def plot_two_roc_curves(roc_curves_list1, roc_curves_list2):
    fig = plt.figure(figsize=(10, 10), dpi=1200)

    mean_tpr1 = 0.0
    mean_fpr1 = np.linspace(0, 1, 100)

    mean_tpr2 = 0.0
    mean_fpr2 = np.linspace(0, 1, 100)


    for i in range(len(roc_curves_list1)):
        fpr1, tpr1 = roc_curves_list1[i][0], roc_curves_list1[i][1]
        mean_tpr1 += interp(mean_fpr1, fpr1, tpr1)
        mean_tpr1[0] = 0.0

        #plt.plot(fpr, tpr, lw=1, label='ROC fold %d (area = %0.2f)' % (i, roc_auc_list[i]))

    for i in range(len(roc_curves_list2)):
        fpr2, tpr2 = roc_curves_list2[i][0], roc_curves_list2[i][1]
        mean_tpr2 += interp(mean_fpr2, fpr2, tpr2)
        mean_tpr2[0] = 0.0

    plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Luck')

    mean_tpr1 /= len(roc_curves_list1)
    mean_tpr1[-1] = 1.0
    mean_auc1 = metrics.auc(mean_fpr1, mean_tpr1)

    mean_tpr2 /= len(roc_curves_list2)
    mean_tpr2[-1] = 1.0
    mean_auc2 = metrics.auc(mean_fpr2, mean_tpr2)

    #print(mean_auc)
    plt.plot(mean_fpr1, mean_tpr1, 'k--',
             label='Mean ROC 1(area = %0.2f)' % mean_auc1, lw=2)

    plt.plot(mean_fpr2, mean_tpr2, 'k:',
             label='Mean ROC 2(area = %0.2f)' % mean_auc1, lw=2)

    plt.xlim([-0.05, 1.05])
    plt.ylim([-0.05, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic example')
    plt.legend(loc="lower right")
    plt.show()