Thanks again for your help and for sharing & maintaining such a great resource!
we recently discussed the partial dependence plots (from
plots.py) and how it can look like the minimum is not where you think it is https://github.com/scikit-optimize/scikit-optimize/issues/626#issuecomment-363197850
cooking up the final example was "fun"
@cschell
or values you have from a previous run
if you only provide
x0 the objective will be evaluated at those points so it is a way of saying "I know some good values you should try at the start"
if you want to write a generic-ish
Checkpoint callback that would be cool to contribute
@iaroslav-ai For your benchmarks, do you think it would be helpful to include algorithms from the RoBO package? They have DNGO, another Bayesian net algorithm and the multi-task one. Downside is for the deep ones, running time will increase and the performance is almost known to be worse than other algorithms on synthetic functions.
What would be the best way to implement a quantised search space ? For example from 0.0 to 2.0 by 0.05 increments . I obviously can just setup an integer search space adn then do all the logic to go from integer to quantised real in my blackbox function
but it might be better for plots / exploratio to do it as a "special" dimension
should i just do a QReal that herits from Dimension and set _rvs to an integer rand and then implement transform and inverse_trasnform as a trnasformer ?
Trying to think what the best way is to use
np.linspace, arange and friends to generate these quantized dimensions
so maybe a new dimension type that is like those would be good
so not quantized but "a range with a fixed number of points in it"
which is basically the same but would cover "ten points between 0 and 10" as well as "ten points between 0 and 3.141" etc
does that make sense?
and I will go outdoors and take advantage of spring sunshine :sun_with_face:
if i say want to make a Dimension of 10 points between 0 and 3.14
inverse_trasnform should return reals between 0 and 3.14
and transform should return Integers between 0 and 9 for example
a bit like for a neural network you want to scale all your input features so they have a similar range
we normalise all ranges to between 0 and 1 when the Gaussian process or random forest see it
as an example
(and then you can play some clever tricks while doing these transformations but really all they are about is going from human friendly to machine friendly and back)
if you had ten points between 0 and 3.141 i'd apply the same transform as the regular
Real does
thanks for your help
Hi, I am trying to use Bayesian Optimization for a parameter tuning scenario. However, my case is slightly different since the parameter bounds have dependencies between other params. As an example, let's say I have two parameters x1 and x2, and I'm trying to optimize y. Let's say x1 is bounded in (10, 200) range. And x2 is always bounded between x1 and 200. Is there any way that I can specify the bound of x2 like this in skopt?
