(self, lc_extractor=extract_HBS_learning_curves, config_ids=None)
return(lc_dict)
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
extracts all learning curves from all run configurations
extracts all learning curves from all run configurations
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)
def get_learning_curves ( self , lc_extractor = extract_HBS_learning_curves , config_ids = None ) : 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 )