

def check_class_weight_balanced_and_bootstrap_multi_output(name):
    ForestClassifier = FOREST_CLASSIFIERS[name]
    _y = np.vstack((y, (np.array(y) * 2))).T
    clf = ForestClassifier(class_weight='balanced', random_state=0)
    clf.fit(X, _y)
    clf = ForestClassifier(class_weight=[{
        (- 1): 0.5,
        1: 1.0,
    }, {
        (- 2): 1.0,
        2: 1.0,
    }], random_state=0)
    clf.fit(X, _y)
    clf = ForestClassifier(class_weight='balanced_subsample', random_state=0)
    clf.fit(X, _y)
    clf = ForestClassifier(class_weight='subsample', random_state=0)
    ignore_warnings(clf.fit)(X, _y)

dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/ensemble/tests/test_forest.py check_class_weight_balanced_and_bootstrap_multi_output/original