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LensKit can work with any data in a :pypandas.DataFrame with the expected columns. LensKit algorithms expect a ratings frame to contain the following columns (in any order):

  • user, containing user identifiers. No requirements are placed on user IDs — if an algorithm requires something specific, such as contiguous 0-based identifiers for indexing into an array — it will use a :pypandas.Index to map them.
  • item, containing item identifiers. The same comments apply as for user.
  • rating, containing user ratings (if available). Implicit-feedback code will not require ratings.

‘Rating’ data can contain other columns as well, and is a catch-all for any user-item interaction data. Algorithms will document any non-standard columns they can make use of.

:pylenskit.algorithms.Recommender.fit can also accept additional data objects as keyword arguments, and algorithms that wrap other algorithms will pass this data through unchanged. Algorithms ignore extra data objects they receive. This allows you to build algorithms that train on data besides user-item interactions, such as user metadata or item content.

Data Loaders

lenskit.datasets

The :pylenskit.datasets module provides utilities for reading a variety of commonly-used LensKit data sets. It does not package or automatically download them, but loads them from a local directory where you have unpacked the data set. Each data set class or function takes a path parameter specifying the location of the data set.

The normal mode of operation for these utilities is to provide a class for the data set; this class then exposes the data set's data as attributes. These attributes are cached internally, so e.g. accessing :pyMovieLens.ratings twice will only load the data file once.

These data files have normalized column names to fit with LensKit's general conventions. These are the following:

  • User ID columns are called user.
  • Item ID columns are called item.
  • Rating columns are called rating.
  • Timestamp columns are called timestamp.

Other column names are unchanged. Data tables that provide information about specific things, such as a table of movie titles, are indexed by the relevant ID (e.g. :pyMovieLens.ratings is indexed by item).

Data sets supported:

  • MovieLens
  • ML100K
  • ML1M
  • ML10M

MovieLens Data Sets

The GroupLens research group provides several data sets extracted from the MovieLens service :citeHarper2015-cx. These can be downloaded from https://grouplens.org/datasets/movielens/.

MovieLens

ML100K

ML1M

ML10M