black-lint fix

Author: Charlotte Tumescheit

chebai/cli.py

@@ -43,7 +43,7 @@ def add_arguments_to_parser(self, parser: LightningArgumentParser):
         def call_data_methods(data: Type[XYBaseDataModule]):
             if data._num_of_labels is None:
                 data.prepare_data()
-                data.setup()    
+                data.setup()
             return data.num_of_labels
 
         parser.link_arguments(
@@ -60,18 +60,27 @@ def call_data_methods(data: Type[XYBaseDataModule]):
         )
 
         for kind in ("train", "val", "test"):
-            for average in ("micro-f1", "macro-f1", "balanced-accuracy", "roc-auc", "f1", "mse", "rmse", "r2"):
-            # When using lightning > 2.5.1 then need to uncomment all metrics that are not used
-            # for average in ("mse", "rmse","r2"): # for regression
-            # for average in ("f1", "roc-auc"): # for binary classification 
-            # for average in ("micro-f1", "macro-f1", "roc-auc"): # for multilabel classification 
-            # for average in ("micro-f1", "macro-f1", "balanced-accuracy", "roc-auc"): # for multilabel classification using balanced-accuracy
+            for average in (
+                "micro-f1",
+                "macro-f1",
+                "balanced-accuracy",
+                "roc-auc",
+                "f1",
+                "mse",
+                "rmse",
+                "r2",
+            ):
+                # When using lightning > 2.5.1 then need to uncomment all metrics that are not used
+                # for average in ("mse", "rmse","r2"): # for regression
+                # for average in ("f1", "roc-auc"): # for binary classification
+                # for average in ("micro-f1", "macro-f1", "roc-auc"): # for multilabel classification
+                # for average in ("micro-f1", "macro-f1", "balanced-accuracy", "roc-auc"): # for multilabel classification using balanced-accuracy
                 parser.link_arguments(
                     "data.num_of_labels",
                     f"model.init_args.{kind}_metrics.init_args.metrics.{average}.init_args.num_labels",
                     apply_on="instantiate",
                 )
-        
+
         parser.link_arguments(
             "data.num_of_labels", "trainer.callbacks.init_args.num_labels"
         )
@@ -84,7 +93,7 @@ def call_data_methods(data: Type[XYBaseDataModule]):
         # parser.link_arguments(
         #     "data.init_args.chebi_version",
         #     "model.init_args.criterion.init_args.data_extractor.init_args.chebi_version",
-        # ) 
+        # )
 
     @staticmethod
     def subcommands() -> Dict[str, Set[str]]:

chebai/models/electra.py

@@ -41,7 +41,7 @@ class ElectraPre(ChebaiBaseNet):
 
     def __init__(self, config: Dict[str, Any] = None, **kwargs: Any):
         super().__init__(config=config, **kwargs)
-      
+
         self.generator_config = ElectraConfig(**config["generator"])
         self.generator = ElectraForMaskedLM(self.generator_config)
         self.discriminator_config = ElectraConfig(**config["discriminator"])