Merge pull request #288 from mlr-org/start_values

feat: allow custom start values for cmaes and nloptr

Author: sumny

R/OptimizerBatchCmaes.R

@@ -16,8 +16,11 @@
 #' \describe{
 #' \item{`sigma`}{`numeric(1)`}
 #' \item{`start_values`}{`character(1)`\cr
-#' Create `random` start values or based on `center` of search space? In the
-#' latter case, it is the center of the parameters before a trafo is applied.}
+#' Create `"random"` start values or based on `"center"` of search space?
+#' In the latter case, it is the center of the parameters before a trafo is applied.
+#' If set to `"custom"`, the start values can be passed via the `start` parameter.}
+#' \item{`start`}{`numeric()`\cr
+#' Custom start values. Only applicable if `start_values` parameter is set to `"custom"`.}
 #' }
 #'
 #' For the meaning of the control parameters, see [adagio::pureCMAES()]. Note
@@ -70,10 +73,11 @@ OptimizerBatchCmaes = R6Class("OptimizerBatchCmaes",
     initialize = function() {
       param_set = ps(
         sigma = p_dbl(default = 0.5),
-        start_values = p_fct(levels = c("random", "center"), tags = "required")
+        start_values = p_fct(default = "random", levels = c("random", "center", "custom")),
+        start = p_uty(default = NULL, depends = start_values == "custom")
       )
-      # old paradox; new paradox can use 'init = "random"'
       param_set$values$start_values = "random"
+      param_set$values$start = NULL
 
       super$initialize(
         id = "cmaes",
@@ -90,15 +94,29 @@ OptimizerBatchCmaes = R6Class("OptimizerBatchCmaes",
   private = list(
     .optimize = function(inst) {
       pv = self$param_set$values
-      pv$par = search_start(inst$search_space, type = pv$start_values)
-      pv$start_values = NULL
+
+      if (pv$start_values == "custom") {
+        pv$par = pv$start
+        pv$start_values = NULL
+        pv$start = NULL
+      } else {
+        pv$par = search_start(inst$search_space, type = pv$start_values)
+        pv$start_values = NULL
+        pv$start = NULL
+      }
+
       pv$stopeval = .Machine$integer.max # make sure pureCMAES does not stop
       pv$stopfitness = -Inf
 
-      if (length(pv$par) < 2) warning("CMA-ES is typically applied to search space dimensions between three and fifty. A lower search space dimension might crash.")
+      if (length(pv$par) < 2L) {
+        warning("CMA-ES is typically applied to search space dimensions between three and fifty. A lower search space dimension might crash.")
+      }
 
-      invoke(adagio::pureCMAES, fun = inst$objective_function, lower = inst$search_space$lower,
-        upper = inst$search_space$upper, .args = pv)
+      invoke(adagio::pureCMAES,
+        fun = inst$objective_function,
+        lower = inst$search_space$lower,
+        upper = inst$search_space$upper,
+        .args = pv)
     }
   )
 )

R/OptimizerBatchNLoptr.R

@@ -16,8 +16,11 @@
 #' \item{`ftol_rel`}{`numeric(1)`}
 #' \item{`ftol_abs`}{`numeric(1)`}
 #' \item{`start_values`}{`character(1)`\cr
-#' Create `random` start values or based on `center` of search space?
-#' In the latter case, it is the center of the parameters before a trafo is applied.}
+#' Create `"random"` start values or based on `"center"` of search space?
+#' In the latter case, it is the center of the parameters before a trafo is applied.
+#' If set to `"custom"`, the start values can be passed via the `start` parameter.}
+#' \item{`start`}{`numeric()`\cr
+#' Custom start values. Only applicable if `start_values` parameter is set to `"custom"`.}
 #' \item{`approximate_eval_grad_f`}{`logical(1)`\cr
 #' Should gradients be numerically approximated via finite differences ([nloptr::nl.grad]).
 #' Only required for certain algorithms.
@@ -103,10 +106,12 @@ OptimizerBatchNLoptr = R6Class("OptimizerBatchNLoptr", inherit = OptimizerBatch,
         xtol_abs = p_dbl(default = 0, lower = 0, upper = Inf, special_vals = list(-1)),
         ftol_rel = p_dbl(default = 0, lower = 0, upper = Inf, special_vals = list(-1)),
         ftol_abs = p_dbl(default = 0, lower = 0, upper = Inf, special_vals = list(-1)),
-        start_values = p_fct(default = "random", levels = c("random", "center")),
+        start_values = p_fct(default = "random", levels = c("random", "center", "custom")),
+        start = p_uty(default = NULL, depends = start_values == "custom"),
         approximate_eval_grad_f = p_lgl(default = FALSE)
       )
       param_set$values$start_values = "random"
+      param_set$values$start = NULL
       param_set$values$approximate_eval_grad_f = FALSE
 
