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Allocate a budget across hubverse model output

Source: R/model_out.R
allocate_model_out.Rd

Runs allocate() once per allocation unit of a hubverse model output table. See as_alloscore_df() for how allocation units are determined.

Usage

allocate_model_out(
  model_out_tbl,
  K,
  target_cols,
  w = 1,
  kappa = 1,
  alpha = 1,
  g = "x",
  ...
)

Arguments

model_out_tbl

a hubverse model output table containing a single output_type, which must be "quantile".

K

vector of budgets. Cannot be supplied via df.

target_cols

character vector naming the task ID columns whose combinations enumerate the targets that share a budget, for example "location".

w

allocation weights: a scalar, a vector named by target, a vector ordered as the targets of each allocation problem, or the name of a column of model_out_tbl holding a per-target weight.

kappa

scale factor.

alpha

normalized loss when the outcome y exceeds the allocation x. Exactly one of alpha and U must be supplied.

g

a non-decreasing increment function, supplied either as a function or as a string in the variable x such as "log(x)".

...

further arguments passed to allocate().

Value

A tibble with one row per allocation unit and value of K, holding the allocation unit columns and the columns returned by allocate(). Unlike allocate() this is a plain tibble rather than an allocated object, since each unit has its own loss functions and weights. A single row of it can still be passed to plot_iterations().

Examples

mot <- dplyr::filter(
  hubExamples::forecast_outputs,
  output_type == "quantile", reference_date == "2022-11-19", horizon == 0
)
allocate_model_out(mot, K = c(100, 500), target_cols = "location")
#> # A tibble: 6 × 17
#>   model_id    reference_date target horizon target_end_date     K xs       x    
#>   <chr>       <date>         <chr>    <int> <date>          <dbl> <list>   <lis>
#> 1 Flusight-b… 2022-11-19     wk in…       0 2022-11-19        100 <gpl_df> <dbl>
#> 2 Flusight-b… 2022-11-19     wk in…       0 2022-11-19        500 <gpl_df> <dbl>
#> 3 MOBS-GLEAM… 2022-11-19     wk in…       0 2022-11-19        100 <gpl_df> <dbl>
#> 4 MOBS-GLEAM… 2022-11-19     wk in…       0 2022-11-19        500 <gpl_df> <dbl>
#> 5 PSI-DICE    2022-11-19     wk in…       0 2022-11-19        100 <gpl_df> <dbl>
#> 6 PSI-DICE    2022-11-19     wk in…       0 2022-11-19        500 <gpl_df> <dbl>
#> # ℹ 9 more variables: qs_OK <lgl>, lamL <dbl>, lamL_seq <list>, lamU <dbl>,
#> #   lamU_seq <list>, lam <dbl>, lam_seq <list>, post_processed <lgl>,
#> #   xdf <list>