Parameter overview#

Overview of parameters used by time_split.split() and plot(). Integrations such as split_pandas() may add or remove parameters, but the base function remains the same unless otherwise stated.

Name

Default

Type

Description

schedule

N/A

Valid Schedule types:

Generates training dates (DatetimeSplitBounds.mid). Examples:

  • ['2019-05-04', '2019-05-11'] | Hand-picked dates.

  • '7d' | every 7 days, aligned to the end of the available data.

  • '0 0 * * MON,FRI' | every Monday and Friday at midnight.
before
after
= ‘7d’
= 1

Valid Span types:

Range before/after schedule timestamps.

The default after=1 stretches the Future data until the next schedule timestamp, simulating models staying in production until a new model takes its place. That is, fold[i].end = fold[i + 1].mid for after=1.

step

= 1

int >= 1

Keep every step:th fold in the schedule. Default (1) =keep all.

n_splits

= 0

int >= 0

Maximum number of folds. Default (0) =keep all.

available

= None

DatetimeIterable

Limits (min, max), or an iterable of datetime-like types that support the built-in min() and max() functions. Binds schedule to a range.

expand_limits

= ‘auto’

Valid ExpandLimits types:

  • Literal ‘auto’ [1]

  • bool

  • ‘round_to[<tolerance]’

Expand available data outward to its likely “true” limits. Disabled if False, True == 'auto'. The tolerance argument is optional; expand_limits='d' performs regular floor(min) / ceil(max) rounding of the limits. Use expand_limits() to experiment.

Example: Passing expand_limits='d<3h' expands (min, max) -limits (derived from available) to the nearest day, at most 3 hours from the original limit.

Later folds are always [2] preferred. For more information about the schedule, before/after and expand_limits-arguments, see the User guide. See the Examples page for plots using the various parameter options.

Footnotes