test_optimisation.py 文件源码

def test_pipeline(get_models, get_transform, get_kernel):

    alg, model = get_models
    trans = get_transform()
    kernel = get_kernel() + WhiteKernel()

    pipe = Pipeline(steps=[(alg, model())])
    param_dict = {}
    if hasattr(model(), 'n_estimators'):
        param_dict[alg + '__n_estimators'] = [5]
    if hasattr(model(), 'kernel'):
        param_dict[alg + '__kernel'] = [kernel]
    param_dict[alg + '__target_transform'] = [trans]

    estimator = GridSearchCV(pipe,
                             param_dict,
                             n_jobs=1,
                             iid=False,
                             pre_dispatch=2,
                             verbose=True,
                             )
    np.random.seed(10)
    estimator.fit(X=1 + np.random.rand(10, 3), y=1. + np.random.rand(10))
    assert estimator.cv_results_['mean_train_score'][0] > -15.0