def get_proposal(
task: Task,
samples: torch.Tensor,
prior_weight: float = 0.01,
bounded: bool = True,
density_estimator: str = "flow",
flow_model: str = "nsf",
**kwargs: Any,
) -> torch.Tensor:
"""Gets proposal distribution by performing density estimation on `samples`
If `prior_weight` > 0., the proposal is defensive, i.e., the prior is mixed in
Args:
task: Task instance
samples: Samples to fit
prior_weight: Prior weight
bounded: If True, will automatically transform proposal density to bounded space
density_estimator: Density estimator
flow_model: Flow to use if `density_estimator` is `flow`
kwargs: Passed on to `get_flow` or `get_kde`
Returns:
Proposal distribution
"""
tic = time.time()
log = sbibm.get_logger(__name__)
log.info("Get proposal distribution called")
prior_dist = task.get_prior_dist()
transform = task._get_transforms(
automatic_transforms_enabled=bounded)["parameters"]
if density_estimator == "flow":
density_estimator_ = get_flow(model=flow_model,
dim_distribution=task.dim_parameters,
**kwargs)
density_estimator_ = train_flow(density_estimator_,
samples,
transform=transform)
elif density_estimator == "kde":
density_estimator_ = get_kde(X=samples, transform=transform, **kwargs)
else:
raise NotImplementedError
proposal_dist = DenfensiveProposal(
dim=task.dim_parameters,
proposal=density_estimator_,
prior=prior_dist,
prior_weight=prior_weight,
)
log.info(f"Proposal distribution is set up, took {time.time()-tic:.3f}sec")
return proposal_dist
def ksd(
task: Task,
num_observation: int,
samples: torch.Tensor,
sig2: Optional[float] = None,
log: bool = True,
) -> torch.Tensor:
"""Gets `log_prob_grad_fn` from task and runs KSD
Args:
task: Task
num_observation: Observation
samples: Samples
sig2: Length scale
log: Whether to log test result
Returns:
The test result is returned
"""
try:
device = get_default_device()
site_name = "parameters"
log_prob_grad_fn = task._get_log_prob_grad_fn(
num_observation=num_observation,
implementation="pyro",
posterior=True,
jit_compile=False,
automatic_transform_enabled=True,
)
samples_transformed = task._get_transforms(automatic_transform_enabled=True)[
site_name
](samples)
def log_prob_grad_numpy(parameters: np.ndarray) -> np.ndarray:
lpg = log_prob_grad_fn(
torch.from_numpy(parameters.astype(np.float32)).to(device)
)
return lpg.detach().cpu().numpy()
test_statistic = ksd_gaussian_kernel(
log_prob_grad_numpy, samples_transformed, sig2=sig2
)
if log:
return math.log(test_statistic)
else:
return test_statistic
except:
return torch.tensor(float("nan"))
def run(
task: Task,
num_samples: int,
num_simulations: int,
num_observation: Optional[int] = None,
observation: Optional[torch.Tensor] = None,
num_rounds: int = 10,
neural_net: str = "nsf",
hidden_features: int = 50,
simulation_batch_size: int = 1000,
training_batch_size: int = 10000,
num_atoms: int = 10,
automatic_transforms_enabled: bool = False,
z_score_x: bool = True,
z_score_theta: bool = True,
) -> Tuple[torch.Tensor, int, Optional[torch.Tensor]]:
"""Runs (S)NPE from `sbi`
Args:
task: Task instance
num_samples: Number of samples to generate from posterior
num_simulations: Simulation budget
num_observation: Observation number to load, alternative to `observation`
observation: Observation, alternative to `num_observation`
num_rounds: Number of rounds
neural_net: Neural network to use, one of maf / mdn / made / nsf
hidden_features: Number of hidden features in network
simulation_batch_size: Batch size for simulator
training_batch_size: Batch size for training network
num_atoms: Number of atoms, -1 means same as `training_batch_size`
automatic_transforms_enabled: Whether to enable automatic transforms
z_score_x: Whether to z-score x
z_score_theta: Whether to z-score theta
Returns:
Samples from posterior, number of simulator calls, log probability of true params if computable
"""
assert not (num_observation is None and observation is None)
assert not (num_observation is not None and observation is not None)
log = logging.getLogger(__name__)
if num_rounds == 1:
log.info(f"Running NPE")
num_simulations_per_round = num_simulations
else:
log.info(f"Running SNPE")
num_simulations_per_round = math.floor(num_simulations / num_rounds)
if simulation_batch_size > num_simulations_per_round:
simulation_batch_size = num_simulations_per_round
log.warn("Reduced simulation_batch_size to num_simulation_per_round")
