roc.py 文件源码

def save_plots(roc_auc, fpr, tpr, nb_classes, path):
    # aggregate all false positive rates
    all_fpr = np.unique(np.concatenate([fpr[i] for i in range(nb_classes)]))

    # interpolate all ROC curves at this points
    mean_tpr = np.zeros_like(all_fpr)
    for i in range(nb_classes):
        mean_tpr += interp(all_fpr, fpr[i], tpr[i])

    # average and compute AUC
    mean_tpr /= nb_classes

    fpr["macro"] = all_fpr
    tpr["macro"] = mean_tpr
    roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

    # plot
    plt.figure()
    plt.plot(fpr["micro"], tpr["micro"], 
        label='micro-average ROC curve (area = {0:0.2f})'.format(roc_auc["micro"]),
        linewidth=2)
    plt.plot(fpr["macro"], tpr["macro"],
        label='macro-average ROC curve (area = {0:0.2f})'.format(roc_auc["macro"]),
        linewidth=2)
    for i in range(nb_classes):
        plt.plot(fpr[i], tpr[i], 
            label='ROC curve of class {0} (area = {1:0.2f})'.format(i, roc_auc[i]))

    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Some extension of Receiver operating characteristic to multi-class')
    plt.legend(loc="lower right")

    plt.savefig(path) # save plot
    plt.close()