[Question] Documentation for Modelbridge.Factory Models

I have been looking through the Ax documentation, such as this one to find specifics about the “out-of-shelf” models that Ax supports, such as ax.modelbridge.factory.get_GPEI. I was wondering where I could find some details about these models that Ax offers, such as what Kernel it is using (I am guessing ARD Matérn 5/2 kernels as it is the default in BoTorch, but I was not able to find it stated in Ax). Could you point me to where this documentation is (or is it in preparation)? Sorry for a such trivial question.

Also, I read from few papers published by Facebook that showed great work in Noisy Expected Improvement (and qNEI). Are there any plans in implementing GP+NEI and GP+qNEI as “out-of-shelf” models in Ax like get_GPEI (or is get_GPEI actually using NEI - I read that ax.models.torch.botorch.BotorchModel uses NEI as default acquisition function, but GPEI just said “EI” in documentation and tutorials)?

[lena-kashtelyan]

Hi @nwrim, for default model components we use (get_GPEI returns the default setup), you can check specifically:

• which BoTorch surrogate model is used here: https://github.com/facebook/Ax/blob/master/ax/models/torch/botorch_defaults.py#L47,
• which acquisition function is used here: https://github.com/facebook/Ax/blob/master/ax/models/torch/botorch.py#L240 (it’s NEI, Noisy Expected Improvement).~~

Edit: the part of my response above is now obsolete since @qingfeng10 updated links above.

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(or is get_GPEI actually using NEI - I read that ax.models.torch.botorch.BotorchModel uses NEI as default acquisition function, but GPEI just said “EI” in documentation and tutorials)?

Yep it is, as linked above, great catch!

We have some documentation on the models we offer off-the-shelf (which you linked above), which states incorrectly that our default GPEI uses EI and not NEI as you point out. We will make a note to change that!

[lena-kashtelyan]

I guess a deeper question is if there’s a way for tuning the greediness of the search.

That seems worthy of a separate issue and a bit more detail so folks on the modeling and optimization side can chime in. If you wouldn’t mind opening one and explaining what you mean in a bit more detail, that would be great, @zeyueN!