Random search

RandomSearch (MultiObjectiveOptimizer)

Source code in blackboxopt/optimizers/random_search.py

class RandomSearch(MultiObjectiveOptimizer):
    def __init__(
        self,
        search_space: ParameterSpace,
        objectives: List[Objective],
        max_steps: int,
        seed: int = None,
    ) -> None:
        """Randomly sample up to `max_steps` configurations from the given search space.

        Args:
            search_space: Space to search
            objectives: The objectives of the optimization.
            max_steps: Max number of evaluation specifications the optimizer generates
                before raising `OptimizationComplete`
            seed: Optional number to seed the random number generator with.
                Defaults to None.
        """
        super().__init__(search_space=search_space, objectives=objectives, seed=seed)

        self.max_steps: int = max_steps
        self.n_steps: int = 0

    def generate_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

__init__(self, search_space, objectives, max_steps, seed=None) special

Randomly sample up to max_steps configurations from the given search space.

Parameters:

Name	Type	Description	Default
search_space	ParameterSpace	Space to search	required
objectives	List[blackboxopt.base.Objective]	The objectives of the optimization.	required
max_steps	int	Max number of evaluation specifications the optimizer generates before raising OptimizationComplete	required
seed	int	Optional number to seed the random number generator with. Defaults to None.	None

Source code in blackboxopt/optimizers/random_search.py

def __init__(
    self,
    search_space: ParameterSpace,
    objectives: List[Objective],
    max_steps: int,
    seed: int = None,
) -> None:
    """Randomly sample up to `max_steps` configurations from the given search space.

    Args:
        search_space: Space to search
        objectives: The objectives of the optimization.
        max_steps: Max number of evaluation specifications the optimizer generates
            before raising `OptimizationComplete`
        seed: Optional number to seed the random number generator with.
            Defaults to None.
    """
    super().__init__(search_space=search_space, objectives=objectives, seed=seed)

    self.max_steps: int = max_steps
    self.n_steps: int = 0

generate_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 generate_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