArHuRVRWPOYUpWEpwsVualSjBle1C1jlc28ow3bWtJWz107fiSXVAxj3ft7uWt5AwFd40AoSuoaRghHzZTGCeiUXXJiPRTYhXStpuXbnttE+UmDaOuIJLagWWd0vc6iDTt46PUtSfJRkc7VP/DqFhaubIwPoLRYUrOL21xyjKrsXKHoHXrTQD0KPG37+RZiBut7wInAOiHERcSq+O4C/q+3CiS6gtvmH9S1pHJ0i3TeDJD03h0v1FEY1ImaMquch90L8zrvqKU9FJNXSsPI4wfw7f95E0uzNV15vUWKQrw4AZ/mOlAwlb6gNCWXPbyGoK5hmCZ+XZDnO1w8UVKUl1R1eShi8vDrW121De3YB1DaKw8TjVPQ5x7qVSgUuafXDJSU8iBw0PpZCNEOHJJS7gH2CCG+CywCioHXgG/11tq6QjabvzV2IpU3Y/35UII/YskRpQrLJeLmoaWrUrOvLaALxxTcRH7xl83pv5AucCBkMG9pPT9bUhc3wtaaYqPjnd9HbILw4XHyQR88MutsKkqK4mNPgnrypOFIJkvZifX7aD0QZmltMwmiIAzw6/zXN89l4ugT3E+gUChySp9JHUkp70z4+Sngqb5ZTfZ46WmyG4ywYWKkMWjpZi55yXmka1BdO2dyUm4r09p6C8vjvO25Tew/FGXBiw1Jo+X9mkYoaqBpwuEhBXSdovyAU629Mx/YFSKmyaIN23mmOrGGJ4aJpKIkMVWqUCh6imNei687pCuicDMYfl0Q9Dl7eqxj7GXaqQYOpiPbBtVUawOyUpjoKnk+wSHbdXRNMH9ZPWFDxtf09FtNPPXtz+H36RQEdKYtXOM4R9gwaeuIxCvxrIeGWxZvintZntbijxWz/uiLo/nFS8me4oCAhilRoT2FopdRBqqb2Ht4En/20tNjYTd2dc1t8dlSXqV2vGgK2hXTU61tV2sHtz/vfRxHV0ksyAhHzU4ZI8Px2jWPb4iruHup8JtROZzykwZx2UOrHdcI+gQPXDkOeyHFoHw/JUV5HAjHhh+u3Pyx61qvv/BU/r8LTlbGSaHoZZSB6iaL1m9n3tI6NAQIyf1XVjKjcnhKg2HlS6w+HLuhsryAcSOOi+vGea3i86IpaK8QdFvb8k0fuoa3BLGCBp8eG5F+yyWjufflzRkLD9LhlhYKRZMLTMKG5NbFtRw3wM+EsiGsnTM5ZYWflacrGzqQ+68cl/RdGBLmPFfreM2eTzq5eIDrWieenjn/p1Aoco8yUN1g0frttuF/sR33B0/XUH7SIAYXBLjxC2UsXNlIQE8tQhuKmlx+znCmji2homRQkrFKR2LfVCZNQctjWvBig2MCcMQ0U4a3IBYC+/03qyjK98c9sDy/ntVMqUxEDImuxYxhou0KRSXfffJtzE5ViFHFBUkVfol5OreJydacp1Q9YX6fjp6QwtJF7HWFQtH7KAPVRVraQ9y5rD7pdQl88Ver0DXI9/sAyQ0TT403grr1T/25uok/Vzfh0+DBmZWOkvJUzbupeqq8aAr6NY0xJUWO4olU4S2I5XrsxvPluo9yapwsXNSQ4hy0FVMsn32hpwpK+3dRu3Nfxp6w0sH5+H0ahu1349MFbR1hGnfvj4cClTelUPQOykB1kabWDmSK8mVJbDyFFX565G+NcemjptYOdJcGVTqPueXZGo4b4KeipIiX6z5i/vIGArpw9ENloxCRLjdl38ArXaSILGZ/IabpW7tzHwUBPa2Ke09jDYCcMfYkntl4WLZpZlVpWsOR7nuwPwR8/vQT+GvD7vhnQlHJ9X98i7B5uJhCafEpFL2DMlBdpHRwfsZGVAv7k3rp4HwORVJ7H2EDbnhiI1FTxlW97dJBVtjKq0JEJiFXi7KhA7nu/JE8sW6H4/VzRxZx6omFTLhnhaPcu68IGwbrt7U4jBPA02/u5OLPDI3n+NyIhVy3Oqoo1zTuZc5zm/BpIqVXGO78mi2v99bFtZ760hQKRfc45ifqdpXiwqBnQVh7+Km4MMiXxwxL+/lDUdN1HpKuifiTfjZTX9MJudq56ytn8doPJ/LFz5yArgkG+HXqmvfzoz/XxMVSw4ZMUmvoTUJRyS9dmobDhuT6/9noKohrCeY+umobILhh4qmsnTOZCWVD4p5oNiHLUFRy31//0d1bUSgUGVAGqhtcUjE042cCerJ47M0Xj+7S9SJGTEV8TeNeR2Ot3+Uadqw5S+FobCNOp8g9uCDA6sYWDFNyMGIQippJfa9+PVbVNzDoS1Jb6EvChpl0b4lq5KGoySN/awRi4VZfF73Bp9/axdW/fyPzBxUKRZdRBqpbpN/cAj6Nl75/ERPKhsRHO0DMCHRlW5w3vRyI6fY5SryljGv6gXOUBBwulLBjl1my42XTjhgw6Ywh/PLrZ/VpuC8V9ntLd+8xT7TrTcnrP2il+gNvQx4VCkX2qBxUN6goGYRPI6kfaIBfwySWTK//8NOkartRxQUUBn3xIgqAgHY41+GGZQaaWjswk8J78L0/bWT2lDJ2ftLhkAu69/KxTCgbkjYkaC8SqNvV5inc9df6j3mt4eOuqgrFCXT2VuVSuyJimhQE9HhRR7oikXnTy7mjG43Jq7bupeqU4u4uWaFQuKAMVDcoLgzy4MxKbl1ci945W+jn0ysYU1KUtvfGrUw6nXGCWGXgHc/X8aXyoa6ffXN7K9f94a34z/brzZ1anjIk6NTkMzwXfkCXJe8cdMeDcSOgC2aeW8q0hWtcNf0SlTlmjR8FMjbGXtcEHVnm1yaePiTzhxQKRZcQmYbU9XeqqqpkdXV1n64hVa9S7c59XPv4BoenNDDo48nrx1O3q83W5Ntz+HUQiCTZnzd+MgU4bECPFm6eUsZ/rdqWNMxw+ewL0/YxtbSHqG9u4/r/eYsUE1OSGHPSQJb/YGKulq5QHFUIITZKKau6cw6Vg8oBqUaGp+u9GXF8Pvn+nv/6I0ayl+LrzMG45WeOdH79eiNGghto9U6lGusOsd/hxNEnctNk7wUsJw4K8r8btqvx7wrorxGyAAAgAElEQVRFD3F07U79BKtIAWJ5qDy/RkFQJ+DTmDutnDWNe/nOE9VZh5NyxYGwQV1zm6sB9WmxoXwFAT0WLqsaHlt/4MiR+4kkzYPyPgH3mvEj8frcsOK9vdz+fB3n/sdrrmX7CoWieygDlWOsnptrH9/AhHtWAMR6kKImfk1w17J6bnk2eYx4QBf4PdRsjz6xICfrXLA8pgZhGdCBQR95fo0HZ1by82mHe6aW1n7I3Gnl/O7acwn6jry/LplK8BMpLgxy/cRTs77O95+uUZ6UQpFjVJFEDnGTILp1cS105oDsoyTsWJNa9x0Mc+viTSnHqef7db4/ZTSz//edbq/VKrV2E1W94D9fJxyVcQWLBcsbePSbVQR0zdOo9/6EJnCU4Hvh+gtP5fHVHyR5Ypl4tf4jrh4/KqtjFApFao68R+J+jFtOR/PQ8WRNap1ROZzHrqtigN89nNYRMbj5z+8ws6qU7gbcOiJRh7qFlZ9ZtGFHkncXuyeZdupv0Nf/+qEgNhyyqbWDxt37WVy9k8bd+zMeU1wY5IErxxHIsgu5cc+Bri5ToVC4oDyoHOKW0+lw8Th8GuialjSGA2K9VWaarqCoCc9UN/EvF4yiflcbb23f16W1ChfB2pb2EI+sbEx6PWwYVJQUce/lY/nxszUkSgnm+/U+GxmfiY5wlDue30Rd82HDdN35I7nrK2elPS7V4MN0XOpBWUShUHhHeVA5xBJmTVdU4Ndh/owxvPGTyTx5/XjWzpkcV8a2ytXnTi2P54VSpX3++Mb2LhsniJWeJypJ1De3obsoQ8yedDoA//jwU9y6EjoihucihN4mKnEYJ4An1u3w5EmVDR3IvBkVnq4zcnA+VacUJ6l4KBSKrqM8qBxj5XRWbv6YO5fVJ6ky+LSYWOvAPJ9jZEPS1Ntp5YwpKWLnJwdzknNKJGyYDiO6pGYXty1OLt4I+jSOLwgw/hevpZ2gu23vwZyvsSep2bmPsqEDM35uTEkRBQGdAxmao3bvD7Fo/fYkFQ81lkOh6DrKg+oBiguDTDrzRFdF8o5IZkHTQxGTBcsbKB2cz/mnFfeYIOtb//zEcX034/Tz6eXctbyhW+Pd+yMnFw/w5OmUDs7H8NDM7tMF85fVO36HqQR5FQqFN5SB6iHs4T63ogevgqbFhcEuq59n4vbn61has8v1+gP8Oo9dV8WYkiLXsN+RzLkji7j2D2/GWwHsPUyJITrr95ipxD5iSPy6N0HeVNdSKBROVIivB7HCffXNn/KdJ6odJdr25tF0ihMAxxcEemyNN/+5hldunsihqDOEZa2nIKATSjNg8UjjM8MKqP9wv+s0Ymt4YWKIzvo9PrVhBwtXbsWnaxwKxwY35vn0eEjW6i2zcGsQtvKMdbvaVDhQociAMlA9TExC5wTuuyI21dZNsNQ+9Tbx/Zb2ED9f0nOafaaMhfoSNRkjhuTfF20kYpggBK7VEUcgWz4+QFB3erR+TaO+uS2ph80yXMWFQYoLg9w05XSOLwgwf1k9eX6dqGlyw8RTuWb8SIoLgwwM+lL+juFwnlEXIp7TcruWQqGIoQxUL5HYEJu4EaV6//HV23KiGp6O595uwq+JpJxZNlNmjxQMEw6abt6iwK9pcYMBsR62+uZPmTj6BMA2+NHWdP3I3xrjk5XT/Y7teUY37CFdhUIRQ+WgepFUorKp3m9pD/HYqm09vq7q7fvoiB4dHlI2DAjo5Pk17r18LBUlg5LCrAcjBt95ojqeo/Iy+DHV7ziTMG82eoEKxbGCMlD9mEUbdnAM2o1e47IxQ3n0m1Xx0FqsGMJZEBKKHq7Gy5QrTIfbsQAFwcNGUnlPCoUTZaD6KalUHY4kMo2O72uef6eZGxe9zYR7VrBow3ZGFRcwf3oFeb7UFZWJ4rpeDYvbsXd/dQxPXX+eo1lboVAcRuWg+ilNrR1HpDirhV8XBH1av85jGZL4MMl0Y9/tXlKmXGI6unOsQnEs0iUPSghRJYS4SghR0PlzgRBCGbsckiokdCTg0+DO6RWujcpHGkFfspeUKZeYju4cq1Aca2RloIQQQ4UQ64E3gacASx3zQeCBHK/tmMYeEjoS5jD5tZjXdHVVKT+b+hmGDQpy+dmlnof/9UesZuUJZUNUQ61C0Qdk6/X8CtgNFAM7bK8/Czycq0UpYthDQis27+Y3r/fPnJQuwK/rdEQMnq5u6uvl5IyoabL5w0+54U/VqqFWoegDsjVQU4ApUsrWhHEN7wMjc7YqRRyrSXTciOM4cVAe85c1gJSEDYkG9IcgoCFjJdlHG1FT8ou/bAb6X0OtpUihclmKo5lsDVQ+EHZ5/QTgUKaDhRBPEjNyBcBHwL1Sysc735sJzAdKgZ3AT6WUL2S5vqOaWeNHcWnFMJpaO9i7/xDffmJjXy+pT/BrkKLfNae4pdD6Q0NtovK98uoURyvZZghWAf9i+1kKIXRgDvC6h+N/CZwspRwEzAD+QwhxrhBiOPAk8CNgEHAr8JQQ4sQs13fUY3lTrQcjfb2UPsPNOAV9wnUCbiDHpe593VDrpnyvVNMVRyvZGqjbgO8IIV4FgsQKIxqACcDtmQ6WUtZLKa1/SbLzv9OIeU37pJR/kTFeBA50vqdwoXLEcX29hC5x+dnDyfNrOR8hIhHccskZ8T4jvy7QBeh67qo0/Lro84ZaL2oWCsXRQlb/eqWUDcBZwBvAK0AesQKJs6WU73s5hxDit0KIg8Bm4EPgJaAa+IcQYoYQQhdCfBUIAZtSnOMGIUS1EKJ6z5492dzCUUPZ0IFcd74z7XfxZ07oo9V4J2IarJ0zmSln5natQkoefG0Lc6eW88iss9FELDfWkcPcmCZgQtmQ+M99MS6jO2oWCsWRRtaPl1LKj6SU86SU06SUl0kpfyal/DCL4/8dGAhcBPwfEJJSGsATxErXQ53//zcp5YEU53hUSlklpaw64YT+vyn3FHd95Sxe++FE7r9iLK/9cCKfO7m4r5eUkaW1H9F6IJzznEnIkByKmMxfVs+nHRECCYrlBUGda8ePINgN1y2g63FPZUnNLibcs8J1plRPYRVGzJ1W3iU1C4XiSCNjkYQQYqLXk0kpV3n8nAGsEUJcC3xPCNEA3At8AXgbOBdYKoT4spSyxuv1j0XKhg6kbOhAWtpD3P/Ke329HE/88Y1/0t7hVmvTfcKG5IfP1GImVDgYpuRfLjiFxW/voqvy8JanYs8DZVPd153KuyU1u7ht8SZ0TWCYkp9PK2fM8CJVxac4qvFSxfc3Yrki69HT+ted+DNA8ujYzNc/DQgAq6SU1Z2vvyWE2ABcDCgD5YGm1g78uhYfA9GfeXLDjswf6gaRBAPk0wRzp5VTNnQg914+lh/+uSZrG+XX4d7LxwKwcvPH6M42i4zVfd2pvGtpD3HLs7WO+7pzWT3rb5/SLeOkStUV/R0vBsoeQxsP3A/cDazrfO184KfECihS0lmRNxlYDnQQMz7f6PyvDfiJEKJSSlkjhDibWAjwt95v5dimdHA+xlEyVDDXRE3JXcvqQcKI4/NjXkiWFuquGWOQwIR7VuDTDg8ctIiYJgUBndqd+9LOgupKP1V9c1uS0Y0YkvrmNiaO7lqhqypVVxwJZDRQUsoW689CiAXAD6SUr9o+sk0I8TGxEN2L6U4FfA/4L2K5r+3AzVLKpZ3nvhNYLIQYCuwBfiGlfCW72zl2sU/l1YWIjXCXoGkiaXM7FglFJXe8UEe+XyPs8n3EStQFXzu7hGc3NiX1QN25rAHDNEnU7i0I6BhSMrOqlGkL17hu+FblnX0YYnb9VO55s8f+vo0Bfp2qU7LLPXbXYCoUvUW2jbrlgJuWzS7gzHQHSin3AJ9P8/5CYGGW61HYSFTLBvjFS//gubd7PoF/pNDh0kTl0+DbF57CH9Z+wEvvfoQmYibBbsfcVOULgjrzp1dQOeI4pj68mlBUum743a28qygZhE8jyTiufr+F1e+3cFFZMX+6/jzHe+nCd903mApF75BtFV89ME8IEf+X1fnnn3e+p+hj7GrZxYVBRqry44xICX9Y+09CUcn+UJSoCUKQUaQ3FDGYdOaJvFT3EaGEyZL23qTuzJGyjn9wZiW+FAWIqxtbqP4gHujIWGGoStUVRwrZelDfI5ZD2iWEsHqUzgIMYGouF6bIDXvae6ZarisMCOh0hA3cAo7jTxnMhg9ae31NAEGfnhRFi5oxsdh0CCFoPRB2HSwZNgzHht/dWVAzKofz7q59PLb6n67vr9q6l6pTij2F7+zhYHtIUnlPiv5GVgZKSvmWEOJUYBaHQ3qLgKdS9Swp+paKkkF9dm2/7sx/RU3JgICeVGCw8Btn8+Nna3tsHfl+nas+W8pTb+7EpwkOJlw/FDXwZVCc8OsCXRMcsoUII4bk/639Z8wFS+CiTmMEOAxDd4zAl8qHpTRQE0+PNRB7Dd+p4YmKI4GuNOoe6GyU/VHnf48p49R/uaRiWK9fUwPu/toYHrhynCOsNW96eVKlYZ5fo765rUcnBx+KGjzxxnZ8AiKGQaI8n6YJfj491vw6wJ/cKTHAr/OrmZVudoin3txByKXo4rXNe7j60XVc8J8rePj1rTTu3t9t1YmqU4q5qCy5IOKisuJ4oUSq8J1VYWi/vhqeqOjvCJlFabIQ4uvp3pdS/l+3V5QlVVVVsrq6OvMHj2Eefn0rD7y6pdeud9LAAMt/MJHiwmA8WV/Q6TnVNbexYHlDPLQ0d2o5dy2vT8rh9CYDgz6evH48BQGdNY17+eVfNjsMZp5fY/nsC/nd3993LTjRhLvyeSJBPVZ9MXvS6VwzfmSXDUP1By08/dZOQHL1Z0cmVfEtrdnlCN/NrCrlmeom1wrDlvYQr9R/xLa9B/hS+dCkc6leKUVXEUJslFJWdescWRqoVI+5EkBKmW2jbrdRBiozLe0hzv/lCsJG706PeujqSmZUDk/quZk79bAKQlNrB9c+voH9oajj2PNOGcz6XspJ+TSYdlYJS2ub8ekxpQZNE+T5dMcGL3CvAuwKAV0wb0YFs8aPysn5ErE/GExbuMYRmszza6ydM5k1jXu5+ekaR07QXhGoeqUU3SEXBipbsVjN/h8xBYjxwGrAsySSoncpLgxy/5Vj0RNiWzkWFE/i+0/X0Lh7f9J4iAUvNsSfyN1CUgCTzxzaw6s7TNSEF2qbMYlJJRkSoobkkVlns3z2hTxT3cShiJmiRN19zEcmwobkjufrWLR+u+v73RWitcJ3B8KGq/p5ffOn3PJMbVLBilURqMZ6KPoD3ZpFIKWMSinfIqYkoVQf+jEzKofz5k+nsPAbZ3PrJaNZ+I1KXv3hxJyPvUhk0YbtacdDFBcGmTutPOm4B1/bwt1fG5OUL+otJLCrtcN1g4dYCXrQpzH/KxVo3VjkvKV1NO7e73gtl0K0qXJS697fSyRFXHLV1r1qrIeiX5CrYTn7ULOb+j3FhUGmjSvhxsmnM23ccAYXBLj54tE5+0vgxt/e20NHxBm+S+y5GVNSREHAGR32axpjSop4646LOXdk38y++unzddQ1tyVt8EGf4LHrzuWNn0xm1vhRjh6noE9jom0kRyaiJlz28Jq4Ecq15+LWgzV3ajl/WPtBymNOKsqjIKCrXilFn5NVmbkQ4pzEl4CTiE3UfSdXi1L0PPb8gq4LdEnKJ+ru8EHLQcfPPo2knhs3HUFrMywuDHJh2RA27tiX8Vq6iDXY5qogUAJ3Lqnj1i+dyYOvbXHkYuwaeDMqh1N+0iD+sPafLN64k+rt2eXOwlGTWxfXctwAP592RNE8CtF6LWBILClvau0goOuEolHXz//ipc0piytUoYSiN8m2Ubcap7K5xXrgX3OyIkWP49bM2VtoQksa+tfU2sHcqeUseLEhaTNsaQ/x+9XbUp5PB3x6bAz87EllnHZiIbc9twnDMF1Hw2dLxIT7X93CvOnljClxH28RG4VRG69E7IqifCgquf5/ql11At08l2wLGBJ7sBK9I41YNWJUEi9Yeaa6ieWzL+RA2FBVfIo+IVsDdUrCzyawR0p5KEfrUfQCbs2cblpvPUHYMHnwlS386JLRrGnc66zum5ZsBNzWascALHvwmxWNXFRWzNo5k3ls9Tb+6+9OwxbQBaWD89m292DyidKtOWpy17IGHruuymEoWtpD1De3OYxTOr74mROZUFbMiMED+N5T7xBO+MLdjFPQlyyL1F2xVzcliRu/UMajq7Y5qin9msaBsMG4EX0TYlUosjVQo4A3pJSO2IAQwgdc4HVgoaJvcUuc94Zxslj05g6e3bgDSUxpwtpkFyxvYO2cyUnhv2zK41c3tlCzozWm8JCAYUq+dnYpIwbn8ePFm8im6j4UNbnuD28CcMU5w7lo9AnMeW4TmhCee7he/cfHrNvWQtSUfLWyhGeq3XSXD5Pn03j0uiomjnZOjc6F2KubsPAjf3NKNqmck6KvyTY/vhI43uX1os73FEcAiYnzgE/r1ij0rhA2kgcLulWJFRcGmT2pLKtz/+b1RldlCkPCA69uYc7/vcsFp2Y3osLO4rd38YOnazgUMZNkkyxS/cNqDxkcipi8UNPMAH/6f36mlK5SVbkSe00UFr738rEEfYIBfp2gT3jOOXW3JF6hSEW2HpQAV63PYkDJHR1B2J+grWbOro5CzxWpNtlrxo/kN69v8ezl1TW3pX0/FJWsbmxJ+5nukOofiR2fJjJ6hvOmV7gaiJ4Se42tuXPWiPT2wKKaeRU9iScDJYRY2vlHCTwphLA/KunAGOCNHK9N0cPYE+eJG96McSfxTHXvzpGaWVWak0R8wKc5lBN6Gy9m/mDYINDpQOX5NSJREzpHfERNmDe9PK3KRK7FXq28lt3zzJTXUoMPFT2NVw/KetwUQCuxke0WYWAN8FgO16XoZdw2vHEjBnPH83W9toZFG3bygymjXQfs5ft9jgR+QUDnK5UlPP3WTocO3syq4Syp+bC3ltwtwp22wDQlf715IoMLAg7dwpb2UNqNvrvq6Ha6ktdSgw8VPY0nAyWl/BaAEOKfwP1KvfzoJHHDmzV+FNIw+dnShl65vmFK1r3fwrRxJY7X3XIuhpT8+JIz+PElZ7Du/b3sbQ9zYdkQyoYO5MKyE/jxs7VHzKj7oC9mkMqGBpMqG3srZNaVvJYafKjoabLV4puvjNOxxbUXnMKJA/29dr2X65K9n+LCIHOnlhPQBQUB3TGRNqaOMZx/mXAKZUMHAjFvcP3tU1j4jUq+dcEorh0/gv/82hgKg72uZewJa1NPpSKRi1EdmejK1N/uTgpWKDKR0YPqnJz7eSllqxDiXdKE2KWUY3O5OEX/4M07LmHu8+/ypw07evxaf23YnRTaWlKziwUvNhDwaYQNybzp5Rm9ikRP5DMlRUQ9KGX4NXLS4JsNZw0fROuBcNIgR4CoYXLZw2sI6k6PqifGYHjJayVeVw0+VPQkGcdtCCHmAfdJKQ8KIe4kvYGan9vlZUaN2+g9rM0pEjX49hNv0taR+528IKjz1PXnxZtDW9pDTLhnheu4iHTJe7djLLWKSNRMWbCoQS9raxym/KQCGj5MH6Cw30dvhAHtBqmvwo+KI5NcjNvI6EHZjY6U8s7uXExxZFNcGMuR/ODpmpycryAgOBB2WgrDlI4cRi6T92OGF7F89oXU7NzHzpZ2frMyWUKp72r/yGicAHRNMH9ZPWFbg7OXaruueDj2EvKwYWBKHI3VqmJP0dNkKxa7Avi6lHJfwuuDgBeklJNzuThF/6KlPZQz4wRgSg1dGAghCPo1DFO6CsnmKnlft6vN4XmcMbSA93anNgpBXSPUy0MeMxExJH5dc+j9pTPYXe1T8qLXqCr2FD1NtkoSXyA2pDCRPOCibq9G0a+pz9AAmy0dEaMz1Cb53axzWDtnctLmmSoRD6QsHHAdMTEtFhazFyBs/6SD/77uXL5z0Skkijr4dcGdM8oJ+npyGEn2fLWyJKXyeyLdGd3hNg8qEVWxp+hpvDbq2sdsjBVCfGL7WQe+BPRuV6eiD+gZOaSoCe82xZzyipKipCfyxET8msa9TLhnRVqvwG3EhFvYb8jAPO6YWs5Zw4u47blNSFMSMmQslLa8gas+Gxs5oWuCcNTkwrIhvPlBCwfCfeNZPf92E187p9Sh4zezqhSIGexMQrtevR43L9Snga5pBHQ1fkPRO3gN8VljNiTwisv7HcBNuVqUon/ipguXK+57ZSsQ81weuHKcqydljd/wql6QacSE3QOwZjpd9vAaQMYLLBJHTgBMuGdF7m48SyImSSKzT23YwZ/fanIYjhmVw7vVp5RKTqm/VOz1RBWjov/h1UCdQuzxeRvwOWCP7b0w8LGUMvshOIojiuLCIHd/dQx3vJC9ukS+X6cjkvmvSMSQ/PCZWnZ/eoivn5MsfdRVr8CLft2BsEFQ1xxjMKyRE5YXVjo4P34eAXT0oaSSRdSEqGnGZYrsBrs7mn2pSsj72iAo/b9jB69KEts7/9i/AvKKXmfWeaPYuns/f1y3PfOHgYCuMW96OSOOz+c7T1R7Gk1hmJK7X9rM3S9t5qGrKx2bT3e8gkw9O26jPaziiqseXefYEOdOLWfe0vqM1+wL7Aa7u31KuZRTygVK/+/YIls1c2v20+eAkSQUTEgpn8jRuhT9mJumnM6f1m939BIJYMJpxax5/7BK+GVnDWXBV86Kbxz3XTGO257bhIbgoAdvCuDHz9Q4Np/uegXpNtw1jXsxbMbPr4tYccXyBseGeOviWkB4avztCyKmSUFAd+SkjpbNW+n/HVtkW2Z+JrCMwyE/o/McESAEKAN1DFBcGORXV1Vy6+JaTFMSMSHo16je0cpPLzuT4wcEqBxxXFx6yMJ6mq9v/rTTm8ocHjNNuPflzVx5bilVpxQ7ztPdHIQ9jwEw57lNjpEemoARg/PRhbM4RBdaT9WLeCbo05K+vwF+HRPJzKpSpi1cc1SGwJT+37FFRiUJx4eFeBnYB3wb+AioJDas8HfAz6SUr/bEItOhlCT6jsbd+7ns4TWOnE2iykOqZPbSml1xLyhsmHx5zFBeyKBCflFZMX+6/rycrN2ex+gIR/nMSYNo/Hg/HbYQ5MCgj2vPG8nvEkbH6wJ8erKB6E00gUPFPejTeOy6KkqK8pi2cE1WyhtHGva/O0ebAT6a6BUliQQ+S0yX74AQwgR8Usq3hRC3AQ8DSovvGCJVUYEVbkmXzHbzglra16cdJLi6sYXqD1rinlRXcctjvNv8adLnwobBo6uS1SaEgG98bgR/fMNbHq4nMCUEdIFPjzU433fFWCpKBrFy88dJHp/9d3I0VL8p/b9jh65M1D3Y+ec9wHDgPaAJyG4ut+KIJ124xUsyOzE38qfrz+P1ho+44cm3MVLkd1Zt3ZvSQHndfN3yGHbyfAKE4FsXnJzkPUGsau5/N+xMef7eImxITBlT4ljTuJfbntuETxNJorPW7+Roqn47mvJqitRkW5VXB4zr/PObwBwhxOeB+UBjpoOFEE8KIT4UQnwqhNgihLje9t4AIcRvhRB7hRBtQohVWa5N0cukG7fgpkRgPcmnY8jAPAb4U4/FmHj6ENfXl9TsYsI9K7j28Q1MuGcFS2tS9427GVY7Z48czNo5kzn/tNSeWn+RQIqasdL8Z6qbOBQxaQ8dNk720SRAl1UljmVa2kM9PupEkZpsPai7gYLOP88FlgMrgb3AVR6O/yXwbSllqLPg4m9CiHeklBuBRzvX8xngE2L5LUU/J1W4JdtktuX9FAT0lMbjorJiV+8p29Jjy7D+4OkaV2n+M4cNpLgwyM5P0htTC78uiBjStXBB4G0EfK4pCOrMn17BpDNPpLgwSO3Ofar6LUuOJo/zSCUrAyWl/Kvtz+8DnxFCHA+0Sg/VFlJKe+OIpUxxmhDiADADKJVSWsmAjdmsTdF3uIVbsikHT9wIZlbFpHz8WmzDn3TGCXznolNShvayKT22DOGEsiE8+2/nccXv1yedb9b4UbS0h7hreeZJwkGf6CxOyOdA2EgSpP23i07lNysyBhdyjmHKuHECVf2WLarfqn/gZWDhUi8nEkIgpZzh4XO/Bf4FyAfeAV4Cvg5sB+YLIb4JfAjcKaV8LsU5bgBuABg5cqSX5Sn6AK8D8BI3gkR5oa7oxrltvm5PxNedP5In1h0exHjd+SMpGzqQ2p370FKUkgd0QdCnx88xcfSJ8ffGjTiOS8cMc9zzhg9aWP9Ba9p7yBUDAjqmTFaF727/2LGG6rfqH3jxoFKXVXUBKeW/CyFuAs4npo4eAkqBMcBzQEnney8KIRqklP9wOcejxEKCVFVV9c9uSQWQOZmdaiM4EDbiQwu9XCPT5pvqiXjtnMlcd97J1Ozc5+jdiqlKuP/VenBmJSOOH5DSeCbe89P/dgG/euU9frOisUdDfkGfIGqYzJte4Tp1V1W/eUd5nP0DLwMLv5Xri3bq9q0RQlwLfI+Y2GwE+A8pZRT4uxBiJXAJkGSgFEcPudoIMm2+6Z6Ix7k0FQPgErXWBZx/WnHWm/sPLzmD6y44Oasm5WyxZKQWvNgQ/39i/kRVv3lDeZz9g6yljnrg+qcBbmFE5RkdA3R1I3ArKbdvvonvuxnCsJHaEDa1djAg4GN/KOp4/eaLR3d5kyouDFKU7yfQw02+blN3b11cy3ED/I5xJm7fYVf7pBKPU/1WilzQawZKCHEiMJlY5V8HcDHwjc7/VgE7gNuFEL8ExgOTgNt6a32KviPbjcDKJfk0QdiQzJtezqzxo5LeT/Qe7r18LD9+tpZIZ+jOME3WNu51rcxyM2hBn8Y147uX80x1XsMw8aCj6wm3qbuhqOS7T76NKSVzp5XT0h7mkZWNjhEdErpUtZZU5HJuKc9sbDoqqt+Ux9m3ZCV11K0LCXECsJhYH5VGrCjiISnlY53vVwCPE1Oj2A7cIaV8PtN5ldTRsUVLe4gJ9/wcdf8AACAASURBVKxwSPkA3P3VMcw6L1Z9d8F/vu5QTQ/6BG/8ZApA0nvpZIC6IqnjxXNwO+/+Q1HPY0x8AjRdI6gnVz1GTNMhcOuVgA4gHHk3LxJJqX4fiXxnwijumD7G83oURz59IXXUZaSUe4DPp3m/nlhxhELhwL7pN7V24HMpr5u/rJ5Lxwxj0YYdSSM9QlHJ46u3cemYkwjoOqHo4bBdusqsrnp2cUMxtZwxw4uSjnU77+Or3vf8fWia4KWbnFWOP5gy2nG+gUFfVsrxMfEJ5/fmpWotkyqHxWNrt/OHN7bz/i+nerlFhQLo+xyUQpGWReu3M39ZPX5dw5CSuVPLXavr/LpGffOnLEzRc/S7v29j8IBA2oKMTHmtdLhVCd7xQh2FQZ2oKZO8L/t5Jz+wkm17Drqe14150yuSijoS19kV5fhEwoaRsVglkyqHHUPC9If+zrLvp3xOPSI5GvJt/RU1gFDRb1m0fjt3vFBH2JAcCBscipgseLGBW744Oumzhoz1facLWd/3ynvMnVbuKs2UjVSSG27STgDtISOtrNAvltd7Nk4+XXD312KhTC8UFwaZOPoE7rsiJkdVEEwtIeXG7EmnZ9xwE+WuArpIu6m829xO4+79Wa2jP9PdvzeK9CgPStEvaWkPMd9FycE0JeNPLebur45xeFb3Xj6WnZ90EEkzRFAQm++0ds7kpIqz7qoGZPIk3MJlLe0hHl/7T0/nD/g0XrrpQgD+uPYDhhQGOXPYQJrbOgBBRcmglGudUTmc8pMGUbNzH3W72jxNQw76NMaNOI6W9lDG78AesiwI6Fz6m1Wkc6pqdu5zL+s/wlBqEz2PMlCKfknMI4FwwuthQ8Y2wTHDKMr3s72lneMLgmhCcNfy9CPYw4bkO09Uc98V4xzhtlyoBtjL5XWRWlHcoqU9xNNv7sDLUN6ArvH1s0/ihj9Vs22vu7elC/jVVZWuRRz23FjYMNEFuPUg+3VBnk/nUNTAME1uXPS25+IQe4jxtkvP5BcvbU752UqPDdj9HaU20fMoA6Xol5QOzo+Xg9sJ+jReqvuIh1dsdX0/E6GoTOoJ6olm4brmNhYsb3Dt7VpSs4tbbOXumQgbJk+/lT50ZEi4+emapKd3t6d8vy4QUsanB+sC7vrKGC4dM4z65rZYzsog3gPm1SuwcjGXn1NKfXMbS1wGUM6sGn5UeE+g1CZ6A2WgFP2S4sIg86ZXuJReSx5Z2dgl42Rh7wmy90jlQjXA8iTGjTiOSyuGuTbC3rZ4U7fWnwoTeHz1NuZ8+TPx19ye8vN8Oo/MOrvzJ2d4sCg/kFWlo4Vb71nFSUXc8/JmpIwNeZxz6Znc8PnTcnrPfYlSm+h5lIFS9FtmnTcKBMxf1oBfFxim5MYvlPG7v3svyU7Fwc4QnOUd9IRqgFsFYFNrB5pIoUKbAx5b/QGXn1Pq0BR0e8q3K0rY6YpXkE7n8PJzS4/qCjelNtGzqCo+Rb9m1vhRrPvJZJ66/jzWzpnMNeNHEs3hsED7EEXL8+nJTaZuVxsdLn1Jfg1e++FEbv/SaIYX5XX5/FFT8uWHVsWrydINlXQj28+DewWjhqC++dNe+U77mlT3qIYddp9eU5LoKZSSxLHHw69v5YFXt+TkXFZ1XFfyItn2v6RSXdCAX199uMChpT3Eeb98vVthQJ8GG356MUC8us7r+BJrDV7vLdV9BX0a911x5MocdQc17DA3ShLKg1IccVwzfiRBX47+6krJtIVrsu5f6Ur/S6peqUQzVFwY5IErx+Hvxi1GTXhs9bb4GqctXMP2lgPxsvpVWz7msVXv87Pn3+V/N2xPespP5/m0tIdYXruLP679gMbd++NeV9DnDF2Gol0bK3+kex72kOf+UDRtH5wiPSoHpTjiKC4Mct8VseR0JGq6lkx7JWxIMCS3PbeJ8pMGefIyutr/Ujo431F8YCGB7ydU4M2oHM5xA/x8+4/VaXu70vHYqm0Ykvgab3m2lg/bDnH/K+8leWe3P1/HQ1e7l6nbWVKzix/+ucZRHj+zqpR7rxjHcQP8fPfJt+P5Pci+7Ppo8DxU+XnuUB6U4ohkRuVw1s6ZzD2Xj2WAv/tFB9KUXPbwGk8ekZsnZM9lpaK4MMh5p7qPrQd4pf4jx88VJUXoetfvTU/QLAwbkl/+ZXPK0OGPn6lxPOVbntby2l2s2rKHxt37ufXZ2qTerWeqm3j07+9TUVKE4TLSpK0j4sl7OFo8D1V+njuUB6U4YikuDDLpzBMxl3R/Tm3IiEklhaOZPaLubEADAqn/yb3SsJtv2MaGOJp/NUEkanLuyONY/0ErmojNUxSaQErpqeE3MyL+lO/Wq6WLWLm4G/e8vJmCoM+xDk1A1DD57pMbMUyZMR/VXc+jv2jiqfLz3KEMlOKIJnEzCBsGUcPS5vOOBg497nQbY3c2oCvPLeXl+t2u761t3JskLTShbAiPfrMKkPHScPtGDLGN/b/XbGNp7eHG2OvOH0nVqOO55dnalKPrk5GUDs5P2avVacNd0YD5yxscx1jGygr5/fjZ2rRh0FRDJS0PLNs+rL4MDary89ygDJTiiCdxM3i5/iPueN7bbCWLxML1TB5RVzegKeXDOGNoAe/tPpD0XtCnO4xiqk03sb+quDDIQ984h+9P3k/Nzn1U2kbYlxTlccXv13ta2wMzKykuDLJqy8ekCiymkkkyBeTrgnByii1OxJDUN7cxcfSJru8nGn5Lcul7T24kYpjMm17BpWOG8Ur9R/y17iNaD0aYdOYJTBtb4poT9JpT7CnSKeH3hbfXXzzMbFBl5oqjkkf//j6/+EtqPbhU+DXQda3Hn8BfeHsnP3xmk8MhsQ8IdCvd9jJAMJHanfu46tF1jvMEfRq3f/kMIoZke8tBKkoGcUnFMAAWbdjBwhVbU3pdAT3mGSVO7/jpl8/kwde2ZBxc+MS/fo6Jo09I+5mW9tBhySWPY4bz/JrzHnWBFCI+1DHx99mXm3VfeHt9cc0jamChQtGbjD+1mMKgTnso87A+Oybwl9ld64vKhq+eMwJN01KGCb3kYxp3J3tMibh5gULA9HHDHRvzkppd3La4Nq1BEMD9V1YCcOviTWgCDBPmzShn1vhRDCvKs4VaTcJR02GAfRpUlAzK+N0UFwYpyg/g0zRCePv9JRrGdDnFvgwH9oUCuts1b3m2lvKTBvV7XURloBRHJaWD84l2oXLAryUrkXcFL0/o6cKEpYPz6Yg442UdkWjc4Pz8hXd5Yv2O+HvXnT+Su75yVtI1vOTLrA0sk7ciIb6pua078X7WNu7l1sW16ELDkCb3XTEuu/ElWSqG6JpggF8nFDXQNOEwWvYqy74ckdEXJehNrR3IhH8LYSNWtXp/P2+kVgZKcVRi35gNU3pWZYhKd68DvIeFsnlCT5enEMJZnSg6S+gad+93GCeAJ9bt4LrzTnZ9IrbPg3LztryObQe4c2kdV39uJOefNoRxLmMz7B7ghLIhvPGTKV0KpaUWC06NBjwy62xKivKZtnCN4z0rp9jXPUp9UYJeENA7PUon4c5G6v48v0oZKMVRy+Gx523sau3gkwNhltbs4r2PkwsULG770hmummqLNuzgkZWNBFLkNOyf7eoTut0ANrV2kOfTiRiHvai8ziKKrSkm0qYaBJjJYBYEYl6HF9a8/wlr3v8ETcCvXeZP5TJ8Nuu8UezZf4hfv97o6fMBv0ZRfoCyoQPTeo192aPUFyXoB8JGUo7Oor83ECsDpTiqWdO41zasz0gbxtKFYNigw0Ktjbv387u/NfL8O83x5+1Qhj6prj6hJ27sc6eVu5RcG7R1hDm5eIDrOdwGAWYymNZ1NS1WnpdYbp8KU8Yae+2VcpD78NmkM4fyu7+/76lYwjBlfB2pwqf9oUept0vQ0xnf/t5ArAyU4qjFbXNOhyFlfEP9zWtbksJodlIZnVyNq1iwvIG5U8tZ8GJs6GFHJIop4cZF7xAxTS4qK2Z1Y0v8HNedP9LVe0pnMOGwQbHIJusTMeGSX68iz6djSJPZk07vknF+veEjlm36kIqSQXz9nFIA6ps/BSQlRflJoU6/FpstVbNzH39t2E3Ap2GYMsnQpAqf9ocepXSh3Z64lmWUIVZQEtQFQhP9voFYGSjFUUs2uRULv6ax7v2WtMYJ4JCtYMFOV57QUxmRMcOLWDtnMvXNn3aWXJvxkN9b21tZ/G/n8c+Wgymr+FraQ7R1RDiUEL47FDXiYcTE5Hm2mBIOdo4P+fVryQrzHS7fkz2U+Y3H1rGlsyfshZpm7k4YFa8LmHTGiaxu3OsaXo2VpMeMWUVJUcrrJH7/vWkgeppsC3KyVbbvS5SBUhy1uHkzmYiYJns9aL+NLXUO/LNvEm7qD9mu0/K6YiXXfgK6Fg8vQsyA+X06V1SNcD2nFbrzaSKpQMTqfYxEDdfkeVcxZEzeyI5I0EayhzI7wlEyRe4MCa9t/hiAb3x2BDdNOd3xfdpDuHbj1d+UJXqKXBXk9FeUgVIctSR6M6GokVH2Z+60csaffHzGc395zEm0tIdY9/5eFm3YzrptrSk/O2xQgLHDi5h05tB4Q6z9idcRgpGx6qpJo4ew7v29xLqPJGHDe9jQHjJ0I2rClAdWsq+j++X0iSQ6ZFZhB0B9c1u81yobr9bij+u2s21vO7+66ux4M3MqBYlMubCuzPLqbyoMfdFT1dsoA6U4qknMN9z89DuO3I2dgoDOmJIiyoYO5NKKE3m5/uOU592yez+fvfs1TyKtH30a5qNP9/DKP/Zwe6cEkyAWvnqwsxJuQtkQCgMaew/EQnh/qf+Yv9iuLwCfJvDrAsOEH108mqbWDlZv+ZiV7+3lrOGD+Fpn/mZZ7a6UUkUWPWGc3DgUNajb1cZVj65DE8KzMkQqVm1t4XN3v8avrqpkVHEBeoKH5tc0anbuS5sLy9a76q/eWC5K5vuj4bWjpI4UxxzVH7Qwe9FGPmqPOF5PlBL6+m/X8PaOth5fz91fHZNVv086NJHsxfQlugCf3n3DlEjQJ/jRF8/glwlyVnl+jeWzL2TawjWuMlFAVhJSuZKc6gm6u7aeNrxqoq5C0QWqTilm/c8u4aGrK8nzawwM+sjza/9/e3ceH1V1NnD898xkZoAEIgZBwyYYFAk1iCggiijaagWXT/Wtdaul1FrFtaJVX5VKq0Xta8Wd2mrdWhW1rrggUoEiroBhUQMim4AEDIQly8x5/7h3wsxkJpnJLPeGPN/PZz46c2/unDkh95lz7nOf0yiZ4cVLj2H6r4dxelkx/iz+pWQqOIG7ghNAwOclxcuAyTFw99tfNHr5F0cfSOd8PzefOgC/V8j3e6N+t6mu5dXStb9yITw13NS/4URay9pbOsWn2qxk0o2H9Cmiz34FvLV0A9k507rfzKtHsmDVFia9sgSPPaV2+qBiXvrMWtSxLmjweQWPCMFQKKqQbH0w1OIVgZuSKLnjof+s5KH/rMQr0N7vpTZouHXsgIaRQaq3Abh98cGWpsw7XVEjWTrFp1QSXlm4Lip1/H+G9ODJ+avTXCax9ehb1IH+BxTw4x90Z/hBRVHrUkWmLc+r2BzVT5eNKuGB2RXNVjnPpsjpvbVbd1G+rqrh/rLw1Faik3xldQ3PLFjNfbO+Is9r3W91y9gBDCwujBsQkr2m4/S1n1xMXWZiik8DlFJJij2pVFbXMGbq+3y7rdbppuVcHlBU4Of4Q7ty1uAebNhWw+bq3QwsLrTvixKKC9uxvmo34//xcaMsxFzqGMjjVyP78uDsiqhKHQOLC8n3e3mjfAMPvFeB1yMEQ4ZxIw5k+EFFrNmyi8mvLwWsm1t99nbxCD6PlQI/aWwp5w2zVkFO9pqOW5IuYr90xd5flm4A1QCFBijlvHeXbuDtpRsZ3ndf3lm6idfLN0RtP+rAzjx0/hHMX7GZd5dtZOn6beQH8vh8XRUODizajECeABJ1H1k7n4ebTx3Aba8tSTuB48ZT+vOTI3okNSJxW9JFvECUqQCq60Ep5QKjB+zP6AHW/U1nDO7J1Ru389YSK0j9qHT/hioPY8q6M6Zszx96+Bus1yPsSHHdKpW80Yd2Zc6XlVEBSoBJr5ZTl4Fuv33GcrburE3qmo7brv3E3rzrtnurNEAplWEl3TomtRBc5AXulz5dy+Pzv8lB69qed5dtgpg7w3ZleOj66JyVeGKy/eIlU7g96cJtATSnaeYi8pSIfCsi20TkSxEZH2efW0TEiMiJuWybUk4oKghQ1nMfLh/dD5+3udtrVUv4vV4mHF9CIK9lpzt/Er8Xf571Hs2lfKeTGp4NldU1LFrzfUN6udsCaK5HUHcAvzTG1IhIf2C2iHxmjPkEQEQOAs4Gvs1xu5RyVFFBgD+fXca1zy9qthyTSk1NMMQpA/ensrom5VGqR+DRnw+huLA9M8o38JeZXxLv1xM0hnOH9uLcob3SWkk5W1K51uT0ciSRHEuSEJFDgNnAlcaY5+zX3gSmAg8C440xM5s7jiZJqL1JOK353plfRhVSLevekbFl3fnTm8uj7jNSzQuPm1rSbX88cyDnDe3d8DxcPX3Wso08/eFq/F4PQWOSTiRwIr08XiAaUdKlyWQNt2Tx5fwalIg8CFwEtAc+A96wXz8bqDHGvBFbATnOMS4GLgbo1atXNpurVE4VFQS4fHS/hN/Eu3Zq1/DttjYYoq4+1KITb1vSkv7xCEw+PTo4hRW293H56H5cPrpfSidxJ9LLEyU9TLtgSJPXmtxS+TznAcoYc6mIXA4MB0YBNSLSEbgdOCnJY0wDpoE1gspSU5VyTLKL7c2r2Mw1zy2MGlUF8jwY4NqTDqb/AZ2Yv2Iz0+Z8TdBtdZBczOf1MLTPvixa831DAEonwGQrO665kU6ipAcwrrrWlIgjWXzGmCAwV0TOB34D9AaeNMascqI9SrUmkcErHLAiV6CNXYxu5MH7Mf7YvixZX8UNLyxiXVXbu7E4VSJwytT38YpQFzJMGFXCI3NWtjjAZCM7LpmAmSjpobS40FXXmhJx9EZdEXkU2AEcB/QA6u1N+wFVwBRjzJSmjqHXoJRKzcTnFvL8p+ucbkarE73wvFWh4qnxQynruU+zP5vpG3RTOV62K0Yk0qquQYlIV+AE4DVgF3Ai8DP7cRvgi9j9I+AaYEau2qdUW7FPvt/pJrRKsV/lU5kSi108M90RSyojsqayBt1yrSmRXE7xGazpvIexEmu+Aa4yxrwSu6OIBIGtxpjqHLZPqTahb5d8p5vQ6gXyJOUAk8n08lTvVwoHosrqGv76/gqWrK9i7GHFDRVQ3Epr8SnVxlRW13DEH5q9g6NV83mFuizdTybAP8YdyciDu2bl+MlqauounpcXruPKfy2Meu2Qbvm8dfWorLSvVU3xKaXcoaggwNRzBnFFzMlqb9GnqB1rvs/ewnsG6ODzRmX4ZUtT14hSGZFVVtfw22cb/76/2LiDd5