(self, t_prof, eval_env_bldr, chief_handle, eval_type, log_conf_interval=False)
Args:
            t_prof (TrainingProfile)
            chief_handle (class instance or ray ActorHandle)
            eval_type (str):                Name of the evaluator
Args:
            t_prof (TrainingProfile)
            chief_handle (class instance or ray ActorHandle)
            eval_type (str):                Name of the evaluator
Args : t_prof ( TrainingProfile ) chief_handle ( class instance or ray ActorHandle ) eval_type ( str ) : Name of the evaluator
def __init__(self, t_prof, eval_env_bldr, chief_handle, eval_type, log_conf_interval=False):
        """
        Args:
            t_prof (TrainingProfile)
            chief_handle (class instance or ray ActorHandle)
            eval_type (str):                Name of the evaluator
        """
        super().__init__(t_prof=t_prof)
        self._eval_env_bldr = eval_env_bldr
        self._chief_handle = chief_handle
        self._chief_info = [None for _ in range(self._t_prof.n_seats)]

        self._is_multi_stack = len(self._t_prof.eval_stack_sizes) > 1

        self._log_conf_interval = log_conf_interval

        self._exp_name_total, self._exp_names_conf = self._create_experiments(self_name=eval_type)

        if self._is_multi_stack:
            self._exp_name_multi_stack = {
                eval_mode:
                    self._ray.get(
                        self._ray.remote(self._chief_handle.create_experiment,
                                         self._t_prof.name
                                         + " " + eval_mode
                                         + "Multi_Stack"
                                         + ": " + eval_type
                                         + " Averaged Total"))
                for eval_mode in self._t_prof.eval_modes_of_algo
            }
            if self._log_conf_interval:
                self._exp_names_multi_stack_conf = {
                    eval_mode:
                        self._ray.get(
                            [
                                self._ray.remote(self._chief_handle.create_experiment,
                                                 self._t_prof.name
                                                 + " " + eval_mode
                                                 + ": " + eval_type
                                                 + " Conf_" + bound_end)
                                for bound_end in ["lower95", "upper95"]
                            ]
                        )
                    for eval_mode in self._t_prof.eval_modes_of_algo
                }
def __init__ ( self , t_prof , eval_env_bldr , chief_handle , eval_type , log_conf_interval = False ) : super ( ) . __init__ ( t_prof = t_prof ) self . _eval_env_bldr = eval_env_bldr self . _chief_handle = chief_handle self . _chief_info = [ None for _ in range ( self . _t_prof . n_seats ) ] self . _is_multi_stack = len ( self . _t_prof . eval_stack_sizes ) > 1 self . _log_conf_interval = log_conf_interval self . _exp_name_total , self . _exp_names_conf = self . _create_experiments ( self_name = eval_type ) if self . _is_multi_stack : self . _exp_name_multi_stack = { eval_mode : self . _ray . get ( self . _ray . remote ( self . _chief_handle . create_experiment , self . _t_prof . name + " " + eval_mode + "Multi_Stack" + ": " + eval_type + " Averaged Total" ) ) for eval_mode in self . _t_prof . eval_modes_of_algo } if self . _log_conf_interval : self . _exp_names_multi_stack_conf = { eval_mode : self . _ray . get ( [ self . _ray . remote ( self . _chief_handle . create_experiment , self . _t_prof . name + " " + eval_mode + ": " + eval_type + " Conf_" + bound_end ) for bound_end in [ "lower95" , "upper95" ] ] ) for eval_mode in self . _t_prof . eval_modes_of_algo }