{ "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": [ "\n", " \n", " \n", "
\n", " \"Open\n", " \n", " \n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Welcome to Machine Learning!\n" ] } ], "source": [ "print(\"Welcome to Machine Learning!\")" ] }, { "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": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
0-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY
1-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY
2-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY
3-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY
4-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY
\n", "
" ], "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": [ "\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": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
count20640.00000020640.00000020640.00000020640.00000020433.00000020640.00000020640.00000020640.00000020640.000000
mean-119.56970435.63186128.6394862635.763081537.8705531425.476744499.5396803.870671206855.816909
std2.0035322.13595212.5855582181.615252421.3850701132.462122382.3297531.899822115395.615874
min-124.35000032.5400001.0000002.0000001.0000003.0000001.0000000.49990014999.000000
25%-121.80000033.93000018.0000001447.750000296.000000787.000000280.0000002.563400119600.000000
50%-118.49000034.26000029.0000002127.000000435.0000001166.000000409.0000003.534800179700.000000
75%-118.01000037.71000037.0000003148.000000647.0000001725.000000605.0000004.743250264725.000000
max-114.31000041.95000052.00000039320.0000006445.00000035682.0000006082.00000015.000100500001.000000
\n", "
" ], "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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": [ "0.10736798530929946\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": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": [ "
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Overall %Stratified %Random %Strat. Error %Rand. Error %
Income Category
13.984.004.240.366.45
231.8831.8830.74-0.02-3.59
335.0635.0534.52-0.01-1.53
417.6317.6418.410.034.42
511.4411.4312.09-0.085.63
\n", "
" ], "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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\n", 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" ] }, "metadata": { "needs_background": "light" }, "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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\n", 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" ] }, "metadata": { "needs_background": "light" }, "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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\n", 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" ] }, "metadata": { "needs_background": "light" }, "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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": "markdown", "metadata": {}, "source": [ "Note: since Pandas 2.0.0, the `numeric_only` argument defaults to `False`, so we need to set it explicitly to True to avoid an error." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "corr_matrix = housing.corr(numeric_only=True)" ] }, { "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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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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\n", 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" ] }, "metadata": { "needs_background": "light" }, "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": [ { "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(numeric_only=True)\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": [ "
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14360-117.8733.628.01266.0NaN375.0183.09.8020<1H OCEAN
\n", "
" ], "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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomeocean_proximity
\n", "
" ], "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": [ "
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longitudelatitudehousing_median_agetotal_roomspopulationhouseholdsmedian_incomeocean_proximity
14452-120.6740.5015.05343.02503.0902.03.5962INLAND
18217-117.9634.0335.02093.01755.0403.03.4115<1H OCEAN
11889-118.0534.0433.01348.01098.0257.04.2917<1H OCEAN
20325-118.8834.1715.04260.01701.0669.05.1033<1H OCEAN
14360-117.8733.628.01266.0375.0183.09.8020<1H OCEAN
\n", "
" ], "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": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomeocean_proximity
14452-120.6740.5015.05343.0434.02503.0902.03.5962INLAND
18217-117.9634.0335.02093.0434.01755.0403.03.4115<1H OCEAN
11889-118.0534.0433.01348.0434.01098.0257.04.2917<1H OCEAN
20325-118.8834.1715.04260.0434.01701.0669.05.1033<1H OCEAN
14360-117.8733.628.01266.0434.0375.0183.09.8020<1H OCEAN
\n", "
" ], "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/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": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_income
14452-120.6740.5015.05343.0434.02503.0902.03.5962
18217-117.9634.0335.02093.0434.01755.0403.03.4115
11889-118.0534.0433.01348.0434.01098.0257.04.2917
20325-118.8834.1715.04260.0434.01701.0669.05.1033
14360-117.8733.628.01266.0434.0375.0183.09.8020
\n", "
" ], "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": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_income
14452-120.6740.5015.05343.0434.02503.0902.03.5962
18217-117.9634.0335.02093.0434.01755.0403.03.4115
11889-118.0534.0433.01348.0434.01098.0257.04.2917
20325-118.8834.1715.04260.0434.01701.0669.05.1033
14360-117.8733.628.01266.0434.0375.0183.09.8020
\n", "
" ], "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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ocean_proximity
13096NEAR BAY
14973<1H OCEAN
3785INLAND
14689INLAND
20507NEAR OCEAN
1286INLAND
18078<1H OCEAN
4396NEAR BAY
\n", "
" ], "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 ''\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_output=False` when creating the `OneHotEncoder` (note: the `sparse` hyperparameter was renamned to `sparse_output` in Scikit-Learn 1.2):" ] }, { "cell_type": "code", "execution_count": 69, "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": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cat_encoder = OneHotEncoder(sparse_output=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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ocean_proximity_INLANDocean_proximity_NEAR BAY
010
101
\n", "
" ], "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": [ "
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ocean_proximity_<2H OCEANocean_proximity_ISLAND
010
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" ], "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": [ "
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ocean_proximity_<1H OCEANocean_proximity_INLANDocean_proximity_ISLANDocean_proximity_NEAR BAYocean_proximity_NEAR OCEAN
