recpack.scenarios.LastItemPrediction

class recpack.scenarios.LastItemPrediction(validation=False, seed=None, n_most_recent_in=2147483647)

Predict a user’s next interaction.

Scenario frequently used in evaluation of sequential recommendation algorithms.

Warning

The scenario can only be used when the dataset has timestamp information, because the order of interactions is needed to correctly split the data.

The scenario splits the data such that the last interaction of a user is the target for prediction, while the earlier ones are used for training and as history.

full_training_data contains all but the most recent interaction for each of the users.
test_data_in: contains the n_most_recent_in interactions before the last interaction of each user.
test_data_out contains the last interaction of all users.
validation_training_data contains all but the most recent interaction of each user in the full training dataset.
validation_data_in contains the n_most_recent_in interactions before the last interaction of each user in the full training dataset.
validation_data_out contains the most recent interaction of each user in the full training dataset

Example

As an example, we split this data with validation = True and n_most_recent_in = 1:

time    0   1   2   3   4   5
Alice   X   X   X
Bob         X   X   X   X

would yield full_training_data:

time    0   1   2   3   4   5
Alice   X   X
Bob         X   X   X

validation_training_data:

time    0   1   2   3   4   5
Alice   X
Bob         X   X

validation_data_in:

time    0   1   2   3   4   5
Alice   X
Bob             X

validation_data_out:

time    0   1   2   3   4   5
Alice       X
Bob                 X

test_data_in:

time    0   1   2   3   4   5
Alice       X
Bob                 X

test_data_out:

time    0   1   2   3   4   5
Alice           X
Bob                     X

Parameters

validation (boolean, optional) – construct validation datasets if True, else split without validation data into only a full training and test dataset.
seed (int, optional) – Seed for randomisation parts of the scenario. This scenario is deterministic, so changing seed does not matter. Defaults to None, so random seed will be generated.
n_most_recent_in (int, optional) – How much of the historic events to use as input history. Defaults to the maximal integer value.

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.