Python语言支持向量机用遗传算法优化

Question

现有大量数据，需求一个程序用python语言编程，对数据进行相应处理，支持向量机评估R值可能较低，故需要使用遗传算法进行优化。求程序。大家的程序部分我进行了采用，发现导入数据后出现了报错，为could not convert string to float '编辑’
查询一些常规解决方法未解决，希望能解决

Answer 1

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import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import r2_score
import random

# 导入数据
data = pd.read_csv('your_data.csv')

# 划分数据集
X = data.drop(columns=['target_column'])
y = data['target_column']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义支持向量机模型
def svm_model(C, gamma):
    model = SVC(kernel='rbf', C=C, gamma=gamma)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    return r2_score(y_test, y_pred)

# 定义遗传算法参数优化函数
def genetic_algorithm():
    # 初始化参数范围
    C_range = [0.1, 1, 10, 100]
    gamma_range = [0.1, 0.01, 0.001, 0.0001]
    
    # 定义种群数量和迭代次数
    population_size = 10
    generations = 50
    
    # 随机初始化种群
    population = []
    for i in range(population_size):
        C = random.choice(C_range)
        gamma = random.choice(gamma_range)
        population.append((C, gamma))
    
    # 进化
    for i in range(generations):
        # 评估种群中每个个体的适应度（即R2分数）
        scores = [svm_model(individual[0], individual[1]) for individual in population]
        
        # 选择精英个体并进行复制
        elite_indices = sorted(range(len(scores)), key=lambda x: scores[x])[-2:]
        elite_population = [population[index] for index in elite_indices] * 3
        
        # 交叉和变异
        new_population = []
        for j in range(population_size):
            parent1, parent2 = random.sample(elite_population, 2)
            C = random.choice([parent1[0], parent2[0]])
            gamma = random.choice([parent1[1], parent2[1]])
            if random.random() < 0.1: # 10%的概率进行变异
                C = random.choice(C_range)
                gamma = random.choice(gamma_range)
            new_population.append((C, gamma))
        
        population = new_population
    
    # 返回最优解
    best_individual = sorted(zip(population, scores), key=lambda x: x[1], reverse=True)[0]
    return best_individual[0]

# 优化支持向量机模型的参数
best_params = genetic_algorithm()

# 训练和评估支持向量机模型
r2 = svm_model(best_params[0], best_params[1])

# 打印R2分数和最优参数
print('R2 score:', r2)
print('Best params:', best_params)