{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning project \n",
    "\n",
    "This notebook is a short version of the [end-to-end Machine Learning project](https://github.com/ageron/handson-ml3/blob/main/02_end_to_end_machine_learning_project.ipynb) provided by Aurélien Geron."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project requires: \n",
    "\n",
    "- Python 3.7 or above\n",
    "- Scikit-Learn ≥ 1.0.1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import sys\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.plotting import scatter_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sklearn\n",
    "from sklearn import set_config\n",
    "set_config(display='diagram')\n",
    "\n",
    "plt.rc('font', size=14)\n",
    "plt.rc('axes', labelsize=14, titlesize=14)\n",
    "plt.rc('legend', fontsize=14)\n",
    "plt.rc('xtick', labelsize=10)\n",
    "plt.rc('ytick', labelsize=10)\n",
    "\n",
    "# Check if you have the correct versions\n",
    "assert sklearn.__version__ >= \"1.0.1\"\n",
    "assert sys.version_info >= (3, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*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.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = pd.read_csv(\"https://raw.githubusercontent.com/kirenz/datasets/master/housing_hml3.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <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": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].___()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>-119.569704</td>\n",
       "      <td>2.003532</td>\n",
       "      <td>-124.3500</td>\n",
       "      <td>-121.8000</td>\n",
       "      <td>-118.4900</td>\n",
       "      <td>-118.01000</td>\n",
       "      <td>-114.3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>32.5400</td>\n",
       "      <td>33.9300</td>\n",
       "      <td>34.2600</td>\n",
       "      <td>37.71000</td>\n",
       "      <td>41.9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>37.00000</td>\n",
       "      <td>52.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1447.7500</td>\n",
       "      <td>2127.0000</td>\n",
       "      <td>3148.00000</td>\n",
       "      <td>39320.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>20433.0</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>296.0000</td>\n",
       "      <td>435.0000</td>\n",
       "      <td>647.00000</td>\n",
       "      <td>6445.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>787.0000</td>\n",
       "      <td>1166.0000</td>\n",
       "      <td>1725.00000</td>\n",
       "      <td>35682.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>280.0000</td>\n",
       "      <td>409.0000</td>\n",
       "      <td>605.00000</td>\n",
       "      <td>6082.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>2.5634</td>\n",
       "      <td>3.5348</td>\n",
       "      <td>4.74325</td>\n",
       "      <td>15.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>206855.816909</td>\n",
       "      <td>115395.615874</td>\n",
       "      <td>14999.0000</td>\n",
       "      <td>119600.0000</td>\n",
       "      <td>179700.0000</td>\n",
       "      <td>264725.00000</td>\n",
       "      <td>500001.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count           mean            std         min  \\\n",
       "longitude           20640.0    -119.569704       2.003532   -124.3500   \n",
       "latitude            20640.0      35.631861       2.135952     32.5400   \n",
       "housing_median_age  20640.0      28.639486      12.585558      1.0000   \n",
       "total_rooms         20640.0    2635.763081    2181.615252      2.0000   \n",
       "total_bedrooms      20433.0     537.870553     421.385070      1.0000   \n",
       "population          20640.0    1425.476744    1132.462122      3.0000   \n",
       "households          20640.0     499.539680     382.329753      1.0000   \n",
       "median_income       20640.0       3.870671       1.899822      0.4999   \n",
       "median_house_value  20640.0  206855.816909  115395.615874  14999.0000   \n",
       "\n",
       "                            25%          50%           75%          max  \n",
       "longitude             -121.8000    -118.4900    -118.01000    -114.3100  \n",
       "latitude                33.9300      34.2600      37.71000      41.9500  \n",
       "housing_median_age      18.0000      29.0000      37.00000      52.0000  \n",
       "total_rooms           1447.7500    2127.0000    3148.00000   39320.0000  \n",
       "total_bedrooms         296.0000     435.0000     647.00000    6445.0000  \n",
       "population             787.0000    1166.0000    1725.00000   35682.0000  \n",
       "households             280.0000     409.0000     605.00000    6082.0000  \n",
       "median_income            2.5634       3.5348       4.74325      15.0001  \n",
       "median_house_value  119600.0000  179700.0000  264725.00000  500001.0000  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.___().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.hist(bins=50, figsize=(12, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"income_cat\"] = pd.cut(housing[\"median_income\"],\n",
    "                               bins=[0., 1.5, 3.0, 4.5, 6., np.inf],\n",
    "                               labels=[1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts().sort_index().plot.bar(rot=0, grid=True)\n",
    "\n",
    "plt.xlabel(\"Income category\")\n",
    "plt.ylabel(\"Number of districts\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stratified split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "strat_train_set, strat_test_set = train_test_split(\n",
    "    housing, test_size=0.2, stratify=___[\"___\"], random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the variable \"income_cat\" from our datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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": [
    "### Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing Geographical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True, alpha=0.2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", \n",
    "             x=\"longitude\", \n",
    "             y=\"latitude\", \n",
    "             grid=True,\n",
    "             s=housing[\"population\"] / 100, \n",
    "             label=\"population\",\n",
    "             c=\"median_house_value\", \n",
    "             cmap=\"jet\", \n",
    "             colorbar=True,\n",
    "             legend=True, \n",
    "             sharex=False, \n",
    "             figsize=(10, 7)\n",
    "             );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The argument `sharex=False` fixes a display bug: without it, the x-axis values and label are not displayed (see: https://github.com/pandas-dev/pandas/issues/10611)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.___()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688380\n",
       "total_rooms           0.137455\n",
       "housing_median_age    0.102175\n",
       "households            0.071426\n",
       "total_bedrooms        0.054635\n",
       "population           -0.020153\n",
       "longitude            -0.050859\n",
       "latitude             -0.139584\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\", \"housing_median_age\"]\n",
    "\n",
    "scatter_matrix(housing[attributes], figsize=(12, 8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", \n",
    "             x=\"median_income\", \n",
    "             y=\"median_house_value\",\n",
    "             alpha=0.1, \n",
    "             grid=True,\n",
    "             figsize=(12, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering\n",
    "\n",
    "Experimenting with Attribute Combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"rooms_per_house\"] = housing[\"total_rooms\"] / housing[\"households\"]\n",
    "housing[\"bedrooms_ratio\"] = housing[\"total_bedrooms\"] / housing[\"total_rooms\"]\n",
    "housing[\"people_per_house\"] = housing[\"population\"] / housing[\"households\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688380\n",
       "rooms_per_house       0.143663\n",
       "total_rooms           0.137455\n",
       "housing_median_age    0.102175\n",
       "households            0.071426\n",
       "total_bedrooms        0.054635\n",
       "population           -0.020153\n",
       "people_per_house     -0.038224\n",
       "longitude            -0.050859\n",
       "latitude             -0.139584\n",
       "bedrooms_ratio       -0.256397\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.___()\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's revert to the original training set and separate the target (note that `strat_train_set.drop()` creates a copy of `strat_train_set` without the column, it doesn't actually modify `strat_train_set` itself, unless you pass `inplace=True`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
    "\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's build a pipeline to preprocess the attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_attribs = [\"longitude\", \"latitude\", \"housing_median_age\", \"total_rooms\",\n",
    "               \"total_bedrooms\", \"population\", \"households\", \"median_income\"]\n",
    "               \n",
    "cat_attribs = [\"ocean_proximity\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# categorical pipeline\n",
    "cat_pipeline = ___(\n",
    "    SimpleImputer(strategy=\"most_frequent\"),\n",
    "    OneHotEncoder(handle_unknown=\"ignore\")\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default numerical pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "default_num_pipeline = ___(\n",
    "    SimpleImputer(strategy=\"median\"),\n",
    "    StandardScaler()\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# custom function to make ratios\n",
    "def column_ratio(X):\n",
    "    return X[:, [___]] / X[:, [___]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# custom function to transfomr ratios\n",
    "def ratio_pipeline(name=None):\n",
    "    return make_pipeline(\n",
    "        SimpleImputer(strategy=\"median\"),\n",
    "        FunctionTransformer(column_ratio),\n",
    "        StandardScaler())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# custom log transformer\n",
    "log_pipeline = make_pipeline(\n",
    "    SimpleImputer(strategy=\"median\"),\n",
    "    FunctionTransformer(np.log),\n",
    "    StandardScaler())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- To learn more about developing scikit-learn estimators, take a look at [this page](https://scikit-learn.org/stable/developers/develop.html)\n",
    "\n",
    "- Here is a template to build your own scikit-learn functions: [template](https://github.com/scikit-learn-contrib/project-template/blob/master/skltemplate/_template.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# custom cluster similarity  \n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.metrics.pairwise import rbf_kernel\n",
    "\n",
    "\n",
    "class ClusterSimilarity(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, n_clusters=10, gamma=1.0, random_state=None):\n",
    "        self.n_clusters = n_clusters\n",
    "        self.gamma = gamma\n",
    "        self.random_state = random_state\n",
    "\n",
    "    def fit(self, X, y=None, sample_weight=None):\n",
    "        self.kmeans_ = KMeans(self.n_clusters, random_state=self.random_state)\n",
    "        self.kmeans_.fit(X, sample_weight=sample_weight)\n",
    "        return self  # always return self!\n",
    "\n",
    "    def transform(self, X):\n",
    "        return rbf_kernel(X, self.kmeans_.cluster_centers_, gamma=self.gamma)\n",
    "    \n",
    "    def get_feature_names_out(self, names=None):\n",
    "        return [f\"Cluster {i} similarity\" for i in range(self.n_clusters)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# custom cluster similarity step \n",
    "cluster_simil = ClusterSimilarity(n_clusters=10, gamma=1., random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform transformations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.compose import make_column_selector\n",
    "\n",
    "\n",
    "preprocessing = ColumnTransformer([\n",
    "        (\"bedrooms_ratio\", ratio_pipeline(\"bedrooms_ratio\"),\n",
