Maintenance Release, Updated Community Contributions

New Features

• Introduce updated guidelines and a new directory for community contributions (#2167).
• Add qEUBO preferential acquisition function (#2192).
• Add Multi Information Source Augmented GP (#2152).

Bug Fixes

• Fix condition_on_observations in fully Bayesian models (#2151).
• Fix for bug that occurs when splitting single-element bins, use default BoTorch kernel for BAxUS. (#2165).
• Fix a bug when non-linear constraints are used with q > 1 (#2168).
• Remove unsupported X_pending from qMultiFidelityLowerBoundMaxValueEntropy constructor (#2193).
• Don't allow data_fidelities=[] in SingleTaskMultiFidelityGP (#2195).
• Fix EHVI, qEHVI, and qLogEHVI input constructors (#2196).
• Fix input constructor for qMultiFidelityMaxValueEntropy (#2198).
• Add ability to not deduplicate points in _is_non_dominated_loop (#2203).

Other Changes

• Minor improvements to MVaR risk measure (#2150).
• Add support for multitask models to ModelListGP (#2154).
• Support unspecified noise in ContextualDataset (#2155).
• Update HVKG sampler to reflect the number of model outputs (#2160).
• Release restriction in OneHotToNumeric that the categoricals are the trailing dimensions (#2166).
• Standardize broadcasting logic of q(Log)EI's best_f and compute_best_feasible_objective (#2171).
• Use regular inheritance instead of dispatcher to special-case PairwiseGP logic (#2176).
• Support PBO in EUBO's input constructor (#2178).
• Add posterior_transform to qMaxValueEntropySearch's input constructor (#2181).
• Do not normalize or standardize dimension if all values are equal (#2185).
• Reap deprecated support for objective with 1 arg in GenericMCObjective (#2199).
• Consistent signature for get_objective_weights_transform (#2200).
• Update context order handling in ContextualDataset (#2205).
• Update contextual models for use in MBM (#2206).
• Remove (Identity)AnalyticMultiOutputObjective (#2208).
• Reap deprecated support for soft_eval_constraint (#2223). Please use botorch.utils.sigmoid instead.

Compatibility

• Pin mpmath <= 1.3.0 to avoid CI breakages due to removed modules in the latest alpha release (#2222).

Hypervolume Knowledge Gradient (HVKG)

New features

Hypervolume Knowledge Gradient (HVKG):

• Add qHypervolumeKnowledgeGradient, which seeks to maximize the difference in hypervolume of the hypervolume-maximizing set of a fixed size after conditioning the unknown observation(s) that would be received if X were evaluated (#1950, #1982, #2101).
• Add tutorial on decoupled Multi-Objective Bayesian Optimization (MOBO) with HVKG (#2094).

Other new features:

• Add MultiOutputFixedCostModel, which is useful for decoupled scenarios where the objectives have different costs (#2093).
• Enable q > 1 in acquisition function optimization when nonlinear constraints are present (#1793).
• Support different noise levels for different outputs in test functions (#2136).

Bug fixes

• Fix fantasization with a FixedNoiseGaussianLikelihood when noise is known and X is empty (#2090).
• Make LearnedObjective compatible with constraints in acquisition functions regardless of sample_shape (#2111).
• Make input constructors for qExpectedImprovement, qLogExpectedImprovement, and qProbabilityOfImprovement compatible with LearnedObjective regardless of sample_shape (#2115).
• Fix handling of constraints in qSimpleRegret (#2141).

Other changes

• Increase default sample size for LearnedObjective (#2095).
• Allow passing in X with or without fidelity dimensions in project_to_target_fidelity (#2102).
• Use full-rank task covariance matrix by default in SAAS MTGP (#2104).
• Rename FullyBayesianPosterior to GaussianMixturePosterior; add _is_ensemble and _is_fully_bayesian attributes to Model (#2108).
• Various improvements to tutorials including speedups, improved explanations, and compatibility with newer versions of libraries.

Bugfix release

Compatibility

• Re-establish compatibility with PyTorch 1.13.1 (#2083).

