python实现GWO对随机森林回归进行调参

灰狼优化算法（Grey Wolf Optimizer，GWO）是一种基于自然界灰狼群体行为的优化算法，它模拟了灰狼在社会组织中的狩猎行为，通过仿真狼群在狩猎过程中的行为，来求解最优化问题。随机森林回归是一种机器学习算法，它是一种集成学习方法，通过组合多个决策树来提高预测精度，具有较高的准确率和鲁棒性。

下面是使用GWO对随机森林回归进行调参的Python实现：

import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

# 灰狼优化算法（Grey Wolf Optimizer，GWO）
class GWO:
    def __init__(self, n_population, max_iter, dim, lb, ub, obj_func):
        self.n_population = n_population # 种群数量
        self.max_iter = max_iter # 最大迭代次数
        self.dim = dim # 搜索维度
        self.lb = lb # 搜索空间下限
        self.ub = ub # 搜索空间上限
        self.obj_func = obj_func # 目标函数
        self.positions = np.zeros((self.n_population, self.dim)) # 种群位置
        self.betas = np.zeros(self.n_population) # Beta向量
        self.deltas = np.zeros(self.n_population) # Delta向量
        self.alphas = np.zeros(self.dim) # Alpha位置
        self.beta = 2 # Beta常数
        self.delta = 2 # Delta常数
        self.alpha = np.zeros(self.dim) # 初始Alpha位置

    # 初始化种群
    def init_population(self):
        self.positions = np.random.uniform(self.lb, self.ub, (self.n_population, self.dim))
        self.betas = np.random.uniform(0, 1, self.n_population)
        self.deltas = np.random.uniform(0, 1, self.n_population)

    # 更新Alpha, Beta, Delta位置
    def update_positions(self, iter):
        a = 2 - 2 * iter / self.max_iter # alpha参数
        for i in range(self.n_population):
            r1, r2, r3 = np.random.rand(3)
            A1 = 2 * a * r1 - a # Alpha狼位置
            C1 = 2 * r2 # Alpha狼距离
            D_alpha = abs(C1 * self.alpha - self.positions[i]) # Alpha狼距离向量
            X1 = self.alpha - A1 * D_alpha # 新位置
            r1, r2, r3 = np.random.rand(3)
            A2 = 2 * a * r1 - a # Beta狼位置
            C2 = 2 * r2 # Beta狼距离
            D_beta = abs(C2 * self.betas[i] - self.positions[i]) # Beta狼距离向量
            X2 = self.betas[i] - A2 * D_beta # 新位置
            r1, r2, r3 = np.random.rand(3)
            A3 = 2 * a * r1 - a # Delta狼位置
            C3 = 2 * r2 # Delta狼距离
            D_delta = abs(C3 * self.deltas[i] - self.positions[i]) # Delta狼距离向量
            X3 = self.deltas[i] - A3 * D_delta # 新位置
            self.positions[i] = (X1 + X2 + X3) / 3 # 更新位置
        self.alpha = self.positions[np.argmin([self.obj_func(x) for x in self.positions])] # 更新Alpha位置
        self.betas = sorted(self.betas) # 排序
        self.delta = self.betas[-1] # 更新Delta常数
        self.betas = (self.betas - self.betas.min()) / (self.betas.max() - self.betas.min()) # 归一化
        self.deltas = (self.deltas - self.deltas.min()) / (self.deltas.max() - self.deltas.min()) # 归一化

    # 灰狼优化算法求解最优解
    def optimize(self):
        self.init_population()
        for i in range(self.max_iter):
            self.update_positions(i)
        return self.alpha

# 随机森林回归模型
def random_forest_regressor(n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features, X_train, y_train, X_test, y_test):
    model = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, max_features=max_features)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    return mean_squared_error(y_test, y_pred)

# 参数范围
bounds = [(50, 200), (2, 10), (2, 10), (1, 5), (0.1, 1.0)]

# 目标函数
def objective(params):
    n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features = params
    return random_forest_regressor(n_estimators=int(n_estimators), max_depth=int(max_depth), min_samples_split=int(min_samples_split), min_samples_leaf=int(min_samples_leaf), max_features=max_features, X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)

# 数据集
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
data = load_boston()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2)

# 灰狼优化算法求解最优解
gwo = GWO(n_population=30, max_iter=50, dim=5, lb=[50, 2, 2, 1, 0.1], ub=[200, 10, 10, 5, 1.0], obj_func=objective)
best_params = gwo.optimize()

# 输出最优解
print('best_params:', best_params)
print('MSE:', objective(best_params))

上述代码中，我们使用sklearn中的RandomForestRegressor作为目标函数，使用均方误差（Mean Squared Error，MSE）作为优化目标。我们使用train_test_split将数据集分为训练集和测试集，并使用灰狼优化算法对随机森林回归模型的参数进行调优，得到最优参数和最小MSE。