lightgbm cv

[Solly7]

Summary

Hi i nee help, still new in machine learning and willing to learn.

I am using lightgbm cv and i need to be able to print out my best AUC score on an each every fold using lgb cv without doing parameter tuning.
i.e fold 1 AUC : 81.2
fold 2 AUc : 83
......
Currently i only see AUC mean per iteration from my cv_results. See my code Below

params = {
'objective': 'binary',
'boosting': 'gbdt',
'max_depth':-1,
'num_leaves': 31, # 2^max_depth - 1
'learning_rate': 0.1,
'min_data_in_leaf':20,
'min_child_samples': 20,
'min_child_weight':0.001,
'verbose': 0,
}

params['metric'] = 'auc'
num_rounds = 5000

lgtrain = lgb.Dataset(train, label=labelTrain)
lgvalid = lgb.Dataset(test, label=labelTest,reference=lgtrain)

#replace this with cv only
cv_results = lgb.cv(params,
lgtrain,
nfold=5,
stratified=False ,
num_boost_round = num_rounds,
early_stopping_rounds = 10, metrics = {'auc'},
seed = 50)

cv_results = pd.DataFrame(cv_results)
cv_results.to_csv('cv_results.csv')

Best_rounds =np.argmax((cv_results['auc-mean']))
best_score = max(cv_results['auc-mean'])

[StrikerRUS]

@Solly7

I am using lightgbm cv and i need to be able to print out my best AUC score on an each every fold using lgb cv without doing parameter tuning.

Unfortunately, it is not possible yet. We already have some feature requests regarding this: #2105, #283. You can comment there with description of desired behavior you want to see in LightGBM.

Also, as I said earlier, as for novice I'd strongly recommend to switch to scikit-learn wrapper and their great tools which make life easier.