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Max-value entropy search, multi-fidelity (cost-aware) optimization
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@Balandat Balandat released this 20 Dec 21:57
v0.2.0
5272700

This release adds the popular Max-value Entropy Search (MES) acquisition function, as well as support for multi-fidelity Bayesian optimization via both the Knowledge Gradient (KG) and MES.

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 (f838eac).
  • Add project_to_target_fidelity and expand_trace_observations utilities for use in multi-fidelity optimization (1ca12ac).

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 (671c93a).
  • 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).
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