Why the function "cal_auc_pauc()" select anomalous data containing all domain(source,target) and not separately ?

[siuoly]

# utils.py
def cal_auc_pauc(y_true, y_pred, domain_list, max_fpr=0.1):
    y_true_s = [y_true[idx] for idx in range(len(y_true)) if domain_list[idx] == "source" or y_true[idx] == 1]
    y_pred_s = [y_pred[idx] for idx in range(len(y_true)) if domain_list[idx] == "source" or y_true[idx] == 1]
    y_true_t = [y_true[idx] for idx in range(len(y_true)) if domain_list[idx] == "target" or y_true[idx] == 1]
    y_pred_t = [y_pred[idx] for idx in range(len(y_true)) if domain_list[idx] == "target" or y_true[idx] == 1]
    auc_s = sklearn.metrics.roc_auc_score(y_true_s, y_pred_s)
    auc_t = sklearn.metrics.roc_auc_score(y_true_t, y_pred_t)
    p_auc = sklearn.metrics.roc_auc_score(y_true, y_pred, max_fpr=max_fpr)
    return auc_s, auc_t, p_auc

I consider that the y_true_s should only contain the source domain data, but in above code it also contain the anomaly target domain data , and the other variable have the same question too. I think that the anomaly and normal data will be imbalance. Is there any reason? Thanks.

[TRHS2025-1-1]

That's what everybody does, no problem