Merge pull request #5252 from Alnusjaponica/update-black

Apply Black 2024 to codebase

optuna/cli.py

@@ -1,6 +1,7 @@
 """Optuna CLI module.
 If you want to add a new command, you also need to update the constant `_COMMANDS`
 """
+
 from argparse import ArgumentParser
 from argparse import Namespace
 import datetime

optuna/multi_objective/trial.py

@@ -102,30 +102,24 @@ def suggest_int(
         return self._trial.suggest_int(name, low, high, step=step, log=log)
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[None]) -> None:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[None]) -> None: ...
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[bool]) -> bool:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[bool]) -> bool: ...
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[int]) -> int:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[int]) -> int: ...
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[float]) -> float:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[float]) -> float: ...
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[str]) -> str:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[str]) -> str: ...
 
     @overload
     def suggest_categorical(
         self, name: str, choices: Sequence[CategoricalChoiceType]
-    ) -> CategoricalChoiceType:
-        ...
+    ) -> CategoricalChoiceType: ...
 
     def suggest_categorical(
         self, name: str, choices: Sequence[CategoricalChoiceType]

optuna/samplers/_brute_force.py

@@ -223,13 +223,15 @@ def after_trial(
         self._populate_tree(
             tree,
             (
-                t
-                if t.number != trial.number
-                else create_trial(
-                    state=state,  # Set current trial as complete.
-                    values=values,
-                    params=trial.params,
-                    distributions=trial.distributions,
+                (
+                    t
+                    if t.number != trial.number
+                    else create_trial(
+                        state=state,  # Set current trial as complete.
+                        values=values,
+                        params=trial.params,
+                        distributions=trial.distributions,
+                    )
                 )
                 for t in trials
             ),

optuna/samplers/_lazy_random_state.py

@@ -4,7 +4,6 @@
 
 
 class LazyRandomState:
-
     """Lazy Random State class.
 
 

optuna/samplers/_nsgaiii/_sampler.py

@@ -83,18 +83,16 @@ def __init__(
         constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None,
         reference_points: np.ndarray | None = None,
         dividing_parameter: int = 3,
-        elite_population_selection_strategy: Callable[
-            [Study, list[FrozenTrial]], list[FrozenTrial]
-        ]
-        | None = None,
-        child_generation_strategy: Callable[
-            [Study, dict[str, BaseDistribution], list[FrozenTrial]], dict[str, Any]
-        ]
-        | None = None,
-        after_trial_strategy: Callable[
-            [Study, FrozenTrial, TrialState, Sequence[float] | None], None
-        ]
-        | None = None,
+        elite_population_selection_strategy: (
+            Callable[[Study, list[FrozenTrial]], list[FrozenTrial]] | None
+        ) = None,
+        child_generation_strategy: (
+            Callable[[Study, dict[str, BaseDistribution], list[FrozenTrial]], dict[str, Any]]
+            | None
+        ) = None,
+        after_trial_strategy: (
+            Callable[[Study, FrozenTrial, TrialState, Sequence[float] | None], None] | None
+        ) = None,
     ) -> None:
         # TODO(ohta): Reconsider the default value of each parameter.
 

optuna/samplers/_tpe/parzen_estimator.py

@@ -115,29 +115,35 @@ def _is_log(dist: BaseDistribution) -> bool:
     def _transform(self, samples_dict: Dict[str, np.ndarray]) -> np.ndarray:
         return np.array(
             [
-                np.log(samples_dict[param])
-                if self._is_log(self._search_space[param])
-                else samples_dict[param]
+                (
+                    np.log(samples_dict[param])
+                    if self._is_log(self._search_space[param])
+                    else samples_dict[param]
+                )
                 for param in self._search_space
             ]
         ).T
 
     def _untransform(self, samples_array: np.ndarray) -> Dict[str, np.ndarray]:
         res = {
-            param: np.exp(samples_array[:, i])
-            if self._is_log(self._search_space[param])
-            else samples_array[:, i]
+            param: (
+                np.exp(samples_array[:, i])
+                if self._is_log(self._search_space[param])
+                else samples_array[:, i]
+            )
             for i, param in enumerate(self._search_space)
         }
         # TODO(contramundum53): Remove this line after fixing log-Int hack.
         return {
-            param: np.clip(
-                dist.low + np.round((res[param] - dist.low) / dist.step) * dist.step,
-                dist.low,
-                dist.high,
+            param: (
+                np.clip(
+                    dist.low + np.round((res[param] - dist.low) / dist.step) * dist.step,
+                    dist.low,
+                    dist.high,
+                )
+                if isinstance(dist, IntDistribution)
+                else res[param]
             )
-            if isinstance(dist, IntDistribution)
-            else res[param]
             for (param, dist) in self._search_space.items()
         }
 

optuna/samplers/nsgaii/_sampler.py

@@ -140,18 +140,16 @@ def __init__(
         swapping_prob: float = 0.5,
         seed: int | None = None,
         constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None,
-        elite_population_selection_strategy: Callable[
-            [Study, list[FrozenTrial]], list[FrozenTrial]
-        ]
-        | None = None,
-        child_generation_strategy: Callable[
-            [Study, dict[str, BaseDistribution], list[FrozenTrial]], dict[str, Any]
-        ]
-        | None = None,
-        after_trial_strategy: Callable[
-            [Study, FrozenTrial, TrialState, Sequence[float] | None], None
-        ]
-        | None = None,
+        elite_population_selection_strategy: (
+            Callable[[Study, list[FrozenTrial]], list[FrozenTrial]] | None
+        ) = None,
+        child_generation_strategy: (
+            Callable[[Study, dict[str, BaseDistribution], list[FrozenTrial]], dict[str, Any]]
+            | None
+        ) = None,
+        after_trial_strategy: (
+            Callable[[Study, FrozenTrial, TrialState, Sequence[float] | None], None] | None
+        ) = None,
     ) -> None:
         # TODO(ohta): Reconsider the default value of each parameter.
 

