

def check_multioutput(name):
    X_train = [[(- 2), (- 1)], [(- 1), (- 1)], [(- 1), (- 2)], [1, 1], [1, 2], [2, 1], [(- 2), 1], [(- 1), 1], [(- 1), 2], [2, (- 1)], [1, (- 1)], [1, (- 2)]]
    y_train = [[(- 1), 0], [(- 1), 0], [(- 1), 0], [1, 1], [1, 1], [1, 1], [(- 1), 2], [(- 1), 2], [(- 1), 2], [1, 3], [1, 3], [1, 3]]
    X_test = [[(- 1), (- 1)], [1, 1], [(- 1), 1], [1, (- 1)]]
    y_test = [[(- 1), 0], [1, 1], [(- 1), 2], [1, 3]]
    est = FOREST_ESTIMATORS[name](random_state=0, bootstrap=False)
    y_pred = est.fit(X_train, y_train).predict(X_test)
    assert_array_almost_equal(y_pred, y_test)
    if (name in FOREST_CLASSIFIERS):
        with np.errstate(divide='ignore'):
            proba = est.predict_proba(X_test)
            assert_equal(len(proba), 2)
            assert_equal(proba[0].shape, (4, 2))
            assert_equal(proba[1].shape, (4, 4))
            log_proba = est.predict_log_proba(X_test)
            assert_equal(len(log_proba), 2)
            assert_equal(log_proba[0].shape, (4, 2))
            assert_equal(log_proba[1].shape, (4, 4))

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