{
 "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\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# 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)"
   ]
  },
  {
   "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": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "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",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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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bVh/gqs9NXtbYuX54hlu0eIyMjOB7b3ocw+lx/KZvrsbQOZUlSZIkSQMpyRHA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fqFUd+6rzujuNEMKnlney24FngrcLdBl2uBdVrf9ltbgRuATwBrBl2uPSj/Kczj79Bh3WaqD3sQ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\nf4eqv0mpNDg3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104f5a198>"
      ]
     },
     "metadata": {},
     "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": [
    "import hashlib\n",
    "\n",
    "def test_set_check(identifier, test_ratio, hash):\n",
    "    return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio\n",
    "\n",
    "def split_train_test_by_id(data, test_ratio, id_column, hash=hashlib.md5):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio, hash))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This version supports both Python 2 and Python 3, instead of just Python 3.\n",
    "def test_set_check(identifier, test_ratio, hash):\n",
    "    return bytearray(hash(np.int64(identifier)).digest())[-1] < 256 * test_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113a2e978>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9SR5LcmuS9T291yV5NMlDSd7W89wL9kqSRm+QLYVVwF/T+ZOczwAuBT6dZEOSs4C9wGXA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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a1efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"median_income\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113b25048>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GmsWamVnJ8uwpBNVDRS8CR4C/AP5rRHwnIg4BS4G7srFLgGV1sWuonjzeAzwOfD4itgHk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\n+f+4CMDPZ7UmDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b380f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"income_cat\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts() / len(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Random</th>\n",
       "      <th>Stratified</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>0.040213</td>\n",
       "      <td>0.039729</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>0.324370</td>\n",
       "      <td>0.318798</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>0.358527</td>\n",
       "      <td>0.350533</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>0.167393</td>\n",
       "      <td>0.176357</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>0.109496</td>\n",
       "      <td>0.114583</td>\n",
       "      <td>-4.318374</td>\n",
       "      <td>0.127011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Overall    Random  Stratified  Rand. %error  Strat. %error\n",
       "1.0  0.039826  0.040213    0.039729      0.973236      -0.243309\n",
       "2.0  0.318847  0.324370    0.318798      1.732260      -0.015195\n",
       "3.0  0.350581  0.358527    0.350533      2.266446      -0.013820\n",
       "4.0  0.176308  0.167393    0.176357     -5.056334       0.027480\n",
       "5.0  0.114438  0.109496    0.114583     -4.318374       0.127011"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure bad_visualization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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OA5Xn03lk1tnU7GzjkZWN/PZvjXTEN81YccWPnqlhw08vBgl3Lq1DEyJp3VIIXpx9IfUf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\njLQs9UoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26e6477710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\")\n",
    "save_fig(\"bad_visualization_plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure better_visualization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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zrWLbLM0Uytxr+n3WnHdx1dpwOMyYrwRkSrBUD0lyg28LhmlOxbfxLIuFilsY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\n1f8iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26e674da20>"
      ]
     },
     "metadata": {},
     "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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure housing_prices_scatterplot\n"
     ]
    },
    {
     "data": {
      "image/png": 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TwmErORffx0YOs299JZUCXrCD3XWbID0A4TBk92I4WSymefHFZtatC3POOT6u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19ARMAcRLokekTjWj1BjSImgqs4tbn1/LS6AZTK4G6CXxyBVZfBikWwcQAaBciZqipcdWGsCLlS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BqFgQykwrCwFq5dBd++HzqHYE49tE+xyguwdm0UTYNk0iCV8t9OMxmPHTtKbN4coKbmzJXf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\nzMzMrOM56DUzMzOzjueg18zMzMw63i/8unXMvu/KqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26e6973358>"
      ]
     },
     "metadata": {},
     "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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure california_housing_prices_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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q7znUrWuxUlJiUlLSPulq3lzJrBkQCNgpr9a4/V47vmZBcYHJvAVhHDZwOz6+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5ryBZ+6xWVRTUBPv0FbTcBrnRZ9IxLT0FTFTnSjV9+wBuBNF7QiQM+Oiuh4d1PJjL/4yPsum8ff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L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\nEvP/eREX3iCYxowAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26e67b4390>"
      ]
     },
     "metadata": {},
     "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",
    "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": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure scatter_matrix_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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jqz93DV/4/ikeOrbIbRtb+ekb1/Phv3oaTYPjv/cubvqD7zJbtPjszYO0xUP83gNDtMeC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\njueHlSdfMl9s8J+/fYEvvhSKbv3IwaF2QFG3MVQVxw+V5lZrjdZ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54+en+ekHt/LEiSWe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8rbVGyH7d1x3hpPMdyf4NBoCp/X5PndXXzn2Ay5kk1zyIentn7w1VNzFMo2J2ayDCbDS0tj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\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X84kGVTRFIh5USFeE67zpCI43+MiSTEARAheHxnNENIW9Q0k6ogEePpsiU7FwfZ+OsMpg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I+Gdea8SwCCNa+4833HhQS6KhPVFXzPp9F22TmcJqmrLFYNvMBD7mSuoppEJqZSadv8qy+/RCqq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\n7b5NTBcbbMuniGkK2YRO2tSREg5NltoF1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WNR1/VjtwySMNbv605XBc4G7i5IoCuuU3Oi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26de2476a0>"
      ]
     },
     "metadata": {},
     "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": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure income_vs_house_value_scatterplot\n"
     ]
    },
    {
     "data": {
      "image/png": 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iJTmaFhvPZu25bXVjJqua0oaC4GVpOZlXaCm4vtOhqt2GEDAraj6Y5PzowZT7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plWGeebnr1pxu3zDVpEpEjGY4NLPbMYIsFD7QK5FHQOMg0rC00SbmfooLc9JoOjsUZLTd87lIZR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\n6Ev+P31A5qx+DrJ5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26d7af9208>"
      ]
     },
     "metadata": {},
     "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": 39,
   "metadata": {
    "collapsed": true
   },
   "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": 40,
   "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": 40,
     "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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RDk+l0fGB2DBeXmlwfa3Ncq3DuekSlin2jIe9txhvyn1+LkvLC0DEhswcmkfP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\nMBj2HP8fo2qc4h2jUWwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26c3f10b00>"
      ]
     },
     "metadata": {},
     "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 42,
     "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": 43,
   "metadata": {
    "collapsed": true
   },
   "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0             NaN   \n",
       "6068     -117.86     34.01                16.0       4632.0             NaN   \n",
       "17923    -121.97     37.35                30.0       1955.0             NaN   \n",
       "13656    -117.30     34.05                 6.0       2155.0             NaN   \n",
       "19252    -122.79     38.48                 7.0       6837.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \n",
       "4629       3296.0      1462.0         2.2708       <1H OCEAN  \n",
       "6068       3038.0       727.0         5.1762       <1H OCEAN  \n",
       "17923       999.0       386.0         4.6328       <1H OCEAN  \n",
       "13656      1039.0       391.0         1.6675          INLAND  \n",
       "19252      3468.0      1405.0         3.1662       <1H OCEAN  "
      ]
     },
     "execution_count": 44,
     "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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, ocean_proximity]\n",
       "Index: []"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows.dropna(subset=[\"total_bedrooms\"])    # option 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  population  \\\n",
       "4629     -118.30     34.07                18.0       3759.0      3296.0   \n",
       "6068     -117.86     34.01                16.0       4632.0      3038.0   \n",
       "17923    -121.97     37.35                30.0       1955.0       999.0   \n",
       "13656    -117.30     34.05                 6.0       2155.0      1039.0   \n",
       "19252    -122.79     38.48                 7.0       6837.0      3468.0   \n",
       "\n",
       "       households  median_income ocean_proximity  \n",
       "4629       1462.0         2.2708       <1H OCEAN  \n",
       "6068        727.0         5.1762       <1H OCEAN  \n",
       "17923       386.0         4.6328       <1H OCEAN  \n",
       "13656       391.0         1.6675          INLAND  \n",
       "19252      1405.0         3.1662       <1H OCEAN  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows.drop(\"total_bedrooms\", axis=1)       # option 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0           433.0   \n",
       "6068     -117.86     34.01                16.0       4632.0           433.0   \n",
       "17923    -121.97     37.35                30.0       1955.0           433.0   \n",
       "13656    -117.30     34.05                 6.0       2155.0           433.0   \n",
       "19252    -122.79     38.48                 7.0       6837.0           433.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \n",
       "4629       3296.0      1462.0         2.2708       <1H OCEAN  \n",
       "6068       3038.0       727.0         5.1762       <1H OCEAN  \n",
       "17923       999.0       386.0         4.6328       <1H OCEAN  \n",
       "13656      1039.0       391.0         1.6675          INLAND  \n",
       "19252      3468.0      1405.0         3.1662       <1H OCEAN  "
      ]
     },
     "execution_count": 47,
     "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": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "imputer = Imputer(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": 49,
   "metadata": {
    "collapsed": true
   },
   "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": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -118.51  ,    34.26  ,    29.    ,  2119.5   ,   433.    ,\n",
       "        1164.    ,   408.    ,     3.5409])"
      ]
     },
     "execution_count": 51,
     "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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -118.51  ,    34.26  ,    29.    ,  2119.5   ,   433.    ,\n",
       "        1164.    ,   408.    ,     3.5409])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
    "                          index = list(housing.index.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0           433.0   \n",
       "6068     -117.86     34.01                16.0       4632.0           433.0   \n",
       "17923    -121.97     37.35                30.0       1955.0           433.0   \n",
       "13656    -117.30     34.05                 6.0       2155.0           433.0   \n",
       "19252    -122.79     38.48                 7.0       6837.0           433.0   \n",
       "\n",
       "       population  households  median_income  \n",
       "4629       3296.0      1462.0         2.2708  \n",
       "6068       3038.0       727.0         5.1762  \n",
       "17923       999.0       386.0         4.6328  \n",
       "13656      1039.0       391.0         1.6675  \n",
       "19252      3468.0      1405.0         3.1662  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.loc[sample_incomplete_rows.index.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'median'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 57,
     "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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17606     <1H OCEAN\n",
       "18632     <1H OCEAN\n",
       "14650    NEAR OCEAN\n",
       "3230         INLAND\n",
       "3555      <1H OCEAN\n",
       "19480        INLAND\n",
       "8879      <1H OCEAN\n",
       "13685        INLAND\n",
       "4937      <1H OCEAN\n",
       "4861      <1H OCEAN\n",
       "Name: ocean_proximity, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use Pandas' `factorize()` method to convert this string categorical feature to an integer categorical feature, which will be easier for Machine Learning algorithms to handle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 2, 0, 2, 0, 2, 0, 0])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoded, housing_categories = housing_cat.factorize()\n",
    "housing_cat_encoded[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['<1H OCEAN', 'NEAR OCEAN', 'INLAND', 'NEAR BAY', 'ISLAND'], dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: earlier versions of the book used the `LabelEncoder` class instead of Pandas' `factorize()` method. This was incorrect: indeed, as its name suggests, the `LabelEncoder` class was designed for labels, not for input features. The code worked because we were handling a single categorical input feature, but it would break if you passed multiple categorical input features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert each categorical value to a one-hot vector using a `OneHotEncoder`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "encoder = OneHotEncoder()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1,1))\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `OneHotEncoder` returns a sparse array by default, but we can convert it to a dense array if needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.,  0.,  0.],\n",
       "       [ 1.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  1.,  0.,  0.],\n",
       "       [ 1.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  1.,  0.]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: earlier versions of the book used the `LabelBinarizer` class at this point. Again, this was incorrect: just like the `LabelEncoder` class, the `LabelBinarizer` class was designed to preprocess labels, not input features. A better solution is to use Scikit-Learn's upcoming `CategoricalEncoder` class: it will soon be added to Scikit-Learn, and in the meantime you can use the code below (copied from [Pull Request #9151](https://github.com/scikit-learn/scikit-learn/pull/9151))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Definition of the CategoricalEncoder class, copied from PR #9151.\n",
    "# Just run this cell, or copy it to your code, do not try to understand it (yet).\n",
    "\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.utils import check_array\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from scipy import sparse\n",
    "\n",
    "class CategoricalEncoder(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"Encode categorical features as a numeric array.\n",
    "    The input to this transformer should be a matrix of integers or strings,\n",
    "    denoting the values taken on by categorical (discrete) features.\n",
    "    The features can be encoded using a one-hot aka one-of-K scheme\n",
    "    (``encoding='onehot'``, the default) or converted to ordinal integers\n",
    "    (``encoding='ordinal'``).\n",
    "    This encoding is needed for feeding categorical data to many scikit-learn\n",
    "    estimators, notably linear models and SVMs with the standard kernels.\n",
    "    Read more in the :ref:`User Guide <preprocessing_categorical_features>`.\n",
    "    Parameters\n",
    "    ----------\n",
    "    encoding : str, 'onehot', 'onehot-dense' or 'ordinal'\n",
    "        The type of encoding to use (default is 'onehot'):\n",
    "        - 'onehot': encode the features using a one-hot aka one-of-K scheme\n",
    "          (or also called 'dummy' encoding). This creates a binary column for\n",
    "          each category and returns a sparse matrix.\n",
    "        - 'onehot-dense': the same as 'onehot' but returns a dense array\n",
