import numpy as np import pytest from sklearn.base import BaseEstimator, TransformerMixin, clone from sklearn.compose import ColumnTransformer, make_column_transformer from sklearn.datasets import load_iris from sklearn.decomposition import PCA, TruncatedSVD from sklearn.feature_extraction.text import CountVectorizer from sklearn.linear_model import LogisticRegression from sklearn.model_selection import TunedThresholdClassifierCV from sklearn.pipeline import FeatureUnion, Pipeline, make_pipeline from sklearn.preprocessing import ( FunctionTransformer, MinMaxScaler, Normalizer, StandardScaler, ) from sklearn.utils._repr_html.estimator import estimator_html_repr from sklearn.utils._repr_html.features import _features_html from sklearn.utils._testing import MinimalTransformer ct = ColumnTransformer([("norm1", Normalizer(), [0, 1])], remainder="passthrough") ct2 = FeatureUnion( [("pca", PCA(n_components=1)), ("svd", TruncatedSVD(n_components=2))] ) rng = np.random.RandomState(42) def test_n_features_not_fitted(): out = estimator_html_repr(ct) assert "2 features" not in out assert "x0" not in out assert "x1" not in out assert "
" not in out def test_with_MinimalTransformer(): """Test works with MinimalTransformer in a pipeline (doesn't inherit from BaseEstimator)""" X, y = np.array([[0, 1], [1, 1]]), np.array([[0, 1]]) model = Pipeline([("transformer", MinimalTransformer())]) model.fit(X, y) out = estimator_html_repr(model) assert "MinimalTransformer" in out @pytest.mark.parametrize( "pandas, feature_cols", [ (True, ["Feature A", "Feature B"]), (False, ["x0", "x1"]), ], ) def test_estimator_html_repr_col_names(pandas, feature_cols): """Test features names are kept with pandas col names and generic.""" if pandas: pd = pytest.importorskip("pandas") X = pd.DataFrame({"Feature A": [0, 2], "Feature B": [1, 1]}) else: X = np.array([[0, 2], [1, 1]]) ct_cloned = clone(ct).fit(X) out = estimator_html_repr(ct_cloned) assert feature_cols[0] in out assert feature_cols[1] in out @pytest.mark.parametrize( "pandas, total_output_features", [ (True, ["norm1__A", "norm1__B", "remainder__C"]), (False, ["norm1__x0", "norm1__x1", "remainder__x2"]), ], ) def test_estimator_html_repr_total_feature_names(pandas, total_output_features): """Test features names are kept with pandas col names and generic.""" if pandas: pd = pytest.importorskip("pandas") X = pd.DataFrame({"A": [0, 2, 3], "B": [1, 1, 3], "C": [3, 5, 4]}) else: X = np.array([[0, 2, 3], [1, 1, 3], [3, 5, 4]]) ct_cloned = clone(ct).fit(X) out = estimator_html_repr(ct_cloned) assert "
" in out assert "3 features
" in out for feature_name in total_output_features: assert feature_name in out def test_estimator_html_col_names_featureunion(): X = [[0.0, 1.0, 3], [2.0, 2.0, 5]] ct2_cloned = clone(ct2) ct2_cloned.fit_transform(X) out = estimator_html_repr(ct2_cloned) assert "pca__pca0" in out assert "svd__truncatedsvd0" in out assert "2 features" in out def test_features_html_with_pipeline(): """Test works with MinimalTransformer in a pipeline and scaler to test number of features.""" pipe = Pipeline([("minimal", MinimalTransformer()), ("scaler", StandardScaler())]) X = rng.randn(10, 3) pipe.fit(X) html = estimator_html_repr(pipe) assert "3 features" in html def test_countvectorizer_output_features(): """Non-regression test for https://github.com/scikit-learn/scikit-learn/issues/33772""" corpus = [ "cat", "dog", "mouse", "bird", ] vectorizer = CountVectorizer() vectorizer.fit_transform(corpus) html = estimator_html_repr(vectorizer) assert "4 features" in html def test_meta_estimator_output_features(): """Non-regression test for https://github.com/scikit-learn/scikit-learn/issues/33887 """ pytest.importorskip("pandas") X, y = load_iris(return_X_y=True, as_frame=True) X, y = X.iloc[:100], y.iloc[:100] preprocessor = make_column_transformer( (StandardScaler(), [0, 1]), (MinMaxScaler(), [2, 3]), ) estimator = make_pipeline(preprocessor, LogisticRegression()) meta_estimator = TunedThresholdClassifierCV( estimator, store_cv_results=True, random_state=0 ).fit(X, y) html = estimator_html_repr(meta_estimator) assert "4 features" in html def test_get_feature_names_out_exception(): """Non-regression test for https://github.com/scikit-learn/scikit-learn/issues/33887 Testing that error in _get_feature_names_out doesn't break and we still get an HTML display with no number of features. """ X = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) union = FeatureUnion( [ ("pca", PCA(n_components=1)), ("identity", FunctionTransformer()), ] ) Xt = union.fit_transform(X) html = estimator_html_repr(union) assert "1 feature" in html assert "
0 features
" not in html assert "identity" in html def test_single_estimator_get_feature_names_out_exception(): """Non-regression test for https://github.com/scikit-learn/scikit-learn/issues/33887 Testing that error in _get_feature_names_out doesn't break hitting single block except branch""" class BrokenTransformer(TransformerMixin, BaseEstimator): def fit(self, X, y=None): self.n_features_in_ = X.shape[1] return self def get_feature_names_out(self, input_features=None): raise RuntimeError("Simulated failure") X = np.array([[1, 2], [3, 4]]) t = BrokenTransformer() t.fit(X) html = estimator_html_repr(t) assert "BrokenTransformer" in html def test_features_html_empty_features(): """Test that _features_html handles empty feature list.""" features = [] html = _features_html(features) assert "" in html def test_features_html_special_characters(): """Test that special characters in feature names are properly escaped.""" features = [ "feature&1", 'feature"2', "feature'3", "feature>4", "feature<5", "", ] html = _features_html(features) assert "&" in html assert "<script>alert('xss')</script>" in html assert ">" in html assert "<" in html assert "6 features" in html def test_features_html_structure(): """Test that HTML structure contains expected elements.""" features = ["feat1", "feat2"] html = _features_html(features) assert "
" in html assert "" in html assert "" in html assert "
" in html assert '' in html assert "" in html assert "" in html assert '