Fix problem saving hysteretic curves models and update those models and results

Code/src/hysteretic_curves/predict_hysteretic_curves.py

@@ -462,6 +462,13 @@ def cv_score_for_params_cached(
     return mean_rmse, std_rmse, arr
 
 
+def strip_compile_prefix(state_dict: dict) -> dict:
+    # torch.compile suele guardar keys como "_orig_mod.xxx"
+    if not any(k.startswith("_orig_mod.") for k in state_dict.keys()):
+        return state_dict
+    return {k.replace("_orig_mod.", "", 1): v for k, v in state_dict.items()}
+
+
 # -------------------------
 # Final training on 100% train_pool (cached) - no val
 # Keep best by TRAIN loss
@@ -561,7 +568,8 @@ def train_final_full_trainpool_cached(  # type: ignore
         epoch_loss = running / max(1, n_batches)
         if epoch_loss < best_train_loss:
             best_train_loss = epoch_loss
-            best_state = {k: v.detach().cpu().clone() for k, v in model.state_dict().items()}
+            raw_state = {k: v.detach().cpu().clone() for k, v in model.state_dict().items()}
+            best_state = strip_compile_prefix(raw_state)
 
     del model, optimizer, criterion, train_loader, train_ds
     gc.collect()
@@ -655,7 +663,7 @@ def main():
         if b_val == 29:
             predict_case = 12
         elif b_val == 34:
-            predict_case = 11
+            predict_case = 12
         else:
             print(f"Warning: No predict_case set for W={w_val}, B={b_val}")
     elif w_val == 3:
@@ -668,7 +676,7 @@ def main():
             print(f"Warning: No predict_case set for W={w_val}, B={b_val}")
     elif w_val == 5:
         n_train_case = 64
-        predict_case = 13
+        predict_case = 70
     else:
         print("Warning: n_train_case and predict_case not set for W != 2, 3, or 5")
 
@@ -875,6 +883,8 @@ def main():
         dropout=best_params_model["dropout"]
     ).to(device)
 
+    final_state = strip_compile_prefix(final_state)
+
     # compile not necessary for predict, but harmless if final was compiled; keep OFF to avoid
     # overhead
     final_model.load_state_dict(final_state)