recpack.algorithms.SVD

class recpack.algorithms.SVD(num_components: int = 100, seed: Optional[int] = None)

Singular Value Decomposition used as a matrix factorization algorithm.

The SVD is computed using TruncatedSVD from sklearn. This computes three matrices \(U, \Sigma, V\) such that \(U \times \Sigma \times V = X\).

Matrix \(U\) is used as the user embedding and the item embeddings are computed as \(\Sigma \times V\).

Parameters

num_components (int) – The size of the embeddings.
seed (int, optional) – The seed for the random state to allow for comparison, defaults to None

Methods

`fit`(X)	Fit the model to the input interaction matrix.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predicts scores, given the interactions in X
`set_params`(**params)	Set the parameters of the estimator.

Attributes

`identifier`	Name of the object.
`name`	Name of the object's class.

Fit the model to the input interaction matrix.

After fitting the model will be ready to use for prediction.

This function will handle some generic bookkeeping for each of the child classes,

The fit function gets timed, and this will get printed
Input data is converted to expected type using call to _transform_predict_input()
The model is trained using the _fit() method
_check_fit_complete() is called to check fitting was succesful

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns: routing – A MetadataRequest encapsulating routing information.
Return type: MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: dict

property identifier

Name of the object.

Name is made by combining the class name with the parameters passed at construction time.

Constructed by recreating the initialisation call. Example: Algorithm(param_1=value)

predict(X: Union[recpack.matrix.interaction_matrix.InteractionMatrix, scipy.sparse._csr.csr_matrix]) → scipy.sparse._csr.csr_matrix

Predicts scores, given the interactions in X

Recommends items for each nonzero user in the X matrix.

This function is a wrapper around the _predict() method, and performs checks on in- and output data to guarantee proper computation.

set_params(**params)

Set the parameters of the estimator.