Source code for hpbandster.core.base_config_generator

import logging
import traceback

[docs]class base_config_generator(object): """ 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. """ 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
[docs] def get_config(self, budget): """ function to sample a new configuration This function is called inside Hyperband to query a new configuration Parameters ---------- budget: float the budget for which this configuration is scheduled returns: (config, info_dict) must return a valid configuration and a (possibly empty) info dict """ raise NotImplementedError('This function needs to be overwritten in %s.'%(self.__class__.__name__))
[docs] def new_result(self, job, update_model=True): """ registers finished runs Every time a run has finished, this function should be called to register it with the result logger. If overwritten, make sure to call this method from the base class to ensure proper logging. Parameters ---------- job: instance of hpbandster.distributed.dispatcher.Job contains all necessary information about the job update_model: boolean determines whether a model inside the config_generator should be updated """ if not job.exception is None: self.logger.warning("job {} failed with exception\n{}".format(, job.exception))