diff --git a/DESCRIPTION b/DESCRIPTION
index 0caa59272..52c8c7c5c 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -44,9 +44,12 @@ Suggests:
processx,
progressr,
redux,
+ reticulate,
RhpcBLASctl,
rush (>= 0.4.1),
testthat (>= 3.0.0)
+Remotes:
+ mlr-org/paradox@cs
Config/testthat/edition: 3
Config/testthat/parallel: false
Encoding: UTF-8
@@ -95,6 +98,7 @@ Collate:
'OptimizerBatchLocalSearch.R'
'OptimizerBatchNLoptr.R'
'OptimizerBatchRandomSearch.R'
+ 'OptimizerBatchSmac3.R'
'Progressor.R'
'mlr_terminators.R'
'Terminator.R'
diff --git a/NAMESPACE b/NAMESPACE
index b3194c483..14ac9d790 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -50,6 +50,7 @@ export(OptimizerBatchIrace)
export(OptimizerBatchLocalSearch)
export(OptimizerBatchNLoptr)
export(OptimizerBatchRandomSearch)
+export(OptimizerBatchSmac3)
export(Terminator)
export(TerminatorClockTime)
export(TerminatorCombo)
diff --git a/R/OptimizerBatchSmac3.R b/R/OptimizerBatchSmac3.R
new file mode 100644
index 000000000..25aebe683
--- /dev/null
+++ b/R/OptimizerBatchSmac3.R
@@ -0,0 +1,200 @@
+#' @title Sequential Model-Based Algorithm Configuration (SMAC3)
+#'
+#' @include Optimizer.R
+#' @name mlr_optimizers_smac
+#'
+#' @description
+#' Calls SMAC3 from Python via the \CRANpkg{reticulate} package.
+#'
+#' @note
+#' All parameters of the search space must have default values.
+#'
+#' @section Parameters:
+#' \describe{
+#' \item{`n_init`}{`integer(1)`\cr
+#' Number of initial configurations to evaluate before starting the optimization.
+#' Defaults to `10` times the number of hyperparameters.}
+#' \item{`facade`}{`character(1)`\cr
+#' Facade to use.
+#' Either `"smac4bb"` (Black-Box Facade) or `"smac4hb"` (Hyperparameter Optimization Facade).
+#' Default is `"smac4bb"`.}
+#' \item{`output_directory`}{`character(1)`\cr
+#' Directory to store the output of SMAC3.
+#' Default is a temporary directory.}
+#' }
+#'
+#' @template section_progress_bars
+#'
+#' @source
+#' `r format_bib("lindauer_2022")`
+#'
+#' @export
+#' @examples
+#' \dontrun{
+#' # define the objective function
+#' fun = function(xs) {
+#' list(y = -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
+#' }
+#'
+#' # set domain (all parameters must have defaults for ConfigSpace)
+#' domain = ps(
+#' x1 = p_dbl(-10, 10, default = 0),
+#' x2 = p_dbl(-5, 5, default = 0)
+#' )
+#'
+#' # set codomain
+#' codomain = ps(
+#' y = p_dbl(tags = "maximize")
+#' )
+#'
+#' # create objective
+#' objective = ObjectiveRFun$new(
+#' fun = fun,
+#' domain = domain,
+#' codomain = codomain,
+#' properties = "deterministic"
+#' )
+#'
+#' # initialize instance
+#' instance = oi(
+#' objective = objective,
+#' terminator = trm("evals", n_evals = 20)
+#' )
+#'
+#' # load optimizer
+#' optimizer = opt("smac")
+#'
+#' # trigger optimization
+#' optimizer$optimize(instance)
+#'
+#' # all evaluated configurations
+#' instance$archive
+#'
+#' # best performing configuration
+#' instance$result
+#' }
+OptimizerBatchSmac3 = R6Class("OptimizerBatchSmac3",
+ inherit = OptimizerBatch,
+ public = list(
+
+ #' @description
+ #' Creates a new instance of this [R6][R6::R6Class] class.
