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Group Tasks into Shards

Source: R/task-shards.R
ssd_scenario_shards.Rd

Group a step's per-task table into a per-shard table: one row per partition_by path cell, carrying the path-axis columns (the tar_map target-name suffix and Hive path) and a tasks list-column of that cell's task rows. Each task row is decorated with seed = scenario$seed and its per-task primer (task_primer() over the step's task_axes()); the decoration is RNG-free (a pure hash, not a draw), so the bare task tables (ssd_scenario_tasks()) keep their no-(seed, primer) contract. The result is the values a tarchetypes::tar_map() consumes to mint one target per shard.

Usage

ssd_scenario_sample_shards(scenario)

ssd_scenario_fit_shards(scenario)

ssd_scenario_hc_shards(scenario)

Arguments

scenario

An ssdsims_scenario from ssd_define_scenario().

Value

A tibble with one row per shard of the step: the path-axis columns and a tasks list-column. Suitable as tarchetypes::tar_map(values = ).

Details

For fit/hc each task row in tasks also carries its parent step's path-axis values and <parent>_id, so the runner opens the matching parent shard by partition path.

Functions

  • ssd_scenario_sample_shards(): Group the sample tasks (ssd_scenario_sample_tasks()) by partition_by$sample.

  • ssd_scenario_fit_shards(): Group the fit tasks (ssd_scenario_fit_tasks()) by partition_by$fit. Each task row in tasks carries its parent sample path-axis values and sample_id, so the runner opens the matching sample shard by partition path.

  • ssd_scenario_hc_shards(): Group the hc tasks (ssd_scenario_hc_tasks()) by partition_by$hc. Each task row in tasks carries its parent fit path-axis values and fit_id, so the runner opens the matching fit shard by partition path.

See also

ssd_run_sample_step() (the matching per-shard step runners).

Examples

data <- ssd_scenario_data(ssddata::ccme_boron)
scenario <- ssd_define_scenario(data, nsim = 2L, seed = 42L)
ssd_scenario_sample_shards(scenario)
#> # A tibble: 2 × 4
#>   dataset      sim replace              tasks
#>   <chr>      <int> <lgl>   <list<tibble[,6]>>
#> 1 ccme_boron     1 TRUE               [1 × 6]
#> 2 ccme_boron     2 TRUE               [1 × 6]
data <- ssd_scenario_data(ssddata::ccme_boron)
scenario <- ssd_define_scenario(
  data,
  nsim = 2L,
  seed = 42L,
  rescale = c(FALSE, TRUE)
)
ssd_scenario_fit_shards(scenario)
#> # A tibble: 4 × 5
#>   dataset      sim  nrow rescale               tasks
#>   <chr>      <int> <int> <lgl>   <list<tibble[,14]>>
#> 1 ccme_boron     1     6 FALSE              [1 × 14]
#> 2 ccme_boron     1     6 TRUE               [1 × 14]
#> 3 ccme_boron     2     6 FALSE              [1 × 14]
#> 4 ccme_boron     2     6 TRUE               [1 × 14]
data <- ssd_scenario_data(ssddata::ccme_boron)
scenario <- ssd_define_scenario(
  data,
  nsim = 2L,
  seed = 42L,
  ci = TRUE
)
ssd_scenario_hc_shards(scenario)
#> # A tibble: 2 × 3
#>   dataset      sim               tasks
#>   <chr>      <int> <list<tibble[,18]>>
#> 1 ccme_boron     1            [1 × 18]
#> 2 ccme_boron     2            [1 × 18]