def run(
task: Task,
num_samples: int,
num_simulations: int,
batch_size: int = 1,
**kwargs: Any,
) -> torch.Tensor:
"""Runtime baseline
Draws `num_simulations` samples from prior and simulates, discards outcomes,
returns tensor of NaNs.
Args:
task: Task instance
num_samples: Number of samples to generate from posterior
num_simulations: Simulation budget
batch_size: Batch size for simulations
Returns:
Random samples from prior
"""
prior = task.get_prior()
simulator = task.get_simulator()
batch_size = min(batch_size, num_simulations)
num_batches = int(num_simulations / batch_size)
for i in tqdm(range(num_batches)):
_ = simulator(prior(num_samples=batch_size))
assert simulator.num_simulations == num_simulations
samples = float("nan") * torch.ones((num_samples, task.dim_parameters))
return samples
def run(
task: Task,
num_samples: int,
**kwargs: Any,
) -> torch.Tensor:
"""Random samples from prior as baseline
Args:
task: Task instance
num_samples: Number of samples to generate from posterior
Returns:
Random samples from prior
"""
log = sbibm.get_logger(__name__)
if "num_simulations" in kwargs:
log.warn(
"`num_simulations` was passed as a keyword but will be ignored, since this is a baseline method."
)
prior = task.get_prior()
return prior(num_samples=num_samples)