如何从Scikit学习中获得针对多类分类的特异性和阴性预测值？

如何解决如何从Scikit学习中获得针对多类分类的特异性和阴性预测值？

当前，scikit-learn的默认分类报告（sklearn.metrics.classification_report-link）不包括特异性和阴性预测值（NPV）。

因此，我做了自己的分类报告功能：

def custom_classification_report(y_true,y_pred):
    tp,fn,fp,tn = confusion_matrix(y_true,y_pred).ravel()
    acc = (tp+tn)/(tp+tn+fp+fn)
    sen = (tp)/(tp+fn)
    sp = (tn)/(tn+fp)
    ppv = (tp)/(tp+fp)
    npv = (tn)/(tn+fn)
    f1 = 2*(sen*ppv)/(sen+ppv)
    fpr = (fp)/(fp+tn)
    tpr = (tp)/(tp+fn)
    return (    '2X2 confusion matrix:',['TP',tp,'FP','FN','TN',tn],'Accuracy:',round(acc,3),'Sensitivity/Recall:',round(sen,'Specificity:',round(sp,'PPV/Precision:',round(ppv,'NPV:',round(npv,'F1-score:',round(f1,'False positive rate:',round(fpr,'True positive rate:',round(tpr,)

def auc_roc(y_true,y_pred_score):
    return ('AUC-ROC:',round(roc_auc_score(y_true,y_pred_score),3))

def avg_precision(y_true,y_pred_score,target_name):
    return ('Average precision:',round(average_precision_score(y_true,pos_label=target_name),3))
    tpr = (tp)/(tp+fn)
    return (    '2X2 confusion matrix:',)

def auc_roc(self,y_true,3))

def avg_precision(self,3))

当我将其用于二进制类分类时，它工作正常-

print('>> Custom classification report:\n',custom_classification_report(y_test,predicted_labels),'\n')

当我将同一行代码print('>> Custom classification report:\n','\n')用于多类分类时，它会给出错误ValueError: too many values to unpack (expected 4)。为什么会这样，如何解决？