Gpflow

This release fixes a performance regression introduced in `2.5.0`. `2.5.0` used features of Python
that `tensorfow < 2.9.0` do not know how to compile, which negatively impacted performance.

Bug Fixes and Other Changes

* Fixed some bugs that prevented TensorFlow compilation and had negative performance impact. (1882)
* Various improvements to documentation. (1875, 1866, 1877, 1879)

Thanks to our Contributors

This release contains contributions from:

jesnie

Fix problem with release process of 2.5.0.

Bug Fixes and Other Changes

* Fix bug in release process.

Thanks to our Contributors

This release contains contributions from:

jesnie

The focus of this release has mostly been bumping the minimally supported versions of Python and
TensorFlow; and development of `gpflow.experimental.check_shapes`.

Breaking Changes

* Dropped support for Python 3.6. New minimum version is 3.7. (1803, 1859)
* Dropped support for TensorFlow 2.2 and 2.3. New minimum version is 2.4. (1803)
* Removed sub-package `gpflow.utilities.utilities`. It was scheduled for deletion in `2.3.0`.
Use `gpflow.utilities` instead. (1804)
* Removed method `Likelihood.predict_density`, which has been deprecated since March 24, 2020.
(1804)
* Removed property `ScalarLikelihood.num_gauss_hermite_points`, which has been deprecated since
September 30, 2020. (1804)

Known Caveats

* Further improvements to type hints - this may reveal new problems in your code-base if
you use a type checker, such as `mypy`. (1795, 1799, 1802, 1812, 1814, 1816)

Major Features and Improvements

* Significant work on `gpflow.experimental.check_shapes`.

- Support anonymous dimensions. (1796)
- Add a hook to let the user register shapes for custom types. (1798)
- Support `Optional` values. (1797)
- Make it configurable. (1810)
- Add accesors for setting/getting previously applied checks. (1815)
- Much improved error messages. (1822)
- Add support for user notes on shapes. (1836)
- Support checking all elements of collections. (1840)
- Enable stand-alone shape checking, without using a decorator. (1845)
- Support for broadcasts. (1849)
- Add support for checking the shapes of intermediate computations. (1853)
- Support conditional shapes. (1855)

* Significant speed-up of the GPR posterior objects. (1809, 1811)

* Significant improvements to documentation. Note the new home page:
https://gpflow.github.io/GPflow/index.html
(1828, 1829, 1830, 1831, 1833, 1841, 1842, 1856, 1857)

Bug Fixes and Other Changes

* Minor improvement to code clarity (variable scoping) in SVGP model. (1800)
* Improving mathematical formatting in docs (SGPR derivations). (1806)
* Allow anisotropic kernels to have negative length-scales. (1843)

Thanks to our Contributors

This release contains contributions from:

ltiao, uri.granta, frgsimpson, st--, jesnie

This release mostly focuses on make posterior objects useful for Bayesian Optimisation.
It also adds a new `experimetal` sub-package, with a tool for annotating tensor shapes.


Breaking Changes

* Slight change to the API of custom posterior objects.
`gpflow.posteriors.AbstractPosterior._precompute` no longer must return an `alpha` and an
`Qinv` - instead it returns any arbitrary tuple of `PrecomputedValue`s.
Correspondingly `gpflow.posteriors.AbstractPosterior._conditional_with_precompute` should no
longer try to access `self.alpha` and `self.Qinv`, but instead is passed the tuple of tensors
returned by `_precompute`, as a parameter. (1763, 1767)

* Slight change to the API of inducing points.
You should no longer override `gpflow.inducing_variables.InducingVariables.__len__`. Override
`gpflow.inducing_variables.InducingVariables.num_inducing` instead. `num_inducing` should return a
`tf.Tensor` which is consistent with previous behaviour, although the type previously was
annotated as `int`. `__len__` has been deprecated. (1766, 1792)

Known Caveats

* Type hints have been added in several places - this may reveal new problems in your code-base if
you use a type checker, such as `mypy`.
(1766, 1769, 1771, 1773, 1775, 1777, 1780, 1783, 1787, 1789)

Major Features and Improvements

* Add new posterior class to enable faster predictions from the VGP model. (1761)
* VGP class bug-fixed to work with variable-sized data. Note you can use
`gpflow.models.vgp.update_vgp_data` to ensure variational parameters are updated sanely. (1774).
* All posterior classes bug-fixed to work with variable data sizes, for Bayesian Optimisation.
(1767)

* Added `experimental` sub-package for features that are still under developmet.
* Added `gpflow.experimental.check_shapes` for checking tensor shapes.
(1760, 1768, 1782, 1785, 1788)

Bug Fixes and Other Changes

* Make `dataclasses` dependency conditional at install time. (1759)
* Simplify calculations of some `predict_f`. (1755)

Thanks to our Contributors

This release contains contributions from:

jesnie, tmct, joacorapela

This is a bug-fix release, primarily for the GPR posterior object.

Bug Fixes and Other Changes

* GPR posterior
* Fix the calculation in the GPR posterior object (1734).
* Fixes leading dimension issues with `GPRPosterior._conditional_with_precompute()` (1747).

* Make `gpflow.optimizers.Scipy` able to handle unused / unconnected variables. (1745).

* Build
* Fixed broken CircleCi build (1738).
* Update CircleCi build to use next-gen Docker images (1740).
* Fixed broken triggering of docs generation (1744).
* Make all slow tests depend on fast tests (1743).
* Make `make dev-install` also install the test requirements (1737).

* Documentation
* Fixed broken link in `README.md` (1736).
* Fix broken build of `cglb.ipynb` (1742).
* Add explanation of how to run notebooks locally (1729).
* Fix formatting in notebook on Heteroskedastic Likelihood (1727).
* Fix broken link in introduction (1718).

* Test suite
* Amends `test_gpr_posterior.py` so it will cover leading dimension uses.



Thanks to our Contributors

This release contains contributions from:

st--, jesnie, johnamcleod, Andrew878

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/SGPR models. (1696, 1711)
* Construct Parameters from other Parameters and retain properties. (1699)
* Add CGLB model (1706)

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)
* Fix SGPR derivation (1688)
* Fix tests which fail with TensorFlow 2.6.0 (1714)

Thanks to our Contributors

This release contains contributions from:

johnamcleod, st--, Andrew878, tadejkrivec, awav, avullo