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_snpe_on_linearGaussian_different_dims(set_seed):
"""Test whether SNPE B/C infer well a simple example with available ground truth.
This example has different number of parameters theta than number of x. Also
this implicitly tests simulation_batch_size=1.
Args:
set_seed: fixture for manual seeding
"""
theta_dim = 3
x_dim = 2
discard_dims = theta_dim - x_dim
x_o = zeros(1, x_dim)
num_samples = 1000
# likelihood_mean will be likelihood_shift+theta
likelihood_shift = -1.0 * ones(x_dim)
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 = SNPE_C(
prior,
density_estimator="maf",
show_progress_bars=False,
)
theta, x = simulate_for_sbi(simulator,
prior,
2000,
simulation_batch_size=1) # type: ignore
_ = inference.append_simulations(theta, x).train()
posterior = inference.build_posterior()
samples = posterior.sample((num_samples, ), x=x_o)
# Compute the c2st and assert it is near chance level of 0.5.
check_c2st(samples, target_samples, alg="snpe_c")
def test_c2st_snl_on_linearGaussian_different_dims(set_seed):
"""Test whether SNL 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 SNL not marked as slow.
Args:
set_seed: fixture for manual seeding
"""
device = "cpu"
configure_default_device(device)
theta_dim = 3
x_dim = 2
discard_dims = theta_dim - x_dim
x_o = ones(1, x_dim)
num_samples = 1000
# likelihood_mean will be likelihood_shift+theta
likelihood_shift = -1.0 * ones(x_dim)
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,
)
simulator = lambda theta: linear_gaussian(theta,
likelihood_shift,
likelihood_cov,
num_discarded_dims=discard_dims)
infer = SNL(
*prepare_for_sbi(simulator, prior),
simulation_batch_size=50,
mcmc_method="slice_np",
show_progress_bars=False,
device=device,
)
posterior = infer(num_rounds=1,
num_simulations_per_round=5000) # type: ignore
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="snle_a")
def test_c2st_snl_on_linearGaussian():
"""Test whether SNL 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 SNL not marked as slow.
"""
theta_dim = 3
x_dim = 2
discard_dims = theta_dim - x_dim
x_o = zeros(1, x_dim)
num_samples = 1000
num_simulations = 3100
# likelihood_mean will be likelihood_shift+theta
likelihood_shift = -1.0 * ones(x_dim)
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,
likelihood_shift,
likelihood_cov,
prior_mean,
prior_cov,
num_discarded_dims=discard_dims,
num_samples=num_samples,
)
simulator, prior = prepare_for_sbi(
lambda theta: linear_gaussian(theta,
likelihood_shift,
likelihood_cov,
num_discarded_dims=discard_dims),
prior,
)
density_estimator = likelihood_nn("maf", num_transforms=3)
inference = SNLE(density_estimator=density_estimator,
show_progress_bars=False)
theta, x = simulate_for_sbi(simulator,
prior,
num_simulations,
simulation_batch_size=50)
likelihood_estimator = inference.append_simulations(theta, x).train()
potential_fn, theta_transform = likelihood_estimator_based_potential(
prior=prior, likelihood_estimator=likelihood_estimator, x_o=x_o)
posterior = MCMCPosterior(
proposal=prior,
potential_fn=potential_fn,
theta_transform=theta_transform,
method="slice_np_vectorized",
num_chains=5,
thin=10,
)
samples = posterior.sample((num_samples, ))
# Compute the c2st and assert it is near chance level of 0.5.
check_c2st(samples, target_samples, alg="snle_a")
def test_c2st_snpe_on_linearGaussian_different_dims():
"""Test whether SNPE B/C infer well a simple example with available ground truth.
This example has different number of parameters theta than number of x. Also
this implicitly tests simulation_batch_size=1. It also impleictly tests whether the
prior can be `None` and whether we can stop and resume training.
"""
theta_dim = 3
x_dim = 2
discard_dims = theta_dim - x_dim
x_o = zeros(1, x_dim)
num_samples = 1000
# likelihood_mean will be likelihood_shift+theta
likelihood_shift = -1.0 * ones(x_dim)
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,
likelihood_shift,
likelihood_cov,
prior_mean,
prior_cov,
num_discarded_dims=discard_dims,
num_samples=num_samples,
)
simulator, prior = prepare_for_sbi(
lambda theta: linear_gaussian(theta,
likelihood_shift,
likelihood_cov,
num_discarded_dims=discard_dims),
prior,
)
# Test whether prior can be `None`.
inference = SNPE_C(prior=None,
density_estimator="maf",
show_progress_bars=False)
# type: ignore
theta, x = simulate_for_sbi(simulator,
prior,
2000,
simulation_batch_size=1)
inference = inference.append_simulations(theta, x)
posterior_estimator = inference.train(
max_num_epochs=10) # Test whether we can stop and resume.
posterior_estimator = inference.train(resume_training=True,
force_first_round_loss=True)
posterior = DirectPosterior(
prior=prior,
posterior_estimator=posterior_estimator).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_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")
