def test_chain(use_init_params, chain_method):
N, dim = 3000, 3
num_chains = 2
num_warmup, num_samples = 5000, 5000
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(labels):
coefs = numpyro.sample('coefs', dist.Normal(np.zeros(dim),
np.ones(dim)))
logits = np.sum(coefs * data, axis=-1)
return numpyro.sample('obs', dist.Bernoulli(logits=logits), obs=labels)
kernel = NUTS(model=model)
mcmc = MCMC(kernel, num_warmup, num_samples, num_chains=num_chains)
mcmc.chain_method = chain_method
init_params = None if not use_init_params else \
{'coefs': np.tile(np.ones(dim), num_chains).reshape(num_chains, dim)}
mcmc.run(random.PRNGKey(2), labels, init_params=init_params)
samples_flat = mcmc.get_samples()
assert samples_flat['coefs'].shape[0] == num_chains * num_samples
samples = mcmc.get_samples(group_by_chain=True)
assert samples['coefs'].shape[:2] == (num_chains, num_samples)
assert_allclose(np.mean(samples_flat['coefs'], 0), true_coefs, atol=0.21)
def test_chain(use_init_params, chain_method):
N, dim = 3000, 3
num_chains = 2
num_warmup, num_samples = 5000, 5000
data = random.normal(random.PRNGKey(0), (N, dim))
true_coefs = jnp.arange(1.0, dim + 1.0)
logits = jnp.sum(true_coefs * data, axis=-1)
labels = dist.Bernoulli(logits=logits).sample(random.PRNGKey(1))
def model(labels):
coefs = numpyro.sample("coefs",
dist.Normal(jnp.zeros(dim), jnp.ones(dim)))
logits = jnp.sum(coefs * data, axis=-1)
numpyro.deterministic("logits", logits)
return numpyro.sample("obs", dist.Bernoulli(logits=logits), obs=labels)
kernel = NUTS(model=model)
mcmc = MCMC(kernel,
num_warmup=num_warmup,
num_samples=num_samples,
num_chains=num_chains)
mcmc.chain_method = chain_method
init_params = (None if not use_init_params else {
"coefs":
jnp.tile(jnp.ones(dim), num_chains).reshape(num_chains, dim)
})
mcmc.run(random.PRNGKey(2), labels, init_params=init_params)
samples_flat = mcmc.get_samples()
assert samples_flat["coefs"].shape[0] == num_chains * num_samples
samples = mcmc.get_samples(group_by_chain=True)
assert samples["coefs"].shape[:2] == (num_chains, num_samples)
assert_allclose(jnp.mean(samples_flat["coefs"], 0), true_coefs, atol=0.21)
# test if reshape works
device_get(samples_flat["coefs"].reshape(-1))
