给我一份excel数据集的GA优化SVR预测的有对比图的代码

好的，以下是一份Python代码示例，用于将Excel数据集进行遗传算法优化的支持向量回归（SVR）预测，并绘制对比图表。

首先，您需要安装以下Python库：pandas、numpy、sklearn、deap、matplotlib。

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 mean_squared_error
from deap import base, creator, tools, algorithms
import matplotlib.pyplot as plt

# 读取Excel数据集
data = pd.read_excel('data.xlsx')

# 提取特征和标签
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 分割数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义适应度函数
def evalSVR(individual):
    svr = SVR(kernel='rbf', C=individual[0], gamma=individual[1], epsilon=individual[2])
    svr.fit(X_train, y_train)
    y_pred = svr.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    return mse,

# 定义遗传算法参数
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", list, fitness=creator.FitnessMin)
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", evalSVR)
toolbox.register("mate", tools.cxBlend, alpha=0.5)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.1)
toolbox.register("select", tools.selTournament, tournsize=3)

# 设置遗传算法参数
pop_size = 50
cx_prob = 0.5
mut_prob = 0.2
ngen = 50

# 运行遗传算法
pop = toolbox.population(n=pop_size)
fitnesses = list(map(toolbox.evaluate, pop))
for ind, fit in zip(pop, fitnesses):
    ind.fitness.values = fit
for g in range(ngen):
    offspring = algorithms.varAnd(pop, toolbox, cx_prob, mut_prob)
    fits = toolbox.map(toolbox.evaluate, offspring)
    for ind, fit in zip(offspring, fits):
        ind.fitness.values = fit
    pop = toolbox.select(offspring, pop_size)
best_ind = tools.selBest(pop, k=1)[0]

# 使用最优个体进行预测
svr = SVR(kernel='rbf', C=best_ind[0], gamma=best_ind[1], epsilon=best_ind[2])
svr.fit(X_train, y_train)
y_pred = svr.predict(X_test)

# 绘制对比图表
fig, ax = plt.subplots()
ax.scatter(y_test, y_pred)
ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=3)
ax.set_xlabel('Measured')
ax.set_ylabel('Predicted')
plt.show()

希望这个代码示例能够帮助到您。请注意，这只是一个简单的示例，您可能需要对代码进行修改以满足您的需求。另外，这个代码示例只是一种可能的方法，您可以根据您的需求使用不同的模型和遗传算法参数。