Apply c-bata's comments

package/samplers/auto_sampler/sampler.py

@@ -26,8 +26,8 @@
     from optuna.trial import FrozenTrial
 
 
-MAXINT32 = (1 << 31) - 1
-SAMPLER_KEY = "auto:sampler"
+_MAXINT32 = (1 << 31) - 1
+_SAMPLER_KEY = "auto:sampler"
 _logger = get_logger(f"optuna.{__name__}")
 
 
@@ -87,7 +87,7 @@ def __init__(
         constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None,
     ) -> None:
         self._rng = LazyRandomState(seed)
-        seed_for_random_sampler = self._rng.rng.randint(MAXINT32)
+        seed_for_random_sampler = self._rng.rng.randint(_MAXINT32)
         sampler: BaseSampler = RandomSampler(seed=seed_for_random_sampler)
         self._thread_local_sampler = _ThreadLocalSampler(sampler)
         self._constraints_func = constraints_func
@@ -105,7 +105,9 @@ def reseed_rng(self) -> None:
         self._sampler.reseed_rng()
 
     def _include_conditional_param(self, study: Study) -> bool:
-        trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE, TrialState.PRUNED))
+        trials = study._get_trials(
+            deepcopy=False, states=(TrialState.COMPLETE, TrialState.PRUNED), use_cache=True
+        )
         if len(trials) == 0:
             return False
 
@@ -118,7 +120,7 @@ def _determine_multi_objective_sampler(
         if isinstance(self._sampler, NSGAIISampler):
             return self._sampler
 
-        seed = self._rng.rng.randint(MAXINT32)
+        seed = self._rng.rng.randint(_MAXINT32)
         return NSGAIISampler(constraints_func=self._constraints_func, seed=seed)
 
     def _determine_single_objective_sampler(
@@ -127,7 +129,7 @@ def _determine_single_objective_sampler(
         if isinstance(self._sampler, TPESampler):
             return self._sampler
 
-        seed = self._rng.rng.randint(MAXINT32)
+        seed = self._rng.rng.randint(_MAXINT32)
         if (
             self._constraints_func is not None
             or any(isinstance(d, CategoricalDistribution) for d in search_space.values())
@@ -144,7 +146,9 @@ def _determine_single_objective_sampler(
                 constant_liar=True,
             )
 
-        complete_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,))
+        complete_trials = study._get_trials(
+            deepcopy=False, states=(TrialState.COMPLETE,), use_cache=True
+        )
         complete_trials.sort(key=lambda trial: trial.datetime_complete)
         if len(complete_trials) < self._N_COMPLETE_TRIALS_FOR_CMAES:
             # Use ``GPSampler`` if search space is numerical and
@@ -195,13 +199,16 @@ def before_trial(self, study: Study, trial: FrozenTrial) -> None:
         # NOTE(nabenabe): Sampler must be updated in this method. If, for example, it is updated in
         # infer_relative_search_space, the sampler for before_trial and that for sample_relative,
         # after_trial might be different, meaning that the sampling routine could be incompatible.
-        if len(study._get_trials(deepcopy=False, states=(TrialState.COMPLETE,))) != 0:
+        if (
+            len(study._get_trials(deepcopy=False, states=(TrialState.COMPLETE,), use_cache=True))
+            != 0
+        ):
             search_space = IntersectionSearchSpace().calculate(study)
             self._sampler = self._determine_sampler(study, trial, search_space)
 
         sampler_name = self._sampler.__class__.__name__
         _logger.debug(f"Sample trial#{trial.number} with {sampler_name}.")
-        study._storage.set_trial_system_attr(trial._trial_id, SAMPLER_KEY, sampler_name)
+        study._storage.set_trial_system_attr(trial._trial_id, _SAMPLER_KEY, sampler_name)
         self._sampler.before_trial(study, trial)
 
     def after_trial(