def __init__(self, interface, **kwargs):
super(NelderMeadController, self).__init__(interface, **kwargs)
self.learner = mll.NelderMeadLearner(
start_datetime=self.start_datetime, **self.remaining_kwargs)
self._update_controller_with_learner_attributes()
self.out_type.append('nelder_mead')
def __init__(
self,
interface,
training_type='differential_evolution',
machine_learner_type='machine_learner',
num_training_runs=None,
no_delay=True,
num_params=None,
min_boundary=None,
max_boundary=None,
trust_region=None,
learner_archive_filename=mll.default_learner_archive_filename,
learner_archive_file_type=mll.default_learner_archive_file_type,
param_names=None,
**kwargs):
super(MachineLearnerController, self).__init__(interface, **kwargs)
self.machine_learner_type = machine_learner_type
self.last_training_cost = None
self.last_training_bad = None
self.last_training_run_flag = False
if num_training_runs is None:
if num_params is None:
self.num_training_runs = 10
else:
self.num_training_runs = max(10, 2 * int(num_params))
else:
self.num_training_runs = int(num_training_runs)
if self.num_training_runs <= 0:
self.log.error(
'Number of training runs must be larger than zero:' +
repr(self.num_training_runs))
raise ValueError
self.no_delay = bool(no_delay)
self.training_type = str(training_type)
if self.training_type == 'random':
self.learner = mll.RandomLearner(
start_datetime=self.start_datetime,
num_params=num_params,
min_boundary=min_boundary,
max_boundary=max_boundary,
trust_region=trust_region,
learner_archive_filename=None,
learner_archive_file_type=learner_archive_file_type,
param_names=param_names,
**self.remaining_kwargs)
elif self.training_type == 'nelder_mead':
self.learner = mll.NelderMeadLearner(
start_datetime=self.start_datetime,
num_params=num_params,
min_boundary=min_boundary,
max_boundary=max_boundary,
learner_archive_filename=None,
learner_archive_file_type=learner_archive_file_type,
param_names=param_names,
**self.remaining_kwargs)
elif self.training_type == 'differential_evolution':
self.learner = mll.DifferentialEvolutionLearner(
start_datetime=self.start_datetime,
num_params=num_params,
min_boundary=min_boundary,
max_boundary=max_boundary,
trust_region=trust_region,
evolution_strategy='rand2',
learner_archive_filename=None,
learner_archive_file_type=learner_archive_file_type,
param_names=param_names,
**self.remaining_kwargs)
else:
self.log.error(
'Unknown training type provided to machine learning controller:'
+ repr(training_type))
self.archive_dict.update({'training_type': self.training_type})
self._update_controller_with_learner_attributes()
def __init__(
self,
interface,
training_type='random',
num_training_runs=None,
no_delay=True,
num_params=None,
min_boundary=None,
max_boundary=None,
trust_region=None,
learner_archive_filename=mll.default_learner_archive_filename,
learner_archive_file_type=mll.default_learner_archive_file_type,
**kwargs):
super(GaussianProcessController, self).__init__(interface, **kwargs)
self.last_training_cost = None
self.last_training_bad = None
self.last_training_run_flag = False
if num_training_runs is None:
if num_params is None:
self.num_training_runs = 10
else:
self.num_training_runs = max(10, 2 * int(num_params))
else:
self.num_training_runs = int(num_training_runs)
if self.num_training_runs <= 0:
self.log.error(
'Number of training runs must be larger than zero:' +
repr(self.num_training_runs))
raise ValueError
self.no_delay = bool(no_delay)
self.training_type = str(training_type)
if self.training_type == 'random':
self.learner = mll.RandomLearner(
start_datetime=self.start_datetime,
num_params=num_params,
min_boundary=min_boundary,
max_boundary=max_boundary,
trust_region=trust_region,
learner_archive_filename=None,
learner_archive_file_type=learner_archive_file_type,
**self.remaining_kwargs)
elif self.training_type == 'nelder_mead':
self.learner = mll.NelderMeadLearner(
start_datetime=self.start_datetime,
num_params=num_params,
min_boundary=min_boundary,
max_boundary=max_boundary,
learner_archive_filename='training_learner_archive',
learner_archive_file_type=learner_archive_file_type,
**self.remaining_kwargs)
else:
self.log.error(
'Unknown training type provided to Gaussian process controller:'
+ repr(training_type))
self.archive_dict.update({'training_type': self.training_type})
self._update_controller_with_learner_attributes()
self.gp_learner = mll.GaussianProcessLearner(
start_datetime=self.start_datetime,
num_params=num_params,
min_boundary=min_boundary,
max_boundary=max_boundary,
trust_region=trust_region,
learner_archive_filename=learner_archive_filename,
learner_archive_file_type=learner_archive_file_type,
**self.remaining_kwargs)
self.gp_learner_params_queue = self.gp_learner.params_out_queue
self.gp_learner_costs_queue = self.gp_learner.costs_in_queue
self.end_gp_learner = self.gp_learner.end_event
self.new_params_event = self.gp_learner.new_params_event
self.remaining_kwargs = self.gp_learner.remaining_kwargs
self.generation_num = self.gp_learner.generation_num