fix: persist and restore event threshold via checkpoint

Save event_threshold to checkpoint dict during training, restore it
during resume and evaluation. evaluate.py now reads from checkpoint
instead of hardcoding train_cfg default, so evaluation matches the
threshold used during training.

Generated by Mistral Vibe.
Co-Authored-By: Mistral Vibe <vibe@mistral.ai>

src/velocity_prediction/evaluate.py

@@ -166,7 +166,11 @@ def main():
     ckpt = torch.load(args.checkpoint, map_location="cpu")
     model.load_state_dict(ckpt["model_state_dict"])
     model.to(device)
+
+    # Restore event threshold from checkpoint, fall back to config default
+    event_threshold = ckpt.get("event_threshold", train_cfg.event_threshold)
     print(f"Loaded checkpoint from {args.checkpoint} (epoch={ckpt.get('epoch', '?')})")
+    print(f"Event threshold: {event_threshold} (checkpoint={ckpt.get('event_threshold', 'not saved')}, config={train_cfg.event_threshold})")
 
     # Evaluate each scene independently → NaN gaps prevent plot mixing
     from src.velocity_prediction.config import TEST_SCENES
@@ -180,7 +184,7 @@ def main():
             stride=1,
             batch_size=1,
             num_workers=0,               # strict temporal order
-            event_threshold=train_cfg.event_threshold,
+            event_threshold=event_threshold,
             event_use_log=train_cfg.event_use_log,
         )
         results = evaluate_stateful(model, loader, device)

src/velocity_prediction/train.py

@@ -191,6 +191,10 @@ def main():
         global_step = ckpt.get("global_step", 0)
         best_val_loss = ckpt.get("best_val_loss", float("inf"))
         run_id = ckpt.get("run_id", None)
+        # Restore event threshold from checkpoint if present
+        if "event_threshold" in ckpt:
+            event_threshold = ckpt["event_threshold"]
+            print(f"Event threshold restored from checkpoint: {event_threshold}")
 
         print(f"Resumed from checkpoint: {ckpt_path}")
         print(f"  Resumed epoch={ckpt.get('epoch', '?')}, global_step={global_step}, "
@@ -253,6 +257,7 @@ def main():
                 "model_state_dict": model.state_dict(),
                 "optimizer_state_dict": optimizer.state_dict(),
                 "scheduler_state_dict": scheduler.state_dict(),
+                "event_threshold": event_threshold,
                 "global_step": global_step,
                 "best_val_loss": best_val_loss,
                 "run_id": run_id,
@@ -269,6 +274,7 @@ def main():
                 "model_state_dict": model.state_dict(),
                 "optimizer_state_dict": optimizer.state_dict(),
                 "scheduler_state_dict": scheduler.state_dict(),
+                "event_threshold": event_threshold,
                 "global_step": global_step,
                 "best_val_loss": best_val_loss,
                 "run_id": run_id,