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Changelog

The release log for BoTorch.

[0.6.1] - Feb 28, 2022

New Features

  • Add Standardize input transform (#1053).
  • Low-rank Cholesky updates for NEI (#1056).
  • Add support for non-linear input constraints (#1067).
  • New MOO problems: MW7 (#1077), disc brake (#1078), penicillin (#1079), RobustToy (#1082), GMM (#1083).

Other Changes

  • Support multi-output models in MES using PosteriorTransform (#904).
  • Add Dispatcher (#1009).
  • Modify qNEHVI to support deterministic models (#1026).
  • Store tensor attributes of input transforms as buffers (#1035).
  • Modify NEHVI to support MTGPs (#1037).
  • Make Normalize input transform input column-specific (#1047).
  • Improve find_interior_point (#1049).
  • Remove deprecated botorch.distributions module (#1061).
  • Avoid costly application of posterior transform in Kronecker & HOGP models (#1076).
  • Support heteroscedastic perturbations in InputPerturbations (#1088).

Performance Improvements

  • Make risk measures more memory efficient (#1034).

Bug Fixes

  • Properly handle empty fixed_features in optimization (#1029).
  • Fix missing weights in VaR risk measure (#1038).
  • Fix find_interior_point for negative variables & allow unbounded problems (#1045).
  • Filter out indefinite bounds in constraint utilities (#1048).
  • Make non-interleaved base samples use intuitive shape (#1057).
  • Pad small diagonalization with zeros for KroneckerMultitaskGP (#1071).
  • Disable learning of bounds in preprocess_transform (#1089).
  • Fix gen_candidates_torch (4079164489613d436d19c7b2df97677d97dfa8dc).
  • Catch runtime errors with ill-conditioned covar (#1095).
  • Fix compare_mc_analytic_acquisition tutorial (#1099).

[0.6.0] - Dec 8, 2021

Compatibility

  • Require PyTorch >=1.9 (#1011).
  • Require GPyTorch >=1.6 (#1011).

New Features

  • New ApproximateGPyTorchModel wrapper for various (variational) approximate GP models (#1012).
  • New SingleTaskVariationalGP stochastic variational Gaussian Process model (#1012).
  • Support for Multi-Output Risk Measures (#906, #965).
  • Introduce ModelList and PosteriorList (#829).
  • New Constraint Active Search tutorial (#1010).
  • Add additional multi-objective optimization test problems (#958).

Other Changes

  • Add covar_module as an optional input of MultiTaskGP models (#941).
  • Add min_range argument to Normalize transform to prevent division by zero (#931).
  • Add initialization heuristic for acquisition function optimization that samples around best points (#987).
  • Update initialization heuristic to perturb a subset of the dimensions of the best points if the dimension is > 20 (#988).
  • Modify apply_constraints utility to work with multi-output objectives (#994).
  • Short-cut t_batch_mode_transform decorator on non-tensor inputs (#991).

Performance Improvements

  • Use lazy covariance matrix in BatchedMultiOutputGPyTorchModel.posterior (#976).
  • Fast low-rank Cholesky updates for qNoisyExpectedHypervolumeImprovement (#747, #995, #996).

Bug Fixes

  • Update error handling to new PyTorch linear algebra messages (#940).
  • Avoid test failures on Ampere devices (#944).
  • Fixes to the Griewank test function (#972).
  • Handle empty base_sample_shape in Posterior.rsample (#986).
  • Handle NotPSDError and hitting maxiter in fit_gpytorch_model (#1007).
  • Use TransformedPosterior for subclasses of GPyTorchPosterior (#983).
  • Propagate best_f argument to qProbabilityOfImprovement in input constructors (f5a5f8b6dc20413e67c6234e31783ac340797a8d).

[0.5.1] - Sep 2, 2021

Compatibility

  • Require GPyTorch >=1.5.1 (#928).

New Features

  • Add HigherOrderGP composite Bayesian Optimization tutorial notebook (#864).
  • Add Multi-Task Bayesian Optimziation tutorial (#867).
  • New multi-objective test problems from (#876).
  • Add PenalizedMCObjective and L1PenaltyObjective (#913).
  • Add a ProximalAcquisitionFunction for regularizing new candidates towards previously generated ones (#919, #924).
  • Add a Power outcome transform (#925).

