fix: add missing roc_curve metric import for evaluation

app/training/train_deep_classifiers.py

@@ -28,7 +28,7 @@ import torch.nn as nn
 from torch.utils.data import DataLoader, TensorDataset
 from sklearn.model_selection import StratifiedKFold
 from sklearn.preprocessing import StandardScaler
-from sklearn.metrics import accuracy_score, roc_auc_score, f1_score, precision_score, recall_score
+from sklearn.metrics import accuracy_score, roc_auc_score, f1_score, precision_score, recall_score, roc_curve
 
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 SEED = 42
@@ -39,6 +39,13 @@ BATCH_SIZE = 64
 LR = 1e-3
 
 
+def _optimal_threshold(y_true: np.ndarray, y_prob: np.ndarray) -> float:
+    """Youden's J: threshold maximising sensitivity + specificity - 1."""
+    fpr, tpr, thresholds = roc_curve(y_true, y_prob)
+    j = tpr - fpr
+    return float(thresholds[np.argmax(j)])
+
+
 def set_seed(seed: int = SEED) -> None:
     np.random.seed(seed)
     torch.manual_seed(seed)