Build one representation for a set of targets — build

Turns a representation and the two tables into the [target, bin, channel] array a learner is fitted on. It is the one place the fitting layer builds an array, so timesift() and predict.timesift() reach a record the same way and a candidate refitted on new targets is built from the settings its own arm was.

Usage

build_representation(rep, series, targets, spec)

Arguments

rep: A representation from native(), grain(), multigrain() or lookback().
series: The long table of readings, or NULL where the targets carry the whole predictor block.
targets: The table of prediction targets.
spec: The resolved settings timesift() carries: the identifier, time, value, anchor and static columns it settled on.

Value

A timesift_matrix.

Details

The rows are the targets, in the order the fitting layer keeps them: sorted by identifier where one target row belongs to each unit, and in the targets' own order where target_time anchors them. Columns named in static are appended as channels holding one value per target, constant across the bins.

Examples

set.seed(1)
t <- seq(as.POSIXct("2021-09-01", tz = "UTC"), by = "hour", length.out = 24 * 90)
units <- sprintf("p%02d", 1:30)
warmth <- rnorm(30)
logger <- data.frame(
  plot = rep(units, each = length(t)), datetime = rep(t, 30),
  temp = as.numeric(vapply(warmth, function(w) w + sin(seq_along(t) / 300), numeric(length(t)))))
plots <- data.frame(plot = units, sp_a = rbinom(30, 1, plogis(2 * warmth)))
fit <- timesift(plots, logger, y = sp_a, id = plot, time = datetime,
                sift = grains("month"), resampling = cv(v = 3L), ensemble = FALSE,
                verbose = FALSE)
x <- build_representation(grain("week"), logger, plots, fit$spec)
dim(x)