Unnest prediction list-cols — layer

For any model that produces forecasts for multiple outcomes, such as multiple aheads, the resulting prediction is a list of forecasts inside a column of the prediction tibble, which is may not be desirable. This layer "lengthens" the result, moving each outcome to a separate row, in the same manner as tidyr::unnest() would. At the moment, the only such engine is smooth_quantile_reg().

Usage

layer_unnest(frosting, ..., id = rand_id("unnest"))

Arguments

frosting: a frosting postprocessor
...: <tidy-select> One or more unquoted expressions separated by commas. Variable names can be used as if they were positions in the data frame, so expressions like x:y can be used to select a range of variables.
id: a random id string

Value

an updated frosting postprocessor

Examples

jhu <- covid_case_death_rates %>%
  filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))

aheads <- 1:7

r <- epi_recipe(jhu) %>%
  step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
  step_epi_ahead(death_rate, ahead = aheads) %>%
  step_epi_naomit()

wf <- epi_workflow(
  r,
  smooth_quantile_reg(
    quantile_levels = c(.05, .1, .25, .5, .75, .9, .95),
    outcome_locations = aheads
  )
) %>%
  fit(jhu)

f <- frosting() %>%
  layer_predict() %>%
  layer_naomit() %>%
  layer_unnest(.pred)

wf1 <- wf %>% add_frosting(f)

p <- forecast(wf1)
p
#> An `epi_df` object, 0 x 3 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2023-03-10
#> Warning: There were 2 warnings in `dplyr::summarize()`.
#> The first warning was:
#> ℹ In argument: `min_t = if (...) NULL`.
#> Caused by warning in `min.default()`:
#> ! no non-missing arguments to min; returning Inf
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
#> Latency (lag between last available observation and epi_df's as_of, by time series):
#> * No time series detected
#> # A tibble: 0 × 3
#> # ℹ 3 variables: geo_value <chr>, time_value <date>, .pred <???>