lenskit.basic.popularity.PopScorer

class lenskit.basic.popularity.PopScorer(config=None, **kwargs)

Bases: lenskit.pipeline.Component[lenskit.data.ItemList], lenskit.training.Trainable

Score items by their popularity. Use with TopN to get a most-popular-items recommender.

Stability:

Caller (see Stability Levels).

Parameters:

• config (object | None)

• kwargs (Any)

config: PopConfig

The component configuration object. Component classes that support configuration must redefine this attribute with their specific configuration class type, which can be a Python dataclass or a Pydantic model class.

items: lenskit.data.Vocabulary

Vocabulary of known items at training time.

item_scores: numpy.ndarray[tuple[int], numpy.dtype[numpy.float32]]

Array of per-item popularity scores.

is_trained()

Query if this component has already been trained.

train(data, options=TrainingOptions())

Train the model to learn its parameters from a training dataset.

Parameters:

• data (lenskit.data.Dataset) – The training dataset.

• options (lenskit.training.TrainingOptions) – The training options.

__call__(items)

Run the pipeline’s operation and produce a result. This is the key method for components to implement.

Parameters:

items (lenskit.data.ItemList)

Return type:

lenskit.data.ItemList