Merge pull request #87 from Alnusjaponica/update-black

Apply Black 2024 to codebase

optuna_integration/_lightgbm_tuner/optimize.py

@@ -90,9 +90,9 @@ def _get_metric_for_objective(self) -> str:
 
     def _get_booster_best_score(self, booster: "lgb.Booster") -> float:
         metric = self._get_metric_for_objective()
-        valid_sets: list["lgb.Dataset"] | tuple[
-            "lgb.Dataset", ...
-        ] | "lgb.Dataset" | None = self.lgbm_kwargs.get("valid_sets")
+        valid_sets: list["lgb.Dataset"] | tuple["lgb.Dataset", ...] | "lgb.Dataset" | None = (
+            self.lgbm_kwargs.get("valid_sets")
+        )
 
         if self.lgbm_kwargs.get("valid_names") is not None:
             if isinstance(self.lgbm_kwargs["valid_names"], str):
@@ -172,9 +172,9 @@ def __init__(
 
         self.trial_count = 0
         self.best_score = best_score
-        self.best_booster_with_trial_number: tuple[
-            "lgb.Booster" | "lgb.CVBooster", int
-        ] | None = None
+        self.best_booster_with_trial_number: tuple["lgb.Booster" | "lgb.CVBooster", int] | None = (
+            None
+        )
         self.step_name = step_name
         self.model_dir = model_dir
 
@@ -945,10 +945,12 @@ def __init__(
         params: dict[str, Any],
         train_set: "lgb.Dataset",
         num_boost_round: int = 1000,
-        folds: Generator[tuple[int, int], None, None]
-        | Iterator[tuple[int, int]]
-        | "BaseCrossValidator"
-        | None = None,
+        folds: (
+            Generator[tuple[int, int], None, None]
+            | Iterator[tuple[int, int]]
+            | "BaseCrossValidator"
+            | None
+        ) = None,
         nfold: int = 5,
         stratified: bool = True,
         shuffle: bool = True,

optuna_integration/chainermn.py

@@ -199,30 +199,24 @@ def func() -> int:
         return self._call_with_mpi(func)
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[None]) -> None:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[None]) -> None: ...  # noqa: E704
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[bool]) -> bool:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[bool]) -> bool: ...  # noqa: E704
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[int]) -> int:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[int]) -> int: ...  # noqa: E704
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[float]) -> float:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[float]) -> float: ...  # noqa: E704
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[str]) -> str:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[str]) -> str: ...  # noqa: E704
 
     @overload
-    def suggest_categorical(
+    def suggest_categorical(  # noqa: E704
         self, name: str, choices: Sequence[CategoricalChoiceType]
-    ) -> CategoricalChoiceType:
-        ...
+    ) -> CategoricalChoiceType: ...
 
     def suggest_categorical(
         self, name: str, choices: Sequence[CategoricalChoiceType]

optuna_integration/dask.py

@@ -81,12 +81,14 @@ def _serialize_frozenstudy(study: FrozenStudy) -> dict:
         "directions": [d.name for d in study._directions],
         "study_id": study._study_id,
         "study_name": study.study_name,
-        "user_attrs": dumps(study.user_attrs)  # type: ignore[no-untyped-call]
-        if study.user_attrs
-        else {},
-        "system_attrs": dumps(study.system_attrs)  # type: ignore[no-untyped-call]
-        if study.system_attrs
-        else {},
+        "user_attrs": (
+            dumps(study.user_attrs) if study.user_attrs else {}  # type: ignore[no-untyped-call]
+        ),
+        "system_attrs": (
+            dumps(study.system_attrs)  # type: ignore[no-untyped-call]
+            if study.system_attrs
+            else {}
+        ),
     }
     return data
 

optuna_integration/pytorch_distributed.py

@@ -191,30 +191,24 @@ def func() -> float:
         return self._call_and_communicate(func, torch.int)
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[None]) -> None:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[None]) -> None: ...  # noqa: E704
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[bool]) -> bool:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[bool]) -> bool: ...  # noqa: E704
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[int]) -> int:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[int]) -> int: ...  # noqa: E704
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[float]) -> float:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[float]) -> float: ...  # noqa: E704
 
     @overload
-    def suggest_categorical(self, name: str, choices: Sequence[str]) -> str:
-        ...
+    def suggest_categorical(self, name: str, choices: Sequence[str]) -> str: ...  # noqa: E704
 
     @overload
-    def suggest_categorical(
+    def suggest_categorical(  # noqa: E704
         self, name: str, choices: Sequence[CategoricalChoiceType]
-    ) -> CategoricalChoiceType:
-        ...
+    ) -> CategoricalChoiceType: ...
 
     @broadcast_properties
     def suggest_categorical(

pyproject.toml

@@ -37,7 +37,7 @@ test = [
   "fakeredis[lua]",
 ]
 checking = [
-  "black<24.0.0",
+  "black",
   "blackdoc",
   "hacking",
   "isort",