ducO1ISgOUUm1Q+OR215vLefbjtXvVulart+yOW+0hUwQ4928fEvBmN+kgmSy9ZK8hLVm/LeFqy28v3agBSinlHuET6cST+zPx5P6s3bqLuvogFz22gOpa60zm9wq3nlZKz84dmLVsY6spZpvtSlEGqK0PUVsfnW4+t2Jzxm7EzdR9U5XVNTy9YDX3z/oq4Q3Lw/vu26I25oIGKKXamKa+mZff9uO4o4CRB+/H5aP78egca0n1vYHXA7HrKHYM5DH+mAP5ZPX37Kqt46Nvqhr9XJ5HqI8YjhgDry5azx0zllNTn5kbcTNx39TLC9dx3fTFUcuMxBKgz37NV953Sk6rmSulnBX5zXx7TT2760Jc98JiKqtrGipbA5T13CduJe7rTzmUT/73RO7/2eEM6VWYVlu8IgTyhHYtqDKebuH3/ICXe396OP6Y964Nhji8977c89NBPHzBkfZih3v4PEQFJ4Ca+hCTX1vaKBD4PB7mr6hk+sdrqNi4PaX2pVtVPPx7bio4AQR8HtdVj4ikIyil2pBE38yfXrA6akn0pqanigoCjCkrZkxZccNoq64+yJS3vuCjVVuTbkuPfdtx8bEHMenVJSl/jnSn8YIhw/CDirj7rD33Ju2uD1IfDHHJU58QDBnuOusw7jqrLCpT7oRDuvJGzIrJidqzsy7IhH9+1vD8wuG9uO30HyTVvnTvm4r3e46UH/ASDBlXVo+IpGnmSrUh8SoQJFoSvSVVDqb9ZwW3z1ie1L4XDO3F85+ujWpLOtr5rNHQj0q78fLCxiv25HmgvS8vbmLDkvVVjHv8o6iahj6v8MENowHrhJ/v93LqfXObHZWE3yvebjOvHtmwmGUyCRUtTbqI93sG63d9y5hSBnYvzHoGoqaZK6VSEu+b+WWjSpj2/sqoE29L68RdfNxB/OSIHtz37lfNJlWcVNqNfy9cn/BbfqqCwRAzrhzJjtogM5duYkftnvXc8wNeHjpvMIXt/XGrKUDjgFIXNCxZX8XIg7tSVBBg0Zrv8Xs9SQWoRDOQcys2s6M2SPm6Kia/vpQ8j1AbNNw6dkDcyuktrfQQ+3uuDYaYcHwJ5w7t5eoRUywNUEq1MbH3zwA8MLsiap90KlsXFQSYdPrAhuUoZi3fyL3vRh9/6jmDKC0ubHSdJR11IZhRvoFzh/YiGDMzVFcforiwfcPoJdb8FZUJjron1MS7LtRUW+K5Y8ZyfF6huiYY9fpNL5WDgfOGNQ5SLeXEwoiZplN8SqmUqxKkKjyNBkJpcaeGk+WI22eyblvmbqoN5Hn47+9OYF7FZq57YTEmZKgJGvxeDyJwy9gBDCyOnt6qrK7h6D/NajQy8gp8eNOJUSf2VxauY2IzmXGJeD3S5JInfq8w/4bRrTKQxKNTfEqpjMj2t+2igkDc0kDrMxicAIwxzF9RyYiSLtx9VhlX2tUTau188pteKiff76EuRMO02tqtu+JO3Z1zZOPFUE8b1J19Ovi55MlP2FkXPQo6svc+LF5XRU3EUsiBPA83nNKfLgUBbnjxc7bX1McesoHPm/q0qhMr9OaSBiilFOBMZevS/fMp37AjY8erDZqGzLmAN/6IZUftnmCFgZMH7h936u7fC9fxwmdrG40mS4s7EYqpveH3Cg9fMKRh5BY7Eq2srmkIkokEjUlpWtWJFXpzTe+DUko55rWrRmXt2DVJ5KL/3k5xv/Mnh9HO5yE/4G3YtqM2GHWfWFg4AaGdz0PHQB7tfB7uPruMooIApw3qzrzrT+Cp8UOZd/0JDQHj3plfNjkt6BW4bFRJ0p+tqfvZ9iY6glJKOeqJcUdy4d8/cuS9w9Nq4SnO95Zv4tZXlkRlAMbLaExljaWKjdt54oPVTbbDGJj2/koemF2R1EgoGyv0upGOoJRSDkuzLEQaIqfVigoCHN+/a+MMwAQZjUUFgbgVN2IttKtzNCUEKY2E0q000VpogFJKOaq0uFPOT0R+r9DO52lUSSE8fRfIEzr4vC1amDDWoCSWhI9UXx/iveWbmgxS8aYZ3V4VoiV0ik8p5aiiggCTzxxoJS3kigivTTgm7n1R1vhJrIGdSX90V9KtI6eV7c8rixqXSIqn3sDvXvycPK80Od2XTuZla8n+0wCllHLcIV0Lcvp+Aa8n6joThO/V2taoAniiquSplCr64YADkg5QYBWkrQ+ZZiuiJ5t5GXkf2potO5n8+tJWkf2nAUop5bj3v9qc0/erDUZfrwmnbHtiahJC/OSD2BTvm08d0Ki+XeQ+O5q4/6kpmUh8eHnhOq59fhF1MVmNmVgWJNs0QCmlHDeyXxemzqpofscMCYZCzKvYzIiSLixZX8V10xdF3WAbqSYYIt+/J/083mKCN/27nIKAl3q7QviIki6N9mmJdBMfKqtruG764kbBKZKbs/80QCmlHDekTxH7d/SzYXttTt6vPgTXPLcQr8eD1yNxg5PfaxVZFWMYc//chqmwREtZhOvrXffCYqZdMKTJ5S6S4fOmn6CxdusuvJ6mr6O5OftPs/iUUq5w/7mDs3bs9j5vo9fqQ9ZigztjrkUB+L1g7HSJmqCJSv9urmisz+MBTNqFcGdccWza14Z6dG6fsP5ffsDr+uw/DVBKKVcY0qeIYX06Z/y4N57Sn0cuGNxoddx4Ovisk/blJxxMu7zooBY5FdZQecLfOPDVhUKUFhdGpYEH8gRfCssA//GMgQkrr6eiqCDAXWcdFvXeeR7r+M+MHxZV7cKNtJq5UspV7nn7C+7N0PWo/ICXZ8YPo6znPlEV22uDQUKGqGszgTwPf71wCKXFnQAaLfgXu4hjOEOvfH0Vk1+LnxUXmek3r2Jzk5XQ/V4BkYRrQ6UjUTX5bMpENXMNUEop1wmfUFMpgdTB72FnbfTJP1FQCQeMppYYSWUJkmTvK6qsruGZBav5v3e+jCo3e/qgAxg3oq/r70tKhQYoNEAptTdLdgn5P54xkIHdC5sczcTTXGDJ1g2tldU1zF9Ryebq3RxTsl9GpvPcRgMUGqCU2ttdN30Rz328Nu62Dj5hzvXRi/y1lioJezsNUGiAUqotqNi4ndtnLGN+xXfU1kPnfB9Xje7H+Uf3cbppKgFdUVcp1SaUdOvI3y86yulmqBzTNHOllFKupAFKKaWUK2mAUkop5UoaoJRSSrmSBiillFKupAFKKaWUK7X6+6BE5Dvgmxy+ZRcgt6uruY/2gfYBaB+A9gEk7oPexpj90jlwqw9QuSYiH6d781lrp32gfQDaB6B9ANntA53iU0op5UoaoJRSSrmSBqjUTXO6AS6gfaB9ANoHoH0AWewDvQallFLKlXQEpZRSypU0QCmllHIlDVBKKaVcSQNUHCIyQUQ+FpEaEXk8ZtswEXlHRLaIyHci8ryIHBDnGH4RWSYi8ZcCdbl0+kBEJopIuYhsF5GvRWRizj9AhqTZDyIiU0Sk0n5MERHJ+YdIQzOf3y8i00VklYgYERkVsz0gIg+LyEa7QouKiAAABxhJREFUj14VkcTrr7tYOv1g7zNYRN4XkWq7P67MVdszJd0+iNgv6fOiBqj41gN/AP4eZ1tnrKyVA4HewHbgsTj7TQS+y1L7ciGdPhDgQnu/k4EJInJONhubRen0w8XAGUAZcBgwFvh1FtuaDU19foC5wPnAhjjbrgSGY332YmArcF8W2pgLLe4HEekCvAk8AhQBJcDb2WlmVqXzbyEstfOiMUYfCR72L+PxZvYZDGyPea0PsAw4BVjr9Odwog9itk8F7nP6s+S6H4D/AhdHPP8l8IHTnyUbnx9YC4yKee0h4M6I56cCXzj9WRzoh9uBJ51uu5N9YL+e8nlRR1DpGwksiXntPuBGYFfum+OIeH0AWNNcwLGJtu9lYvuhFFgU8XyR/Vpb8TdghIgUi0gH4DxghsNtcsIwYIuI/FdENtlTnb2cbpQDUj4vaoBKg4gcBtyCNWwNv3Ym4DXGvORYw3IoXh/EmIT17yzeNOheI0E/FABVEc+rgILWdh0qDV8Ba4B1wDbgUOA2R1vkjB7Az7GmPHsBXwP/dLRFOdbS82KbC1AiMtu+iBfvMTeF45RgfRu80hgzx34tH7gTuCI7rc+MbPZBzPYJWNeiTjXG1GTuE2RGDvqhGugU8bwTUG3s+Q6nZerzN+EBIIB13SUfeBEXjqBy0A+7gJeMMR8ZY3YDvweOFpHCDBw7I7LZB+mcF/PSeePWyBgzKt1jiEhvYCYw2RjzZMSmflgXzOfYX5L9QKGIbACGGWNWpfvemZDlPghvHwf8DhhpjHFlJmMO+mEJVoLEh/bzMlw01ZmJz9+MQcBNxpgtACJyH3CbiHQxxrhmiYoc9MNiIPJLiSu+oETKch+0+LzY5kZQyRCRPBFpB3gBr4i0E5E8e1t3YBZwvzHm4ZgfLQd6Yv1hDgLGAxvt/1+Tq/ZnQhp9gIich3Vh+CRjzMpctjvT0ukH4AngGhHpLiLFwG+Bx3PU9Ixo6vPb2wP2dgC/vT08hfkRcKGIFIqID7gUWO+m4JSsNPvhMeBMERlk98PNwFxjTBWtSBp90PLzotMZIW58YF03MTGPSfa2W+3n1ZGPBMcZRSvN4kunD7Dm2Otitj/s9GdyoB8Ea2pji/24E7v+ZWt5NPX57e2r4mw/0N5WBDwNbAK+x0pDPsrpz5TrfrC3/wbrWtxW4FWgp9OfKdd9ELFf0udFLRarlFLKlXSKTymllCtpgFJKKeVKGqCUUkq5kgYopZRSrqQBSimllCtpgFJKKeVKGqCUiiAij4vIazl+z4tEpDqLx68WkYuydXylskUDlFLOexboG34iIpNEpNzB9ijlCm2uFp9SbmOM2UXbWZpFqaTpCEqpBOzaYn8Ra4nu3SLygYgcE7F9lF3tebSILBCRnWItiT045jjjRGS1vf1VEblUREzE9oYpPnsq7lagNKKa9EX2NiMiZ8Uce5WIXBvxvMSuTL1bRL4QkTFxPld3EfmXiGy1H6+LSL/M9JpSmaMBSqnE7gR+CowDDgc+B94UkQNi9rsDq3L7YKASeDpcKFREhgOPYi09MQh4BWu5hUSeBf4MfAEcYD+eTaaxIuIBXsL6ux5ut3sS1pIX4X06AO8Bu4Hj7P2+BWba25RyDZ3iUyoOew2b3wDjjTGv269dApwAXAb8b8TuNxtj3rP3uQ2rKGp3rKWvrwDeNsZMsff9UkSOBH4V732NMbvs0VS9MWZDis0+ERgA9DHGrLbbcxUQuUbVOVhFbH9h7EKcIvJrrIKuY4DnUnxPpbJGR1BKxXcQ4APmhV8wxgSB+VhBINLiiP9fb/+3q/3f/uxZDypsQeaaGeVQYF04OEW8Vyji+RFAH2C7nd1XjbXSb2esz6yUa+gISqnUxS4BUBdnWza+/Bms0U8kX4rH8AALsUZSsba0pFFKZYuOoJSKbwVQC4wIvyAiXqxrNktTOM5y4MiY145q5mdqsRaFi/Ud1jWpcHu6RT4HlgHdRaRnzHtF/p1/CpQAm40xFTEPDVDKVTRAKRWHMWYH8BAwRUR+LCKH2s+7AQ+mcKipwA9FZKKI9BORXwJnNvMzq4DeIjJYRLqISDjJYRZwmYgMEZHDsVbn3R3xczOxAuIT9uqtw4F7gPqIfZ7GWs30ZRE5TkT6iMhIEfmzZvIpt9EApVRi12Nl0D2GNS12GHCyMebbZA9gjJmPlRBxBda1qjOAKUQHllgvAG8A72KNmn5mv/5bYCUwG5iOlR24KeK9QljBz4N17ekJ4A9ATcQ+O4GR9nGexwpo/8C6BrU12c+lVC7oirpK5ZiI3AOcaIz5gdNtUcrNNElCqSwTkYnAO0A1Vir4JcCNjjZKqVZAR1BKZZmIPAuMAgqBr4FHgHuN/vEp1SQNUEoppVxJkySUUkq5kgYopZRSrqQBSimllCtpgFJKKeVKGqCUUkq50v8DOcs3jLQs9UoAAAAASUVORK5CYII=\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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure better_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\", 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": 34,
   "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": 35,
   "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(PROJECT_ROOT_DIR + '/images/end_to_end_project/california.png')\n",
    "ax = housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", figsize=(10,7),\n",
    "                       s=housing['population']/100, label=\"Population\",\n",
    "                       c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"),\n",
    "                       colorbar=False, alpha=0.4,\n",
    "                      )\n",
    "plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5,\n",
    "           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()\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": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 39,
   "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": "code",
   "execution_count": 40,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Note: there was a bug in the previous cell, in the definition of the `rooms_per_household` attribute. This explains why the correlation value below differs slightly from the value in the book (unless you are reading the latest version)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 41,
     "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": 42,
   "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": 43,
   "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": 43,
     "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": 44,
   "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": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </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",
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       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
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       "    <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": 45,
     "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows.dropna(subset=[\"total_bedrooms\"])    # option 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
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       "      <th>4629</th>\n",
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       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
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       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows.drop(\"total_bedrooms\", axis=1)       # option 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = housing[\"total_bedrooms\"].median()\n",
    "sample_incomplete_rows[\"total_bedrooms\"].fillna(median, inplace=True) # option 3\n",
    "sample_incomplete_rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: Since Scikit-Learn 0.20, the `sklearn.preprocessing.Imputer` class was replaced by the `sklearn.impute.SimpleImputer` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    from sklearn.impute import SimpleImputer # Scikit-Learn 0.20+\n",
    "except ImportError:\n",
    "    from sklearn.preprocessing import Imputer as SimpleImputer\n",
    "\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": 50,
   "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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(copy=True, fill_value=None, missing_values=nan,\n",
       "       strategy='median', verbose=0)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 52,
     "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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
    "                          index = list(housing.index.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
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       "      <th>median_income</th>\n",
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       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
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       "      <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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.loc[sample_incomplete_rows.index.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'median'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -121.89     37.29                38.0       1568.0           351.0   \n",
       "1    -121.93     37.05                14.0        679.0           108.0   \n",
       "2    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3    -119.61     36.31                25.0       1847.0           371.0   \n",
       "4    -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "   population  households  median_income  \n",