+ initialize = function() {
+ param_set = ps(
+ n_init = p_int(lower = 1L),
+ facade = p_fct(levels = c("smac4bb", "smac4hb"), init = "smac4bb"),
+ output_directory = p_uty(init = tempdir())
+ )
+ super$initialize(
+ id = "smac",
+ param_set = param_set,
+ param_classes = c("ParamDbl", "ParamInt", "ParamFct", "ParamLgl"),
+ properties = c("dependencies", "single-crit"),
+ packages = "reticulate",
+ label = "Sequential Model-Based Algorithm Configuration",
+ man = "bbotk::mlr_optimizers_smac"
+ )
+ }
+ ),
+
+ private = list(
+ .optimize = function(inst) {
+ assert_python_packages(c("smac", "ConfigSpace"))
+ smac = reticulate::import("smac")
+
+ pv = self$param_set$values
+ search_space = inst$search_space
+
+ # convert paradox search space to ConfigSpace
+ cs = paramset_to_configspace(search_space)
+
+ terminator = inst$terminator
+ if (inherits(terminator, "TerminatorEvals")) {
+ n_trials = terminator$param_set$values$n_evals
+ } else {
+ # use a large number for other terminators
+ n_trials = .Machine$integer.max
+ }
+
+ n_init = pv$n_init %??% (10L * search_space$length)
+
+ scenario = smac$Scenario(
+ configspace = cs,
+ deterministic = TRUE,
+ n_trials = as.integer(n_trials),
+ seed = as.integer(sample.int(.Machine$integer.max, 1L)),
+ output_directory = pv$output_directory
+ )
+
+ if (pv$facade == "smac4bb") {
+ facade_class = smac$BlackBoxFacade
+ } else {
+ facade_class = smac$HyperparameterOptimizationFacade
+ }
+
+ intensifier = facade_class$get_intensifier(
+ scenario,
+ max_config_calls = 1L
+ )
+
+ # create initial design
+ initial_design = facade_class$get_initial_design(
+ scenario,
+ n_configs = as.integer(n_init)
+ )
+
+ # create smac optimizer
+ # use a dummy target function since we use the ask-tell interface
+ # SMAC validates the function signature, so we create a Python function directly
+ reticulate::py_run_string("def _dummy_target_fn(config, seed): return 0.0")
+ dummy_fn = reticulate::py$`_dummy_target_fn`
+
+ smac_optimizer = facade_class(
+ scenario = scenario,
+ target_function = dummy_fn,
+ intensifier = intensifier,
+ initial_design = initial_design,
+ overwrite = TRUE
+ )
+
+ # import TrialValue for reporting results
+ TrialValue = smac$runhistory$dataclasses$TrialValue
+
+ repeat {
+ # ask for next configuration
+ trial_info = tryCatch(
+ smac_optimizer$ask(),
+ error = function(e) NULL
+ )
+
+ if (is.null(trial_info)) {
+ break
+ }
+
+ # extract configuration as named list
+ config = trial_info$config
+ config_dict = reticulate::py_to_r(config$get_dictionary())
+
+ # inactive parameters are not in the config dictionary
+ # create data.table with all parameters (inactive ones are NA)
+ all_params = search_space$ids()
+ xdt = setDT(lapply(set_names(all_params), function(p) config_dict[[p]] %??% NA))
+
+ # fix logical parameters (ConfigSpace uses strings "TRUE"/"FALSE")
+ lgl_params = search_space$ids(class = "ParamLgl")
+ if (length(lgl_params)) {
+ xdt[, (lgl_params) := lapply(.SD, as.logical), .SDcols = lgl_params]
+ }
+
+ res = inst$eval_batch(xdt)
+ cost = res[[inst$archive$cols_y]] * inst$objective_multiplicator
+
+ # tell smac the result
+ trial_value = TrialValue(cost = cost, time = 0.0)
+ smac_optimizer$tell(trial_info, trial_value)
+ }
+ }
+ )
+)
+
+mlr_optimizers$add("smac", OptimizerBatchSmac3)
diff --git a/R/assertions.R b/R/assertions.R
index eb1df6949..5028bece4 100644
--- a/R/assertions.R
+++ b/R/assertions.R
@@ -180,3 +180,24 @@ assert_archive_batch = function(archive, null_ok = FALSE) {
if (null_ok && is.null(archive)) return(NULL)
assert_r6(archive, "ArchiveBatch")
}
+
+#' @title Assert Python Packages
+#'
+#' @description
+#' Assert that the given Python packages are available.
