version 0.2.3

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: mlr3mbo
 Title: Flexible Bayesian Optimization
-Version: 0.2.2
+Version: 0.2.3
 Authors@R: c(
     person("Lennart", "Schneider", , "lennart.sch@web.de", role = c("cre", "aut"),
            comment = c(ORCID = "0000-0003-4152-5308")),
@@ -39,12 +39,12 @@ License: LGPL-3
 URL: https://mlr3mbo.mlr-org.com, https://github.com/mlr-org/mlr3mbo
 BugReports: https://github.com/mlr-org/mlr3mbo/issues
 Depends: R (>= 3.1.0)
-Imports: bbotk (>= 0.5.4), checkmate (>= 2.0.0), data.table, lgr (>=
+Imports: bbotk (>= 1.0.0), checkmate (>= 2.0.0), data.table, lgr (>=
         0.3.4), mlr3 (>= 0.14.0), mlr3misc (>= 0.11.0), mlr3tuning (>=
-        0.14.0), paradox (>= 0.10.0), spacefillr, R6 (>= 2.4.1)
+        1.0.0), paradox (>= 1.0.0), spacefillr, R6 (>= 2.4.1)
 Suggests: DiceKriging, emoa, fastGHQuad, knitr, lhs, mlr3learners (>=
         0.5.4), mlr3pipelines (>= 0.4.2), nloptr, ranger, rgenoud,
-        rmarkdown, rpart, stringi, testthat (>= 3.0.0),
+        rmarkdown, rpart, stringi, testthat (>= 3.0.0)
 ByteCompile: no
 Encoding: UTF-8
 Config/testthat/edition: 3
@@ -65,7 +65,7 @@ Collate: 'mlr_acqfunctions.R' 'AcqFunction.R' 'AcqFunctionAEI.R'
         'bibentries.R' 'helper.R' 'loop_function.R' 'mbo_defaults.R'
         'sugar.R' 'zzz.R'
 VignetteBuilder: knitr
-Packaged: 2024-03-01 18:58:03 UTC; lps
+Packaged: 2024-06-30 19:24:36 UTC; marc
 Author: Lennart Schneider [cre, aut] (<https://orcid.org/0000-0003-4152-5308>),
   Jakob Richter [aut] (<https://orcid.org/0000-0003-4481-5554>),
   Marc Becker [aut] (<https://orcid.org/0000-0002-8115-0400>),
@@ -81,4 +81,4 @@ Author: Lennart Schneider [cre, aut] (<https://orcid.org/0000-0003-4152-5308>),
   Luis Paquete [cph]
 Maintainer: Lennart Schneider <lennart.sch@web.de>
 Repository: CRAN
-Date/Publication: 2024-03-01 19:40:02 UTC
+Date/Publication: 2024-07-01 08:50:02 UTC