    "                           [\"total_bedrooms\", \"total_rooms\"]),\n",
    "\n",
    "        (\"rooms_per_house\", ratio_pipeline(\"rooms_per_house\"),\n",
    "                            [\"total_rooms\", \"households\"]),\n",
    "\n",
    "        (\"people_per_house\", ratio_pipeline(\"people_per_house\"),\n",
    "                             [\"population\", \"households\"]),\n",
    "\n",
    "        (\"log\", log_pipeline, [\"total_bedrooms\", \"total_rooms\",\n",
    "                               \"population\", \"households\", \"median_income\"]),\n",
    "\n",
    "        (\"geo\", cluster_simil, [\"latitude\", \"longitude\"]),\n",
    "        \n",
    "        (\"cat\", cat_pipeline, make_column_selector(dtype_include=object)),\n",
    "    ],\n",
    "    remainder=default_num_pipeline)  # one column remaining: housing_median_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 24)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = preprocessing.fit_transform(housing)\n",
    "\n",
    "housing_prepared.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = make_pipeline(\n",
    "            preprocessing, \n",
    "            ___()\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                             (&#x27;standardscaler&#x27;,\n",
       "                                                              StandardScaler())]),\n",
       "                                   transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d...\n",
       "                                                   &#x27;households&#x27;,\n",
       "                                                   &#x27;median_income&#x27;]),\n",
       "                                                 (&#x27;geo&#x27;,\n",
       "                                                  ClusterSimilarity(random_state=42),\n",
       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;)])),\n",
       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-221\" type=\"checkbox\" ><label for=\"sk-estimator-id-221\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                             (&#x27;standardscaler&#x27;,\n",
       "                                                              StandardScaler())]),\n",
       "                                   transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d...\n",
       "                                                   &#x27;households&#x27;,\n",
       "                                                   &#x27;median_income&#x27;]),\n",
       "                                                 (&#x27;geo&#x27;,\n",
       "                                                  ClusterSimilarity(random_state=42),\n",
       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;)])),\n",
       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-222\" type=\"checkbox\" ><label for=\"sk-estimator-id-222\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                             SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                            (&#x27;standardscaler&#x27;,\n",
       "                                             StandardScaler())]),\n",
       "                  transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler...\n",
       "                                 [&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;,\n",
       "                                  &#x27;households&#x27;, &#x27;median_income&#x27;]),\n",
       "                                (&#x27;geo&#x27;, ClusterSimilarity(random_state=42),\n",
       "                                 [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-223\" type=\"checkbox\" ><label for=\"sk-estimator-id-223\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms_ratio</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-224\" type=\"checkbox\" ><label for=\"sk-estimator-id-224\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-225\" type=\"checkbox\" ><label for=\"sk-estimator-id-225\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-226\" type=\"checkbox\" ><label for=\"sk-estimator-id-226\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-227\" type=\"checkbox\" ><label for=\"sk-estimator-id-227\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">rooms_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-228\" type=\"checkbox\" ><label for=\"sk-estimator-id-228\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-229\" type=\"checkbox\" ><label for=\"sk-estimator-id-229\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-230\" type=\"checkbox\" ><label for=\"sk-estimator-id-230\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-231\" type=\"checkbox\" ><label for=\"sk-estimator-id-231\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">people_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-232\" type=\"checkbox\" ><label for=\"sk-estimator-id-232\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-233\" type=\"checkbox\" ><label for=\"sk-estimator-id-233\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-234\" type=\"checkbox\" ><label for=\"sk-estimator-id-234\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-235\" type=\"checkbox\" ><label for=\"sk-estimator-id-235\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">log</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-236\" type=\"checkbox\" ><label for=\"sk-estimator-id-236\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-237\" type=\"checkbox\" ><label for=\"sk-estimator-id-237\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;ufunc &#x27;log&#x27;&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-238\" type=\"checkbox\" ><label for=\"sk-estimator-id-238\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-239\" type=\"checkbox\" ><label for=\"sk-estimator-id-239\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">geo</label><div class=\"sk-toggleable__content\"><pre>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-240\" type=\"checkbox\" ><label for=\"sk-estimator-id-240\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ClusterSimilarity</label><div class=\"sk-toggleable__content\"><pre>ClusterSimilarity(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-241\" type=\"checkbox\" ><label for=\"sk-estimator-id-241\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-242\" type=\"checkbox\" ><label for=\"sk-estimator-id-242\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-243\" type=\"checkbox\" ><label for=\"sk-estimator-id-243\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-244\" type=\"checkbox\" ><label for=\"sk-estimator-id-244\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-245\" type=\"checkbox\" ><label for=\"sk-estimator-id-245\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-246\" type=\"checkbox\" ><label for=\"sk-estimator-id-246\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-247\" type=\"checkbox\" ><label for=\"sk-estimator-id-247\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
       "                                                              SimpleImputer(strategy='median')),\n",
       "                                                             ('standardscaler',\n",
       "                                                              StandardScaler())]),\n",
       "                                   transformers=[('bedrooms_ratio',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('functiontransformer',\n",
       "                                                                   FunctionTransformer(func=<function column_ratio at 0x186eac9d...\n",
       "                                                   'households',\n",
       "                                                   'median_income']),\n",
       "                                                 ('geo',\n",
       "                                                  ClusterSimilarity(random_state=42),\n",
       "                                                  ['latitude', 'longitude']),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('onehotencoder',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30>)])),\n",
       "                ('linearregression', LinearRegression())])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.fit(___, ___)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_predictions = lin_reg.predict(___)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68695.46147809878"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "lin_rmse = mean_squared_error(housing_labels, housing_predictions,\n",
    "                              squared=False)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try the full preprocessing pipeline on a few training instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([244400., 371700., 128400.,  94700., 328200.])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions[:5].round(-2)  # -2 = rounded to the nearest hundred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare against the actual values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([458300., 483800., 101700.,  96100., 361800.])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_labels.iloc[:5].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-46.7%, -23.2%, 26.3%, -1.5%, -9.3%\n"
     ]
    }
   ],
   "source": [
    "# extra code – computes the error ratios discussed in the book\n",
    "error_ratios = housing_predictions[:5].round(-2) / housing_labels.iloc[:5].values - 1\n",
    "print(\", \".join([f\"{100 * ratio:.1f}%\" for ratio in error_ratios]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = make_pipeline(\n",
    "    preprocessing, \n",
    "    ___(random_state=42)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                             (&#x27;standardscaler&#x27;,\n",
       "                                                              StandardScaler())]),\n",
       "                                   transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d...\n",
       "                                                 (&#x27;geo&#x27;,\n",
       "                                                  ClusterSimilarity(random_state=42),\n",
       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;)])),\n",
       "                (&#x27;decisiontreeregressor&#x27;,\n",
       "                 DecisionTreeRegressor(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-248\" type=\"checkbox\" ><label for=\"sk-estimator-id-248\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                             (&#x27;standardscaler&#x27;,\n",
       "                                                              StandardScaler())]),\n",
       "                                   transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d...\n",
       "                                                 (&#x27;geo&#x27;,\n",
       "                                                  ClusterSimilarity(random_state=42),\n",
       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;)])),\n",
       "                (&#x27;decisiontreeregressor&#x27;,\n",
       "                 DecisionTreeRegressor(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-249\" type=\"checkbox\" ><label for=\"sk-estimator-id-249\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                             SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                            (&#x27;standardscaler&#x27;,\n",
       "                                             StandardScaler())]),\n",
       "                  transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler...\n",
       "                                 [&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;,\n",
       "                                  &#x27;households&#x27;, &#x27;median_income&#x27;]),\n",
       "                                (&#x27;geo&#x27;, ClusterSimilarity(random_state=42),\n",
