class_docstr: The config generator determines how new configurations are sampled. This can take very different levels of
  complexity, from random sampling to the construction of complex empirical prediction models for promising
  configurations.
class_docstr_tok: ["The","config","generator","determines","how","new","configurations","are","sampled",".","This","can","take","very","different","levels","of","complexity",",","from","random","sampling","to","the","construction","of","complex","empirical","prediction","models","for","promising","configurations","."]
docstr: Parameters
  ----------
  
  directory: string
          where the results are logged
  logger: hpbandster.utils.result_logger_v??
          the logger to store the data, defaults to v1
  overwrite: bool
          whether or not existing data will be overwritten
  logger: logging.logger
          for some debug output
docstr_tok: ["Parameters","--","--","--","--","--","directory",":","string","where","the","results","are","logged","logger",":","hpbandster.utils.result_logger_v","?","?","the","logger","to","store","the","data",",","defaults","to","v1","overwrite",":","bool","whether","or","not","existing","data","will","be","overwritten","logger",":","logging.logger","for","some","debug","output"]
code: def __init__(self, logger=None):
      """
      Parameters
      ----------
  
      directory: string
              where the results are logged
      logger: hpbandster.utils.result_logger_v??
              the logger to store the data, defaults to v1
      overwrite: bool
              whether or not existing data will be overwritten
      logger: logging.logger
              for some debug output
  
      """
  
      if logger is None:
          self.logger = logging.getLogger("hpbandster")
      else:
          self.logger = logger
  
imports: import logging
  import traceback
label_desc: Use image node_id 1 to create a new base_config_generator object from inherited base classes: object with example: obj = base_config_generator(logger)