Merge branch 'main' of https://gitty.informatik.hs-mannheim.de/3016498/ANLP_WS24_CA2

BalancedCELoss.py

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+import torch
+import torch.nn as nn
+import numpy as np
+
+class BalancedCELoss(nn.Module):
+    def __init__(self, alpha=0.1):
+        super(BalancedCELoss, self).__init__()
+        self.bce_loss = nn.CrossEntropyLoss()
+        self.alpha = alpha
+
+    def forward(self, predictions, targets):
+        # detect num of unique classes
+        num_classes = len(torch.unique(targets))
+        if num_classes == 1:
+        # If only one class than split it into two classes
+            predictions = torch.cat((1 - predictions, predictions), dim=1)
+
+
+        # Calculate the standard binary cross-entropy loss
+        bce_loss = self.bce_loss(predictions, targets)
+
+        predictions = torch.argmax(predictions, dim=1)
+
+        # Calculate the number of predictions for each class
+        class_0_preds_n = predictions[predictions == 0]
+        class_1_preds_n = predictions[predictions == 1]
+
+        # Calculate the number of labels for each class based on predictions
+        class_0_labels_n = targets[targets == 0]
+        class_1_labels_n = targets[targets == 1]
+
+        preds_ratio_0 = len(class_0_preds_n) / len(predictions)
+        preds_ratio_1 = len(class_1_preds_n) / len(predictions)
+
+        labels_ratio_0 = len(class_0_labels_n) / len(targets)
+        labels_ratio_1 = len(class_1_labels_n) / len(targets)
+
+        # Calculate the imbalance penalty
+        imbalance_penalty = np.abs(preds_ratio_0 - labels_ratio_0) + np.abs(preds_ratio_1 - labels_ratio_1)
+        
+        # Combine the BCE loss with the imbalance penalty
+        total_loss = bce_loss + self.alpha * imbalance_penalty
+        
+        return total_loss