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import warnings import numpy as np import pytest from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.datasets import ( load_diabetes, load_iris, make_classification, make_multilabel_classification, ) from sklearn.ensemble import IsolationForest from sklearn.inspection import DecisionBoundaryDisplay from sklearn.inspection._plot.decision_boundary import _check_boundary_response_method from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import scale from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor from sklearn.utils._testing import ( _convert_container, assert_allclose, assert_array_equal, ) X, y = make_classification( n_informative=1, n_redundant=1, n_clusters_per_class=1, n_features=2, random_state=42, ) def load_iris_2d_scaled(): X, y = load_iris(return_X_y=True) X = scale(X)[:, :2] return X, y @pytest.fixture(scope="module") def fitted_clf(): return LogisticRegression().fit(X, y) def test_input_data_dimension(pyplot): """Check that we raise an error when `X` does not have exactly 2 features.""" X, y = make_classification(n_samples=10, n_features=4, random_state=0) clf = LogisticRegression().fit(X, y) msg = "n_features must be equal to 2. Got 4 instead." with pytest.raises(ValueError, match=msg): DecisionBoundaryDisplay.from_estimator(estimator=clf, X=X) def test_check_boundary_response_method_error(): """Check that we raise an error for the cases not supported by `_check_boundary_response_method`. """ class MultiLabelClassifier: classes_ = [np.array([0, 1]), np.array([0, 1])] err_msg = "Multi-label and multi-output multi-class classifiers are not supported" with pytest.raises(ValueError, match=err_msg): _check_boundary_response_method(MultiLabelClassifier(), "predict", None) class MulticlassClassifier: classes_ = [0, 1, 2] err_msg = "Multiclass classifiers are only supported when `response_method` is" for response_method in ("predict_proba", "decision_function"): with pytest.raises(ValueError, match=err_msg): _check_boundary_response_method( MulticlassClassifier(), response_method, None ) @pytest.mark.parametrize( "estimator, response_method, class_of_interest, expected_prediction_method", [ (DecisionTreeRegressor(), "predict", None, "predict"), (DecisionTreeRegressor(), "auto", None, "predict"), (LogisticRegression().fit(load_iris_2d_scaled()), "predict", None, "predict"), (LogisticRegression().fit(load_iris_2d_scaled()), "auto", None, "predict"), ( LogisticRegression().fit(load_iris_2d_scaled()), "predict_proba", 0, "predict_proba", ), ( LogisticRegression().fit(load_iris_2d_scaled()), "decision_function", 0, "decision_function", ), ( LogisticRegression().fit(X, y), "auto", None, ["decision_function", "predict_proba", "predict"], ), (LogisticRegression().fit(X, y), "predict", None, "predict"), ( LogisticRegression().fit(X, y), ["predict_proba", "decision_function"], None, ["predict_proba", "decision_function"], ), ], ) def test_check_boundary_response_method( estimator, response_method, class_of_interest, expected_prediction_method ): """Check the behaviour of `_check_boundary_response_method` for the supported cases. """ prediction_method = _check_boundary_response_method( estimator, response_method, class_of_interest ) assert prediction_method == expected_prediction_method @pytest.mark.parametrize("response_method", ["predict_proba", "decision_function"]) def test_multiclass_error(pyplot, response_method): """Check multiclass errors.""" X, y = make_classification(n_classes=3, n_informative=3, random_state=0) X = X[:, [0, 1]] lr = LogisticRegression().fit(X, y) msg = ( "Multiclass classifiers are only supported when `response_method` is 'predict'" " or 'auto'" ) with