def test_api_snl_on_linearGaussian(num_dim: int, set_seed):
"""Test API for inference on linear Gaussian model using SNL.
Avoids expensive computations by training on few simulations and generating few
posterior samples.
Args:
num_dim: parameter dimension of the gaussian model
"""
num_samples = 10
x_o = zeros(num_dim)
prior_mean = zeros(num_dim)
prior_cov = eye(num_dim)
prior = MultivariateNormal(loc=prior_mean, covariance_matrix=prior_cov)
simulator, prior = prepare_for_sbi(diagonal_linear_gaussian, prior)
inference = SNL(
prior,
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().set_default_x(x_o)
posterior.sample(sample_shape=(num_samples, ),
x=x_o,
mcmc_parameters={"thin": 3})
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
"""
theta_dim = 3
x_dim = 2
discard_dims = theta_dim - x_dim
x_o = ones(1, x_dim)
num_samples = 1000
num_simulations = 5000
# 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)
simulator, prior = prepare_for_sbi(simulator, prior)
inference = SNL(
prior,
show_progress_bars=False,
)
theta, x = simulate_for_sbi(simulator,
prior,
num_simulations,
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="snle_a")
def test_c2st_snle_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 = SNL(
*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_snl_sampling_methods(sampling_method: str, prior_str: str,
set_seed):
"""Runs SNL on linear Gaussian and tests sampling from posterior via mcmc.
Args:
mcmc_method: which mcmc method to use for sampling
prior_str: use gaussian or uniform prior
set_seed: fixture for manual seeding
"""
num_dim = 2
num_samples = 10
num_trials = 2
# HMC with uniform prior needs good likelihood.
num_simulations = 10000 if sampling_method == "hmc" else 1000
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"
else:
sample_with = "mcmc"
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 = SNL(prior, show_progress_bars=False)
theta, x = simulate_for_sbi(simulator,
prior,
num_simulations,
simulation_batch_size=50)
_ = inference.append_simulations(theta, x).train(max_num_epochs=5)
posterior = inference.build_posterior(
sample_with=sample_with,
mcmc_method=sampling_method).set_default_x(x_o)
posterior.sample(
sample_shape=(num_samples, ),
x=x_o,
mcmc_parameters={
"thin": 3,
"num_chains": num_chains
},
)
def test_api_snl_sampling_methods(mcmc_method: str, prior_str: str, set_seed):
"""Runs SNL on linear Gaussian and tests sampling from posterior via mcmc.
Args:
mcmc_method: which mcmc method to use for sampling
prior_str: use gaussian or uniform prior
set_seed: fixture for manual seeding
"""
num_dim = 2
num_samples = 10
x_o = zeros((1, num_dim))
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))
infer = SNL(
*prepare_for_sbi(diagonal_linear_gaussian, prior),
simulation_batch_size=50,
mcmc_method="slice_np",
show_progress_bars=False,
)
posterior = infer(num_rounds=1,
num_simulations_per_round=200,
max_num_epochs=5)
posterior.sample(sample_shape=(num_samples, ),
x=x_o,
mcmc_parameters={"thin": 3})
def test_api_snl_on_linearGaussian(num_dim: int, set_seed):
"""Test API for inference on linear Gaussian model using SNL.
Avoids expensive computations by training on few simulations and generating few
posterior samples.
Args:
num_dim: parameter dimension of the gaussian model
"""
num_samples = 10
x_o = zeros(num_dim)
prior_mean = zeros(num_dim)
prior_cov = eye(num_dim)
prior = MultivariateNormal(loc=prior_mean, covariance_matrix=prior_cov)
infer = SNL(
*prepare_for_sbi(diagonal_linear_gaussian, prior),
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=(num_samples, ),
x=x_o,
mcmc_parameters={"thin": 3})
def test_c2st_snl_on_linearGaussian(num_dim: int, prior_str: str, set_seed):
"""Test SNL on linear Gaussian, comparing to ground truth posterior via c2st.
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)
simulator = lambda theta: linear_gaussian(theta, likelihood_shift,
likelihood_cov)
infer = SNL(
*prepare_for_sbi(simulator, prior),
mcmc_method="slice_np",
show_progress_bars=False,
)
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"snle_a-{prior_str}-prior")
# TODO: we do not have a test for SNL log_prob(). This is because the output
# TODO: density is not normalized, so KLd does not make sense.
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"
def test_api_snl_sampling_methods(mcmc_method: str, prior_str: str, set_seed):
"""Runs SNL on linear Gaussian and tests sampling from posterior via mcmc.
