diff --git a/DESCRIPTION b/DESCRIPTION
index 48499663..5ec5f74e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -59,6 +59,7 @@ Suggests:
lhs,
mlr3learners (>= 0.5.4),
mlr3pipelines (>= 0.4.2),
+ mlr3hyperband (>= 0.4.4),
nloptr,
ranger,
rgenoud,
@@ -95,6 +96,7 @@ Collate:
'ResultAssigner.R'
'ResultAssignerArchive.R'
'ResultAssignerSurrogate.R'
+ 'SamplerMbo.R'
'Surrogate.R'
'SurrogateLearner.R'
'SurrogateLearnerCollection.R'
diff --git a/NAMESPACE b/NAMESPACE
index 9b8e3b96..b862088e 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -21,6 +21,7 @@ export(OptimizerMbo)
export(ResultAssigner)
export(ResultAssignerArchive)
export(ResultAssignerSurrogate)
+export(SamplerMbo)
export(Surrogate)
export(SurrogateLearner)
export(SurrogateLearnerCollection)
diff --git a/R/SamplerMbo.R b/R/SamplerMbo.R
new file mode 100644
index 00000000..078c921b
--- /dev/null
+++ b/R/SamplerMbo.R
@@ -0,0 +1,167 @@
+#' @title Sampler Suitable for Combining Hyperband With Model Based Optimization
+#'
+#' @description
+#' This class provides a [paradox::Sampler] that allows for sampling points relying on Model Based Optimization.
+#' Using this sampler together with [mlr3hyperband::OptimizerBatchHyperband] or [mlr3hyperband::TunerBatchHyperband] allows for constructing an optimizer
+#' that performs a combination of Hyperband and Model Based Optimization similarly as the BOHB algorithm in the context of multifidelity optimization.
+#'
+#' @export
+#' @examples
+#'if (requireNamespace("mlr3learners") &
+#' requireNamespace("DiceKriging") &
+#' requireNamespace("rgenoud") &
+#' requireNamespace("mlr3hyperband")) {
+#'
+#' library(bbotk)
+#' library(paradox)
+#' library(mlr3hyperband)
+#'
+#' # Augmented Branin function
+#' fun = function(xs) {
+#' y =
+#' (
+#' xs[["x2"]] - ((5.1 / (4 * pi^2)) - 0.1 * (1 - (xs[["fidelity"]] / 100))) *
+#' xs[["x1"]]^2 + (5 / pi) * xs[["x1"]] - 6
+#' ) ^ 2 +
+#' 10 * (1 - (1 / (8 * pi))) * cos(xs[["x1"]]) + 10
+#' list(y = y)
+#' }
+#' domain = ps(
+#' x1 = p_dbl(lower = -5, upper = 10),
+#' x2 = p_dbl(lower = 0, upper = 15),
+#' fidelity = p_int(lower = 10, upper = 100, tags = "budget"))
+#' codomain = ps(y = p_dbl(tags = "minimize"))
+#' objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)
+#'
+#' instance = OptimInstanceSingleCrit$new(objective = objective, terminator = trm("none"))
+#'
+#' surrogate = default_surrogate(instance)
+#'
+#' acq_function = acqf("ei")
+#'
+#' acq_optimizer = acqo(
+#' optimizer = opt("random_search", batch_size = 100),
+#' terminator = trm("evals", n_evals = 100))
+#'
+#' sampler = SamplerMbo$new(
+#' instance = instance,
+#' surrogate = surrogate,
+#' acq_function = acq_function,
+#' acq_optimizer = acq_optimizer)
+#'
+#' optimizer = opt("hyperband", eta = 3, sampler = sampler, repetitions = 2)
+#' optimizer$optimize(instance)
+#' }
+SamplerMbo = R6Class("SamplerMbo", inherit = paradox::Sampler,
+ public = list(
+ #' @field instance ([bbotk::OptimInstance)\cr
+ #' The instance that is to be optimized.
+ instance = NULL,
+
+ #' @field budget_id (`character(1)`)\cr
+ #' Id of the budget parameter part of the search space of the instance (i.e., the single numeric or integer parameter tagged via `"budget"`).
+ budget_id = NULL,
+
+ #' @field search_space_sampler ([paradox::Domain])\cr
+ #' Search space the sampler operates on.
+ #' This is the same as the search space of the [bbotk::OptimInstance] to be optimized but excludes the parameter specified via `budget_id`.
