Lenskit

This release is just to fix a build problem in 0.11.0 that prevented automatic package publication.

This release brings a number of functionality and performance improvements. Highlights include:

- Refactoring the `Bias` model and using it consistently instead of re-implementing pieces in matrix factorizers
- Support new ratings from a user in both ALS recommenders
- Fix crash when TensorFlow 1 is installed

The main-channel Conda packages for this release have disabled MKL support in macOS, due to environmental factors causing the build to fail. LensKit will still work fine in MKL environments, it just won't use its MKL-based k-NN acceleration on macOS. Linux and Windows are still unchanged. With 0.11, we will also begin publishing packages to `conda-forge`; we expect MKL acceleration to work in that environment.

What’s Changed

* Update actions/core for CI build (205) mdekstrand
* ALS: Refactor common matrix & fix tests (204) mdekstrand
* added new ratings for predict method in ImplicitMF (202) carlos10seg
* Remove BiasedMFPredictor in favor of Bias (201) mdekstrand
* Fix failures with unexpected parallel package installs (199) mdekstrand
* [DOC/FIX] Correction in ImplicitMF docstring (196) ShwetanshuSingh
* Add known-rating predictor (182) (184) carlos10seg
* Move Bias class into `bias` package (175) (183) carlos10seg
* Bump actions/core from 1.2.3 to 1.2.6 in /.github/actions/conda-env (194) dependabot
* Bump Numba support to 0.51 (186) mdekstrand
* Fix ALS run-time training (114) for empty rating series (187) carlos10seg
* Add run-time training to ALS BiasedMF (114) (173) carlos10seg
* Add transform_user and inverse_transform_user methods to bias. (181) carlos10seg

This release makes some improvements to multi-process support and item-item kNN resource use.

What’s Changed

* Manage random seeds in subprocesses (179) mdekstrand
* Support Numba 0.50 (178) mdekstrand
* Use parallel blocks for SciPy-based item-item CF training (177) mdekstrand
* Improve MP worker detection and disable item-item parallelism when run under MP (176) mdekstrand
* Use Hypothesis for testing and clean up tests (172) mdekstrand
* Remove unused math routines (171) mdekstrand

Highlights of this release are significant improvements to parallel processing (we no longer use joblib), shared memory, and our first TensorFlow integrations.

What’s Changed

* Reorganize and improve documentation (169) mdekstrand
* Improve RecListAnalysis performance and parallelize (164) mdekstrand
* Make persistence configurable & reduce open file count (165) mdekstrand
* Fix Python versions and conda environments in CI builds (163) mdekstrand
* Add TensorFlow support (159) mdekstrand
* Improve parallel configuration and docs (161) mdekstrand
* Use setup.cfg for all dev deps, including in Conda (160) mdekstrand
* Add fit_transform API to Bias (158) mdekstrand
* Remove dead code and add tests (157) mdekstrand
* Add scikit-learn SVD (156) mdekstrand
* Remove old sharing and file APIs (155) mdekstrand
* Use ProcessPoolExecutor instead of joblib (154) mdekstrand
* Add 'persist' API to sharing (153) mdekstrand

This release has some performance and improvements, including full Python 3.8, Pandas 1.0, and Numba 0.49 testing.

This is the **last** release we expect to use JobLib to parallelize batch prediction and recommendation. Any Python scripts that call the batch routines (`batch.predict`, `batch.recommend`, or `MultiEval`) need to be *import-protected*: their code needs to be in functions, and only invoked with a `__name__` guard:

if __name__ = '__main__':
do_stuff()

Unprotected scripts (where the code is just in the script, and runs when the script is imported as a module) will probably still work with LensKit 0.9, but will not work in the next version of LensKit. Jupyter notebooks should be just fine - when they are run, the IPython kernel is actually running, and it is properly protected.

What’s Changed

* Improving testing with minimal dependencies (151) mdekstrand
* Skip predictions when no ratings to predict (149) mdekstrand
* Use BinPickle for sharing (148) mdekstrand
* Support iterating over training iterations (144) mdekstrand
* Fix for Numba 0.49 compatibility (146) mdekstrand
* Use GitHub Actions for CI (143) mdekstrand
* Use declarative configuration for builds (142) mdekstrand
* Add model stores for batch multiprocessing (139) mdekstrand
* Improve top-N metric performance (140) mdekstrand
* Fix Conda Python 3.8 testing (138) mdekstrand
* Unify configuration points (137) mdekstrand
* Clean up RNG infrastructure (136) mdekstrand
* Add configurable RNG infrastructure (135) mdekstrand
* Remove deprecated and unused features (134) mdekstrand
* Version bumps - Pandas 1.0 and Python 3.8 (133) mdekstrand

This release cleans up dependency problems to make it easier to reliably install LensKit. We remove
some unused utility code that had compatibility problems.

- Remove `CSR.sort_values` - we were no longer using this function, and it failed to compile with Numba 0.46.
- Change dependency versions