+#'
+#' @param packages (`character()`)\cr
+#' Python packages to check.
+#' @param python_version (`character(1)`)\cr
+#' Python version to use. If `NULL`, the default Python version is used.
+#'
+#' @return (`character()`)\cr
+#' Invisibly returns the input `packages` vector if all requested Python packages are available; otherwise throws an error listing the missing packages.
+assert_python_packages = function(packages, python_version = NULL) {
+ reticulate::py_require(packages, python_version = python_version)
+ available = map_lgl(packages, reticulate::py_module_available)
+ if (any(!available)) {
+ stopf("Package %s not available.", as_short_string(packages[!available]))
+ }
+ invisible(packages)
+}
diff --git a/R/bibentries.R b/R/bibentries.R
index fe4baab48..a98a8c1cf 100644
--- a/R/bibentries.R
+++ b/R/bibentries.R
@@ -61,5 +61,16 @@ bibentries = c(
eprint = "1903.04703",
archivePrefix = "arXiv",
primaryClass = "cs.LG"
+ ),
+
+ lindauer_2022 = bibentry("article",
+ title = "{SMAC3}: A Versatile Bayesian Optimization Package for Hyperparameter Optimization",
+ author = "Marius Lindauer and Katharina Eggensperger and Matthias Feurer and Andre Biedenkapp and Difan Deng and Carolin Benjamins and Tim Ruhkopf and Rene Sass and Frank Hutter",
+ year = "2022",
+ journal = "Journal of Machine Learning Research",
+ volume = "23",
+ number = "54",
+ pages = "1--9",
+ url = "http://jmlr.org/papers/v23/21-0888.html"
)
)
diff --git a/man/assert_python_packages.Rd b/man/assert_python_packages.Rd
new file mode 100644
index 000000000..a9375ef21
--- /dev/null
+++ b/man/assert_python_packages.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/assertions.R
+\name{assert_python_packages}
+\alias{assert_python_packages}
+\title{Assert Python Packages}
+\usage{
+assert_python_packages(packages, python_version = NULL)
+}
+\arguments{
+\item{packages}{(\code{character()})\cr
+Python packages to check.}
+
+\item{python_version}{(\code{character(1)})\cr
+Python version to use. If \code{NULL}, the default Python version is used.}
+}
+\value{
+(\code{character()})\cr
+Invisibly returns the input \code{packages} vector if all requested Python packages are available; otherwise throws an error listing the missing packages.
+}
+\description{
+Assert that the given Python packages are available.
