Python: representations

How a series becomes the array a learner reads, and the set a run is compared across.

All of the Python reference

native()

native(stats='mean', year_start='01-01')

The record unreduced: one bin per reading.

grain()

grain(g, stats='mean', year_start='01-01')

One calendar grain, named, or supplied as a function of the reading instants returning each reading’s bin start, which is reported as custom.

multigrain()

multigrain(grains=None, stats='mean', year_start='01-01')

Several grains flattened and bound side by side into one block of features.

Left at None the grains are the ones the record supports, the set auto_grains names. A caller who does not want the record unreduced among them names the grains instead.

lookback()

lookback(span, lag='0 days', bins=1, stats='mean')

A stretch of record of fixed length, ending a fixed lag before each target’s own instant.

grains()

grains(*g, stats='mean', year_start='01-01')

A sift over calendar grains, named or read off the record with "auto".

lookbacks()

lookbacks(*spans, lag='0 days', bins=1, stats='mean')

A sift over lookbacks of several lengths, all sharing a lag and a number of bins.

Representation

Representation(label, kind, stats, grain, grains, span, lag, bins, sequence, year_start)

One reduction, named but not yet built.

sequence says whether the bins are ordered in time and mean something to a convolution: the record unreduced, a calendar grain and a lookback cut into several bins are sequences; a block of features bound side by side is not.

Attributes:

• label - str
• kind - str
• stats - tuple[str, …]
• grain - object
• grains - tuple[str, …] | None
• span - object
• lag - object
• bins - int
• sequence - bool
• year_start - str

Sift

The representations a set of candidates runs across, as a mapping of label to spec.

as_sift()

as_sift(x)

A sift, whether it arrived as one, as a representation, as a grain name, or as a list.

expand_sift()

expand_sift(sift, series, spec: TimesiftSpec)

The sift with "auto" replaced by the grains the record supports.

auto_grains()

auto_grains(series, spec: TimesiftSpec, stats=('mean',), year_start='01-01')

The named grains that give the record at least two bins, from the finest to the coarsest.

The count comes from the calendar in the core rather than from arithmetic here, so a grain is admitted on the same rule that will bin it. It is read off one reading per distinct instant, which carries the record’s whole span and its gaps at the cost of a single unit’s memory, in the zone the time column carries, so a grain is counted on the clock it will be binned by.

build_representation()

build_representation(rep: Representation, series, targets, spec: TimesiftSpec)

The array one representation names, for these targets, in their own row order.