def test_c2st_multi_round_snr_on_linearGaussian_vi(num_trials: int):
"""Test SNL on linear Gaussian, comparing to ground truth posterior via c2st."""
num_dim = 2
x_o = zeros((num_trials, num_dim))
num_samples = 500
num_simulations_per_round = 1000 * num_trials
# 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, likelihood_shift, likelihood_cov, prior_mean, prior_cov
)
target_samples = gt_posterior.sample((num_samples,))
simulator, prior = prepare_for_sbi(
lambda theta: linear_gaussian(theta, likelihood_shift, likelihood_cov), prior
)
inference = SNRE_B(show_progress_bars=False)
theta, x = simulate_for_sbi(
simulator, prior, num_simulations_per_round, simulation_batch_size=50
)
ratio_estimator = inference.append_simulations(theta, x).train()
potential_fn, theta_transform = ratio_estimator_based_potential(
prior=prior, ratio_estimator=ratio_estimator, x_o=x_o
)
posterior1 = VIPosterior(
potential_fn=potential_fn,
theta_transform=theta_transform,
)
posterior1.train()
theta, x = simulate_for_sbi(
simulator, posterior1, num_simulations_per_round, simulation_batch_size=50
)
ratio_estimator = inference.append_simulations(theta, x).train()
potential_fn, theta_transform = ratio_estimator_based_potential(
prior=prior, ratio_estimator=ratio_estimator, x_o=x_o
)
posterior = VIPosterior(
potential_fn=potential_fn,
theta_transform=theta_transform,
q=posterior1,
)
posterior.train()
samples = posterior.sample(sample_shape=(num_samples,))
# Check performance based on c2st accuracy.
check_c2st(samples, target_samples, alg="multi-round-snl")
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
"""
prior = MultivariateNormal(loc=zeros(num_dim), covariance_matrix=eye(num_dim))
simulator, prior = prepare_for_sbi(diagonal_linear_gaussian, prior)
inference = SNRE_B(
classifier="resnet",
show_progress_bars=False,
)
theta, x = simulate_for_sbi(simulator, prior, 1000, simulation_batch_size=50)
ratio_estimator = inference.append_simulations(theta, x).train(max_num_epochs=5)
for num_trials in [1, 2]:
x_o = zeros(num_trials, num_dim)
potential_fn, theta_transform = ratio_estimator_based_potential(
ratio_estimator=ratio_estimator, prior=prior, x_o=x_o
)
posterior = MCMCPosterior(
potential_fn=potential_fn,
theta_transform=theta_transform,
proposal=prior,
num_chains=2,
)
posterior.sample(sample_shape=(10,))
def test_inference_with_2d_x(embedding, method):
num_dim = 2
num_samples = 10
num_simulations = 100
prior = utils.BoxUniform(zeros(num_dim), torch.ones(num_dim))
simulator, prior = prepare_for_sbi(simulator_2d, prior)
theta_o = torch.ones(1, num_dim)
if method == SNPE:
net_provider = utils.posterior_nn(
model="mdn",
embedding_net=embedding(),
)
num_trials = 1
elif method == SNLE:
net_provider = utils.likelihood_nn(model="mdn",
embedding_net=embedding())
num_trials = 2
else:
net_provider = utils.classifier_nn(
model="mlp",
z_score_theta="structured", # Test that structured z-scoring works.
embedding_net_x=embedding(),
)
num_trials = 2
if method == SNRE:
inference = method(classifier=net_provider, show_progress_bars=False)
else:
inference = method(density_estimator=net_provider,
show_progress_bars=False)
theta, x = simulate_for_sbi(simulator, prior, num_simulations)
estimator = inference.append_simulations(theta,
x).train(training_batch_size=100,
max_num_epochs=10)
x_o = simulator(theta_o.repeat(num_trials, 1))
if method == SNLE:
potential_fn, theta_transform = likelihood_estimator_based_potential(
estimator, prior, x_o)
elif method == SNPE:
potential_fn, theta_transform = posterior_estimator_based_potential(
estimator, prior, x_o)
elif method == SNRE:
potential_fn, theta_transform = ratio_estimator_based_potential(
estimator, prior, x_o)
else:
raise NotImplementedError
posterior = MCMCPosterior(
potential_fn=potential_fn,
theta_transform=theta_transform,
proposal=prior,
method="slice_np_vectorized",
num_chains=2,
)
posterior.potential(
posterior.sample((num_samples, ), show_progress_bars=False))
def test_training_and_mcmc_on_device(
method,
model,
data_device,
mcmc_method,
training_device,
prior_device,
prior_type="gaussian",
):
"""Test training on devices.
