Remove Black due to conflicts with ruff; Add ruff action

.github/workflows/precommit-action.yml

@@ -0,0 +1,19 @@
+name: Pre-commit Check
+
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+
+jobs:
+  pre-commit:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+
+    - uses: actions/setup-python@v5
+      with:
+        python-version: '3.10'
+
+    - name: Run pre-commit
+      uses: pre-commit/action@v3.0.1

.pre-commit-config.yaml

@@ -1,31 +1,19 @@
 repos:
--   repo: https://github.com/psf/black
-    rev: "25.1.0"
-    hooks:
-    -   id: black
-    -   id: black-jupyter # for formatting jupyter-notebook
+# Use `pre-commit autoupdate` to update all the hook.
 
--   repo: https://github.com/pycqa/isort
-    rev: 5.13.2
-    hooks:
-      - id: isort
-        name: isort (python)
-        args: ["--profile=black"]
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  # Ruff version. https://docs.astral.sh/ruff/integrations/#pre-commit
+  rev: v0.14.11
+  hooks:
+    # Run the linter.
+    - id: ruff-check
+      args: [ --fix ]
+    # Run the formatter.
+    - id: ruff-format
 
--   repo: https://github.com/asottile/seed-isort-config
-    rev: v2.2.0
-    hooks:
-    -   id: seed-isort-config
-
--   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
-    hooks:
-    -   id: check-yaml
-    -   id: end-of-file-fixer
-    -   id: trailing-whitespace
-
--   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.2
-    hooks:
-    -   id: ruff
-        args: [--fix]
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v6.0.0
+  hooks:
+    - id: check-yaml
+    - id: end-of-file-fixer
+    - id: trailing-whitespace

chebai/loss/bce_weighted.py

@@ -32,18 +32,20 @@ def __init__(
             data_extractor = data_extractor.labeled
         self.data_extractor = data_extractor
 
-        assert (
-            isinstance(beta, float) and beta > 0.0
-        ), f"Beta parameter must be a float with value greater than 0.0, for loss class {self.__class__.__name__}."
+        assert isinstance(beta, float) and beta > 0.0, (
+            f"Beta parameter must be a float with value greater than 0.0, for loss class {self.__class__.__name__}."
+        )
 
-        assert (
-            self.data_extractor is not None
-        ), f"Data extractor must be provided if this loss class ({self.__class__.__name__}) is used."
+        assert self.data_extractor is not None, (
+            f"Data extractor must be provided if this loss class ({self.__class__.__name__}) is used."
+        )
 
         assert all(
             os.path.exists(os.path.join(self.data_extractor.processed_dir, file_name))
             for file_name in self.data_extractor.processed_file_names
-        ), "Dataset files not found. Make sure the dataset is processed before using this loss."
+        ), (
+            "Dataset files not found. Make sure the dataset is processed before using this loss."
+        )
 
         assert (
             isinstance(self.data_extractor, _ChEBIDataExtractor)

chebai/loss/focal_loss.py

@@ -36,9 +36,9 @@ def __init__(
             and alpha is not None
             and isinstance(alpha, (list, torch.Tensor))
         ):
-            assert (
-                num_classes is not None
-            ), "num_classes must be specified for multi-class classification"
+            assert num_classes is not None, (
+                "num_classes must be specified for multi-class classification"
+            )
             if isinstance(alpha, list):
                 self.alpha = torch.Tensor(alpha)
             else:

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/preprocessing/datasets/chebi.py

@@ -144,9 +144,9 @@ def __init__(
         **kwargs,
     ):
         if bool(augment_smiles):
-            assert (
-                int(aug_smiles_variations) > 0
-            ), "Number of variations must be greater than 0"
+            assert int(aug_smiles_variations) > 0, (
+                "Number of variations must be greater than 0"
+            )
             aug_smiles_variations = int(aug_smiles_variations)
 
             if not kwargs.get("splits_file_path", None):