       "0       710.0       339.0         2.7042  \n",
       "1       306.0       113.0         6.4214  \n",
       "2       936.0       462.0         2.8621  \n",
       "3      1460.0       353.0         1.8839  \n",
       "4      4459.0      1463.0         3.0347  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's preprocess the categorical input feature, `ocean_proximity`:"
   ]
  },
  {
   "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>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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing[['ocean_proximity']]\n",
    "housing_cat.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: earlier versions of the book used the `LabelEncoder` class or Pandas' `Series.factorize()` method to encode string categorical attributes as integers. However, the `OrdinalEncoder` class that was introduced in Scikit-Learn 0.20 (see [PR #10521](https://github.com/scikit-learn/scikit-learn/issues/10521)) is preferable since it is designed for input features (`X` instead of labels `y`) and it plays well with pipelines (introduced later in this notebook). If you are using an older version of Scikit-Learn (<0.20), then you can import it from `future_encoders.py` instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    from sklearn.preprocessing import OrdinalEncoder\n",
    "except ImportError:\n",
    "    from future_encoders import OrdinalEncoder # Scikit-Learn < 0.20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_encoder = OrdinalEncoder()\n",
    "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoded[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_encoder.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: earlier versions of the book used the `LabelBinarizer` or `CategoricalEncoder` classes to convert each categorical value to a one-hot vector. It is now preferable to use the `OneHotEncoder` class. Since Scikit-Learn 0.20 it can handle string categorical inputs (see [PR #10521](https://github.com/scikit-learn/scikit-learn/issues/10521)), not just integer categorical inputs. If you are using an older version of Scikit-Learn, you can import the new version from `future_encoders.py`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try:\n",
    "    from sklearn.preprocessing import OrdinalEncoder # just to raise an ImportError if Scikit-Learn < 0.20\n",
    "    from sklearn.preprocessing import OneHotEncoder\n",
    "except ImportError:\n",
    "    from future_encoders import OneHotEncoder # Scikit-Learn < 0.20\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": 64,
   "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": 64,
     "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": 65,
   "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": 65,
     "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": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a custom transformer to add extra attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
       "       'total_bedrooms', 'population', 'households', 'median_income',\n",
       "       'ocean_proximity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# get the right column indices: safer than hard-coding indices 3, 4, 5, 6\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = [\n",
    "    list(housing.columns).index(col)\n",
    "    for col in (\"total_rooms\", \"total_bedrooms\", \"population\", \"households\")]\n",
    "\n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True): # no *args or **kwargs\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y=None):\n",
    "        return self  # nothing else to do\n",
    "    def transform(self, X, y=None):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household, population_per_household,\n",
    "                         bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]\n",
    "\n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can use Scikit-Learn's `FunctionTransformer` class that lets you easily create a transformer based on a transformation function (thanks to [Hanmin Qin](https://github.com/qinhanmin2014) for suggesting this code). Note that we need to set `validate=False` because the data contains non-float values (`validate` will default to `False` in Scikit-Learn 0.22)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import FunctionTransformer\n",
    "\n",
    "def add_extra_features(X, add_bedrooms_per_room=True):\n",
    "    rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "    population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "    if 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 = FunctionTransformer(add_extra_features, validate=False,\n",
    "                                 kw_args={\"add_bedrooms_per_room\": False})\n",
    "housing_extra_attribs = attr_adder.fit_transform(housing.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38</td>\n",
       "      <td>1568</td>\n",
       "      <td>351</td>\n",
       "      <td>710</td>\n",
       "      <td>339</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4.62537</td>\n",
       "      <td>2.0944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14</td>\n",
       "      <td>679</td>\n",
       "      <td>108</td>\n",
       "      <td>306</td>\n",
       "      <td>113</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>6.00885</td>\n",
       "      <td>2.70796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-117.2</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31</td>\n",
       "      <td>1952</td>\n",
       "      <td>471</td>\n",
       "      <td>936</td>\n",
       "      <td>462</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4.22511</td>\n",
       "      <td>2.02597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25</td>\n",
       "      <td>1847</td>\n",
       "      <td>371</td>\n",
       "      <td>1460</td>\n",
       "      <td>353</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5.23229</td>\n",
       "      <td>4.13598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17</td>\n",
       "      <td>6592</td>\n",
       "      <td>1525</td>\n",
       "      <td>4459</td>\n",
       "      <td>1463</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.04785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  longitude latitude housing_median_age total_rooms total_bedrooms population  \\\n",
       "0   -121.89    37.29                 38        1568            351        710   \n",
       "1   -121.93    37.05                 14         679            108        306   \n",
       "2    -117.2    32.77                 31        1952            471        936   \n",
       "3   -119.61    36.31                 25        1847            371       1460   \n",
       "4   -118.59    34.23                 17        6592           1525       4459   \n",
       "\n",
       "  households median_income ocean_proximity rooms_per_household  \\\n",
       "0        339        2.7042       <1H OCEAN             4.62537   \n",
       "1        113        6.4214       <1H OCEAN             6.00885   \n",
       "2        462        2.8621      NEAR OCEAN             4.22511   \n",
       "3        353        1.8839          INLAND             5.23229   \n",
       "4       1463        3.0347       <1H OCEAN             4.50581   \n",
       "\n",
       "  population_per_household  \n",
       "0                   2.0944  \n",
       "1                  2.70796  \n",
       "2                  2.02597  \n",
       "3                  4.13598  \n",
       "4                  3.04785  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs = pd.DataFrame(\n",
    "    housing_extra_attribs,\n",
    "    columns=list(housing.columns)+[\"rooms_per_household\", \"population_per_household\"])\n",
    "housing_extra_attribs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's build a pipeline for preprocessing the numerical attributes (note that we could use `CombinedAttributesAdder()` instead of `FunctionTransformer(...)` if we preferred):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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', FunctionTransformer(add_extra_features, validate=False)),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: earlier versions of the book applied different transformations to different columns using a solution based on a `DataFrameSelector` transformer and a `FeatureUnion` (see below). It is now preferable to use the `ColumnTransformer` class that was introduced in Scikit-Learn 0.20. If you are using an older version of Scikit-Learn, you can import it from `future_encoders.py`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    from sklearn.compose import ColumnTransformer\n",
    "except ImportError:\n",
    "    from future_encoders import ColumnTransformer # Scikit-Learn < 0.20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 75,
   "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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 16)"
      ]
     },
     "execution_count": 76,
     "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": 77,
   "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 (again, we could use `CombinedAttributesAdder()` instead of `FunctionTransformer(...)` if we preferred):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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', FunctionTransformer(add_extra_features, validate=False)),\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": 79,
   "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": 80,
   "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": 80,
     "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": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 81,
     "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": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 82,
     "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": 83,
   "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": 84,
   "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": 85,
   "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": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_data_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68628.19819848922"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49439.89599001897"
      ]
     },
     "execution_count": 87,
     "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": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 88,
     "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": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 89,
     "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": [
