class_docstr: Base class for evaluating methods for optimisation. 
class_docstr_tok: ["Base","class","for","evaluating","methods","for","optimisation","."]
docstr: Runs some routines to set up saving. 
docstr_tok: ["Runs","some","routines","to","set","up","saving","."]
code: def _set_up_saving(self):
      """Runs some routines to set up saving."""
      # Store methods and the options in to_be_saved
      self.to_be_saved.max_capital = self.max_capital
      self.to_be_saved.num_workers = self.worker_manager.num_workers
      self.to_be_saved.methods = self.methods
      self.to_be_saved.method_options = (
          self.method_options
      )  # Some error created here.
      self.to_be_saved.time_distro_str = (
          self.worker_manager.get_time_distro_info()
      )
      # Data about the problem
      self.to_be_saved.true_maxval = (
          self.func_caller.maxval
          if self.func_caller.maxval is not None
          else -np.inf
      )
      self.to_be_saved.true_argmax = (
          self.func_caller.argmax
          if self.func_caller.argmax is not None
          else "not-known"
      )
      self.to_be_saved.domain_type = self.domain.get_type()
      self.to_be_saved.is_noisy = self.func_caller.is_noisy()
      # For the results
      self.data_to_be_saved = [
          "query_step_idxs",
          "query_points",
          "query_vals",
          "query_true_vals",
          "query_send_times",
          "query_receive_times",
          "query_eval_times",
          "query_worker_ids",
          "curr_opt_vals",
          "curr_opt_points",
          "curr_true_opt_vals",
          "curr_true_opt_points",
          "num_jobs_per_worker",
      ]
      self.data_to_be_saved_if_available = [
          "query_fidels",
          "query_cost_at_fidels",
          "query_at_fidel_to_opts",
          "prev_eval_points",
          "prev_eval_vals",
      ]
      self.data_not_to_be_mat_saved.extend(
          [
              "method_options",
              "query_points",
              "curr_opt_points",
              "curr_true_opt_points",
          ]
      )
      self.data_not_to_be_pickled.extend(["method_options"])
      for data_type in self.data_to_be_saved:
          setattr(
              self.to_be_saved,
              data_type,
              self._get_new_empty_results_array(),
          )
      for data_type in self.data_to_be_saved_if_available:
          setattr(
              self.to_be_saved,
              data_type,
              self._get_new_empty_results_array(),
          )
  
imports: from argparse import Namespace
  from datetime import datetime
  import os
  import numpy
  from .blackbox_optimiser import OptInitialiser
  from ..utils.method_evaluator import BaseMethodEvaluator
label_desc: Use image node_id 3 for calling the OptMethodEvaluator obj's underlying member method code with example usage: obj._set_up_saving() without return types