class_docstr: 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 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
class_docstr_tok: ["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","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"]
docstr: 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. 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.
  
  Returns
  -------
  challengers : Iterator[Configuration]
      An iterable consisting of configurations.
docstr_tok: ["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",".","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",".","Returns","--","--","--","-","challengers",":","Iterator","[","Configuration","]","An","iterable","consisting","of","configurations","."]
code: 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. 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.
  
      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
  
file_path: SMAC3/smac/acquisition/maximizer/abstract_acqusition_maximizer.py
imports: from __future__ import annotations
  from abc import abstractmethod
  from typing import Any, Iterator
  import numpy
  from ConfigSpace import Configuration, ConfigurationSpace
  from smac.acquisition.function.abstract_acquisition_function import AbstractAcquisitionFunction
  from smac.acquisition.maximizer.helpers import ChallengerList
  from smac.random_design.abstract_random_design import AbstractRandomDesign
  from smac.utils.logging import get_logger
label_desc: Use image node_id 5 for calling the AbstractAcquisitionMaximizer obj's underlying member method code with example usage: obj.maximize(previous_configs, n_points, random_design) and returns: challengers, unknown