CODEWITHSUNDEEP
scikit-learnbayesianhyperparametersscikit-optimize
Boom
Boom

Reputation: 1315

BayesSearchCV parameters

I just read about Bayesian optimization and I want to try it.

I installed scikit-optimize and checked the API, and I'm confused:

  1. I read that Bayesian optimization starts with some initialize samples.

    • I can't see where I can change this number ? (BayesSearchCV)
    • n_points will change the number of parameter settings to sample in parallel and n_iter is the number of iterations (and if I'm not wrong the iterations can't run in parallel, the algorithm improve the parameters after every iteration)

  2. I read that we can use different acquisition functions. I can't see where I can change the acquisition function in BayesSearchCV ?

Upvotes: 0

Views: 2147

Answers (1)

donglinjy
donglinjy

Reputation: 1079

Is this something you are looking for?

BayesSearchCV(..., optimizer_kwargs={'n_initial_points': 20, 'acq_func': 'gp_hedge'}, ...)

skopt.Optimizer is the one actually doing the hyperparameter optimization.

BayesSearchCV will build Optimzier with optimizer_kwargs parameters.

https://github.com/scikit-optimize/scikit-optimize/blob/de32b5fd2205a1e58526f3cacd0422a26d315d0f/skopt/searchcv.py#L551

Upvotes: 2

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