Keep unique solutions in a solution set

Return a reduced solutionset-class object containing one representative from each group of equivalent solutions.

Solutions can be considered equivalent according to either their decision vectors or their objective values.

Usage

solution_unique(
  x,
  by = c("decisions", "objectives"),
  keep = c("first", "last"),
  objectives = NULL,
  tolerance = sqrt(.Machine$double.eps)
)

Arguments

x: A solutionset-class object returned by solve.
by: Character. Definition of uniqueness. One of "decisions" or "objectives".
keep: Character. Which representative to retain from each group of equivalent solutions. If "first", retain the first solution in the current run order. If "last", retain the last.
objectives: Optional character vector of objective names to compare when by = "objectives". If NULL, all available objectives are used. This argument is ignored when by = "decisions".
tolerance: Non-negative numeric tolerance used when comparing objective values. It is only used when by = "objectives".

Value

A new solutionset-class object containing one representative from each group of equivalent stored solutions, together with any runs that did not produce a stored solution.

Details

solution_unique() is a solution-set management function. It removes repeated solutions while consistently filtering the run table, design table, stored run-level solutions, and summary tables.

Two definitions of uniqueness are supported:

by = "decisions" compares the complete stored decision vector of each solution. Two solutions are considered equivalent when their decision vectors are identical. This identifies repeated planning-unit or planning-unit/action configurations, even when they were generated by different runs or multi-objective parameter combinations.
by = "objectives" compares the selected objective values. Two solutions are considered equivalent when all compared objective values are equal within the specified numerical tolerance. Such solutions may still have different decision vectors.

Consequently, uniqueness in objective space and uniqueness in decision space are not equivalent. Two spatially different solutions may produce the same objective values, while repeated runs may generate exactly the same decision vector.

Only runs with a stored solution_id can be assessed. Runs without a stored solution, such as infeasible runs, are preserved unchanged. This retains the full run history while removing only duplicated stored solutions.

The function does not renumber run_id or solution_id. The representative retained from each duplicate group keeps its original identifiers, preserving traceability to the original run design.

For by = "objectives", numerical equality is assessed using a relative comparison:

$$ |a-b| \leq \epsilon \max(1, |a|, |b|), $$

where $\epsilon$ is specified by tolerance. This avoids treating insignificant floating-point differences as distinct objective points.

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 = 7,
      include_extremes = TRUE
    ),
    normalize_weights = TRUE
  )

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

  solutions <- solve(problem)

  # Keep one representative for each distinct decision vector
  unique_decisions <- solution_unique(
    solutions,
    by = "decisions"
  )

  # Keep one representative for each distinct objective point
  unique_objectives <- solution_unique(
    solutions,
    by = "objectives"
  )

  # Compare only selected objectives
  unique_cost_benefit <- solution_unique(
    solutions,
    by = "objectives",
    objectives = c("cost", "benefit")
  )

  # Keep the last representative of each duplicate group
  unique_last <- solution_unique(
    solutions,
    by = "decisions",
    keep = "last"
  )

  # Compare the number of stored solutions
  sum(!is.na(get_runs(solutions)$solution_id))
  sum(!is.na(get_runs(unique_decisions)$solution_id))
  sum(!is.na(get_runs(unique_objectives)$solution_id))

  # Typical cleaning workflow
  if (requireNamespace("moocore", quietly = TRUE)) {
    clean_solutions <- solutions |>
      solution_filter(
        feasible_only = TRUE,
        nondominated = TRUE
      ) |>
      solution_unique(
        by = "decisions"
      )
  }
}

Usage

Arguments

Value

Details

See also

Examples