QPSO-XGBOOST多分类python

好的，QPSO-XGBoost是一种结合了量子粒子群优化算法（QPSO）和XGBoost算法的分类方法。它可以用于解决多分类问题。以下是一个使用Python实现QPSO-XGBoost多分类的示例代码：

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import xgboost as xgb
from qpsopy import QPSO

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义QPSO-XGBoost分类器
class QPSOXGB:
    def __init__(self, n_class, lr, max_depth, n_estimators, gamma, subsample, colsample_bytree, reg_alpha, reg_lambda):
        self.n_class = n_class
        self.lr = lr
        self.max_depth = max_depth
        self.n_estimators = n_estimators
        self.gamma = gamma
        self.subsample = subsample
        self.colsample_bytree = colsample_bytree
        self.reg_alpha = reg_alpha
        self.reg_lambda = reg_lambda

    def fit(self, X, y):
        dtrain = xgb.DMatrix(X, label=y)
        params = {
            'objective': 'multi:softmax',
            'num_class': self.n_class,
            'learning_rate': self.lr,
            'max_depth': self.max_depth,
            'n_estimators': self.n_estimators,
            'gamma': self.gamma,
            'subsample': self.subsample,
            'colsample_bytree': self.colsample_bytree,
            'reg_alpha': self.reg_alpha,
            'reg_lambda': self.reg_lambda
        }
        bst = xgb.train(params, dtrain)
        self.bst = bst

    def predict(self, X):
        dtest = xgb.DMatrix(X)
        pred = self.bst.predict(dtest)
        return pred

# 定义适应度函数
def fitness(QPSOXGB, X_train, y_train):
    QPSOXGB.fit(X_train, y_train)
    y_pred = QPSOXGB.predict(X_train)
    fitness_val = np.sum(y_pred == y_train)/len(y_train)
    return fitness_val

# 定义搜索范围
search_range = {
    'lr': (0.001, 0.1),
    'max_depth': (3, 10),
    'n_estimators': (50, 300),
    'gamma': (0, 1),
    'subsample': (0.5, 1),
    'colsample_bytree': (0.5, 1),
    'reg_alpha': (0, 10),
    'reg_lambda': (0, 10)
}

# 定义QPSO算法
q = QPSO(
    n_particles=20,
    n_iterations=50,
    fitness_func=fitness,
    QPSOXGB=QPSOXGB,
    search_range=search_range,
    X_train=X_train,
    y_train=y_train
)

# 运行QPSO算法
q.run()

# 在测试集上评估模型
y_pred = q.QPSOXGB.predict(X_test)
accuracy = np.sum(y_pred == y_test)/len(y_test)
print('Test accuracy:', accuracy)

在这个例子中，我们使用sklearn中的鸢尾花数据集，将其分割为训练集和测试集，并定义了一个QPSO-XGBoost分类器。然后，我们定义了适应度函数，它将训练QPSO-XGBoost分类器并返回其在训练集上的准确度。我们还定义了搜索范围和QPSO算法，最后在测试集上评估了模型的准确度。