Bug Fixes

  • Batch mode fix for HigherOrderGP initialization (#856).
  • Improve CategoricalKernel precision (#857).
  • Fix an issue with qMultiFidelityKnowledgeGradient.evaluate (#858).
  • Fix an issue with transforms with HigherOrderGP. (#889)
  • Fix initial candidate generation when parameter constraints are on different device (#897).
  • Fix bad in-place op in _generate_unfixed_lin_constraints (#901).
  • Fix an input transform bug in fantasize call (#902).
  • Fix outcome transform bug in batched_to_model_list (#917).

Other Changes

  • Make variance optional for TransformedPosterior.mean (#855).
  • Support transforms in DeterministicModel (#869).
  • Support batch_shape in RandomFourierFeatures (#877).
  • Add a maximize flag to PosteriorMean (#881).
  • Ignore categorical dimensions when validating training inputs in MixedSingleTaskGP (#882).
  • Refactor HigherOrderGPPosterior for memory efficiency (#883).
  • Support negative weights for minimization objectives in get_chebyshev_scalarization (#884).
  • Move train_inputs transforms to model.train/eval calls (#894).

[0.5.0] - Jun 29, 2021

Compatibility

  • Require PyTorch >=1.8.1 (#832).
  • Require GPyTorch >=1.5 (#848).
  • Changes to how input transforms are applied: transform_inputs is applied in model.forward if the model is in train mode, otherwise it is applied in the posterior call (#819, #835).

New Features

  • Improved multi-objective optimization capabilities:
    • qNoisyExpectedHypervolumeImprovement acquisition function that improves on qExpectedHypervolumeImprovement in terms of tolerating observation noise and speeding up computation for large q-batches (#797, #822).
    • qMultiObjectiveMaxValueEntropy acqusition function (913aa0e510dde10568c2b4b911124cdd626f6905, #760).
    • Heuristic for reference point selection (#830).
    • FastNondominatedPartitioning for Hypervolume computations (#699).
    • DominatedPartitioning for partitioning the dominated space (#726).
    • BoxDecompositionList for handling box decompositions of varying sizes (#712).
    • Direct, batched dominated partitioning for the two-outcome case (#739).
    • get_default_partitioning_alpha utility providing heuristic for selecting approximation level for partitioning algorithms (#793).
    • New method for computing Pareto Frontiers with less memory overhead (#842, #846).
  • New qLowerBoundMaxValueEntropy acquisition function (a.k.a. GIBBON), a lightweight variant of Multi-fidelity Max-Value Entropy Search using a Determinantal Point Process approximation (#724, #737, #749).
  • Support for discrete and mixed input domains:
    • CategoricalKernel for categorical inputs (#771).
    • MixedSingleTaskGP for mixed search spaces (containing both categorical and ordinal parameters) (#772, #847).
    • optimize_acqf_discrete for optimizing acquisition functions over fully discrete domains (#777).
    • Extend optimize_acqf_mixed to allow batch optimization (#804).
  • Support for robust / risk-aware optimization:
    • Risk measures for robust / risk-averse optimization (#821).
    • AppendFeatures transform (#820).
    • InputPerturbation input transform for for risk averse BO with implementation errors (#827).
    • Tutorial notebook for Bayesian Optimization of risk measures (#823).
    • Tutorial notebook for risk-averse Bayesian Optimization under input perturbations (#828).
  • More scalable multi-task modeling and sampling:
    • KroneckerMultiTaskGP model for efficient multi-task modeling for block-design settings (all tasks observed at all inputs) (#637).
    • Support for transforms in Multi-Task GP models (#681).
    • Posterior sampling based on Matheron's rule for Multi-Task GP models (#841).
  • Various changes to simplify and streamline integration with Ax:
    • Handle non-block designs in TrainingData (#794).
    • Acquisition function input constructor registry (#788, #802, #845).
  • Random Fourier Feature (RFF) utilties for fast (approximate) GP function sampling (#750).
  • DelaunayPolytopeSampler for fast uniform sampling from (simple) polytopes (#741).
  • Add evaluate method to ScalarizedObjective (#795).

Bug Fixes

  • Handle the case when all features are fixed in optimize_acqf (#770).
  • Pass fixed_features to initial candidate generation functions (#806).
  • Handle batch empty pareto frontier in FastPartitioning (#740).
  • Handle empty pareto set in is_non_dominated (#743).
  • Handle edge case of no or a single observation in get_chebyshev_scalarization (#762).
  • Fix an issue in gen_candidates_torch that caused problems with acqusition functions using fantasy models (#766).
  • Fix HigherOrderGP dtype bug (#728).
  • Normalize before clamping in Warp input warping transform (#722).
  • Fix bug in GP sampling (#764).

Other Changes

  • Modify input transforms to support one-to-many transforms (#819, #835).
  • Make initial conditions for acquisition function optimization honor parameter constraints (#752).
  • Perform optimization only over unfixed features if fixed_features is passed (#839).
  • Refactor Max Value Entropy Search Methods (#734).
  • Use Linear Algebra functions from the torch.linalg module (#735).
  • Use PyTorch's Kumaraswamy distribution (#746).
  • Improved capabilities and some bugfixes for batched models (#723, #767).
  • Pass callback argument to scipy.optim.minimize in gen_candidates_scipy (#744).
  • Modify behavior of X_pending in in multi-objective acqusiition functions (#747).
  • Allow multi-dimensional batch shapes in test functions (#757).
  • Utility for converting batched multi-output models into batched single-output models (#759).
  • Explicitly raise NotPSDError in _scipy_objective_and_grad (#787).
  • Make raw_samples optional if batch_initial_conditions is passed (#801).
  • Use powers of 2 in qMC docstrings & examples (#812).

[0.4.0] - Feb 23, 2021

Compatibility

  • Require PyTorch >=1.7.1 (#714).
  • Require GPyTorch >=1.4 (#714).

New Features

  • HigherOrderGP - High-Order Gaussian Process (HOGP) model for high-dimensional output regression (#631, #646, #648, #680).
  • qMultiStepLookahead acquisition function for general look-ahead optimization approaches (#611, #659).
  • ScalarizedPosteriorMean and project_to_sample_points for more advanced MFKG functionality (#645).
  • Large-scale Thompson sampling tutorial (#654, #713).
  • Tutorial for optimizing mixed continuous/discrete domains (application to multi-fidelity KG with discrete fidelities) (#716).
  • GPDraw utility for sampling from (exact) GP priors (#655).
  • Add X as optional arg to call signature of MCAcqusitionObjective (#487).
  • OSY synthetic test problem (#679).

Bug Fixes

  • Fix matrix multiplication in scalarize_posterior (#638).
  • Set X_pending in get_acquisition_function in qEHVI (#662).
  • Make contextual kernel device-aware (#666).
  • Do not use an MCSampler in MaxPosteriorSampling (#701).
  • Add ability to subset outcome transforms (#711).

Performance Improvements

  • Batchify box decomposition for 2d case (#642).

Other Changes

  • Use scipy distribution in MES quantile bisect (#633).
  • Use new closure definition for GPyTorch priors (#634).
  • Allow enabling of approximate root decomposition in posterior calls (#652).
  • Support for upcoming 21201-dimensional PyTorch SobolEngine (#672, #674).
  • Refactored various MOO utilities to allow future additions (#656, #657, #658, #661).
  • Support input_transform in PairwiseGP (#632).
  • Output shape checks for t_batch_mode_transform (#577).
  • Check for NaN in gen_candidates_scipy (#688).
  • Introduce base_sample_shape property to Posterior objects (#718).

[0.3.3] - Dec 8, 2020

Contextual Bayesian Optimization, Input Warping, TuRBO, sampling from polytopes.

Compatibility

  • Require PyTorch >=1.7 (#614).
  • Require GPyTorch >=1.3 (#614).

New Features

Bug fixes

  • Fix bounds of HolderTable synthetic function (#596).
  • Fix device issue in MOO tutorial (#621).

Other changes

  • Add train_inputs option to qMaxValueEntropy (#593).
  • Enable gpytorch settings to override BoTorch defaults for fast_pred_var and debug (#595).
  • Rename set_train_data_transform -> preprocess_transform (#575).
  • Modify _expand_bounds() shape checks to work with >2-dim bounds (#604).
  • Add batch_shape property to models (#588).
  • Modify qMultiFidelityKnowledgeGradient.evaluate() to work with project, expand and cost_aware_utility (#594).
  • Add list of papers using BoTorch to website docs (#617).

