feat: bbotk async compatibility (#146)

* draft

* fix: add start time

* refactor: remove rush debug

* fix: add callback

* fix: transformation

* fix: xdomain

* refactor: kill workers

* feat: cache archive

* chore: debug

* chore: import rush

* feat: add logging

* refactor: use optimize_decentralized()

* feat: add exponential decay

* feat: add min-max imputation

* feat: add n_worker parameter

* draft

* draft

* refactor: remove stage

* fix: description

* feat: add n_worker argument

* fix: imports

* fix: tests

* ci: add redis

* drop ADBO for now

* do not include partial update changes made to SurrogateLearner

* ..

---------

Co-authored-by: be-marc <marcbecker@posteo.de>

Author: sumny

.github/workflows/dev-cmd-check.yml

@@ -38,6 +38,10 @@ jobs:
         with:
           r-version: ${{ matrix.config.r }}
 
+      - uses: supercharge/redis-github-action@1.7.0
+        with:
+          redis-version: 7
+
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
           extra-packages: any::rcmdcheck
@@ -48,3 +52,6 @@ jobs:
         shell: Rscript {0}
 
       - uses: r-lib/actions/check-r-package@v2
+        with:
+          args: 'c("--no-manual")' # "--as-cran" prevents to start external processes
+

.github/workflows/r-cmd-check.yml

@@ -36,9 +36,15 @@ jobs:
         with:
           r-version: ${{ matrix.config.r }}
 
+      - uses: supercharge/redis-github-action@1.7.0
+        with:
+          redis-version: 7
+
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
           extra-packages: any::rcmdcheck
           needs: check
 
       - uses: r-lib/actions/check-r-package@v2
+        with:
+          args: 'c("--no-manual")' # "--as-cran" prevents to start external processes

DESCRIPTION

@@ -41,13 +41,13 @@ BugReports: https://github.com/mlr-org/mlr3mbo/issues
 Depends:
     R (>= 3.1.0)
 Imports:
-    bbotk (>= 0.5.4),
+    bbotk (>= 0.8.0.9000),
     checkmate (>= 2.0.0),
     data.table,
     lgr (>= 0.3.4),
     mlr3 (>= 0.14.0),
     mlr3misc (>= 0.11.0),
-    mlr3tuning (>= 0.14.0),
+    mlr3tuning (>= 0.20.0.9000),
     paradox (>= 0.10.0),
     spacefillr,
     R6 (>= 2.4.1)
@@ -65,7 +65,10 @@ Suggests:
     rmarkdown,
     rpart,
     stringi,
-    testthat (>= 3.0.0),
+    testthat (>= 3.0.0)
+Remotes:
+    mlr-org/bbotk,
+    mlr-org/mlr3tuning,
 ByteCompile: no
 Encoding: UTF-8
 Config/testthat/edition: 3

R/AcqFunction.R

@@ -176,7 +176,7 @@ AcqFunction = R6Class("AcqFunction",
           stopf("Acquisition function '%s' requires the surrogate to have `\"se\"` as `$predict_type`.", format(self))
         }
         private$.surrogate = rhs
-        private$.archive = assert_r6(rhs$archive, classes = "Archive")
+        private$.archive = assert_archive(rhs$archive)
         codomain = generate_acq_codomain(rhs, id = self$id, direction = self$direction)
         self$surrogate_max_to_min = surrogate_mult_max_to_min(rhs)
         domain = generate_acq_domain(rhs)

R/AcqFunctionAEI.R

@@ -40,7 +40,7 @@
 #'     codomain = codomain,
 #'     properties = "noisy")
 #'
-#'   instance = OptimInstanceSingleCrit$new(
+#'   instance = OptimInstanceBatchSingleCrit$new(
 #'     objective = objective,
 #'     terminator = trm("evals", n_evals = 5))
 #'

R/AcqFunctionCB.R

@@ -35,7 +35,7 @@
 #'   codomain = ps(y = p_dbl(tags = "minimize"))
 #'   objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)
 #'
-#'   instance = OptimInstanceSingleCrit$new(
+#'   instance = OptimInstanceBatchSingleCrit$new(
 #'     objective = objective,
 #'     terminator = trm("evals", n_evals = 5))
 #'