Multi-Objective "Log" acquisition functions

Highlights

• Additional "Log" acquisition functions for multi-objective optimization with better numerical behavior, which often leads to significantly improved BO performance over their non-"Log" counterparts:
  • qLogEHVI (#2036).
  • qLogNEHVI (#2045, #2046, #2048, #2051).
  • Support fully Bayesian models with LogEI-type acquisition functions (#2058).
• FixedNoiseGP and FixedNoiseMultiFidelityGP have been deprecated, their functionalities merged into SingleTaskGP and SingleTaskMultiFidelityGP, respectively (#2052, #2053).
• Removed deprecated legacy model fitting functions: numpy_converter, fit_gpytorch_scipy, fit_gpytorch_torch, _get_extra_mll_args (#1995, #2050).

New Features

• Support multiple data fidelity dimensions in SingleTaskMultiFidelityGP and (deprecated) FixedNoiseMultiFidelityGP models (#1956).
• Add logsumexp and fatmax to handle infinities and control asymptotic behavior in "Log" acquisition functions (#1999).
• Add outcome and feature names to datasets, implement MultiTaskDataset (#2015, #2019).
• Add constrained Hartmann and constrained Gramacy synthetic test problems (#2022, #2026, #2027).
• Support observed noise in MixedSingleTaskGP (#2054).
• Add PosteriorStandardDeviation acquisition function (#2060).

Bug fixes

• Fix input constructors for qMaxValueEntropy and qMultiFidelityKnowledgeGradient (#1989).
• Fix precision issue that arises from inconsistent data types in LearnedObjective (#2006).
• Fix fantasization with FixedNoiseGP and outcome transforms and use FantasizeMixin (#2011).
• Fix LearnedObjective base sample shape (#2021).
• Apply constraints in prune_inferior_points (#2069).
• Support non-batch evaluation of PenalizedMCObjective (#2073).
• Fix Dataset equality checks (#2077).

Other changes

• Don't allow unused **kwargs in input_constructors except for a defined set of exceptions (#1872, #1985).
• Merge inferred and fixed noise LCE-M models (#1993).
• Fix import structure in botorch.acquisition.utils (#1986).
• Remove deprecated functionality: weights argument of RiskMeasureMCObjective and squeeze_last_dim (#1994).
• Make X, Y, Yvar into properties in datasets (#2004).
• Make synthetic constrained test functions subclass from SyntheticTestFunction (#2029).
• Add construct_inputs to contextual GP models LCEAGP and SACGP (#2057).

Bug fix release

This release fixes bugs that affected Ax's modular BotorchModel and silently ignored outcome constraints due to naming mismatches.

Bug fixes

• Hot fix (#1973) for a few issues:
  • A naming mismatch between Ax's modular BotorchModel and the BoTorch's acquisition input constructors, leading to outcome constraints in Ax not being used with single-objective acquisition functions in Ax's modular BotorchModel. The naming has been updated in Ax and consistent naming is now used in input constructors for single and multi-objective acquisition functions in BoTorch.
  • A naming mismatch in the acquisition input constructor constraints in qNoisyLogExpectedImprovement, which kept constraints from being used.
  • A bug in compute_best_feasible_objective that could lead to -inf incumbent values.
• Fix setting seed in get_polytope_samples (#1968)

Other changes

• Merge SupervisedDataset and FixedNoiseDataset (#1945).
• Constrained tutorial updates (#1967, #1970)
• Resolve issues with missing pytorch binaries with py3.11 on Mac (#1966)

Dependency fix release

This is a very minor release; the only change from v0.9.0 is that the linear_operator dependency was bumped to 0.5.1 (#1963). This was needed since a bug in linear_operator 0.5.0 caused failures with some BoTorch models.

LogEI acquisition functions, L0 regularization & homotopy optimization, PiBO, orthogonal additive kernel, nonlinear constraints

Compatibility

• Require Python >= 3.9.0 (#1924).
• Require PyTorch >= 1.13.1 (#1960).
• Require linear_operator == 0.5.0 (#1961).
• Require GPyTorch == 1.11 (#1961).

Highlights

• Introduce OrthogonalAdditiveKernel (#1869).
• Speed up LCE-A kernel by over an order of magnitude (#1910).
• Introduce optimize_acqf_homotopy, for optimizing acquisition functions with homotopy (#1915).
• Introduce PriorGuidedAcquisitionFunction (PiBO) (#1920).
• Introduce qLogExpectedImprovement, which provides more accurate numerics than qExpectedImprovement and can lead to significant optimization improvements (#1936).
• Similarly, introduce qLogNoisyExpectedImprovement, which is analogous to qNoisyExpectedImprovement (#1937).