chebai/preprocessing/datasets/pubchem.py

@@ -408,9 +408,7 @@ def download(self):
                 print("Selecting most dissimilar values from random subsets...")
                 for i in tqdm.tqdm(range(self.n_random_subsets)):
                     smiles_i = random_smiles[
-                        i
-                        * len(random_smiles)
-                        // self.n_random_subsets : (i + 1)
+                        i * len(random_smiles) // self.n_random_subsets : (i + 1)
                         * len(random_smiles)
                         // self.n_random_subsets
                     ]

chebai/preprocessing/migration/chebi_data_migration.py

@@ -308,7 +308,6 @@ def _old_raw_dir(self) -> str:
 
 
 class Main:
-
     def migrate(
         self,
         datamodule: Optional[_ChEBIDataExtractor] = None,

chebai/preprocessing/reader.py

@@ -215,7 +215,6 @@ def _read_data(self, raw_data: str) -> List[int]:
             return None
 
     def _back_to_smiles(self, smiles_encoded):
-
         token_file = self.reader.token_path
         token_coding = {}
         counter = 0

chebai/result/analyse_sem.py

@@ -642,9 +642,9 @@ def run_all(
                 for file in os.listdir(os.path.join(ckpt_dir, run_name)):
                     if f"epoch={epoch}_" in file or f"epoch={epoch}." in file:
                         ckpt_path = os.path.join(os.path.join(ckpt_dir, run_name, file))
-                assert (
-                    ckpt_path is not None
-                ), f"Failed to find checkpoint for epoch {epoch} in {os.path.join(ckpt_dir, run_name)}"
+                assert ckpt_path is not None, (
+                    f"Failed to find checkpoint for epoch {epoch} in {os.path.join(ckpt_dir, run_name)}"
+                )
             print(f"Starting run {run_name} (epoch {epoch})")
 
             for dataset, dataset_key in prediction_datasets:

chebai/result/base.py

@@ -22,9 +22,9 @@ def close(self):
         pass
 
     def __init_subclass__(cls, **kwargs):
-        assert (
-            cls._identifier() not in PROCESSORS
-        ), f"ResultProcessor {cls.__name__} does not have a unique identifier"
+        assert cls._identifier() not in PROCESSORS, (
+            f"ResultProcessor {cls.__name__} does not have a unique identifier"
+        )
         PROCESSORS[cls._identifier()] = cls
 
     def process_prediction(self, proc_id, features, labels, pred, ident):

chebai/result/classification.py

@@ -120,7 +120,6 @@ def metrics_classification_multilabel(
     labels: Tensor,
     device: torch.device,
 ):
-
     if device != labels.device:
         device = labels.device
 
@@ -145,7 +144,6 @@ def metrics_classification_binary(
     labels: Tensor,
     device: torch.device,
 ):
-
     if device != labels.device:
         device = labels.device
 

chebai/result/generate_class_properties.py

@@ -242,7 +242,9 @@ def generate(
             "train",
             "val",
             "test",
-        ], f"Given data partition invalid: {data_partition}, Choose one of the value among `train`, `val`, `test` "
+        ], (
+            f"Given data partition invalid: {data_partition}, Choose one of the value among `train`, `val`, `test` "
+        )
         generator = ClassesPropertiesGenerator()
         generator.generate_props(
             data_partition,

chebai/result/utils.py

@@ -655,9 +655,9 @@ def load_data_instance(data_cls_path: str, data_cls_kwargs: dict):
     assert isinstance(data_cls_kwargs, dict), "data_cls_kwargs must be a dict"
     data_cls = load_class(data_cls_path)
     assert isinstance(data_cls, type), f"{data_cls} is not a class."
-    assert issubclass(
-        data_cls, XYBaseDataModule
-    ), f"{data_cls} must inherit from XYBaseDataModule"
+    assert issubclass(data_cls, XYBaseDataModule), (
+        f"{data_cls} must inherit from XYBaseDataModule"
+    )
     return data_cls(**data_cls_kwargs)
 
 
@@ -682,17 +682,17 @@ def load_model_for_inference(
     lightning_cls = load_class(model_cls_path)
 
     assert isinstance(lightning_cls, type), f"{lightning_cls} is not a class."
-    assert issubclass(
-        lightning_cls, ChebaiBaseNet
-    ), f"{lightning_cls} must inherit from ChebaiBaseNet"
+    assert issubclass(lightning_cls, ChebaiBaseNet), (
+        f"{lightning_cls} must inherit from ChebaiBaseNet"
+    )
     try:
         model = lightning_cls.load_from_checkpoint(model_ckpt_path, **model_load_kwargs)
     except Exception as e:
         raise RuntimeError(f"Error loading model {model_name} \n Error: {e}") from e
 
