【python】蜜蜂算法优化决策树回归的超参数

蜜蜂算法的基本思想是，将参数空间视为蜂巢，并在参数空间中展开搜索，将搜索任务分配给一群“蜜蜂”。每个“蜜蜂”在搜索空间中进行搜索，并与其他蜜蜂共享其搜索结果。通过某些操作，蜜蜂会找到最优解并返回给算法，最终获得全局最优解。

蜜蜂算法的优点是可以在大型搜索空间中有效运行，同时能够克服局部最优解。它已被应用于各种优化问题，包括线性和非线性问题、连续和离散问题等。

决策树模型既可以做分类也可以做回归。

回归就是根据特征向量来决定对应的输出值。回归树就是将特征空间划分成若干单元，每一个划分单元有一个特定的输出。对于测试数据，只要按照特征将其归到某个单元，便得到对应的输出值。

from sklearn.tree import DecisionTreeRegressor
from sklearn.datasets import load_boston
from sklearn.model_selection import cross_val_score
import numpy as np

# 加载数据集
from sklearn.datasets import load_diabetes
X, y = load_diabetes(return_X_y=True)

# 定义回归树模型结构
def fit_model(params):
    max_depth = int(params[0])
    min_samples_split = int(params[1])
    reg = DecisionTreeRegressor(max_depth=max_depth, min_samples_split=min_samples_split)
    return np.mean(cross_val_score(reg, X, y, cv=5, n_jobs=-1))

# 定义目标函数
def objective(x):
    return -1.0 * fit_model(x)

class Bee:
    def __init__(self, x0):
        self.position = x0
        self.error = objective(x0)

    def waggle_dance(self, prob, neighbour):
        x_new = np.copy(self.position)
        if np.random.rand() < prob:
            k = np.random.randint(len(self.position))
            x_new[k] = neighbour[k % len(neighbour)]
        return Bee(x_new)

def run_bee_algorithm():
    # 超参数定义
    MIN_POSITION = [1, 1]
    MAX_POSITION = [20, 20]
    NPARAMS = 2
    NBEES = 50
    MAX_CYCLE = 30
    N_NEIGHBOURS = 3
    P_LOCAL = 0.5
    P_GLOBAL = 0.2
    P_NEIGHBOUR = 0.4
    P_MAKENEW = 0.01

    cycle = 0
    bees = [Bee(np.random.uniform(MIN_POSITION, MAX_POSITION, NPARAMS)) for i in range(NBEES)]
    while cycle < MAX_CYCLE:
        for i in range(NBEES):
            if np.random.uniform(0, 1) < P_LOCAL:
                neighbour = sorted(bees, key=lambda bee: bee.error)[np.random.randint(N_NEIGHBOURS)].position
                new_bee = bees[i].waggle_dance(P_NEIGHBOUR, neighbour)
                if(new_bee.error < bees[i].error):
                    bees[i] = new_bee
            elif np.random.uniform(0, 1) < P_GLOBAL:
                best_bee = sorted(bees, key=lambda bee: bee.error)[0]
                new_bee = bees[i].waggle_dance(P_NEIGHBOUR, best_bee.position)
                if(new_bee.error < bees[i].error):
                    bees[i] = new_bee
            else:
                new_bee = Bee(np.random.uniform(MIN_POSITION, MAX_POSITION, NPARAMS))
                if np.random.uniform(0, 1) > P_MAKENEW and new_bee.error < bees[i].error:
                    bees[i] = new_bee
        cycle += 1

    best_bee = sorted(bees, key=lambda bee: bee.error)[0]
    return best_bee.position

if __name__ == '__main__':
    best_params = run_bee_algorithm()
    print('最优参数：', best_params)