pre-commit format

Author: aditya0by0

README.md

@@ -84,7 +84,7 @@ python3  chebai/result/prediction.py predict_from_file --checkpoint_path=[path-t
 * **`--save_to`** *(optional)*: Predictions will be saved to the path as CSV file. The CSV will contain one row per SMILES string and one column per predicted class. Default path will be the current working directory with file name as `predictions.csv`.
 
 * **`--classes_path`** *(optional)*: Path to the dataset’s `classes.txt` file, which maps model output indices to ChEBI IDs.
-  * Checkpoints created after PR #135 will have the classification labels stored in them and hence this parameter is not required. 
+  * Checkpoints created after PR #135 will have the classification labels stored in them and hence this parameter is not required.
   * If provided, the CSV columns will be named using the ChEBI IDs.
   * If omitted, then script will located the file automatically. If unable to locate then the columns will be numbered sequentially.
 

chebai/preprocessing/datasets/base.py

@@ -96,9 +96,9 @@ def __init__(
         self.prediction_kind = prediction_kind
         self.data_limit = data_limit
         self.label_filter = label_filter
-        assert (balance_after_filter is not None) or (self.label_filter is None), (
-            "Filter balancing requires a filter"
-        )
+        assert (balance_after_filter is not None) or (
+            self.label_filter is None
+        ), "Filter balancing requires a filter"
         self.balance_after_filter = balance_after_filter
         self.num_workers = num_workers
         self.persistent_workers: bool = bool(persistent_workers)
@@ -108,13 +108,13 @@ def __init__(
         self.use_inner_cross_validation = (
             inner_k_folds > 1
         )  # only use cv if there are at least 2 folds
-        assert fold_index is None or self.use_inner_cross_validation is not None, (
-            "fold_index can only be set if cross validation is used"
-        )
+        assert (
+            fold_index is None or self.use_inner_cross_validation is not None
+        ), "fold_index can only be set if cross validation is used"
         if fold_index is not None and self.inner_k_folds is not None:
-            assert fold_index < self.inner_k_folds, (
-                "fold_index can't be larger than the total number of folds"
-            )
+            assert (
+                fold_index < self.inner_k_folds
+            ), "fold_index can't be larger than the total number of folds"
         self.fold_index = fold_index
         self._base_dir = base_dir
         self.n_token_limit = n_token_limit
@@ -137,9 +137,9 @@ def num_of_labels(self):
 
     @property
     def feature_vector_size(self):
-        assert self._feature_vector_size is not None, (
-            "size of feature vector must be set"
-        )
+        assert (
+            self._feature_vector_size is not None
+        ), "size of feature vector must be set"
         return self._feature_vector_size
 
     @property

chebai/result/prediction.py

@@ -67,9 +67,9 @@ def __init__(
         )
         if self._classification_labels is not None:
             print(f"Loaded {len(self._classification_labels)} classification labels.")
-            assert len(self._classification_labels) > 0, (
-                "Classification labels list is empty."
-            )
+            assert (
+                len(self._classification_labels) > 0
+            ), "Classification labels list is empty."
             assert len(self._classification_labels) == self._model.out_dim, (
                 f"Number of class labels ({len(self._classification_labels)}) does not match "
                 f"the model output dimension ({self._model.out_dim})."