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()
.
Base Algorithm¶
Algorithms follow the SciKit fitpredict paradigm for estimators, except they know natively how to work with Pandas objects.
The Algorithm
interface defines common methods.

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

fit
(ratings, *args, **kwargs)¶ Train a model using the specified ratings (or similar) data.
Parameters:  ratings (pandas.DataFrame) – The ratings data.
 args – Additional training data the algorithm may require.
 kwargs – Additional training data the algorithm may require.
Returns: The algorithm object.

get_params
(deep=True)¶ Get the parameters for this algorithm (as in scikitlearn). Algorithm parameters should match constructor argument names.
The default implementation returns all attributes that match a constructor parameter name. It should be compatible with
scikit.base.BaseEstimator.get_params()
method so that LensKit alogrithms can be cloned withscikit.base.clone()
as well aslenskit.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)
Note
We are rethinking the ergonomics of this interface, and it may change in LensKit 0.6. We expect
keep compatibility in the lenskit.batch.recommend()
API, though.

class
lenskit.algorithms.
Recommender
¶ Recommends lists of items for users.

classmethod
adapt
(algo)¶ Ensure that an algorithm is a
Recommender
. If it is not a recommender, it is wrapped in alenskit.basic.TopN
with a default candidate selector.Parameters: algo (Predictor) – the underlying rating predictor.

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 (arraylike) – The set of valid candidate items; if
None
, a default set will be used. For many algorithms, this is theirCandidateSelector
.  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 ascore
column.Return type:

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

candidates
(user, ratings=None)¶ Select candidates for the user.
Parameters:  user – The user key or ID.
 ratings (pandas.Series or arraylike) – 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 arraylike 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¶

class
lenskit.algorithms.
Predictor
¶ 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.
predict
(pairs, ratings=None)¶ Compute predictions for useritem pairs. This method is designed to be compatible with the general SciKit paradigm; applications typically want to use
predict_for_user()
.Parameters:  pairs (pandas.DataFrame) – The useritem pairs, as
user
anditem
columns.  ratings (pandas.DataFrame) – useritem rating data to replace memorized data.
Returns: The predicted scores for each useritem pair.
Return type:  pairs (pandas.DataFrame) – The useritem pairs, as

predict_for_user
(user, items, ratings=None)¶ Compute predictions for a user and items.
Parameters:  user – the user ID
 items (arraylike) – 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:
