recpack.algorithms.WeightedMatrixFactorization

class recpack.algorithms.WeightedMatrixFactorization(confidence_scheme: str = 'minimal', alpha: int = 40, epsilon: float = 1e-08, num_components: int = 100, regularization: float = 0.01, iterations: int = 20, batch_size: int = 100)

WMF Algorithm by Yifan Hu, Yehuda Koren and Chris Volinsky et al.

As described in Hu, Yifan, Yehuda Koren, and Chris Volinsky. “Collaborative filtering for implicit feedback datasets.” 2008 Eighth IEEE International Conference on Data Mining. Ieee, 2008

Based on the input data a confidence of the interaction is computed. Parametrized by alpha and epsilon (hyper parameters)

If the chosen confidence scheme is 'minimal', confidence is computed as c(u,i) = 1 + alpha * r(u,i).
If the chosen confidence scheme is 'log-scaling', confidence is computed as c(u,i) = 1 + alpha * log(1 + r(u,i)/epsilon)

Since the data during fitting is assumed to be implicit, this confidence will be the same for all interactions, and as such leaving the HP to the defaults works well enough.

Parameters

confidence_scheme (string, optional) – Which confidence scheme should be used to calculate the confidence matrix. Options are [“minimal”, “log-scaling”]. Defaults to “minimal”
alpha (int, optional) – Scaling parameter for generating confidences from ratings. Defaults to 40.
epsilon (float, optional) – Small value to avoid division by zero, used to compute a confidence from a rating. Only used in case cs is set to ‘log-scaling’ Defaults to 1e-8
num_components (int, optional) – Dimension of the embeddings of both user- and item-factors. Defaults to 100
regularization (float, optional) – Regularization parameter used to calculate the Least Squares. Defaults to 0.01
iterations (int, optional) – Number of iterations to execute the ALS calculations. Defaults to 20
batch_size (int, optional) – Number of users/items to process in every mini batch. Defaults to 100

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

`CONFIDENCE_SCHEMES`	Allowed values for confidence scheme parameter
`identifier`	Name of the object.
`name`	Name of the object's class.

CONFIDENCE_SCHEMES = ['minimal', 'log-scaling']: Allowed values for confidence scheme parameter

fit(X: Union[recpack.matrix.interaction_matrix.InteractionMatrix, scipy.sparse._csr.csr_matrix]) → recpack.algorithms.base.Algorithm

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

Parameters: X (Matrix) – The interactions to fit the model on.
Returns: self, fitted algorithm
Return type: Algorithm

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)

property name: Name of the object’s class.

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.

Checks that model is fitted correctly
checks the output using _check_prediction() function

Parameters: X (Matrix) – interactions to predict from.
Returns: The recommendation scores in a sparse matrix format.
Return type: csr_matrix

set_params(**params)

Set the parameters of the estimator.

Parameters: params (dict) – Estimator parameters