    "# Fine-tune your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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": 89,
   "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": 90,
   "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: 2731.674001798348\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=10` to avoid a warning about the fact that the default value is going to change to 100 in Scikit-Learn 0.22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=10, random_state=42)\n",
    "forest_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21933.31414779769"
      ]
     },
     "execution_count": 92,
     "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": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [51646.44545909 48940.60114882 53050.86323649 54408.98730149\n",
      " 50922.14870785 56482.50703987 51864.52025526 49760.85037653\n",
      " 55434.21627933 53326.10093303]\n",
      "Mean: 52583.72407377466\n",
      "Standard deviation: 2298.353351147122\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
    "                                scoring=\"neg_mean_squared_error\", cv=10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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": 94,
     "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": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111094.6308539982"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_reg = SVR(kernel=\"linear\")\n",
    "svm_reg.fit(housing_prepared, housing_labels)\n",
    "housing_predictions = svm_reg.predict(housing_prepared)\n",
    "svm_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "svm_rmse = np.sqrt(svm_mse)\n",
    "svm_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]}, {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 96,
     "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', 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": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 30}"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features=8, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=30, n_jobs=None, oob_score=False, random_state=42,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 98,
     "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": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63669.05791727153 {'max_features': 2, 'n_estimators': 3}\n",
      "55627.16171305252 {'max_features': 2, 'n_estimators': 10}\n",
      "53384.57867637289 {'max_features': 2, 'n_estimators': 30}\n",
      "60965.99185930139 {'max_features': 4, 'n_estimators': 3}\n",
      "52740.98248528835 {'max_features': 4, 'n_estimators': 10}\n",
      "50377.344409590376 {'max_features': 4, 'n_estimators': 30}\n",
      "58663.84733372485 {'max_features': 6, 'n_estimators': 3}\n",
      "52006.15355973719 {'max_features': 6, 'n_estimators': 10}\n",
      "50146.465964159885 {'max_features': 6, 'n_estimators': 30}\n",
      "57869.25504027614 {'max_features': 8, 'n_estimators': 3}\n",
      "51711.09443660957 {'max_features': 8, 'n_estimators': 10}\n",
      "49682.25345942335 {'max_features': 8, 'n_estimators': 30}\n",
      "62895.088889905004 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54658.14484390074 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "59470.399594730654 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "52725.01091081235 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "57490.612956065226 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "51009.51445842374 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "cvres = grid_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>0.060251</td>\n",
       "      <td>0.001252</td>\n",
       "      <td>0.004077</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 3}</td>\n",
       "      <td>-3.837622e+09</td>\n",
       "      <td>-4.147108e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.053749e+09</td>\n",
       "      <td>1.519609e+08</td>\n",
       "      <td>18</td>\n",
       "      <td>-1.064113e+09</td>\n",
       "      <td>-1.105142e+09</td>\n",
       "      <td>-1.116550e+09</td>\n",
       "      <td>-1.112342e+09</td>\n",
       "      <td>-1.129650e+09</td>\n",
       "      <td>-1.105559e+09</td>\n",
       "      <td>2.220402e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.195211</td>\n",
       "      <td>0.001845</td>\n",
       "      <td>0.010179</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 10}</td>\n",
       "      <td>-3.047771e+09</td>\n",
       "      <td>-3.254861e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.094381e+09</td>\n",
       "      <td>1.327046e+08</td>\n",
       "      <td>11</td>\n",
       "      <td>-5.927175e+08</td>\n",
       "      <td>-5.870952e+08</td>\n",
       "      <td>-5.776964e+08</td>\n",
       "      <td>-5.716332e+08</td>\n",
       "      <td>-5.802501e+08</td>\n",
       "      <td>-5.818785e+08</td>\n",
       "      <td>7.345821e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.589625</td>\n",
       "      <td>0.003846</td>\n",
       "      <td>0.030403</td>\n",
       "      <td>0.003266</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 30}</td>\n",
       "      <td>-2.689185e+09</td>\n",
       "      <td>-3.021086e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.849913e+09</td>\n",
       "      <td>1.626879e+08</td>\n",
       "      <td>9</td>\n",
       "      <td>-4.381089e+08</td>\n",
       "      <td>-4.391272e+08</td>\n",
       "      <td>-4.371702e+08</td>\n",
       "      <td>-4.376955e+08</td>\n",
       "      <td>-4.452654e+08</td>\n",
       "      <td>-4.394734e+08</td>\n",
       "      <td>2.966320e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.098321</td>\n",
       "      <td>0.001313</td>\n",
       "      <td>0.003398</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 3}</td>\n",
       "      <td>-3.730181e+09</td>\n",
       "      <td>-3.786886e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.716852e+09</td>\n",
       "      <td>1.631421e+08</td>\n",
       "      <td>16</td>\n",
       "      <td>-9.865163e+08</td>\n",
       "      <td>-1.012565e+09</td>\n",
       "      <td>-9.169425e+08</td>\n",
       "      <td>-1.037400e+09</td>\n",
       "      <td>-9.707739e+08</td>\n",
       "      <td>-9.848396e+08</td>\n",
       "      <td>4.084607e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.322361</td>\n",
       "      <td>0.002503</td>\n",
       "      <td>0.010031</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 10}</td>\n",
       "      <td>-2.666283e+09</td>\n",
       "      <td>-2.784511e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.781611e+09</td>\n",
       "      <td>1.268562e+08</td>\n",
       "      <td>8</td>\n",
       "      <td>-5.097115e+08</td>\n",
       "      <td>-5.162820e+08</td>\n",
       "      <td>-4.962893e+08</td>\n",
       "      <td>-5.436192e+08</td>\n",
       "      <td>-5.160297e+08</td>\n",
       "      <td>-5.163863e+08</td>\n",
       "      <td>1.542862e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.964709</td>\n",
       "      <td>0.003208</td>\n",
       "      <td>0.026985</td>\n",
       "      <td>0.000617</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 30}</td>\n",
       "      <td>-2.387153e+09</td>\n",
       "      <td>-2.588448e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.537877e+09</td>\n",
       "      <td>1.214603e+08</td>\n",
       "      <td>3</td>\n",
       "      <td>-3.838835e+08</td>\n",
       "      <td>-3.880268e+08</td>\n",
       "      <td>-3.790867e+08</td>\n",
       "      <td>-4.040957e+08</td>\n",
       "      <td>-3.845520e+08</td>\n",
       "      <td>-3.879289e+08</td>\n",
       "      <td>8.571233e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.132895</td>\n",
       "      <td>0.003371</td>\n",
       "      <td>0.003408</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 3}</td>\n",
       "      <td>-3.119657e+09</td>\n",
       "      <td>-3.586319e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.441447e+09</td>\n",
       "      <td>1.893141e+08</td>\n",
       "      <td>14</td>\n",
       "      <td>-9.245343e+08</td>\n",
       "      <td>-8.886939e+08</td>\n",
       "      <td>-9.353135e+08</td>\n",
       "      <td>-9.009801e+08</td>\n",
       "      <td>-8.624664e+08</td>\n",
       "      <td>-9.023976e+08</td>\n",
       "      <td>2.591445e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.443695</td>\n",
       "      <td>0.005027</td>\n",
       "      <td>0.010141</td>\n",
       "      <td>0.000682</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 10}</td>\n",
       "      <td>-2.549663e+09</td>\n",
       "      <td>-2.782039e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.704640e+09</td>\n",
       "      <td>1.471542e+08</td>\n",
       "      <td>6</td>\n",
       "      <td>-4.980344e+08</td>\n",
       "      <td>-5.045869e+08</td>\n",
       "      <td>-4.994664e+08</td>\n",
       "      <td>-4.990325e+08</td>\n",
       "      <td>-5.055542e+08</td>\n",
       "      <td>-5.013349e+08</td>\n",
       "      <td>3.100456e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.343336</td>\n",
       "      <td>0.004695</td>\n",
       "      <td>0.027562</td>\n",
       "      <td>0.001805</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 30}</td>\n",
       "      <td>-2.370010e+09</td>\n",
       "      <td>-2.583638e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.514668e+09</td>\n",
       "      <td>1.285063e+08</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.838538e+08</td>\n",
       "      <td>-3.804711e+08</td>\n",
       "      <td>-3.805218e+08</td>\n",
       "      <td>-3.856095e+08</td>\n",
       "      <td>-3.901917e+08</td>\n",
       "      <td>-3.841296e+08</td>\n",
       "      <td>3.617057e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.169927</td>\n",
       "      <td>0.001055</td>\n",
       "      <td>0.003458</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 3}</td>\n",
       "      <td>-3.353504e+09</td>\n",
       "      <td>-3.348552e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.348851e+09</td>\n",
       "      <td>1.241864e+08</td>\n",
       "      <td>13</td>\n",
       "      <td>-9.228123e+08</td>\n",
       "      <td>-8.553031e+08</td>\n",
       "      <td>-8.603321e+08</td>\n",
       "      <td>-8.881964e+08</td>\n",
       "      <td>-9.151287e+08</td>\n",
       "      <td>-8.883545e+08</td>\n",
       "      <td>2.750227e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.570730</td>\n",
       "      <td>0.004385</td>\n",
       "      <td>0.010258</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 10}</td>\n",
       "      <td>-2.571970e+09</td>\n",
       "      <td>-2.718994e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.674037e+09</td>\n",
       "      <td>1.392720e+08</td>\n",
       "      <td>5</td>\n",
       "      <td>-4.932416e+08</td>\n",
       "      <td>-4.815238e+08</td>\n",
       "      <td>-4.730979e+08</td>\n",
       "      <td>-5.155367e+08</td>\n",
       "      <td>-4.985555e+08</td>\n",
       "      <td>-4.923911e+08</td>\n",
       "      <td>1.459294e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.724103</td>\n",
       "      <td>0.005320</td>\n",
       "      <td>0.027100</td>\n",
       "      <td>0.000618</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 30}</td>\n",
       "      <td>-2.357390e+09</td>\n",
       "      <td>-2.546640e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.468326e+09</td>\n",
       "      <td>1.091647e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.841658e+08</td>\n",
       "      <td>-3.744500e+08</td>\n",
       "      <td>-3.773239e+08</td>\n",
       "      <td>-3.882250e+08</td>\n",
       "      <td>-3.810005e+08</td>\n",
       "      <td>-3.810330e+08</td>\n",
       "      <td>4.871017e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.093079</td>\n",
       "      <td>0.001465</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 2, 'n_est...</td>\n",
       "      <td>-3.785816e+09</td>\n",
       "      <td>-4.166012e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.955792e+09</td>\n",
       "      <td>1.900966e+08</td>\n",
       "      <td>17</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.309217</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>0.011903</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 2, 'n_est...</td>\n",
       "      <td>-2.810721e+09</td>\n",
       "      <td>-3.107789e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.987513e+09</td>\n",
       "      <td>1.539231e+08</td>\n",
       "      <td>10</td>\n",
       "      <td>-6.056477e-02</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-2.967449e+00</td>\n",
       "      <td>-6.056027e-01</td>\n",
       "      <td>1.181156e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.124233</td>\n",
       "      <td>0.002787</td>\n",
       "      <td>0.004212</td>\n",
       "      <td>0.000185</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 3, 'n_est...</td>\n",
       "      <td>-3.618324e+09</td>\n",
       "      <td>-3.441527e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.536728e+09</td>\n",
       "      <td>7.795196e+07</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-6.072840e+01</td>\n",
       "      <td>-1.214568e+01</td>\n",
       "      <td>2.429136e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.408420</td>\n",
       "      <td>0.003067</td>\n",
       "      <td>0.012991</td>\n",
       "      <td>0.000480</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 3, 'n_est...</td>\n",
       "      <td>-2.757999e+09</td>\n",
       "      <td>-2.851737e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.779927e+09</td>\n",
       "      <td>6.286611e+07</td>\n",
       "      <td>7</td>\n",
       "      <td>-2.089484e+01</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-5.465556e+00</td>\n",
       "      <td>-5.272080e+00</td>\n",
       "      <td>8.093117e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.154712</td>\n",
       "      <td>0.002472</td>\n",
       "      <td>0.004148</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 4, 'n_est...</td>\n",
       "      <td>-3.134040e+09</td>\n",
       "      <td>-3.559375e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.305171e+09</td>\n",
       "      <td>1.879203e+08</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.512536</td>\n",
       "      <td>0.001934</td>\n",
       "      <td>0.012041</td>\n",
       "      <td>0.000309</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 4, 'n_est...</td>\n",
       "      <td>-2.525578e+09</td>\n",
       "      <td>-2.710011e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.601971e+09</td>\n",
       "      <td>1.088031e+08</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-1.514119e-02</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-3.028238e-03</td>\n",
       "      <td>6.056477e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.060251      0.001252         0.004077        0.000397   \n",
       "1        0.195211      0.001845         0.010179        0.000394   \n",
       "2        0.589625      0.003846         0.030403        0.003266   \n",
       "3        0.098321      0.001313         0.003398        0.000078   \n",
       "4        0.322361      0.002503         0.010031        0.000395   \n",
       "5        0.964709      0.003208         0.026985        0.000617   \n",
       "6        0.132895      0.003371         0.003408        0.000064   \n",
       "7        0.443695      0.005027         0.010141        0.000682   \n",
       "8        1.343336      0.004695         0.027562        0.001805   \n",
       "9        0.169927      0.001055         0.003458        0.000110   \n",
       "10       0.570730      0.004385         0.010258        0.000573   \n",
       "11       1.724103      0.005320         0.027100        0.000618   \n",
       "12       0.093079      0.001465         0.004737        0.000253   \n",
       "13       0.309217      0.002585         0.011903        0.000335   \n",
       "14       0.124233      0.002787         0.004212        0.000185   \n",
       "15       0.408420      0.003067         0.012991        0.000480   \n",
       "16       0.154712      0.002472         0.004148        0.000244   \n",
       "17       0.512536      0.001934         0.012041        0.000309   \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  \\\n",
       "0       -4.147108e+09       ...           -4.053749e+09    1.519609e+08   \n",
       "1       -3.254861e+09       ...           -3.094381e+09    1.327046e+08   \n",
       "2       -3.021086e+09       ...           -2.849913e+09    1.626879e+08   \n",
       "3       -3.786886e+09       ...           -3.716852e+09    1.631421e+08   \n",
       "4       -2.784511e+09       ...           -2.781611e+09    1.268562e+08   \n",
       "5       -2.588448e+09       ...           -2.537877e+09    1.214603e+08   \n",
       "6       -3.586319e+09       ...           -3.441447e+09    1.893141e+08   \n",
       "7       -2.782039e+09       ...           -2.704640e+09    1.471542e+08   \n",
       "8       -2.583638e+09       ...           -2.514668e+09    1.285063e+08   \n",
       "9       -3.348552e+09       ...           -3.348851e+09    1.241864e+08   \n",
       "10      -2.718994e+09       ...           -2.674037e+09    1.392720e+08   \n",
       "11      -2.546640e+09       ...           -2.468326e+09    1.091647e+08   \n",
       "12      -4.166012e+09       ...           -3.955792e+09    1.900966e+08   \n",
       "13      -3.107789e+09       ...           -2.987513e+09    1.539231e+08   \n",
       "14      -3.441527e+09       ...           -3.536728e+09    7.795196e+07   \n",
       "15      -2.851737e+09       ...           -2.779927e+09    6.286611e+07   \n",
       "16      -3.559375e+09       ...           -3.305171e+09    1.879203e+08   \n",
       "17      -2.710011e+09       ...           -2.601971e+09    1.088031e+08   \n",
       "\n",
       "    rank_test_score  split0_train_score  split1_train_score  \\\n",
       "0                18       -1.064113e+09       -1.105142e+09   \n",
       "1                11       -5.927175e+08       -5.870952e+08   \n",
       "2                 9       -4.381089e+08       -4.391272e+08   \n",
       "3                16       -9.865163e+08       -1.012565e+09   \n",
       "4                 8       -5.097115e+08       -5.162820e+08   \n",
       "5                 3       -3.838835e+08       -3.880268e+08   \n",
       "6                14       -9.245343e+08       -8.886939e+08   \n",
       "7                 6       -4.980344e+08       -5.045869e+08   \n",
       "8                 2       -3.838538e+08       -3.804711e+08   \n",
       "9                13       -9.228123e+08       -8.553031e+08   \n",
       "10                5       -4.932416e+08       -4.815238e+08   \n",
       "11                1       -3.841658e+08       -3.744500e+08   \n",
       "12               17       -0.000000e+00       -0.000000e+00   \n",
       "13               10       -6.056477e-02       -0.000000e+00   \n",
       "14               15       -0.000000e+00       -0.000000e+00   \n",
       "15                7       -2.089484e+01       -0.000000e+00   \n",
       "16               12       -0.000000e+00       -0.000000e+00   \n",
       "17                4       -0.000000e+00       -1.514119e-02   \n",
       "\n",
       "    split2_train_score  split3_train_score  split4_train_score  \\\n",
       "0        -1.116550e+09       -1.112342e+09       -1.129650e+09   \n",
       "1        -5.776964e+08       -5.716332e+08       -5.802501e+08   \n",
       "2        -4.371702e+08       -4.376955e+08       -4.452654e+08   \n",
       "3        -9.169425e+08       -1.037400e+09       -9.707739e+08   \n",
       "4        -4.962893e+08       -5.436192e+08       -5.160297e+08   \n",
       "5        -3.790867e+08       -4.040957e+08       -3.845520e+08   \n",
       "6        -9.353135e+08       -9.009801e+08       -8.624664e+08   \n",
       "7        -4.994664e+08       -4.990325e+08       -5.055542e+08   \n",
       "8        -3.805218e+08       -3.856095e+08       -3.901917e+08   \n",
       "9        -8.603321e+08       -8.881964e+08       -9.151287e+08   \n",
       "10       -4.730979e+08       -5.155367e+08       -4.985555e+08   \n",
       "11       -3.773239e+08       -3.882250e+08       -3.810005e+08   \n",
       "12       -0.000000e+00       -0.000000e+00       -0.000000e+00   \n",
       "13       -0.000000e+00       -0.000000e+00       -2.967449e+00   \n",
       "14       -0.000000e+00       -0.000000e+00       -6.072840e+01   \n",
       "15       -0.000000e+00       -0.000000e+00       -5.465556e+00   \n",
       "16       -0.000000e+00       -0.000000e+00       -0.000000e+00   \n",
       "17       -0.000000e+00       -0.000000e+00       -0.000000e+00   \n",
       "\n",
       "    mean_train_score  std_train_score  \n",
       "0      -1.105559e+09     2.220402e+07  \n",
       "1      -5.818785e+08     7.345821e+06  \n",
       "2      -4.394734e+08     2.966320e+06  \n",
