ENH Specify categorical features with feature names in HGBDT #24889
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glemaitre
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Nov 10, 2022
glemaitre
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Nov 10, 2022
sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
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glemaitre
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glemaitre
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thomasjpfan
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Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
lorentzenchr
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lorentzenchr
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Nov 12, 2022
| "on data without feature names." | ||
| ) | ||
| is_categorical = np.zeros(n_features, dtype=bool) | ||
| feature_names = self.feature_names_in_.tolist() |
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Is this conversion to a list necessary?
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Arrays do not have the index method. Not sure how to implement this while staying in numpy and making it easy to raise the error message timely at the same time.
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Also, the feature names list should never be to long (few hundred values) for HGBDT models in practice because those models tend to perform poorly when n_features >> n_samples.
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Thanks for the explanation.
|
Thank for the final fixes @lorentzenchr! |
This was referenced Nov 13, 2022
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Similar to #24855 but for the
categorical_featuresparameter as stated in #24852 (comment).Note that this works well with the
.set_output("pandas")of this release.However it requires disabling the verbose column names of the column transformer :)
Note: in the future we might directly inspect dataframe column dtypes in HGBDT and have an "auto" mode to trigger native categorical support for explicitly encoded categorical dtyped columns, but this will be the topic for a later PR.