MNT add n_features_in_ through the feature_extraction module #20180
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jeremiedbb
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Jun 1, 2021
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| # test idf transform with incompatible n_features | ||
| X = [[1, 1, 5], | ||
| [1, 1, 0]] | ||
| t3.fit(X) | ||
| X_incompt = [[1, 3], | ||
| [1, 3]] | ||
| with pytest.raises(ValueError): | ||
| t3.transform(X_incompt) |
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This is now checked through the common tests
sklearn/utils/estimator_checks.py
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| if ("2darray" not in tags["X_types"] and "sparse" not in tags["X_types"] or | ||
| tags["no_validation"]): |
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TfidfTransformer has tags["X_types"] = "sparse". I guess it does not hurt to allow it as well since sparse is 2d
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Part of #19333
the feature_extraction module contains CountVectorizer, DictVectorizer, FeatureHasher, HashingVectorizer, PatchExtractor, TfidfTransformer and TfidfVectorizer.
n_features_in_is only relevant for the TfidfTransformer.