# 如下,评价函数为l1,程序会自动将预测值和标签传入eval_metric中,并返回score

gbm = lgb.LGBMRegressor(num_leaves=31,

                        learning_rate=0.05,

                        n_estimators=20)

gbm.fit(X_train, y_train,

        eval_set=[(X_test, y_test)],

        eval_metric='l1',

        early_stopping_rounds=5)

# 如下为自定义的评价指标

def rmsle(y_true, y_pred):

    return 'RMSLE', np.sqrt(np.mean(np.power(np.log1p(y_pred) - np.log1p(y_true), 2))), False

gbm.fit(X_train, y_train,

        eval_set=[(X_test, y_test)],

        eval_metric=rmsle,

        early_stopping_rounds=5)

# 如下可以同时用两个评价指标,输出的时候也是输出两个分数

def rae(y_true, y_pred):

    return 'RAE', np.sum(np.abs(y_pred - y_true)) / np.sum(np.abs(np.mean(y_true) - y_true)), False

gbm.fit(X_train, y_train,

        eval_set=[(X_test, y_test)],

        eval_metric=lambda y_true, y_pred: [rmsle(y_true, y_pred), rae(y_true, y_pred)],

        early_stopping_rounds=5)