    "          instead of a sparse matrix.\n",
    "        - 'ordinal': encode the features as ordinal integers. This results in\n",
    "          a single column of integers (0 to n_categories - 1) per feature.\n",
    "    categories : 'auto' or a list of lists/arrays of values.\n",
    "        Categories (unique values) per feature:\n",
    "        - 'auto' : Determine categories automatically from the training data.\n",
    "        - list : ``categories[i]`` holds the categories expected in the ith\n",
    "          column. The passed categories are sorted before encoding the data\n",
    "          (used categories can be found in the ``categories_`` attribute).\n",
    "    dtype : number type, default np.float64\n",
    "        Desired dtype of output.\n",
    "    handle_unknown : 'error' (default) or 'ignore'\n",
    "        Whether to raise an error or ignore if a unknown categorical feature is\n",
    "        present during transform (default is to raise). When this is parameter\n",
    "        is set to 'ignore' and an unknown category is encountered during\n",
    "        transform, the resulting one-hot encoded columns for this feature\n",
    "        will be all zeros.\n",
    "        Ignoring unknown categories is not supported for\n",
    "        ``encoding='ordinal'``.\n",
    "    Attributes\n",
    "    ----------\n",
    "    categories_ : list of arrays\n",
    "        The categories of each feature determined during fitting. When\n",
    "        categories were specified manually, this holds the sorted categories\n",
    "        (in order corresponding with output of `transform`).\n",
    "    Examples\n",
    "    --------\n",
    "    Given a dataset with three features and two samples, we let the encoder\n",
    "    find the maximum value per feature and transform the data to a binary\n",
    "    one-hot encoding.\n",
    "    >>> from sklearn.preprocessing import CategoricalEncoder\n",
    "    >>> enc = CategoricalEncoder(handle_unknown='ignore')\n",
    "    >>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])\n",
    "    ... # doctest: +ELLIPSIS\n",
    "    CategoricalEncoder(categories='auto', dtype=<... 'numpy.float64'>,\n",
    "              encoding='onehot', handle_unknown='ignore')\n",
    "    >>> enc.transform([[0, 1, 1], [1, 0, 4]]).toarray()\n",
    "    array([[ 1.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.],\n",
    "           [ 0.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.]])\n",
    "    See also\n",
    "    --------\n",
    "    sklearn.preprocessing.OneHotEncoder : performs a one-hot encoding of\n",
    "      integer ordinal features. The ``OneHotEncoder assumes`` that input\n",
    "      features take on values in the range ``[0, max(feature)]`` instead of\n",
    "      using the unique values.\n",
    "    sklearn.feature_extraction.DictVectorizer : performs a one-hot encoding of\n",
    "      dictionary items (also handles string-valued features).\n",
    "    sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot\n",
    "      encoding of dictionary items or strings.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, encoding='onehot', categories='auto', dtype=np.float64,\n",
    "                 handle_unknown='error'):\n",
    "        self.encoding = encoding\n",
    "        self.categories = categories\n",
    "        self.dtype = dtype\n",
    "        self.handle_unknown = handle_unknown\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        \"\"\"Fit the CategoricalEncoder to X.\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like, shape [n_samples, n_feature]\n",
    "            The data to determine the categories of each feature.\n",
    "        Returns\n",
    "        -------\n",
    "        self\n",
    "        \"\"\"\n",
    "\n",
    "        if self.encoding not in ['onehot', 'onehot-dense', 'ordinal']:\n",
    "            template = (\"encoding should be either 'onehot', 'onehot-dense' \"\n",
    "                        \"or 'ordinal', got %s\")\n",
    "            raise ValueError(template % self.handle_unknown)\n",
    "\n",
    "        if self.handle_unknown not in ['error', 'ignore']:\n",
    "            template = (\"handle_unknown should be either 'error' or \"\n",
    "                        \"'ignore', got %s\")\n",
    "            raise ValueError(template % self.handle_unknown)\n",
    "\n",
    "        if self.encoding == 'ordinal' and self.handle_unknown == 'ignore':\n",
    "            raise ValueError(\"handle_unknown='ignore' is not supported for\"\n",
    "                             \" encoding='ordinal'\")\n",
    "\n",
    "        X = check_array(X, dtype=np.object, accept_sparse='csc', copy=True)\n",
    "        n_samples, n_features = X.shape\n",
    "\n",
    "        self._label_encoders_ = [LabelEncoder() for _ in range(n_features)]\n",
    "\n",
    "        for i in range(n_features):\n",
    "            le = self._label_encoders_[i]\n",
    "            Xi = X[:, i]\n",
    "            if self.categories == 'auto':\n",
    "                le.fit(Xi)\n",
    "            else:\n",
    "                valid_mask = np.in1d(Xi, self.categories[i])\n",
    "                if not np.all(valid_mask):\n",
    "                    if self.handle_unknown == 'error':\n",
    "                        diff = np.unique(Xi[~valid_mask])\n",
    "                        msg = (\"Found unknown categories {0} in column {1}\"\n",
    "                               \" during fit\".format(diff, i))\n",
    "                        raise ValueError(msg)\n",
    "                le.classes_ = np.array(np.sort(self.categories[i]))\n",
    "\n",
    "        self.categories_ = [le.classes_ for le in self._label_encoders_]\n",
    "\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        \"\"\"Transform X using one-hot encoding.\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like, shape [n_samples, n_features]\n",
    "            The data to encode.\n",
    "        Returns\n",
    "        -------\n",
    "        X_out : sparse matrix or a 2-d array\n",
    "            Transformed input.\n",
    "        \"\"\"\n",
    "        X = check_array(X, accept_sparse='csc', dtype=np.object, copy=True)\n",
    "        n_samples, n_features = X.shape\n",
    "        X_int = np.zeros_like(X, dtype=np.int)\n",
    "        X_mask = np.ones_like(X, dtype=np.bool)\n",
    "\n",
    "        for i in range(n_features):\n",
    "            valid_mask = np.in1d(X[:, i], self.categories_[i])\n",
    "\n",
    "            if not np.all(valid_mask):\n",
    "                if self.handle_unknown == 'error':\n",
    "                    diff = np.unique(X[~valid_mask, i])\n",
    "                    msg = (\"Found unknown categories {0} in column {1}\"\n",
    "                           \" during transform\".format(diff, i))\n",
    "                    raise ValueError(msg)\n",
    "                else:\n",
    "                    # Set the problematic rows to an acceptable value and\n",
    "                    # continue `The rows are marked `X_mask` and will be\n",
    "                    # removed later.\n",
    "                    X_mask[:, i] = valid_mask\n",
    "                    X[:, i][~valid_mask] = self.categories_[i][0]\n",
    "            X_int[:, i] = self._label_encoders_[i].transform(X[:, i])\n",
    "\n",
    "        if self.encoding == 'ordinal':\n",
    "            return X_int.astype(self.dtype, copy=False)\n",
    "\n",
    "        mask = X_mask.ravel()\n",
    "        n_values = [cats.shape[0] for cats in self.categories_]\n",
    "        n_values = np.array([0] + n_values)\n",
    "        indices = np.cumsum(n_values)\n",
    "\n",
    "        column_indices = (X_int + indices[:-1]).ravel()[mask]\n",
    "        row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),\n",
    "                                n_features)[mask]\n",
    "        data = np.ones(n_samples * n_features)[mask]\n",
    "\n",
    "        out = sparse.csc_matrix((data, (row_indices, column_indices)),\n",
    "                                shape=(n_samples, indices[-1]),\n",
    "                                dtype=self.dtype).tocsr()\n",
    "        if self.encoding == 'onehot-dense':\n",
    "            return out.toarray()\n",
    "        else:\n",
    "            return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `CategoricalEncoder` expects a 2D array containing one or more categorical input features. We need to reshape `housing_cat` to a 2D array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from sklearn.preprocessing import CategoricalEncoder # in future versions of Scikit-Learn\n",
    "\n",
    "cat_encoder = CategoricalEncoder()\n",
    "housing_cat_reshaped = housing_cat.values.reshape(-1, 1)\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat_reshaped)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default encoding is one-hot, and it returns a sparse array. You can use `toarray()` to get a dense array:"
   ]
  },
  {
   "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": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can specify the encoding to be `\"onehot-dense\"` to get a dense matrix rather than a sparse matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder = CategoricalEncoder(encoding=\"onehot-dense\")\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat_reshaped)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'], dtype=object)]"
      ]
     },
     "execution_count": 67,
     "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": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# column index\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6\n",
    "\n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y=None):\n",
    "        return self  # nothing else to do\n",
    "    def transform(self, X, y=None):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household, population_per_household,\n",
    "                         bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]\n",
    "\n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs = pd.DataFrame(housing_extra_attribs, 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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('imputer', Imputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And a transformer to just select a subset of the Pandas DataFrame columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# Create a class to select numerical or categorical columns \n",
    "# since Scikit-Learn doesn't handle DataFrames yet\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's join all these components into a big pipeline that will preprocess both the numerical and the categorical features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(num_attribs)),\n",
    "        ('imputer', Imputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(cat_attribs)),\n",
    "        ('cat_encoder', CategoricalEncoder(encoding=\"onehot-dense\")),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipeline\", num_pipeline),\n",
    "        (\"cat_pipeline\", cat_pipeline),\n",
    "    ])"
   ]
  },
  {
   "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 = full_pipeline.fit_transform(housing)\n",
    "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": [
    "# Select and train a model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 77,
     "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": 78,
   "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 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": 79,
   "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": 80,
   "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": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_data_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68628.198198489219"
      ]
     },
     "execution_count": 81,
     "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": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49439.895990018973"
      ]
     },
     "execution_count": 82,
     "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": 83,
   "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": 83,
     "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": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 84,
     "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": 85,
   "metadata": {
    "collapsed": true
   },
   "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": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 70232.0136482   66828.46839892  72444.08721003  70761.50186201\n",
      "  71125.52697653  75581.29319857  70169.59286164  70055.37863456\n",
      "  75370.49116773  71222.39081244]\n",
      "Mean: 71379.0744771\n",
      "Standard deviation: 2458.31882043\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": 87,
   "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: 69052.4613635\n",
      "Standard deviation: 2731.6740018\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": "code",
   "execution_count": 88,
   "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=1,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "forest_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21941.911027380233"
      ]
     },
     "execution_count": 89,
     "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": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 51650.94405471  48920.80645498  52979.16096752  54412.74042021\n",
      "  50861.29381163  56488.55699727  51866.90120786  49752.24599537\n",
      "  55399.50713191  53309.74548294]\n",
      "Mean: 52564.1902524\n",
      "Standard deviation: 2301.87380392\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": 91,
   "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": 91,
     "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": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111094.6308539982"
      ]
     },
     "execution_count": 92,
     "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": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\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=10, n_jobs=1,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid=[{'max_features': [2, 4, 6, 8], 'n_estimators': [3, 10, 30]}, {'max_features': [2, 3, 4], 'n_estimators': [3, 10], 'bootstrap': [False]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    # try 12 (3×4) combinations of hyperparameters\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    # then try 6 (2×3) combinations with bootstrap set as False\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "# train across 5 folds, that's a total of (12+6)*5=90 rounds of training \n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error')\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best hyperparameter combination found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 30}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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=1, oob_score=False, random_state=42,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 95,
     "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": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63647.854446 {'max_features': 2, 'n_estimators': 3}\n",
      "55611.5015988 {'max_features': 2, 'n_estimators': 10}\n",
      "53370.0640736 {'max_features': 2, 'n_estimators': 30}\n",
      "60959.1388585 {'max_features': 4, 'n_estimators': 3}\n",
      "52740.5841667 {'max_features': 4, 'n_estimators': 10}\n",
      "50374.1421461 {'max_features': 4, 'n_estimators': 30}\n",
      "58661.2866462 {'max_features': 6, 'n_estimators': 3}\n",
      "52009.9739798 {'max_features': 6, 'n_estimators': 10}\n",
      "50154.1177737 {'max_features': 6, 'n_estimators': 30}\n",
      "57865.3616801 {'max_features': 8, 'n_estimators': 3}\n",
      "51730.0755087 {'max_features': 8, 'n_estimators': 10}\n",
      "49694.8514333 {'max_features': 8, 'n_estimators': 30}\n",
      "62874.4073931 {'max_features': 2, 'n_estimators': 3, 'bootstrap': False}\n",
      "54643.4998083 {'max_features': 2, 'n_estimators': 10, 'bootstrap': False}\n",
      "59437.8922859 {'max_features': 3, 'n_estimators': 3, 'bootstrap': False}\n",
      "52735.3582936 {'max_features': 3, 'n_estimators': 10, 'bootstrap': False}\n",
      "57490.0168279 {'max_features': 4, 'n_estimators': 3, 'bootstrap': False}\n",
      "51008.2615672 {'max_features': 4, 'n_estimators': 10, 'bootstrap': False}\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": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>mean_score_time</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>param_bootstrap</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.045302</td>\n",
       "      <td>0.002430</td>\n",
       "      <td>-4.051049e+09</td>\n",
       "      <td>-1.106013e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 3}</td>\n",
       "      <td>18</td>\n",
       "      <td>-3.850668e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.194135e+09</td>\n",
       "      <td>-1.116843e+09</td>\n",
       "      <td>-3.906732e+09</td>\n",
       "      <td>-1.112813e+09</td>\n",
       "      <td>-4.169669e+09</td>\n",
       "      <td>-1.129842e+09</td>\n",
       "      <td>0.001158</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>1.431223e+08</td>\n",
       "      <td>2.173798e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.145982</td>\n",
       "      <td>0.007629</td>\n",