MD5

@@ -1,87 +1,87 @@
-557f1c8465ddc57e585402f8a38e124c *DESCRIPTION
+e6fbe87f2abf000d2763f4cc8a4c867c *DESCRIPTION
 659570bff6c7a240b79ca1e95ad918a3 *NAMESPACE
-6e3248eb59fffce00d46dd79dee4be68 *NEWS.md
-c8d0f6dc7cf4787606af175b28bcd312 *R/AcqFunction.R
-d55747e853f48a429c2ed2c9ec749b8f *R/AcqFunctionAEI.R
-a31675bb95fded08eebd36e0449bc344 *R/AcqFunctionCB.R
-ee825551ffb723560aad39ea18b5dffb *R/AcqFunctionEHVI.R
-33d970063047ce2abd6e47eb3d2b87c2 *R/AcqFunctionEHVIGH.R
-ed5814b603ac28ad34912b4b4e67e18d *R/AcqFunctionEI.R
-4c09b8c22944b76dfe0bde62a54fcbd7 *R/AcqFunctionEIPS.R
-f0f2d58f229c946a917bd9dd98ea0cc9 *R/AcqFunctionMean.R
-b6f4eeb300e6e3cd4f2d689546d468f0 *R/AcqFunctionPI.R
-ff8046fbd78bef89c167a32447347618 *R/AcqFunctionSD.R
-0c7275f397533db15a56a9073d80c089 *R/AcqFunctionSmsEgo.R
-ba3dc0d4c42695edc5714b7798d04a36 *R/AcqOptimizer.R
-8f973911849770925264805cad0dedd5 *R/OptimizerMbo.R
-2f3d76fbbfa852b8be910a615d9d22a8 *R/ResultAssigner.R
-632869d8fe18408e994f8db2287c8b29 *R/ResultAssignerArchive.R
-ab546dbb9d2026d2bc40d26bb07a8206 *R/ResultAssignerSurrogate.R
-d429f72244661751e3a4a503dbf4ce28 *R/Surrogate.R
-74eea3c851b57d0a570a4ea9bab9763c *R/SurrogateLearner.R
-b5dfed5af61a29f3f48204c7f4c1f9dc *R/SurrogateLearnerCollection.R
-d6e95fdaa7ae94e0c4061d4acc4c9bd0 *R/TunerMbo.R
+9d7a775783168a5f51e59ff4bec80b52 *NEWS.md
+a452db8bcdcaae3d3ed268022f325a96 *R/AcqFunction.R
+b6eba6fbe83e36517a21794abcf93b0c *R/AcqFunctionAEI.R
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+4cc225b1a5b19935b0ed7ad0063c7dba *R/AcqFunctionEHVI.R
+1ce114fedabb34d2716a630033d9e665 *R/AcqFunctionEHVIGH.R
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+81345eef5ba1abb259c6a05706862b91 *R/AcqFunctionPI.R
+59fe01c2ba253e941712f15b69428bf0 *R/AcqFunctionSD.R
+69a8bba45eb8e559b4e859088b0bb554 *R/AcqFunctionSmsEgo.R
+618711518fdaddce5cc133cdc515e3e0 *R/AcqOptimizer.R
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 e29b9365d05b79b2eff13a5302b30341 *README.md
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-3a1adc31796e72191b990e58aa42bf02 *build/vignette.rds
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 ea4320ef6038c0877356de25a9811552 *man/default_rf.Rd
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-229235d25e5313b882eaaf1bd18a6abe *man/mlr_loop_functions_mpcl.Rd
-31d20971ba45f993c3d260087e9142f9 *man/mlr_loop_functions_parego.Rd
-f426b778ebb7b0f0f0fdb6ee6efe1d38 *man/mlr_loop_functions_smsego.Rd
-562bb30a695a24f14fa64b79c2223ae2 *man/mlr_optimizers_mbo.Rd
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+fe1f01676ee093ff09f47c84c1193676 *man/mlr_loop_functions_mpcl.Rd
+e343ac841fccc647417305e5a0a0bec6 *man/mlr_loop_functions_parego.Rd
+6c672b4fb0f5ac58cfbbaec669d88645 *man/mlr_loop_functions_smsego.Rd
+5f35ab463131ba02a386a25f0eb77847 *man/mlr_optimizers_mbo.Rd
 ba797f17adbdaa9aba828ac2df71c451 *man/mlr_result_assigners.Rd
-0e4fa403f602b6adef1e8d235cb74acb *man/mlr_result_assigners_archive.Rd
-39c4c6b6463314a37cc244c73a454603 *man/mlr_result_assigners_surrogate.Rd
-a65ec4d136a5b7caa797d02d92132363 *man/mlr_tuners_mbo.Rd
+9c1c30bc6f703a8fcd02bc3f508a9ceb *man/mlr_result_assigners_archive.Rd
+21627f2b3d39cda0787bde71c72bb999 *man/mlr_result_assigners_surrogate.Rd
+aec8d5d3bbcfd1a4359e10fe6996f583 *man/mlr_tuners_mbo.Rd
 ba9da51f30790221bafd73210616d8b8 *man/ras.Rd
 f0d04e6f8b94c60dbb759a6e0b7a04ca *man/srlrn.Rd
 9dc74efc7eb063907095dfc6f4f866e6 *src/avl.c
@@ -91,7 +91,7 @@ a0e0709cade443b0b98aef77789ad709 *src/hv.h
 bd1614c43015948c4899211149642bbe *src/infill.c
 6f035caf4b01cda7501193dbb0ff606c *src/init.c
 6880a777b6f6d949ea9e2800525d2489 *tests/testthat.R
-0784c3ee7142bdb6d8fb4c6240a256a7 *tests/testthat/helper.R
+b90237ad95b559f71170a811cec40bfc *tests/testthat/helper.R
 b7b86e732aff6af13fc090f7e35bcd08 *tests/testthat/setup.R
 aa52628686a9dc29317eef7e5bab28bd *tests/testthat/teardown.R
 a0cc32933fe64d967233d351256a2287 *tests/testthat/test_AcqFunction.R
@@ -105,23 +105,23 @@ addeb05768a540df2417b385f96899a3 *tests/testthat/test_AcqFunctionCB.R
 31fd21d468787ea36d351912f4c42328 *tests/testthat/test_AcqFunctionPI.R
 3d9be670bf128fc0476239720fb1515e *tests/testthat/test_AcqFunctionSD.R
 6eb380da7962eb11dda9a36a249d22fa *tests/testthat/test_AcqFunctionSmsEgo.R
-2ed6eca42d3485ccabeb0dd19783fc09 *tests/testthat/test_AcqOptimizer.R
+10bec38c5a6afee7dae22633a7dc79a5 *tests/testthat/test_AcqOptimizer.R
 d916d4d7d0f5adc282520bb979c4b50f *tests/testthat/test_OptimizerMbo.R
 535f3f5cb1858fe761ae2b6f0f3e64d9 *tests/testthat/test_ResultAssigner.R
 e0ad379818751924cddd98595a368047 *tests/testthat/test_ResultAssignerArchive.R
 e8baf3cd297d9972c886e4892a4cd508 *tests/testthat/test_ResultAssignerSurrogate.R
 c67db0f4e3dc05a7ecbb2d80502a3cd2 *tests/testthat/test_SurrogateLearner.R
 fc71d5000ec9c4406f3e8edd2a39f90f *tests/testthat/test_SurrogateLearnerCollection.R
-26264559a9497bba72e743adc26ff1c5 *tests/testthat/test_TunerMbo.R
-43fee2663c14ac80ef4323607af35278 *tests/testthat/test_bayesopt_ego.R
+85f0c60b84d1be635487f081f4930b08 *tests/testthat/test_TunerMbo.R
+2ef641c9b18e5f2201e261464ef4e505 *tests/testthat/test_bayesopt_ego.R
 e2a5e8f1724e7ddd88118bb4ede046e4 *tests/testthat/test_bayesopt_emo.R
 970ea275cdbf6c776f0a153a2a410540 *tests/testthat/test_bayesopt_mpcl.R
 ce288f0dacad84216f0c1963faa3a3e0 *tests/testthat/test_bayesopt_parego.R
 8d59c418545030ecf0431d3a3859bf40 *tests/testthat/test_bayesopt_smsego.R
-61d2d2993fc51b2d339ec8940f1e9dc4 *tests/testthat/test_mbo_defaults.R
+1146f08c80a0714b4b4844994132186e *tests/testthat/test_mbo_defaults.R
 2b5a81afe21dbdc2fe6d53d3f9ba2362 *tests/testthat/test_mlr_acqfunctions.R
 4ee7d12da08fc13d3a646448b3df8e1a *tests/testthat/test_mlr_loop_functions.R
 5457a7195716cbdff8ea1d9bfbec3955 *tests/testthat/test_mlr_result_assigners.R
 99acf74b68b2de2e1d4b231c2d1317ef *tests/testthat/test_sugar.R
-2a41d257cf00bf0e517018249ec23ce0 *vignettes/mlr3mbo.Rmd
+51796f1fe3bc6894ddd19ff7fe88fb60 *vignettes/mlr3mbo.Rmd
 bb11ba039268face77ea2a0fda1348d1 *vignettes/references.bib

