定义pso-在进行xgboost多分类交叉验证python代码

以下是一个基于pyswarms库和scikit-learn库的XGBoost多分类交叉验证的Python代码示例：

import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score, GridSearchCV
from pyswarms.single.global_best import GlobalBestPSO

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

# 定义PSO适应度函数
def pso_fit(params, X, y):
    params = params.astype(int)
    clf = xgb.XGBClassifier(max_depth=params[0], learning_rate=params[1], n_estimators=params[2], min_child_weight=params[3], subsample=params[4], colsample_bytree=params[5], gamma=params[6])
    score = cross_val_score(clf, X, y, cv=5, scoring='accuracy').mean()
    return 1 - score

# 设置PSO参数范围
pso_options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9}

# 设置XGBoost模型参数范围
params_range = (np.array([3, 0, 50, 1, 0.1, 0.1, 0]), np.array([10, 1, 1000, 10, 1, 1, 5]))

# 进行PSO优化
optimizer = GlobalBestPSO(n_particles=10, dimensions=7, options=pso_options, bounds=params_range)
best_params = optimizer.optimize(pso_fit, iters=10, X=X, y=y)

# 定义XGBoost分类器
clf = xgb.XGBClassifier(max_depth=best_params[0], learning_rate=best_params[1], n_estimators=best_params[2], min_child_weight=best_params[3], subsample=best_params[4], colsample_bytree=best_params[5], gamma=best_params[6])

# 使用GridSearchCV函数进行模型参数调优
param_grid = {'max_depth': range(3, 11), 'learning_rate': [0.1, 0.01], 'n_estimators': range(50, 1001, 50), 'min_child_weight': range(1, 11), 'subsample': np.arange(0.1, 1.1, 0.1), 'colsample_bytree': np.arange(0.1, 1.1, 0.1), 'gamma': np.arange(0, 5, 0.5)}
grid_search = GridSearchCV(clf, param_grid, cv=5, scoring='accuracy')
grid_search.fit(X, y)

# 输出最优模型参数和交叉验证分数
print('Best parameters:', grid_search.best_params_)
print('Cross validation score:', grid_search.best_score_)

注意，这里的PSO适应度函数返回的是1-score，因为pyswarms库的优化目标是最小化适应度函数。同时，为了避免参数数值类型错误，需要将参数向量转换为整数类型。最后，这里使用GridSearchCV函数进行模型参数调优，可以得到最优的XGBoost模型参数和交叉验证分数。