def test_bayesian_score():
# sinusoidal data with heteroscedastic noise
x_train = np.linspace(-3, 3, 300, dtype=np.float32).reshape((300, 1))
noise = tfp.distributions.MultivariateNormalDiag(loc=5 *
tf.math.sin(2 * x_train),
scale_diag=abs(x_train))
y_train = noise.sample().numpy()
mle = NormalizingFlowNetwork(1,
n_flows=0,
hidden_sizes=(6, 6),
trainable_base_dist=True)
mle.fit(x_train, y_train, epochs=20, verbose=0)
mle.map_mode = False
# deterministic, so should be the same
# mle furthermore has no regularisation loss / KL-divs added, therefore evaluate and nll are the same
assert bayesian_log_likelihood_score(
DummyWrapper(mle), x_train,
y_train) == pytest.approx(-mle.evaluate(x_train, y_train))
be = BayesNormalizingFlowNetwork(
n_dims=1,
kl_weight_scale=1.0 / x_train.shape[0],
n_flows=0,
hidden_sizes=(6, 6),
trainable_base_dist=True,
)
be.fit(x_train, y_train, epochs=200, verbose=0)
score = bayesian_log_likelihood_score(DummyWrapper(be), x_train, y_train)
loss = sum([be.evaluate(x_train, y_train) for _ in range(50)]) / 50
# as the loss has the KL div to the prior added to it, it's negative has to be smaller than the nll score
assert score > -loss
def test_mle_score():
x_train = np.linspace(-1, 1, 10).reshape((10, 1))
y_train = np.linspace(-1, 1, 10).reshape((10, 1))
mle = NormalizingFlowNetwork(1, n_flows=0, hidden_sizes=(6, 6), trainable_base_dist=True)
mle.fit(x_train, y_train, epochs=10, verbose=0)
# deterministic, so should be the same
assert mle_log_likelihood_score(DummyWrapper(mle), x_train, y_train) == pytest.approx(
-mle.evaluate(x_train, y_train)
)