probabilistic reparameterization

[sdaulton]

Summary: Probabilistic reparameterization

Differential Revision: D41629217

[facebook-github-bot]

This pull request was exported from Phabricator. Differential Revision: D41629217

[facebook-github-bot]

This pull request was exported from Phabricator. Differential Revision: D41629217

[exs-hkenlay]

Here the variable is defined as gen_kws but line 296 uses gen_kwargs

[sdaulton]

thanks! I have fixed this in the underlying PR. #1655

[sdaulton]

Regarding your comment in the PR repo about pure categorical spaces. Here is a WIP notebook on using pure categorical spaces. Not this implementation and notebook is still in development
discrete_mixed_bo_categorical.ipynb.txt

[facebook-github-bot]

This pull request was exported from Phabricator. Differential Revision: D41629217

[codecov]

Codecov Report

Merging #1533 (8be7d28) into main (63dd0cd) will decrease coverage by 2.71%.
The diff coverage is 12.71%.

❗ Current head 8be7d28 differs from pull request most recent head 7ce1389. Consider uploading reports for the commit 7ce1389 to get more accurate results

@@             Coverage Diff             @@
##              main    #1533      +/-   ##
===========================================
- 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)

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[exs-hkenlay]

Hello @sdaulton. Thank you for providing the notebook. However it does not seem to work for me (I'm using commit e313e4c). I get an error in the BO loop at the line

new_x_pr, new_obj_pr = optimize_acqf_pr_and_get_observation(
    ei_pr, analytic=False
)

Which gives IndexError: index 0 is out of bounds for dimension 0 with size 0. This error comes from line 351 of botorch/acquisition/probabilistic_reparameterization.py. I set a flag in my debugger for this line and find unnormalized_X has shape (1, 17) but both self.integer_indices and self.integer_bounds are an empty torch.tensor([]) which is what causes this index error.

I tried adding the line integer_indices=list(categorical_features.keys()) in optimize_acqf_pr_and_get_observation so pr_acq_func is given by

pr_acq_func = MCProbabilisticReparameterization(
    acq_function=acq_func,
    one_hot_bounds=one_hot_bounds,
    categorical_features=categorical_features,
    integer_indices = list(categorical_features.keys()), # new line here
    batch_limit=128,
    mc_samples=4 if SMOKE_TEST else 128,
)

This seems to get the code to progress but then a different error is triggered on line 366 of botorch/acquisition/probabilistic_reparameterization.py. The error is RuntimeError: The expanded size of the tensor (1) must match the existing size (3) at non-singleton dimension 1. Target sizes: [1, 1]. Tensor sizes: [3].

Am I using the correct commit? You can run the notebook without errors?

[facebook-github-bot]

This pull request was exported from Phabricator. Differential Revision: D41629217

[sdaulton]

@hkenlay Sorry about that. I forgot to include the changes in there for categoricals. Let me know if you still have issues on 7ce1389

[exs-hkenlay]

Works great, and really exciting work, thanks @sdaulton.

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[dsanyal]

Hello @sdaulton and @Balandat, thanks for this very exciting piece of work! Is there any timeline when this PR might be merged? :)

[sdaulton]

Not at the moment. It's been fairly low priority. If this is of interest, I can work on prioritizing getting it ready

[dsanyal]

Not at the moment. It's been fairly low priority. If this is of interest, I can work on prioritizing getting it ready

Thanks @sdaulton. I recently went through the paper, fantastic piece of work! I'd be very interested to try it out with botorch, especially to check out in practice how the new approach results in a speed-up over the brute-force combinatorical approach for mixed search spaces where the discrete parameter space contains several dimensions and levels.

[Ruan-Yixiang]

@hkenlay Sorry about that. I forgot to include the changes in there for categoricals. Let me know if you still have issues on 7ce1389

I recently read your interesting work. @sdaulton I try to use it in parallel chemical reaction optimization. In the notebook you provide I changed q=1 to q=2 in function optimize_acqf_pr_and_get_observation:

    candidates, _ = optimize_acqf(
        acq_function=pr_acq_func,
        bounds=standard_bounds,
        q=2,
        num_restarts=NUM_RESTARTS,
        raw_samples=RAW_SAMPLES,  # used for intialization heuristic
        options={
            "batch_limit": 5,
            "maxiter": 200,
            "rel_tol": float("-inf"),  # run for a full 200 steps
        },
        # use Adam for Monte Carlo PR
        gen_candidates=gen_candidates_torch if not analytic else gen_candidates_scipy,
    )

which raises error: AssertionError: Expected X to be `batch_shape x q=1 x d`, but got X with shape torch.Size([1024, 128, 2, 17]).
Did I make some mistake? Do you have the case whose batch size > 1 and space is mixed?
Thanks!

[sdaulton]

Hi @Ruan-Yixiang, We didn't test q>1, so it is quite possible that this PR needs updating to support q>1. The MC formulation of PR could get pretty expensive if one is also using a MC acquisition function too (e.g. for q>1). It would be interesting to test out PR in the batch setting and determine ways of speeding things up.

[Ruan-Yixiang]

thanks for your apply! @sdaulton I may continue trying it. By the way, I tested the wall time of MC PR. It is strange that gen_candidates_torch in 4090 GPU is much slower (about 3 times) than gen_candidates_scipy in i9-13900k CPU. Do you know why? And have you compared the wall time of PR to Botorch's optimize_acqf_mixed which is achieved by enumerating combinations of categorical variables?

[dsanyal]

@sdaulton Hi, may I ask if this PR is in progress? :)
Again, many thanks for your efforts!