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)
