streamsight.settings.NPastInteractionTimestampSplitter

class streamsight.settings.NPastInteractionTimestampSplitter(t, t_upper: int | None = None, n_seq_data: int = 1, include_all_past_data: bool = False)

Bases: TimestampSplitter

Splits with n past interactions based on a timestamp.

Splits the data into unlabeled and ground truth data based on a timestamp. Historical data contains last n_seq_data interactions before the timestamp t and the future interaction contains interactions after the timestamp t.

Attribute definition

past_interaction: List of unlabeled data. Interval is [0, t).
future_interaction: Data used for training the model. Interval is [t, t+t_upper) or [t,inf].
n_seq_data: Number of last interactions to provide as data for model to make prediction. These interactions are past interactions from before the timestamp t.

param t:: Timestamp to split on in seconds since epoch.
type t:: int
param t_upper:: Seconds past t. Upper bound on the timestamp of interactions. Defaults to None (infinity).
type t_upper:: int, optional
param n_seq_data:: Number of last interactions to provide as data for model to make prediction.
type n_seq_data:: int, optional
param include_all_past_data:: If True, include all past data in the past_interaction.
type include_all_past_data:: bool, optional
return:: A 2-tuple containing the past_interaction and future_interaction matrices.
rtype:: Tuple[InteractionMatrix, InteractionMatrix]

__init__(t, t_upper: int | None = None, n_seq_data: int = 1, include_all_past_data: bool = False)

Methods

`__init__`(t[, t_upper, n_seq_data, ...])
`split`(data)	Splits data such that the following definition holds:
`update_split_point`(t)

Attributes

`identifier`	String identifier of the splitter object, contains name and parameter values.
`name`	The name of the splitter.

_abc_impl = <_abc._abc_data object>

property identifier: String identifier of the splitter object, contains name and parameter values.

property name: The name of the splitter.

split(data: InteractionMatrix) → Tuple[InteractionMatrix, InteractionMatrix]

Splits data such that the following definition holds:

past_interaction : List of unlabeled data. Interval is [0, t).
future_interaction : Data used for training the model. Interval is [t, t+t_upper) or [t,inf].

Parameters:: data (InteractionMatrix) – Interaction matrix to be split. Must contain timestamps.
Returns:: A 2-tuple containing the past_interaction and future_interaction matrices.
Return type:: Tuple[InteractionMatrix, InteractionMatrix]

update_split_point(t: int)