blackboxopt.optimizers.space_filling

SpaceFilling (MultiObjectiveOptimizer)

Sobol sequence based, space filling optimizer.

Parameters:

Name	Type	Description	Default
search_space	ParameterSpace	The search space to optimize	required
objectives	List[blackboxopt.base.Objective]	The objectives of the optimization	required
seed	int	The sobol sequence is Owen scrambled and can be seeded for reproducibility	None

Source code in blackboxopt/optimizers/space_filling.py

class SpaceFilling(MultiObjectiveOptimizer):
    """Sobol sequence based, space filling optimizer.

    Args:
        search_space: The search space to optimize
        objectives: The objectives of the optimization
        seed: The sobol sequence is Owen scrambled and can be seeded for reproducibility
    """

    def __init__(
        self,
        search_space: ParameterSpace,
        objectives: List[Objective],
        seed: int = None,
    ) -> None:
        super().__init__(search_space=search_space, objectives=objectives, seed=seed)
        self.sobol = Sobol(d=len(self.search_space), scramble=True, seed=seed)

    def generate_evaluation_specification(self) -> EvaluationSpecification:
        vector = self.sobol.random().flatten()
        configuration = self.search_space.from_numerical(vector)
        return EvaluationSpecification(configuration=configuration)

generate_evaluation_specification(self)

Get next configuration and settings to evaluate.

Exceptions:

Type	Description
OptimizationComplete	When the optimization run is finished, e.g. when the budget has been exhausted.
OptimizerNotReady	When the optimizer is not ready to propose a new evaluation specification.

Source code in blackboxopt/optimizers/space_filling.py

def generate_evaluation_specification(self) -> EvaluationSpecification:
    vector = self.sobol.random().flatten()
    configuration = self.search_space.from_numerical(vector)
    return EvaluationSpecification(configuration=configuration)

report(self, evaluations) inherited

Report one or more evaluated evaluation specifications.

NOTE: Not all optimizers support reporting results for evaluation specifications that were not proposed by the optimizer.

Parameters:

Name	Type	Description	Default
evaluations	Union[blackboxopt.evaluation.Evaluation, Iterable[blackboxopt.evaluation.Evaluation]]	A single evaluated evaluation specifications, or an iterable of many.	required

Source code in blackboxopt/optimizers/space_filling.py

def report(self, evaluations: Union[Evaluation, Iterable[Evaluation]]) -> None:
    _evals = [evaluations] if isinstance(evaluations, Evaluation) else evaluations

    call_functions_with_evaluations_and_collect_errors(
        [functools.partial(validate_objectives, objectives=self.objectives)],
        _evals,
    )