Updated my documentations

Author: sot176

CollaborativeCoding/dataloaders/uspsh5_7_9.py

@@ -8,26 +8,45 @@
 
 class USPSH5_Digit_7_9_Dataset(Dataset):
     """
-    Custom USPS dataset class that loads images with digits 7-9 from an .h5 file.
+    This class loads a subset of the USPS dataset, specifically images of digits 7, 8, and 9, from an HDF5 file.
+    It allows for applying transformations to the images and provides methods to retrieve images and their corresponding labels.
 
     Parameters
     ----------
-    h5_path : str
-        Path to the USPS `.h5` file.
+    data_path : str or Path
+        Path to the directory containing the USPS `.h5` file. This file should contain the data in the "train" or "test" group.
+
+    sample_ids : list of int
+        A list of sample indices to be used from the dataset. This allows for filtering or selecting a subset of the full dataset.
+
+    train : bool, optional, default=False
+        If `True`, the dataset is loaded in training mode (using the "train" group). If `False`, the dataset is loaded in test mode (using the "test" group).
 
     transform : callable, optional, default=None
-        A transform function to apply on images. If None, no transformation is applied.
+        A transformation function to apply to each image. If `None`, no transformation is applied. Typically used for data augmentation or normalization.
+
+    nr_channels : int, optional, default=1
+        The number of channels in the image. USPS images are typically grayscale, so this should generally be set to 1. This parameter allows for potential future flexibility.
 
     Attributes
     ----------
     images : numpy.ndarray
-        The filtered images corresponding to digits 7-9.
+        Array of images corresponding to digits 7, 8, and 9 from the USPS dataset. The images are loaded from the HDF5 file and filtered based on the labels.
 
     labels : numpy.ndarray
-        The filtered labels corresponding to digits 7-9.
+        Array of labels corresponding to the images. Only labels of digits 7, 8, and 9 are retained, and they are mapped to 0, 1, and 2 for classification tasks.
 
     transform : callable, optional
-        A transform function to apply to the images.
+        A transformation function to apply to the images. This is passed as an argument during initialization.
+
+    label_shift : function
+        A function to shift the labels for classification purposes. It maps the original labels (7, 8, 9) to (0, 1, 2).
+
+    label_restore : function
+        A function to restore the original labels (7, 8, 9) from the shifted labels (0, 1, 2).
+
+    num_classes : int
+        The number of unique labels in the dataset, which is 3 (for digits 7, 8, and 9).
     """
 
     def __init__(
@@ -36,14 +55,25 @@ def __init__(
         super().__init__()
         """
         Initializes the USPS dataset by loading images and labels from the given `.h5` file.
-
+        
+        The dataset is filtered to only include images of digits 7, 8, and 9, which are mapped to labels 0, 1, and 2 respectively for classification purposes.
+        
         Parameters
         ----------
-        h5_path : str
-            Path to the USPS `.h5` file.
-            
+        data_path : str or Path
+          Path to the directory containing the USPS `.h5` file.
+        
+        sample_ids : list of int
+          List of sample indices to load from the dataset.
+        
+        train : bool, optional, default=False
+          If `True`, loads the training data from the HDF5 file. If `False`, loads the test data.
+        
         transform : callable, optional, default=None
-            A transform function to apply on images.
+          A function to apply transformations to the images. If None, no transformation is applied.
+        
+        nr_channels : int, optional, default=1
+          The number of channels in the image. Defaults to 1 for grayscale images.
         """
         self.filename = "usps.h5"
         path = data_path if isinstance(data_path, Path) else Path(data_path)
@@ -72,27 +102,33 @@ def __len__(self):
         """
         Returns the total number of samples in the dataset.
 
+        This method is required for PyTorch's Dataset class, as it allows PyTorch to determine the size of the dataset.
+
         Returns
         -------
         int
-            The number of images in the dataset.
+            The number of images in the dataset (after filtering for digits 7, 8, and 9).
         """
+
         return len(self.images)
 
     def __getitem__(self, id):
         """
         Returns a sample from the dataset given an index.
 
+        This method is required for PyTorch's Dataset class, as it allows indexing into the dataset to retrieve specific samples.
+
         Parameters
         ----------
         idx : int
-            The index of the sample to retrieve.
+           The index of the sample to retrieve from the dataset.
 
         Returns
         -------
         tuple
-            - image (PIL Image): The image at the specified index.
-            - label (int): The label corresponding to the image.
+           A tuple containing:
+           - image (PIL Image): The image at the specified index.
+           - label (int): The label corresponding to the image, shifted to be in the range [0, 2] for classification.
         """
         # Convert to PIL Image (USPS images are typically grayscale 16x16)
         image = Image.fromarray(self.images[id].astype(np.uint8), mode="L")

CollaborativeCoding/metrics/F1.py

@@ -5,18 +5,67 @@
 
 class F1Score(nn.Module):
     """
-    F1 Score implementation with support for both macro and micro averaging.
-    This class computes the F1 score during training using either macro or micro averaging.
+    Computes the F1 score for classification tasks with support for both macro and micro averaging.
+
+    This class allows you to compute the F1 score during training or evaluation. You can select between two methods of averaging:
+    - **Micro Averaging**: Computes the F1 score globally, treating each individual prediction as equally important.
+    - **Macro Averaging**: Computes the F1 score for each class individually and then averages the scores.
+
     Parameters
     ----------
     num_classes : int
         The number of classes in the classification task.
 
