diff --git a/R/AcqFunction.R b/R/AcqFunction.R index 261bc1f8..f359135f 100644 --- a/R/AcqFunction.R +++ b/R/AcqFunction.R @@ -82,15 +82,15 @@ AcqFunction = R6Class("AcqFunction", }, #' @description - #' Evaluates multiple input values on the objective function. + #' Evaluates multiple input values on the acquisition function. #' #' @param xss (`list()`)\cr #' A list of lists that contains multiple x values, e.g. #' `list(list(x1 = 1, x2 = 2), list(x1 = 3, x2 = 4))`. #' #' @return data.table::data.table() that contains one y-column for - #' single-objective functions and multiple y-columns for multi-objective functions, - #' e.g. `data.table(y = 1:2)` or `data.table(y1 = 1:2, y2 = 3:4)`. + #' single-objective acquisition functions and multiple y-columns for multi-objective + #' acquisition functions, e.g. `data.table(y = 1:2)` or `data.table(y1 = 1:2, y2 = 3:4)`. eval_many = function(xss) { if (self$check_values) lapply(xss, self$domain$assert) res = invoke(private$.fun, rbindlist(xss, use.names = TRUE, fill = TRUE), .args = self$constants$values) @@ -105,8 +105,8 @@ AcqFunction = R6Class("AcqFunction", #' One point per row, e.g. `data.table(x1 = c(1, 3), x2 = c(2, 4))`. #' #' @return data.table::data.table() that contains one y-column for - #' single-objective functions and multiple y-columns for multi-objective - #' functions, e.g. `data.table(y = 1:2)` or `data.table(y1 = 1:2, y2 = 3:4)`. + #' single-objective acquisition functions and multiple y-columns for multi-objective + #' acquisition functions, e.g. `data.table(y = 1:2)` or `data.table(y1 = 1:2, y2 = 3:4)`. eval_dt = function(xdt) { if (self$check_values) self$domain$assert_dt(xdt) res = invoke(private$.fun, xdt, .args = self$constants$values) @@ -118,7 +118,7 @@ AcqFunction = R6Class("AcqFunction", active = list( #' @field direction (`"same"` | `"minimize"` | `"maximize"`)\cr #' Optimization direction of the acquisition function relative to the direction of the - #' objective function of the [bbotk::OptimInstance]. + #' objective function of the [bbotk::OptimInstance] related to the passed [bbotk::Archive]. #' Must be `"same"`, `"minimize"`, or `"maximize"`. direction = function(rhs) { if (missing(rhs)) { diff --git a/R/AcqOptimizer.R b/R/AcqOptimizer.R index 1802ff85..5fb8544e 100644 --- a/R/AcqOptimizer.R +++ b/R/AcqOptimizer.R @@ -12,7 +12,7 @@ #' result in computing the q- or multi-Expected Improvement) but rather the top `n_candidates` are selected from the #' [bbotk::ArchiveBatch] of the acquisition function [bbotk::OptimInstanceBatch]. #' Note that setting `n_candidates > 1` is usually not a sensible idea but it is still supported for experimental reasons. -#' Note that in the case of the acquisition function [bbotk::OptimInstanceBatch] being multi-criteria, due to using an [AcqFunctionMulti], +#' Note that in the case of the acquisition function [bbotk::OptimInstanceBatch] being multi-objective, due to using an [AcqFunctionMulti], #' selection of the best candidates is performed via non-dominated-sorting. #' Default is `1`. #' } @@ -26,7 +26,7 @@ #' the actual [bbotk::OptimInstance] (which is contained in the archive of the [AcqFunction])? #' This is sensible when using a population based acquisition function optimizer, e.g., local search or mutation. #' Default is `FALSE`. -#' Note that in the case of the [bbotk::OptimInstance] being multi-criteria, selection of the best point(s) is performed via non-dominated-sorting. +#' Note that in the case of the [bbotk::OptimInstance] being multi-objective, selection of the best point(s) is performed via non-dominated-sorting. #' } #' \item{`warmstart_size`}{`integer(1) | "all"`\cr #' Number of best points selected from the [bbotk::Archive] of the actual [bbotk::OptimInstance] that are to be used for warm starting. diff --git a/man/AcqFunction.Rd b/man/AcqFunction.Rd index 24f48a24..eb0fca42 100644 --- a/man/AcqFunction.Rd +++ b/man/AcqFunction.Rd @@ -34,7 +34,7 @@ Other Acquisition Function: \describe{ \item{\code{direction}}{(\code{"same"} | \code{"minimize"} | \code{"maximize"})\cr Optimization direction of the acquisition function relative to the direction of the -objective function of the \link[bbotk:OptimInstance]{bbotk::OptimInstance}. +objective function of the \link[bbotk:OptimInstance]{bbotk::OptimInstance} related to the passed \link[bbotk:Archive]{bbotk::Archive}. Must be \code{"same"}, \code{"minimize"}, or \code{"maximize"}.