{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 2 – End-to-end Machine Learning project**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*This notebook contains all the sample code and solutions to the exercises in chapter 2.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/ageron/handson-ml3/blob/main/02_end_to_end_machine_learning_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml3/blob/main/02_end_to_end_machine_learning_project.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End-to-end Machine Learning project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project requires Python 3.7 or above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "assert sys.version_info >= (3, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also requires Scikit-Learn ≥ 1.0.1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from packaging import version\n",
    "import sklearn\n",
    "\n",
    "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the 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": "markdown",
   "metadata": {},
   "source": [
    "## Download the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import tarfile\n",
    "import urllib.request\n",
    "\n",
    "def load_housing_data():\n",
    "    tarball_path = Path(\"datasets/housing.tgz\")\n",
    "    if not tarball_path.is_file():\n",
    "        Path(\"datasets\").mkdir(parents=True, exist_ok=True)\n",
    "        url = \"https://github.com/ageron/data/raw/main/housing.tgz\"\n",
    "        urllib.request.urlretrieve(url, tarball_path)\n",
    "        with tarfile.open(tarball_path) as housing_tarball:\n",
    "            housing_tarball.extractall(path=\"datasets\")\n",
    "    return pd.read_csv(Path(\"datasets/housing/housing.csv\"))\n",
    "\n",
    "housing = load_housing_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Take a Quick Look at the Data Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell is not shown either in the book. It creates the `images/end_to_end_project` folder (if it doesn't already exist), and it defines the `save_fig()` function which is used through this notebook to save the figures in high-res for the book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extra code – code to save the figures as high-res PNGs for the book\n",
    "\n",
    "IMAGES_PATH = Path() / \"images\" / \"end_to_end_project\"\n",
    "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# extra code – the next 5 lines define the default font sizes\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",
    "housing.hist(bins=50, figsize=(12, 8))\n",
    "save_fig(\"attribute_histogram_plots\")  # extra code\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def shuffle_and_split_data(data, test_ratio):\n",
    "    shuffled_indices = np.random.permutation(len(data))\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "    test_indices = shuffled_indices[:test_set_size]\n",
    "    train_indices = shuffled_indices[test_set_size:]\n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set, test_set = shuffle_and_split_data(housing, 0.2)\n",
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To ensure that this notebook's outputs remain the same every time we run it, we need to set the random seed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sadly, this won't guarantee that this notebook will output exactly the same results as in the book, since there are other possible sources of variation. The most important is the fact that algorithms get tweaked over time when libraries evolve. So please tolerate some minor differences: hopefully, most of the outputs should be the same, or at least in the right ballpark."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: another source of randomness is the order of Python sets: it is based on Python's `hash()` function, which is randomly \"salted\" when Python starts up (this started in Python 3.3, to prevent some denial-of-service attacks). To remove this randomness, the solution is to set the `PYTHONHASHSEED` environment variable to `\"0\"` _before_ Python even starts up. Nothing will happen if you do it after that. Luckily, if you're running this notebook on Colab, the variable is already set for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from zlib import crc32\n",
    "\n",
    "def is_id_in_test_set(identifier, test_ratio):\n",
    "    return crc32(np.int64(identifier)) < test_ratio * 2**32\n",
    "\n",
    "def split_data_with_id_hash(data, test_ratio, id_column):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_: is_id_in_test_set(id_, test_ratio))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index()  # adds an `index` column\n",
    "train_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, \"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]\n",
    "train_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, \"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set[\"total_bedrooms\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find the probability that a random sample of 1,000 people contains less than 48.5% female or more than 53.5% female when the population's female ratio is 51.1%, we use the [binomial distribution](https://en.wikipedia.org/wiki/Binomial_distribution). The `cdf()` method of the binomial distribution gives us the probability that the number of females will be equal or less than the given value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10736798530929913\n"
     ]
    }
   ],
   "source": [
    "# extra code – shows how to compute the 10.7% proba of getting a bad sample\n",
    "\n",
    "from scipy.stats import binom\n",
    "\n",
    "sample_size = 1000\n",
    "ratio_female = 0.511\n",
    "proba_too_small = binom(sample_size, ratio_female).cdf(485 - 1)\n",
    "proba_too_large = 1 - binom(sample_size, ratio_female).cdf(535)\n",
    "print(proba_too_small + proba_too_large)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you prefer simulations over maths, here's how you could get roughly the same result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1071"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – shows another way to estimate the probability of bad sample\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "samples = (np.random.rand(100_000, sample_size) < ratio_female).sum(axis=1)\n",
    "((samples < 485) | (samples > 535)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "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",
    "plt.xlabel(\"Income category\")\n",
    "plt.ylabel(\"Number of districts\")\n",
    "save_fig(\"housing_income_cat_bar_plot\")  # extra code\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "splitter = StratifiedShuffleSplit(n_splits=10, test_size=0.2, random_state=42)\n",
    "strat_splits = []\n",
    "for train_index, test_index in splitter.split(housing, housing[\"income_cat\"]):\n",
    "    strat_train_set_n = housing.iloc[train_index]\n",
    "    strat_test_set_n = housing.iloc[test_index]\n",
    "    strat_splits.append([strat_train_set_n, strat_test_set_n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "strat_train_set, strat_test_set = strat_splits[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's much shorter to get a single stratified split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "strat_train_set, strat_test_set = train_test_split(\n",
    "    housing, test_size=0.2, stratify=housing[\"income_cat\"], random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114341\n",
       "1    0.039971\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall %</th>\n",
       "      <th>Stratified %</th>\n",
       "      <th>Random %</th>\n",
       "      <th>Strat. Error %</th>\n",
       "      <th>Rand. Error %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.98</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.24</td>\n",
       "      <td>0.36</td>\n",
       "      <td>6.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.88</td>\n",
       "      <td>31.88</td>\n",
       "      <td>30.74</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-3.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.06</td>\n",
       "      <td>35.05</td>\n",
       "      <td>34.52</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.63</td>\n",
       "      <td>17.64</td>\n",
       "      <td>18.41</td>\n",
       "      <td>0.03</td>\n",
       "      <td>4.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.44</td>\n",
       "      <td>11.43</td>\n",
       "      <td>12.09</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>5.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Overall %  Stratified %  Random %  Strat. Error %  \\\n",
       "Income Category                                                      \n",
       "1                     3.98          4.00      4.24            0.36   \n",
       "2                    31.88         31.88     30.74           -0.02   \n",
       "3                    35.06         35.05     34.52           -0.01   \n",
       "4                    17.63         17.64     18.41            0.03   \n",
       "5                    11.44         11.43     12.09           -0.08   \n",
       "\n",
       "                 Rand. Error %  \n",
       "Income Category                 \n",
       "1                         6.45  \n",
       "2                        -3.59  \n",
       "3                        -1.53  \n",
       "4                         4.42  \n",
       "5                         5.63  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – computes the data for Figure 2–10\n",
    "\n",
    "def income_cat_proportions(data):\n",
    "    return data[\"income_cat\"].value_counts() / len(data)\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "\n",
    "compare_props = pd.DataFrame({\n",
    "    \"Overall %\": income_cat_proportions(housing),\n",
    "    \"Stratified %\": income_cat_proportions(strat_test_set),\n",
    "    \"Random %\": income_cat_proportions(test_set),\n",
    "}).sort_index()\n",
    "compare_props.index.name = \"Income Category\"\n",
    "compare_props[\"Strat. Error %\"] = (compare_props[\"Stratified %\"] /\n",
    "                                   compare_props[\"Overall %\"] - 1)\n",
    "compare_props[\"Rand. Error %\"] = (compare_props[\"Random %\"] /\n",
    "                                  compare_props[\"Overall %\"] - 1)\n",
    "(compare_props * 100).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for set_ in (strat_train_set, strat_test_set):\n",
    "    set_.drop(\"income_cat\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discover and Visualize the Data to Gain Insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Geographical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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)\n",
    "save_fig(\"bad_visualization_plot\")  # extra code\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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)\n",
    "save_fig(\"better_visualization_plot\")  # extra code\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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\", x=\"longitude\", y=\"latitude\", grid=True,\n",
    "             s=housing[\"population\"] / 100, label=\"population\",\n",
    "             c=\"median_house_value\", cmap=\"jet\", colorbar=True,\n",
    "             legend=True, sharex=False, figsize=(10, 7))\n",
    "save_fig(\"housing_prices_scatterplot\")  # extra code\n",
    "plt.show()"
   ]
  },
  {
   "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": [
    "The next cell generates the first figure in the chapter (this code is not in the book). It's just a beautified version of the previous figure, with an image of California added in the background, nicer label names and no grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates the first figure in the chapter\n",
    "\n",
    "# Download the California image\n",
    "filename = \"california.png\"\n",
    "if not (IMAGES_PATH / filename).is_file():\n",
    "    homl3_root = \"https://github.com/ageron/handson-ml3/raw/main/\"\n",
    "    url = homl3_root + \"images/end_to_end_project/\" + filename\n",
    "    print(\"Downloading\", filename)\n",
    "    urllib.request.urlretrieve(url, IMAGES_PATH / filename)\n",
    "\n",
    "housing_renamed = housing.rename(columns={\n",
    "    \"latitude\": \"Latitude\", \"longitude\": \"Longitude\",\n",
    "    \"population\": \"Population\",\n",
    "    \"median_house_value\": \"Median house value (ᴜsᴅ)\"})\n",
    "housing_renamed.plot(\n",
    "             kind=\"scatter\", x=\"Longitude\", y=\"Latitude\",\n",
    "             s=housing_renamed[\"Population\"] / 100, label=\"Population\",\n",
    "             c=\"Median house value (ᴜsᴅ)\", cmap=\"jet\", colorbar=True,\n",
    "             legend=True, sharex=False, figsize=(10, 7))\n",
    "\n",
    "california_img = plt.imread(IMAGES_PATH / filename)\n",
    "axis = -124.55, -113.95, 32.45, 42.05\n",
    "plt.axis(axis)\n",
    "plt.imshow(california_img, extent=axis)\n",
    "\n",
    "save_fig(\"california_housing_prices_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking for Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_34715/2466220658.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  corr_matrix = housing.corr()\n"
     ]
    }
   ],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688380\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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n",
    "              \"housing_median_age\"]\n",
    "scatter_matrix(housing[attributes], figsize=(12, 8))\n",
    "save_fig(\"scatter_matrix_plot\")  # extra code\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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=\"median_income\", y=\"median_house_value\",\n",
    "             alpha=0.1, grid=True)\n",
    "save_fig(\"income_vs_house_value_scatterplot\")  # extra code\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experimenting with Attribute Combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_34715/826279322.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  corr_matrix = housing.corr()\n"
     ]
    },
    {
     "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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare the Data for Machine Learning Algorithms"
   ]
  },
  {
   "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": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the book 3 options are listed to handle the NaN values:\n",
    "\n",
    "```python\n",
    "housing.dropna(subset=[\"total_bedrooms\"], inplace=True)    # option 1\n",
    "\n",
    "housing.drop(\"total_bedrooms\", axis=1)       # option 2\n",
    "\n",
    "median = housing[\"total_bedrooms\"].median()  # option 3\n",
    "housing[\"total_bedrooms\"].fillna(median, inplace=True)\n",
    "```\n",
    "\n",
    "For each option, we'll create a copy of `housing` and work on that copy to avoid breaking `housing`. We'll also show the output of each option, but filtering on the rows that originally contained a NaN value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14452</th>\n",
       "      <td>-120.67</td>\n",
       "      <td>40.50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5343.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2503.0</td>\n",
       "      <td>902.0</td>\n",
       "      <td>3.5962</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18217</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>34.03</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2093.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1755.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>3.4115</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11889</th>\n",
       "      <td>-118.05</td>\n",
       "      <td>34.04</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1348.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>4.2917</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20325</th>\n",
       "      <td>-118.88</td>\n",
       "      <td>34.17</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4260.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1701.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>5.1033</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14360</th>\n",
       "      <td>-117.87</td>\n",
       "      <td>33.62</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1266.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>375.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>9.8020</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "14452    -120.67     40.50                15.0       5343.0             NaN   \n",
       "18217    -117.96     34.03                35.0       2093.0             NaN   \n",
       "11889    -118.05     34.04                33.0       1348.0             NaN   \n",
       "20325    -118.88     34.17                15.0       4260.0             NaN   \n",
       "14360    -117.87     33.62                 8.0       1266.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \n",
