test_optimisation.py 文件源码

def test_krige_pipeline(get_krige_method, get_variogram_model):
    pipe = Pipeline(steps=[('krige', Krige(method=get_krige_method))])
    param_dict = {'krige__variogram_model': [get_variogram_model]}

    estimator = GridSearchCV(pipe,
                             param_dict,
                             n_jobs=1,
                             iid=False,
                             pre_dispatch=2,
                             verbose=True
                            )
    np.random.seed(1)
    X = np.random.randint(0, 400, size=(20, 2)).astype(float)
    y = 5*np.random.rand(20)
    estimator.fit(X=X, y=y)
    assert estimator.cv_results_['mean_train_score'][0] > -1.0