0.6.0 (In Progress)¶
See the GitHub milestone for a summary of what’s happening!
loadmethods 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
- Several bug fixes and testing improvements
These changes should not affect you if you are only consuming LensKit’s algorithm and evaluation capabilities.
- Rewrite the
CSRclass to be more ergonomic from Python, at the expense of making the NumPy jitclass indirect. It is available in the
.Nattribute. Big improvement: it is now picklable.
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
We also have some new capabilities:
- Ben Frederickson’s Implicit library
A number of improvements, including replacing Cython/OpenMP with Numba and adding ALS.
A lot of fixes to get ready for RecSys.