Model Input Standardization Using TrainingData
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TrainingData in BoTorch CodebaseTrainingData
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Summary: Pull Request resolved: pytorch#477 Different GP models take different kwargs as inputs into their constructors. To standardize the inputs, we create a `TrainingData` dataclass in conjunction with a classmethod `construct_inputs()`. Reviewed By: Balandat Differential Revision: D22395030 fbshipit-source-id: e58bead479d0b649552145c947c86caa33e341a2
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Summary: Pull Request resolved: pytorch#477 Different GP models take different kwargs as inputs into their constructors. To standardize the inputs, we create a `TrainingData` dataclass in conjunction with a classmethod `construct_inputs()`. Reviewed By: Balandat Differential Revision: D22395030 fbshipit-source-id: e4e7b3c4e132cf73527a24ff45318c5eb54512f2
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Summary: Pull Request resolved: pytorch#477 Different GP models take different kwargs as inputs into their constructors. To standardize the inputs, we create a `TrainingData` dataclass in conjunction with a classmethod `construct_inputs()`. Reviewed By: Balandat Differential Revision: D22395030 fbshipit-source-id: be89da3e2878993d8ba8972e48712762e9c3ccc8
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Summary: Pull Request resolved: pytorch#477 Different GP models take different kwargs as inputs into their constructors. To standardize the inputs, we create a `TrainingData` dataclass in conjunction with a classmethod `construct_inputs()`. Reviewed By: Balandat Differential Revision: D22395030 fbshipit-source-id: eae9a65cb1e0142c2d8e19757b0b813f2cbd7b49
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Summary: Pull Request resolved: pytorch#477 Different GP models take different kwargs as inputs into their constructors. To standardize the inputs, we create a `TrainingData` dataclass in conjunction with a classmethod `construct_inputs()`. Reviewed By: Balandat Differential Revision: D22395030 fbshipit-source-id: c94b342d763e24c44a71fe2beb0483b2aead2d7b
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Summary: Pull Request resolved: pytorch#477 Different GP models take different kwargs as inputs into their constructors. To standardize the inputs, we create a `TrainingData` dataclass in conjunction with a classmethod `construct_inputs()`. Reviewed By: Balandat Differential Revision: D22395030 fbshipit-source-id: 38e6283a2b86fdfc69060eaa579d5b1d152c7475
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Codecov Report
@@ Coverage Diff @@
## master #477 +/- ##
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Coverage 100.00% 100.00%
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Files 83 84 +1
Lines 5205 5225 +20
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+ Hits 5205 5225 +20
Continue to review full report at Codecov.
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This pull request has been merged in 10a71ae. |
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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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Summary: Different GP models take different kwargs as inputs into their constructors. To standardize the inputs, we create a
TrainingDatadataclass in conjunction with a classmethodconstruct_inputs().Differential Revision: D22395030