feat: add AcqOptimizerCmaes

DESCRIPTION

@@ -93,6 +93,7 @@ Collate:
     'AcqFunctionStochasticEI.R'
     'AcqOptimizer.R'
     'mlr_acqoptimizers.R'
+    'AcqOptimizerCmaes.R'
     'AcqOptimizerDirect.R'
     'AcqOptimizerLbfgsb.R'
     'AcqOptimizerLocalSearch.R'

NAMESPACE

@@ -23,6 +23,7 @@ export(AcqFunctionSmsEgo)
 export(AcqFunctionStochasticCB)
 export(AcqFunctionStochasticEI)
 export(AcqOptimizer)
+export(AcqOptimizerCmaes)
 export(AcqOptimizerDirect)
 export(AcqOptimizerLbfgsb)
 export(AcqOptimizerLocalSearch)

R/AcqOptimizerCmaes.R

@@ -0,0 +1,147 @@
+#' @title CMA-ES Acquisition Function Optimizer
+#'
+#' @include AcqOptimizer.R mlr_acqoptimizers.R
+#'
+#' @description
+#' CMA-ES acquisition function optimizer.
+#' Calls `cmaes()` from \CRANpkg{libcmaesr}.
+#' The default algorithm is `"abipop"` with unlimited restarts and a budget of `100 * D^2` function evaluations, where `D` is the dimension of the search space.
+#' For the meaning of the control parameters, see `libcmaesr::cmaes_control()`.
+#'
+#' @section Termination Parameters:
+#' The following termination parameters can be used.
+#'
+#' \describe{
+#' \item{`max_fevals`}{`integer(1)`\cr
+#'   Maximum number of function evaluations.
+#'   Deactivate with `NA`.
+#'   Default is `100 * D^2`, where `D` is the dimension of the search space.}
+#' \item{`max_iter`}{`integer(1)`\cr
+#'   Maximum number of iterations.
+#'   Deactivate with `NA`.}
+#' \item{`ftarget`}{`numeric(1)`\cr
+#'   Target function value.
+#'   Deactivate with `NA`.}
+#' \item{`f_tolerance`}{`numeric(1)`\cr
+#'   Function tolerance.
+#'   Deactivate with `NA`.}
+#' \item{`x_tolerance`}{`numeric(1)`\cr
+#'   Parameter tolerance.
+#'   Deactivate with `NA`.}
+#' }
+#'
+#' @export
+#' @examples
+#' if (requireNamespace("libcmaesr")) {
+#'   acqo("cmaes")
+#' }
+AcqOptimizerCmaes = R6Class("AcqOptimizerCmaes",
+  inherit = AcqOptimizer,
+  public = list(
+
+    #' @description
+    #' Creates a new instance of this [R6][R6::R6Class] class.
+    #'
+    #' @param acq_function (`NULL` | [AcqFunction]).
+    initialize = function(acq_function = NULL) {
+      self$acq_function = assert_r6(acq_function, "AcqFunction", null.ok = TRUE)
+      param_set = ps(
+        algo          = p_fct(init = "abipop", levels = c(
+          "cmaes",
+          "ipop",
+          "bipop",
+          "acmaes",
+          "aipop",
+          "abipop",
+          "sepcmaes",
+          "sepipop",
+          "sepbipop",
+          "sepacmaes",
+          "sepaipop",
+          "sepabipop",
+          "vdcma",
+          "vdipopcma",
+          "vdbipopcma")),
+        lambda        = p_int(lower = 1L, default = NA_integer_, special_vals = list(NA_integer_)),
+        sigma         = p_dbl(default = NA_real_, special_vals = list(NA_real_)),
+        max_restarts  = p_int(lower = 1L, special_vals = list(NA), init = 1e5L),
+        tpa           = p_int(default = NA_integer_, special_vals = list(NA_integer_)),
+        tpa_dsigma    = p_dbl(default = NA_real_, special_vals = list(NA_real_)),
+        seed          = p_int(default = NA_integer_, special_vals = list(NA_integer_)),
+        quiet         = p_lgl(default = FALSE),
+        # internal termination criteria
+        max_fevals    = p_int(lower = 1L, special_vals = list(NA_integer_)),
+        max_iter      = p_int(lower = 1L, default = NA_integer_, special_vals = list(NA_integer_)),
+        ftarget       = p_dbl(default = NA_real_, special_vals = list(NA_real_)),
+        f_tolerance   = p_dbl(default = NA_real_, special_vals = list(NA_real_)),
+        x_tolerance   = p_dbl(default = NA_real_, special_vals = list(NA_real_)),
+        catch_errors = p_lgl(init = TRUE)
+      )
+      private$.param_set = param_set
+    },
+
+    #' @description
+    #' Optimize the acquisition function.
+    #'
+    #' @return [data.table::data.table()] with 1 row per candidate.
+    optimize = function() {
+      pv = self$param_set$values
+
+      if (is.null(pv$max_fevals)) {
+        pv$max_fevals = 100 * self$acq_function$domain$length^2
+      }
+
+      fun = get_private(self$acq_function)$.fun
+      constants = self$acq_function$constants$values
+      direction = self$acq_function$codomain$direction
+
+      control = invoke(libcmaesr::cmaes_control, maximize = direction == -1L, .args = pv[names(pv) %in% formalArgs(libcmaesr::cmaes_control)])
+
+      wrapper = function(xmat) {
+        xdt = set_names(as.data.table(xmat), self$acq_function$domain$ids())
+        mlr3misc::invoke(fun, xdt = xdt, .args = constants)[[1]]
+      }
+
+      lower = self$acq_function$domain$lower
+      upper = self$acq_function$domain$upper
+      x0 = as.numeric(self$acq_function$archive$best()[, self$acq_function$domain$ids(), with = FALSE])
+
+      # add saveguard_epsilon to x0
+      saveguard_epsilon = 1e-5
+      x0[x0 < lower] = x0[x0 < lower] + saveguard_epsilon
+      x0[x0 > upper] = x0[x0 > upper] - saveguard_epsilon
+
+      optimize = function() {
+        libcmaesr::cmaes(
+          objective = wrapper,
+          x0 = x0,
+          lower = lower,
+          upper = upper,
+          batch = TRUE,
+          control = control)
+      }
+
+      if (pv$catch_errors) {
+        tryCatch({
+          res = optimize()
+        }, error = function(error_condition) {
+          lg$warn(error_condition$message)
+          stop(set_class(list(message = error_condition$message, call = NULL), classes = c("acq_optimizer_error", "mbo_error", "error", "condition")))
+        })
+      } else {
+        res = optimize()
+      }
+      as.data.table(as.list(set_names(c(res$x, res$y * direction), c(self$acq_function$domain$ids(), self$acq_function$codomain$ids()))))
+    }
+  ),
+
+  active = list(
+    #' @template field_print_id
+    print_id = function(rhs) {
+      assert_ro_binding(rhs)
+      "(OptimizerBatchCmaes)"
+    }
+  )
+)
+
+mlr_acqoptimizers$add("cmaes", AcqOptimizerCmaes)

tests/testthat/test_AcqOptimizerCmaes.R

@@ -0,0 +1,45 @@
+test_that("AcqOptimizerCmaes works", {
+  instance = oi(OBJ_1D, terminator = trm("evals", n_evals = 5L))
+  design = generate_design_grid(instance$search_space, resolution = 4L)$data
+  instance$eval_batch(design)
+
+  surrogate = srlrn(REGR_KM_DETERM, archive = instance$archive)
+  acqfun = acqf("ei", surrogate = surrogate)
+  acqopt = AcqOptimizerCmaes$new(acq_function = acqfun)
+  acqopt$param_set$set_values(max_fevals = 100L)
+  acqfun$surrogate$update()
+  acqfun$update()
+  expect_data_table(acqopt$optimize(), nrows = 1L)
+  expect_list(acqopt$state)
+})
+
+test_that("AcqOptimizerCmaes works with 2D", {
+  instance = oi(OBJ_2D, terminator = trm("evals", n_evals = 5L))
+  design = generate_design_grid(instance$search_space, resolution = 4L)$data
+  instance$eval_batch(design)
+
+  surrogate = srlrn(REGR_KM_DETERM, archive = instance$archive)
+  acqfun = acqf("ei", surrogate = surrogate)
+  acqopt = AcqOptimizerCmaes$new(acq_function = acqfun)
+  acqopt$param_set$set_values(max_fevals = 100L)
+  acqfun$surrogate$update()
+  acqfun$update()
+  expect_data_table(acqopt$optimize(), nrows = 1L)
+  expect_list(acqopt$state)
+  expect_list(acqopt$state)
+})
+
+test_that("AcqOptimizerCmaes works with instance", {
+  instance = oi(OBJ_1D, terminator = trm("evals", n_evals = 10L))
+  design = generate_design_grid(instance$search_space, resolution = 4L)$data
+  instance$eval_batch(design)
+
+  surrogate = srlrn(REGR_KM_DETERM, archive = instance$archive)
+  acqfun = acqf("ei", surrogate = surrogate)
+  acqopt = AcqOptimizerCmaes$new(acq_function = acqfun)
+  acqopt$param_set$set_values(max_fevals = 100L)
+
+  optimizer = opt("mbo", acq_optimizer = acqopt, acq_function = acqfun, surrogate = surrogate)
+  expect_data_table(optimizer$optimize(instance), nrow = 1L)
+})
+