       "      <td>-3.092639e+09</td>\n",
       "      <td>-5.819353e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 10}</td>\n",
       "      <td>11</td>\n",
       "      <td>-3.052380e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.124982e+09</td>\n",
       "      <td>-5.780873e+08</td>\n",
       "      <td>-2.865117e+09</td>\n",
       "      <td>-5.713421e+08</td>\n",
       "      <td>-3.169914e+09</td>\n",
       "      <td>-5.797944e+08</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>1.306954e+08</td>\n",
       "      <td>7.584886e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.447888</td>\n",
       "      <td>0.023658</td>\n",
       "      <td>-2.848364e+09</td>\n",
       "      <td>-4.396234e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 30}</td>\n",
       "      <td>9</td>\n",
       "      <td>-2.692176e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.943808e+09</td>\n",
       "      <td>-4.374429e+08</td>\n",
       "      <td>-2.619893e+09</td>\n",
       "      <td>-4.374715e+08</td>\n",
       "      <td>-2.968460e+09</td>\n",
       "      <td>-4.451903e+08</td>\n",
       "      <td>0.002696</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>1.604534e+08</td>\n",
       "      <td>2.883885e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.075246</td>\n",
       "      <td>0.002313</td>\n",
       "      <td>-3.716017e+09</td>\n",
       "      <td>-9.850011e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 3}</td>\n",
       "      <td>16</td>\n",
       "      <td>-3.729600e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.736527e+09</td>\n",
       "      <td>-9.172986e+08</td>\n",
       "      <td>-3.404974e+09</td>\n",
       "      <td>-1.035901e+09</td>\n",
       "      <td>-3.914186e+09</td>\n",
       "      <td>-9.711998e+08</td>\n",
       "      <td>0.000913</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>1.690029e+08</td>\n",
       "      <td>4.047487e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.245712</td>\n",
       "      <td>0.007718</td>\n",
       "      <td>-2.781569e+09</td>\n",
       "      <td>-5.160154e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 10}</td>\n",
       "      <td>8</td>\n",
       "      <td>-2.667093e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.891599e+09</td>\n",
       "      <td>-4.960301e+08</td>\n",
       "      <td>-2.613393e+09</td>\n",
       "      <td>-5.422542e+08</td>\n",
       "      <td>-2.949550e+09</td>\n",
       "      <td>-5.158794e+08</td>\n",
       "      <td>0.001580</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>1.278498e+08</td>\n",
       "      <td>1.498960e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.734909</td>\n",
       "      <td>0.023360</td>\n",
       "      <td>-2.537554e+09</td>\n",
       "      <td>-3.878685e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 30}</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.387199e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.663178e+09</td>\n",
       "      <td>-3.789712e+08</td>\n",
       "      <td>-2.397951e+09</td>\n",
       "      <td>-4.036920e+08</td>\n",
       "      <td>-2.649850e+09</td>\n",
       "      <td>-3.846171e+08</td>\n",
       "      <td>0.004653</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>1.209935e+08</td>\n",
       "      <td>8.424973e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.103654</td>\n",
       "      <td>0.002362</td>\n",
       "      <td>-3.441147e+09</td>\n",
       "      <td>-9.030212e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 3}</td>\n",
       "      <td>14</td>\n",
       "      <td>-3.119576e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.587747e+09</td>\n",
       "      <td>-9.360639e+08</td>\n",
       "      <td>-3.331544e+09</td>\n",
       "      <td>-9.025026e+08</td>\n",
       "      <td>-3.577062e+09</td>\n",
       "      <td>-8.612945e+08</td>\n",
       "      <td>0.003427</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>1.884229e+08</td>\n",
       "      <td>2.639683e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.342629</td>\n",
       "      <td>0.007915</td>\n",
       "      <td>-2.705037e+09</td>\n",
       "      <td>-5.014210e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 10}</td>\n",
       "      <td>6</td>\n",
       "      <td>-2.553481e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.762945e+09</td>\n",
       "      <td>-4.996537e+08</td>\n",
       "      <td>-2.519522e+09</td>\n",
       "      <td>-4.989516e+08</td>\n",
       "      <td>-2.906270e+09</td>\n",
       "      <td>-5.063617e+08</td>\n",
       "      <td>0.005475</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>1.464963e+08</td>\n",
       "      <td>3.357661e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.029902</td>\n",
       "      <td>0.023183</td>\n",
       "      <td>-2.515436e+09</td>\n",
       "      <td>-3.840197e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 30}</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.371924e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.607962e+09</td>\n",
       "      <td>-3.805596e+08</td>\n",
       "      <td>-2.351220e+09</td>\n",
       "      <td>-3.856159e+08</td>\n",
       "      <td>-2.662399e+09</td>\n",
       "      <td>-3.904866e+08</td>\n",
       "      <td>0.004482</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>1.283580e+08</td>\n",
       "      <td>3.796810e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.130636</td>\n",
       "      <td>0.002393</td>\n",
       "      <td>-3.348400e+09</td>\n",
       "      <td>-8.884890e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 3}</td>\n",
       "      <td>13</td>\n",
       "      <td>-3.351347e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.396841e+09</td>\n",
       "      <td>-8.596460e+08</td>\n",
       "      <td>-3.131753e+09</td>\n",
       "      <td>-8.893698e+08</td>\n",
       "      <td>-3.509451e+09</td>\n",
       "      <td>-9.146734e+08</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>1.226683e+08</td>\n",
       "      <td>2.730057e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.441364</td>\n",
       "      <td>0.007754</td>\n",
       "      <td>-2.676001e+09</td>\n",
       "      <td>-4.923247e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 10}</td>\n",
       "      <td>5</td>\n",
       "      <td>-2.572358e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.844608e+09</td>\n",
       "      <td>-4.730979e+08</td>\n",
       "      <td>-2.462797e+09</td>\n",
       "      <td>-5.154156e+08</td>\n",
       "      <td>-2.777049e+09</td>\n",
       "      <td>-4.979127e+08</td>\n",
       "      <td>0.003378</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>1.393253e+08</td>\n",
       "      <td>1.446900e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.332903</td>\n",
       "      <td>0.023311</td>\n",
       "      <td>-2.469578e+09</td>\n",
       "      <td>-3.809175e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 30}</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.358884e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.591134e+09</td>\n",
       "      <td>-3.772512e+08</td>\n",
       "      <td>-2.319816e+09</td>\n",
       "      <td>-3.881153e+08</td>\n",
       "      <td>-2.528200e+09</td>\n",
       "      <td>-3.807496e+08</td>\n",
       "      <td>0.006315</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>1.089395e+08</td>\n",
       "      <td>4.853344e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.070815</td>\n",
       "      <td>0.002721</td>\n",
       "      <td>-3.953191e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 3, 'bootst...</td>\n",
       "      <td>17</td>\n",
       "      <td>-3.792367e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.050371e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-3.668520e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-4.087237e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.001584</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>1.898516e+08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.235981</td>\n",
       "      <td>0.009598</td>\n",
       "      <td>-2.985912e+09</td>\n",
       "      <td>-6.056027e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 10, 'boots...</td>\n",
       "      <td>10</td>\n",
       "      <td>-2.808029e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.125519e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-2.788623e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-3.100391e+09</td>\n",
       "      <td>-2.967449e+00</td>\n",
       "      <td>0.002296</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>1.535103e+08</td>\n",
       "      <td>1.181156e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.095853</td>\n",
       "      <td>0.002854</td>\n",
       "      <td>-3.532863e+09</td>\n",
       "      <td>-1.214568e+01</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_features': 3, 'n_estimators': 3, 'bootst...</td>\n",
       "      <td>15</td>\n",
       "      <td>-3.604830e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.552984e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-3.610963e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-3.455760e+09</td>\n",
       "      <td>-6.072840e+01</td>\n",
       "      <td>0.001642</td>\n",
       "      <td>0.000212</td>\n",
       "      <td>7.251650e+07</td>\n",
       "      <td>2.429136e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.315569</td>\n",
       "      <td>0.009573</td>\n",
       "      <td>-2.781018e+09</td>\n",
       "      <td>-5.272080e+00</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_features': 3, 'n_estimators': 10, 'boots...</td>\n",
       "      <td>7</td>\n",
       "      <td>-2.756941e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.831963e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-2.672258e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-2.793018e+09</td>\n",
       "      <td>-5.465556e+00</td>\n",
       "      <td>0.002867</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>6.329307e+07</td>\n",
       "      <td>8.093117e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.120213</td>\n",
       "      <td>0.003053</td>\n",
       "      <td>-3.305102e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 3, 'bootst...</td>\n",
       "      <td>12</td>\n",
       "      <td>-3.143457e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.440323e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-3.047980e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-3.337950e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.002222</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>1.867866e+08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.402905</td>\n",
       "      <td>0.009938</td>\n",
       "      <td>-2.601843e+09</td>\n",
       "      <td>-3.028238e-03</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 10, 'boots...</td>\n",
       "      <td>4</td>\n",
       "      <td>-2.531436e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.606596e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-2.437626e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-2.726341e+09</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000843</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>1.082086e+08</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  mean_score_time  mean_test_score  mean_train_score  \\\n",
       "0        0.045302         0.002430    -4.051049e+09     -1.106013e+09   \n",
       "1        0.145982         0.007629    -3.092639e+09     -5.819353e+08   \n",
       "2        0.447888         0.023658    -2.848364e+09     -4.396234e+08   \n",
       "3        0.075246         0.002313    -3.716017e+09     -9.850011e+08   \n",
       "4        0.245712         0.007718    -2.781569e+09     -5.160154e+08   \n",
       "5        0.734909         0.023360    -2.537554e+09     -3.878685e+08   \n",
       "6        0.103654         0.002362    -3.441147e+09     -9.030212e+08   \n",
       "7        0.342629         0.007915    -2.705037e+09     -5.014210e+08   \n",
       "8        1.029902         0.023183    -2.515436e+09     -3.840197e+08   \n",
       "9        0.130636         0.002393    -3.348400e+09     -8.884890e+08   \n",
       "10       0.441364         0.007754    -2.676001e+09     -4.923247e+08   \n",
       "11       1.332903         0.023311    -2.469578e+09     -3.809175e+08   \n",
       "12       0.070815         0.002721    -3.953191e+09      0.000000e+00   \n",
       "13       0.235981         0.009598    -2.985912e+09     -6.056027e-01   \n",
       "14       0.095853         0.002854    -3.532863e+09     -1.214568e+01   \n",
       "15       0.315569         0.009573    -2.781018e+09     -5.272080e+00   \n",
       "16       0.120213         0.003053    -3.305102e+09      0.000000e+00   \n",
       "17       0.402905         0.009938    -2.601843e+09     -3.028238e-03   \n",
       "\n",
       "   param_bootstrap param_max_features param_n_estimators  \\\n",
       "0              NaN                  2                  3   \n",
       "1              NaN                  2                 10   \n",
       "2              NaN                  2                 30   \n",
       "3              NaN                  4                  3   \n",
       "4              NaN                  4                 10   \n",
       "5              NaN                  4                 30   \n",
       "6              NaN                  6                  3   \n",
       "7              NaN                  6                 10   \n",
       "8              NaN                  6                 30   \n",
       "9              NaN                  8                  3   \n",
       "10             NaN                  8                 10   \n",
       "11             NaN                  8                 30   \n",
       "12           False                  2                  3   \n",
       "13           False                  2                 10   \n",
       "14           False                  3                  3   \n",
       "15           False                  3                 10   \n",
       "16           False                  4                  3   \n",
       "17           False                  4                 10   \n",
       "\n",
       "                                               params  rank_test_score  \\\n",
       "0              {'max_features': 2, 'n_estimators': 3}               18   \n",
       "1             {'max_features': 2, 'n_estimators': 10}               11   \n",
       "2             {'max_features': 2, 'n_estimators': 30}                9   \n",
       "3              {'max_features': 4, 'n_estimators': 3}               16   \n",
       "4             {'max_features': 4, 'n_estimators': 10}                8   \n",
       "5             {'max_features': 4, 'n_estimators': 30}                3   \n",
       "6              {'max_features': 6, 'n_estimators': 3}               14   \n",
       "7             {'max_features': 6, 'n_estimators': 10}                6   \n",
       "8             {'max_features': 6, 'n_estimators': 30}                2   \n",
       "9              {'max_features': 8, 'n_estimators': 3}               13   \n",
       "10            {'max_features': 8, 'n_estimators': 10}                5   \n",
       "11            {'max_features': 8, 'n_estimators': 30}                1   \n",
       "12  {'max_features': 2, 'n_estimators': 3, 'bootst...               17   \n",
       "13  {'max_features': 2, 'n_estimators': 10, 'boots...               10   \n",
       "14  {'max_features': 3, 'n_estimators': 3, 'bootst...               15   \n",
       "15  {'max_features': 3, 'n_estimators': 10, 'boots...                7   \n",
       "16  {'max_features': 4, 'n_estimators': 3, 'bootst...               12   \n",
       "17  {'max_features': 4, 'n_estimators': 10, 'boots...                4   \n",
       "\n",
       "    split0_test_score       ...         split2_test_score  split2_train_score  \\\n",
       "0       -3.850668e+09       ...             -4.194135e+09       -1.116843e+09   \n",
       "1       -3.052380e+09       ...             -3.124982e+09       -5.780873e+08   \n",
       "2       -2.692176e+09       ...             -2.943808e+09       -4.374429e+08   \n",
       "3       -3.729600e+09       ...             -3.736527e+09       -9.172986e+08   \n",
       "4       -2.667093e+09       ...             -2.891599e+09       -4.960301e+08   \n",
       "5       -2.387199e+09       ...             -2.663178e+09       -3.789712e+08   \n",
       "6       -3.119576e+09       ...             -3.587747e+09       -9.360639e+08   \n",
       "7       -2.553481e+09       ...             -2.762945e+09       -4.996537e+08   \n",
       "8       -2.371924e+09       ...             -2.607962e+09       -3.805596e+08   \n",
       "9       -3.351347e+09       ...             -3.396841e+09       -8.596460e+08   \n",
       "10      -2.572358e+09       ...             -2.844608e+09       -4.730979e+08   \n",
       "11      -2.358884e+09       ...             -2.591134e+09       -3.772512e+08   \n",
       "12      -3.792367e+09       ...             -4.050371e+09       -0.000000e+00   \n",
       "13      -2.808029e+09       ...             -3.125519e+09       -0.000000e+00   \n",
