4744e062ffe801fe2c7c1faa2bda385f293d5b37,sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py,,test_binomial_deviance,#,22

Before Change

    test_data = [(np.array([1.0, 1.0, 1.0]), np.array([100.0, 100.0, 100.0])),
                 (np.array([0.0, 0.0, 0.0]), np.array([100.0, 100.0, 100.0])),
                 (np.array([0.0, 0.0, 0.0]),
                  np.array([-100.0, -100.0, -100.0])),
                 (np.array([1.0, 1.0, 1.0]),
                  np.array([-100.0, -100.0, -100.0]))]

After Change

        z = 2 * y - 1
        return 2 * np.mean(np.log(1 + np.exp(-z * raw_pred)))

    test_data = product(
        (np.array([0., 0, 0]), np.array([1., 1, 1])),
        (np.array([-5., -5, -5]), np.array([3., 3, 3])))

    for datum in test_data:
        assert bd(*datum) == approx(alt_dev(*datum))

Instances