       "14452      2503.0       902.0         3.5962          INLAND  \n",
       "18217      1755.0       403.0         3.4115       <1H OCEAN  \n",
       "11889      1098.0       257.0         4.2917       <1H OCEAN  \n",
       "20325      1701.0       669.0         5.1033       <1H OCEAN  \n",
       "14360       375.0       183.0         9.8020       <1H OCEAN  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_rows_idx = housing.isnull().any(axis=1)\n",
    "housing.loc[null_rows_idx].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, ocean_proximity]\n",
       "Index: []"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_option1 = housing.copy()\n",
    "\n",
    "housing_option1.dropna(subset=[\"total_bedrooms\"], inplace=True)  # option 1\n",
    "\n",
    "housing_option1.loc[null_rows_idx].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14452</th>\n",
       "      <td>-120.67</td>\n",
       "      <td>40.50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5343.0</td>\n",
       "      <td>2503.0</td>\n",
       "      <td>902.0</td>\n",
       "      <td>3.5962</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18217</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>34.03</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2093.0</td>\n",
       "      <td>1755.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>3.4115</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11889</th>\n",
       "      <td>-118.05</td>\n",
       "      <td>34.04</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1348.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>4.2917</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20325</th>\n",
       "      <td>-118.88</td>\n",
       "      <td>34.17</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4260.0</td>\n",
       "      <td>1701.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>5.1033</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14360</th>\n",
       "      <td>-117.87</td>\n",
       "      <td>33.62</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1266.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>9.8020</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  population  \\\n",
       "14452    -120.67     40.50                15.0       5343.0      2503.0   \n",
       "18217    -117.96     34.03                35.0       2093.0      1755.0   \n",
       "11889    -118.05     34.04                33.0       1348.0      1098.0   \n",
       "20325    -118.88     34.17                15.0       4260.0      1701.0   \n",
       "14360    -117.87     33.62                 8.0       1266.0       375.0   \n",
       "\n",
       "       households  median_income ocean_proximity  \n",
       "14452       902.0         3.5962          INLAND  \n",
       "18217       403.0         3.4115       <1H OCEAN  \n",
       "11889       257.0         4.2917       <1H OCEAN  \n",
       "20325       669.0         5.1033       <1H OCEAN  \n",
       "14360       183.0         9.8020       <1H OCEAN  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_option2 = housing.copy()\n",
    "\n",
    "housing_option2.drop(\"total_bedrooms\", axis=1, inplace=True)  # option 2\n",
    "\n",
    "housing_option2.loc[null_rows_idx].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14452</th>\n",
       "      <td>-120.67</td>\n",
       "      <td>40.50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5343.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>2503.0</td>\n",
       "      <td>902.0</td>\n",
       "      <td>3.5962</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18217</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>34.03</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2093.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1755.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>3.4115</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11889</th>\n",
       "      <td>-118.05</td>\n",
       "      <td>34.04</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1348.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>4.2917</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20325</th>\n",
       "      <td>-118.88</td>\n",
       "      <td>34.17</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4260.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1701.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>5.1033</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14360</th>\n",
       "      <td>-117.87</td>\n",
       "      <td>33.62</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1266.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>9.8020</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "14452    -120.67     40.50                15.0       5343.0           434.0   \n",
       "18217    -117.96     34.03                35.0       2093.0           434.0   \n",
       "11889    -118.05     34.04                33.0       1348.0           434.0   \n",
       "20325    -118.88     34.17                15.0       4260.0           434.0   \n",
       "14360    -117.87     33.62                 8.0       1266.0           434.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \n",
       "14452      2503.0       902.0         3.5962          INLAND  \n",
       "18217      1755.0       403.0         3.4115       <1H OCEAN  \n",
       "11889      1098.0       257.0         4.2917       <1H OCEAN  \n",
       "20325      1701.0       669.0         5.1033       <1H OCEAN  \n",
       "14360       375.0       183.0         9.8020       <1H OCEAN  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_option3 = housing.copy()\n",
    "\n",
    "median = housing[\"total_bedrooms\"].median()\n",
    "housing_option3[\"total_bedrooms\"].fillna(median, inplace=True)  # option 3\n",
    "\n",
    "housing_option3.loc[null_rows_idx].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy=\"median\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separating out the numerical attributes to use the `\"median\"` strategy (as it cannot be calculated on text attributes like `ocean_proximity`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num = housing.select_dtypes(include=[np.number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SimpleImputer(strategy=&#x27;median&#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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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></div>"
      ],
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2125.    ,  434.    , 1167.    ,\n",
       "        408.    ,    3.5385])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that this is the same as manually computing the median of each attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2125.    ,  434.    , 1167.    ,\n",
       "        408.    ,    3.5385])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
       "       'total_bedrooms', 'population', 'households', 'median_income'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.feature_names_in_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
    "                          index=housing_num.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14452</th>\n",
       "      <td>-120.67</td>\n",
       "      <td>40.50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5343.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>2503.0</td>\n",
       "      <td>902.0</td>\n",
       "      <td>3.5962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18217</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>34.03</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2093.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1755.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>3.4115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11889</th>\n",
       "      <td>-118.05</td>\n",
       "      <td>34.04</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1348.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>4.2917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20325</th>\n",
       "      <td>-118.88</td>\n",
       "      <td>34.17</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4260.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1701.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>5.1033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14360</th>\n",
       "      <td>-117.87</td>\n",
       "      <td>33.62</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1266.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>9.8020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "14452    -120.67     40.50                15.0       5343.0           434.0   \n",
       "18217    -117.96     34.03                35.0       2093.0           434.0   \n",
       "11889    -118.05     34.04                33.0       1348.0           434.0   \n",
       "20325    -118.88     34.17                15.0       4260.0           434.0   \n",
       "14360    -117.87     33.62                 8.0       1266.0           434.0   \n",
       "\n",
       "       population  households  median_income  \n",
       "14452      2503.0       902.0         3.5962  \n",
       "18217      1755.0       403.0         3.4115  \n",
       "11889      1098.0       257.0         4.2917  \n",
       "20325      1701.0       669.0         5.1033  \n",
       "14360       375.0       183.0         9.8020  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.loc[null_rows_idx].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'median'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
    "                          index=housing_num.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14452</th>\n",
       "      <td>-120.67</td>\n",
       "      <td>40.50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5343.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>2503.0</td>\n",
       "      <td>902.0</td>\n",
       "      <td>3.5962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18217</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>34.03</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2093.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1755.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>3.4115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11889</th>\n",
       "      <td>-118.05</td>\n",
       "      <td>34.04</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1348.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>4.2917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20325</th>\n",
       "      <td>-118.88</td>\n",
       "      <td>34.17</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4260.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1701.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>5.1033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14360</th>\n",
       "      <td>-117.87</td>\n",
       "      <td>33.62</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1266.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>9.8020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "14452    -120.67     40.50                15.0       5343.0           434.0   \n",
       "18217    -117.96     34.03                35.0       2093.0           434.0   \n",
       "11889    -118.05     34.04                33.0       1348.0           434.0   \n",
       "20325    -118.88     34.17                15.0       4260.0           434.0   \n",
       "14360    -117.87     33.62                 8.0       1266.0           434.0   \n",
       "\n",
       "       population  households  median_income  \n",
       "14452      2503.0       902.0         3.5962  \n",
       "18217      1755.0       403.0         3.4115  \n",
       "11889      1098.0       257.0         4.2917  \n",
       "20325      1701.0       669.0         5.1033  \n",
       "14360       375.0       183.0         9.8020  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.loc[null_rows_idx].head()  # not shown in the book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn import set_config\n",
    "#\n",
    "# set_config(transform_output=\"pandas\")  # scikit-learn >= 1.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's drop some outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import IsolationForest\n",
    "\n",
    "isolation_forest = IsolationForest(random_state=42)\n",
    "outlier_pred = isolation_forest.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  1,  1, ...,  1,  1,  1])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you wanted to drop outliers, you would run the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#housing = housing.iloc[outlier_pred == 1]\n",
    "#housing_labels = housing_labels.iloc[outlier_pred == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Text and Categorical Attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's preprocess the categorical input feature, `ocean_proximity`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13096</th>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3785</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14689</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20507</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18078</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4396</th>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "13096        NEAR BAY\n",
       "14973       <1H OCEAN\n",
       "3785           INLAND\n",
       "14689          INLAND\n",
       "20507      NEAR OCEAN\n",
       "1286           INLAND\n",
       "18078       <1H OCEAN\n",
       "4396         NEAR BAY"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing[[\"ocean_proximity\"]]\n",
    "housing_cat.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "ordinal_encoder = OrdinalEncoder()\n",
    "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [4.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [3.]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoded[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_encoder.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "cat_encoder = OneHotEncoder()\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the `OneHotEncoder` class returns a sparse array, but we can convert it to a dense array if needed by calling the `toarray()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 1., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can set `sparse=False` when creating the `OneHotEncoder`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 1., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder = OneHotEncoder(sparse=False)\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity_INLAND</th>\n",
       "      <th>ocean_proximity_NEAR BAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ocean_proximity_INLAND  ocean_proximity_NEAR BAY\n",
       "0                       1                         0\n",
       "1                       0                         1"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.DataFrame({\"ocean_proximity\": [\"INLAND\", \"NEAR BAY\"]})\n",
    "pd.get_dummies(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder.transform(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity_&lt;2H OCEAN</th>\n",
       "      <th>ocean_proximity_ISLAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ocean_proximity_<2H OCEAN  ocean_proximity_ISLAND\n",
       "0                          1                       0\n",
       "1                          0                       1"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_unknown = pd.DataFrame({\"ocean_proximity\": [\"<2H OCEAN\", \"ISLAND\"]})\n",
    "pd.get_dummies(df_test_unknown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0.]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder.handle_unknown = \"ignore\"\n",
    "cat_encoder.transform(df_test_unknown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ocean_proximity'], dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder.feature_names_in_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ocean_proximity_<1H OCEAN', 'ocean_proximity_INLAND',\n",
       "       'ocean_proximity_ISLAND', 'ocean_proximity_NEAR BAY',\n",
       "       'ocean_proximity_NEAR OCEAN'], dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_output = pd.DataFrame(cat_encoder.transform(df_test_unknown),\n",
    "                         columns=cat_encoder.get_feature_names_out(),\n",
    "                         index=df_test_unknown.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity_&lt;1H OCEAN</th>\n",
       "      <th>ocean_proximity_INLAND</th>\n",
       "      <th>ocean_proximity_ISLAND</th>\n",
       "      <th>ocean_proximity_NEAR BAY</th>\n",
       "      <th>ocean_proximity_NEAR OCEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ocean_proximity_<1H OCEAN  ocean_proximity_INLAND  ocean_proximity_ISLAND  \\\n",
       "0                        0.0                     0.0                     0.0   \n",
       "1                        0.0                     0.0                     1.0   \n",
