Gpflow

Latest version: v2.9.1

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2.2.1

Bug Fixes

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

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--

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

2.1.4

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).

2.1.3

Improvements
* kullback_leiblers.gauss_kl: when running TensorFlow >= 2.2, we avoid a tf.tile() that was a workaround for broadcasting issues in TensorFlow <= 2.1; the workaround is now only executed when running in TensorFlow 2.1 (1585).

Bugfixes

* model.predict_y/predict_log_density now explicitly raise NotImplementedError when passed full_(output_)cov=True (1582). Actual implementation of the full_(output_)cov=True case remains an open issue (1461).
* bugfix for conditionals.multioutput.util.independent_interdomain_conditional (1583).

Docs and infrastructure
* CI/infrastructure updates (1580, 1581, 1587, 1592).
* heteroskedastic notebook cleaned up, links added (1579).
* intro_to_gpflow2 notebook updated to account for saved_model now working (1586).

2.1.2

New features and improvements
* Likelihoods now use the new Quadrature class (1566, 1571)
* Multi-latent likelihoods for heteroskedastic models (1559)
* isort for consistent imports (1572)

Bugfixes
* Fix-up to our workaround for https://github.com/tensorflow/probability/issues/547 for tensorflow-probability==0.11.1 (#1574)
* Add missing `packaging` dependency (1568)

Deprecations
* You should no longer use `likelihood.num_gauss_hermite_points`. Interact with its `quadrature` attribute instead.

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