def test_unpack_transform():
x = np.ones(3)
unpack_fn = lambda x: {'key': x} # noqa: E731
transform = transforms.UnpackTransform(unpack_fn)
y = transform(x)
z = transform.inv(y)
assert_allclose(y['key'], x)
assert_allclose(z, x)
def test_iaf():
# test for substitute logic for exposed methods `sample_posterior` and `get_transforms`
N, dim = 3000, 3
data = random.normal(random.PRNGKey(0), (N, dim))
true_coefs = np.arange(1., dim + 1.)
logits = np.sum(true_coefs * data, axis=-1)
labels = dist.Bernoulli(logits=logits).sample(random.PRNGKey(1))
def model(data, labels):
coefs = numpyro.sample('coefs', dist.Normal(np.zeros(dim),
np.ones(dim)))
offset = numpyro.sample('offset', dist.Uniform(-1, 1))
logits = offset + np.sum(coefs * data, axis=-1)
return numpyro.sample('obs', dist.Bernoulli(logits=logits), obs=labels)
adam = optim.Adam(0.01)
rng_key_init = random.PRNGKey(1)
guide = AutoIAFNormal(model)
svi = SVI(model, guide, adam, AutoContinuousELBO())
svi_state = svi.init(rng_key_init, data, labels)
params = svi.get_params(svi_state)
x = random.normal(random.PRNGKey(0), (dim + 1, ))
rng_key = random.PRNGKey(1)
actual_sample = guide.sample_posterior(rng_key, params)
actual_output = guide.get_transform(params)(x)
flows = []
for i in range(guide.num_flows):
if i > 0:
flows.append(transforms.PermuteTransform(np.arange(dim + 1)[::-1]))
arn_init, arn_apply = AutoregressiveNN(
dim + 1, [dim + 1, dim + 1],
permutation=np.arange(dim + 1),
skip_connections=guide._skip_connections,
nonlinearity=guide._nonlinearity)
arn = partial(arn_apply, params['auto_arn__{}$params'.format(i)])
flows.append(InverseAutoregressiveTransform(arn))
flows.append(transforms.UnpackTransform(guide._unpack_latent))
transform = transforms.ComposeTransform(flows)
_, rng_key_sample = random.split(rng_key)
expected_sample = transform(
dist.Normal(np.zeros(dim + 1), 1).sample(rng_key_sample))
expected_output = transform(x)
assert_allclose(actual_sample['coefs'], expected_sample['coefs'])
assert_allclose(
actual_sample['offset'],
transforms.biject_to(constraints.interval(-1, 1))(
expected_sample['offset']))
check_eq(actual_output, expected_output)
