Algorithm Interfaces#

LKPY’s batch routines and utility support for managing algorithms expect algorithms to implement consistent interfaces. This page describes those interfaces.

The interfaces are realized as abstract base classes with the Python abc module. Implementations must be registered with their interfaces, either by subclassing the interface or by calling abc.ABCMeta.register().

Serialization#

Like SciKit models, all LensKit algorithms support pickle, and this is how we recommend saving models to disk for later use.

Todo

It would be good to support e.g. safetensors.

Base Algorithm#

Algorithms follow the SciKit fit-predict paradigm for estimators, except they know natively how to work with Pandas objects.

The Algorithm interface defines common methods.

class lenskit.algorithms.Algorithm#

Bases: object

Base class for LensKit algorithms. These algorithms follow the SciKit design pattern for estimators.

Canonical:

lenskit.Algorithm

IGNORED_PARAMS = []#

Names of parameters to ignore in get_params().

EXTRA_PARAMS = []#

Names of extra parameters to include in get_params(). Useful when the constructor takes **kwargs.

abstract fit(ratings, **kwargs)#

Train a model using the specified ratings (or similar) data.

Parameters:
  • ratings (pandas.DataFrame) – The ratings data.

  • kwargs – Additional training data the algorithm may require. Algorithms should avoid using the same keyword arguments for different purposes, so that they can be more easily hybridized.

Returns:

The algorithm object.

get_params(deep=True)#

Get the parameters for this algorithm (as in scikit-learn). Algorithm parameters should match constructor argument names.

The default implementation returns all attributes that match a constructor parameter name. It should be compatible with sklearn.base.BaseEstimator.get_params() method so that LensKit alogrithms can be cloned with sklearn.base.clone() as well as lenskit.util.clone().

Returns:

the algorithm parameters.

Return type:

dict

Recommendation#

The Recommender interface provides an interface to generating recommendations. Not all algorithms implement it; call Recommender.adapt() on an algorithm to get a recommender for any algorithm that at least implements Predictor. For example:

pred = Bias(damping=5)
rec = Recommender.adapt(pred)

If the algorithm already implements Recommender, it is returned, so it is safe to always call Recommender.adapt() before fitting an algorithm you will need for top-N recommendations to mak sure it is suitable.

class lenskit.algorithms.Recommender#

Bases: Algorithm

Recommends lists of items for users.

abstract recommend(user, n=None, candidates=None, ratings=None)#

Compute recommendations for a user.

Parameters:
  • user – the user ID

  • n (int) – the number of recommendations to produce (None for unlimited)

  • candidates (array-like) – The set of valid candidate items; if None, a default set will be used. For many algorithms, this is their CandidateSelector.

  • ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.

Returns:

a frame with an item column; if the recommender also produces scores, they will be in a score column.

Return type:

pandas.DataFrame

classmethod adapt(algo)#

Ensure that an algorithm is a Recommender. If it is not a recommender, it is wrapped in a lenskit.basic.TopN with a default candidate selector.

Note

Since 0.6.0, since algorithms are fit directly, you should call this method before calling Algorithm.fit(), unless you will always be passing explicit candidate sets to recommend().

Parameters:

algo (Predictor) – the underlying rating predictor.

Candidate Selection#

Some recommenders use a candidate selector to identify possible items to recommend. These are also treated as algorithms, mainly so that they can memorize users’ prior ratings to exclude them from recommendation.

class lenskit.algorithms.CandidateSelector#

Bases: Algorithm

Select candidates for recommendation for a user, possibly with some additional ratings.

UnratedItemCandidateSelector is the default and most common implementation of this interface.

abstract candidates(user, ratings=None)#

Select candidates for the user.

Parameters:
  • user – The user key or ID.

  • ratings (pandas.Series or array-like) – Ratings or items to use instead of whatever ratings were memorized for this user. If a pandas.Series, the series index is used; if it is another array-like it is assumed to be an array of items.

static rated_items(ratings)#

Utility function for converting a series or array into an array of item IDs. Useful in implementations of candidates().

Rating Prediction#

The Predictor class impelemnts ‘rating prediction’, as well as any other personalized item scoring that may not be predictions of actual ratings. Most algorithms actually implement this interface.

class lenskit.algorithms.Predictor#

Bases: Algorithm

Predicts user ratings of items. Predictions are really estimates of the user’s like or dislike, and the Predictor interface makes no guarantees about their scale or granularity.

Canonical:

lenskit.Predictor

predict(pairs, ratings=None)#

Compute predictions for user-item pairs. This method is designed to be compatible with the general SciKit paradigm; applications typically want to use predict_for_user().

Parameters:
Returns:

The predicted scores for each user-item pair.

Return type:

pandas.Series

abstract predict_for_user(user, items, ratings=None)#

Compute predictions for a user and items.

Parameters:
  • user – the user ID

  • items (array-like) – the items to predict

  • ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.

Returns:

scores for the items, indexed by item id.

Return type:

pandas.Series