Source: scikit-learn Version: 1.1.2+dfsg-5 Severity: normal scikit-learn fails debci tests when running with scipy 1.8.
The failing test is test_mlp_regressor_dtypes_casting in test_mlp.py It's not a "large" failure, just that the tolerance rtol=0.0001 is not met by a small amount. rtol=0.0002 would pass. The error message is ______________________ test_mlp_regressor_dtypes_casting _______________________ def test_mlp_regressor_dtypes_casting(): mlp_64 = MLPRegressor( alpha=1e-5, hidden_layer_sizes=(5, 3), random_state=1, max_iter=50 ) mlp_64.fit(X_digits[:300], y_digits[:300]) pred_64 = mlp_64.predict(X_digits[300:]) mlp_32 = MLPRegressor( alpha=1e-5, hidden_layer_sizes=(5, 3), random_state=1, max_iter=50 ) mlp_32.fit(X_digits[:300].astype(np.float32), y_digits[:300]) pred_32 = mlp_32.predict(X_digits[300:].astype(np.float32)) > assert_allclose(pred_64, pred_32, rtol=1e-04) E AssertionError: E Not equal to tolerance rtol=0.0001, atol=0 E E Mismatched elements: 1 / 60 (1.67%) E Max absolute difference: 1.77346709e-06 E Max relative difference: 0.00013333 E x: array([-1.624248e-02, 2.327707e+00, 6.674963e-01, 4.904700e-01, E 6.739288e-01, 3.166697e+00, 4.548126e-01, 6.674963e-01, E -3.220949e-02, -6.899952e-01, 6.674963e-01, -6.329127e-01,... E y: array([-1.624250e-02, 2.327706e+00, 6.674960e-01, 4.904711e-01, E 6.739284e-01, 3.166698e+00, 4.548138e-01, 6.674960e-01, E -3.220773e-02, -6.899955e-01, 6.674960e-01, -6.329128e-01,... /usr/lib/python3/dist-packages/sklearn/neural_network/tests/test_mlp.py:872: AssertionError