Get objective values from a solution set

Extract objective values from the runs stored in a solutionset-class object.

Usage

get_objectives(x, format = c("long", "wide"), feasible_only = FALSE)

Arguments

x

A solutionset-class object returned by solve.

format

Character. Output representation, either "long" or "wide". Defaults to "long".

feasible_only

Logical. If TRUE, extract values only from runs whose status is interpreted as usable. Defaults to FALSE.

Value

If format = "long", a data.frame with columns run_id, solution_id, objective, and value.

If format = "wide", a data.frame with run_id, solution_id, and one column per objective.

Details

Objective values are read from run-table columns named value_<objective>, where <objective> is the registered objective alias.

Runs without a stored solution may contain missing objective values. Use feasible_only = TRUE, or filter the SolutionSet beforehand, when only solved runs should be included.

In long format, every run-objective combination occupies one row. In wide format, every run occupies one row and every objective occupies one column.

See also

get_runs, get_objective_specs, frontier_extremes, frontier_distances

Examples

pu <- data.frame(
  id = 1:4,
  cost = c(1, 2, 3, 4)
)

features <- data.frame(
  id = 1:2,
  name = c("sp1", "sp2")
)

dist_features <- data.frame(
  pu = c(1, 1, 2, 3, 4),
  feature = c(1, 2, 2, 1, 2),
  amount = c(5, 2, 3, 4, 1)
)

actions <- data.frame(
  id = c("conservation", "restoration")
)

effects <- data.frame(
  action = rep(actions$id, each = 2),
  feature = rep(features$id, times = 2),
  multiplier = c(
    1.0, 1.0,
    1.5, 1.5
  )
)

problem <- create_problem(
  pu = pu,
  features = features,
  dist_features = dist_features,
  cost = "cost"
) |>
  add_actions(
    actions = actions,
    cost = c(
      conservation = 1,
      restoration = 2
    )
  ) |>
  add_effects(
    effects = effects,
    effect_type = "after"
  ) |>
  add_constraint_targets_relative(0.05) |>
  add_objective_min_cost(alias = "cost") |>
  add_objective_max_benefit(alias = "benefit") |>
  set_method_weighted_sum(
    aliases = c("cost", "benefit"),
    runs = run_grid(
      n = 5,
      include_extremes = TRUE
    ),
    normalize_weights = TRUE
  )

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

  solutions <- solve(problem)

  # Long format
  get_objectives(solutions)

  # Wide format
  get_objectives(
    solutions,
    format = "wide"
  )

  # Objective values from usable runs only
  get_objectives(
    solutions,
    feasible_only = TRUE
  )
}
#>    run_id solution_id objective value
#> 1       1          s1      cost   2.0
#> 2       2          s2      cost   3.0
#> 3       3          s3      cost   3.0
#> 4       4          s4      cost  12.0
#> 5       5          s5      cost  18.0
#> 6       1          s1   benefit   0.0
#> 7       2          s2   benefit   3.5
#> 8       3          s3   benefit   3.5
#> 9       4          s4   benefit   7.0
#> 10      5          s5   benefit   7.5