       "\n",
       "   ocean_proximity_NEAR BAY  ocean_proximity_NEAR OCEAN  \n",
       "0                       0.0                         0.0  \n",
       "1                       0.0                         0.0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "min_max_scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "housing_num_min_max_scaled = min_max_scaler.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "std_scaler = StandardScaler()\n",
    "housing_num_std_scaled = std_scaler.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates Figure 2–17\n",
    "fig, axs = plt.subplots(1, 2, figsize=(8, 3), sharey=True)\n",
    "housing[\"population\"].hist(ax=axs[0], bins=50)\n",
    "housing[\"population\"].apply(np.log).hist(ax=axs[1], bins=50)\n",
    "axs[0].set_xlabel(\"Population\")\n",
    "axs[1].set_xlabel(\"Log of population\")\n",
    "axs[0].set_ylabel(\"Number of districts\")\n",
    "save_fig(\"long_tail_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we replace each value with its percentile?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – just shows that we get a uniform distribution\n",
    "percentiles = [np.percentile(housing[\"median_income\"], p)\n",
    "               for p in range(1, 100)]\n",
    "flattened_median_income = pd.cut(housing[\"median_income\"],\n",
    "                                 bins=[-np.inf] + percentiles + [np.inf],\n",
    "                                 labels=range(1, 100 + 1))\n",
    "flattened_median_income.hist(bins=50)\n",
    "plt.xlabel(\"Median income percentile\")\n",
    "plt.ylabel(\"Number of districts\")\n",
    "plt.show()\n",
    "# Note: incomes below the 1st percentile are labeled 1, and incomes above the\n",
    "# 99th percentile are labeled 100. This is why the distribution below ranges\n",
    "# from 1 to 100 (not 0 to 100)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import rbf_kernel\n",
    "\n",
    "age_simil_35 = rbf_kernel(housing[[\"housing_median_age\"]], [[35]], gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates Figure 2–18\n",
    "\n",
    "ages = np.linspace(housing[\"housing_median_age\"].min(),\n",
    "                   housing[\"housing_median_age\"].max(),\n",
    "                   500).reshape(-1, 1)\n",
    "gamma1 = 0.1\n",
    "gamma2 = 0.03\n",
    "rbf1 = rbf_kernel(ages, [[35]], gamma=gamma1)\n",
    "rbf2 = rbf_kernel(ages, [[35]], gamma=gamma2)\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_xlabel(\"Housing median age\")\n",
    "ax1.set_ylabel(\"Number of districts\")\n",
    "ax1.hist(housing[\"housing_median_age\"], bins=50)\n",
    "\n",
    "ax2 = ax1.twinx()  # create a twin axis that shares the same x-axis\n",
    "color = \"blue\"\n",
    "ax2.plot(ages, rbf1, color=color, label=\"gamma = 0.10\")\n",
    "ax2.plot(ages, rbf2, color=color, label=\"gamma = 0.03\", linestyle=\"--\")\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "ax2.set_ylabel(\"Age similarity\", color=color)\n",
    "\n",
    "plt.legend(loc=\"upper left\")\n",
    "save_fig(\"age_similarity_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "target_scaler = StandardScaler()\n",
    "scaled_labels = target_scaler.fit_transform(housing_labels.to_frame())\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(housing[[\"median_income\"]], scaled_labels)\n",
    "some_new_data = housing[[\"median_income\"]].iloc[:5]  # pretend this is new data\n",
    "\n",
    "scaled_predictions = model.predict(some_new_data)\n",
    "predictions = target_scaler.inverse_transform(scaled_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[131997.15275877],\n",
       "       [299359.35844434],\n",
       "       [146023.37185694],\n",
       "       [138840.33653057],\n",
       "       [192016.61557639]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import TransformedTargetRegressor\n",
    "\n",
    "model = TransformedTargetRegressor(LinearRegression(),\n",
    "                                   transformer=StandardScaler())\n",
    "model.fit(housing[[\"median_income\"]], housing_labels)\n",
    "predictions = model.predict(some_new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([131997.15275877, 299359.35844434, 146023.37185694, 138840.33653057,\n",
       "       192016.61557639])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create simple transformers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import FunctionTransformer\n",
    "\n",
    "log_transformer = FunctionTransformer(np.log, inverse_func=np.exp)\n",
    "log_pop = log_transformer.transform(housing[[\"population\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "rbf_transformer = FunctionTransformer(rbf_kernel,\n",
    "                                      kw_args=dict(Y=[[35.]], gamma=0.1))\n",
    "age_simil_35 = rbf_transformer.transform(housing[[\"housing_median_age\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.81118530e-13],\n",
       "       [8.20849986e-02],\n",
       "       [6.70320046e-01],\n",
       "       ...,\n",
       "       [9.55316054e-22],\n",
       "       [6.70320046e-01],\n",
       "       [3.03539138e-04]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_simil_35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "sf_coords = 37.7749, -122.41\n",
    "sf_transformer = FunctionTransformer(rbf_kernel,\n",
    "                                     kw_args=dict(Y=[sf_coords], gamma=0.1))\n",
    "sf_simil = sf_transformer.transform(housing[[\"latitude\", \"longitude\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.999927  ],\n",
       "       [0.05258419],\n",
       "       [0.94864161],\n",
       "       ...,\n",
       "       [0.00388525],\n",
       "       [0.05038518],\n",
       "       [0.99868067]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_simil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5 ],\n",
       "       [0.75]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio_transformer = FunctionTransformer(lambda X: X[:, [0]] / X[:, [1]])\n",
    "ratio_transformer.transform(np.array([[1., 2.], [3., 4.]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.utils.validation import check_array, check_is_fitted\n",
    "\n",
    "class StandardScalerClone(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, with_mean=True):  # no *args or **kwargs!\n",
    "        self.with_mean = with_mean\n",
    "\n",
    "    def fit(self, X, y=None):  # y is required even though we don't use it\n",
    "        X = check_array(X)  # checks that X is an array with finite float values\n",
    "        self.mean_ = X.mean(axis=0)\n",
    "        self.scale_ = X.std(axis=0)\n",
    "        self.n_features_in_ = X.shape[1]  # every estimator stores this in fit()\n",
    "        return self  # always return self!\n",
    "\n",
    "    def transform(self, X):\n",
    "        check_is_fitted(self)  # looks for learned attributes (with trailing _)\n",
    "        X = check_array(X)\n",
    "        assert self.n_features_in_ == X.shape[1]\n",
    "        if self.with_mean:\n",
    "            X = X - self.mean_\n",
    "        return X / self.scale_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\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": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "cluster_simil = ClusterSimilarity(n_clusters=10, gamma=1., random_state=42)\n",
    "similarities = cluster_simil.fit_transform(housing[[\"latitude\", \"longitude\"]],\n",
    "                                           sample_weight=housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.  , 0.14, 0.  , 0.  , 0.  , 0.08, 0.  , 0.99, 0.  , 0.6 ],\n",
       "       [0.63, 0.  , 0.99, 0.  , 0.  , 0.  , 0.04, 0.  , 0.11, 0.  ],\n",
       "       [0.  , 0.29, 0.  , 0.  , 0.01, 0.44, 0.  , 0.7 , 0.  , 0.3 ]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarities[:3].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates Figure 2–19\n",
    "\n",
    "housing_renamed = housing.rename(columns={\n",
    "    \"latitude\": \"Latitude\", \"longitude\": \"Longitude\",\n",
    "    \"population\": \"Population\",\n",
    "    \"median_house_value\": \"Median house value (ᴜsᴅ)\"})\n",
    "housing_renamed[\"Max cluster similarity\"] = similarities.max(axis=1)\n",
    "\n",
    "housing_renamed.plot(kind=\"scatter\", x=\"Longitude\", y=\"Latitude\", grid=True,\n",
    "                     s=housing_renamed[\"Population\"] / 100, label=\"Population\",\n",
    "                     c=\"Max cluster similarity\",\n",
    "                     cmap=\"jet\", colorbar=True,\n",
    "                     legend=True, sharex=False, figsize=(10, 7))\n",
    "plt.plot(cluster_simil.kmeans_.cluster_centers_[:, 1],\n",
    "         cluster_simil.kmeans_.cluster_centers_[:, 0],\n",
    "         linestyle=\"\", color=\"black\", marker=\"X\", markersize=20,\n",
    "         label=\"Cluster centers\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "save_fig(\"district_cluster_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformation Pipelines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's build a pipeline to preprocess the numerical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    (\"impute\", SimpleImputer(strategy=\"median\")),\n",
    "    (\"standardize\", StandardScaler()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                (&#x27;standardscaler&#x27;, StandardScaler())])</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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                (&#x27;standardscaler&#x27;, StandardScaler())])</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" 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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" 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>"
      ],
      "text/plain": [
       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
       "                ('standardscaler', StandardScaler())])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import set_config\n",
    "\n",
    "set_config(display='diagram')\n",
    "\n",
    "num_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.42,  1.01,  1.86,  0.31,  1.37,  0.14,  1.39, -0.94],\n",
       "       [ 0.6 , -0.7 ,  0.91, -0.31, -0.44, -0.69, -0.37,  1.17]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_prepared = num_pipeline.fit_transform(housing_num)\n",
    "housing_num_prepared[:2].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def monkey_patch_get_signature_names_out():\n",
    "    \"\"\"Monkey patch some classes which did not handle get_feature_names_out()\n",
    "       correctly in Scikit-Learn 1.0.*.\"\"\"\n",
    "    from inspect import Signature, signature, Parameter\n",
    "    import pandas as pd\n",
    "    from sklearn.impute import SimpleImputer\n",
    "    from sklearn.pipeline import make_pipeline, Pipeline\n",
    "    from sklearn.preprocessing import FunctionTransformer, StandardScaler\n",
    "\n",
    "    default_get_feature_names_out = StandardScaler.get_feature_names_out\n",
    "\n",
    "    if not hasattr(SimpleImputer, \"get_feature_names_out\"):\n",
    "      print(\"Monkey-patching SimpleImputer.get_feature_names_out()\")\n",
    "      SimpleImputer.get_feature_names_out = default_get_feature_names_out\n",
    "\n",
    "    if not hasattr(FunctionTransformer, \"get_feature_names_out\"):\n",
    "        print(\"Monkey-patching FunctionTransformer.get_feature_names_out()\")\n",
    "        orig_init = FunctionTransformer.__init__\n",
    "        orig_sig = signature(orig_init)\n",
    "\n",
    "        def __init__(*args, feature_names_out=None, **kwargs):\n",
    "            orig_sig.bind(*args, **kwargs)\n",
    "            orig_init(*args, **kwargs)\n",
    "            args[0].feature_names_out = feature_names_out\n",
    "\n",
    "        __init__.__signature__ = Signature(\n",
    "            list(signature(orig_init).parameters.values()) + [\n",
    "                Parameter(\"feature_names_out\", Parameter.KEYWORD_ONLY)])\n",
    "\n",
    "        def get_feature_names_out(self, names=None):\n",
    "            if callable(self.feature_names_out):\n",
    "                return self.feature_names_out(self, names)\n",
    "            assert self.feature_names_out == \"one-to-one\"\n",
    "            return default_get_feature_names_out(self, names)\n",
    "\n",
    "        FunctionTransformer.__init__ = __init__\n",
    "        FunctionTransformer.get_feature_names_out = get_feature_names_out\n",
    "\n",
    "monkey_patch_get_signature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_housing_num_prepared = pd.DataFrame(\n",
    "    housing_num_prepared, columns=num_pipeline.get_feature_names_out(),\n",
    "    index=housing_num.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13096</th>\n",
       "      <td>-1.423037</td>\n",
       "      <td>1.013606</td>\n",
       "      <td>1.861119</td>\n",
       "      <td>0.311912</td>\n",
       "      <td>1.368167</td>\n",
       "      <td>0.137460</td>\n",
       "      <td>1.394812</td>\n",
       "      <td>-0.936491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>0.596394</td>\n",
       "      <td>-0.702103</td>\n",
       "      <td>0.907630</td>\n",
       "      <td>-0.308620</td>\n",
       "      <td>-0.435925</td>\n",
       "      <td>-0.693771</td>\n",
       "      <td>-0.373485</td>\n",
       "      <td>1.171942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "13096  -1.423037  1.013606            1.861119     0.311912        1.368167   \n",
       "14973   0.596394 -0.702103            0.907630    -0.308620       -0.435925   \n",
       "\n",
       "       population  households  median_income  \n",
       "13096    0.137460    1.394812      -0.936491  \n",
       "14973   -0.693771   -0.373485       1.171942  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_housing_num_prepared.head(2)  # extra code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('simpleimputer', SimpleImputer(strategy='median')),\n",
       " ('standardscaler', StandardScaler())]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pipeline.steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pipeline[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#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-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" 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></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median'))])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pipeline[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SimpleImputer(strategy=&#x27;median&#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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" 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></div>"
      ],
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pipeline.named_steps[\"simpleimputer\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                (&#x27;standardscaler&#x27;, StandardScaler())])</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-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                (&#x27;standardscaler&#x27;, StandardScaler())])</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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" 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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" 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>"
      ],
      "text/plain": [
       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
       "                ('standardscaler', StandardScaler())])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pipeline.set_params(simpleimputer__strategy=\"median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "num_attribs = [\"longitude\", \"latitude\", \"housing_median_age\", \"total_rooms\",\n",
    "               \"total_bedrooms\", \"population\", \"households\", \"median_income\"]\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "cat_pipeline = make_pipeline(\n",
    "    SimpleImputer(strategy=\"most_frequent\"),\n",
    "    OneHotEncoder(handle_unknown=\"ignore\"))\n",
    "\n",
    "preprocessing = ColumnTransformer([\n",
    "    (\"num\", num_pipeline, num_attribs),\n",
    "    (\"cat\", cat_pipeline, cat_attribs),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_selector, make_column_transformer\n",
    "\n",
    "preprocessing = make_column_transformer(\n",
    "    (num_pipeline, make_column_selector(dtype_include=np.number)),\n",
    "    (cat_pipeline, make_column_selector(dtype_include=object)),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_prepared = preprocessing.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>pipeline-1__longitude</th>\n",
       "      <th>pipeline-1__latitude</th>\n",
       "      <th>pipeline-1__housing_median_age</th>\n",
       "      <th>pipeline-1__total_rooms</th>\n",
       "      <th>pipeline-1__total_bedrooms</th>\n",
       "      <th>pipeline-1__population</th>\n",
       "      <th>pipeline-1__households</th>\n",
