Get run-level metadata from a solution set

Extract the run table from a solutionset-class object.

Usage

get_runs(x)

Arguments

x: A solutionset-class object returned by solve.

Value

A data.frame with one row per attempted optimization run. The table contains run metadata and the numeric mapping between run_id and solution_id, but not objective-value columns.

Details

A run represents an attempted optimization solve. Each run has a unique run_id. Only runs that produce a stored solution receive a solution_id.

The solution_id is numeric and matches the corresponding run_id. Therefore, if a run fails or is infeasible, its solution_id is NA; if a later run succeeds, its solution_id keeps the same value as its run_id.

This function is the user-facing place where the relationship between attempted runs and stored solutions is reported.

Objective values are not returned by get_runs(). To extract objective values, use get_objectives.

Examples

# Load a complete simulated planning problem.
example_data <- load_sim_multiaction()

problem <- create_problem(
  pu = example_data$planning_units,
  features = example_data$features,
  dist_features = example_data$dist_features,
  cost = "cost"
) |>
  add_actions(
    example_data$actions,
    cost = example_data$action_costs
  ) |>
  add_effects(
    example_data$effects,
    effect_type = "delta"
  ) |>
  add_constraint_targets_relative(0.05) |>
  add_objective_min_cost(alias = "cost", include_pu_cost = FALSE) |>
  add_objective_max_benefit(alias = "benefit") |>
  set_method_weighted_sum(
    aliases = c("cost", "benefit"),
    runs = set_runs_grid(n = 3),
    normalize_weights = TRUE
  )

if (requireNamespace("rcbc", quietly = TRUE)) {
  problem <- set_solver_cbc(
    problem,
    verbose = FALSE
  )

  solutions <- solve(problem)

  get_runs(solutions)
}
#>   run_id solution_id  status runtime gap
#> 1      1           1 optimal    0.02   0
#> 2      2           2 optimal    0.01   0
#> 3      3           3 optimal    0.00   0