215 lines
8.9 KiB
Python
215 lines
8.9 KiB
Python
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"""
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================================================
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Categorical Feature Support in Gradient Boosting
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================================================
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.. currentmodule:: sklearn
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In this example, we will compare the training times and prediction
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performances of :class:`~ensemble.HistGradientBoostingRegressor` with
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different encoding strategies for categorical features. In
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particular, we will evaluate:
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- dropping the categorical features
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- using a :class:`~preprocessing.OneHotEncoder`
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- using an :class:`~preprocessing.OrdinalEncoder` and treat categories as
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ordered, equidistant quantities
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- using an :class:`~preprocessing.OrdinalEncoder` and rely on the :ref:`native
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category support <categorical_support_gbdt>` of the
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:class:`~ensemble.HistGradientBoostingRegressor` estimator.
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We will work with the Ames Lowa Housing dataset which consists of numerical
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and categorical features, where the houses' sales prices is the target.
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"""
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print(__doc__)
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# %%
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# Load Ames Housing dataset
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# -------------------------
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# First, we load the ames housing data as a pandas dataframe. The features
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# are either categorical or numerical:
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from sklearn.datasets import fetch_openml
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X, y = fetch_openml(data_id=41211, as_frame=True, return_X_y=True)
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n_categorical_features = (X.dtypes == 'category').sum()
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n_numerical_features = (X.dtypes == 'float').sum()
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print(f"Number of samples: {X.shape[0]}")
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print(f"Number of features: {X.shape[1]}")
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print(f"Number of categorical features: {n_categorical_features}")
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print(f"Number of numerical features: {n_numerical_features}")
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# %%
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# Gradient boosting estimator with dropped categorical features
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# -------------------------------------------------------------
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# As a baseline, we create an estimator where the categorical features are
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# dropped:
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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.pipeline import make_pipeline
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from sklearn.compose import make_column_transformer
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from sklearn.compose import make_column_selector
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dropper = make_column_transformer(
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('drop', make_column_selector(dtype_include='category')),
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remainder='passthrough')
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hist_dropped = make_pipeline(dropper,
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HistGradientBoostingRegressor(random_state=42))
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# %%
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# Gradient boosting estimator with one-hot encoding
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# -------------------------------------------------
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# Next, we create a pipeline that will one-hot encode the categorical features
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# and let the rest of the numerical data to passthrough:
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from sklearn.preprocessing import OneHotEncoder
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one_hot_encoder = make_column_transformer(
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(OneHotEncoder(sparse=False, handle_unknown='ignore'),
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make_column_selector(dtype_include='category')),
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remainder='passthrough')
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hist_one_hot = make_pipeline(one_hot_encoder,
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HistGradientBoostingRegressor(random_state=42))
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# %%
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# Gradient boosting estimator with ordinal encoding
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# -------------------------------------------------
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# Next, we create a pipeline that will treat categorical features as if they
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# were ordered quantities, i.e. the categories will be encoded as 0, 1, 2,
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# etc., and treated as continuous features.
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from sklearn.preprocessing import OrdinalEncoder
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import numpy as np
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ordinal_encoder = make_column_transformer(
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(OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=np.nan),
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make_column_selector(dtype_include='category')),
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remainder='passthrough')
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hist_ordinal = make_pipeline(ordinal_encoder,
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HistGradientBoostingRegressor(random_state=42))
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# %%
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# Gradient boosting estimator with native categorical support
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# -----------------------------------------------------------
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# We now create a :class:`~ensemble.HistGradientBoostingRegressor` estimator
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# that will natively handle categorical features. This estimator will not treat
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# categorical features as ordered quantities.
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#
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# Since the :class:`~ensemble.HistGradientBoostingRegressor` requires category
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# values to be encoded in `[0, n_unique_categories - 1]`, we still rely on an
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# :class:`~preprocessing.OrdinalEncoder` to pre-process the data.
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#
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# The main difference between this pipeline and the previous one is that in
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# this one, we let the :class:`~ensemble.HistGradientBoostingRegressor` know
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# which features are categorical.
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# The ordinal encoder will first output the categorical features, and then the
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# continuous (passed-through) features
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categorical_mask = ([True] * n_categorical_features +
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[False] * n_numerical_features)
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hist_native = make_pipeline(
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ordinal_encoder,
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HistGradientBoostingRegressor(random_state=42,
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categorical_features=categorical_mask)
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)
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# %%
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# Model comparison
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# ----------------
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# Finally, we evaluate the models using cross validation. Here we compare the
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# models performance in terms of
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# :func:`~metrics.mean_absolute_percentage_error` and fit times.
