fix handling of 0-label predictions

chebai/result/analyse_sem.py

@@ -217,10 +217,16 @@ def set_label_names(self, label_names):
             self.label_successors = self.label_successors.unsqueeze(0)
 
     def __call__(self, preds):
+        if preds.shape[1] == 0:
+            # no labels predicted
+            return preds
+        # preds shape: (n_samples, n_labels)
         preds_sum_orig = torch.sum(preds)
         # step 1: apply implications: for each class, set prediction to max of itself and all successors
         preds = preds.unsqueeze(1)
         preds_masked_succ = torch.where(self.label_successors, preds, 0)
+        # preds_masked_succ shape: (n_samples, n_labels, n_labels)
+
         preds = preds_masked_succ.max(dim=2).values
         if torch.sum(preds) != preds_sum_orig:
             print(f"Preds change (step 1): {torch.sum(preds) - preds_sum_orig}")

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "chebai"
-version = "1.0.2"
+version = "1.0.3"
 description = "ChEBai is a deep learning library designed for the integration of deep learning methods with chemical ontologies, particularly ChEBI."
 authors = [
     { name = "MGlauer", email = "[email protected]" }