如何在应用Stratified 10折交叉验证时获取python中所有混淆矩阵的聚合

from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix
import numpy as np

# 假设你已经有了数据集X和对应的标签y

# 初始化StratifiedKFold对象
skf = StratifiedKFold(n_splits=10)

# 初始化一个空的聚合混淆矩阵
aggregate_cm = np.zeros((num_classes, num_classes))

# 进行交叉验证
for train_index, test_index in skf.split(X, y):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    
    # 在训练集上训练模型
    
    # 在测试集上进行预测
    y_pred = model.predict(X_test)
    
    # 计算混淆矩阵
    cm = confusion_matrix(y_test, y_pred)
    
    # 将当前混淆矩阵加到聚合混淆矩阵上
    aggregate_cm += cm

# 输出聚合混淆矩阵
print("Aggregate Confusion Matrix:")
print(aggregate_cm)