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 10:09:26 浏览: 26
以下是一份使用Excel数据集的GA优化SVR预测代码和对比图。请注意,此代码假定您已经安装了必要的库(如numpy,pandas,sklearn等)。
```python
import pandas as pd
import numpy as np
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from deap import creator, base, tools, algorithms
# 读取Excel数据集
data = pd.read_excel('dataset.xlsx')
# 提取特征和标签
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
# 特征缩放
sc = StandardScaler()
X = sc.fit_transform(X)
# 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)
# 定义适应度函数
def evaluate(individual):
# 提取个体中的超参数
C = individual[0]
gamma = individual[1]
epsilon = individual[2]
# 训练SVR模型
svr = SVR(C=C, gamma=gamma, epsilon=epsilon)
svr.fit(X_train, y_train)
# 预测测试集
y_pred = svr.predict(X_test)
# 计算均方根误差
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
# 返回适应度(RMSE越小越好)
return 1 / rmse,
# 创建遗传算法工具箱
creator.create('FitnessMax', base.Fitness, weights=(1.0,))
creator.create('Individual', list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register('attr_float', np.random.uniform, 0, 10)
toolbox.register('individual', tools.initRepeat, creator.Individual, toolbox.attr_float, n=3)
toolbox.register('population', tools.initRepeat, list, toolbox.individual)
toolbox.register('evaluate', evaluate)
toolbox.register('mate', tools.cxUniform, indpb=0.5)
toolbox.register('mutate', tools.mutGaussian, mu=0, sigma=1, indpb=0.2)
toolbox.register('select', tools.selTournament, tournsize=3)
# 运行遗传算法
population = toolbox.population(n=50)
NGEN = 100
for gen in range(NGEN):
offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)
fits = toolbox.map(toolbox.evaluate, offspring)
for fit, ind in zip(fits, offspring):
ind.fitness.values = fit
population = toolbox.select(offspring, k=len(population))
best_individual = tools.selBest(population, k=1)[0]
best_params = list(map(float, best_individual))
# 使用最佳超参数训练SVR模型
best_svr = SVR(C=best_params[0], gamma=best_params[1], epsilon=best_params[2])
best_svr.fit(X_train, y_train)
# 预测和可视化结果
import matplotlib.pyplot as plt
plt.scatter(range(len(y_test)), y_test, color='red', label='真实值')
plt.plot(range(len(y_test)), best_svr.predict(X_test), color='blue', label='预测值')
plt.title('SVR预测结果')
plt.xlabel('样本序号')
plt.ylabel('目标变量')
plt.legend()
plt.show()
```
这段代码将读取名为“dataset.xlsx”的Excel文件,并使用遗传算法来寻找最佳的SVR超参数。然后使用最佳超参数拟合模型并预测测试集。最后,使用matplotlib库将真实值和预测值绘制在同一张图上以进行比较。