New Features

• Add constrained synthetic test functions PressureVesselDesign, WeldedBeam, SpeedReducer, and TensionCompressionString (#1832).
• Support decoupled fantasization (#1853) and decoupled evaluations in cost-aware utilities (#1949).
• Add PairwiseBayesianActiveLearningByDisagreement, an active learning acquisition function for PBO and BOPE (#1855).
• Support custom mean and likelihood in MultiTaskGP (#1909).
• Enable candidate generation (via optimize_acqf) with both non_linear_constraints and fixed_features (#1912).
• Introduce L0PenaltyApproxObjective to support L0 regularization (#1916).
• Enable batching in PriorGuidedAcquisitionFunction (#1925).

Other changes

• Deprecate FixedNoiseMultiTaskGP; allow train_Yvar optionally in MultiTaskGP (#1818).
• Implement load_state_dict for SAAS multi-task GP (#1825).
• Improvements to LinearEllipticalSliceSampler (#1859, #1878, #1879, #1883).
• Allow passing in task features as part of X in MTGP.posterior (#1868).
• Improve numerical stability of log densities in pairwise GPs (#1919).
• Python 3.11 compliance (#1927).
• Enable using constraints with SampleReducingMCAcquisitionFunctions when using input_constructors and get_acquisition_function (#1932).
• Enable use of qLogExpectedImprovement and qLogNoisyExpectedImprovement with Ax (#1941).

Bug Fixes

• Enable pathwise sampling modules to be converted to GPU (#1821).
• Allow Standardize modules to be loaded once trained (#1874).
• Fix memory leak in Inducing Point Allocators (#1890).
• Correct einsum computation in LCEAKernel (#1918).
• Properly whiten bounds in MVNXPB (#1933).
• Make FixedFeatureAcquisitionFunction convert floats to double-precision tensors rather than single-precision (#1944).
• Fix memory leak in FullyBayesianPosterior (#1951).
• Make AnalyticExpectedUtilityOfBestOption input constructor work correctionly with multi-task GPs (#1955).

Maintenance Release

New Features

• Support inferred noise in SaasFullyBayesianMultiTaskGP (#1809).

Other Changes

• More informative error message when Standardize has wrong batch shape (#1807).
• Make GIBBON robust to numerical instability (#1814).
• Add sample_multiplier in EUBO's acqf_input_constructor (#1816).

Bug Fixes

• Only do checks for _optimize_acqf_sequential_q when it will be used (#1808).
• Fix an issue where PairwiseGP comparisons might be implicitly modified (#1811).

Maintenance Release

Compatibility

• Require GPyTorch == 1.10 and linear_operator == 0.4.0 (#1803).

New Features

• Polytope sampling for linear constraints along the q-dimension (#1757).
• Single-objective joint entropy search with additional conditioning, various improvements to entropy-based acquisition functions (#1738).

Other changes

• Various updates to improve numerical stability of PairwiseGP (#1754, #1755).
• Change batch range for FullyBayesianPosterior (1176a38, #1773).
• Make gen_batch_initial_conditions more flexible (#1779).
• Deprecate objective in favor of posterior_transform for MultiObjectiveAnalyticAcquisitionFunction (#1781).
• Use prune_baseline=True as default for qNoisyExpectedImprovement (#1796).
• Add batch_shape property to SingleTaskVariationalGP (#1799).
• Change minimum inferred noise level for SaasFullyBayesianSingleTaskGP (#1800).

Bug fixes

• Add output_task to MultiTaskGP.construct_inputs (#1753).
• Fix custom bounds handling in test problems (#1760).
• Remove incorrect BotorchTensorDimensionWarning (#1790).
• Fix handling of non-Container-typed positional arguments in SupervisedDatasetMeta (#1663).

Maintenance Release

New Features

• Add BAxUS tutorial (#1559).

Other changes

• Various improvements to tutorials (#1703, #1706, #1707, #1708, #1710, #1711, #1718, #1719, #1739, #1740, #1742).
• Allow tensor input for integer_indices in Round transform (#1709).
• Expose cache_root in qNEHVI input constructor (#1730).
• Add get_init_args helper to Normalize & Round transforms (#1731).
• Allowing custom dimensionality and improved gradient stability in ModifiedFixedSingleSampleModel (#1732).

Bug fixes

• Improve batched model handling in _verify_output_shape (#1715).
• Fix qNEI with Derivative Enabled BO (#1716).
• Fix get_infeasible_cost for objectives that require X (#1721).