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)
