混沌麻雀优化算法优化XGBoost算法的代码

混沌麻雀优化算法（Chaotic Sparrow Optimization，CSO）是一种基于混沌理论的优化算法，可以用于优化各种问题，包括机器学习模型的参数优化。下面是使用CSO优化XGBoost算法的代码示例：

import numpy as np
from xgboost import XGBRegressor
from sklearn.datasets import load_boston
from sklearn.metrics import mean_squared_error

# 加载波士顿房价数据集
boston = load_boston()
X, y = boston.data, boston.target

# 定义CSO算法
class CSO:
    def __init__(self, n_particles, n_features, c1, c2):
        self.n_particles = n_particles
        self.n_features = n_features
        self.c1 = c1
        self.c2 = c2
        self.positions = np.random.rand(n_particles, n_features) * 10 - 5
        self.velocities = np.zeros((n_particles, n_features))
        self.personal_best_positions = self.positions.copy()
        self.personal_best_scores = np.zeros(n_particles)
        self.global_best_position = None
        self.global_best_score = np.inf
        
    def update(self, objective_func):
        # 计算适应度值
        scores = np.array([objective_func(p) for p in self.positions])
        
        # 更新个体最优位置和全局最优位置
        for i in range(self.n_particles):
            if scores[i] < self.personal_best_scores[i]:
                self.personal_best_positions[i] = self.positions[i].copy()
                self.personal_best_scores[i] = scores[i]
            if scores[i] < self.global_best_score:
                self.global_best_position = self.positions[i].copy()
                self.global_best_score = scores[i]
        
        # 更新速度和位置
        r1 = np.random.rand(self.n_particles, self.n_features)
        r2 = np.random.rand(self.n_particles, self.n_features)
        self.velocities = self.velocities + self.c1 * r1 * (self.personal_best_positions - self.positions) \
            + self.c2 * r2 * (self.global_best_position - self.positions)
        self.positions = self.positions + self.velocities
        
    def optimize(self, objective_func, n_iterations):
        for i in range(n_iterations):
            self.update(objective_func)
        return self.global_best_position

# 定义XGBoost的目标函数
def xgb_objective(params):
    n_estimators = int(params[0])
    max_depth = int(params[1])
    learning_rate = params[2]
    reg_alpha = params[3]
    reg_lambda = params[4]
    subsample = params[5]
    colsample_bytree = params[6]
    
    model = XGBRegressor(
        n_estimators=n_estimators,
        max_depth=max_depth,
        learning_rate=learning_rate,
        reg_alpha=reg_alpha,
        reg_lambda=reg_lambda,
        subsample=subsample,
        colsample_bytree=colsample_bytree
    )
    model.fit(X, y)
    y_pred = model.predict(X)
    score = mean_squared_error(y, y_pred)
    return score

# 运行CSO算法进行参数优化
cso = CSO(n_particles=50, n_features=7, c1=2, c2=2)
best_params = cso.optimize(xgb_objective, n_iterations=100)

# 使用最优参数训练XGBoost模型
best_n_estimators = int(best_params[0])
best_max_depth = int(best_params[1])
best_learning_rate = best_params[2]
best_reg_alpha = best_params[3]
best_reg_lambda = best_params[4]
best_subsample = best_params[5]
best_colsample_bytree = best_params[6]
best_model = XGBRegressor(
    n_estimators=best_n_estimators,
    max_depth=best_max_depth,
    learning_rate=best_learning_rate,
    reg_alpha=best_reg_alpha,
    reg_lambda=best_reg_lambda,
    subsample=best_subsample,
    colsample_bytree=best_colsample_bytree
)
best_model.fit(X, y)
y_pred = best_model.predict(X)
print("MSE:", mean_squared_error(y, y_pred))

在上面的代码中，我们定义了一个CSO类，包括粒子群的初始化、更新和优化过程。优化过程中使用了XGBoost的目标函数xgb_objective，这个函数将XGBoost的参数作为输入，训练模型并计算均方误差作为适应度值。最后，我们使用优化得到的最优参数训练XGBoost模型并计算均方误差。