This test does not check training speeds.
"""
num_dim = 2
num_samples = 10
num_simulations = 100
max_num_epochs = 5
x_o = zeros(1, num_dim).to(data_device)
likelihood_shift = -1.0 * ones(num_dim).to(prior_device)
likelihood_cov = 0.3 * eye(num_dim).to(prior_device)
if prior_type == "gaussian":
prior_mean = zeros(num_dim).to(prior_device)
prior_cov = eye(num_dim).to(prior_device)
prior = MultivariateNormal(loc=prior_mean, covariance_matrix=prior_cov)
else:
prior = BoxUniform(
low=-2 * torch.ones(num_dim),
high=2 * torch.ones(num_dim),
device=prior_device,
)
def simulator(theta):
return linear_gaussian(theta, likelihood_shift, likelihood_cov)
training_device = process_device(training_device)
if method in [SNPE_A, SNPE_C]:
kwargs = dict(
density_estimator=utils.posterior_nn(model=model, num_transforms=2)
)
elif method == SNLE:
kwargs = dict(
density_estimator=utils.likelihood_nn(model=model, num_transforms=2)
)
elif method in (SNRE_A, SNRE_B):
kwargs = dict(classifier=utils.classifier_nn(model=model))
else:
raise ValueError()
inferer = method(show_progress_bars=False, device=training_device, **kwargs)
proposals = [prior]
# Test for two rounds.
for _ in range(2):
theta, x = simulate_for_sbi(simulator, proposals[-1], num_simulations)
theta, x = theta.to(data_device), x.to(data_device)
estimator = inferer.append_simulations(theta, x).train(
training_batch_size=100, max_num_epochs=max_num_epochs
)
if method == SNLE:
potential_fn, theta_transform = likelihood_estimator_based_potential(
estimator, prior, x_o
)
elif method == SNPE_A or method == SNPE_C:
potential_fn, theta_transform = posterior_estimator_based_potential(
estimator, prior, x_o
)
elif method == SNRE_A or method == SNRE_B:
potential_fn, theta_transform = ratio_estimator_based_potential(
estimator, prior, x_o
)
else:
raise ValueError
if mcmc_method == "rejection":
posterior = RejectionPosterior(
proposal=prior,
potential_fn=potential_fn,
device=training_device,
)
elif mcmc_method == "direct":
posterior = DirectPosterior(
posterior_estimator=estimator, prior=prior
).set_default_x(x_o)
else:
posterior = MCMCPosterior(
potential_fn=potential_fn,
theta_transform=theta_transform,
proposal=prior,
method=mcmc_method,
device=training_device,
)
proposals.append(posterior)
# Check for default device for inference object
weights_device = next(inferer._neural_net.parameters()).device
assert torch.device(training_device) == weights_device
samples = proposals[-1].sample(sample_shape=(num_samples,))
proposals[-1].potential(samples)
def test_c2st_sre_on_linearGaussian():
"""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.
"""
theta_dim = 3
x_dim = 2
discard_dims = theta_dim - x_dim
num_samples = 1000
num_simulations = 2100
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)
simulator, prior = prepare_for_sbi(
lambda theta: linear_gaussian(
theta, likelihood_shift, likelihood_cov, num_discarded_dims=discard_dims
),
prior,
)
inference = SNRE_B(
classifier="resnet",
show_progress_bars=False,
)
theta, x = simulate_for_sbi(
simulator, prior, num_simulations, simulation_batch_size=100
)
ratio_estimator = inference.append_simulations(theta, x).train()
num_trials = 1
x_o = zeros(num_trials, x_dim)
target_samples = samples_true_posterior_linear_gaussian_mvn_prior_different_dims(
x_o,
likelihood_shift,
likelihood_cov,
prior_mean,
prior_cov,
num_discarded_dims=discard_dims,
num_samples=num_samples,
)
potential_fn, theta_transform = ratio_estimator_based_potential(
ratio_estimator=ratio_estimator, prior=prior, x_o=x_o
)
posterior = MCMCPosterior(
potential_fn=potential_fn,
theta_transform=theta_transform,
proposal=prior,
thin=5,
num_chains=2,
)
samples = posterior.sample((num_samples,))
# Compute the c2st and assert it is near chance level of 0.5.
check_c2st(samples, target_samples, alg=f"snre-{num_trials}trials")
def test_api_sre_sampling_methods(sampling_method: str, prior_str: str):
"""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"
"""
num_dim = 2
num_samples = 10
num_trials = 2
num_simulations = 2100
x_o = zeros((num_trials, num_dim))
