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Use HeteroskedasticTFP likelihood
Release notes for all past releases are available in the ['Releases' section](https://github.com/GPflow/GPflow/releases) of the GPflow GitHub Repo. [HOWTO_RELEASE.md](HOWTO_RELEASE.md) explains just that. # Release x.y.z (template for future releases) <INSERT SMALL BLURB ABOUT RELEASE FOCUS AREA AND POTENTIAL TOOLCHAIN CHANGES> ## Breaking Changes * <DOCUMENT BREAKING CHANGES HERE> * <THIS SECTION SHOULD CONTAIN API AND BEHAVIORAL BREAKING CHANGES> ## Known Caveats * <CAVEATS REGARDING THE RELEASE (BUT NOT BREAKING CHANGES).> * <ADDING/BUMPING DEPENDENCIES SHOULD GO HERE> * <KNOWN LACK OF SUPPORT ON SOME PLATFORM SHOULD GO HERE> ## Major Features and Improvements * <INSERT MAJOR FEATURE HERE, USING MARKDOWN SYNTAX> * <IF RELEASE CONTAINS MULTIPLE FEATURES FROM SAME AREA, GROUP THEM TOGETHER> ## Bug Fixes and Other Changes * <SIMILAR TO ABOVE SECTION, BUT FOR OTHER IMPORTANT CHANGES / BUG FIXES> * <IF A CHANGE CLOSES A GITHUB ISSUE, IT SHOULD BE DOCUMENTED HERE> * <NOTES SHOULD BE GROUPED PER AREA> ## Thanks to our Contributors This release contains contributions from: <INSERT>, <NAME>, <HERE>, <USING>, <GITHUB>, <HANDLE> # Release 2.2.2 (next upcoming release in progress) <INSERT SMALL BLURB ABOUT RELEASE FOCUS AREA AND POTENTIAL TOOLCHAIN CHANGES> ## Known Caveats * <CAVEATS REGARDING THE RELEASE (BUT NOT BREAKING CHANGES).> * <ADDING/BUMPING DEPENDENCIES SHOULD GO HERE> * <KNOWN LACK OF SUPPORT ON SOME PLATFORM SHOULD GO HERE> ## Major Features and Improvements * Refactor posterior base class to support other model types. (#1695) * Add new posterior class to enable faster predictions from the GPR model. (#1696) * Construct Parameters from other Parameters and retain properties. (#1699) ## Bug Fixes and Other Changes * Fix unit test failure when using TensorFlow 2.5.0 (#1684) * Upgrade black formatter to version 20.8b1 (#1694) * Remove erroneous DeprecationWarnings (#1693) ## Thanks to our Contributors This release contains contributions from: johnamcleod, st--, Andrew878 # Release 2.2.1 Bugfix for creating the new posterior objects with `PrecomputeCacheType.VARIABLE`. # Release 2.2.0 The main focus of this release is the new "Posterior" object introduced by PR #1636, which allows for a significant speed-up of post-training predictions with the `SVGP` model (partially resolving #1599). * For end-users, by default nothing changes; see Breaking Changes below if you have written your own _implementations_ of `gpflow.conditionals.conditional`. * After training an `SVGP` model, you can call `model.posterior()` to obtain a Posterior object that precomputes all quantities not depending on the test inputs (e.g. Choleskty of Kuu), and provides a `posterior.predict_f()` method that reuses these cached quantities. `model.predict_f()` computes exactly the same quantities as before and does **not** give any speed-up. * `gpflow.conditionals.conditional()` forwards to the same "fused" code-path as before. ## Breaking Changes * `gpflow.conditionals.conditional.register` is deprecated and should not be called outside of the GPflow core code. If you have written your own implementations of `gpflow.conditionals.conditional()`, you have two options to use your code with GPflow 2.2: 1. Temporary work-around: Instead of `gpflow.models.SVGP`, use the backwards-compatible `gpflow.models.svgp.SVGP_deprecated`. 2. Convert your conditional() implementation into a subclass of `gpflow.posteriors.AbstractPosterior`, and register `get_posterior_class()` instead (see the "Variational Fourier Features" notebook for an example). ## Known Caveats * The Posterior object is currently only available for the `SVGP` model. We would like to extend this to the other models such as `GPR`, `SGPR`, or `VGP`, but this effort is beyond what we can currently provide. If you would be willing to contribute to those efforts, please get in touch! * The Posterior object does not currently provide the `GPModel` convenience functions such as `predict_f_samples`, `predict_y`, `predict_log_density`. Again, if you're willing to contribute, get in touch! ## Thanks to our Contributors This release contains contributions from: stefanosele, johnamcleod, st-- # Release 2.1.5 ## Known Caveats * GPflow requires TensorFlow >= 2.2. ## Deprecations * The `gpflow.utilities.utilities` submodule has been deprecated and will be removed in GPflow 2.3. User code should access functions directly through `gpflow.utilities` instead (#1650). ## Major Features and Improvements * Improves compatibility between monitoring API and Scipy optimizer (#1642). * Adds `_add_noise_cov` method to GPR model class to make it more easily extensible (#1645). ## Bug Fixes * Fixes a bug in ModelToTensorBoard (#1619) when `max_size=-1` (#1619) * Fixes a dynamic shape issue in the quadrature code (#1626). * Fixes #1651, a bug in `fully_correlated_conditional_repeat` (#1652). * Fixes #1653, a bug in the "fallback" code path for multioutput Kuf (#1654). * Fixes a bug in the un-whitened code path for the fully correlated conditional function (#1662). * Fixes a bug in `independent_interdomain_conditional` (#1663). * Fixes an issue with the gpflow.config API documentation (#1664). * Test suite * Fixes the test suite for TensorFlow 2.4 / TFP 0.12 (#1625). * Fixes mypy call (#1637). * Fixes a bug in test_method_equivalence.py (#1649). ## Thanks to our Contributors This release contains contributions from: johnamcleod, st--, vatsalaggarwal, sam-willis, vdutor