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

load
(file)¶ Load a fit algorithm from a file. The default implementation unpickles the object and transplants its parameters and model into this object.
Parameters: file (pathlike) – the file to load.

save
(file)¶ Save a fit algorithm to a file. The default implementation pickles the object.
Parameters: file (pathlike) – the file to save.

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.

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.
 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:

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:
