test: helper

Author: sumny

tests/testthat/helper.R

@@ -9,24 +9,24 @@ FUN_1D = function(xs) {
   list(y = as.numeric(xs)^2)
 }
 FUN_1D_CODOMAIN = ps(y = p_dbl(tags = "minimize"))
-OBJ_1D = ObjectiveRFun$new(fun = FUN_1D, domain = PS_1D, codomain = FUN_1D_CODOMAIN, properties = "single-crit")
+OBJ_1D = bbotk::ObjectiveRFun$new(fun = FUN_1D, domain = PS_1D, codomain = FUN_1D_CODOMAIN, properties = "single-crit")
 
 FUN_1D_2 = function(xs) {
   list(y1 = as.numeric(xs)^2, y2 = - sqrt(abs(as.numeric(xs))))
 }
 FUN_1D_2_CODOMAIN = ps(y1 = p_dbl(tags = "minimize"), y2 = p_dbl(tags = "minimize"))
-OBJ_1D_2 = ObjectiveRFun$new(fun = FUN_1D_2, domain = PS_1D, codomain = FUN_1D_2_CODOMAIN, properties = "multi-crit")
+OBJ_1D_2 = bbotk::ObjectiveRFun$new(fun = FUN_1D_2, domain = PS_1D, codomain = FUN_1D_2_CODOMAIN, properties = "multi-crit")
 
 # Simple 1D Functions with noise
 FUN_1D_NOISY = function(xs) {
   list(y = as.numeric(xs)^2 + rnorm(1, sd = 0.5))
 }
-OBJ_1D_NOISY = ObjectiveRFun$new(fun = FUN_1D_NOISY, domain = PS_1D, codomain = FUN_1D_CODOMAIN, properties = c("single-crit", "noisy"))
+OBJ_1D_NOISY = bbotk::ObjectiveRFun$new(fun = FUN_1D_NOISY, domain = PS_1D, codomain = FUN_1D_CODOMAIN, properties = c("single-crit", "noisy"))
 
 FUN_1D_2_NOISY = function(xs) {
   list(y1 = as.numeric(xs)^2 + rnorm(1, sd = 0.5), y2 = sqrt(abs(as.numeric(xs))) + rnorm(1, sd = 0.5))
 }
-OBJ_1D_2_NOISY = ObjectiveRFun$new(fun = FUN_1D_2, domain = PS_1D, codomain = FUN_1D_2_CODOMAIN, properties = c("multi-crit", "noisy"))
+OBJ_1D_2_NOISY = bbotk::ObjectiveRFun$new(fun = FUN_1D_2, domain = PS_1D, codomain = FUN_1D_2_CODOMAIN, properties = c("multi-crit", "noisy"))
 
 # Mixed 1D Functions
 PS_1D_MIXED = ps(
@@ -44,16 +44,16 @@ FUN_1D_MIXED = function(xs) {
   }
   list(y = (xs$x1 - switch(xs$x2, "a" = 0, "b" = 1, "c" = 2)) %% xs$x3 + (if (xs$x4) xs$x1 else pi))
 }
-OBJ_1D_MIXED = ObjectiveRFun$new(fun = FUN_1D_MIXED, domain = PS_1D_MIXED, properties = "single-crit")
-OBJ_1D_MIXED_DEPS = ObjectiveRFun$new(fun = FUN_1D_MIXED, domain = PS_1D_MIXED_DEPS, properties = "single-crit")
+OBJ_1D_MIXED = bbotk::ObjectiveRFun$new(fun = FUN_1D_MIXED, domain = PS_1D_MIXED, properties = "single-crit")
+OBJ_1D_MIXED_DEPS = bbotk::ObjectiveRFun$new(fun = FUN_1D_MIXED, domain = PS_1D_MIXED_DEPS, properties = "single-crit")
 
 FUN_1D_2_MIXED = function(xs) {
   if (is.null(xs$x2)) {
     xs$x2 = "a"
   }
   list(y1 = (xs$x1 - switch(xs$x2, "a" = 0, "b" = 1, "c" = 2)) %% xs$x3 + (if (xs$x4) xs$x1 else pi), y2 = xs$x1)
 }
-OBJ_1D_2_MIXED = ObjectiveRFun$new(fun = FUN_1D_2_MIXED, domain = PS_1D_MIXED, codomain = FUN_1D_2_CODOMAIN, properties = "multi-crit")
+OBJ_1D_2_MIXED = bbotk::ObjectiveRFun$new(fun = FUN_1D_2_MIXED, domain = PS_1D_MIXED, codomain = FUN_1D_2_CODOMAIN, properties = "multi-crit")
 
 # Simple 2D Functions
 PS_2D = ps(
@@ -69,14 +69,14 @@ FUN_2D = function(xs) {
   list(y = y)
 }
 FUN_2D_CODOMAIN = ps(y = p_dbl(tags = c("minimize", "random_tag")))
-OBJ_2D = ObjectiveRFun$new(fun = FUN_2D, domain = PS_2D, properties = "single-crit")
+OBJ_2D = bbotk::ObjectiveRFun$new(fun = FUN_2D, domain = PS_2D, properties = "single-crit")
 
 # Simple 2D Function with noise
 FUN_2D_NOISY = function(xs) {
   y = sum(as.numeric(xs)^2) + rnorm(1, sd = 0.5)
   list(y = y)
 }
-OBJ_2D_NOISY = ObjectiveRFun$new(fun = FUN_2D_NOISY, domain = PS_2D, properties = c("single-crit", "noisy"))
+OBJ_2D_NOISY = bbotk::ObjectiveRFun$new(fun = FUN_2D_NOISY, domain = PS_2D, properties = c("single-crit", "noisy"))
 
 # Instance helper
 MAKE_INST = function(objective = OBJ_2D, search_space = PS_2D, terminator = trm("evals", n_evals = 10L)) {
@@ -109,7 +109,7 @@ if (requireNamespace("mlr3learners") && requireNamespace("DiceKriging") && requi
 REGR_FEATURELESS = lrn("regr.featureless")
 REGR_FEATURELESS$encapsulate("callr", lrn("regr.featureless"))
 
-OptimizerError = R6Class("OptimizerError",
+OptimizerError = R6::R6Class("OptimizerError",
   inherit = OptimizerBatch,
   public = list(
 
@@ -129,7 +129,7 @@ OptimizerError = R6Class("OptimizerError",
   )
 )
 
-LearnerRegrError = R6Class("LearnerRegrError",
+LearnerRegrError = R6::R6Class("LearnerRegrError",
   inherit = LearnerRegr,
   public = list(