def test_logistic_regression(auto_class, Elbo):
N, dim = 3000, 3
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(data, labels):
coefs = numpyro.sample("coefs",
dist.Normal(0, 1).expand([dim]).to_event())
logits = numpyro.deterministic("logits", jnp.sum(coefs * data,
axis=-1))
with numpyro.plate("N", len(data)):
return numpyro.sample("obs",
dist.Bernoulli(logits=logits),
obs=labels)
adam = optim.Adam(0.01)
rng_key_init = random.PRNGKey(1)
guide = auto_class(model, init_loc_fn=init_strategy)
svi = SVI(model, guide, adam, Elbo())
svi_state = svi.init(rng_key_init, data, labels)
# smoke test if analytic KL is used
if auto_class is AutoNormal and Elbo is TraceMeanField_ELBO:
_, mean_field_loss = svi.update(svi_state, data, labels)
svi.loss = Trace_ELBO()
_, elbo_loss = svi.update(svi_state, data, labels)
svi.loss = TraceMeanField_ELBO()
assert abs(mean_field_loss - elbo_loss) > 0.5
def body_fn(i, val):
svi_state, loss = svi.update(val, data, labels)
return svi_state
svi_state = fori_loop(0, 2000, body_fn, svi_state)
params = svi.get_params(svi_state)
if auto_class not in (AutoDAIS, AutoIAFNormal, AutoBNAFNormal):
median = guide.median(params)
assert_allclose(median["coefs"], true_coefs, rtol=0.1)
# test .quantile method
if auto_class is not AutoDelta:
median = guide.quantiles(params, [0.2, 0.5])
assert_allclose(median["coefs"][1], true_coefs, rtol=0.1)
# test .sample_posterior method
posterior_samples = guide.sample_posterior(random.PRNGKey(1),
params,
sample_shape=(1000, ))
expected_coefs = jnp.array([0.97, 2.05, 3.18])
assert_allclose(jnp.mean(posterior_samples["coefs"], 0),
expected_coefs,
rtol=0.1)
def test_logistic_regression(auto_class, Elbo):
N, dim = 3000, 3
data = random.normal(random.PRNGKey(0), (N, dim))
true_coefs = jnp.arange(1., dim + 1.)
logits = jnp.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(jnp.zeros(dim), jnp.ones(dim)))
logits = jnp.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 = auto_class(model, init_loc_fn=init_strategy)
svi = SVI(model, guide, adam, Elbo())
svi_state = svi.init(rng_key_init, data, labels)
# smoke test if analytic KL is used
if auto_class is AutoNormal and Elbo is TraceMeanField_ELBO:
_, mean_field_loss = svi.update(svi_state, data, labels)
svi.loss = Trace_ELBO()
_, elbo_loss = svi.update(svi_state, data, labels)
svi.loss = TraceMeanField_ELBO()
assert abs(mean_field_loss - elbo_loss) > 0.5
def body_fn(i, val):
svi_state, loss = svi.update(val, data, labels)
return svi_state
svi_state = fori_loop(0, 2000, body_fn, svi_state)
params = svi.get_params(svi_state)
if auto_class not in (AutoIAFNormal, AutoBNAFNormal):
median = guide.median(params)
assert_allclose(median['coefs'], true_coefs, rtol=0.1)
# test .quantile method
median = guide.quantiles(params, [0.2, 0.5])
assert_allclose(median['coefs'][1], true_coefs, rtol=0.1)
# test .sample_posterior method
posterior_samples = guide.sample_posterior(random.PRNGKey(1),
params,
sample_shape=(1000, ))
assert_allclose(jnp.mean(posterior_samples['coefs'], 0),
true_coefs,
rtol=0.1)
