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Posterior, MCSampler & Closure Refactors, Entropy Search Acquisition Functions

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@saitcakmak saitcakmak released this 07 Dec 00:01
v0.8.0
7c1b2f5

Highlights

This release includes some backwards incompatible changes.

  • Refactor Posterior and MCSampler modules to better support non-Gaussian distributions in BoTorch (#1486).
    • Introduced a TorchPosterior object that wraps a PyTorch Distribution object and makes it compatible with the rest of Posterior API.
    • PosteriorList no longer accepts Gaussian base samples. It should be used with a ListSampler that includes the appropriate sampler for each posterior.
    • The MC acquisition functions no longer construct a Sobol sampler by default. Instead, they rely on a get_sampler helper, which dispatches an appropriate sampler based on the posterior provided.
    • The resample and collapse_batch_dims arguments to MCSamplers have been removed. The ForkedRNGSampler and StochasticSampler can be used to get the same functionality.
    • Refer to the PR for additional changes. We will update the website documentation to reflect these changes in a future release.
  • #1191 refactors much of botorch.optim to operate based on closures that abstract away how losses (and gradients) are computed. By default, these closures are created using multiply-dispatched factory functions (such as get_loss_closure), which may be customized by registering methods with an associated dispatcher (e.g. GetLossClosure). Future releases will contain tutorials that explore these features in greater detail.

New Features

  • Add mixed optimization for list optimization (#1342).
  • Add entropy search acquisition functions (#1458).
  • Add utilities for straight-through gradient estimators for discretization functions (#1515).
  • Add support for categoricals in Round input transform and use STEs (#1516).
  • Add closure-based optimizers (#1191).

Other Changes

  • Do not count hitting maxiter as optimization failure & update default maxiter (#1478).
  • BoxDecomposition cleanup (#1490).
  • Deprecate torch.triangular_solve in favor of torch.linalg.solve_triangular (#1494).
  • Various docstring improvements (#1496, #1499, #1504).
  • Remove __getitem__ method from LinearTruncatedFidelityKernel (#1501).
  • Handle Cholesky errors when fitting a fully Bayesian model (#1507).
  • Make eta configurable in apply_constraints (#1526).
  • Support SAAS ensemble models in RFFs (#1530).
  • Deprecate botorch.optim.numpy_converter (#1191).
  • Deprecate fit_gpytorch_scipy and fit_gpytorch_torch (#1191).

Bug Fixes

  • Enforce use of float64 in NdarrayOptimizationClosure (#1508).
  • Replace deprecated np.bool with equivalent bool (#1524).
  • Fix RFF bug when using FixedNoiseGP models (#1528).
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