0
$\begingroup$

I am trying to use lightGBM's cv() function for tuning my model for a regression problem. My main model is lightgbm.LGBMRegressor(). However, I am encountering the errors which is a bit confusing given that I am in a regression mode and NOT classification mode. Below are the code snippet and part of the trace.

param_grid = {
    'class_weight': [None, 'balanced'],
    'num_leaves': list(range(30, 150)),
    'learning_rate': list(np.logspace(np.log(0.005), np.log(0.2), base = np.exp(1), num = 1000)),
    'subsample_for_bin': list(range(20000, 300000, 20000)),
    'min_child_samples': list(range(20, 500, 5)),
    'reg_alpha': list(np.linspace(0, 1)),
    'reg_lambda': list(np.linspace(0, 1)),
    'colsample_bytree': list(np.linspace(0.6, 1, 10)),
    'objective': 'regression'
 } 
lgb_train = lgb.Dataset(X_train,y_train)
cv_results = lgb.cv(params, lgb_train, num_boost_round = 10000, nfold = 10, metrics = 'rmse', shuffle=False,
           early_stopping_rounds = 100, verbose_eval = False, seed = 50 )

Part of the trace of the error I am getting is

---------------------------------------------------------------------------
LightGBMError                             Traceback (most recent call last)
<ipython-input-84-3f76f5345fda> in <module>
      1 # Perform cross validation with 10 folds
      2 cv_results = lgb.cv(params, lgb_train, num_boost_round = 10000, nfold = 10, metrics = 'rmse', shuffle=False,
----> 3            early_stopping_rounds = 100, verbose_eval = False, seed = 50, )

/anaconda3/envs/py36/lib/python3.6/site-packages/lightgbm/engine.py in cv(params, train_set, num_boost_round, folds, nfold, stratified, shuffle, metrics, fobj, feval, init_model, feature_name, categorical_feature, early_stopping_rounds, fpreproc, verbose_eval, show_stdv, seed, callbacks)
    456     cvfolds = _make_n_folds(train_set, folds=folds, nfold=nfold,
    457                             params=params, seed=seed, fpreproc=fpreproc,
--> 458                             stratified=stratified, shuffle=shuffle)
    459 
    460     # setup callbacks

/anaconda3/envs/py36/lib/python3.6/site-packages/lightgbm/engine.py in _make_n_folds(full_data, folds, nfold, params, seed, fpreproc, stratified, shuffle)
    315         else:
    316             tparam = params
--> 317         cvbooster = Booster(tparam, train_set)
    318         cvbooster.add_valid(valid_set, 'valid')
    319         ret.append(cvbooster)

/anaconda3/envs/py36/lib/python3.6/site-packages/lightgbm/basic.py in __init__(self, params, train_set, model_file, silent)
   1552                 train_set.construct().handle,
   1553                 c_str(params_str),
-> 1554                 ctypes.byref(self.handle)))
   1555             # save reference to data
   1556             self.train_set = train_set

/anaconda3/envs/py36/lib/python3.6/site-packages/lightgbm/basic.py in _safe_call(ret)
     44     """
     45     if ret != 0:
---> 46         raise LightGBMError(decode_string(_LIB.LGBM_GetLastError()))
     47 
     48 

LightGBMError: Unknown objective type name: r

I don't understand the error. Moreover, if I remove the 'objective':'regressor', then we are getting the error: y variable is only 1 class which seems to me to be referring to a classifier.

Any help would be great.

Thanks

$\endgroup$
6
  • $\begingroup$ Could you provide the values in params? $\endgroup$ Apr 16 '19 at 18:23
  • $\begingroup$ it should be param_grid and not just params $\endgroup$
    – user62198
    Apr 16 '19 at 23:16
  • $\begingroup$ does that fix it? $\endgroup$
    – oW_
    Apr 16 '19 at 23:18
  • $\begingroup$ If you are doing a hyperparameter search, it might help to see that code; I don't think you can just pass the grid dictionary into lgb.cv (?). $\endgroup$ Apr 17 '19 at 1:13
  • 1
    $\begingroup$ That makes sense inside the grid; it's expecting a list, and so it was treating the string as the list of individual characters (hence "objective type r"). $\endgroup$ Apr 18 '19 at 2:26
1
$\begingroup$

Answer

In the param_grid dictionary, pass every hyper-parameter as array e.g., in your code above, you missed-out to place 'objective': 'regression' part of param_grid as an array. Even though, you have are using single value place it as array i.e. 'objective': ['regression']

Therefore, you can update below piece of code in your script and re-run. It will remove the error. `

param_grid = {
    'class_weight': [None, 'balanced'],
    'num_leaves': list(range(30, 150)),
    'learning_rate': list(np.logspace(np.log(0.005), np.log(0.2), base = np.exp(1), num = 1000)),
    'subsample_for_bin': list(range(20000, 300000, 20000)),
    'min_child_samples': list(range(20, 500, 5)),
    'reg_alpha': list(np.linspace(0, 1)),
    'reg_lambda': list(np.linspace(0, 1)),
    'colsample_bytree': list(np.linspace(0.6, 1, 10)),
    'objective': ['regression'] # <<< CHANGE REQUIRED TO remove ERROR
 } 
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.