Support batched best_f #487
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Summary: For multi-step with batched costs we need to support a batched `best_f`. Reviewed By: danielrjiang Differential Revision: D22592699 fbshipit-source-id: 419b18c699dfd93ae489d8e6dc54fe675b6d21e4
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This pull request was exported from Phabricator. Differential Revision: D22592699 |
Codecov Report
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This pull request has been merged in adf9c38. |
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Sep 11, 2020
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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Feb 5, 2021
Summary: Pull Request resolved: facebook/Ax#487 Pull Request resolved: #664 This was requested in #663. Reviewed By: qingfeng10 Differential Revision: D25938012 fbshipit-source-id: c47db2ec449eb598cec383965e95622beefeddef
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Aug 28, 2023
…ureMCObjective` and `squeeze_last_dim` (#1994) Summary: ## Motivation * `utils.transforms.squeeze_last_dim` was deprecated prior to 0.7.0 (#487 ), so since we are at 0.9.2 it can be deleted. * The `weights` argument of `acquisition.risk_measures.RiskMeasureMCObjective` was deprecated in 0.7.2 (#1400 ). Technically, the deprecation message only says that `weights` should be None rather than that it should not be passed, but I think it's okay to just remove it. Where it was not the last argument, I added a `*` to require that subsequent arguments be keyword-only. * ### Have you read the [Contributing Guidelines on pull requests](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md#pull-requests)? Yes Pull Request resolved: #1994 Test Plan: Existing units ## Related PRs #487, #1400 Reviewed By: Balandat Differential Revision: D48738152 Pulled By: esantorella fbshipit-source-id: 55bee2937e55b4d993c8d1cfc68330e1eb54a8ea
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Summary: For multi-step with batched costs we need to support a batched
best_f.Reviewed By: danielrjiang
Differential Revision: D22592699