Make PreferentialGPSampler the default for preferential optimization

optuna_dashboard/preferential/_study.py

@@ -7,9 +7,9 @@ from typing import Iterable
 
 import optuna
 from optuna import logging
+from optuna._imports import try_import
 from optuna.distributions import BaseDistribution
 from optuna.samplers import BaseSampler
-from optuna.samplers import RandomSampler
 from optuna.trial import FrozenTrial
 from optuna.trial import TrialState
 from optuna_dashboard.preferential._system_attrs import get_n_generate
@@ -20,6 +20,10 @@ from optuna_dashboard.preferential._system_attrs import report_preferences
 from optuna_dashboard.preferential._system_attrs import set_n_generate
 
 
+with try_import() as _imports:
+    from optuna_dashboard.preferential.samplers.gp import PreferentialGPSampler
+
+
 _logger = logging.get_logger(__name__)
 _SYSTEM_ATTR_PREFERENTIAL_STUDY = "preference:is_preferential"
 
@@ -340,11 +344,10 @@ def create_study(
 
         sampler:
             A sampler object that implements background algorithm for value suggestion.
-            If :obj:`None` is specified, `RandomSampler`_ is used. Please note that
-            most Optuna samplers does not work efficiently for preferential optimization.
-
-            .. _RandomSampler: https://optuna.readthedocs.io/en/stable/reference/\
-            samplers/generated/optuna.samplers.RandomSampler.html
+            If :obj:`None` is specified,
+            :class:`~optuna_dashboard.preferential.samplers.gp.PreferentialGPSampler` is used.
+            Please note that most Optuna samplers does not work efficiently for preferential
+            optimization.
 
         study_name:
             Study's name. If this argument is set to None, a unique name is generated
@@ -361,9 +364,13 @@ def create_study(
         A :class:`~optuna_dashboard.preferential.PreferentialStudy` object.
     """
     try:
+        if sampler is None:
+            _imports.check()  # If BoTorch is not installed, raise ImportError.
+            sampler = PreferentialGPSampler()
+
         study = optuna.create_study(
             storage=storage,
-            sampler=sampler or RandomSampler(),
+            sampler=sampler,
             study_name=study_name,
         )
         study._storage.set_study_system_attr(
@@ -433,18 +440,19 @@ def load_study(
             :func:`~optuna.study.create_study` for further details.
         sampler:
             A sampler object that implements background algorithm for value suggestion.
-            If :obj:`None` is specified, `RandomSampler`_ is used. Please note that
-            most Optuna samplers does not work efficiently for preferential optimization.
-
-            .. _RandomSampler: https://optuna.readthedocs.io/en/stable/reference/samplers/\
-            generated/optuna.samplers.RandomSampler.html
+            If :obj:`None` is specified,
+            :class:`~optuna_dashboard.preferential.samplers.gp.PreferentialGPSampler` is used.
+            Please note that most Optuna samplers does not work efficiently for preferential
+            optimization.
 
     Returns:
         A :class:`~optuna_dashboard.preferential.PreferentialStudy` object.
     """
-    study = optuna.load_study(
-        study_name=study_name, storage=storage, sampler=sampler or RandomSampler()
-    )
+    if sampler is None:
+        _imports.check()  # If BoTorch is not installed, raise ImportError.
+        sampler = PreferentialGPSampler()
+
+    study = optuna.load_study(study_name=study_name, storage=storage, sampler=sampler)
     system_attrs = study._storage.get_study_system_attrs(study._study_id)
     if not system_attrs.get(_SYSTEM_ATTR_PREFERENTIAL_STUDY):
         raise ValueError("The study is not a PreferentialStudy.")