class_docstr: Bayesian Optimiser using `GPyOpt` as a backend.
class_docstr_tok: ["Bayesian","Optimiser","using","`","GPyOpt","`","as","a","backend","."]
docstr: Initialise the optimiser's domain.
  
  Args:
      domain: :class:`Domain`. The domain of the objective function.
      seed: (optional) :obj:`int`. The seed of the optimiser. Used for
          reproducibility purposes.
docstr_tok: ["Initialise","the","optimiser","'s","domain",".","Args",":","domain",":",":","class",":","`","Domain","`",".","The","domain","of","the","objective","function",".","seed",":","(","optional",")",":","obj",":","`","int","`",".","The","seed","of","the","optimiser",".","Used","for","reproducibility","purposes","."]
code: def __init__(self, domain, seed=None):
      """Initialise the optimiser's domain.
  
      Args:
          domain: :class:`Domain`. The domain of the objective function.
          seed: (optional) :obj:`int`. The seed of the optimiser. Used for
              reproducibility purposes.
      """
      np.random.seed(seed)
      domain = Domain(domain.as_dict(), seed=seed)
      super(BayesianOptimisation, self).__init__(domain)
      converted_and_mappers = self._convert_to_gpyopt_domain(
          self.domain
      )
      (
          self.gpyopt_domain,
          self._categorical_value_mapper,
          self._discrete_type_mapper,
      ) = converted_and_mappers
      self._inv_categorical_value_mapper = {
          name: {v: k for k, v in mapping.items()}
          for name, mapping in self._categorical_value_mapper.items()
      }
      self._data_x = np.array([[]])
      self._data_fx = np.array([[]])
      self.__is_empty_data = True
  
imports: from multiprocessing import cpu_count
  from typing import Any, Dict, List, Sequence, Tuple, Type, TypeVar, Union
  import GPy
  import GPyOpt
  import numpy
  from GPyOpt.core import errors
  from hypertunity import utils
  from hypertunity.domain import Domain, Sample
  from hypertunity.optimisation.base import EvaluationScore, ExhaustedSearchSpaceError, Optimiser
label_desc: Use image node_id 1 to create a new BayesianOptimisation object from inherited base classes: Optimiser with example: obj = BayesianOptimisation(domain, seed)