style: apply automated linter fixes

src/safeds/data/tabular/containers/_tagged_table.py

@@ -252,7 +252,7 @@ def add_columns_as_features(self, columns: list[Column] | Table) -> TaggedTable:
             super().add_columns(columns),
             target_name=self.target.name,
             feature_names=self.features.column_names
-                          + [col.name for col in (columns.to_columns() if isinstance(columns, Table) else columns)],
+            + [col.name for col in (columns.to_columns() if isinstance(columns, Table) else columns)],
         )
 
     # ------------------------------------------------------------------------------------------------------------------
@@ -686,8 +686,8 @@ def replace_column(self, old_column_name: str, new_columns: list[Column]) -> Tag
                     self.features.column_names
                     if old_column_name not in self.features.column_names
                     else self.features.column_names[: self.features.column_names.index(old_column_name)]
-                         + [col.name for col in new_columns]
-                         + self.features.column_names[self.features.column_names.index(old_column_name) + 1:]
+                    + [col.name for col in new_columns]
+                    + self.features.column_names[self.features.column_names.index(old_column_name) + 1 :]
                 ),
             )
 
@@ -747,7 +747,7 @@ def slice_rows(
     def sort_columns(
         self,
         comparator: Callable[[Column, Column], int] = lambda col1, col2: (col1.name > col2.name)
-                                                                         - (col1.name < col2.name),
+        - (col1.name < col2.name),
     ) -> TaggedTable:
         """
         Sort the columns of a `TaggedTable` with the given comparator and return a new `TaggedTable`.

src/safeds/data/tabular/transformation/_imputer.py

@@ -153,11 +153,9 @@ def fit(self, table: Table, column_names: list[str] | None) -> Imputer:
                     multiple_most_frequent[name] = table.get_column(name).mode()
             if len(multiple_most_frequent) > 0:
                 warnings.warn(
-                    (
-                        "There are multiple most frequent values in a column given to the Imputer.\nThe lowest values"
-                        " are being chosen in this cases. The following columns have multiple most frequent"
-                        f" values:\n{multiple_most_frequent}"
-                    ),
+                    "There are multiple most frequent values in a column given to the Imputer.\nThe lowest values"
+                    " are being chosen in this cases. The following columns have multiple most frequent"
+                    f" values:\n{multiple_most_frequent}",
                     UserWarning,
                     stacklevel=2,
                 )

src/safeds/data/tabular/transformation/_label_encoder.py

@@ -56,12 +56,9 @@ def fit(self, table: Table, column_names: list[str] | None) -> LabelEncoder:
 
         if table.keep_only_columns(column_names).remove_columns_with_non_numerical_values().number_of_columns > 0:
             warnings.warn(
-                (
-                    "The columns"
-                    f" {table.keep_only_columns(column_names).remove_columns_with_non_numerical_values().column_names} contain"
-                    " numerical data. The LabelEncoder is designed to encode non-numerical values into numerical"
-                    " values"
-                ),
+                "The columns"
+                f" {table.keep_only_columns(column_names).remove_columns_with_non_numerical_values().column_names} contain"
+                " numerical data. The LabelEncoder is designed to encode non-numerical values into numerical values",
                 UserWarning,
                 stacklevel=2,
             )

src/safeds/data/tabular/transformation/_one_hot_encoder.py

@@ -111,12 +111,9 @@ def fit(self, table: Table, column_names: list[str] | None) -> OneHotEncoder:
             > 0
         ):
             warnings.warn(
-                (
-                    "The columns"
-                    f" {table._as_table().keep_only_columns(column_names).remove_columns_with_non_numerical_values().column_names} contain"
-                    " numerical data. The OneHotEncoder is designed to encode non-numerical values into numerical"
-                    " values"
-                ),
+                "The columns"
+                f" {table._as_table().keep_only_columns(column_names).remove_columns_with_non_numerical_values().column_names} contain"
+                " numerical data. The OneHotEncoder is designed to encode non-numerical values into numerical values",
                 UserWarning,
                 stacklevel=2,
             )

src/safeds/ml/classical/_util_sklearn.py

@@ -52,11 +52,9 @@ def fit(model: Any, tagged_table: TaggedTable) -> None:
     if len(non_numerical_column_names) != 0:
         raise NonNumericColumnError(
             str(non_numerical_column_names),
-            (
-                "You can use the LabelEncoder or OneHotEncoder to transform your non-numerical data to numerical"
-                " data.\nThe OneHotEncoder should be used if you work with nominal data. If your data contains too many"
-                " different values\nor is ordinal, you should use the LabelEncoder."
-            ),
+            "You can use the LabelEncoder or OneHotEncoder to transform your non-numerical data to numerical"
+            " data.\nThe OneHotEncoder should be used if you work with nominal data. If your data contains too many"
+            " different values\nor is ordinal, you should use the LabelEncoder.",
         )
 
     null_containing_column_names = set(tagged_table.features.column_names) - set(
@@ -65,10 +63,8 @@ def fit(model: Any, tagged_table: TaggedTable) -> None:
     if len(null_containing_column_names) != 0:
         raise MissingValuesColumnError(
             str(null_containing_column_names),
-            (
-                "You can use the Imputer to replace the missing values based on different strategies.\nIf you want to"
-                " remove the missing values entirely you can use the method `Table.remove_rows_with_missing_values`."
-            ),
+            "You can use the Imputer to replace the missing values based on different strategies.\nIf you want to"
+            " remove the missing values entirely you can use the method `Table.remove_rows_with_missing_values`.",
         )
 
     try:
@@ -138,11 +134,9 @@ def predict(model: Any, dataset: Table, feature_names: list[str] | None, target_
     if len(non_numerical_column_names) != 0:
         raise NonNumericColumnError(
             str(non_numerical_column_names),
-            (
-                "You can use the LabelEncoder or OneHotEncoder to transform your non-numerical data to numerical"
-                " data.\nThe OneHotEncoder should be used if you work with nominal data. If your data contains too many"
-                " different values\nor is ordinal, you should use the LabelEncoder.\n"
-            ),
+            "You can use the LabelEncoder or OneHotEncoder to transform your non-numerical data to numerical"
+            " data.\nThe OneHotEncoder should be used if you work with nominal data. If your data contains too many"
+            " different values\nor is ordinal, you should use the LabelEncoder.\n",
         )
 
     null_containing_column_names = set(dataset.keep_only_columns(feature_names).column_names) - set(
@@ -151,10 +145,8 @@ def predict(model: Any, dataset: Table, feature_names: list[str] | None, target_
     if len(null_containing_column_names) != 0:
         raise MissingValuesColumnError(
             str(null_containing_column_names),
-            (
-                "You can use the Imputer to replace the missing values based on different strategies.\nIf you want to"
-                " remove the missing values entirely you can use the method `Table.remove_rows_with_missing_values`."
-            ),
+            "You can use the Imputer to replace the missing values based on different strategies.\nIf you want to"
+            " remove the missing values entirely you can use the method `Table.remove_rows_with_missing_values`.",
         )
 
     dataset_df = dataset.keep_only_columns(feature_names)._data