test_gradient_boosting.py 文件源码

def test_feature_importances():
    X = np.array(boston.data, dtype=np.float32)
    y = np.array(boston.target, dtype=np.float32)

    for presort in True, False:
        clf = GradientBoostingRegressor(n_estimators=100, max_depth=5,
                                        min_samples_split=2, random_state=1,
                                        presort=presort)
        clf.fit(X, y)
        assert_true(hasattr(clf, 'feature_importances_'))

        # XXX: Remove this test in 0.19 after transform support to estimators
        # is removed.
        X_new = assert_warns(
            DeprecationWarning, clf.transform, X, threshold="mean")
        assert_less(X_new.shape[1], X.shape[1])
        feature_mask = (
            clf.feature_importances_ > clf.feature_importances_.mean())
        assert_array_almost_equal(X_new, X[:, feature_mask])