用python3语言写遗传算法优化lightgbm参数的代码

下面是一个示例代码，它使用遗传算法来优化LightGBM的参数：

import numpy as np
import lightgbm as lgb
from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
import random

# 设置遗传算法的参数
population_size = 100
generations = 20
mutation_prob = 0.1

# 定义遗传算法中用到的参数
param_grid = {
    'boosting_type': ['gbdt', 'dart'],
    'num_leaves': list(range(20, 150)),
    'learning_rate': list(np.logspace(np.log10(0.005), np.log10(0.5), base = 10, num = 1000)),
    'subsample_for_bin': list(range(20000, 300000, 20000)),
    'min_child_samples': list(range(20, 500, 5)),
    'reg_alpha': list(np.linspace(0, 1)),
    'reg_lambda': list(np.linspace(0, 1)),
    'colsample_bytree': list(np.linspace(0.6, 1, 10)),
    'subsample': list(np.linspace(0.5, 1, 100)),
    'is_unbalance': [True, False]
}

# 加载数据集
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 遗传算法
def genetic_algorithm(population_size, generations, mutation_prob):
    population = []
    for i in range(population_size):
        individual = {key: random.choice(value) for key, value in param_grid.items()}
        population.append(individual)
    
    for generation in range(generations):
        print(f"Generation {generation}")
        scores = []
        for individual in population:
            model = lgb.LGBMClassifier(boosting_type=individual['boosting_type'],
                                       num_leaves=individual['num_leaves'],
                                       learning_rate=individual['learning_rate'],
                                       subsample_for_bin=individual['subsample_for_bin'],
                                       min_child_samples=individual['min_child_samples'],
                                       reg_alpha=individual['reg_alpha'],
                                       reg_lambda=individual['reg_lambda'],
                                       colsample_bytree=individual['colsample_bytree'],
                                       subsample=individual['subsample'],
                                       is_unbalance=individual['is_unbalance'])