def test_marginal_likelihood_linear_model(linear_model, one_point_design):
pyro.set_rng_seed(42)
pyro.clear_param_store()
# Pre-train (large learning rate)
marginal_likelihood_eig(linear_model,
one_point_design,
"y",
"w",
num_samples=10,
num_steps=250,
marginal_guide=marginal_guide,
cond_guide=likelihood_guide,
optim=optim.Adam({"lr": 0.1}))
# Finesse (small learning rate)
estimated_eig = marginal_likelihood_eig(linear_model,
one_point_design,
"y",
"w",
num_samples=10,
num_steps=250,
marginal_guide=marginal_guide,
cond_guide=likelihood_guide,
optim=optim.Adam({"lr": 0.01}),
final_num_samples=500)
expected_eig = linear_model_ground_truth(linear_model, one_point_design,
"y", "w")
assert_equal(estimated_eig, expected_eig, prec=5e-2)
def test_marginal_likelihood_finite_space_model(
finite_space_model, one_point_design, true_eig
):
pyro.set_rng_seed(42)
pyro.clear_param_store()
# Pre-train (large learning rate)
marginal_likelihood_eig(
finite_space_model,
one_point_design,
"y",
"theta",
num_samples=10,
num_steps=250,
marginal_guide=marginal_guide,
cond_guide=likelihood_guide,
optim=optim.Adam({"lr": 0.1}),
)
# Finesse (small learning rate)
estimated_eig = marginal_likelihood_eig(
finite_space_model,
one_point_design,
"y",
"theta",
num_samples=10,
num_steps=250,
marginal_guide=marginal_guide,
cond_guide=likelihood_guide,
optim=optim.Adam({"lr": 0.01}),
final_num_samples=1000,
)
assert_equal(estimated_eig, true_eig, prec=1e-2)