def test_api_sre_on_linearGaussian(num_dim: int):
"""Test inference API of SRE with linear Gaussian model.
Avoids intense computation for fast testing of API etc.
Args:
num_dim: parameter dimension of the Gaussian model
"""
x_o = zeros(num_dim)
prior = MultivariateNormal(loc=zeros(num_dim),
covariance_matrix=eye(num_dim))
simulator, prior = prepare_for_sbi(diagonal_linear_gaussian, prior)
inference = SRE(
prior,
classifier="resnet",
show_progress_bars=False,
)
theta, x = simulate_for_sbi(simulator,
prior,
1000,
simulation_batch_size=50)
_ = inference.append_simulations(theta, x).train(max_num_epochs=5)
posterior = inference.build_posterior()
posterior.sample(sample_shape=(10, ),
x=x_o,
mcmc_parameters={"num_chains": 2})
def test_api_sre_sampling_methods(mcmc_method: str, prior_str: str, set_seed):
"""Test leakage correction both for MCMC and rejection sampling.
Args:
mcmc_method: which mcmc method to use for sampling
prior_str: one of "gaussian" or "uniform"
set_seed: fixture for manual seeding
"""
num_dim = 2
x_o = zeros(num_dim)
if prior_str == "gaussian":
prior = MultivariateNormal(loc=zeros(num_dim),
covariance_matrix=eye(num_dim))
else:
prior = utils.BoxUniform(low=-1.0 * ones(num_dim), high=ones(num_dim))
simulator, prior = prepare_for_sbi(diagonal_linear_gaussian, prior)
inference = SRE(
prior,
classifier="resnet",
show_progress_bars=False,
)
theta, x = simulate_for_sbi(simulator,
prior,
200,
simulation_batch_size=50)
_ = inference.append_simulations(theta, x).train(max_num_epochs=5)
posterior = inference.build_posterior(mcmc_method=mcmc_method)
posterior.sample(sample_shape=(10, ), x=x_o)
def test_c2st_sre_on_linearGaussian_different_dims(set_seed):
"""Test whether SRE infers well a simple example with available ground truth.
This example has different number of parameters theta than number of x. This test
also acts as the only functional test for SRE not marked as slow.
Args:
set_seed: fixture for manual seeding
"""
theta_dim = 3
x_dim = 2
discard_dims = theta_dim - x_dim
x_o = ones(1, x_dim)
num_samples = 1000
likelihood_shift = -1.0 * ones(
x_dim) # likelihood_mean will be likelihood_shift+theta
likelihood_cov = 0.3 * eye(x_dim)
prior_mean = zeros(theta_dim)
prior_cov = eye(theta_dim)
prior = MultivariateNormal(loc=prior_mean, covariance_matrix=prior_cov)
target_samples = samples_true_posterior_linear_gaussian_mvn_prior_different_dims(
x_o[0],
likelihood_shift,
likelihood_cov,
prior_mean,
prior_cov,
num_discarded_dims=discard_dims,
num_samples=num_samples,
)
def simulator(theta):
return linear_gaussian(theta,
likelihood_shift,
likelihood_cov,
num_discarded_dims=discard_dims)
simulator, prior = prepare_for_sbi(simulator, prior)
inference = SRE(
prior,
classifier="resnet",
show_progress_bars=False,
)
theta, x = simulate_for_sbi(simulator,
prior,
5000,
simulation_batch_size=50)
_ = inference.append_simulations(theta, x).train()
posterior = inference.build_posterior()
samples = posterior.sample((num_samples, ),
x=x_o,
mcmc_parameters={"thin": 3})
# Compute the c2st and assert it is near chance level of 0.5.
check_c2st(samples, target_samples, alg="snpe_c")
def test_c2st_sre_external_data_on_linearGaussian(set_seed):
"""Test whether SNPE C infers well a simple example with available ground truth.
Args:
set_seed: fixture for manual seeding
"""
num_dim = 2
device = "cpu"
configure_default_device(device)
x_o = zeros(1, num_dim)
num_samples = 1000
# likelihood_mean will be likelihood_shift+theta
likelihood_shift = -1.0 * ones(num_dim)
likelihood_cov = 0.3 * eye(num_dim)
prior_mean = zeros(num_dim)
prior_cov = eye(num_dim)
prior = MultivariateNormal(loc=prior_mean, covariance_matrix=prior_cov)
gt_posterior = true_posterior_linear_gaussian_mvn_prior(
x_o[0], likelihood_shift, likelihood_cov, prior_mean, prior_cov)
target_samples = gt_posterior.sample((num_samples, ))
def simulator(theta):
return linear_gaussian(theta, likelihood_shift, likelihood_cov)
infer = SRE(
*prepare_for_sbi(simulator, prior),
simulation_batch_size=1000,
show_progress_bars=False,
device=device,
)
external_theta = prior.sample((1000, ))
external_x = simulator(external_theta)
infer.provide_presimulated(external_theta, external_x)
posterior = infer(
num_rounds=1,
num_simulations_per_round=1000,
training_batch_size=100,
).set_default_x(x_o)
samples = posterior.sample((num_samples, ))