+ search_space_sampler = NULL,
+
+ #' @field rho (`numeric(1)`)\cr
+ #' Fraction of configurations that should be sampled uniformly at random.
+ rho = NULL,
+
+ #' @field Nmin (`integer(1)`)\cr
+ #' Minimum number of observations required for any budget level for the model based proposal mechanism to take action.
+ Nmin = NULL,
+
+ #' @field tmp_archive ([bbotk::Archive])\cr
+ #' Temporal archive which is a copy of the archive of the [bbotk::OptimInstance] used for updating the surrogate model within the model based proposal mechanism.
+ tmp_archive = NULL,
+
+ #' @template field_surrogate
+ surrogate = NULL,
+
+ #' @template field_acq_function
+ acq_function = NULL,
+
+ #' @template field_acq_optimizer
+ acq_optimizer = NULL,
+
+ #' @field sampler_random ([paradox::SamplerUnif])\cr
+ #' The sampler responsible for proposing points uniformly at random based on the fraction specified as `rho`.
+ sampler_random = NULL,
+
+ #' @description
+ #' Creates a new instance of this [R6][R6::R6Class] class.
+ #'
+ #' @param instance ([bbotk::OptimInstance)\cr
+ #' The instance that is to be optimized.
+ #' Note that this is required as the model based sampling requires access to the evaluations logged in the [bbotk::Archive].
+ #' @template param_surrogate
+ #' @template param_acq_function
+ #' @template param_acq_optimizer
+ #' @param rho (`numeric(1)`)\cr
+ #' Fraction of configurations that should be sampled uniformly at random.
+ #' @param Nmin (`integer(1)`)\cr
+ #' Minimum number of observations required for any budget level for the model based proposal mechanism to take action.
+ #' Default is to use the number of parameters of the search space of the instance + 2.
+ initialize = function(instance, surrogate, acq_function, acq_optimizer, rho = 0.25, Nmin = NULL) {
+ self$instance = assert_r6(instance, classes = "OptimInstance")
+ self$budget_id = self$instance$search_space$ids(tags = "budget")
+ if (length(self$budget_id) != 1L) {
+ stop('Exactly one parameter must be tagged with "budget."')
+ }
+ assert_choice(self$instance$search_space$class[[self$budget_id]], c("ParamInt", "ParamDbl"))
+ self$search_space_sampler = self$instance$search_space$clone(deep = TRUE)$subset(setdiff(self$instance$search_space$ids(), self$budget_id))
+ self$rho = assert_number(rho, lower = 0, upper = 1)
+ if (is.null(Nmin)) {
+ Nmin = self$instance$search_space$length + 2L # budget param is in search space therefore d - 1 + 1 which is d
+ }
+ self$Nmin = assert_int(Nmin, lower = 1L)
+ self$tmp_archive = self$instance$archive$clone(deep = TRUE)
+ self$surrogate = assert_r6(surrogate, classes = "Surrogate")
+ self$acq_function = assert_r6(acq_function, classes = "AcqFunction")
+ self$acq_optimizer = assert_r6(acq_optimizer, classes = "AcqOptimizer")
+ self$surrogate$archive = self$tmp_archive
+ self$surrogate$cols_x = setdiff(surrogate$cols_x, self$budget_id)
+ self$acq_function$surrogate = self$surrogate
+ self$acq_optimizer$acq_function = self$acq_function
+ self$sampler_random = SamplerUnif$new(self$search_space_sampler)
+ super$initialize(self$search_space_sampler)
+ # FIXME: check if instance, surrogate, acq_function and acq_optimizer work for the instance
+ }
+ ),
+ private = list(
+ .sample = function(n) {
+ # we need at the minimum Nmin observations for any budget
+ # so if we have less then Nmin in total we surely cannot do a model based proposal
+ if (self$instance$archive$n_evals < self$Nmin) {
+ return(self$sampler_random$sample(n)$data)
+ }
+ budget_table = self$instance$archive$data[, .N, by = eval(self$budget_id)][N >= self$Nmin]
+ if (nrow(budget_table) == 0L) {
+ return(self$sampler_random$sample(n)$data)
+ }
+ # a fraction of rho of the n candidate points are proposed uniformly at random
+ propose_random = runif(n, min = 0, max = 1) <= self$rho
+ n_random = sum(propose_random)
+ xdt = if (n_random < n) {
