def create_new_study(self, study_name=None):
# type: (Optional[str]) -> int
if study_name is not None and self._redis.exists(
self._key_study_name(study_name)):
raise exceptions.DuplicatedStudyError
if not self._redis.exists("study_counter"):
# We need the counter to start with 0.
self._redis.set("study_counter", -1)
study_id = self._redis.incr("study_counter", 1)
# We need the trial_number counter to start with 0.
self._redis.set("study_id:{:010d}:trial_number".format(study_id), -1)
if study_name is None:
study_name = "{}{:010d}".format(DEFAULT_STUDY_NAME_PREFIX,
study_id)
with self._redis.pipeline() as pipe:
pipe.multi()
pipe.set(self._key_study_name(study_name), pickle.dumps(study_id))
pipe.set("study_id:{:010d}:study_name".format(study_id),
pickle.dumps(study_name))
pipe.set(
"study_id:{:010d}:direction".format(study_id),
pickle.dumps(structs.StudyDirection.NOT_SET),
)
study_summary = structs.StudySummary(
study_name=study_name,
direction=structs.StudyDirection.NOT_SET,
best_trial=None,
user_attrs={},
system_attrs={},
n_trials=0,
datetime_start=datetime.now(),
study_id=study_id,
)
pipe.rpush("study_list", pickle.dumps(study_id))
pipe.set(self._key_study_summary(study_id),
pickle.dumps(study_summary))
pipe.execute()
return study_id
def get_all_study_summaries(self):
# type: () -> List[structs.StudySummary]
best_trial = None
n_complete_trials = len([t for t in self.trials if t.state == structs.TrialState.COMPLETE])
if n_complete_trials > 0:
best_trial = self.get_best_trial(IN_MEMORY_STORAGE_STUDY_ID)
datetime_start = None
if len(self.trials) > 0:
datetime_start = min([t.datetime_start for t in self.trials])
return [structs.StudySummary(
study_id=IN_MEMORY_STORAGE_STUDY_ID,
study_name=self.study_name,
direction=self.direction,
best_trial=best_trial,
user_attrs=copy.deepcopy(self.study_user_attrs),
system_attrs=copy.deepcopy(self.study_system_attrs),
n_trials=len(self.trials),
datetime_start=datetime_start
)]
def get_all_study_summaries(self):
# type: () -> List[structs.StudySummary]
session = self.scoped_session()
study_models = models.StudyModel.all(session)
trial_models = models.TrialModel.all(session)
param_models = models.TrialParamModel.all(session)
value_models = models.TrialValueModel.all(session)
trial_user_attribute_models = models.TrialUserAttributeModel.all(
session)
trial_system_attribute_models = models.TrialSystemAttributeModel.all(
session)
study_summaries = []
for study_model in study_models:
# Filter model objects by study.
study_trial_models = [
t for t in trial_models if t.study_id == study_model.study_id
]
# Get best trial.
completed_trial_models = [
t for t in study_trial_models
if t.state is structs.TrialState.COMPLETE
]
best_trial = None
if len(completed_trial_models) > 0:
if study_model.direction == structs.StudyDirection.MAXIMIZE:
best_trial_model = max(completed_trial_models,
key=lambda t: t.value)
else:
best_trial_model = min(completed_trial_models,
key=lambda t: t.value)
best_param_models = [
p for p in param_models
if p.trial_id == best_trial_model.trial_id
]
best_value_models = [
v for v in value_models
if v.trial_id == best_trial_model.trial_id
]
best_trial_user_models = [
u for u in trial_user_attribute_models
if u.trial_id == best_trial_model.trial_id
]
best_trial_system_models = [
s for s in trial_system_attribute_models
if s.trial_id == best_trial_model.trial_id
]
# Merge model objects related to the best trial.
best_trial = self._merge_trials_orm(
[best_trial_model], best_param_models, best_value_models,
best_trial_user_models, best_trial_system_models)[0]
# Find datetime_start.
datetime_start = None
if len(study_trial_models) > 0:
datetime_start = min(
[t.datetime_start for t in study_trial_models])
attributes = models.StudySystemAttributeModel.where_study_id(
study_model.study_id, session)
system_attrs = {
attr.key: json.loads(attr.value_json)
for attr in attributes
}
# Consolidate StudySummary.
study_summaries.append(
structs.StudySummary(
study_name=study_model.study_name,
direction=self.get_study_direction(study_model.study_id),
best_trial=best_trial,
user_attrs=self.get_study_user_attrs(study_model.study_id),
system_attrs=system_attrs,
n_trials=len(study_trial_models),
datetime_start=datetime_start,
study_id=study_model.study_id))
# Terminate transaction explicitly to avoid connection timeout during transaction.
self._commit(session)
return study_summaries
