LightGBM错误。ValueError。对于早期停止，至少需要一个数据集和评估度量来进行评估

Question

我试图用gridsearch训练一个LightGBM，当我试图训练模型时，我得到以下错误。

ValueError: For early stopping, at least one dataset and eval metric is required for evaluation

我已经提供了验证数据集和评估指标。不知道为什么我还是得到这个问题。这是我的代码。

train_data  = rtotal[rtotal['train_Y'] == 1]
test_data   = rtotal[rtotal['train_Y'] == 0]

trainData, validData = train_test_split(train_data, test_size=0.007, random_state = 123)

#train data prep
X_train = trainData.iloc[:,2:71]
y_train = trainData.loc[:,['a_class']]

#validation data prep
X_valid = validData.iloc[:,2:71]
y_valid = validData.loc[:,['a_class']]

#X_test
X_test = test_data.iloc[:,2:71]

import lightgbm as lgb
from sklearn.model_selection import GridSearchCV

gridParams = {
    'learning_rate': [0.005],
    'n_estimators': [40],
    'num_leaves': [16,32, 64],
    'objective' : ['multiclass'],
    'random_state' : [501],
    'num_boost_round' : [3000],
    'colsample_bytree' : [0.65, 0.66],
    'subsample' : [0.7,0.75],
    'reg_alpha' : [1,1.2],
    'reg_lambda' : [1,1.2,1.4],
    }

lgb_estimator = lgb.LGBMClassifier(boosting_type = 'gbdt', 
                                   n_estimators=500, 
                                   objective = 'multiclass', 
                                   learning_rate =  0.05, num_leaves =  64,  
                                   eval_metric  = 'multi_logloss',
                                   verbose_eval=20, 
                                   eval_set = [X_valid, y_valid],
                                   early_stopping_rounds=100)

g_lgbm = GridSearchCV(estimator=lgb_estimator, param_grid=gridParams, n_jobs = 3, cv= 3)

lgb_model = g_lgbm.fit(X=X_train, y=y_train)

Answer 1

从我提供的代码中可以看出，你有几个问题。

1. 你把你的分类定义为多类，但并不是这样的，因为你把你的输出定义为一列，我相信在这一列中可能有几个标签。

2. 如果你想提前停止，你需要提供验证集，正如错误信息中明确指出的那样。而且你需要在一个适合的方法中进行。

如果你针对这些错误修正你的代码，它就会愉快地运行。

gridParams = { 
    'learning_rate': [0.005],
    'n_estimators': [40],
    'num_leaves': [16,32, 64], 
    'random_state' : [501],
    'num_boost_round' : [3000],
    'colsample_bytree' : [0.65, 0.66],
    'subsample' : [0.7,0.75],
    'reg_alpha' : [1,1.2],
    'reg_lambda' : [1,1.2,1.4], 
    }

lgb_estimator = lgb.LGBMClassifier(boosting_type = 'gbdt', 
                                   n_estimators=500, 
                                   learning_rate =  0.05, num_leaves =  64,  
                                   eval_metric  = 'logloss',
                                   verbose_eval=20, 
                                   early_stopping_rounds=10)

g_lgbm = GridSearchCV(estimator=lgb_estimator, param_grid=gridParams, n_jobs = 3, cv= 3)

lgb_model = g_lgbm.fit(X=X_train, y=y_train, eval_set = (X_valid, y_valid))

...
[370]   valid_0's binary_logloss: 0.422895
[371]   valid_0's binary_logloss: 0.423064
[372]   valid_0's binary_logloss: 0.422681
[373]   valid_0's binary_logloss: 0.423206
[374]   valid_0's binary_logloss: 0.423142
[375]   valid_0's binary_logloss: 0.423414
[376]   valid_0's binary_logloss: 0.423338
[377]   valid_0's binary_logloss: 0.423864
[378]   valid_0's binary_logloss: 0.42381
[379]   valid_0's binary_logloss: 0.42409
[380]   valid_0's binary_logloss: 0.423476
[381]   valid_0's binary_logloss: 0.423759
[382]   valid_0's binary_logloss: 0.423804
Early stopping, best iteration is:
[372]   valid_0's binary_logloss: 0.422681