       "                                 [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-250\" type=\"checkbox\" ><label for=\"sk-estimator-id-250\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms_ratio</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-251\" type=\"checkbox\" ><label for=\"sk-estimator-id-251\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-252\" type=\"checkbox\" ><label for=\"sk-estimator-id-252\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-253\" type=\"checkbox\" ><label for=\"sk-estimator-id-253\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-254\" type=\"checkbox\" ><label for=\"sk-estimator-id-254\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">rooms_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-255\" type=\"checkbox\" ><label for=\"sk-estimator-id-255\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-256\" type=\"checkbox\" ><label for=\"sk-estimator-id-256\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-257\" type=\"checkbox\" ><label for=\"sk-estimator-id-257\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-258\" type=\"checkbox\" ><label for=\"sk-estimator-id-258\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">people_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-259\" type=\"checkbox\" ><label for=\"sk-estimator-id-259\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-260\" type=\"checkbox\" ><label for=\"sk-estimator-id-260\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-261\" type=\"checkbox\" ><label for=\"sk-estimator-id-261\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-262\" type=\"checkbox\" ><label for=\"sk-estimator-id-262\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">log</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-263\" type=\"checkbox\" ><label for=\"sk-estimator-id-263\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-264\" type=\"checkbox\" ><label for=\"sk-estimator-id-264\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;ufunc &#x27;log&#x27;&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-265\" type=\"checkbox\" ><label for=\"sk-estimator-id-265\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-266\" type=\"checkbox\" ><label for=\"sk-estimator-id-266\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">geo</label><div class=\"sk-toggleable__content\"><pre>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-267\" type=\"checkbox\" ><label for=\"sk-estimator-id-267\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ClusterSimilarity</label><div class=\"sk-toggleable__content\"><pre>ClusterSimilarity(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-268\" type=\"checkbox\" ><label for=\"sk-estimator-id-268\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-269\" type=\"checkbox\" ><label for=\"sk-estimator-id-269\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-270\" type=\"checkbox\" ><label for=\"sk-estimator-id-270\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-271\" type=\"checkbox\" ><label for=\"sk-estimator-id-271\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-272\" type=\"checkbox\" ><label for=\"sk-estimator-id-272\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-273\" type=\"checkbox\" ><label for=\"sk-estimator-id-273\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-274\" type=\"checkbox\" ><label for=\"sk-estimator-id-274\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
       "                                                              SimpleImputer(strategy='median')),\n",
       "                                                             ('standardscaler',\n",
       "                                                              StandardScaler())]),\n",
       "                                   transformers=[('bedrooms_ratio',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('functiontransformer',\n",
       "                                                                   FunctionTransformer(func=<function column_ratio at 0x186eac9d...\n",
       "                                                 ('geo',\n",
       "                                                  ClusterSimilarity(random_state=42),\n",
       "                                                  ['latitude', 'longitude']),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('onehotencoder',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30>)])),\n",
       "                ('decisiontreeregressor',\n",
       "                 DecisionTreeRegressor(random_state=42))])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg.fit(___, ___)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = tree_reg.predict(___)\n",
    "\n",
    "tree_rmse = mean_squared_error(housing_labels, housing_predictions,\n",
    "                              squared=False)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# we only use cv=3 instead of cv=10 to speed up the computation\n",
    "tree_rmses = ___(tree_reg, housing, housing_labels,\n",
    "                              scoring=\"neg_root_mean_squared_error\", cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        3.000000\n",
       "mean     68282.891053\n",
       "std       1486.492256\n",
       "min      66810.075215\n",
       "25%      67532.990434\n",
       "50%      68255.905652\n",
       "75%      69019.298971\n",
       "max      69782.692291\n",
       "dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(tree_rmses).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        3.000000\n",
       "mean     69778.756842\n",
       "std       1629.907725\n",
       "min      67980.530959\n",
       "25%      69088.686886\n",
       "50%      70196.842814\n",
       "75%      70677.869784\n",
       "max      71158.896754\n",
       "dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_rmses = ___(lin_reg, housing, housing_labels,\n",
    "                              scoring=\"neg_root_mean_squared_error\", cv=3)\n",
    "\n",
    "pd.Series(lin_rmses).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we set cv=3 instead of cv=10:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = make_pipeline(preprocessing,\n",
    "                           RandomForestRegressor(random_state=42))\n",
    "                           \n",
    "forest_rmses = -cross_val_score(forest_reg, housing, housing_labels,\n",
    "                                scoring=\"neg_root_mean_squared_error\", cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        3.000000\n",
       "mean     47889.465303\n",
       "std        619.080587\n",
       "min      47392.624982\n",
       "25%      47542.700149\n",
       "50%      47692.775316\n",
       "75%      48137.885464\n",
       "max      48582.995613\n",
       "dtype: float64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(forest_rmses).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare this RMSE measured using cross-validation (the \"validation error\") with the RMSE measured on the training set (the \"training error\"):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17474.619286483998"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_reg.fit(housing, housing_labels)\n",
    "\n",
    "housing_predictions = forest_reg.predict(housing)\n",
    "\n",
    "forest_rmse = mean_squared_error(housing_labels, housing_predictions,\n",
    "                                 squared=False)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training error is much lower than the validation error, which usually means that the model has overfit the training set. Another possible explanation may be that there's a mismatch between the training data and the validation data, but it's not the case here, since both came from the same dataset that we shuffled and split in two parts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "full_pipeline = Pipeline([\n",
    "    (\"preprocessing\", preprocessing),\n",
    "    (\"random_forest\", RandomForestRegressor(random_state=42)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [\n",
    "    {'preprocessing__geo__n_clusters': [5, 8, 10],\n",
    "     'random_forest__max_features': [4, 6, 8]},\n",
    "    {'preprocessing__geo__n_clusters': [10, 15],\n",
    "     'random_forest__max_features': [6, 8, 10]},\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(full_pipeline, param_grid, cv=3,\n",
    "                           scoring='neg_root_mean_squared_error')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** the following cell may take a few minutes to run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "             estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                        ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                     SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                    (&#x27;standardscaler&#x27;,\n",
       "                                                                                     StandardScaler())]),\n",
       "                                                          transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                         (&#x27;functiontransformer&#x27;,\n",
       "                                                                                          FunctionTransformer(func=&lt;function...\n",
       "                                                                         &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;)])),\n",
       "                                       (&#x27;random_forest&#x27;,\n",
       "                                        RandomForestRegressor(random_state=42))]),\n",
       "             param_grid=[{&#x27;preprocessing__geo__n_clusters&#x27;: [5, 8, 10],\n",
       "                          &#x27;random_forest__max_features&#x27;: [4, 6, 8]},\n",
       "                         {&#x27;preprocessing__geo__n_clusters&#x27;: [10, 15],\n",
       "                          &#x27;random_forest__max_features&#x27;: [6, 8, 10]}],\n",
       "             scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-275\" type=\"checkbox\" ><label for=\"sk-estimator-id-275\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                        ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                     SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                    (&#x27;standardscaler&#x27;,\n",
       "                                                                                     StandardScaler())]),\n",
       "                                                          transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                         (&#x27;functiontransformer&#x27;,\n",
       "                                                                                          FunctionTransformer(func=&lt;function...\n",
       "                                                                         &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;)])),\n",
       "                                       (&#x27;random_forest&#x27;,\n",
       "                                        RandomForestRegressor(random_state=42))]),\n",
       "             param_grid=[{&#x27;preprocessing__geo__n_clusters&#x27;: [5, 8, 10],\n",
       "                          &#x27;random_forest__max_features&#x27;: [4, 6, 8]},\n",
       "                         {&#x27;preprocessing__geo__n_clusters&#x27;: [10, 15],\n",
       "                          &#x27;random_forest__max_features&#x27;: [6, 8, 10]}],\n",
       "             scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-276\" type=\"checkbox\" ><label for=\"sk-estimator-id-276\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                             (&#x27;standardscaler&#x27;,\n",
       "                                                              StandardScaler())]),\n",
       "                                   transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;))...\n",
       "                                                   &#x27;median_income&#x27;]),\n",
       "                                                 (&#x27;geo&#x27;,\n",
       "                                                  ClusterSimilarity(random_state=42),\n",
       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;)])),\n",
       "                (&#x27;random_forest&#x27;, RandomForestRegressor(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-277\" type=\"checkbox\" ><label for=\"sk-estimator-id-277\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessing: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                             SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                            (&#x27;standardscaler&#x27;,\n",
