NSections Issue with Train Dataset about lightautoml HOT 10 CLOSED

Hi @AlexanderLavelle,

Could you please share the code you have used to receive the error?

As I can see you have 17k+ features - are they real or you one-hot-encoded some variables?

Alex

from lightautoml.

automl = TabularAutoML(task=task, 
                       timeout=TIMEOUT,
                       #cpu_limit=N_THREADS,
                       reader_params={'n_jobs': N_THREADS, 'cv': N_FOLDS, 'random_state': RANDOM_STATE},
                       #general_params={'use_algos': [['lgb', 'cb', 'LinearLBFGS', 'linear_l1', 'xgb']]}
                      )
oof_pred = automl.fit_predict(newTrainDum, roles=roles)

Is the line just above. The 17k features are 20 features and OHE feats are: ['id', 'yr_built', 'yr_renovated', 'zipcode']

The dataset is the toy King County housing dataset

from lightautoml.

@AlexanderLavelle,

Now all is clear. I'll state some moments I can see from your code below:

• if you do not set the cpu_limit param, we will use the default one which is equal to 4 vCPU cores (as you can see from the beginning of the log)
• as for the use_algos - for now we have only 5 variants here: 'linear_l2', 'lgb', 'lgb_tuned', 'cb', 'cb_tuned', but you still can combine them on the different stacking levels if necessary
• and the main part - to use our TabularAutoML preset you have no need to do the preprocessing: we can work with the categorical features in their raw edition, we work with unfilled Nans in the dataset etc. You can just use the raw dataset version to train the model and receive the result.

Hope this helps.

Alex

from lightautoml.

@alexmryzhkov - I uncommented cpu_limit to properly utilize 12 threads. I backtracked and used the "wholeDf" with no OHE -- but this has resulted in the same issue. I have set

roles = {
    #'drop': 'id2', #done when I thought it needed a drop column
    #'group': 'breath_id',  #from the kaggle root of formatting
    #'category': autoMLcat,  #just commented out to test, not working either way 
    'target': 'logPrice',
}

but I still end up with the same error.

For use_algos, that line is commented out -- I will say that it's not quite clear from the documentation how to implement the various algorithms -- for instance, I used "LinearLBFGS" as a result of the documentation rather than the example on Kaggle.

In terms of processing / category: I fed categorical feats with the no dummy df (wholeDf) and I fed [dummy cats + orig cats] to roles for "newTrainDum", but no matter what I am receiving the same error.

Perhaps I am just giving it too fine a tune for a beginner? Should I just try to run it in a naive style?

from lightautoml.

Hi @AlexanderLavelle,

Please check my notebook on the King County dataset - if it works with you, cool. If you have any questions about that - please feel free to ask.

Alex

from lightautoml.

@alexmryzhkov I rewrote my notebook to better follow the flow of initializing the CV. It worked, which is great! I think the root of the problem may have been trying to set torch.device to 'cuda'?

Either way, thank you for your notebook and confirmation on the dataset!

from lightautoml.

@AlexanderLavelle if you set torch.device to cuda, do you want to train models on GPU? If yes, you have no need to do that - if your environment has properly installed GPU and torch, our LightAutoML will automatically train CatBoost models on GPU (for other models there will be almost no improvement, especially in Kaggle Kernels).

Alex

from lightautoml.

@alexmryzhkov I would like to train on GPU, top to bottom. I have sklearn-intelex and GPU versions of lightgbm on my local machine -- so in theory, any dataset within my 4GB nvidia card (planning to upgrade soon), I would like to have the pipeline do every calculation on GPU for speed. As far as gpu/enhanced sklearn (intelex), I have received notices that lightautoml will use the intelex augmented 'auc'.

from lightautoml.

Hi @AlexanderLavelle,

Currently we do not have the full GPU pipeline but we are working on it ☺️
The only parts, which can work on the GPU for now are the models.

Alex

from lightautoml.

Stale issue message

from lightautoml.