       "14      -3.604830e+09       ...             -3.552984e+09       -0.000000e+00   \n",
       "15      -2.756941e+09       ...             -2.831963e+09       -0.000000e+00   \n",
       "16      -3.143457e+09       ...             -3.440323e+09       -0.000000e+00   \n",
       "17      -2.531436e+09       ...             -2.606596e+09       -0.000000e+00   \n",
       "\n",
       "    split3_test_score  split3_train_score  split4_test_score  \\\n",
       "0       -3.906732e+09       -1.112813e+09      -4.169669e+09   \n",
       "1       -2.865117e+09       -5.713421e+08      -3.169914e+09   \n",
       "2       -2.619893e+09       -4.374715e+08      -2.968460e+09   \n",
       "3       -3.404974e+09       -1.035901e+09      -3.914186e+09   \n",
       "4       -2.613393e+09       -5.422542e+08      -2.949550e+09   \n",
       "5       -2.397951e+09       -4.036920e+08      -2.649850e+09   \n",
       "6       -3.331544e+09       -9.025026e+08      -3.577062e+09   \n",
       "7       -2.519522e+09       -4.989516e+08      -2.906270e+09   \n",
       "8       -2.351220e+09       -3.856159e+08      -2.662399e+09   \n",
       "9       -3.131753e+09       -8.893698e+08      -3.509451e+09   \n",
       "10      -2.462797e+09       -5.154156e+08      -2.777049e+09   \n",
       "11      -2.319816e+09       -3.881153e+08      -2.528200e+09   \n",
       "12      -3.668520e+09       -0.000000e+00      -4.087237e+09   \n",
       "13      -2.788623e+09       -0.000000e+00      -3.100391e+09   \n",
       "14      -3.610963e+09       -0.000000e+00      -3.455760e+09   \n",
       "15      -2.672258e+09       -0.000000e+00      -2.793018e+09   \n",
       "16      -3.047980e+09       -0.000000e+00      -3.337950e+09   \n",
       "17      -2.437626e+09       -0.000000e+00      -2.726341e+09   \n",
       "\n",
       "    split4_train_score  std_fit_time  std_score_time  std_test_score  \\\n",
       "0        -1.129842e+09      0.001158        0.000211    1.431223e+08   \n",
       "1        -5.797944e+08      0.000994        0.000199    1.306954e+08   \n",
       "2        -4.451903e+08      0.002696        0.000297    1.604534e+08   \n",
       "3        -9.711998e+08      0.000913        0.000055    1.690029e+08   \n",
       "4        -5.158794e+08      0.001580        0.000156    1.278498e+08   \n",
       "5        -3.846171e+08      0.004653        0.000110    1.209935e+08   \n",
       "6        -8.612945e+08      0.003427        0.000044    1.884229e+08   \n",
       "7        -5.063617e+08      0.005475        0.000176    1.464963e+08   \n",
       "8        -3.904866e+08      0.004482        0.000140    1.283580e+08   \n",
       "9        -9.146734e+08      0.000712        0.000129    1.226683e+08   \n",
       "10       -4.979127e+08      0.003378        0.000204    1.393253e+08   \n",
       "11       -3.807496e+08      0.006315        0.000131    1.089395e+08   \n",
       "12       -0.000000e+00      0.001584        0.000097    1.898516e+08   \n",
       "13       -2.967449e+00      0.002296        0.000120    1.535103e+08   \n",
       "14       -6.072840e+01      0.001642        0.000212    7.251650e+07   \n",
       "15       -5.465556e+00      0.002867        0.000179    6.329307e+07   \n",
       "16       -0.000000e+00      0.002222        0.000241    1.867866e+08   \n",
       "17       -0.000000e+00      0.000843        0.000070    1.082086e+08   \n",
       "\n",
       "    std_train_score  \n",
       "0      2.173798e+07  \n",
       "1      7.584886e+06  \n",
       "2      2.883885e+06  \n",
       "3      4.047487e+07  \n",
       "4      1.498960e+07  \n",
       "5      8.424973e+06  \n",
       "6      2.639683e+07  \n",
       "7      3.357661e+06  \n",
       "8      3.796810e+06  \n",
       "9      2.730057e+07  \n",
       "10     1.446900e+07  \n",
       "11     4.853344e+06  \n",
       "12     0.000000e+00  \n",
       "13     1.181156e+00  \n",
       "14     2.429136e+01  \n",
       "15     8.093117e+00  \n",
       "16     0.000000e+00  \n",
       "17     6.056477e-03  \n",
       "\n",
       "[18 rows x 23 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(grid_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\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=10, n_jobs=1,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f26e6519ac8>, 'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f26de22e080>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score=True, scoring='neg_mean_squared_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 98,
     "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": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49147.1524172 {'max_features': 7, 'n_estimators': 180}\n",
      "51396.8768969 {'max_features': 5, 'n_estimators': 15}\n",
      "50797.0573732 {'max_features': 3, 'n_estimators': 72}\n",
      "50840.744514 {'max_features': 5, 'n_estimators': 21}\n",
      "49276.1753033 {'max_features': 7, 'n_estimators': 122}\n",
      "50775.4633168 {'max_features': 3, 'n_estimators': 75}\n",
      "50681.383925 {'max_features': 3, 'n_estimators': 88}\n",
      "49612.1525305 {'max_features': 5, 'n_estimators': 100}\n",
      "50473.0175142 {'max_features': 3, 'n_estimators': 150}\n",
      "64458.2538503 {'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": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  7.33442355e-02,   6.29090705e-02,   4.11437985e-02,\n",
       "         1.46726854e-02,   1.41064835e-02,   1.48742809e-02,\n",
       "         1.42575993e-02,   3.66158981e-01,   5.64191792e-02,\n",
       "         1.08792957e-01,   5.33510773e-02,   1.03114883e-02,\n",
       "         1.64780994e-01,   6.02803867e-05,   1.96041560e-03,\n",
       "         2.85647464e-03])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.36615898061813418, 'median_income'),\n",
       " (0.16478099356159051, 'INLAND'),\n",
       " (0.10879295677551573, 'pop_per_hhold'),\n",
       " (0.073344235516012421, 'longitude'),\n",
       " (0.062909070482620302, 'latitude'),\n",
       " (0.056419179181954007, 'rooms_per_hhold'),\n",
       " (0.053351077347675809, '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",
       " (0.0028564746373201579, 'NEAR OCEAN'),\n",
       " (0.0019604155994780701, 'NEAR BAY'),\n",
       " (6.0280386727365991e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_attribs = [\"rooms_per_hhold\", \"pop_per_hhold\", \"bedrooms_per_room\"]\n",
    "cat_encoder = cat_pipeline.named_steps[\"cat_encoder\"]\n",
    "cat_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": 102,
   "metadata": {
    "collapsed": true
   },
   "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": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47766.003966433083"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rmse"
   ]
  },
  {
   "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": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 210644.60459286,  317768.80697211,  210956.43331178,\n",
       "         59218.98886849,  189747.55849879])"
      ]
     },
     "execution_count": 104,
     "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": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_model = full_pipeline_with_predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "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": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26e61afe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26d40dd748>"
      ]
     },
     "metadata": {},
     "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": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "[CV] C=10.0, kernel=linear ...........................................\n",
      "[CV] C=10.0, kernel=linear ...........................................\n",
      "[CV] C=10.0, kernel=linear ...........................................\n",
      "[CV] C=10.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=10.0, kernel=linear, total=   6.5s\n",
      "[CV] C=10.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=10.0, kernel=linear, total=   6.5s\n",
      "[CV] C=30.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=10.0, kernel=linear, total=   6.6s\n",
      "[CV] C=30.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=10.0, kernel=linear, total=   6.6s\n",
      "[CV] C=30.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=30.0, kernel=linear, total=   6.4s\n",
      "[CV] C=30.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=30.0, kernel=linear, total=   6.5s\n",
      "[CV] C=30.0, kernel=linear ...........................................\n",
      "[CV] ............................ C=10.0, kernel=linear, total=   6.6s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ............................ C=30.0, kernel=linear, total=   6.7s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ............................ C=30.0, kernel=linear, total=   6.4s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ............................ C=30.0, kernel=linear, total=   6.6s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   6.4s\n",
      "[CV] C=100.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   6.4s\n",
      "[CV] C=300.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   6.4s\n",
      "[CV] C=300.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   6.3s\n",
      "[CV] C=300.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=100.0, kernel=linear, total=   6.5s\n",
      "[CV] C=300.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=300.0, kernel=linear, total=   6.5s\n",
      "[CV] C=300.0, kernel=linear ..........................................\n",
      "[CV] ........................... C=300.0, kernel=linear, total=   6.5s\n",
      "[CV] C=1000.0, kernel=linear .........................................\n",
      "[CV] ........................... C=300.0, kernel=linear, total=   6.6s\n",
      "[CV] C=1000.0, kernel=linear .........................................\n",
      "[CV] ........................... C=300.0, kernel=linear, total=   6.6s\n",
      "[CV] C=1000.0, kernel=linear .........................................\n",
      "[CV] ........................... C=300.0, kernel=linear, total=   6.5s\n",
      "[CV] C=1000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=1000.0, kernel=linear, total=   6.6s\n",
      "[CV] C=1000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=1000.0, kernel=linear, total=   6.7s\n",
      "[CV] C=3000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=1000.0, kernel=linear, total=   6.9s\n",
      "[CV] C=3000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=1000.0, kernel=linear, total=   6.8s\n",
      "[CV] C=3000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=1000.0, kernel=linear, total=   6.6s\n",
      "[CV] C=3000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=3000.0, kernel=linear, total=   7.3s\n",
      "[CV] C=3000.0, kernel=linear .........................................\n",
      "[CV] .......................... C=3000.0, kernel=linear, total=   7.1s\n",
      "[CV] C=10000.0, kernel=linear ........................................\n",
      "[CV] .......................... C=3000.0, kernel=linear, total=   7.3s\n",
      "[CV] C=10000.0, kernel=linear ........................................\n",
      "[CV] .......................... C=3000.0, kernel=linear, total=   7.6s\n",
      "[CV] C=10000.0, kernel=linear ........................................\n",
      "[CV] .......................... C=3000.0, kernel=linear, total=   7.2s\n",
      "[CV] C=10000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=10000.0, kernel=linear, total=   9.4s\n",
      "[CV] C=10000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=10000.0, kernel=linear, total=   9.6s\n",
      "[CV] C=30000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=10000.0, kernel=linear, total=   9.0s\n",
      "[CV] C=30000.0, kernel=linear ........................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:  1.4min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ......................... C=10000.0, kernel=linear, total=   9.8s\n",
      "[CV] C=30000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=10000.0, kernel=linear, total=   8.6s\n",
      "[CV] C=30000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=30000.0, kernel=linear, total=  15.3s\n",
      "[CV] C=30000.0, kernel=linear ........................................\n",
      "[CV] ......................... C=30000.0, kernel=linear, total=  15.4s\n",
      "[CV] gamma=0.01, C=1.0, kernel=rbf ...................................\n",
      "[CV] ......................... C=30000.0, kernel=linear, total=  16.1s\n",
      "[CV] gamma=0.01, C=1.0, kernel=rbf ...................................\n",
      "[CV] ......................... C=30000.0, kernel=linear, total=  15.4s\n",
      "[CV] gamma=0.01, C=1.0, kernel=rbf ...................................\n",
      "[CV] ......................... C=30000.0, kernel=linear, total=  13.5s\n",
      "[CV] gamma=0.01, C=1.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=0.01, C=1.0, kernel=rbf, total=  24.5s\n",
      "[CV] gamma=0.01, C=1.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=0.01, C=1.0, kernel=rbf, total=  24.6s\n",
      "[CV] gamma=0.03, C=1.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=0.01, C=1.0, kernel=rbf, total=  24.8s\n",
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      "[CV] .................... gamma=0.01, C=1.0, kernel=rbf, total=  11.4s\n",
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      "[CV] gamma=0.1, C=1.0, kernel=rbf ....................................\n",
      "[CV] .................... gamma=0.03, C=1.0, kernel=rbf, total=  11.4s\n",
      "[CV] gamma=0.1, C=1.0, kernel=rbf ....................................\n",
      "[CV] .................... gamma=0.03, C=1.0, kernel=rbf, total=  11.5s\n",
      "[CV] gamma=0.1, C=1.0, kernel=rbf ....................................\n",
      "[CV] .................... gamma=0.03, C=1.0, kernel=rbf, total=  11.3s\n",
      "[CV] gamma=0.1, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.1, C=1.0, kernel=rbf, total=  11.4s\n",
      "[CV] gamma=0.1, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.1, C=1.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=0.3, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.1, C=1.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=0.3, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.1, C=1.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=0.3, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.1, C=1.0, kernel=rbf, total=  11.3s\n",
      "[CV] gamma=0.3, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.3, C=1.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=0.3, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.3, C=1.0, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=1.0, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.3, C=1.0, kernel=rbf, total=  11.1s\n",
      "[CV] gamma=1.0, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.3, C=1.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=1.0, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.3, C=1.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=1.0, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=1.0, C=1.0, kernel=rbf, total=  10.6s\n",
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      "[CV] ..................... gamma=1.0, C=1.0, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=3.0, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=1.0, C=1.0, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=3.0, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=1.0, C=1.0, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=3.0, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=1.0, C=1.0, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=3.0, C=1.0, kernel=rbf ....................................\n",
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      "[CV] gamma=3.0, C=1.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=3.0, C=1.0, kernel=rbf, total=  12.5s\n",
      "[CV] gamma=0.01, C=3.0, kernel=rbf ...................................\n",
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      "[CV] gamma=0.01, C=3.0, kernel=rbf ...................................\n",
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      "[CV] gamma=0.01, C=3.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=0.01, C=3.0, kernel=rbf, total=  11.8s\n",
      "[CV] gamma=0.03, C=3.0, kernel=rbf ...................................\n",
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      "[CV] gamma=0.03, C=3.0, kernel=rbf ...................................\n",
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      "[CV] gamma=0.03, C=3.0, kernel=rbf ...................................\n",
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      "[CV] gamma=0.03, C=3.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=0.03, C=3.0, kernel=rbf, total=  11.8s\n",
      "[CV] gamma=0.03, C=3.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=0.03, C=3.0, kernel=rbf, total=  11.5s\n",
      "[CV] gamma=0.1, C=3.0, kernel=rbf ....................................\n",
      "[CV] .................... gamma=0.03, C=3.0, kernel=rbf, total=  11.7s\n",
      "[CV] gamma=0.1, C=3.0, kernel=rbf ....................................\n",
      "[CV] .................... gamma=0.03, C=3.0, kernel=rbf, total=  11.6s\n",
