def check_importances(name, criterion, X, y):
    ForestEstimator = FOREST_ESTIMATORS[name]

    est = ForestEstimator(n_estimators=20, criterion=criterion,
                          random_state=0)
    est.fit(X, y)
    importances = est.feature_importances_
    n_important = np.sum(importances > 0.1)
    assert_equal(importances.shape[0], 10)
    assert_equal(n_important, 3)

    # XXX: Remove this test in 0.19 after transform support to estimators
    # is removed.
    X_new = assert_warns(
        DeprecationWarning, est.transform, X, threshold="mean")
    assert_less(0 < X_new.shape[1], X.shape[1])

    # Check with parallel
    importances = est.feature_importances_
    est.set_params(n_jobs=2)
    importances_parrallel = est.feature_importances_
    assert_array_almost_equal(importances, importances_parrallel)

    # Check with sample weights
    sample_weight = check_random_state(0).randint(1, 10, len(X))
    est = ForestEstimator(n_estimators=20, random_state=0, criterion=criterion)
    est.fit(X, y, sample_weight=sample_weight)
    importances = est.feature_importances_
    assert_true(np.all(importances >= 0.0))

    for scale in [0.5, 10, 100]:
        est = ForestEstimator(n_estimators=20, random_state=0, criterion=criterion)
        est.fit(X, y, sample_weight=scale * sample_weight)
        importances_bis = est.feature_importances_
        assert_less(np.abs(importances / importances_bis).mean(), 0.001)
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/ensemble/tests/test_forest.py/check_importances/operator `-` -> `/`