test_classification.py 文件源码

def test_brier_score_loss():
    # Check brier_score_loss function
    y_true = np.array([0, 1, 1, 0, 1, 1])
    y_pred = np.array([0.1, 0.8, 0.9, 0.3, 1., 0.95])
    true_score = linalg.norm(y_true - y_pred) ** 2 / len(y_true)

    assert_almost_equal(brier_score_loss(y_true, y_true), 0.0)
    assert_almost_equal(brier_score_loss(y_true, y_pred), true_score)
    assert_almost_equal(brier_score_loss(1. + y_true, y_pred),
                        true_score)
    assert_almost_equal(brier_score_loss(2 * y_true - 1, y_pred),
                        true_score)
    assert_raises(ValueError, brier_score_loss, y_true, y_pred[1:])
    assert_raises(ValueError, brier_score_loss, y_true, y_pred + 1.)
    assert_raises(ValueError, brier_score_loss, y_true, y_pred - 1.)