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Robust Multi-Objective BO, Multi-Objective Multi-Fidelity BO, Scalable Constrained BO, Improvements to Ax Integration

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@saitcakmak saitcakmak released this 15 Jul 17:08
v0.6.5
f998e06

Compatibility

  • Require PyTorch >=1.10 (#1293).
  • Require GPyTorch >=1.7 (#1293).

New Features

  • Add MOMF (Multi-Objective Multi-Fidelity) acquisition function (#1153).
  • Support PairwiseLogitLikelihood and modularize PairwiseGP (#1193).
  • Add in transformed weighting flag to Proximal Acquisition function (#1194).
  • Add FeasibilityWeightedMCMultiOutputObjective (#1202).
  • Add outcome_transform to FixedNoiseMultiTaskGP (#1255).
  • Support Scalable Constrained Bayesian Optimization (#1257).
  • Support SaasFullyBayesianSingleTaskGP in prune_inferior_points (#1260).
  • Implement MARS as a risk measure (#1303).
  • Add MARS tutorial (#1305).

Other Changes

  • Add Bilog outcome transform (#1189).
  • Make get_infeasible_cost return a cost value for each outcome (#1191).
  • Modify risk measures to accept List[float] for weights (#1197).
  • Support SaasFullyBayesianSingleTaskGP in prune_inferior_points_multi_objective (#1204).
  • BotorchContainers and BotorchDatasets: Large refactor of the original TrainingData API to allow for more diverse types of datasets (#1205, #1221).
  • Proximal biasing support for multi-output SingleTaskGP models (#1212).
  • Improve error handling in optimize_acqf_discrete with a check that choices is non-empty (#1228).
  • Handle X_pending properly in FixedFeatureAcquisition (#1233, #1234).
  • PE and PLBO support in Ax (#1240, #1241).
  • Remove model.train call from get_X_baseline for better caching (#1289).
  • Support inf values in bounds argument of optimize_acqf (#1302).

Bug Fixes

  • Update get_gp_samples to support input / outcome transforms (#1201).
  • Fix cached Cholesky sampling in qNEHVI when using Standardize outcome transform (#1215).
  • Make task_feature as required input in MultiTaskGP.construct_inputs (#1246).
  • Fix CUDA tests (#1253).
  • Fix FixedSingleSampleModel dtype/device conversion (#1254).
  • Prevent inappropriate transforms by putting input transforms into train mode before converting models (#1283).
  • Fix sample_points_around_best when using 20 dimensional inputs or prob_perturb (#1290).
  • Skip bound validation in optimize_acqf if inequality constraints are specified (#1297).
  • Properly handle RFFs when used with a ModelList with individual transforms (#1299).
  • Update PosteriorList to support deterministic-only models and fix event_shape (#1300).

Documentation

  • Add a note about observation noise in the posterior in fit_model_with_torch_optimizer notebook (#1196).
  • Fix custom botorch model in Ax tutorial to support new interface (#1213).
  • Update MOO docs (#1242).
  • Add SMOKE_TEST option to MOMF tutorial (#1243).
  • Fix ModelListGP.condition_on_observations/fantasize bug (#1250).
  • Replace space with underscore for proper doc generation (#1256).
  • Update PBO tutorial to use EUBO (#1262).
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