def GetSuggestions(self, request, context):
"""
Main function to provide suggestion.
"""
algorithm_name, config = OptimizerConfiguration.convertAlgorithmSpec(
request.experiment.spec.algorithm)
if algorithm_name != "bayesianoptimization":
raise Exception(
"Failed to create the algorithm: {}".format(algorithm_name))
if self.is_first_run:
search_space = HyperParameterSearchSpace.convert(
request.experiment)
self.base_service = BaseSkoptService(
base_estimator=config.base_estimator,
n_initial_points=config.n_initial_points,
acq_func=config.acq_func,
acq_optimizer=config.acq_optimizer,
random_state=config.random_state,
search_space=search_space)
self.is_first_run = False
trials = Trial.convert(request.trials)
new_trials = self.base_service.getSuggestions(trials,
request.request_number)
return api_pb2.GetSuggestionsReply(
parameter_assignments=Assignment.generate(new_trials))
def ValidateAlgorithmSettings(self, request, context):
algorithm_name = request.experiment.spec.algorithm.algorithm_name
if algorithm_name == "grid":
search_space = HyperParameterSearchSpace.convert(
request.experiment)
for param in search_space.params:
if param.type == DOUBLE:
if param.step == "" or param.step is None:
return self._set_validate_context_error(
context, "param {} step is nil".format(param.name))
return api_pb2.ValidateAlgorithmSettingsReply()
def GetSuggestions(self, request, context):
"""
Main function to provide suggestion.
"""
with self.lock:
if self.study is None:
self.search_space = HyperParameterSearchSpace.convert(request.experiment)
self.study = self._create_study(request.experiment.spec.algorithm, self.search_space)
trials = Trial.convert(request.trials)
if len(trials) != 0:
self._tell(trials)
list_of_assignments = self._ask(request.request_number)
return api_pb2.GetSuggestionsReply(
parameter_assignments=Assignment.generate(list_of_assignments)
)
def GetSuggestions(self, request, context):
"""
Main function to provide suggestion.
"""
if self.is_first_run:
search_space = HyperParameterSearchSpace.convert(
request.experiment)
self.base_service = BaseChocolateService(
algorithm_name=request.experiment.spec.algorithm.
algorithm_name,
search_space=search_space)
self.is_first_run = False
trials = Trial.convert(request.trials)
new_assignments = self.base_service.getSuggestions(
trials, request.request_number)
return api_pb2.GetSuggestionsReply(
parameter_assignments=Assignment.generate(new_assignments))
def GetSuggestions(self, request, context):
"""
Main function to provide suggestion.
"""
name, config = OptimizerConfiguration.convert_algorithm_spec(
request.experiment.spec.algorithm)
if self.is_first_run:
search_space = HyperParameterSearchSpace.convert(
request.experiment)
self.base_service = BaseHyperoptService(algorithm_name=name,
algorithm_conf=config,
search_space=search_space)
self.is_first_run = False
trials = Trial.convert(request.trials)
new_assignments = self.base_service.getSuggestions(
trials, request.current_request_number)
return api_pb2.GetSuggestionsReply(
parameter_assignments=Assignment.generate(new_assignments))
def ValidateAlgorithmSettings(self, request, context):
algorithm_name = request.experiment.spec.algorithm.algorithm_name
if algorithm_name == "grid":
search_space = HyperParameterSearchSpace.convert(
request.experiment)
available_space = {}
for param in search_space.params:
if param.type == INTEGER:
available_space[param.name] = range(
int(param.min),
int(param.max) + 1, int(param.step))
elif param.type == DOUBLE:
if param.step == "" or param.step is None:
return self._set_validate_context_error(
context,
"Param: {} step is nil".format(param.name))
double_list = np.arange(
float(param.min),
float(param.max) + float(param.step),
float(param.step))
if double_list[-1] > float(param.max):
double_list = double_list[:-1]
available_space[param.name] = double_list
elif param.type == CATEGORICAL or param.type == DISCRETE:
available_space[param.name] = param.list
num_combinations = len(
list(itertools.product(*available_space.values())))
max_trial_count = request.experiment.spec.max_trial_count
if max_trial_count > num_combinations:
return self._set_validate_context_error(
context,
"Max Trial Count: {} > all possible search space combinations: {}"
.format(max_trial_count, num_combinations))
return api_pb2.ValidateAlgorithmSettingsReply()