blackboxopt.optimizers.random_search

RandomSearch

get_evaluation_specification(self)

[summary]

Exceptions:

Type	Description
OptimizationComplete	Raised if the optimizer's max_steps are reached.

Returns:

Type	Description
EvaluationSpecification	[description]

Source code in blackboxopt/optimizers/random_search.py

def get_evaluation_specification(self) -> EvaluationSpecification:
    """[summary]

    Raises:
        OptimizationComplete: Raised if the optimizer's `max_steps` are reached.

    Returns:
        [description]
    """
    if self.n_steps >= self.max_steps:
        raise OptimizationComplete()

    eval_spec = EvaluationSpecification(
        configuration=self.search_space.sample(),
        settings={},
        optimizer_info={"step": self.n_steps},
    )
    self.n_steps += 1

    return eval_spec

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	required

Source code in blackboxopt/optimizers/random_search.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,
    )