for each class class_names = np.unique(y_train) y_scores = tree.predict_proba(X_test) y_pred = tree.predict(X_test) macro_auc = roc_auc_score(y_test, y_scores, multi_class='ovo', average='macro') y_test = label_binarize(y_test, classes=range(3)) y_pred = label_binarize(y_pred, classes=range(3)) micro_auc = roc_auc_score(y_test, y_scores, average='micro') #micro_auc = roc_auc_score(y_test, y_scores, multi_class='ovr', average='micro') # calculate ROC curve fpr = dict() tpr = dict() roc_auc = dict() for i in range(3): # 遍历三个类别 fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_pred[:, i]) roc_auc[i] = auc(fpr[i], tpr[i]) return reports, matrices, micro_auc, macro_auc, fpr, tpr, roc_auc根据上述代码怎么调整下列代码fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_pred.ravel()) roc_auc["micro"] = auc(fpr["micro"], tpr["micro"]) # Compute macro-average ROC curve and ROC area（方法一） # First aggregate all false positive rates all_fpr = np.unique(np.concatenate([fpr_avg[i] for i in range(3)])) # Then interpolate all ROC curves at this points mean_tpr = np.zeros_like(all_fpr) for i in range(3): mean_tpr += interp(all_fpr, fpr_avg[i], tpr_avg[i]) # Finally average it and compute AUC mean_tpr /= 3 fpr_avg["macro"] = all_fpr tpr_avg["macro"] = mean_tpr macro_auc_avg["macro"] = macro_auc_avg # Plot all ROC curves lw = 2 plt.figure() plt.plot(fpr_avg["micro"], tpr_avg["micro"], label='micro-average ROC curve (area = {0:0.2f})' ''.format(micro_auc_avg["micro"]), color='deeppink', linestyle=':', linewidth=4) plt.plot(fpr_avg["macro"], tpr_avg["macro"], label='macro-average ROC curve (area = {0:0.2f})' ''.format(macro_auc_avg["macro"]), color='navy', linestyle=':', linewidth=4) colors = cycle(['aqua', 'darkorange', 'cornflowerblue']) for i, color in zip(range(3), colors): plt.plot(fpr_avg[i], tpr_avg[i], color=color, lw=lw, label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i, roc_auc_avg[i])) plt.plot([0, 1], [0, 1], 'k--', lw=lw) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('DF') plt.legend(loc="lower right") plt.show()

根据上述代码，可以将下列代码进行如下调整：

fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_pred.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

# Compute macro-average ROC curve and ROC area
# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(3)]))
# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(3):
    mean_tpr += interp(all_fpr, fpr[i], tpr[i])
# Finally average it and compute AUC
mean_tpr /= 3
fpr_avg["macro"] = all_fpr
tpr_avg["macro"] = mean_tpr
macro_auc_avg = roc_auc_score(y_test, y_scores, multi_class='ovo', average='macro')
roc_auc_avg["macro"] = macro_auc_avg

# Plot all ROC curves
lw = 2
plt.figure()
plt.plot(fpr_avg["micro"], tpr_avg["micro"], label='micro-average ROC curve (area = {0:0.2f})'
         ''.format(micro_auc), color='deeppink', linestyle=':', linewidth=4)
plt.plot(fpr_avg["macro"], tpr_avg["macro"], label='macro-average ROC curve (area = {0:0.2f})'
         ''.format(macro_auc_avg), color='navy', linestyle=':', linewidth=4)
colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
for i, color in zip(range(3), colors):
    plt.plot(fpr_avg[i], tpr_avg[i], color=color, lw=lw,
             label='ROC curve of class {0} (area = {1:0.2f})'.format(i, roc_auc[i]))
plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('DF')
plt.legend(loc="lower right")
plt.show()

首先，`macro_auc_avg` 应该是一个变量，用来存储计算得到的宏平均 ROC AUC 值，因此需要对其进行定义和赋值。其次，`roc_auc_avg` 应该是一个字典，用来存储每个类别的 ROC AUC 值，因此需要将其定义为一个空字典。在计算宏平均 ROC 曲线和 ROC AUC 值时，应该使用 `roc_auc_score` 函数来计算。最后，在绘制 ROC 曲线时，变量名应该改为 `roc_auc`，而不是 `roc_auc_avg`。