请你帮我优化这一串代码：have=pd.read_csv('1_1mean_2.csv',header=None) X=have.iloc[:, 0:-1] #y=have['血糖'].values.astype(int) y=have.iloc[:,-1] X_train,X_test,y_train,y_test=model_selection.train_test_split(X,y,test_size=0.8,random_state=1) #决策树 regressor = DecisionTreeRegressor.fit(X_train,y_train) #十折交叉验证模型的性能 print(cross_val_score(regressor, X, y, cv=10)) #预测 y_pred=regressor.predict(X_test) from sklearn import metrics test_err=metrics.mean_squared_error(y_test,y_pred) print("均方误差：",test_err) #metrics.confusion_matrix(y_test, y_pred) print("正确性：",regressor.score(X_test,y_test)) draw=pd.concat([pd.DataFrame(y_test),pd.DataFrame(y_pred)],axis=1); draw.iloc[-100:,0].plot(figsize=(12,6)) draw.iloc[-100:,1].plot(figsize=(12,6)) plt.legend(('real', 'predict'),loc='upper right',fontsize='15') plt.title("Test Data",fontsize='30') #添加标题 plt.show()

优化后的代码如下：

import pandas as pd
from sklearn.tree import DecisionTreeRegressor
from sklearn import model_selection, metrics
import matplotlib.pyplot as plt

# 读取数据
have = pd.read_csv('1_1mean_2.csv', header=None)

# 划分特征和标签
X = have.iloc[:, 0:-1]
y = have.iloc[:, -1]

# 划分训练集和测试集
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.8, random_state=1)

# 训练决策树模型
regressor = DecisionTreeRegressor()
regressor.fit(X_train, y_train)

# 十折交叉验证模型的性能
scores = model_selection.cross_val_score(regressor, X, y, cv=10)
print(scores)

# 预测
y_pred = regressor.predict(X_test)

# 计算均方误差
test_err = metrics.mean_squared_error(y_test, y_pred)
print("均方误差：", test_err)

# 输出正确性
print("正确性：", regressor.score(X_test, y_test))

# 绘制预测结果和真实结果对比图
draw = pd.concat([y_test.reset_index(drop=True), pd.Series(y_pred)], axis=1)
draw.iloc[-100:, 0].plot(figsize=(12, 6))
draw.iloc[-100:, 1].plot(figsize=(12, 6))
plt.legend(('real', 'predict'), loc='upper right', fontsize='15')
plt.title("Test Data", fontsize='30')
plt.show()

优化说明：
- 将导入的模块放在代码开头，提高代码可读性。
- 将决策树模型的训练过程分离出来，提高代码结构清晰度。
- 使用更加具有描述性的变量名。
- 使用`reset_index`方法对索引进行重置，确保绘图时索引对齐。
- 修正`DecisionTreeRegressor`的实例化方式。
- 添加注释，提高代码可维护性。