def cal_metrics(y_trues, y_preds, threshold=0.5):
'''
This function is utilized to calculate the performance of the methods
Args:
y_trues (list): the list of the ground-truth labels, which contains the int data
y_preds (list): the list of the predicted results, which contains the float data
threshold (float, optional):
'best': calculate the best results
'auto': calculate the results corresponding to the thresholds of EER
float: calculate the results of the specific thresholds
'''
metrics = EasyDict()
fpr, tpr, thresholds = roc_curve(y_trues, y_preds)
metrics.AUC = auc(fpr, tpr)
metrics.EER = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
metrics.Thre = float(interp1d(fpr, thresholds)(metrics.EER))
if threshold == 'best':
_, best_metrics = find_best_threshold(y_trues, y_preds)
return best_metrics
elif threshold == 'auto':
threshold = metrics.Thre
prediction = (np.array(y_preds) > threshold).astype(int)
res = confusion_matrix(y_trues, prediction, labels=[0, 1])
TP, FN = res[0, :]
FP, TN = res[1, :]
metrics.ACC = (TP + TN) / len(y_trues)
TP_rate = float(TP / (TP + FN))
TN_rate = float(TN / (TN + FP))
metrics.APCER = float(FP / (TN + FP))
metrics.BPCER = float(FN / (FN + TP))
metrics.ACER = (metrics.APCER + metrics.BPCER) / 2
return metrics
