recpack.algorithms.Algorithm

class recpack.algorithms.Algorithm

Base class for all recpack algorithm implementations.

This class provides fit and predict methods that handle generic pre and post-conditions. Usually a new algorithm will have to implement just the _fit() and _predict() methods.

Methods

`_assert_has_timestamps`(*matrices)	Make sure that the matrices all have timestamp information.
`_assert_is_interaction_matrix`(*matrices)	Make sure that the passed matrices are all an InteractionMatrix.
`_check_fit_complete`()	Helper function to check if model was correctly fitted
`_check_prediction`(X_pred, X)	Checks that the predictions matches expectations
`_fit`(X)	Stub implementation for fitting an algorithm.
`_predict`(X)	Stub for predicting scores to users
`_transform_fit_input`(X)	Transform the training data to expected type
`_transform_predict_input`(X)	Transform the input of predict to expected type
`fit`(X)	Fit the model to the input interaction matrix.
`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.

_assert_has_timestamps(*matrices: recpack.matrix.interaction_matrix.InteractionMatrix): Make sure that the matrices all have timestamp information.

_assert_is_interaction_matrix(*matrices: Union[recpack.matrix.interaction_matrix.InteractionMatrix, scipy.sparse._csr.csr_matrix]) → None: Make sure that the passed matrices are all an InteractionMatrix.

_check_fit_complete()

Helper function to check if model was correctly fitted

Uses the sklear check_is_fitted function, https://scikit-learn.org/stable/modules/generated/sklearn.utils.validation.check_is_fitted.html

_check_prediction(X_pred: scipy.sparse._csr.csr_matrix, X: scipy.sparse._csr.csr_matrix) → None

Checks that the predictions matches expectations

Checks implemented:

Check that all users with history got at least 1 recommendation

For failing checks a warning is printed.

Parameters

X_pred (csr_matrix) – Predictions made for all nonzero users in X
X (csr_matrix) – User-item interaction matrix used as input to predict

_fit(X: scipy.sparse._csr.csr_matrix)

Stub implementation for fitting an algorithm.

Will be called by the fit() wrapper. Child classes should implement this function.

Parameters: X (csr_matrix) – User-item interaction matrix to fit the model to
Raises: NotImplementedError – Implement this method in the child class

_predict(X: scipy.sparse._csr.csr_matrix) → scipy.sparse._csr.csr_matrix

Stub for predicting scores to users

Will be called by the predict() wrapper. Child classes should implement this function.

Parameters: X (csr_matrix) – User-item interaction matrix used as input to predict
Raises: NotImplementedError – Implement this method in the child class
Returns: Predictions made for all nonzero users in X
Return type: csr_matrix

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

Transform the training data to expected type

Data will be turned into a binary csr matrix.

Parameters: X (Matrix) – User-item interaction matrix to fit the model to
Returns: Transformed user-item interaction matrix to fit the model
Return type: csr_matrix

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

Transform the input of predict to expected type

Data will be turned into a binary csr matrix.

Parameters: X (Matrix) – User-item interaction matrix used as input to predict
Returns: Transformed user-item interaction matrix used as input to predict
Return type: csr_matrix

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

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