       "      <th>pipeline-1__median_income</th>\n",
       "      <th>pipeline-2__ocean_proximity_&lt;1H OCEAN</th>\n",
       "      <th>pipeline-2__ocean_proximity_INLAND</th>\n",
       "      <th>pipeline-2__ocean_proximity_ISLAND</th>\n",
       "      <th>pipeline-2__ocean_proximity_NEAR BAY</th>\n",
       "      <th>pipeline-2__ocean_proximity_NEAR OCEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13096</th>\n",
       "      <td>-1.423037</td>\n",
       "      <td>1.013606</td>\n",
       "      <td>1.861119</td>\n",
       "      <td>0.311912</td>\n",
       "      <td>1.368167</td>\n",
       "      <td>0.137460</td>\n",
       "      <td>1.394812</td>\n",
       "      <td>-0.936491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>0.596394</td>\n",
       "      <td>-0.702103</td>\n",
       "      <td>0.907630</td>\n",
       "      <td>-0.308620</td>\n",
       "      <td>-0.435925</td>\n",
       "      <td>-0.693771</td>\n",
       "      <td>-0.373485</td>\n",
       "      <td>1.171942</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pipeline-1__longitude  pipeline-1__latitude  \\\n",
       "13096              -1.423037              1.013606   \n",
       "14973               0.596394             -0.702103   \n",
       "\n",
       "       pipeline-1__housing_median_age  pipeline-1__total_rooms  \\\n",
       "13096                        1.861119                 0.311912   \n",
       "14973                        0.907630                -0.308620   \n",
       "\n",
       "       pipeline-1__total_bedrooms  pipeline-1__population  \\\n",
       "13096                    1.368167                0.137460   \n",
       "14973                   -0.435925               -0.693771   \n",
       "\n",
       "       pipeline-1__households  pipeline-1__median_income  \\\n",
       "13096                1.394812                  -0.936491   \n",
       "14973               -0.373485                   1.171942   \n",
       "\n",
       "       pipeline-2__ocean_proximity_<1H OCEAN  \\\n",
       "13096                                    0.0   \n",
       "14973                                    1.0   \n",
       "\n",
       "       pipeline-2__ocean_proximity_INLAND  pipeline-2__ocean_proximity_ISLAND  \\\n",
       "13096                                 0.0                                 0.0   \n",
       "14973                                 0.0                                 0.0   \n",
       "\n",
       "       pipeline-2__ocean_proximity_NEAR BAY  \\\n",
       "13096                                   1.0   \n",
       "14973                                   0.0   \n",
       "\n",
       "       pipeline-2__ocean_proximity_NEAR OCEAN  \n",
       "13096                                     0.0  \n",
       "14973                                     0.0  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – shows that we can get a DataFrame out if we want\n",
    "housing_prepared_fr = pd.DataFrame(\n",
    "    housing_prepared,\n",
    "    columns=preprocessing.get_feature_names_out(),\n",
    "    index=housing.index)\n",
    "housing_prepared_fr.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def column_ratio(X):\n",
    "    return X[:, [0]] / X[:, [1]]\n",
    "\n",
    "def ratio_name(function_transformer, feature_names_in):\n",
    "    return [\"ratio\"]  # feature names out\n",
    "\n",
    "def ratio_pipeline():\n",
    "    return make_pipeline(\n",
    "        SimpleImputer(strategy=\"median\"),\n",
    "        FunctionTransformer(column_ratio, feature_names_out=ratio_name),\n",
    "        StandardScaler())\n",
    "\n",
    "log_pipeline = make_pipeline(\n",
    "    SimpleImputer(strategy=\"median\"),\n",
    "    FunctionTransformer(np.log, feature_names_out=\"one-to-one\"),\n",
    "    StandardScaler())\n",
    "cluster_simil = ClusterSimilarity(n_clusters=10, gamma=1., random_state=42)\n",
    "default_num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"),\n",
    "                                     StandardScaler())\n",
    "preprocessing = ColumnTransformer([\n",
    "        (\"bedrooms\", ratio_pipeline(), [\"total_bedrooms\", \"total_rooms\"]),\n",
    "        (\"rooms_per_house\", ratio_pipeline(), [\"total_rooms\", \"households\"]),\n",
    "        (\"people_per_house\", ratio_pipeline(), [\"population\", \"households\"]),\n",
    "        (\"log\", log_pipeline, [\"total_bedrooms\", \"total_rooms\", \"population\",\n",
    "                               \"households\", \"median_income\"]),\n",
    "        (\"geo\", cluster_simil, [\"latitude\", \"longitude\"]),\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": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(16512, 24)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = preprocessing.fit_transform(housing)\n",
    "housing_prepared.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bedrooms__ratio', 'rooms_per_house__ratio',\n",
       "       'people_per_house__ratio', 'log__total_bedrooms',\n",
       "       'log__total_rooms', 'log__population', 'log__households',\n",
       "       'log__median_income', 'geo__Cluster 0 similarity',\n",
       "       'geo__Cluster 1 similarity', 'geo__Cluster 2 similarity',\n",
       "       'geo__Cluster 3 similarity', 'geo__Cluster 4 similarity',\n",
       "       'geo__Cluster 5 similarity', 'geo__Cluster 6 similarity',\n",
       "       'geo__Cluster 7 similarity', 'geo__Cluster 8 similarity',\n",
       "       'geo__Cluster 9 similarity', 'cat__ocean_proximity_<1H OCEAN',\n",
       "       'cat__ocean_proximity_INLAND', 'cat__ocean_proximity_ISLAND',\n",
       "       'cat__ocean_proximity_NEAR BAY', 'cat__ocean_proximity_NEAR OCEAN',\n",
       "       'remainder__housing_median_age'], dtype=object)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select and Train a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Evaluating on the Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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>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&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6...\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 0x7f6bd716ed70&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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" 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&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6...\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 0x7f6bd716ed70&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-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" 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&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                                                                      func=&lt;function column_ratio...\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 0x7f6bd716ed70&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-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms</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-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" 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-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" 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-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" 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-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" 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-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" 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-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" 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-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" 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-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" 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-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" 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-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" 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-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&#x27;one-to-one&#x27;, 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-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" 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-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" 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-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" 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-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" 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 0x7f6bd716ed70&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-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" 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-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" 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-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" 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-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" 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-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" 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-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" 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',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('functiontransformer',\n",
       "                                                                   FunctionTransformer(feature_names_out=<function ratio_name at 0x7f6...\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 0x7f6bd716ed70>)])),\n",
       "                ('linearregression', LinearRegression())])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = make_pipeline(preprocessing, LinearRegression())\n",
    "lin_reg.fit(housing, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try the full preprocessing pipeline on a few training instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([243700., 372400., 128800.,  94400., 328300.])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = lin_reg.predict(housing)\n",
    "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": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([458300., 483800., 101700.,  96100., 361800.])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_labels.iloc[:5].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-46.8%, -23.0%, 26.6%, -1.8%, -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": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68687.89176589991"
      ]
     },
     "execution_count": 123,
     "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": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 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-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 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-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 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-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 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-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" 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&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6...\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 0x7f6bd716ed70&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-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" 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&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6...\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 0x7f6bd716ed70&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-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" 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&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                                                                      func=&lt;function column_ratio...\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 0x7f6bd716ed70&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-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms</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-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" 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-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" 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-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" 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-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" 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-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" 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-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" 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-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" 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-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" 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-53\" type=\"checkbox\" ><label for=\"sk-estimator-id-53\" 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-54\" type=\"checkbox\" ><label for=\"sk-estimator-id-54\" 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-55\" type=\"checkbox\" ><label for=\"sk-estimator-id-55\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&#x27;one-to-one&#x27;, 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-56\" type=\"checkbox\" ><label for=\"sk-estimator-id-56\" 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-57\" type=\"checkbox\" ><label for=\"sk-estimator-id-57\" 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-58\" type=\"checkbox\" ><label for=\"sk-estimator-id-58\" 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-59\" type=\"checkbox\" ><label for=\"sk-estimator-id-59\" 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 0x7f6bd716ed70&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-60\" type=\"checkbox\" ><label for=\"sk-estimator-id-60\" 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-61\" type=\"checkbox\" ><label for=\"sk-estimator-id-61\" 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-62\" type=\"checkbox\" ><label for=\"sk-estimator-id-62\" 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-63\" type=\"checkbox\" ><label for=\"sk-estimator-id-63\" 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-64\" type=\"checkbox\" ><label for=\"sk-estimator-id-64\" 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-65\" type=\"checkbox\" ><label for=\"sk-estimator-id-65\" 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',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('functiontransformer',\n",
       "                                                                   FunctionTransformer(feature_names_out=<function ratio_name at 0x7f6...\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 0x7f6bd716ed70>)])),\n",
       "                ('decisiontreeregressor',\n",
       "                 DecisionTreeRegressor(random_state=42))])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = make_pipeline(preprocessing, DecisionTreeRegressor(random_state=42))\n",
    "tree_reg.fit(housing, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = tree_reg.predict(housing)\n",
    "tree_rmse = mean_squared_error(housing_labels, housing_predictions,\n",
    "                              squared=False)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Better Evaluation Using Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "tree_rmses = -cross_val_score(tree_reg, housing, housing_labels,\n",
    "                              scoring=\"neg_root_mean_squared_error\", cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       10.000000\n",
       "mean     66868.027288\n",
       "std       2060.966425\n",
       "min      63649.536493\n",
       "25%      65338.078316\n",
       "50%      66801.953094\n",
       "75%      68229.934454\n",
       "max      70094.778246\n",
       "dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(tree_rmses).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count       10.000000\n",
       "mean     69858.018195\n",
       "std       4182.205077\n",
       "min      65397.780144\n",
       "25%      68070.536263\n",
       "50%      68619.737842\n",
       "75%      69810.076342\n",
       "max      80959.348171\n",
       "dtype: float64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – computes the error stats for the linear model\n",
    "lin_rmses = -cross_val_score(lin_reg, housing, housing_labels,\n",
    "                              scoring=\"neg_root_mean_squared_error\", cv=10)\n",
    "pd.Series(lin_rmses).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** the following cell may take a few minutes to run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = make_pipeline(preprocessing,\n",
    "                           RandomForestRegressor(random_state=42))\n",
    "forest_rmses = -cross_val_score(forest_reg, housing, housing_labels,\n",