if training_batch_size > num_simulations_per_round:
training_batch_size = num_simulations_per_round
log.warn("Reduced training_batch_size to num_simulation_per_round")
prior = task.get_prior_dist()
if observation is None:
observation = task.get_observation(num_observation)
simulator = task.get_simulator(max_calls=num_simulations)
transforms = task._get_transforms(
automatic_transforms_enabled)["parameters"]
if automatic_transforms_enabled:
prior = wrap_prior_dist(prior, transforms)
simulator = wrap_simulator_fn(simulator, transforms)
density_estimator_fun = posterior_nn(
model=neural_net.lower(),
hidden_features=hidden_features,
z_score_x=z_score_x,
z_score_theta=z_score_theta,
)
inference_method = inference.SNPE_C(
prior, density_estimator=density_estimator_fun)
posteriors = []
proposal = prior
for _ in range(num_rounds):
theta, x = inference.simulate_for_sbi(
simulator,
proposal,
num_simulations=num_simulations_per_round,
simulation_batch_size=simulation_batch_size,
)
density_estimator = inference_method.append_simulations(
theta, x, proposal=proposal).train(
num_atoms=num_atoms,
training_batch_size=training_batch_size,
retrain_from_scratch_each_round=False,
discard_prior_samples=False,
use_combined_loss=False,
show_train_summary=True,
)
posterior = inference_method.build_posterior(density_estimator,
sample_with_mcmc=False)
proposal = posterior.set_default_x(observation)
posteriors.append(posterior)
posterior = wrap_posterior(posteriors[-1], transforms)
assert simulator.num_simulations == num_simulations
samples = posterior.sample((num_samples, )).detach()
if num_observation is not None:
true_parameters = task.get_true_parameters(
num_observation=num_observation)
log_prob_true_parameters = posterior.log_prob(true_parameters)
return samples, simulator.num_simulations, log_prob_true_parameters
else:
return samples, simulator.num_simulations, None
def run(
task: Task,
num_samples: int,
num_observation: Optional[int] = None,
observation: Optional[torch.Tensor] = None,
num_chains: int = 10,
num_warmup: int = 10000,
kernel: str = "slice",
kernel_parameters: Optional[Dict[str, Any]] = None,
thinning: int = 1,
diagnostics: bool = True,
available_cpu: int = 1,
mp_context: str = "fork",
jit_compile: bool = False,
automatic_transforms_enabled: bool = True,
initial_params: Optional[torch.Tensor] = None,
**kwargs: Any,
) -> torch.Tensor:
"""Runs MCMC using Pyro on potential function
Produces `num_samples` while accounting for warmup (burn-in) and thinning.
Note that the actual number of simulations is not controlled for with MCMC since
algorithms are only used as a reference method in the benchmark.
MCMC is run on the potential function, which returns the unnormalized
negative log posterior probability. Note that this requires a tractable likelihood.
Pyro is used to automatically construct the potential function.
Args:
task: Task instance
num_samples: Number of samples to generate from posterior
num_observation: Observation number to load, alternative to `observation`
observation: Observation, alternative to `num_observation`
num_chains: Number of chains
num_warmup: Warmup steps, during which parameters of the sampler are adapted.
Warmup samples are not returned by the algorithm.
kernel: HMC, NUTS, or Slice
kernel_parameters: Parameters passed to kernel
thinning: Amount of thinning to apply, in order to avoid drawing
correlated samples from the chain
diagnostics: Flag for diagnostics
available_cpu: Number of CPUs used to parallelize chains
mp_context: multiprocessing context, only fork might work
jit_compile: Just-in-time (JIT) compilation, can yield significant speed ups
automatic_transforms_enabled: Whether or not to use automatic transforms
initial_params: Parameters to initialize at
Returns:
Samples from posterior
"""
assert not (num_observation is None and observation is None)
assert not (num_observation is not None and observation is not None)
tic = time.time()
log = sbibm.get_logger(__name__)
hook_fn = None
if diagnostics:
log.info(f"MCMC sampling for observation {num_observation}")
tb_writer, tb_close = tb_make_writer(
logger=log,
basepath=
f"tensorboard/pyro_{kernel.lower()}/observation_{num_observation}",
)
hook_fn = tb_make_hook_fn(tb_writer)
if "num_simulations" in kwargs:
warnings.warn(
"`num_simulations` was passed as a keyword but will be ignored, see docstring for more info."
