Replace option with argument

Co-authored-by: Naoto Mizuno <[email protected]>

optuna/_experimental.py

@@ -26,9 +26,9 @@
 """
 
 
-def warn_experimental_option(option_name: str) -> None:
+def warn_experimental_argument(option_name: str) -> None:
     warnings.warn(
-        f"``{option_name}`` option is an experimental feature."
+        f"Argument ``{option_name}`` is an experimental feature."
         " The interface can change in the future.",
         ExperimentalWarning,
     )

optuna/importance/__init__.py

@@ -3,7 +3,7 @@
 from typing import List
 from typing import Optional
 
-from optuna._experimental import warn_experimental_option
+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
@@ -118,5 +118,5 @@ def get_param_importances(
         else:
             return dict((param, value / s) for (param, value) in res.items())
     else:
-        warn_experimental_option("normalize")
+        warn_experimental_argument("normalize")
         return res

optuna/samplers/_cmaes.py

@@ -19,7 +19,7 @@
 
 import optuna
 from optuna import logging
-from optuna._experimental import warn_experimental_option
+from optuna._experimental import warn_experimental_argument
 from optuna._imports import _LazyImport
 from optuna._transform import _SearchSpaceTransform
 from optuna.distributions import BaseDistribution
@@ -281,22 +281,22 @@ def __init__(
         self._source_trials = source_trials
 
         if self._restart_strategy:
-            warn_experimental_option("restart_strategy")
+            warn_experimental_argument("restart_strategy")
 
         if self._consider_pruned_trials:
-            warn_experimental_option("consider_pruned_trials")
+            warn_experimental_argument("consider_pruned_trials")
 
         if self._use_separable_cma:
-            warn_experimental_option("use_separable_cma")
+            warn_experimental_argument("use_separable_cma")
 
         if self._source_trials is not None:
-            warn_experimental_option("source_trials")
+            warn_experimental_argument("source_trials")
 
         if self._with_margin:
-            warn_experimental_option("with_margin")
+            warn_experimental_argument("with_margin")
 
         if self._lr_adapt:
-            warn_experimental_option("lr_adapt")
+            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

@@ -12,7 +12,7 @@
 
 import numpy as np
 
-from optuna._experimental import warn_experimental_option
+from optuna._experimental import warn_experimental_argument
 from optuna._hypervolume import WFG
 from optuna._hypervolume.hssp import _solve_hssp
 from optuna.distributions import BaseDistribution
@@ -322,24 +322,24 @@ def __init__(
         self._parzen_estimator_cls = _ParzenEstimator
 
         if multivariate:
-            warn_experimental_option("multivariate")
+            warn_experimental_argument("multivariate")
 
         if group:
             if not multivariate:
                 raise ValueError(
                     "``group`` option can only be enabled when ``multivariate`` is enabled."
                 )
-            warn_experimental_option("group")
+            warn_experimental_argument("group")
             self._group_decomposed_search_space = _GroupDecomposedSearchSpace(True)
 
         if constant_liar:
-            warn_experimental_option("constant_liar")
+            warn_experimental_argument("constant_liar")
 
         if constraints_func is not None:
-            warn_experimental_option("constraints_func")
+            warn_experimental_argument("constraints_func")
 
         if categorical_distance_func is not None:
-            warn_experimental_option("categorical_distance_func")
+            warn_experimental_argument("categorical_distance_func")
 
     def reseed_rng(self) -> None:
         self._rng.rng.seed()

optuna/samplers/nsgaii/_sampler.py

@@ -8,7 +8,7 @@
 from typing import TYPE_CHECKING
 
 import optuna
-from optuna._experimental import warn_experimental_option
+from optuna._experimental import warn_experimental_argument
 from optuna.distributions import BaseDistribution
 from optuna.samplers._base import BaseSampler
 from optuna.samplers._lazy_random_state import LazyRandomState
@@ -160,15 +160,15 @@ def __init__(
             raise ValueError("`population_size` must be greater than or equal to 2.")
 
         if constraints_func is not None:
-            warn_experimental_option("constraints_func")
+            warn_experimental_argument("constraints_func")
         if after_trial_strategy is not None:
-            warn_experimental_option("after_trial_strategy")
+            warn_experimental_argument("after_trial_strategy")
 
         if child_generation_strategy is not None:
-            warn_experimental_option("child_generation_strategy")
+            warn_experimental_argument("child_generation_strategy")
 
         if elite_population_selection_strategy is not None:
-            warn_experimental_option("elite_population_selection_strategy")
+            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._experimental import warn_experimental_option
+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
@@ -252,7 +252,7 @@ def _get_pareto_front_info(
         )
 
     if constraints_func is not None:
-        warn_experimental_option("constraints_func")
+        warn_experimental_argument("constraints_func")
         feasible_trials = []
         infeasible_trials = []
         for trial in study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)):