def test_transformed_potential_energy():
beta_dist = dist.Beta(np.ones(5), np.ones(5))
transform = transforms.AffineTransform(3, 4)
inv_transform = transforms.AffineTransform(-0.75, 0.25)
z = random.normal(random.PRNGKey(0), (5,))
pe_expected = -dist.TransformedDistribution(beta_dist, transform).log_prob(z)
potential_fn = lambda x: -beta_dist.log_prob(x) # noqa: E731
pe_actual = transformed_potential_energy(potential_fn, inv_transform, z)
assert_allclose(pe_actual, pe_expected)
def actual_model(data):
alpha = numpyro.sample("alpha", dist.Uniform(0, 1))
with numpyro.handlers.reparam(config={"loc": TransformReparam()}):
loc = numpyro.sample(
"loc",
dist.TransformedDistribution(
dist.Uniform(0, 1), transforms.AffineTransform(0, alpha)),
)
numpyro.sample("obs", dist.Normal(loc, 0.1), obs=data)
def test_transformed_transformed_distribution():
loc, scale = -2, 3
dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.PowerTransform(2.))
dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(-2, 3))
assert isinstance(dist2.base_dist, dist.Normal)
assert len(dist2.transforms) == 2
assert isinstance(dist2.transforms[0], transforms.PowerTransform)
assert isinstance(dist2.transforms[1], transforms.AffineTransform)
rng_key = random.PRNGKey(0)
assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))
intermediates = dist2.sample_with_intermediates(rng_key)
assert len(intermediates) == 2
if len(event_shape) == 1:
expected = onp.linalg.slogdet(jax.jacobian(transform)(x))[1]
inv_expected = onp.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]
else:
expected = np.log(np.abs(grad(transform)(x)))
inv_expected = np.log(np.abs(grad(transform.inv)(y)))
assert_allclose(actual, expected, atol=1e-6)
assert_allclose(actual, -inv_expected, atol=1e-6)
@pytest.mark.parametrize('transformed_dist', [
dist.TransformedDistribution(dist.Normal(np.array([2., 3.]), 1.), transforms.ExpTransform()),
dist.TransformedDistribution(dist.Exponential(np.ones(2)), [
transforms.PowerTransform(0.7),
transforms.AffineTransform(0., np.ones(2) * 3)
]),
])
def test_transformed_distribution_intermediates(transformed_dist):
sample, intermediates = transformed_dist.sample_with_intermediates(random.PRNGKey(1))
assert_allclose(transformed_dist.log_prob(sample, intermediates), transformed_dist.log_prob(sample))
def test_transformed_transformed_distribution():
loc, scale = -2, 3
dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.PowerTransform(2.))
dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(-2, 3))
assert isinstance(dist2.base_dist, dist.Normal)
assert len(dist2.transforms) == 2
assert isinstance(dist2.transforms[0], transforms.PowerTransform)
assert isinstance(dist2.transforms[1], transforms.AffineTransform)
inv_expected = onp.linalg.slogdet(jax.jacobian(
transform.inv)(y))[1]
else:
expected = np.log(np.abs(grad(transform)(x)))
inv_expected = np.log(np.abs(grad(transform.inv)(y)))
assert_allclose(actual, expected, atol=1e-6)
assert_allclose(actual, -inv_expected, atol=1e-6)
@pytest.mark.parametrize('transformed_dist', [
dist.TransformedDistribution(dist.Normal(np.array([2., 3.]), 1.),
transforms.ExpTransform()),
dist.TransformedDistribution(dist.Exponential(np.ones(2)), [
transforms.PowerTransform(0.7),
transforms.AffineTransform(0.,
np.ones(2) * 3)
]),
])
def test_transformed_distribution_intermediates(transformed_dist):
sample, intermediates = transformed_dist.sample_with_intermediates(
random.PRNGKey(1))
assert_allclose(transformed_dist.log_prob(sample, intermediates),
transformed_dist.log_prob(sample))
def test_transformed_transformed_distribution():
loc, scale = -2, 3
dist1 = dist.TransformedDistribution(dist.Normal(2, 3),
transforms.PowerTransform(2.))
dist2 = dist.TransformedDistribution(dist1,
transforms.AffineTransform(-2, 3))
def actual_model(data):
alpha = numpyro.sample('alpha', dist.Uniform(0, 1))
with numpyro.handlers.reparam(config={'loc': TransformReparam()}):
loc = numpyro.sample('loc', dist.TransformedDistribution(
dist.Uniform(0, 1), transforms.AffineTransform(0, alpha)))
numpyro.sample('obs', dist.Normal(loc, 0.1), obs=data)