    "                                scoring=\"neg_root_mean_squared_error\", cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       10.000000\n",
       "mean     47019.561281\n",
       "std       1033.957120\n",
       "min      45458.112527\n",
       "25%      46464.031184\n",
       "50%      46967.596354\n",
       "75%      47325.694987\n",
       "max      49243.765795\n",
       "dtype: float64"
      ]
     },
     "execution_count": 130,
     "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": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "17474.619286483998"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_reg.fit(housing, housing_labels)\n",
    "housing_predictions = forest_reg.predict(housing)\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-Tune Your Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** the following cell may take a few minutes to run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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>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&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                         (&#x27;functiontransformer&#x27;,\n",
       "                                                                                          FunctionTransformer(feature_names_out=&lt;f...\n",
       "                                                                         &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f6bd716ed70&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-66\" type=\"checkbox\" ><label for=\"sk-estimator-id-66\" 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&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                         (&#x27;functiontransformer&#x27;,\n",
       "                                                                                          FunctionTransformer(feature_names_out=&lt;f...\n",
       "                                                                         &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f6bd716ed70&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-67\" type=\"checkbox\" ><label for=\"sk-estimator-id-67\" 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&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe...\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 0x7f6bd716ed70&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-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" 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&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                                                                      func=&lt;function column_ratio...\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 0x7f6bd716ed70&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-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms</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-70\" type=\"checkbox\" ><label for=\"sk-estimator-id-70\" 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-71\" type=\"checkbox\" ><label for=\"sk-estimator-id-71\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-72\" type=\"checkbox\" ><label for=\"sk-estimator-id-72\" 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-73\" type=\"checkbox\" ><label for=\"sk-estimator-id-73\" 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-74\" type=\"checkbox\" ><label for=\"sk-estimator-id-74\" 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-75\" type=\"checkbox\" ><label for=\"sk-estimator-id-75\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-76\" type=\"checkbox\" ><label for=\"sk-estimator-id-76\" 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-77\" type=\"checkbox\" ><label for=\"sk-estimator-id-77\" 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-78\" type=\"checkbox\" ><label for=\"sk-estimator-id-78\" 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-79\" type=\"checkbox\" ><label for=\"sk-estimator-id-79\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-80\" type=\"checkbox\" ><label for=\"sk-estimator-id-80\" 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-81\" type=\"checkbox\" ><label for=\"sk-estimator-id-81\" 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-82\" type=\"checkbox\" ><label for=\"sk-estimator-id-82\" 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-83\" type=\"checkbox\" ><label for=\"sk-estimator-id-83\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&#x27;one-to-one&#x27;, 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-84\" type=\"checkbox\" ><label for=\"sk-estimator-id-84\" 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-85\" type=\"checkbox\" ><label for=\"sk-estimator-id-85\" 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-86\" type=\"checkbox\" ><label for=\"sk-estimator-id-86\" 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-87\" type=\"checkbox\" ><label for=\"sk-estimator-id-87\" 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 0x7f6bd716ed70&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-88\" type=\"checkbox\" ><label for=\"sk-estimator-id-88\" 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-89\" type=\"checkbox\" ><label for=\"sk-estimator-id-89\" 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-90\" type=\"checkbox\" ><label for=\"sk-estimator-id-90\" 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-91\" type=\"checkbox\" ><label for=\"sk-estimator-id-91\" 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-92\" type=\"checkbox\" ><label for=\"sk-estimator-id-92\" 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-93\" type=\"checkbox\" ><label for=\"sk-estimator-id-93\" 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',\n",
       "                                                                         Pipeline(steps=[('simpleimputer',\n",
       "                                                                                          SimpleImputer(strategy='median')),\n",
       "                                                                                         ('functiontransformer',\n",
       "                                                                                          FunctionTransformer(feature_names_out=<f...\n",
       "                                                                         <sklearn.compose._column_transformer.make_column_selector object at 0x7f6bd716ed70>)])),\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": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "full_pipeline = Pipeline([\n",
    "    (\"preprocessing\", preprocessing),\n",
    "    (\"random_forest\", RandomForestRegressor(random_state=42)),\n",
    "])\n",
    "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",
    "]\n",
    "grid_search = GridSearchCV(full_pipeline, param_grid, cv=3,\n",
    "                           scoring='neg_root_mean_squared_error')\n",
    "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": 133,
   "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__keep_empty_features', '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'...\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": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'preprocessing__geo__n_clusters': 15, 'random_forest__max_features': 6}"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "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;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&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe...\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 0x7f6bdbef4970&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-94\" type=\"checkbox\" ><label for=\"sk-estimator-id-94\" 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&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe...\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 0x7f6bdbef4970&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-95\" type=\"checkbox\" ><label for=\"sk-estimator-id-95\" 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&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                                                                      func=&lt;function column_ratio...\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 0x7f6bdbef4970&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-96\" type=\"checkbox\" ><label for=\"sk-estimator-id-96\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms</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-97\" type=\"checkbox\" ><label for=\"sk-estimator-id-97\" 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-98\" type=\"checkbox\" ><label for=\"sk-estimator-id-98\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-99\" type=\"checkbox\" ><label for=\"sk-estimator-id-99\" 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-100\" type=\"checkbox\" ><label for=\"sk-estimator-id-100\" 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-101\" type=\"checkbox\" ><label for=\"sk-estimator-id-101\" 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-102\" type=\"checkbox\" ><label for=\"sk-estimator-id-102\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-103\" type=\"checkbox\" ><label for=\"sk-estimator-id-103\" 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-104\" type=\"checkbox\" ><label for=\"sk-estimator-id-104\" 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-105\" type=\"checkbox\" ><label for=\"sk-estimator-id-105\" 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-106\" type=\"checkbox\" ><label for=\"sk-estimator-id-106\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-107\" type=\"checkbox\" ><label for=\"sk-estimator-id-107\" 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-108\" type=\"checkbox\" ><label for=\"sk-estimator-id-108\" 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-109\" type=\"checkbox\" ><label for=\"sk-estimator-id-109\" 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-110\" type=\"checkbox\" ><label for=\"sk-estimator-id-110\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&#x27;one-to-one&#x27;, 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-111\" type=\"checkbox\" ><label for=\"sk-estimator-id-111\" 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-112\" type=\"checkbox\" ><label for=\"sk-estimator-id-112\" 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-113\" type=\"checkbox\" ><label for=\"sk-estimator-id-113\" 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-114\" type=\"checkbox\" ><label for=\"sk-estimator-id-114\" 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 0x7f6bdbef4970&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-115\" type=\"checkbox\" ><label for=\"sk-estimator-id-115\" 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-116\" type=\"checkbox\" ><label for=\"sk-estimator-id-116\" 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-117\" type=\"checkbox\" ><label for=\"sk-estimator-id-117\" 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-118\" type=\"checkbox\" ><label for=\"sk-estimator-id-118\" 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-119\" type=\"checkbox\" ><label for=\"sk-estimator-id-119\" 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-120\" type=\"checkbox\" ><label for=\"sk-estimator-id-120\" 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',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('functiontransformer',\n",
       "                                                                   FunctionTransformer(feature_names_out=<function ratio_name at 0x7f6bdbe...\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 0x7f6bdbef4970>)])),\n",
       "                ('random_forest',\n",
       "                 RandomForestRegressor(max_features=6, random_state=42))])"
      ]
     },
     "execution_count": 135,
     "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": 136,
   "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": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_res = pd.DataFrame(grid_search.cv_results_)\n",
    "cv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\n",
    "\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\", \"split0_test_score\",\n",
    "                 \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\n",
    "score_cols = [\"split0\", \"split1\", \"split2\", \"mean_test_rmse\"]\n",
    "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\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": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.experimental import enable_halving_search_cv\n",
    "from sklearn.model_selection import HalvingRandomSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try 30 (`n_iter` × `cv`) random combinations of hyperparameters:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** the following cell may take a few minutes to run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 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-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 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-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 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-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 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-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" 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&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                               (&#x27;functiontransformer&#x27;,\n",
       "                                                                                                FunctionTransformer(feature_names_...\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 0x7f6bd9735c30&gt;,\n",
       "                                        &#x27;random_forest__max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7f6bdbd0a2f0&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-121\" type=\"checkbox\" ><label for=\"sk-estimator-id-121\" 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&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                               (&#x27;functiontransformer&#x27;,\n",
       "                                                                                                FunctionTransformer(feature_names_...\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 0x7f6bd9735c30&gt;,\n",
       "                                        &#x27;random_forest__max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7f6bdbd0a2f0&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-122\" type=\"checkbox\" ><label for=\"sk-estimator-id-122\" 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&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe...\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 0x7f6bd716ed70&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-123\" type=\"checkbox\" ><label for=\"sk-estimator-id-123\" 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&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                                                                      func=&lt;function column_ratio...\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 0x7f6bd716ed70&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-124\" type=\"checkbox\" ><label for=\"sk-estimator-id-124\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms</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-125\" type=\"checkbox\" ><label for=\"sk-estimator-id-125\" 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-126\" type=\"checkbox\" ><label for=\"sk-estimator-id-126\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-127\" type=\"checkbox\" ><label for=\"sk-estimator-id-127\" 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-128\" type=\"checkbox\" ><label for=\"sk-estimator-id-128\" 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-129\" type=\"checkbox\" ><label for=\"sk-estimator-id-129\" 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-130\" type=\"checkbox\" ><label for=\"sk-estimator-id-130\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-131\" type=\"checkbox\" ><label for=\"sk-estimator-id-131\" 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-132\" type=\"checkbox\" ><label for=\"sk-estimator-id-132\" 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-133\" type=\"checkbox\" ><label for=\"sk-estimator-id-133\" 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-134\" type=\"checkbox\" ><label for=\"sk-estimator-id-134\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-135\" type=\"checkbox\" ><label for=\"sk-estimator-id-135\" 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-136\" type=\"checkbox\" ><label for=\"sk-estimator-id-136\" 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-137\" type=\"checkbox\" ><label for=\"sk-estimator-id-137\" 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-138\" type=\"checkbox\" ><label for=\"sk-estimator-id-138\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&#x27;one-to-one&#x27;, 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-139\" type=\"checkbox\" ><label for=\"sk-estimator-id-139\" 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-140\" type=\"checkbox\" ><label for=\"sk-estimator-id-140\" 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-141\" type=\"checkbox\" ><label for=\"sk-estimator-id-141\" 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-142\" type=\"checkbox\" ><label for=\"sk-estimator-id-142\" 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 0x7f6bd716ed70&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-143\" type=\"checkbox\" ><label for=\"sk-estimator-id-143\" 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-144\" type=\"checkbox\" ><label for=\"sk-estimator-id-144\" 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-145\" type=\"checkbox\" ><label for=\"sk-estimator-id-145\" 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-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\">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-148\" type=\"checkbox\" ><label for=\"sk-estimator-id-148\" 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',\n",
       "                                                                               Pipeline(steps=[('simpleimputer',\n",
       "                                                                                                SimpleImputer(strategy='median')),\n",
       "                                                                                               ('functiontransformer',\n",
       "                                                                                                FunctionTransformer(feature_names_...\n",
       "                                             ('random_forest',\n",
       "                                              RandomForestRegressor(random_state=42))]),\n",
       "                   param_distributions={'preprocessing__geo__n_clusters': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7f6bd9735c30>,\n",
       "                                        'random_forest__max_features': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7f6bdbd0a2f0>},\n",
       "                   random_state=42, scoring='neg_root_mean_squared_error')"
      ]
     },
     "execution_count": 138,
     "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=50),\n",
    "                  'random_forest__max_features': randint(low=2, high=20)}\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",
    "rnd_search.fit(housing, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>1</th>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>41287</td>\n",
       "      <td>42071</td>\n",
       "      <td>42627</td>\n",
       "      <td>41995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>41690</td>\n",
       "      <td>42513</td>\n",
       "      <td>43224</td>\n",
       "      <td>42475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>16</td>\n",
       "      <td>42223</td>\n",
       "      <td>42959</td>\n",
       "      <td>43321</td>\n",
       "      <td>42834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>41818</td>\n",
       "      <td>43094</td>\n",
       "      <td>43817</td>\n",
       "      <td>42910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>42264</td>\n",
       "      <td>42996</td>\n",
       "      <td>43830</td>\n",
       "      <td>43030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  n_clusters max_features  split0  split1  split2  mean_test_rmse\n",
       "1         45            9   41287   42071   42627           41995\n",
       "8         32            7   41690   42513   43224           42475\n",
       "0         41           16   42223   42959   43321           42834\n",
       "5         42            4   41818   43094   43817           42910\n",
       "2         23            8   42264   42996   43830           43030"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – displays the random search results\n",
    "cv_res = pd.DataFrame(rnd_search.cv_results_)\n",
    "cv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\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",
    "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\n",
    "cv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n",
    "cv_res.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bonus section: how to choose the sampling distribution for a hyperparameter**\n",
    "\n",
    "* `scipy.stats.randint(a, b+1)`: for hyperparameters with _discrete_ values that range from a to b, and all values in that range seem equally likely.\n",
    "* `scipy.stats.uniform(a, b)`: this is very similar, but for _continuous_ hyperparameters.\n",
    "* `scipy.stats.geom(1 / scale)`: for discrete values, when you want to sample roughly in a given scale. E.g., with scale=1000 most samples will be in this ballpark, but ~10% of all samples will be <100 and ~10% will be >2300.\n",
    "* `scipy.stats.expon(scale)`: this is the continuous equivalent of `geom`. Just set `scale` to the most likely value.\n",
    "* `scipy.stats.loguniform(a, b)`: when you have almost no idea what the optimal hyperparameter value's scale is. If you set a=0.01 and b=100, then you're just as likely to sample a value between 0.01 and 0.1 as a value between 10 and 100.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are plots of the probability mass functions (for discrete variables), and probability density functions (for continuous variables) for `randint()`, `uniform()`, `geom()` and `expon()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – plots a few distributions you can use in randomized search\n",
    "\n",
    "from scipy.stats import randint, uniform, geom, expon\n",
    "\n",
    "xs1 = np.arange(0, 7 + 1)\n",
    "randint_distrib = randint(0, 7 + 1).pmf(xs1)\n",
    "\n",
    "xs2 = np.linspace(0, 7, 500)\n",
    "uniform_distrib = uniform(0, 7).pdf(xs2)\n",
    "\n",
    "xs3 = np.arange(0, 7 + 1)\n",
    "geom_distrib = geom(0.5).pmf(xs3)\n",
    "\n",
    "xs4 = np.linspace(0, 7, 500)\n",
    "expon_distrib = expon(scale=1).pdf(xs4)\n",
    "\n",
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.bar(xs1, randint_distrib, label=\"scipy.randint(0, 7 + 1)\")\n",
    "plt.ylabel(\"Probability\")\n",
    "plt.legend()\n",
    "plt.axis([-1, 8, 0, 0.2])\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.fill_between(xs2, uniform_distrib, label=\"scipy.uniform(0, 7)\")\n",
    "plt.ylabel(\"PDF\")\n",
    "plt.legend()\n",
    "plt.axis([-1, 8, 0, 0.2])\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.bar(xs3, geom_distrib, label=\"scipy.geom(0.5)\")\n",
    "plt.xlabel(\"Hyperparameter value\")\n",
    "plt.ylabel(\"Probability\")\n",
    "plt.legend()\n",
    "plt.axis([0, 7, 0, 1])\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.fill_between(xs4, expon_distrib, label=\"scipy.expon(scale=1)\")\n",
    "plt.xlabel(\"Hyperparameter value\")\n",
    "plt.ylabel(\"PDF\")\n",
    "plt.legend()\n",
    "plt.axis([0, 7, 0, 1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the PDF for `expon()` and `loguniform()` (left column), as well as the PDF of log(X) (right column). The right column shows the distribution of hyperparameter _scales_. You can see that `expon()` favors hyperparameters with roughly the desired scale, with a longer tail towards the smaller scales. But `loguniform()` does not favor any scale, they are all equally likely:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – shows the difference between expon and loguniform\n",
    "\n",
    "from scipy.stats import loguniform\n",
    "\n",
    "xs1 = np.linspace(0, 7, 500)\n",
    "expon_distrib = expon(scale=1).pdf(xs1)\n",
    "\n",
    "log_xs2 = np.linspace(-5, 3, 500)\n",
    "log_expon_distrib = np.exp(log_xs2 - np.exp(log_xs2))\n",
    "\n",
    "xs3 = np.linspace(0.001, 1000, 500)\n",
    "loguniform_distrib = loguniform(0.001, 1000).pdf(xs3)\n",
    "\n",
    "log_xs4 = np.linspace(np.log(0.001), np.log(1000), 500)\n",
    "log_loguniform_distrib = uniform(np.log(0.001), np.log(1000)).pdf(log_xs4)\n",
    "\n",
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.fill_between(xs1, expon_distrib,\n",
    "                 label=\"scipy.expon(scale=1)\")\n",
    "plt.ylabel(\"PDF\")\n",
    "plt.legend()\n",
    "plt.axis([0, 7, 0, 1])\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.fill_between(log_xs2, log_expon_distrib,\n",
    "                 label=\"log(X) with X ~ expon\")\n",
    "plt.legend()\n",
    "plt.axis([-5, 3, 0, 1])\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.fill_between(xs3, loguniform_distrib,\n",
    "                 label=\"scipy.loguniform(0.001, 1000)\")\n",
    "plt.xlabel(\"Hyperparameter value\")\n",
    "plt.ylabel(\"PDF\")\n",
    "plt.legend()\n",
    "plt.axis([0.001, 1000, 0, 0.005])\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.fill_between(log_xs4, log_loguniform_distrib,\n",
    "                 label=\"log(X) with X ~ loguniform\")\n",
    "plt.xlabel(\"Log of hyperparameter value\")\n",
    "plt.legend()\n",
    "plt.axis([-8, 1, 0, 0.2])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze the Best Models and Their Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.07, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.19, 0.04, 0.01, 0.  ,\n",
       "       0.01, 0.01, 0.01, 0.01, 0.01, 0.  , 0.01, 0.01, 0.01, 0.  , 0.01,\n",
       "       0.01, 0.01, 0.01, 0.01, 0.  , 0.  , 0.02, 0.01, 0.01, 0.01, 0.02,\n",
       "       0.01, 0.  , 0.02, 0.03, 0.01, 0.01, 0.01, 0.01, 0.01, 0.02, 0.01,\n",
       "       0.01, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.02, 0.01, 0.  , 0.07,\n",
       "       0.  , 0.  , 0.  , 0.01])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = rnd_search.best_estimator_  # includes preprocessing\n",
    "feature_importances = final_model[\"random_forest\"].feature_importances_\n",
    "feature_importances.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.18694559869103852, 'log__median_income'),\n",
       " (0.0748194905715524, 'cat__ocean_proximity_INLAND'),\n",
       " (0.06926417748515576, 'bedrooms__ratio'),\n",
       " (0.05446998753775219, 'rooms_per_house__ratio'),\n",
       " (0.05262301809680712, 'people_per_house__ratio'),\n",
       " (0.03819415873915732, 'geo__Cluster 0 similarity'),\n",
       " (0.02879263999929514, 'geo__Cluster 28 similarity'),\n",
       " (0.023530192521380392, 'geo__Cluster 24 similarity'),\n",
       " (0.020544786346378206, 'geo__Cluster 27 similarity'),\n",
       " (0.019873052631077512, 'geo__Cluster 43 similarity'),\n",
       " (0.018597511022930273, 'geo__Cluster 34 similarity'),\n",
       " (0.017409085415656868, 'geo__Cluster 37 similarity'),\n",
       " (0.015546519677632162, 'geo__Cluster 20 similarity'),\n",
       " (0.014230331127504292, 'geo__Cluster 17 similarity'),\n",
       " (0.0141032216204026, 'geo__Cluster 39 similarity'),\n",
       " (0.014065768027447325, 'geo__Cluster 9 similarity'),\n",
       " (0.01354220782825315, 'geo__Cluster 4 similarity'),\n",
       " (0.01348963625822907, 'geo__Cluster 3 similarity'),\n",
       " (0.01338319626383868, 'geo__Cluster 38 similarity'),\n",
       " (0.012240533790212824, 'geo__Cluster 31 similarity'),\n",
       " (0.012089046542256785, 'geo__Cluster 7 similarity'),\n",
       " (0.01152326329703204, 'geo__Cluster 23 similarity'),\n",
       " (0.011397459905603558, 'geo__Cluster 40 similarity'),\n",
       " (0.011282340924816439, 'geo__Cluster 36 similarity'),\n",
       " (0.01104139770781063, 'remainder__housing_median_age'),\n",
       " (0.010671123191312802, 'geo__Cluster 44 similarity'),\n",
       " (0.010296376177202627, 'geo__Cluster 5 similarity'),\n",
       " (0.010184798445004483, 'geo__Cluster 42 similarity'),\n",
       " (0.010121853542225083, 'geo__Cluster 11 similarity'),\n",
       " (0.009795219101117579, 'geo__Cluster 35 similarity'),\n",
       " (0.00952581084310724, 'geo__Cluster 10 similarity'),\n",
       " (0.009433209165984823, 'geo__Cluster 13 similarity'),\n",
       " (0.00915075361116215, 'geo__Cluster 1 similarity'),\n",
       " (0.009021485619463173, 'geo__Cluster 30 similarity'),\n",
       " (0.00894936224917583, 'geo__Cluster 41 similarity'),\n",
       " (0.008901832702357514, 'geo__Cluster 25 similarity'),\n",
       " (0.008897504713401587, 'geo__Cluster 29 similarity'),\n",
       " (0.0086846298524955, 'geo__Cluster 21 similarity'),\n",
       " (0.008061104590483955, 'geo__Cluster 15 similarity'),\n",
       " (0.00786048176566994, 'geo__Cluster 16 similarity'),\n",
       " (0.007793633130749198, 'geo__Cluster 22 similarity'),\n",
       " (0.007501766442066527, 'log__total_rooms'),\n",
       " (0.0072024111938241275, 'geo__Cluster 32 similarity'),\n",
       " (0.006947156598995616, 'log__population'),\n",
       " (0.006800076770899128, 'log__households'),\n",
       " (0.006736105364684462, 'log__total_bedrooms'),\n",
       " (0.006315268213499131, 'geo__Cluster 33 similarity'),\n",
       " (0.005796398579893261, 'geo__Cluster 14 similarity'),\n",
       " (0.005234954623294958, 'geo__Cluster 6 similarity'),\n",
       " (0.0045514083468621595, 'geo__Cluster 12 similarity'),\n",
       " (0.004546042080216035, 'geo__Cluster 18 similarity'),\n",
       " (0.004314514641115755, 'geo__Cluster 2 similarity'),\n",
       " (0.003953528110719969, 'geo__Cluster 19 similarity'),\n",
       " (0.003297404747742136, 'geo__Cluster 26 similarity'),\n",
       " (0.00289453474290887, 'cat__ocean_proximity_<1H OCEAN'),\n",
       " (0.0016978863168109126, 'cat__ocean_proximity_NEAR OCEAN'),\n",
       " (0.0016391131530559377, 'geo__Cluster 8 similarity'),\n",
       " (0.00015061247730531558, 'cat__ocean_proximity_NEAR BAY'),\n",
       " (7.301686597099842e-05, 'cat__ocean_proximity_ISLAND')]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(zip(feature_importances,\n",
    "           final_model[\"preprocessing\"].get_feature_names_out()),\n",
    "           reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Your System on the Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41424.40026462184\n"
     ]
    }
   ],
   "source": [
    "X_test = strat_test_set.drop(\"median_house_value\", axis=1)\n",
    "y_test = strat_test_set[\"median_house_value\"].copy()\n",
    "\n",
    "final_predictions = final_model.predict(X_test)\n",
    "\n",
    "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": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([39275.40861216, 43467.27680583])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "confidence = 0.95\n",
    "squared_errors = (final_predictions - y_test) ** 2\n",
    "np.sqrt(stats.t.interval(confidence, len(squared_errors) - 1,\n",
    "                         loc=squared_errors.mean(),\n",
    "                         scale=stats.sem(squared_errors)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could compute the interval manually like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39275.40861216077, 43467.2768058342)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – shows how to compute a confidence interval for the RMSE\n",
    "m = len(squared_errors)\n",
    "mean = squared_errors.mean()\n",
    "tscore = stats.t.ppf((1 + confidence) / 2, df=m - 1)\n",
    "tmargin = tscore * squared_errors.std(ddof=1) / np.sqrt(m)\n",
    "np.sqrt(mean - tmargin), np.sqrt(mean + tmargin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we could use a z-score rather than a t-score. Since the test set is not too small, it won't make a big difference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39276.05610140007, 43466.691749969636)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – computes a confidence interval again using a z-score\n",
    "zscore = stats.norm.ppf((1 + confidence) / 2)\n",
    "zmargin = zscore * squared_errors.std(ddof=1) / np.sqrt(m)\n",
    "np.sqrt(mean - zmargin), np.sqrt(mean + zmargin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model persistence using joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the final model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['my_california_housing_model.pkl']"
      ]
     },
     "execution_count": 148,
     "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": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "\n",
    "# extra code – excluded for conciseness\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.metrics.pairwise import rbf_kernel\n",
    "\n",
    "def column_ratio(X):\n",
    "    return X[:, [0]] / X[:, [1]]\n",
    "\n",
    "#class ClusterSimilarity(BaseEstimator, TransformerMixin):\n",
    "#    [...]\n",
    "\n",
    "final_model_reloaded = joblib.load(\"my_california_housing_model.pkl\")\n",
    "\n",
    "new_data = housing.iloc[:5]  # pretend these are new districts\n",
    "predictions = final_model_reloaded.predict(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([442737.15, 457566.06, 105965.  ,  98462.  , 332992.01])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You could use pickle instead, but joblib is more efficient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1."
