Run an identification to completion with a simulator

Repeats "choose the next alternative, evaluate it, update" until every alternative is certified or the budget is exhausted.

Usage

pareto_run(design, simulate, batch = 1L, initial = 2L, max_rounds = 1e+05)

Arguments

design: A pareto_design().
simulate: A function function(alternative, n) returning a data frame (or matrix) with n rows and one column per objective, in the design's objective order or with the objective names.
batch: Evaluations per call for the chosen alternative.
initial: Evaluations of every alternative before the adaptive phase.
max_rounds: Safety cap on the number of adaptive rounds.

Value

The final paretoinfer_state.

Examples

design <- pareto_design(alternatives = 4, objectives = 2, bounds = c(0, 1), epsilon = 0.05,
                        max_evaluations = 400)
means <- tibble::tribble(
  ~alternative, ~y1, ~y2,
  "A1",         0.2, 0.7,
  "A2",         0.5, 0.4,
  "A3",         0.7, 0.2,
  "A4",         0.6, 0.6
)
sim <- function(alternative, n, h = 0.1) {
  mu <- means[means$alternative == alternative, ]
  tibble::tibble(y1 = runif(n, mu$y1 - h, mu$y1 + h),
                 y2 = runif(n, mu$y2 - h, mu$y2 + h))
}
set.seed(1)
state <- design |> pareto_run(sim)
state
#> 
#> ── Pareto identification ───────────────────────────────────────────────────────
#> 4 alternatives, 2 objectives, alpha 0.05, epsilon 0.05, 0.05.
#> 307 evaluations, cost 307.
#> • Estimated frontier: "A1", "A2", and "A3"
#> • Plausible Pareto set: "A1", "A2", and "A3" (certified optimal: A1, A2, A3)
#> • Certified dominated: A4
#> ℹ Stopped: identified.