-    assert isinstance(
-        model, ChebaiBaseNet
-    ), f"Model: {model}(Model Name: {model_name}) is not a ChebaiBaseNet instance."
+    assert isinstance(model, ChebaiBaseNet), (
+        f"Model: {model}(Model Name: {model_name}) is not a ChebaiBaseNet instance."
+    )
     model.eval()
     model.freeze()
     return model

chebai/train.py

@@ -277,9 +277,9 @@ def batchify(x: List, y: List) -> List:
     ]
 
 
-def load_data() -> (
-    Tuple[List[Molecule], List[torch.Tensor], List[Molecule], List[torch.Tensor]]
-):
+def load_data() -> Tuple[
+    List[Molecule], List[torch.Tensor], List[Molecule], List[torch.Tensor]
+]:
     """
     Load and preprocess the data.
 

chebai/trainer/CustomTrainer.py

@@ -159,7 +159,6 @@ def log_dir(self) -> Optional[str]:
 
 
 class LoadDataLaterFitLoop(_FitLoop):
-
     def on_advance_start(self) -> None:
         """Calls the hook ``on_train_epoch_start`` **before** the dataloaders are setup. This is necessary
          so that the dataloaders can get information from the model. For example: The on_train_epoch_start

configs/data/moleculenet/bace_moleculenet.yml

@@ -2,4 +2,4 @@ class_path: chebai.preprocessing.datasets.molecule_classification.BaceChem
 init_args:
   batch_size: 32
   validation_split: 0.05
-  test_split: 0.15
+  test_split: 0.15

configs/data/moleculenet/hiv_moleculenet.yml

@@ -2,4 +2,4 @@ class_path: chebai.preprocessing.datasets.molecule_classification.HIVChem
 init_args:
   batch_size: 32
   validation_split: 0.05
-  test_split: 0.15
+  test_split: 0.15

configs/data/moleculenet/lipo_moleculenet.yml

@@ -2,4 +2,4 @@ class_path: chebai.preprocessing.datasets.molecule_regression.LipoChem
 init_args:
   batch_size: 32
   validation_split: 0.05
-  test_split: 0.15
+  test_split: 0.15

configs/data/moleculenet/muv_moleculenet.yml

@@ -2,4 +2,4 @@ class_path: chebai.preprocessing.datasets.molecule_classification.MUVChem
 init_args:
   batch_size: 32
   validation_split: 0.05
-  test_split: 0.15
+  test_split: 0.15

configs/data/moleculenet/sider_moleculenet.yml

@@ -2,4 +2,4 @@ class_path: chebai.preprocessing.datasets.molecule_classification.SiderChem
 init_args:
   batch_size: 10
   validation_split: 0.05
-  test_split: 0.15
+  test_split: 0.15

configs/loss/bce.yml

@@ -1,3 +1,3 @@
 class_path: chebai.loss.bce_weighted.BCEWeighted
 init_args:
-  beta: 1000
+  beta: 1000

configs/loss/bce_new.yml

@@ -1 +1 @@
-class_path: torch.nn.BCEWithLogitsLoss
+class_path: torch.nn.BCEWithLogitsLoss

configs/loss/bce_try.yml

@@ -1 +1 @@
-class_path: torch.nn.BCELoss
+class_path: torch.nn.BCELoss

configs/loss/focal_loss_12.yml

@@ -1,4 +1,4 @@
 class_path: chebai.loss.focal_loss.FocalLoss
 init_args:
   task_type: multi-label
-  num_classes: 12
+  num_classes: 12

configs/loss/mae.yml

@@ -1 +1 @@
-class_path: torch.nn.L1Loss
+class_path: torch.nn.L1Loss

configs/loss/mse.yml

@@ -1 +1 @@
-class_path: torch.nn.MSELoss
+class_path: torch.nn.MSELoss