Attach a tailor Post-Processor to One Output or Step of a Multi-Output or Multistep Workflow

workflows::add_tailor() cannot be used on a kerasnip multi-output or multistep workflow: tailor::fit() selects outcome/estimate via [[, which requires exactly one, flat, numeric column, and both a multi-output recipe (output_1 + output_2 ~ .) and a multistep model's nested .pred list-column violate that (see vignette("multi_output_postprocessing")). kerasnip_add_tailor() is a kerasnip-owned analogue that attaches a tailor post-processor to a single named output or forecast step, using kerasnip_output_view() or kerasnip_step_view() internally.

Usage

kerasnip_add_tailor(x, tailor, output = NULL, step = NULL, var = NULL)

Arguments

x: An unfitted workflow whose model has more than one outcome (multi-output) or is a multistep forecasting model.
tailor: A tailor::tailor() specification.
output: A string, the name of the outcome column to post-process (multi-output models).
step: An integer, the forecast step to post-process (multistep models).
var: A string, the forecasted variable to post-process; only needed with step if the model forecasts more than one variable.

Value

A kerasnip_tailored_workflow, to be trained with fit().

Details

At fit() time, the underlying model is trained as usual; the relevant view is then used to fit the tailor against that output's/step's predictions (on data_calibration if supplied, otherwise on data, mirroring workflows::add_tailor()'s data-usage convention). At predict() time, the full prediction is generated, the target output's/step's value(s) are replaced with the tailor-adjusted values, and everything else (other outputs; other steps in the same nested tibble) is left untouched.

Exactly one of output or step must be supplied: output for a multi-output model, step (and var, if more than one variable is forecast) for a multistep model.

Examples

if (FALSE) { # \dontrun{
tlr <- tailor::tailor() |> tailor::adjust_numeric_calibration()

# multi-output
tailored_wf <- kerasnip_add_tailor(wf, tlr, output = "output_1")

# multistep
tailored_wf <- kerasnip_add_tailor(wf, tlr, step = 2)

fit_obj <- fit(tailored_wf, data = train_data, data_calibration = cal_data)
predict(fit_obj, new_data = test_data)
} # }