feat: support callbacks in AcqOptimizer

DESCRIPTION

@@ -66,13 +66,14 @@ Suggests:
     rpart,
     stringi,
     testthat (>= 3.0.0)
+Remotes: mlr-org/bbotk
 ByteCompile: no
 Encoding: UTF-8
 Config/testthat/edition: 3
 Config/testthat/parallel: false
 NeedsCompilation: yes
 Roxygen: list(markdown = TRUE, r6 = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Collate:
     'mlr_acqfunctions.R'
     'AcqFunction.R'

R/AcqOptimizer.R

@@ -93,16 +93,21 @@ AcqOptimizer = R6Class("AcqOptimizer",
     #' @field acq_function ([AcqFunction]).
     acq_function = NULL,
 
+    #' @field callbacks (`NULL` | list of [mlr3misc::Callback]).
+    callbacks = NULL,
+
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     #'
     #' @param optimizer ([bbotk::Optimizer]).
     #' @param terminator ([bbotk::Terminator]).
     #' @param acq_function (`NULL` | [AcqFunction]).
-    initialize = function(optimizer, terminator, acq_function = NULL) {
+    #' @param callbacks (`NULL` | list of [mlr3misc::Callback])
+    initialize = function(optimizer, terminator, acq_function = NULL, callbacks = NULL) {
       self$optimizer = assert_r6(optimizer, "Optimizer")
       self$terminator = assert_r6(terminator, "Terminator")
       self$acq_function = assert_r6(acq_function, "AcqFunction", null.ok = TRUE)
+      self$callbacks = assert_callbacks(as_callbacks(callbacks))
       ps = ps(
         n_candidates = p_int(lower = 1, default = 1L),
         logging_level = p_fct(levels = c("fatal", "error", "warn", "info", "debug", "trace"), default = "warn"),
@@ -146,7 +151,7 @@ AcqOptimizer = R6Class("AcqOptimizer",
       logger$set_threshold(self$param_set$values$logging_level)
       on.exit(logger$set_threshold(old_threshold))
 
-      instance = OptimInstanceBatchSingleCrit$new(objective = self$acq_function, search_space = self$acq_function$domain, terminator = self$terminator, check_values = FALSE)
+      instance = OptimInstanceBatchSingleCrit$new(objective = self$acq_function, search_space = self$acq_function$domain, terminator = self$terminator, check_values = FALSE, callbacks = self$callbacks)
 
       # warmstart
       if (self$param_set$values$warmstart) {

R/sugar.R

@@ -20,7 +20,7 @@
 #' @param cols_y (`NULL` | `character()`)\cr
 #'   Column id(s) in the [bbotk::Archive] that should be used as a target.
 #'   If a list of [mlr3::LearnerRegr] is provided as the `learner` argument and `cols_y` is
-#'   specified as well, as many column names as learners must be provided. 
+#'   specified as well, as many column names as learners must be provided.
 #'   Can also be `NULL` in which case this is automatically inferred based on the archive.
 #' @param ... (named `list()`)\cr
 #' Named arguments passed to the constructor, to be set as parameters in the
@@ -90,6 +90,8 @@ acqf = function(.key, ...) {
 #' @param acq_function (`NULL` | [AcqFunction])\cr
 #'   [AcqFunction] that is to be used.
 #'   Can also be `NULL`.
+#' @param callbacks (`NULL` | list of [mlr3misc::Callback])
+#'   Callbacks used during acquisition function optimization.
 #' @param ... (named `list()`)\cr
 #' Named arguments passed to the constructor, to be set as parameters in the
 #' [paradox::ParamSet].
@@ -101,9 +103,9 @@ acqf = function(.key, ...) {
 #' library(bbotk)
 #' acqo(opt("random_search"), trm("evals"), catch_errors = FALSE)
 #' @export
-acqo = function(optimizer, terminator, acq_function = NULL, ...) {
+acqo = function(optimizer, terminator, acq_function = NULL, callbacks = NULL, ...) {
   dots = list(...)
-  acqopt = AcqOptimizer$new(optimizer = optimizer, terminator = terminator, acq_function = acq_function)
+  acqopt = AcqOptimizer$new(optimizer = optimizer, terminator = terminator, acq_function = acq_function, callbacks = callbacks)
   acqopt$param_set$values = insert_named(acqopt$param_set$values, dots)
   acqopt
 }

tests/testthat/test_AcqOptimizer.R

@@ -120,3 +120,32 @@ test_that("AcqOptimizer deep clone", {
   expect_true(address(acqopt1$terminator) != address(acqopt2$terminator))
 })
 
+test_that("AcqOptimizer callbacks", {
+  domain = ps(x = p_dbl(lower = 10, upper = 20, trafo = function(x) x - 15))
+  objective = ObjectiveRFunDt$new(
+    fun = function(xdt) data.table(y = xdt$x ^ 2),
+    domain = domain,
+    codomain = ps(y = p_dbl(tags = "minimize")),
+    check_values = FALSE
+  )
+  instance = MAKE_INST(objective = objective, search_space = domain, terminator = trm("evals", n_evals = 5L))
+  design = MAKE_DESIGN(instance)
+  instance$eval_batch(design)
+  callback = callback_batch("mlr3mbo.acqopt_time",
+    on_optimization_begin = function(callback, context) {
+      callback$state$begin = Sys.time()
+    },
+    on_optimization_end = function(callback, context) {
+      callback$state$end = Sys.time()
+      attr(callback$state$outer_instance, "acq_opt_runtime") = as.numeric(callback$state$end - callback$state$begin)
+    }
+  )
+  callback$state$outer_instance = instance
+  acqfun = AcqFunctionEI$new(SurrogateLearner$new(REGR_FEATURELESS, archive = instance$archive))
+  acqopt = AcqOptimizer$new(opt("random_search", batch_size = 10L), trm("evals", n_evals = 10L), acq_function = acqfun, callbacks = callback)
+  acqfun$surrogate$update()
+  acqfun$update()
+  res = acqopt$optimize()
+  expect_number(attr(instance, "acq_opt_runtime"))
+})
+