fix: update evaluation to use optimal threshold and adjust BCEWithLogitsLoss for class imbalance

app/training/train_deep_classifiers.py

@@ -272,13 +272,15 @@ def evaluate_cv(
         print(f"    Fold {fold+1}: AUC={auc:.4f}")
 
     elapsed = time.time() - t0
-    y_pred = (all_probs > 0.5).astype(int)
+    threshold = _optimal_threshold(y, all_probs)
+    y_pred = (all_probs >= threshold).astype(int)
     return {
         "accuracy": round(float(accuracy_score(y, y_pred)), 4),
         "precision": round(float(precision_score(y, y_pred, zero_division=0)), 4),
         "recall": round(float(recall_score(y, y_pred, zero_division=0)), 4),
         "f1": round(float(f1_score(y, y_pred, zero_division=0)), 4),
         "roc_auc": round(float(roc_auc_score(y, all_probs)), 4),
+        "optimal_threshold": round(threshold, 4),
         "fold_aucs": [round(a, 4) for a in aucs],
         "train_time_sec": round(elapsed, 1),
     }
@@ -356,7 +358,10 @@ def train_final_model(
     n_features = X.shape[1]
     model = model_class(n_features).to(DEVICE)
     optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-4)
-    criterion = nn.BCEWithLogitsLoss()
+    n_pos = max(int(y_tr.sum()), 1)
+    n_neg = len(y_tr) - n_pos
+    pos_weight = torch.tensor([n_neg / n_pos], dtype=torch.float32).to(DEVICE)
+    criterion = nn.BCEWithLogitsLoss(pos_weight=pos_weight)
     loader = DataLoader(
         TensorDataset(
             torch.tensor(X_tr, dtype=torch.float32),