       "3      -9.848396e+08     4.084607e+07  \n",
       "4      -5.163863e+08     1.542862e+07  \n",
       "5      -3.879289e+08     8.571233e+06  \n",
       "6      -9.023976e+08     2.591445e+07  \n",
       "7      -5.013349e+08     3.100456e+06  \n",
       "8      -3.841296e+08     3.617057e+06  \n",
       "9      -8.883545e+08     2.750227e+07  \n",
       "10     -4.923911e+08     1.459294e+07  \n",
       "11     -3.810330e+08     4.871017e+06  \n",
       "12      0.000000e+00     0.000000e+00  \n",
       "13     -6.056027e-01     1.181156e+00  \n",
       "14     -1.214568e+01     2.429136e+01  \n",
       "15     -5.272080e+00     8.093117e+00  \n",
       "16      0.000000e+00     0.000000e+00  \n",
       "17     -3.028238e-03     6.056477e-03  \n",
       "\n",
       "[18 rows x 23 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(grid_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid='warn', n_iter=10, n_jobs=None,\n",
       "          param_distributions={'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1210939e8>, 'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x121093710>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_squared_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 101,
     "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": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49150.657232934034 {'max_features': 7, 'n_estimators': 180}\n",
      "51389.85295710133 {'max_features': 5, 'n_estimators': 15}\n",
      "50796.12045980556 {'max_features': 3, 'n_estimators': 72}\n",
      "50835.09932039744 {'max_features': 5, 'n_estimators': 21}\n",
      "49280.90117886215 {'max_features': 7, 'n_estimators': 122}\n",
      "50774.86679035961 {'max_features': 3, 'n_estimators': 75}\n",
      "50682.75001237282 {'max_features': 3, 'n_estimators': 88}\n",
      "49608.94061293652 {'max_features': 5, 'n_estimators': 100}\n",
      "50473.57642831875 {'max_features': 3, 'n_estimators': 150}\n",
      "64429.763804893395 {'max_features': 5, 'n_estimators': 2}\n"
     ]
    }
   ],
   "source": [
    "cvres = rnd_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.3661589806181342, 'median_income'),\n",
       " (0.1647809935615905, 'INLAND'),\n",
       " (0.10879295677551573, 'pop_per_hhold'),\n",
       " (0.07334423551601242, 'longitude'),\n",
       " (0.0629090704826203, 'latitude'),\n",
       " (0.05641917918195401, 'rooms_per_hhold'),\n",
       " (0.05335107734767581, 'bedrooms_per_room'),\n",
       " (0.041143798478729635, 'housing_median_age'),\n",
       " (0.014874280890402767, 'population'),\n",
       " (0.014672685420543237, 'total_rooms'),\n",
       " (0.014257599323407807, 'households'),\n",
       " (0.014106483453584102, 'total_bedrooms'),\n",
       " (0.010311488326303787, '<1H OCEAN'),\n",
       " (0.002856474637320158, 'NEAR OCEAN'),\n",
       " (0.00196041559947807, 'NEAR BAY'),\n",
       " (6.028038672736599e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 104,
     "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": "code",
   "execution_count": 105,
   "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": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47730.22690385927"
      ]
     },
     "execution_count": 106,
     "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": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([45685.10470776, 49691.25001878])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confidence = 0.95\n",
    "squared_errors = (final_predictions - y_test) ** 2\n",
    "mean = squared_errors.mean()\n",
    "m = len(squared_errors)\n",
    "\n",
    "np.sqrt(stats.t.interval(confidence, m - 1,\n",
    "                         loc=np.mean(squared_errors),\n",
    "                         scale=stats.sem(squared_errors)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could compute the interval manually like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45685.10470776014, 49691.25001877871)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tscore = stats.t.ppf((1 + confidence) / 2, df=m - 1)\n",
    "tmargin = tscore * squared_errors.std(ddof=1) / np.sqrt(m)\n",
    "np.sqrt(mean - tmargin), np.sqrt(mean + tmargin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we could use a z-scores rather than t-scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45685.717918136594, 49690.68623889426)"
      ]
     },
     "execution_count": 110,
     "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": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([210644.60459286, 317768.80697211, 210956.43331178,  59218.98886849,\n",
       "       189747.55849879])"
      ]
     },
     "execution_count": 111,
     "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": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model = full_pipeline_with_predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.externals import joblib\n",
    "joblib.dump(my_model, \"my_model.pkl\") # DIFF\n",
    "#...\n",
    "my_model_loaded = joblib.load(\"my_model.pkl\") # DIFF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example SciPy distributions for `RandomizedSearchCV`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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": {
    "collapsed": true
   },
   "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": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=4)]: Done 154 tasks      | elapsed:  9.4min\n",
      "[Parallel(n_jobs=4)]: Done 250 out of 250 | elapsed: 15.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid='warn', n_jobs=4,\n",
       "       param_grid=[{'kernel': ['linear'], 'C': [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0, 10000.0, 30000.0]}, {'kernel': ['rbf'], 'C': [1.0, 3.0, 10.0, 30.0, 100.0, 300.0, 1000.0], 'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_squared_error', verbose=2)"
      ]
     },
     "execution_count": 115,
     "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_jobs=4)\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": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70363.90313964167"
      ]
     },
     "execution_count": 116,
     "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": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 30000.0, 'kernel': 'linear'}"
      ]
     },
     "execution_count": 117,
     "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": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=4)]: Done 154 tasks      | elapsed: 12.1min\n",
      "[Parallel(n_jobs=4)]: Done 250 out of 250 | elapsed: 19.6min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "          estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          fit_params=None, iid='warn', n_iter=50, n_jobs=4,\n",
       "          param_distributions={'kernel': ['linear', 'rbf'], 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x13a243278>, 'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x13a243e48>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_squared_error',\n",
       "          verbose=2)"
      ]
     },
     "execution_count": 118,
     "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, n_jobs=4, 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": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54767.99053704408"
      ]
     },
     "execution_count": 119,
     "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": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 157055.10989448498, 'gamma': 0.26497040005002437, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 120,
     "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": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "expon_distrib = expon(scale=1.)\n",
    "samples = expon_distrib.rvs(10000, random_state=42)\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(121)\n",
    "plt.title(\"Exponential distribution (scale=1.0)\")\n",
    "plt.hist(samples, bins=50)\n",
    "plt.subplot(122)\n",
    "plt.title(\"Log of this distribution\")\n",
    "plt.hist(np.log(samples), bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zPWUfA86JiJ9HxFMRcX9E3A8cA/RExHUR8TgpCJtXuLXQCcDSiNgcEXeRrna+KKc1ymtmZmbWNRoGZfneW/OBGZJ+K+k+SRdJeg7pdgxP34g43/V+LTBX0lRgd/rfqHh1zsNAeWuU4WRJqySt6u3tHWwdzczMzEqvmZ6ymcAuwFuBQ4ADgZeR7pU3iXSPx6ItpCHOSYXX1Wk0yNtPRFwSEfMjYv6MGR1xApaZmZlZP80EZZUrKH8+ItbnG8V+GjiKdAPqKVXrTwG25TSq0itpNMhrZmZm1lUaBmURUbmTffHS/5XnPcC8ykJJE0l3iO/J+dYX0/PznkZ5B10LMzMzszbX7ET/rwL/IOn5ea7YB4DvANcDB0haIGkCcBZwZ0SsyfmuABZLmpon8J8ELM9pjfKamZmZdY1mg7KlwC+Au4G7gP8HnBcRvcAC4DxgM3AwsLCQ72zS5P11wC3AhRGxEqCJvGZmZmZdo6kr+kfEE8Ap+VGddiNQ8zIWEbEDODE/aqXXzTtSZp/x3X6v/3DBm0Zzd2ZmZjX5/5E14huSm5mZmZWAgzIzMzOzEnBQZmZmZlYCDsrMzMzMSsBBmZmZmVkJNHX2pZmZWaerPjsSfIakjS33lJmZmZmVgHvKzMzMrCV87bb+3FNmZmZmVgLuKTMz6zCeG2XWnhyUmZlZS3joyqw/B2VmZmZdyD2q5eM5ZWZmZmYl4KDMzMzMrAQclJmZmZmVgIMyMzMzsxJwUGZmZmZWAj770szMAF+iwqzVHJSZmVnTfBmF5tR6n8wa8fClmZmZWQm4p8zMzGwQPMxro8VBmZmZdZx2DZzatdztqmzD8Q7KzMzamOcuWTfq1ODVQZmZmXWldg1oy9a7YyPHE/3NzMzMSqCpoEzSzZIel9SXH78upB0raZ2k7ZJukDStkDZN0vU5bZ2kY6u2WzevmZmZWTcZzPDlqRHxleICSXOBLwNvAu4ALgEuBhbmVb4A/BGYCRwIfFfS6ojoaSKvmdmokjSe1O68DpgGrAU+EhHfz+mHk9qxvYDbgUURsa6Q94vAW4FHgU9GxKfHvBIl0Or5Pe06DGlWbbjDl8cB346IWyOiDzgTOEbSZEkTgQXAmRHRFxE/Ab4FHN8o7zDLZGbWrHHAvcBrgOcBi4FvSJotaTqwgtQ2TQNWAdcW8i4B9gNmAYcBp0s6cuyKbmadZjBB2fmSNkq6TdJr87K5wOrKChGxltQztn9+PBkRdxe2sTrnaZTXzGzURcT2iFgSEX+IiKci4jvA74GDgGOAnoi4LiIeJwVh8yTNydlPAJZGxOaIuAu4FFg09rUws07R7PDlh4H/IgVNC4FvSzoQmARsqVp3CzAZ+BOwtU4aDfL2I+lk4GSAvfbaq8kim5kNjqSZpAPDHuB99D9w3C5pLTBX0oPA7sX0/Pzo0S7jUIfqWj3EaEMzkkOzHuYtv6aCsoi4vfDycklvB44C+oApVatPAbYBTw2QRoO81fu/hDTnjPnz50czZTYzGwxJuwBXA5dHxBpJk4DeqtUqB46TCq+r02ptu3QHlv4HbVZbKw9ghnqdsgBEOpqcV1koaW9gPHA3KSgbJ2m/iPhNXmVezkODvGZmY0bSTsCVpNGAU/PigQ4c+wqvH69KexYfWDbH19+y0dQOByINgzJJuwIHA7cATwJ/BxwKvB/YBfiZpENIZ1CeA6yIiG057wrgHEnvJp19+RbgVXnTVw+U18xsLEgScBnpLPGjIuKJnNRDmjdWWW8isA9pntlmSetJB5Y/zKsUDzpHRDv8ExltHnYtn9EMnrs9MG+mp2wX4FxgDmme2Brg6MoEfknvJQVYuwE3Au8s5D0FWAY8BGwC3hcRPQD5shgD5TUzGwtfBF4MvC4iHissvx64UNIC4LvAWcCdEbEmp18BLJa0ihTQnYTbMMD/WM2GqmFQFhG9wMsHSL8GuKZO2sMMMPF1oLxmZqNN0izgPcAOYEPqNAPgPRFxdQ7ILgKuIl2nrHgdxbNJAd064DHgExGxcqzK3m7c6ze6RqpH0QF1a/nel2bWtfKFYDVA+o2kUYJaaTuAE/PDxoiDO+tkDsrMzMzqaNcgsF3L3e18Q3IzMzOzEnBPmZmZdTz3HFk7cFBmZmZmdTmgHTsevjQzMzMrAQdlZmZmZiXg4UszMzMrrW4aPnVPmZmZmVkJuKfMzMzMRtRY3xmgU+5E4J4yMzMzsxJwT5mZmdXUTXN5zOoZy14495SZmZmZlYB7yszMzFqg23oiu62+Q+GeMjMzM7MScFBmZmZmVgIOyszMzMxKwHPKzMzMrOO04xw2B2VmZmbD0I7//Eea34OR4eFLMzMzsxJwUGZmZmZWAg7KzMzMzErAQZmZmZlZCTgoMzMzMysBB2VmZmZmJeCgzMzMzKwEBhWUSdpP0uOSriosO1bSOknbJd0gaVohbZqk63PaOknHVm2vbl4zMzOzbjLYnrIvAL+ovJA0F/gycDwwE3gUuLhq/T/mtOOAL+Y8zeQ1MzMz6xpNX9Ff0kLgEeCnwL558XHAtyPi1rzOmcBdkiYDTwELgAMiog/4iaRvkYKwMwbKGxHbRqR2ZmZmZm2iqZ4ySVOAc4DTqpLmAqsrLyJiLalnbP/8eDIi7i6svzrnaZS3ev8nS1olaVVvb28zRTYzMzNrK80OXy4FLouI+6qWTwK2VC3bAkzOaVvrpDXK209EXBIR8yNi/owZM5osspmZmVn7aDh8KelA4HXAy2ok9wFTqpZNAbaRhi/rpTXKa2ZmZtZVmplT9lpgNnCPJEg9XDtLegmwEphXWVHS3sB44G5SUDZO0n4R8Zu8yjygJz/vGSCvmZmZWVdpJii7BPh64fUHSUHa+4DnAz+TdAhwB2ne2YrKRH1JK4BzJL0bOBB4C/CqvJ2rB8prZmZm1k0azimLiEcjYkPlQRp2fDwieiOiB3gvKcB6iDQf7JRC9lOA5+S0rwHvy3loIq+ZmZlZ1xj0Ff0jYklEvKPw+pqI2CsiJkbEWyLi4ULawxFxdE7bKyKuqdpW3bxmZqNN0qn5zO4dkpYXls+WFJL6Co8zC+njJS2TtFXSBknVZ6abmQ1a09cpMzPrQA8A5wJvIPXqV9s1Ip6ssXwJsB8wC3gBcJOk/4qIlaNVUDPrfL73pZl1rYhYERE3AJsGmfUEYGlEbI6Iu4BLgUUjXT4z6y4OyszM6lsn6T5JX5U0HUDSVGB3Che/pv+Fsc3MhsRBmZnZs20EXk4anjyIdCLS1TltUv5bvPh1zQtfV/iuJGbWDAdlZmZVIqIvIlZFxJMR8SBwKnBEvq9vX16tePHrAS987buSmFkzHJSZmTUW+e9OEbEZWE/h4tf0vzC2mdmQOCgzs64laZykCcDOpDuVTMjLDpb0Ikk7SdoN+Bxwc0RUhiyvABZLmippDnASsLwllTCzjuGgzMy62WLgMeAM4B35+WJgb9Jt5LYB/wnsAN5eyHc2sBZYB9wCXOjLYZjZcPk6ZWbWtSJiCemaY7V8bYB8O4AT88PMbES4p8zMzMysBByUmZmZmZWAgzIzMzOzEnBQZmZmZlYCDsrMzMzMSsBBmZmZmVkJOCgzMzMzKwEHZWZmZmYl4KDMzMzMrAQclJmZmZmVgIMyMzMzsxJwUGZmZmZWAg7KzMzMzErAQZmZmZlZCTgoMzMzMysBB2VmZmZmJdBUUCbpKknrJW2VdLekdxfSDpe0RtKjkm6SNKuQNl7Sspxvg6TTqrZbN6+ZmZlZN2m2p+x8YHZETAH+BjhX0kGSpgMrgDOBacAq4NpCviXAfsAs4DDgdElHAjSR18zMzKxrjGtmpYjoKb7Mj32Ag4CeiLgOQNISYKOkORGxBjgBWBQRm4HNki4FFgErgWMa5DUzMzPrGk3PKZN0saRHgTXAeuB7wFxgdWWdiNgOrAXmSpoK7F5Mz8/n5ud18w6pJmZmZmZtrOmgLCJOASYDh5CGHXcAk4AtVatuyetNKryuTqNB3n4knSxplaRVvb29zRbZzMzMrG0M6uzLiPhTRPwEeCHwPqAPmFK12hRgW06jKr2SRoO81fu9JCLmR8T8GTNmDKbIZmZmZm1hqJfEGEeaU9YDzKsslDSxsjzPI1tfTM/PK/PT6uYdYpnMzMzM2lbDoEzS8yUtlDRJ0s6S3gC8Hfi/wPXAAZIWSJoAnAXcWZiofwWwWNJUSXOAk4DlOa1RXjMzM7Ou0UxPWZCGKu8DNgOfAv5XRHwrInqBBcB5Oe1gYGEh79mkyfvrgFuACyNiJUATec3MzMy6RsNLYuTg6TUDpN8IzKmTtgM4MT8GldfMzMysm/g2S2ZmZmYl4KDMzMzMrAQclJmZmZmVgIMyMzMzsxJwUGZmZmZWAg7KzMzMzErAQZmZmZlZCTgoMzMzMysBB2VmZmZmJeCgzMy6lqRTJa2StEPS8qq0wyWtkfSopJskzSqkjZe0TNJWSRsknTbmhTezjuOgzMy62QPAucCy4kJJ04EVwJnANGAVcG1hlSXAfsAs4DDgdElHjkF5zayDOSgzs64VESsi4gZgU1XSMUBPRFwXEY+TgrB5kir36j0BWBoRmyPiLuBSYNEYFdvMOpSDMjOzZ5sLrK68iIjtwFpgrqSpwO7F9Px8br2NSTo5D5Ou6u3tHaUim1m7c1BmZvZsk4AtVcu2AJNzGlXplbSaIuKSiJgfEfNnzJgxogU1s87hoMzM7Nn6gClVy6YA23IaVemVNDOzIXNQZmb2bD3AvMoLSROBfUjzzDYD64vp+XnPmJbQzDqOgzIz61qSxkmaAOwM7CxpgqRxwPXAAZIW5PSzgDsjYk3OegWwWNLUPPn/JGB5C6pgZh3EQZmZdbPFwGPAGcA78vPFEdELLADOAzYDBwMLC/nOJk38XwfcAlwYESvHsNxm1oHGtboAZmatEhFLSJe7qJV2IzCnTtoO4MT8MDMbEe4pMzMzMysBB2VmZmZmJeCgzMzMzKwEHJSZmZmZlYCDMjMzM7MScFBmZmZmVgIOyszMzMxKoGFQJmm8pMskrZO0TdKvJL2xkH64pDWSHpV0k6RZVXmXSdoqaYOk06q2XTevmZmZWTdppqdsHHAv8BrgeaQrYH9D0mxJ04EVwJnANGAVcG0h7xJgP2AWcBhwuqQjAZrIa2ZmZtY1Gl7RPyK20/+K19+R9HvgIGA30g16rwOQtATYKGlOvkfcCcCifAPfzZIuBRYBK4FjGuQ1MzMz6xqDnlMmaSawP9ADzAVWV9JyALcWmCtpKrB7MT0/n5uf181bY58nS1olaVVvb+9gi2xmZmZWeoMKyiTtAlwNXJ57syYBW6pW2wJMzmlUpVfSaJC3n4i4JCLmR8T8GTNmDKbIZmZmZm2h6aBM0k7AlcAfgVPz4j5gStWqU4BtOY2q9Epao7xmZmZmXaWpoEySgMuAmcCCiHgiJ/UA8wrrTQT2Ic0V2wysL6bn5z2N8g6pJmZmZmZtrNmesi8CLwb+OiIeKyy/HjhA0gJJE4CzgDsLE/WvABZLmippDnASsLzJvGZmZmZdo5nrlM0C3gMcCGyQ1Jcfx0VEL7AAOA/YDBwMLCxkP5s0eX8dcAtwYUSsBGgir5mZmVnXaOaSGOsADZB+IzCnTtoO4MT8GFReMzMzs27i2yyZmZmZlYCDMjMzM7MScFBmZmZmVgIOyszMzMxKwEGZmZmZWQk4KDMzMzMrAQdlZmZmZiXgoMzMzMysBByUmZmZmZWAgzIzMzOzEnBQZmZmZlYCDsrMzMzMSsBBmZmZmVkJOCgzMzMzKwEHZWZmZmYl4KDMzMzMrAQclJmZmZmVgIMyM7MBSLpZ0uOS+vLj14W0YyWtk7Rd0g2SprWyrGbW3hyUmZk1dmpETMqPFwFImgt8GTgemAk8ClzcwjKaWZsb1+oCmJm1qeOAb0fErQCSzgTukjQ5Ira1tmhm1o7cU2Zm1tj5kjZKuk3Sa/OyucDqygoRsRb4I7B/dWZJJ0taJWlVb2/vmBTYzNqPgzIzs4F9GNgb2BO4BPi2pH2AScCWqnW3AJOrNxARl0TE/IiYP2PGjNEur5m1KQdlZmYDiIjbI2JbROyIiMuB24CjgD5gStXqUwAPXZrZkDgoMzMbnAAE9ADzKgsl7Q2MB+4llmhpAAAKbklEQVRuUbnMrM15or+ZWR2SdgUOBm4BngT+DjgUeD+wC/AzSYcAdwDnACs8yd/MhqqpnjJJp+ZJqjskLa9KO1zSGkmPSrpJ0qxC2nhJyyRtlbRB0mnN5jUzK4FdgHOBXmAj8A/A0RFxd0T0AO8FrgYeIs0lO6VVBTWz9tfs8OUDpIZpWXGhpOnACuBMYBqwCri2sMoSYD9gFnAYcLqkI5vMa2bWUhHRGxEvj4jJEbFrRLwiIn5YSL8mIvaKiIkR8ZaIeLiV5TWz9tZUUBYRKyLiBmBTVdIxQE9EXBcRj5OCsHmS5uT0E4ClEbE5Iu4CLgUWNZnXzMzMrGsMd6J/9XV6tgNrgbmSpgK7F9Pz87mN8lbvxNf4MTMzs0433KBsoOv0TCq8rk5rlLcfX+PHzMzMOt1wg7KBrtPTV3hdndYor5mZmVlXGW5QVn2dnonAPqS5YpuB9cX0/LynUd5hlsnMzMys7TR7SYxxkiYAOwM7S5ogaRxwPXCApAU5/SzgzohYk7NeASyWNDVP4D8JWJ7TGuU1MzMz6xrN9pQtBh4DzgDekZ8vjoheYAFwHrCZdJHFhYV8Z5Mm768jXXzxwohYCelU8wZ5zczMzLpGU1f0j4glpEtW1Eq7Eah5GYuI2AGcmB+DymtmZmbWTXzvSzMzM7MScFBmZmZmVgJdd0Py2Wd891nL/nDBm1pQEjMzM7NnuKfMzMzMrAQclJmZmZmVgIMyMzMzsxJwUGZmZmZWAg7KzMzMzErAQZmZmZlZCTgoMzMzMysBB2VmZmZmJeCgzMzMzKwEHJSZmZmZlUDX3WapFt96yczMzFrNPWVmZmZmJeCgzMzMzKwEHJSZmZmZlYCDMjMzM7MS8ET/Oqon/3viv5mZmY0m95SZmZmZlYCDMjMzM7MS8PBlk3wtMzMzMxtNDsqGwYGamZmZjRQHZSOsmRMEfBKBmZmZVXNQNspq9aaZmZmZVXNQVgLNBm7uUTMzM+tcLQ/KJE0DLgOOADYCH4mIa1pbqnIay143B4Bmjbn9MrOR1PKgDPgC8EdgJnAg8F1JqyOip7XF6m5DDQCbmUPXbL5mtEOg6hNCOprbLzMbMS0NyiRNBBYAB0REH/ATSd8CjgfOaGXZbGiGGiS1w9y7kSxjO9R3NHVCUOr2y8xGWqt7yvYHnoyIuwvLVgOvKa4k6WTg5PyyT9KvB7GP6aRhhU7XDfXshjpCF9RTnwAGV89Zo1aYoWuq/YJht2GjrVO+b51SD+icunRsPXIb1qym269WB2WTgK1Vy7YAk4sLIuIS4JKh7EDSqoiYP7TitY9uqGc31BFczzbSVPsFw2vDRlsHfA5A59QDOqcursfgtfo2S33AlKplU4BtLSiLmdlguP0ysxHV6qDsbmCcpP0Ky+YBniRrZmXn9svMRlRLg7KI2A6sAM6RNFHSXwJvAa4cwd2UcshgFHRDPbuhjuB6toUxar/GQlt/DgWdUg/onLq4HoOkiBirfdUuQLrOzzLg9cAm4Axf58fM2oHbLzMbSS0PyszMzMys9XPKzMzMzAwHZWZmZmal0LFBmaRpkq6XtF3SOknHtrpM9Ui6WdLjkvry49eFtGNz+bdLuiHPYamkDVjH4eQdgTqdKmmVpB2SllelHS5pjaRHJd0kaVYhbbykZZK2Stog6bSxyDvS9ZQ0W1IUPtM+SWe2Yz3z/i7L35Ntkn4l6Y2jXdZWfJ7dSNJ+uf25qtVlGQ5JCyXdldu0tZIOaXWZBiu3G9+TtDl/5y+S1OrriQ5oqG192QzQlr9C0g8lPSypV9J1knYftYJEREc+gK8B15Iu8Phq0kUd57a6XHXKejPw7hrL55KueXRorsc1wNebqeNw8o5QnY4Bjga+CCwvLJ+e9/U2YAJwIfDzQvr5wI+BqcCLgQ3AkaOddxTqORsIYFydfG1TT2AisCTXaSfgzfm7NbvTPs9ufAA/yO/zVa0uyzDq8HpgHfCK/B3dE9iz1eUaQj2+ByzP3+kXAP8B/GOry9WgzENq68v2GKAeb8x1mAI8l3Riz8pRK0er34hRenMnkm4SvH9h2ZXABa0uW53y3kztoOzjwDWF1/vkek1uVMfh5B3hup1b9QU/Gfhp1Wf1GDAnv34AOKKQvpQcTI5m3lGo52wGDsrasp6F7d5Juu9jR36e3fIAFgLfIAXd7RyU/RR4V6vLMQL1uAs4qvD6QuDLrS5Xk2UfVFtf1kd1PWqk/w9g22jtv1OHL+vdk25ui8rTjPMlbZR0m6TX5mVzSeUGICLWkoMpGtdxOHlHU3W5tgNrgbmSpgK7F9MZuE4jkndEalXfOkn3SfqqpOkA7V5PSTNJ36Ge0SprGerZ6SRNAc4BTmu0bplJ2hmYD8yQ9Nv8e7tI0nNaXbYh+CywUNJzJe1J6qVZ2eIyDVWn/kYPZRQvEN2pQVnT96QriQ8De5O63C8Bvi1pH1I9tlStW6lHozoOJ+9oalQuqtIHU6eh5h0NG4GXk25Ee1Dez9WFslT2X6sspa2npF1I9bg8ItaMYlnL9nl2oqXAZRFxX6sLMkwzgV2AtwKHAAcCLwMWt7JQQ3QrKWjZCtwHrAJuaGmJhq7jfqOSXgqcBXxotPbRqUFZW92TLiJuj4htEbEjIi4HbgOOYuB6NKrjcPKOpkbloip9MHUaat4RFxF9EbEqIp6MiAeBU4EjJE0eZllbVk9JO5GGuf9Iqs9olrVUn2enkXQg8DrgM60uywh4LP/9fESsj4iNwKdJbWjbyL+vlaS7REwkzcmaCnyileUaho76jUraF/g+8P6I+PFo7adTg7J2vyddACKVd15loaS9gfGk+jWq43Dyjqbqck0kzXfriYjNwPpiOgPXaUTyjkitGqtcpXmndqynJAGXkXolFkTEE6NZ1jb4PNvda0nzHu+RtAH4ILBA0h2tLNRQ5O/KfTzzG6PqebuYBuwFXJQP0DcBX6XNgsuCjvmN5rNGbwSWRsTo3kat1ZPqRnGy3tdJZxhOBP6Skp59CewKvIF0dso44DhgO2nOTqUb+5Bcj6vofwZl3ToOJ+8I1WtcrtP5pN6VSv1m5H0tyMs+Qf8z7i4AbiEdIc4h/WOunHE3anlHoZ4HAy8iHfjsRjrT9aY2rueXgJ8Dk6qWd9Tn2S0P0llkLyg8PgV8E5jR6rINsT7nAL8Anp+/Lz8m/QNtedkGWY/fAWfkNmRX4HoKJ2yV8TFAG9hWv9EB6rEnaS7cB8ekHK1+I0bxDZ5GGovfDtwDHNvqMtUp54zcmGwDHsn/+F5fSD82l3878G/AtGbrOJy8I1CvJaSj1eJjSU57HbCGNOxwMzC7kG886ZTjrcCDwGlV2x2VvCNdT+DtwO/z+7seuAJ4QTvWkzQvLoDHSUMSlcdxnfZ5duuD9j/7chfgYlIbugH4HDCh1eUaQj0OzN/lzaR5qd8AZra6XA3KXLMNzGlt8xsdoC0/Oz8vtn19o1UO3/vSzMzMrAQ6dU6ZmZmZWVtxUGZmZmZWAg7KzMzMzErAQZmZmZlZCTgoMzMzMysBB2VmZmZmJeCgzMzMzKwEHJSZmZmZlcD/B9nYUTepRxtlAAAAAElFTkSuQmCC\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": 123,
   "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": 124,
   "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": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  7,  9, 12])"
      ]
     },
     "execution_count": 125,
     "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": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['longitude', 'latitude', 'median_income', 'pop_per_hhold',\n",
       "       'INLAND'], dtype='<U18')"
      ]
     },
     "execution_count": 126,
     "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": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.3661589806181342, 'median_income'),\n",
       " (0.1647809935615905, 'INLAND'),\n",
       " (0.10879295677551573, 'pop_per_hhold'),\n",
       " (0.07334423551601242, 'longitude'),\n",
       " (0.0629090704826203, 'latitude')]"
      ]
     },
     "execution_count": 127,
     "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": 128,
   "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": 129,
   "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": 130,
   "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": 130,
     "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": 131,
   "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": 131,
     "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": 132,
   "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": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('preparation', ColumnTransformer(n_jobs=1, remainder='drop', sparse_threshold=0.3,\n",
       "         transformer_weights=None,\n",
       "         transformers=[('num', Pipeline(memory=None,\n",
       "     steps=[('imputer', SimpleImputer(copy=True, fill_value=None, missing_values=nan,\n",
       "       strategy='median', verbose=0... gamma=0.26497040005002437, kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 133,
     "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": 134,
   "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": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=4)]: Done 154 tasks      | elapsed:  9.7min\n",
      "[Parallel(n_jobs=4)]: Done 240 out of 240 | elapsed: 19.8min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=Pipeline(memory=None,\n",
       "     steps=[('preparation', ColumnTransformer(n_jobs=1, remainder='drop', sparse_threshold=0.3,\n",
       "         transformer_weights=None,\n",
       "         transformers=[('num', Pipeline(memory=None,\n",
       "     steps=[('imputer', SimpleImputer(copy=True, fill_value=None, missing_values=nan,\n",
       "       strategy='median', verbose=0... gamma=0.26497040005002437, kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False))]),\n",
       "       fit_params=None, iid='warn', n_jobs=4,\n",
       "       param_grid=[{'preparation__num__imputer__strategy': ['mean', 'median', 'most_frequent'], 'feature_selection__k': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_squared_error', verbose=2)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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_jobs=4)\n",
    "grid_search_prep.fit(housing, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'feature_selection__k': 15,\n",
       " 'preparation__num__imputer__strategy': 'most_frequent'}"
      ]
     },
     "execution_count": 137,
     "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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