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test_svi.py
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test_svi.py
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# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
from numpy.testing import assert_allclose
import pytest
from jax import jit, random, value_and_grad
import jax.numpy as jnp
from jax.test_util import check_close
import numpyro
from numpyro import optim
import numpyro.distributions as dist
from numpyro.distributions import constraints
from numpyro.distributions.transforms import AffineTransform, SigmoidTransform
from numpyro.handlers import substitute
from numpyro.infer import ELBO, SVI, RenyiELBO
from numpyro.util import fori_loop
@pytest.mark.parametrize('alpha', [0., 2.])
def test_renyi_elbo(alpha):
def model(x):
numpyro.sample("obs", dist.Normal(0, 1), obs=x)
def guide(x):
pass
def elbo_loss_fn(x):
return ELBO().loss(random.PRNGKey(0), {}, model, guide, x)
def renyi_loss_fn(x):
return RenyiELBO(alpha=alpha, num_particles=10).loss(random.PRNGKey(0), {}, model, guide, x)
elbo_loss, elbo_grad = value_and_grad(elbo_loss_fn)(2.)
renyi_loss, renyi_grad = value_and_grad(renyi_loss_fn)(2.)
assert_allclose(elbo_loss, renyi_loss, rtol=1e-6)
assert_allclose(elbo_grad, renyi_grad, rtol=1e-6)
@pytest.mark.parametrize('elbo', [
ELBO(),
RenyiELBO(num_particles=10),
])
def test_beta_bernoulli(elbo):
data = jnp.array([1.0] * 8 + [0.0] * 2)
def model(data):
f = numpyro.sample("beta", dist.Beta(1., 1.))
numpyro.sample("obs", dist.Bernoulli(f), obs=data)
def guide(data):
alpha_q = numpyro.param("alpha_q", 1.0,
constraint=constraints.positive)
beta_q = numpyro.param("beta_q", 1.0,
constraint=constraints.positive)
numpyro.sample("beta", dist.Beta(alpha_q, beta_q))
adam = optim.Adam(0.05)
svi = SVI(model, guide, adam, elbo)
svi_state = svi.init(random.PRNGKey(1), data)
assert_allclose(adam.get_params(svi_state.optim_state)['alpha_q'], 0.)
def body_fn(i, val):
svi_state, _ = svi.update(val, data)
return svi_state
svi_state = fori_loop(0, 1000, body_fn, svi_state)
params = svi.get_params(svi_state)
assert_allclose(params['alpha_q'] / (params['alpha_q'] + params['beta_q']), 0.8, atol=0.05, rtol=0.05)
def test_jitted_update_fn():
data = jnp.array([1.0] * 8 + [0.0] * 2)
def model(data):
f = numpyro.sample("beta", dist.Beta(1., 1.))
numpyro.sample("obs", dist.Bernoulli(f), obs=data)
def guide(data):
alpha_q = numpyro.param("alpha_q", 1.0,
constraint=constraints.positive)
beta_q = numpyro.param("beta_q", 1.0,
constraint=constraints.positive)
numpyro.sample("beta", dist.Beta(alpha_q, beta_q))
adam = optim.Adam(0.05)
svi = SVI(model, guide, adam, ELBO())
svi_state = svi.init(random.PRNGKey(1), data)
expected = svi.get_params(svi.update(svi_state, data)[0])
actual = svi.get_params(jit(svi.update)(svi_state, data=data)[0])
check_close(actual, expected, atol=1e-5)
def test_param():
# this test the validity of model/guide sites having
# param constraints contain composed transformed
rng_keys = random.split(random.PRNGKey(0), 5)
a_minval = 1
c_minval = -2
c_maxval = -1
a_init = jnp.exp(random.normal(rng_keys[0])) + a_minval
b_init = jnp.exp(random.normal(rng_keys[1]))
c_init = random.uniform(rng_keys[2], minval=c_minval, maxval=c_maxval)
d_init = random.uniform(rng_keys[3])
obs = random.normal(rng_keys[4])
def model():
a = numpyro.param('a', a_init, constraint=constraints.greater_than(a_minval))
b = numpyro.param('b', b_init, constraint=constraints.positive)
numpyro.sample('x', dist.Normal(a, b), obs=obs)
def guide():
c = numpyro.param('c', c_init, constraint=constraints.interval(c_minval, c_maxval))
d = numpyro.param('d', d_init, constraint=constraints.unit_interval)
numpyro.sample('y', dist.Normal(c, d), obs=obs)
adam = optim.Adam(0.01)
svi = SVI(model, guide, adam, ELBO())
svi_state = svi.init(random.PRNGKey(0))
params = svi.get_params(svi_state)
assert_allclose(params['a'], a_init)
assert_allclose(params['b'], b_init)
assert_allclose(params['c'], c_init)
assert_allclose(params['d'], d_init)
actual_loss = svi.evaluate(svi_state)
assert jnp.isfinite(actual_loss)
expected_loss = dist.Normal(c_init, d_init).log_prob(obs) - dist.Normal(a_init, b_init).log_prob(obs)
# not so precisely because we do transform / inverse transform stuffs
assert_allclose(actual_loss, expected_loss, rtol=1e-6)
def test_elbo_dynamic_support():
x_prior = dist.TransformedDistribution(
dist.Normal(), [AffineTransform(0, 2), SigmoidTransform(), AffineTransform(0, 3)])
x_guide = dist.Uniform(0, 3)
def model():
numpyro.sample('x', x_prior)
def guide():
numpyro.sample('x', x_guide)
adam = optim.Adam(0.01)
x = 2.
guide = substitute(guide, param_map={'x': x})
svi = SVI(model, guide, adam, ELBO())
svi_state = svi.init(random.PRNGKey(0))
actual_loss = svi.evaluate(svi_state)
assert jnp.isfinite(actual_loss)
expected_loss = x_guide.log_prob(x) - x_prior.log_prob(x)
assert_allclose(actual_loss, expected_loss)