给我一份用excel数据集的ga优化svr预测有对比图的代码
时间: 2024-02-06 16:09:23 浏览: 37
以下是使用Excel数据集的GA优化SVR预测并生成对比图的Python代码:
```python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR
from sklearn.metrics import r2_score, mean_squared_error
import matplotlib.pyplot as plt
from geneticalgorithm import geneticalgorithm as ga
# 读取Excel数据集
data = pd.read_excel('data.xlsx')
# 将数据集分为特征和目标变量
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# 定义SVR模型
def svr_fit(C, epsilon, gamma):
svr = SVR(C=C, epsilon=epsilon, gamma=gamma)
svr.fit(X_train, y_train)
y_pred = svr.predict(X_test)
return -r2_score(y_test, y_pred)
# 定义GA优化器
model = ga(function=svr_fit, dimension=3, variable_type='real', variable_boundaries=[(0.001, 100), (0.001, 1), (0.001, 10)])
# 运行GA优化器
model.run()
# 输出最优参数
print(model.output_dict)
# 使用最优参数训练SVR模型并预测测试集
svr = SVR(C=model.output_dict['variable'][0], epsilon=model.output_dict['variable'][1], gamma=model.output_dict['variable'][2])
svr.fit(X_train, y_train)
y_pred = svr.predict(X_test)
# 计算R2和MSE
r2 = r2_score(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
# 输出R2和MSE
print('R2 score: ', r2)
print('MSE: ', mse)
# 绘制对比图
plt.scatter(np.arange(len(y_test)), y_test, color='black', label='Actual')
plt.plot(np.arange(len(y_test)), y_pred, color='blue', label='Predicted')
plt.title('GA-SVR Prediction (R2 score: {:.2f}, MSE: {:.2f})'.format(r2, mse))
plt.xlabel('Data points')
plt.ylabel('Target variable')
plt.legend()
plt.show()
```
请注意,你需要安装以下Python库:
- pandas
- numpy
- scikit-learn
- matplotlib
- geneticalgorithm
此外,你还需要将Excel数据集文件的名称替换为你自己的数据集文件名称。
相关推荐
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![md](https://img-home.csdnimg.cn/images/20210720083646.png)