       "                                             StandardScaler())]),\n",
       "                  transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler...\n",
       "                                 [&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;,\n",
       "                                  &#x27;households&#x27;, &#x27;median_income&#x27;]),\n",
       "                                (&#x27;geo&#x27;, ClusterSimilarity(random_state=42),\n",
       "                                 [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-278\" type=\"checkbox\" ><label for=\"sk-estimator-id-278\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms_ratio</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-279\" type=\"checkbox\" ><label for=\"sk-estimator-id-279\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-280\" type=\"checkbox\" ><label for=\"sk-estimator-id-280\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-281\" type=\"checkbox\" ><label for=\"sk-estimator-id-281\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-282\" type=\"checkbox\" ><label for=\"sk-estimator-id-282\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">rooms_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-283\" type=\"checkbox\" ><label for=\"sk-estimator-id-283\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-284\" type=\"checkbox\" ><label for=\"sk-estimator-id-284\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-285\" type=\"checkbox\" ><label for=\"sk-estimator-id-285\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-286\" type=\"checkbox\" ><label for=\"sk-estimator-id-286\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">people_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-287\" type=\"checkbox\" ><label for=\"sk-estimator-id-287\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-288\" type=\"checkbox\" ><label for=\"sk-estimator-id-288\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-289\" type=\"checkbox\" ><label for=\"sk-estimator-id-289\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-290\" type=\"checkbox\" ><label for=\"sk-estimator-id-290\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">log</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-291\" type=\"checkbox\" ><label for=\"sk-estimator-id-291\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-292\" type=\"checkbox\" ><label for=\"sk-estimator-id-292\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;ufunc &#x27;log&#x27;&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-293\" type=\"checkbox\" ><label for=\"sk-estimator-id-293\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-294\" type=\"checkbox\" ><label for=\"sk-estimator-id-294\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">geo</label><div class=\"sk-toggleable__content\"><pre>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-295\" type=\"checkbox\" ><label for=\"sk-estimator-id-295\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ClusterSimilarity</label><div class=\"sk-toggleable__content\"><pre>ClusterSimilarity(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-296\" type=\"checkbox\" ><label for=\"sk-estimator-id-296\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-297\" type=\"checkbox\" ><label for=\"sk-estimator-id-297\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-298\" type=\"checkbox\" ><label for=\"sk-estimator-id-298\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-299\" type=\"checkbox\" ><label for=\"sk-estimator-id-299\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-300\" type=\"checkbox\" ><label for=\"sk-estimator-id-300\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-301\" type=\"checkbox\" ><label for=\"sk-estimator-id-301\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-302\" type=\"checkbox\" ><label for=\"sk-estimator-id-302\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3,\n",
       "             estimator=Pipeline(steps=[('preprocessing',\n",
       "                                        ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
       "                                                                                     SimpleImputer(strategy='median')),\n",
       "                                                                                    ('standardscaler',\n",
       "                                                                                     StandardScaler())]),\n",
       "                                                          transformers=[('bedrooms_ratio',\n",
       "                                                                         Pipeline(steps=[('simpleimputer',\n",
       "                                                                                          SimpleImputer(strategy='median')),\n",
       "                                                                                         ('functiontransformer',\n",
       "                                                                                          FunctionTransformer(func=<function...\n",
       "                                                                         <sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30>)])),\n",
       "                                       ('random_forest',\n",
       "                                        RandomForestRegressor(random_state=42))]),\n",
       "             param_grid=[{'preprocessing__geo__n_clusters': [5, 8, 10],\n",
       "                          'random_forest__max_features': [4, 6, 8]},\n",
       "                         {'preprocessing__geo__n_clusters': [10, 15],\n",
       "                          'random_forest__max_features': [6, 8, 10]}],\n",
       "             scoring='neg_root_mean_squared_error')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(housing, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can get the full list of hyperparameters available for tuning by looking at `full_pipeline.get_params().keys()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['memory', 'steps', 'verbose', 'preprocessing', 'random_forest', 'preprocessing__n_jobs', 'preprocessing__remainder__memory', 'preprocessing__remainder__steps', 'preprocessing__remainder__verbose', 'preprocessing__remainder__simpleimputer', 'preprocessing__remainder__standardscaler', 'preprocessing__remainder__simpleimputer__add_indicator', 'preprocessing__remainder__simpleimputer__copy', 'preprocessing__remainder__simpleimputer__fill_value', 'preprocessing__remainder__simpleimputer__missing_values', 'preprocessing__remainder__simpleimputer__strategy', 'preprocessing__remainder__simpleimputer__verbose', 'preprocessing__remainder__standardscaler__copy', 'preprocessing__remainder__standardscaler__with_mean', 'preprocessing__remainder__standardscaler__with_std', 'preprocessing__remainder', 'preprocessing__sparse_threshold', 'preprocessing__transformer_weights', 'preprocessing__transformers', 'preprocessing__verbose', 'preprocessing__verbose_feature_names_out', 'preprocessing__be...\n"
     ]
    }
   ],
   "source": [
    "# extra code – shows part of the output of get_params().keys()\n",
    "print(str(full_pipeline.get_params().keys())[:1000] + \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best hyperparameter combination found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'preprocessing__geo__n_clusters': 15, 'random_forest__max_features': 6}"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                             (&#x27;standardscaler&#x27;,\n",
       "                                                              StandardScaler())]),\n",
       "                                   transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;))...\n",
       "                                                  ClusterSimilarity(n_clusters=15,\n",
       "                                                                    random_state=42),\n",
       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186ef0250&gt;)])),\n",
       "                (&#x27;random_forest&#x27;,\n",
       "                 RandomForestRegressor(max_features=6, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-330\" type=\"checkbox\" ><label for=\"sk-estimator-id-330\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                             (&#x27;standardscaler&#x27;,\n",
       "                                                              StandardScaler())]),\n",
       "                                   transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;))...\n",
       "                                                  ClusterSimilarity(n_clusters=15,\n",
       "                                                                    random_state=42),\n",
       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186ef0250&gt;)])),\n",
       "                (&#x27;random_forest&#x27;,\n",
       "                 RandomForestRegressor(max_features=6, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-331\" type=\"checkbox\" ><label for=\"sk-estimator-id-331\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessing: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                             SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                            (&#x27;standardscaler&#x27;,\n",
       "                                             StandardScaler())]),\n",
       "                  transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler...\n",
       "                                 [&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;,\n",
       "                                  &#x27;households&#x27;, &#x27;median_income&#x27;]),\n",
       "                                (&#x27;geo&#x27;,\n",
       "                                 ClusterSimilarity(n_clusters=15,\n",
       "                                                   random_state=42),\n",
       "                                 [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186ef0250&gt;)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-332\" type=\"checkbox\" ><label for=\"sk-estimator-id-332\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms_ratio</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-333\" type=\"checkbox\" ><label for=\"sk-estimator-id-333\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-334\" type=\"checkbox\" ><label for=\"sk-estimator-id-334\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-335\" type=\"checkbox\" ><label for=\"sk-estimator-id-335\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-336\" type=\"checkbox\" ><label for=\"sk-estimator-id-336\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">rooms_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-337\" type=\"checkbox\" ><label for=\"sk-estimator-id-337\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-338\" type=\"checkbox\" ><label for=\"sk-estimator-id-338\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-339\" type=\"checkbox\" ><label for=\"sk-estimator-id-339\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-340\" type=\"checkbox\" ><label for=\"sk-estimator-id-340\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">people_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-341\" type=\"checkbox\" ><label for=\"sk-estimator-id-341\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-342\" type=\"checkbox\" ><label for=\"sk-estimator-id-342\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-343\" type=\"checkbox\" ><label for=\"sk-estimator-id-343\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-344\" type=\"checkbox\" ><label for=\"sk-estimator-id-344\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">log</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-345\" type=\"checkbox\" ><label for=\"sk-estimator-id-345\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-346\" type=\"checkbox\" ><label for=\"sk-estimator-id-346\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;ufunc &#x27;log&#x27;&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-347\" type=\"checkbox\" ><label for=\"sk-estimator-id-347\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-348\" type=\"checkbox\" ><label for=\"sk-estimator-id-348\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">geo</label><div class=\"sk-toggleable__content\"><pre>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-349\" type=\"checkbox\" ><label for=\"sk-estimator-id-349\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ClusterSimilarity</label><div class=\"sk-toggleable__content\"><pre>ClusterSimilarity(n_clusters=15, random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-350\" type=\"checkbox\" ><label for=\"sk-estimator-id-350\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x186ef0250&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-351\" type=\"checkbox\" ><label for=\"sk-estimator-id-351\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-352\" type=\"checkbox\" ><label for=\"sk-estimator-id-352\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-353\" type=\"checkbox\" ><label for=\"sk-estimator-id-353\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-354\" type=\"checkbox\" ><label for=\"sk-estimator-id-354\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-355\" type=\"checkbox\" ><label for=\"sk-estimator-id-355\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-356\" type=\"checkbox\" ><label for=\"sk-estimator-id-356\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=6, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessing',\n",
