Release 2.6.1

This is a bug-fixes release, due to problems with model saving in 2.6.0.

Breaking Changes

• Removed gpflow.utilities.ops.cast. Use tf.cast instead.

Bug Fixes and Other Changes

• Fixed bug related to tf.saved_model and methods wrapped in @check_shapes.
• Some documentation formatting fixes.

Thanks to our Contributors

This release contains contributions from:

jesnie

Release 2.6.0

The major theme for this release is heteroskedastic likelihoods. Changes have unfortunately caused some breaking changes, but makes it much easier to use heteroskedastic likelihoods, either by plugging together built-in GPflow classes, or when writing your own. See our updated notebook, for examples on how to use this.

Breaking Changes

• All likelihood methods now take an extra X argument. If you have written custom likelihoods or you have custom code calling likelihoods directly you will need to add this extra argument.
• On the CGLB model the xnew parameters has changed name to Xnew, to be consistent with the other models.
• On the GPLVM model the variance returned by predict_f with full_cov=True has changed shape from [batch..., N, N, P] to [batch..., P, N, N] to be consistent with the other models.
• gpflow.likelihoods.Gaussian.DEFAULT_VARIANCE_LOWER_BOUND has been replaced with gpflow.likelihoods.scalar_continuous.DEFAULT_LOWER_BOUND.
• Change to InducingVariables API. InducingVariables must now have a shape property.
• gpflow.experimental.check_shapes.get_shape.register has been replaced with gpflow.experimental.check_shapes.register_get_shape.
• check_shapes will no longer automatically wrap shape checking in tf.compat.v1.flags.tf_decorator.make_decorator. This is likely to affect you if you use check_shapes with custom Keras models. If you require the decorator you can manually enable it with check_shapes(..., tf_decorator=True).

Known Caveats

• Shape checking is now, by default, disabled within tf.function. Use set_enable_check_shapes to change this behaviour. See the API documentation for more details.

Major Features and Improvements

• Improved handling of variable noise
  • All likelihood methods now take an X argument, allowing you to easily implement heteroskedastic likelihoods.
  • The Gaussian likelihood can now be parametrized by either a variance or a scale
  • Some existing likelihoods can now take a function (of X) instead of a parameter, allowing them to become heteroskedastic. The parameters are:
    • Gaussian variance
    • Gaussian scale
    • StudentT scale
    • Gamma shape
    • Beta scale
  • The GPR and SGPR can now be configured with a custom Gaussian likelihood, allowing you to make them heteroskedastic.
  • See the updated notebook.
  • gpflow.mean_functions has been renamed gpflow.functions, but with an alias, to avoid breaking changes.
• gpflow.experimental.check_shapes
  • Can now be in three different states - ENABLED, EAGER_MODE_ONLY, and DISABLE. The default is EAGER_MODE_ONLY, which only performs shape checks when the code is not compiled. Compiling the shape checking code is a major bottleneck and this provides a significant speed-up for performance sensitive parts of the code.
  • Now supports multiple variable-rank dimensions at the same time, e.g. cov: [n..., n...].
  • Now supports single broadcast dimensions to have size 0 or 1, instead of only 1.
  • Now supports variable-rank dimensions to be broadcast, even if they're not leading.
  • Now supports is None and is not None as checks for conditional shapes.
  • Now uses custom function register_get_shape instead of get_shape.register, for better compatibility with TensorFlow.
  • Now supports checking the shapes of InducingVariables.
  • Now adds documentation to function arguments that has declared shapes, but no other documentation.
  • All of GPflow is now consistently shape-checked.
• All built-in kernels now consistently support broadcasting.

Bug Fixes and Other Changes

• Tested with TensorFlow 2.10.
• Add support for Apple Silicon Macs (arm64) via the tensorflow-macos dependency. (#1850)
• New implementation of GPR and SGPR posterior objects. This primarily improves numerical stability. (#1960)
  • For the GPR this is also a speed improvement when using a GPU.
  • For the SGPR this is a mixed bag, performance-wise.
• Improved checking and error reporting for the models than do not support full_cov and full_output_cov.
• Documentation improvements:
  • Improved MCMC notebook.
  • Deleted notebooks that had no contents.
  • Fixed some broken formatting.

Thanks to our Contributors

This release contains contributions from:

jesnie, corwinpro, st--, vdutor

Release 2.5.2

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

Release 2.5.1

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

Release 2.5.0

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

Release 2.4.0

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 PrecomputedValues. 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

Release 2.3.1

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

Bug Fixes

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--