lenskit.funksvd

FunkSVD (biased MF).

Functions

make_params(niters, lrate, reg, range)
train(ctx, params, model, timer)

Classes

Context(*args, **kwargs)
FunkSVD(features[, iterations, lrate, reg, ...])	Algorithm class implementing FunkSVD matrix factorization.
Model(*args, **kwargs)	Internal model class for training SGD MF.

class lenskit.funksvd.Model(*args, **kwargs)

Bases: Model

Internal model class for training SGD MF.

class lenskit.funksvd.FunkSVD(features, iterations=100, *, lrate=0.001, reg=0.015, damping=5, range=None, bias=True, random_state=None)

Bases: MFPredictor[ndarray]

Algorithm class implementing FunkSVD matrix factorization. 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.

Parameters:

• features (int) – the number of features to train

• iterations (int) – the number of iterations to train each feature

• lrate (float) – the learning rate

• reg (float) – the regularization factor

• damping (float | tuple[float, float]) – damping factor for the underlying mean

• bias (Bias | None) – the underlying bias model to fit. If True, then a bias.Bias model is fit with damping.

• range (tuple[float, float] | None) – the (min, max) rating values to clamp ratings, or None to leave predictions unclamped.

• random_state – The random state for shuffling the data prior to training.

fit(data, **kwargs)

Train a FunkSVD model.

Parameters:

• ratings – the ratings data frame.

• data (Dataset)

predict_for_user(user, items, ratings=None)

Compute predictions for a user and items.

Parameters:

• user – the user ID

• items (array-like) – the items to predict

• ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.

Returns:

scores for the items, indexed by item id.

Return type:

pandas.Series