)
# Prepare model and transforms
conditioned_model = task._get_pyro_model(num_observation=num_observation,
observation=observation)
transforms = task._get_transforms(
num_observation=num_observation,
observation=observation,
automatic_transforms_enabled=automatic_transforms_enabled,
)
kernel_parameters = kernel_parameters if kernel_parameters is not None else {}
kernel_parameters["jit_compile"] = jit_compile
kernel_parameters["transforms"] = transforms
log.info("Using kernel: {name}({parameters})".format(
name=kernel,
parameters=",".join([f"{k}={v}"
for k, v in kernel_parameters.items()]),
))
if kernel.lower() == "nuts":
mcmc_kernel = NUTS(model=conditioned_model, **kernel_parameters)
elif kernel.lower() == "hmc":
mcmc_kernel = HMC(model=conditioned_model, **kernel_parameters)
elif kernel.lower() == "slice":
mcmc_kernel = Slice(model=conditioned_model, **kernel_parameters)
else:
raise NotImplementedError
if initial_params is not None:
site_name = "parameters"
initial_params = {site_name: transforms[site_name](initial_params)}
else:
initial_params = None
mcmc_parameters = {
"num_chains": num_chains,
"num_samples": thinning * num_samples,
"warmup_steps": num_warmup,
"available_cpu": available_cpu,
"initial_params": initial_params,
}
log.info("Calling MCMC with: MCMC({name}_kernel, {parameters})".format(
name=kernel,
parameters=",".join([f"{k}={v}" for k, v in mcmc_parameters.items()]),
))
mcmc = MCMC(mcmc_kernel, hook_fn=hook_fn, **mcmc_parameters)
mcmc.run()
toc = time.time()
log.info(f"Finished MCMC after {toc-tic:.3f} seconds")
log.info(f"Automatic transforms {mcmc.transforms}")
log.info(f"Apply thinning of {thinning}")
mcmc._samples = {
"parameters": mcmc._samples["parameters"][:, ::thinning, :]
}
num_samples_available = (mcmc._samples["parameters"].shape[0] *
mcmc._samples["parameters"].shape[1])
if num_samples_available < num_samples:
warnings.warn("Some samples will be included multiple times")
samples = mcmc.get_samples(
num_samples=num_samples,
group_by_chain=False)["parameters"].squeeze()
else:
samples = mcmc.get_samples(
group_by_chain=False)["parameters"].squeeze()
idx = torch.randperm(samples.shape[0])[:num_samples]
samples = samples[idx, :]
assert samples.shape[0] == num_samples
if diagnostics:
mcmc.summary()
tb_ess(tb_writer, mcmc)
tb_r_hat(tb_writer, mcmc)
tb_marginals(tb_writer, mcmc)
tb_acf(tb_writer, mcmc)
tb_posteriors(tb_writer, mcmc)
tb_plot_posterior(tb_writer, samples, tag="posterior/final")
tb_close()
return samples
def run(
task: Task,
num_samples: int,
num_simulations: int,
num_observation: Optional[int] = None,
observation: Optional[torch.Tensor] = None,
num_rounds: int = 10,
neural_net: str = "resnet",
hidden_features: int = 50,
simulation_batch_size: int = 1000,
training_batch_size: int = 10000,
num_atoms: int = 10,
automatic_transforms_enabled: bool = True,
mcmc_method: str = "slice_np_vectorized",
mcmc_parameters: Dict[str, Any] = {
"num_chains": 100,
"thin": 10,
"warmup_steps": 100,
"init_strategy": "sir",
"sir_batch_size": 1000,
"sir_num_batches": 100,
},
z_score_x: bool = True,
z_score_theta: bool = True,
variant: str = "B",
) -> Tuple[torch.Tensor, int, Optional[torch.Tensor]]:
"""Runs (S)NRE from `sbi`
Args:
task: Task instance
num_samples: Number of samples to generate from posterior
num_observation: Observation number to load, alternative to `observation`
observation: Observation, alternative to `num_observation`
num_simulations: Simulation budget
num_rounds: Number of rounds
neural_net: Neural network to use, one of linear / mlp / resnet
hidden_features: Number of hidden features in network
simulation_batch_size: Batch size for simulator
training_batch_size: Batch size for training network
num_atoms: Number of atoms, -1 means same as `training_batch_size`
automatic_transforms_enabled: Whether to enable automatic transforms
mcmc_method: MCMC method
mcmc_parameters: MCMC parameters
z_score_x: Whether to z-score x
z_score_theta: Whether to z-score theta
variant: Can be used to switch between SNRE-A (AALR) and -B (SRE)
