Merge pull request optuna#5434 from nabenabe0928/code-fix/use-experim…

…ental-template

Bundle experimental feature warning

optuna/_experimental.py

@@ -26,6 +26,14 @@
 """
 
 
+def warn_experimental_argument(option_name: str) -> None:
+    warnings.warn(
+        f"Argument ``{option_name}`` is an experimental feature."
+        " The interface can change in the future.",
+        ExperimentalWarning,
+    )
+
+
 def _validate_version(version: str) -> None:
     if not isinstance(version, str) or len(version.split(".")) != 3:
         raise ValueError(

optuna/importance/__init__.py

@@ -2,9 +2,8 @@
 from typing import Dict
 from typing import List
 from typing import Optional
-import warnings
 
-from optuna.exceptions import ExperimentalWarning
+from optuna._experimental import warn_experimental_argument
 from optuna.importance._base import BaseImportanceEvaluator
 from optuna.importance._fanova import FanovaImportanceEvaluator
 from optuna.importance._mean_decrease_impurity import MeanDecreaseImpurityImportanceEvaluator
@@ -119,9 +118,5 @@ def get_param_importances(
         else:
             return dict((param, value / s) for (param, value) in res.items())
     else:
-        warnings.warn(
-            "`normalize` option is an experimental feature."
-            " The interface can change in the future.",
-            ExperimentalWarning,
-        )
+        warn_experimental_argument("normalize")
         return res

optuna/samplers/_cmaes.py

@@ -14,18 +14,17 @@
 from typing import Tuple
 from typing import TYPE_CHECKING
 from typing import Union
-import warnings
 
 import numpy as np
 
 import optuna
 from optuna import logging
+from optuna._experimental import warn_experimental_argument
 from optuna._imports import _LazyImport
 from optuna._transform import _SearchSpaceTransform
 from optuna.distributions import BaseDistribution
 from optuna.distributions import FloatDistribution
 from optuna.distributions import IntDistribution
-from optuna.exceptions import ExperimentalWarning
 from optuna.samplers import BaseSampler
 from optuna.samplers._lazy_random_state import LazyRandomState
 from optuna.search_space import IntersectionSearchSpace
@@ -282,46 +281,22 @@ def __init__(
         self._source_trials = source_trials
 
         if self._restart_strategy:
-            warnings.warn(
-                "`restart_strategy` option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("restart_strategy")
 
         if self._consider_pruned_trials:
-            warnings.warn(
-                "`consider_pruned_trials` option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("consider_pruned_trials")
 
         if self._use_separable_cma:
-            warnings.warn(
-                "`use_separable_cma` option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("use_separable_cma")
 
         if self._source_trials is not None:
-            warnings.warn(
-                "`source_trials` option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("source_trials")
 
         if self._with_margin:
-            warnings.warn(
-                "`with_margin` option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("with_margin")
 
         if self._lr_adapt:
-            warnings.warn(
-                "`lr_adapt` option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("lr_adapt")
 
         if source_trials is not None and (x0 is not None or sigma0 is not None):
             raise ValueError(

optuna/samplers/_tpe/sampler.py

@@ -10,11 +10,11 @@
 
 import numpy as np
 
+from optuna._experimental import warn_experimental_argument
 from optuna._hypervolume import WFG
 from optuna._hypervolume.hssp import _solve_hssp
 from optuna.distributions import BaseDistribution
 from optuna.distributions import CategoricalChoiceType
-from optuna.exceptions import ExperimentalWarning
 from optuna.logging import get_logger
 from optuna.samplers._base import _CONSTRAINTS_KEY
 from optuna.samplers._base import _process_constraints_after_trial
@@ -320,44 +320,24 @@ def __init__(
         self._parzen_estimator_cls = _ParzenEstimator
 
         if multivariate:
-            warnings.warn(
-                "``multivariate`` option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("multivariate")
 
         if group:
             if not multivariate:
                 raise ValueError(
                     "``group`` option can only be enabled when ``multivariate`` is enabled."
                 )
-            warnings.warn(
-                "``group`` option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("group")
             self._group_decomposed_search_space = _GroupDecomposedSearchSpace(True)
 
         if constant_liar:
-            warnings.warn(
-                "``constant_liar`` option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("constant_liar")
 
         if constraints_func is not None:
-            warnings.warn(
-                "The ``constraints_func`` option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("constraints_func")
 
         if categorical_distance_func is not None:
-            warnings.warn(
-                "The ``categorical_distance_func`` option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("categorical_distance_func")
 
     def reseed_rng(self) -> None:
         self._rng.rng.seed()

optuna/samplers/nsgaii/_sampler.py

@@ -6,11 +6,10 @@
 import hashlib
 from typing import Any
 from typing import TYPE_CHECKING
-import warnings
 
 import optuna
+from optuna._experimental import warn_experimental_argument
 from optuna.distributions import BaseDistribution
-from optuna.exceptions import ExperimentalWarning
 from optuna.samplers._base import BaseSampler
 from optuna.samplers._lazy_random_state import LazyRandomState
 from optuna.samplers._random import RandomSampler
@@ -161,31 +160,15 @@ def __init__(
             raise ValueError("`population_size` must be greater than or equal to 2.")
 
         if constraints_func is not None:
-            warnings.warn(
-                "The constraints_func option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("constraints_func")
         if after_trial_strategy is not None:
-            warnings.warn(
-                "The after_trial_strategy option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("after_trial_strategy")
 
         if child_generation_strategy is not None:
-            warnings.warn(
-                "The child_generation_strategy option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("child_generation_strategy")
 
         if elite_population_selection_strategy is not None:
-            warnings.warn(
-                "The elite_population_selection_strategy option is an experimental feature."
-                " The interface can change in the future.",
-                ExperimentalWarning,
-            )
+            warn_experimental_argument("elite_population_selection_strategy")
 
         if crossover is None:
             crossover = UniformCrossover(swapping_prob)

optuna/visualization/_pareto_front.py

@@ -7,7 +7,7 @@
 import warnings
 
 import optuna
-from optuna.exceptions import ExperimentalWarning
+from optuna._experimental import warn_experimental_argument
 from optuna.study import Study
 from optuna.study._multi_objective import _get_pareto_front_trials_by_trials
 from optuna.trial import FrozenTrial
@@ -252,11 +252,7 @@ def _get_pareto_front_info(
         )
 
     if constraints_func is not None:
-        warnings.warn(
-            "``constraints_func`` argument is an experimental feature."
-            " The interface can change in the future.",
-            ExperimentalWarning,
-        )
+        warn_experimental_argument("constraints_func")
         feasible_trials = []
         infeasible_trials = []
         for trial in study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)):