Comments (4)
I think we should be able to easily expose a transform_observation_data()
method to make this less hacky - not sure why we haven't done this until now. @bletham is there a reason for this or just not something that was needed so far ?
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Some transforms for ObservationData require knowing ObservationFeatures. Particularly StratifiedStandardizeY, which standardizes Y but stratified on some conditions on X (https://github.com/facebook/Ax/blob/main/ax/modelbridge/transforms/stratified_standardize_y.py#L34). It is used for multi-task modeling, where data from different tasks may have very different scales and so need to be standardized separately.
This is why transform_observation_data
is not a required method for a Transform to implement, and in fact StratifiedStandardizeY does not implement that and implements only transform_observations
.
We could implement a transform_observation_data
on the modelbridge, and it would just need to throw an error if the modelbridge has any transforms that do not implement transform_observation_data
.
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HI @IanDelbridge, yes, that's correct, and your solution is as well. It's actually not that hacky at all, all things considered. Ideally, we'd have an automated way of transforming the inputs to the acquisition functions, but since with the modular setup you could have any number and kinds of those with different inputs it's not obvious how to tell what to transform.
Maybe a convenience feature could be something like adding a acq_options_to_transform
argument to evaluate_acquisition_function
and then apply what you're doing manually here under the hood and then pass the union of the transformed and the acq_options
dict to the acquisition function? Do you think that would be a convenient API? Though that would also require distinguishing between python native and Tensor type inputs...
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Hi Max, thanks, I appreciate your response!
I think the part that sets off alarms telling me my solution is a hack is the way I am doing the transform. Is there a better way to apply the transform and inverse transforms without making fake observation features? Something like gp_pi.transform_observation_data(ObservationData(...))
?
acq_options_to_transform
would work from my perspective as an API but I'm also thinking about un-transforming the acquisition function value, specifically thinking about the UCB
return value.
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Related Issues (20)
- `StandardizeY` transform requires non-empty data." when using SAASBO
- Plotting outside of a notebook HOT 1
- Setting search space step size in Ax Service API HOT 10
- Problem when Sobol falls back to HitAndRunPolytopeSampler HOT 5
- Arms from previous batch keep appearing in new batches HOT 5
- EHVI & NEHVI break with more than 7 objectives HOT 4
- Multi-objective experiments generate duplicated data HOT 5
- [Question] Multiobjective optimization where one target has no optimization direction (but required range)
- How to expose the default acquisition function being used by AxClient() HOT 3
- Fix core docs related to linear constraints HOT 3
- Improve string normalization for constraint string parsing
- Replacement for `Models.GPKG` HOT 2
- Ax serice API for GPU
- Looking for suggestions for the objectives data HOT 3
- batch of suggestions HOT 14
- MOO Tutorial potentially incorrect model HOT 2
- Constraints on FixedParameters are not allowed HOT 6
- RegistryBundle can't be imported HOT 7
- [Bug] Arguments passed to BoTorch optimize_acqf don't work (in an intuitive way) or don't make sense HOT 1
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