class_docstr: Object returned by the HB_master.run function
  
  This class offers a simple API to access the information from
  a Hyperband run.
class_docstr_tok: ["Object","returned","by","the","HB_master.run","function","This","class","offers","a","simple","API","to","access","the","information","from","a","Hyperband","run","."]
docstr: extracts all learning curves from all run configurations
  
  Parameters
  ----------
          lc_extractor: callable
                  a function to return a list of learning_curves.
                  defaults to hpbanster.HB_result.extract_HP_learning_curves
          config_ids: list of valid config ids
                  if only a subset of the config ids is wanted
  
  Returns
  -------
          dict
                  a dictionary with the config_ids as keys and the
                  learning curves as values
docstr_tok: ["extracts","all","learning","curves","from","all","run","configurations","Parameters","--","--","--","--","--","lc_extractor",":","callable","a","function","to","return","a","list","of","learning_curves",".","defaults","to","hpbanster.HB_result.extract_HP_learning_curves","config_ids",":","list","of","valid","config","ids","if","only","a","subset","of","the","config","ids","is","wanted","Returns","--","--","--","-","dict","a","dictionary","with","the","config_ids","as","keys","and","the","learning","curves","as","values"]
code: def get_learning_curves(
      self, lc_extractor=extract_HBS_learning_curves, config_ids=None
  ):
      """
      extracts all learning curves from all run configurations
  
      Parameters
      ----------
              lc_extractor: callable
                      a function to return a list of learning_curves.
                      defaults to hpbanster.HB_result.extract_HP_learning_curves
              config_ids: list of valid config ids
                      if only a subset of the config ids is wanted
  
      Returns
      -------
              dict
                      a dictionary with the config_ids as keys and the
                      learning curves as values
      """
  
      config_ids = (
          self.data.keys() if config_ids is None else config_ids
      )
  
      lc_dict = {}
  
      for id in config_ids:
          runs = self.get_runs_by_id(id)
          lc_dict[id] = lc_extractor(runs)
  
      return lc_dict
  
imports: import copy
  import os
  import json
  from hpbandster.core.base_iteration import Datum
label_desc: Use image node_id 6 for calling the Result obj's underlying member method code with example usage: obj.get_learning_curves(lc_extractor, config_ids) and returns: lc_dict