Add support for batch qMC sampling #510
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Coverage 100.00% 100.00%
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Balandat
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Aug 7, 2020
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Thanks for the feedback! I made the changes |
Balandat
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Aug 7, 2020
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Summary: #### 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` (c80c4fd) * 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, 3621198) * Acqusiition function constructors now accept catch-all `**kwargs` options (#478, e5b6935) * Use `psd_safe_cholesky` in `qMaxValueEntropy` for better numerical stabilty (#518) * Added `WeightedMCMultiOutputObjective` (81d91fd) * 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) Pull Request resolved: #542 Reviewed By: sdaulton Differential Revision: D23645619 Pulled By: Balandat fbshipit-source-id: 0384f266cbd517aacd5778a6e2680336869bb31c
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Motivation
See #507. Batch qMC samples are useful for generating raw_samples while optimizing batch GP models / acquisition functions.
Have you read the Contributing Guidelines on pull requests?
Yes
Test Plan
Added unit tests to verify that the samples have proper shape (passed).
Used the script in #507 (comment) to verify that each batch of samples has low discrepancy.