lenskit.hpf.HPFScorer

class lenskit.hpf.HPFScorer(config=None, **kwargs)

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

Hierarchical Poisson factorization, provided by hpfrec

Todo

Right now, this uses the ‘rating’ as a count. Actually use counts (🐞 656).

Stability:

Experimental

Parameters:

• features – the number of features

• kwargs (Any) – additional arguments to pass to hpfrec.HPF.

• config (object | None)

config: HPFConfig

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.

users: lenskit.data.Vocabulary

user_features: numpy.ndarray[tuple[int, int], numpy.dtype[numpy.float64]]

items: lenskit.data.Vocabulary

item_features: numpy.ndarray[tuple[int, int], numpy.dtype[numpy.float64]]

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__(query, items)

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

Parameters:

• query (lenskit.data.QueryInput)

• items (lenskit.data.ItemList)

Return type:

lenskit.data.ItemList