[0.3.2] - Oct 23, 2020

Maintenance Release

New Features

  • Add PenalizedAcquisitionFunction wrapper (#585)
  • Input transforms
    • Reversible input transform (#550)
    • Rounding input transform (#562)
    • Log input transform (#563)
  • Differentiable approximate rounding for integers (#561)

Bug fixes

  • Fix sign error in UCB when maximize=False (a4bfacbfb2109d3b89107d171d2101e1995822bb)
  • Fix batch_range sample shape logic (#574)

Other changes

  • Better support for two stage sampling in preference learning (0cd13d0cb49b1ac8d0971e42f1f0e9dd6126fd9a)
  • Remove noise term in PairwiseGP and add ScaleKernel by default (#571)
  • Rename prior to task_covar_prior in MultiTaskGP and FixedNoiseMultiTaskGP (8e42ea82856b165a7df9db2a9b6f43ebd7328fc4)
  • Support only transforming inputs on training or evaluation (#551)
  • Add equals method for InputTransform (#552)

[0.3.1] - Sep 15, 2020

Maintenance Release

New Features

  • Constrained Multi-Objective tutorial (#493)
  • Multi-fidelity Knowledge Gradient tutorial (#509)
  • Support for batch qMC sampling (#510)
  • New evaluate method for qKnowledgeGradient (#515)

Compatibility

  • Require PyTorch >=1.6 (#535)
  • Require GPyTorch >=1.2 (#535)
  • Remove deprecated botorch.gen module (#532)

Bug fixes

  • Fix bad backward-indexing of task_feature in MultiTaskGP (#485)
  • Fix bounds in constrained Branin-Currin test function (#491)
  • Fix max_hv for C2DTLZ2 and make Hypervolume always return a float (#494)
  • Fix bug in draw_sobol_samples that did not use the proper effective dimension (#505)
  • Fix constraints for q>1 in qExpectedHypervolumeImprovement (c80c4fdb0f83f0e4f12e4ec4090d0478b1a8b532)
  • Only use feasible observations in partitioning for qExpectedHypervolumeImprovement in get_acquisition_function (#523)
  • Improved GPU compatibility for PairwiseGP (#537)

Performance Improvements

  • Reduce memory footprint in qExpectedHypervolumeImprovement (#522)
  • Add (q)ExpectedHypervolumeImprovement to nonnegative functions [for better initialization] (#496)

Other changes

  • Support batched best_f in qExpectedImprovement (#487)
  • Allow to return full tree of solutions in OneShotAcquisitionFunction (#488)
  • Added construct_inputs class method to models to programmatically construct the inputs to the constructor from a standardized TrainingData representation (#477, #482, 3621198d02195b723195b043e86738cd5c3b8e40)
  • Acquisition function constructors now accept catch-all **kwargs options (#478, e5b69352954bb10df19a59efe9221a72932bfe6c)
  • Use psd_safe_cholesky in qMaxValueEntropy for better numerical stabilty (#518)
  • Added WeightedMCMultiOutputObjective (81d91fd2e115774e561c8282b724457233b6d49f)
  • Add ability to specify outcomes to all multi-output objectives (#524)
  • Return optimization output in info_dict for fit_gpytorch_scipy (#534)
  • Use setuptools_scm for versioning (#539)

[0.3.0] - July 6, 2020

Multi-Objective Bayesian Optimization

New Features

  • Multi-Objective Acquisition Functions (#466)
    • q-Expected Hypervolume Improvement
    • q-ParEGO
    • Analytic Expected Hypervolume Improvement with auto-differentiation
  • Multi-Objective Utilities (#466)
    • Pareto Computation
    • Hypervolume Calculation
    • Box Decomposition algorithm
  • Multi-Objective Test Functions (#466)
    • Suite of synthetic test functions for multi-objective, constrained optimization
  • Multi-Objective Tutorial (#468)
  • Abstract ConstrainedBaseTestProblem (#454)
  • Add optimize_acqf_list method for sequentially, greedily optimizing 1 candidate from each provided acquisition function (d10aec911b241b208c59c192beb9e4d572a092cd)

Bug fixes

  • Fixed re-arranging mean in MultiTask MO models (#450).

