LightGBM Regressor Custom feval loss function return as list error for single output答案

【问题标题】：LightGBMRegressor Custom eval loss fuction return as list error for single outputLightGBM Regressor Custom feval loss function return as list error for single output
【发布时间】：2020-11-24 15:25:47
【问题描述】：

我想在我的 lightGBM 模型中使用自定义 eval 函数。我的代码如下：

X = df.loc[:, "VALUE_1":"TVALUE_33"]
y_true = df.loc[:, "VALUE_34"]
   
X_train, X_test, y_train, y_test = train_test_split(X, y_true, test_size= 0.30, random_state = 333)

def mle_loss(y_true, y_pred):
    y_true = tf.cast(y_true, dtype= tf.float32)
    
    # get params for probability distributions
    mu = y_pred.mean()
    sigma = y_pred.std()
    mu = tf.cast(mu, tf.float32)
    sigma = tf.cast(sigma, tf.float32)
        
    total_dist = tfp.distributions.Normal(loc=mu, scale=sigma)
    total_log_prob = total_dist.log_prob(y_true)[0]
        
    return ["mle_loss", total_log_prob.numpy(), True]

hyper_params_11 = {
    'task': 'train',
    'boosting_type': 'gbdt',
    'objective': 'regression',
    'metric':  "custom",
    'subsample_freq': 250, 
    'subsample': 0.8, 
    'num_leaves': 16,
    'min_split_gain': 50,
    'min_child_samples': 25,
    'max_depth': 5, 
    'max_bin': 1024, 
    'learning_rate': 0.001, 
    'colsample_bytree': 0.5,
    "num_iterations": 10000,
    "lambda_l1" : 1,
    "lambda_l2" : 1
    }

gbm_model_1 = lgb.LGBMRegressor(**hyper_params_1)
gbm_model_1.fit(X_train, y_train,
        eval_set=[(X_test, y_test), (X_train,y_train)],
        eval_metric = mle_loss,
        early_stopping_rounds=100
               )

不幸的是，运行这段代码时出现错误 ValueError: too many values to unpack (expected 3)。当我在没有 eval_set 和 early_stopping_rounds 的情况下运行它时，它工作正常。你能帮帮我吗？

谢谢

【问题讨论】：

标签： python eval lightgbm custom-function

【解决方案1】：

只需将返回值更改为元组

def mle_loss(y_true, y_pred):
    ....
        
    return ("mle_loss", total_log_prob.numpy(), True)

作为列表返回会抛出错误，例如，

feval_ret = ['mle_loss', -1.4523773, True]

if isinstance(feval_ret, list):
    for eval_name, val, is_higher_better in feval_ret:
        pass

会抛出你提到的错误

ValueError: too many values to unpack (expected 3)

根据docs，这是因为它期望单个元素的元组或多个元素的元组列表

返回 (eval_name, eval_result, is_higher_better) 或列表 (eval_name, eval_result, is_higher_better)

【讨论】：