       "                 ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
       "                                                              SimpleImputer(strategy='median')),\n",
       "                                                             ('standardscaler',\n",
       "                                                              StandardScaler())]),\n",
       "                                   transformers=[('bedrooms_ratio',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('functiontransformer',\n",
       "                                                                   FunctionTransformer(func=<function column_ratio at 0x186eac9d0>))...\n",
       "                                                  ClusterSimilarity(n_clusters=15,\n",
       "                                                                    random_state=42),\n",
       "                                                  ['latitude', 'longitude']),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('onehotencoder',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x186ef0250>)])),\n",
       "                ('random_forest',\n",
       "                 RandomForestRegressor(max_features=6, random_state=42))])"
      ]
     },
     "execution_count": 153,
     "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": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_res = pd.DataFrame(grid_search.cv_results_)\n",
    "\n",
    "cv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# extra code – these few lines of code just make the DataFrame look nicer\n",
    "cv_res = cv_res[[\"param_preprocessing__geo__n_clusters\",\n",
    "                 \"param_random_forest__max_features\", \n",
    "                 \"split0_test_score\",\n",
    "                 \"split1_test_score\", \n",
    "                 \"split2_test_score\", \n",
    "                 \"mean_test_score\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_clusters</th>\n",
       "      <th>max_features</th>\n",
       "      <th>split0</th>\n",
       "      <th>split1</th>\n",
       "      <th>split2</th>\n",
       "      <th>mean_test_rmse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>43460</td>\n",
       "      <td>43919</td>\n",
       "      <td>44748</td>\n",
       "      <td>44042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>44132</td>\n",
       "      <td>44075</td>\n",
       "      <td>45010</td>\n",
       "      <td>44406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>44374</td>\n",
       "      <td>44286</td>\n",
       "      <td>45316</td>\n",
       "      <td>44659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>44683</td>\n",
       "      <td>44655</td>\n",
       "      <td>45657</td>\n",
       "      <td>44999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>44683</td>\n",
       "      <td>44655</td>\n",
       "      <td>45657</td>\n",
       "      <td>44999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_clusters max_features  split0  split1  split2  mean_test_rmse\n",
       "12         15            6   43460   43919   44748           44042\n",
       "13         15            8   44132   44075   45010           44406\n",
       "14         15           10   44374   44286   45316           44659\n",
       "7          10            6   44683   44655   45657           44999\n",
       "9          10            6   44683   44655   45657           44999"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_cols = [\"split0\", \"split1\", \"split2\", \"mean_test_rmse\"]\n",
    "\n",
    "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\n",
    "\n",
    "cv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n",
    "\n",
    "cv_res.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Randomized Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.experimental import enable_halving_search_cv\n",
    "from sklearn.model_selection import HalvingRandomSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** the following cell may take a few minutes to run:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try 30 (`n_iter` × `cv`) random combinations of hyperparameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3,\n",
       "                   estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                              ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                           SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                          (&#x27;standardscaler&#x27;,\n",
       "                                                                                           StandardScaler())]),\n",
       "                                                                transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                               (&#x27;functiontransformer&#x27;,\n",
       "                                                                                                FunctionTransformer(func=&lt;fu...\n",
       "                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;)])),\n",
       "                                             (&#x27;random_forest&#x27;,\n",
       "                                              RandomForestRegressor(random_state=42))]),\n",
       "                   param_distributions={&#x27;preprocessing__geo__n_clusters&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x186f11a00&gt;,\n",
       "                                        &#x27;random_forest__max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x186ec66a0&gt;},\n",
       "                   random_state=42, scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-357\" type=\"checkbox\" ><label for=\"sk-estimator-id-357\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3,\n",
       "                   estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                              ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                           SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                          (&#x27;standardscaler&#x27;,\n",
       "                                                                                           StandardScaler())]),\n",
       "                                                                transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                               (&#x27;functiontransformer&#x27;,\n",
       "                                                                                                FunctionTransformer(func=&lt;fu...\n",
       "                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;)])),\n",
       "                                             (&#x27;random_forest&#x27;,\n",
       "                                              RandomForestRegressor(random_state=42))]),\n",
       "                   param_distributions={&#x27;preprocessing__geo__n_clusters&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x186f11a00&gt;,\n",
       "                                        &#x27;random_forest__max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x186ec66a0&gt;},\n",
       "                   random_state=42, scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-358\" type=\"checkbox\" ><label for=\"sk-estimator-id-358\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                             (&#x27;standardscaler&#x27;,\n",
       "                                                              StandardScaler())]),\n",
       "                                   transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;))...\n",
       "                                                   &#x27;median_income&#x27;]),\n",
       "                                                 (&#x27;geo&#x27;,\n",
       "                                                  ClusterSimilarity(random_state=42),\n",
       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;)])),\n",
       "                (&#x27;random_forest&#x27;, RandomForestRegressor(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-359\" type=\"checkbox\" ><label for=\"sk-estimator-id-359\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessing: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                             SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                            (&#x27;standardscaler&#x27;,\n",
       "                                             StandardScaler())]),\n",
       "                  transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler...\n",
       "                                 [&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;,\n",
       "                                  &#x27;households&#x27;, &#x27;median_income&#x27;]),\n",
       "                                (&#x27;geo&#x27;, ClusterSimilarity(random_state=42),\n",
       "                                 [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-360\" type=\"checkbox\" ><label for=\"sk-estimator-id-360\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms_ratio</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-361\" type=\"checkbox\" ><label for=\"sk-estimator-id-361\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-362\" type=\"checkbox\" ><label for=\"sk-estimator-id-362\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-363\" type=\"checkbox\" ><label for=\"sk-estimator-id-363\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-364\" type=\"checkbox\" ><label for=\"sk-estimator-id-364\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">rooms_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-365\" type=\"checkbox\" ><label for=\"sk-estimator-id-365\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-366\" type=\"checkbox\" ><label for=\"sk-estimator-id-366\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-367\" type=\"checkbox\" ><label for=\"sk-estimator-id-367\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-368\" type=\"checkbox\" ><label for=\"sk-estimator-id-368\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">people_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-369\" type=\"checkbox\" ><label for=\"sk-estimator-id-369\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-370\" type=\"checkbox\" ><label for=\"sk-estimator-id-370\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x186eac9d0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-371\" type=\"checkbox\" ><label for=\"sk-estimator-id-371\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-372\" type=\"checkbox\" ><label for=\"sk-estimator-id-372\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">log</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-373\" type=\"checkbox\" ><label for=\"sk-estimator-id-373\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-374\" type=\"checkbox\" ><label for=\"sk-estimator-id-374\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;ufunc &#x27;log&#x27;&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-375\" type=\"checkbox\" ><label for=\"sk-estimator-id-375\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-376\" type=\"checkbox\" ><label for=\"sk-estimator-id-376\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">geo</label><div class=\"sk-toggleable__content\"><pre>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-377\" type=\"checkbox\" ><label for=\"sk-estimator-id-377\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ClusterSimilarity</label><div class=\"sk-toggleable__content\"><pre>ClusterSimilarity(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-378\" type=\"checkbox\" ><label for=\"sk-estimator-id-378\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-379\" type=\"checkbox\" ><label for=\"sk-estimator-id-379\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-380\" type=\"checkbox\" ><label for=\"sk-estimator-id-380\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-381\" type=\"checkbox\" ><label for=\"sk-estimator-id-381\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-382\" type=\"checkbox\" ><label for=\"sk-estimator-id-382\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-383\" type=\"checkbox\" ><label for=\"sk-estimator-id-383\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-384\" type=\"checkbox\" ><label for=\"sk-estimator-id-384\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=3,\n",
