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probabilistic reparameterization tutorial #1534

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sdaulton
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Summary: see title

Differential Revision: D41629553

@facebook-github-bot facebook-github-bot added CLA Signed Do not delete this pull request or issue due to inactivity. fb-exported labels Nov 30, 2022
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This pull request was exported from Phabricator. Differential Revision: D41629553

@jduerholt jduerholt mentioned this pull request Dec 15, 2022
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facebook-github-bot pushed a commit that referenced this pull request Dec 27, 2022
@jduerholt @facebook-github-bot
remove input transform checks (#1568)
6090284 
Summary:
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## Motivation

As I am currently refactoring our internal codebase, I had a look at sdaulton PR regarding probabilistic reparameterization.
From my understanding one has to use it by representing the categoricals by a one hot encoding for the reparmeterized ACQF and then eventually transforming the input to a numerical represenation via `OneHotToNumeric` especially when one wants to use it togehter with `MixedSingleTaskGP`. Currently MixedSingleTaskGP is very strict on which input transforms are allowed. This PR lifts the restrictions to make it usable with  OneHotToNumeric`.

Note that the transform also has to be instantiated with `transform_on_train = False` and `train_X` has to be transformed before it is passed to the constructor of `MixedSingleTaskGP`, else the indices for the different kernels are mixed up.

### Have you read the [Contributing Guidelines on pull requests](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md#pull-requests)?

Yes.

Pull Request resolved: #1568

Test Plan:
Unit tests.

## Related PRs

#1534

Reviewed By: esantorella

Differential Revision: D42230252

Pulled By: Balandat

fbshipit-source-id: b6a0a12d926fbab9890a75438eb60ef849441149
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This pull request was exported from Phabricator. Differential Revision: D41629553

sdaulton added a commit to sdaulton/botorch that referenced this pull request Feb 4, 2023
@sdaulton @facebook-github-bot
probabilistic reparameterization tutorial (pytorch#1534)
05b9764 
Summary:
Pull Request resolved: pytorch#1534

see title

Differential Revision: D41629553

fbshipit-source-id: 2ecd5870ecbb769c7157a29f68cfbafb6a76dd04
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This pull request was exported from Phabricator. Differential Revision: D41629553

sdaulton added a commit to sdaulton/botorch that referenced this pull request Feb 8, 2023
@sdaulton @facebook-github-bot
probabilistic reparameterization tutorial (pytorch#1534)
b6b64d9 
Summary:
Pull Request resolved: pytorch#1534

see title

Differential Revision: D41629553

fbshipit-source-id: 19856a6df6f88eb0493b4eedc6d53ec5ea141b72
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codecov bot commented Feb 8, 2023 •
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Codecov Report

Merging #1534 (2a4554f) into main (63dd0cd) will decrease coverage by 2.71%.
The diff coverage is 12.71%.

❗ Current head 2a4554f differs from pull request most recent head 9412010. Consider uploading reports for the commit 9412010 to get more accurate results

@@             Coverage Diff             @@
##              main    #1534      +/-   ##
===========================================
- Coverage   100.00%   97.29%   -2.71%     
===========================================
  Files          169      171       +2     
  Lines        14518    14949     +431     
===========================================
+ Hits         14518    14544      +26     
- Misses           0      405     +405
Impacted Files Coverage Δ
...ch/acquisition/probabilistic_reparameterization.py 0.00% <0.00%> (ø)
botorch/models/transforms/input.py 67.79% <5.42%> (-32.21%) ⬇️
botorch/models/transforms/factory.py 71.79% <8.33%> (-28.21%) ⬇️
botorch/acquisition/fixed_feature.py 100.00% <100.00%> (ø)
botorch/acquisition/penalized.py 100.00% <100.00%> (ø)
botorch/acquisition/proximal.py 100.00% <100.00%> (ø)
botorch/acquisition/utils.py 100.00% <100.00%> (ø)
botorch/acquisition/wrapper.py 100.00% <100.00%> (ø)

📣 We’re building smart automated test selection to slash your CI/CD build times. Learn more

sdaulton and others added 4 commits February 14, 2023 16:32
@sdaulton @facebook-github-bot
acquisition function wrapper (pytorch#1532)
d529c24 
Summary:
Pull Request resolved: pytorch#1532

Add a wrapper for modifying inputs/outputs. This is useful for not only probabilistic reparameterization, but will also simplify other integrated AFs (e.g. MCMC) as well as fixed feature AFs and things like prior-guided AFs

Differential Revision: https://internalfb.com/D41629186

fbshipit-source-id: 51b84765e58c17cda63bc582bfe30d0ca13955b5
@sdaulton @facebook-github-bot
Add isinstance_af
17c4ad8 
Summary: Creates a new helper method for checking both if a given AF is an instance of a class or if the given AF wraps a base AF that is an instance of a class

Differential Revision: D43127722

fbshipit-source-id: 13b9d54b05de09d2b2ed86406a921a38fcedab13
@sdaulton @facebook-github-bot
probabilistic reparameterization (pytorch#1533)
86aa360 
Summary:
Pull Request resolved: pytorch#1533

Probabilistic reparameterization

Differential Revision: https://internalfb.com/D41629217

fbshipit-source-id: f0719b974a8b9de4a1fe8fb62a9c73e9a1fbb551
@sdaulton @facebook-github-bot
probabilistic reparameterization tutorial (pytorch#1534)
9412010 
Summary:
Pull Request resolved: pytorch#1534

see title

Differential Revision: D41629553

fbshipit-source-id: 522f1fc245c268b4de33524c8a7addd1a8bf15b7
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This pull request was exported from Phabricator. Differential Revision: D41629553

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Hi @sdaulton!

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