Comments (5)
Dear @zaikunzhang ,
As a user of this package I think the scikit-optimize
original repository is not maintained anymore. However, I am working on an updated fork of this package available here within an hyperparameter optimization software called DeepHyper. Also, I was wondering if your approach could be used to optimize the acquisition function within Bayesian optimization. In replacement of lbfgs
in scikit-optimize for example (would it perform better?).
Let me know if you are interested to try this out. I have implemented genetic algorithm recently to compare against lbfgs.
Best regards,
Romain EGELE,
Ph.D. at Universite Paris-Saclay.
from scikit-optimize.
Hi,
Is it better to wait for scipy/scipy#18118 and libprima/prima#52 to be completed to use PRIMA with Python or is there a Python binding already usable?
Yes, you can wait for libprima/prima#52 .
Does it work with mixed-variable spaces (Real, Discrete, Categorical)?
No.
Is there an "asynchronous" interface of the type ask/tell or suggest/observe to use prima in an asynchronous parallel setting?
Not directly. Maybe you can implement that using a callback function, but I am not sure.
Can it perform batch vectorized optimization? (instead of inferring the function once per input point, we infer a list of points directly, it can be faster in Python to avoid slow Python for loops)
Since PRIMA solvers are intrinsically sequential (except for the initialization), I guess the answer is no. However, I do not think calling PRIMA will involve Python for loops --- it will be a single line of Python code, behind which is the Fortran code.
Thank you.
Zaikun
from scikit-optimize.
Hi Romain @Deathn0t ,
The solvers in PRIMA can solve black-box optimization problems without requiring derivatives (first-order information). They are quite capable in solving hyper-parameter tuning problems. So they are good solvers to be included in DeepHyper.
if your approach could be used to optimize the acquisition function within Bayesian optimization. In replacement of lbfgs in scikit-optimize for example (would it perform better?).
Sure. Whether it will outperform lbfgs or not, it depends on the properties of the problem. If your problem is a black-box optimization problem, the PRIMA solvers are the state of the art --- they definitely outperform genetic algorithms. However, it your problem can provide first-order information, then gradient-based algorithms will perform better.
Thanks,
Zaikun
from scikit-optimize.
Thanks for your reply @zaikunzhang . Also, I have the following questions:
- Is it better to wait for scipy/scipy#18118 and libprima/prima#52 to be completed to use PRIMA with Python or is there a Python binding already usable?
- Does it work with mixed-variable spaces (Real, Discrete, Categorical)?
- Is there an "asynchronous" interface of the type
ask/tell
orsuggest/observe
to use prima in an asynchronous parallel setting? - Can it perform batch vectorized optimization? (instead of inferring the function once per input point, we infer a list of points directly, it can be faster in Python to avoid slow Python for loops)
Thanks,
Romain.
from scikit-optimize.
Thank you for the detailed replies @zaikunzhang !
from scikit-optimize.
Related Issues (20)
- BayesSearchCV: default value mentioned for cv is wrong in the doc HOT 1
- np.int no longer works with numpy >= 1.24 HOT 2
- InvalidParameterError HOT 3
- mse needs updating (to friedman_mse?) HOT 1
- Question: Are plot_convergence and plot_objective supposed to look like identical plots?
- gp_minimize callback arguments / return value
- Bug Latin Hypercube sampling
- space.Integer and space.Float not allowed to be constant
- `np.int` was a deprecated alias for the builtin `int`. HOT 17
- First and last values of each variable in the space are sampled 2 times less than the others
- Repeated error Using Forest_optimize HOT 1
- Something about earlystopping HOT 1
- Optimization space and initial points in x0 use inconsistent dimensions.
- How to introduce customized stop criteria HOT 1
- LOO in BayesSearchCV
- Error in strategy-comparison.ipynb
- AttributeError: 'Sum' object has no attribute 'gradient_x' when using sci-kit learn RBF for sci-kit optimize gp_minimize HOT 3
- Question: How to initialize a model in 4D search space, but run trials in only 2D for the first 100 trials through ask-tell interface. Then expand to 4D.
- Reproducibility when using BayesSearchCV with MLPRegressor
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