[MNT] reformt docstring of in datasets

abl/dataset/bridge_dataset.py

@@ -1,32 +1,35 @@
-from typing import Any, List, Tuple
+from typing import Any, List, Optional, Tuple
 
 from torch.utils.data import Dataset
 
 
 class BridgeDataset(Dataset):
-    def __init__(self, X: List[Any], Z: List[Any], Y: List[Any]):
-        """Initialize a basic dataset.
+    """Dataset used in ``BaseBridge``.
 
-        Parameters
-        ----------
-        X : List[Any]
-            A list of objects representing the input data.
-        Z : List[Any]
-            A list of objects representing the symbol.
-        Y : List[Any]
-            A list of objects representing the label.
-        """
+    Parameters
+    ----------
+    X : List[List[Any]]
+        A list of objects representing the input data.
+    gt_pseudo_label : List[List[Any]], optional
+        A list of objects representing the ground truth label of each element in ``X``.
+    Y : List[Any]
+        A list of objects representing the ground truth of the reasoning result of each instance in ``X``.
+    """
+
+    def __init__(
+        self, X: List[List[Any]], gt_pseudo_label: Optional[List[List[Any]]], Y: List[Any]
+    ):
         if (not isinstance(X, list)) or (not isinstance(Y, list)):
             raise ValueError("X and Y should be of type list.")
         if len(X) != len(Y):
             raise ValueError("Length of X and Y must be equal.")
-        
+
         self.X = X
-        self.Z = Z
+        self.gt_pseudo_label = gt_pseudo_label
         self.Y = Y
 
-        if self.Z is None:
-            self.Z = [None] * len(self.X)
+        if self.gt_pseudo_label is None:
+            self.gt_pseudo_label = [None] * len(self.X)
 
     def __len__(self):
         """Return the length of the dataset.
@@ -38,7 +41,7 @@ class BridgeDataset(Dataset):
         """
         return len(self.X)
 
-    def __getitem__(self, index: int) -> Tuple[Any, Any]:
+    def __getitem__(self, index: int) -> Tuple[List, List, Any]:
         """Get an item from the dataset.
 
         Parameters
@@ -48,14 +51,14 @@ class BridgeDataset(Dataset):
 
         Returns
         -------
-        Tuple[Any, Any]
+        Tuple[List, List, Any]
             A tuple containing the input and output data at the specified index.
         """
         if index >= len(self):
             raise ValueError("index range error")
 
         X = self.X[index]
-        Z = self.Z[index]
+        gt_pseudo_label = self.gt_pseudo_label[index]
         Y = self.Y[index]
 
-        return (X, Z, Y)
+        return (X, gt_pseudo_label, Y)

abl/dataset/classification_dataset.py

@@ -5,21 +5,21 @@ from torch.utils.data import Dataset
 
 
 class ClassificationDataset(Dataset):
+    """
+    Dataset used for classification task.
+
+    Parameters
+    ----------
+    X : List[Any]
+        The input data.
+    Y : List[int]
+        The target data.
+    transform : Callable[..., Any], optional
+        A function/transform that takes in an object and returns a transformed version. Defaults to None.
+    """
     def __init__(
         self, X: List[Any], Y: List[int], transform: Callable[..., Any] = None
     ):
-        """
-        Initialize the dataset used for classification task.
-
-        Parameters
-        ----------
-        X : List[Any]
-            The input data.
-        Y : List[int]
-            The target data.
-        transform : Callable[..., Any], optional
-            A function/transform that takes in an object and returns a transformed version. Defaults to None.
-        """
         if (not isinstance(X, list)) or (not isinstance(Y, list)):
             raise ValueError("X and Y should be of type list.")
         if len(X) != len(Y):

abl/dataset/prediction_dataset.py

@@ -5,17 +5,17 @@ from torch.utils.data import Dataset
 
 
 class PredictionDataset(Dataset):
+    """
+    Dataset used for prediction.
+
+    Parameters
+    ----------
+    X : List[Any]
+        The input data.
+    transform : Callable[..., Any], optional
+        A function/transform that takes in an object and returns a transformed version. Defaults to None.
+    """
     def __init__(self, X: List[Any], transform: Callable[..., Any] = None):
-        """
-        Initialize the dataset used for classification task.
-
-        Parameters
-        ----------
-        X : List[Any]
-            The input data.
-        transform : Callable[..., Any], optional
-            A function/transform that takes in an object and returns a transformed version. Defaults to None.
-        """
         if not isinstance(X, list):
             raise ValueError("X should be of type list.")
 

abl/dataset/regression_dataset.py

@@ -5,16 +5,17 @@ from torch.utils.data import Dataset
 
 
 class RegressionDataset(Dataset):
+    """
+    Dataset used for regression task.
+
+    Parameters
+    ----------
+    X : List[Any]
+        A list of objects representing the input data.
+    Y : List[Any]
+        A list of objects representing the output data.
+    """
     def __init__(self, X: List[Any], Y: List[Any]):
-        """Initialize a basic dataset.
-
-        Parameters
-        ----------
-        X : List[Any]
-            A list of objects representing the input data.
-        Y : List[Any]
-            A list of objects representing the output data.
-        """
         if (not isinstance(X, list)) or (not isinstance(Y, list)):
             raise ValueError("X and Y should be of type list.")
         if len(X) != len(Y):