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()
.
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)

class
lenskit.algorithms.
Recommender
¶ Recommends items for a user.

classmethod
adapt
(algo)¶ Adapt an algorithm to be a recommender.
Parameters: algo – the algorithm to adapt. If the algorithm implements Recommender
, it is returned asis; if it implementsPredictor
, then a topN recommender using the predictor’s scores is returned.Returns: a recommendation interface to algo
.Return type: Recommender

recommend
(model, user, n=None, candidates=None, ratings=None)¶ Compute recommendations for a user.
Parameters:  model – the trained model to use. Either
None
or the ratings matrix if the algorithm has no concept of training.  user – the user ID
 n (int) – the number of recommendations to produce (
None
for unlimited)  candidates (arraylike) – the set of valid candidate items.
 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:  model – the trained model to use. Either

classmethod
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
(model, user, items, ratings=None)¶ Compute predictions for a user and items.
Parameters:  model – the trained model to use. Either
None
or the ratings matrix if the algorithm has no concept of training.  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:  model – the trained model to use. Either

Model Training¶
Most algorithms have some concept of a trained model. The Trainable
interface captures the
ability of a model to be trained and saved to disk.

class
lenskit.algorithms.
Trainable
¶ Models that can be trained and have their models saved.

train
(ratings)¶ Train the model on rating/consumption data. Training methods that require additional data may accept it as additional parameters or via class members.
Parameters: ratings (pandas.DataFrame) – rating data, as a matrix with columns ‘user’, ‘item’, and ‘rating’. The user and item identifiers may be of any type. Returns: the trained model (of an implementationdefined type).
