additions to documentation

Author: sfluegel

chebai/callbacks/epoch_metrics.py

@@ -19,6 +19,9 @@ def custom_reduce_fx(input: torch.Tensor) -> torch.Tensor:
 class MacroF1(torchmetrics.Metric):
     """
     Computes the Macro F1 score, which is the unweighted mean of F1 scores for each class.
+    This implementation differs from torchmetrics.classification.MultilabelF1Score in the behaviour for undefined
+    values (i.e., classes where TP+FN=0). The torchmetrics implementation sets these classes to a default value.
+    Here, the mean is only taken over classes which have at least one positive sample.
 
     Args:
         num_labels (int): Number of classes/labels.

chebai/cli.py

@@ -28,7 +28,8 @@ def __init__(self, save_config_kwargs: dict, parser_kwargs: dict):
 
     def add_arguments_to_parser(self, parser: LightningArgumentParser):
         """
-        Add custom arguments to the argument parser.
+        Link input parameters that are used by different classes (e.g. number of labels)
+        see https://lightning.ai/docs/pytorch/stable/cli/lightning_cli_expert.html#argument-linking
 
         Args:
             parser (LightningArgumentParser): Argument parser instance.

chebai/loss/semantic.py

@@ -160,7 +160,7 @@ class DisjointLoss(ImplicationLoss):
     Disjoint Loss module, extending ImplicationLoss.
 
     Args:
-        path_to_disjointness (str): Path to the disjointness data file.
+        path_to_disjointness (str): Path to the disjointness data file (a csv file containing pairs of disjoint classes)
         data_extractor (Union[_ChEBIDataExtractor, LabeledUnlabeledMixed]): Data extractor for labels.
         base_loss (torch.nn.Module, optional): Base loss function. Defaults to None.
         disjoint_loss_weight (float, optional): Weight of disjointness loss. Defaults to 100.

chebai/preprocessing/datasets/base.py

@@ -229,7 +229,7 @@ def dataloader(self, kind: str, **kwargs) -> DataLoader:
             dataset = dataset[: self.data_limit]
         return DataLoader(
             dataset,
-            collate_fn=self.reader.collater,
+            collate_fn=self.reader.collator,
             batch_size=self.batch_size,
             **kwargs,
         )
@@ -312,8 +312,8 @@ def val_dataloader(self, *args, **kwargs) -> Union[DataLoader, List[DataLoader]]
         Returns the validation DataLoader.
 
         Args:
-            *args: Additional positional arguments.
-            **kwargs: Additional keyword arguments.
+            *args: Additional positional arguments (unused).
+            **kwargs: Additional keyword arguments, passed to dataloader().
 
         Returns:
             Union[DataLoader, List[DataLoader]]: A DataLoader object for validation data.
@@ -331,8 +331,8 @@ def test_dataloader(self, *args, **kwargs) -> Union[DataLoader, List[DataLoader]
         Returns the test DataLoader.
 
         Args:
-            *args: Additional positional arguments.
-            **kwargs: Additional keyword arguments.
+            *args: Additional positional arguments (unused).
+            **kwargs: Additional keyword arguments, passed to dataloader().
 
         Returns:
             Union[DataLoader, List[DataLoader]]: A DataLoader object for test data.
@@ -346,8 +346,8 @@ def predict_dataloader(
         Returns the predict DataLoader.
 
         Args:
-            *args: Additional positional arguments.
-            **kwargs: Additional keyword arguments.
+            *args: Additional positional arguments (unused).
+            **kwargs: Additional keyword arguments, passed to dataloader().
 
         Returns:
             Union[DataLoader, List[DataLoader]]: A DataLoader object for prediction data.
@@ -520,7 +520,7 @@ def dataloader(self, kind: str, **kwargs) -> DataLoader:
         ]
         return DataLoader(
             dataset,
-            collate_fn=self.reader.collater,
+            collate_fn=self.reader.collator,
             batch_size=self.batch_size,
             **kwargs,
         )