def _do_dummy_prediction(self, datamanager, num_run):
# When using partial-cv it makes no sense to do dummy predictions
if self._resampling_strategy in ['partial-cv',
'partial-cv-iterative-fit']:
return num_run
self._logger.info("Starting to create dummy predictions.")
memory_limit = int(self._ml_memory_limit)
scenario_mock = unittest.mock.Mock()
scenario_mock.wallclock_limit = self._time_for_task
# This stats object is a hack - maybe the SMAC stats object should
# already be generated here!
stats = Stats(scenario_mock)
stats.start_timing()
ta = ExecuteTaFuncWithQueue(backend=self._backend,
autosklearn_seed=self._seed,
resampling_strategy=self._resampling_strategy,
initial_num_run=num_run,
logger=self._logger,
stats=stats,
metric=self._metric,
memory_limit=memory_limit,
disable_file_output=self._disable_evaluator_output,
**self._resampling_strategy_arguments)
status, cost, runtime, additional_info = \
ta.run(1, cutoff=self._time_for_task)
if status == StatusType.SUCCESS:
self._logger.info("Finished creating dummy predictions.")
else:
self._logger.error('Error creating dummy predictions: %s ',
str(additional_info))
return ta.num_run
def _do_dummy_prediction(self, datamanager, num_run):
# When using partial-cv it makes no sense to do dummy predictions
if self._resampling_strategy in ['partial-cv',
'partial-cv-iterative-fit']:
return num_run
self._logger.info("Starting to create dummy predictions.")
memory_limit = int(self._ml_memory_limit)
scenario_mock = unittest.mock.Mock()
scenario_mock.wallclock_limit = self._time_for_task
# This stats object is a hack - maybe the SMAC stats object should
# already be generated here!
stats = Stats(scenario_mock)
stats.start_timing()
ta = ExecuteTaFuncWithQueue(backend=self._backend,
autosklearn_seed=self._seed,
resampling_strategy=self._resampling_strategy,
initial_num_run=num_run,
logger=self._logger,
stats=stats,
metric=self._metric,
memory_limit=memory_limit,
disable_file_output=self._disable_evaluator_output,
**self._resampling_strategy_arguments)
status, cost, runtime, additional_info = \
ta.run(1, cutoff=self._time_for_task)
if status == StatusType.SUCCESS:
self._logger.info("Finished creating dummy predictions.")
else:
self._logger.error('Error creating dummy predictions: %s ',
str(additional_info))
return ta.num_run