# Test for multiple chains is cheap when vectorized.
num_chains = 3 if sampling_method == "slice_np_vectorized" else 1
if sampling_method == "rejection":
sample_with = "rejection"
elif (
"slice" in sampling_method
or "nuts" in sampling_method
or "hmc" in sampling_method
):
sample_with = "mcmc"
else:
sample_with = "vi"
if prior_str == "gaussian":
prior = MultivariateNormal(loc=zeros(num_dim), covariance_matrix=eye(num_dim))
else:
prior = utils.BoxUniform(-1.0 * ones(num_dim), ones(num_dim))
simulator, prior = prepare_for_sbi(diagonal_linear_gaussian, prior)
inference = SNRE_B(classifier="resnet", show_progress_bars=False)
theta, x = simulate_for_sbi(
simulator, prior, num_simulations, simulation_batch_size=50
)
ratio_estimator = inference.append_simulations(theta, x).train(max_num_epochs=5)
potential_fn, theta_transform = ratio_estimator_based_potential(
ratio_estimator=ratio_estimator, prior=prior, x_o=x_o
)
if sample_with == "rejection":
posterior = RejectionPosterior(potential_fn=potential_fn, proposal=prior)
elif (
"slice" in sampling_method
or "nuts" in sampling_method
or "hmc" in sampling_method
):
posterior = MCMCPosterior(
potential_fn,
proposal=prior,
theta_transform=theta_transform,
method=sampling_method,
thin=3,
num_chains=num_chains,
)
else:
posterior = VIPosterior(
potential_fn=potential_fn,
theta_transform=theta_transform,
vi_method=sampling_method,
)
posterior.train(max_num_iters=10)
posterior.sample(sample_shape=(num_samples,))
def test_c2st_sre_variants_on_linearGaussian(
num_dim: int,
num_trials: int,
prior_str: str,
method_str: str,
):
"""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"
"""
x_o = zeros(num_trials, num_dim)
num_samples = 500
num_simulations = 2600 if num_trials == 1 else 40500
# `likelihood_mean` will be `likelihood_shift + theta`.
likelihood_shift = -1.0 * ones(num_dim)
likelihood_cov = 0.8 * 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)
else:
prior = utils.BoxUniform(-2.0 * ones(num_dim), 2.0 * ones(num_dim))
def simulator(theta):
return linear_gaussian(theta, likelihood_shift, likelihood_cov)
simulator, prior = prepare_for_sbi(simulator, prior)
kwargs = dict(
classifier="resnet",
show_progress_bars=False,
)
inference = SNRE_B(**kwargs) if method_str == "sre" else AALR(**kwargs)
# Should use default `num_atoms=10` for SRE; `num_atoms=2` for AALR
theta, x = simulate_for_sbi(
simulator, prior, num_simulations, simulation_batch_size=50
)
ratio_estimator = inference.append_simulations(theta, x).train()
potential_fn, theta_transform = ratio_estimator_based_potential(
ratio_estimator=ratio_estimator, prior=prior, x_o=x_o
)
posterior = MCMCPosterior(
potential_fn=potential_fn,
theta_transform=theta_transform,
proposal=prior,
method="slice_np_vectorized",
thin=5,
num_chains=5,
)
samples = posterior.sample(sample_shape=(num_samples,))
# Get posterior samples.
if prior_str == "gaussian":
gt_posterior = true_posterior_linear_gaussian_mvn_prior(
x_o, likelihood_shift, likelihood_cov, prior_mean, prior_cov
)
target_samples = gt_posterior.sample((num_samples,))
else:
target_samples = samples_true_posterior_linear_gaussian_uniform_prior(
x_o, likelihood_shift, likelihood_cov, prior=prior, num_samples=num_samples
)
# Check performance based on c2st accuracy.
check_c2st(
samples, target_samples, alg=f"sre-{prior_str}-{method_str}-{num_trials}trials"
)
map_ = posterior.map(num_init_samples=1_000, init_method="proposal")
# 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, likelihood_shift, likelihood_cov, prior_mean, prior_cov
)
max_dkl = 0.15
assert (
dkl < max_dkl
), f"KLd={dkl} is more than 2 stds above the average performance."
assert ((map_ - gt_posterior.mean) ** 2).sum() < 0.5
if prior_str == "uniform":
# Check whether the returned probability outside of the support is zero.
posterior_prob = get_prob_outside_uniform_prior(posterior, prior, num_dim)
assert (
posterior_prob == 0.0
), "The posterior probability outside of the prior support is not zero"
assert ((map_ - ones(num_dim)) ** 2).sum() < 0.5