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rics.ml.time_split.integration.polars#

Integration with the Polars library.

Examples

Splitting a polars.DataFrame with split_polars().

>>> import polars as pl
>>> from datetime import date
>>> ts = pl.datetime_range(
...     date.fromisoformat("2022-01-01"),
...     date.fromisoformat("2022-01-10"),
...     interval="1h",
...     eager=True,
... )
>>> df = pl.DataFrame({"timestamp": ts, "ints": range(len(ts))})
>>> df.sample(5, seed=1999)
shape: (5, 2)
┌─────────────────────┬──────┐
│ timestamp           ┆ ints │
│ ---                 ┆ ---  │
│ datetime[μs]        ┆ i64  │
╞═════════════════════╪══════╡
│ 2022-01-06 16:00:00 ┆ 136  │
│ 2022-01-08 17:00:00 ┆ 185  │
│ 2022-01-01 06:00:00 ┆ 6    │
│ 2022-01-01 05:00:00 ┆ 5    │
│ 2022-01-03 08:00:00 ┆ 56   │
└─────────────────────┴──────┘

To split the frame, pass it to split_polars along with the column to split on. The schedule keyword argument is required, but log_progress is not.

>>> for fold in split_polars(
...     df, schedule="1d", log_progress="progress", time_column="timestamp"
... ):  
...     print(
...         f"Summary of fold {tuple(map(pd.Timestamp.isoformat, fold.bounds))}:"
...         f"\n  {fold.data['ints'].mean()=}"
...         f"\n  {fold.future_data['ints'].mean()=}",
...     )
INFO:progress:Begin fold 1/2: '2022-01-01' <= [schedule: '2022-01-08' (Saturday)] < '2022-01-09'.
Summary of fold ('2022-01-01T00:00:00', '2022-01-08T00:00:00', '2022-01-09T00:00:00'):
  fold.data['ints'].mean()=83.5
  fold.future_data['ints'].mean()=179.5
INFO:progress:Finished fold 1/2: [schedule: '2022-01-08' (Saturday)] after 542μs.
INFO:progress:Begin fold 2/2: '2022-01-02' <= [schedule: '2022-01-09' (Sunday)] < '2022-01-10'.
Summary of fold ('2022-01-02T00:00:00', '2022-01-09T00:00:00', '2022-01-10T00:00:00'):
  fold.data['ints'].mean()=107.5
  fold.future_data['ints'].mean()=203.5
INFO:progress:Finished fold 2/2: [schedule: '2022-01-09' (Sunday)] after 433μs.

Functions

split_polars(data, time_column, *[, ...])

Split a polars frame.
split_polars(data: DataFrame, time_column: str, *, log_progress: str | bool | dict[str, Any] | Logger | LoggerAdapter = False, **kwargs: Unpack[DatetimeIndexSplitterKwargs]) → Iterable[DatetimeSplit[DataFrame]][source]#

Split a polars frame.

Parameters:

  • data – A polars.DataFrame.

  • time_column – A column to split on.

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

  • **kwargs – See split(). The available keyword is managed by the integration.

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

Yields:

Tuples (data, future_data, bounds).

Raises:

TypeError – If time_column does not denote a datetime index-like field.

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