def test_posterior_predictive():
true_probs = torch.ones(5) * 0.7
num_trials = torch.ones(5) * 1000
num_success = dist.Binomial(num_trials, true_probs).sample()
conditioned_model = poutine.condition(model, data={"obs": num_success})
nuts_kernel = NUTS(conditioned_model, adapt_step_size=True)
mcmc_run = MCMC(nuts_kernel, num_samples=1000,
warmup_steps=200).run(num_trials)
posterior_predictive = TracePredictive(model, mcmc_run,
num_samples=10000).run(num_trials)
marginal_return_vals = posterior_predictive.marginal().empirical["_RETURN"]
assert_equal(marginal_return_vals.mean, torch.ones(5) * 700, prec=30)
def test_posterior_predictive_svi_auto_diag_normal_guide():
true_probs = torch.ones(5) * 0.7
num_trials = torch.ones(5) * 1000
num_success = dist.Binomial(num_trials, true_probs).sample()
conditioned_model = poutine.condition(model, data={"obs": num_success})
opt = optim.Adam(dict(lr=0.1))
loss = Trace_ELBO()
guide = AutoDiagonalNormal(conditioned_model)
svi_run = SVI(conditioned_model,
guide,
opt,
loss,
num_steps=1000,
num_samples=100).run(num_trials)
posterior_predictive = TracePredictive(model, svi_run,
num_samples=10000).run(num_trials)
marginal_return_vals = posterior_predictive.marginal().empirical["_RETURN"]
assert_close(marginal_return_vals.mean, torch.ones(5) * 700, rtol=0.05)
def test_posterior_predictive_svi_one_hot():
pseudocounts = torch.ones(3) * 0.1
true_probs = torch.tensor([0.15, 0.6, 0.25])
classes = dist.OneHotCategorical(true_probs).sample((10000, ))
opt = optim.Adam(dict(lr=0.1))
loss = Trace_ELBO()
guide = AutoDelta(one_hot_model)
svi_run = SVI(one_hot_model,
guide,
opt,
loss,
num_steps=1000,
num_samples=1000).run(pseudocounts, classes=classes)
posterior_predictive = TracePredictive(one_hot_model,
svi_run,
num_samples=10000).run(pseudocounts)
marginal_return_vals = posterior_predictive.marginal().empirical["_RETURN"]
assert_close(marginal_return_vals.mean, true_probs.unsqueeze(0), rtol=0.1)