calculcate with \emph{sklearn.metrics.roc\_auc\_score} Higher AUC score->better $AUC=1.0$->Perfect seperation $AUC=0.5$->Random model $AUC=0.0$->Inverse seperation (every anomaly is normal, and every normal sample is anomalous)