Other changes

  • Move gpt_posterior_settings into models.utils (#449)
  • Allow specifications of batch dims to collapse in samplers (#457)
  • Remove outcome transform before model-fitting for sequential model fitting in MO models (#458)

[0.2.5] - May 14, 2020

Bugfix Release

Bug fixes

  • Fixed issue with broken wheel build (#444).

Other changes

  • Changed code style to use absolute imports throughout (#443).

[0.2.4] - May 12, 2020

Bugfix Release

Bug fixes

  • There was a mysterious issue with the 0.2.3 wheel on pypi, where part of the botorch/optim/utils.py file was not included, which resulted in an ImportError for many central components of the code. Interestingly, the source dist (built with the same command) did not have this issue.
  • Preserve order in ChainedOutcomeTransform (#440).

New Features

  • Utilities for estimating the feasible volume under outcome constraints (#437).

[0.2.3] - Apr 27, 2020

Pairwise GP for Preference Learning, Sampling Strategies.

Compatibility

  • Require PyTorch >=1.5 (#423).
  • Require GPyTorch >=1.1.1 (#425).

New Features

  • Add PairwiseGP for preference learning with pair-wise comparison data (#388).
  • Add SamplingStrategy abstraction for sampling-based generation strategies, including MaxPosteriorSampling (i.e. Thompson Sampling) and BoltzmannSampling (#218, #407).

Deprecations

  • The existing botorch.gen module is moved to botorch.generation.gen and imports from botorch.gen will raise a warning (an error in the next release) (#218).

Bug fixes

  • Fix & update a number of tutorials (#394, #398, #393, #399, #403).
  • Fix CUDA tests (#404).
  • Fix sobol maxdim limitation in prune_baseline (#419).

Other changes

  • Better stopping criteria for stochastic optimization (#392).
  • Improve numerical stability of LinearTruncatedFidelityKernel (#409).
  • Allow batched best_f in qExpectedImprovement and qProbabilityOfImprovement (#411).
  • Introduce new logger framework (#412).
  • Faster indexing in some situations (#414).
  • More generic BaseTestProblem (9e604fe2188ac85294c143d249872415c4d95823).

[0.2.2] - Mar 6, 2020

Require PyTorch 1.4, Python 3.7 and new features for active learning, multi-fidelity optimization, and a number of bug fixes.

Compatibility

  • Require PyTorch >=1.4 (#379).
  • Require Python >=3.7 (#378).

New Features

  • Add qNegIntegratedPosteriorVariance for Bayesian active learning (#377).
  • Add FixedNoiseMultiFidelityGP, analogous to SingleTaskMultiFidelityGP (#386).
  • Support scalarize_posterior for m>1 and q>1 posteriors (#374).
  • Support subset_output method on multi-fidelity models (#372).
  • Add utilities for sampling from simplex and hypersphere (#369).

Bug fixes

  • Fix TestLoader local test discovery (#376).
  • Fix batch-list conversion of SingleTaskMultiFidelityGP (#370).
  • Validate tensor args before checking input scaling for more informative error messaages (#368).
  • Fix flaky qNoisyExpectedImprovement test (#362).
  • Fix test function in closed-loop tutorial (#360).
  • Fix num_output attribute in BoTorch/Ax tutorial (#355).

Other changes

  • Require output dimension in MultiTaskGP (#383).
  • Update code of conduct (#380).
  • Remove deprecated joint_optimize and sequential_optimize (#363).

[0.2.1] - Jan 15, 2020

Minor bug fix release.

New Features

  • Add a static method for getting batch shapes for batched MO models (#346).

Bug fixes

  • Revamp qKG constructor to avoid issue with missing objective (#351).
  • Make sure MVES can support sampled costs like KG (#352).

Other changes

  • Allow custom module-to-array handling in fit_gpytorch_scipy (#341).

[0.2.0] - Dec 20, 2019

Max-value entropy acquisition function, cost-aware / multi-fidelity optimization, subsetting models, outcome transforms.

Compatibility

  • Require PyTorch >=1.3.1 (#313).
  • Require GPyTorch >=1.0 (#342).