   ]
  },
  {
   "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": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 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-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 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-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 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-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 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-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" 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&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                         (&#x27;functiontransformer&#x27;,\n",
       "                                                                                          FunctionTransformer(feature_names_out=&lt;f...\n",
       "                                                                         &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f6bd716ed70&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-149\" type=\"checkbox\" ><label for=\"sk-estimator-id-149\" 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&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                         (&#x27;functiontransformer&#x27;,\n",
       "                                                                                          FunctionTransformer(feature_names_out=&lt;f...\n",
       "                                                                         &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f6bd716ed70&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-150\" type=\"checkbox\" ><label for=\"sk-estimator-id-150\" 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&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe...\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 0x7f6bd716ed70&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-151\" type=\"checkbox\" ><label for=\"sk-estimator-id-151\" 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&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                                                                      func=&lt;function column_ratio...\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 0x7f6bd716ed70&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-152\" type=\"checkbox\" ><label for=\"sk-estimator-id-152\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms</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-153\" type=\"checkbox\" ><label for=\"sk-estimator-id-153\" 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-154\" type=\"checkbox\" ><label for=\"sk-estimator-id-154\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-155\" type=\"checkbox\" ><label for=\"sk-estimator-id-155\" 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-156\" type=\"checkbox\" ><label for=\"sk-estimator-id-156\" 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-157\" type=\"checkbox\" ><label for=\"sk-estimator-id-157\" 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-158\" type=\"checkbox\" ><label for=\"sk-estimator-id-158\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-159\" type=\"checkbox\" ><label for=\"sk-estimator-id-159\" 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-160\" type=\"checkbox\" ><label for=\"sk-estimator-id-160\" 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-161\" type=\"checkbox\" ><label for=\"sk-estimator-id-161\" 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-162\" type=\"checkbox\" ><label for=\"sk-estimator-id-162\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-163\" type=\"checkbox\" ><label for=\"sk-estimator-id-163\" 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-164\" type=\"checkbox\" ><label for=\"sk-estimator-id-164\" 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-165\" type=\"checkbox\" ><label for=\"sk-estimator-id-165\" 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-166\" type=\"checkbox\" ><label for=\"sk-estimator-id-166\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&#x27;one-to-one&#x27;, 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-167\" type=\"checkbox\" ><label for=\"sk-estimator-id-167\" 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-168\" type=\"checkbox\" ><label for=\"sk-estimator-id-168\" 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-169\" type=\"checkbox\" ><label for=\"sk-estimator-id-169\" 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-170\" type=\"checkbox\" ><label for=\"sk-estimator-id-170\" 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 0x7f6bd716ed70&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-171\" type=\"checkbox\" ><label for=\"sk-estimator-id-171\" 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-172\" type=\"checkbox\" ><label for=\"sk-estimator-id-172\" 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-173\" type=\"checkbox\" ><label for=\"sk-estimator-id-173\" 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-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\">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-176\" type=\"checkbox\" ><label for=\"sk-estimator-id-176\" 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',\n",
       "                                                                         Pipeline(steps=[('simpleimputer',\n",
       "                                                                                          SimpleImputer(strategy='median')),\n",
       "                                                                                         ('functiontransformer',\n",
       "                                                                                          FunctionTransformer(feature_names_out=<f...\n",
       "                                                                         <sklearn.compose._column_transformer.make_column_selector object at 0x7f6bd716ed70>)])),\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": 151,
     "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",
    "grid_search = GridSearchCV(svr_pipeline, param_grid, cv=3,\n",
    "                           scoring='neg_root_mean_squared_error')\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": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69814.13888570886"
      ]
     },
     "execution_count": 152,
     "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": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'svr__C': 10000.0, 'svr__kernel': 'linear'}"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear kernel seems better than the RBF kernel. Notice that the value of `C` is the maximum tested value. When this happens you definitely want to launch the grid search again with higher values for `C` (removing the smallest values), because it is likely that higher values of `C` will be better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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>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&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                               (&#x27;functiontransformer&#x27;,\n",
       "                                                                                                FunctionTransformer(feature_names_...\n",
       "                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f6bd716ed70&gt;)])),\n",
       "                                             (&#x27;svr&#x27;, SVR())]),\n",
       "                   n_iter=50,\n",
       "                   param_distributions={&#x27;svr__C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7f6bd730b1c0&gt;,\n",
       "                                        &#x27;svr__gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7f6bd73278b0&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-177\" type=\"checkbox\" ><label for=\"sk-estimator-id-177\" 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&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                               (&#x27;functiontransformer&#x27;,\n",
       "                                                                                                FunctionTransformer(feature_names_...\n",
       "                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f6bd716ed70&gt;)])),\n",
       "                                             (&#x27;svr&#x27;, SVR())]),\n",
       "                   n_iter=50,\n",
       "                   param_distributions={&#x27;svr__C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7f6bd730b1c0&gt;,\n",
       "                                        &#x27;svr__gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7f6bd73278b0&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-178\" type=\"checkbox\" ><label for=\"sk-estimator-id-178\" 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&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe...\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 0x7f6bd716ed70&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-179\" type=\"checkbox\" ><label for=\"sk-estimator-id-179\" 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&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                                                                      func=&lt;function column_ratio...\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 0x7f6bd716ed70&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-180\" type=\"checkbox\" ><label for=\"sk-estimator-id-180\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms</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-181\" type=\"checkbox\" ><label for=\"sk-estimator-id-181\" 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-182\" type=\"checkbox\" ><label for=\"sk-estimator-id-182\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-183\" type=\"checkbox\" ><label for=\"sk-estimator-id-183\" 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-184\" type=\"checkbox\" ><label for=\"sk-estimator-id-184\" 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-185\" type=\"checkbox\" ><label for=\"sk-estimator-id-185\" 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-186\" type=\"checkbox\" ><label for=\"sk-estimator-id-186\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-187\" type=\"checkbox\" ><label for=\"sk-estimator-id-187\" 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-188\" type=\"checkbox\" ><label for=\"sk-estimator-id-188\" 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-189\" type=\"checkbox\" ><label for=\"sk-estimator-id-189\" 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-190\" type=\"checkbox\" ><label for=\"sk-estimator-id-190\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-191\" type=\"checkbox\" ><label for=\"sk-estimator-id-191\" 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-192\" type=\"checkbox\" ><label for=\"sk-estimator-id-192\" 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-193\" type=\"checkbox\" ><label for=\"sk-estimator-id-193\" 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-194\" type=\"checkbox\" ><label for=\"sk-estimator-id-194\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&#x27;one-to-one&#x27;, 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-195\" type=\"checkbox\" ><label for=\"sk-estimator-id-195\" 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-196\" type=\"checkbox\" ><label for=\"sk-estimator-id-196\" 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-197\" type=\"checkbox\" ><label for=\"sk-estimator-id-197\" 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-198\" type=\"checkbox\" ><label for=\"sk-estimator-id-198\" 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 0x7f6bd716ed70&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-199\" type=\"checkbox\" ><label for=\"sk-estimator-id-199\" 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-200\" type=\"checkbox\" ><label for=\"sk-estimator-id-200\" 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-201\" type=\"checkbox\" ><label for=\"sk-estimator-id-201\" 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-202\" type=\"checkbox\" ><label for=\"sk-estimator-id-202\" 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-203\" type=\"checkbox\" ><label for=\"sk-estimator-id-203\" 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-204\" type=\"checkbox\" ><label for=\"sk-estimator-id-204\" 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',\n",
       "                                                                               Pipeline(steps=[('simpleimputer',\n",
       "                                                                                                SimpleImputer(strategy='median')),\n",
       "                                                                                               ('functiontransformer',\n",
       "                                                                                                FunctionTransformer(feature_names_...\n",
       "                                                                               <sklearn.compose._column_transformer.make_column_selector object at 0x7f6bd716ed70>)])),\n",
       "                                             ('svr', SVR())]),\n",
       "                   n_iter=50,\n",
       "                   param_distributions={'svr__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7f6bd730b1c0>,\n",
       "                                        'svr__gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7f6bd73278b0>,\n",
       "                                        'svr__kernel': ['linear', 'rbf']},\n",
       "                   random_state=42, scoring='neg_root_mean_squared_error')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import expon, loguniform\n",
    "\n",
    "# see https://docs.scipy.org/doc/scipy/reference/stats.html\n",
    "# for `expon()` and `loguniform()` 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': loguniform(20, 200_000),\n",
    "        'svr__gamma': expon(scale=1.0),\n",
    "    }\n",
    "\n",
    "rnd_search = RandomizedSearchCV(svr_pipeline,\n",
    "                                param_distributions=param_distribs,\n",
    "                                n_iter=50, cv=3,\n",
    "                                scoring='neg_root_mean_squared_error',\n",
    "                                random_state=42)\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": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55853.88100158656"
      ]
     },
     "execution_count": 155,
     "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": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'svr__C': 157055.10989448498,\n",
       " 'svr__gamma': 0.26497040005002437,\n",
       " 'svr__kernel': 'rbf'}"
      ]
     },
     "execution_count": 156,
     "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": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80066"
      ]
     },
     "execution_count": 157,
     "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 `loguniform()` 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."
   ]
  },
  {
   "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": 158,
   "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": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/workspaces/data_mining/.venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count        3.000000\n",
       "mean     56211.362086\n",
       "std       1922.002801\n",
       "min      54150.008630\n",
       "25%      55339.929908\n",
       "50%      56529.851186\n",
       "75%      57242.038814\n",
       "max      57954.226441\n",
       "dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)\n",
    "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": [
    "## 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Try creating a custom transformer that trains a k-Nearest Neighbors regressor (`sklearn.neighbors.KNeighborsRegressor`) in its `fit()` method, and outputs the model's predictions in its `transform()` method. Then add this feature to the preprocessing pipeline, using latitude and longitude as the inputs to this transformer. This will add a feature in the model that corresponds to the housing median price of the nearest districts._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than restrict ourselves to k-Nearest Neighbors regressors, let's create a transformer that accepts any regressor. For this, we can extend the `MetaEstimatorMixin` and have a required `estimator` argument in the constructor. The `fit()` method must work on a clone of this estimator, and it must also save `feature_names_in_`. The `MetaEstimatorMixin` will ensure that `estimator` is listed as a required parameters, and it will update `get_params()` and `set_params()` to make the estimator's hyperparameters available for tuning. Lastly, we create a `get_feature_names_out()` method: the output column name is the ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.base import MetaEstimatorMixin, clone\n",
    "\n",
    "class FeatureFromRegressor(MetaEstimatorMixin, BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, estimator):\n",
    "        self.estimator = estimator\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        estimator_ = clone(self.estimator)\n",
    "        estimator_.fit(X, y)\n",
    "        self.estimator_ = estimator_\n",
    "        self.n_features_in_ = self.estimator_.n_features_in_\n",
    "        if hasattr(self.estimator, \"feature_names_in_\"):\n",
    "            self.feature_names_in_ = self.estimator.feature_names_in_\n",
    "        return self  # always return self!\n",
    "    \n",
    "    def transform(self, X):\n",
    "        check_is_fitted(self)\n",
    "        predictions = self.estimator_.predict(X)\n",
    "        if predictions.ndim == 1:\n",
    "            predictions = predictions.reshape(-1, 1)\n",
    "        return predictions\n",
    "\n",
    "    def get_feature_names_out(self, names=None):\n",
    "        check_is_fitted(self)\n",
    "        n_outputs = getattr(self.estimator_, \"n_outputs_\", 1)\n",
    "        estimator_class_name = self.estimator_.__class__.__name__\n",
    "        estimator_short_name = estimator_class_name.lower().replace(\"_\", \"\")\n",
    "        return [f\"{estimator_short_name}_prediction_{i}\"\n",
    "                for i in range(n_outputs)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's ensure it complies to Scikit-Learn's API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils.estimator_checks import check_estimator\n",
    "\n",
    "check_estimator(FeatureFromRegressor(KNeighborsRegressor()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good! Now let's test it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[412500.33333333],\n",
       "       [435250.        ],\n",
       "       [105100.        ],\n",
       "       ...,\n",
       "       [148800.        ],\n",
       "       [500001.        ],\n",
       "       [234333.33333333]])"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_reg = KNeighborsRegressor(n_neighbors=3, weights=\"distance\")\n",
    "knn_transformer = FeatureFromRegressor(knn_reg)\n",
    "geo_features = housing[[\"latitude\", \"longitude\"]]\n",
