test_model_selection_sklearn.py 文件源码

def test_gridsearch_no_predict():
    # test grid-search with an estimator without predict.
    # slight duplication of a test from KDE
    def custom_scoring(estimator, X):
        return 42 if estimator.bandwidth == .1 else 0
    X, _ = make_blobs(cluster_std=.1, random_state=1,
                      centers=[[0, 1], [1, 0], [0, 0]])
    search = dcv.GridSearchCV(KernelDensity(),
                              param_grid=dict(bandwidth=[.01, .1, 1]),
                              scoring=custom_scoring)
    search.fit(X)
    assert search.best_params_['bandwidth'] == .1
    assert search.best_score_ == 42