class_docstr: The class for driving the Bayesian optimization according to the configuration.
class_docstr_tok: ["The","class","for","driving","the","Bayesian","optimization","according","to","the","configuration","."]
docstr: Runs the optimization using the previously loaded elements.
docstr_tok: ["Runs","the","optimization","using","the","previously","loaded","elements","."]
code: def run(self):
      """
      Runs the optimization using the previously loaded elements.
      """
  
      space = self._get_space()
      obj_func = self._get_obj(space)
      model = self._get_model()
      acq = self._get_acquisition(model, space)
      acq_eval = self._get_acq_evaluator(acq)
  
      from ..experiment_design import initial_design
  
      X_init = initial_design(
          self.config["initialization"]["type"],
          space,
          self.config["initialization"]["num-eval"],
      )
  
      from ..methods import ModularBayesianOptimization
  
      bo = ModularBayesianOptimization(
          model, space, obj_func, acq, acq_eval, X_init
      )
  
      bo.run_optimization(
          max_iter=self.config["resources"]["maximum-iterations"],
          max_time=self.config["resources"]["max-run-time"]
          if self.config["resources"]["max-run-time"] != "NA"
          else np.inf,
          eps=self.config["resources"]["tolerance"],
          verbosity=True,
      )
      return bo
  
file_path: GPyOpt/GPyOpt/interface/driver.py
imports: import numpy
  import time
  from ..methods import BayesianOptimization
label_desc: Use image node_id 8 for calling the BODriver obj's underlying member method code with example usage: obj.run() and returns: bo