      "[CV] gamma=0.1, C=3.0, kernel=rbf ....................................\n",
      "[CV] .................... gamma=0.03, C=3.0, kernel=rbf, total=  11.7s\n",
      "[CV] gamma=0.1, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.1, C=3.0, kernel=rbf, total=  12.0s\n",
      "[CV] gamma=0.1, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.1, C=3.0, kernel=rbf, total=  11.5s\n",
      "[CV] gamma=0.3, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.1, C=3.0, kernel=rbf, total=  11.7s\n",
      "[CV] gamma=0.3, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.1, C=3.0, kernel=rbf, total=  11.5s\n",
      "[CV] gamma=0.3, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.1, C=3.0, kernel=rbf, total=  11.9s\n",
      "[CV] gamma=0.3, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.3, C=3.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=0.3, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.3, C=3.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=1.0, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.3, C=3.0, kernel=rbf, total=  11.1s\n",
      "[CV] gamma=1.0, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.3, C=3.0, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=1.0, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=0.3, C=3.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=1.0, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=1.0, C=3.0, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=1.0, C=3.0, kernel=rbf ....................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..................... gamma=1.0, C=3.0, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=3.0, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=1.0, C=3.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=3.0, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=1.0, C=3.0, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=3.0, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=1.0, C=3.0, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=3.0, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=3.0, C=3.0, kernel=rbf, total=  11.8s\n",
      "[CV] gamma=3.0, C=3.0, kernel=rbf ....................................\n",
      "[CV] ..................... gamma=3.0, C=3.0, kernel=rbf, total=  11.9s\n",
      "[CV] gamma=0.01, C=10.0, kernel=rbf ..................................\n",
      "[CV] ..................... gamma=3.0, C=3.0, kernel=rbf, total=  11.8s\n",
      "[CV] gamma=0.01, C=10.0, kernel=rbf ..................................\n",
      "[CV] ..................... gamma=3.0, C=3.0, kernel=rbf, total=  11.8s\n",
      "[CV] gamma=0.01, C=10.0, kernel=rbf ..................................\n",
      "[CV] ..................... gamma=3.0, C=3.0, kernel=rbf, total=  11.9s\n",
      "[CV] gamma=0.01, C=10.0, kernel=rbf ..................................\n",
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      "[CV] gamma=0.01, C=10.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.01, C=10.0, kernel=rbf, total=  11.5s\n",
      "[CV] gamma=0.03, C=10.0, kernel=rbf ..................................\n",
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      "[CV] gamma=0.03, C=10.0, kernel=rbf ..................................\n",
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      "[CV] gamma=0.03, C=10.0, kernel=rbf ..................................\n",
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      "[CV] gamma=0.1, C=10.0, kernel=rbf ...................................\n",
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      "[CV] gamma=0.1, C=10.0, kernel=rbf ...................................\n",
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      "[CV] gamma=0.1, C=10.0, kernel=rbf ...................................\n",
      "[CV] ................... gamma=0.03, C=10.0, kernel=rbf, total=  11.5s\n",
      "[CV] gamma=0.1, C=10.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=0.1, C=10.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=0.1, C=10.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=0.1, C=10.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=0.3, C=10.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=0.1, C=10.0, kernel=rbf, total=  11.5s\n",
      "[CV] gamma=0.3, C=10.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=0.1, C=10.0, kernel=rbf, total=  11.3s\n",
      "[CV] gamma=0.3, C=10.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=0.1, C=10.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=0.3, C=10.0, kernel=rbf ...................................\n",
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      "[CV] gamma=0.3, C=10.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=0.3, C=10.0, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=1.0, C=10.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=0.3, C=10.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=1.0, C=10.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=0.3, C=10.0, kernel=rbf, total=  11.7s\n",
      "[CV] gamma=1.0, C=10.0, kernel=rbf ...................................\n",
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      "[CV] gamma=1.0, C=10.0, kernel=rbf ...................................\n",
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      "[CV] gamma=3.0, C=10.0, kernel=rbf ...................................\n",
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      "[CV] gamma=3.0, C=10.0, kernel=rbf ...................................\n",
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      "[CV] gamma=3.0, C=10.0, kernel=rbf ...................................\n",
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      "[CV] gamma=3.0, C=10.0, kernel=rbf ...................................\n",
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      "[CV] gamma=0.01, C=30.0, kernel=rbf ..................................\n",
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      "[CV] gamma=0.03, C=30.0, kernel=rbf ..................................\n",
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      "[CV] gamma=0.1, C=30.0, kernel=rbf ...................................\n",
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      "[CV] gamma=0.3, C=30.0, kernel=rbf ...................................\n",
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      "[CV] gamma=0.3, C=30.0, kernel=rbf ...................................\n",
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      "[CV] gamma=0.3, C=30.0, kernel=rbf ...................................\n",
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      "[CV] gamma=1.0, C=30.0, kernel=rbf ...................................\n",
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      "[CV] gamma=1.0, C=30.0, kernel=rbf ...................................\n",
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      "[CV] gamma=1.0, C=30.0, kernel=rbf ...................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .................... gamma=0.3, C=30.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=1.0, C=30.0, kernel=rbf ...................................\n",
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      "[CV] gamma=3.0, C=30.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=1.0, C=30.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=3.0, C=30.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=1.0, C=30.0, kernel=rbf, total=  10.5s\n",
      "[CV] gamma=3.0, C=30.0, kernel=rbf ...................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 154 tasks      | elapsed:  9.9min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .................... gamma=1.0, C=30.0, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=3.0, C=30.0, kernel=rbf ...................................\n",
      "[CV] .................... gamma=3.0, C=30.0, kernel=rbf, total=  11.7s\n",
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      "[CV] .................... gamma=3.0, C=30.0, kernel=rbf, total=  12.0s\n",
      "[CV] gamma=0.01, C=100.0, kernel=rbf .................................\n",
      "[CV] .................... gamma=3.0, C=30.0, kernel=rbf, total=  12.0s\n",
      "[CV] gamma=0.01, C=100.0, kernel=rbf .................................\n",
      "[CV] .................... gamma=3.0, C=30.0, kernel=rbf, total=  12.0s\n",
      "[CV] gamma=0.01, C=100.0, kernel=rbf .................................\n",
      "[CV] .................... gamma=3.0, C=30.0, kernel=rbf, total=  12.2s\n",
      "[CV] gamma=0.01, C=100.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.01, C=100.0, kernel=rbf, total=  11.5s\n",
      "[CV] gamma=0.01, C=100.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.01, C=100.0, kernel=rbf, total=  11.5s\n",
      "[CV] gamma=0.03, C=100.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.01, C=100.0, kernel=rbf, total=  11.6s\n",
      "[CV] gamma=0.03, C=100.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.01, C=100.0, kernel=rbf, total=  11.5s\n",
      "[CV] gamma=0.03, C=100.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.03, C=100.0, kernel=rbf, total=  11.3s\n",
      "[CV] gamma=0.03, C=100.0, kernel=rbf .................................\n",
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      "[CV] gamma=0.03, C=100.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.03, C=100.0, kernel=rbf, total=  11.8s\n",
      "[CV] gamma=0.1, C=100.0, kernel=rbf ..................................\n",
      "[CV] .................. gamma=0.03, C=100.0, kernel=rbf, total=  11.6s\n",
      "[CV] gamma=0.1, C=100.0, kernel=rbf ..................................\n",
      "[CV] .................. gamma=0.03, C=100.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=0.1, C=100.0, kernel=rbf ..................................\n",
      "[CV] .................. gamma=0.03, C=100.0, kernel=rbf, total=  11.5s\n",
      "[CV] gamma=0.1, C=100.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.1, C=100.0, kernel=rbf, total=  11.1s\n",
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      "[CV] gamma=0.3, C=100.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.1, C=100.0, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=0.3, C=100.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.1, C=100.0, kernel=rbf, total=  11.1s\n",
      "[CV] gamma=0.3, C=100.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.1, C=100.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=0.3, C=100.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.3, C=100.0, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=0.3, C=100.0, kernel=rbf ..................................\n",
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      "[CV] gamma=1.0, C=100.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.3, C=100.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=1.0, C=100.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.3, C=100.0, kernel=rbf, total=  11.1s\n",
      "[CV] gamma=1.0, C=100.0, kernel=rbf ..................................\n",
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      "[CV] gamma=3.0, C=100.0, kernel=rbf ..................................\n",
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      "[CV] ................... gamma=3.0, C=100.0, kernel=rbf, total=  11.9s\n",
      "[CV] gamma=0.01, C=300.0, kernel=rbf .................................\n",
      "[CV] ................... gamma=3.0, C=100.0, kernel=rbf, total=  11.8s\n",
      "[CV] gamma=0.01, C=300.0, kernel=rbf .................................\n",
      "[CV] ................... gamma=3.0, C=100.0, kernel=rbf, total=  12.0s\n",
      "[CV] gamma=0.01, C=300.0, kernel=rbf .................................\n",
      "[CV] ................... gamma=3.0, C=100.0, kernel=rbf, total=  11.9s\n",
      "[CV] gamma=0.01, C=300.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.01, C=300.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=0.01, C=300.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.01, C=300.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=0.03, C=300.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.01, C=300.0, kernel=rbf, total=  11.3s\n",
      "[CV] gamma=0.03, C=300.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.01, C=300.0, kernel=rbf, total=  11.5s\n",
      "[CV] gamma=0.03, C=300.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.01, C=300.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=0.03, C=300.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.03, C=300.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=0.03, C=300.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.03, C=300.0, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=0.1, C=300.0, kernel=rbf ..................................\n",
      "[CV] .................. gamma=0.03, C=300.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=0.1, C=300.0, kernel=rbf ..................................\n",
      "[CV] .................. gamma=0.03, C=300.0, kernel=rbf, total=  11.1s\n",
      "[CV] gamma=0.1, C=300.0, kernel=rbf ..................................\n",
      "[CV] .................. gamma=0.03, C=300.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=0.1, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.1, C=300.0, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=0.1, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.1, C=300.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=0.3, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.1, C=300.0, kernel=rbf, total=  11.1s\n",
      "[CV] gamma=0.3, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.1, C=300.0, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=0.3, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.1, C=300.0, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=0.3, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.3, C=300.0, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=0.3, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.3, C=300.0, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=1.0, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.3, C=300.0, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=1.0, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.3, C=300.0, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=1.0, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=0.3, C=300.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=1.0, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=1.0, C=300.0, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=1.0, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=1.0, C=300.0, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=3.0, C=300.0, kernel=rbf ..................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................... gamma=1.0, C=300.0, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=3.0, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=1.0, C=300.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=3.0, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=1.0, C=300.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=3.0, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=3.0, C=300.0, kernel=rbf, total=  12.2s\n",
      "[CV] gamma=3.0, C=300.0, kernel=rbf ..................................\n",
      "[CV] ................... gamma=3.0, C=300.0, kernel=rbf, total=  12.1s\n",
      "[CV] gamma=0.01, C=1000.0, kernel=rbf ................................\n",
      "[CV] ................... gamma=3.0, C=300.0, kernel=rbf, total=  11.8s\n",
      "[CV] gamma=0.01, C=1000.0, kernel=rbf ................................\n",
      "[CV] ................... gamma=3.0, C=300.0, kernel=rbf, total=  12.2s\n",
      "[CV] gamma=0.01, C=1000.0, kernel=rbf ................................\n",
      "[CV] ................... gamma=3.0, C=300.0, kernel=rbf, total=  12.4s\n",
      "[CV] gamma=0.01, C=1000.0, kernel=rbf ................................\n",
      "[CV] ................. gamma=0.01, C=1000.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=0.01, C=1000.0, kernel=rbf ................................\n",
      "[CV] ................. gamma=0.01, C=1000.0, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=0.03, C=1000.0, kernel=rbf ................................\n",
      "[CV] ................. gamma=0.01, C=1000.0, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=0.03, C=1000.0, kernel=rbf ................................\n",
      "[CV] ................. gamma=0.01, C=1000.0, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=0.03, C=1000.0, kernel=rbf ................................\n",
      "[CV] ................. gamma=0.01, C=1000.0, kernel=rbf, total=  11.1s\n",
      "[CV] gamma=0.03, C=1000.0, kernel=rbf ................................\n",
      "[CV] ................. gamma=0.03, C=1000.0, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.03, C=1000.0, kernel=rbf ................................\n",
      "[CV] ................. gamma=0.03, C=1000.0, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=0.1, C=1000.0, kernel=rbf .................................\n",
      "[CV] ................. gamma=0.03, C=1000.0, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.1, C=1000.0, kernel=rbf .................................\n",
      "[CV] ................. gamma=0.03, C=1000.0, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=0.1, C=1000.0, kernel=rbf .................................\n",