pytest.raises(ValueError, match=msg): DecisionBoundaryDisplay.from_estimator(lr, X, response_method=response_method) @pytest.mark.parametrize("response_method", ["auto", "predict"]) def test_multiclass(pyplot, response_method): """Check multiclass gives expected results.""" grid_resolution = 10 eps = 1.0 X, y = make_classification(n_classes=3, n_informative=3, random_state=0) X = X[:, [0, 1]] lr = LogisticRegression(random_state=0).fit(X, y) disp = DecisionBoundaryDisplay.from_estimator( lr, X, response_method=response_method, grid_resolution=grid_resolution, eps=1.0 ) x0_min, x0_max = X[:, 0].min() - eps, X[:, 0].max() + eps x1_min, x1_max = X[:, 1].min() - eps, X[:, 1].max() + eps xx0, xx1 = np.meshgrid( np.linspace(x0_min, x0_max, grid_resolution), np.linspace(x1_min, x1_max, grid_resolution), ) response = lr.predict(np.c_[xx0.ravel(), xx1.ravel()]) assert_allclose(disp.response, response.reshape(xx0.shape)) assert_allclose(disp.xx0, xx0) assert_allclose(disp.xx1, xx1) @pytest.mark.parametrize( "kwargs, error_msg", [ ( {"plot_method": "hello_world"}, r"plot_method must be one of contourf, contour, pcolormesh. Got hello_world" r" instead.", ), ( {"grid_resolution": 1}, r"grid_resolution must be greater than 1. Got 1 instead", ), ( {"grid_resolution": -1}, r"grid_resolution must be greater than 1. Got -1 instead", ), ({"eps": -1.1}, r"eps must be greater than or equal to 0. Got -1.1 instead"), ], ) def test_input_validation_errors(pyplot, kwargs, error_msg, fitted_clf): """Check input validation from_estimator.""" with pytest.raises(ValueError, match=error_msg): DecisionBoundaryDisplay.from_estimator(fitted_clf, X, *kwargs) def test_display_plot_input_error(pyplot, fitted_clf): """Check input validation for `plot`.""" disp = DecisionBoundaryDisplay.from_estimator(fitted_clf, X, grid_resolution=5) with pytest.raises(ValueError, match="plot_method must be 'contourf'"): disp.plot(plot_method="hello_world") @pytest.mark.parametrize( "response_method", ["auto", "predict", "predict_proba", "decision_function"] ) @pytest.mark.parametrize("plot_method", ["contourf", "contour"]) def test_decision_boundary_display_classifier( pyplot, fitted_clf, response_method, plot_method ): """Check that decision boundary is correct.""" fig, ax = pyplot.subplots() eps = 2.0 disp = DecisionBoundaryDisplay.from_estimator( fitted_clf, X, grid_resolution=5, response_method=response_method, plot_method=plot_method, eps=eps, ax=ax, ) assert isinstance(disp.surface_, pyplot.matplotlib.contour.QuadContourSet) assert disp.ax_ == ax assert disp.figure_ == fig x0, x1 = X[:, 0], X[:, 1] x0_min, x0_max = x0.min() - eps, x0.max() + eps x1_min, x1_max = x1.min() - eps, x1.max() + eps assert disp.xx0.min() == pytest.approx(x0_min) assert disp.xx0.max() == pytest.approx(x0_max) assert disp.xx1.min() == pytest.approx(x1_min) assert disp.xx1.max() == pytest.approx(x1_max) fig2, ax2 = pyplot.subplots() # change plotting method for second plot disp.plot(plot_method="pcolormesh", ax=ax2, shading="auto") assert isinstance(disp.surface_, pyplot.matplotlib.collections.QuadMesh) assert disp.ax_ == ax2 assert disp.figure_ == fig2 @pytest.mark.parametrize("response_method", ["auto", "predict", "decision_function"]) @pytest.mark.parametrize("plot_method", ["contourf", "contour"]) def test_decision_boundary_display_outlier_detector( pyplot, response_method, plot_method ): """Check that decision boundary is correct for outlier detector.""" fig, ax = pyplot.subplots() eps = 2.0 outlier_detector = IsolationForest(random_state=0).fit(X, y) disp = DecisionBoundaryDisplay.from_estimator( outlier_detector, X, grid_resolution=5, response_method=response_method, plot_method=plot_method, eps=eps, ax=ax, ) assert