+ # we order the budget table by the budget id and take the largest budget that has at least Nmin observations
+ setorderv(budget_table, cols = self$budget_id, order = -1L)
+ data = self$instance$archive$data[get(self$budget_id) == budget_table[1L, ][[self$budget_id]], ]
+ # we then do one iteration of BO based on this subset of observations excluding the budget id as a parameter
+ self$tmp_archive$data = data
+ self$acq_function$surrogate$update()
+ self$acq_function$update()
+ self$acq_optimizer$param_set$values$n_candidates = n - n_random
+ xdt = self$acq_optimizer$optimize()[, self$search_space_sampler$ids(), with = FALSE]
+ xdt_random = self$sampler_random$sample(n_random)$data
+ rbind(xdt, xdt_random)
+ } else {
+ self$sampler_random$sample(n_random)$data
+ }
+ xdt
+ }
+ )
+)
diff --git a/R/zzz.R b/R/zzz.R
index 003b52c1..64370aaf 100644
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -44,7 +44,7 @@ register_mlr3tuning = function() {
} # nocov end
# static code checks should not complain about commonly used data.table columns
-utils::globalVariables("y_scal")
+utils::globalVariables(c("N", "y_scal"))
if (!("mlr3mbo" %in% Sys.getenv("DEVTOOLS_LOAD"))) {
leanify_package()
diff --git a/man/SamplerMbo.Rd b/man/SamplerMbo.Rd
new file mode 100644
index 00000000..baf916a3
--- /dev/null
+++ b/man/SamplerMbo.Rd
@@ -0,0 +1,172 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/SamplerMbo.R
+\name{SamplerMbo}
+\alias{SamplerMbo}
+\title{Sampler Suitable for Combining Hyperband With Model Based Optimization}
+\description{
+This class provides a \link[paradox:Sampler]{paradox::Sampler} that allows for sampling points relying on Model Based Optimization.
+Using this sampler together with \link[mlr3hyperband:mlr_optimizers_hyperband]{mlr3hyperband::OptimizerBatchHyperband} or \link[mlr3hyperband:mlr_tuners_hyperband]{mlr3hyperband::TunerBatchHyperband} allows for constructing an optimizer
+that performs a combination of Hyperband and Model Based Optimization similarly as the BOHB algorithm in the context of multifidelity optimization.
+}
+\examples{
+if (requireNamespace("mlr3learners") &
+ requireNamespace("DiceKriging") &
+ requireNamespace("rgenoud") &
+ requireNamespace("mlr3hyperband")) {
+
+ library(bbotk)
+ library(paradox)
+ library(mlr3hyperband)
+
+ # Augmented Branin function
+ fun = function(xs) {
+ y =
+ (
+ xs[["x2"]] - ((5.1 / (4 * pi^2)) - 0.1 * (1 - (xs[["fidelity"]] / 100))) *
+ xs[["x1"]]^2 + (5 / pi) * xs[["x1"]] - 6
+ ) ^ 2 +
+ 10 * (1 - (1 / (8 * pi))) * cos(xs[["x1"]]) + 10
+ list(y = y)
+ }
+ domain = ps(
+ x1 = p_dbl(lower = -5, upper = 10),
+ x2 = p_dbl(lower = 0, upper = 15),
+ fidelity = p_int(lower = 10, upper = 100, tags = "budget"))
+ codomain = ps(y = p_dbl(tags = "minimize"))
+ objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)
+
+ instance = OptimInstanceSingleCrit$new(objective = objective, terminator = trm("none"))
+
+ surrogate = default_surrogate(instance)
+
+ acq_function = acqf("ei")
+
+ acq_optimizer = acqo(
+ optimizer = opt("random_search", batch_size = 100),
+ terminator = trm("evals", n_evals = 100))
+
+ sampler = SamplerMbo$new(
+ instance = instance,
+ surrogate = surrogate,
+ acq_function = acq_function,
+ acq_optimizer = acq_optimizer)
+
+ optimizer = opt("hyperband", eta = 3, sampler = sampler, repetitions = 2)
+ optimizer$optimize(instance)
+}
+}
+\section{Super class}{
+\code{\link[paradox:Sampler]{paradox::Sampler}} -> \code{SamplerMbo}
+}
+\section{Public fields}{
+\if{html}{\out{}}
+\describe{
+\item{\code{instance}}{([bbotk::OptimInstance)\cr
+The instance that is to be optimized.}
+
+\item{\code{budget_id}}{(\code{character(1)})\cr
+Id of the budget parameter part of the search space of the instance (i.e., the single numeric or integer parameter tagged via \code{"budget"}).}
+
+\item{\code{search_space_sampler}}{(\link[paradox:Domain]{paradox::Domain})\cr
+Search space the sampler operates on.
