Convert hubverse model output into forecasts alloscore2 can allocate against

Groups a hubverse model output table into allocation problems, turns each target's predictive quantiles into a cdf and quantile function using distfromq::make_p_fn() and distfromq::make_q_fn(), and attaches the observed outcomes from oracle_output.

Usage

as_alloscore_df(model_out_tbl, oracle_output = NULL, target_cols)

Arguments

model_out_tbl: a hubverse model output table containing a single output_type, which must be "quantile".
oracle_output: hubverse oracle output, holding the observed values in an oracle_value column. Optional when only allocating.
target_cols: character vector naming the task ID columns whose combinations enumerate the targets that share a budget, for example "location".

Value

A tibble with one row per allocation unit, the allocation unit columns, and a forecasts list column. Each element of forecasts is a tibble with one row per target holding the target_cols, a target_names key, the predictive quantile levels ps and values qs, the cdf F and quantile function Q, and – when oracle_output was supplied – the observed outcome y.

Details

An allocation problem is a set of targets that share one budget. target_cols names the task ID columns whose combinations enumerate those targets; every other task ID column, together with model_id, defines the allocation unit, and one allocation problem is solved per combination of those. This mirrors the way a hubverse compound_taskid_set distinguishes task IDs that vary within a group from those held constant.

Examples

mot <- dplyr::filter(hubExamples::forecast_outputs, output_type == "quantile")
adf <- as_alloscore_df(
  mot,
  oracle_output = hubExamples::forecast_oracle_output,
  target_cols = "location"
)
adf
#> # A tibble: 24 × 6
#>    model_id          reference_date target     horizon target_end_date forecasts
#>  * <chr>             <date>         <chr>        <int> <date>          <list>   
#>  1 Flusight-baseline 2022-11-19     wk inc fl…       0 2022-11-19      <tibble> 
#>  2 Flusight-baseline 2022-11-19     wk inc fl…       1 2022-11-26      <tibble> 
#>  3 Flusight-baseline 2022-11-19     wk inc fl…       2 2022-12-03      <tibble> 
#>  4 Flusight-baseline 2022-11-19     wk inc fl…       3 2022-12-10      <tibble> 
#>  5 Flusight-baseline 2022-12-17     wk inc fl…       0 2022-12-17      <tibble> 
#>  6 Flusight-baseline 2022-12-17     wk inc fl…       1 2022-12-24      <tibble> 
#>  7 Flusight-baseline 2022-12-17     wk inc fl…       2 2022-12-31      <tibble> 
#>  8 Flusight-baseline 2022-12-17     wk inc fl…       3 2023-01-07      <tibble> 
#>  9 MOBS-GLEAM_FLUH   2022-11-19     wk inc fl…       0 2022-11-19      <tibble> 
#> 10 MOBS-GLEAM_FLUH   2022-11-19     wk inc fl…       1 2022-11-26      <tibble> 
#> # ℹ 14 more rows
adf$forecasts[[1]]
#> # A tibble: 2 × 7
#>   location target_names     y ps        qs        F      Q     
#>   <chr>    <chr>        <dbl> <list>    <list>    <list> <list>
#> 1 25       25              79 <dbl [7]> <dbl [7]> <fn>   <fn>  
#> 2 48       48            1230 <dbl [7]> <dbl [7]> <fn>   <fn>