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Run a Scenario with the Baseline Loop Runner

Source: R/task-lists.R
ssd_run_scenario_baseline.Rd

Executes the three task tables in dependency order - sample, fit, then hc - by looping over each table with purrr::pmap() and looking up each task's parent result by the parent's <step>_id foreign key. The fit step truncates its parent sample inline (head(sample, nrow)) before fitting. The runner does no task expansion of its own (it consumes ssd_scenario_tasks()); it just threads outputs forward and returns the collected per-step results.

Usage

ssd_run_scenario_baseline(scenario)

Arguments

scenario

An ssdsims_scenario from ssd_define_scenario().

Value

A named list with sample, fit, and hc elements: each the corresponding task table augmented with a list column of per-task results (sample draws, fits objects, and hc tibbles).

Details

This is the no-frills baseline: it runs in-process, with no targets dependency, no shard grouping or partition_by, and no Parquet I/O.

It is reproducible without an external seed. The runner opens one local_dqrng_backend() scope and seeds each sample/fit/hc task exactly once through its *_data_task_primer() wrapper, with seed = scenario$seed and a per-task primer derived from the task's canonical identity (task_primer() over the task_axes(step) columns). Because each task's (seed, primer) pair fully determines its RNG starting point, two runs of a scenario with a fixed seed yield identical results, and a task's result is independent of the order in which tasks run. These same *_data_task_primer() wrappers are the per-task entry point a future targets shard body and the replay helper (TARGETS-DESIGN.md §7) reuse.

The scenario retains the data frames it was built from, so the runner reads them directly - no separate data argument. min_pmix names are resolved to their materialised functions off the scenario via scenario_min_pmix() (resolved once, at construction), not by a runtime ssdtools/global-env search.

Examples

data <- ssd_scenario_data(ssddata::ccme_boron)
scenario <- ssd_define_scenario(
  data,
  nsim = 1L,
  nrow = 6L,
  seed = 42L,
  dists = ssd_distset(lnorm = "lnorm")
)
out <- ssd_run_scenario_baseline(scenario)
out$hc
#> <ssdsims_tasks: hc>
#>   axes:  dataset, sim, replace, nrow, rescale, computable, at_boundary_ok, min_pmix, range_shape1, range_shape2, nboot, ci_method, parametric, distset
#>   tasks: 1
#> # A tibble: 1 × 20
#>   dataset      sim replace  nrow rescale computable at_boundary_ok min_pmix    
#>   <chr>      <int> <lgl>   <int> <lgl>   <lgl>      <lgl>          <chr>       
#> 1 ccme_boron     1 TRUE        6 FALSE   FALSE      TRUE           ssd_min_pmix
#> # ℹ 12 more variables: range_shape1 <list>, range_shape2 <list>, nboot <int>,
#> #   ci_method <chr>, parametric <lgl>, distset <chr>, hc_id <chr>,
#> #   fit_id <chr>, hc <list>, .start <dttm>, .end <dttm>, .host <chr>