Bundle experimental feature warning

optuna/_experimental.py

@@ -26,6 +26,14 @@
 """
 
 
+def warn_experimental_option(option_name: str) -> None:
+ warnings.warn(
+ f"``{option_name}`` option 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

@@ -4,6 +4,7 @@
 from typing import Optional
 import warnings
 
+from optuna._experimental import warn_experimental_option
 from optuna.exceptions import ExperimentalWarning
 from optuna.importance._base import BaseImportanceEvaluator
 from optuna.importance._fanova import FanovaImportanceEvaluator
@@ -119,9 +120,5 @@ 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_option("normalize")
  return res

optuna/samplers/_cmaes.py

@@ -20,6 +20,7 @@
 
 import optuna
 from optuna import logging
+from optuna._experimental import warn_experimental_option
 from optuna._imports import _LazyImport
 from optuna._transform import _SearchSpaceTransform
 from optuna.distributions import BaseDistribution
@@ -282,46 +283,22 @@ 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_option("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_option("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_option("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_option("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_option("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_option("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

@@ -12,6 +12,7 @@
 
 import numpy as np
 
+from optuna._experimental import warn_experimental_option
 from optuna._hypervolume import WFG
 from optuna._hypervolume.hssp import _solve_hssp
 from optuna.distributions import BaseDistribution
@@ -322,44 +323,24 @@ 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_option("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_option("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_option("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_option("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_option("categorical_distance_func")
 
  def reseed_rng(self) -> None:
  self._rng.rng.seed()

optuna/samplers/nsgaii/_sampler.py

@@ -9,6 +9,7 @@
 import warnings
 
 import optuna
+from optuna._experimental import warn_experimental_option
 from optuna.distributions import BaseDistribution
 from optuna.exceptions import ExperimentalWarning
 from optuna.samplers._base import BaseSampler
@@ -161,31 +162,15 @@ 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_option("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_option("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_option("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_option("elite_population_selection_strategy")
 
  if crossover is None:
  crossover = UniformCrossover(swapping_prob)

optuna/visualization/_pareto_front.py

@@ -7,6 +7,7 @@
 import warnings
 
 import optuna
+from optuna._experimental import warn_experimental_option
 from optuna.exceptions import ExperimentalWarning
 from optuna.study import Study
 from optuna.study._multi_objective import _get_pareto_front_trials_by_trials
@@ -252,11 +253,7 @@ 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_option("constraints_func")
  feasible_trials = []
  infeasible_trials = []
  for trial in study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)):