isinstance(disp.surface_, pyplot.matplotlib.contour.QuadContourSet) assert disp.ax_ == ax assert disp.figure_ == fig x0, x1 = X[:, 0], X[:, 1] x0_min, x0_max = x0.min() - eps, x0.max() + eps x1_min, x1_max = x1.min() - eps, x1.max() + eps assert disp.xx0.min() == pytest.approx(x0_min) assert disp.xx0.max() == pytest.approx(x0_max) assert disp.xx1.min() == pytest.approx(x1_min) assert disp.xx1.max() == pytest.approx(x1_max) @pytest.mark.parametrize("response_method", ["auto", "predict"]) @pytest.mark.parametrize("plot_method", ["contourf", "contour"]) def test_decision_boundary_display_regressor(pyplot, response_method, plot_method): """Check that we can display the decision boundary for a regressor.""" X, y = load_diabetes(return_X_y=True) X = X[:, :2] tree = DecisionTreeRegressor().fit(X, y) fig, ax = pyplot.subplots() eps = 2.0 disp = DecisionBoundaryDisplay.from_estimator( tree, X, response_method=response_method, ax=ax, eps=eps, plot_method=plot_method, ) assert isinstance(disp.surface_, pyplot.matplotlib.contour.QuadContourSet) assert disp.ax_ == ax assert disp.figure_ == fig x0, x1 = X[:, 0], X[:, 1] x0_min, x0_max = x0.min() - eps, x0.max() + eps x1_min, x1_max = x1.min() - eps, x1.max() + eps assert disp.xx0.min() == pytest.approx(x0_min) assert disp.xx0.max() == pytest.approx(x0_max) assert disp.xx1.min() == pytest.approx(x1_min) assert disp.xx1.max() == pytest.approx(x1_max) fig2, ax2 = pyplot.subplots() # change plotting method for second plot disp.plot(plot_method="pcolormesh", ax=ax2, shading="auto") assert isinstance(disp.surface_, pyplot.matplotlib.collections.QuadMesh) assert disp.ax_ == ax2 assert disp.figure_ == fig2 @pytest.mark.parametrize( "response_method, msg", [ ( "predict_proba", "MyClassifier has none of the following attributes: predict_proba", ), ( "decision_function", "MyClassifier has none of the following attributes: decision_function", ), ( "auto", ( "MyClassifier has none of the following attributes: decision_function, " "predict_proba, predict" ), ), ( "bad_method", "MyClassifier has none of the following attributes: bad_method", ), ], ) def test_error_bad_response(pyplot, response_method, msg): """Check errors for bad response.""" class MyClassifier(ClassifierMixin, BaseEstimator): def fit(self, X, y): self.fitted_ = True self.classes_ = [0, 1] return self clf = MyClassifier().fit(X, y) with pytest.raises(AttributeError, match=msg): DecisionBoundaryDisplay.from_estimator(clf, X, response_method=response_method) @pytest.mark.parametrize("response_method", ["auto", "predict", "predict_proba"]) def test_multilabel_classifier_error(pyplot, response_method): """Check that multilabel classifier raises correct error.""" X, y = make_multilabel_classification(random_state=0) X = X[:, :2] tree = DecisionTreeClassifier().fit(X, y) msg = "Multi-label and multi-output multi-class classifiers are not supported" with pytest.raises(ValueError, match=msg): DecisionBoundaryDisplay.from_estimator( tree, X, response_method=response_method, ) @pytest.mark.parametrize("response_method", ["auto", "predict", "predict_proba"]) def test_multi_output_multi_class_classifier_error(pyplot, response_method): """Check that multi-output multi-class classifier raises correct error.""" X = np.asarray([[0, 1], [1, 2]]) y = np.asarray([["tree", "cat"], ["cat", "tree"]]) tree = DecisionTreeClassifier().fit(X, y) msg = "Multi-label and multi-output multi-class classifiers are not supported" with pytest.raises(ValueError, match=msg): DecisionBoundaryDisplay.from_estimator( tree, X, response_method=response_method, ) def test_multioutput_regressor_error(pyplot): """Check that multioutput regressor raises correct error.""" X = np.asarray([[0, 