用python实现lgb模型的StratifiedKFold
时间: 2024-03-17 16:42:29 浏览: 102
以下是用Python实现lgb模型的StratifiedKFold的示例代码:
```python
import lightgbm as lgb
from sklearn.model_selection import StratifiedKFold
# 假设数据集的特征矩阵为 X,标签为 y
# 定义模型参数
params = {
'boosting_type': 'gbdt',
'objective': 'binary',
'metric': 'auc',
'num_leaves': 31,
'learning_rate': 0.05,
'feature_fraction': 0.9,
'bagging_fraction': 0.8,
'bagging_freq': 5,
'verbose': -1,
'random_state': 2021
}
# 定义StratifiedKFold交叉验证
n_splits = 5
skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=2021)
# 定义输出变量
oof_preds = np.zeros(X.shape[0])
# 开始交叉验证
for fold, (train_idx, valid_idx) in enumerate(skf.split(X, y)):
print("Fold", fold+1)
X_train, X_valid = X[train_idx], X[valid_idx]
y_train, y_valid = y[train_idx], y[valid_idx]
# 定义训练数据
lgb_train = lgb.Dataset(X_train, y_train)
lgb_valid = lgb.Dataset(X_valid, y_valid)
# 训练模型
model = lgb.train(params, lgb_train, valid_sets=[lgb_valid],
num_boost_round=10000, early_stopping_rounds=100, verbose_eval=100)
# 对验证集进行预测
valid_preds = model.predict(X_valid, num_iteration=model.best_iteration)
oof_preds[valid_idx] = valid_preds
print("-" * 50)
# 输出交叉验证结果
print("Overall AUC:", roc_auc_score(y, oof_preds))
```
在这个示例中,我们使用了lightgbm作为模型,同时使用了sklearn中的StratifiedKFold来进行交叉验证。示例中的模型参数可以根据具体任务进行调整。
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