NEWS.md

@@ -1,3 +1,7 @@
+# mlr3mbo 0.2.3
+
+* compatibility: Work with new bbotk and mlr3tuning version 1.0.0.
+
 # mlr3mbo 0.2.2
 
 * refactor: compatibility with upcoming paradox upgrade.

R/AcqFunction.R

@@ -47,8 +47,6 @@ AcqFunction = R6Class("AcqFunction",
         check_packages_installed(packages, msg = sprintf("Package '%%s' required but not installed for acquisition function '%s'", sprintf("<%s:%s>", "AcqFunction", id)))
       }
       private$.requires_predict_type_se = assert_flag(requires_predict_type_se)
-      private$.label = assert_string(label, na.ok = TRUE)
-      private$.man = assert_string(man, na.ok = TRUE)
       private$.packages = packages
       self$direction = assert_choice(direction, c("same", "minimize", "maximize"))
       if (is.null(surrogate)) {
@@ -64,7 +62,7 @@ AcqFunction = R6Class("AcqFunction",
         self$surrogate_max_to_min = surrogate_mult_max_to_min(surrogate)
         domain = generate_acq_domain(surrogate)
       }
-      super$initialize(id = id, domain = domain, codomain = codomain, constants = constants)
+      super$initialize(id = id, domain = domain, codomain = codomain, constants = constants, label = label, man = man)
     },
 
     #' @description
@@ -176,7 +174,7 @@ 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))
 #'

R/AcqFunctionMean.R

@@ -27,7 +27,7 @@
 #'   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/AcqFunctionPI.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/AcqFunctionSD.R

@@ -27,7 +27,7 @@
 #'   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/AcqFunctionSmsEgo.R

@@ -40,7 +40,7 @@
 #'   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))
 #'