python如何将多个模型的ROC曲线绘制在一张图(含图例)

 def multi_models_roc(names, sampling_methods, colors, X_test, y_test, save=True, dpin=100):
        """
        将多个机器模型的roc图输出到一张图上
        
        Args:
            names: list, 多个模型的名称
            sampling_methods: list, 多个模型的实例化对象
            save: 选择是否将结果保存（默认为png格式）
            
        Returns:
            返回图片对象plt
        """
        plt.figure(figsize=(20, 20), dpi=dpin)

        for (name, method, colorname) in zip(names, sampling_methods, colors):
            
            method.fit(X_train, y_train)
            y_test_preds = method.predict(X_test)
            y_test_predprob = method.predict_proba(X_test)[:,1]
            fpr, tpr, thresholds = roc_curve(y_test, y_test_predprob, pos_label=1)
            
            plt.plot(fpr, tpr, lw=5, label='{} (AUC={:.3f})'.fORMat(name, auc(fpr, tpr)),color = colorname)
            plt.plot([0, 1], [0, 1], '--', lw=5, color = 'grey')
            plt.axis('square')
            plt.xlim([0, 1])
            plt.ylim([0, 1])
            plt.xlabel('False Positive Rate',fontsize=20)
            plt.ylabel('True Positive Rate',fontsize=20)
            plt.title('ROC Curve',fontsize=25)
            plt.legend(loc='lower right',fontsize=20)

        if save:
            plt.savefig('multi_models_roc.png')
            
        return plt

names = ['LoGIStic Regression',
         'Random Forest',
         'XGBoost',
         'AdaBoost',
         'GBDT',
         'LGBM']

sampling_methods = [clf_lr,
                    clf_rf,
                    clf_xgb,
                    clf_adb,
                    clf_gbdt,
                    clf_lgbm
                   ]

colors = ['crimson',
          'orange',
          'Gold',
          'mediumseagreen',
          'steelblue', 
          'mediumpurple'  
         ]

#ROC curves
train_roc_graph = multi_models_roc(names, sampling_methods, colors, X_train, y_train, save = True)
train_roc_graph.savefig('ROC_Train_all.png')

fpr, tpr, thresholds = roc_curve(y_test, y_test_predprob, pos_label=1)

 plt.plot(fpr, tpr, lw=5, label='{} (AUC={:.3f})'.format(name, auc(fpr, tpr)),color = colorname)

clf = RandomForestClassifier(n_estimators = 100, max_depth=3, min_samples_split=0.2, random_state=0)

y_test_predprob = method.predict_proba(X_test)[:,1]
fpr, tpr, thresholds = roc_curve(y_test, y_test_predprob, pos_label=1)

plt.legend(loc='lower right')
plt.legend(fontsize=10)

plt.legend(loc='lower right',fontsize=10)

# 8：2划分训练集测试集
X, y = df.drop(target,axis=1), df[target]
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=0)