+This is the same as the search space of the \link[bbotk:OptimInstance]{bbotk::OptimInstance} to be optimized but excludes the parameter specified via \code{budget_id}.}
+
+\item{\code{rho}}{(\code{numeric(1)})\cr
+Fraction of configurations that should be sampled uniformly at random.}
+
+\item{\code{Nmin}}{(\code{integer(1)})\cr
+Minimum number of observations required for any budget level for the model based proposal mechanism to take action.}
+
+\item{\code{tmp_archive}}{(\link[bbotk:Archive]{bbotk::Archive})\cr
+Temporal archive which is a copy of the archive of the \link[bbotk:OptimInstance]{bbotk::OptimInstance} used for updating the surrogate model within the model based proposal mechanism.}
+
+\item{\code{surrogate}}{(\link{Surrogate} | \code{NULL})\cr
+The surrogate.}
+
+\item{\code{acq_function}}{(\link{AcqFunction} | \code{NULL})\cr
+The acquisition function.}
+
+\item{\code{acq_optimizer}}{(\link{AcqOptimizer} | \code{NULL})\cr
+The acquisition function optimizer.}
+
+\item{\code{sampler_random}}{(\link[paradox:SamplerUnif]{paradox::SamplerUnif})\cr
+The sampler responsible for proposing points uniformly at random based on the fraction specified as \code{rho}.}
+}
+\if{html}{\out{
}}
+}
+\section{Methods}{
+\subsection{Public methods}{
+\itemize{
+\item \href{#method-SamplerMbo-new}{\code{SamplerMbo$new()}}
+\item \href{#method-SamplerMbo-clone}{\code{SamplerMbo$clone()}}
+}
+}
+\if{html}{\out{
+Inherited methods
+
+
}}
+\if{html}{\out{}}
+\if{latex}{\out{\hypertarget{method-SamplerMbo-new}{}}}
+\subsection{Method \code{new()}}{
+Creates a new instance of this \link[R6:R6Class]{R6} class.
+\subsection{Usage}{
+\if{html}{\out{}}\preformatted{SamplerMbo$new(
+ instance,
+ surrogate,
+ acq_function,
+ acq_optimizer,
+ rho = 0.25,
+ Nmin = NULL
+)}\if{html}{\out{
}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{}}
+\describe{
+\item{\code{instance}}{([bbotk::OptimInstance)\cr
+The instance that is to be optimized.
+Note that this is required as the model based sampling requires access to the evaluations logged in the \link[bbotk:Archive]{bbotk::Archive}.}
+
+\item{\code{surrogate}}{(\link{Surrogate} | \code{NULL})\cr
+The surrogate.}
+
+\item{\code{acq_function}}{(\link{AcqFunction} | \code{NULL})\cr
+The acquisition function.}
+
+\item{\code{acq_optimizer}}{(\link{AcqOptimizer} | \code{NULL})\cr
+The acquisition function optimizer.}
+
+\item{\code{rho}}{(\code{numeric(1)})\cr
+Fraction of configurations that should be sampled uniformly at random.}
+
+\item{\code{Nmin}}{(\code{integer(1)})\cr
+Minimum number of observations required for any budget level for the model based proposal mechanism to take action.
+Default is to use the number of parameters of the search space of the instance + 2.}
+}
+\if{html}{\out{
}}
+}
+}
+\if{html}{\out{
}}
+\if{html}{\out{}}
+\if{latex}{\out{\hypertarget{method-SamplerMbo-clone}{}}}
+\subsection{Method \code{clone()}}{
+The objects of this class are cloneable with this method.
+\subsection{Usage}{
+\if{html}{\out{}}\preformatted{SamplerMbo$clone(deep = FALSE)}\if{html}{\out{
}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{}}
+\describe{
+\item{\code{deep}}{Whether to make a deep clone.}
+}
+\if{html}{\out{
}}
+}
+}
+}