Release Notes

0.6.0 (In Progress)

See the GitHub milestone for a summary of what’s happening!

  • The save and load methods on algorithms have been removed. Just pickle fitted models to save their data. This is what SciKit does, see no need to deviate.
  • Top-N evaluation has been completely revamped to make it easier to correctly implement and run evaluation metrics. Batch recommend no longer attaches ratings to recommendations.
  • Batch recommend & predict functions now take nprocs as keyword-only.
  • Several bug fixes and testing improvements

Internal Changes

These changes should not affect you if you are only consuming LensKit’s algorithm and evaluation capabilities.

  • Rewrite the CSR class to be more ergonomic from Python, at the expense of making the NumPy jitclass indirect. It is available in the .N attribute. Big improvement: it is now picklable.

0.5.0

LensKit 0.5.0 modifies the algorithm APIs to follow the SciKit design patterns instead of our previous custom patterns. Highlights of this change:

  • Algorithms are trained in-place — we no longer have distinct model objects.
  • Model data is stored as attributes on the algorithm object that end in _.
  • Instead of writing model = algo.train_model(ratings), call algo.fit(ratings).

We also have some new capabilities:

  • Ben Frederickson’s Implicit library

0.3.0

A number of improvements, including replacing Cython/OpenMP with Numba and adding ALS.

0.2.0

A lot of fixes to get ready for RecSys.

0.1.0

Hello, world!