       "                   estimator=Pipeline(steps=[('preprocessing',\n",
       "                                              ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
       "                                                                                           SimpleImputer(strategy='median')),\n",
       "                                                                                          ('standardscaler',\n",
       "                                                                                           StandardScaler())]),\n",
       "                                                                transformers=[('bedrooms_ratio',\n",
       "                                                                               Pipeline(steps=[('simpleimputer',\n",
       "                                                                                                SimpleImputer(strategy='median')),\n",
       "                                                                                               ('functiontransformer',\n",
       "                                                                                                FunctionTransformer(func=<fu...\n",
       "                                                                               <sklearn.compose._column_transformer.make_column_selector object at 0x186e9fd30>)])),\n",
       "                                             ('random_forest',\n",
       "                                              RandomForestRegressor(random_state=42))]),\n",
       "                   param_distributions={'preprocessing__geo__n_clusters': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x186f11a00>,\n",
       "                                        'random_forest__max_features': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x186ec66a0>},\n",
       "                   random_state=42, scoring='neg_root_mean_squared_error')"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {'preprocessing__geo__n_clusters': randint(low=3, high=10),\n",
    "                  'random_forest__max_features': randint(low=2, high=10)}\n",
    "\n",
    "rnd_search = RandomizedSearchCV(\n",
    "    full_pipeline, param_distributions=param_distribs, n_iter=10, cv=3,\n",
    "    scoring='neg_root_mean_squared_error', random_state=42)\n",
    "\n",
    "\n",
    "rnd_search.fit(housing, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_clusters</th>\n",
       "      <th>max_features</th>\n",
       "      <th>split0</th>\n",
       "      <th>split1</th>\n",
       "      <th>split2</th>\n",
       "      <th>mean_test_rmse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>45311</td>\n",
       "      <td>44727</td>\n",
       "      <td>46099</td>\n",
       "      <td>45379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>45721</td>\n",
       "      <td>45397</td>\n",
       "      <td>46743</td>\n",
       "      <td>45953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>45887</td>\n",
       "      <td>45597</td>\n",
       "      <td>46504</td>\n",
       "      <td>45996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>45633</td>\n",
       "      <td>45815</td>\n",
       "      <td>46702</td>\n",
       "      <td>46050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>45887</td>\n",
       "      <td>45820</td>\n",
       "      <td>46809</td>\n",
       "      <td>46172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  n_clusters max_features  split0  split1  split2  mean_test_rmse\n",
       "0          9            5   45311   44727   46099           45379\n",
       "4          9            3   45721   45397   46743           45953\n",
       "7          7            5   45887   45597   46504           45996\n",
       "3          7            6   45633   45815   46702           46050\n",
       "1          7            8   45887   45820   46809           46172"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – displays the random search results\n",
    "cv_res = pd.DataFrame(rnd_search.cv_results_)\n",
    "\n",
    "cv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\n",
    "\n",
    "cv_res = cv_res[[\"param_preprocessing__geo__n_clusters\",\n",
    "                 \"param_random_forest__max_features\", \"split0_test_score\",\n",
    "                 \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\n",
    "\n",
    "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\n",
    "\n",
    "cv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n",
    "\n",
    "cv_res.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Errors\n",
    "\n",
    "Analyze Errors of Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08, 0.06, 0.08, 0.01, 0.01, 0.01, 0.01, 0.25, 0.04, 0.03, 0.04,\n",
       "       0.03, 0.04, 0.06, 0.02, 0.03, 0.02, 0.01, 0.12, 0.  , 0.  , 0.01,\n",
       "       0.03])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = rnd_search.best_estimator_  # includes preprocessing\n",
    "\n",
    "feature_importances = final_model[\"random_forest\"].feature_importances_\n",
    "\n",
    "feature_importances.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate on Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = strat_test_set.drop(\"median_house_value\", axis=1)\n",
    "y_test = strat_test_set[\"median_house_value\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_predictions = final_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44556.01747258438\n"
     ]
    }
   ],
   "source": [
    "final_rmse = mean_squared_error(y_test, final_predictions, squared=False)\n",
    "print(final_rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compute a 95% confidence interval for the test RMSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42373.48736315, 46636.51953908])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "confidence = 0.95\n",
    "\n",
    "squared_errors = (final_predictions - y_test) ** 2\n",
    "\n",
    "np.sqrt(stats.t.interval(confidence, len(squared_errors) - 1,\n",
    "                         loc=squared_errors.mean(),\n",
    "                         scale=stats.sem(squared_errors)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save model\n",
    "\n",
    "Model persistence using joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the final model:\n",
    "\n",
    "Note that the model is pretty huge:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['my_california_housing_model.pkl']"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "joblib.dump(final_model, \"my_california_housing_model.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can deploy this model to production. For example, the following code could be a script that would run in production:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model_reloaded = joblib.load(\"my_california_housing_model.pkl\")\n",
    "\n",
    "new_data = housing.iloc[:5]  # pretend these are new districts\n",
    "\n",
    "predictions = final_model_reloaded.predict(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([429545.11      , 462980.09      , 108348.47761194,  99628.        ,\n",
       "       345977.07      ])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _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). Note that SVMs don't scale well to large datasets, so you should probably train your model on just the first 5,000 instances of the training set and use only 3-fold cross-validation, or else it will take hours. Don't worry about what the hyperparameters mean for now (see the SVM notebook if you're interested). How does the best `SVR` predictor perform?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                        ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                     SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                    (&#x27;standardscaler&#x27;,\n",
       "                                                                                     StandardScaler())]),\n",
       "                                                          transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                         (&#x27;functiontransformer&#x27;,\n",
       "                                                                                          FunctionTransformer(func=&lt;function...\n",
       "                                                                         &lt;sklearn.compose._column_transformer.make_column_selector object at 0x17f06eaf0&gt;)])),\n",
       "                                       (&#x27;svr&#x27;, SVR())]),\n",
       "             param_grid=[{&#x27;svr__C&#x27;: [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0,\n",
       "                                     10000.0, 30000.0],\n",
       "                          &#x27;svr__kernel&#x27;: [&#x27;linear&#x27;]},\n",
       "                         {&#x27;svr__C&#x27;: [1.0, 3.0, 10.0, 30.0, 100.0, 300.0,\n",
       "                                     1000.0],\n",
       "                          &#x27;svr__gamma&#x27;: [0.01, 0.03, 0.1, 0.3, 1.0, 3.0],\n",
       "                          &#x27;svr__kernel&#x27;: [&#x27;rbf&#x27;]}],\n",
       "             scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-138\" type=\"checkbox\" ><label for=\"sk-estimator-id-138\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                        ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                     SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                    (&#x27;standardscaler&#x27;,\n",
       "                                                                                     StandardScaler())]),\n",
       "                                                          transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                         (&#x27;functiontransformer&#x27;,\n",
       "                                                                                          FunctionTransformer(func=&lt;function...\n",
       "                                                                         &lt;sklearn.compose._column_transformer.make_column_selector object at 0x17f06eaf0&gt;)])),\n",
       "                                       (&#x27;svr&#x27;, SVR())]),\n",
       "             param_grid=[{&#x27;svr__C&#x27;: [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0,\n",
       "                                     10000.0, 30000.0],\n",
       "                          &#x27;svr__kernel&#x27;: [&#x27;linear&#x27;]},\n",
       "                         {&#x27;svr__C&#x27;: [1.0, 3.0, 10.0, 30.0, 100.0, 300.0,\n",
       "                                     1000.0],\n",
       "                          &#x27;svr__gamma&#x27;: [0.01, 0.03, 0.1, 0.3, 1.0, 3.0],\n",
       "                          &#x27;svr__kernel&#x27;: [&#x27;rbf&#x27;]}],\n",
       "             scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-139\" type=\"checkbox\" ><label for=\"sk-estimator-id-139\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                             (&#x27;standardscaler&#x27;,\n",
       "                                                              StandardScaler())]),\n",
