def run_wrapper(
self,
run_info: RunInfo,
) -> Tuple[RunInfo, RunValue]:
"""
wrapper function for ExecuteTARun.run_wrapper() to cap the target algorithm
runtime if it would run over the total allowed runtime.
Parameters
----------
run_info : RunInfo
Object that contains enough information to execute a configuration run in
isolation.
Returns
-------
RunInfo:
an object containing the configuration launched
RunValue:
Contains information about the status/performance of config
"""
if self.budget_type is None:
if run_info.budget != 0:
raise ValueError(
'If budget_type is None, budget must be.0, but is %f' %
run_info.budget)
else:
if run_info.budget == 0:
run_info = run_info._replace(budget=100)
elif run_info.budget <= 0 or run_info.budget > 100:
raise ValueError(
'Illegal value for budget, must be >0 and <=100, but is %f'
% run_info.budget)
if self.budget_type not in ('subsample', 'iterations', 'mixed'):
raise ValueError(
"Illegal value for budget type, must be one of "
"('subsample', 'iterations', 'mixed'), but is : %s" %
self.budget_type)
remaining_time = self.stats.get_remaing_time_budget()
if remaining_time - 5 < run_info.cutoff:
run_info = run_info._replace(cutoff=int(remaining_time - 5))
if run_info.cutoff < 1.0:
return run_info, RunValue(
status=StatusType.STOP,
cost=self.worst_possible_result,
time=0.0,
additional_info={},
starttime=time.time(),
endtime=time.time(),
)
elif (run_info.cutoff != int(np.ceil(run_info.cutoff))
and not isinstance(run_info.cutoff, int)):
run_info = run_info._replace(cutoff=int(np.ceil(run_info.cutoff)))
return super().run_wrapper(run_info=run_info)
def run_wrapper(
self,
run_info: RunInfo,
) -> Tuple[RunInfo, RunValue]:
"""
wrapper function for ExecuteTARun.run_wrapper() to cap the target algorithm
runtime if it would run over the total allowed runtime.
Args:
run_info (RunInfo): Object that contains enough information
to execute a configuration run in isolation.
Returns:
RunInfo:
an object containing the configuration launched
RunValue:
Contains information about the status/performance of config
"""
if self.budget_type is None:
if run_info.budget != 0:
raise ValueError(
'If budget_type is None, budget must be.0, but is %f' %
run_info.budget)
else:
if run_info.budget == 0:
# SMAC can return budget zero for intensifiers that don't have a concept
# of budget, for example a simple bayesian optimization intensifier.
# Budget determines how our pipeline trains, which can be via runtime or epochs
epochs_budget = self.pipeline_config.get('epochs', np.inf)
runtime_budget = self.pipeline_config.get('runtime', np.inf)
run_info = run_info._replace(
budget=min(epochs_budget, runtime_budget))
elif run_info.budget <= 0:
raise ValueError(
'Illegal value for budget, must be greater than zero but is %f'
% run_info.budget)
if self.budget_type not in ('epochs', 'runtime'):
raise ValueError(
"Illegal value for budget type, must be one of "
"('epochs', 'runtime'), but is : %s" % self.budget_type)
remaining_time = self.stats.get_remaing_time_budget()
if remaining_time - 5 < run_info.cutoff:
run_info = run_info._replace(cutoff=int(remaining_time - 5))
if run_info.cutoff < 1.0:
return run_info, RunValue(
status=StatusType.STOP,
cost=self.worst_possible_result,
time=0.0,
additional_info={},
starttime=time.time(),
endtime=time.time(),
)
elif (run_info.cutoff != int(np.ceil(run_info.cutoff))
and not isinstance(run_info.cutoff, int)):
run_info = run_info._replace(cutoff=int(np.ceil(run_info.cutoff)))
self.logger.info("Starting to evaluate configuration %s" %
run_info.config.config_id)
run_info, run_value = super().run_wrapper(run_info=run_info)
return run_info, run_value
