Bump botorch from 0.10.0 to 0.13.0

[dependabot]

Bumps botorch from 0.10.0 to 0.13.0.

Release notes

Sourced from botorch's releases.

Maintenance Release, Website Upgrade, BO with Relevance Pursuit, LatentKroneckerGP and MAP-SAAS Models

Highlights

• BoTorch website has been upgraded to utilize Docusaurus v3, with the API reference being hosted by ReadTheDocs. The tutorials now expose an option to open with Colab, for easy access to a runtime with modifiable tutorials. The old versions of the website can be found at archive.botorch.org (#2653).
• RobustRelevancePursuitSingleTaskGP, a robust Gaussian process model that adaptively identifies outliers and leverages Bayesian model selection (paper) (#2608, #2690, #2707).
• LatentKroneckerGP, a scalable model for data on partially observed grids, like the joint modeling of hyper-parameters and partially completed learning curves in AutoML (paper) (#2647).
• Add MAP-SAAS model, which utilizes the sparse axis-aligned subspace priors (paper) with MAP model fitting (#2694).

Compatibility

• Require GPyTorch==1.14 and linear_operator==0.6 (#2710).
• Remove support for anaconda (official package) (#2617).
• Remove mpmath dependency pin (#2640).
• Updates to optimization routines to support SciPy>1.15:
  • Use threadpoolctl in minimize_with_timeout to prevent CPU oversubscription (#2712).
  • Update optimizer output parsing to make model fitting compatible with SciPy>1.15 (#2667).

New Features

• Add support for priors in OAK Kernel (#2535).
• Add BatchBroadcastedTransformList, which broadcasts a list of InputTransforms over batch shapes (#2558).
• InteractionFeatures input transform (#2560).
• Implement percentile_of_score, which takes inputs data and score, and returns the percentile of values in data that are below score (#2568).
• Add optimize_acqf_mixed_alternating, which supports optimization over mixed discrete & continuous spaces (#2573).
• Add support for PosteriorTransform to get_optimal_samples and optimize_posterior_samples (#2576).
• Support inequality constraints & X_avoid in optimize_acqf_discrete (#2593).
• Add ability to mix batch initial conditions and internal IC generation (#2610).
• Add qPosteriorStandardDeviation acquisition function (#2634).
• TopK downselection for initial batch generation. (#2636).
• Support optimization over mixed spaces in optimize_acqf_homotopy (#2639).
• Add InfeasibilityError exception class (#2652).
• Support InputTransforms in SparseOutlierLikelihood and get_posterior_over_support (#2659).
• StratifiedStandardize outcome transform (#2671).
• Add center argument to Normalize (#2680).
• Add input normalization step in Warp input transform (#2692).
• Support mixing fully Bayesian & SingleTaskGP models in ModelListGP (#2693).
• Add abstract fully Bayesian GP class and fully Bayesian linear GP model (#2696, #2697).
• Tutorial on BO constrained by probability of classification model (#2700).

Bug Fixes

• Fix error in decoupled_mobo tutorial due to torch/numpy issues (#2550).
• Raise error for MTGP in batch_cross_validation (#2554).
• Fix posterior method in BatchedMultiOutputGPyTorchModel for tracing JIT (#2592).
• Replace hard-coded double precision in test_functions with default dtype (#2597).
• Remove as_tensor argument of set_tensors_from_ndarray_1d (#2615).
• Skip fixed feature enumerations in optimize_acqf_mixed that can't satisfy the parameter constraints (#2614).

... (truncated)

Changelog

Sourced from botorch's changelog.

[0.13.0] -- Feb 3, 2025

Highlights

• BoTorch website has been upgraded to utilize Docusaurus v3, with the API reference being hosted by ReadTheDocs. The tutorials now expose an option to open with Colab, for easy access to a runtime with modifiable tutorials. The old versions of the website can be found at archive.botorch.org (#2653).
• RobustRelevancePursuitSingleTaskGP, a robust Gaussian process model that adaptively identifies outliers and leverages Bayesian model selection (paper) (#2608, #2690, #2707).
• LatentKroneckerGP, a scalable model for data on partially observed grids, like the joint modeling of hyper-parameters and partially completed learning curves in AutoML (paper) (#2647).
• Add MAP-SAAS model, which utilizes the sparse axis-aligned subspace priors (paper) with MAP model fitting (#2694).

Compatibility

• Require GPyTorch==1.14 and linear_operator==0.6 (#2710).
• Remove support for anaconda (official package) (#2617).
• Remove mpmath dependency pin (#2640).
• Updates to optimization routines to support SciPy>1.15:
  • Use threadpoolctl in minimize_with_timeout to prevent CPU oversubscription (#2712).
  • Update optimizer output parsing to make model fitting compatible with SciPy>1.15 (#2667).

New Features

• Add support for priors in OAK Kernel (#2535).
• Add BatchBroadcastedTransformList, which broadcasts a list of InputTransforms over batch shapes (#2558).
• InteractionFeatures input transform (#2560).
• Implement percentile_of_score, which takes inputs data and score, and returns the percentile of values in data that are below score (#2568).
• Add optimize_acqf_mixed_alternating, which supports optimization over mixed discrete & continuous spaces (#2573).
• Add support for PosteriorTransform to get_optimal_samples and optimize_posterior_samples (#2576).
• Support inequality constraints & X_avoid in optimize_acqf_discrete (#2593).
• Add ability to mix batch initial conditions and internal IC generation (#2610).
• Add qPosteriorStandardDeviation acquisition function (#2634).
• TopK downselection for initial batch generation. (#2636).
• Support optimization over mixed spaces in optimize_acqf_homotopy (#2639).
• Add InfeasibilityError exception class (#2652).
• Support InputTransforms in SparseOutlierLikelihood and get_posterior_over_support (#2659).
• StratifiedStandardize outcome transform (#2671).
• Add center argument to Normalize (#2680).
• Add input normalization step in Warp input transform (#2692).
• Support mixing fully Bayesian & SingleTaskGP models in ModelListGP (#2693).
• Add abstract fully Bayesian GP class and fully Bayesian linear GP model (#2696, #2697).
• Tutorial on BO constrained by probability of classification model (#2700).

Bug Fixes

• Fix error in decoupled_mobo tutorial due to torch/numpy issues (#2550).
• Raise error for MTGP in batch_cross_validation (#2554).
• Fix posterior method in BatchedMultiOutputGPyTorchModel for tracing JIT (#2592).
• Replace hard-coded double precision in test_functions with default dtype (#2597).
• Remove as_tensor argument of set_tensors_from_ndarray_1d (#2615).

... (truncated)

Commits

• 6d6fc5d Changelog for 0.13.0 (#2714)
• 16fbe2c Add license to files (#2722)
• b197bf1 Fix broken links (#2720)
• 37f3f7d Specify permissions when deviating from defaults (#2721)
• 8814f68 Add Region Averaged Acquisition Functions (#2699)
• d34eca3 Bump nanoid from 3.3.7 to 3.3.8 in /website (#2716)
• 740d71d Bump katex from 0.16.11 to 0.16.21 in /website (#2717)
• 651390e Update url to official project (#2718)
• b3f6a38 Upgrade Website (#2715)
• acae12d Unpin scipy & use threadpoolctl in minimize_with_timeout (#2712)
• Additional commits viewable in compare view

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