diff --git a/NAMESPACE b/NAMESPACE
index ebfc18f3..93177b1c 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -66,6 +66,9 @@ import(mlr3misc)
import(paradox)
importFrom(R6,R6Class)
importFrom(methods,formalArgs)
+importFrom(mlr3misc,clbk)
+importFrom(mlr3misc,clbks)
+importFrom(mlr3misc,mlr_callbacks)
importFrom(utils,bibentry)
importFrom(utils,capture.output)
importFrom(utils,head)
diff --git a/NEWS.md b/NEWS.md
index fd556a36..575c7039 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,7 @@
# bbotk (development version)
+* refactor: Optimize the runtime of `archive$best()` method with partial sorting.
+
# bbotk 0.7.3
* fix: `OptimInstance$print()` errored when the search space was empty.
diff --git a/R/Archive.R b/R/Archive.R
index ccfc9c7b..81c4e90f 100644
--- a/R/Archive.R
+++ b/R/Archive.R
@@ -82,41 +82,48 @@ Archive = R6Class("Archive",
},
#' @description
- #' Returns the best scoring evaluation(s). For single-crit optimization,
- #' the solution that minimizes / maximizes the objective function.
+ #' Returns the best scoring evaluation(s).
+ #' For single-crit optimization, the solution that minimizes / maximizes the objective function.
#' For multi-crit optimization, the Pareto set / front.
#'
#' @param batch (`integer()`)\cr
- #' The batch number(s) to limit the best results to. Default is
- #' all batches.
+ #' The batch number(s) to limit the best results to.
+ #' Default is all batches.
#' @param n_select (`integer(1L)`)\cr
- #' Amount of points to select. Ignored for multi-crit optimization.
+ #' Amount of points to select.
+ #' Ignored for multi-crit optimization.
#'
#' @return [data.table::data.table()]
- best = function(batch = NULL, n_select = 1) {
- if (self$n_batch == 0L) return(data.table())
- if (is.null(batch)) batch = seq_len(self$n_batch)
+ best = function(batch = NULL, n_select = 1L) {
+ if (!self$n_batch) return(data.table())
assert_subset(batch, seq_len(self$n_batch))
+ assert_int(n_select, lower = 1L)
- tab = self$data[get("batch_nr") %in% batch, ]
- assert_int(n_select, lower = 1L, upper = nrow(tab))
+ tab = if (is.null(batch)) self$data else self$data[list(batch), , on = "batch_nr"]
- max_to_min = self$codomain$maximization_to_minimization
if (self$codomain$target_length == 1L) {
- setorderv(tab, self$cols_y, order = max_to_min, na.last = TRUE)
- res = tab[seq_len(n_select), ]
+ if (n_select == 1L) {
+ # use which_max to find the best point
+ y = tab[[self$cols_y]] * -self$codomain$maximization_to_minimization
+ ii = which_max(y, ties_method = "random")
+ tab[ii]
+ } else {
+ # use partial sort to find the best points
+ y = tab[[self$cols_y]] * self$codomain$maximization_to_minimization
+ i = sort(y, partial = n_select)[n_select]
+ ii = which(y <= i)
+ tab[ii]
+ }
} else {
+ # use non-dominated sorting to find the best points
ymat = t(as.matrix(tab[, self$cols_y, with = FALSE]))
- ymat = max_to_min * ymat
- res = tab[!is_dominated(ymat)]
+ ymat = self$codomain$maximization_to_minimization * ymat
+ tab[!is_dominated(ymat)]
}
-
- return(res)
},
#' @description
- #' Calculate best points w.r.t. non dominated sorting with hypervolume
- #' contribution.
+ #' Calculate best points w.r.t. non dominated sorting with hypervolume contribution.
#'
#' @param batch (`integer()`)\cr
#' The batch number(s) to limit the best points to. Default is
diff --git a/man/Archive.Rd b/man/Archive.Rd
index 4c4008fb..8294d7d7 100644
--- a/man/Archive.Rd
+++ b/man/Archive.Rd
@@ -134,22 +134,23 @@ Optimal outcome.}
\if{html}{\out{}}
\if{latex}{\out{\hypertarget{method-Archive-best}{}}}
\subsection{Method \code{best()}}{
-Returns the best scoring evaluation(s). For single-crit optimization,
-the solution that minimizes / maximizes the objective function.
+Returns the best scoring evaluation(s).
+For single-crit optimization, the solution that minimizes / maximizes the objective function.
