sherpa-ai/sherpa

Bayesian optimization with Random Forest

mikolajwojciuk opened this issue · 1 comments

mikolajwojciuk commented

Hi there,

I am having a problem with implementing Bayesian optimization with Random Forest model, no matter how I do set up Sherpa study, i constantly get an error saying:

"InvalidConfigError: local_penalization evaluator can only be used with GP models"

My Sherpa config:

algorithm = sherpa.algorithms.GPyOpt(model_type='RF',acquisition_type='MPI',verbosity=True,max_num_trials=8)
study = sherpa.Study(parameters=parameters,
algorithm=algorithm,
lower_is_better=True,
disable_dashboard = True)

P.S. Overall great library!

LarsHH commented

Hi @mikolajwojciuk !

Apologies for the slow reply. It looks like the issue is due to the evaluator_type in GPyOpt, i.e. how GPyOpt chooses to evaluate concurrent trials. I didn't realize RF didn't work with local_penalization which I had hardcoded as the option for the evaluator type. Unless you have otherwise resolved the issue, could you try setting max_concurrent=1. That is, for your code:

algorithm = sherpa.algorithms.GPyOpt(model_type='RF',acquisition_type='MPI',verbosity=True,max_num_trials=8, max_concurrent=1)

In that case GPyOpt should ignore the setting. If that doesn't work, you could try going to this line

sherpa/sherpa/algorithms/bayesian_optimization.py

Line 138 in ff6466e

evaluator_type='local_penalization',
in your code and setting it to evaluator_type='random'.

Thanks for raising the issue.

Best,
Lars

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