Open mikolajwojciuk opened 3 years ago
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 https://github.com/sherpa-ai/sherpa/blob/ff6466e99717983f9f394ba72b63f17343e32bdc/sherpa/algorithms/bayesian_optimization.py#L138 in your code and setting it to evaluator_type='random'
.
Thanks for raising the issue.
Best, Lars
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!