def _init_optimizer(self): """ Initializes the optimizer if it does not exist. """ self._logger.debug("Initializing optimizer. Current state is %s" % self._optimizer) self._optimizer = check_optimizer( self._optimizer, self._experiment, optimizer_arguments=self._optimizer_arguments) self._logger.debug("Initialized optimizer. State afterwards is %s" % self._optimizer)
def get_next_candidate(self): """ Returns the Candidate next to evaluate. Returns ------- next_candidate : Candidate or None The Candidate object that should be evaluated next. May be None. """ self.logger.info("Returning next candidate.") self.optimizer = check_optimizer(self.optimizer, optimizer_arguments=self.optimizer_arguments) if not self.experiment.candidates_pending: self.experiment.candidates_pending.extend(self.optimizer.get_next_candidates(self.experiment)) next_candidate = self.experiment.candidates_pending.pop() self.logger.info("next candidate found: %s" % next_candidate) return next_candidate
def get_next_candidate(self): """ Returns the Candidate next to evaluate. Returns ------- next_candidate : Candidate or None The Candidate object that should be evaluated next. May be None. """ self.logger.info("Returning next candidate.") self.optimizer = check_optimizer(self.optimizer, optimizer_arguments=self.optimizer_arguments) if not self.experiment.candidates_pending: self.experiment.candidates_pending.extend( self.optimizer.get_next_candidates(self.experiment)) next_candidate = self.experiment.candidates_pending.pop() self.logger.info("next candidate found: %s" %next_candidate) return next_candidate
def _init_optimizer(self): """ Initializes the optimizer if it does not exist. """ self._optimizer= check_optimizer(self._optimizer, self._experiment, optimizer_arguments=self._optimizer_arguments)