def test_binomial_deviance():
    # Check binomial deviance loss.
    # Check against alternative definitions in ESLII.
    bd = BinomialDeviance(2)

    # pred has the same BD for y in {0, 1}
    assert_equal(bd(np.array([0.0]), np.array([0.0])),
                 bd(np.array([1.0]), np.array([0.0])))

    assert_almost_equal(bd(np.array([1.0, 1.0, 1.0]),
                           np.array([100.0, 100.0, 100.0])),
                        0.0)
    assert_almost_equal(bd(np.array([1.0, 0.0, 0.0]),
                           np.array([100.0, -100.0, -100.0])), 0)

    # check if same results as alternative definition of deviance (from ESLII)
    alt_dev = lambda y, pred: np.mean(np.logaddexp(0.0, -2.0 *
                                                   (2.0 * y - 1) * pred))
    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]))]

    for datum in test_data:
        assert_almost_equal(bd(*datum), alt_dev(*datum))

    # check the gradient against the
    alt_ng = lambda y, pred: (1 + np.exp(2 * (2 * y - 1) * pred)) / (2 * y - 1)
    for datum in test_data:
        assert_almost_equal(bd.negative_gradient(*datum), alt_ng(*datum))
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py/test_binomial_deviance/swapped operands of `/`