REFACTOR: reformat code with newest black

Author: j-ittner

src/facet/explanation/_explanation.py

@@ -344,6 +344,7 @@ def to_expression(self) -> Expression:
 # Exact explainer factory
 #
 
+
 # noinspection PyPep8Naming
 class _ExactExplainer(
     shap.explainers.Exact,  # type: ignore

src/facet/inspection/shap/_shap.py

@@ -290,7 +290,6 @@ def _reset_fit(self) -> None:
         self.output_names_ = None
 
     def _make_explainer(self, features: pd.DataFrame) -> BaseExplainer:
-
         # prepare the background dataset
 
         background_dataset: Optional[pd.DataFrame]

src/facet/inspection/shap/sklearn/_sklearn.py

@@ -241,7 +241,6 @@ def _convert_shap_tensors_to_list(
         shap_tensors: Union[npt.NDArray[np.float_], List[npt.NDArray[np.float_]]],
         n_outputs: int,
     ) -> List[npt.NDArray[np.float_]]:
-
         if n_outputs == 1 and isinstance(shap_tensors, list) and len(shap_tensors) == 2:
             # in the binary classification case, we will proceed with SHAP values
             # for class 0 only, since values for class 1 will just be the same
@@ -273,7 +272,6 @@ def _convert_shap_to_df(
         observation_idx: pd.Index,
         feature_idx: pd.Index,
     ) -> List[pd.DataFrame]:
-
         if self.interaction_values:
             # return a list of data frame [(obs x features) x features],
             # one for each of the outputs

src/facet/selection/_parameters.py

@@ -185,7 +185,6 @@ def get_parameters(self, prefix: Optional[str] = None) -> ParameterDict:
         }
 
     def _validate_parameter(self, name: str, value: ParameterSet) -> None:
-
         if name not in self._params:
             raise AttributeError(
                 f"unknown parameter name for "
@@ -222,7 +221,6 @@ def __dir__(self) -> Iterable[str]:
 
     def __getattr__(self, key: str) -> Any:
         if not key.startswith("_"):
-
             result: Union[ParameterSpace[Any], ParameterSet, None]
 
             result = self._children.get(key, None)
@@ -241,7 +239,6 @@ def __iter__(self) -> Iterator[Tuple[List[str], ParameterSet]]:
     def _iter_parameters(
         self, path_prefix: List[str]
     ) -> Iterator[Tuple[List[str], ParameterSet]]:
-
         yield from (
             ([*path_prefix, name], value) for name, value in self._values.items()
         )

src/facet/validation/_validation.py

@@ -115,7 +115,6 @@ def split(
                 test: npt.NDArray[np.int_] = indices[test_mask]
                 # make sure test is not empty, else sample another train set
                 if len(test) > 0:
-
                     yield train, test
                     break
 
@@ -254,7 +253,6 @@ def _select_train_indices(
         random_state: np.random.RandomState,
         y: Union[npt.NDArray[Any], pd.Series, pd.DataFrame, None],
     ) -> npt.NDArray[np.int_]:
-
         mean_block_size = self.mean_block_size
         if mean_block_size < 1:
             # if mean block size was set as a percentage, calculate the actual mean