New Features

  • Add cost-aware KnowledgeGradient (qMultiFidelityKnowledgeGradient) for multi-fidelity optimization (#292).
  • Add qMaxValueEntropy and qMultiFidelityMaxValueEntropy max-value entropy search acquisition functions (#298).
  • Add subset_output functionality to (most) models (#324).
  • Add outcome transforms and input transforms (#321).
  • Add outcome_transform kwarg to model constructors for automatic outcome transformation and un-transformation (#327).
  • Add cost-aware utilities for cost-sensitive acquisiiton functions (#289).
  • Add DeterminsticModel and DetermisticPosterior abstractions (#288).
  • Add AffineFidelityCostModel (f838eacb4258f570c3086d7cbd9aa3cf9ce67904).
  • Add project_to_target_fidelity and expand_trace_observations utilties for use in multi-fidelity optimization (1ca12ac0736e39939fff650cae617680c1a16933).

Performance Improvements

  • New prune_baseline option for pruning X_baseline in qNoisyExpectedImprovement (#287).
  • Do not use approximate MLL computation for deterministic fitting (#314).
  • Avoid re-evaluating the acquisition function in gen_candidates_torch (#319).
  • Use CPU where possible in gen_batch_initial_conditions to avoid memory issues on the GPU (#323).

Bug fixes

  • Properly register NoiseModelAddedLossTerm in HeteroskedasticSingleTaskGP (671c93a203b03ef03592ce322209fc5e71f23a74).
  • Fix batch mode for MultiTaskGPyTorchModel (#316).
  • Honor propagate_grads argument in fantasize of FixedNoiseGP (#303).
  • Properly handle diag arg in LinearTruncatedFidelityKernel (#320).

Other changes

  • Consolidate and simplify multi-fidelity models (#308).
  • New license header style (#309).
  • Validate shape of best_f in qExpectedImprovement (#299).
  • Support specifying observation noise explicitly for all models (#256).
  • Add num_outputs property to the Model API (#330).
  • Validate output shape of models upon instantiating acquisition functions (#331).

Tests

  • Silence warnings outside of explicit tests (#290).
  • Enforce full sphinx docs coverage in CI (#294).

[0.1.4] - Oct 1, 2019

Knowledge Gradient acquisition function (one-shot), various maintenance

Breaking Changes

  • Require explicit output dimensions in BoTorch models (#238)
  • Make joint_optimize / sequential_optimize return acquisition function values (#149) [note deprecation notice below]
  • standardize now works on the second to last dimension (#263)
  • Refactor synthetic test functions (#273)

New Features

  • Add qKnowledgeGradient acquisition function (#272, #276)
  • Add input scaling check to standard models (#267)
  • Add cyclic_optimize, convergence criterion class (#269)
  • Add settings.debug context manager (#242)

Deprecations

  • Consolidate sequential_optimize and joint_optimize into optimize_acqf (#150)

Bug fixes

  • Properly pass noise levels to GPs using a FixedNoiseGaussianLikelihood (#241) [requires gpytorch > 0.3.5]
  • Fix q-batch dimension issue in ConstrainedExpectedImprovement (6c067185f56d3a244c4093393b8a97388fb1c0b3)
  • Fix parameter constraint issues on GPU (#260)

Minor changes

  • Add decorator for concatenating pending points (#240)
  • Draw independent sample from prior for each hyperparameter (#244)
  • Allow dim > 1111 for gen_batch_initial_conditions (#249)
  • Allow optimize_acqf to use q>1 for AnalyticAcquisitionFunction (#257)
  • Allow excluding parameters in fit functions (#259)
  • Track the final iteration objective value in fit_gpytorch_scipy (#258)
  • Error out on unexpected dims in parameter constraint generation (#270)
  • Compute acquisition values in gen_ functions w/o grad (#274)

Tests

  • Introduce BotorchTestCase to simplify test code (#243)
  • Refactor tests to have monolithic cuda tests (#261)

[0.1.3] - Aug 9, 2019

Compatibility & maintenance release

Compatibility

  • Updates to support breaking changes in PyTorch to boolean masks and tensor comparisons (#224).
  • Require PyTorch >=1.2 (#225).
  • Require GPyTorch >=0.3.5 (itself a compatibility release).