Args:
mcmc_method: which mcmc method to use for sampling
prior_str: use gaussian or uniform prior
set_seed: fixture for manual seeding
"""
num_dim = 2
num_samples = 10
x_o = zeros((1, num_dim))
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 = SNL(
prior,
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).set_default_x(x_o)
posterior.sample(sample_shape=(num_samples, ),
x=x_o,
mcmc_parameters={"thin": 3})
def test_c2st_multi_round_snl_on_linearGaussian(set_seed):
"""Test SNL on linear Gaussian, comparing to ground truth posterior via c2st.
Args:
set_seed: fixture for manual seeding
"""
num_dim = 2
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)
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, ))
simulator = lambda theta: linear_gaussian(theta, likelihood_shift,
likelihood_cov)
simulator, prior = prepare_for_sbi(simulator, prior)
inference = SNL(
prior,
show_progress_bars=False,
)
theta, x = simulate_for_sbi(simulator,
prior,
750,
simulation_batch_size=50)
_ = inference.append_simulations(theta, x).train()
posterior1 = inference.build_posterior(mcmc_method="slice_np_vectorized",
mcmc_parameters={
"thin": 5,
"num_chains": 20
}).set_default_x(x_o)
theta, x = simulate_for_sbi(simulator,
posterior1,
750,
simulation_batch_size=50)
_ = inference.append_simulations(theta, x).train()
posterior = inference.build_posterior().copy_hyperparameters_from(
posterior1)
samples = posterior.sample(sample_shape=(num_samples, ),
mcmc_parameters={"thin": 3})
# Check performance based on c2st accuracy.
check_c2st(samples, target_samples, alg="multi-round-snl")
def test_c2st_multi_round_snl_on_linearGaussian(set_seed):
"""Test SNL on linear Gaussian, comparing to ground truth posterior via c2st.
Args:
set_seed: fixture for manual seeding
"""
num_dim = 2
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)
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, ))
simulator = lambda theta: linear_gaussian(theta, likelihood_shift,
likelihood_cov)
infer = SNL(
*prepare_for_sbi(simulator, prior),
simulation_batch_size=50,
mcmc_method="slice",
show_progress_bars=False,
)
posterior1 = infer(num_simulations=500).set_default_x(x_o)
posterior = infer(num_simulations=500,
proposal=posterior1).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="multi-round-snl")
def test_c2st_snl_on_linearGaussian_different_dims_and_trials(
num_dim: int, prior_str: str, set_seed):
"""Test SNL on linear Gaussian, comparing to ground truth posterior via c2st.
Args:
num_dim: parameter dimension of the gaussian model
prior_str: one of "gaussian" or "uniform"
set_seed: fixture for manual seeding
"""
num_samples = 500
num_simulations = 7500
trials_to_test = [1, 5, 10]
# likelihood_mean will be likelihood_shift+theta
likelihood_shift = -1.0 * ones(num_dim)
# Use increased cov to avoid too small posterior cov for many trials.
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))
simulator, prior = prepare_for_sbi(
lambda theta: linear_gaussian(theta, likelihood_shift, likelihood_cov),
prior)
inference = SNL(prior, show_progress_bars=False)
theta, x = simulate_for_sbi(simulator,
prior,
num_simulations,
simulation_batch_size=50)
_ = inference.append_simulations(theta, x).train()
# Test inference amortized over trials.
for num_trials in trials_to_test:
x_o = zeros((num_trials, num_dim))
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,
)
posterior = inference.build_posterior(
mcmc_method="slice_np_vectorized").set_default_x(x_o)
samples = posterior.sample(sample_shape=(num_samples, ),
mcmc_parameters={
"thin": 3,
"num_chains": 2
})
# Check performance based on c2st accuracy.
check_c2st(samples,
target_samples,
alg=f"snle_a-{prior_str}-prior-{num_trials}-trials")
map_ = posterior.map(num_init_samples=1_000, init_method="prior")
# TODO: we do not have a test for SNL log_prob(). This is because the output
# TODO: density is not normalized, so KLd does not make sense.
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
else:
assert ((map_ - gt_posterior.mean)**2).sum() < 0.5
