548ac5868bd37d4cbfd69771cb95f3eefa80f934,autosklearn/smbo.py,AutoMLSMBO,run_smbo,#AutoMLSMBO#Any#,568

Before Change

                num_run += 1
                self.logger.info("Finished SUBSET training sucessfully "
                                 "with ratio %f", ratio)
                break

        // == METALEARNING suggestions
        // we start by evaluating the defaults on the full dataset again
        // and add the suggestions from metalearning behind it

After Change

                                 "info: %s ", num_run, duration, result,
                                 str(status), additional_run_info)

                if i < 2:
                    if status != StatusType.SUCCESS:
                        self.logger.info("A CONFIG did not finish "
                                         " for subset ratio %f -> going smaller",
                                         ratio)
                        continue
                    else:
                        num_run += 1
                        self.logger.info("Finished SUBSET training sucessfully "
                                         "with ratio %f", ratio)
                        break
                else:
                    if status != StatusType.SUCCESS:
                        self.logger.info("A CONFIG did not finish "
                                         " for subset ratio %f.",
                                         ratio)
                        num_run += 1
                        continue
                    else:
                        num_run += 1
                        self.logger.info("Finished SUBSET training sucessfully "
                                         "with ratio %f", ratio)
                        break

        // == METALEARNING suggestions
        // we start by evaluating the defaults on the full dataset again
        // and add the suggestions from metalearning behind it
        metalearning_configurations = default_cfgs \
                                      + self.collect_metalearning_suggestions_with_limits()

        // == first, evaluate all metelearning and default configurations

Instances