update KGE literals

dicee/knowledge_graph_embeddings.py

@@ -1632,10 +1632,9 @@ def train_literals(self, path, rel_to_predict = []):
                 filtered_rows = dataset.train_set[dataset.train_set[:, 1] == rel_idx]
                 h_idx, _, t_indx = filtered_rows.T.tolist()
                 head_entites = [dataset.idx_to_entity[idx] for idx in h_idx]
-                literal_values = [dataset.idx_to_entity[idx] for idx in t_indx]
-                head_embeddings = self.get_transductive_entity_embeddings(head_entites)
-                assert len(head_embeddings) == len(literal_values)
-                inputs = torch.tensor(head_embeddings)
+                literal_values = [float(dataset.idx_to_entity[idx]) for idx in t_indx]
+                inputs  = self.get_entity_embeddings(head_entites)
+                assert len(inputs) == len(literal_values)
                 weights = torch.randn(inputs.shape[1], requires_grad=True)
                 y = torch.tensor([int(x) for x in literal_values])
 
@@ -1648,19 +1647,14 @@ def train_literals(self, path, rel_to_predict = []):
                     if weights.grad is not None:
                         weights.grad.zero_()
 
-                    # Step 4: Forward pass (compute predicted output yhat)
                     product = inputs * weights # Dot product of weights and inputs
                     yhat = product.sum(dim=1)
-                    # # Step 5: Compute the loss (squared error)
-                    #loss = torch.mean((yhat - y) ** 2)
+
                     loss = loss_fn(yhat, y.float())
 
-
-                    # # Step 6: Backpropagation (compute gradients)
                     loss.backward()
 
-                    # # Step 7: Update weights manually using gradient descent
-                    with torch.no_grad():  # Temporarily disable gradient tracking for manual update
+                    with torch.no_grad(): 
                         weights -= learning_rate * weights.grad
 
                     # # Step 8: Store and print the loss for tracking