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CHANGELOG.md

@@ -6,6 +6,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ## \[Unreleased\]
 
 ### Added
+
 - Added support for materializing dataset for train and test dataframe separately([#470](https://github.com/pyg-team/pytorch-frame/issues/470))
 - Added support for PyTorch 2.5 ([#464](https://github.com/pyg-team/pytorch-frame/pull/464))
 - Added a benchmark script to compare PyTorch Frame with PyTorch Tabular ([#398](https://github.com/pyg-team/pytorch-frame/pull/398), [#444](https://github.com/pyg-team/pytorch-frame/pull/444))

torch_frame/data/dataset.py

@@ -571,7 +571,7 @@ def materialize(
                 :obj:`path`. If :obj:`path` is :obj:`None`, this will
                 materialize the dataset without caching.
                 (default: :obj:`None`)
-            col_stats (Dict[str, Dict[StatType, Any]], optional): optional 
+            col_stats (Dict[str, Dict[StatType, Any]], optional): optional
             col_stats provided by the user. If not provided, the statistics
             is calculated from the dataframe itself. (default: :obj:`None`)
 
@@ -604,9 +604,9 @@ def materialize(
                     sep=self.col_to_sep.get(col, None),
                     time_format=self.col_to_time_format.get(col, None),
                 )
-            # For a target column, sort categories lexicographically such that
-            # we do not accidentally swap labels in binary classification
-            # tasks.
+                # For a target column, sort categories lexicographically such that
+                # we do not accidentally swap labels in binary classification
+                # tasks.
                 if col == self.target_col and stype == torch_frame.categorical:
                     index, value = self._col_stats[col][StatType.COUNT]
                     if len(index) == 2: