[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

src/pytorch_tabular/categorical_encoders.py

@@ -62,8 +62,8 @@ def transform(self, X):
                 not X[self.cols].isnull().any().any()
             ), "`handle_missing` = `error` and missing values found in columns to encode."
         X_encoded = X.copy(deep=True)
-        category_cols = X_encoded.select_dtypes(include='category').columns
-        X_encoded[category_cols] = X_encoded[category_cols].astype('object')          
+        category_cols = X_encoded.select_dtypes(include="category").columns
+        X_encoded[category_cols] = X_encoded[category_cols].astype("object")
         for col, mapping in self._mapping.items():
             X_encoded[col] = X_encoded[col].fillna(NAN_CATEGORY).map(mapping["value"])
 
@@ -269,4 +269,4 @@ def save_as_object_file(self, path):
 
     def load_from_object_file(self, path):
         for k, v in pickle.load(open(path, "rb")).items():
-            setattr(self, k, v)
+            setattr(self, k, v)

src/pytorch_tabular/tabular_datamodule.py

@@ -301,14 +301,14 @@ def _update_config(self, config) -> InferredConfig:
         else:
             raise ValueError(f"{config.task} is an unsupported task.")
         if self.train is not None:
-            category_cols = self.train[config.categorical_cols].select_dtypes(include='category').columns
-            self.train[category_cols] = self.train[category_cols].astype('object')
+            category_cols = self.train[config.categorical_cols].select_dtypes(include="category").columns
+            self.train[category_cols] = self.train[category_cols].astype("object")
             categorical_cardinality = [
                 int(x) + 1 for x in list(self.train[config.categorical_cols].fillna("NA").nunique().values)
             ]
         else:
-            category_cols = self.train_dataset.data[config.categorical_cols].select_dtypes(include='category').columns
-            self.train_dataset.data[category_cols] = self.train_dataset.data[category_cols].astype('object')
+            category_cols = self.train_dataset.data[config.categorical_cols].select_dtypes(include="category").columns
+            self.train_dataset.data[category_cols] = self.train_dataset.data[category_cols].astype("object")
             categorical_cardinality = [
                 int(x) + 1 for x in list(self.train_dataset.data[config.categorical_cols].nunique().values)
             ]