class_docstr: Bayesian optimization with Gaussian Processes
class_docstr_tok: ["Bayesian","optimization","with","Gaussian","Processes"]
code: def __init__(
      self,
      target_metric=None,
      iterations=1,
      verbose=1,
      read_experiments=True,
      reporter_parameters=None,
      warn_on_re_ask=False,
      base_estimator="GP",
      n_initial_points=10,
      acquisition_function="gp_hedge",
      acquisition_optimizer="auto",
      random_state=32,
      acquisition_function_kwargs=None,
      acquisition_optimizer_kwargs=None,
      n_random_starts="DEPRECATED",
      callbacks=None,
      base_estimator_kwargs=None,
  ):
      _validate_estimator(
          base_estimator, "GP", GaussianProcessRegressor
      )
      base_estimator_kwargs = base_estimator_kwargs or {}
      base_estimator_kwargs.setdefault("noise", "gaussian")
  
      super().__init__(
          target_metric=target_metric,
          iterations=iterations,
          verbose=verbose,
          read_experiments=read_experiments,
          reporter_parameters=reporter_parameters,
          warn_on_re_ask=warn_on_re_ask,
          base_estimator=base_estimator,
          n_initial_points=n_initial_points,
          acquisition_function=acquisition_function,
          acquisition_optimizer=acquisition_optimizer,
          random_state=random_state,
          acquisition_function_kwargs=acquisition_function_kwargs,
          acquisition_optimizer_kwargs=acquisition_optimizer_kwargs,
          n_random_starts=n_random_starts,
          callbacks=callbacks,
          base_estimator_kwargs=base_estimator_kwargs,
      )
  
imports: from hyperparameter_hunter import __version__
  from hyperparameter_hunter.optimization.protocol_core import SKOptPro
  from hyperparameter_hunter.utils.version_utils import Deprecated
  from functools import partial
  from typing import Optional, Type, Union
  from sklearn.base import BaseEstimator
  from skopt.learning.gaussian_process.gpr import GaussianProcessRegressor
  from skopt.learning.gbrt import GradientBoostingQuantileRegressor
  from skopt.learning.forest import RandomForestRegressor, ExtraTreesRegressor
label_desc: Use image node_id 1 to create a new BayesianOptPro object from inherited base classes: SKOptPro with example: obj = BayesianOptPro(target_metric, iterations, verbose, read_experiments, reporter_parameters, warn_on_re_ask, base_estimator, n_initial_points, acquisition_function, acquisition_optimizer, random_state, acquisition_function_kwargs, acquisition_optimizer_kwargs, n_random_starts, callbacks, base_estimator_kwargs)