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](https://gpflow.github.io/GPflow/2.6.0/notebooks/advanced/varying_noise.html), 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](https://gpflow.github.io/GPflow/2.6.0/api/gpflow/experimental/check_shapes/index.html#speed-and-interactions-with-tf-function) 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](https://gpflow.github.io/GPflow/2.6.0/notebooks/advanced/varying_noise.html).
- `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 `InducingVariable`s.
- 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