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Random search

RandomSearch #

RandomSearch(
    configspace: ConfigurationSpace,
    acquisition_function: (
        AbstractAcquisitionFunction | None
    ) = None,
    challengers: int = 5000,
    seed: int = 0,
)

Bases: AbstractAcquisitionMaximizer

Get candidate solutions via random sampling of configurations.

Source code in smac/acquisition/maximizer/abstract_acquisition_maximizer.py 
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def __init__(
    self,
    configspace: ConfigurationSpace,
    acquisition_function: AbstractAcquisitionFunction | None = None,
    challengers: int = 5000,
    seed: int = 0,
):
    self._configspace = configspace
    self._acquisition_function = acquisition_function
    self._challengers = challengers
    self._seed = seed
    self._rng = np.random.RandomState(seed=seed)

acquisition_function property writable #

acquisition_function: AbstractAcquisitionFunction | None

The acquisition function used for maximization.

meta property #

meta: dict[str, Any]

Return the meta-data of the created object.

maximize #

maximize(
    previous_configs: list[Configuration],
    n_points: int | None = None,
    random_design: AbstractRandomDesign | None = None,
) -> Iterator[Configuration]

Maximize acquisition function using _maximize, implemented by a subclass.

Parameters#

previous_configs: list[Configuration] Previous evaluated configurations. n_points: int, defaults to None Number of points to be sampled & number of configurations to be returned. If n_points is not specified, self._challengers is used. Semantics depend on concrete implementation. random_design: AbstractRandomDesign, defaults to None Part of the returned ChallengerList such that we can interleave random configurations by a scheme defined by the random design. The method random_design.next_iteration() is called at the end of this function.

Returns#

challengers : Iterator[Configuration] An iterable consisting of configurations.

Source code in smac/acquisition/maximizer/abstract_acquisition_maximizer.py 
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def maximize(
    self,
    previous_configs: list[Configuration],
    n_points: int | None = None,
    random_design: AbstractRandomDesign | None = None,
) -> Iterator[Configuration]:
    """Maximize acquisition function using `_maximize`, implemented by a subclass.

    Parameters
    ----------
    previous_configs: list[Configuration]
        Previous evaluated configurations.
    n_points: int, defaults to None
        Number of points to be sampled & number of configurations to be returned. If `n_points` is not specified,
        `self._challengers` is used. Semantics depend on concrete implementation.
    random_design: AbstractRandomDesign, defaults to None
        Part of the returned ChallengerList such that we can interleave random configurations
        by a scheme defined by the random design. The method `random_design.next_iteration()`
        is called at the end of this function.

    Returns
    -------
    challengers : Iterator[Configuration]
        An iterable consisting of configurations.
    """
    if n_points is None:
        n_points = self._challengers

    def next_configs_by_acquisition_value() -> list[Configuration]:
        assert n_points is not None
        # since maximize returns a tuple of acquisition value and configuration,
        # and we only need the configuration, we return the second element of the tuple
        # for each element in the list
        return [t[1] for t in self._maximize(previous_configs, n_points)]

    challengers = ChallengerList(
        self._configspace,
        next_configs_by_acquisition_value,
        random_design,
    )

    if random_design is not None:
        random_design.next_iteration()

    return challengers