写一段支持向量机回归的shap模型解释代码

import pandas as pd
import numpy as np
import shap
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR

# 加载波士顿房价数据集
boston = load_boston()
X, y = boston.data, boston.target
feature_names = boston.feature_names

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# SVM回归模型训练
svm = SVR(kernel='linear', C=1.0, epsilon=0.1)
svm.fit(X_train, y_train)

# 使用shap解释SVM回归模型
explainer = shap.KernelExplainer(svm.predict, X_train)
shap_values = explainer.shap_values(X_test)

# 打印特征重要性
shap.summary_plot(shap_values, X_test, feature_names=feature_names)