+}
diff --git a/man/mlr_optimizers_async_design_points.Rd b/man/mlr_optimizers_async_design_points.Rd
index 9a0c790e6..8750eae7a 100644
--- a/man/mlr_optimizers_async_design_points.Rd
+++ b/man/mlr_optimizers_async_design_points.Rd
@@ -82,7 +82,7 @@ as.data.table(instance$archive)
}
}
\section{Super classes}{
-\code{\link[bbotk:Optimizer]{bbotk::Optimizer}} -> \code{bbotk::OptimizerAsync} -> \code{OptimizerAsyncDesignPoints}
+\code{\link[bbotk:Optimizer]{bbotk::Optimizer}} -> \code{\link[bbotk:OptimizerAsync]{bbotk::OptimizerAsync}} -> \code{OptimizerAsyncDesignPoints}
}
\section{Methods}{
\subsection{Public methods}{
diff --git a/man/mlr_optimizers_async_grid_search.Rd b/man/mlr_optimizers_async_grid_search.Rd
index 909bd25be..86094ed0f 100644
--- a/man/mlr_optimizers_async_grid_search.Rd
+++ b/man/mlr_optimizers_async_grid_search.Rd
@@ -88,7 +88,7 @@ as.data.table(instance$archive)
}
}
\section{Super classes}{
-\code{\link[bbotk:Optimizer]{bbotk::Optimizer}} -> \code{bbotk::OptimizerAsync} -> \code{OptimizerAsyncGridSearch}
+\code{\link[bbotk:Optimizer]{bbotk::Optimizer}} -> \code{\link[bbotk:OptimizerAsync]{bbotk::OptimizerAsync}} -> \code{OptimizerAsyncGridSearch}
}
\section{Methods}{
\subsection{Public methods}{
diff --git a/man/mlr_optimizers_async_random_search.Rd b/man/mlr_optimizers_async_random_search.Rd
index 3bd77dc6a..0d1193129 100644
--- a/man/mlr_optimizers_async_random_search.Rd
+++ b/man/mlr_optimizers_async_random_search.Rd
@@ -78,7 +78,7 @@ as.data.table(instance$archive)
}
}
\section{Super classes}{
-\code{\link[bbotk:Optimizer]{bbotk::Optimizer}} -> \code{bbotk::OptimizerAsync} -> \code{OptimizerAsyncRandomSearch}
+\code{\link[bbotk:Optimizer]{bbotk::Optimizer}} -> \code{\link[bbotk:OptimizerAsync]{bbotk::OptimizerAsync}} -> \code{OptimizerAsyncRandomSearch}
}
\section{Methods}{
\subsection{Public methods}{
diff --git a/man/mlr_optimizers_smac.Rd b/man/mlr_optimizers_smac.Rd
new file mode 100644
index 000000000..df77d7c78
--- /dev/null
+++ b/man/mlr_optimizers_smac.Rd
@@ -0,0 +1,135 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/OptimizerBatchSmac3.R
+\name{mlr_optimizers_smac}
+\alias{mlr_optimizers_smac}
+\alias{OptimizerBatchSmac3}
+\title{Sequential Model-Based Algorithm Configuration (SMAC3)}
+\source{
+Lindauer M, Eggensperger K, Feurer M, Biedenkapp A, Deng D, Benjamins C, Ruhkopf T, Sass R, Hutter F (2022).
+\dQuote{SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.}
+\emph{Journal of Machine Learning Research}, \bold{23}(54), 1--9.
+\url{http://jmlr.org/papers/v23/21-0888.html}.
+}
+\description{
+Calls SMAC3 from Python via the \CRANpkg{reticulate} package.
+}
+\note{
+All parameters of the search space must have default values.
+}
+\section{Parameters}{
+
+\describe{
+\item{\code{n_init}}{\code{integer(1)}\cr
+Number of initial configurations to evaluate before starting the optimization.
+Defaults to \code{10} times the number of hyperparameters.}
+\item{\code{facade}}{\code{character(1)}\cr
+Facade to use.
+Either \code{"smac4bb"} (Black-Box Facade) or \code{"smac4hb"} (Hyperparameter Optimization Facade).
+Default is \code{"smac4bb"}.}
+\item{\code{output_directory}}{\code{character(1)}\cr
+Directory to store the output of SMAC3.
+Default is a temporary directory.}
+}
+}
+
+\section{Progress Bars}{
+
+\verb{$optimize()} supports progress bars via the package \CRANpkg{progressr}
+combined with a \link{Terminator}. Simply wrap the function in
+\code{progressr::with_progress()} to enable them. We recommend to use package
+\CRANpkg{progress} as backend; enable with \code{progressr::handlers("progress")}.
