def test_robust_max_multiclass_symmetric(num_classes, num_points, tol,
epsilon):
"""
This test is based on the observation that for
symmetric inputs the class predictions must have equal probability.
"""
rng = np.random.RandomState(1)
p = 1. / num_classes
F = tf.ones((num_points, num_classes), dtype=default_float())
Y = tf.convert_to_tensor(rng.randint(num_classes, size=(num_points, 1)),
dtype=default_float())
likelihood = MultiClass(num_classes)
likelihood.invlink.epsilon = tf.convert_to_tensor(epsilon,
dtype=default_float())
mu, _ = likelihood.predict_mean_and_var(F, F)
pred = likelihood.predict_density(F, F, Y)
variational_expectations = likelihood.variational_expectations(F, F, Y)
expected_mu = (p * (1. - epsilon) + (1. - p) * epsilon /
(num_classes - 1)) * np.ones((num_points, 1))
expected_log_density = np.log(expected_mu)
# assert_allclose() would complain about shape mismatch
assert (np.allclose(mu, expected_mu, tol, tol))
assert (np.allclose(pred, expected_log_density, 1e-3, 1e-3))
validation_variational_expectation = (p * np.log(1. - epsilon) +
(1. - p) * np.log(epsilon /
(num_classes - 1)))
assert_allclose(
variational_expectations,
np.ones((num_points, 1)) * validation_variational_expectation, tol,
tol)
def test_robust_max_multiclass_predict_density(num_classes, num_points,
mock_prob, expected_prediction,
tol, epsilon):
class MockRobustMax(gpflow.likelihoods.RobustMax):
def prob_is_largest(self, Y, Fmu, Fvar, gh_x, gh_w):
return tf.ones((num_points, 1), dtype=default_float()) * mock_prob
likelihood = MultiClass(num_classes,
invlink=MockRobustMax(num_classes, epsilon))
F = tf.ones((num_points, num_classes))
rng = np.random.RandomState(1)
Y = tf.cast(rng.randint(num_classes, size=(num_points, 1)),
dtype=default_int())
prediction = likelihood.predict_density(F, F, Y)
assert_allclose(prediction, expected_prediction, tol, tol)