# Compute the c2st and assert it is near chance level of 0.5.
check_c2st(samples, target_samples, alg="snpe_c")
def test_api_sre_on_linearGaussian(num_dim: int):
"""Test inference API of SRE with linear Gaussian model.
Avoids intense computation for fast testing of API etc.
Args:
num_dim: parameter dimension of the Gaussian model
"""
x_o = zeros(num_dim)
prior = MultivariateNormal(loc=zeros(num_dim),
covariance_matrix=eye(num_dim))
infer = SRE(
*prepare_for_sbi(diagonal_linear_gaussian, prior),
classifier="resnet",
simulation_batch_size=50,
mcmc_method="slice_np",
show_progress_bars=False,
)
posterior = infer(num_rounds=1,
num_simulations_per_round=1000,
max_num_epochs=5)
posterior.sample(sample_shape=(10, ),
x=x_o,
mcmc_parameters={"num_chains": 2})
def test_c2st_sre_on_linearGaussian(
num_dim: int,
prior_str: str,
method_str: str,
set_seed,
):
"""Test c2st accuracy of inference with SRE on linear Gaussian model.
Args:
num_dim: parameter dimension of the gaussian model
prior_str: one of "gaussian" or "uniform"
set_seed: fixture for manual seeding
"""
x_o = zeros(1, num_dim)
num_samples = 500
# `likelihood_mean` will be `likelihood_shift + theta`.
likelihood_shift = -1.0 * ones(num_dim)
likelihood_cov = 0.3 * eye(num_dim)
if prior_str == "gaussian":
prior_mean = zeros(num_dim)
prior_cov = eye(num_dim)
prior = MultivariateNormal(loc=prior_mean, covariance_matrix=prior_cov)
gt_posterior = true_posterior_linear_gaussian_mvn_prior(
x_o[0], likelihood_shift, likelihood_cov, prior_mean, prior_cov)
target_samples = gt_posterior.sample((num_samples, ))
else:
prior = utils.BoxUniform(-2.0 * ones(num_dim), 2.0 * ones(num_dim))
target_samples = samples_true_posterior_linear_gaussian_uniform_prior(
x_o,
likelihood_shift,
likelihood_cov,
prior=prior,
num_samples=num_samples)
def simulator(theta):
return linear_gaussian(theta, likelihood_shift, likelihood_cov)
simulator, prior = prepare_for_sbi(simulator, prior)
kwargs = dict(
simulator=simulator,
prior=prior,
classifier="resnet",
simulation_batch_size=50,
mcmc_method="slice_np",
show_progress_bars=False,
)
infer = SRE(**kwargs) if method_str == "sre" else AALR(**kwargs)
# Should use default `num_atoms=10` for SRE; `num_atoms=2` for AALR
posterior = infer(num_rounds=1,
num_simulations_per_round=1000).set_default_x(x_o)
samples = posterior.sample(sample_shape=(num_samples, ),
mcmc_parameters={"thin": 3})
# Check performance based on c2st accuracy.
check_c2st(samples, target_samples, alg=f"sre-{prior_str}-{method_str}")
# Checks for log_prob()
if prior_str == "gaussian" and method_str == "aalr":
# For the Gaussian prior, we compute the KLd between ground truth and
# posterior. We can do this only if the classifier_loss was as described in
# Hermans et al. 2020 ('aalr') since Durkan et al. 2020 version only allows
# evaluation up to a constant.
# For the Gaussian prior, we compute the KLd between ground truth and posterior
dkl = get_dkl_gaussian_prior(posterior, x_o[0], likelihood_shift,
likelihood_cov, prior_mean, prior_cov)
max_dkl = 0.1 if num_dim == 1 else 0.8
assert (
dkl < max_dkl
), f"KLd={dkl} is more than 2 stds above the average performance."
if prior_str == "uniform":
# Check whether the returned probability outside of the support is zero.
posterior_prob = get_prob_outside_uniform_prior(posterior, num_dim)
assert (
posterior_prob == 0.0
), "The posterior probability outside of the prior support is not zero"