Returns:
Samples from posterior, number of simulator calls, log probability of true params if computable
"""
assert not (num_observation is None and observation is None)
assert not (num_observation is not None and observation is not None)
log = logging.getLogger(__name__)
if num_rounds == 1:
log.info(f"Running NRE")
num_simulations_per_round = num_simulations
else:
log.info(f"Running SNRE")
num_simulations_per_round = math.floor(num_simulations / num_rounds)
if simulation_batch_size > num_simulations_per_round:
simulation_batch_size = num_simulations_per_round
log.warn("Reduced simulation_batch_size to num_simulation_per_round")
if training_batch_size > num_simulations_per_round:
training_batch_size = num_simulations_per_round
log.warn("Reduced training_batch_size to num_simulation_per_round")
prior = task.get_prior_dist()
if observation is None:
observation = task.get_observation(num_observation)
simulator = task.get_simulator(max_calls=num_simulations)
transforms = task._get_transforms(
automatic_transforms_enabled)["parameters"]
if automatic_transforms_enabled:
prior = wrap_prior_dist(prior, transforms)
simulator = wrap_simulator_fn(simulator, transforms)
classifier = classifier_nn(
model=neural_net.lower(),
hidden_features=hidden_features,
z_score_x=z_score_x,
z_score_theta=z_score_theta,
)
if variant == "A":
inference_class = inference.SNRE_A
inference_method_kwargs = {}
elif variant == "B":
inference_class = inference.SNRE_B
inference_method_kwargs = {"num_atoms": num_atoms}
else:
raise NotImplementedError
inference_method = inference_class(classifier=classifier, prior=prior)
posteriors = []
proposal = prior
mcmc_parameters["warmup_steps"] = 25
for r in range(num_rounds):
theta, x = inference.simulate_for_sbi(
simulator,
proposal,
num_simulations=num_simulations_per_round,
simulation_batch_size=simulation_batch_size,
)
density_estimator = inference_method.append_simulations(
theta, x, from_round=r).train(
training_batch_size=training_batch_size,
retrain_from_scratch_each_round=False,
discard_prior_samples=False,
show_train_summary=True,
**inference_method_kwargs,
)
if r > 1:
mcmc_parameters["init_strategy"] = "latest_sample"
posterior = inference_method.build_posterior(
density_estimator,
mcmc_method=mcmc_method,
mcmc_parameters=mcmc_parameters)
# Copy hyperparameters, e.g., mcmc_init_samples for "latest_sample" strategy.
if r > 0:
posterior.copy_hyperparameters_from(posteriors[-1])
proposal = posterior.set_default_x(observation)
posteriors.append(posterior)
posterior = wrap_posterior(posteriors[-1], transforms)
assert simulator.num_simulations == num_simulations
samples = posterior.sample((num_samples, )).detach()
return samples, simulator.num_simulations, None
def run(
task: Task,
num_samples: int,
num_simulations: int,
num_observation: Optional[int] = None,
observation: Optional[torch.Tensor] = None,
population_size: Optional[int] = None,
distance: Optional[str] = "l2",
initial_round_factor: int = 5,
batch_size: int = 1000,
epsilon_decay: Optional[float] = 0.5,
kernel: Optional[str] = "gaussian",
kernel_variance_scale: Optional[float] = 0.5,
population_strategy: Optional[str] = "constant",
use_last_pop_samples: bool = False,
num_workers: int = 1,
sass: bool = False,
sass_sample_weights: bool = False,
sass_feature_expansion_degree: int = 1,
sass_fraction: float = 0.5,
lra: bool = False,
lra_sample_weights: bool = True,
kde_bandwidth: Optional[str] = None,
kde_sample_weights: bool = False,
) -> Tuple[torch.Tensor, int, Optional[torch.Tensor]]:
"""ABC-SMC using pyabc toolbox
Args:
task: Task instance
num_samples: Number of samples to generate from posterior
num_simulations: Simulation budget
num_observation: Observation number to load, alternative to `observation`
observation: Observation, alternative to `num_observation`
population_size: If None, uses heuristic: 1000 if `num_simulations` is greater
than 10k, else 100
distance: Distance function, options = {l1, l2, mse}
epsilon_decay: Decay for epsilon, quantile based.
