save

mlmc/data/dataset_classes.py

@@ -312,7 +312,7 @@ class SingleLabelDataset(MultiLabelDataset):
         y = y if isinstance(y, str) else y[0]
         return SingleLabelDataset(
             x = df[x].applymap(str).agg(sep.join, axis=1).to_list(),
-            y = [[str(l)] for l in df[y].tolist()],
+            y = df[y].tolist(),
             classes=classes if classes is not None else {cls:i for i, cls in enumerate(sorted(df[y].map(str).unique()))}
         )
 

mlmc/loss/__init__.py

-from .loss_labelwise_ranking import RelativeRankingLoss
+from .loss_labelwise_ranking import RelativeRankingLoss, RelativeRankingLossEncourage
 from .loss_mask_noise_wrapper import MaskNoiseWrapper
\ No newline at end of file

mlmc/loss/loss_labelwise_ranking.py

@@ -24,3 +24,53 @@ class RelativeRankingLoss(torch.nn.Module):
         l1 = torch.relu(-inputs_rel[target_rel > 0] + self.margin)
         l2 = torch.relu(inputs_rel[target_rel < 0] + self.margin)
         return l1.mean() + l2.mean()
+
+class RelativeRankingLossAlt(torch.nn.Module):
+    """
+    This considers only relative similarities, y = 1, x1 should be large than x2.
+    """
+    def __init__(self, margin=0.5, act=None):
+        """
+        Initialize RelativeRankingLoss
+        :param margin:  margin of the loss function
+        :param act:  If necessary an additional activation function before applying the loss.
+        """
+        super(RelativeRankingLossAlt, self).__init__()
+        self.margin = margin
+        self.act = act
+
+    def forward(self, inputs, targets):
+        if self.act is not None:
+            inputs = self.act(inputs)
+        if targets.shape != inputs.shape:
+            targets = torch.nn.functional.one_hot(targets, inputs.shape[-1])
+        inputs_rel = (inputs[..., None,:] - inputs[..., None])
+        target_rel = (targets[..., None,:] - targets[..., None])
+        l = ((- inputs_rel * target_rel + self.margin).relu()[target_rel!=0]).mean()
+        return l
+
+
+class RelativeRankingLossEncourage(torch.nn.Module):
+    """
+    This considers only relative similarities, y = 1, x1 should be large than x2.
+    """
+    def __init__(self, margin=0.5, act=None):
+        """
+        Initialize RelativeRankingLoss
+        :param margin:  margin of the loss function
+        :param act:  If necessary an additional activation function before applying the loss.
+        """
+        super(RelativeRankingLossEncourage, self).__init__()
+        self.margin = margin
+        self.act = act
+
+    def forward(self, inputs, targets):
+        if self.act is not None:
+            inputs = self.act(inputs)
+        if targets.shape != inputs.shape:
+            targets = torch.nn.functional.one_hot(targets, inputs.shape[-1])
+        inputs_rel = (inputs[..., None,:] - inputs[..., None])
+        target_rel = (targets[..., None,:] - targets[..., None])
+        l = (- inputs_rel * target_rel).relu().sum()/ (target_rel!=0).sum()
+        margins = ( self.margin - inputs_rel * target_rel).relu().sum() / (target_rel!=0).sum()
+        return l + margins