给我一段PSO-lightGBM的代码

以下是一个简单的PSO-lightGBM代码示例：

import lightgbm as lgb
import numpy as np
from pyswarm import pso

# 数据准备
train_data = lgb.Dataset(X_train, label=y_train)
test_data = lgb.Dataset(X_test, label=y_test)

# PSO模型参数空间
def pso_parameter_space():
    space = [(100, 1000), # num_iterations
             (0.01, 0.5), # learning_rate
             (1, 20), # num_leaves
             (10, 100), # min_data_in_leaf
             (0.1, 0.9), # feature_fraction
             (0.1, 0.9), # bagging_fraction
             (0, 10), # bagging_freq
             (0.1, 0.9), # lambda_l1
             (0.1, 0.9), # lambda_l2
             (0.1, 0.9), # min_gain_to_split
             (10, 100)] # max_depth
    return space

# 定义PSO目标函数
def pso_objective_function(x):
    params = {'objective': 'binary',
              'metric': 'binary_logloss',
              'num_iterations': int(x[0]),
              'learning_rate': x[1],
              'num_leaves': int(x[2]),
              'min_data_in_leaf': int(x[3]),
              'feature_fraction': x[4],
              'bagging_fraction': x[5],
              'bagging_freq': int(x[6]),
              'lambda_l1': x[7],
              'lambda_l2': x[8],
              'min_gain_to_split': x[9],
              'max_depth': int(x[10])}
    model = lgb.train(params, train_data)
    y_pred = model.predict(X_test)
    loss = np.mean((y_pred - y_test) ** 2)
    return loss

# PSO优化
lb = np.array([s[0] for s in pso_parameter_space()])
ub = np.array([s[1] for s in pso_parameter_space()])
x_opt, f_opt = pso(pso_objective_function, lb, ub, swarmsize=50, maxiter=100)

# 输出最佳参数
print('Best parameters:', x_opt)

该代码定义了一个PSO模型参数空间，将目标函数设置为LightGBM模型的均方误差，然后使用pyswarm库中的pso函数进行PSO优化。最终输出最佳参数。