1], [1, 2]]) y = np.asarray([[0, 1], [4, 1]]) tree = DecisionTreeRegressor().fit(X, y) with pytest.raises(ValueError, match="Multi-output regressors are not supported"): DecisionBoundaryDisplay.from_estimator(tree, X, response_method="predict") @pytest.mark.parametrize( "response_method", ["predict_proba", "decision_function", ["predict_proba", "predict"]], ) def test_regressor_unsupported_response(pyplot, response_method): """Check that we can display the decision boundary for a regressor.""" X, y = load_diabetes(return_X_y=True) X = X[:, :2] tree = DecisionTreeRegressor().fit(X, y) err_msg = "should either be a classifier to be used with response_method" with pytest.raises(ValueError, match=err_msg): DecisionBoundaryDisplay.from_estimator(tree, X, response_method=response_method) @pytest.mark.filterwarnings( # We expect to raise the following warning because the classifier is fit on a # NumPy array "ignore:X has feature names, but LogisticRegression was fitted without" ) def test_dataframe_labels_used(pyplot, fitted_clf): """Check that column names are used for pandas.""" pd = pytest.importorskip("pandas") df = pd.DataFrame(X, columns=["col_x", "col_y"]) # pandas column names are used by default _, ax = pyplot.subplots() disp = DecisionBoundaryDisplay.from_estimator(fitted_clf, df, ax=ax) assert ax.get_xlabel() == "col_x" assert ax.get_ylabel() == "col_y" # second call to plot will have the names fig, ax = pyplot.subplots() disp.plot(ax=ax) assert ax.get_xlabel() == "col_x" assert ax.get_ylabel() == "col_y" # axes with a label will not get overridden fig, ax = pyplot.subplots() ax.set(xlabel="hello", ylabel="world") disp.plot(ax=ax) assert ax.get_xlabel() == "hello" assert ax.get_ylabel() == "world" # labels get overridden only if provided to the `plot` method disp.plot(ax=ax, xlabel="overwritten_x", ylabel="overwritten_y") assert ax.get_xlabel() == "overwritten_x" assert ax.get_ylabel() == "overwritten_y" # labels do not get inferred if provided to `from_estimator` _, ax = pyplot.subplots() disp = DecisionBoundaryDisplay.from_estimator( fitted_clf, df, ax=ax, xlabel="overwritten_x", ylabel="overwritten_y" ) assert ax.get_xlabel() == "overwritten_x" assert ax.get_ylabel() == "overwritten_y" def test_string_target(pyplot): """Check that decision boundary works with classifiers trained on string labels.""" iris = load_iris() X = iris.data[:, [0, 1]] # Use strings as target y = iris.target_names[iris.target] log_reg = LogisticRegression().fit(X, y) # Does not raise DecisionBoundaryDisplay.from_estimator( log_reg, X, grid_resolution=5, response_method="predict", ) @pytest.mark.parametrize("constructor_name", ["pandas", "polars"]) def test_dataframe_support(pyplot, constructor_name): """Check that passing a dataframe at fit and to the Display does not raise warnings. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/23311 * https://github.com/scikit-learn/scikit-learn/issues/28717 """ df = _convert_container( X, constructor_name=constructor_name, columns_name=["col_x", "col_y"] ) estimator = LogisticRegression().fit(df, y) with warnings.catch_warnings(): # no warnings linked to feature names validation should be raised warnings.simplefilter("error", UserWarning) DecisionBoundaryDisplay.from_estimator(estimator, df, response_method="predict") @pytest.mark.parametrize("response_method", ["predict_proba", "decision_function"]) def test_class_of_interest_binary(pyplot, response_method): """Check the behaviour of passing `class_of_interest` for plotting the output of `predict_proba` and `decision_function` in the binary case. """ iris = load_iris() X = iris.data[:100, :2] y = iris.target[:100] assert_array_equal(np.unique(y), [0, 1]) estimator = LogisticRegression().fit(X, y) # We will check that `class_of_interest=None` is equivalent to # `class_of_interest=estimator.classes_[1]` disp_default = DecisionBoundaryDisplay.from_estimator( estimator, X, response_method=response_method, class_of_interest=None, ) disp_class_1 = DecisionBoundaryDisplay.from_estimator( estimator, X, response_method=response_method, class_of_interest=estimator.classes_[1], ) assert_allclose(disp_default.response, disp_class_1.response) # we can check that `_get_response_values` modifies the response when targeting # the other class, i.e. 1 - p(y=1\|x) for `predict_proba` and -decision_function # for `decision_function`. disp_class_0 = DecisionBoundaryDisplay.from_estimator( estimator, X, response_method=response_method, class_of_interest=estimator.classes_[0], ) if response_method == "predict_proba": assert_allclose(disp_default.response, 1 - disp_class_0.response) else: assert response_method == "decision_function" assert_allclose(disp_default.response, -disp_class_0.response) @pytest.mark.parametrize("response_method", ["predict_proba", "decision_function"]) def test_class_of_interest_multiclass(pyplot, response_method): """Check the behaviour of passing `class_of_interest` for plotting the output of `predict_proba` and `decision_function` in the multiclass case. """ iris = load_iris() X = iris.data[:, :2] y = iris.target # the target are numerical labels class_of_interest_idx = 2 estimator = LogisticRegression().fit(X, y) disp = DecisionBoundaryDisplay.from_estimator( estimator, X, response_method=response_method, class_of_interest=class_of_interest_idx, ) # we will check that we plot the expected values as response grid = np.concatenate([disp.xx0.reshape(-1, 1), disp.xx1.reshape(-1, 1)], axis=1) response = getattr(estimator, response_method)(grid)[:, class_of_interest_idx] assert_allclose(response.reshape(disp.response.shape), disp.response) # make the same test but this time using target as strings y = iris.target_names[iris.target] estimator = LogisticRegression().fit(X, y) disp = DecisionBoundaryDisplay.from_estimator( estimator, X, response_method=response_method, class_of_interest=iris.target_names[class_of_interest_idx], ) grid = np.concatenate([disp.xx0.reshape(-1, 1), disp.xx1.reshape(-1, 1)], axis=1) response = getattr(estimator, response_method)(grid)[:, class_of_interest_idx] assert_allclose(response.reshape(disp.response.shape), disp.response) # check that we raise an error for unknown labels # this test should already be handled in `_get_response_values` but we can have this # test here as well err_msg = "class_of_interest=2 is not a valid label: It should be one of" with pytest.raises(ValueError, match=err_msg): DecisionBoundaryDisplay.from_estimator( estimator, X, response_method=response_method, class_of_interest=class_of_interest_idx, ) # TODO: remove this test when we handle multiclass with class_of_interest=None # by showing the max of the decision function or the max of the predicted # probabilities. err_msg = "Multiclass classifiers are only supported" with pytest.raises(ValueError, match=err_msg): DecisionBoundaryDisplay.from_estimator( estimator, X, response_method=response_method, class_of_interest=None, ) def test_subclass_named_constructors_return_type_is_subclass(pyplot): """Check that named constructors return the correct type when subclassed. Non-regression test for: https://github.com/scikit-learn/scikit-learn/pull/27675 """ clf = LogisticRegression().fit(X, y) class SubclassOfDisplay(DecisionBoundaryDisplay): pass curve = SubclassOfDisplay.from_estimator(estimator=clf, X=X) assert isinstance(curve, SubclassOfDisplay)

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