def test_vnmc_linear_model(linear_model, one_point_design):
pyro.set_rng_seed(42)
pyro.clear_param_store()
# Pre-train (large learning rate)
vnmc_eig(linear_model,
one_point_design,
"y",
"w",
num_samples=[9, 3],
num_steps=250,
guide=posterior_guide,
optim=optim.Adam({"lr": 0.1}))
# Finesse (small learning rate)
estimated_eig = vnmc_eig(linear_model,
one_point_design,
"y",
"w",
num_samples=[9, 3],
num_steps=250,
guide=posterior_guide,
optim=optim.Adam({"lr": 0.01}),
final_num_samples=[500, 100])
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_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_dv_linear_model(linear_model, one_point_design):
pyro.set_rng_seed(42)
pyro.clear_param_store()
donsker_varadhan_eig(
linear_model,
one_point_design,
"y",
"w",
num_samples=100,
num_steps=500,
T=dv_critic,
optim=optim.Adam({"lr": 0.1}),
)
estimated_eig = donsker_varadhan_eig(
linear_model,
one_point_design,
"y",
"w",
num_samples=100,
num_steps=650,
T=dv_critic,
optim=optim.Adam({"lr": 0.001}),
final_num_samples=2000,
)
expected_eig = linear_model_ground_truth(linear_model, one_point_design, "y", "w")
assert_equal(estimated_eig, expected_eig, prec=5e-2)
def test_lfire_linear_model(linear_model, one_point_design):
pyro.set_rng_seed(42)
pyro.clear_param_store()
estimated_eig = lfire_eig(linear_model, one_point_design, "y", "w", num_y_samples=2, num_theta_samples=50,
num_steps=1200, classifier=make_lfire_classifier(50), optim=optim.Adam({"lr": 0.0025}),
final_num_samples=100)
expected_eig = linear_model_ground_truth(linear_model, one_point_design, "y", "w")
assert_equal(estimated_eig, expected_eig, prec=5e-2)
def test_laplace_linear_model(linear_model, one_point_design):
pyro.set_rng_seed(42)
pyro.clear_param_store()
# You can use 1 final sample here because linear models have a posterior entropy that is independent of `y`
estimated_eig = laplace_eig(linear_model, one_point_design, "y", "w",
guide=laplace_guide, num_steps=250, final_num_samples=1,
optim=optim.Adam({"lr": 0.05}),
loss=Trace_ELBO().differentiable_loss)
expected_eig = linear_model_ground_truth(linear_model, one_point_design, "y", "w")
assert_equal(estimated_eig, expected_eig, prec=5e-2)
def test_nmc_eig_linear_model(linear_model, one_point_design):
pyro.set_rng_seed(42)
pyro.clear_param_store()
estimated_eig = nmc_eig(linear_model,
one_point_design,
"y",
"w",
M=60,
N=60 * 60)
expected_eig = linear_model_ground_truth(linear_model, one_point_design,
"y", "w")
assert_equal(estimated_eig, expected_eig, prec=5e-2)
def test_eig_lm(model, design, observation_labels, target_labels, estimator,
args, eig, allow_error):
pyro.set_rng_seed(42)
pyro.clear_param_store()
y = estimator(model, design, observation_labels, target_labels, *args)
if model is bernoulli_model:
y_true = bernoulli_ground_truth(model,
design,
observation_labels,
target_labels,
eig=eig)
else:
y_true = linear_model_ground_truth(model,
design,
observation_labels,
target_labels,
eig=eig)
logger.debug(estimator.__name__)
logger.debug(y)
logger.debug(y_true)
error = torch.max(torch.abs(y - y_true))
assert error < allow_error
