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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

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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

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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

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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

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.

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

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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

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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

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Bug Fixes

Bugfix for creating the new posterior objects with PrecomputeCacheType.VARIABLE.

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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--

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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

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Improvements

  • Replace len(inducing_variable) with inducing_variable.num inducing property (#1594).
    Adds support for inducing variables with dynamically changing shape (compatibility with tf.function).
    (Resolves #1578.)

  • HeteroskedasticTFPConditional should construct tensors at class-construction, not at module-import time (#1598).