test_model_assessment.py 文件源码

def test_model_assessment():
    X, y = make_classification(n_samples=40, n_features=100, n_informative=2,
                               n_classes=2, n_redundant=0)
    pipe = Pipeline([('enet', ElasticNetFeatureSelection()),
                     ('ridge', RidgeClassifier())])

    ma = ModelAssessment(GridSearchCV(pipe, {'enet__l1_ratio': [2]})).fit(X, y)
    assert len(ma.cv_results_) == 0