Comments (3)
See usage.py
in the examples, but for the xgboost example:
print(xgbBO.res['max'])
print(xgbBO.res['all'])
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Your question is a bit too vague for me to help with, as such I suggest you taking a look at the papers linked in the README, and watching the youtube lecture also linked there, to get a feel for the algorithm. Other than that the examples should cover the basic functionally of the package.
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Sorry for the misunderstanding, next time I would try to explain my question in more details.
My question has a pure practical background, what I wanted to ask is:
how to extract the best parameters from optimizer, f.i. with RandomizedSearchCV from scklearn we can extract parameters very simple:
RandomizedSearchCV.best_params_
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Related Issues (20)
- `FloatingPointError: underflow encountered in exp` in `optimizer.maximize()` HOT 4
- Save optimizer state and load again in the Suggest-Evaluate-Register Paradigm HOT 1
- Replace custom colour implementation with colorama colours HOT 1
- Constrained optimization does not allow duplicate points HOT 3
- JSONLogger bug in Basic Tour example HOT 2
- Implement `gp_hedge` acquisition function HOT 10
- support multiple parameters HOT 2
- Values outside pbounds HOT 7
- How to specify pbounds when there are no variable names per se HOT 7
- Make n_restarts_optimizer dynamic HOT 2
- pbounds step size HOT 2
- Manually provide observed samples as init points HOT 2
- Pass *args, *kwargs as non-optimized arguments to the cost function HOT 3
- Verbose=2 doesn't work in terminal or bash HOT 1
- 'str object has no attribute reproduce' HOT 5
- add supported python versions badge to readme HOT 1
- NotUniqueError encountered for constrained problems. HOT 4
- [Question] can I use ML model as a black_box function? HOT 2
- random_state not working as expected when using constraints HOT 4
- How to use specific init_points HOT 3
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