chore(deps): update pre-commit hook psf/black to v24 - abandoned

.pre-commit-config.yaml

@@ -50,7 +50,7 @@ repos:
     hooks:
       - id: isort
   - repo: https://github.com/psf/black
-    rev: 23.12.1
+    rev: 24.1.0
     hooks:
       - id: black
   - repo: https://github.com/codespell-project/codespell

src/sklearn_utilities/eval_set.py

@@ -131,23 +131,25 @@ def __init__(
             self,
             estimator: TEstimator,
             *,
-            tqdm_cls: Literal[
-                "auto",
-                "autonotebook",
-                "std",
-                "notebook",
-                "asyncio",
-                "keras",
-                "dask",
-                "tk",
-                "gui",
-                "rich",
-                "contrib.slack",
-                "contrib.discord",
-                "contrib.telegram",
-                "contrib.bells",
-            ]
-            | type[tqdm.std.tqdm] = "auto",
+            tqdm_cls: (
+                Literal[
+                    "auto",
+                    "autonotebook",
+                    "std",
+                    "notebook",
+                    "asyncio",
+                    "keras",
+                    "dask",
+                    "tk",
+                    "gui",
+                    "rich",
+                    "contrib.slack",
+                    "contrib.discord",
+                    "contrib.telegram",
+                    "contrib.bells",
+                ]
+                | type[tqdm.std.tqdm]
+            ) = "auto",
             tqdm_kwargs: dict[str, Any] | None = None,
             verbose: bool = True,
         ) -> None:

src/sklearn_utilities/pandas/dataframe_wrapper.py

@@ -25,20 +25,24 @@ def to_frame_or_series(
             return Series(
                 array,
                 index=base_index if array.shape[0] == len(base_index) else None,
-                name=base_columns_or_name
-                if not isinstance(base_columns_or_name, Index)
-                else None,
+                name=(
+                    base_columns_or_name
+                    if not isinstance(base_columns_or_name, Index)
+                    else None
+                ),
             )
         if array.ndim == 2:
             return DataFrame(
                 array,
                 index=base_index if array.shape[0] == len(base_index) else None,
-                columns=base_columns_or_name
-                if (
-                    isinstance(base_columns_or_name, Index)
-                    and array.shape[1] == len(base_columns_or_name)
-                )
-                else None,
+                columns=(
+                    base_columns_or_name
+                    if (
+                        isinstance(base_columns_or_name, Index)
+                        and array.shape[1] == len(base_columns_or_name)
+                    )
+                    else None
+                ),
             )
     except Exception as e:
         warnings.warn(f"Could not convert {array} to DataFrame or Series: {e}")

src/sklearn_utilities/pandas/multioutput.py

@@ -115,9 +115,12 @@ def predict(
         check_is_fitted(self)
         X = X[self.feature_names_in_]
         preds = [est.predict(X, **predict_params) for est in self.estimators_]
-        preds_: DataFrame | Series | NDArray[Any] | tuple[
-            DataFrame | Series | NDArray[Any], ...
-        ]
+        preds_: (
+            DataFrame
+            | Series
+            | NDArray[Any]
+            | tuple[DataFrame | Series | NDArray[Any], ...]
+        )
         if any(isinstance(pred, tuple) for pred in preds):
             # list of tuples of arrays to tuples of arrays
             preds_ = tuple(np.array(pred).T for pred in zip(*preds))

src/sklearn_utilities/proba/compose_var.py

@@ -37,14 +37,12 @@ def fit(self, X: TX, y: TY, **fit_params: Any) -> Self:
     @overload
     def predict(
         self, X: TX, return_std: Literal[False] = ..., **predict_params: Any
-    ) -> TY:
-        ...
+    ) -> TY: ...
 
     @overload
     def predict(
         self, X: TX, return_std: Literal[True], **predict_params: Any
-    ) -> tuple[TY, TY]:
-        ...
+    ) -> tuple[TY, TY]: ...
 
     def predict(
         self, X: TX, return_std: bool = False, **predict_params: Any

src/sklearn_utilities/reindex_missing_columns.py

@@ -16,8 +16,9 @@ class ReindexMissingColumns(BaseEstimator, TransformerMixin):
     def __init__(
         self,
         *,
-        if_missing: Literal["warn", "raise"]
-        | Callable[[Index[Any], Index[Any]], None] = "warn",
+        if_missing: (
+            Literal["warn", "raise"] | Callable[[Index[Any], Index[Any]], None]
+        ) = "warn",
         reindex_kwargs: dict[
             Literal["method", "copy", "level", "fill_value", "limit", "tolerance"], Any
         ] = {},

src/sklearn_utilities/report_non_finite.py

@@ -28,8 +28,9 @@ def __init__(
         plot: bool = True,
         calc_corr: bool = False,
         callback: Callable[[dict[str, DataFrame | Series]], None] | None = None,
-        callback_figure: Callable[[Figure], None]
-        | None = lambda fig: Path("sklearn_utilities_info/ReportNonFinite").mkdir(  # type: ignore
+        callback_figure: Callable[[Figure], None] | None = lambda fig: Path(
+            "sklearn_utilities_info/ReportNonFinite"
+        ).mkdir(  # type: ignore
             parents=True, exist_ok=True
         )
         or fig.savefig(

src/sklearn_utilities/torch/pca.py

@@ -121,9 +121,9 @@ def __init__(
         *,
         qr: bool = False,
         svd_flip: bool | None = None,
-        device: torch.device | int | str = "cuda"
-        if torch.cuda.is_available()
-        else "cpu",
+        device: torch.device | int | str = (
+            "cuda" if torch.cuda.is_available() else "cpu"
+        ),
         dtype: torch.dtype = torch.float32,
         **kwargs: Any,
     ) -> None:

src/sklearn_utilities/torch/skorch/proba.py

@@ -297,22 +297,24 @@ def predict(
         X: TX,
         *,
         return_std: bool = False,
-        type_: Literal[
-            "mean",
-            "median",
-            "nanmean",
-            "nanmedian",
-            "var",
-            "std",
-            "ptp",
-            "nanvar",
-            "nanstd",
-        ]
-        | tuple[
-            Literal["mean", "median", "nanmean", "nanmedian"],
-            Literal["var", "std", "ptp", "nanvar", "nanstd"],
-        ]
-        | None = None,
+        type_: (
+            Literal[
+                "mean",
+                "median",
+                "nanmean",
+                "nanmedian",
+                "var",
+                "std",
+                "ptp",
+                "nanvar",
+                "nanstd",
+            ]
+            | tuple[
+                Literal["mean", "median", "nanmean", "nanmedian"],
+                Literal["var", "std", "ptp", "nanvar", "nanstd"],
+            ]
+            | None
+        ) = None,
         **predict_params: Any,
     ) -> TY | tuple[TY, TY]:
         ts_axis_ = self.estimator.criterion.ts_axis_