rics.ml.time_split.integration.sklearn

Integration with the scikit-learn library.

Classes

ScikitLearnSplitter(schedule, *[, before, ...])

A scikit-learn compatible datetime splitter.

class ScikitLearnSplitter(schedule: DatetimeIndex | Iterable[str | Timestamp | datetime | date | datetime64] | str | Timedelta | timedelta | timedelta64, *, before: int | Literal['all'] | str | Timedelta | timedelta | timedelta64 = '7d', after: int | Literal['all'] | str | Timedelta | timedelta | timedelta64 = 1, n_splits: int | None = None, flex: bool | Literal['auto'] | str = 'auto', step: int = 1, log_progress: str | bool | dict[str, Any] | Logger | LoggerAdapter = False, verify_xy: bool = True)

Bases: object

A scikit-learn compatible datetime splitter.

This class may be used to create temporal folds from heterogeneous/unaggregated data, typically used for training models (e.g. on raw transaction data). If your data is a well-formed time series, consider using the TimeSeriesSplit class from scikit-learn instead.

If a pandas type is passed to the ScikitLearnSplitter.split()-method, the index will be used.

Parameters:

• schedule – A collection of timestamps, a pandas offset alias, or a cron expression.

• before – Range before schedule timestamps. Either a pandas offset alias, an integer (schedule-based offsets), or ‘all’ (requires available data).

• after – Range after schedule timestamps. Either a pandas offset alias, an integer (schedule-based offsets), or ‘all’ (requires available data).

• step – Select a subset of folds, preferring folds later in the schedule.

• n_splits – Maximum number of folds, preferring folds later in the schedule.

• flex – A pandas offset alias used to expand available data to its likely “true” limits. Pass False to disable.

• log_progress – Controls logging of fold progress. See log_split_progress() for details.

• verify_xy – If True, split X and y independently and verify that they are equal.

For more information about the schedule, before/after and flex-arguments, see the User guide.

get_n_splits(X: Iterable[str | Timestamp | datetime | date | datetime64] | None = None, y: Iterable[str | Timestamp | datetime | date | datetime64] | None = None, groups: Any = None) → int

Returns the number of splitting iterations in the cross-validator.

Equivalent to len(list(split(X, y, groups)).

Parameters:

• X – Training data (features).

• y – Target variable.

• groups – Always ignored, exists for compatibility.

Returns:

Number of splits with given arguments.

Raises:

• ValueError – If both X and y are None.

• ValueError – If splits of X and y are not equal when verify_xy=True.

split(X: Iterable[str | Timestamp | datetime | date | datetime64] | None = None, y: Iterable[str | Timestamp | datetime | date | datetime64] | None = None, groups: Any = None) → Iterable[tuple[Sequence[int], Sequence[int]]]

Generate indices to split data into training and test set.

Parameters:

• X – Training data (features).

• y – Target variable.

• groups – Always ignored, exists for compatibility.

Yields:

The training/test set indices for that split.

Raises:

• ValueError – If both X and y are None.

• ValueError – If splits of X and y are not equal when verify_xy=True.

• TypeError – If X or y have an index-attribute, but index elements are not datetime-like.