recpack.scenarios.Scenario

class recpack.scenarios.Scenario(validation=False, seed=None)

Base class for defining an evaluation scenario.

A scenario is a set of steps that splits data into training, validation and test datasets. The test dataset is made up of two components: a fold-in set of interactions that is used to predict another held-out set of interactions. The creation of the validation dataset, from the full training dataset, should follow the same splitting strategy as the one used to create training and test datasets from the full dataset.

Parameters

validation (boolean, optional) – Create validation datasets when True, else split into training and test datasets.
seed (int, optional) – Seed for randomisation parts of the scenario. Defaults to None, so random seed will be generated.

Methods

split(data_m)

Splits data_m according to the scenario.

Attributes

`full_training_data`	The full training dataset, which should be used for a final training after hyper parameter optimisation.
`test_data`	The test dataset.
`test_data_in`	Fold-in part of the test dataset
`test_data_out`	Held-out part of the test dataset
`validation_data`	The validation dataset.
`validation_data_in`	Fold-in part of the validation dataset
`validation_data_out`	Held-out part of the validation dataset
`validation_training_data`	The training data to be used during validation.

property full_training_data: recpack.matrix.interaction_matrix.InteractionMatrix

The full training dataset, which should be used for a final training after hyper parameter optimisation.

Returns: Interaction Matrix of training interactions.
Return type: InteractionMatrix

split(data_m: recpack.matrix.interaction_matrix.InteractionMatrix) → None

Splits data_m according to the scenario.

After splitting properties training_data, validation_data and test_data can be used to retrieve the splitted data.

Parameters: data_m – Interaction matrix that should be split.

property test_data: Tuple[recpack.matrix.interaction_matrix.InteractionMatrix, recpack.matrix.interaction_matrix.InteractionMatrix]

The test dataset. Consist of a fold-in and hold-out set of interactions.

Data is processed such that both matrices contain the exact same users. Users that were present in only one of the matrices and not in the other are removed.

Returns: Test data matrices as InteractionMatrix in, InteractionMatrix out.
Return type: Tuple[InteractionMatrix, InteractionMatrix]

property test_data_in: Fold-in part of the test dataset

property test_data_out: Held-out part of the test dataset

property validation_data: Optional[Tuple[recpack.matrix.interaction_matrix.InteractionMatrix, recpack.matrix.interaction_matrix.InteractionMatrix]]

The validation dataset. Consist of a fold-in and hold-out set of interactions.

Data is processed such that both matrices contain the exact same users. Users that were present in only one of the matrices and not in the other are removed.

Returns: Validation data matrices as InteractionMatrix in, InteractionMatrix out.
Return type: Tuple[InteractionMatrix, InteractionMatrix]

property validation_data_in: Fold-in part of the validation dataset

property validation_data_out: Held-out part of the validation dataset

property validation_training_data: recpack.matrix.interaction_matrix.InteractionMatrix: The training data to be used during validation.