lenskit.sklearn.svd.BiasedSVDScorer#

class lenskit.sklearn.svd.BiasedSVDScorer(config=None, **kwargs)#

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

Biased matrix factorization for explicit feedback using SciKit-Learn’s TruncatedSVD. It operates by first computing the bias, then computing the SVD of the bias residuals.

You’ll generally want one of the iterative SVD implementations such as lenskit.als.BiasedMFScorer; this is here primarily as an example and for cases where you want to evaluate a pure SVD implementation.

Stability:
Caller (see Stability Levels).
Parameters:

  • config (object | None)

  • kwargs (Any)

config: BiasedSVDConfig#

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.

bias: lenskit.basic.BiasModel#
factorization: sklearn.decomposition.TruncatedSVD#
users: lenskit.data.Vocabulary#
items: lenskit.data.Vocabulary#
user_components: numpy.typing.NDArray[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:
__call__(query, items)#

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

Parameters:
Return type:

lenskit.data.ItemList