请提供使用python实现基于传算法工具箱GEAPT进行遗传算法优化OneClassSVM分类器参数的示例代码

以下是使用Python实现基于遗传算法工具箱GEAPT进行遗传算法优化OneClassSVM分类器参数的示例代码：

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import OneClassSVM
from geapt import GeneticProgramming

# 加载数据集
data = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2)

# 定义适应度函数，使用OneClassSVM进行分类，并返回分类准确率
def fitness_function(individual):
    clf = OneClassSVM(kernel=individual, nu=0.1)
    clf.fit(X_train)
    accuracy = clf.score(X_test)
    return accuracy,

# 定义遗传算法的参数和限制条件
function_set = ['add', 'sub', 'mul', 'div', 'sqrt', 'log', 'sin', 'cos']
parameters = {'kernel': function_set}
pset = GeneticProgramming.create_function_set(parameters)
search_space = {'kernel': pset}

# 构造遗传算法对象，并进行优化
ga = GeneticProgramming(population_size=100, generations=50, crossover_prob=0.8, mutation_prob=0.1, elitism=True)
ga.search(search_space, fitness_function)

# 输出最优参数
best_params = ga.best_individual()[0]
print("Best Parameters: {}".format(best_params))

请注意，此代码仅是示例，并不保证在所有情况下都能正常工作。具体实现可能需要进行适当的修改和调整。