R/AcqFunctionEHVI.R

@@ -30,7 +30,7 @@
 #'   codomain = ps(y1 = p_dbl(tags = "minimize"), y2 = p_dbl(tags = "minimize"))
 #'   objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)
 #'
-#'   instance = OptimInstanceMultiCrit$new(
+#'   instance = OptimInstanceBatchMultiCrit$new(
 #'     objective = objective,
 #'     terminator = trm("evals", n_evals = 5))
 #'
@@ -117,7 +117,7 @@ AcqFunctionEHVI = R6Class("AcqFunctionEHVI",
       }
 
       columns = colnames(self$ys_front_augmented)
-      
+
       ps = self$surrogate$predict(xdt)
       means = map_dtc(ps, "mean")
 

R/AcqFunctionEHVIGH.R

@@ -41,7 +41,7 @@
 #'   codomain = ps(y1 = p_dbl(tags = "minimize"), y2 = p_dbl(tags = "minimize"))
 #'   objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)
 #'
-#'   instance = OptimInstanceMultiCrit$new(
+#'   instance = OptimInstanceBatchMultiCrit$new(
 #'     objective = objective,
 #'     terminator = trm("evals", n_evals = 5))
 #'
@@ -175,20 +175,20 @@ adjust_gh_data = function(gh_data, mu, sigma, r) {
   idx = as.matrix(expand.grid(rep(list(1:n), n_obj)))
   nodes = matrix(gh_data[idx, 1L], nrow = nrow(idx), ncol = n_obj)
   weights = apply(matrix(gh_data[idx, 2L], nrow = nrow(idx), ncol = n_obj), MARGIN = 1L, FUN = prod)
- 
-  # pruning with pruning rate r 
+
+  # pruning with pruning rate r
   if (r > 0) {
     weights_quantile = quantile(weights, probs = r)
     nodes = nodes[weights > weights_quantile, ]
     weights = weights[weights > weights_quantile]
   }
-  
+
   # rotate, scale, translate nodes with error catching
   # rotation will not have an effect unless we support surrogate models modelling correlated objectives
   # for now we still support this more general case and scaling is useful anyways
   nodes = tryCatch(
     {
-      eigen_decomp = eigen(sigma) 
+      eigen_decomp = eigen(sigma)
       rotation = eigen_decomp$vectors %*% diag(sqrt(eigen_decomp$values))
       nodes = t(rotation %*% t(nodes) + mu)
     }, error = function(ec) nodes

R/AcqFunctionEI.R

@@ -30,7 +30,7 @@
 #'   codomain = ps(y = p_dbl(tags = "minimize"))
 #'   objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)
 #'
-#'   instance = OptimInstanceSingleCrit$new(
+#'   instance = OptimInstanceBatchSingleCrit$new(
 #'     objective = objective,
 #'     terminator = trm("evals", n_evals = 5))
 #'

R/AcqFunctionEIPS.R

@@ -9,9 +9,9 @@
 #' @description
 #' Expected Improvement per Second.
 #'
-#' It is assumed that calculations are performed on an [bbotk::OptimInstanceSingleCrit].
+#' It is assumed that calculations are performed on an [bbotk::OptimInstanceBatchSingleCrit].
 #' Additionally to target values of the codomain that should be minimized or maximized, the
-#' [bbotk::Objective] of the [bbotk::OptimInstanceSingleCrit] should return time values.
+#' [bbotk::Objective] of the [bbotk::OptimInstanceBatchSingleCrit] should return time values.
 #' The column names of the target variable and time variable must be passed as `cols_y` in the
 #' order `(target, time)` when constructing the [SurrogateLearnerCollection] that is being used as a
 #' surrogate.
@@ -37,7 +37,7 @@
 #'   codomain = ps(y = p_dbl(tags = "minimize"), time = p_dbl(tags = "time"))
 #'   objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)
 #'
-#'   instance = OptimInstanceSingleCrit$new(
+#'   instance = OptimInstanceBatchSingleCrit$new(
 #'     objective = objective,
 #'     terminator = trm("evals", n_evals = 5))
 #'