小编Sud*_*mar的帖子

当我尝试使用 metrics.roc_auc_score 时，我得到ValueError: multiclass format is not supported.

import lightgbm as lgb
from sklearn import metrics
def train_model(train, valid):

    dtrain = lgb.Dataset(train, label=y_train)
    dvalid = lgb.Dataset(valid, label=y_valid)

    param = {'num_leaves': 64, 'objective': 'binary', 
             'metric': 'auc', 'seed': 7}
    print("Training model!")
    bst = lgb.train(param, dtrain, num_boost_round=1000, valid_sets=[dvalid], 
                    early_stopping_rounds=10, verbose_eval=False)

    valid_pred = bst.predict(valid)
    print('Valid_pred: ')
    print(valid_pred)
    print('y_valid:')
    print(y_valid)
    valid_score = metrics.roc_auc_score(y_valid, valid_pred)
    print(f"Validation AUC score: {valid_score:.4f}")
    return bst

bst = train_model(X_train_final, X_valid_final)

valid_pred 和 y_valid 是：

Training model!
Valid_pred: 
[1. 1. …

如何按第一列升序和第二列降序对 NumPy 中的二维数组进行排序？

例如，

a = array([[9, 2, 3],
           [4, 5, 6],
           [7, 0, 5],
           [7, 1, 6]])

结果 ：

array([[4, 5, 6],
       [7, 1, 6],
       [7, 0, 5],
       [9, 2, 3]])