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Improved Multi-Objective Optimization, Support for categorical/mixed domains, robust/risk-aware optimization, efficient MTGP sampling
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@Balandat Balandat released this 29 Jun 19:31
v0.5.0
01c4707

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 (913aa0e, #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).
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