test_feature_selection.py 文件源码

def test_multiprocessing():
    generator = check_random_state(0)
    data = genData(n_samples=200, n_features=4, n_redundant=2,strRel=2,
                    n_repeated=0, class_sep=1, flip_y=0, random_state=generator)

    X_orig, y = data
    X_orig = StandardScaler().fit(X_orig).transform(X_orig)

    X = np.c_[X_orig, generator.normal(size=(len(X_orig), 6))]
    y = list(y)   # regression test: list should be supported

    # Test using the score function
    fri = EnsembleFRI(FRIClassification(random_state=generator),n_bootstraps=5,n_jobs=2, random_state=generator)
    fri.fit(X, y)
    # non-regression test for missing worst feature:
    assert len(fri.allrel_prediction_) == X.shape[1]
    assert len(fri.interval_) == X.shape[1]

    # All strongly relevant features have a lower bound > 0
    assert np.all(fri.interval_[0:2,0]>0)
    # All weakly relevant features should have a lower bound 0
    assert np.any(fri.interval_[2:4,0]>0) == False