lenskit.sklearn.nmf.NMFScorer#
- class lenskit.sklearn.nmf.NMFScorer(config=None, **kwargs)#
Bases:
lenskit.pipeline.Component[lenskit.data.ItemList],lenskit.training.TrainableNon-negative matrix factorization for implicit feedback using SciKit-Learn’s
sklearn.decomposition.non_negative_factorization(). It computes the user and item embedding matrices using an indicator matrix as the input.- Stability:
- Caller (see Stability Levels).
- Parameters:
config (object | None)
kwargs (Any)
- config: NMFConfig#
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#
- items: lenskit.data.Vocabulary#
- user_components: lenskit.data.types.NPMatrix#
- item_components: lenskit.data.types.NPMatrix#
- 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: