def main(args):
pyro.enable_validation(__debug__)
if args.cuda:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
# Generate synthetic data.
pyro.set_rng_seed(args.seed)
x_train, t_train, y_train, _ = generate_data(args)
# Train.
pyro.set_rng_seed(args.seed)
pyro.clear_param_store()
cevae = CEVAE(feature_dim=args.feature_dim,
latent_dim=args.latent_dim,
hidden_dim=args.hidden_dim,
num_layers=args.num_layers,
num_samples=10)
cevae.fit(x_train, t_train, y_train,
num_epochs=args.num_epochs,
batch_size=args.batch_size,
learning_rate=args.learning_rate,
learning_rate_decay=args.learning_rate_decay,
weight_decay=args.weight_decay)
# Evaluate.
x_test, t_test, y_test, true_ite = generate_data(args)
true_ate = true_ite.mean()
print("true ATE = {:0.3g}".format(true_ate.item()))
naive_ate = y_test[t_test == 1].mean() - y_test[t_test == 0].mean()
print("naive ATE = {:0.3g}".format(naive_ate))
if args.jit:
cevae = cevae.to_script_module()
est_ite = cevae.ite(x_test)
est_ate = est_ite.mean()
print("estimated ATE = {:0.3g}".format(est_ate.item()))
def test_serialization(jit, feature_dim, outcome_dist):
x, t, y = generate_data(num_data=32, feature_dim=feature_dim)
if outcome_dist == "exponential":
y.clamp_(min=1e-20)
cevae = CEVAE(feature_dim, outcome_dist=outcome_dist, num_samples=1000, hidden_dim=32)
cevae.fit(x, t, y, num_epochs=4, batch_size=8)
pyro.set_rng_seed(0)
expected_ite = cevae.ite(x)
if jit:
traced_cevae = cevae.to_script_module()
f = io.BytesIO()
torch.jit.save(traced_cevae, f)
f.seek(0)
loaded_cevae = torch.jit.load(f)
else:
f = io.BytesIO()
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
torch.save(cevae, f)
f.seek(0)
loaded_cevae = torch.load(f)
pyro.set_rng_seed(0)
actual_ite = loaded_cevae.ite(x)
assert_close(actual_ite, expected_ite, atol=0.1)