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局部搜索

LocalSearch #

LocalSearch(
    configspace: ConfigurationSpace,
    acquisition_function: AbstractAcquisitionFunction
    | None = None,
    challengers: int = 5000,
    max_steps: int | None = None,
    n_steps_plateau_walk: int = 10,
    vectorization_min_obtain: int = 2,
    vectorization_max_obtain: int = 64,
    seed: int = 0,
)

基类: AbstractAcquisitionMaximizer

SMAC 局部搜索的实现。

参数#

configspace : ConfigurationSpace acquisition_function : AbstractAcquisitionFunction challengers : int, 默认为 5000 挑战者数量。 max_steps: int | None, 默认为 None 局部搜索将执行的最大迭代次数。 n_steps_plateau_walk: int, 默认为 10 局部搜索终止前在平稳期行走的步数。 vectorization_min_obtain : int, 默认为 2 对于每个局部搜索的向量化调用，一次性获得的最小邻居数量。可以调整此参数以减少 SMAC 的开销。 vectorization_max_obtain : int, 默认为 64 对于每个局部搜索的向量化调用，一次性获得的最大邻居数量。可以调整此参数以减少 SMAC 的开销。 seed : int, 默认为 0

源代码位于 smac/acquisition/maximizer/local_search.py 
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def __init__(
    self,
    configspace: ConfigurationSpace,
    acquisition_function: AbstractAcquisitionFunction | None = None,
    challengers: int = 5000,
    max_steps: int | None = None,
    n_steps_plateau_walk: int = 10,
    vectorization_min_obtain: int = 2,
    vectorization_max_obtain: int = 64,
    seed: int = 0,
) -> None:
    super().__init__(
        configspace,
        acquisition_function,
        challengers=challengers,
        seed=seed,
    )

    self._max_steps = max_steps
    self._n_steps_plateau_walk = n_steps_plateau_walk
    self._vectorization_min_obtain = vectorization_min_obtain
    self._vectorization_max_obtain = vectorization_max_obtain

acquisition_function property writable #

acquisition_function: AbstractAcquisitionFunction | None

用于最大化的采集函数。

maximize #

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

使用 _maximize（由子类实现）最大化采集函数。

参数#

previous_configs: list[Configuration] 先前评估过的配置。 n_points: int, 默认为 None 要采样的点数和要返回的配置数量。如果未指定 n_points，则使用 self._challengers。语义取决于具体实现。 random_design: AbstractRandomDesign, 默认为 None 返回的 ChallengerList 的一部分，以便我们可以通过随机设计定义的方案交错插入随机配置。此函数末尾会调用 random_design.next_iteration() 方法。

返回值#

challengers : Iterator[Configuration] 由配置组成的迭代器。

源代码位于 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