      "[CV] ................. gamma=0.03, C=1000.0, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=0.1, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.1, C=1000.0, kernel=rbf, total=  10.5s\n",
      "[CV] gamma=0.1, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.1, C=1000.0, kernel=rbf, total=  10.5s\n",
      "[CV] gamma=0.3, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.1, C=1000.0, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=0.3, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.1, C=1000.0, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.3, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.1, C=1000.0, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=0.3, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.3, C=1000.0, kernel=rbf, total=  10.5s\n",
      "[CV] gamma=0.3, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.3, C=1000.0, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=1.0, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.3, C=1000.0, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=1.0, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.3, C=1000.0, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=1.0, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=0.3, C=1000.0, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=1.0, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=1.0, C=1000.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=1.0, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=1.0, C=1000.0, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=3.0, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=1.0, C=1000.0, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=3.0, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=1.0, C=1000.0, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=3.0, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=1.0, C=1000.0, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=3.0, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=3.0, C=1000.0, kernel=rbf, total=  11.8s\n",
      "[CV] gamma=3.0, C=1000.0, kernel=rbf .................................\n",
      "[CV] .................. gamma=3.0, C=1000.0, kernel=rbf, total=  11.9s\n",
      "[CV] .................. gamma=3.0, C=1000.0, kernel=rbf, total=  12.4s\n",
      "[CV] .................. gamma=3.0, C=1000.0, kernel=rbf, total=  11.7s\n",
      "[CV] .................. gamma=3.0, C=1000.0, kernel=rbf, total=  11.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 250 out of 250 | elapsed: 16.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=4,\n",
       "       param_grid=[{'C': [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0, 10000.0, 30000.0], 'kernel': ['linear']}, {'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0], 'C': [1.0, 3.0, 10.0, 30.0, 100.0, 300.0, 1000.0], 'kernel': ['rbf']}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_mean_squared_error', verbose=2)"
      ]
     },
     "execution_count": 108,
     "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": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70363.903139641669"
      ]
     },
     "execution_count": 109,
     "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": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 30000.0, 'kernel': 'linear'}"
      ]
     },
     "execution_count": 110,
     "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": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "[CV] gamma=3.01012143092, C=629.782329591, kernel=linear .............\n",
      "[CV] gamma=3.01012143092, C=629.782329591, kernel=linear .............\n",
      "[CV] gamma=3.01012143092, C=629.782329591, kernel=linear .............\n",
      "[CV] gamma=3.01012143092, C=629.782329591, kernel=linear .............\n",
      "[CV]  gamma=3.01012143092, C=629.782329591, kernel=linear, total=   6.6s\n",
      "[CV] gamma=3.01012143092, C=629.782329591, kernel=linear .............\n",
      "[CV]  gamma=3.01012143092, C=629.782329591, kernel=linear, total=   6.6s\n",
      "[CV] gamma=0.908446969632, C=26290.2064643, kernel=rbf ...............\n",
      "[CV]  gamma=3.01012143092, C=629.782329591, kernel=linear, total=   6.7s\n",
      "[CV] gamma=0.908446969632, C=26290.2064643, kernel=rbf ...............\n",
      "[CV]  gamma=3.01012143092, C=629.782329591, kernel=linear, total=   6.8s\n",
      "[CV] gamma=0.908446969632, C=26290.2064643, kernel=rbf ...............\n",
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      "[CV] gamma=0.908446969632, C=26290.2064643, kernel=rbf ...............\n",
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      "[CV] gamma=0.908446969632, C=26290.2064643, kernel=rbf ...............\n",
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      "[CV] gamma=0.0598387686087, C=84.1410790058, kernel=rbf ..............\n",
      "[CV]  gamma=0.908446969632, C=26290.2064643, kernel=rbf, total=  25.9s\n",
      "[CV] gamma=0.0598387686087, C=84.1410790058, kernel=rbf ..............\n",
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      "[CV] gamma=0.0598387686087, C=84.1410790058, kernel=rbf ..............\n",
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      "[CV] gamma=0.0598387686087, C=84.1410790058, kernel=rbf ..............\n",
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      "[CV] gamma=0.0598387686087, C=84.1410790058, kernel=rbf ..............\n",
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      "[CV] gamma=0.154161967467, C=432.378848131, kernel=linear ............\n",
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      "[CV] gamma=0.154161967467, C=432.378848131, kernel=linear ............\n",
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      "[CV] gamma=0.154161967467, C=432.378848131, kernel=linear ............\n",
      "[CV]  gamma=0.0598387686087, C=84.1410790058, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=0.154161967467, C=432.378848131, kernel=linear ............\n",
      "[CV]  gamma=0.0598387686087, C=84.1410790058, kernel=rbf, total=  11.4s\n",
      "[CV] gamma=0.154161967467, C=432.378848131, kernel=linear ............\n",
      "[CV]  gamma=0.154161967467, C=432.378848131, kernel=linear, total=   6.6s\n",
      "[CV] gamma=3.50355747516, C=24.1750829461, kernel=rbf ................\n",
      "[CV]  gamma=0.154161967467, C=432.378848131, kernel=linear, total=   6.6s\n",
      "[CV] gamma=3.50355747516, C=24.1750829461, kernel=rbf ................\n",
      "[CV]  gamma=0.154161967467, C=432.378848131, kernel=linear, total=   6.5s\n",
      "[CV] gamma=3.50355747516, C=24.1750829461, kernel=rbf ................\n",
      "[CV]  gamma=0.154161967467, C=432.378848131, kernel=linear, total=   6.6s\n",
      "[CV] gamma=3.50355747516, C=24.1750829461, kernel=rbf ................\n",
      "[CV] . gamma=3.50355747516, C=24.1750829461, kernel=rbf, total=  12.6s\n",
      "[CV] gamma=3.50355747516, C=24.1750829461, kernel=rbf ................\n",
      "[CV] . gamma=3.50355747516, C=24.1750829461, kernel=rbf, total=  12.5s\n",
      "[CV] gamma=0.000779069236658, C=113564.039406, kernel=rbf ............\n",
      "[CV] . gamma=3.50355747516, C=24.1750829461, kernel=rbf, total=  12.7s\n",
      "[CV] gamma=0.000779069236658, C=113564.039406, kernel=rbf ............\n",
      "[CV] . gamma=3.50355747516, C=24.1750829461, kernel=rbf, total=  12.4s\n",
      "[CV] gamma=0.000779069236658, C=113564.039406, kernel=rbf ............\n",
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      "[CV] gamma=0.000779069236658, C=113564.039406, kernel=rbf ............\n",
      "[CV] . gamma=3.50355747516, C=24.1750829461, kernel=rbf, total=  12.4s\n",
      "[CV] gamma=0.000779069236658, C=113564.039406, kernel=rbf ............\n",
      "[CV]  gamma=0.000779069236658, C=113564.039406, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=0.36275372946, C=108.304882388, kernel=rbf ................\n",
      "[CV]  gamma=0.000779069236658, C=113564.039406, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=0.36275372946, C=108.304882388, kernel=rbf ................\n",
      "[CV]  gamma=0.000779069236658, C=113564.039406, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=0.36275372946, C=108.304882388, kernel=rbf ................\n",
      "[CV]  gamma=0.000779069236658, C=113564.039406, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.36275372946, C=108.304882388, kernel=rbf ................\n",
      "[CV] . gamma=0.36275372946, C=108.304882388, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=0.36275372946, C=108.304882388, kernel=rbf ................\n",
      "[CV] . gamma=0.36275372946, C=108.304882388, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=0.0233325235983, C=21.3449536726, kernel=linear ...........\n",
      "[CV]  gamma=0.0233325235983, C=21.3449536726, kernel=linear, total=   6.5s\n",
      "[CV] gamma=0.0233325235983, C=21.3449536726, kernel=linear ...........\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:  2.4min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . gamma=0.36275372946, C=108.304882388, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=0.0233325235983, C=21.3449536726, kernel=linear ...........\n",
      "[CV] . gamma=0.36275372946, C=108.304882388, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=0.0233325235983, C=21.3449536726, kernel=linear ...........\n",
      "[CV] . gamma=0.36275372946, C=108.304882388, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.0233325235983, C=21.3449536726, kernel=linear ...........\n",
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      "[CV] gamma=0.150234528727, C=5603.27031743, kernel=rbf ...............\n",
      "[CV]  gamma=0.0233325235983, C=21.3449536726, kernel=linear, total=   6.5s\n",
      "[CV] gamma=0.150234528727, C=5603.27031743, kernel=rbf ...............\n",
      "[CV]  gamma=0.0233325235983, C=21.3449536726, kernel=linear, total=   6.5s\n",
      "[CV] gamma=0.150234528727, C=5603.27031743, kernel=rbf ...............\n",
      "[CV]  gamma=0.0233325235983, C=21.3449536726, kernel=linear, total=   6.4s\n",
      "[CV] gamma=0.150234528727, C=5603.27031743, kernel=rbf ...............\n",
      "[CV]  gamma=0.150234528727, C=5603.27031743, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.150234528727, C=5603.27031743, kernel=rbf ...............\n",
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      "[CV] gamma=0.26497040005, C=157055.109894, kernel=rbf ................\n",
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      "[CV] gamma=0.26497040005, C=157055.109894, kernel=rbf ................\n",
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      "[CV] gamma=0.26497040005, C=157055.109894, kernel=rbf ................\n",
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      "[CV] gamma=0.26497040005, C=157055.109894, kernel=rbf ................\n",
      "[CV] . gamma=0.26497040005, C=157055.109894, kernel=rbf, total=  25.8s\n",
      "[CV] gamma=0.26497040005, C=157055.109894, kernel=rbf ................\n",
      "[CV] . gamma=0.26497040005, C=157055.109894, kernel=rbf, total=  27.3s\n",
      "[CV] gamma=0.222735862129, C=27652.4643587, kernel=linear ............\n",
      "[CV] . gamma=0.26497040005, C=157055.109894, kernel=rbf, total=  30.9s\n",
      "[CV] gamma=0.222735862129, C=27652.4643587, kernel=linear ............\n",
      "[CV] . gamma=0.26497040005, C=157055.109894, kernel=rbf, total=  25.5s\n",
      "[CV] gamma=0.222735862129, C=27652.4643587, kernel=linear ............\n",
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      "[CV] gamma=0.222735862129, C=27652.4643587, kernel=linear ............\n",
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      "[CV] gamma=0.222735862129, C=27652.4643587, kernel=linear ............\n",
      "[CV] . gamma=0.26497040005, C=157055.109894, kernel=rbf, total=  27.8s\n",
      "[CV] gamma=0.628789100541, C=171377.395704, kernel=linear ............\n",
      "[CV]  gamma=0.222735862129, C=27652.4643587, kernel=linear, total=  15.6s\n",
      "[CV] gamma=0.628789100541, C=171377.395704, kernel=linear ............\n",
      "[CV]  gamma=0.222735862129, C=27652.4643587, kernel=linear, total=  14.1s\n",
      "[CV] gamma=0.628789100541, C=171377.395704, kernel=linear ............\n",
      "[CV]  gamma=0.222735862129, C=27652.4643587, kernel=linear, total=  12.4s\n",
      "[CV] gamma=0.628789100541, C=171377.395704, kernel=linear ............\n",
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      "[CV] gamma=0.628789100541, C=171377.395704, kernel=linear ............\n",
      "[CV]  gamma=0.628789100541, C=171377.395704, kernel=linear, total=  57.9s\n",
      "[CV] gamma=0.186961251977, C=5385.29382017, kernel=linear ............\n",
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      "[CV] gamma=0.186961251977, C=5385.29382017, kernel=linear ............\n",
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      "[CV] gamma=0.186961251977, C=5385.29382017, kernel=linear ............\n",
      "[CV]  gamma=0.186961251977, C=5385.29382017, kernel=linear, total=   8.0s\n",
      "[CV] gamma=0.186961251977, C=5385.29382017, kernel=linear ............\n",
      "[CV]  gamma=0.186961251977, C=5385.29382017, kernel=linear, total=   8.2s\n",
      "[CV] gamma=0.186961251977, C=5385.29382017, kernel=linear ............\n",
      "[CV]  gamma=0.186961251977, C=5385.29382017, kernel=linear, total=   8.2s\n",
      "[CV] gamma=2.85079687894, C=22.5990321662, kernel=rbf ................\n",
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      "[CV] gamma=2.85079687894, C=22.5990321662, kernel=rbf ................\n",
      "[CV]  gamma=0.186961251977, C=5385.29382017, kernel=linear, total=   8.1s\n",
      "[CV] gamma=2.85079687894, C=22.5990321662, kernel=rbf ................\n",
      "[CV] . gamma=2.85079687894, C=22.5990321662, kernel=rbf, total=  11.8s\n",
      "[CV] gamma=2.85079687894, C=22.5990321662, kernel=rbf ................\n",
      "[CV]  gamma=0.628789100541, C=171377.395704, kernel=linear, total=  39.8s\n",
      "[CV] gamma=2.85079687894, C=22.5990321662, kernel=rbf ................\n",
      "[CV] . gamma=2.85079687894, C=22.5990321662, kernel=rbf, total=  11.8s\n",
      "[CV] gamma=0.363287859969, C=34246.7519463, kernel=linear ............\n",
      "[CV] . gamma=2.85079687894, C=22.5990321662, kernel=rbf, total=  11.8s\n",
      "[CV] gamma=0.363287859969, C=34246.7519463, kernel=linear ............\n",
      "[CV] . gamma=2.85079687894, C=22.5990321662, kernel=rbf, total=  11.9s\n",
      "[CV] gamma=0.363287859969, C=34246.7519463, kernel=linear ............\n",
      "[CV] . gamma=2.85079687894, C=22.5990321662, kernel=rbf, total=  11.6s\n",
      "[CV] gamma=0.363287859969, C=34246.7519463, kernel=linear ............\n",
      "[CV]  gamma=0.363287859969, C=34246.7519463, kernel=linear, total=  16.4s\n",
      "[CV] gamma=0.363287859969, C=34246.7519463, kernel=linear ............\n",
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      "[CV] gamma=0.275787054226, C=167.727895608, kernel=rbf ...............\n",
      "[CV]  gamma=0.363287859969, C=34246.7519463, kernel=linear, total=  16.2s\n",
      "[CV] gamma=0.275787054226, C=167.727895608, kernel=rbf ...............\n",
      "[CV]  gamma=0.363287859969, C=34246.7519463, kernel=linear, total=  16.9s\n",
      "[CV] gamma=0.275787054226, C=167.727895608, kernel=rbf ...............\n",
      "[CV]  gamma=0.363287859969, C=34246.7519463, kernel=linear, total=  14.6s\n",
      "[CV] gamma=0.275787054226, C=167.727895608, kernel=rbf ...............\n",
      "[CV]  gamma=0.275787054226, C=167.727895608, kernel=rbf, total=  11.1s\n",
      "[CV] gamma=0.275787054226, C=167.727895608, kernel=rbf ...............\n",
      "[CV]  gamma=0.275787054226, C=167.727895608, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.683547228134, C=61.543605425, kernel=linear .............\n",
      "[CV]  gamma=0.275787054226, C=167.727895608, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=0.683547228134, C=61.543605425, kernel=linear .............\n",
      "[CV]  gamma=0.683547228134, C=61.543605425, kernel=linear, total=   6.4s\n",
      "[CV] gamma=0.683547228134, C=61.543605425, kernel=linear .............\n",
      "[CV]  gamma=0.275787054226, C=167.727895608, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.683547228134, C=61.543605425, kernel=linear .............\n",
      "[CV]  gamma=0.275787054226, C=167.727895608, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=0.683547228134, C=61.543605425, kernel=linear .............\n",
      "[CV]  gamma=0.683547228134, C=61.543605425, kernel=linear, total=   6.3s\n",
      "[CV] gamma=0.496036536049, C=98.7389738992, kernel=rbf ...............\n",
      "[CV]  gamma=0.683547228134, C=61.543605425, kernel=linear, total=   6.5s\n",
      "[CV] gamma=0.496036536049, C=98.7389738992, kernel=rbf ...............\n",
      "[CV]  gamma=0.683547228134, C=61.543605425, kernel=linear, total=   6.4s\n",
      "[CV] gamma=0.496036536049, C=98.7389738992, kernel=rbf ...............\n",
      "[CV]  gamma=0.683547228134, C=61.543605425, kernel=linear, total=   6.3s\n",
      "[CV] gamma=0.496036536049, C=98.7389738992, kernel=rbf ...............\n",
      "[CV]  gamma=0.496036536049, C=98.7389738992, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.496036536049, C=98.7389738992, kernel=rbf ...............\n",
      "[CV]  gamma=0.496036536049, C=98.7389738992, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.373546581658, C=8935.50563595, kernel=rbf ...............\n",