} \item{\code{surrogate_max_to_min}}{(\code{-1} | \code{1})\cr @@ -165,7 +165,7 @@ Can be implemented by subclasses. \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-AcqFunction-eval_many}{}}} \subsection{Method \code{eval_many()}}{ -Evaluates multiple input values on the objective function. +Evaluates multiple input values on the acquisition function. \subsection{Usage}{ \if{html}{\out{
}}\preformatted{AcqFunction$eval_many(xss)}\if{html}{\out{
}} } @@ -181,8 +181,8 @@ A list of lists that contains multiple x values, e.g. } \subsection{Returns}{ data.table::data.table() that contains one y-column for -single-objective functions and multiple y-columns for multi-objective functions, -e.g. \code{data.table(y = 1:2)} or \code{data.table(y1 = 1:2, y2 = 3:4)}. +single-objective acquisition functions and multiple y-columns for multi-objective +acquisition functions, e.g. \code{data.table(y = 1:2)} or \code{data.table(y1 = 1:2, y2 = 3:4)}. } } \if{html}{\out{
}} @@ -204,8 +204,8 @@ One point per row, e.g. \code{data.table(x1 = c(1, 3), x2 = c(2, 4))}.} } \subsection{Returns}{ data.table::data.table() that contains one y-column for -single-objective functions and multiple y-columns for multi-objective -functions, e.g. \code{data.table(y = 1:2)} or \code{data.table(y1 = 1:2, y2 = 3:4)}. +single-objective acquisition functions and multiple y-columns for multi-objective +acquisition functions, e.g. \code{data.table(y = 1:2)} or \code{data.table(y1 = 1:2, y2 = 3:4)}. } } \if{html}{\out{
}} diff --git a/man/AcqOptimizer.Rd b/man/AcqOptimizer.Rd index ed484834..627fc195 100644 --- a/man/AcqOptimizer.Rd +++ b/man/AcqOptimizer.Rd @@ -16,7 +16,7 @@ Note that this does not affect how the acquisition function itself is calculated result in computing the q- or multi-Expected Improvement) but rather the top \code{n_candidates} are selected from the \link[bbotk:ArchiveBatch]{bbotk::ArchiveBatch} of the acquisition function \link[bbotk:OptimInstanceBatch]{bbotk::OptimInstanceBatch}. Note that setting \code{n_candidates > 1} is usually not a sensible idea but it is still supported for experimental reasons. -Note that in the case of the acquisition function \link[bbotk:OptimInstanceBatch]{bbotk::OptimInstanceBatch} being multi-criteria, due to using an \link{AcqFunctionMulti}, +Note that in the case of the acquisition function \link[bbotk:OptimInstanceBatch]{bbotk::OptimInstanceBatch} being multi-objective, due to using an \link{AcqFunctionMulti}, selection of the best candidates is performed via non-dominated-sorting. Default is \code{1}. } @@ -30,7 +30,7 @@ Should the acquisition function optimization be warm-started by evaluating the b the actual \link[bbotk:OptimInstance]{bbotk::OptimInstance} (which is contained in the archive of the \link{AcqFunction})? This is sensible when using a population based acquisition function optimizer, e.g., local search or mutation. Default is \code{FALSE}. -Note that in the case of the \link[bbotk:OptimInstance]{bbotk::OptimInstance} being multi-criteria, selection of the best point(s) is performed via non-dominated-sorting. +Note that in the case of the \link[bbotk:OptimInstance]{bbotk::OptimInstance} being multi-objective, selection of the best point(s) is performed via non-dominated-sorting. } \item{\code{warmstart_size}}{\code{integer(1) | "all"}\cr Number of best points selected from the \link[bbotk:Archive]{bbotk::Archive} of the actual \link[bbotk:OptimInstance]{bbotk::OptimInstance} that are to be used for warm starting.