给我一份excel数据集的GA优化SVR预测的有对比图的代码

以下是Python代码示例，用于GA优化SVR预测Excel数据集并生成对比图：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.svm import SVR
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import r2_score

# 读取Excel数据集
data = pd.read_excel('data.xlsx')
X = data.iloc[:,:-1].values
y = data.iloc[:,-1].values

# 特征缩放
sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transform(y.reshape(-1,1)).reshape(-1)

# SVR模型
def svr_model(C, gamma, epsilon):
    svr = SVR(C=C, gamma=gamma, epsilon=epsilon)
    svr.fit(X_train, y_train)
    y_pred = svr.predict(X_test)
    return r2_score(y_test, y_pred)

# GA优化
from geneticalgorithm import geneticalgorithm as ga

varbound = np.array([[10**-2, 10**2]]*3)
model_ga = ga(function=svr_model, dimension=3, variable_type='real', variable_boundaries=varbound)
model_ga.run()

# 训练SVR模型
C, gamma, epsilon = model_ga.output_dict['variable']
svr = SVR(C=C, gamma=gamma, epsilon=epsilon)
svr.fit(X, y)

# 预测并还原结果
y_pred = sc_y.inverse_transform(svr.predict(X))
y_true = data.iloc[:,-1].values

# 绘制对比图
plt.plot(y_true, label='True')
plt.plot(y_pred, label='Predicted')
plt.legend()
plt.show()

请确保已安装以下依赖项：

- pandas
- matplotlib
- sklearn
- geneticalgorithm

如果您尚未安装geneticalgorithm，请使用以下命令安装：

!pip install geneticalgorithm

请将代码中的`data.xlsx`替换为您的Excel数据集文件名并运行代码。