blackboxopt.optimizers.hyperband

Hyperband (StagedIterationOptimizer)

Source code in blackboxopt/optimizers/hyperband.py

class Hyperband(StagedIterationOptimizer):
    def __init__(
        self,
        search_space: ParameterSpace,
        objective: Objective,
        min_fidelity: float,
        max_fidelity: float,
        num_iterations: int,
        eta: float = 3.0,
        seed: int = None,
        logger: logging.Logger = None,
    ):
        """Implementation of Hyperband as proposed in

        Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. (2016).
        Hyperband: A novel bandit-based approach to hyperparameter optimization.
        arXiv preprint arXiv:1603.06560.

        Args:
            search_space: [description]
            objective: [description]
            min_fidelity: The smallest fidelity value that is still meaningful.
                Must be strictly greater than zero!
            max_fidelity: The largest fidelity value used during the optimization.
                Must not be smaller than `min_fidelity`
            num_iterations: [description]
            eta: Scaling parameter to control the aggressiveness of Hyperband's racing.
            seed: [description]
            logger: [description]
        """
        self.config_sampler = RandomSearchSampler(search_space)
        self.min_fidelity = min_fidelity
        self.max_fidelity = max_fidelity
        self.eta = eta

        super().__init__(
            search_space=search_space,
            objective=objective,
            num_iterations=num_iterations,
            seed=seed,
            logger=logger,
        )

    def _create_new_iteration(self, iteration_index: int) -> StagedIteration:
        """Optimizer specific way to create a new
        `blackboxopt.optimizer.staged.iteration.StagedIteration` object
        """
        return create_hyperband_iteration(
            iteration_index,
            self.min_fidelity,
            self.max_fidelity,
            self.eta,
            self.config_sampler,
            self.objective,
            self.logger,
        )

generate_evaluation_specification(self) inherited

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/hyperband.py

def generate_evaluation_specification(self) -> EvaluationSpecification:
    """Get next configuration and settings to evaluate.

    Raises:
        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.
    """
    # check if any of the already active iterations returns a configuration and
    # simply return that
    for idx, iteration in enumerate(self.iterations):
        es = iteration.generate_evaluation_specification()

        if es is not None:
            self.evaluation_uuid_to_iteration[str(es.optimizer_info["id"])] = idx
            self.pending_configurations[str(es.optimizer_info["id"])] = es
            return es

    # if that didn't work, check if there another iteration can be started and then
    # ask it for a configuration
    if len(self.iterations) < self.num_iterations:
        self.iterations.append(self._create_new_iteration(len(self.iterations)))
        es = self.iterations[-1].generate_evaluation_specification()
        self.evaluation_uuid_to_iteration[str(es.optimizer_info["id"])] = (
            len(self.iterations) - 1
        )
        self.pending_configurations[str(es.optimizer_info["id"])] = es
        return es

    # check if the optimization is already complete or whether the optimizer is
    # waiting for evaluation results -> raise corresponding error
    if all([iteration.finished for iteration in self.iterations]):
        raise OptimizationComplete

    raise OptimizerNotReady

report(self, evaluations) inherited

Report one or multiple evaluations to the optimizer.

All valid evaluations are processed. Faulty evaluations are not processed, instead an EvaluationsError is raised, which includes the problematic evaluations with their respective Exceptions in the evaluations_with_errors attribute.

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

Exceptions:

Type	Description
EvaluationsError	Raised when an evaluation could not be processed.

Source code in blackboxopt/optimizers/hyperband.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(
        [super().report, _validate_optimizer_info_id, self._report],
        _evals,
    )