refactor: ♻️ Add get_class_weights function

src/cellmap_data/multidataset.py

@@ -105,11 +105,10 @@ def get_subset_random_sampler(
             indices = indices[torch.randperm(len(indices), generator=rng)]
             return torch.utils.data.SubsetRandomSampler(indices, generator=rng)
 
-    def get_dataset_weights(self):
+    def get_class_weights(self):
         """
-        Returns the weights for each dataset in the multi-dataset based on the number of samples in each dataset.
+        Returns the class weights for the multi-dataset based on the number of samples in each class.
         """
-
         if len(self.classes) > 1:
             class_counts = {c: 0 for c in self.classes}
             class_count_sum = 0
@@ -122,7 +121,14 @@ def get_dataset_weights(self):
                 c: 1 - (class_counts[c] / class_count_sum) for c in self.classes
             }
         else:
-            class_weights = {self.classes[0]: 1}
+            class_weights = {self.classes[0]: 0.1}  # less than 1 to avoid overflow
+        return class_weights
+
+    def get_dataset_weights(self):
+        """
+        Returns the weights for each dataset in the multi-dataset based on the number of samples in each dataset.
+        """
+        class_weights = self.get_class_weights()
 
         dataset_weights = {}
         for dataset in self.datasets: