粒子群优化算法xgboost多分类python

以下是使用粒子群优化算法优化XGBoost模型参数进行多分类的Python实现：

import xgboost as xgb
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import math
import random

# 粒子群优化算法
class PSO:
    def __init__(self, dim, size, iter_num, bound, c1, c2):
        self.__dim = dim
        self.__size = size
        self.__iter_num = iter_num
        self.__bound = bound
        self.__c1 = c1
        self.__c2 = c2
        self.__swarm = [Particle(dim, bound) for _ in range(size)]
        self.__best_pos = self.__swarm[0].get_best_pos()
        self.__best_fitness_value = float('-inf')

    def update(self, x_train, y_train):
        for i in range(self.__size):
            params = self.__swarm[i].get_pos()
            param_dict = {'objective': 'multi:softmax',
                          'num_class': 3,
                          'max_depth': int(params[0]),
                          'learning_rate': params[1],
                          'min_child_weight': int(params[2]),
                          'subsample': params[3],
                          'colsample_bytree': params[4],
                          'gamma': params[5]}
            xgb_model = xgb.XGBClassifier(**param_dict)
            xgb_model.fit(x_train, y_train)
            y_pred = xgb_model.predict(x_train)
            fitness_value = accuracy_score(y_train, y_pred)

            if fitness_value > self.__swarm[i].get_fitness_value():
                self.__swarm[i].set_best_pos(params)
                self.__swarm[i].set_fitness_value(fitness_value)

            if fitness_value > self.__best_fitness_value:
                self.__best_pos = params
                self.__best_fitness_value = fitness_value

            vel = self.__swarm[i].get_vel()
            pos = self.__swarm[i].get_pos()
            best_pos = self.__swarm[i].get_best_pos()

            for j in range(self.__dim):
                vel[j] = vel[j] + self.__c1 * random.random() * (best_pos[j] - pos[j]) + \
                         self.__c2 * random.random() * (self.__best_pos[j] - pos[j])
                pos[j] = pos[j] + vel[j]

                if pos[j] < self.__bound[j][0]:
                    pos[j] = self.__bound[j][0]
                    vel[j] = 0
                elif pos[j] > self.__bound[j][1]:
                    pos[j] = self.__bound[j][1]
                    vel[j] = 0

            self.__swarm[i].set_pos(pos)
            self.__swarm[i].set_vel(vel)

    def run(self, x_train, y_train):
        for i in range(self.__iter_num):
            self.update(x_train, y_train)
        return self.__best_pos

class Particle:
    def __init__(self, dim, bound):
        self.__pos = [random.uniform(bound[i][0], bound[i][1]) for i in range(dim)]
        self.__vel = [random.uniform(-1, 1) for _ in range(dim)]
        self.__best_pos = self.__pos[:]
        self.__fitness_value = float('-inf')

    def set_pos(self, pos):
        self.__pos = pos

    def get_pos(self):
        return self.__pos

    def set_vel(self, vel):
        self.__vel = vel

    def get_vel(self):
        return self.__vel

    def set_best_pos(self, pos):
        self.__best_pos = pos

    def get_best_pos(self):
        return self.__best_pos

    def set_fitness_value(self, value):
        self.__fitness_value = value

    def get_fitness_value(self):
        return self.__fitness_value

# 加载数据
iris = load_iris()
x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)

# 设置参数边界
bound = [(2, 10), (0.01, 0.3), (1, 10), (0.5, 1), (0.5, 1), (0, 1)]

# 使用粒子群优化算法寻找最优参数
pso = PSO(dim=6, size=50, iter_num=100, bound=bound, c1=2, c2=2)
best_params = pso.run(x_train, y_train)

# 训练最优模型并预测测试集
param_dict = {'objective': 'multi:softmax',
              'num_class': 3,
              'max_depth': int(best_params[0]),
              'learning_rate': best_params[1],
              'min_child_weight': int(best_params[2]),
              'subsample': best_params[3],
              'colsample_bytree': best_params[4],
              'gamma': best_params[5]}
xgb_model = xgb.XGBClassifier(**param_dict)
xgb_model.fit(x_train, y_train)
y_pred = xgb_model.predict(x_test)

# 计算准确率并输出
accuracy = accuracy_score(y_test, y_pred)
print('accuracy: ', accuracy)

这个例子中使用了sklearn中的鸢尾花数据集进行训练和测试，目标是进行多分类。首先，需要定义一个PSO类和一个Particle类分别实现粒子群优化算法和粒子类。在PSO类中，每个粒子的位置表示XGBoost模型的参数，需要将其转换成字典形式传入XGBoost模型进行训练，并计算训练集的准确率作为适应度值。然后，根据适应度值更新每个粒子的最佳位置和最佳适应度值，同时更新全局最佳位置和最佳适应度值。在update方法中，根据粒子的速度和位置更新粒子的位置和速度，并进行边界限制。在run方法中，进行多次迭代，最终得到最优解，即最佳参数。最后，使用最佳参数训练XGBoost模型，并对测试集进行预测，计算准确率并输出。