       super$initialize(
@@ -125,8 +130,15 @@ OptimizerBatchNLoptr = R6Class("OptimizerBatchNLoptr", inherit = OptimizerBatch,
     .optimize = function(inst) {
       pv = self$param_set$values
 
-      pv$x0 = search_start(inst$search_space, type = pv$start_values)
-      pv$start_values = NULL
+      if (pv$start_values == "custom") {
+        pv$x0 = pv$start
+        pv$start_values = NULL
+        pv$start = NULL
+      } else {
+        pv$x0 = search_start(inst$search_space, type = pv$start_values)
+        pv$start_values = NULL
+        pv$start = NULL
+      }
 
       if (pv$approximate_eval_grad_f) {
         eval_grad_f = function(x) {

R/zzz.R

@@ -30,6 +30,6 @@
   }
 } # nocov end
 
-utils::globalVariables("batch_nr")
+utils::globalVariables(c("batch_nr", "start_values"))
 
 leanify_package()

man/mlr_optimizers_cmaes.Rd

@@ -20,12 +20,17 @@ test_that("OptimizerBatchCmaes", {
   instance = OptimInstanceBatchSingleCrit$new(
     objective = objective,
     search_space = search_space,
-    terminator = trm("evals", n_evals = 10))
+    terminator = trm("evals", n_evals = 10L))
 
   z = test_optimizer(instance, "cmaes", real_evals = 10L)
 
   expect_class(z$optimizer, "OptimizerBatchCmaes")
   expect_snapshot(z$optimizer)
 
   expect_error(test_optimizer_2d("cmaes", term_evals = 10L), "multi-crit objectives")
+
+  instance$archive$clear()
+  optimizer = opt("cmaes", start_values = "custom", start = c(-9.1, 1.3))
+  optimizer$optimize(instance)
+  # start values are used for the initial mean vector so a deterministic test is not applicable
 })

man/mlr_optimizers_nloptr.Rd

@@ -12,4 +12,29 @@ test_that("OptimizerBatchNLoptr", {
     xtol_rel = -1, xtol_abs = -1, ftol_rel = -1, ftol_abs = -1, term_evals = 5L)
   expect_class(z$optimizer, "OptimizerBatchNLoptr")
   expect_snapshot(z$optimizer)
+
+  search_space = domain = ps(
+    x1 = p_dbl(-10, 10),
+    x2 = p_dbl(-5, 5)
+  )
+
+  codomain = ps(y = p_dbl(tags = "maximize"))
+
+  objective_function = function(xs) {
+    c(y = -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
+  }
+
+  objective = ObjectiveRFun$new(
+    fun = objective_function,
+    domain = domain,
+    codomain = codomain)
+
+  instance = OptimInstanceBatchSingleCrit$new(
+    objective = objective,
+    search_space = search_space,
+    terminator = trm("evals", n_evals = 10L))
+
+  optimizer = opt("nloptr", algorithm = "NLOPT_LN_BOBYQA", start_values = "custom", start = c(-9.1, 1.3))
+  optimizer$optimize(instance)
+  expect_equal(unlist(instance$archive$data[1L, c("x1", "x2")]), c(x1 = -9.1, x2 = 1.3))
 })