+}
+
+\examples{
+\dontrun{
+# define the objective function
+fun = function(xs) {
+ list(y = -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
+}
+
+# set domain (all parameters must have defaults for ConfigSpace)
+domain = ps(
+ x1 = p_dbl(-10, 10, default = 0),
+ x2 = p_dbl(-5, 5, default = 0)
+)
+
+# set codomain
+codomain = ps(
+ y = p_dbl(tags = "maximize")
+)
+
+# create objective
+objective = ObjectiveRFun$new(
+ fun = fun,
+ domain = domain,
+ codomain = codomain,
+ properties = "deterministic"
+)
+
+# initialize instance
+instance = oi(
+ objective = objective,
+ terminator = trm("evals", n_evals = 20)
+)
+
+# load optimizer
+optimizer = opt("smac")
+
+# trigger optimization
+optimizer$optimize(instance)
+
+# all evaluated configurations
+instance$archive
+
+# best performing configuration
+instance$result
+}
+}
+\section{Super classes}{
+\code{\link[bbotk:Optimizer]{bbotk::Optimizer}} -> \code{\link[bbotk:OptimizerBatch]{bbotk::OptimizerBatch}} -> \code{OptimizerBatchSmac3}
+}
+\section{Methods}{
+\subsection{Public methods}{
+\itemize{
+\item \href{#method-OptimizerBatchSmac3-new}{\code{OptimizerBatchSmac3$new()}}
+\item \href{#method-OptimizerBatchSmac3-clone}{\code{OptimizerBatchSmac3$clone()}}
+}
+}
+\if{html}{\out{
+Inherited methods
+
+
}}
+\if{html}{\out{}}
+\if{latex}{\out{\hypertarget{method-OptimizerBatchSmac3-new}{}}}
+\subsection{Method \code{new()}}{
+Creates a new instance of this \link[R6:R6Class]{R6} class.
+\subsection{Usage}{
+\if{html}{\out{}}\preformatted{OptimizerBatchSmac3$new()}\if{html}{\out{
}}
+}
+
+}
+\if{html}{\out{
}}
+\if{html}{\out{}}
+\if{latex}{\out{\hypertarget{method-OptimizerBatchSmac3-clone}{}}}
+\subsection{Method \code{clone()}}{
+The objects of this class are cloneable with this method.
+\subsection{Usage}{
+\if{html}{\out{}}\preformatted{OptimizerBatchSmac3$clone(deep = FALSE)}\if{html}{\out{
}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{}}
+\describe{
+\item{\code{deep}}{Whether to make a deep clone.}
+}
+\if{html}{\out{
}}
+}
+}
+}
diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml
index 1735017a1..f80a9db63 100644
--- a/pkgdown/_pkgdown.yml
+++ b/pkgdown/_pkgdown.yml
@@ -86,3 +86,4 @@ reference:
- trafo_xs
- terminated_error
- choose_search_space
+ - assert_python_packages
diff --git a/tests/testthat/teardown.R b/tests/testthat/teardown.R
index 5bbca3170..d2aeb0452 100644
--- a/tests/testthat/teardown.R
+++ b/tests/testthat/teardown.R
@@ -1,3 +1,9 @@
options(old_opts)
lg_bbotk$set_threshold(old_threshold_bbotk)
lg_rush$set_threshold(old_threshold_rush)
+
+if (requireNamespace("reticulate", quietly = TRUE)) {
+ python_temp_dir = dirname(reticulate::py_run_string("import tempfile; x=tempfile.NamedTemporaryFile().name", local = TRUE)$x)
+ detritus = list.files(python_temp_dir, pattern = "^uv-setuptools-[0-9A-Fa-f]{16}\\.lock$", full.names = TRUE)
+ if (length(detritus)) unlink(detritus)
+}
diff --git a/tests/testthat/test_OptimizerBatchSmac3.R b/tests/testthat/test_OptimizerBatchSmac3.R
new file mode 100644
index 000000000..0292b3b32
--- /dev/null
+++ b/tests/testthat/test_OptimizerBatchSmac3.R
@@ -0,0 +1,134 @@