      "[CV]  gamma=0.496036536049, C=98.7389738992, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.373546581658, C=8935.50563595, kernel=rbf ...............\n",
      "[CV]  gamma=0.496036536049, C=98.7389738992, kernel=rbf, total=  11.1s\n",
      "[CV] gamma=0.373546581658, C=8935.50563595, kernel=rbf ...............\n",
      "[CV]  gamma=0.496036536049, C=98.7389738992, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.373546581658, C=8935.50563595, kernel=rbf ...............\n",
      "[CV]  gamma=0.373546581658, C=8935.50563595, kernel=rbf, total=  10.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] gamma=0.373546581658, C=8935.50563595, kernel=rbf ...............\n",
      "[CV]  gamma=0.373546581658, C=8935.50563595, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=0.838636245625, C=135.767758248, kernel=linear ............\n",
      "[CV]  gamma=0.373546581658, C=8935.50563595, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=0.838636245625, C=135.767758248, kernel=linear ............\n",
      "[CV]  gamma=0.373546581658, C=8935.50563595, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=0.838636245625, C=135.767758248, kernel=linear ............\n",
      "[CV]  gamma=0.838636245625, C=135.767758248, kernel=linear, total=   6.4s\n",
      "[CV] gamma=0.838636245625, C=135.767758248, kernel=linear ............\n",
      "[CV]  gamma=0.838636245625, C=135.767758248, kernel=linear, total=   6.6s\n",
      "[CV] gamma=0.838636245625, C=135.767758248, kernel=linear ............\n",
      "[CV]  gamma=0.373546581658, C=8935.50563595, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=1.49224537714, C=151136.202825, kernel=rbf ................\n",
      "[CV]  gamma=0.838636245625, C=135.767758248, kernel=linear, total=   6.8s\n",
      "[CV] gamma=1.49224537714, C=151136.202825, kernel=rbf ................\n",
      "[CV]  gamma=0.838636245625, C=135.767758248, kernel=linear, total=   6.6s\n",
      "[CV] gamma=1.49224537714, C=151136.202825, kernel=rbf ................\n",
      "[CV]  gamma=0.838636245625, C=135.767758248, kernel=linear, total=   6.6s\n",
      "[CV] gamma=1.49224537714, C=151136.202825, kernel=rbf ................\n",
      "[CV] . gamma=1.49224537714, C=151136.202825, kernel=rbf, total= 1.8min\n",
      "[CV] gamma=1.49224537714, C=151136.202825, kernel=rbf ................\n",
      "[CV] . gamma=1.49224537714, C=151136.202825, kernel=rbf, total= 1.9min\n",
      "[CV] gamma=2.61263365142, C=761.43167585, kernel=linear ..............\n",
      "[CV]  gamma=2.61263365142, C=761.43167585, kernel=linear, total=   6.6s\n",
      "[CV] gamma=2.61263365142, C=761.43167585, kernel=linear ..............\n",
      "[CV]  gamma=2.61263365142, C=761.43167585, kernel=linear, total=   6.7s\n",
      "[CV] gamma=2.61263365142, C=761.43167585, kernel=linear ..............\n",
      "[CV] . gamma=1.49224537714, C=151136.202825, kernel=rbf, total= 2.3min\n",
      "[CV] gamma=2.61263365142, C=761.43167585, kernel=linear ..............\n",
      "[CV]  gamma=2.61263365142, C=761.43167585, kernel=linear, total=   6.7s\n",
      "[CV] gamma=2.61263365142, C=761.43167585, kernel=linear ..............\n",
      "[CV]  gamma=2.61263365142, C=761.43167585, kernel=linear, total=   6.6s\n",
      "[CV] gamma=0.0926554589531, C=97392.8188304, kernel=linear ...........\n",
      "[CV] . gamma=1.49224537714, C=151136.202825, kernel=rbf, total= 2.5min\n",
      "[CV] gamma=0.0926554589531, C=97392.8188304, kernel=linear ...........\n",
      "[CV]  gamma=2.61263365142, C=761.43167585, kernel=linear, total=   6.6s\n",
      "[CV] gamma=0.0926554589531, C=97392.8188304, kernel=linear ...........\n",
      "[CV]  gamma=0.0926554589531, C=97392.8188304, kernel=linear, total=  32.9s\n",
      "[CV] gamma=0.0926554589531, C=97392.8188304, kernel=linear ...........\n",
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      "[CV] gamma=0.0926554589531, C=97392.8188304, kernel=linear ...........\n",
      "[CV]  gamma=0.0926554589531, C=97392.8188304, kernel=linear, total=  50.7s\n",
      "[CV] gamma=3.24861427024, C=2423.07599849, kernel=linear .............\n",
      "[CV]  gamma=0.0926554589531, C=97392.8188304, kernel=linear, total=  27.2s\n",
      "[CV] gamma=3.24861427024, C=2423.07599849, kernel=linear .............\n",
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      "[CV] gamma=3.24861427024, C=2423.07599849, kernel=linear .............\n",
      "[CV]  gamma=0.0926554589531, C=97392.8188304, kernel=linear, total=  33.6s\n",
      "[CV] gamma=3.24861427024, C=2423.07599849, kernel=linear .............\n",
      "[CV]  gamma=3.24861427024, C=2423.07599849, kernel=linear, total=   7.1s\n",
      "[CV] gamma=3.24861427024, C=2423.07599849, kernel=linear .............\n",
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      "[CV] gamma=0.316560443209, C=717.363299726, kernel=linear ............\n",
      "[CV]  gamma=3.24861427024, C=2423.07599849, kernel=linear, total=   7.4s\n",
      "[CV] gamma=0.316560443209, C=717.363299726, kernel=linear ............\n",
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      "[CV] gamma=0.316560443209, C=717.363299726, kernel=linear ............\n",
      "[CV]  gamma=3.24861427024, C=2423.07599849, kernel=linear, total=   7.1s\n",
      "[CV] gamma=0.316560443209, C=717.363299726, kernel=linear ............\n",
      "[CV]  gamma=0.316560443209, C=717.363299726, kernel=linear, total=   6.6s\n",
      "[CV] gamma=0.316560443209, C=717.363299726, kernel=linear ............\n",
      "[CV]  gamma=0.316560443209, C=717.363299726, kernel=linear, total=   6.8s\n",
      "[CV] gamma=3.35972844566, C=4446.66752118, kernel=rbf ................\n",
      "[CV]  gamma=0.316560443209, C=717.363299726, kernel=linear, total=   6.7s\n",
      "[CV] gamma=3.35972844566, C=4446.66752118, kernel=rbf ................\n",
      "[CV]  gamma=0.316560443209, C=717.363299726, kernel=linear, total=   6.5s\n",
      "[CV] gamma=3.35972844566, C=4446.66752118, kernel=rbf ................\n",
      "[CV] . gamma=1.49224537714, C=151136.202825, kernel=rbf, total= 2.2min\n",
      "[CV] gamma=3.35972844566, C=4446.66752118, kernel=rbf ................\n",
      "[CV] . gamma=3.35972844566, C=4446.66752118, kernel=rbf, total=  12.3s\n",
      "[CV] gamma=3.35972844566, C=4446.66752118, kernel=rbf ................\n",
      "[CV] . gamma=3.35972844566, C=4446.66752118, kernel=rbf, total=  12.4s\n",
      "[CV] gamma=0.151898147821, C=2963.56412121, kernel=linear ............\n",
      "[CV] . gamma=3.35972844566, C=4446.66752118, kernel=rbf, total=  12.3s\n",
      "[CV] gamma=0.151898147821, C=2963.56412121, kernel=linear ............\n",
      "[CV] . gamma=3.35972844566, C=4446.66752118, kernel=rbf, total=  12.3s\n",
      "[CV] gamma=0.151898147821, C=2963.56412121, kernel=linear ............\n",
      "[CV]  gamma=0.151898147821, C=2963.56412121, kernel=linear, total=   7.2s\n",
      "[CV] gamma=0.151898147821, C=2963.56412121, kernel=linear ............\n",
      "[CV]  gamma=0.151898147821, C=2963.56412121, kernel=linear, total=   7.6s\n",
      "[CV] gamma=0.151898147821, C=2963.56412121, kernel=linear ............\n",
      "[CV]  gamma=0.151898147821, C=2963.56412121, kernel=linear, total=   7.7s\n",
      "[CV] gamma=0.0157599448359, C=91.6426738169, kernel=linear ...........\n",
      "[CV] . gamma=3.35972844566, C=4446.66752118, kernel=rbf, total=  12.8s\n",
      "[CV] gamma=0.0157599448359, C=91.6426738169, kernel=linear ...........\n",
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      "[CV] gamma=0.0157599448359, C=91.6426738169, kernel=linear ...........\n",
      "[CV]  gamma=0.151898147821, C=2963.56412121, kernel=linear, total=   7.1s\n",
      "[CV] gamma=0.0157599448359, C=91.6426738169, kernel=linear ...........\n",
      "[CV]  gamma=0.0157599448359, C=91.6426738169, kernel=linear, total=   6.3s\n",
      "[CV] gamma=0.0157599448359, C=91.6426738169, kernel=linear ...........\n",
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      "[CV] gamma=0.221539440506, C=24547.6019757, kernel=rbf ...............\n",
      "[CV]  gamma=0.0157599448359, C=91.6426738169, kernel=linear, total=   6.8s\n",
      "[CV] gamma=0.221539440506, C=24547.6019757, kernel=rbf ...............\n",
      "[CV]  gamma=0.0157599448359, C=91.6426738169, kernel=linear, total=   6.4s\n",
      "[CV] gamma=0.221539440506, C=24547.6019757, kernel=rbf ...............\n",
      "[CV]  gamma=0.0157599448359, C=91.6426738169, kernel=linear, total=   6.3s\n",
      "[CV] gamma=0.221539440506, C=24547.6019757, kernel=rbf ...............\n",
      "[CV]  gamma=0.221539440506, C=24547.6019757, kernel=rbf, total=  11.3s\n",
      "[CV] gamma=0.221539440506, C=24547.6019757, kernel=rbf ...............\n",
      "[CV]  gamma=0.221539440506, C=24547.6019757, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=0.221697602314, C=22.7692794106, kernel=rbf ...............\n",
      "[CV]  gamma=0.221539440506, C=24547.6019757, kernel=rbf, total=  11.1s\n",
      "[CV] gamma=0.221697602314, C=22.7692794106, kernel=rbf ...............\n",
      "[CV]  gamma=0.221539440506, C=24547.6019757, kernel=rbf, total=  11.1s\n",
      "[CV] gamma=0.221697602314, C=22.7692794106, kernel=rbf ...............\n",
      "[CV]  gamma=0.221539440506, C=24547.6019757, kernel=rbf, total=  11.3s\n",
      "[CV] gamma=0.221697602314, C=22.7692794106, kernel=rbf ...............\n",
      "[CV]  gamma=0.221697602314, C=22.7692794106, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=0.221697602314, C=22.7692794106, kernel=rbf ...............\n",
      "[CV]  gamma=0.221697602314, C=22.7692794106, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=1.47521452604, C=16483.8505298, kernel=linear .............\n",
      "[CV]  gamma=0.221697602314, C=22.7692794106, kernel=rbf, total=  10.9s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] gamma=1.47521452604, C=16483.8505298, kernel=linear .............\n",
      "[CV]  gamma=1.47521452604, C=16483.8505298, kernel=linear, total=  10.4s\n",
      "[CV] gamma=1.47521452604, C=16483.8505298, kernel=linear .............\n",
      "[CV]  gamma=0.221697602314, C=22.7692794106, kernel=rbf, total=  11.2s\n",
      "[CV] gamma=1.47521452604, C=16483.8505298, kernel=linear .............\n",
      "[CV]  gamma=0.221697602314, C=22.7692794106, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=1.47521452604, C=16483.8505298, kernel=linear .............\n",
      "[CV]  gamma=1.47521452604, C=16483.8505298, kernel=linear, total=  11.1s\n",
      "[CV] gamma=1.05290408458, C=101445.668813, kernel=rbf ................\n",
      "[CV]  gamma=1.47521452604, C=16483.8505298, kernel=linear, total=  11.3s\n",
      "[CV] gamma=1.05290408458, C=101445.668813, kernel=rbf ................\n",
      "[CV]  gamma=1.47521452604, C=16483.8505298, kernel=linear, total=  10.2s\n",
      "[CV] gamma=1.05290408458, C=101445.668813, kernel=rbf ................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 154 tasks      | elapsed: 13.2min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  gamma=1.47521452604, C=16483.8505298, kernel=linear, total=  11.9s\n",
      "[CV] gamma=1.05290408458, C=101445.668813, kernel=rbf ................\n",
      "[CV] . gamma=1.05290408458, C=101445.668813, kernel=rbf, total=  51.0s\n",
      "[CV] gamma=1.05290408458, C=101445.668813, kernel=rbf ................\n",
      "[CV] . gamma=1.05290408458, C=101445.668813, kernel=rbf, total=  47.6s\n",
      "[CV] gamma=0.976301191712, C=56681.8085903, kernel=rbf ...............\n",
      "[CV] . gamma=1.05290408458, C=101445.668813, kernel=rbf, total= 1.0min\n",
      "[CV] gamma=0.976301191712, C=56681.8085903, kernel=rbf ...............\n",
      "[CV] . gamma=1.05290408458, C=101445.668813, kernel=rbf, total= 1.1min\n",
      "[CV] gamma=0.976301191712, C=56681.8085903, kernel=rbf ...............\n",
      "[CV]  gamma=0.976301191712, C=56681.8085903, kernel=rbf, total=  21.3s\n",
      "[CV] gamma=0.976301191712, C=56681.8085903, kernel=rbf ...............\n",
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      "[CV] gamma=0.976301191712, C=56681.8085903, kernel=rbf ...............\n",
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      "[CV] gamma=0.463335116798, C=48.1582239093, kernel=rbf ...............\n",
      "[CV] . gamma=1.05290408458, C=101445.668813, kernel=rbf, total=  53.6s\n",
      "[CV] gamma=0.463335116798, C=48.1582239093, kernel=rbf ...............\n",
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      "[CV] gamma=0.463335116798, C=48.1582239093, kernel=rbf ...............\n",
      "[CV]  gamma=0.463335116798, C=48.1582239093, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=0.463335116798, C=48.1582239093, kernel=rbf ...............\n",
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      "[CV] gamma=0.463335116798, C=48.1582239093, kernel=rbf ...............\n",
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      "[CV] gamma=1.30787578396, C=399.726815571, kernel=rbf ................\n",
      "[CV]  gamma=0.463335116798, C=48.1582239093, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=1.30787578396, C=399.726815571, kernel=rbf ................\n",
      "[CV]  gamma=0.463335116798, C=48.1582239093, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=1.30787578396, C=399.726815571, kernel=rbf ................\n",
      "[CV]  gamma=0.463335116798, C=48.1582239093, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=1.30787578396, C=399.726815571, kernel=rbf ................\n",
      "[CV] . gamma=1.30787578396, C=399.726815571, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=1.30787578396, C=399.726815571, kernel=rbf ................\n",
      "[CV] . gamma=1.30787578396, C=399.726815571, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=0.823810520491, C=251.140738863, kernel=linear ............\n",
      "[CV] . gamma=1.30787578396, C=399.726815571, kernel=rbf, total=  10.5s\n",
      "[CV] gamma=0.823810520491, C=251.140738863, kernel=linear ............\n",
      "[CV]  gamma=0.823810520491, C=251.140738863, kernel=linear, total=   6.3s\n",
      "[CV] gamma=0.823810520491, C=251.140738863, kernel=linear ............\n",
      "[CV] . gamma=1.30787578396, C=399.726815571, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.823810520491, C=251.140738863, kernel=linear ............\n",
      "[CV]  gamma=0.823810520491, C=251.140738863, kernel=linear, total=   6.4s\n",
      "[CV] gamma=0.823810520491, C=251.140738863, kernel=linear ............\n",
      "[CV] . gamma=1.30787578396, C=399.726815571, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=1.24912634432, C=60.1737364289, kernel=linear .............\n",
      "[CV]  gamma=0.823810520491, C=251.140738863, kernel=linear, total=   6.6s\n",
      "[CV] gamma=1.24912634432, C=60.1737364289, kernel=linear .............\n",
      "[CV]  gamma=0.823810520491, C=251.140738863, kernel=linear, total=   6.6s\n",
      "[CV] gamma=1.24912634432, C=60.1737364289, kernel=linear .............\n",
      "[CV]  gamma=1.24912634432, C=60.1737364289, kernel=linear, total=   6.5s\n",
      "[CV] gamma=1.24912634432, C=60.1737364289, kernel=linear .............\n",
      "[CV]  gamma=0.823810520491, C=251.140738863, kernel=linear, total=   6.8s\n",
      "[CV] gamma=1.24912634432, C=60.1737364289, kernel=linear .............\n",
      "[CV]  gamma=1.24912634432, C=60.1737364289, kernel=linear, total=   6.9s\n",
      "[CV] gamma=0.269167751462, C=15415.1615449, kernel=rbf ...............\n",
      "[CV]  gamma=1.24912634432, C=60.1737364289, kernel=linear, total=   6.5s\n",
      "[CV] gamma=0.269167751462, C=15415.1615449, kernel=rbf ...............\n",
      "[CV]  gamma=1.24912634432, C=60.1737364289, kernel=linear, total=   6.4s\n",
      "[CV] gamma=0.269167751462, C=15415.1615449, kernel=rbf ...............\n",
      "[CV]  gamma=1.24912634432, C=60.1737364289, kernel=linear, total=   6.5s\n",
      "[CV] gamma=0.269167751462, C=15415.1615449, kernel=rbf ...............\n",
      "[CV]  gamma=0.269167751462, C=15415.1615449, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=0.269167751462, C=15415.1615449, kernel=rbf ...............\n",
      "[CV]  gamma=0.269167751462, C=15415.1615449, kernel=rbf, total=  11.3s\n",
      "[CV] gamma=0.739678838777, C=1888.914851, kernel=linear ..............\n",
      "[CV]  gamma=0.269167751462, C=15415.1615449, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=0.739678838777, C=1888.914851, kernel=linear ..............\n",
      "[CV]  gamma=0.269167751462, C=15415.1615449, kernel=rbf, total=  11.3s\n",
      "[CV] gamma=0.739678838777, C=1888.914851, kernel=linear ..............\n",
      "[CV]  gamma=0.739678838777, C=1888.914851, kernel=linear, total=   7.0s\n",
      "[CV] gamma=0.739678838777, C=1888.914851, kernel=linear ..............\n",
      "[CV]  gamma=0.739678838777, C=1888.914851, kernel=linear, total=   7.1s\n",
      "[CV] gamma=0.739678838777, C=1888.914851, kernel=linear ..............\n",
      "[CV]  gamma=0.269167751462, C=15415.1615449, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=0.578634378499, C=55.5383891123, kernel=linear ............\n",
      "[CV]  gamma=0.739678838777, C=1888.914851, kernel=linear, total=   7.4s\n",
      "[CV] gamma=0.578634378499, C=55.5383891123, kernel=linear ............\n",
      "[CV]  gamma=0.739678838777, C=1888.914851, kernel=linear, total=   7.1s\n",
