AbstractAcquisitionMaximizer(configspace[, ...])

Abstract class for the acquisition maximization.


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.

  • configspace (ConfigurationSpace) –

  • acquisition_function (AbstractAcquisitionFunction) –

  • challengers (int, defaults to 5000) – Number of configurations to sample from the configuration space to get the acquisition function value for, thus challenging the current incumbent and becoming a candidate for the next function evaluation.

  • 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.

  • previous_configs (list[Configuration]) – Previous evaluated configurations.

  • n_points (int, defaults to None) – Number of points to be sampled. If n_points is not specified, self._challengers is used.

  • 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.


challengers – An iterable consisting of configurations.

Return type:


property meta: dict[str, Any]

Return the meta-data of the created object.