+skip_if_not_installed("reticulate")
+
+test_that("OptimizerBatchSmac3", {
+ search_space = ps(
+ x = p_dbl(lower = -1, upper = 1, default = 0)
+ )
+
+ fun = function(xs) {
+ list(y = as.numeric(xs)^2)
+ }
+
+ objective = ObjectiveRFun$new(
+ fun = fun,
+ domain = search_space,
+ properties = "single-crit"
+ )
+
+ terminator = trm("evals", n_evals = 5L)
+ instance = OptimInstanceBatchSingleCrit$new(
+ objective = objective,
+ search_space = search_space,
+ terminator = terminator
+ )
+
+ optimizer = opt("smac")
+ expect_class(optimizer, "OptimizerBatchSmac3")
+
+ optimizer$optimize(instance)
+
+ expect_data_table(instance$archive$data, min.rows = 5L)
+ expect_equal(instance$archive$n_evals, 5L)
+
+ x_opt = instance$result_x_domain
+ y_opt = instance$result_y
+ expect_list(x_opt, len = 1)
+ expect_named(x_opt, "x")
+ expect_numeric(y_opt, len = 1)
+ expect_named(y_opt, "y")
+})
+
+test_that("OptimizerBatchSmac3 with 2d search space", {
+ search_space = ps(
+ x1 = p_dbl(lower = -1, upper = 1, default = 0),
+ x2 = p_dbl(lower = -1, upper = 1, default = 0)
+ )
+
+ fun = function(xs) {
+ list(y = sum(as.numeric(xs)^2))
+ }
+
+ objective = ObjectiveRFun$new(
+ fun = fun,
+ domain = search_space,
+ properties = "single-crit"
+ )
+
+ terminator = trm("evals", n_evals = 10L)
+ instance = OptimInstanceBatchSingleCrit$new(
+ objective = objective,
+ search_space = search_space,
+ terminator = terminator
+ )
+
+ optimizer = opt("smac", n_init = 5L)
+ optimizer$optimize(instance)
+
+ expect_data_table(instance$archive$data, min.rows = 10L)
+ expect_equal(instance$archive$n_evals, 10L)
+})
+
+test_that("OptimizerBatchSmac3 with mixed parameter types", {
+ search_space = ps(
+ x1 = p_dbl(lower = -1, upper = 1, default = 0),
+ x2 = p_int(lower = 1L, upper = 10L, default = 5L),
+ x3 = p_fct(levels = c("a", "b", "c"), default = "a"),
+ x4 = p_lgl(default = TRUE)
+ )
+
+ fun = function(xs) {
+ y = xs$x1^2 + xs$x2 / 10
+ if (xs$x3 == "a") y = y + 1
+ if (xs$x4) y = y + 0.5
+ list(y = y)
+ }
+
+ objective = ObjectiveRFun$new(
+ fun = fun,
+ domain = search_space,
+ properties = "single-crit"
+ )
+
+ terminator = trm("evals", n_evals = 10L)
+ instance = OptimInstanceBatchSingleCrit$new(
+ objective = objective,
+ search_space = search_space,
+ terminator = terminator
+ )
+
+ optimizer = opt("smac", n_init = 5L)
+ optimizer$optimize(instance)
+
+ expect_data_table(instance$archive$data, min.rows = 10L)
+ expect_equal(instance$archive$n_evals, 10L)
+})
+
+test_that("OptimizerBatchSmac3 with dependencies", {
+ search_space = ps(
+ x1 = p_fct(levels = c("a", "b"), default = "a"),
+ x2 = p_dbl(lower = -1, upper = 1, default = 0, depends = x1 == "a")
+ )
+
+ fun = function(xs) {
+ y = if (xs$x1 == "a") xs$x2^2 else 1
+ list(y = y)
+ }
+
+ objective = ObjectiveRFun$new(
+ fun = fun,
+ domain = search_space,
+ properties = "single-crit"
+ )
+
+ terminator = trm("evals", n_evals = 10L)
+ instance = OptimInstanceBatchSingleCrit$new(
+ objective = objective,
+ search_space = search_space,
+ terminator = terminator
+ )
+
+ optimizer = opt("smac", n_init = 5L)
+ optimizer$optimize(instance)
+
+ expect_data_table(instance$archive$data, min.rows = 10L)
+})