.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/ensemble/plot_gradient_boosting_categorical.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py: ================================================ Categorical Feature Support in Gradient Boosting ================================================ .. currentmodule:: sklearn In this example, we will compare the training times and prediction performances of :class:`~ensemble.HistGradientBoostingRegressor` with different encoding strategies for categorical features. In particular, we will evaluate: - dropping the categorical features - using a :class:`~preprocessing.OneHotEncoder` - using an :class:`~preprocessing.OrdinalEncoder` and treat categories as ordered, equidistant quantities - using an :class:`~preprocessing.OrdinalEncoder` and rely on the :ref:`native category support ` of the :class:`~ensemble.HistGradientBoostingRegressor` estimator. We will work with the Ames Lowa Housing dataset which consists of numerical and categorical features, where the houses' sales prices is the target. .. GENERATED FROM PYTHON SOURCE LINES 24-26 .. code-block:: default print(__doc__) .. GENERATED FROM PYTHON SOURCE LINES 27-31 Load Ames Housing dataset ------------------------- First, we load the ames housing data as a pandas dataframe. The features are either categorical or numerical: .. GENERATED FROM PYTHON SOURCE LINES 31-42 .. code-block:: default from sklearn.datasets import fetch_openml X, y = fetch_openml(data_id=41211, as_frame=True, return_X_y=True) n_categorical_features = (X.dtypes == 'category').sum() n_numerical_features = (X.dtypes == 'float').sum() print(f"Number of samples: {X.shape[0]}") print(f"Number of features: {X.shape[1]}") print(f"Number of categorical features: {n_categorical_features}") print(f"Number of numerical features: {n_numerical_features}") .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /home/circleci/project/sklearn/datasets/_openml.py:849: UserWarning: Version 1 of dataset ames-housing is inactive, meaning that issues have been found in the dataset. Try using a newer version from this URL: https://www.openml.org/data/v1/download/20649135/ames-housing.arff warn("Version {} of dataset {} is inactive, meaning that issues have " Number of samples: 2930 Number of features: 80 Number of categorical features: 46 Number of numerical features: 34 .. GENERATED FROM PYTHON SOURCE LINES 43-47 Gradient boosting estimator with dropped categorical features ------------------------------------------------------------- As a baseline, we create an estimator where the categorical features are dropped: .. GENERATED FROM PYTHON SOURCE LINES 47-60 .. code-block:: default from sklearn.experimental import enable_hist_gradient_boosting # noqa from sklearn.ensemble import HistGradientBoostingRegressor from sklearn.pipeline import make_pipeline from sklearn.compose import make_column_transformer from sklearn.compose import make_column_selector dropper = make_column_transformer( ('drop', make_column_selector(dtype_include='category')), remainder='passthrough') hist_dropped = make_pipeline(dropper, HistGradientBoostingRegressor(random_state=42)) .. GENERATED FROM PYTHON SOURCE LINES 61-65 Gradient boosting estimator with one-hot encoding ------------------------------------------------- Next, we create a pipeline that will one-hot encode the categorical features and let the rest of the numerical data to passthrough: .. GENERATED FROM PYTHON SOURCE LINES 65-76 .. code-block:: default from sklearn.preprocessing import OneHotEncoder one_hot_encoder = make_column_transformer( (OneHotEncoder(sparse=False, handle_unknown='ignore'), make_column_selector(dtype_include='category')), remainder='passthrough') hist_one_hot = make_pipeline(one_hot_encoder, HistGradientBoostingRegressor(random_state=42)) .. GENERATED FROM PYTHON SOURCE LINES 77-82 Gradient boosting estimator with ordinal encoding ------------------------------------------------- Next, we create a pipeline that will treat categorical features as if they were ordered quantities, i.e. the categories will be encoded as 0, 1, 2, etc., and treated as continuous features. .. GENERATED FROM PYTHON SOURCE LINES 82-94 .. code-block:: default from sklearn.preprocessing import OrdinalEncoder import numpy as np ordinal_encoder = make_column_transformer( (OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=np.nan), make_column_selector(dtype_include='category')), remainder='passthrough') hist_ordinal = make_pipeline(ordinal_encoder, HistGradientBoostingRegressor(random_state=42)) .. GENERATED FROM PYTHON SOURCE LINES 95-108 Gradient boosting estimator with native categorical support ----------------------------------------------------------- We now create a :class:`~ensemble.HistGradientBoostingRegressor` estimator that will natively handle categorical features. This estimator will not treat categorical features as ordered quantities. Since the :class:`~ensemble.HistGradientBoostingRegressor` requires category values to be encoded in `[0, n_unique_categories - 1]`, we still rely on an :class:`~preprocessing.OrdinalEncoder` to pre-process the data. The main difference between this pipeline and the previous one is that in this one, we let the :class:`~ensemble.HistGradientBoostingRegressor` know which features are categorical. .. GENERATED FROM PYTHON SOURCE LINES 108-120 .. code-block:: default # The ordinal encoder will first output the categorical features, and then the # continuous (passed-through) features categorical_mask = ([True] * n_categorical_features + [False] * n_numerical_features) hist_native = make_pipeline( ordinal_encoder, HistGradientBoostingRegressor(random_state=42, categorical_features=categorical_mask) ) .. GENERATED FROM PYTHON SOURCE LINES 121-126 Model comparison ---------------- Finally, we evaluate the models using cross validation. Here we compare the models performance in terms of :func:`~metrics.mean_absolute_percentage_error` and fit times. .. GENERATED FROM PYTHON SOURCE LINES 126-159 .. code-block:: default from sklearn.model_selection import cross_validate import matplotlib.pyplot as plt scoring = "neg_mean_absolute_percentage_error" dropped_result = cross_validate(hist_dropped, X, y, cv=3, scoring=scoring) one_hot_result = cross_validate(hist_one_hot, X, y, cv=3, scoring=scoring) ordinal_result = cross_validate(hist_ordinal, X, y, cv=3, scoring=scoring) native_result = cross_validate(hist_native, X, y, cv=3, scoring=scoring) def plot_results(figure_title): fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8)) plot_info = [('fit_time', 'Fit times (s)', ax1, None), ('test_score', 'Mean Absolute Percentage Error', ax2, (0, 0.20))] x, width = np.arange(4), 0.9 for key, title, ax, y_limit in plot_info: items = [dropped_result[key], one_hot_result[key], ordinal_result[key], native_result[key]] ax.bar(x, [np.mean(np.abs(item)) for item in items], width, yerr=[np.std(item) for item in items], color=['C0', 'C1', 'C2', 'C3']) ax.set(xlabel='Model', title=title, xticks=x, xticklabels=["Dropped", "One Hot", "Ordinal", "Native"], ylim=y_limit) fig.suptitle(figure_title) plot_results("Gradient Boosting on Adult Census") .. image:: /auto_examples/ensemble/images/sphx_glr_plot_gradient_boosting_categorical_001.png :alt: Gradient Boosting on Adult Census, Fit times (s), Mean Absolute Percentage Error :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 160-173 We see that the model with one-hot-encoded data is by far the slowest. This is to be expected, since one-hot-encoding creates one additional feature per category value (for each categorical feature), and thus more split points need to be considered during fitting. In theory, we expect the native handling of categorical features to be slightly slower than treating categories as ordered quantities ('Ordinal'), since native handling requires :ref:`sorting categories `. Fitting times should however be close when the number of categories is small, and this may not always be reflected in practice. In terms of prediction performance, dropping the categorical features leads to poorer performance. The three models that use categorical features have comparable error rates, with a slight edge for the native handling. .. GENERATED FROM PYTHON SOURCE LINES 175-196 Limitting the number of splits ------------------------------ In general, one can expect poorer predictions from one-hot-encoded data, especially when the tree depths or the number of nodes are limited: with one-hot-encoded data, one needs more split points, i.e. more depth, in order to recover an equivalent split that could be obtained in one single split point with native handling. This is also true when categories are treated as ordinal quantities: if categories are `A..F` and the best split is `ACF - BDE` the one-hot-encoder model will need 3 split points (one per category in the left node), and the ordinal non-native model will need 4 splits: 1 split to isolate `A`, 1 split to isolate `F`, and 2 splits to isolate `C` from `BCDE`. How strongly the models' performances differ in practice will depend on the dataset and on the flexibility of the trees. To see this, let us re-run the same analysis with under-fitting models where we artificially limit the total number of splits by both limitting the number of trees and the depth of each tree. .. GENERATED FROM PYTHON SOURCE LINES 196-210 .. code-block:: default for pipe in (hist_dropped, hist_one_hot, hist_ordinal, hist_native): pipe.set_params(histgradientboostingregressor__max_depth=3, histgradientboostingregressor__max_iter=15) dropped_result = cross_validate(hist_dropped, X, y, cv=3, scoring=scoring) one_hot_result = cross_validate(hist_one_hot, X, y, cv=3, scoring=scoring) ordinal_result = cross_validate(hist_ordinal, X, y, cv=3, scoring=scoring) native_result = cross_validate(hist_native, X, y, cv=3, scoring=scoring) plot_results("Gradient Boosting on Adult Census (few and small trees)") plt.show() .. image:: /auto_examples/ensemble/images/sphx_glr_plot_gradient_boosting_categorical_002.png :alt: Gradient Boosting on Adult Census (few and small trees), Fit times (s), Mean Absolute Percentage Error :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 211-216 The results for these under-fitting models confirm our previous intuition: the native category handling strategy performs the best when the splitting budget is constrained. The two other strategies (one-hot encoding and treating categories as ordinal values) lead to error values comparable to the baseline model that just dropped the categorical features altogether. .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 22.461 seconds) .. _sphx_glr_download_auto_examples_ensemble_plot_gradient_boosting_categorical.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_gradient_boosting_categorical.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_gradient_boosting_categorical.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_gradient_boosting_categorical.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_