kernel: Kernel distribution used to perturb the particles.
kernel_variance_scale: Scaling factor for kernel variance.
sass: If True, summary statistics are learned as in
Fearnhead & Prangle 2012.
sass_sample_weights: Whether to weigh SASS samples
sass_feature_expansion_degree: Degree of polynomial expansion of the summary
statistics.
sass_fraction: Fraction of simulation budget to use for sass.
lra: If True, posterior samples are adjusted with
linear regression as in Beaumont et al. 2002.
lra_sample_weights: Whether to weigh LRA samples
kde_bandwidth: If not None, will resample using KDE when necessary, set
e.g. to "cv" for cross-validated bandwidth selection
kde_sample_weights: Whether to weigh KDE samples
Returns:
Samples from posterior, number of simulator calls, log probability of true params if computable
"""
assert not (num_observation is None and observation is None)
assert not (num_observation is not None and observation is not None)
log = sbibm.get_logger(__name__)
db = "sqlite:///" + os.path.join(
tempfile.gettempdir(),
f"pyabc_{time.time()}_{random.randint(0, 1e9)}.db")
# Wrap sbibm prior and simulator for pyABC
prior = wrap_prior(task)
simulator = PyAbcSimulator(task)
distance_str = distance
if observation is None:
observation = task.get_observation(num_observation)
# Population size strategy
if population_size is None:
population_size = 100
if num_simulations > 10_000:
population_size = 1000
# Find initial epsilon with rej abc run.
initial_round_size = clip_int(
value=initial_round_factor * population_size,
minimum=population_size,
maximum=max(0.5 * num_simulations, population_size),
)
log.info(
f"Running REJ-ABC with {initial_round_size} samples to find initial epsilon."
)
_, distances = run_rejection_abc(task, initial_round_size, population_size,
observation, distance_str, batch_size)
initial_epsilon = distances[-1].item()
# Wrap observation and distance for pyabc.
distance = get_distance(distance_str)
observation = np.atleast_1d(np.array(observation, dtype=float).squeeze())
# Define quantile based epsilon decay.
epsilon = pyabc.epsilon.QuantileEpsilon(initial_epsilon=initial_epsilon,
alpha=epsilon_decay)
# Perturbation kernel
transition = pyabc.transition.MultivariateNormalTransition(
scaling=kernel_variance_scale)
population_size = min(population_size, num_simulations)
if population_strategy == "constant":
population_size_strategy = population_size
elif population_strategy == "adaptive":
raise NotImplementedError("Not implemented atm.")
population_size_strategy = pyabc.populationstrategy.AdaptivePopulationSize(
start_nr_particles=population_size,
max_population_size=int(10 * population_size),
min_population_size=int(0.1 * population_size),
)
# Multiprocessing
if num_workers > 1:
sampler = pyabc.sampler.MulticoreParticleParallelSampler(
n_procs=num_workers)
else:
sampler = pyabc.sampler.SingleCoreSampler(check_max_eval=False)
# Collect kwargs
kwargs = dict(
parameter_priors=[prior],
distance_function=distance,
population_size=population_size_strategy,
transitions=[transition],
eps=epsilon,
sampler=sampler,
)
# Semi-automatic summary statistics.
if sass:
num_pilot_simulations = int(sass_fraction * num_simulations)
log.info(f"SASS pilot run with {num_pilot_simulations} simulations.")
kwargs["models"] = [simulator]
# Run pyabc with fixed budget.
pilot_theta, pilot_weights = run_pyabc(
task,
db,
num_pilot_simulations,
observation,
pyabc_kwargs=kwargs,
use_last_pop_samples=use_last_pop_samples,
distance_str=distance_str,
batch_size=batch_size,
)
# Regression
# TODO: Posterior does not return xs, which we would need for
# regression adjustment. So we will resimulate, which is
# unneccessary. Should ideally change `inference_method` to return xs
# if requested instead. This step thus does not count towards budget
pilot_x = task.get_simulator(max_calls=None)(pilot_theta)
# Run SASS.
sumstats_transform = get_sass_transform(
theta=pilot_theta,
x=pilot_x,
expansion_degree=sass_feature_expansion_degree,
sample_weight=pilot_weights if sass_sample_weights else None,
)
# Update simulator to use sass summary stats.
def sumstats_simulator(theta):
# Pyabc simulator returns dict.
x = simulator(theta)["data"].reshape(1, -1)
# Transform return Tensor.
sx = sumstats_transform(x)
return dict(data=sx.numpy().squeeze())
observation = sumstats_transform(observation.reshape(1, -1))
observation = np.atleast_1d(
np.array(observation, dtype=float).squeeze())
log.info(f"Finished learning summary statistics.")
