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:
Correct
Clear
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.
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.