Merge pull request #39 from ChEB-AI/protein_prediction

Protein function prediction with GO

Author: sfluegel05

chebai/preprocessing/bin/protein_token/tokens.txt

@@ -0,0 +1,20 @@
+M
+S
+I
+G
+A
+T
+R
+L
+Q
+N
+D
+K
+Y
+P
+C
+F
+W
+E
+V
+H

chebai/preprocessing/collate.py

@@ -41,19 +41,41 @@ def __call__(self, data: List[Dict]) -> XYData:
 
 
 class RaggedCollator(Collator):
-    """Collator for handling ragged data samples."""
+    """
+    Collator for handling ragged data samples, designed to support scenarios where some labels may be missing (None).
+
+    This class is specifically designed for preparing batches of "ragged" data, where the samples may have varying sizes,
+    such as molecular representations or variable-length protein sequences. Additionally, it supports cases where some
+    of the data samples might be partially labeled, which is useful for certain loss functions that allow training
+    with incomplete or fuzzy data (e.g., fuzzy loss).
+
+    During batching, the class pads the data samples to a uniform length, applies appropriate masks to differentiate
+    between valid and padded elements, and ensures that label misalignment is handled by filtering out unlabelled
+    data points. The indices of valid labels are stored in the `non_null_labels` field, which can be used later for
+    metrics computation such as F1-score or MSE, especially in cases where some data points lack labels.
+
+    Reference: https://github.com/ChEB-AI/python-chebai/pull/48#issuecomment-2324393829
+    """
 
     def __call__(self, data: List[Union[Dict, Tuple]]) -> XYData:
-        """Collate ragged data samples (i.e., samples of unequal size such as string representations of molecules) into
-        a batch.
+        """
+        Collate ragged data samples (i.e., samples of unequal size, such as molecular sequences) into a batch.
+
+        Handles both fully and partially labeled data, where some samples may have `None` as their label. The indices
+        of non-null labels are stored in the `non_null_labels` field, which is used to filter out predictions for
+        unlabeled data during evaluation (e.g., F1, MSE). For models supporting partially labeled data, this method
+        ensures alignment between features and labels.
 
         Args:
-            data (List[Union[Dict, Tuple]]): List of ragged data samples.
+            data (List[Union[Dict, Tuple]]): List of ragged data samples. Each sample can be a dictionary or tuple
+            with 'features', 'labels', and 'ident'.
 
         Returns:
-            XYData: Batched data with appropriate padding and masks.
+            XYData: A batch of padded sequences and labels, including masks for valid positions and indices of
+            non-null labels for metric computation.
         """
         model_kwargs: Dict = dict()
+        # Indices of non-null labels are stored in key `non_null_labels` of loss_kwargs.
         loss_kwargs: Dict = dict()
 
         if isinstance(data[0], tuple):
@@ -64,18 +86,23 @@ def __call__(self, data: List[Union[Dict, Tuple]]) -> XYData:
                 *((d["features"], d["labels"], d.get("ident")) for d in data)
             )
         if any(x is not None for x in y):
+            # If any label is not None: (None, None, `1`, None)
             if any(x is None for x in y):
+                # If any label is None: (`None`, `None`, 1, `None`)
                 non_null_labels = [i for i, r in enumerate(y) if r is not None]
                 y = self.process_label_rows(
                     tuple(ye for i, ye in enumerate(y) if i in non_null_labels)
                 )
                 loss_kwargs["non_null_labels"] = non_null_labels
             else:
+                # If all labels are not None: (`0`, `2`, `1`, `3`)
                 y = self.process_label_rows(y)
         else:
+            # If all labels are None : (`None`, `None`, `None`, `None`)
             y = None
             loss_kwargs["non_null_labels"] = []
 
+        # Calculate the lengths of each sequence, create a binary mask for valid (non-padded) positions
         lens = torch.tensor(list(map(len, x)))
         model_kwargs["mask"] = torch.arange(max(lens))[None, :] < lens[:, None]
         model_kwargs["lens"] = lens
@@ -89,7 +116,11 @@ def __call__(self, data: List[Union[Dict, Tuple]]) -> XYData:
         )
 
     def process_label_rows(self, labels: Tuple) -> torch.Tensor:
-        """Process label rows by padding sequences.
+        """
+        Process label rows by padding sequences to ensure uniform shape across the batch.
+
+        This method pads the label rows, converting sequences of labels of different lengths into a uniform tensor.
+        It ensures that `None` values in the labels are handled by substituting them with a default value(e.g.,`False`).
 
         Args:
             labels (Tuple): Tuple of label rows.