smac.acquisition.maximizer.abstract_acqusition_maximizer

Classes

AbstractAcquisitionMaximizer(configspace[, ...])

Abstract class for the acquisition maximization.

Interfaces

class smac.acquisition.maximizer.abstract_acqusition_maximizer.AbstractAcquisitionMaximizer(configspace, acquisition_function=None, challengers=5000, seed=0)[source]

Bases: object

Abstract class for the acquisition maximization.

In order to use this class it has to be subclassed and the method _maximize must be implemented.

Parameters:

  • configspace (ConfigurationSpace acquisition_function : AbstractAcquisitionFunction)

  • challengers (int, defaults to 5000 Number of configurations sampled during the optimization process,)

  • Also (details depend on the used maximizer.)

  • maximize. (the number of configurations that is returned by calling)

  • seed (int, defaults to 0)

property acquisition_function: AbstractAcquisitionFunction | None

The acquisition function used for maximization.

maximize(previous_configs, n_points=None, random_design=None)[source]

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 – An iterable consisting of configurations.

Return type:

Iterator[Configuration]

property meta: dict[str, Any]

Return the meta-data of the created object.

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