test_pipeline.py 文件源码

def test_fit_predict_on_pipeline():
    # test that the fit_predict method is implemented on a pipeline
    # test that the fit_predict on pipeline yields same results as applying
    # transform and clustering steps separately
    iris = load_iris()
    scaler = StandardScaler()
    km = KMeans(random_state=0)

    # first compute the transform and clustering step separately
    scaled = scaler.fit_transform(iris.data)
    separate_pred = km.fit_predict(scaled)

    # use a pipeline to do the transform and clustering in one step
    pipe = Pipeline([('scaler', scaler), ('Kmeans', km)])
    pipeline_pred = pipe.fit_predict(iris.data)

    assert_array_almost_equal(pipeline_pred, separate_pred)