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Changelog for v0.7.2 (#1424)
Summary: Pull Request resolved: #1424 Reviewed By: mpolson64 Differential Revision: D39853181 Pulled By: mpolson64 fbshipit-source-id: 2a04b5918cbddf654f7f36561915ac87c893cadc
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CHANGELOG.md

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The release log for BoTorch.
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## [0.7.2] - Sep 27, 2022
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#### New Features
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* A full refactor of model fitting methods (#1134).
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* This introduces a new `fit_gpytorch_mll` method that multiple-dispatches
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on the model type. Users may register custom fitting routines for different
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combinations of MLLs, Likelihoods, and Models.
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* Unlike previous fitting helpers, `fit_gpytorch_mll` does **not** pass
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`kwargs` to `optimizer` and instead introduces an optional `optimizer_kwargs`
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argument.
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* When a model fitting attempt fails, `botorch.fit` methods restore modules to their
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original states.
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* `fit_gpytorch_mll` throws a `ModelFittingError` when all model fitting attempts fail.
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* Upon returning from `fit_gpytorch_mll`, `mll.training` will be `True` if fitting failed
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and `False` otherwise.
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* Allow custom bounds to be passed in to `SyntheticTestFunction` (#1415).
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#### Deprecations
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* Deprecate weights argument of risk measures in favor of a `preprocessing_function` (#1400),
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* Deprecate `fit_gyptorch_model`; to be superseded by `fit_gpytorch_mll`.
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#### Other Changes
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* Support risk measures in MOO input constructors (#1401).
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#### Bug Fixes
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* Fix fully Bayesian state dict loading when there are more than 10 models (#1405).
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* Fix `batch_shape` property of `SaasFullyBayesianSingleTaskGP` (#1413).
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* Fix `model_list_to_batched` ignoring the `covar_module` of the input models (#1419).
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## [0.7.1] - Sep 13, 2022
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#### Compatibility

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