recpack.algorithms.NMFItemToItem

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

Computes similarities between items as the similarity between their NMF item embeddings.

First, item embeddings are computed using the recpack.algorithms.NMF algorithm. The similarity matrix is constructed by computing the dot product between the item embeddings.

Parameters

• num_components (int) – The size of the latent dimension

• 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(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