def test_robust_max_multiclass_predict_log_density(
num_classes, num_points, mock_prob, expected_prediction, tol, epsilon
):
class MockRobustMax(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 = to_default_int(rng.randint(num_classes, size=(num_points, 1)))
prediction = likelihood.predict_log_density(F, F, Y)
assert_allclose(prediction, expected_prediction, tol, tol)
def test_softmax_bernoulli_equivalence(num, dimF, dimY):
dF = np.vstack(
(np.random.randn(num - 3,
dimF), np.array([[-3.0, 0.0], [3, 0.0], [0.0, 0.0]])))
dY = np.vstack((np.random.randn(num - 3, dimY), np.ones((3, dimY)))) > 0
F = to_default_float(dF)
Fvar = tf.exp(
tf.stack([F[:, 1], -10.0 + tf.zeros(F.shape[0], dtype=F.dtype)],
axis=1))
F = tf.stack([F[:, 0], tf.zeros(F.shape[0], dtype=F.dtype)], axis=1)
Y = to_default_int(dY)
Ylabel = 1 - Y
softmax_likelihood = Softmax(dimF)
bernoulli_likelihood = Bernoulli(invlink=tf.sigmoid)
softmax_likelihood.num_monte_carlo_points = int(
0.3e7) # Minimum number of points to pass the test on CircleCI
bernoulli_likelihood.num_gauss_hermite_points = 40
assert_allclose(
softmax_likelihood.conditional_mean(F)[:, :1],
bernoulli_likelihood.conditional_mean(F[:, :1]),
)
assert_allclose(
softmax_likelihood.conditional_variance(F)[:, :1],
bernoulli_likelihood.conditional_variance(F[:, :1]),
)
assert_allclose(
softmax_likelihood.log_prob(F, Ylabel),
bernoulli_likelihood.log_prob(F[:, :1], Y.numpy()),
)
mean1, var1 = softmax_likelihood.predict_mean_and_var(F, Fvar)
mean2, var2 = bernoulli_likelihood.predict_mean_and_var(
F[:, :1], Fvar[:, :1])
assert_allclose(mean1[:, 0, None], mean2, rtol=2e-3)
assert_allclose(var1[:, 0, None], var2, rtol=2e-3)
ls_ve = softmax_likelihood.variational_expectations(F, Fvar, Ylabel)
lb_ve = bernoulli_likelihood.variational_expectations(
F[:, :1], Fvar[:, :1], Y.numpy())
assert_allclose(ls_ve, lb_ve, rtol=5e-3)