Algorithm Implementation Tips

Implementing algorithms is fun, but there are a few things that are good to keep in mind.

In general, development follows the following:

1. Correct

2. Clear

3. Fast

In that order. Further, we always want LensKit to be usable in an easy fashion. Code implementing algorithms, however, may be quite complex in order to achieve good performance.

Performance

We use Numba to optimize critical code paths and provide parallelism in a number of cases, such as ALS training. See the ALS source code for examples.

We also use the CSR package for sparse matrices that are usable from Numba-accelerated code, and to provide unified access to important sparse matrix operations that use MKL acceleration when available. Previous versions of LensKit included the MKL code directly, but we have moved that logic over into CSR.

If you are working on an algorithm implementation that needs access to additional MKL operations, please add the relevant operations to CSR to keep LensKit pure Python + Numba. We do not have plans to re-add the MKL wrapper logic to the LensKit core.

Pickling and Sharing

LensKit uses binpickle quite a bit to save and reload models and to share model data between concurrent processes. This generally just works, and you don’t need to implement any particular save/load logic in order to have your algorithm be savable and sharable.

There are a few exceptions, though.

If your algorithm updates state after fitting, this should not be pickled. An example of this would be caching predictions or recommendations to save time in subsequent calls. Only the model parameters and estimated parameters should be pickled. If you have caches or other ephemeral structures, override __getstate__ and __setstate__ to exclude them from the saved data and to initialize caches to empty values on unpickling.

If your model excludes secondary data structures from pickling, such as a reverse index of user-item interactions, then you should only exclude them when pickling for serialization. When pickling for model sharing (see lenskit.sharing.in_share_context()), you should include the derived structures so they can also be shared.

If your algorithm uses subsidiary models as a part of the training process, but does not need them for prediction or recommendation, then consider overriding __getstate__ to remove the underlying model or replace it with a cloned copy (with lenskit.util.clone()) to reduce serialized disk space (and deserialized memory use).

Random Number Generation

LensKit uses seedbank for managing RNG seeds and constructing random number generation.

In general, algorithms using randomization should have an rng_spec parameter that takes a seed or RNG, and pass this to seedbank.numpy_rng() to get a random number generator. Algorithms that use randomness at predict or recommendation time, not just training time, should support the value 'user' for the rng parameter, and if it is passed, derive a new seed for each user using seedbank.derive_seed() to allow reproducibility in the face of parallelism for common experimental designs. lenskit.util.derivable_rng() automates this logic.