optuna/storages/_journal/storage.py

@@ -74,8 +74,7 @@ class JournalStorage(BaseStorage):
             import optuna
 
 
-            def objective(trial):
-                ...
+            def objective(trial): ...
 
 
             storage = optuna.storages.JournalStorage(

optuna/storages/_rdb/alembic/versions/v0.9.0.a.py

@@ -5,6 +5,7 @@
 Create Date: 2019-03-12 12:30:31.178819
 
 """
+
 from alembic import op
 import sqlalchemy as sa
 

optuna/storages/_rdb/alembic/versions/v1.2.0.a.py

@@ -5,6 +5,7 @@
 Create Date: 2020-02-05 15:17:41.458947
 
 """
+
 from alembic import op
 import sqlalchemy as sa
 

optuna/storages/_rdb/alembic/versions/v1.3.0.a.py

@@ -5,6 +5,7 @@
 Create Date: 2020-02-14 16:23:04.800808
 
 """
+
 import json
 
 from alembic import op

optuna/storages/_rdb/alembic/versions/v2.4.0.a.py

@@ -5,6 +5,7 @@
 Create Date: 2020-11-17 02:16:16.536171
 
 """
+
 from alembic import op
 import sqlalchemy as sa
 from typing import Any

optuna/storages/_rdb/alembic/versions/v2.6.0.a_.py

@@ -5,6 +5,7 @@
 Create Date: 2021-03-01 11:30:32.214196
 
 """
+
 from alembic import op
 import sqlalchemy as sa
 

optuna/storages/_rdb/alembic/versions/v3.0.0.a.py

@@ -5,6 +5,7 @@
 Create Date: 2021-11-21 23:48:42.424430
 
 """
+
 from typing import Any
 from typing import List
 

optuna/storages/_rdb/alembic/versions/v3.0.0.b.py

@@ -5,6 +5,7 @@
 Create Date: 2022-04-27 16:31:42.012666
 
 """
+
 import enum
 
 from alembic import op

optuna/storages/_rdb/alembic/versions/v3.0.0.c.py

@@ -5,6 +5,7 @@
 Create Date: 2022-05-16 17:17:28.810792
 
 """
+
 import enum
 
 import numpy as np

optuna/storages/_rdb/alembic/versions/v3.0.0.d.py

@@ -5,6 +5,7 @@
 Create Date: 2022-06-02 09:57:22.818798
 
 """
+
 import enum
 
 import numpy as np

optuna/storages/_rdb/alembic/versions/v3.2.0.a_.py

@@ -5,6 +5,7 @@
 Create Date: 2023-02-25 13:21:00.730272
 
 """
+
 from alembic import op
 
 

optuna/trial/_base.py

@@ -55,35 +55,29 @@ def suggest_int(
 
     @overload
     @abc.abstractmethod
-    def suggest_categorical(self, name: str, choices: Sequence[None]) -> None:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[None]) -> None: ...
 
     @overload
     @abc.abstractmethod
-    def suggest_categorical(self, name: str, choices: Sequence[bool]) -> bool:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[bool]) -> bool: ...
 
     @overload
     @abc.abstractmethod
-    def suggest_categorical(self, name: str, choices: Sequence[int]) -> int:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[int]) -> int: ...
 
     @overload
     @abc.abstractmethod
-    def suggest_categorical(self, name: str, choices: Sequence[float]) -> float:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[float]) -> float: ...
 
     @overload
     @abc.abstractmethod
-    def suggest_categorical(self, name: str, choices: Sequence[str]) -> str:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[str]) -> str: ...
 
     @overload
     @abc.abstractmethod
     def suggest_categorical(
         self, name: str, choices: Sequence[CategoricalChoiceType]
-    ) -> CategoricalChoiceType:
-        ...
+    ) -> CategoricalChoiceType: ...
 
     @abc.abstractmethod
     def suggest_categorical(

optuna/trial/_fixed.py

@@ -98,30 +98,24 @@ def suggest_int(
         return int(self._suggest(name, IntDistribution(low, high, log=log, step=step)))
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[None]) -> None:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[None]) -> None: ...
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[bool]) -> bool:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[bool]) -> bool: ...
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[int]) -> int:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[int]) -> int: ...
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[float]) -> float:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[float]) -> float: ...
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[str]) -> str:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[str]) -> str: ...
 
     @overload
     def suggest_categorical(
         self, name: str, choices: Sequence[CategoricalChoiceType]
-    ) -> CategoricalChoiceType:
-        ...
+    ) -> CategoricalChoiceType: ...
 
     def suggest_categorical(
         self, name: str, choices: Sequence[CategoricalChoiceType]

optuna/trial/_frozen.py

@@ -235,30 +235,24 @@ def suggest_int(
         return int(self._suggest(name, IntDistribution(low, high, log=log, step=step)))
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[None]) -> None:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[None]) -> None: ...
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[bool]) -> bool:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[bool]) -> bool: ...
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[int]) -> int:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[int]) -> int: ...
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[float]) -> float:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[float]) -> float: ...
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[str]) -> str:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[str]) -> str: ...
 
     @overload
     def suggest_categorical(
         self, name: str, choices: Sequence[CategoricalChoiceType]
-    ) -> CategoricalChoiceType:
-        ...
+    ) -> CategoricalChoiceType: ...
 
     def suggest_categorical(
         self, name: str, choices: Sequence[CategoricalChoiceType]