test_model_selection.py 文件源码

def test_pipeline_fit_failure():
    X, y = make_classification(n_samples=100, n_features=10, random_state=0)

    pipe = Pipeline([('bad', FailingClassifier()),
                     ('good1', MockClassifier()),
                     ('good2', MockClassifier())])

    grid = {'bad__parameter': [0, 1, 2]}
    gs = dcv.GridSearchCV(pipe, grid, refit=False)

    # Check that failure raises if error_score is `'raise'`
    with pytest.raises(ValueError):
        gs.fit(X, y)

    # Check that grid scores were set to error_score on failure
    gs.error_score = float('nan')
    with pytest.warns(FitFailedWarning):
        gs.fit(X, y)

    check_scores_all_nan(gs, 'bad__parameter')