New Features

  • Add FixedFeatureAcquisitionFunction wrapper that simplifies optimizing acquisition functions over a subset of input features (#219).
  • Add ScalarizedObjective for scalarizing posteriors (#210).
  • Change default optimization behavior to use L-BFGS-B by for box constraints (#207).

Bug fixes

  • Add validation to candidate generation (#213), making sure constraints are strictly satisfied (rater than just up to numerical accuracy of the optimizer).

Minor changes

  • Introduce AcquisitionObjective base class (#220).
  • Add propagate_grads context manager, replacing the propagate_grads kwarg in model posterior() calls (#221)
  • Add batch_initial_conditions argument to joint_optimize() for warm-starting the optimization (ec3365a37ed02319e0d2bb9bea03aee89b7d9caa).
  • Add return_best_only argument to joint_optimize() (#216). Useful for implementing advanced warm-starting procedures.

[0.1.2] - July 9, 2019

Maintenance release

Bug fixes

  • Avoid [PyTorch bug]((pytorch/pytorch#22353) resulting in bad gradients on GPU by requiring GPyTorch >= 0.3.4
  • Fixes to resampling behavior in MCSamplers (#204)

Experimental Features

  • Linear truncated kernel for multi-fidelity bayesian optimization (#192)
  • SingleTaskMultiFidelityGP for GP models that have fidelity parameters (#181)

[0.1.1] - June 27, 2019

API updates, more robust model fitting

Breaking changes

  • rename botorch.qmc to botorch.sampling, move MC samplers from acquisition.sampler to botorch.sampling.samplers (#172)

New Features

  • Add condition_on_observations and fantasize to the Model level API (#173)
  • Support pending observations generically for all MCAcqusitionFunctions (#176)
  • Add fidelity kernel for training iterations/training data points (#178)
  • Support for optimization constraints across q-batches (to support things like sample budget constraints) (2a95a6c3f80e751d5cf8bc7240ca9f5b1529ec5b)
  • Add ModelList <-> Batched Model converter (#187)
  • New test functions
    • basic: neg_ackley, cosine8, neg_levy, neg_rosenbrock, neg_shekel (e26dc7576c7bf5fa2ba4cb8fbcf45849b95d324b)
    • for multi-fidelity BO: neg_aug_branin, neg_aug_hartmann6, neg_aug_rosenbrock (ec4aca744f65ca19847dc368f9fee4cc297533da)

Improved functionality:

  • More robust model fitting
    • Catch gpytorch numerical issues and return NaN to the optimizer (#184)
    • Restart optimization upon failure by sampling hyperparameters from their prior (#188)
    • Sequentially fit batched and ModelListGP models by default (#189)
    • Change minimum inferred noise level (e2c64fef1e76d526a33951c5eb75ac38d5581257)
  • Introduce optional batch limit in joint_optimize to increases scalability of parallel optimization (baab5786e8eaec02d37a511df04442471c632f8a)
  • Change constructor of ModelListGP to comply with GPyTorch’s IndependentModelList constructor (a6cf739e769c75319a67c7525a023ece8806b15d)
  • Use torch.random to set default seed for samplers (rather than random) to making sampling reproducible when setting torch.manual_seed (ae507ad97255d35f02c878f50ba68a2e27017815)

Performance Improvements

  • Use einsum in LinearMCObjective (22ca29535717cda0fcf7493a43bdf3dda324c22d)
  • Change default Sobol sample size for MCAquisitionFunctions to be base-2 for better MC integration performance (5d8e81866a23d6bfe4158f8c9b30ea14dd82e032)
  • Add ability to fit models in SumMarginalLogLikelihood sequentially (and make that the default setting) (#183)
  • Do not construct the full covariance matrix when computing posterior of single-output BatchedMultiOutputGPyTorchModel (#185)

Bug fixes

  • Properly handle observation_noise kwarg for BatchedMultiOutputGPyTorchModels (#182)
  • Fix a issue where f_best was always max for NoisyExpectedImprovement (de8544a75b58873c449b41840a335f6732754c77)
  • Fix bug and numerical issues in initialize_q_batch (844dcd1dc8f418ae42639e211c6bb8e31a75d8bf)
  • Fix numerical issues with inv_transform for qMC sampling (#162)

Other

  • Bump GPyTorch minimum requirement to 0.3.3

[0.1.0] - April 30, 2019

First public beta release.