Refactor training logic to improve early stopping mechanism and variable naming

train.py

@@ -294,7 +294,7 @@ class Trainer:
 
     def train(self) -> None:
         history = []
-        best_val_loss = float('inf')
+        best_val_score = float('inf')
         patience_counter = 0
         for epoch in range(1, self.cfg.max_epochs + 1):
             self.model.train()
@@ -302,6 +302,7 @@ class Trainer:
             running_reg = 0.0
             pbar = tqdm(self.train_loader,
                         desc=f"Epoch {epoch}/{self.cfg.max_epochs} - Training", ncols=100)
+            batch_count = 0
             for batch in pbar:
                 (
                     event_seq,
@@ -445,7 +446,7 @@ class Trainer:
             # Check for improvement
             if val_nll < best_val_score:
                 best_val_score = val_nll
-                patient_counter = 0
+                patience_counter = 0
                 tqdm.write("  ✓ New best validation score. Saving checkpoint.")
 
                 torch.save({
@@ -456,21 +457,13 @@ class Trainer:
                     "optimizer_state_dict": self.optimizer.state_dict(),
                 }, self.best_path)
             else:
-                patient_counter += 1
-                if epoch+1 >= self.cfg.min_epochs and patient_counter >= self.cfg.patient_epochs:
+                patience_counter += 1
+                if epoch >= self.cfg.warmup_epochs and patience_counter >= self.cfg.patience:
                     tqdm.write(
-                        f"\n⚠ No improvement in validation score for {patient_counter} epochs. Early stopping.")
+                        f"\n⚠ No improvement in validation score for {patience_counter} epochs. Early stopping.")
                     return
                 tqdm.write(
-                    f"  No improvement (patience: {patient_counter}/{self.cfg.patient_epochs})")
-
-            torch.save({
-                "epoch": epoch,
-                "global_step": self.global_step,
-                "model_state_dict": self.ema_model.state_dict(),
-                "criterion_state_dict": self.criterion.state_dict(),
-                "optimizer_state_dict": self.optimizer.state_dict(),
-            }, self.last_path)
+                    f"  No improvement (patience: {patience_counter}/{self.cfg.patience})")
 
         tqdm.write("\n🎉 Training complete!")