chore(pre-commit.ci): auto fixes

Author: pre-commit-ci[bot]

README.md

@@ -117,4 +117,5 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
 This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!
 
 [^bokbokbok]: Inspired by [orchardbirds/bokbokbok](https://github.com/orchardbirds/bokbokbok)
+
 [^autograd]: Inspired by [TomerRonen34/treeboost_autograd](https://github.com/TomerRonen34/treeboost_autograd)

src/boost_loss/base.py

@@ -343,7 +343,7 @@ def eval_metric_lgb(
         self,
         y_true: NDArray | lgb.Dataset | xgb.DMatrix,
         y_pred: NDArray | lgb.Dataset | xgb.DMatrix,
-        sample_weight: NDArray | lgb.Dataset | xgb.DMatrix | None = None
+        sample_weight: NDArray | lgb.Dataset | xgb.DMatrix | None = None,
         # not used, exists for eval_metric_xgb_sklearn
     ) -> tuple[str, float, bool]:
         """LightGBM-compatible interface"""

src/boost_loss/regression/regression.py

@@ -204,9 +204,7 @@ def grad(self, y_true: NDArray, y_pred: NDArray) -> NDArray:
         return -y_true / (1 + np.exp(y_true * y_pred))
 
     def hess(self, y_true: NDArray, y_pred: NDArray) -> NDArray:
-        return (
-            y_true**2 * np.exp(y_true * y_pred) / (1 + np.exp(y_true * y_pred)) ** 2
-        )
+        return y_true**2 * np.exp(y_true * y_pred) / (1 + np.exp(y_true * y_pred)) ** 2
 
 
 class MSLELoss(LossBase):

src/boost_loss/regression/sklearn.py

@@ -217,32 +217,37 @@ def predict_raw(self, X: Any, **predict_params: Any) -> NDArray[Any]:
     def predict(
         self,
         X: Any,
-        type_: Literal["mean", "median", "var", "std", "range", "mae", "mse"]
-        | None = None,
+        type_: (
+            Literal["mean", "median", "var", "std", "range", "mae", "mse"] | None
+        ) = None,
         return_std: Literal[False] = False,
         **predict_params: Any,
-    ) -> NDArray[Any]:
-        ...
+    ) -> NDArray[Any]: ...
 
     @overload
     def predict(
         self,
         X: Any,
-        type_: tuple[
-            Literal["mean", "median"], Literal["var", "std", "range", "mae", "mse"]
-        ]
-        | None = None,
+        type_: (
+            tuple[
+                Literal["mean", "median"], Literal["var", "std", "range", "mae", "mse"]
+            ]
+            | None
+        ) = None,
         return_std: Literal[True] = ...,
         **predict_params: Any,
-    ) -> tuple[NDArray[Any], NDArray[Any]]:
-        ...
+    ) -> tuple[NDArray[Any], NDArray[Any]]: ...
 
     def predict(
         self,
         X: Any,
-        type_: Literal["mean", "median", "var", "std", "range", "mae", "mse"]
-        | tuple[Literal["mean", "median"], Literal["var", "std", "range", "mae", "mse"]]
-        | None = None,
+        type_: (
+            Literal["mean", "median", "var", "std", "range", "mae", "mse"]
+            | tuple[
+                Literal["mean", "median"], Literal["var", "std", "range", "mae", "mse"]
+            ]
+            | None
+        ) = None,
         return_std: bool = False,
         **predict_params: Any,
     ) -> NDArray[Any] | tuple[NDArray[Any], NDArray[Any]]:

src/boost_loss/sklearn.py

@@ -30,8 +30,7 @@ def apply_custom_loss(
     target_transformer: None = ...,
     recursive: bool = ...,
     recursive_strict: bool = ...,
-) -> TEstimator:
-    ...
+) -> TEstimator: ...
 
 
 @overload
@@ -46,8 +45,7 @@ def apply_custom_loss(
     target_transformer: BaseEstimator = ...,
     recursive: bool = ...,
     recursive_strict: bool = ...,
-) -> TransformedTargetRegressor:
-    ...
+) -> TransformedTargetRegressor: ...
 
 
 def apply_custom_loss(