      "[CV] gamma=0.578634378499, C=55.5383891123, kernel=linear ............\n",
      "[CV]  gamma=0.578634378499, C=55.5383891123, kernel=linear, total=   6.7s\n",
      "[CV] gamma=0.578634378499, C=55.5383891123, kernel=linear ............\n",
      "[CV]  gamma=0.578634378499, C=55.5383891123, kernel=linear, total=   6.5s\n",
      "[CV] gamma=0.578634378499, C=55.5383891123, kernel=linear ............\n",
      "[CV]  gamma=0.739678838777, C=1888.914851, kernel=linear, total=   6.9s\n",
      "[CV] gamma=1.01172955093, C=26.7144808239, kernel=rbf ................\n",
      "[CV]  gamma=0.578634378499, C=55.5383891123, kernel=linear, total=   7.0s\n",
      "[CV] gamma=1.01172955093, C=26.7144808239, kernel=rbf ................\n",
      "[CV]  gamma=0.578634378499, C=55.5383891123, kernel=linear, total=   6.3s\n",
      "[CV] gamma=1.01172955093, C=26.7144808239, kernel=rbf ................\n",
      "[CV]  gamma=0.578634378499, C=55.5383891123, kernel=linear, total=   6.7s\n",
      "[CV] gamma=1.01172955093, C=26.7144808239, kernel=rbf ................\n",
      "[CV] . gamma=1.01172955093, C=26.7144808239, kernel=rbf, total=  10.9s\n",
      "[CV] gamma=1.01172955093, C=26.7144808239, kernel=rbf ................\n",
      "[CV] . gamma=1.01172955093, C=26.7144808239, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=1.18913702221, C=3582.05527805, kernel=linear .............\n",
      "[CV] . gamma=1.01172955093, C=26.7144808239, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=1.18913702221, C=3582.05527805, kernel=linear .............\n",
      "[CV] . gamma=1.01172955093, C=26.7144808239, kernel=rbf, total=  10.8s\n",
      "[CV] gamma=1.18913702221, C=3582.05527805, kernel=linear .............\n",
      "[CV] . gamma=1.01172955093, C=26.7144808239, kernel=rbf, total=  10.6s\n",
      "[CV] gamma=1.18913702221, C=3582.05527805, kernel=linear .............\n",
      "[CV]  gamma=1.18913702221, C=3582.05527805, kernel=linear, total=   7.7s\n",
      "[CV] gamma=1.18913702221, C=3582.05527805, kernel=linear .............\n",
      "[CV]  gamma=1.18913702221, C=3582.05527805, kernel=linear, total=   7.6s\n",
      "[CV] gamma=0.528281974883, C=198.700478181, kernel=linear ............\n",
      "[CV]  gamma=1.18913702221, C=3582.05527805, kernel=linear, total=   7.8s\n",
      "[CV] gamma=0.528281974883, C=198.700478181, kernel=linear ............\n",
      "[CV]  gamma=1.18913702221, C=3582.05527805, kernel=linear, total=   7.2s\n",
      "[CV] gamma=0.528281974883, C=198.700478181, kernel=linear ............\n",
      "[CV]  gamma=1.18913702221, C=3582.05527805, kernel=linear, total=   7.5s\n",
      "[CV] gamma=0.528281974883, C=198.700478181, kernel=linear ............\n",
      "[CV]  gamma=0.528281974883, C=198.700478181, kernel=linear, total=   6.3s\n",
      "[CV] gamma=0.528281974883, C=198.700478181, kernel=linear ............\n",
      "[CV]  gamma=0.528281974883, C=198.700478181, kernel=linear, total=   6.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] gamma=2.86213836765, C=129.800060414, kernel=linear .............\n",
      "[CV]  gamma=0.528281974883, C=198.700478181, kernel=linear, total=   6.5s\n",
      "[CV] gamma=2.86213836765, C=129.800060414, kernel=linear .............\n",
      "[CV]  gamma=0.528281974883, C=198.700478181, kernel=linear, total=   6.5s\n",
      "[CV] gamma=2.86213836765, C=129.800060414, kernel=linear .............\n",
      "[CV]  gamma=0.528281974883, C=198.700478181, kernel=linear, total=   6.3s\n",
      "[CV] gamma=2.86213836765, C=129.800060414, kernel=linear .............\n",
      "[CV]  gamma=2.86213836765, C=129.800060414, kernel=linear, total=   6.7s\n",
      "[CV] gamma=2.86213836765, C=129.800060414, kernel=linear .............\n",
      "[CV]  gamma=2.86213836765, C=129.800060414, kernel=linear, total=   6.3s\n",
      "[CV] gamma=0.1758083585, C=288.426929959, kernel=rbf .................\n",
      "[CV]  gamma=2.86213836765, C=129.800060414, kernel=linear, total=   6.5s\n",
      "[CV] gamma=0.1758083585, C=288.426929959, kernel=rbf .................\n",
      "[CV]  gamma=2.86213836765, C=129.800060414, kernel=linear, total=   6.4s\n",
      "[CV] gamma=0.1758083585, C=288.426929959, kernel=rbf .................\n",
      "[CV]  gamma=2.86213836765, C=129.800060414, kernel=linear, total=   6.5s\n",
      "[CV] gamma=0.1758083585, C=288.426929959, kernel=rbf .................\n",
      "[CV] .. gamma=0.1758083585, C=288.426929959, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.1758083585, C=288.426929959, kernel=rbf .................\n",
      "[CV] .. gamma=0.1758083585, C=288.426929959, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=0.350456725533, C=6287.03948943, kernel=linear ............\n",
      "[CV] .. gamma=0.1758083585, C=288.426929959, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=0.350456725533, C=6287.03948943, kernel=linear ............\n",
      "[CV] .. gamma=0.1758083585, C=288.426929959, kernel=rbf, total=  11.1s\n",
      "[CV] gamma=0.350456725533, C=6287.03948943, kernel=linear ............\n",
      "[CV]  gamma=0.350456725533, C=6287.03948943, kernel=linear, total=   8.2s\n",
      "[CV] gamma=0.350456725533, C=6287.03948943, kernel=linear ............\n",
      "[CV]  gamma=0.350456725533, C=6287.03948943, kernel=linear, total=   8.1s\n",
      "[CV] gamma=0.350456725533, C=6287.03948943, kernel=linear ............\n",
      "[CV]  gamma=0.350456725533, C=6287.03948943, kernel=linear, total=   8.7s\n",
      "[CV] gamma=1.62796894074, C=61217.0442134, kernel=rbf ................\n",
      "[CV] .. gamma=0.1758083585, C=288.426929959, kernel=rbf, total=  10.7s\n",
      "[CV] gamma=1.62796894074, C=61217.0442134, kernel=rbf ................\n",
      "[CV]  gamma=0.350456725533, C=6287.03948943, kernel=linear, total=   8.2s\n",
      "[CV] gamma=1.62796894074, C=61217.0442134, kernel=rbf ................\n",
      "[CV]  gamma=0.350456725533, C=6287.03948943, kernel=linear, total=   7.9s\n",
      "[CV] gamma=1.62796894074, C=61217.0442134, kernel=rbf ................\n",
      "[CV] . gamma=1.62796894074, C=61217.0442134, kernel=rbf, total=  39.0s\n",
      "[CV] gamma=1.62796894074, C=61217.0442134, kernel=rbf ................\n",
      "[CV] . gamma=1.62796894074, C=61217.0442134, kernel=rbf, total=  43.5s\n",
      "[CV] gamma=2.14797959306, C=926.97876841, kernel=rbf .................\n",
      "[CV] . gamma=1.62796894074, C=61217.0442134, kernel=rbf, total=  40.8s\n",
      "[CV] gamma=2.14797959306, C=926.97876841, kernel=rbf .................\n",
      "[CV] . gamma=1.62796894074, C=61217.0442134, kernel=rbf, total=  41.6s\n",
      "[CV] gamma=2.14797959306, C=926.97876841, kernel=rbf .................\n",
      "[CV] .. gamma=2.14797959306, C=926.97876841, kernel=rbf, total=  11.1s\n",
      "[CV] gamma=2.14797959306, C=926.97876841, kernel=rbf .................\n",
      "[CV] .. gamma=2.14797959306, C=926.97876841, kernel=rbf, total=  11.1s\n",
      "[CV] gamma=2.14797959306, C=926.97876841, kernel=rbf .................\n",
      "[CV] .. gamma=2.14797959306, C=926.97876841, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=2.26424264929, C=33946.1570649, kernel=linear .............\n",
      "[CV] .. gamma=2.14797959306, C=926.97876841, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=2.26424264929, C=33946.1570649, kernel=linear .............\n",
      "[CV] .. gamma=2.14797959306, C=926.97876841, kernel=rbf, total=  11.0s\n",
      "[CV] gamma=2.26424264929, C=33946.1570649, kernel=linear .............\n",
      "[CV] . gamma=1.62796894074, C=61217.0442134, kernel=rbf, total=  39.4s\n",
      "[CV] gamma=2.26424264929, C=33946.1570649, kernel=linear .............\n",
      "[CV]  gamma=2.26424264929, C=33946.1570649, kernel=linear, total=  16.3s\n",
      "[CV] gamma=2.26424264929, C=33946.1570649, kernel=linear .............\n",
      "[CV]  gamma=2.26424264929, C=33946.1570649, kernel=linear, total=  15.9s\n",
      "[CV] gamma=0.31763590853, C=84789.8294774, kernel=linear .............\n",
      "[CV]  gamma=2.26424264929, C=33946.1570649, kernel=linear, total=  14.6s\n",
      "[CV] gamma=0.31763590853, C=84789.8294774, kernel=linear .............\n",
      "[CV]  gamma=2.26424264929, C=33946.1570649, kernel=linear, total=  15.1s\n",
      "[CV] gamma=0.31763590853, C=84789.8294774, kernel=linear .............\n",
      "[CV]  gamma=2.26424264929, C=33946.1570649, kernel=linear, total=  16.9s\n",
      "[CV] gamma=0.31763590853, C=84789.8294774, kernel=linear .............\n",
      "[CV]  gamma=0.31763590853, C=84789.8294774, kernel=linear, total=  29.9s\n",
      "[CV] gamma=0.31763590853, C=84789.8294774, kernel=linear .............\n",
      "[CV]  gamma=0.31763590853, C=84789.8294774, kernel=linear, total=  40.3s\n",
      "[CV]  gamma=0.31763590853, C=84789.8294774, kernel=linear, total=  33.2s\n",
      "[CV]  gamma=0.31763590853, C=84789.8294774, kernel=linear, total=  44.2s\n",
      "[CV]  gamma=0.31763590853, C=84789.8294774, kernel=linear, total=  24.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 250 out of 250 | elapsed: 20.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "          estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),\n",
       "          fit_params=None, iid=True, n_iter=50, n_jobs=4,\n",
       "          param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f26de25b828>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f26de25b8d0>, 'kernel': ['linear', 'rbf']},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score=True, scoring='neg_mean_squared_error',\n",
       "          verbose=2)"
      ]
     },
     "execution_count": 111,
     "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-0.19.0/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": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54767.99053704409"
      ]
     },
     "execution_count": 112,
     "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": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 157055.10989448498, 'gamma': 0.26497040005002437, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 113,
     "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": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HxGOk+0XOy2kHkwK6iyLda/LzpFE3tW4ya2ZmZtb2BtvH7E9J98g6S9JaSfdJml1In07P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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26d7ac8a20>"
      ]
     },
     "metadata": {},
     "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": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EQZmZdbPFwGPAGcA78vPFEdELLADOAzYDBwMLC/nOJk38XwfcAlwYESvHsNxm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\nlcD/B9nYUTepRxtlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26d7ac80f0>"
      ]
     },
     "metadata": {},
     "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": 116,
   "metadata": {
    "collapsed": true
   },
   "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": 117,
   "metadata": {
    "collapsed": true
   },
   "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": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  7,  9, 12])"
      ]
     },
     "execution_count": 118,
     "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": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['longitude', 'latitude', 'median_income', 'pop_per_hhold', 'INLAND'],\n",
       "      dtype='<U18')"
      ]
     },
     "execution_count": 119,
     "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": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.36615898061813418, 'median_income'),\n",
       " (0.16478099356159051, 'INLAND'),\n",
       " (0.10879295677551573, 'pop_per_hhold'),\n",
       " (0.073344235516012421, 'longitude'),\n",
       " (0.062909070482620302, 'latitude')]"
      ]
     },
     "execution_count": 120,
     "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": 121,
   "metadata": {
    "collapsed": true
   },
   "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": 122,
   "metadata": {
    "collapsed": true
   },
   "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": 123,
   "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": 123,
     "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": 124,
   "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": 124,
     "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": 125,
   "metadata": {
    "collapsed": true
   },
   "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": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('preparation', FeatureUnion(n_jobs=1,\n",
       "       transformer_list=[('num_pipeline', Pipeline(memory=None,\n",
       "     steps=[('selector', DataFrameSelector(attribute_names=['longitude', 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income'])), ('... gamma=0.26497040005002437, kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 126,
     "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": 127,
   "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": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 15 candidates, totalling 75 fits\n",
      "[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=3 \n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:  1.9min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[CV]  preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=6, total=  10.5s\n",
      "[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7 \n",
      "[CV]  preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=6, total=  11.3s\n",
      "[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7 \n",
      "[CV]  preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7, total=  10.9s\n",
      "[CV] preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7 \n",
      "[CV]  preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7, total=  10.5s\n",
      "[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7 \n",
      "[CV]  preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7, total=  10.9s\n",
      "[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7 \n",
      "[CV]  preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7, total=  11.1s\n",
      "[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7 \n",
      "[CV]  preparation__num_pipeline__imputer__strategy=mean, feature_selection__k=7, total=  10.7s\n",
      "[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7 \n",
      "[CV]  preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7, total=  11.5s\n",
      "[CV] preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7 \n",
      "[CV]  preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7, total=  10.8s\n",
      "[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7 \n",
      "[CV]  preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7, total=  10.8s\n",
      "[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7 \n",
      "[CV]  preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7, total=  10.4s\n",
      "[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7 \n",
      "[CV]  preparation__num_pipeline__imputer__strategy=median, feature_selection__k=7, total=  10.6s\n",
      "[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7 \n",
      "[CV]  preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7, total=  11.9s\n",
      "[CV] preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7 \n",
      "[CV]  preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7, total=  11.2s\n",
      "[CV]  preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7, total=  11.7s\n",
      "[CV]  preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7, total=  11.1s\n",
      "[CV]  preparation__num_pipeline__imputer__strategy=most_frequent, feature_selection__k=7, total=  11.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done  75 out of  75 | elapsed:  4.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=Pipeline(memory=None,\n",
       "     steps=[('preparation', FeatureUnion(n_jobs=1,\n",
       "       transformer_list=[('num_pipeline', Pipeline(memory=None,\n",
       "     steps=[('selector', DataFrameSelector(attribute_names=['longitude', 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income'])), ('... gamma=0.26497040005002437, kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False))]),\n",
       "       fit_params=None, iid=True, n_jobs=4,\n",
       "       param_grid=[{'preparation__num_pipeline__imputer__strategy': ['mean', 'median', 'most_frequent'], 'feature_selection__k': [3, 4, 5, 6, 7]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_mean_squared_error', verbose=2)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = [\n",
    "        {'preparation__num_pipeline__imputer__strategy': ['mean', 'median', 'most_frequent'],\n",
    "         'feature_selection__k': [3, 4, 5, 6, 7]}\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": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'feature_selection__k': 7,\n",
       " 'preparation__num_pipeline__imputer__strategy': 'median'}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_prep.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! It seems that we had the right imputer strategy (mean), and apparently only the top 7 features are useful (out of 9), the last 2 seem to just add some noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 9)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations! You already know quite a lot about Machine Learning. :)"
   ]
  }
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