For multi-crit optimization, the Pareto set / front.
\subsection{Usage}{
-\if{html}{\out{}}\preformatted{Archive$best(batch = NULL, n_select = 1)}\if{html}{\out{
}}
+\if{html}{\out{}}\preformatted{Archive$best(batch = NULL, n_select = 1L)}\if{html}{\out{
}}
}
\subsection{Arguments}{
\if{html}{\out{}}
\describe{
\item{\code{batch}}{(\code{integer()})\cr
-The batch number(s) to limit the best results to. Default is
-all batches.}
+The batch number(s) to limit the best results to.
+Default is all batches.}
\item{\code{n_select}}{(\code{integer(1L)})\cr
-Amount of points to select. Ignored for multi-crit optimization.}
+Amount of points to select.
+Ignored for multi-crit optimization.}
}
\if{html}{\out{
}}
}
@@ -161,8 +162,7 @@ Amount of points to select. Ignored for multi-crit optimization.}
\if{html}{\out{}}
\if{latex}{\out{\hypertarget{method-Archive-nds_selection}{}}}
\subsection{Method \code{nds_selection()}}{
-Calculate best points w.r.t. non dominated sorting with hypervolume
-contribution.
+Calculate best points w.r.t. non dominated sorting with hypervolume contribution.
\subsection{Usage}{
\if{html}{\out{}}\preformatted{Archive$nds_selection(batch = NULL, n_select = 1, ref_point = NULL)}\if{html}{\out{
}}
}
diff --git a/tests/testthat/test_Archive.R b/tests/testthat/test_Archive.R
index 83eb69ab..36dece5b 100644
--- a/tests/testthat/test_Archive.R
+++ b/tests/testthat/test_Archive.R
@@ -120,3 +120,55 @@ test_that("deep clone works", {
expect_different_address(a1$search_space, a2$search_space)
expect_different_address(a1$codomain, a2$codomain)
})
+
+test_that("best method works with maximization", {
+ codomain = FUN_2D_CODOMAIN
+ codomain$params$y$tags = "maximize"
+
+ archive = Archive$new(PS_2D, FUN_2D_CODOMAIN)
+ xdt = data.table(x1 = runif(5), x2 = runif(5))
+ xss_trafoed = list(list(x1 = runif(5), x2 = runif(5)))
+ ydt = data.table(y = c(1, 0.25, 2, 0.5, 0.3))
+ archive$add_evals(xdt, xss_trafoed, ydt)
+
+ expect_equal(archive$best()$y, 2)
+})
+
+test_that("best method works with minimization", {
+ codomain = FUN_2D_CODOMAIN
+ codomain$params$y$tags = "minimize"
+
+ archive = Archive$new(PS_2D, FUN_2D_CODOMAIN)
+ xdt = data.table(x1 = runif(5), x2 = runif(5))
+ xss_trafoed = list(list(x1 = runif(5), x2 = runif(5)))
+ ydt = data.table(y = c(1, 0.25, 2, 0.5, 0.3))
+ archive$add_evals(xdt, xss_trafoed, ydt)
+
+ expect_equal(archive$best()$y, 0.25)
+})
+
+test_that("best method returns top n results with maximization", {
+ codomain = FUN_2D_CODOMAIN
+ codomain$params$y$tags = "maximize"
+
+ archive = Archive$new(PS_2D, FUN_2D_CODOMAIN)
+ xdt = data.table(x1 = runif(5), x2 = runif(5))
+ xss_trafoed = list(list(x1 = runif(5), x2 = runif(5)))
+ ydt = data.table(y = c(1, 0.25, 2, 0.5, 0.3))
+ archive$add_evals(xdt, xss_trafoed, ydt)
+
+ expect_equal(archive$best(n_select = 2)$y, c(1, 2))
+})
+
+test_that("best method returns top n results with minimization", {
+ codomain = FUN_2D_CODOMAIN
+ codomain$params$y$tags = "minimize"
+
+ archive = Archive$new(PS_2D, FUN_2D_CODOMAIN)
+ xdt = data.table(x1 = runif(5), x2 = runif(5))
+ xss_trafoed = list(list(x1 = runif(5), x2 = runif(5)))
+ ydt = data.table(y = c(1, 0.25, 2, 0.5, 0.3))
+ archive$add_evals(xdt, xss_trafoed, ydt)
+
+ expect_equal(archive$best(n_select = 2)$y, c(0.25, 0.3))
+})