else:
sumstats_simulator = simulator
num_pilot_simulations = 0
population_size = min(population_size, num_simulations)
log.info("""Running ABC-SMC-pyabc with {} simulations""".format(
num_simulations - num_pilot_simulations))
kwargs["models"] = [sumstats_simulator]
# Run pyabc with fixed budget.
particles, weights = run_pyabc(
task,
db,
num_simulations=num_simulations - num_pilot_simulations,
observation=observation,
pyabc_kwargs=kwargs,
use_last_pop_samples=use_last_pop_samples,
distance_str=distance_str,
batch_size=batch_size,
)
if lra:
log.info(f"Running linear regression adjustment.")
# TODO: Posterior does not return xs, which we would need for
# regression adjustment. So we will resimulate, which is
# unneccessary. Should ideally change `inference_method` to return xs
# if requested instead.
xs = task.get_simulator(max_calls=None)(particles)
# NOTE: If posterior is bounded we should do the regression in
# unbounded space, as described in https://arxiv.org/abs/1707.01254
transform_to_unbounded = True
transforms = task._get_transforms(transform_to_unbounded)["parameters"]
# Update the particles with LRA.
particles = run_lra(
theta=particles,
x=xs,
observation=torch.tensor(observation,
dtype=torch.float32).unsqueeze(0),
sample_weight=weights if lra_sample_weights else None,
transforms=transforms,
)
# TODO: Maybe set weights uniform because they can't be updated?
# weights = torch.ones(particles.shape[0]) / particles.shape[0]
if kde_bandwidth is not None:
samples = particles
log.info(
f"KDE on {samples.shape[0]} samples with bandwidth option {kde_bandwidth}"
)
kde = get_kde(
samples,
bandwidth=kde_bandwidth,
sample_weight=weights if kde_sample_weights else None,
)
samples = kde.sample(num_samples)
else:
log.info(f"Sampling {num_samples} samples from trace")
samples = sample_with_weights(particles,
weights,
num_samples=num_samples)
log.info(f"Unique samples: {torch.unique(samples, dim=0).shape[0]}")
return samples, simulator.simulator.num_simulations, None
def run(
task: Task,
num_samples: int,
num_simulations: int,
num_simulations_per_step: int = 100,
num_observation: Optional[int] = None,
observation: Optional[torch.Tensor] = None,
automatic_transforms_enabled: bool = False,
mcmc_method: str = "slice_np",
mcmc_parameters: Dict[str, Any] = {},
diag_eps: float = 0.0,
) -> (torch.Tensor, int, Optional[torch.Tensor]):
"""Runs (S)NLE from `sbi`
Args:
task: Task instance
num_observation: Observation number to load, alternative to `observation`
observation: Observation, alternative to `num_observation`
num_samples: Number of samples to generate from posterior
num_simulations: Simulation budget
num_simulations_per_step: Number of simulations per MCMC step
automatic_transforms_enabled: Whether to enable automatic transforms
mcmc_method: MCMC method
mcmc_parameters: MCMC parameters
diag_eps: Epsilon applied to diagonal
Returns:
Samples from posterior, number of simulator calls, log probability of true params if computable
"""
assert not (num_observation is None and observation is None)
assert not (num_observation is not None and observation is not None)
log = logging.getLogger(__name__)
log.info(f"Running SL")
prior = task.get_prior_dist()
if observation is None:
observation = task.get_observation(num_observation)
simulator = task.get_simulator()
transforms = task._get_transforms(automatic_transforms_enabled)["parameters"]
prior = wrap_prior_dist(prior, transforms)
simulator = wrap_simulator_fn(simulator, transforms)
likelihood_estimator = SynthLikNet(
simulator=simulator,
num_simulations_per_step=num_simulations_per_step,
diag_eps=diag_eps,
)
posterior = LikelihoodBasedPosterior(
method_family="snle",
neural_net=likelihood_estimator,
prior=prior,
x_shape=observation.shape,
mcmc_parameters=mcmc_parameters,
)
posterior.set_default_x(observation)
posterior = wrap_posterior(posterior, transforms)
# assert simulator.num_simulations == num_simulations
samples = posterior.sample((num_samples,)).detach()
return samples, simulator.num_simulations, None
