recpack.scenarios.Timed

class recpack.scenarios.Timed(t, t_validation: Optional[int] = None, delta_out: int = 2147483647, delta_in: int = 2147483647, validation: bool = False, seed: Optional[int] = None)

Predict users’ future interactions, given information about historical interactions.

full_training_data is constructed by using all interactions whose timestamps are in the interval [t - delta_in, t[
test_data_in are events with timestamps in [t - delta_in, t[.
test_data_out are events with timestamps in [t, t + delta_out[.
validation_training_data are all interactions with timestamps in [t_validation - delta_in, t_validation[.
validation_data_in are interactions with timestamps in [t_validation - delta_in, t_validation[
validation_data_out are interactions with timestamps in [t_validation, min(t, t_validation + delta_out)[.

Warning

The scenario can only be used when the dataset has timestamp information.

Example

As an example, we split this data with t = 4, t_validation = 2 ``delta_in = None (infinity), delta_out = 2``, and validation = True:

time    0   1   2   3   4   5   6
Alice   X   X               X
Bob         X   X   X   X
Carol   X   X       X       X   X

would yield full_training_data:

time    0   1   2   3   4   5   6
Alice   X   X
Bob         X   X   X
Carol   X   X       X

validation_training_data:

time    0   1   2   3   4   5   6
Alice   X   X
Bob         X
Carol   X   X

validation_data_in:

time    0   1   2   3   4   5   6
Bob         X
Carol   X   X

validation_data_out:

time    0   1   2   3   4   5   6
Bob             X   X
Carol           X

test_data_in:

time    0   1   2   3   4   5   6
Alice   X   X
Carol   X   X       X

test_data_out:

time    0   1   2   3   4   5   6
Alice                       X
Carol                       X   X

Parameters

t (int) – Timestamp to split target dataset test_data_out from the remainder of the data.
t_validation (int, optional) – Timestamp to split validation_data_out from validation_training_data. Required if validation is True.
delta_out (int, optional) – Size of interval in seconds for both validation_data_out and test_data_out. Both sets will contain interactions that occurred within delta_out seconds after the splitting timestamp. Defaults to maximal integer value (acting as infinity).
delta_in (int, optional) – Size of interval in seconds for full_training_data, validation_training_data, validation_data_in and test_data_in. All sets will contain interactions that occurred within delta_out seconds before the splitting timestamp. Defaults to maximal integer value (acting as infinity).
validation (boolean, optional) – Assign a portion of the full training dataset to validation data if True, else split without validation data into only a training and test dataset.
seed (int, optional) – Seed for randomisation parts of the scenario. Timed scenario is deterministic, so changing seed should not matter. 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.