AbstractAdaptiveSampler

class AbstractAdaptiveSampler(initial_models=10, batch_size=1)

Bases: AbstractSampler

Adaptive samplers iterate until a budget is reached or convergence is met.

Parameters:

• initial_models (int)

• batch_size (int)

initial_models: int = 10

batch_size: int = 1

init(eval_fn, d, key, options)

Initialize the sampler state and perform the first evaluations.

Return type:

SamplerState

terminate(state, target_N)

Determine whether the sampling loop should stop.

Parameters:

• state (SamplerState)

• target_N (int)

Return type:

bool

abstractmethod step(eval_fn, d, state, key, options)

Perform one iteration of the sampling algorithm.

Parameters:

• eval_fn (Callable[[Array], Array])

• d (int)

• state (SamplerState)

• key (Array)

• options (dict[str, Any])

Return type:

SamplerState