       "                                   transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function column_ratio at 0x17ef9de50&gt;))...\n",
       "                                                   &#x27;total_rooms&#x27;, &#x27;population&#x27;,\n",
       "                                                   &#x27;households&#x27;,\n",
       "                                                   &#x27;median_income&#x27;]),\n",
       "                                                 (&#x27;geo&#x27;,\n",
       "                                                  ClusterSimilarity(random_state=42),\n",
       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x17f06eaf0&gt;)])),\n",
       "                (&#x27;svr&#x27;, SVR())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-140\" type=\"checkbox\" ><label for=\"sk-estimator-id-140\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessing: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                             SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                            (&#x27;standardscaler&#x27;,\n",
       "                                             StandardScaler())]),\n",
       "                  transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function column_ratio at 0x17ef9de50&gt;)),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler...\n",
       "                                 [&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;,\n",
       "                                  &#x27;households&#x27;, &#x27;median_income&#x27;]),\n",
       "                                (&#x27;geo&#x27;, ClusterSimilarity(random_state=42),\n",
       "                                 [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x17f06eaf0&gt;)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-141\" type=\"checkbox\" ><label for=\"sk-estimator-id-141\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms_ratio</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-142\" type=\"checkbox\" ><label for=\"sk-estimator-id-142\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-143\" type=\"checkbox\" ><label for=\"sk-estimator-id-143\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x17ef9de50&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-144\" type=\"checkbox\" ><label for=\"sk-estimator-id-144\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-145\" type=\"checkbox\" ><label for=\"sk-estimator-id-145\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">rooms_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-146\" type=\"checkbox\" ><label for=\"sk-estimator-id-146\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-147\" type=\"checkbox\" ><label for=\"sk-estimator-id-147\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x17ef9de50&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-148\" type=\"checkbox\" ><label for=\"sk-estimator-id-148\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-149\" type=\"checkbox\" ><label for=\"sk-estimator-id-149\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">people_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-150\" type=\"checkbox\" ><label for=\"sk-estimator-id-150\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-151\" type=\"checkbox\" ><label for=\"sk-estimator-id-151\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x17ef9de50&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-152\" type=\"checkbox\" ><label for=\"sk-estimator-id-152\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-153\" type=\"checkbox\" ><label for=\"sk-estimator-id-153\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">log</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-154\" type=\"checkbox\" ><label for=\"sk-estimator-id-154\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-155\" type=\"checkbox\" ><label for=\"sk-estimator-id-155\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;ufunc &#x27;log&#x27;&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-156\" type=\"checkbox\" ><label for=\"sk-estimator-id-156\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-157\" type=\"checkbox\" ><label for=\"sk-estimator-id-157\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">geo</label><div class=\"sk-toggleable__content\"><pre>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-158\" type=\"checkbox\" ><label for=\"sk-estimator-id-158\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ClusterSimilarity</label><div class=\"sk-toggleable__content\"><pre>ClusterSimilarity(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-159\" type=\"checkbox\" ><label for=\"sk-estimator-id-159\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x17f06eaf0&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-160\" type=\"checkbox\" ><label for=\"sk-estimator-id-160\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-161\" type=\"checkbox\" ><label for=\"sk-estimator-id-161\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-162\" type=\"checkbox\" ><label for=\"sk-estimator-id-162\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-163\" type=\"checkbox\" ><label for=\"sk-estimator-id-163\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-164\" type=\"checkbox\" ><label for=\"sk-estimator-id-164\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-165\" type=\"checkbox\" ><label for=\"sk-estimator-id-165\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3,\n",
       "             estimator=Pipeline(steps=[('preprocessing',\n",
       "                                        ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
       "                                                                                     SimpleImputer(strategy='median')),\n",
       "                                                                                    ('standardscaler',\n",
       "                                                                                     StandardScaler())]),\n",
       "                                                          transformers=[('bedrooms_ratio',\n",
       "                                                                         Pipeline(steps=[('simpleimputer',\n",
       "                                                                                          SimpleImputer(strategy='median')),\n",
       "                                                                                         ('functiontransformer',\n",
       "                                                                                          FunctionTransformer(func=<function...\n",
       "                                                                         <sklearn.compose._column_transformer.make_column_selector object at 0x17f06eaf0>)])),\n",
       "                                       ('svr', SVR())]),\n",
       "             param_grid=[{'svr__C': [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0,\n",
       "                                     10000.0, 30000.0],\n",
       "                          'svr__kernel': ['linear']},\n",
       "                         {'svr__C': [1.0, 3.0, 10.0, 30.0, 100.0, 300.0,\n",
       "                                     1000.0],\n",
       "                          'svr__gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0],\n",
       "                          'svr__kernel': ['rbf']}],\n",
       "             scoring='neg_root_mean_squared_error')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "param_grid = [\n",
    "        {'svr__kernel': ['linear'], 'svr__C': [10., 30., 100., 300., 1000.,\n",
    "                                               3000., 10000., 30000.0]},\n",
    "        {'svr__kernel': ['rbf'], 'svr__C': [1.0, 3.0, 10., 30., 100., 300.,\n",
    "                                            1000.0],\n",
    "         'svr__gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0]},\n",
    "    ]\n",
    "\n",
    "svr_pipeline = Pipeline([(\"preprocessing\", preprocessing), (\"svr\", SVR())])\n",
    "\n",
    "grid_search = GridSearchCV(svr_pipeline, param_grid, cv=3,\n",
    "                           scoring='neg_root_mean_squared_error')\n",
    "\n",
    "grid_search.fit(housing.iloc[:5000], housing_labels.iloc[:5000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best model achieves the following score (evaluated using 3-fold cross validation):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69814.13878034828"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_grid_search_rmse = -grid_search.best_score_\n",
    "svr_grid_search_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's much worse than the `RandomForestRegressor` (but to be fair, we trained the model on much less data). Let's check the best hyperparameters found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'svr__C': 10000.0, 'svr__kernel': 'linear'}"
      ]
     },
     "execution_count": 71,
     "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. Randomized Search CV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Try replacing the `GridSearchCV` with a `RandomizedSearchCV`._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** the following cell will take several minutes to run. You can specify `verbose=2` when creating the `RandomizedSearchCV` if you want to see the training details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3,\n",
       "                   estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                              ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                           SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                          (&#x27;standardscaler&#x27;,\n",
       "                                                                                           StandardScaler())]),\n",
       "                                                                transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                               (&#x27;functiontransformer&#x27;,\n",
       "                                                                                                FunctionTransformer(func=&lt;fu...\n",
       "                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x17f06eaf0&gt;)])),\n",
       "                                             (&#x27;svr&#x27;, SVR())]),\n",
       "                   param_distributions={&#x27;svr__C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x17ebcf070&gt;,\n",
       "                                        &#x27;svr__gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x17ebf1be0&gt;,\n",
       "                                        &#x27;svr__kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;]},\n",
       "                   random_state=42, scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-166\" type=\"checkbox\" ><label for=\"sk-estimator-id-166\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3,\n",
       "                   estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                              ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                           SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                          (&#x27;standardscaler&#x27;,\n",
       "                                                                                           StandardScaler())]),\n",
       "                                                                transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                               (&#x27;functiontransformer&#x27;,\n",
       "                                                                                                FunctionTransformer(func=&lt;fu...\n",
       "                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x17f06eaf0&gt;)])),\n",
       "                                             (&#x27;svr&#x27;, SVR())]),\n",
       "                   param_distributions={&#x27;svr__C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x17ebcf070&gt;,\n",
       "                                        &#x27;svr__gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x17ebf1be0&gt;,\n",
       "                                        &#x27;svr__kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;]},\n",