    "knn_transformer.fit_transform(geo_features, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And what does its output feature name look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['kneighborsregressor_prediction_0']"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_transformer.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, now let's include this transformer in our preprocessing pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import clone\n",
    "\n",
    "transformers = [(name, clone(transformer), columns)\n",
    "                for name, transformer, columns in preprocessing.transformers]\n",
    "geo_index = [name for name, _, _ in transformers].index(\"geo\")\n",
    "transformers[geo_index] = (\"geo\", knn_transformer, [\"latitude\", \"longitude\"])\n",
    "\n",
    "new_geo_preprocessing = ColumnTransformer(transformers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_geo_pipeline = Pipeline([\n",
    "    ('preprocessing', new_geo_preprocessing),\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": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         3.000000\n",
       "mean     104866.322819\n",
       "std        2966.688335\n",
       "min      101535.315061\n",
       "25%      103687.330297\n",
       "50%      105839.345534\n",
       "75%      106531.826698\n",
       "max      107224.307862\n",
       "dtype: float64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pipe_rmses = -cross_val_score(new_geo_pipeline,\n",
    "                                  housing.iloc[:5000],\n",
    "                                  housing_labels.iloc[:5000],\n",
    "                                  scoring=\"neg_root_mean_squared_error\",\n",
    "                                  cv=3)\n",
    "pd.Series(new_pipe_rmses).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yikes, that's terrible! Apparently the cluster similarity features were much better. But perhaps we should tune the `KNeighborsRegressor`'s hyperparameters? That's what the next exercise is about."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Automatically explore some preparation options using `RandomSearchCV`._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "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(transformers=[(&#x27;bedrooms&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                               (&#x27;functiontransformer&#x27;,\n",
       "                                                                                                FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                                                                                                                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&gt;)),\n",
       "                                                                                               (&#x27;standardscaler&#x27;,\n",
       "                                                                                                StandardSc...\n",
       "                   param_distributions={&#x27;preprocessing__geo__estimator__n_neighbors&#x27;: range(1, 30),\n",
       "                                        &#x27;preprocessing__geo__estimator__weights&#x27;: [&#x27;distance&#x27;,\n",
       "                                                                                   &#x27;uniform&#x27;],\n",
       "                                        &#x27;svr__C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7f6bc984c8b0&gt;,\n",
       "                                        &#x27;svr__gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7f6bc984ca90&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-205\" type=\"checkbox\" ><label for=\"sk-estimator-id-205\" 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(transformers=[(&#x27;bedrooms&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                               (&#x27;functiontransformer&#x27;,\n",
       "                                                                                                FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                                                                                                                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&gt;)),\n",
       "                                                                                               (&#x27;standardscaler&#x27;,\n",
       "                                                                                                StandardSc...\n",
       "                   param_distributions={&#x27;preprocessing__geo__estimator__n_neighbors&#x27;: range(1, 30),\n",
       "                                        &#x27;preprocessing__geo__estimator__weights&#x27;: [&#x27;distance&#x27;,\n",
       "                                                                                   &#x27;uniform&#x27;],\n",
       "                                        &#x27;svr__C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7f6bc984c8b0&gt;,\n",
       "                                        &#x27;svr__gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7f6bc984ca90&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-206\" type=\"checkbox\" ><label for=\"sk-estimator-id-206\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;bedrooms&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;functiontransformer&#x27;,\n",
       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                                                                                       func=&lt;function column_ratio at 0x7f6bdbe6cf70&gt;)),\n",
       "                                                                  (&#x27;standardscaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;total_bedrooms&#x27;,\n",
       "                                                   &#x27;total...\n",
       "                                                  FeatureFromRegressor(estimator=KNeighborsRegressor(n_neighbors=3,\n",
       "                                                                                                     weights=&#x27;distance&#x27;)),\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 0x7f6bd716ed70&gt;)])),\n",
       "                (&#x27;svr&#x27;, SVR(C=157055.10989448498, gamma=0.26497040005002437))])</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-207\" type=\"checkbox\" ><label for=\"sk-estimator-id-207\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessing: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;bedrooms&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                                                                      func=&lt;function column_ratio at 0x7f6bdbe6cf70&gt;)),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler())]),\n",
       "                                 [&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]),\n",
       "                                (&#x27;rooms_per_house&#x27;,\n",
       "                                 Pipe...\n",
       "                                  &#x27;households&#x27;, &#x27;median_income&#x27;]),\n",
       "                                (&#x27;geo&#x27;,\n",
       "                                 FeatureFromRegressor(estimator=KNeighborsRegressor(n_neighbors=3,\n",
       "                                                                                    weights=&#x27;distance&#x27;)),\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 0x7f6bd716ed70&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-208\" type=\"checkbox\" ><label for=\"sk-estimator-id-208\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms</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-209\" type=\"checkbox\" ><label for=\"sk-estimator-id-209\" 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-210\" type=\"checkbox\" ><label for=\"sk-estimator-id-210\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-211\" type=\"checkbox\" ><label for=\"sk-estimator-id-211\" 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-212\" type=\"checkbox\" ><label for=\"sk-estimator-id-212\" 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-213\" type=\"checkbox\" ><label for=\"sk-estimator-id-213\" 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-214\" type=\"checkbox\" ><label for=\"sk-estimator-id-214\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-215\" type=\"checkbox\" ><label for=\"sk-estimator-id-215\" 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-216\" type=\"checkbox\" ><label for=\"sk-estimator-id-216\" 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-217\" type=\"checkbox\" ><label for=\"sk-estimator-id-217\" 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-218\" type=\"checkbox\" ><label for=\"sk-estimator-id-218\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x7f6bdbe6cee0&gt;,\n",
       "                    func=&lt;function column_ratio at 0x7f6bdbe6cf70&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-219\" type=\"checkbox\" ><label for=\"sk-estimator-id-219\" 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-220\" type=\"checkbox\" ><label for=\"sk-estimator-id-220\" 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-221\" type=\"checkbox\" ><label for=\"sk-estimator-id-221\" 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-222\" type=\"checkbox\" ><label for=\"sk-estimator-id-222\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&#x27;one-to-one&#x27;, 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-223\" type=\"checkbox\" ><label for=\"sk-estimator-id-223\" 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-224\" type=\"checkbox\" ><label for=\"sk-estimator-id-224\" 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 sk-dashed-wrapped\"><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-225\" type=\"checkbox\" ><label for=\"sk-estimator-id-225\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor(n_neighbors=3, weights=&#x27;distance&#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-226\" type=\"checkbox\" ><label for=\"sk-estimator-id-226\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor(n_neighbors=3, weights=&#x27;distance&#x27;)</pre></div></div></div></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\">cat</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f6bd716ed70&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-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;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-229\" type=\"checkbox\" ><label for=\"sk-estimator-id-229\" 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></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\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR(C=157055.10989448498, gamma=0.26497040005002437)</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(transformers=[('bedrooms',\n",
       "                                                                               Pipeline(steps=[('simpleimputer',\n",
       "                                                                                                SimpleImputer(strategy='median')),\n",
       "                                                                                               ('functiontransformer',\n",
       "                                                                                                FunctionTransformer(feature_names_out=<function ratio_name at 0x7f6bdbe6cee0>,\n",
       "                                                                                                                    func=<function column_ratio at 0x7f6bdbe6cf70>)),\n",
       "                                                                                               ('standardscaler',\n",
       "                                                                                                StandardSc...\n",
       "                   param_distributions={'preprocessing__geo__estimator__n_neighbors': range(1, 30),\n",
       "                                        'preprocessing__geo__estimator__weights': ['distance',\n",
       "                                                                                   'uniform'],\n",
       "                                        'svr__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7f6bc984c8b0>,\n",
       "                                        'svr__gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7f6bc984ca90>},\n",
       "                   random_state=42, scoring='neg_root_mean_squared_error')"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_distribs = {\n",
    "    \"preprocessing__geo__estimator__n_neighbors\": range(1, 30),\n",
    "    \"preprocessing__geo__estimator__weights\": [\"distance\", \"uniform\"],\n",
    "    \"svr__C\": loguniform(20, 200_000),\n",
    "    \"svr__gamma\": expon(scale=1.0),\n",
    "}\n",
    "\n",
    "new_geo_rnd_search = RandomizedSearchCV(new_geo_pipeline,\n",
    "                                        param_distributions=param_distribs,\n",
    "                                        n_iter=50,\n",
    "                                        cv=3,\n",
    "                                        scoring='neg_root_mean_squared_error',\n",
    "                                        random_state=42)\n",
    "new_geo_rnd_search.fit(housing.iloc[:5000], housing_labels.iloc[:5000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106768.04614723712"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_geo_rnd_search_rmse = -new_geo_rnd_search.best_score_\n",
    "new_geo_rnd_search_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh well... at least we tried! It looks like the cluster similarity features are definitely better than the KNN feature. But perhaps you could try having both? And maybe training on the full training set would help as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Try to implement the `StandardScalerClone` class again from scratch, then add support for the `inverse_transform()` method: executing `scaler.inverse_transform(scaler.fit_transform(X))` should return an array very close to `X`. Then add support for feature names: set `feature_names_in_` in the `fit()` method if the input is a DataFrame. This attribute should be a NumPy array of column names. Lastly, implement the `get_feature_names_out()` method: it should have one optional `input_features=None` argument. If passed, the method should check that its length matches `n_features_in_`, and it should match `feature_names_in_` if it is defined, then `input_features` should be returned. If `input_features` is `None`, then the method should return `feature_names_in_` if it is defined or `np.array([\"x0\", \"x1\", ...])` with length `n_features_in_` otherwise._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.utils.validation import check_array, check_is_fitted\n",
    "\n",
    "class StandardScalerClone(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, with_mean=True):  # no *args or **kwargs!\n",
    "        self.with_mean = with_mean\n",
    "\n",
    "    def fit(self, X, y=None):  # y is required even though we don't use it\n",
    "        X_orig = X\n",
    "        X = check_array(X)  # checks that X is an array with finite float values\n",
    "        self.mean_ = X.mean(axis=0)\n",
    "        self.scale_ = X.std(axis=0)\n",
    "        self.n_features_in_ = X.shape[1]  # every estimator stores this in fit()\n",
    "        if hasattr(X_orig, \"columns\"):\n",
    "            self.feature_names_in_ = np.array(X_orig.columns, dtype=object)\n",
    "        return self  # always return self!\n",
    "\n",
    "    def transform(self, X):\n",
    "        check_is_fitted(self)  # looks for learned attributes (with trailing _)\n",
    "        X = check_array(X)\n",
    "        if self.n_features_in_ != X.shape[1]:\n",
    "            raise ValueError(\"Unexpected number of features\")\n",
    "        if self.with_mean:\n",
    "            X = X - self.mean_\n",
    "        return X / self.scale_\n",
    "    \n",
    "    def inverse_transform(self, X):\n",
    "        check_is_fitted(self)\n",
    "        X = check_array(X)\n",
    "        if self.n_features_in_ != X.shape[1]:\n",
    "            raise ValueError(\"Unexpected number of features\")\n",
    "        X = X * self.scale_\n",
    "        return X + self.mean_ if self.with_mean else X\n",
    "    \n",
    "    def get_feature_names_out(self, input_features=None):\n",
    "        if input_features is None:\n",
    "            return getattr(self, \"feature_names_in_\",\n",
    "                           [f\"x{i}\" for i in range(self.n_features_in_)])\n",
    "        else:\n",
    "            if len(input_features) != self.n_features_in_:\n",
    "                raise ValueError(\"Invalid number of features\")\n",
    "            if hasattr(self, \"feature_names_in_\") and not np.all(\n",
    "                self.feature_names_in_ == input_features\n",
    "            ):\n",
    "                raise ValueError(\"input_features ≠ feature_names_in_\")\n",
    "            return input_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test our custom transformer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils.estimator_checks import check_estimator\n",
    " \n",
    "check_estimator(StandardScalerClone())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No errors, that's a great start, we respect the Scikit-Learn API."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's ensure the transformation works as expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "X = np.random.rand(1000, 3)\n",
    "\n",
    "scaler = StandardScalerClone()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "assert np.allclose(X_scaled, (X - X.mean(axis=0)) / X.std(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about setting `with_mean=False`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScalerClone(with_mean=False)\n",
    "X_scaled_uncentered = scaler.fit_transform(X)\n",
    "\n",
    "assert np.allclose(X_scaled_uncentered, X / X.std(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And does the inverse work?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScalerClone()\n",
    "X_back = scaler.inverse_transform(scaler.fit_transform(X))\n",
    "\n",
    "assert np.allclose(X, X_back)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about the feature names out?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert np.all(scaler.get_feature_names_out() == [\"x0\", \"x1\", \"x2\"])\n",
    "assert np.all(scaler.get_feature_names_out([\"a\", \"b\", \"c\"]) == [\"a\", \"b\", \"c\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if we fit a DataFrame, are the feature in and out ok?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"a\": np.random.rand(100), \"b\": np.random.rand(100)})\n",
    "scaler = StandardScalerClone()\n",
    "X_scaled = scaler.fit_transform(df)\n",
    "\n",
    "assert np.all(scaler.feature_names_in_ == [\"a\", \"b\"])\n",
    "assert np.all(scaler.get_feature_names_out() == [\"a\", \"b\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All good! That's all for today! 😀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations! You already know quite a lot about Machine Learning. :)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "nav_menu": {
   "height": "279px",
   "width": "309px"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}