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