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" ], "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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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, n_init=10,\n", " 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": "markdown", "metadata": {}, "source": [ "Note: The default value for the `n_init` hyperparameter above will change from 10 to `\"auto\"` in Scikit-Learn 1.4, so I'm setting it explicitly to 10 to keep this notebook stable." ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "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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\n", 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" ] }, "metadata": { "needs_background": "light" }, "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/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": [ "
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" ], "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/plain": [ "StandardScaler()" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num_pipeline[1]" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "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/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/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": [ "
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pipeline-1__longitudepipeline-1__latitudepipeline-1__housing_median_agepipeline-1__total_roomspipeline-1__total_bedroomspipeline-1__populationpipeline-1__householdspipeline-1__median_incomepipeline-2__ocean_proximity_<1H OCEANpipeline-2__ocean_proximity_INLANDpipeline-2__ocean_proximity_ISLANDpipeline-2__ocean_proximity_NEAR BAYpipeline-2__ocean_proximity_NEAR OCEAN
13096-1.4230371.0136061.8611190.3119121.3681670.1374601.394812-0.9364910.00.00.01.00.0
149730.596394-0.7021030.907630-0.308620-0.435925-0.693771-0.3734851.1719421.00.00.00.00.0
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" ], "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": [ { "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": [ { "data": { "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=)])),\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": [ { "data": { "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=)])),\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": [], "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": [ { "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": [], "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": [ { "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": [ { "data": { "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=)])),\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__missing_values', 'preprocessing__remainder__simpleimputer__strategy', 'preprocessing__remainder__simpleimputer__verbose', 'preprocessing__remainder__standardscaler__copy', 'preprocessing__remainder__standardscaler__with_mean', 'preprocessing__remainder__standardscaler__with_std', 'preprocessing__remainder', 'preprocessing__sparse_threshold', 'preprocessing__transformer_weights', 'preprocessing__transformers', 'preprocessing__verbose', 'preprocessing__verbose_feature_names_out', 'preprocessing__be...\n" ] } ], "source": [ "# extra code – shows part of the output of get_params().keys()\n", "print(str(full_pipeline.get_params().keys())[:1000] + \"...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The best hyperparameter combination found:" ] }, { "cell_type": "code", "execution_count": 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/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=)])),\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": [ "
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n_clustersmax_featuressplit0split1split2mean_test_rmse
1215643460439194474844042
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" ], "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": [ { "data": { "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", " )])),\n", " ('random_forest',\n", " RandomForestRegressor(random_state=42))]),\n", " param_distributions={'preprocessing__geo__n_clusters': ,\n", " 'random_forest__max_features': },\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": [ "
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n_clustersmax_featuressplit0split1split2mean_test_rmse
145941287421504262742021
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" ], "text/plain": [ " n_clusters max_features split0 split1 split2 mean_test_rmse\n", "1 45 9 41287 42150 42627 42021\n", "8 32 7 41690 42542 43224 42485\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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\n", 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" ] }, "metadata": { "needs_background": "light" }, "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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": [ { "data": { "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=)])),\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": [ "69814.13889867254" ] }, "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": [ { "data": { "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", " )])),\n", " ('svr', SVR())]),\n", " n_iter=50,\n", " param_distributions={'svr__C': ,\n", " 'svr__gamma': ,\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": [ "55853.88100300133" ] }, "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": [ { "data": { "text/plain": [ "count 3.000000\n", "mean 56211.362086\n", "std 1922.002802\n", "min 54150.008629\n", "25% 55339.929909\n", "50% 56529.851189\n", "75% 57242.038815\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([[456667. ],\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 104992.095758\n", "std 3112.486560\n", "min 101550.880533\n", "25% 103682.876337\n", "50% 105814.872141\n", "75% 106712.703370\n", "max 107610.534600\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/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=,\n", " func=)),\n", " ('standardscaler',\n", " StandardScaler()...\n", " param_distributions={'preprocessing__geo__estimator__n_neighbors': range(1, 30),\n", " 'preprocessing__geo__estimator__weights': ['distance',\n", " 'uniform'],\n", " 'svr__C': ,\n", " 'svr__gamma': },\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": [ "106775.63787128967" ] }, "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": 170, "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": 171, "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": 172, "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": 173, "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": 174, "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": 175, "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 (ipykernel)", "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": "3.10.13" }, "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 }