class_docstr: Class to handle extra configurations in the definition of the BayesianOptimization class
class_docstr_tok: ["Class","to","handle","extra","configurations","in","the","definition","of","the","BayesianOptimization","class"]
docstr: Acquisition chooser from the available options. Guide the optimization through sequential or parallel evalutions of the objective.
docstr_tok: ["Acquisition","chooser","from","the","available","options",".","Guide","the","optimization","through","sequential","or","parallel","evalutions","of","the","objective","."]
code: def evaluator_creator(
      self,
      evaluator_type,
      acquisition,
      batch_size,
      model_type,
      model,
      space,
      acquisition_optimizer,
  ):
      """
      Acquisition chooser from the available options. Guide the optimization through sequential or parallel evalutions of the objective.
      """
      acquisition_transformation = self.kwargs.get(
          "acquisition_transformation", "none"
      )
  
      if batch_size == 1 or evaluator_type == "sequential":
          return Sequential(acquisition)
  
      elif batch_size > 1 and (
          evaluator_type == "random" or evaluator_type is None
      ):
          return RandomBatch(acquisition, batch_size)
  
      elif batch_size > 1 and evaluator_type == "thompson_sampling":
          return ThompsonBatch(acquisition, batch_size)
  
      elif evaluator_type == "local_penalization":
          if model_type not in [
              "GP",
              "sparseGP",
              "GP_MCMC",
              "warpedGP",
          ]:
              raise InvalidConfigError(
                  "local_penalization evaluator can only be used with GP models"
              )
  
          if not isinstance(acquisition, AcquisitionLP):
              acquisition_lp = AcquisitionLP(
                  model,
                  space,
                  acquisition_optimizer,
                  acquisition,
                  acquisition_transformation,
              )
          return LocalPenalization(acquisition_lp, batch_size)
  
file_path: GPyOpt/GPyOpt/util/arguments_manager.py
imports: from ..models.gpmodel import GPModel, GPModel_MCMC
  from ..models.rfmodel import RFModel
  from ..models.warpedgpmodel import WarpedGPModel
  from ..models.input_warped_gpmodel import InputWarpedGPModel
  from ..core.evaluators import Sequential, RandomBatch, LocalPenalization, ThompsonBatch
  from ..acquisitions import AcquisitionEI, AcquisitionMPI, AcquisitionLCB, AcquisitionEI_MCMC, AcquisitionMPI_MCMC, AcquisitionLCB_MCMC, AcquisitionLP
  from ..core.errors import InvalidConfigError
label_desc: Use image node_id 2 for calling the ArgumentsManager obj's underlying member method code with example usage: obj.evaluator_creator(evaluator_type, acquisition, batch_size, model_type, model, space, acquisition_optimizer) and returns: Sequential, RandomBatch, ThompsonBatch, LocalPenalization