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from sklearn.model_selection import cross_validate
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import matplotlib.pyplot as plt
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scoring = "neg_mean_absolute_percentage_error"
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dropped_result = cross_validate(hist_dropped, X, y, cv=3, scoring=scoring)
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one_hot_result = cross_validate(hist_one_hot, X, y, cv=3, scoring=scoring)
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ordinal_result = cross_validate(hist_ordinal, X, y, cv=3, scoring=scoring)
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native_result = cross_validate(hist_native, X, y, cv=3, scoring=scoring)
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def plot_results(figure_title):
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8))
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plot_info = [('fit_time', 'Fit times (s)', ax1, None),
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('test_score', 'Mean Absolute Percentage Error', ax2,
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(0, 0.20))]
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x, width = np.arange(4), 0.9
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for key, title, ax, y_limit in plot_info:
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items = [dropped_result[key], one_hot_result[key], ordinal_result[key],
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native_result[key]]
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ax.bar(x, [np.mean(np.abs(item)) for item in items],
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width, yerr=[np.std(item) for item in items],
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color=['C0', 'C1', 'C2', 'C3'])
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ax.set(xlabel='Model', title=title, xticks=x,
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xticklabels=["Dropped", "One Hot", "Ordinal", "Native"],
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ylim=y_limit)
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fig.suptitle(figure_title)
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plot_results("Gradient Boosting on Adult Census")
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# %%
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# We see that the model with one-hot-encoded data is by far the slowest. This
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# is to be expected, since one-hot-encoding creates one additional feature per
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# category value (for each categorical feature), and thus more split points
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# need to be considered during fitting. In theory, we expect the native
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# handling of categorical features to be slightly slower than treating
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# categories as ordered quantities ('Ordinal'), since native handling requires
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# :ref:`sorting categories <categorical_support_gbdt>`. Fitting times should
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# however be close when the number of categories is small, and this may not
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# always be reflected in practice.
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#
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# In terms of prediction performance, dropping the categorical features leads
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# to poorer performance. The three models that use categorical features have
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# comparable error rates, with a slight edge for the native handling.
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# %%
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# Limitting the number of splits
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# ------------------------------
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#
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# In general, one can expect poorer predictions from one-hot-encoded data,
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# especially when the tree depths or the number of nodes are limited: with
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# one-hot-encoded data, one needs more split points, i.e. more depth, in order
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# to recover an equivalent split that could be obtained in one single split
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# point with native handling.
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#
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# This is also true when categories are treated as ordinal quantities: if
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# categories are `A..F` and the best split is `ACF - BDE` the one-hot-encoder
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# model will need 3 split points (one per category in the left node), and the
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# ordinal non-native model will need 4 splits: 1 split to isolate `A`, 1 split
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# to isolate `F`, and 2 splits to isolate `C` from `BCDE`.
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#
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# How strongly the models' performances differ in practice will depend on the
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# dataset and on the flexibility of the trees.
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#
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# To see this, let us re-run the same analysis with under-fitting models where
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# we artificially limit the total number of splits by both limitting the number
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# of trees and the depth of each tree.
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for pipe in (hist_dropped, hist_one_hot, hist_ordinal, hist_native):
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pipe.set_params(histgradientboostingregressor__max_depth=3,
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histgradientboostingregressor__max_iter=15)
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dropped_result = cross_validate(hist_dropped, X, y, cv=3, scoring=scoring)
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one_hot_result = cross_validate(hist_one_hot, X, y, cv=3, scoring=scoring)
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ordinal_result = cross_validate(hist_ordinal, X, y, cv=3, scoring=scoring)
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native_result = cross_validate(hist_native, X, y, cv=3, scoring=scoring)
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plot_results("Gradient Boosting on Adult Census (few and small trees)")
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plt.show()
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# %%
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# The results for these under-fitting models confirm our previous intuition:
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# the native category handling strategy performs the best when the splitting
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# budget is constrained. The two other strategies (one-hot encoding and
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# treating categories as ordinal values) lead to error values comparable
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# to the baseline model that just dropped the categorical features altogether.
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