-    macro_averaging : bool, default=False
-        If True, computes the macro-averaged F1 score. If False, computes the micro-averaged F1 score.
+    macro_averaging : bool, optional, default=False
+        If True, computes the macro-averaged F1 score. If False, computes the micro-averaged F1 score. Default is micro averaging.
+
+    Attributes
+    ----------
+    num_classes : int
+        The number of classes in the classification task.
+
+    macro_averaging : bool
+        A flag to determine whether to compute the macro-averaged or micro-averaged F1 score.
+
+    y_true : list
+        A list to store true labels for the current batch.
+
+    y_pred : list
+        A list to store predicted labels for the current batch.
+
+    Methods
+    -------
+    forward(target, preds)
+        Stores predictions and true labels for computing the F1 score during training or evaluation.
+
+    compute_f1()
+        Computes and returns the F1 score based on the stored predictions and true labels.
+
+    _micro_F1(target, preds)
+        Computes the micro-averaged F1 score based on the global true positive, false positive, and false negative counts.
+
+    _macro_F1(target, preds)
+        Computes the macro-averaged F1 score by calculating the F1 score per class and then averaging across all classes.
+
+    __returnmetric__()
+        Computes and returns the F1 score (Micro or Macro) as specified.
+
+    __reset__()
+        Resets the stored predictions and true labels, preparing for the next batch or epoch.
     """
 
     def __init__(self, num_classes, macro_averaging=False):
+        """
+        Initializes the F1Score object with the number of classes and averaging mode.
+
+        Parameters
+        ----------
+        num_classes : int
+           The number of classes in the classification task.
+
+        macro_averaging : bool, optional, default=False
+           If True, compute the macro-averaged F1 score. If False, compute the micro-averaged F1 score.
+        """
         super().__init__()
         self.num_classes = num_classes
         self.macro_averaging = macro_averaging
@@ -25,14 +74,15 @@ def __init__(self, num_classes, macro_averaging=False):
 
     def forward(self, target, preds):
         """
-        Stores predictions and targets for computing the F1 score.
+        Stores the true labels and predictions to compute the F1 score.
 
         Parameters
         ----------
-        preds : torch.Tensor
-            Predicted logits (shape: [batch_size, num_classes]).
         target : torch.Tensor
             True labels (shape: [batch_size]).
+
+        preds : torch.Tensor
+            Predicted logits (shape: [batch_size, num_classes]).
         """
         preds = torch.argmax(preds, dim=-1)  # Convert logits to class indices
         self.y_true.append(target.detach())
@@ -47,7 +97,7 @@ def compute_f1(self):
         Returns
         -------
         torch.Tensor
-            The computed F1 score.
+            The computed F1 score. Returns NaN if no predictions or targets are available.
         """
         if not self.y_true or not self.y_pred:  # Check if empty
             return torch.tensor(np.nan)
@@ -63,7 +113,7 @@ def compute_f1(self):
         )
 
     def _micro_F1(self, target, preds):
-        """Computes Micro F1 Score (global TP, FP, FN)."""
+        """Computes the Micro-averaged F1 score (global TP, FP, FN)."""
         tp = torch.sum(preds == target).float()
         fp = torch.sum(preds != target).float()
         fn = fp  # Since all errors are either FP or FN
@@ -75,7 +125,7 @@ def _micro_F1(self, target, preds):
         return f1
 
     def _macro_F1(self, target, preds):
-        """Computes Macro F1 Score in a vectorized way (no loops)."""
+        """Computes the Macro-averaged F1 score."""
         num_classes = self.num_classes
         target = target.long()  # Ensure target is a LongTensor
         preds = preds.long()
@@ -100,12 +150,12 @@ def _macro_F1(self, target, preds):
 
     def __returnmetric__(self):
         """
-        Computes and returns the F1 score (Micro or Macro).
+        Computes and returns the F1 score (Micro or Macro) based on the stored predictions and targets.
 
         Returns
         -------
         torch.Tensor
-            The computed F1 score.
+            The computed F1 score. Returns NaN if no predictions or targets are available.
         """
         if not self.y_true or not self.y_pred:  # Check if empty
             return torch.tensor(np.nan)
@@ -121,6 +171,6 @@ def __returnmetric__(self):
         )
 
     def __reset__(self):
-        """Resets stored predictions and targets."""
+        """Resets the stored predictions and targets for the next batch or epoch."""
         self.y_true = []
         self.y_pred = []