lenskit.funksvd#
FunkSVD (biased MF).
Classes
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Configuration for |
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FunkSVD explicit-feedback matrix factoriation. |
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- class lenskit.funksvd.FunkSVDConfig(*, embedding_size=50, epochs=100, learning_rate=0.001, regularization=0.015, damping=5.0, range=None)#
Bases:
BaseModel
Configuration for
FunkSVDScorer
.- Parameters:
- model_config: ClassVar[ConfigDict] = {}#
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class lenskit.funksvd.FunkSVDTrainingParams(learning_rate: float, regularization: float, rating_min: float, rating_max: float)#
Bases:
object
- class lenskit.funksvd.FunkSVDTrainingData(users: pyarrow.lib.Int32Array, items: pyarrow.lib.Int32Array, ratings: pyarrow.lib.FloatArray)#
Bases:
object
- Parameters:
users (Int32Array)
items (Int32Array)
ratings (FloatArray)
- class lenskit.funksvd.FunkSVDScorer(*args, **kwargs)#
Bases:
Trainable
,Component
[ItemList
, …]FunkSVD explicit-feedback matrix factoriation. FunkSVD is a regularized biased matrix factorization technique trained with featurewise stochastic gradient descent.
See the base class
MFPredictor
for documentation on the estimated parameters you can extract from a trained model.Deprecated since version LKPY: This scorer is kept around for historical comparability, but ALS
BiasedMF
is usually a better option.- Stability:
- Caller (see Stability Levels).
- train(data, options=TrainingOptions(retrain=True, device=None, rng=None))#
Train a FunkSVD model.
- Parameters:
ratings – the ratings data frame.
data (Dataset)
options (TrainingOptions)