@pytest.fixture
def dummy_feats_and_labels():
    feats = np.array([[0.7057, -5.4981, 8.3368, -2.8715],
                      [2.4391, 6.4417, -0.80743, -0.69139],
                      [-0.2062, 9.2207, -3.7044, -6.8103],
                      [4.2586, 11.2962, -4.0943, -4.3457],
                      [-2.343, 12.9516, 3.3285, -5.9426],
                      [-2.0545, -10.8679, 9.4926, -1.4116],
                      [2.2279, 4.0951, -4.8037, -2.1112],
                      [-6.1632, 8.7096, -0.21621, -3.6345],
                      [0.52374, 3.644, -4.0746, -1.9909],
                      [1.5077, 1.9596, -3.0584, -0.12243]
                      ])
    labels = np.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
    return feats, labels

def test_dt_overfit(dummy_feats_and_labels):
    feats, labels = dummy_feats_and_labels
    dt = DecisionTree()
    dt.fit(feats, labels)
    pred = np.round(dt.predict(feats))

    assert np.array_equal(labels, pred), 'DecisionTree should fit data perfectly and prediction should == labels.'