2025 Releases

2025 brought breaking changes to across the LensKit APIs to improve ergonomics, correctness-by-default, and flexibility. It also adopts SPEC0, a standard for supported versions of scientific Python libraries, and changes the LensKit version number scheme to “SemCalVer”. See Migrating from LensKit 0.x for information on how to upgrade your code.

2025.3.0 (in progress)

• Replace broken “softmax” sampler with a proper stochastic sampler (⛙ 667).

• sample_negatives() now accepts "popular" as an alias for "popularity".

• lenskit.basic now exports the configuration classes for basic algorithms (⛙ 672).

• Several bug fixes for logging in niche setups (including ray clusters) (⛙ 673).

2025.2.0

Some small quality-of-life improvements (and removing invalid API compat).

• Add lenskit.pipeline.PipelineCache to share components between pipelines (⛙ 605).

• Only warn once for users without test data in bulk analysis (⛙ 664, 🐞 663).

• Allow a Pandas data frame to be passed as the test data to the batch recommender (⛙ 660).

  Note

  This removes extra keyword arguments from the convenience batch.recommend, etc. functions that were leftovers from LensKit 0.14 and no longder did anything.

• Support auto-detecting keys in lenskit.data.ItemListCollection.from_df() (⛙ 659).

2025.1.1

LensKit 2025.1.1 was released March 7, 2025.

The changes in this release are too numerous and fundamental to fully document in traditional release notes. See the following for release update documentation:

• Migrating from LensKit 0.x for conceptual changes and how to upgrade your code.

• The notes below for behavior changes (e.g. new defaults, new metric capabilities), and small bits not covered in the migration guide.

• The full changelog in the Git history and issue/PR milestone.

Breaking Changes

LensKit 2025 has many breaking changes, with the migration guide (Migrating from LensKit 0.x) documenting the major ones. Below are some smaller ones not covered by that document:

• Where Pandas data frames are still used, the standard user and item columns have been renamed to user_id and item_id respectively, with user_num and item_num for 0-based user and item numbers. This is to remove ambiguity about how users and items are being referenced.

• The Popular recommender has been removed in favor of PopScore.

• The DCG metric has been removed, as it is basically never used and was not useful as a part of the NDCG implementation.

New Features (incremental)

• Many LensKit components (batch running, model training, etc.) now report progress the progress API in lenskit.logging.progress, and can be connected to Jupyter or Rich.

• Added RBP top-N metric (⛙ 334).

• Added command-line tool to fetch datasets (⛙ 347).

Metric Behavior Changes

Important

Some LensKit metric default has been changed; this results in values different from those computed by previous versions, either more correct or more consistent with common practice.

• The NDCG metric now defaults to ignore rating values.

Model Behavior Changes

Most models will exhibit some changes, hopefully mostly in performance, due to moving to PyTorch. There are some deliberate behavior changes in this new version, however, documented here.

• ALS models only use Cholesky decomposition (previously selected with the erroneously-named method="lu" option); conjugate gradient and coordinate descent are no longer available. Cholesky decomposition is faster on PyTorch than it was with Numba, and is easier to maintain.

• The default minimum similarity for UserUser is now \(10^{-6}\).

• k-NN algorithms no longer support negative similarities; min_sim is clamped to be at least the smallest normal in 32-bit floating point (\(1.75 \times 10^{-38}\)).

• The implicit bridge algorithms no longer look at rating values when they are present.

• Bias is no longer optional for BiasedMFScorer and FunkSVD; both are inherently biased models, and FunkSVD is not commonly used.

• lenskit.hpf.HPF no longer uses ratings as synthetic counts by default.

Bug Fixes

• Fixed bug in NDCG list truncation (🐞 309, ⛙ 312).

• Corrected documentation errors for recall() and hit() (⛙ 369 by @lukas-wegmeth).

Dependencies and Maintenance

• Bumped minimum supported dependencies as per SPEC0 (Python 3.11, NumPy 1.24, Pandas 2.0, SciPy 1.10).

• Added support for Pandas 2 (⛙ 364) and Python 3.12.

• Improved Apple testing to include vanilla Python and Apple Silicon (⛙ 366).

• Updated build environment, dependency setup, taskrunning, and CI to more consistent and maintainable.

• Removed legacy random code and SeedBank usage in favor of SPEC 7 (see Random Seeds).

• Code is now auto-formatted with Ruff.