       "                   random_state=42, scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-167\" type=\"checkbox\" ><label for=\"sk-estimator-id-167\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                             (&#x27;standardscaler&#x27;,\n",
       "                                                              StandardScaler())]),\n",
       "                                   transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function column_ratio at 0x17ef9de50&gt;))...\n",
       "                                                   &#x27;total_rooms&#x27;, &#x27;population&#x27;,\n",
       "                                                   &#x27;households&#x27;,\n",
       "                                                   &#x27;median_income&#x27;]),\n",
       "                                                 (&#x27;geo&#x27;,\n",
       "                                                  ClusterSimilarity(random_state=42),\n",
       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x17f06eaf0&gt;)])),\n",
       "                (&#x27;svr&#x27;, SVR())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-168\" type=\"checkbox\" ><label for=\"sk-estimator-id-168\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessing: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                             SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                            (&#x27;standardscaler&#x27;,\n",
       "                                             StandardScaler())]),\n",
       "                  transformers=[(&#x27;bedrooms_ratio&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function column_ratio at 0x17ef9de50&gt;)),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler...\n",
       "                                 [&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;,\n",
       "                                  &#x27;households&#x27;, &#x27;median_income&#x27;]),\n",
       "                                (&#x27;geo&#x27;, ClusterSimilarity(random_state=42),\n",
       "                                 [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x17f06eaf0&gt;)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-169\" type=\"checkbox\" ><label for=\"sk-estimator-id-169\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms_ratio</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-170\" type=\"checkbox\" ><label for=\"sk-estimator-id-170\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-171\" type=\"checkbox\" ><label for=\"sk-estimator-id-171\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x17ef9de50&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-172\" type=\"checkbox\" ><label for=\"sk-estimator-id-172\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-173\" type=\"checkbox\" ><label for=\"sk-estimator-id-173\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">rooms_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-174\" type=\"checkbox\" ><label for=\"sk-estimator-id-174\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-175\" type=\"checkbox\" ><label for=\"sk-estimator-id-175\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x17ef9de50&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-176\" type=\"checkbox\" ><label for=\"sk-estimator-id-176\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-177\" type=\"checkbox\" ><label for=\"sk-estimator-id-177\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">people_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-178\" type=\"checkbox\" ><label for=\"sk-estimator-id-178\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-179\" type=\"checkbox\" ><label for=\"sk-estimator-id-179\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function column_ratio at 0x17ef9de50&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-180\" type=\"checkbox\" ><label for=\"sk-estimator-id-180\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-181\" type=\"checkbox\" ><label for=\"sk-estimator-id-181\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">log</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-182\" type=\"checkbox\" ><label for=\"sk-estimator-id-182\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-183\" type=\"checkbox\" ><label for=\"sk-estimator-id-183\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;ufunc &#x27;log&#x27;&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-184\" type=\"checkbox\" ><label for=\"sk-estimator-id-184\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-185\" type=\"checkbox\" ><label for=\"sk-estimator-id-185\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">geo</label><div class=\"sk-toggleable__content\"><pre>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-186\" type=\"checkbox\" ><label for=\"sk-estimator-id-186\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ClusterSimilarity</label><div class=\"sk-toggleable__content\"><pre>ClusterSimilarity(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-187\" type=\"checkbox\" ><label for=\"sk-estimator-id-187\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x17f06eaf0&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-188\" type=\"checkbox\" ><label for=\"sk-estimator-id-188\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-189\" type=\"checkbox\" ><label for=\"sk-estimator-id-189\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-190\" type=\"checkbox\" ><label for=\"sk-estimator-id-190\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-191\" type=\"checkbox\" ><label for=\"sk-estimator-id-191\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-192\" type=\"checkbox\" ><label for=\"sk-estimator-id-192\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-193\" type=\"checkbox\" ><label for=\"sk-estimator-id-193\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=3,\n",
       "                   estimator=Pipeline(steps=[('preprocessing',\n",
       "                                              ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
       "                                                                                           SimpleImputer(strategy='median')),\n",
       "                                                                                          ('standardscaler',\n",
       "                                                                                           StandardScaler())]),\n",
       "                                                                transformers=[('bedrooms_ratio',\n",
       "                                                                               Pipeline(steps=[('simpleimputer',\n",
       "                                                                                                SimpleImputer(strategy='median')),\n",
       "                                                                                               ('functiontransformer',\n",
       "                                                                                                FunctionTransformer(func=<fu...\n",
       "                                                                               <sklearn.compose._column_transformer.make_column_selector object at 0x17f06eaf0>)])),\n",
       "                                             ('svr', SVR())]),\n",
       "                   param_distributions={'svr__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x17ebcf070>,\n",
       "                                        'svr__gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x17ebf1be0>,\n",
       "                                        'svr__kernel': ['linear', 'rbf']},\n",
       "                   random_state=42, scoring='neg_root_mean_squared_error')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import expon, reciprocal\n",
    "\n",
    "# see https://docs.scipy.org/doc/scipy/reference/stats.html\n",
    "# for `expon()` and `reciprocal()` documentation and more probability distribution functions.\n",
    "\n",
    "# Note: gamma is ignored when kernel is \"linear\"\n",
    "param_distribs = {\n",
    "        'svr__kernel': ['linear', 'rbf'],\n",
    "        'svr__C': reciprocal(20, 200_000),\n",
    "        'svr__gamma': expon(scale=1.0),\n",
    "    }\n",
    "\n",
    "# we set n_iter to 10 \n",
    "rnd_search = RandomizedSearchCV(svr_pipeline,\n",
    "                                param_distributions=param_distribs,\n",
    "                                n_iter=10, cv=3,\n",
    "                                scoring='neg_root_mean_squared_error',\n",
    "                                random_state=42)\n",
    "\n",
    "rnd_search.fit(housing.iloc[:5000], housing_labels.iloc[:5000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best model achieves the following score (evaluated using 3-fold cross validation):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55853.881003350914"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_rnd_search_rmse = -rnd_search.best_score_\n",
    "svr_rnd_search_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that's really much better, but still far from the `RandomForestRegressor`'s performance. Let's check the best hyperparameters found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'svr__C': 157055.10989448498,\n",
       " 'svr__gamma': 0.26497040005002437,\n",
       " 'svr__kernel': 'rbf'}"
      ]
     },
     "execution_count": 74,
     "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": [
    "Note that we used the `expon()` distribution for `gamma`, with a scale of 1, so `RandomSearch` mostly searched for values roughly of that scale: about 80% of the samples were between 0.1 and 2.3 (roughly 10% were smaller and 10% were larger):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80066"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "s = expon(scale=1).rvs(100_000)  # get 100,000 samples\n",
    "((s > 0.105) & (s < 2.29)).sum() / 100_000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We used the `reciprocal()` distribution for `C`, meaning we did not have a clue what the optimal scale of `C` was before running the random search. It explored the range from 20 to 200 just as much as the range from 2,000 to 20,000 or from 20,000 to 200,000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. SelectFromModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Try adding a `SelectFromModel` transformer in the preparation pipeline to select only the most important attributes._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a new pipeline that runs the previously defined preparation pipeline, and adds a `SelectFromModel` transformer based on a `RandomForestRegressor` before the final regressor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "selector_pipeline = Pipeline([\n",
    "    ('preprocessing', preprocessing),\n",
    "    ('selector', SelectFromModel(RandomForestRegressor(random_state=42),\n",
    "                                 threshold=0.005)),  # min feature importance\n",
    "    ('svr', SVR(C=rnd_search.best_params_[\"svr__C\"],\n",
    "                gamma=rnd_search.best_params_[\"svr__gamma\"],\n",
    "                kernel=rnd_search.best_params_[\"svr__kernel\"])),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "selector_rmses = -cross_val_score(selector_pipeline,\n",
    "                                  housing.iloc[:5000],\n",
    "                                  housing_labels.iloc[:5000],\n",
    "                                  scoring=\"neg_root_mean_squared_error\",\n",
    "                                  cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        3.000000\n",
       "mean     56211.362089\n",
       "std       1922.002815\n",
       "min      54150.008611\n",
       "25%      55339.929912\n",
       "50%      56529.851213\n",
       "75%      57242.038829\n",
       "max      57954.226445\n",
       "dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.Series(selector_rmses).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh well, feature selection does not seem to help. But maybe that's just because the threshold we used was not optimal. Perhaps try tuning it using random search or grid search?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations! You already know quite a lot about Machine Learning. :)"
   ]
  }
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