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.id, job.exception))