定义pso—xgboost多分类交叉验证python
时间: 2023-09-11 21:08:39 浏览: 38
好的,下面是一个完整的pso-xgboost多分类交叉验证的Python代码:
```python
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold
import xgboost as xgb
from pyswarm import pso
# 加载数据集
data = pd.read_csv('data.csv')
X = data.drop('label', axis=1)
y = data['label']
# 定义目标函数
def objective_function(params):
params = {'n_estimators': int(params[0]),
'max_depth': int(params[1]),
'learning_rate': params[2],
'subsample': params[3],
'gamma': params[4]}
kf = KFold(n_splits=5)
scores = []
for train_index, test_index in kf.split(X):
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
model = xgb.XGBClassifier(**params)
model.fit(X_train, y_train)
score = model.score(X_test, y_test)
scores.append(score)
return -np.mean(scores)
# 定义参数范围
lower_bound = [10, 1, 0.01, 0.1, 0]
upper_bound = [1000, 10, 1, 1, 10]
# 使用pso算法搜索最优参数
best_params, best_score = pso(objective_function, lower_bound, upper_bound, swarmsize=50, maxiter=100)
# 使用最优参数建立模型并进行预测
best_params = {'n_estimators': int(best_params[0]),
'max_depth': int(best_params[1]),
'learning_rate': best_params[2],
'subsample': best_params[3],
'gamma': best_params[4]}
model = xgb.XGBClassifier(**best_params)
model.fit(X, y)
y_pred = model.predict(X)
# 输出结果
print('Best parameters:', best_params)
print('Best score:', -best_score)
print('Accuracy:', np.mean(y == y_pred))
```
在上述代码中,我们使用了pso算法来搜索xgboost的最优参数,并使用k折交叉验证来评估模型的性能。最后,我们使用最优参数建立模型并进行预测,输出了最优参数、最优得分和准确率。您可以根据自己的需求进行修改和扩展。