用lgb模型进行6分类，并使用StratifiedKFold，评价指标为：macro-f1

以下是用lgb模型进行6分类，并使用StratifiedKFold，评价指标为macro-f1的示例代码：

import lightgbm as lgb
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import f1_score

# 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': 'multiclass',
    'num_class': 6,
    'metric': 'multi_logloss',
    '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])
class_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.argmax(axis=1)
    class_preds[valid_idx] = valid_preds.max(axis=1)
    
    print("-" * 50)

# 输出交叉验证结果
macro_f1 = f1_score(y, oof_preds, average='macro')
print("Overall Macro-F1:", macro_f1)

在这个示例中，我们使用了sklearn中的f1_score函数来计算macro-f1。在计算f1_score时，需要将参数average设为'macro'。最终输出结果为整个数据集上的macro-f1。