poli.objective_repository.gfp_cbas.register.GFPCBasBlackBox

class poli.objective_repository.gfp_cbas.register.GFPCBasBlackBox(problem_type: Literal['gp', 'vae', 'elbo'], functional_only: bool = False, ignore_stops: bool = True, unique=True, n_starting_points: int = 1, batch_size: Optional[int] = None, parallelize: bool = False, num_workers: Optional[int] = None, seed: Optional[int] = None, evaluation_budget: int = inf, force_isolation: bool = False, negate: bool = False)

__init__(problem_type: Literal['gp', 'vae', 'elbo'], functional_only: bool = False, ignore_stops: bool = True, unique=True, n_starting_points: int = 1, batch_size: Optional[int] = None, parallelize: bool = False, num_workers: Optional[int] = None, seed: Optional[int] = None, evaluation_budget: int = inf, force_isolation: bool = False, negate: bool = False)

Initialize the AbstractBlackBox object.

Parameters

• batch_size (int, optional) – The batch size for parallel execution, by default None.

• parallelize (bool, optional) – Flag indicating whether to parallelize the execution, by default False.

• num_workers (int, optional) – The number of workers for parallel execution, by default we use half the available CPUs.

• evaluation_budget (int, optional) – The maximum number of evaluations allowed for the black box function, by default float(“inf”).

Methods

__init__(problem_type[, functional_only, ...])	Initialize the AbstractBlackBox object.
reset_evaluation_budget()	Resets the evaluation budget by setting the number of evaluations made to 0.
set_observer(observer)	Set the observer object for recording observations during evaluation.
terminate()	Terminate the black box optimization problem.