gwo-xgboost回归

### 使用 GWO 灰狼优化器结合 XGBoost 进行回归分析 #### 安装必要的库 为了使用 Python 实现 GWO 和 XGBoost 的组合，需安装 `xgboost` 库以及其他辅助库。 ```bash pip install xgboost numpy pandas scikit-learn ``` #### 导入所需模块并准备数据集 加载所需的 Python 模块，并准备好要使用的训练和测试数据集。 ```python import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from xgboost import XGBRegressor from sklearn.metrics import mean_squared_error, r2_score ``` #### 初始化 XGBoost 回归模型 创建一个基础的 XGBoost 回归模型实例，该模型将在后续过程中被 GWO 优化其超参数设置[^1]。 ```python def create_model(params=None): if params is None: model = XGBRegressor() else: model = XGBRegressor(**params) return model ``` #### 设计适应度函数 定义适合于评估个体解质量的目标函数。此函数接收一组候选参数作为输入，并返回相应的性能指标得分。 ```python def fitness_function(solution): # 将解决方案转换成字典形式以便传递给XGBoost param_dict = { 'n_estimators': int(np.round(solution[0])), 'max_depth': int(np.round(solution[1])), 'learning_rate': solution[2], 'subsample': min(1., max(0., solution[3])) } regressor = create_model(param_dict) # 训练模型 regressor.fit(X_train, y_train) predictions = regressor.predict(X_test) mse = mean_squared_error(y_test, predictions) return -mse # 负号表示最大化负均方误差即最小化正向均方误差 ``` #### 编写 GWO 主体逻辑 构建完整的 GWO 流程来搜索最佳的 XGBoost 参数配置。这里简化展示了核心部分；实际应用中可能还需要考虑更多细节如种群初始化策略等。 ```python class GreyWolfOptimizer(): def __init__(self, n_wolves=5, dim=4, lb=[10, 1, 0.01, 0], ub=[1000, 10, 0.3, 1]): self.n_wolves = n_wolves self.dim = dim self.lb = np.array(lb).reshape(-1,) self.ub = np.array(ub).reshape(-1,) ... def optimize(self, iterations=100): alpha_pos = beta_pos = delta_pos = np.zeros((self.dim,)) alpha_fit = beta_fit = delta_fit = float('-inf') wolves_positions = ... # 随机生成初始位置 for t in range(iterations): for i in range(self.n_wolves): current_fitness = fitness_function(wolves_positions[i]) if current_fitness > alpha_fit: delta_fit = beta_fit; delta_pos = beta_pos.copy() beta_fit = alpha_fit; beta_pos = alpha_pos.copy() alpha_fit = current_fitness; alpha_pos = wolves_positions[i].copy() elif (current_fitness > beta_fit and current_fitness <= alpha_fit): delta_fit = beta_fit; delta_pos = beta_pos.copy() beta_fit = current_fitness; beta_pos = wolves_positions[i].copy() elif (current_fitness > delta_fit and current_fitness <= beta_fit): delta_fit = current_fitness; delta_pos = wolves_positions[i].copy() a = 2 * (iterations-t)/iterations for i in range(self.n_wolves): A1 = 2*a*np.random.rand() - a; C1 = 2*np.random.rand(); D_alpha = abs(C1*alpha_pos - wolves_positions[i]); X1 = alpha_pos - A1*D_alpha; A2 = 2*a*np.random.rand() - a; C2 = 2*np.random.rand(); D_beta = abs(C2*beta_pos - wolves_positions[i]); X2 = beta_pos - A2*D_beta; A3 = 2*a*np.random.rand() - a; C3 = 2*np.random.rand(); D_delta = abs(C3*delta_pos - wolves_positions[i]); X3 = delta_pos - A3*D_delta; wolves_positions[i] = (X1 + X2 + X3) / 3 # 边界处理... best_params = {'n_estimators': round(alpha_pos[0]), 'max_depth': round(alpha_pos[1]), 'learning_rate': alpha_pos[2], 'subsample': alpha_pos[3]} return best_params ``` 通过上述方法可以有效地利用 GWO 来找到更优的 XGBoost 模型参数，从而提高预测精度和泛化能力[^2]。